\r\n\tFood insecurity results in fear of hunger and starvation that ultimately affects one’s ability to work for sustainability and economic growth of the country. In addition to this, food insecurity results in various chronic diseases due to reduce immunity that ultimately, a burned on the county economy. Therefore, this book will intend to discuss in detail about the food insecurity challenges and their effect on the quality of life. This book will also aim to provide an overview about the new trends and future prospective that help to resolve the food security issues.
",isbn:"978-1-80356-942-0",printIsbn:"978-1-80356-941-3",pdfIsbn:"978-1-80356-943-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"090302a30e461cee643ec49675c811ec",bookSignature:"Dr. Muhammad Haseeb Ahmad, Dr. Muhammad Imran and Dr. Muhammad Kamran Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11475.jpg",keywords:"Nutrition, Poverty, Hunger, Food Waste Utilization, Innovative Technologies, Food Processing, Genetically Modified Food, Policy Making, Trade Reforms, Climate Change, Agriculture Productivity, Disease Resistant Crops",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 7th 2022",dateEndSecondStepPublish:"May 5th 2022",dateEndThirdStepPublish:"July 4th 2022",dateEndFourthStepPublish:"September 22nd 2022",dateEndFifthStepPublish:"November 21st 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"An emerging scientist in the field of food science and technology with special expertise in development of rapid and nondestructive technologies, chemometrics and data mining.",coeditorOneBiosketch:"Muhammad Imran has expertise in extrusion technology, microencapsulation, lipids chemistry, sensory evaluation and food process engineering.",coeditorTwoBiosketch:"A renowned scientist with expertise in Novel food processing technologies.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"292145",title:"Dr.",name:"Muhammad",middleName:null,surname:"Haseeb Ahmad",slug:"muhammad-haseeb-ahmad",fullName:"Muhammad Haseeb Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/292145/images/system/292145.png",biography:"Dr. Muhammad Haseeb Ahmad is currently an assistant professor in the Department of Food Science, Government College University Faisalabad, Pakistan. 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1. Introduction
The features of Flash memory include electrically erasable, no back-up power to protect data, in-circuit programming, high-density memory, low-cost and so on, which rapidly increase the using of Flash memory in embedded system.
The programming methods of Flash memory include Programmer Mode and In-Circuit Programmer Mode. Programmer Mode means erasing/programming Flash by programming tool (programmer), with the purpose of writing programs into MCU
. In-Circuit Programmer Mode means erasing/programming some region of Flash by MCU’s internal programs during run time, with the purpose of saving relevant data and preventing from lost after power off. Take AW60/XS128/MCF52233 in Freescale 8/16/32bits S08/S12/ColdFire serials’ MCUs for example, we elaborate the In-Circuit Programming method of Flash memory in this chapter. The programming method of the other MCUs in the whole Freescale S08/S12/ColdFire MCU family is similar. Besides, we discuss the protection mechanisms and security operations for AW60/XS128/MCF52233 Flash memory. Some instances are also provided in this chapter.
1.1. Flash memory characteristics
The most perfect memory should be a high-speed, non-volatile, low-cost and high-density memory. But only one or several specialties are implemented in general memory. With the maturity of its technology, flash memory has become an ideal memory in recent years. It is endowed with characteristics such as electrical erasure, data preservation without power supply, in-system programming, high storage density, low power consumption and low cost. These are just what MCU are expecting, because MCU with internal flash memory introduced in earlier years has some shortages in reliability and stability. With the maturity of the Flash technology, now more and more above characteristics are integrated to MCU and become an important part of it. Hence flash memory makes MCU progress enormously.
Flash memory is really a high-density, high-performance reading/writing memory with non-volatility, low-power and high-reliability and has the following characteristics comparing with old solid state memory.
Non-volatility: Flash memory protects data without power supply the same as magnetic storage.
Easy-updating: Comparing with old EEPROM
EEPROM—Electrically Programmable Read-Only-Memory
, the electrical erasure of flash memory shortens the programming cycle for developers and makes end users’ updating memory become true.
Low-cost, high-density and reliability: The parameters are much better than EEPROM (or E2PROM).
1.2. Flash memory program concepts
In many embedded systems, the memory which can protect program parameters and important data without external power supply is necessary as EEPROM previously was. The ColdFire MCU family provides the function of in-system programming of flash memory in user mode instead of EEPROM, hence making the circuit design simpler and cost lower.
However, different from the reading/writing of generic RAM, flash memory operations need two special processes—Erase and Program. The former, which converts all bits to 1, consists of mass erase and page erase. The latter, which converts bit to 0, can program only one word at a time. During erasing and programming, voltage higher than the power is usually needed and it is generated by Coldfire MCU inner electric charge pump. Besides, before programming, it should be insured that the program field has not been written after last erasure. That is, the field is blank (the content is $FF). So generally, erase should be carried out before perform.
1.3. In-circuit programming concepts of flash memory
In-circuit programming of flash memory in user mode (U-ICP) is a technique by which user programs stored in flash memory can modify data or programs also stored in the flash memory during run time. The electrically erasable characteristics of flash memory allow such programs to execute erase or write functions. This important branch of computer technology, an outgrowth of embedded systems development, makes it possible to update embedded programs, provide power-off protection and the recovery of important parameters, and modify the static parameters of embedded applications. In addition, U-ICP improves the expansibility and upgradeability of embedded systems. Portions of flash memory can substitute for the traditional EEPROM functions mentioned above, increasing system stability. And make the circuit design simpler and cost lower.
U-ICP was introduced to MCU technology by the semiconductor department of Motorola (now called Freescale) in 2000, and has been widely applied and developed ever since. However, different from the reading/writing of generic RAM, flash memory operations need two special processes—Erase and Program. The former, which converts all bits to 1, consists of mass erase and page erase. The latter, which converts bit to 0, can program only one word at a time. During erasing and programming, voltage higher than the power is usually needed and it is generated by Freescale S08/S12/CordFire MCU inner electric charge pump. Besides, before programming, it should be insured that the program field has not been written after last erasure. That is, the field is blank (the content is $FF). So generally, erase should be carried out before perform.
U-ICP can erase and reprogram other regions of flash memory by executing internal flash functions, but these may also prove unstable at high voltage. This problem, which is indicated in the data sheet of the Freescale S08/S12/CordFire MCU family, has not yet been solved by hardware design. It can be solved on the software level, by proper design of the U-ICP bottom driver program. So we describe an embedded software engineering rule that should improve the stability of the general erase and write functions in U-ICP for any flash device.
2. Programming flash in freescale MC9S08AW60 MCU
The flash memory in-circuit programming implement for 8bit MC9S08AW60 MCU will be explained in this section, as follows:
2.1. How to operate MC9S08AW60 flash memory
2.1.1. MC9S08AW60 Flash memory-mapping
S08 serials MCUs’ addressable address space is 64Kbyte, which ranges from $0000 ~ $FFFF. This addressing range is divided into different sectors. Each sector has different function. The memory map of AW60 MCU is shown in Fig.1, which includes the address distribution of 2KB RAM
RAM—Random Access Memory
, 2 parts of Flash memory and some I/O image registers.
As can be seen from the Fig.1, the Flash memory of AW60 is divided into two parts in this 64K memory address space. These addresses range from $0870~$17FF(3984 bytes) and $1860~$FFFF(59296 bytes). Among $1860~$FFFF only the addresses range from $1860~$FFAF can be used to erase and program user program. The addresses range from $FFB0~$FFBF are the 16 bytes non-volatile registers region and the addresses range from $FFC0~$FFFF are the 64 bytes interrupt vector region.
Flash memory is organized by page and row in the chip. The size of each page is 512 bytes. And the size of each row is 64 bytes. There are about 60K bytes of Flash memory address space in AW60, the page addresses are rounding 512 in $0000~$FFFF. For example, the first page’s address of Flash memory in the 3984bytes region ($0870~$17FF) is $1000~$11FF, instead of $0870~$0A6F.
Figure 1.
The memory map of AW60 MCU
For S08 serials MCU ( AW60, etc.), we can do mass erasing operation for the Flash memory, or can erasing one page(512 bytes) from a certain start address. But we can’t only erase a certain byte or some bytes which are less than 512 bytes. Noting this feature, it is important for the data arranging. The programming operation of AW60 is based on row (64 bytes). The data which can be programmed continuously at a time is only within one row. Certainly, the region that has not been erased can’t be programmed.
As can be seen from the above, in order to program Flash memory, we should prepare a set of data and move them into RAM, then erase the corresponding region of Flash memory, so programming operation can be done. Because erasing /programming a certain byte of Flash memory will influence the follow-up one page, it is necessary to reasonably arrange the relevant data of erasing region before erasing/programming the Flash.
2.1.2. MC9S08AW60 FLASH registers and control bits
In AW60, Erasing and programming operations relate to registers such as FCDIV、FOPT、FCNFG、FPROT、FSTAT and FCMD. Their corresponding addresses are $1820、$1821、$1823、$1824、$1825 and $1826. For the detailed function and use of these registers, please refer to the Reference Manual “MC9S08AW60 Data Sheet (HCS08 Microcontrollers)”.
2.1.3. Flash programming procedure
The execution steps of Flash commands
Write a data in an address of Flash. The address and data information will be locked into Flash interface. For blank check command, the data information is an arbitrary value; For page erase command, the address information is either one of the address in erase page (512 bytes) addresses; For blank check and mass erase commands, the address information is either one of the address in flash.
Write the commands which are needed to be executed into FCMD.
Execute commands. The FCBEF bit of FSTAT register is set, simultaneously execute the commands in the FCMD.
The flowcharts of the Flash programming
When programming flash, we need follow strict timing process. Fig.2 gives the programming flowchart with the other flash commands ( not include the command “burst mode byte program”). Writing a byte with burst mode command is very different from the execution with other commands. Burst mode means a lot of continuous data need to be written into Flash. Each time a write command has been executed, the writing high voltage in flash will not be removed, which will speed up the write speed of data; But for other commands, the high voltage is given to ensure the command executing, and the high voltage is immediately removed when the command ended. The programming flowchart with burst mode command is shown in Fig.3.
Flash Memory Illegal Operations
In the following processing, an error occurs, and the FACCERR bit is automatically set.
Writing the flash memory before initializing FCDIV register.
Writing the flash memory while FCBEF is not set.
Writing the second command to the FCMD register before executing the previously written command。
After write the flash memory, initializing the other flash control registers in addition to FCMD.
Writing an invalid flash normal mode command to the FCMD register.
Try to operate the other registers in addition to FSTAT after finished writing command values to FCMD.
MCU enters into STOP mode when execute the command.
When MCU is in secure state, erase pages or write flash memory with background debug interface (If MCU is in encrypted state, we can only execute blank check or mass erase commands with background debug interface).
Aborting a command write sequence by writing 0 to the FCBEF flag.
We first give the AW60\'s Flash programming subroutine in this section. Then put forward the Flash in-circuit programming instance in user mode. And verify the result by the serial communication mode with PC.
