The Murdoch University PAS meter program for assignable registers and time of use logging (TOU).
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Traditionally meters have been used for revenue recovery on utility scale distribution networks with meters read manually on a monthly basis. Meters and data only accounted for a single direction of energy flow. Smart meters measure a spectrum of energy consumption information at intervals and communicate the information to the utility remotely. They are effective tools for gathering of data, monitoring long-term trends and responding short term or live events or incentives mechanisms such as demand-side management both locally and remotely. New technology in smart meters enables the measurement of two-way energy flows [1]. Monitoring of data allows for a higher degree of control on energy use, which will be important in the emerging post carbon urban environments [2] with the impact of distributed renewable energy generation, battery storage and electric vehicles as well as feedback on energy use patterns to consumers.
\nBuildings and districts are an appropriate focus for smart metering infrastructure in the urban environment. While properties and buildings have traditionally been metered for revenue recovery purposes, energy management of these buildings has not been available. In the way we account for money, we should account for energy; energy in its own right carries a direct cost with it to the end user. Along with carrying a cost, energy also carries carbon emissions. Smart meters are a vital component to the making and management of post carbon urban environments [3].
\nSmart meter requires accessible communications for robust data availability and storage to enable advanced architecture and applications. Preferably, it is best to use a smart meter with multiple communication ports (at a minimum Modbus, TCP/IP, IR). Smart meters need to be installed and calibrated to national peak measurement body standards (e.g. in Australia, the National Measurement Institution or NMI) to enable utility billing. The smart meter should have a configurable program to optimise data measured and collected to meet the requirements of application (i.e. within the South West Interconnected System (SWIS), demand intervals have been extended from a 15-min period to a 30-min period).
\nThe meter should have the ability to bring in additional data sources (i.e. water, air, gas, electric and steam, WAGES) through either direct pulse inputs or through a local wireless communication protocol such as Zigbee and its appropriate sub-meter hardware. In addition, smart meters should have configurable on board logging that enables backup of meter data locally if communications or power failures occur, which can cause loss of data and revenue. Meters and data collection needs to adhere to legislation on data collection and usage. Consumers’ data are typically protected, and permission is required to use it (check your local laws and regulations to ensure compliance).
\nIn this chapter, we will describe smart meter architecture and management systems for buildings and district energy and water use, and then, we will provide two case studies as to how this is applied in practice. The latter is done with specific industry focus and products.
\nSmart meter architecture needs multiple levels providing multiple points of reconciliation. Hierarchies should inform the levels of consumption and more importantly demand within the distribution network and for total and isolated services (i.e. Internal/External Lighting, Mechanical services, power etc.). Smart meters should separately meter all generation inputs into the system (i.e. renewable energy, Co/Tri-generation). Using hybrid communication architecture will bring in meters at the lowest cost and highest data reliability. (Direct meter to TCP/IP; Meter via Modbus to gateway to TCP/IP; Gateway to enable localised data logging [where proprietary meter software is not used to pull data logs direct from the meter] and pass through of centralised/decentralised [i.e. cloud based] software packages to contact meters/gateway to collate data).
\nAccuracy of data is important for smart metering metrology characteristics. International standards assist to ensure accurate measurements. Varying reference conditions such as low loads, poor power factor and/or verification of meter accuracy affect performance outcomes. Accuracy standards such as Class 0.5S to AS/IEC 62053-22 improve levels of reliability thus ensuring confidence in measurements in varying conditions.
\nThe use of smart meters can leverage off existing WiFi technology networks to reduce costs. GSM technology is expensive and has ongoing running costs (bring data back over TCP/IP backbone; and architecture complies with BEEC/NABERS & Green Star/BREEAM/LEED) (Figure 1).
\nLarge building and campus electricity smart meter architecture.
Centralised energy management systems (EMSs) are used to support the reporting of energy consumption monitoring, a primary requirement as a component of the management cycle for feedback and continual improvement. EMSs directly support the billing and revenue recovery from end-use consumers. It allows customers to understand where the costs are allocated from their bill and therefore where energy and cost savings can be made. EMSs assist the implementation of demand-side management, often with alarms linked to either a manual intervention or more preferable automated actions implemented through the building management system (BMS).
\nDemand and particularly the capacity charge of the bill is the most effective way to reduce energy consumption (predictive based upon historical consumption and ambient conditions particularly for the Perth climate as HVAC often accounts for ∼40% of a sites consumption). EMSs can set benchmarks for energy savings, enable forecasting, ensure benchmarks are achieved and energy efficiency measures are tracked and continue to perform as installed and commissioned.
\nDashboards are typically used as a primary method to advise building occupants on a live basis when demand-side management is in effect and what measures are being undertaken. This is particularly important to manage stakeholder expectations during these critical demand management events where large amounts of money can be saved on capacity charges. The capacity charges are price signals from the utilities to reduce peak demand of the site when the grid is at maximum demand.
\nThere are several key demand-side management measures to be communicated to building occupants. One example is reducing the lighting demand in a staged process, with the lowest level not to significantly affect work and in line with occupational health and safety. Another example is expanding set point ranges for thermal comfort to reduce pressure on the chilled water supply and the need to turn on additional chilled water supply to cope with escalating demand. A change in the global set point of buildings of 2–3°C during demand events when the external ambient temperature is >38°C for a period of 2–5 h would likely not negatively affect occupant comfort. An additional measure would be the shutting of a building CHW supply or air-conditioning entirely for 30 min periods. The Australian National University in Canberra using SATEC meters have successfully trialled this technique to manage the load. They advised that ambient temperatures did not escalate and stayed within 1°C. An important benefit is reduced fan and pump power and energy use of mechanical services. Buildings could run on a cyclical demand management process, depending upon the event in question. Another example is the pre-cooling of buildings before 8 am during periods of consecutive 38°C ambient temperatures. (See the CIBSE Energy Efficiency in Buildings for more tested ideas as well as for pros and cons) [4].
\nSmart meters are integral to integrated and distributed renewable energy systems in buildings. If an existing building is considering renewable energy, smart meter data can be analysed to size the system to produce the greatest benefits to the owner/occupier. Demand profile in the form of 15 or 30 min interval data can be used to determine seasonal consumption patterns, base and peak loads. Renewable energy production can then be modelled on an equivalent 15 or 30 min interval data and compared on a temperature and humidity normalised basis to the predictive model to gauge the optimal pairing of renewable production to consumption behaviour.
\nWhile smart meters provide accurate data, in general unless the occupier is planning on going carbon neutral or implementing battery storage, renewable energy should only ever be sized to 70–75% of average daily load on the normalised comparison data. This provides a buffer to enable future energy efficiencies to reduce the overall consumption of the premises. However, this needs to be optimised with the degradation of PV and other renewable energies over the life cycle of the system, typically 20–30 years for PV.
\nWith the introduction of energy storage, buildings are now able to more effectively utilise their renewable energy production and target energy generated to best meet their own needs. The largest benefit to large consumers is the ability to target market signals such as demand-side management programs and to reduce their own capacity charges. Other benefits offered include the ability to store the energy rather than export it to the grid often for little or no rebate from the utility supplier. Meters form an integral part in the monitoring and management integrated renewable energy and storage systems. Live data inform all decision making and supports tuning of these management systems to improve performance and reduce both carbon emissions [5] and consumers’ bills.
\nTraditional residential metering has involved an electricity, gas and water meter, which is read on a monthly, bimonthly, quarterly or biannual basis. This standard of data does not provide sufficient detail of consumption patterns to enable energy or water management [6]. It is vital that utilities and embedded network owners provide consumers access to live and/or 15–30 min interval data [7]. The data should be easily accessible either by an in-home display (IHD), mobile application or website [8].
\nConsumers are then informed on a real-time basis of how their behaviour contributes to their utility bills and are empowered to manage their own utility bills [9]. In Australia, residential electricity prices have increased 60–80% over the past 10 years, which has put extra additional strain on consumers’ budgets, in particular pensioners and low-income households, the most vulnerable to fluctuations in the community. Data should be easily accessible and easily configurable at the tip of a finger for consumers, particularly for those who are not technology literate or who do not have access to the internet, a computer or smart phone. In this latter case, affordable IHDs would be the best solution [8].
\nUtilities and energy service companies can then configure the data to provide useful feedback to consumers in water and energy efficiency and savings programs. This can be done through the IHDs, online programs, smart phone apps or over-the-phone coaching programs [3].
\nThe typical structure, applications and interactions of residential smart metering are laid out in Figure 2.
\nTypical residential smart meter architecture and applications.
In this section, we introduce two case studies that show how the above principles are applied in practice at two very different locations. Case study 1 is in Perth, Western Australia on the Murdoch University Campus where the Carbon View Smart Meter Project was implemented and provides a commercial/institutional example. The second case study is described in Central Park, Sydney, New South Wales, Australia. This is an inner city mixed use development and this provides a residential apartment example.
\nIn both case studies, the same types of meters have been used, being the SATEC EM 133 smart meter, in very different applications. The difference at each site is the programming. Although at Murdoch University the SATEC meters were programmed for a 15-min demand interval and one decimal place (to account for large users), and the meters at Central Park were programmed for a 30-min demand interval and four decimal places for the smaller residential users. For example, the latter provides significant figures for the water meters down to liters. This is specifically because of the type of pulse output from the water meters at this site.
\nMurdoch University has approximately 135,000 m2 gross floor area (GFA) across 165 buildings on the South Street Campus. The university distributes electricity from the western power grid through two gate feeders through two interconnected HV ring mains at 22 kV with no step down. Each of these independent feeders is metered by western power. The internal distribution network is broken down into two distinct electrical ring mains with a total of 26 sub-stations, which reduce HV (high voltage) to LV (low voltage). The locations and layout of the HV distribution network can be seen in Figure 3. Electricity is then reticulated out to the end use either directly or through building’s internal LV distribution network at 240/415 V.
\nMurdoch University Typical HV Schematic Distribution Single Line Diagram, each ring main has an open point not represented in the diagram which is moved to balance the ring and to enable maintenance.
Murdoch University has recently embarked upon a smart meter roll out to upgrade its existing varied and aging meter embedded network infrastructure. Murdoch University undertook a review of its electricity metering embedded network and found that its old mechanical meters were at end of life and required replacement. After external consultation by JD Shute Pty Ltd, the SATEC EM133 meter was chosen as the universities’ meter of choice for all installations as a standardised product. The university initially purchased 200 m to be rolled out from 2015 to replace existing end of life metering which included central chilled water plant and major tenancies.
\nThe SATEC EM133 meter.
A significant amount of work was put in through the consolation phase on the meter selection to ensure that the meter had the capability to meet the needs of the university. One of the primary governing factors as to the choice of the SATEC smart meter was the need for a NMI-approved DIN-rail mount meter. At the time of selection, the SATEC EM133 meter was the first meter on the market available, which met the criteria [10].
