Energy delivery estimates of green energy sources.
\r\n\t
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"71738",title:"Sustainable Advanced Manufacturing of Printed Electronics: An Environmental Consideration",doi:"10.5772/intechopen.91979",slug:"sustainable-advanced-manufacturing-of-printed-electronics-an-environmental-consideration",body:'Sustainable and renewable green energy and materials as an alternative to fossil fuels that take millions of years to be developed have been the most important challenge for all industries to secure the future energy demands, environment, and human health [1]. Burning fossil fuels for energy, production and transportation of fossil fuel-based materials, industrial/agricultural activities, as well as growing population yield greenhouse gasses (GHGs) that trap heat in the atmosphere [2]. The GHGs remain in the air for various amounts of time, from a few to thousands of years, causing global heating and drastic changes in climate [3, 4]. Therefore, innovations in all fields are critically important to reduce the GHGs, unsustainable energy and material usage, cost, toxic waste, and pollution which are the potential risks on human health and environment [5].
Worldwide energy consumption by source recorded to be an average of 18.4 trillion watts (TW) in 2018 [6]. Figure 1 represents that the majority of the energy was based on fossil energy sources. For the future, the total consumption is projected to be 27.6 TW by 2050 and 43.0 TW by 2100 [7]. Researchers help formulating solutions to increase green energy production that comes from the natural sources such as solar, wind, ocean or tidal, hydropower, biomass, and geothermal energy. They are also called C-neutral sources [7]. Among these, solar energy is the largest source that enables more energy in an hour to the Earth than all of the energy consumed by humans in an entire year (if only this energy could be stored) [1]. Each energy sources have different potential to provide the projected power need. The theoretical delivery potentials of green sources in Table 1 represents that using direct radiation from the sun is by far the only biggest source of energy [7].
Global energy consumption [
Green energy source | Theoretical potential (TW) |
---|---|
Solar energy | 89,000 TWp |
Wind | 1000 TWm |
Geothermal | 44 TWt |
Hydropower | 12 TWm |
Ocean tidal | 2.4 TWm |
Energy delivery estimates of green energy sources.
Subscripts denote mechanical, photonic, and thermal.
The sun is a massive reactor where hydrogen atoms are fused into helium. The energy from this reaction is released into space in the form of radiation that creates electromagnetic energy—the entire range of light that exist (Figure 2). By using various technologies (solar panels and photovoltaics (PV)), the solar radiation can be turned into heat and electricity [8].
Electromagnetic spectrum [
Part of the light radiated from the sun does not reach to the Earth due to various reasons [10]. Some portion for instance is reflected from the atmosphere back into the space, called
Schematics of a conventional solar panel at different tilting angles.
The basic working principle of solar panel is converting light energy directly into electricity through the photovoltaic effect. The panel is usually constructed by an n-type and p-type semiconductor material (silicon based in general) between the two metal conductor layers (Figure 4). The n-type semiconductor has extra electrons that carry negative charge, while the positive p-type semiconductor has missing electrons. When the light photons are absorbed, the extra electrons of the n-type get free (the so-called holes) and forced to travel in the electron transport layer by the top conductor. Meanwhile, the conductor on the bottom layer forces the missing positive electrons to travel in the transport layer. These moving electron–hole pairs induce the DC electric current formation that is converted into AC in the inverter unit of the solar system. In the case of solar heating (Figure 5), a panel of tubes heats up the water through the absorbed light energy and redistributes into the building for heating, air conditioning, and hot water usage.
Illustration of sunlight conversion to electricity.
Illustration of traditional solar water heating (edited) [
Between the global energy need for electricity and heating, 10% is estimated to be for illumination purpose, while 90% is for the heat used to make products and to heat and cool buildings and homes and the energy used to drive motor vehicles [1]. One of the most critical factors is understanding not only how to produce the green energy, but also how to remanufacture, reduce, and reuse/recycle electronic products that use this energy. Thereby, as much as the effort goes into producing green energy from natural sources, the same effort is needed for electronic manufacturing since an electronic circuit is found in a surprising number of devices that we use in our daily life: from lighting to domestic/industrial appliances; from computers and its accessories to communication devices and cameras; from vehicle electronics to medical devices; or from the products that use displaying units, controlling apparatus, and switches to alarm systems and toys. The circuitry use is almost endless.
Traditional electronic manufacturing requires multiple production steps as illustrated in Figure 6 [16]. First, a functional layer must be deposited on a substrate, typically through a chemical vapor or physical vapor process, for a copper layer production. The most common substrate used for the circuit board is a glass fiber-reinforced epoxy resin. Then, a photoresist layer is deposited on the substrate and experiences exposing, developing and curing processes. The next is the use of harsh etching chemicals to remove the photoresist layer and the unwanted metal that is not covered by the photoresist. The last step is striping the resist material and cleaning all the residues away. The entire manufacturing processes are highly time- and energy consuming, costly, and inherently wasteful. One main approach for green electronic manufacturing is called the three R’s—“remanufacture, reduce, reuse/recycle”—focused on minimizing the use of energy, hazardous materials, toxic waste and pollution, and coolant consumption while machining, while promoting product take-back policies, the use of reusable/recyclable components, recycled feedstock in plastic parts, and lead-fee production [14].