2.2.1. The erasing and programming c language subroutines of flash memory
For the Flash programming subroutines are not solidified in the internal monitoring ROM
ROM—Read Only Memory
of AW60, the initial loaded user program should contain the flash erasing and programming subroutines in order to do in-circuit programming for Flash. Because these subroutines resident in Flash, when running the erasing/programming subroutines, the whole Flash region will be added programming voltage that is higher than the normal operating voltage, which results in instable Flash region’s reading, and may lead to program’s error running. In order to make the erasing/programming subroutines running normally, these subroutines should be moved into RAM and run in RAM. Therefore, a buffer should be opened up in RAM to store these subroutines. The following sample program gives a convenient method to save the machine codes which are generated by erasing/programming subroutines in RAM. The machine codes include 57 bytes. Necessarily, you can directly call these codes to implement the Flash in-circuit erasing/programming. For the detailed Erasing and Programming subroutines, please refer to the program in our program directory “
..\\Flash_Program\\S08(AW60)-Flash
”You can download the program directory “Flash_Program” in our website (http://sumcu.suda.edu.cn/flash.htm), which involves three Flash programming instances in three subdirectory (“\\S08(AW60)-Flash”, “\\ S12X(XS128)-Flash” and “\\ ColdFire(MCF52233)-Flash”).. These subroutines contain the following operations:
Some public operation of erasing/programming processing
For the codes of erasing/programming operation must run in RAM, so we write the C language program according to Fig.2. After being compiled, the corresponding machine code bytes are saved in the array PGM (volatile unsigned char PGM[57]). Therefore we need not copy the codes to RAM to realize the erasing/programming operations.
Page erasing subroutine (Flash_PageErase)
In this subroutine we should calculate the page’s top address by the page number, and change the Flash command to erasing command 0x40, then call and execute the erasing codes.
Flash programming subroutine
In this subroutine we calculate the top programming address according to the page number and page offset, then change the Flash command to erasing command 0x20, and program flash one byte by one byte.
2.2.2. Programming essentials of erasing /programming subroutines
Using Flash in-circuit programming technology eliminates the need for external EEPROM, which not only simplify the circuit design, but also improve the stability of system. We compile the Flash programs and save them in Flash. When you need to use these codes, they will be copied to RAM. Just because of this special procedure, we put forward the following notes according to the experience which is accumulated in the actual programming and debugging, and project development process.
There are 57 bytes in RAM to store the erasing/programming machine codes, don’t forget to calculate when using RAM.
The region that has been erased for one time and has not been programmed can be programmed by calling Flash programming subroutine again, but the region that has been programmed can’t be programmed again if it hasn’t been erased.
For we do erasing for one page (512 bytes) each time, so we should arrange the data reasonably to avoid wrong erasing.
The start address of the page should be defined according to the rules of the FPROT register.
It is invalid to do in-circuit programming for the protection block set in FPROT.
2.2.3. Validate flash memory implements
In order to more clearly understand the method of Flash memory in-circuit programming, we give a flash memory validating project. Its function is as the following: the MCU receives formatted data from PC
PC—Personal Computer
by SCI
SCI—Serial Communication Interface
tools and erases, programs or reads its flash memory. The PC software is any SCI testing tool.
Now, list some flash operating commands using the SCI debug (as shown in Table 1):
Commands
Functions
?
MCU sends some items to PC
E:8
Erase page 8
R:8:0:4
Read 4 bytes the word 0 of page 8
W:8:0:4:A,C,B,D
Write “ACBD” (4 bytes) to the word 0 of page 8
B:8,7,6,5,4,3,2,1
Set the Flash back door key, the password is "87654321"
M:8,7,6,5,4,3,2,1
Verify the Flash back door key, the password is "87654321"
D
Delete passwords
U
Encryption lift
P:8
Protect block, protect the addresses from 8 to 0xFFFF
Table 1.
Flash operating commands using the SCI debug
Above examples only give the program data less than one page (512 bytes). Only slightly modify the program, you can program data that exceeds one page.
2.3. Protection mechanisms and security operations of MC9S08AW60 flash memory
2.3.1. Protection mechanisms
Being a non-volatile memory (NVM), flash memory may be used by programmers to store some important parameters and data. To prevent from erasing or programming these significant regions by accident, the MC9S08AW60 MCU supplies protection mechanisms for its flash memory. That’s to say, it can’t erase or program the protected region.
The Flash Protect Register (FPROT and NVPROT [1]) is interrelated with the protection mechanisms of S08 flash memory. And for the register’s bit definition and programming method you can refer to the reference manual [1]. By setting this register, we can protect the Flash memory.
For the programming codes for setting block protection, please refer to the program in our program directory “..\\Flash_Program\\S08(AW60)-Flash”
2.3.2. Security operations
The debug module is added in the S08 series MCUs, which increased the practicality of the chip, and brought risks to the security of the chip. In order to ensure the safety of the chip, the security mechanisms are much more complex.
S08 series MCUs use hardware mechanisms to prevent unauthorized users trying to access the Flash and RAM memory data. Having been set security, Flash and RAM are all counted as secure resources. But the direct page register, high-end page register and background debugging module are all counted as unsecure resources. During executing process, we can access any memory data, but can’t access secure sources by background debugging interface or unsafe method.
The security can be set by the nonvolatile data bit SEC01:SEC00 in FOPT. Table.2 gives the security state of MCU.
SEC01:SEC00
state
0:0
Secure
0:1
Secure
1:0
Unsecure
1:1
Secure
Table 2.
Security state
Set MCU to Security Mode
To prevent the programs in the flash memory from being read out illegally, the MCU should be set in security mode. Two methods for locking the flash memory are shown in the following.
Method A. Lock the MCU by modifying the security configuration field in the file isr.c(that is, modify the values of the FOPT’s address 0xFFBF and the key’s address 0xFFB0~0xFFB7 ).
Method B. we can lock the flash memory by calling the custom subroutine Flash_Secure to modify relevant address matters when the program is running. By modify the content in the address of NVOPT, the value of this register are automatically loaded in FPOT while the system is set. For the detailed Flash_secure subroutine, please refer to the program in our program directory “..\\Flash_Program\\S08(AW60)-Flash”
Unlock from Security Mode
If we want to program locked S08 serials MCU again, we should unlock it. Here two methods are provided to unlock it.
Method A. Use the BDM interface of our writer, mass erase the locked MCU. (The writer is designed by our lab.)
Method B. Call the subroutine Flash_KEY_Match to erase password or flash by memory-resident program. For the detailed Flash_KEY_Match subroutine, please refer to the program in our program directory “..\\Flash_Program\\S08(AW60)-Flash”
3. Programming flash memory in freescale S12XS128
The flash memory in-circuit programming implement for 16bit S12XS128 MCU will be explained in this section, as follows:
3.1. How to operate S12XS128 flash memory
3.1.1. The paging mechanism and MMC module in XS128 flash
The paging mechanism of S12XS memory
Take XS128 of S12XS serials MCU for example, XS128 contains 8KB RAM, 8KB D-Flash and 128KB P-Flash. But the basic address line of S12XS serials MCUs is 16bits, which determine its addressing scope range from 0x0000~0xFFFF. So the size of addressing space is 216B=64KB. That is, in most case MCU can only “see” these 64KB memory space.
As shown in Fig.4, the 64KB address space in S12XS serials MCUs is divided into four parts: I/O register, data Flash memory (D-Flash, also called as EEPROM), RAM and program Flash memory (P-Flash, directly called as Flash). The I/O register region ranges from 0x0000~0x07FF (2KB). D-Flash region ranges from 0x0800~0x0FFF (2K). RAM region ranges from 0x1000~0x3FFF (12K). P-Flash region ranges from 0x4000~0xFFFF (48K).
Figure 4.
S12XS’s 64KB address space
In order to expand the memory space when using 16 bits address line, S12XS serials MCU integrates MMC (Memory Mapping Control) module, which expand the addressing space from 64KB (16bits) to 8MB (23bits) by using paging management mechanism.
Address analyzing and addressing are managed by the MMC module in XS128. The main functions of MMC module include address mapping, controlling the operation mode of MCU, multi-agent (MCU and BDM) priority addressing, choosing the internal resource and controlling internal bus (which include memory space and peripheral resources) etc.
When we provide a certain address, whether it is a local 16bits address or a global 23 bits address, it will be analysized by MMC and assigned automatically to PPAGE or EPAGE, then gain a remainning 16bits address so that 16bits machine can directly calculate the address space and addess. Without these registers, 16bits machine should calculate twice to deal with 23bits address. Using these registers can improve the addressing efficiency. The whole procedure is automatically completed by MMC without user’s special care. Users only need to provide correct address.
There is a Global Page Index Register (GPAGE) in MMC. The highest bit of this register is fixed to 0, so GPAGE actually become a 7-bits register. MCU expands its 16 bits address to be 23 bits by means of GPAGE register. The 23 bits global address is composed of 7 bits GPAGE value [22:16] and CPU local address [15:0]. Meanwhile, specified 23 bits address read/write instructions are added in the instruction system of CPU. Only when CPU
Figure 5.
S12XS’s 8MB expanded address space
performs a global command GPAGE register is used. GPAGE provides a method to addressing 8MB space by using 23 bits global address. At this time the 8MB continuous addresses are distributed as Fig.5. The specific distribution is also shown as below:
0x00_0000~0x00_07FF 2KB I/O register address space
0x00_0800~0x0F_FFFF 64KB×16-2KB=1MB-2KB RAM space
0x10_0000~0x13_FFFF 64KB×4=256KB D-Flash space
0x14_0000~0x3F_FFFF 64KB×44=2816KB unused space
0x40_0000~0x7F_FFFF 64KB×64=4MB Flash space
Besides, in order to manage and use D-Flash, RAM and P-Flash, MMC adds three memory page registers: Data FLASH Page Index Register (EPAGE)[2], RAM Page Index Register (RPAGE)[2] and Program Page Index Register (PPAGE)[2], which are used to addressing corresponding expanded region. CPU opens up several windows in its 64KB addressing space. By using above page registers, CPU can map the memory space out of 64KB into these windows in 64KB space at any time. Meanwhile the window which is not used temporarily will be exchanged out. By using this method CPU can expand its addressing space. Besides, these page registers are also used to addressing the global address.
Paging memory mapping of XS128
For specific chip, not all the address spaces correspond to actual physical memory. For example, XS128 involves 8KB RAM, 8KB D-Flash and 128KB P-Flash. The address spaces used by these actual physical memories have been determined when chip is designed.
The global addresses of 8KB RAM in XS128 range from 0x0F_E000~0x0F_FFFF. RPAGE=0xFE~0xFF. When chip is reset, the default value in RPAGE is 0xFD, which is a invalid value. That is, addressing 0x1000~0x1FFF will make mistakes and produce illegal address interrupt. When MCU is initialized, we can initialize RPAGE to be 0xFE. So directly addressing 0x2000~0x2FFF will be same to addressing with global address 0x0F_E000~0x0F_EFFF. The global addresses of 8KB D-Flash range from 0x10_0000~0x10_1FFF, EPAGE=0x00~0x07. And the global addresses of 128KB P-Flash range from 0x7E_0000~0x7F_FFFF, PPAGE=0xF8~0xFF. Only these memory address resources mentioned above can be used in actual programming. The operation for the memory addresses outside of these addresses has no meaning.
The conversion of Local address, Logical address and Global address
Correctly comprehending the Local address, Logical address and Global address is the foundation to flexibly apply XS128 Flash module. In fact, for 16bits address line MCU, logical address is just the traditional 64Kb address space 0x0000~0xFFFF. Logical address is the expanded 24bits address. Its general format is 0xXX_XXXX. The two hexadecimal bits before “_” are the value of PPAGE or EPAGE (Page number). The other four hexadecimal bits behind “_” are the corresponding window address. For example, 0xFE_8000 is a logical address. 0xFE is P-Flash’s page number, and 0x8000 is the P-Flash’s corresponding window address in 64KB memory space. Global address is the physical space’s address used to save data. The global address of XS128 has 23 bits. That is, range from 0x00_0000~0x7F_FFFF. For example, if PPAGE=0xFE, addressing any address in the local address space 0x8000~0xBFFF actually means addressing the logical address space 0xFE_8000~0xFE_BFFF, while the corresponding global address space is 0x7F_8000~0x7F_BFFF. These two addresses are equivalent, and they are only two kinds of index patterns.
The conversion of logical address and global address in P-Flash is shown as Fig.6
Figure 6.