\nInstantaneous sub-cycle meter analysis and phase rotation vector diagram.
The SATEC EM133 meter, Figure 4, provides the university with flexibility as to what information can be programmed, stored and logged locally and at what frequency as well as live Modbus values, which can be pulled from the meter remotely by an EMS. The SATEC meter is provided with free software that provides access to program, interrogate and download data logs. This licence-free power analysis software (PAS) enables a sub-cycle snapshot and phase diagram to be produced (Figure 5), which is very useful for commissioning and electrical investigations.
\nThe variety of existing meters installed at Murdoch University.
It is recommended that within DBs that 0–100Amp direct meters are placed between the main switch and the chassis or end use rather than on the source side of the main switch as the entire DB including the other services will have to be isolated should the meter fail or require replacement. Therefore, this configuration of direct connect in line metering significantly reduces the impact and stakeholder engagement for facilities managers during shutdowns.
\nThe alternative CT type meters while they take up a significantly larger amount of space within the DBs due to the installation with Class 0.5 s CTs allow for installed test links to be isolated and the power from the source to the measured load to remain on while the meter is being replaced. CTs are also required for measurement of loads >100 A. The university’s choices of CTs for CT type installations are the:\n
Type ‘S’, 200/5 Amp CT 5VA Burden SCT200-IPD; and,
Type ‘T’, 800/5 Amp CT 15VA Burden TCT800-IPD.
These CTs are Class 0.5ME2 and 0.5S extended range CTs, which enable the SCT200 CTs to be installed on measurement application up to 400 A loads and the TCT800 CTs to be used on applications up to 1600 A. With the use of extended range CT\'s, the smart meters are required to be of the extended range design. SATEC\'s EM133 supports extended range CT\'s thus compliance to 5A/10A style CTs is assured. The majority of metering hardware is designed as 5A/6A which would not be suitable for extended range CT\'s.
\nThe third alternative installation methodology used at the university is the SATEC EM 133 meter installed with high-accuracy current sensors (HACS) as the installed equipment is NMI compliant as a Class 1 system. This meter configuration has only recently become available since NMI installation and provides the additional benefit to the university particularly with the ability to use HACS split core CTs on existing electrical infrastructure rather than having to disconnect to slide the IPD CTs over the cables and then re-terminate the cables. The HACS CTs range from 5 to 1200 A with various split core and solid core configurations. HACS CT installations with the EM 133 meter are NMI compliant up to 200 m with the CT wiring only transmitting 40 mA.
\nWhen developing a metering architecture and strategy for the existing campus building, it was important to be flexible yet also future proof the strategy for the expansion within the campus. While a significant amount of planning and stakeholder engagement with academics and industry experts took place, changes to the base program still occurred post-installation. The original program had four decimal places and was meant for residential applications, as the base program for the SATEC EM133AR was designed for the Central Park installation measuring water down to 0.0001 kWh intervals. This problem was noticed when regular meter readings of large consumers were lower than the previous read. It was then discovered through the use of the 30-min interval logging that the meter had been clocking at 9999.9999 kWh, which in some instances was every few days. Thanks to historical log files because no data were compromised nor lost in the process.
\nThe MU program has since been tailored for commercial applications and reduced to 1 decimal place. As a result, the largest consumer on campus will clock in around 25 years time rather than have constant clocking in some cases every 2–3 days with large consumers. Choosing this number of decimal places does impact pulse counted units such as water and gas. Water will, therefore, have the lowest resolution down to 0.1, which will equate to 100 L. Gas likewise will clock at 0.1 of an m3.
\nInvestigations were undertaken to determine the most cost effective and robust connection for the automated meter reading software. Initially, the plan to bring metering online to the BMS utilising the BMS backbone through upgraded PLC in the form of Schneider Automation Servers was not feasible as there was a limitation to the number of Modbus points that could be logged. Another finding was that the automation server would not function as a gateway device to allow other software packages to contact the meter. Therefore, the license free and very useful commissioning and meter management tool PAS could not be used remotely for commissioning, meter data log reclamation, programming and detailed engineering analysis through its sub-cycle waveform capture.
\nThe university meter program supports the functions in Table 1 in relation to the eight assignable registers within the meter as well as providing time-of-use (TOU) data in line with the Western Australian SWIS grid on peak and off peak tariff times. That TOU billing is represented below:\n
On peak—Monday to Friday 8 AM to 10 PM—Tariff 2 (displayed as T2 on meter display); and
Off peak—Weekends and after hours—Tariff 1 (displayed as T1 on meter display).
The information in Table 1 is also represented on the meter display and logged daily at the following configurations. Current totals for the target registers are displayed in the meter for all time. The previous and second previous read values at the end of the daily, weekly, monthly and quarterly period are displayed along with the period use consumption values for each target register as sub-menus on the meter display.
\nMeter program registers | \nUnit | \nSource input | \nTarget | \n
---|---|---|---|
Total electricity | \nkWh | \nkWh import | \nRegister 1 | \n
Phase 1 | \nkWh | \nkWh L1 import | \nRegister 2 | \n
Phase 2 | \nkWh | \nkWh L2 import | \nRegister 3 | \n
Phase 3 | \nkWh | \nkWh L3 import | \nRegister 4 | \n
Kvarh | \nKvarh | \nKvarh import | \nRegister 5 | \n
kVAh | \nkVAh | \nkVAh import | \nRegister 6 | \n
Gas | \nm3 | \nDI1 | \nRegister 7 | \n
Water | \nm3 | \nDI2 | \nRegister 8 | \n
The Murdoch University PAS meter program for assignable registers and time of use logging (TOU).
TCP/IP was then considered against a typical Modbus RS485 meter communication connection to a gateway. While the university would have liked to go direct with the SATEC meter TCP/IP module onto its fiber backbone, the cost per module and per switch was high enough for the university to choose to utilise a hybrid solution to maintain cost-effectiveness across the smart meter roll out.
\nThe university chose the Schneider Com X 510 gateway, which can log up to 30 m, but has been limited to around 12 m due to the number of Modbus points logged and the high polling frequency required. The university is still working on finding the limit to the number of meters per gateway and total distance for RS485 to optimise cost-effectiveness and ensure that live polling is equal to or less than 500 ms. This is particularly the case as the SATEC EM 133 driver being viewed and logged on the Schneider Power Manager Software has not been undertaken before. One of the biggest benefits of choosing the Schneider Com X 510 gateway other than the competitive price was that the software is intrinsically linked and viewed on MU’s smart structure building operations (SBO) despite being installed on a standalone server and on a secure VLAN. Therefore, SBO works as a high-level Web integration package for the BMS, EMS and, in the future, the lighting controls of the university, which will all be integrated into a demand-management page on the BMS.
\nInstalled Embedded Network Installations in 2015 at Murdoch University: from top left and left to right each row (a) 9× SATEC EM133 meters installed on main distribution board in one building, (b) chart showing output from the Schneider Electric COM510 energy server webpage, (c) showing the 3× energy servers with ethernet and modbus connections, (d) and (e) showing the EM133 smart meter installation using IPD CTs, test links and voltage fuses.
Building design and specification of meters and electrical installations have varied over 40 years as building codes have changed leading to variation in metering installed across the campus, which can be seen in Figure 6. Within buildings, electrical risers, disparate DBs and in slab conduit can limit the ability to run Modbus RS485 cable in an open loop to the nearest Com X 510 device. As a result, meters in this situation are installed with the SATEC EM133 Ethernet module, which can leverage off of the universities’ Ethernet network; the Ethernet module that ‘bolts’ onto the side of the SATEC EM133R meter. While this module increases the installation cost of the meter significantly, the meter can still be logged at a high level either by the Com X 510 or at the EMS itself using 4 TCP Sockets. In some locations, the Com X 510 gateways have leveraged off of the universities’
PV and renewable energy metering applications will always be connected through the Ethernet module to ensure the highest resolution for research and to ensure automated protection mechanisms are not affected by network traffic or WIFI outages.
\nTypical Murdoch University Embedded Meter Network SLD, automated meter reading system and theoretical energy management framework. The arrows depict metered energy flows and type. The 5 level and end use breakdown is important for energy management, NABERS and Green Star.
The embedded network meter hierarchy in Figure 1 has been adapted and represented along with the individual SLD-type diagram in Figure 8, which also displays the communications methodology in place at Murdoch University. The embedded network diagram also shows the breakdown of end use services in line with NABERS and Green Star requirements as well as the interactions between the meters, the EMS and the BMS to form the management feedback loop for the control of consumption and internal and external conditions for occupation and operation of the facility.
\nThe outcome is the system that has live consumption information, internal building feedback from equipment and sensors and building and tenant occupant feedback and room booking information. The deployment of sensors as part of this system must be done in a thoughtful and strategic manner to satisfy feedback requirements and to optimise data collection for this purpose [10]. In the end, this has leveraged existing systems to enable the energy and water efficient operation of the Murdoch University facilities at the lowest operational cost and environmental impact.
\nCentral Park is an urban village located in Chippendale, Sydney, New South Wales (NSW), Australia, on the former old Kent Brewery site. The $2 billion mixed use development comprises two 5 Star Green Star-rated commercial and residential towers. The SATEC EM133AR meter [11] was chosen by the developer as the best fit for the project. The requirements were for a DIN-mounted Class 0.5 s, according to AS/IEC 62053-22, direct connect meter to be installed within each apartment for the monitoring of multiple electrical sources including hot/cold water usage [12].
\nCentral Park operates a ‘tri-generation system’ and a membrane bio-reactor for the advanced water treatment (recycled water) system to support self-sufficiency and sustainable energy and water consumption. Therefore, fan coil units (FCU) are used instead of individual air-conditioning systems for each residential unit. The objective of measuring cold water and hot water is to allocate the heating/chilling cost to each tenant based on their consumption. The EM133AR was deployed to measure 3x single-phase supplies in each apartment, as well as the cold/hot water. Overall, each meter supports 7x sub-meters per apartment (Figure 9):\n
Total electricity;
Light + power;
FCU #1;
FCU #2;
FCU #1 + FCU #2;
Cold water; and
Hot water.
An example of an alternative raw data configuration inputs to the meter and configuration to support the 7x sub-meters per apartment is represented below:\n
Phase 1—light and power (kWh);
Phase 2—fan coil unit #1 (kWh);
Phase 3—fan coil unit #2 (kWh);
Phase 2 + 3—fan coil unit #1 + #2 (kWh);
Digital input 1—cold water (kL);
Digital input 2—hot water (kL);
Digital input 3—recycled or other type water (kL); and
Digital input 4—gas meter (m3).
