Leaf concentrations of Cu, Fe, Mn, and Zn of soybean affected by cobalt and molybdenum application mode and
\r\n\t
",isbn:"978-1-80356-477-7",printIsbn:"978-1-80356-476-0",pdfIsbn:"978-1-80356-478-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"b306ce94998737c764d08736e76d60e1",bookSignature:"Dr. Alyssa A Brewer and Dr. Brian Barton",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11636.jpg",keywords:"Mammalian, Primate, Human, Genetics and Epigenetics, Individual Variability, Adaptation, Cortical Reorganization, Cortical Recovery, Visual Field Map, Sensorimotor Maps, Bottom-Up Sensory Processing, Top-Down Visual Attention",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 3rd 2022",dateEndSecondStepPublish:"May 4th 2022",dateEndThirdStepPublish:"July 3rd 2022",dateEndFourthStepPublish:"September 21st 2022",dateEndFifthStepPublish:"November 20th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Brewer is a Stanford-trained physician-scientist, tenured Associate Professor at UC Irvine, and Director of the mind space Lab, who uses cutting-edge computational neuroimaging to study the organization and plasticity of the human sensory cortex.",coeditorOneBiosketch:"Dr. Barton is a UCI-trained cognitive scientist who has pioneered the study of auditory field maps in the human cortex and currently focuses his research on computational neuroimaging measurements of audiovisual cortical processing and organization.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"115304",title:"Dr.",name:"Alyssa",middleName:"A",surname:"Brewer",slug:"alyssa-brewer",fullName:"Alyssa Brewer",profilePictureURL:"https://mts.intechopen.com/storage/users/115304/images/system/115304.jpg",biography:"Dr. Alyssa A. Brewer completed her undergraduate degrees at Stanford University, with a B.S. with Honors in Biological Sciences and an A.B. in Comparative Literature with interdisciplinary Honors in Humanities. She continued on at Stanford in a dual-degree graduate program, graduating with an M.D. and a Ph.D. in Neuroscience in 2007. Her work in graduate school with Brian Wandell, Ph.D., focused on computational neuroimaging measurements of visual cortex organization and plasticity in humans and macaque. She now is an Associate Professor in the Departments of Cognitive Sciences and Language Science, by courtesy, at the University of California, Irvine. 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Brewer, M.D., Ph.D. He continued on at UCI as a post-doctoral scholar for three years with Drs. Brewer, Greg Hickok, Ph.D., and Kourosh Saberi, Ph.D., applying his vision work to measurements of the organization of human auditory cortex. 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\nThis explains why the soybean plant is very demanding on nitrogen (N). It is estimated that 80 kg of N is needed to produce 1000 kg of soybean grains. Therefore, to obtain high yields, the biological fixation of N2 (BNF) should be as efficient as possible [2–7].
\nThe process of BNF in Brazil is responsible for nitrogen accumulated by plants; it represents about 200 kg ha-1 N [8], which is no longer applied via mineral fertilizers. It reduces the cost of production [9].
\nIn addition, the use of selected and efficient bradyrhizobia inoculant and cobalt (Co) and molybdenum (Mo) nutrition contributes decisively in the BNF [10]. Cobalt and molybdenum are essential for BNF [11]. The first B12 vitamin is essential for the processing of BNF and other parts of the molybdoenzymes, used in absorption and metabolism of nitrogen [12]. The application of Mo and especially Mo + Co increases BNF [13].
\nIn Brazil, soybean generally responds positively to fertilization with Mo in soils of low fertility and in fertile soils depleted of Mo due to long‐term cropping. The micronutrient can be supplied by seed treatment. However, the toxicity of Mo sources to
Considering the main current limitations and potential of BNF in soybean crop and benefits attributed to various crops by inoculation with
Bacteria promoters of plant growth (BPPG) correspond to a group of beneficial microorganisms to plants due to the ability to colonize the surface of roots, rhizosphere, phyllosphere, and internal plant tissues [16, 17]. The BPPG can stimulate plant growth in several ways. The most relevant are BNF capacity [18], increase in nitrate reductase activity when the BPPG grows endophytically plants [19], production of hormones such as auxins, cytokinins, gibberellins, and ethylene, and a variety of other molecules [20], phosphate solubilization [21], and act as biological control agent of pathogens [22]. In general, it is believed that the benefit of BPPG to plant growth is caused by a combination of all these mechanisms [23].
\nBased on the above information and the lack of research about the interaction between co‐inoculation with
The experiment was conducted in the 2014/2015 season in an experimental area that belongs to the UNESP Engineering Faculty located in Selvíria, MS/Brazil, with the following geographical coordinates, 20o22′S and 51o22′W and an altitude of 335 m. The experimental area soil was classified as Distroferric Red Oxisol with clay texture (the granulometric analysis indicated values of particle size of 420, 50 kg-1, and 530 g of sand, silt, and clay, respectively), according to Embrapa (2013) [25], which has been cultivated with annual cultures over 27 years, with the last 10 years in the direct tillage system. Before soybean sowing, corn was cultivated in the area. The annual average temperature was 23.5°C, the annual average pluvial precipitation was 1370 mm, and the annual average relative air humidity was between 70% and 80%.