The conversion of logical address and global address
As shown in Figure.6, the logical address ranges from 0xFC_8000~0xFC_BFFF. The corresponding value of PPAGE is 0xFC. When the logical address is converted into corresponding global address, the top bit [22] in the 23bits address is fixed to 1, the following 8bits [21:14] are the value of PPAGE, that is 0xFC. The lower 14bits are local address, which ranges from 0x0000~0x3FFF. So the corresponding global address ranges from 0x7F_0000~0x7F_3FFF.
On the contrary, the global address ranges from 0x7F_0000~0x7F_3FFF. The corresponding value of PPAGE is the value of [21:14] (0xFC). Bit [22]=1 means a P-Flash page’s global address. The lower 16bits of logical address ranges from 0x8000~0xBFFF ( P-Flash window address region). So the corresponding logical address ranges from 0xFC_8000~0xFC_BFFF.
Fig.7 provides the relation between local address and global address. The local address of P-Flash in XS128 ranges from 0x4000~0xFFFF. 0x8000~0xBFFF is P-Flash window address region. Its corresponding global address region is 0x7E_0000~0x7F_FFFF. 0x4000~0x7FFF and 0xC000~0xFFFF can directly addressing the global physical addresses (0x7F_4000~0x7F_7FFF and 0x7F_C000~0x7F_FFFF). The local address of D-Flash range from 0x0800~0x0FFF, which is used for EPAGE address mapping window. And the corresponding global address for this window range from 0x10_0000~0x10_1FFF.
Figure 7.
The relation between the local address and global address in XS128
3.1.2. S12XS128 flash memory registers
In XS128 MCU, the relative registers for Flash programming include general registers and dedicated registers. Setting the general register can simultaneously set the characteristics of two Flash parts. While the dedicated register can only give service to a single Flash part at a certain time interval, the corresponding dedicated registers of the two Flash parts share the same address, so we should illustrate which Flash part is operated before using it.
There are 5 registers used in erasing and programming operation, which include FCLKDIV, FCNFG, FSTAT, FCCOBIX /FCCOB etc. FCLKDIV and FCNFG are general registers. FSTAT and FCCOBIX/FCCOB are dedicated registers. For the detailed function and use of these registers, please refer to the Reference Manual “MC9S12XS256 Reference Manual”[2].
3.1.3. XS128 special command mode NVM
For Loading of Flash commands, XS128 is different from the other Freescale MCUs (include DG128). The other MCUs mostly use a command register, which can be writen erasing/programming command codes directly. However, XS128 improve the previous mechanism. It loads the commands and parameters by using FCCOBIX register cooperate with FCCOB register.
The essence of NVM command mode is using the indexed FCCOB register to provide a command code and relevant parameter for memory controller. Users first according to need to set up all needed FCCOB registers domain, then initialize the execution of command by setting the CCIF bit in FSTAT register. When users clear the CCIF bit in FSTAT register, all the parameters in FCCOB register will be locked, which can’t be modified before the completion of command execution. (When command finished, CCIF is set to be 1).
In NVM command mode, the general command formats of FCCOB are shown as Fig.8
Figure 8.
The general command formats of FCCOB
Users can load commands by assigning FCCOB and FCCOBIX register according to the specific command formats. For the detailed programming method, please refer to the follow-up section which gives specific erasing/programming subroutines.
3.1.4. Flash programming procedure
In general, the erasing/programming operation of Flash involves four steps as below.
Set FCLKDIV register
For the detailed setting, please refer to the introduction of FCLKDIV register in the above section.
Caution: If the frequency is less than 1MHz, the Flash erasing/programming operation will be unsuccessfully. Too high setting of FDIV may damage Flash memory module. But too low setting may lead to unsuccessfully erasing and incomplete programming for Flash memory units. So users should choice appropriate Clock Divider.
Set the corresponding commands and parameters for FCCOB and FCCOBIX registers as needed
Set the CCIF bit in FSTAT register
Judge whether errors occur during the running of commands
Fig.9 gives a general Flash programming flowchart. According to this flowchart we can erase/program Flash successfully. We only need to pay attention to that part of Flash codes for P-FLASH operation should be moved in RAM.
We provide a D-FLASH In-circuit Programming Instance in our program directory “..\\Flash_Program\\ S12X(XS128)-Flash”, which contains the following parts:
3.2.1. Preparation for D-FLASH programming
XS128 contains 8Kb D-FLASH spaces, which is divided into 8 pages (1KB/page). The minimum erasable unit in programming is a sector, which is 256 bytes. There are 32 sectors in D-FLASH. For the detailed blocking codes, please refer to the head file EEPROM.h in our program directory “..\\Flash_Program\\ S12X(XS128)-Flash”.
3.2.2. Some common operation for erasing/programming procedure
The erasing/programming programs for D-FLASH need not run in RAM. For this kind of memory mode with multi paging mechanism, it is necessary to design a function which calculate the specific address with sector number and block number. Besides, we should set the FCLKDIV register before erasing/programming D-FLASH. And we also should detect the error flags to judge whether command run successfully.
3.2.3. Erasing subroutine
First we calculate the first address of erasing sector by sector number, load this first address and the D-FLASH sector erasing command CMD_D_ERASE_SECTOR (0x12) into FCCOB register by NVM command mode. Then execute the erasing command.
3.2.4. Programming subroutine
Calculate the first address of the programming sector by sector number and offset block number, load this first address and the D-FLASH sector programming command CMD_D_PROGRAM (0x11) into FCCOB register by NVM command mode. Then execute the programming command.
3.2.5. Reading/writing data
In regard to the reading/writing for content of Flash region, special additional remarks should be provided here.
The address space of Flash usually corresponds with multiple addresses. Here we elaborate the using method of these addresses, which apply some technique of C language.
Reading Flash can adopt the following 3 addressing patterns.
Addressing by Local Address. That is, addressing through 64KB address space. The addresses range from 0x0000~0xFFFF.
For example: Data= *(volatile uint8 *)0x0400;
Addressing by Logical Address (Global Logical Address). The addresses cooperated by EPAGE range from 0x0800~0x0c00, which can be addressed by the format “__eptr”. Caution: “__eptr” includes two underlines.
For example: Data= *(volatile uint8 * __eptr)0x00_0800;
Addressing by Global address (Global Physical Address). According to the actual physical location of the whole memory, access the memory with the format “__far”. Caution: “__far” includes two underlines.
For example: Data= *(volatile uint8 * __far)0x10_0000;
Caution: A sector is the minimum unit to erase. For D-FLASH, the minimum size is 256 bytes.
XS128 contains 128Kb P-FLASH spaces, which is divided into 8 pages (16KB/page). The minimum erasable unit in programming is a sector, which is 1024 bytes. There are 128 sectors in P-FLASH. The programming procedure of P-FLASH is similar to that of D-FLASH. So we omit the detailed description for this P-FLASH programming instance. For the detailed program codes, please refer to the program in our program directory “..\\Flash_Program\\ S12X(XS128)-Flash”.
3.4. Protection mechanisms and security operations Of XS128 flash memory
3.4.1. Protection mechanisms
The registers relate with XS128 Flash’s protection mechanisms include FPROT (Flash Protection Register) and DFPROT (D-Flash Protection Register). After set the protection registers, the protected region can’t be erased or programmed.
3.4.2. Security operations
The debugging module in XS128 improves the practical applicability of MCU, but simultaneously brings about hidden danger to the security of MCU. The common users may easily steal the programs from MCU by BDM. In order to prevent software piracy, XS128 brings in complex security mechanism to guarantee the security of MCU. When the MCU is encrypted, the common users can’t read any content in memory by BDM
BDM-- Background Debug Monitor
(Only messy codes can be read.) But the programs running in MCU can access arbitrary resources of MCU, and can decrypt MCU by using the back-door key access mechanism provided by MCU.
Set MCU to Security Mode
To prevent the programs in the flash memory from being read out illegally, the MCU should be set in security mode. The corresponding register is FSEC (Flash Security Register). If it is reset, FSEC register automatically load value from the configuration address 0x7F_FF0F. All bits of FSEC are related to the security of device, and these bits are read-only.
Unlock from Security Mode
Can’t unlock by BDM
As manual states we can’t unlock MCU by BDM with backdoor key access mechanism. Facts also show that we can’t unlock MCU and obtain valid data by BDM. It is worth noting that we can entirely erase the locked MCU by BDM, while the flag bit FPVIOL of FSTAT register will be set. If we don’t want to secure MCU now, we should program immediately by changing the later two bits of the byte in 0x7F_FF0F as the value 1:0. So after the next reset MCU will be in unlocked state.
The only way to unlock MCU—using backdoor key access mechanism.
Programs like buried treasure locked in chip. Treasure pretenders have tried every means to get it, but they are always blocked by an indestructible security door. Only intelligent master owns the key to open this security door. This is the so-called backdoor key access mechanisms.
How to start and use this kind of mechanism?
First, 8 bytes backdoor key together with the programs should be programmed into MCU. That is, 8 bytes key should be successively programmed into the addresses 0x7F_FF00~0x7F_FF07.
After that, the bits KEYEN[1:0] of FSEC register should be set as the value 10 to enable the backdoor key access mechanism.
Concerning how to unlock:
First, prepare to match the key. This step will use the FLASH backdoor key comparison command 0x0C. The backdoor key comparison command and 8 bytes key can be set to FCCOB. And setting flag bit CCIF can enable the comparison. If the comparison is successful, the security state will temporarily be unlocked. If the comparison is unsuccessful, the next comparison can be done only after reset, otherwise none operation can be done. Besides, if the comparison is successful, SEC[1:0] will be 10 which means unlocked state. If at this time users want to disable the encryption function, the bits KEY[1:0] should be set as disabled state to disable the backdoor key comparison function.
4. Programming flash in freescale MCF52233 flash
The flash memory in-circuit programming implement for 32bits MCF52233 MCU will be explained in this section, as follows:
4.1. How to operate coldfire flash memory
4.1.1. The basic concepts of MCF52233 flash memory
ColdFire Flash Module (CFM) is made up of 4 arrays, and each consists of 32K*16 bits, thus composing a flash memory space of 256 Kbytes, as is shown in Fig.10. Inner flash controller needs 2 cycles to access to the flash memory, but since across accessing enabled, it can read the flash consecutively with a higher frequency. Only one cycle is needed for reading each word.
Figure 10.
CFM Block Diagram
In MCF52233, the 256KB flash memory space is divided into 32 8KB sectors. Each section has 4 pages and each page is of 2KB. When programming, note that the erase is carried out by page. That is to say, at least one page needs to be erased at a time. 2 words (4 bytes) are performed at a time.
The 32-bit MCF52233 has 32 address buses, and can address 4GB space. In principle, the initial address of MCF52233 is alterable. By setting the corresponding register, the 256KB 32-bit flash can be located to any continuous space. However, in practice its start address is set to 0x0000_0000. And it is suggested not to alter the address.
4.1.2. ColdFire flash memory registers
Erasing and programming relate to registers such as FLASHBAR, CFMCLKD, CFMMCR, CFMPROT, CFMSEC, CFMUSTAT and CFMCMD. For the detailed function and use of these registers, please refer to the Reference Manual “MCF52235 ColdFire integrated Microcontroller Reference Manual”[3].
4.1.3. ColdFire flash memory erase and program implements
For ColdFire MCU, the entire flash memory or only one page (2KB) at the start address can be erased. That is, more than one byte or 2KB is erased at a time. To perform, a row of data should be prepared and put into the RAM first. Only after erasing the corresponding region in Flash memory can perform be carried out. Furthermore, the erasing or performing of any byte influences the page it is in, so before that it is necessary to arrange relevant data in the erasing region by linking files. In other words, the page which is being programmed cannot be erased. Below is a detailed procedure of Coldfire Flash memory erase and program. The corresponding sub-program instances are also provided in our program directory “..\\Flash_Program\\ ColdFire(MCF52233)-Flash”.
