Hyperspectral airborne sensors used in water quality assessment.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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In the present era of globalization, outsourcing proves to be one of the prominent and emerging business practices widely adopted by the firms around the world in order to stay competitive [1]. There are various definitions and explanations of outsourcing in the literature, all mostly conveying a similar meaning. Outsourcing is an abbreviation for “outside resource using” which essentially means using external parties in the value chain of a firm [2]. Outsourcing is defined as a management approach in which an enterprise delegates its operational responsibilities to an external party which was prior performed in-house [3]. Tadelis [4] defines outsourcing as “the transfer of a business activity or function to an external provider (or vendor) who takes control of the activity’s inputs, and then performs that function off the company’s balance sheet and sells the activity/function back to the company”. Outsourcing to third-party firms when based within the same continent or substantially in the similar cultural environment is termed as near shore outsourcing whereas third-party vendors when based on a different continent or substantially in a different cultural environment is termed as offshore outsourcing [5, 6].
Hätönen and Eriksson [7] and Zhu et al. [8] define outsourcing as one of the strategies that are being recently practised among most of the manufacturing and service industries so as to gain competitive advantage. Considering the potential benefits of such a business practice in terms of catering to the customer requirements in reduced time and cost, a wide application of outsourcing has been noticed by industries all over the globe. Though originated in the eighteenth-century, such business endeavour has gained prominence during the 1980s. Since then there has been a massive paradigm shift in such a business outlook [9, 10]. Traditionally, outsourcing practices were confined to peripheral jobs like cleaning, catering, and security that has shifted to potential core jobs like design, research and development, manufacturing, mining, human resource, sales and marketing [11, 12, 13, 14, 15]. A large number of firms view outsourcing as a value-addition process and a means to achieve business transformation [16, 17]. Thus, the motivation of outsourcing that was solely based on cost criteria has gradually transformed into a strategy-based approach more likely to be known as transformational outsourcing. In this reference certain concepts like vested outsourcing, crowdsourcing, white collar outsourcing has emerged in the era of 21st century.
Outsourcing has offered several benefits to the organizations that have enabled managers to use it as a strategic tool to be ahead in the competitive race [18]. The motivation for outsourcing differs from one organization to the other and accordingly a wide spectrum of possible benefits is witnessed in the existing literature. Outsourcing has been a proven mechanism in offering a plethora of strategic benefits including skilled workforce, state-of-the-art technology, cost reduction, greater flexibility are to name a few [19, 20]. Organizations have experienced several other benefits of outsourcing, some of them are mentioned below [21]:
Outsourcing non-value-added activities to third-party service providers allow the companies to focus on their core activities. Outsourcing non-strategic activities allow the client firms to invest in capital, resources, and time to the areas that contribute to the competitiveness of the firm [22, 23, 24].
Outsourcing allows the companies to achieve cost-savings in terms of reduced overheads and consequent training costs by delegating low-skilled and labour-intensive activities to low-cost locations [25, 26].
Outsourcing enables organizations to achieve cost-savings by capitalizing on economies of scale gained through production efficiencies and specialized personnel of the outsourced firm [22, 27].
Outsourcing allows achievement of improved and quality services due to service provider’s standardized and consistent service levels which ensures an improved and appropriate level of service through their specialized equipment and expertise [28, 29].
Outsourcing enables organizations to convert fixed cost into variable cost. Components that are required occasionally are often selected as the candidates for outsourcing since maintaining capacity for such items may lead to cost incurrence throughout the year [30, 31].
Outsourcing enables organizations to gain access to the state-of-the-art and most effective technology, innovation, proven methodologies, and specialized capabilities of the outsourced firm [10, 27, 29].
Outsourcing enables firms to deliver products/services at a much-reduced time. Reduced cycle time leads to better responsiveness in catering to the ever-changing customer needs through the utilization of state-of-the-art technology, specialized knowledge, and expertise workforce [2, 32, 33].
Outsourcing brings in greater flexibility. Outsourcing is beneficial at times when sudden necessity arises for a certain resource that may be either human or equipment that are not required on a full-time basis [30, 34, 35].
Outsourcing is helpful at times in terms of sharing risks when conditions like market fluctuations, volatile financial conditions, and change in government regulations occur [32, 35].