\nThe experimental design was carried out in a randomized blocks with six treatments and four replications. The treatments were as follows: (1) control (without soybean inoculation with
In all treatments, the inoculation with Rhizobium was performed in seeds at a dose of 200 ml ha-1 (strains: SEMIA 5019 (
Chemical properties of the soil in the tillable layer were determined before 2014, before the soybean experiment began. The methods proposed by Raij et al. [26] provided the following results: 10 mg dm‐3 of P (resin), 5 mg dm-3 of S‐SO4, 22 g dm-3 of organic matter (OM), pH(CaCl2) of 5.3, 2.4 mmolc dm-3 of K+, 21.0 mmolc dm-3 of Ca2+, 18.0 mmolc dm-3 of Mg2+, 28.0 mmolc dm‐3 of H+Al, 3.2 mg dm-3 of Cu, 22.0 mg dm-3 of Fe, 24.2 mg dm-3 of Mn, 1.2 mg dm-3 of Zn (diethylenetriaminepentaacetic acid (DTPA)), 0.16 mg dm-3 of B (hot water), and 60% base saturation. Based on soil analysis and soybean crop fertilization recommendation [27], the fertilization was done in the seed furrows with 96 kg P2O5 ha-1 (in the form of triple superphosphate) and 70 kg ha-1 K2O (in the form of potassium chloride).
\nThe seeds were treated with the fungicide Thiram + Carbendazim at a dosage of 30 + 70 g active ingredient (a.i.) per 100 kg seed, respectively, after drying the seeds, and were inoculated with Rhizobium, and depending on the treatment the seed was inoculated with
The experiments were conducted in a no‐tillage system. The area was irrigated by a central pivot sprinkler system when necessary. The water coverage was 14 mm over a period of around 72 h. The control of weeds, pests, and diseases prevention was carried out when necessary in soybean crop. The plants were harvested 120 days after soybean emergence.
\nConcentrations of N, P, K, Ca, Mg, S, Cu, Fe, Mn, and Zn were measured in soybean plant leaves. The third upper trifoliate leaves (30 plants) in the flowering soybean plants (R2 stage) were collected according to the methodology described in Ambrosano et al. [27]. The determination of nutrients was carried out as described by Malavolta [28]. The leaf chlorophyll index (LCI) was determined indirectly after application of the treatments and when the plants were in the flowering (R2 stage), in 10 plants per plot through readings in the third upper trifoliate leaves, using a digital chlorophyll CFL 1030 Falker (Falker Agricultural Automation, Porto Alegre, Brazil).
\nThe leaf area of 10 leaves per plot was measured using the software ImageJ 1:45 (2011), according to the methodology described by Bauermann [29]. At the time of harvest, 10 soybean plants representing were collected for counting the number of grains per pod, grains per plant, and mass of 100 grains. The mass was determined on a precision scale of 0.01 g and corrected for 13% moisture (wet basis). The soybean was harvested from the plants in the useful area of each plot and grain yield was calculated after mechanical threshing. Data were transformed into kg ha-1 and corrected for 13% moisture (wet basis). The results of all the evaluations were subjected to analysis of variance and the Tukey test at 5% probability to compare the averages of treatments, using the Sisvar program.
The seed inoculated with
Treatments | N | P | K | Ca | Mg | S |
---|---|---|---|---|---|---|
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ g kg-1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ | ||||||
Control | 48.65 b | 4.01 a | 19.12 a | 9.70 a | 5.67 ab | 3.06 a |
Co, Mo seed | 50.91 ab | 3.75 a | 19.98 a | 8.94 a | 4.55 b | 3.08 a |
Co, Mo + Azos seed | 56.21 a | 4.16 a | 20.42 a | 8.64 a | 4.73 ab | 3.16 a |
Azos foliar | 49.00 b | 4.75 a | 19.92 a | 9.51 a | 5.63 ab | 3.36 a |
Co, Mo foliar | 55.95 a | 4.01 a | 18.38 a | 8.74 a | 5.32 ab | 3.35 a |
Co, Mo + Azos leaf | 54.30 ab | 4.28 a | 18.70 a | 8.99 a | 5.83 a | 3.64 a |
Overall average | 52.50 | 4.16 | 19.42 | 9.09 | 5.29 | 3.28 |
CV (%) | 4.34 | 9.42 | 5.42 | 8.75 | 7.77 | 12.73 |
LSD (5%) | 6.46 | 1.11 | 2.99 | 2.26 | 1.17 | 1.18 |
Leaf concentrations of Cu, Fe, Mn, and Zn of soybean affected by cobalt and molybdenum application mode and
Means followed by the same letter in the column do not differ by the Tukey test at 5%. CV: coefficient of variation; LSD: least significant difference.