Common Operations for Erase and Program
If the CFMCLKD register is written, the DIVLD bit is set automatically. If the DIVLD bit is 0, the CFMCLKD register has not been written since last reset. No command can be executed if the CFMCLKD register has not been written.
Before starting a command write sequence, the ACCERR and PVIOL flags in the CFMUSTAT register must be cleared.
Erase
set the clock frequency division by writing the CFMCLKD register. Clear error flags, and set the sector number. These operations take place at the beginning of all operations, and have been packaged into a subroutine which can be called directly.
locate the sector to be erased. Write a value to any location in that sector.
write 0x40 to command register CFMCMD (section 10.2.1 “CFM Registers”).
write a “1” to the command buffer empty interrupt flag (CBEIF) of register CFMUSTAT. This clears the flag and launches the flash command described in step three.
wait for the command to be accomplished. This is indicated by the command complete interrupt flag (CCIF), which is also located in status register CFMUSTAT. This bit is set when the command is completed.
Program
If we need to write some words to a specific start address in flash memory (note: the address should be clean—non-written), detailed steps are as follows.
is the same as in the erasing operation.
set the start address. The process of writing words is then divided into sub-steps as follows:
select a word (provide the source address and the target address).
write a “1” to CBEIF in register CFMUSTAT, clearing the flag bit and executing the flash command.
wait for the command to be accomplished (the CBEIF flag of register CFMUSTAT is 1), meanwhile the next command is receivable only.
if data remain to be written, increase the source and target addresses then go to step B.
Notes: the register CFMCLKD is set only once anterior erase operation and in no any program case. Don’t erase any region which stores codes.
Flash Memory Illegal Operations
Writing to the flash memory before initializing CFMCLKD; Writing to the flash memory while CBEIF is not set; Writing to a flash block with a data size other than 32 bits; After writing to the even flash block, writing an additional word to the flash memory during the flash command write sequence other than the odd flash block; Writing an invalid flash normal mode command to the CFMCMD register (out of the 5 values); Writing to any CFM register other than CFMCMD after writing to the flash memory; Writing a second command to the CFMCMD register before executing the previously written command;. Writing to any CFM register other than CFMUSTAT (to clear CBEIF) after writing to the command register, CFMCMD; entering stop mode with some commands uncompleted. Upon entering STOP mode, any active command is aborted; Aborting a command write sequence by writing a 0 to the CBEIF flag after writing to the flash memory or after writing a command to the CFMCMD register but before the command is launched.
The PVIOL flag is set during the command write sequence if any of the following illegal operations are performed, causing the command write sequence to immediately abort: Writing a program command if the address to program is in a protected flash logical sector; Writing a page erase command if the address to erase is in a protected flash logical sector; Writing a mass erase command while any protection is enabled. If a read operation is attempted on a flash logical block while a command is active on that logical block (CCIF=0), the read operation returns invalid data and the ACCERR flag in the CFMUSTAT register is not set.
For predigesting programming, various illegal operation types listed above are ignored in practice and are simply classified as: completed or aborted.
4.2. Validate ColdFire flash memory implements
The validate ColdFire flash memory application in our network site is as the following: the MCU receives formatted data from PC by SCI interface and erases, programs or reads its flash memory. The PC software is SCI debug or our testing tool. For the detailed codes and running windows, please refer to the program in our program directory
“..\\Flash_Program\\ ColdFire(MCF52233)-Flash”.
Now, list some flash operating commands using the SCI debug:
Commands Functions
? MCU sends some items to PC
E:8 Erase page 8
R:8:0:4 Read 4 bytes the word 0 of page 8
W:8:0:4:A,C,B,D Write “ACBD” (4 bytes) to the word 0 of page 8
P:8,7,6,5,4,3,2,1 Encrypt Flash and the password is "87654321"
D Delete passwords
Above examples only give the program data less than one page (2048 bytes). Flash memory application for data that exceeds one page can been found in the aforesaid network site.
4.3. CFM protection mechanisms and security operations
4.3.1. CFM protection mechanisms
The CFMPROT register (refer to the reference manual[3]) is interrelated with the protection mechanisms of ColdFire flash memory which is divided to 32 sectors and each controlled by a flag of CFMPROT—the sector is presumed as in protected state while the corresponding flag is set to 1—there will be Illegal when erasing or programming the sector. Note to get it back to the protected state after erasing or programming the sector. In erase subroutine Flash_Page_Erase and program subroutine Flash_Page_Write, the Flash_Protect(page,FALSE) releases the sector from the protected state, but the MCF_CFM_CFMPROT = 0xffffffff reverses that.
4.3.2. CFM security operations
The ColdFire 0x0400~0x0417 is the flash configuration field whose security word is read automatically after each reset and is stored in the CFMSEC register. If the low 2 bytes of the CFMSEC register offset (0x0414~0x0417) in the file vectors.s is equal to 0x4ac8, the MCU is in its security mode and programs in the flash memory can’t be read, erased or programmed by 32-bit ColdFire programming writer in the BDM mode. Whereas it allows the password matching while the high 2 bytes is 0xc000. If the 32-bit ColdFire programming writer is set in JTAG mode, the password can be released by erasing the page 0 (the programs in the flash memory can’t be used any longer), and then the flash memory can erase or program in the BDM mode again.
Set MCU to Security Mode
To prevent the programs in the flash memory from being read out illegally, the MCU should be set in security mode. Two methods for locking the flash memory are shown in the following.
Method A. Lock the MCU by modifying the security configuration field in the file vectors.s.
Method B. we can lock the flash memory by calling the custom subroutine Flash_Secure to modify relevant address matters when the program is running. For the locked subroutine, please refer to the program directory “..\\Flash_Program\\ ColdFire(MCF52233)-Flash”.
Unlock from Security Mode
We must unlock the MCU first then can write into the program if it has been locked, because locked ColdFire family can’t be mass erased by BDM. And here two methods are provided to unlock it.
First, after setting the writer into JTAG mode, mass erase the locked MCU. Refer to “32-bit ColdFire writer” in our network site (http://sumcu.suda.edu.cn) for details.
Second, call the subroutine Flash_Delete_Key to erase password or flash by memory-resident program. (the subroutine Flash_Delete_Key is shown in the program directory “..\\Flash_Program\\ ColdFire(MCF52233)-Flash”)
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Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Flash memory characteristics",level:"2"},{id:"sec_2_2",title:"1.2. Flash memory program concepts",level:"2"},{id:"sec_3_2",title:"1.3. In-circuit programming concepts of flash memory",level:"2"},{id:"sec_5",title:"2. Programming flash in freescale MC9S08AW60 MCU",level:"1"},{id:"sec_5_2",title:"2.1. How to operate MC9S08AW60 flash memory",level:"2"},{id:"sec_5_3",title:"2.1.1. MC9S08AW60 Flash memory-mapping",level:"3"},{id:"sec_6_3",title:"2.1.2. MC9S08AW60 FLASH registers and control bits",level:"3"},{id:"sec_7_3",title:"2.1.3. Flash programming procedure",level:"3"},{id:"sec_9_2",title:"2.2. MC9S08AW60 flash memory in-circuit programming instance",level:"2"},{id:"sec_9_3",title:"2.2.1. The erasing and programming c language subroutines of flash memory",level:"3"},{id:"sec_10_3",title:"2.2.2. Programming essentials of erasing /programming subroutines",level:"3"},{id:"sec_11_3",title:"Table 1.",level:"3"},{id:"sec_13_2",title:"2.3. 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Erasing subroutine",level:"3"},{id:"sec_25_3",title:"3.2.4. Programming subroutine",level:"3"},{id:"sec_26_3",title:"3.2.5. Reading/writing data",level:"3"},{id:"sec_28_2",title:"3.3. XS128 P-FLASH in-circuit programming instance",level:"2"},{id:"sec_29_2",title:"3.4. Protection mechanisms and security operations Of XS128 flash memory",level:"2"},{id:"sec_29_3",title:"3.4.1. Protection mechanisms",level:"3"},{id:"sec_30_3",title:"3.4.2. Security operations",level:"3"},{id:"sec_33",title:"4. Programming flash in freescale MCF52233 flash",level:"1"},{id:"sec_33_2",title:"4.1. How to operate coldfire flash memory",level:"2"},{id:"sec_33_3",title:"4.1.1. The basic concepts of MCF52233 flash memory",level:"3"},{id:"sec_34_3",title:"4.1.2. ColdFire flash memory registers",level:"3"},{id:"sec_35_3",title:"4.1.3. ColdFire flash memory erase and program implements",level:"3"},{id:"sec_37_2",title:"4.2. Validate ColdFire flash memory implements",level:"2"},{id:"sec_38_2",title:"4.3. CFM protection mechanisms and security operations",level:"2"},{id:"sec_38_3",title:"4.3.1. CFM protection mechanisms",level:"3"},{id:"sec_39_3",title:"4.3.2. CFM security operations",level:"3"}],chapterReferences:[{id:"B1",body:'Freescale: MC9S08AW60Data Sheet,Rev.2,2006'},{id:"B2",body:'Freescale: MC9S12XS256 Reference Manual, Rev.2009'},{id:"B3",body:'Freescale: MCF52235 ColdFire integrated Microcontroller Reference Manual,Rev.4, 2007'},{id:"B4",body:'WANG Yi-huai, LIU Xiao-sheng, Embedded systems-design and application on HCS12 MCUs, Beihang, University Press, 2008'},{id:"B5",body:'Yihuai Wang,Zhigui Lin.Stable In circuit Programming of Flash Memory in Freescale’s MC9S12 MCU family. Proceedings-ICMTMA\n\t\t\t\t\t2010Volume III:477480IEEE Computer Society,2010'}],footnotes:[{id:"fn1",explanation:"MCU—Microcontroller Unit"},{id:"fn2",explanation:"EEPROM—Electrically Programmable Read-Only-Memory"},{id:"fn3",explanation:"RAM—Random Access Memory"},{id:"fn4",explanation:"ROM—Read Only Memory"},{id:"fn5",explanation:"..\\Flash_Program\\S08(AW60)-Flash"},{id:"fn6",explanation:"PC—Personal Computer"},{id:"fn7",explanation:"SCI—Serial Communication Interface"},{id:"fn8",explanation:"BDM-- Background Debug Monitor"}],contributors:[{corresp:"yes",contributorFullName:"Yihuai Wang",address:"",affiliation:'
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1. Introduction
Tropical forests are crucial for mitigating climate change, but many forests continue to be driven from carbon sinks to sources through human activities. To support more sustainable forest uses, therefore forests carbon needs to be measured and monitored at high spatial and temporal resolution. Tropical forest is one of the key ecosystems in addressing issues relating to climate change as it known to store large amount of carbon [1]. Retrieving tropical forest carbon over large areas has been challenging since decades due to the limited data resource, accessibility, and numerous technical issues. Remote sensing has been used actively for forest carbon estimation since the last three decades and it is proven to be effective [2, 3]. Although there are issues and arguments raised in the estimation accuracy, research is continuously being carried out. Optical or synthetic aperture radar (SAR) system has its own potential in retrieving biomass, but several issues remain unaddressed. While optical remote sensing is usually hindered by cloud, SAR systems are always limited by signal saturation at high biomass levels [4]. However, optical sensors offer better solutions for biomass assessment. Various spectral signatures and several vegetation indices can be derived from multispectral images can make the interpretation of biophysical properties of forests can be carried out conveniently. These are the most significant difference between optical and SAR systems that has made optical satellite data preferable in vegetation studies.