Meters installed at Central Park: (a) typical complete installation of meters for gas, electricity, hot water, cold water, (b) close up view of the gas and water meters, (c) showing electricity meter EM133AR installed in residential DB with Ethernet output to building IP backbone.
SATEC developed an algorithm specifically for the Central Park project based on the Green Star principles for daily/weekly/monthly reporting. SATEC also added ‘quarterly’ so that the EM133AR profiles for ‘electricity and water’ can easily be displayed as ‘daily/weekly/monthly/quarterly’ and accessible at the meter and at the Modbus/TCP/IP interface levels. The meter was programmed to provide the information over 3× periods, thus available on the meter is profiled:\n
Today, yesterday and day prior to yesterday;
This week, last week and week prior to last week;
This month, last month and month prior to last month; and
This quarter, last quarter and quarter prior to last quarter.
The methodology of the in-depth meter program above was designed to comply with the Green Star’s Metering and Monitoring Credit. The credit specifies that the Green Star requirements are in line with the CIBSE TM39 Building Energy Metering [4] for best practice in the design of energy metering and sub -metering. Utility meters must meet metering guidelines under the weights and measures legislation, as outlined under the current national measurement regulations.
\nNon-utility meters (including sub-meters) must follow the same requirements to those described in the most current validating non-utility meters for NABERS ratings protocol, issued by the NSW Office of Environment and Heritage.
\nThe Green Star credit requires all residential premises and tenancies to have their own utility grade meter and access to the meter reliant on accuracy standards. However, points are not awarded to the project without an automated monitoring system installed, which, as a minimum, must be capable of (Green
One Central Park & Park Lane design topology.
Collecting data from all meters;
Alerting to missing data due to failures;
Recording and processing of data on energy use or water consumption at user adjustable intervals;
Raising an alarm when the energy or water use increases beyond certain parameters and automatically and instantly issue an alert the facilities manager;
Providing a breakdown of the information by building system (mechanical, electrical, etc.), or by space (or by tenanted floor);
Including the consumption water or energy, the load versus time (load profile), and the power factor (in the case of energy); and
Producing, as a minimum, a quarterly report that is automatically emailed to the facilities manager responsible for the building.
The SATEC program outlined above provides a virtual utilisation type technique or a distributed logic approach reducing processing power from software platforms particularly for the daily/ weekly/ monthly/ quarterly element. While the program attributes in the meter offer these convenient summaries, real-time data are available through the Modbus register map along with asset information assisting in managing metering assets according to ISO 55000 (Figures 10–12).
\nCentral Park—Proposed Building 8 Solutions (Mechanical, Electrical, Hydraulic Services).
SATEC proposed typical typology for Smart Metering for Central Park Sydney (SATEC EM133AR ‘Smart Metering’ for Central Park Building 8 Rev 2 04).
The purpose of the embedded network is to allow the developer/owner to have increased financial revenue from their infrastructure. This trend is becoming more popular, as developments and owners are becoming more aware of the benefits that can be gained by owning and operating their utility networks internal to their facilities. This is particularly the case for multi-unit residential developments. SATEC (Australia) Pty Ltd advised that as of December 2015 the embedded network is still not fully operational at Central Park, as the billing provider was not fully utilising the functionality. SATEC (Australia) Pty Ltd further noted that it was possible for them to fully utilise the system, but there were some managerial issues still to overcome.
\nCommon area metering is now more used widely utilised in commercial buildings, mainly due to NABERS and Green Star. Once NABERS became mandatory under the commercial building disclosure legislation at the point of sale, lease or sub-lease for office space greater than 2000 m2, metering sales and installations increased dramatically according to SATEC (Australia) Pty Ltd. Comparing to New Zealand, where NABERS is voluntary, fewer owners install embedded network metering systems than Australia. The CBD legislation is under review by the federal government with the recommendation that the threshold of 2000 m2 be reduced to 1000 m2, which will further drive the installation of these embedded meter networks from A grade buildings into B and C grades building stock within Australia. The Green Building Council has linked NABERS and Green Star ratings directly to the improvement in return on assets calculations, as specifically expressed in a meeting in Christchurch.
\nWhile NABERS energy and water assessments require 12 months of utility meter readings for the building for a whole building reading, a base building rating requires 12 months of tenant meter data to subtract from the buildings total consumption. The outcome is the base building consumption, which is the standardised based upon the climate using the postcode, net lettable area (NLA) and occupancy data to provide the final NABERS energy base building rating.
\nAlternatively, Green Star requires under Section 6.0.1 Metering Distinct Uses or Floors of its Design & As Built v1.1 rating tool that metering shall be provided to allow for monitoring of the relevant areas or functions of the project. Stating that in most cases floor-by-floor metering will suffice if the entire floor has a single use and that if a floor has multiple uses, the different uses shall be metered. Therefore, should a floor be composed of office space and a seminar room, both spaces shall be separately sub-metered. If a floor has multiple tenants or owners, each tenancy or property shall also be separately sub-metered.
\nGreen Star also has specific requirements where an energy load for a single item exceeds 5% of the total energy use for the building, or 100 kW, it must be independently metered. Supplementary equipment can also be installed on the same measured circuit as the major use item. However, the total combined energy use of any systems connected to the major use item must not contribute more than 10 kVA to the overall energy use.
\nExamples of systems that are considered to be common uses for energy are provided by the Green Star rating tool, but are not limited to:\n
Chillers;
Air handling units, fans and humidification;
Server and computer equipment;
Water reuse systems;
Kitchen plant and equipment;
Specialist lighting for stages, etc.; and
Specialist equipment.
Embedded networks within Australian buildings will gradually change, with the market competition reviews being conducted by the Australian Energy Market Commission (AEMC) and the Western Australia (WA) stand-alone grid known as the South West Interconnected System (SWIS) being harmonised into the national management framework under changes being made out of the WA Electricity Market Review in particular.
\nThe outcomes of that will be increased competition in metering to be more likely, and advanced metering will be used instead of low-cost metering. The ability for consumers to ‘opt-out’ of the embedded network, or meter churn, will be a big issue in the future. One way of overcoming this is to leverage improved communication such as Ethernet (TCP/IP). Ethernet is a ‘multi-master’ communications platform and widely used throughout many industries.
\nMetrology measuring devices have improved from RS232 communications, which are an example of 1:1 relationships. The communications or RS232 are most demonstrated in networks whereby remote communications is leveraged by modems supporting—2/3G, 4G, GPRS, etc. Metering encompasses further complicated communication networks such as RS485 (e.g. daisy-chain) topology configurations. In particular, Modbus RTU protocol delivered over RS485 methods could limit physical connections with respect to individual modern digital metering systems.
\nMethods over TCP/IP leverage improved communication methods. For example, a typical ‘modem/meter relationship’ is limited to a 1:32 ratio in respect to a Modbus RTU protocol implementation. Ethernet leverages modern improvements in communication methods allowing for improved transparent communication methods. The National Broadband Network (NBN) in Australia will inhibit a transition to a modern information enabled generation.
\nAt Central Park in Sydney, Ethernet is used on all meters. The meter has data logging and event logging, as well as a daily/weekly/monthly/quarterly profile. Developed for Green Star reporting principles, the benefit is now the meter is ‘the data server’. It performs the ‘distributed logic’ and alleviates the burden from any given control system such as the building management system (BMS) or building management control system (BMCS). It allows for more efficient communications and works to guarantee that all profiled data are done by the physics of the real-time clock, compared to a traditional ‘polling system’.
\nA traditional ‘polling system’ is unable to assemble all data correctly across these many meters. Central Park in Sydney has more than 1500 m or devices installed in the digital metering network. Each meter has 7×7× sub-meters per apartment which equates to 10,500 sub-meters on a digital network. The main benefit of Ethernet is the multi-master support. The SATEC TCP/IP module has four TCP sockets available for different software systems to consume the data from the meters simultaneously, which cannot be done with traditional RS485/RS232 communication topologies.
\nIn the case of ‘meter churn’, or a consumer replacing their respective meter used for trade purposes such as ‘billing’, future communication developments will enhance the overall consumer experience respective to regional Internet capabilities. Overall ‘meter churn’ within the Australian jurisdiction, or others, can be improved exponentially, and with effective methods , the overall efficiencies and flexibility in network design improve the following:\n
BMS, BMCS, and EMS;
Billing system;
In-home display, and;
Metering programming software (E.g. Power Analysis Software—PAS).
In the future, should a consumer choose to opt-out of the embedded network, an energy retailer could easily access the data directly from the meter through use of TCP/IP Ethernet communications. If ADSL, ADSL+, GPRS, NBN or 2/3/4G are required, then it is with TCP/IP communications whereby enhancements can be made. A simple ‘port forward’ is managed though IT layers proving a clear and transparent visualisation of the respective metering system. With the use of ‘multiple TCP socket’, a true disciplined ‘multi-master application’ can be achieved and overall data limitation through traditional methods can be realised.
\nSmart metering at both the residential and large facility scale should be robust and commissioned correctly. Utilities and urban developers need to engage with communities and consumers to better understand how smart meters and data feedback can best achieve societal goals [13]. This will then ensure accurate and live consumption information is easily accessible to the consumers and utilities where appropriate to enable the most efficient consumption, generation and storage of electricity, water and gas.
\nThese goals can be achieved by choosing meters with standardised high-level communication inputs and outputs, which provide connectivity for additional sub-metered services such as water and gas and ultimately connectivity to a reporting system to the consumer. While the connectivity ultimately should be TCP/IP, for internal university campuses, utilising Modbus and open-source master gateway devices has proven to be a cost-effective structure. Where large-scale multi-tenanted residential or commercial tenancies involved, direct TCP/IP connectivity to the meter provides for future proofing within the Australian market due review of all aspects of the energy landscape and particularly how metering can be leveraged by both consumers and in particular networks and generators.
\nEnergy management systems provide both residential and large energy consumers with the ability to capture, monitor and control their energy consumption and therefore their expenditure whether this is by reducing consumption directly through energy efficiency upgrades or behaviour change to optimise consumption against price signals from the utilities. Accuracy of data is a key element in order to represent the measured outcome for analysis.
\nEnergy management systems combined with structured metering also enable consumers with renewable energy generation such as photovoltaic (PV) panels to monitor their own generation, consumption, import and export. As battery storage becomes integrated with renewable energy generation, consumers will have the ability to consume cheaper renewable energy than can be bought from the grid and sell energy back to the grid at the most economically viable times. While uncertainty surrounds the grid and its impact on rising electricity prices, smart metering, intelligent control systems and utilities offering consumers more amenity and the ability for consumers to participate in the wholesale market will ensure the smart grid can contribute to future carbon neutral [14] urban environments.