Subtractive manufacturing steps [
Printing has received immense attention due to the additive nature of the manufacturing [15]. During printing, functional materials (or the so-called conductive/smart inks) can be patterned by selective deposition on where they are needed by the print heads in the case of digital printing, such as inkjet, 3D printing, and aerosol (Figure 7) or by the printing plates. Different functional materials are printed in a layer-on-layer manner, then followed by a curing process that forms necking between the pigment particles [16]. The additive approach and high production capability of the printing presses significantly reduce production cost, number of manufacturing steps, and the need for energy, time, and consumables, as well as offer great reduction in waste compared to traditional photolithography manufacturing. Printing allows sheet-to-sheet or roll-to-roll mass production; thereby electronics can be manufactured not only on rigid, but also on thin, lightweight, flexible, and large area substrates [17, 18, 19, 20, 21, 22]. Printing technologies and the pluriformity of substrates open up launching brand new products that could have never existed before and realize bendable, rollable, wearable, or elastically stretchable devices. These printed electronics (PE) are environmentally friendly when compared to the traditional electronic methods. PEs are lightweight and not made with extremely harsh etching chemicals, and they do not occupy a massive amount of space in landfills. If the aim is to be green and sustainable, then printing technologies are certainly the future.
Additive manufacturing steps [
Printing techniques include conventional printing systems that require an intermediate printing plate to transfer a pattern (flexography, gravure, screen, and offset lithography) and nonimpact printing systems that print the pattern directly onto the substrates (digital and 3D) (Figure 8) [23]. However, nonprinting systems (liquid dispensing, aerosol) and coating systems (rod, blade, air knife metering) can also be used to dispense functional materials. The difference between the printing technologies originates from the ink characteristics (i.e., viscosity, rheology, surface tension), substrate types (i.e., papers, films, textiles), and printability properties (i.e., ink film thickness, resolution, speed, line quality) [24, 25]. Table 2 shows some of the printed feature size capabilities of printing processes for PE applications.
Classification of printing technologies based on printing plate requirement [
In recent years, the advancements in digital inkjet printing technologies have shown great promises for printed electronics. Akin to more conventional additive manufacturing strategies such as using screen printing, digital inkjet printing has been rapidly and successfully applied for rapid prototyping, low-volume production, and hybrid integration of critical components for a wide range of optoelectronic applications including energy harvesting, wearables, and biomedical sensors [26, 27].
There are PE components that have been researched and fabricated using these printing techniques such as solar cells, displays, and transistors [28, 29, 30, 31]. Similar to subtractive electronic manufacturing, PE components (Figure 9) require specific inks to provide functionalities like conductivity, resistivity, semi-conductivity, or color change by heat, light, moisture, pressure, or spoilage.
Printed electronics samples: circuit printed with nano-silver ink [
The materials listed in Table 3 present common functional pigments used in ink formulations. A typical ink formulation includes binders, vehicles, and additives besides the pigments [32]. Binders are the chemicals binding formulation ingredients to each other and to the substrate. Vehicle is the liquid portion of the formulation that carries the ink onto the substrates. Generally, ink formulations are classified based on the vehicle type, such as water-based, solvent-based, ultraviolet light/electron beam (UV/EB) based, or soy-based. Additives are used for supplementary properties, such as promoting stability, or preventing oxidation, flocculation, etc. [33]. In PE applications, binders and some of the additives act as an insulator and reduce the conductivity. There are studies that suggest binder-free formulations that reverse this impact and enhance conductivity [34].
Property | Screen | Flexo | Gravure | Inkjet |
---|---|---|---|---|
Viscosity (cP) | 500–5000 | 50–500 | 100–1000 | 10–20 |
Minimum trace width (μ) | 30–50 | 5–50 | 5–25 | 3–20 |
Minimum trace spacing (μ) | 50–100 | 20–30 | 10–25 | 10–20 |
Ink film thickness (μ) | 0.5–200 | 0.25–4 | 0.25–6 | 0.05–20 |
Common printed feature size of printing processes for PE applications.
Functionality | Pigment type |
---|---|
Conductors | Copper, silver, gold, carbon, aluminum, nickel, indium tin oxide, tin, graphene, graphene oxide, PEDOT:PSS, polyaniline, iron, graphite [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58] |
Semiconductors | Zinc oxide, silicon, zinc selenide, indium-gallium-zinc oxide, cadmium selenide, gallium arsenide, MALH [59, 60, 61, 62, 63, 64] |
Resistors | Aluminum oxide, hafnium dioxide, poly(4-vinylphenol), spin-on glass, parylene, solid electrolytes [65, 66, 67, 68, 69, 70, 71, 72] |
Functional pigment examples for PE applications [16].