While the world has growing demand energy sector, it is crucial that nations put a collective effort to reduce anthropogenic greenhouse (GHG) emission and limit the global warming below 2οC above pre-industrial times, thus prevent catastrophic effects of global climate change. Mitigating the consequences of global climate change may be a critical societal objective now and within the forthcoming decades. Tropical countries contribute to carbon emissions mainly through deforestation and forest degradation, which accounts for approximately 10% of the world’s annual total carbon emissions [5]. National and international initiatives such as reducing emission from deforestation and forest degradation, and forest conservation (REDD+) and carbon offset are dedicated to mitigating the impacts of global warming. To achieve this objective, each nation’s carbon emissions resulting from deforestation and forest degradation need to be quantified and tracked over time. At such large geographic scales, a precise, cost-effective, and high-resolution means to monitor changes in aboveground carbon stocks is needed. This chapter is focusing on the roles of space borne remote sensing, especially free-access satellite data in assessing biomass of forest in various ecosystems in Malaysia, i.e., inland dipterocarp forests, mangrove forest, and peat swamp forest.
1.1 Forests in Malaysia
Major forest types in Malaysia are lowland dipterocarp forest, hill dipterocarp forest, upper hill dipterocarp forest, oak-laurel forest, montane ericaceous forest, peat swamp forest and mangrove forest. In addition, there also smaller areas of freshwater swamp forest, melaleuca forest, heath forest, forest on limestone and forest on quartz ridges. Considering the composition of these forests in Malaysia, the types can be generalized into three types, which are inland, peat swamp and mangroves.
The forests in Malaysia are mostly dominated by trees from the Dipterocarpaceae family, hence the term ‘dipterocarp’ forests. The dipterocarp forest occurs on dry land just above sea level to an altitude of about 900 meters. The term also refers to the fact that most of the largest trees in this forest belong to Dipterocarpaceae family. This type of forest can be classified according to altitude into lowland dipterocarp forest, up to 300 m above sea level, and hill dipterocarp forest found in elevation of between 300 m and 750 m above sea level, and the upper dipterocarp forests, from 750 m to 1,200 m above sea level. However, in Sarawak and Sabah both the lowland and hill dipterocarp forests are known as mixed-dipterocarp forest.
Currently, lowland dipterocarp forest is very few left outside of protected areas such as parks and wildlife reserves. While most of the country was covered with lowland forest in the past, today the majority has been cleared for other land uses. The few remaining pockets are under the gazetted land as Forest Reserves. Moreover, forest in this regime is also being central attraction for timber extractions. There is a real need to put more effort in saving and protecting this precious habitat type. Fortunately, some State (i.e., Provincial) Governments have halted land clearing for agriculture. It is vital that all remaining forest areas are protected. In this way, this valuable natural habitat can be managed on a sustainable basis.
1.2 Reducing emission from deforestation and forest degradation, and forest conservation (REDD+) in Malaysia
The REDD+ mechanism was agreed at the 15th Session of the Conference of Parties (COP 15) United Nations Framework Convention on Climate Change (UNFCCC), 2009 in Copenhagen. The REDD+ mechanism includes reducing emissions from deforestation, forest degradation, conservation, sustainable management of forest and carbon stock enhancement. It was also agreed that parties implementing REDD+ would need an effective national strategy or action plan and a transparent national forest monitoring and governance system. Ultimately, this mechanism was created to provide an incentive for developing countries to protect, better manage, and wisely use their forest resources, thereby contributing to the global fight against climate change.
Following COP 15, the progress in the REDD+ negotiations have been relatively rapid, with the most significant developments occurring in the last couple of years. Seven important decisions were adopted in 2014 for REDD+ governing methodological issues on safeguards, measurement, reporting and verification (MRV), development of national forest monitoring systems, addressing drivers of deforestation, and technical assessment of reference levels. In addition, the modalities for institutional arrangements at the national level for REDD+ implementation and results-based payments were also agreed.
Consensus on REDD+ was reached at the UNFCCC’s COP 15, which agreed on the need to provide positive incentives. This is followed by the Warsaw Framework for REDD+ providing guidance on all the requirements to obtain Results Based Payments (RBP). The agreed REDD+ to capture activities are (i) reduction of emissions from deforestation, (ii) reduction of emissions from forest degradation, (iii) conservation of forest carbon stocks, (iv) pursuance of sustainable management of forests, and (v) enhancement of forest carbon stock.
Malaysia’s forests can be categorized according to the degree of protection and land use classification. Management of forest land falls under three broad categories, which are: (i) Protected Areas/Totally Protected Area which consist of, national and state parks, wildlife sanctuaries, and nature reserves, (ii) Permanent Reserved Forests (PRFs) /Permanent Forest Estate (PFEs)/Permanent Forest Reserves (PFR), which are primarily natural forests to be maintained and managed sustainably for production and protection, and (iii) Stateland forest which are forest land reserved for future development purposes.
REDD+ is more than just a means of assigning monetary value to forest carbon stocks. It is also about ensuring the livelihoods of those whose culture, survival and heritage depend on the forests themselves.
1.3 The Paris agreement
The Paris Agreement builds on the Convention by bringing all nations together for the first time to commit to ambitious efforts to prevent climate change and adapt to its effects, with increased support for developing countries. As a result, it sets a new direction for the global climate effort.
The main goal of Paris Agreement is to enhance the global response to the issue of climate change by keeping a global temperature rise this century not more than 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5°C [6]. In addition, the agreement intends to improve countries’ ability to deal with the effects of climate change. Appropriate financial flows, a new technology framework, and expanded capacity building frameworks will be put in place to achieve these lofty goals, allowing developing countries and the most vulnerable countries to pursue their own national ambitions. Through a more rigorous transparency structure, the Agreement also provides for increased action and support transparency.
1.4 Nationally determined contribution (NCD)
Nationally determined contributions (NDCs) are at the core of the Paris Agreement and the achievement of these long-term goals. Malaysia intends to reduce the emission intensity of the greenhouse gas by 45% by 2030; in which corresponding to GDP [7]. In this circumstance, developed countries should involve 35% on an unconditional basis and a further 10% is condition upon receipt of climate finance for advanced technology transformation in construction capacity enhancement. In fact, this can assist in monitoring GHG.
Since the Paris Climate Agreement was signed in late 2016, governments all over the world have been submitting plans for reducing CO2 emissions through their NDCs. The NDCs was previously known as “Intended Nationally Determined Contributions” (INDC) and it is submitted to the United Nations Framework Convention on Climate Change (UNFCCC) once the countries ratified to the Paris Agreement. Currently, 197 parties to the Convention had submitted their INDCs and 150 had ratified it including Malaysia.
There has been no baseline prediction or quantified analysis of baseline measures provided, but Malaysia’s NDC indicated a 2005 as base-year emission level of 288 Mil Mg CO2e, which includes emissions of 25 Mil Mg CO2 from the LULUCF sector.
In 2014, Malaysia produced an Emissions Intensity Reduction Roadmap. According to the report, the country has chances across many sectors to fulfill the reduction target of a 40% decrease in GDP emissions intensity [8]. However, even if these opportunities exist, significant work would be necessary to achieve these emissions reductions, given the challenges of a 4.8 percent yearly rate for the per capita emissions between years 2000 and 2030. [9]. Energy for transportation is expected to rise at a pace of 5.3 percent per year over the next 25 years, making it the fastest-growing sector. Malaysia’s ultimate energy needs are predicted to treble by 2030, compared to present levels of consumption.
While the “ambitious scenario” indicates that Malaysia would be able to meet its Paris agreement NDC reduction target, substantial assistance from international funders is required. When both LULUCF emissions and removals are included, the GHG emission intensity per GDP in 2030 increases when compared to 2005 levels. This is since increase in removals by the LULUCF sector is much lower than the increase in emissions from the other sectors.
To achieve this target, a carbon offset project must be developed. Forest conservation is one of the options that can be explored since the forests are able to sequester CO2 at considerable amount. Several options have been recognized by the Verified Carbon Standards (VCS) that there are 7 types of projects related to forest conservation that can be intervened as carbon offset project (Table 1) [10].
No.
Project type
Definitions
1
Avoided planned deforestation
Avoided or stopping any logging/ plantation concession that involves deforestation. Carbon stock refers to the carbon stored in trees, whereas abatement refers to the net reduction in greenhouse gas emissions as a result of a project.
2
Wetland restoration and conservation
Increasing GHG removals by restoring wetland ecosystem by rewetting or avoiding the degradation of wetlands.
3
From low to high productivity forest
Convert low-productivity forests to high-productivity forests to increase carbon sequestration. Improved stocking density in low-productivity forests can help to boost carbon stores.
4
Conversion of logged forests to protected forests
Converting logged forests by eliminating harvesting of timber, biomass carbon stocks are protected, and can increase as the forest grows and/or continues to grow.
5
Reduced impact logging
Switching from conventional logging to RIL during timber harvesting. Carbon stocks can be increases by reducing damage to other trees, improve selection of trees, improve logging plan, etc.
6
Afforestation / Reforestation
Increase carbon sequestration via planting or human-assisted natural vegetation to develop, increase, or restore vegetative cover (forest or non-forest).
7
Extending the rotation age of evenly aged managed forests
Extending the forest rotation age or cutting cycle and increase carbon stocks. No fixed period of years to be extended, but generally the longer the period, the more average carbon stock increases.
Table 1.
Types of potential carbon offset project in Malaysia.
1.5 Carbon offset initiative
Carbon offsetting is the process of compensating for CO2 pollution (carbon footprint) by avoiding similar pollution from occurring elsewhere. One carbon offset entail compensating for the emission of 1 Mg of CO2 into the atmosphere by preventing the emission of 1 Mg of CO2 somewhere on Earth. The underlying concept is that developed countries pay poor countries (or assist them in other ways) to reduce global emissions on their behalf. In theory, carbon offsetting can assist the world to combat global warming if offsets are used to fund good, long-term environmental projects that would not have occurred otherwise.
There are dozens of different techniques to reduce carbon dioxide emissions, ranging from energy efficiency and renewable energy to forest planting. The most popular projects are those involving renewable energy; the most contentious are those involving forestry [11].
Malaysia has a lengthy history of forest management. However, some forest areas have been damaged as a result of prior management practices. The cost of restoring and rehabilitating these forests is high, as is the cost of caring for them. Each project type will have different implications on the cost, carbon benefits, biodiversity benefits, social benefits, and risk of failures. Figure 1 illustrates how these implications could occur when a project type is chosen as carbon offset project [12].
Figure 1.
Different restoration strategies can be used for different purposes and have different trade-offs.
2. Methodological framework
This study includes estimation of the national greenhouse gas emission trends from 2005 through 2020. This is to ensure that the LULUCF sector is the right area to venture for the carbon offset project. This is because the LULUCF sector also does emit CO2 in various manners. Therefore, instead of CO2 sink, it can be a source of CO2 emission as well at some extend. To ensure that the GHGs reported in this study is comparable to UNFCCC, the estimates presented here were calculated by using methodologies consistent with those recommended in the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. This study will not be used as regard to the carbon offset under the Paris Agreement and other international obligations but can be considered as an index that shows the extent of Malaysia mitigation measures implemented to combat the global warming.
2.1 Activity data
Remotely sensed data was used in this study for the years between 2005 and 2020 to estimate greenhouse gas emissions/removals, with 5-year interval. The reason for using these data due to its availability as independent data, it has a consistent time series and compatible with alternative data sources. In addition, the activity data obtained from Landsat satellite images are not found in any publication. Since the optical images over Malaysia are always hindered by cloud covers, a considerable number of datasets were required to produce a seamless mosaic of the images (without clouds). The Landsat images, covering the entire Malaysia that were used in this study are summarized in Tables 2 and 3 and Figure 2.