\nMaterial on the Central Park case study was kindly supplied by Dean Davis, SATEC (Australia) Pty Ltd, P.O. Box 82, Mulgoa, New South Wales 2745, dean@satec-global.com.au | www.satec-global.com.au, Tel. +61 2 4774 2959 | Fax. +61 2 4774 0249, Mobile. (Australia) +61 431 421 716.
\nRice (
Parts of rough rice grain. 1-Scutelium (Cotyledon); 2-Coleoptile; 3-Epicotyl (Plumule); 4-Apical meristem; 5-Radicle; 6-Coleorhiza; 7-Pericarp; 8-Tegmen (Seed coat); 9-Aleurone layer; 10-Subaleurone layer; 11-Starchy endosperm; 12-Lemma; 13-Palea; 14-Sterile lemmas; 15-Rachilla; 16-Part of pedicel. Adapted from: [
Many characteristics of grain quality, such as milling behaviour, appearance, nutritional properties, and cooking qualities, have been routinely evaluated [8]. The evaluation methods of rice varieties are based on their chemical composition, namely (protein, moisture, fat, and ash), apparent amylose concentration, gelatinization temperature, gel consistency and dough viscosity. These procedures are based on standardized methods, which are often considered to be slow and expensive [8]. The classification and characterization of different types of rice depends on several physicochemical parameters, namely, biometric data and protein, fat, ash, moisture, starch, amylose, among other.
Starch is one of main components in rice grain, being the essential carbohydrate reserve in the grain, and so its impact in the evaluated physico-chemical parameters. Starch is a complex polysaccharide of α-D-glucose units exclusively, which are joined by a sequence of α-D-(1,4)-glucosidic linkages thus giving rise to a linear or helical chain, being composed by two classes of glucose polymers: amylopectin and amylose. Amylose is a linear polymer of D-glucose units, and amylopectin is a highly branched polymer of glucose. These are referred to as amylose (20–30%). The much less frequent α-(1,6)-glucosidic linkages form the branch points between the chains thereby creating highly branched domains, denominated amylopectin (70–80%) [9]. Amylose is considered the most important determinant of the eating quality of rice and based on their contents, rice varieties can be classified as: waxy (0–2%); very low (3–12%); low (13–20%); intermediate (21–25%) and high (>26%) [10]. The classical and still commonly used method for the amylose and amylopectin determination is the iodine reaction coupled with potentiometric or amperometric titration. There are also other methods such as: differential scanning calorimetry [11], potentiometric [12], spectrophotometric [13], and chromatographic [14, 15] that can be used for classification and a detailed analysis. The fine structure of amylose, both molecular size and chain-length distribution, are also significant factors of the hardness of cooked rice [16]. Amylose content is correlated with the retrogradation behavior, influencing the textural properties of cooked rice and the viscoelasticity dynamic of rice starch gel [17]. The elongation of grains, volume expansion as well as water absorption characteristics are accounted for cooked rice quality [18].
Proteins and lipid content are also characteristics currently accepted to define rice quality [19]. After starch, the protein is the second main component of rice, being found by four fractions: albumin (soluble in water), globulin (soluble in salt), glutelin (soluble in alkali), which represents the dominant protein in brown rice and white rice, and prolamine (soluble alcohol), a secondary protein in all rice mill fractions [20, 21]. Lipids are the third major component of brown rice, next to carbohydrates and protein, playing a major role in the quality of rice during processing and storage. Fats or lipids are mainly concentrated in the outer bran layer of brown rice, up to 20% by mass; therefore, the lipids content of brown rice is greater than that of milled rice [19, 22].
Appearance quality is how the rice appears after milling and it is associated with grain length, width, length-width ratio (shape) and translucency/chalkiness of the endosperm. Generally, most markets prefer translucent rice as opposed to chalky ones. Appearance quality has a direct influence on marketability and success of commercial varieties. The physical properties of rice grain include all of its external or integral characteristics, such as its appearance (size, shape, smoothness, colour), weight, hardness, volume, flow properties and so on (Figure 2).
Rice grains aspects.
Rice classification and consequent analysis is a comprehensive quality indicator not only in terms of the appearance but also for its cooking and processing qualities. Physical properties of rice are fundamental in all activities related to the production, preservation and utilisation of rice [23]. The parameters such as dimensions, density, hardness, friction and mechanical properties are affected by the moisture content of the grain and its degree of milling, and also to a small extent by temperature. Cereal research, as well as grading and evaluation of food products, have encouraged the development of non-destructive, rapid and accurate analytical techniques to evaluate grain quality and safety being characterized by a huge amount of experimental data that must be accurately analysed [24]. Different types of rice vary in terms of size, shape, color and constitution, which cannot be accurately identified by human visualization. Often, rice seed cultivars, characterized by high quality, can be faked using low quality cultivars or confused with other cultivars, which complicates rice quality, yield and value. For this reason, the identification of rice seed cultivars is extremely important.
Grain appearance is characterized by biometric parameters (length, width, length/width ratio), total whiteness, vitreous whiteness, and chalkiness, being considered as crucial factor that affects its market acceptability. Grain shape can be described by biometric parameters, which are closely associated with grain weight [25, 26]. The ratio of the length and the width is used internationally to describe the shape and class of the variety. Grain weight provides information about the size and density of the grain. Grains of different density mill differently, and are likely to retain moisture differently and cook differently. Uniform grain weight is important for consistent grain quality [27]. Chalkiness, an opaque white discoloration of the endosperm, reduces the value of head rice kernels and decreases the ratio of head to broken rice produced during the milling process [28]. Viscosity is a characteristic that indicates some of the cooking properties of rice, being evaluated by Rapid Visco Analysis (RVA), which mimics the process of cooking and monitors the changes to a slurry of rice flour and water, during the test. Starch viscosity curves are useful for breeding because the shape of the curve is unique to each class of rice [29]. The primary RVA parameters include peak viscosity, PV (first peak viscosity after gelatinization); trough or hot paste viscosity, HPV (paste viscosity at the end of the 95 °C holding period) and final or cool paste viscosity, CPV (paste viscosity at the end of the test) [30]. The breakdown (BD = PV − HPV); setback (SB = CPV − PV); consistency (CS = CPV – HPV); set back ratio (SBR = CPV/HPV) and stability (ST = HPV/PV) are considered as secondary parameters, once are derived from primary ones [30, 31, 32]. Other factors include peak time (time required to reach peak viscosity), and pasting temperature (temperature of initial viscosity increase) [33].
Industrial processing parameters such as the milling yield husked, milling yield milled, and milling industrial can influence positive and negatively the acceptability of rice by the industrials, can also affect the commercial value of rice. Rice yield and milling quality determine the economic value of rice from the field to the mill and in the industrial market. The rice commercial quality depends on several parameters that are evaluated separately or are involved several time-consuming experimental procedures. The evaluation of some parameters are related to biochemical or biological properties that allow more esasily its determination or prediction. Milling quality aspects affected by temperature during rice ripening include chalkiness, immature kernels, kernel dimensions, fissuring, protein content, amylose content, and amylopectin chain length [10]. Rice milling process can be subjected to dehusking of paddy which results in brown rice, and removing the bran from the kernel by polishing the brown rice to yield white rice. The milling quality of rice determines the yield and appearance of the rice after the milling process.
Beer’s law is generally applied in analytical spectroscopy to correlate the concentrations of standard samples with corresponding analyte absorbances to develop the calibration curve that is later used to evaluate the concentration of analyte of unknown samples, typically at lambda (λmax). Variation in other wavelengths/wavenumber regions is often not considered but contains significant information that may be selected to represent analyte absorption fingerprint signatures and spectral profiles for ultimate pattern recognition and/or quantification of analytes in unknown samples.
Analytical infrared spectra are focus on the absorption or reflection of the electromagnetic radiation can be divided in three regions of IR: near IR (NIR) in the 12.000–4000 cm−1 region, mid IR (MIR) in the 4000–400 cm−1 region, and far IR (FIR) beyond 400 cm−1 (Figure 3). The MIR region (4000–400 cm−1) is a well-recognized and reliable method through which different compounds can be identified and quantified, being used for biological applications, which includes the so-called fingerprint regions representative for lipids, proteins, amide I/II, carbohydrates, and nucleic acids (Figure 3). FIR spectroscopy (400–20 cm−1) provides information on the highly ordered structures such as fibrillar formation and protein dynamics [35] since it is more sensitive to the vibrations from the peptide skeletons and hydrogen bonds than MIR [36]. NIR, known also “far-visible spectroscopy” or “overtone vibrational spectroscopy”, can measure the chemical composition of biological materials using the diffuse reflectance or transmittance of the sample at several wavelengths [37]. The NIR spectrum, from 12.000 to 4000 cm−1 lies between the visible and mid-infrared regions of the electromagnetic spectrum, is characterized by a number of absorption bands that vary in intensity due to energy absorption by specific functional groups in a sample [38].
Infrared spectral region (adapted by Balan et al. [
NIR is a spectroscopic technique used to study of hydrogen bonding because it evaluates the overtones and combinations of the molecule’s vibrational modes, principally those involving hydrogen. NIR spectroscopy can measure the concentration of components, characterized by different molecular composition such as protein, water, or starch [39]. The chemical bonds present in food and crop components such as fats, water, and carbohydrates are easily detected by NIR spectroscopy due to the specificity of the radiation, in terms of the groups of interest such as N-H, C-H, and O-H bonds. Due to the macromolecular complexity of the rice sample, it is normal for these bands to overlap one another.
The transmission and reflection are defined as the two major modes of NIR spectroscopy, that are used based on physical state of the sample. Transmission modes are more suitable for liquids, thin solids, and thick solids when inspecting a food item for its ripeness, or whether it contains pests or defects. In another side, reflectance mode is applied for measuring content in whole grains such as lipids, starch, amylose, protein, moisture, and oil content. Low reflectivity indicates that energy diffuses readily beneath the surface of most samples, including visually opaque samples. Low absorptivity represents that NIR light energy easily penetrates the samples without fast attenuation [40]. This technique is extensively used in breeding procedures for quality improvement of any cereals, and crop management, receivable testing, and on-line process control [41, 42].
The NIR methodology presents some advantages such as no sample preparation or pre-treatment process, no need for dangerous reagents or solvents, and no disposal problem, either. These advantages can eliminate sampling errors caused by manual sample handling and reagent contamination. The samples also can be used in additional studies, being carried out by technically untrained personnel. On the other hand, through NIR analysis, it is possible to obtain a set of spectra, simultaneously, in a certain range of wavelengths, which may serve as a basis for the development of specific calibration curves for each analyte. In the calibration process are transformed during modelling using, for this purpose, chemometric techniques that use a representative set of training to use the program to discriminate slight differences that exist in the specific spectra of the sample [43]. A single spectrum can be subjected to many different calibration models, to measure any number of constituents.