Increases in total nitrogen biologically fixed by plant through symbiosis with rhizobia, associated with
The treatments in this research provided similar leaf concentrations of P, K, Ca, and S (Table 1). However, there was a higher concentration of Mg in the leaves when Co and Mo and
The leaf chlorophyll index (LCI) and leaf Fe and Cu concentrations were not affected by treatments (Table 2). This can be explained by adequate leaf N concentrations obtained for soybean crop. Zuffo et al. [30] also observed that the use of
Treatments | LCI | Cu | Fe | Mn | Zn |
---|---|---|---|---|---|
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ mg kg-1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ | |||||
Control | 43.79 a | 9.00 a | 156.33 a | 90.00 ab | 47.67 b |
Co, Mo seed | 44.12 a | 8.33 a | 163.33 a | 71.00 b | 50.00 ab |
Co, Mo + Azos seed | 44.52 a | 8.67 a | 189.67 a | 70.33 b | 47.67 b |
Azos foliar | 43.56 a | 10.33 a | 212.33 a | 85.00 ab | 53.33 a |
Co, Mo foliar | 44.23 a | 9.33 a | 191.00 a | 84.67 ab | 46.00 b |
Co, Mo + Azos leaf | 44.15 a | 10.67 a | 184.00 a | 97.67 a | 50.00 ab |
Overall average | 44.06 | 9.39 | 182.78 | 83.11 | 49.11 |
CV (%) | 3.11 | 9.46 | 27.89 | 11.06 | 3.89 |
LSD (5%) | 1.55 | 2.52 | 144.60 | 26.08 | 5.42 |
Leaf chlorophyll index (LCI) and leaf concentrations of Cu, Fe, Mn, and Zn of soybean affected by cobalt and molybdenum application mode and
Means followed by the same letter in the column do not differ by the Tukey test at 5%. CV: coefficient of variation; LSD: least significant difference.
The results are different from those found by other authors using corn plants, who found that the LCI was higher in the treatments with diazotrophs than in the treatments without inoculation. Corn plants that were inoculated with
Leaf application of Co and Mo and foliar inoculation with
The leaf area of soybean was greater in treatment with the application of
Treatments | Leaf area (cm2) | Grains per pod | Grains per plant | Mass of 100 grains (g) | Grains yield (kg ha-1) |
---|---|---|---|---|---|
Control | 64.05 ab | 3.00 a | 176.30 a | 14.68 b | 5550 b |
Co, Mo seed | 62.90 b | 2.55 b | 145.20 a | 15.88 ab | 6083 ab |
Co, Mo + Azos seed | 68.35 ab | 2.78 ab | 155.67 a | 16.10 a | 6557 a |
Azos foliar | 77.80 a | 2.65 b | 156.90 a | 14.80 ab | 5355 b |
Co, Mo foliar | 69.75 ab | 2.80 ab | 185.07 a | 14.60 b | 5685 ab |
Co, Mo + Azos leaf | 67.50 ab | 2.65 b | 138.47 a | 14.88 ab | 5602 ab |
Overall average | 68.39 | 2.74 | 159.60 | 15.15 | 5805 |
CV (%) | 4.89 | 4.87 | 14.61 | 3.76 | 7.27 |
LSD (5%) | 14.26 | 0.31 | 66.14 | 1.31 | 970 |
Leaf area, grain per pod, grains per plant, mass of 100 grains, and grains yield of soybean affected by cobalt and molybdenum application mode and
Means followed by the same letter in the column do not differ by the Tukey test at 5%. CV: coefficient of variation; LSD: least significant difference.
The control treatment provided greater number of grains per pod, and the number of grains per pod did not differ between treatment with Co and Mo of the leaf and treatment of inoculation of the seed with
Seed inoculated with
These results may be due to several mechanisms, which are the anticipation in the BNF of the nodes, an increase in the dry weight of nodes, promoting the occurrence of nodulation heterologous through the increased formation of hair root and secondary roots, an increase in infection sites, inhibition of plant pathogens and production of phytohormones and influences in the partition of dry matter between the roots and shoots [24]. Yet, pondering Hungria et al. [15], these results caused by co‐inoculation bacteria promoters of plant growth and Rhizobia appear to be under the influence of specific signals among bacterial genotypes involved and the genotype of the host plant. It is important to do more related studies on the response of the co‐inoculation depending on the genotypes, aiming at the development of more responsive genotypes.