Satellite
Sensor
Date of acquisition
Time series (year)
Landsat-5
Thematic Mapper (TM)
January 2004–December 2006
2005
January 2009–December 2011
2010
Landsat-8
Operational Land Imager (OLI)
January 2014–December 2016
2015
January 2018 – March 2020
2020
Table 2.
Satellite images that were used as activity data.
Landsat Scene
Region
West Malaysia
East Malaysia
State
Peninsular Malaysia
Sarawak
Sabah
Scene ID (Path/Row)
128/055–057 127/056–058 126/056–059 125/058–059
121/058–059 120/058–059 119/057–059 118/058–059
118/055–057 117/055–057 116/056–057
Scenes required to cover Malaysia
12
9
8
Scenes acquired to produce cloud-free data
60
45
40
Scenes acquired to produce time series data (4 series)
240
180
160
Total scenes acquired
580
Table 3.
Summary of Landsat scenes datasets required to produce seamless mosaics over the entire Malaysia.
Figure 2.
Landsat scenes coverages over the entire Malaysia.
2.2 Production of seamless mosaic, time series Landsat images
According to Table 2, only 29 scenes of Landsat images are required to produce a mosaic that cover the entire Malaysia. However, being in the tropical regions, Malaysia is always covered by clouds that is almost impossible to be removed completely. Therefore, several images acquired at different dates over the same scenes are required to produce a cloudless image. The study has set a limit of five best images of the same scenes acquired circa three years of the targeted year to be used for further processes. These images must have <30% cloud cover and acquired within the specific periods (Table 3). Even though Landsat has 16-day repeat cycle, which are producing about 22 images over the same scene in a year, it is still difficult to find the best five images within 3 years. This is due to the heavy cloud covers in the atmosphere of Malaysia, especially at the mountainous areas and during the monsoon season (October – February). Cloud covers in most of the scenes are ranging from 10 to 90% and therefore, the chance to obtain <30% cloud cover is very small.
However, this issue has been solved by having several good quality scenes. The clouds on these images were detected and masked by using F_Mask algorithm [13]. Figure 3 shows the example of cloud masking a process was carried out to produce a cloudless mosaic of the scene 126/058 (Figure 4) that were acquired from various dates. This process is repeated for the other scenes and throughout the intervals (2005, 2010, 2015 and 2020). Altogether 580 scenes were processed to produce a seamless mosaic image for each time series. The final product is shown in Figure 5. Although the image looks clean, there is about ∼1% of hollow pixels still appear on the images, especially at the highlands and top of mountains areas. This is due to the clouds that always there all the time regardless weather conditions and seasons.
Figure 3.
Cloud masking process of Landsat scene 126/058.
Figure 4.
Cloudless image of Landsat scene 126/058.
Figure 5.
Cloudless image of Landsat images over Malaysia.
2.3 Image classification
Forest includes all land with woody vegetation consistent with threshold (minimum mapping unit (MMU) is 0.5 ha, minimum crown cover is 30% or minimum height at maturity is 5 m) used to define forest land in the national statistic. It also includes system with vegetation structure that currently falls below threshold, but in situ could potentially reach the threshold value is expected to exceed (the threshold of forest land category is sub-divided at the national level into managed and unmanaged and by ecosystem type as specified in the IPCC Guidelines) [14]. In this study, forests are divided into three major ecosystem types, which are inland forest, peat swamp forest and mangrove forest. These areas were further divided into Permanent Reserved Forests (PRFs)/Permanent Forest Estate (PFEs)/Permanent Forest Reserves (PFR) as managed category and the remaining areas outside the managed areas as stateland forest [15]. Figure 6 illustrates how the forests is defined and various conditions (due to management practices and natural disturbances) that possibly occur in the forests in Malaysia.
Figure 6.
Common structure of forests in Malaysia.
Understanding these conditions and management practices in forestry sector in Malaysia are crucial before these forests are interpreted and classified on the satellite images. Having several secondary data before hands are desirable and can facilitated the classification processes. Spatial information such as boundary of the PRFs, the management regimes, types and locations of varying ecosystems are required to ensure that the classification is performed accurately. In this case, the classification was performed to delineate forests from other land features. This process was performed by using traditional supervised classification method. Several training sets were selected on the images. Unchanged forest areas, which were determined from secondary spatial data were used as forest training sets, and the other land cover classes were determined from the image interpretation. The same training set was used for forest class for all time series.
The biggest challenge in the image classification was about to deal with huge data size and to produce classification results with minimal uncertainties. Manual editing of the classification results was typical and need to be done repeatedly, which is a tedious process and time consuming. However, the results are satisfying, and the example of the classification results are depicted in Figure 7 for the year 2020. The classification results in pixels form were converted to vector format (.shp) for further analysis and post-classification detection process. From the vector data, the areas for each forest type classified from the images were determined. Example forest area statistics derived from the vector data is summarized in Table 4 for the year 2020.
Figure 7.
Forests in Malaysia classified from the images.
Region
Forest Cover (ha)
Total Forest Cover (ha) (d) = (a) + (b) + (c)
Land Area* (ha) (e)
Percentage (%) (f) = (d)/(e)*100
Inland Forest (a)
Peat Swamp Forest (b)
Mangrove Forest (c)
Peninsular Malaysia
5,338,082
243,504
110,953
5,692,539
13,100,367
43.5
Sarawak
7,328,029
320,207
139,890
7,788,126
12,444,951
62.6
Sabah
4,273,536
97,276
378,195
4,749,007
7,390,224
64.3
Total
16,939,647
660,987
629,038
18,229,673
32,935,542
55.3
Table 4.
Composition of forest cover in Malaysia (2020).
Sources: Department of Survey and Mapping Malaysia, Lands and Surveys Department, Sabah and Department of Land and Survey, Sarawak.
2.4 CO2 emissions calculation
Carbon dioxide (CO2) is the main greenhouse gas that plays critical roles in regulating the earth’s climate. According to IPCC, there are two basic approach to estimate CO2 emissions/removals, which is Gain-Loss Method (GLM) and Stock-Difference Method (SDM). Calculation methods for this study are determined by SDM at Tier-2 level by using CO2 based on [16]. The result is then multiplied by 44/12 or equal to 3.67 unit of carbon (C). Since the emission from the forestry activities are considered, the CO2 in this study is attributed only from the forest carbon stock, and it is not equivalent to emission from other gases. Therefore, the reported emission is in carbon dioxide (CO2) and not carbon dioxide equivalent (CO2e).
2006 IPCC Guidelines offer a default methodology that includes default emission factors for Tier-1 [14]. Tier-1 level is designed to be the simplest to use, for which equations and default parameter values (e.g., emission and stock change factors) are provided by 2006 IPCC Guidelines. The emission factor is derived from readily available statistical information, which often globally available sources of activity data estimates (e.g., deforestation rates, global forest cover maps, etc.) although these data are usually spatially coarse.
Meanwhile, Tier-2 level use the same or similar activity data to Tier-1 level but applies emission and stock change factors that are based on country- or region-specific data. Country-defined emission factors are more appropriate for the local climatic regions and land use system. In many cases the Tier-2 could also be applied at a higher level of temporal and spatial resolution and more disaggregated activity data, where the activity statistics are further split into sub-categories.
Higher-order approaches are utilized at the Tier-3 level, such as models and inventory measurement systems suited to national circumstances, repeated over time, and driven by high-resolution activity data disaggregated at the subnational level. Higher-order approaches produce more accurate estimations than lower-tier approaches.
Estimated carbon stock (Mg C) in the stock change method is obtained by multiplying the forest area (ha) by the carbon stock per unit area (Mg C ha−1). The carbon stocks of the entire project area at a given time are obtained by calculating the products of the carbon stocks per unit area for each forest type and the area occupied by that type and then summing the results over all forest types.
Ct=∑i=1nAi×CiE1
Where:
Ct = total carbon stock at a certain time t (Mg C)
Ai = area occupied by forest type i (ha)
Ci = carbon stock per unit area of type i (Mg C ha−1)
The emission is calculated as the difference of carbon stocks for a given forest area at two points of time, which is expressed as
ΔC=Ct1×Ct2/t2−t1E2
Where:
ΔC = annual carbon stock change in biomass (Mg C yr.−1)
Ct1 = carbon stock at time 1 (Mg C)
Ct2 = carbon stock at time 2 (Mg C)
3. Results and discussion
3.1 Changes of forest cover
Deforestation is defined as human induced permanent conversion of forest land to non-forest, i.e., all the forest stands are cut, and the land is cleared and used for another purpose. Temporary change in land use, like one rotation tree crop (up to 25 years) within forest reserves are not considered as deforestation [17]. In a broader term, deforestation converts forest land to alternative, permanent, non-forested land to be used in agriculture, grazing or urban development or clearing of any area of its natural vegetation cover, which normally leads to a decrease in plant population, resulting in a loss of plant biodiversity [18]. Deforestation is caused by multiple drivers and pressures, including conversion for agricultural uses, infrastructure development, wood extraction, agricultural product prices, and a complex set of additional instructional and location-specific factors [7], which can be extremely important in certain localities.
A crude estimate showed that the total forest loss in Malaysia during years 2000–2012 amounted to 14.4% of its year 2000 forest cover [19]. Oil palm expansion was the major reason that contributed to the figure. The oil palm plantation area in Malaysia increased from 5.59 to 11.56 Mil. ha from 2000 to 2018, an increase of 5.98 Mil ha with a growth rate of 106.96%. The area of oil palm plantations in West Malaysia increased by 2.53 Mil ha, with a growth rate of 82.77%; in East Malaysia, the area increased by 3.45 Mil ha, with a growth rate of 136.14% [20]. The growth of oil palm accelerated between years 2000–2010 and become decelerated starting from 2010 onwards. In addition to that, the deforestation was caused by rubber plantation, construction of hydro-electric dam reservoirs, mining activities, forest fire, illegal logging, shifting cultivation, and natural disasters such as tsunami and erosion.
In contrast, the study found that the deforestation from 2005 to 2020 was amounted to the loss only of 1,087,030 ha (5.6%) of its year 2005 forest cover, with the annual rate of deforestation at 0.37% yr.−1. Hence the study proved that the reported rate by [19] was not right. The forest cover has reduced from 19,316,702 ha in 2005 to 18,229,672 ha in year 2020 (Table 5). This was attributed to reduction in about 3.4% of total forest extents in Malaysia due to the conversion of forest to agricultural lands and settlement, which were mainly under the stateland forest that are designated for development purposes.
Year
Forest cover (ha)
Percentage cover (%)
Inland forest
Peat swamp forest
Mangrove forest
Total
2005
17,949,753
700,401
666,547
19,316,702
58.7
2010
17,329,165
676,186
643,502
18,648,853
56.6
2015
17,088,338
666,789
634,559
18,389,686
55.8
2020
16,939,647
660,987
629,038
18,229,672
55.3
Table 5.
Forest cover in Malaysia (ha).
Given the landmass of Malaysia was at 32,935,542 ha.
Aboveground biomass (AGB) comprises all living aboveground vegetation including stems, branches, twigs, and leaves. It is the most important pool of carbon forest types. In this study, a published allometric equation was used to calculate AGB for inland forests [21]. This equation was calibrated based on trees sampled in lowland and hill forests in west Peninsular Malaysia. Wood densities were obtained from the Global Wood Density Database [22]. A biomass expansion factor of 0.47 was used to convert the biomass into carbon stock. Previous study indicated that the average values for carbon stock from all carbon pools in major types of forest in Malaysia as summarized in Table 6 [16]. A comprehensive review of carbon stock in various forest types and conditions in Malaysia was also made by [23, 24]. However, only the aboveground component of carbon stock is used for the emission calculation in this study.