Though outsourcing has been considered as a strategic tool in providing organizations with a competitive advantage, there are quite a few drawbacks which adversely affects the firm in form of cost escalation and inherent risks [30, 36]. Some of the outsourcing risks are highlighted as follows:
Outsourcing may lead to loss of core competencies of the firm. It has been witnessed that firms often indulge in an aggressive outsourcing in view of the short-term cost advantage failing to realize the significance of such an activity in contributing to the long-run competitiveness of the firm [32]. Delegating a potential activity may be vulnerable in terms of the service provider becoming a competitor in the near future [21].
Dependency on the service provider sometimes leads to opportunism demonstrated by the service provider which is another risk factor in an outsourcing relationship. Opportunism occurs when individuals act deceitfully and in a self-seeking manner as and when need arises [37, 38]. Such negative behaviour influences an outsourcing relationship by increasing cost and decreasing revenue [39].
Client organization investing in specialized assets and resources (tangible and intangible) that are specific to that relationship often encounter difficulties in switching providers known as lock-in situation. Interruption of supply, delivery of inferior quality of products, unexpected cost escalations, and non-performance of the service provider are some of the complications encountered by the client organization in such situation [37].
One of the primary motives behind outsourcing is to gain cost advantage. However, there are several unexpected costs associated with outsourcing such as, cost of monitoring, implementing, negotiating, coordinating, enforcing and terminating the existing exchange agreements that goes unnoticed and unreported while taking an outsourcing decision [32].
Possession of proprietary knowledge and methods, customer specific data, organizational know-how are examples of intellectual capital that need to be identified and protected through contract clauses when engaging a third party as they can be easily copied and thereby prone to risks and leakages [40, 41].
The academic literature on outsourcing in the Indian context mostly deals with the outsourcing of information technology/information systems (IT/IS) and business process outsourcing (BPO) that are outsourced to Indian IT firms by the multinational companies (MNCs) located abroad. However, studies on outsourcing practices followed by the Indian firms may be inadequate in the extant literature. It was observed that the decision of outsourcing is often taken in an aggressive manner with an emphasis on short-term cost advantage rather than giving due consideration in realizing the significant contribution of such decisions over the long-term competitiveness of the organization [42]. The present study provides a structured approach to investigate the appropriateness of outsourcing, as a strategic decision, in accord with the organizational strategy for performance improvement in the context of an Indian coal mining organization. Therefore, the objectives of the present study are to provide a general overview of outsourcing in the backdrop of the Indian outsourcing scenario focussing on the coal mining organization and manufacturing and service industries in general. The study then discusses the significant contribution of the coal mining organization in reference to the growing importance of coal in the country. Subsequently, the relevance of outsourcing in view of the improved organizational performances for the Indian coal mining organization has been elucidated. Finally, the study proposes an outsourcing decision model that may provide a comprehensive approach towards evaluating the appropriateness of such strategic decision as outsourcing in consistent with the organizational strategy for performance improvement for the coal mining organization in India.
As aforementioned, the extant literature on outsourcing in the Indian context mostly highlights outsourcing of IT, IS, and BPO that are offshored to Indian IT firms by the MNCs located overseas [43, 44]. However, studies on outsourcing practices by the Indian firms may be inadequate in the extant literature. According to the outsourcing survey by Deloitte, the business functions that are recently being outsourced are IT, operations, finance, human resources, legal, real estate/facilities, procurement, and sales/marketing support [45]. As reported in this survey, while IT, finance, and operations are expected to be heavily outsourced in offshore locations, business functions such as procurement, human resources, sales and marketing, legal, and real estate/facilities are likely to be outsourced within the national boundaries (India). Some of the publicly known cases of outsourcing by Indian firms are (a) IT outsourcing contract to IBM by Airtel [46], (b) outsourcing of business process and technology by Indian banking sector [47], (c) outsourcing of mining operations by Hindustan copper Ltd. and Mahanadi Coalfields Ltd. [48, 49], (d) outsourcing of back-office operations to Tata Consultancy Services (TCS) by Passport Seva, Ministry of External Affairs [50], (e) outsourcing of passenger services by Indian Railways [51], and (f) human resources management system outsourcing contract to TCS by the Indian Railways [52]. Some of the recent cases of outsourcing reported in Indian context that are more transformational in nature are relocation of high-tech industries like pharmaceutical industry outsourced to India [53], study of professional service outsourcing in India while examining the impacts of task traits (complexity, connectivity and security) and their alignments with inter-firm governance control mechanisms in improving service capabilities by Jayaraman and Liu [54], the impact of employer branding strategy on employee engagement consideration a case of a business process outsourcing (BPO) in India [43], and R & D offshore outsourcing to India, the service provider, taking into account their innovation performance gained through learning from their clients for a biopharmaceutical industry [55].