Different techniques such as machine vision and Visible/Near-Infrared spectroscopy have been developed and applied to determine and characterize rice varieties and evaluate the biochemical characteristics. Traditional techniques used for rice variety evaluation such as High-pressure Liquid Chromatography (HPLC) or Gas chromatography-mass spectrometry (GC-MS) are time-consuming and hard to apply [44]. NIR spectroscopy, compared to the traditional analysis methods, is characterized by many advantages, such as is easy-to-use, real-time analysis, fast and accurate, highly reproducible results, non-destructive sampling, no sample preparation, multiple components analysis with a single measurement, high precision and non-destructive detection, being widely used in the measurement of agricultural and food products [45, 46].
Over the years, several multivariate regression analysis methods have been developed in order to provide significant information from spectral data, due in part to the limitations of univariate spectral analysis. The processing of spectral data for chemical analysis usually uses the field of statistics and advanced mathematics for an analysis in terms of multivariate regression of spectral data. Simultaneous investigation of several wavenumbers or wavenumbers for biochemical analysis can be carried out through multivariate regression techniques, as these allow the analysis of different sample components without the need for spectral resolution and spectral deconvolutions. Pre-processing methods allowed eliminating noise caused by spectral data, which allow to remove the non-informative variability present in the spectra. Data pre-processing techniques such as normal variable transformation (SNV), multiplicative dispersion correction (MSC) and smoothing derivative are required for raw NIR spectra for proper qualitative classification and development of quantitative calibration models. MSC is used to compensate for particle size effects as it rotates the spectra to remove part of that effect, adjusting as close to the average spectrum as possible [47]. The first and second derivatives are calculated according to the Savitzky–Golay approach using a 19 point window and a 2nd or 3rd order polynomial, which allows to remove noise such as baseline drift, large, reverse and so on [48, 49, 50] (Figure 4).
Rice NIR spectra data without treatment (a); and after pre-processing procedure: baseline correction; (b, c) and first derivative process. (Adapted from Sampaio et al. [
Machine learning is one of the most promising technologies in the field of artificial intelligence, that involve the use of algorithms that allow machines to learn by imitating the way humans learn step. Machine learning based on experimental data allows to optimize grouping or classification, developing models that allow to predict the behavior or properties of systems. There are two main types of machine learning: the supervised and the unsupervised process. Supervised machine learning uses algorithms that “learn” from the labeled data entered by a person without an algorithm. The algorithm generates expected output data as long as the input has been labelled and prior primary. There are two types of data that can be used in the development of the algorithm: (a) classification, which classifies an object into different classes, for example, it allows determining the type of rice according to its physical characteristics; (b) Regression, predicts a numerical value such as the concentration of any biochemical parameters such as the protein, lipids, or carbohydrates, etc. Supervised learning consists of learning a function from training examples, based on their attributes (inputs) and labels (outputs). In the unsupervised machine learning, unlike the previous case, there is no human intervention, and the algorithms learn process is based on the data with unlabeled elements, looking for patterns between them without human intervention. In this case two types of algorithms have been developed: (a) clustering, classifies the output data into groups according to its similarity; (b) association, the algorithm discovers rules within the data set. In semi-supervised learning, both labeled and unlabeled data is used for training, with usually only a small amount of labeled data, but a large amount of unlabeled data. Instead, the learning system receives some sort of a reward after each action, and the goal is to maximize the cumulative reward for the whole process. The much recognized machine learning methods are: Principal Component Analysis (PCA), the most basic feature extraction unsupervised techniques, based on the analysis of the variance of features within the full spectrum; the clustering unsupervised methods, used to identify biological subtypes within a sample, such as Hierarchical Cluster Analysis (HCA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), discriminant analysis (DA), Partial Least-Squares-Discriminant Analysis (PLS-DA), Partial Least-Squares (PLS), and Support Vector Machines (SVM).
Principal Component Analysis (PCA) is an unsupervised technique that allows the dimensionality reduction of the multivariate data to
A Discriminant Analysis is a strategy that has been used successfully for a qualitative analysis, being called pattern recognition. This methodology aims to classify groups as groups into well-defined groups according to the similarities of a “training set” despite limited knowledge of the composition of those belonging to the group. Johnson and Wichern [54] concluded that the use of discriminant analysis uses several variables and analyzed how to solve the grouping together. The development of calibration models in discriminant analysis is based on two methods: Mahalanobis distances, considered the unit distance vector in multidimensional space, and PCA coupled with Mahalanobis distances [54, 55]. The Mahalanobis distance can be defined by an ellipsoid in a multidimensional space that circumscribes the data. This method is based on a matrix that represents the inverse of the matrix formed by combining the covariance matrices within the group of all groups, which is generated by combining information from all different materials of interest in a single matrix. Studies developed by and Williams considered the Mahalanobis distance as the mathematical number that defines the position, size and shape of the ellipsoid for all clusters [38]. According to of statistical perspective, the Mahalanobis distance considers the sample variability to be valid, while the Euclidean distance method does not consider the variability of values in all dimensions to be valid. The Mahalanobis distances look at not only variation between the responses at the same wavelengths, but also at the inter-wavelength variations. Instead of treating all values equally when calculating the distance from the mean point, it weights the differences by the range of variability in the direction of the sample point. The place of each cluster in multidimensional space is defined by the mean value of the absorbances (the group mean) at each wavelength. Dunmire and Williams indicated that the sample can be classified clearly if it falls within three times the Mahalanobis distance from the respective centroid and at least six times the Mahalanobis distance from the ellipses of other groups [38]. Meanwhile, the Mahalanobis distance represents a multidimensional distance
where
Partial Least Squares-Discriminant Analysis (PLS-DA) is defined as a linear classification method that permits to estimate the predictive models based on partial least squares regression algorithm that follows for latent variables with maximum covariance, representing the significative sources of data variability with linear combinations of the original variables is considered an example of machine learning tool applied to conduct a global cellular analysis of bioprocess as an exploratory technique, gaining increasing attention as a useful feature selector and classifier [56, 57, 58, 59, 60]. Multivariate classification methods aimed at finding mathematical models able to recognize the membership of each sample to its appropriate class, by a set of measurements. PLS-DA have shown promising results in the detection of food adulteration without identifying specific compounds [61]. PLS-DA is a discriminant classifier, being particularly suitable for handling correlated features (e.g., spectroscopic variables). The predicted value is a number, but not a dummy integer. Thus, a cut off value needs to be set to determine which class the sample belongs to. PLS-DA is computed based to full cross validation methods. More specifically, a predictor block is used to estimate (by PLS) a binary response called dummy Y (a binary response matrix encoding the class-belonging). Mathematically, the regression relation between the data matrix X and the dummy vector y for a two-class case is represented by the model represented in Eq. (2)
where
Support Vector Machine (SVM) is a widely used supervised statistical learning algorithm, considered as a nonlinear classification technique, which works with supervised learning models that analyze data used for classification and regression analysis, producing linear boundaries between objects groups in a transformed space of the
Partial Least Squares (PLS) regression and principal component regression (PCR) are examples of quantitative regression algorithms that are currently used for linear data, being considered as factor-based models. PLS and PCR use information from all wavelengths in the entire NIR spectrum to predict sample composition, instead of using a few selected wavelengths. PLS is similar to PCR but more sensitive in terms of variations in sample concentration. Studies performed by Wehling described that PLS and PCR, based on data reduction approaches, allowed to decrease a huge number of variables to a much smaller number of new variables that account for most of the variability in the samples [66]. The amount of a constituent in samples can then be predicted by these new variables. PLS is the most widely used supervised multivariate data analysis method that estimates and quantify components in a specific sample. Each training example is defined as a pair (
The matrices containing the data provided by the NIR spectra, denominated by
where
Soft Independent Modeling of Class Analogy (SIMCA) is a supervised discriminant analysis method based on PCA [77]. This methodology is a class-modeling approach, meaning that, in defining the class boundaries, the method focuses on the similarities among samples from the same category [61, 78]. For each class, a PCA model is created and consequently the residual variance of the modeled class with the residual variance of the unknown sample is compared to determine which category the sample belongs to. The number of PCs used in each class should be selected to achieve the best classification results. SIMCA results are presented in terms of “sensitivity” and “specificity”, where the former specifies the percentage of samples truly belonging to the category correctly accepted by the class model, while the latter expresses the percentage of the objects from other classes which have been correctly rejected. SIMCA starts from a principal component analysis (PCA) of only the training objects belonging to the category to be modeled, to “capture” the regular variability due to the similarities among samples of the same class [79, 80]. Once the PCA is calculated, objects are accepted or rejected by the class-model based to their reduced distance from the class space, referred as
where T2 is the Mahalanobis distance of the sample from the center of the class space and Q is its orthogonal distance from the PC subspace. These values are divided by T20.95 and Q0.95, which are the 95th percentiles of the T2 and Q0.95 distributions, obtaining the reduced T2 (T2red) and the reduced Q (Qred), respectively [79]. Due to the normalization, T2 and Q limit values are equal to 1; a sample will then be accepted by the class model if
Random Forest (RF) is a novel machine learning algorithm that presents many decision trees, and each tree is grown from a bootstrap sample of the response variable. The optimal split is chosen from a random subset of variables at each node of the tree, and then extends the tree to the maximum extent without cutting. Prediction procedure can be performed from new data by combining the outputs of all trees. RF is suitable and fast to deal with a large amount of data, showing the advantages to reduce variance and achieve comparable classification accuracy [82, 83].
Artificial Neural Networks (ANNs) is defined a non-parametric regression models that capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. ANNs are applied for classification and function mapping difficulties which are tolerant of some inaccuracy and have lots of training data available, but to which hard and fast rules cannot easily be applied [84]. In the ANN the input layer is linked to an output layer, either directly or through one or numerous hidden layers of interconnected neurons. The amount of hidden layers defines the depth of a ANN, and the width depends on the amount of neurons of each layer. Rapid optimization algorithms are used to iteratively develop forward and backward passes for minimization of a loss function and to learn the weights and biases of the layer. The activation functions are applied to the present values of the weights at each layer in the forward pass. The final result of a forward pass is new predicted outputs. The backward pass computes the error derivatives among the expected outputs and the real outputs. These errors are then disseminated backwards updating the weights and calculating new error terms for each layer. Iterative repetitions of this process is designated as back-propagation [85]. A neural network is an adaptable system that learns relationships from the input and output data sets and then can predict a previously unseen data set of similar characteristics to the input set [86, 87]. Multilayer perceptron (MLP) and radial basis function (RBF) are widely used neural network architecture in literature for regression problems [88, 89, 90]. MLPs are usually used for prediction and classification using suitable training algorithms for the network weights. The MLP trained with the use of back propagation learning algorithm. Figure 5a represents a three-layer structure (MLP) the most basic ANN and its minimum configuration that consists of three layers of nodes (1) input layer, (2) hidden layer, and (3) output layer. The input layer accepts the data and the hidden layer processes them and finally the output layer displays the resultant outputs of the model [91, 92]. Each node, with the exception of the input, is a neuron that is based on a non-linear activation function. The MLP can be regarded as a hierarchical mathematical function planning some set of input values to output values via many simpler functions. Normally, the nodes are fully linked between layers and therefore the quantity of parameters quickly increases to huge numbers with a considerable risk of overfitting [93]. The RBF is considered the most broadly used structural design in ANN and simpler than MLP neural network (Figure 5b). The RBF has also an input, hidden and output layer. There are different types of radial basis functions, but the most widely used type is the Gaussian function.