\nIn an important research by Campos et al. [13], they concluded that there are no Mo and Co effects on nodulation in soil with established
Leaf application of Co and Mo and foliar inoculation with
Seed inoculated with
This research demonstrated that co‐inoculation with
Climate change is the shift in the state of climate that can be observed through variability of its components and lasts for a relatively long period of time, typically decades [1]. Long-term changes in climate are caused by natural factors, such as modulations of the solar cycle, as well as human factors. The current climate change is largely attributed to the high concentration of anthropogenically generated CO2 in the atmosphere. As at September 2020, the atmospheric CO2 concentration was around 414 ppm, a concentration way above a historic maximum of 300 ppm estimated at around 300 000 years ago [2]. The burning of fossil fuels and deforestation have accelerated the concentration of CO2 and other greenhouse gases in the atmosphere. The increasing human population may result in more deforestation to establish farmland and human settlements. In the near term, the concentration of atmospheric CO2 is expected to continue rising, together with the resultant consequences of climate change.
A warming climate has undesirable consequences for biodiversity in many ecosystems and negatively impacts livelihoods, especially in developing countries with less resilient systems. Polar ice is melting at a rapid rate, increasing sea level that threatens to flood coastal communities and small islands. In the Arctic, summer sea ice extent has declined by 45% over the last 30 years, glaciers have lost their protective cap of perennial ice, permafrost is thawing rapidly and coastlines are experiencing high wave action and erosion [3]. Climate change may increase frequency and severity of pest and disease outbreaks [4]. Extreme weather events, such as floods, storms, hurricanes, droughts, wild fires and heatwaves are being frequently experienced with devastating consequences on livelihoods and ecosystems [5]. Severe and more frequent droughts in Zimbabwe and the increasing incidences of malaria, a disease that affects about 50% of the world’s population, have all been attributed to climate change [5, 6]. In addition to impacting livelihoods at personal, family and community levels, climate change also impacts business activities.
International, regional and national actions, conventions, agreements and policies have been drafted to reverse and mitigate risks and impacts of climate change. Over 180 countries which form parties of the United Nations Convention on Climate Change (UNCCC) drafted the Paris Agreement in 2015 to tackle climate change. One of the objectives of the Paris Agreement is to hold the increase in global average temperature below 2°C above preindustrial levels [7]. All parties agreed to determine national contributions to reduce emission of greenhouse gases that will help achieve the objective. The nationally and voluntarily determined targets are renewed incrementally after five years. Limiting global average temperature increase to below 2°C above preindustrial level is strongly predicted to significantly reduce risks and impacts of climate change.
Regionally, under the EU’s European green deal, the European climate law sets a goal of achieving carbon neutrality by 2050. The European climate pact encourages governments and local authorities to engage all citizens in climate change action, while the 2030 target plan aims to reduce greenhouse gas emissions by at least 55% by 2030 [8]. Other regional and continental organizations, such as the African Union, the Arab states and the Organization of American States, have similar plans to curb emission of greenhouse gases [9, 10, 11]. Nationally, governments are implementing different measures, including giving tax credits to companies that switch to clean energy, such as wind and solar energy. Countries are also tightening regulations on deforestation and vehicle fuel standards. International, regional and national not-for-profit organizations and civil society groups are aggressively involved in diverse activities to combat climate change, including advocacy, education and litigation.
Socially responsible investing (SRI) is a theme that advances investor morals and values. SRI is not necessarily a new theme as it can be traced back centuries ago. SRI is integrating personal values and societal concerns with investment decisions [12, 13, 14]. Restrictions to investing, called negative screening, in companies linked to weapons, tobacco, alcohol, gambling and slavery imposed by the church during the middle ages marked the early roots of SRI [15]. Under SRI, an investor intentionally invests to effect desirable social change. In recent history, investment restrictions were imposed on companies linked to colonialism, the Vietnam war, ponography and racism, among other issues [16]. It is emerging, though, that although negative screening can be useful to express ethical, religious or moral values of investors through their investment portfolios, for many, it may prove to be too restrictive [17].
Closely aligned with SRI is environmental, social and governance (ESG) investing, which involves integrating environmental, social and governance factors into fundamental investment analysis with the belief that the factors are material to financial performance. ESG investing takes a broader view than SRI by examining whether environmental, social and governance factors are important to performance, and therefore to the investment performance of a long-term portfolio [17]. In 1996, after recognizing the importance of ESG investing, the UN launched the United Nations Principles for Responsible Investment (UNPRI). The UNPRI is an investor led initiative to support investors when incorporating environmental, social and governance (ESG) factors into their investment decisions [18].
The aim of the chapter is to describe factors that are likely to elevate ESG investing in reducing greenhouse gas emissions. Due to its growing acceptance among investors, ESG investing should be recognized, just like international, regional and national initiatives, as vital to reduce risks and impacts of climate change. The objectives of the chapter are to (i) describe the concept of ESG investing, (ii) describe factors increasing the contribution of ESG investing in reducing emission of greenhouse gases, and (iii) discuss challenges of ESG investing. With rising consumer activism among a world population that is getting increasingly younger on average and more environmentally conscious, investors are likely to be under growing pressure to invest responsibly.