In terms of solar energy harvesting, lighting and displays, and sensing applications, the research has pioneered new and better low-cost and printable optoelectronic materials and devices. Methylammonium lead halide (MALH) perovskites for instance (e.g., CH3NH3PbX3, X either I, Cl or Br) have shown great potential due to their unique optoelectronic properties and the ability to replace P-N junctions for various applications including light-emitting diodes, solar cells, and photodetectors [64]. The power-conversion efficiency of photovoltaic devices has been reported to increase from 3.8% in 2009 [73] to 22.1% in 2016 [74, 75]. Such progress is largely attributed to improved processing and longer charge-carrier lifetimes directly related to increased material quality. Yet, fundamental challenges including low carrier mobilities still prevent the fabrication of large-area devices with performances competing with state-of-the-art technologies [76].
The thermal and mechanical stability of substrates are critically important for the precise registration of functional ink layers upon each other to create PE components (i.e., electroluminescence lamp, capacitors, organic light-emitting diodes). Polymer films [77], papers [19, 78], flexible glass [79], textiles [80], and metal [81] have been given significant consideration as a substrate material. In PE applications, substrates either act as a base material to mechanically support electrical components such as circuit board [82], or as a top material for touch panels and display and lighting applications [83], or as an interlayer in batteries as separator membranes [84].
The quality and type of the substrate affect electrical, optical, mechanical, and magnetic properties of the functional ink layer [85] as well as economics [48]; therefore, its properties need to be engineered depending on the application. For instance, defects on the surface of the substrates may lead to pinholes and block electron flow in circuitry [77]. General requirements of substrates for PE include flexibility, transparency, surface smoothness, low thermal expansion, stiffness, heat resistance, low cost, thinness, and lightweight [86]. Table 4 presents different properties of substrates having the same 100 μm thickness for flexible backplane applications (glass, plastic films [PEN and PI], and stainless steel) [22].
Property | Unit | Glass | Plastics | Stainless steel |
---|---|---|---|---|
Weight | g/m2 | 250 | 120 | 800 |
Safe bending radius | cm | 40 | 4 | 4 |
R2R processable | — | Unlikely | Yes | Yes |
Transparency | — | Yes | Yes/some | No |
Maximum process temperature | °C | 600 | 180–300 | 1000 |
Coefficient thermal expansion | ppm/°C | 4 | 16 | 10 |
Elastic modulus | GPa | 70 | 5 | 200 |
Permeable to oxygen, water vapor | — | No | Yes | No |
Coefficient of hydrolytic expansion | ppm/RH% | None | 11 | None |
Electrical conductivity | — | None | None | High |
Thermal conductivity | W/m°C | 1 | 0.1–0.2 | 16 |
Comparison of substrate properties for flexible backplane application.
In printing industry, drying is performed with an oven or via UV lamps; however, the inks used for PE applications require higher temperatures for densification or crystallization to function properly. Once the functional ink layer is printed, the post-printing process is needed to enable connectivity in pigment particles, so the printed ink layer can conduct electricity (Figure 7). The connectivity is accomplished by means of one or multiple processes: drying, curing, sintering, reactive chemistry transformation, and annealing. These post processes change ink structure by volatilizing vehicle of the ink and form interparticle necking between the pigment particles that grow particle grain to form continuous functional layer, while decomposing the binder (Figure 10) [87, 88]. The main types of post-printing methods are listed as microwave heating and electrical, spark plasma, laser, and photonic sintering [89].
Illustration of interparticle necking.
The heat applied during curing volatilizes vehicle component of an ink formulation that allows functional pigments to contact each other. Sintering, on the other hand, is the process of pigment grain growth in the crystalline structure in the printed ink layer. However, the terms are used interchangeably. Sintering process requires optimization for each substrate as well as ink formulations since the chemical composition of the ink, particle size, shape and distribution, or degree of agglomeration varies. Different sintering parameters such as temperature, energy, time, or the atmosphere (ambient vs. inert) also cause variation in the performance of the same material. Figure 11 shows an example of the effect of photonic energy variation on the sheet resistance of printed nickel ink [16]. As the energy applied increases, the sheet resistance decreases.
Sheet resistance of printed Ni ink.
Photonic sintering has attracted great attention within the main types of sintering methods due to the instant heating applied during exposure, followed by an instant cooling, which is advantageous especially for the substrates with low glass transition temperatures. A pulse light from a xenon gas-filled flash lamp heats the functional ink layer in milliseconds beyond the maximum working temperature of the substrate. Then, the heat is removed rapidly in the interface of the substrate via conduction thanks to the thermal mass of the substrate and prevents structural degradation [90]. Three transient sintering conditions that are essential for an optimum photonic processing have been reported:
ink film thickness < substrate thickness
pulse of photonic light duration < thermal equilibration time of substrate
thermal equilibration time of ink film < pulse of photonic light duration
The thermal equilibration time of materials (
The thermal properties in Table 5, three transient conditions in Table 6, and the thermal profile presented in Figure 12 exemplify photonic sintering of a 36-μm-thick nickel ink film printed on a 250-μm-thick solid bleached sulfate (SBS) paperboard that is processed at ∼5 J/m2 photonic energy that provides 12 Ω/⃞ of sheet resistance [16]. A millisecond of two overlapped light pulse heats the surface of printed nickel ink to a temperature between 350 and 500°C. Thanks to the rapid cooling, the temperature in the interface, 20 μm depth of the paperboard, reaches only 200–320°C. Although the ignition temperature of paper is 233°C, the heat equilibrates below 200°C in less than two milliseconds, which is too short for substrate to observe any deformation. Photonic sintering enables significant reduction both in processing time and energy in comparison to conventional oven that uses high temperature and processing time ranging from several minutes to hours.