A comparative study of artificial neural network (MLP, RBF) models for rice biochemical parameters prediction. Simple configuration of (a) MLP and; (b) RBF neural networks [
Multiple Linear Regression (MLR) is a commonly used machine learning algorithm that allows to determine a mathematical relationship among a number of random variables, analyzing how multiple independent variables are related to one dependent variable. Since each of the independent factors has been determined to predict the dependent variable, information about the multiple variables is used to develop an accurate prediction about the level of effect they have on the outcome variable. The model generates a relationship in the form of a straight line (linear) that best approximates all the individual data points. The most important advantage of MLR is it helps us to understand the relationships among variables present in the dataset. This will further help in understanding the correlation between dependent and independent variables. MLR is one of the oldest regression methods, being used to establish linear relationships between several independent variables (
where
There are several studies that discribe the quantitative analysis by NIR spectroscopy in different types of food, providing an exceptional method for the evaluation of chemical composition (
There are several studies based on NIR to predict viscosity properties of rice. Delwiche et al. developed calibration models on whole-grain milled rice using PLS regression to predict viscosity properties of a flour-water paste as recorded by the RVA, that determine the cooking and processing characteristics of rice [102]. Meadows and Barton later used NIR to predict RVA data in rice flour [103]. A PLS regression of NIR spectra
Studies developed by Osborne et al. using near infrared transmission spectroscopy allowed to discriminate between Basmati and other long-grain rice samples. A discriminant rule was derived using the Fisher linear discriminant function calculated from the first few principal component scores of the NIR spectra [107]. The discriminant rule was assessed by cross-validation. Based on this study, nine Basmati varieties and 53 other rice samples were classified correctly from NIR spectra, but 8% of the Basmatis and 14% of the others were misclassified on the basis of spectra of individual grains. NIR spectroscopy technique also offers effective quantitative capability for moisture, fat, protein and gluten content in rice cookies [108].
According to studies performed by Chen et al., the NIR diffuse reflectance spectroscopy of multi-grain seeds, a spectral discriminant analysis method for the variety identification of multi-grain rice seed was developed using the PLS-DA [109]. Due to the slight differences of seeds spectra in various varieties, it’s necessary to propose the novel and valid methods. In this study, the SNV pretreatment combined with wavelength-screening methods improved the accuracy of the discriminant models. The selected optimal wavelength model was the combination of 54 discrete wavelengths within NIR region. NIR spectral discrimination total recognition accuracy rates reached 94.3% for a study that involves the identification of one type of differentiation (negative and excellent hybrid variety) and several interference groups (positive, four pure groups and four mixed groups).
The Hyperspectral Imaging (HSI) technique coupled with visible (vis) and/or NIR spectroscopy is generally used to identify or inspect different substances of seed by recognizing the molecular bonds in the sample, being considered the most feasible methods for rapidly and non-destructively detecting the substances of agricultural products, combining the technologies of spectroscopy and digital imaging. Studies developed by He et al. used the system NIR-HSI combined with multiple data preprocessing methods [110]. This approach allowed simultaneously to obtain spectral and spatial information from testing samples in the form of a hypercube constituted by two spatial dimensions and one spectral dimension. The HSI technique has the ability to collect hyperspectral information from samples of different sizes and shapes based on the spatial data. The detection speed of HSI is faster than that of point-based techniques, as many samples can be scanned and analyzed at the same time by using an HSI camera [111]. The classification models was developed to identify the vitality of rice seeds, presenting a great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky–Golay preprocessing reached a significant classification accuracy of 93.67% by spectral data. In terms of the non-viable seeds identification from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths achieved a significant classification achievement (94.38% accuracy), and can be adopted as an optimal combination to identify non-viable seeds from viable seeds. In another study, carried out by Barnaby et al., NIR hyperspectral image consists of numerous bands with small spectrum gaps (every 4 nm in our study) and can assess grain traits such as fat, starch, protein, moisture, color, and many other physicochemical compounds at once [101]. Genome wide association study allowed to confirm known genes and to identify new genes that can affect grain quality traits based on hyperspectral imaging technique. The PLS-DA models of hyperspectral data identify spectral ranges that distinguished genetic and production environment differences, and this data can support to resolve the genetics of complex traits such as rice grain quality.
The nitrogen content is an important chemical indicator used for monitoring and management of plant due to its role in photosynthesis, productivity as well as its effect on carbon and oxygen cycle. The nitrogen content can be measured by laboratory analysis, meanwhile, its spectral reflectance of NIR (700–1075 nm) in the field was measured using hand held spectroradiometer. Studies performed by Afandia et al. evaluated nitrogen content in rice crop based on NIR reflectance using ANN [111]. The reported study allowed to conclude that the organic molecules (nitrogen, water, etc) present a specific absorption pattern in the NIR region and the comparison between measured and model estimation of nitrogen content presented a RMSE of 0.32.
A study developed by Lin et al., based on the imaging method, a system constituted by a NIR camera, filters, an automatically exchange filters device, and the imaging processing techniques allowed to detect the rice protein content based on the spectrum absorption. The NIR data allowed to establish the calibration model based on MLR, PLS, and ANN analysis models. In the MLR model, the NIR imaging system used the calibration model that take in account 5 wavelengths (880 nm, 910 nm, 920 nm, 1000 nm, and 1014 nm) to predict the rice protein content, and had R2 validation (0.782) and standard error of predicition (SEP) 0.274%, and respectively. The NIR imaging system used 15 filters ranging from 870 to 1014 nm in the PLS model, the predictive results expressed a significant performance (R2val = 0.782, and SEP = 0.274%) comparatively tothe MLR model. The ANN model, the net input using the 5 spectrum wavelengths selected by the MLR, simplified the model, and the predicting results (R2val = 0.806, and SEP = 0.266%) were similar to those of the PLS. The prediction results indicated that the developed NIR imaging system has the advantages of simple, convenient operation, and high detection accuracy as well as it presents commercial potential in non-destructive high accurate predicting capability detection of rice protein content [112].
NIR spectroscopy was used to develop a new discrimination method of varieties of rice. The several variables compressed by PCA were used as inputs of multiple discriminant analysis (MDA). The study showed that the combinantion of spectroscopy and computer data processing technology based on PCA and MDA for the identification of rice from different areas allowed to identify correctly about 98% for the calibration process, and 100% for the prediction process. These results showed that the proposed alternative method is a feasible way for the identification of the specific production areas of rice [113].
NIR spectroscopy has been widely used in the evaluation of agricultural products due to its many advantages, such as being easy-to-use, non-destructive, fast and accurate, providing highly reproducible results, requiring minimum or, often, no sample preparation, and allowing the analysis of several constituents based on a single measurement. As consequence of the importance of rice at global level, in the literature it is possible to find several studies aimed at their analysis and characterization. Due to environmental reasons and the rice the market, non-destructive approaches are generally preferred. NIR spectroscopy has emerged as an important tool to determine fraud, adulteration, contamination in grains and flours. A substantial instrumental improvements (e.g., hyperspectral imaging, FT-NIR) and advances in data analysis (e.g., deep learning) have allowed for the development of screening methods for detecting the presence of pests (e.g., rice weevil) across a range of stored grains [114, 115, 116].
Direct spectroscopic measurements have been widely applied for several foods and commodities, especially in the grain, cereal products, such for classification of rice [117, 118, 119, 120, 121]. Furthermore, in the structure of the evaluation of rice quality, NIR spectroscopy has been used for the discrimination of rice [122, 123]; varieties classificationand transgenic rice detection [124]; the physico-chemical properties quantification (such as moisture content, sound whole kernel, whiteness, translucency, color, and amylogram characteristics) [125]; cultivars classification [126], protein and amylose content prediction [127, 128]; wax rice detection [129]; and eating quality prediction [130]. Barnaby et al. correlated the grain chalk of rice to the genomic regions of NIR spectra [101]. These spectral regions can be applied in the automation of grain chalk quantification and potentially for other grain products as well [131].
Rapid and nondestructive detection of rice authenticity and quality were performed based on hand-held NIR spectrometer coupled with the appropriate chemometrics. The selection of different preprocessing methods with PCA and modeling with KNN and SVM multivariate calibration model showed that MSC + PCA plus KNN showed superiority in this study with more than 90% classification rate for all categories of rice samples studied. Based on these results, the hand-held spectrometer associated to an appropriate multivariate calibration model could be used for quick and non-destructive detection of rice quality and authenticity [132].
Food fraud remains a significant problem for food regulators, importers, merchants, law enforcement personnel, and the consumer. A key feature of food fraud is the use of a lower value ingredient to imitate an authentic product. NIR analysis technology, PLS-DA, and SVM have been used to detect whether high-quality rice was mixed with other varieties of rice. NIR spectral data analyzed using PLS-DA and a SVM algorithm, was shown to be a feasible method (5% detection limit) for the rapid identification of fraudulent rice varieties blended with authentic Wuchang rice samples [133].
Studies performed by Liu et al. showed that those techniques represent a significant support to qualitative discrimination [133]. PLS was used to establish the quantitative analysis model to support in the recognition of the degree of fraud. As consequence of the direct correlation between the results of NIR analysis and the homogeneity of the samples, four groups of samples with different physical forms (full granules, 40 mesh, 70 mesh, and 100 mesh) were prepared. Regarding qualitative analysis, the performance of the model has no obvious relationship with the physical state of the sample, the qualitative model of PLS-DA and SVM can detect the fraudulent rice with a 5% detection limit. The determination coefficient and root mean square errors of the optimal prediction result were 0.96 and 2.93, respectively. Based on this study, NIR analysis technology can be considered as a reliable and fast strategy to determine if the premium high-quality rice is adultered with inferior categories of rice.