Forest type
Carbon stock (Mg C ha−1)
Above-ground
Below-ground
Dead-wood
Litter
Soil
Total
Inland forest
174.49
35.22
4.92
1.29
48.40
264.32
Peat swamp forest
168.63
35.95
21.40
2.19
188.10
416.27
Mangrove forest
135.45
48.57
22.12
3.88
54.87
264.89
Table 6.
Carbon stock in all carbon pools in major types of forests in Malaysia (Mg C ha−1).
The most important parameters that play roles that produce variations in carbon stock estimations are (i) the use of different allometric equations in the estimations, (ii) application of different sampling design/protocols, (iii) levels of disturbances in the forest, (iv) harvesting/ logging practices in production forest, and (iv) the selection of study sites. These influence the process of selecting project sites for carbon offset project.
3.3 CO2 emission from deforestation
Assuming the CO2 emission occurred was a result from the changes of forest cover, the emission from year 2005 to 2020 was about 689.26 Mil. Mg CO2, with an annual rate of emission at 45.95 Mil. Mg CO2 yr.−1. This was equal to carbon loss of about 12.53 Mil. Mg C. Table 7 summarizes the trend of CO2 emission that occurred between years 2005 and 2020. The trend indicates that the deforestation accelerated between years 2005 and 2010 and slowed down between year 2010 and 2020. This was mainly due to awareness and mitigation action among government towards REDD+ interventions and enhancement of management practices towards various conservation efforts.
Time Series
CO2 emission (Mil. Mg CO2)
Total
Inland forest
Peat swamp forest
Mangrove forest
2005–2010
397.05
14.97
11.45
423.47
2010–2015
154.08
5.81
4.44
164.33
2015–2020
95.13
3.59
2.74
101.46
Table 7.
CO2 emission resulted from deforestation Malaysia (2005–2020).
Although there are a few assumptions and generalizations were included in the estimations, the reported figures can present an overall scenario of CO2 emission resulted from deforestation activities in Malaysia.
3.4 CO2 emission in forest land remaining forest land
Although deforestation attributed much to the CO2 emission in LULUCF sector, the remaining forests are still playing roles in CO2 sequestration while they regrow. However, the rate of CO2 sequestration is very slow and is greatly depending on the overall management practices applied within the forests. This is also typically occurred within the PRFs where some areas are designated for production purpose with sustainable forest management (SFM) practices. The average rate of sequestration for the major types of forests in Malaysia is summarized in Table 8.
Forest type
Growth rate AGB (Mg ha−1 yr.−1)
Carbon sequestration* (Mg C ha−1 yr.−1)
Inland
9.3
4.37
Peat swamp
9.2
4.23
Mangrove
11
5.17
Table 8.
Rate of carbon sequestration in major forest types in Malaysia [17].
Carbon conversion factor: 0.47.
Analysis of CO2 emission and removals from LULUCF sector in Malaysia evidenced that the activity data used are very important to determine the emission and removals within the forest land remaining forest land category. In this case, data such as logging history records, net production (timber volume), and annual allowable coupe (AAC) acquired from the respective forestry departments were used estimate emission from upstream forest operations. Peninsular has contributed net removal from the category was about −0.14 Mil. Mg CO2 within 5 years from 2005 to 2010. Then, it was followed by net emissions at 14.31 Mil. Mg CO2 which occurred between year 2010 to 2015 as compared to year 2015 to 2020, which accounted net emissions for 23.53 Mil. Mg CO2. Meanwhile, Sabah has contributed net removal from forest land remaining forest land about −16.22 Mil. Mg CO2 within 5 years from 2005 to 2010. Then, it continued to remove emission at −0.49 Mil. Mg CO2 between years 2010 and 2015, and even much greater between years 2015 and 2020, with the accounted net removal of −72.62 Mil. Mg CO2. Sarawak has contributed net removal of about −11.83 Mil. Mg CO2 in 5 years between 2005 and 2010 and continued to the years between 2010 and 2015 with a net removal at −90.44 Mil. Mg CO2. However, it emitted back within the years 2015–2020 with the net emission of 48.87 Mil. Mg CO2. Table 9 and Figure 8 summarize the fluctuations in the net emission and removal that have occurred within years 2005 to 2020.
Year
Net (Mil. Mg CO2)
Peninsular Malaysia
Sabah
Sarawak
Total (entire Malaysia)
2005–2010
−0.14
−16.22
−11.83
−28.19
2010–2015
14.31
−0.49
−90.44
−76.62
2015–2020
23.53
−72.62
48.87
−0.22
Table 9.
Summary of CO2 emission for forest land remaining forest land.
Note: -ve sign is net removal and + ve sign is net emission.
Figure 8.
Net CO2 emission/removal in Malaysia.
Principally there is not fix trend in the net emission and removal within the category of forest land remaining forest land in each region in Malaysia. This indicates that the activities within the forests are dynamic and unpredictable. Some areas could produce emission, but some other areas sequester carbon and thus resulting in removals. However, taking Malaysia as a whole, there is a trend of continuous removals from year 2005 to 2020 that has been produced by the remaining forests in Malaysia (Figure 9).
Figure 9.
Net CO2 removal in Malaysia.
4. Requirements to offset the emission
It is concluded that the rapid logging operations within the forest can be very dynamic, thus the forests in Malaysia not only remove CO2 but also produce emissions. These indicated that the forest areas that have been serving for timber production and logging are continuously fluctuating in terms of emission and removals. This area is worth for a carbon offset program because the intervention can stand on all pillars of SFM; ecological, economic, and socio-cultural. The forests’ ability to attract investment and support commercially sustainable forest uses is unaffected in the present and future.
Analysis indicated that the LULUCF sector in Malaysia is still producing the net emission at 101.24 Mil. Mg CO2 at the end series of year 2020 (Table 10). Based on this figure, Malaysia needs to stop completely deforestation activities and restore about 27.6 Mil. Mg C, which is equal to 158,240 ha of natural forest to offset the emission from LULUCF sector in the country. Otherwise, Malaysia must limit logging activities and retain about 631,838 ha of the logging areas to regrow naturally for at least 10 years. This is almost impossible since Malaysia is a developing country and still depending much on the forests for timber productions [25]. While the remaining stateland forests can be demolished at any time for development purposes. Therefore, the finding of this study suggesting that the project types that have potentially suitable for carbon offset program are (i) avoided planned deforestation, (ii) conversion of logged forests to protected forests, (iii) extending the rotation age of evenly aged managed forests, and (iv) avoided deforestation on wetlands (conservation).
Time Series
Net CO2 emission/removal (Mil. Mg CO2)
Net CO2 emission from deforestation
Net CO2 removal in forest land remaining forest land
Net CO2 emission
2005–2010
423.47
−28.19
395.28
2010–2015
164.33
−76.62
87.71
2015–2020
101.46
−0.22
101.24
Table 10.
Summary of CO2 emission for forest land remaining forest land.
The continuing removals indicated that the forest sector contributed greatly to the sinks through forest land remaining forest land. In this case, there are activities occurred within the forestland that attributed to the CO2 removals, which could be driven by the good of management practices, such as Forest Stewardship Council (FSC) Certification Scheme. The FSC will conduct an independent review of forest management methods in order to ensure sustainable management practises, sustainable management of Malaysia’s natural forest, and to meet demand for certified timber products.
Forest enrichment activities are being made by the forest industry between 2016 and 2020 to rehabilitate degraded forests. Ongoing projects such as the Central Forest Spine (CFS) in Peninsular Malaysia and the Heart of Borneo (HoB) in Sabah and Sarawak serve as facilitators for improving forest connectivity, reducing fragmentation, and improving natural resource management. The forestry sector implemented a REDD+ strategy in 2017 to ensure that at least 50% of Malaysia’s land mass is forested, which was accomplished by improving sustainable forest management, conservation activities, and seeking synergies with activities under the National Policy on Biological Diversity 2016–2025 [17].
Nonetheless, the current net removals are still not sufficient to offset the emission that has been produced by the deforestation activities in Malaysia. While the LULUCF sector is producing emission, the other sectors such as energy, transportation, agriculture, solid waste, and others are also emitting CO2 to the atmosphere at even greater amount. Therefore, carbon offset can not only depend on forests. Appropriate mitigation actions need to put in proper place within the individual sector so that the climate change mitigation can be achieved, and the targeted reduction of global temperature is materialized.
5. Conclusion
The study demonstrated that the use of remote sensing data, coupled with the other supporting data are viable for assessing forest carbon and emissions in forestry sector in Malaysia. Although there were technical issues regarding the data, with appropriate image processing methods, the issues have been well addressed. Landsat satellite images that have been acquired between years 2005 and 2020 with 5-year intervals were processed to produce seamless, wall to wall images over Malaysia. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses.
Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. The study found that the deforestation from 2005 to 2020 was amounted to the loss of 1,087,030 ha (5.6%) of its year 2005 forest cover, with the annual rate of deforestation at 0.37% yr.−1. This has contributed to the total CO2 emission of 101.46 Mil. Mg CO2. The study also estimated the total CO2 emission and removals within the forest land remaining forest land. It was revealed that the forests also produced emission in terms of timber production activities. However, the overall estimates showed that this category is still able to sequester carbon and provide removals at a sum of 105.03 Mil. Mg CO2 for the period of 15 years (2005–2020).
The study exposed suggested that Malaysia must stop completely deforestation activities and restore about 27.6 Mil. Mg C to achieve the net-zero emission. This is equal to 158,240 ha of natural forest or 631,838 ha of the logging areas to need to be left regrown naturally for at least 10 years. The study also suggested that the project types that have potentially suitable for carbon offset program in Malaysia are (i) avoided planned deforestation, (ii) conversion of logged forests to protected forests, (iii) extending the rotation age of evenly aged managed forests, and (iv) avoided deforestation on wetlands (conservation).
The study proved that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring deforestation in Malaysia. Although cloud covers are the major issue for optical imagery datasets, current development in remote sensing, computer technologies and processing algorithms for images analysis can provide solutions for the issues.