Factors | |||||
---|---|---|---|---|---|
Nickel ink | 440 | 8908 | 0.000036 | 90.9 | 1.4 × 10−5 |
SBS | 1400 | 900 | 0.000250 | 0.05 | 3.9 × 10−2 |
Thermal properties of the ink and substrate.
Factors | |
---|---|
Ink film thickness < substrate thickness | 36 μm < 250 μm |
Pulse light time < substrate thermal equilibration time | 0.001 s < 0.039 s |
Ink thermal equilibration time < pulse light time | 0.000014 s < 0.001 s |
Three transient conditions.
Thermal profile simulation of nickel ink on a SBS paperboard.
In today’s world, technological innovations accelerate at a much faster rate than before due to the more networked environment, advanced computers, data analytics, artificial intelligence (AI) tools, Internet of Things (IoT), and the speed of connectivity considering the ubiquitous information and communication technologies through Internet access, cloud computing, and smartphones [5, 91]. The innovations in the printed electronics area derive mostly from flexible and conformable disruptive device designs and structures (single or multilayer circuit constructions, sensors), formulation of materials (inks, substrates), and manufacturing process design (printing, post-printing, assembly). The performance of printed devices is mainly dependent on the complex ink formulation, adhesion, and the interactions between the inks and substrates to produce materials that can withstand post-printing, assembly, and environmental processes [48, 92, 93]. Therefore, most material providers follow closed innovation model and keep proprietary rights for their complex material formulation, processes, and methods to stabilize their position in the market [5].
The most common issues with the traditional screen-printed circuit manufacturing market are the limitation of printing finely spaced traces (Figure 13(a)), the printing process that requires new platemaking phase at each design and client change (Figure 13(b-c)), and the usage of high ink amount during printing which generates large amount of waste materials that has fairly complex disposal handling process (Figure 13(d)). In contrast, digitally printed electronic circuit manufacturing (Figure 13(e)) allows instantaneous design modification by simply changing the Gerber design file as well as dramatically reduces the material consumption [94]. The digital inkjet system is a rapidly emerging technology that could be in-lined to a hybrid automated component assembly pick-and-place robot systems (Figure 13(f)) where large-scale advanced manufacturing strategies can be explored as a potential way to reach seamless manufacturing of high volumes and open entirely new markets. However, the inkjet printing of functional inks requires a highly complex process optimization. Figure 14 exemplifies a concrete process optimization done in ÉTS laboratory for only one silver ink formulation.
(a) Commercial flexible hybrid electronic product that cannot be manufactured using screen printing (courtesies of Molex), (b) typical industrial grade screen-printing machine currently used for flexible circuit board manufacturing, (c) conventional screen stencil currently used for electronic board manufacturing, (d) typical screen printing application that uses tremendous amount of expensive functional inks, (e) industrial grade high-throughput digital inkjet printer vs. research grade in sectors like the graphic and consumer packaging industries (courtesy of Fujifilm), and (f) industrial grade fully automated pick-and-place robot for the assembly of electronic components of the circuit board (courtesies of C2MI and Varitron).
Digital inkjet printing process mapping and optimization. (a) Research grade Ceradrop digital inkjet printing system at ÉTS; (b) precise control of a wide range of critical jetting parameters; (c) examples of inkjet-printed features using Ag ink on Kapton® for different printing lattices (drop placement configurations), drop interpenetration, and substrate temperatures; (d) example of one-layer and two-layer process mappings looking at the Ag features’ thickness and uniformity for different substrate temperatures and drop interpenetrations in the hexagonal shifted lattice configuration; (e) typical high-resolution laser-scanning microscope image of the features obtained using a hexagonal shifted lattice with 50% interpenetration at 25°C substrate temperature; (f) example of a typical multivariate design-of-experiment analysis for process optimization (adapted from [
The massive push for intelligent cyber-physical systems associated with the Industry 4.0 and the IoT revolutions aims at taking better-informed decisions in real time, based on more complete and readily acquired sets of data. For the very reason, conformable printed sensors are deployed to collect data from places that are critical and difficult to access for energy, biomedical, transportations, manufacturing, smart building, or wearable electronics applications where better and cheaper flexible hybrid electronics circuits used as ubiquitous sensing elements would play a comprehensive role in ways that rigid devices cannot [95].