Different preprocessing approache were used for NIR signals pretreatment. Besides considering raw data, the first derivative (Savitzky–Golay approach, 15 points window, 2nd order polynomial), second derivative (Savitzky–Golay approach, 15 points window, 3rd order polynomial), and standard normal variate (SNV) were also evaluated (Figure 6). NIR data were further mean-centered prior to the creation of any calibration model. The most suitable preprocessing approach, together with the optimal complexity (number of LVs or PCs to be extracted) of any classification model, were defined based on a cross-validation procedure. PLS-DA selection, specifically, was based on the combination of pre-processing and model complexity leading to the lowest mean classification error, whereas for SIMCA the maximum efficiency was sought. A study developed by Duy Le Nguyen Doan investigate the possibility of combination NIR spectroscopy and chemometric classifiers with the aim of detecting adulterated rice samples [134]. Two different strategies were exploited: discriminant classifier (PLS-DA), and class-modelling technique (SIMCA). Both strategies provided different results; in particular, SIMCA appeared unable to solve the investigated problem. On the other hand, PLS-DA analysis showed to be a suitable approach. These results indicate that the high within-class variability can have an impact on the possibility of detecting low levels of adulteration; simultaneously, was also suggested that the proposed approach could be useful for detecting samples adulterated. Then, this study demonstrates that the combination of NIR spectroscopy and PLS-DA can represent an effective, rapid and non-destructive tool for the determination of adulteration in jasmine rice [134].
NIR spectra (a) raw spectra of samples, (b) mean spectra of authentic (red line) and adulterated samples (blue line). (Adapted from [
Fast determination of heavy metals is necessary and important to ensure the safety of crops. The potential of NIR spectroscopy coupled with chemometric technology for quantitative analysis of cadmium in rice was investigated. The spectrum was pre-processed using first derivation to reduce the baseline shift and several chemometric techniques, such as iPLS, mwPLS, siPLS, and biPLS were proposed to extract and optimize spectral interval from full-spectrum data. The PLS models based on four chemometric algorithms outperformed the full-spectrum PLS model then developed. Among the techniques, biPLS performed better with the optimal subinterval selection [135].
Heavy metals are spectrally featureless so that spectral responses could not be directly used for the assessment of heavy metals in rice. With a close combination of protein, crude fiber, and other ingredients, heavy metals present significant correlation with protein in rice [136]. The detection of heavy metal concentration in grain is mostly realized by physical and chemical direct methods that can exactly obtain the residual levels of heavy metal; however, it is time consuming, cumbersome, and inefficient. On the basis of the hypothesis that heavy metal concentration could be spectrally estimated through the correlation between heavy metal concentration and protein contents, the objectives of this study are to: (1) build quantitative model for the quick prediction of both heavy metal and protein content, and (2) to evaluate the feasibility of near-infrared spectroscopy in assessing heavy metal concentration in coarse rice.
Protecting people from heavy metal contamination is an important public-health concern and a major national environmental issue. The NIR spectral technique is used to identify heavy metal concentration such as lead (Pb) and copper (Cu) in rice. The NIR spectral data were treated by some methods, including, logarithm, baseline correction, standard normal variate, multiple scatter correction, first derivates, and continuum removal. The lead (Pb) was accumulated in rice at a high level (17.05) compared with the others heavy metals. MSC-PLSR models were developed, respectively, for Pb (R2 = 0.49, RMSE = 2.01 mg/kg) and Cu (R2 = 0.29, RMSE = 0.75 mg/kg). It is achievable to identify Pb and Cu content in rice by using NIR spectral technique. However, further studies should be performed on the application of spectral technique in discriminating the other heavy metals in rice due to the limitations of few samples and particles size interference.
Based on the reported studies, it was possible to develop a robust classification, authentication or fraud detection model for rice samples considering their specific physicochemical properties and using machine learning tools such as PLS-DA, KNN, ANN, and SVM among other methodologies applied to NIR spectroscopy data, revealing the pattern and relationship of each variety and chemical similarities, according to their specific properties. The classification models developed using several models allow to classify with high confidence rice varieties using the spectral data. The results show that the use of these chemometric tools, combined with spectroscopy capabilities, can facilitate the process of classification and identification of different rice types. The rice discrimination by their origin, harvest season, state of conservation as well as the presence of contaminants and adulteration issues based on robust classification methods can facilitate the creation of a data base, a useful tool for rice authenticity that can increase the confidence and producer-consumer engagement in rice-based foods.
Acknowledge of funding: The study was supported by project TRACE-RICE -Tracing rice and valorising side streams along Mediterranean blockchain, grant n° 1934, (call 2019, Section 1 Agrofood) of the PRIMA Programme supported under Horizon 2020, the European Union’s Framework Programme for Research and Innovation, and Research Unit, UIDB/04551/2020 (GREEN-IT, Bioresources for Sustainability).
The authors declare no conflict of interest.
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Cultivated forms belong to the species “Manihot esculenta Crantz” and “Manihot utilissima Pohl.” Africa produces about 50–80 million tonnes of cassava annually; this translates into an average of more than 300 calories per day for more than 200 million people. Cassava can grow on relatively marginal soils and erratic rainfall conditions in southeastern, Nigeria. It quickly adapts and integrates into the traditional farming system, is easy to cultivate and process and it is available all year round acting as a buffer against crop failure. These characteristics make this root crop a necessary component of the farming system in many areas of Africa south of the Sahara. Some of the principal recommended cultivated varieties in Nigeria include; TME 419, TMS 90257, TMS 91934, TMS 81/00110, TMS 82/00661, TMS 30001, TMS 30555, TMS 30572 and local cultivars—Nwugo, Nwaiwa, Ekpe and Okotorowa that are popular in southeastern Nigeria. Cassava is expected to play increased role in Africa’s struggle to attain food and nutrition security through increased production and utilization. This paper examines the ecophysiology, production principles, pest and disease management, uses and constraint hampering cassava production in southeast Nigeria.",book:{id:"6308",slug:"cassava",title:"Cassava",fullTitle:"Cassava"},signatures:"Martin A.N. Anikwe and Ejike E. Ikenganyia",authors:[{id:"28328",title:"Prof.",name:"Martin",middleName:null,surname:"Anikwe",slug:"martin-anikwe",fullName:"Martin Anikwe"},{id:"220543",title:"MSc.",name:"Ejike",middleName:null,surname:"Ikenganyia",slug:"ejike-ikenganyia",fullName:"Ejike Ikenganyia"}]},{id:"57918",title:"Review of Various Harvesting Options for Cassava",slug:"review-of-various-harvesting-options-for-cassava",totalDownloads:3183,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Harvesting plays a critical role in the cassava production value chain. A review of some existing cassava harvesting options is necessary to facilitate the proper adaption and uptake of improved harvesting methods applicable to farmers from different parts of the globe. In terms of capacity, manual, semi-manual and fully mechanised harvesting options respectively require about 22–51 man-hha-1, 16-45 man-hha-1 and 1–4 man-hha-1. An added advantage with mechanised options is that the field is left ploughed after harvesting with savings on fuel, time and cost. Mechanised harvesters work best on ridged fields with minimal trash or weeds and relatively dry soils (12–16% d.b. moisture content). Earlier attempts at mechanised harvesting have been affected by constraints such as soil characteristics, nature and size of tubers, depth and width of cluster and bond between tubers and the soil, leading to high tuber damage. Though less research attention is given to cassava harvesting mechanisation, that aspect of the global cassava transformation agenda has always been the problem. There is still room for improvement in the provision of appropriate harvesting options for cassava worldwide and a more concerted effort from both the government and private sector is vital.",book:{id:"6308",slug:"cassava",title:"Cassava",fullTitle:"Cassava"},signatures:"Shadrack Kwadwo Amponsah, Ahmad Addo and Byju\nGangadharan",authors:[{id:"203117",title:"Dr.",name:"Shadrack",middleName:"Kwadwo",surname:"Amponsah",slug:"shadrack-amponsah",fullName:"Shadrack Amponsah"},{id:"220625",title:"Prof.",name:"Ahmad",middleName:null,surname:"Addo",slug:"ahmad-addo",fullName:"Ahmad Addo"},{id:"220626",title:"Dr.",name:"Gangadharan",middleName:null,surname:"Byju",slug:"gangadharan-byju",fullName:"Gangadharan Byju"}]},{id:"45885",title:"Nitrogen Fixation in Sugarcane",slug:"nitrogen-fixation-in-sugarcane",totalDownloads:4097,totalCrossrefCites:6,totalDimensionsCites:11,abstract:null,book:{id:"3806",slug:"advances-in-biology-and-ecology-of-nitrogen-fixation",title:"Advances in Biology and Ecology of Nitrogen Fixation",fullTitle:"Advances in Biology and Ecology of Nitrogen Fixation"},signatures:"Takuji Ohyama, Atsushi Momose, Norikuni Ohtake, Kuni Sueyoshi,\nTakashi Sato, Yasuhiro Nakanishi, Constancio A. Asis Jr., Soraya\nRuamsungsri and Shotaro Ando",authors:[{id:"30061",title:"Prof.",name:"Takuji",middleName:null,surname:"Ohyama",slug:"takuji-ohyama",fullName:"Takuji Ohyama"}]},{id:"56558",title:"Introductory Chapter: Cassava as a Staple Food",slug:"introductory-chapter-cassava-as-a-staple-food",totalDownloads:2070,totalCrossrefCites:7,totalDimensionsCites:7,abstract:null,book:{id:"6308",slug:"cassava",title:"Cassava",fullTitle:"Cassava"},signatures:"Viduranga Y. Waisundara",authors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara"}]},{id:"46074",title:"Effects of Rhizobium Inoculation on Nitrogen Fixation and Growth of Leguminous Green Manure Crop Hairy Vetch (Vicia villosa Roth)",slug:"effects-of-rhizobium-inoculation-on-nitrogen-fixation-and-growth-of-leguminous-green-manure-crop-hai",totalDownloads:3139,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"3806",slug:"advances-in-biology-and-ecology-of-nitrogen-fixation",title:"Advances in Biology and Ecology of Nitrogen Fixation",fullTitle:"Advances in Biology and Ecology of Nitrogen Fixation"},signatures:"Takashi Sato",authors:[{id:"41434",title:"Dr.",name:"Takashi",middleName:null,surname:"Sato",slug:"takashi-sato",fullName:"Takashi Sato"}]}],onlineFirstChaptersFilter:{topicId:"373",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81798",title:"Protein Metabolism in Plants to Survive against Abiotic Stress",slug:"protein-metabolism-in-plants-to-survive-against-abiotic-stress",totalDownloads:11,totalDimensionsCites:0,doi:"10.5772/intechopen.102995",abstract:"Plants are frequently subjected to several abiotic environmental stresses under natural conditions causing profound impacts on agricultural yield and quality. Plants can themselves develop a wide variety of efficient mechanisms to respond environmental challenges. Tolerance and acclimation of plants are always related to significant changes in protein, cellular localization, posttranscription, and posttranslational modifications. Protein response pathways as well as pathways unique to a given stress condition shared by plants under different stressed environment are discussed in this chapter. The various signaling of protein such as fluctuation, overexpression, and silencing of the protein gene are observed to be modulated in drought-tolerant plants. Similarly, gene expression, RNA processing, and metabolic process take place to cope with drought conditions. For adaption in water-submerged conditions, plants undergo reactive oxygen species (ROS), cell wall modification, proteolysis, and post-recovery protein metabolism. Heat shock protein and protein and lipid contents vary and play pivotal role in resisting low and high temperatures. In a nutshell, this paper provides an overview of several modification, synthesis, degradation, and metabolism of protein in plants to cope with and revive again to normal growing conditions against abiotic stress, emphasizing drought, submerged, extreme cold, and heat temperatures.