ESG investing is the consideration of environmental, social and governance factors (Table 1), alongside financial factors, when evaluating risks, opportunities and sustainability of investments [20, 21]. ESG investors believe that ESG factors are drivers of a company’s long-term value, risk and return, and the factors indicate long-term sustainability [21].
Environmental | Social | Governance |
---|---|---|
Air emissions | Adequate housing | Antitrust violations |
Air quality | Abortion services | Auditor independence |
Biodiversity protection | Child labor | Board independence |
Community health | Consumer privacy | Board diversity |
Community safety | Minorities employment | Disclosure of risk |
Community security | Human rights | Executive compensation |
Energy conservation | Indigenous people rights | Oversight strategy |
Fossil fuels | Income equality | Reporting transparency |
Hazardous materials use | Slavery | Voting rights |
Land contamination | Unionism | |
Natural resources use | ||
Renewable energy use | ||
Waste generation | ||
Waste recycling |
List of potential environmental, social and governance (ESG) factors considered under ESG investing [19].
The “E” in ESG investing considers how a company takes care of the natural or physical environment. Environmental factors (Table 1) consider a company’s utilization of natural resources and impacts of its direct operations and supply chains on the environment. The environmental factors examine a company’s environmental disclosure, impact and efforts to reduce carbon emissions, issues which represent risks and opportunities for a company [22]. Conservation of biodiversity, pollution, waste generation and community health are common environmental factors that pose risks and opportunities. For example, companies that violate waste disposal regulations are prone to costly litigation and criminal prosecution while those implicated in biodiversity loss may experience negative publicity and customer backlash. The 2010 BP oil spill in the Gulf of Mexico brought a record fine and furious negative publicity to the company. Investors may choose to avoid oil and gas companies altogether because of such environmental risks associated with their operations.
Reduction of greenhouse gas emissions is becoming a significant positive screening environmental factor in light of the current climate change. Climate change is expected to increase the occurrence of catastrophic events and, therefore, it imposes a realistic financial risk, especially to companies that are inadequately prepared and poorly resourced [22]. Carbon emissions have been categorized into scopes 1–3. Scope 1 are carbon emissions directly linked to the activities of a company and they mostly occur at premises of the company [23]. For example, carbon emissions resulting from baking of bread at a bakery or burning of coal at a power plant constitute scope 1 emissions for the bakery and power plant. Emissions associated with the supply of electricity are scope 2. All other indirect emissions not associated with electricity constitute scope 3 [23]. Scope 3 may emanate from downstream (consumers) and or upstream activities (suppliers). Since on average more than 75% of an industry sector’s carbon footprint is from scope 3 sources, companies are now being encouraged to target this scope across their supply chain [24]. Overall, a company’s strategy to reduce carbon emission depends on the targeted scope.
Social factors (Table 1) relate to how a company manages relationships with its workforce, suppliers, customers, communities and political environment it operates under. Human rights, community outreach, diversity policies, modern slavery, child labor, working conditions and racial disparities are some of the social factors important to a company’s long-term performance [25]. Investors may be influenced by whether a company provides safe and healthy working conditions, or if it donates time, money or other resources to communities where it operates. Unsafe working conditions or a disregard for community or customer concerns are potential grave financial risks. In contrast, companies that treat employees well and donate to communities are judged as less risky and they can benefit from higher productivity and attraction of top talent.
The UN’s International Bill of Human Rights (IBHR) and the International Labor Organization (ILO) Core Conventions set out social factors that are important for long-term financial performance. The United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) helps companies to avoid conflict with indigenous populations. The seemingly unending birth pangs of the Keystone XL pipeline in the US and the incessant negative publicity directed at Rio Tinto after the company blew up ancient caves in western Australia despite opposition from Aboriginal communities may be consequences of overlooking indigenous peoples’ rights. Such disregard of social factors may turn out to be financially costly to investors.
Governance factors (Table 1) are concerned with a company’s decision-making, from policymaking to the distribution of rights and responsibilities among different participants, including the board of directors, managers and shareholders. Governance factors indicate the rules and procedures for companies, and allow investors to screen for appropriate governance practices as they would for environmental and social factors. A corporation’s purpose, the role and makeup of boards of directors, shareholder rights and how corporate performance is measured are core elements of corporate governance structures [26]. Gender diversity and equity are becoming important to investors who are increasingly demanding better representation of women and people of color on corporate boards and in executive ranks, as well as equal compensation and promotion prospects.