In this chapter, a general vision on energy sources and how an emerging field of printed electronics could consolidate green energy and environment is presented. An electronic circuit is found in a surprising number of devices that we use in our daily life. Manufacturing digitally printed electronic circuits is a sustainable method that dramatically reduces high energy consumption and toxic etching chemical usage relative to traditional electronic manufacturing. Advanced printed electronics is truly a transdisciplinary research and production landscape that benefit greatly from strongly intertwined interrelationships between multiple diverse complementary fields, including material formulation engineering, printable electronic devices architecturing, computational robotics and process automation, and AI and process optimization. It is important to adopt an agile mindset for a complete ecosystem to conduct transformative R&D for disruptive and advanced printed electronics manufacturing solutions.
This study was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) under the 2214-A program.
There is no conflict of interest.
Global business has become more competitive than before. The technology and dynamic life increase the opportunities and risks for several firms. Accordingly, financial statements and financial analysis must be developed to assess the company’s performance relative to its past performance or relative to its industrial competitors [1, 2, 3, 4, 5, 6]. The financial statements are annual reports containing essential information about the firm, including income, cash flows, and current financial condition, illustrating the assets, liabilities, and owners’ equity. However, if the financial information is not analyzed well, it will not help the company’s success and management decision-making. In addition, a firm should be prepared for the uncertainties and opportunities in the future; therefore, the financial analysis can support oversight of the future business [3, 7, 8].
The financial analysis uses financial statements to evaluate the firm’s overall performance, assess the equity securities, value opportunities, and risk, grow company earnings, and increase the cash flow. This study discusses financial statements, the difference and similarities between US GAAP and IFRS, financial data collection, research methodology, and analysis. In addition, a case study of one of the recent international companies, which is Tesla Motors, will be explained, and financial analysis and results will be applied to it [9, 10].
The used financial analysis method is financial ratios analysis. In this research, the profitability ratio, liquidity ratio, leverage ratio, and activity ratio will be applied to the financial statement of Tesla Motors. This study aims to evaluate the financial position of Tesla Motors through ratios and formulas to analyze the efficiency and business risk of the enterprise.
A financial statement consists three main statements that provide essential details and information about the company’s performance—income statements, balance sheets, and cash flow statements. The statements are analyzed annually using financial analysis techniques to continuously compare the firm effectiveness with previous years and compare it with the competitors from the same industry [11, 12, 13].
The income statement is defined as the profit and loss statements representing the cost of sales, total operating expenses, net profit to the net sales over a certain period, and earnings per share. The cost of sales contains the cost of merchandise, production, materials purchase expenses, research and development costs, and total operating expenses, including administrative and distribution expenses. To increase the net profit of the firm, expenses must be decreased, and sales have to be increased. The return of investment, financial flexibility, operating capabilities, and risk are essential information gathered from the income statement. The firm’s overall performance is measured by the return of investment, where the enterprise’s ability to adapt to consequences and opportunities is defined as financial flexibility. Moreover, the ability to maintain operations at the desired level is considered the operating capability, and risk is defined as the uncertainty related to the firm’s future. In summary, an Income statement supports the stakeholders and managers in evaluating the past performance, predicting future performance, and reducing the risk and uncertainty in achieving future cash flows [14].
The balance sheet statement is referred to as the statement of financial position. The primary role of the balance sheet is to report the firm’s assets, “economic resources,” liabilities, “economic obligations,” and equity over a particular period where total assets should be equal to total liabilities and equity “residual claims of owners.” The assets are shown concerning its cash liquidity, and the liabilities are related to its maturity date. The balance sheet can be measured by several values based on the relevance and reliability of desired attributes—a one-time cost, present cost, present market value, net realizable value, and the current value of future cash flows. At a specific balance sheet date, the current or present cost is the cash required to attain the asset, whereas the current market value is the amount of cash gained from selling the asset. In addition, the net realizable value is represented as the cash obtained from the sale of a future asset. The benefit of the balance sheet is to gather information and data about obligations, resources, and net resources equity. As well as it supports predicting the time, cost amounts, potential, and uncertainty of future cash flows [14].
The cash flow statement is a classification of cash payments and cash receipts issued by financing, operating, and investing activities. Each firm prepares the cash flow statement annually and compares the current year with previous years to evaluate the overall performance and plan the organization’s expenditures. The information and details provided by the cash flow statement report to stakeholders, lenders, and investors are cash that comes from or is used in operating and financing activities and the change of cash, whether increasing or decreasing in a particular period. In addition, the statement of cash flow support making economic decisions about the firm. The financing activities related to a firm are treasury stock, which describes the reacquisition of earlier issued shares, stock issuance, dividends payment to stakeholders, debt financing, and debt repayment. Investing activities contain fixed assets, debt sale or purchase, and equity securities of entities. Additionally, the operating activities are related to manufacturing companies and the sale of goods [14].