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Bharti Thapa and Abhisek Shrestha"},{id:"80923",title:"Salt Stress Tolerance in Rice and Wheat: Physiological and Molecular Mechanism",slug:"salt-stress-tolerance-in-rice-and-wheat-physiological-and-molecular-mechanism",totalDownloads:57,totalDimensionsCites:0,doi:"10.5772/intechopen.101529",abstract:"Salinity is a major obstacle to global grain crop production, especially rice and wheat. The identification and improvement of salt-tolerant rice and wheat depending upon the genetic diversity and salt stress response could be a promising solution to deal with soil salinity and the increasing food demands. Plant responses to salt stress occur at the organismic, cellular, and molecular levels and the salt stress tolerance in those crop plant involving (1) regulation of ionic homeostasis, (2) maintenance of osmotic potential, (3) ROS scavenging and antioxidant enzymes activity, and (4) plant hormonal regulation. In this chapter, we summarize the recent research progress on these four aspects of plant morpho-physiological and molecular response, with particular attention to ionic, osmolytic, enzymatic, hormonal and gene expression regulation in rice and wheat plants. Moreover, epigenetic diversity could emerge as novel of phenotypic variations to enhance plant adaptation to an adverse environmental conditions and develop stable stress-resilient crops. The information summarized here will be useful for accelerating the breeding of salt-tolerant rice. This information may help in studies to reveal the mechanism of plant salt tolerance, screen high efficiency and quality salt tolerance in crops.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Mohammad Hasanuzzaman"},{id:"80091",title:"Molecular Defense Mechanisms in Plants to Tolerate Toxic Action of Heavy Metal Environmental Pollution",slug:"molecular-defense-mechanisms-in-plants-to-tolerate-toxic-action-of-heavy-metal-environmental-polluti",totalDownloads:21,totalDimensionsCites:0,doi:"10.5772/intechopen.102330",abstract:"Toxic action of heavy metals on plants growing in contaminated soils intensified the research on detoxification and sequestering mechanisms existing in plants to understand and manipulate defense mechanisms that confer tolerance against metal ions. Increased biosynthesis of plant biomolecules to confer tolerance during toxic action of heavy metals is an intrinsic ability of plants. Induced formation of low-molecular weight amino acids, peptides or proteines as chelators such as proline (Pro), glutathione (GSH), phytochelatins (PCs) or metallothioneins (MTs) under heavy metal stress enhances metal binding and detoxification capability of plants. In addition, proline and GSH related enzymes such as GSH reductase, GSH peroxidases and glutathione S-transferases are also key components of the antioxidant defense system in the cells to scavenge reactive oxygen species (ROS). Protective action of oxidized fatty acids oxylipins at toxic levels of heavy metals is considered to activate detoxification processes as signaling molecules.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Istvan Jablonkai"},{id:"80723",title:"Intra-Annual Variation in Leaf Anatomical Traits of an Overwintering Shrub of High Elevations of Himalaya",slug:"intra-annual-variation-in-leaf-anatomical-traits-of-an-overwintering-shrub-of-high-elevations-of-him",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.102016",abstract:"Trait variability in response to seasonal variations can be hypothesised as an advantageous strategy for overwintering shrubs. This hypothesis was tested by elucidating patterns of trait variation in an evergreen alpine shrub, Rhododendron anthopogon D. Don. The study site was established at Rohtang (3990 m a.s.l.) in western Himalaya. Its leaves were sampled at 10 time points spanning a period of 1 year (beginning from 22-August-2017 to 14-August-2018) for estimating anatomical traits using light and scanning electron microscopy. The data were analysed using one-way analysis of variance, and the trait-temperature relationships were analysed using linear regression. The results indicated a lower variability in the anatomical traits. A few traits (e.g. cuticle thickness and epidermal scales) were found to be significantly correlated with temperature (p < 0.05). Our analysis revealed increase in cuticle thickness and a decrease in epidermal scales (size) during low-temperature conditions. The lesser variability found in anatomical traits of overwintering shrub could be explained as ‘evolutionary gained adaptive traits’.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Nikita Rathore, Dinesh Thakur, Nang Elennie Hopak and Amit Chawla"},{id:"80587",title:"Morpho-Anatomical Adaptation against Salinity",slug:"morpho-anatomical-adaptation-against-salinity",totalDownloads:57,totalDimensionsCites:0,doi:"10.5772/intechopen.101681",abstract:"Plants tolerant of NaCl, implement several adjustments to acclimate to salt stress, such as biochemical, physiological, and morphological modifications. Besides, plants also adjust to saline circumstances by altering their anatomical structure of roots, leaves, and morphological modifications. The leaf and roots are among the essential plant organs and are involved in the transport of water and minerals used for photosynthesis. From a plant physiology perspective, water use efficiency in the quantity of CO2 fixed in photosynthesis compared to the leaf anatomy. In this review, we provide a comparative account of the morphology of the leaf and root under normal and salt stress circumstances. There is little information on the ultrastructure changes elicited in response to salt stress. The analysis expands our knowledge of how salt may impact the leaves and root anatomy.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Smita Srivastava"},{id:"80201",title:"Nutrients Deficit and Water Stress in Plants: New Concept Solutions Using Olive Solid Waste",slug:"nutrients-deficit-and-water-stress-in-plants-new-concept-solutions-using-olive-solid-waste",totalDownloads:76,totalDimensionsCites:0,doi:"10.5772/intechopen.101523",abstract:"Great efforts were deployed by researchers to mobilize water resources while is becoming rarer and to control with efficiency the water besides nutrient needs for the plant. Autonomous water and nutritional anti-stress device for plants (AWANASD) based on the recovery of rainwater patented by Medhioub et al. fits into this general framework. Scientific efforts were also dedicated to preserve the environment and minimize energy consumption through using agricultural waste materials in different fields. This chapter provides a new concept based on the use of the olive solid waste in AWANASD as water storage and nutrient elements for plants giving rise to the new system called AWANASD-OSW.",book:{id:"10905",title:"Plant Defense Mechanisms",coverURL:"https://cdn.intechopen.com/books/images_new/10905.jpg"},signatures:"Samir Medhioub, Slah Bouraoui, Ali Ellouze and Hassen Sabeur"}],onlineFirstChaptersTotal:9},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:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,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:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,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:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",isOpenForSubmission:!0,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"28",title:"Animal Reproductive Biology and Technology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",isOpenForSubmission:!0,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). 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She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}}]},{type:"book",id:"8524",title:"Lactation in Farm Animals",subtitle:"Biology, Physiological Basis, Nutritional Requirements, and Modelization",coverURL:"https://cdn.intechopen.com/books/images_new/8524.jpg",slug:"lactation-in-farm-animals-biology-physiological-basis-nutritional-requirements-and-modelization",publishedDate:"January 22nd 2020",editedByType:"Edited by",bookSignature:"Naceur M'Hamdi",hash:"2aa2a9a0ec13040bbf0455e34625504e",volumeInSeries:3,fullTitle:"Lactation in Farm Animals - Biology, Physiological Basis, Nutritional Requirements, and Modelization",editors:[{id:"73376",title:"Dr.",name:"Naceur",middleName:null,surname:"M'Hamdi",slug:"naceur-m'hamdi",fullName:"Naceur M'Hamdi",profilePictureURL:"https://mts.intechopen.com/storage/users/73376/images/system/73376.jpg",biography:"Naceur M’HAMDI is Associate Professor at the National Agronomic Institute of Tunisia, University of Carthage. He is also Member of the Laboratory of genetic, animal and feed resource and member of Animal science Department of INAT. He graduated from Higher School of Agriculture of Mateur, University of Carthage, in 2002 and completed his masters in 2006. Dr. M’HAMDI completed his PhD thesis in Genetic welfare indicators of dairy cattle at Higher Institute of Agronomy of Chott-Meriem, University of Sousse, in 2011. He worked as assistant Professor of Genetic, biostatistics and animal biotechnology at INAT since 2013.",institutionString:null,institution:null}]},{type:"book",id:"8460",title:"Reproductive Biology and Technology in Animals",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/8460.jpg",slug:"reproductive-biology-and-technology-in-animals",publishedDate:"April 15th 2020",editedByType:"Edited by",bookSignature:"Juan Carlos Gardón Poggi and Katy Satué Ambrojo",hash:"32ef5fe73998dd723d308225d756fa1e",volumeInSeries:4,fullTitle:"Reproductive Biology and Technology in Animals",editors:[{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null}]}]},openForSubmissionBooks:{paginationCount:1,paginationItems:[{id:"11601",title:"Econometrics - Recent Advances and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11601.jpg",hash:"bc8ab49e2cf436c217a49ca8c12a22eb",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 13th 2022",isOpenForSubmission:!0,editors:[{id:"452331",title:"Dr.",name:"Brian",surname:"Sloboda",slug:"brian-sloboda",fullName:"Brian Sloboda"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:0,paginationItems:[]},subseriesFiltersForOFChapters:[],publishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"11392",title:"Leadership in a Changing World",subtitle:"A Multidimensional Perspective",coverURL:"https://cdn.intechopen.com/books/images_new/11392.jpg",slug:"leadership-in-a-changing-world-a-multidimensional-perspective",publishedDate:"May 11th 2022",editedByType:"Edited by",bookSignature:"Muhammad Mohiuddin, Bilal Khalid, Md. Samim Al Azad and Slimane Ed-dafali",hash:"86a6d33cf601587e591064ce92effc02",volumeInSeries:1,fullTitle:"Leadership in a Changing World - A Multidimensional Perspective",editors:[{id:"418514",title:"Dr.",name:"Muhammad",middleName:null,surname:"Mohiuddin",slug:"muhammad-mohiuddin",fullName:"Muhammad Mohiuddin",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038UqSfQAK/Profile_Picture_2022-05-13T10:39:03.jpg",institutionString:null,institution:{name:"Université Laval",institutionURL:null,country:{name:"Canada"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Business and Management",value:86,count:1}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. 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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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