A company that has robust governance structures is transparent and fair, and it operates within regulations and policies. Good governance mitigates risks of mismanagement, corruption and regulatory penalties. The Volkswagen emission cheating scandal that was revealed in 2015 in the US may be a result of governance failure. Volkswagen installed a software that was programmed to allow their vehicles to pass testing performed by the Environmental Protection Agency (EPA) but pollute up to 40x the federal maximum when on the road [27]. The governance failure of the company potentially had a negative effect on its stock price and financial performance, on top of reputational damage [28].
Investors and asset managers use seven strategies to integrate ESG factors in their investments. Selection of the strategies is influenced by investor objectives. The strategies are Best-in-class, Exclusions, ESG integration, Impact investing, Norms-based screening, Sustainability-themed and Engagement and voting [29]. Descriptions of the strategies below are largely according to [29], although there are other bodies that use slightly different nomenclature.
Best-in-class involves the selection of companies with the best ESG practices. The best companies are identified by ESG analysis, and they are usually selected by comparing ESG ratings provided by different rating agencies. Asset managers invest in companies with the best ESG ratings.
This is an approach where companies are excluded based on the values of an investor. Companies engaging in activities deemed negative to society are excluded. Activities and or products that may result in exclusion include controversial weapons, pornography, tobacco and alcohol, fossil fuel extraction and nuclear energy. In the past, there were divestment from companies involved in the Vietnam war, colonialism, slavery, apartheid as well as those that were complicit in racism.
ESG integration in the sense of ESG strategies is the consideration of ESG factors alongside financial factors, in the analysis of investments. The integration process is influenced by the potential impact of ESG factors on financial performance. The investor believes that a company will have good financial returns if it has high ESG ratings.
Impact investments are made into companies with a clear intention to generate an ESG impact alongside a financial return. A major characteristic is the intention of the investor to have measurable ESG impacts from the investment. The financial return ranges from below-market to market rate.
Norms-based screening is an approach that involves excluding companies that violate international norms and conventions. These norms and conventions are defined by international organizations. They include the UN Global Compact Principles, the Universal Declaration of Human Rights, ILO Declaration on Fundamental Principles and Rights at Work, and the United Nations Convention against Corruption.
Themed investing is the selection of companies involved in sustainable development challenges. Sustainability themes include renewable energy, waste recycling, clean water provision and human health.
Engagement refers to all interactions between an investor and investee to address ESG issues and business strategy. The objective of engagement is to exert influence on ESG issues. Voting is the investor’s practice of exerting voting rights at annual general meetings, where commonly ESG issues are taken into consideration. Engagement and voting is a long-term strategy seeking to influence corporate culture and increase disclosure.
ESG investing is likely to contribute significantly to steering companies to reduce their carbon footprint. There is increasing evidence that companies which incorporate ESG factors are likely to have higher financial performance, climate change issues are becoming popular among younger investors, the number of investors pledging to evaluate ESG factors when making investment decisions is increasing, and investor and regulatory ESG promoting initiatives are increasing. Taken collectively, these observations are likely to elevate the impact of ESG investing in combating climate change.
There are competing views on the impact of ESG investing on financial performance. One view is that companies incur costs from socially responsible actions that put them at an economic disadvantage compared to other firms that are less socially responsible. A second, contrasting view is that costs associated with ESG factors are necessary because companies overall benefit through high employee morale and productivity [30]. The pioneering studies of the impact of ESG investing, then SRI, on financial performance can be traced to [31]. In the study by [31], fourteen companies were selected based on their social responsibility credentials, and the rate of their return on common stock was calculated [31]. The stocks of the selected companies appreciated at a higher rate compared to major market indices [32]. This difference in performance was attributed to responsible investing [32]. A number of subsequent studies have validated the positive correlation between social responsibility and financial performance.
In a review of 2 200 studies on effect of ESG investing on corporate financial performance, 90% concluded a positive correlation [33]. The positive correlation was observed to be stable over time, especially in North America and emerging markets [33]. Age of a company is an important variable since old equipment and buildings might incur more expenses in making them environmentally and socially compatible with modern regulatory standards. After correcting for age of a company, a positive relationship was observed between social responsibility and financial performance [34]. When a Morgan Stanley Capital International (MSCI) ESG rating was applied to study corporate returns over a 10-year period, 2007–2017, it was observed that ESG-rated companies paid higher dividends and showed higher valuation levels [21, 35]. Among 100 American companies, employee satisfaction was associated with positive risk-adjusted returns at a statistically significant level [36]. Similarly, an evaluation of two equity portfolios that differed in eco-efficiency showed that the high-ranked portfolio provided substantially higher average returns than its low-ranked counterpart over the 1995–2003 period [37].
Although there are also some studies refuting the non-negative correlation between ESG investing and financial performance [38, 39, 40], orientation towards long-term ESG investing is important for investors to fulfill their fiduciary duties and achieving objectives of society [41]. Companies can build goodwill through ESG investing which can protect against reputational damage from catastrophic occurrences. The widely reported positive financial performance will likely influence more asset managers and investors to consider ESG factors, thereby becoming material in combating climate change.