The above three statements can be prepared in accordance with two types of the conceptual framework, which are The International Financial Reporting Standards (IFRS), which is used the worldwide, and the United States Generally Accepted Accounting Principles (US GAAP), which was used in the US but recently it has been used by some firms in the UK and India. Both representations have similarities and differences in finance and account aspects. Some differences and similarities in financial aspects are illustrated in the table below (Similarities and Differences A comparison of IFRS, US GAAP, and UK GAAP*, 2005) (see Table 1).
Financial Statement | IFRS | US GAAP |
---|---|---|
Income statement |
|
|
Balance sheet |
|
|
Cash flow statements |
|
|
IFRS and US GAAP conceptual frameworks: Similarities and differences in financial statements preparation.
Sources: The Author.
To evaluate firm performance, it is complimentary to analyze the presented data and compare it with historical data or/and other competitors from the same industry. Thus, the basis and elements of comparison must be clarified to ensure an entity’s excellent performance and effectiveness. Analytical techniques can assess the firm’s capabilities to generate and grow the cash flow and earnings. Additionally, it supports identifying the cash flow and earnings risks for current and future times.
For example, one of the main aspects of comparison is the firm profitability compared with other companies. In most cases, there will be differences between the companies in the firm size, presenting financial information or/and the currency of financial data. Therefore, comparing the firms based on the net income will provide the right and valuable results. An alternative methodology was created, a ratio analysis technique that expresses one value concerning another value that enables more sufficient and accurate comparison and results. Furthermore, performing the standard size of financial statements eliminate the size factor, which provides improper results.
Regarding the issue of currency differences that appear from comparing international companies, an alternative method rather than using ratio analysis is using global exchange rates and unifying the currency in financial status at the end of a particular period. In addition to that, the enterprise compares its performance over time. Using the ratio analysis, which is horizontal financial statements that compare the current year to a based year and implement the results as a graph, shows the significant changes in the firm’s effectiveness and performance [14].
The primary objectives of using ratio analysis are as follows:
Assess the past performance, evaluate the current financial position, and predict future opportunities and risks.
Support analysis to determine earnings and free cash flow.
Examine the firm’s financial flexibility and ability to provide the cash needed to grow the firm and meet the obligations in normal or unexpected circumstances.
Improve management’s ability to make better decisions related to enterprise growth (Henry, Robinson, and Van Greuning, n.d.).
Types of ratio Analysis:
The automobile industry is the producer of electric, hybrid, and gasoline-powered vehicles and one of the largest industries that affect the economy and culture of the world. Moreover, it opened a broader market area for many businesses and commerce by using vehicles in transporting people and goods. Based on the worldwide statistics, the leading countries for the production of passenger cars in 2018 are represented in the figure below. The total global sales of passenger cars reached 62 million vehicles in 2018, and the United States produced around 2.8 million vehicles. Accordingly, the US is considered one of the largest automobile markets in production and sales.
The most produced and selling brands of vehicles in the US automobile industry are Ford, Volkswagen, Toyota, Hyundai, and Chevrolet. All mentioned models are fuel-based vehicles where a new generation of alternative energy resources was developed in the US to produce and sell hybrid and electric vehicles. One of the leading global producers of electric cars is Tesla Motors. This research discusses an overview of Tesla Motors, methodology, and analysis of Tesla’s financial statements (see Figure 1) [15].
Leading countries for the production of cars in 2018 [
Tesla Motors is an international manufacturing automotive and energy company founded in 2003 and based in California, US. The company is founded by Martin Eberhard, Marc Tarpenning, Elon Musk, J. B. Straubel, and Ian Wright. The organization aims to establish a sustainable energy eco-system by creating affordable vehicles and building unique energy solutions like solar roofs, power walls, and power packs. Tesla’s automotive and energy solution enables the consumers to manage the generation, consumption, and storage of renewable energy. Tesla Motors achieved a financial turnover of around 21.5 billion US dollars in the fiscal year of 2018 and 45,000 employees in 30 worldwide branches.
Due to the massive competition in the automotive industry, the global economy affecting the business, and the competitive prices, Tesla Motors added a unique value to its customers by alternating fuel-based vehicles with electric vehicles. Although Tesla avoids the risk of increasing the oil prices, technological and political environments significantly impact Tesla vehicle prices. Therefore, the financial and non-financial performance of Tesla should be analyzed carefully to support in making critical decisions and to determine the future risk and potential of the company [17].
The historical financial information and data of Tesla Motors provide a better understanding of its financial position and cash flow forecast. Moreover, by comparing the annual financial reports, the created value of Tesla and performance relative to peers can be examined. The financial information contains annual reports of Tesla’s income statement, balance sheet, and cash flow. Those financial details are authenticated and published by Tesla Motors company. In this research, the financial data duration will be analyzed, including the years from 2015 to 2018. A copy of detailed Tesla financial statements is attached in appendix A. Furthermore, the model used to evaluate Tesla’s financial performance is described in (Figure 2).
The research methodology.
The financial technique used in this research is the ratios analysis technique, which provides financial measurements and results to indicate the performance of Tesla Motors. The main four ratios for financial data analysis are liquidity ratios, assets management ratios, profitability ratios, and debt management ratios (Appendix B). Each ratio contains various formulas that describe an essential principle of finance and account, represented in the table below (see Table 2).