Acknowledgments
This study was conducted in collaboration between Geoinformation Programme, Forest Research Institute Malaysia (FRIM) and National Petroleum Limited (PETRONAS) under the project entitled “Screening Study for Carbon Offset Opportunities in Malaysia”. Thanks to Forestry Department Peninsular Malaysia (FDPM), Sabah Forestry Department (SFD) and Forest Department of Sarawak (FDS) for providing supporting related data and statistics that was used for the calculations in this study. The publication of this chapter is supported by the FRIM’s research and development (R&D) fund under the 12th Malaysia Plan from the Government of Malaysia through the Ministry of Energy and Natural Resources, Malaysia. Thanks also to the USGS (https://earthexplorer.usgs.gov) that provide free-access Landsat images for this study.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"Landsat images, tropical forests, deforestation, carbon offset, climate change",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77534.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77534.xml",downloadPdfUrl:"/chapter/pdf-download/77534",previewPdfUrl:"/chapter/pdf-preview/77534",totalDownloads:212,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 18th 2021",dateReviewed:"June 17th 2021",datePrePublished:"July 13th 2021",datePublished:null,dateFinished:"July 13th 2021",readingETA:"0",abstract:"Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77534",risUrl:"/chapter/ris/77534",signatures:"Hamdan Omar, Thirupathi Rao Narayanamoorthy, Norsheilla Mohd Johan Chuah, Nur Atikah Abu Bakar and Muhamad Afizzul Misman",book:{id:"10854",type:"book",title:"Remote Sensing",subtitle:null,fullTitle:"Remote Sensing",slug:null,publishedDate:null,bookSignature:"Prof. Dr. Maged Marghany",coverURL:"https://cdn.intechopen.com/books/images_new/10854.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-545-2",printIsbn:"978-1-83969-544-5",pdfIsbn:"978-1-83969-546-9",isAvailableForWebshopOrdering:!0,editors:[{id:"96666",title:"Prof.",name:"Dr. Maged",middleName:null,surname:"Marghany",slug:"dr.-maged-marghany",fullName:"Dr. Maged Marghany"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Forests in Malaysia",level:"2"},{id:"sec_2_2",title:"1.2 Reducing emission from deforestation and forest degradation, and forest conservation (REDD+) in Malaysia",level:"2"},{id:"sec_3_2",title:"1.3 The Paris agreement",level:"2"},{id:"sec_4_2",title:"1.4 Nationally determined contribution (NCD)",level:"2"},{id:"sec_5_2",title:"1.5 Carbon offset initiative",level:"2"},{id:"sec_7",title:"2. Methodological framework",level:"1"},{id:"sec_7_2",title:"2.1 Activity data",level:"2"},{id:"sec_8_2",title:"2.2 Production of seamless mosaic, time series Landsat images",level:"2"},{id:"sec_9_2",title:"2.3 Image classification",level:"2"},{id:"sec_10_2",title:"2.4 CO2 emissions calculation",level:"2"},{id:"sec_12",title:"3. Results and discussion",level:"1"},{id:"sec_12_2",title:"3.1 Changes of forest cover",level:"2"},{id:"sec_13_2",title:"3.2 Carbon stock of forests in Malaysia",level:"2"},{id:"sec_14_2",title:"3.3 CO2 emission from deforestation",level:"2"},{id:"sec_15_2",title:"3.4 CO2 emission in forest land remaining forest land",level:"2"},{id:"sec_17",title:"4. Requirements to offset the emission",level:"1"},{id:"sec_18",title:"5. Conclusion",level:"1"},{id:"sec_19",title:"Acknowledgments",level:"1"},{id:"sec_22",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Brendan M, Cyril FK, Heather K, William RM, Houghton RA., Russell AM, Hole D, Sonia H. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitigation and Adaptation Strategies for Global Change, 2020. 25: 763–787'},{id:"B2",body:'Lu D. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 2006. 27(7): 1297-1328'},{id:"B3",body:'Nurul Ain MZ, Zulkiflee AL. Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling. Geocarto International, 2017. 32(7): 701-716'},{id:"B4",body:'Hamdan O, Muhamad Afizzul M, Abd Rahman K. Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia. Applied Sciences. 2017. 7, 675'},{id:"B5",body:'Pearson TR, Brown S, Murray L, Sidman G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance and Management, 2017. 12, 3'},{id:"B6",body:'IPCC. Global Warming of 1.5°C. An IPCC Special Report [Internet]. 2018 Available from: https://www.ipcc.ch/sr15/ [Accessed: 2020-07-24]'},{id:"B7",body:'UNFCCC. Malaysia\'s Submission on Reference Levels for REDD+. [Internet]. 2020. Available from: https://www4.unfccc.int/sites/submissions/INDC [Accessed: 2020-02-12]'},{id:"B8",body:'Ministry of Natural Resources and Environment Malaysia (NRE). A Roadmap of Emissions Intensity Reduction in Malaysia. 2014. Putrajaya, Malaysia'},{id:"B9",body:'Lopez G, Laan T. Biofuels—At what cost? Government Support for Biodiesel in Malaysia; International Institute for Sustainable Development: Geneva, Switzerland [Internet]. 2008. Available from: https://www.iisd.org/gsi/sites/default/files/Final_Malaysia_2.pdf [Accessed: 2020-06-03]'},{id:"B10",body:'Verified Carbon Standard (VCS). The VCS Program. [Internet]. 2020. Available from: https://www.offsetguide.org/ [Accessed: 2020-04-20]'},{id:"B11",body:'Chris W. Carbon offets. [Internet]. 2016. Available from: https://www.explainthatstuff.com/carbonoffsets [Accessed: 2020-02-22]'},{id:"B12",body:'Crouzeilles R, Alexandre N, Bodin B, Beyer HL, Guariguata MR, Chazdon RL. How to Deliver Forest Restoration at Scale: Recommendations for unlocking the potential of the most cost-effective way to restore forests in the fight against climate change and biodiversity loss. Conservation Letters. 2019'},{id:"B13",body:'Qiu S, Zhu Z, He B. Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery. Remote Sensing of Environment, 2019. 231: 111205'},{id:"B14",body:'IPCC. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. [Internet]. 2019. Available from: https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/ [Accessed: 2020-08-16]'},{id:"B15",body:'Ministry of Natural Resources and Environment Malaysia (NRE). National REDD Plus Strategy. 2018. Putrajaya, Malaysia'},{id:"B16",body:'Hamdan O, Norsheilla MJC, Ismail P, Samsudin M, Wan Abdul Hamid Shukri WAR, Azmer M. Forest Reference Emission Level for REDD+ in Pahang, Malaysia. FRIM Research Pamphlet No. 141, 2018. 97 pp'},{id:"B17",body:'Ministry of Energy, Science, Technology, Environment and Climate Change (MESTECC). Malaysia, Third National Communication and Second Biennial Update Report to the UNFCCC. 2018. Putrajaya, Malaysia'},{id:"B18",body:'Omran A, Schwarz-Herion O. Deforestation in Malaysia: The Current Practice and the Way Forward. In: Omran A., Schwarz-Herion O. (eds) Sustaining our Environment for Better Future. 2020. Springer, Singapore'},{id:"B19",body:'Butler R. Malaysia has the world’s highest deforestation rate, reveals Google forest map. [Internet]. 2013. Available from: http://news.mongabay.com/2013/1115-worlds-highest-deforestation-rate [Accessed: 2020-07-21]'},{id:"B20",body:'Li W, Fu D, Su F, Xiao Y. Spatial–Temporal Evolution and Analysis of the Driving Force of Oil Palm Patterns in Malaysia from 2000 to 2018. ISPRS Int. J. Geo-Inf. 2020. 9: 280'},{id:"B21",body:'Chave J, Réjou-Méchain M, Búrquez A. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Change Biol, 2014. 20: 3177-3190. doi:10.1111/gcb.12629'},{id:"B22",body:'Chave J, Andalo C, Brown S. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 2005. 145, 87–99'},{id:"B23",body:'Kho LK, Jepsen MR. Carbon stock of oil palm plantations and tropical forests in Malaysia: A review. Singapore Journal of Tropical Geography, 2015. 36: 249–266'},{id:"B24",body:'Syafinie AM, Ainuddin AN. Aboveground Biomass and Carbon Stock Estimation in Logged-Over Lowland Tropical Forest in Malaysia. International Journal of Agriculture, Forestry and Plantation, 2015. 1: 1- 14'},{id:"B25",body:'Deutch J. Is Net Zero Carbon 2050 Possible? Joule, 2020. 4: 2237–2243'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Hamdan Omar",address:"hamdanomar@frim.gov.my",affiliation:'
Forest Research Institute Malaysia (FRIM), Malaysia
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Oxidative stress has been considered a major contributory factor to the diseases. They are mainly derived from oxygen (reactive oxygen species (ROS)) and nitrogen (reactive nitrogen species (RNS)) and are generated in our body by various endogenous systems and exposure to different physicochemical conditions or pathophysiological states. Free radical damage to protein can result in loss of enzyme activity. There are epidemiological evidences correlating higher intake of components/foods with antioxidant abilities to lower incidence of various human morbidities or mortalities. The sources and origin of antioxidants which include fruits and vegetables, meats, poultry, and fish were treated in this study. The classification and characteristics of antioxidant, its measurements and level in food and free radicals, were also documented. The chemistry of antioxidants which includes chain reactions, molecular structures, food antioxidants and reaction mechanisms, biochemical activity, therapeutic properties, and future choice of antioxidants was reported in this review.",book:{id:"8323",slug:"traditional-and-complementary-medicine",title:"Traditional and Complementary Medicine",fullTitle:"Traditional and Complementary Medicine"},signatures:"Arun Rasheed and Rinshana Fathima Abdul Azeez",authors:[{id:"277345",title:"Dr.",name:"Arun",middleName:null,surname:"Rasheed",slug:"arun-rasheed",fullName:"Arun Rasheed"}]},{id:"66587",title:"Introductory Chapter: Plant Extracts",slug:"introductory-chapter-plant-extracts",totalDownloads:1781,totalCrossrefCites:3,totalDimensionsCites:3,abstract:null,book:{id:"8593",slug:"plant-extracts",title:"Plant Extracts",fullTitle:"Plant Extracts"},signatures:"Aman Dekebo",authors:[{id:"191684",title:"Dr.",name:"Aman",middleName:null,surname:"Dekebo",slug:"aman-dekebo",fullName:"Aman Dekebo"}]}],onlineFirstChaptersFilter:{topicId:"199",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:30,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. 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Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. from Integral University, Lucknow, India, with his work titled ‘Development and evaluation of silymarin nanoformulation for hepatic carcinoma’. Currently, he is an Assistant Professor of Pharmaceutics, at the Faculty of Pharmacy, Integral University. He has been teaching PharmD, BPharm, and MPharm students and conducting research in the novel drug delivery domain. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than twenty-four original journal articles, two edited books, four book chapters, and several scientific articles to his credit. He is a member of the American Association for Cancer Research, the International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"333824",title:"Dr.",name:"Ahmad Farouk",middleName:null,surname:"Musa",slug:"ahmad-farouk-musa",fullName:"Ahmad Farouk Musa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333824/images/22684_n.jpg",biography:"Dato’ Dr Ahmad Farouk Musa\nMD, MMED (Surgery) (Mal), Fellowship in Cardiothoracic Surgery (Monash Health, Aust), Graduate Certificate in Higher Education (Aust), Academy of Medicine (Mal)\n\n\n\nDato’ Dr Ahmad Farouk Musa obtained his Doctor of Medicine from USM in 1992. He then obtained his Master of Medicine in Surgery from the same university in the year 2000 before subspecialising in Cardiothoracic Surgery at Institut Jantung Negara (IJN), Kuala Lumpur from 2002 until 2005. He then completed his Fellowship in Cardiothoracic Surgery at Monash Health, Melbourne, Australia in 2008. He has served in the Malaysian army as a Medical Officer with the rank of Captain upon completing his Internship before joining USM as a trainee lecturer. He is now serving as an academic and researcher at Monash University Malaysia. He is a life-member of the Malaysian Association of Thoracic & Cardiovascular Surgery (MATCVS) and a committee member of the MATCVS Database. He is also a life-member of the College of Surgeons, Academy of Medicine of Malaysia; a life-member of Malaysian Medical Association (MMA), and a life-member of Islamic Medical Association of Malaysia (IMAM). Recently he was appointed as an Interim Chairperson of Examination & Assessment Subcommittee of the UiTM-IJN Cardiothoracic Surgery Postgraduate Program. As an academic, he has published numerous research papers and book chapters. He has also been appointed to review many scientific manuscripts by established journals such as the British Medical Journal (BMJ). He has presented his research works at numerous local and international conferences such as the European Association for Cardiothoracic Surgery (EACTS) and the European Society of Cardiovascular Surgery (ESCVS), to name a few. He has also won many awards for his research presentations at meetings and conferences like the prestigious International Invention, Innovation & Technology Exhibition (ITEX); Design, Research and Innovation Exhibition, the National Conference on Medical Sciences and the Annual Scientific Meetings of the Malaysian Association for Thoracic and Cardiovascular Surgery. He was awarded the Darjah Setia Pangkuan Negeri (DSPN) by the Governor of Penang in July, 2015.",institutionString:null,institution:{name:"Monash University Malaysia",country:{name:"Malaysia"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. 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Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. 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Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/19383",hash:"",query:{},params:{id:"19383"},fullPath:"/chapters/19383",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()