Companies are under pressure from shareholders to maximize productivity, and from consumers, workers and communities at large to consider ESG factors [39]. Consumers are increasingly becoming conscious of environmental issues, and are showing preferences for environmentally friendly products. Companies are, therefore, not only concerned about financial performance, but also impact of their activities on the environment, social wellbeing of workers and the broader community. The concern is reflected by the increasing number of companies that are publicly declaring their commitment to ESG investing [16]. In 2019, ESG-themed mutual funds had a net inflow of US $20 billion, an increase from the previous year by a factor of four [42]. Since the formation of UNPRI, the number of signatories has increased to over 3 000 at the end of 2020, with their assets under management at US $103.4 trillion (Figure 1).
Increase in number of signatories to the United Nations principles for responsible investment (UNPRI) and assets under management (source: UNPRI).
As at January 2021, 61 large companies from diverse sectors had committed to stakeholder capitalism metrics (SCI), a set of environmental, social and governance metrics and disclosures released by the World Economic Forum (WFP) and its international business council (IBC) that measure long-term value creation for stakeholders [43, 44]. In making these commitments, influential companies signaled that ESG factors are increasingly becoming important to success and long-term viability. The commitments to SCI and UNPRI represent the intent of leading global companies to integrate ESG factors into their core strategy and operations which is likely to result in tangible action towards reduction of their carbon footprint.
The number of policy initiatives to encourage ESG investing has also increased in tandem with the number of investors and assets. For example, the sustainable banking network (SBN) was formed in 2012 by financial sector regulatory agencies and banks in emerging market economies (EME). The aims of the SBN are to support companies to adapt to environmental and social sustainability and to contribute to national development goals [21]. In 2015, the taskforce on climate-related financial disclosures (TCFD) was formed by the G20. The task of TCFD is to identify information needed by investors to asses climate-related risks, among other sustainability issues. The TCFD drafted recommendations on climate-related financial disclosures which have been widely adopted [45]. The high-level expert group (HLEG) on sustainable finance was formed by the European Commission in 2016 to channel public and private capital flows towards sustainable investments and to protect stability of financial systems from climate change related risks [21]. These and other policy initiatives are likely to influence more asset managers and investors towards ESG investing.
Environmental factors topped the list of individual investors as far back as 1991 [46]. A 1991 survey of 4 000 individual investors in two mutual funds that incorporate ESG factors in investment decisions established that environmental and labor issues were a top priority [46]. The majority of the investors were generally young (below 44 years of age) and better educated, with at least a college degree. In a similar study among members of the American Association of Individual Investors (AAII), it was observed that environmental factors dominate in their decision making [14]. A company’s environmental performance and the environmental impacts of its products are important considerations for ESG investors. With the continued increase in the concentration of atmospheric greenhouse gases, environmental factors, particularly climate change, are likely to attract most investor concern [47, 48].
Climate change activism, especially among the younger population, is making international headlines. Climate change threatens both financial performance of companies and social well-being of communities. Rising sea level will impact nearly 40% of the US population and other coastal communities around the world [48]. Hurricanes, storms, heat waves and floods will disrupt company activities and community cohesion. Investors and asset managers are likely to continue reducing climate change risk in their portfolios, especially with carbon-intensive companies [48].
Investors worried about impacts of climate change are taking their own initiatives to reduce greenhouse gas emissions. Climate Action 100+ is an investor-led initiative to ensure the world’s largest corporate greenhouse gas emitters take necessary actions to reduce emissions. The Investor Agenda aims to accelerate actions that are critical to tackling climate change and achieving goals of the Paris Agreement. The Investor Agenda encourages investors to set science-based portfolio emissions reduction targets that allow global net-zero emissions by 2050 or sooner, with credible intermediate targets. It provides actions to meet the emissions reduction targets that align with the goals of the Paris Agreement. The investor led initiatives and the resultant decarbonation actions by companies are vital in reducing climate change risks and exposure.
ESG investing has been fraught with challenges since its early days. The lack of common terms, huge volumes of data to be processed from company reports and other sources, and heterogeneity of rating methods are noteworthy drawbacks.
The variety of terms that are used under ESG investing may create confusion among investors. For example, the term “environment” to describe a fund with positive environmental impact may alternatively be substituted with “sustainable”, “green” and “eco” [16]. There are also terms that are sometimes used interchangeably with ESG investing, such as SRI, responsible investment, sustainable investment, impact investing and ethical investing [16]. There is need to resolve such terminology variability so that evaluation can be more consistent. Apart from terminology ambiguities, scoring some ESG factors may be highly influenced by context. For example, a company that accords its workers a worshipping hour may score highly under social factors in a predominantly religious society than in an atheistic one. A company may be negatively screened in one community while positively screened in another due to cultural, religious, ideological and ethical differences.