Liquidity Ratios | Asset Management Ratios | Profitability Ratios | Debt Management Ratios |
---|---|---|---|
1. Current Ratio | 1. Accounts Receivable Turnover | 1. Net Profit Margin | 1. Debt Ratio |
2. Quick Ratio | 2. Inventory Turnover Ratio | 2. Gross Profit Margin Ratio | 2. Time Interest Earned |
3. Cash Ratio | 3. Accounts Payable Turnover | 3. Operating Profit Margin |
Financial ratios analysis.
The liquidity ratio indicates the strong ability to use its asset to cover its short-term debts. The three liquidity ratios used in this research are current ratio, quick ratio, acid test, and cash ratio.
The current ratio formula is performed by dividing the current assets by the current liabilities for the same year. The current asset consists of cash and cash equivalents, restricted cash, net accounts receivable, inventory, prepaid expenses, and other current assets, where current liability includes Accounts payable, accrued liabilities, deferred revenue, resale value guarantee, customer deposits, current portion of long-term debt and capital leases (see Table 3 and Figure 3).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Current Assets | $ 8,306,308 | $ 6,570,520 | $ 6,259,796 | $ 2,782,006 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Current Ratio | 0.8313 | 0.8561 | 1.0743 | 0.9897 |
Current ratio analysis.
Current ratio graph.
The acid test or quick ratio is calculated by eliminating the inventories from current assets and dividing them by current liabilities (see Table 4 and Figure 4).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
(Current Assets-Inventories) | $ 5,192,862 | $ 4,306,983 | $ 4,192,342 | $ 1,594,168 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Acid Test Ratio | 0.5197 | 0.5612 | 0.7195 | 0.5671 |
Acid test ratio.
Acid test ratio graph.
A cash ratio is a type of measurement, which evaluates the strong ability to cover its current liability by only its cash and cash equivalent (see Table 5 and Figure 5).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Cash and Cash Equivalent | $ 3,685,618 | $ 3,367,914 | $ 3,393,216 | $ 1,196,908 |
Current Liability | $ 9,992,136 | $ 7,674,670 | $ 5,827,005 | $ 2,811,035 |
Cash Ratio | 0.3689 | 0.4388 | 0.5823 | 0.4258 |
Cash ratio.
Cash ratio graph.
The most important financial ratios for the manufacturing company are asset management because it effectively measures the enterprise usage and control of its assets. It consists many ratios, but in this research, the accounts receivable turnover, inventory turnover, accounts Payable turnover, and total asset turnover will be implemented on General Motors’ financial statements.
The accounts receivable turnover measures the number of cash collection times during a particular period, and it is calculated by dividing the sales by the average account receivable (see Table 6 and Figure 6).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Average Accounts Receivable | $ 949,022 | $ 515,381 | $ 499,142 | $ 168,965 |
Accounts Receivable Turnover Ratio | 22.61 | 22.82 | 14.02 | 23.95 |
Accounts receivable turnover ratio.
Accounts receivable turnover ratio graph.
This ratio is calculated several times inventories are sold and restocked yearly. All manufacturers have Inventories to keep unsold stocks which cost them significant value until the materials are sold out. It is measured by dividing the cost of goods sold over the average inventories (see Table 7 and Figure 7).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Cost of Goods Sold | $ 17,419,247 | $ 9,536,264 | $ 5,400,875 | $ 3,122,522 |
Average Inventories | $ 2,688,491 | $ 2,165,495 | $ 1,672,646 | $ 1,115,756 |
Inventory Turnover Ratio | 6.48 | 4.40 | 3.23 | 2.80 |
Inventory turnover ratio.
Inventory turnover ratio graph.
Since raw materials are considered the main expenses of manufacturing firms, the accounts payable turnover measures the speed of paying the purchasing of raw materials or inventories on the account. The account payable turnover is calculated by dividing the purchases over average accounts payable. The below formula calculates the value of the purchase (see Table 8 and Figure 8).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Purchases | $ 18,269,156 | $ 9,732,347 | $ 6,190,491 | $ 3,446,685 |
Average Accounts Payable | $ 3,404,451 | $ 2,390,250 | $ 1,860,341 | $ 916,148 |
Accounts Payable Turnover Ratio | 5.366 | 4.072 | 3.328 | 3.762 |
Accounts payable turnover ratio.
Accounts payable turnover ratio graph.
Purchases = Cost of goods sold + [(Ending inventory) – (Beginning inventory)].
The company’s overall efficiency and performance are evaluated by the profitability ratio, where it concentrates on measuring the assets and controlling the expenses to generate a reasonable rate of return. In addition, it analyses the firm current operational performance compared to previous years. The net profit margin, gross profit margin ratio, and operating profit margin ratio will be performed on the financial statements of Tesla Motors.