To score ESG factors, huge volume of data needs to be processed. These data may be mined from company reports, regulatory agencies’ reports and other mandatory and or non-obligatory disclosures, a very laborious and cumbersome process for investors. Scoring of factors is to some extent subjective. Some investors may score environmental factors highly compared to social factors while others score vice versa. An environmentally-friendly company could mistreat workers [42], and thus it is challenging to balance such factors when evaluating the company. There is also a risk of evaluating greenwashed and impact-washed data. Greenwashing is a false claim to deceive consumers into believing that a company’s products and actions are environmentally friendly [48]. Impact washing is promoting the positive impact of a company to society while it engages in other damaging activities [48]. For example, a mining company may tout how its solar power plant will result in decarbonation, while simultaneously the company will be releasing toxic chemicals that cause deforestation. To circumvent the problem of scouring over enormous amount of data, asset managers and investors usually rely on ESG indices prepared by vendors. There is a possibility in future of using artificial intelligence and machine learning methods to score the huge volume of ESG data [48].
ESG rating agencies analyze publicly available data reported by a company, sector-specific NGOs, government agencies, trade unions and other sources to produce an ESG rating for a company. The rating agencies are paid by investors who use the ratings to evaluate investment decisions. Leading international ESG rating agencies include Vigeo Eiris, MSCI, ISS-oekom, Inrate and Sustainalytics. The rating criteria, however, differ among agencies and business sectors. As no common standard exists for ESG rating, each agency develops its own method, and as a result, ratings from different agencies sometimes disagree [42]. ESG investing is a rapidly evolving field, and rating agencies are adapting to the evolution by making continuous improvements and changes to their rating criteria.
There are ongoing efforts to create standards in ESG investing in order to have consistence and reliability. In Europe, the EU taxonomy was drafted to provide a list of environmentally sustainable economic activities. It is a classification system that enables grouping of economic activities that are crucial in climate change mitigation and adaptation [49]. The EU taxonomy is crucial in scaling up sustainable investment and implementation of the European green deal. It provides appropriate definitions to companies, investors and policymakers on which economic activities can be considered environmentally sustainable. It is, therefore, expected to protect investors from greenwashing and impact washing, help companies to transition to decarbonation, and move investments where they are most needed [49]. Internationally, the SCI is another important endeavor to create universal standards in ESG investing. The SCI are a set of 21 universal, comparable disclosures that were derived from voluntary standards and are focused on people, planet, prosperity and principles of governance that companies can report on regardless of sector or region [43, 44]. The regional and international standardization efforts will likely lead to convergence of ESG rating methods.
Environmental, social and governance factors are becoming vital determinants of investment decisions. It is apparent that environmental factors, especially climate change, pose a substantial financial risk to companies. Increasing frequency of extreme events, rising sea level and destruction of biodiversity are some consequences of climate change that impact financial performance. ESG investing is likely to influence more companies to implement strategies to reduce their carbon footprint. Reduction of scope emissions will contribute to holding the increase in average global temperatures to below 2°C above preindustrial levels, a goal of the Paris Agreement. Reduction of scope emissions will also help to achieve net carbon neutrality by 2050, a target considered crucial to combat the current climate change. The high financial performance of ESG investing, increase in the number of ESG investors and financial assets, environmental consciousness among younger investors, and regulatory and investor ESG promoting initiatives are likely to influence more companies to implement strategies towards carbon neutrality. The lack of ESG scoring standards, terminology variability and enormous amount of data to process are some challenges of ESG investing. There are initiatives, however, to have standards in ESG rating, and attempts are also being made at using artificial intelligence to process huge volumes of data. Despite the challenges, and going forward, ESG investing is likely to play an important role in combating climate change.
Appreciation goes to Grim, D.M. of The Vanguard Group, and Barrett, J. of UNPRI for providing updated information and statistics.
The author declares no conflict of interest.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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In particular, Alzheimer's disease (AD) models have been designed to test the hypothesis that certain lesions are associated with functional and morphological changes beginning with memory loss and impairment in activities of daily life. This review compares and evaluates the phenotypes of different AD animal models, on the basis of the specific objectives of each study, with the purpose of encompassing their contributions to the comprehension of the AD signs and symptoms in humans. All these models contribute to the comprehension of the human AD mechanisms regarding the heterogeneity of AD phenotypes: the overlap between AD and age‐related changes, the variability of AD onset (early or late), the probable reactiveness of amyloid‐β and tau proteins, the scarcity of senile plaques and/or neurofibrillary tangles in some AD cases, the spatial correlation of the pathology and cerebral blood vessels, and the immunological responses (microglial aging) and synaptopathy. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. 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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. 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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. 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