The net profit margin is calculated by dividing the net profit after tax over the net sales. For any automotive company, the higher the net profit margin, the better the performance (see Table 9 and Figure 9).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Net Profit after Tax | $ 1,062,582 | $ 2,240,578 | $ 773,046 | $ 888,663 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Net Profit Margin (Percentage) | 4.951% | 19.055% | 11.043% | 21.964% |
Net profit margin.
Net profit margin graph.
A gross profit margin serves as the source of paying additional expenses and savings for the future to assess financial health. The gross profit margin ratio is calculated by dividing the gross profit over sales (see Table 10 and Figure 10).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Gross Profit Margin | $ 1,004,745 | $ 2,209,032 | $ 746,348 | $ 875,624 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Gross Profit Margin Ratio (Percentage) | 4.682% | 18.786% | 10.662% | 21.642% |
Gross profit margin ratio.
Gross profit margin ratio graph.
This ratio is calculated by dividing the operating profits over sales (see Table 11 and Figure 11).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Operating Profits | $ 388,073 | $ 1,632,086 | $ 667,340 | $ 716,629 |
Sales | $ 21,461,268 | $ 11,758,751 | $ 7,000,132 | $ 4,046,025 |
Operating Profit Margin Ratio | 0.018 | 0.139 | 0.095 | 0.177 |
Operating profit margin ratio.
Operating profit margin ratio figure.
The degree of safety afforded to creditors is financial leverage or debt financing. There are two methods to obtain the enterprise debt by determining the borrowed funds used to finance assets on the balance sheet. The other is by obtaining the fixed charges covered by the operating profits in the income statement.
The debt ratio is calculated by dividing total debt over total assets, where total debt contains current liabilities and long-term debt (see Table 12 and Figure 12).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
Total Debt | $ 13,433,874 | $ 15,348,310 | $ 10,923,162 | $ 4,125,915 |
Total Assets | $ 29,739,614 | $ 28,655,372 | $ 22,664,076 | $ 8,067,939 |
Debt Ratio | 0.452 | 0.536 | 0.482 | 0.511 |
Debt ratio.
Debt ratio graph.
The time interest earned is measured by dividing the earnings “EBIT” before interest tax by the interest charged. The ratio indicates the enterprise’s ability to meet the interest payment (see Table 13 and Figure 13).
Category/Year | 2018 | 2017 | 2016 | 2015 |
---|---|---|---|---|
EBIT | $ 4,340,986 | $ 2,208,596 | $ 1,600,685 | $ 917,671 |
Interest Charges | $ 663,071 | $ 471,259 | $ 198,810 | $ 118,851 |
Time Interest Earned Ratio | 6.547 | 4.687 | 8.051 | 7.721 |
Time interest earned ratio.
Time interest earned graph.
An overview of financial statements, financial presentation methods, and financial analysis was discussed. A real-life case study on Tesla Motors was implemented to perform the financial analysis and concluded the results of its financial statements and analyses to evaluate its performance.
This study concludes that Tesla Motors continuously suffers from losses. Tesla Motors has a high value of assets since they concentrate on adding value to the customers and inventing unique electric vehicles. In addition, the automobile industry is too competitive where vehicle manufacturers compete to drive the attention of various stakeholders in the market. Furthermore, the new idea of shifting from fuel-based vehicles to electric-based vehicles needs significant duration to convince stakeholders to purchase the developed electric cars. However, this research proves that Tesla Motors made low gross profits where it decreased from 21.642% in 2015 to 4.682% in 2018. The decrement is due to high maintenance costs, research and development cost, selling expenses, and administrative expenses. Furthermore, the interest percentage is too high where Tesla Motors is accumulating the losses, which leads to increasing the interest expenses of the current year. The financial ratios support Tesla Motors to highlight the current firm position and provide the potential threats and opportunities in the future.
This study concludes that Tesla has changed their strategy to become the most worldwide sales of purely battery electric vehicles, capturing 23% of the market and 16% of the plug-in electric battery in the market for 2020. It has also developed a significant installer of photovoltaic systems through its subsidiary Tesla Energy in the United States. One of the largest global battery energy-storage systems suppliers is Tesla Energy, with 3.99 gigawatt-hours (GWh) installed in 2021.
This study also concludes that Tesla has changed its production strategy over time. It started to produce its first car model, the Roadster sports car, in 2009, which was followed by the Model S sedan in 2012, the Model X SUV in 2015, the Model 3 sedan in 2017, and the Model Y crossover in 2020. However, the Model 3 is the best-selling plug-in electric car in the global market, and, in the mid of 2021, it became the first electric car sale with 1 million units globally. The sale strategy thus has been developed. The global sales of Tesla increased to 936,222 cars in 2021, with an 87% increase over the previous year, and cumulative sales for all years totaled 2.3 million cars at the end of 2021. By the end of 2021, The market capitalization of Tesla reached $1 trillion to hold the rank 6 in US market history.
The year 2017–2018
The year 2017–2016
The year 2016–2015
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\n1. RETRACTIONS
\\n\\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\\n\\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
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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:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{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. 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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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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/129252",hash:"",query:{},params:{id:"129252"},fullPath:"/profiles/129252",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()