Mineral and mining sectors play a pivotal role in the economic development of a country as they are the principal source of raw materials for an array of industries. Among them, coal is the most dominating energy resource and remains as the lifeline for fuelling Indian industries since its first use in the 1700s. Coal mining constitutes a share of 80% of the total mining in India while the remaining 20% includes mining of various other ores such as gold, copper, iron, lead, bauxite, zinc, etc. [56]. At present coal contributes about 52% to the India’s total commercial energy needs and about 66% of the country’s power generation and is expected to remain the most viable energy resource contributing to sustainable economic growth for the years to come [56, 57, 58, 59]. As India is among the top three fastest-growing economies in the world, the coal mining industry in India plays a substantial role in fulfilling the uprising demand of coal from the increasing power plants, steel, and cement industries.
The state-owned coal mining organization of India came into existence in the year 1975 after the Coal Mines (Nationalization) Act in the year 1973 taking over the private coal mines by the Government. For the purpose of this study, this organization is referred to as the Indian coal mining organization (ICMO). ICMO owns seven coal producing subsidiaries along with a mine planning and consultancy company located in eight provincial states in India. They are Eastern Coalfields Limited (ECL), Bharat Coking Coal Limited (BCCL), Central Coalfields Limited (CCL), South Eastern Coalfields Limited (SECL), Western Coalfields Limited (WCL), Northern Coalfields Limited (NCL), Mahanadi Coalfields Limited (MCL), and Central Mine Planning and Design Institute Limited (CMPDIL) [57]. Ministry of Coal (MoC) is responsible for the development and implementation of policies and strategies for the entire coal sector that are exercised through ICMO and its subsidiaries along with Singareni Collieries Company Limited (SCCL) which is a public-sector undertaking company jointly governed by Government of Andhra Pradesh and the Government of India [56]. ICMO is of strategic importance to the country because of several reasons as mentioned below [57]:
India is the third largest coal-producing country in the world after China and USA where ICMO contributes to about 81.1% of India’s overall coal production and is the single largest coal producer in the world.
Out of the 52% of India’s primary commercial energy which is coal-based, ICMO alone contributes to around 40% of the primary commercial energy requirement.
ICMO accounts for about 74% of the Indian coal market.
ICMO maintains the stability of coal prices to the Indian coal customers.
The main business of the organization is based on the identification of coal reserves, coal exploration, design, optimization and application of operational activities for excavation of coal while distributing them to industries across the nation as per the demand.
As coal is an important source of primary energy in India, the demand for coal has always been on the rise. The demand for coal has further aggravated in view of the increasing power sectors and steel and cement industries growing nationwide. Despite the fact that India has been ranked fourth in terms of the total coal reserve and is also considered as the third largest coal producing country in the world, India is still chasing to encounter the escalating demand of coal.
As per the Coal India Report (2014–2015), between 2007–2008 and 2014–2015, the demand for coal in India has increased from 492.5 million tons (MT) to 787.03 MT showing an increase of 60% over a period of seven years. Figure 1 illustrates the year-wise details of overall demand for coal and the corresponding share of ICMO, SCCL, and other indigenous sources contributing to the overall demand while the gap is met through import [57].
Year-wise demand and supply of coal.
From Figure 1 it may be observed that the share of demand met by ICMO, SCCL, and other indigenous sources have always been substantially below the expected demand which has necessitated the coal mining organization to depend on coal imports from the neighbourhood countries. The gap, bridged by import between the period 2007–2008 and 2012–2013 has increased from 30.61 MT to 192.54 MT, showing a rise of 529% over a period of five years.
The Ministry of Coal articulates several reasons for such dependency on imported coal. As stated, increasing unavailability of indigenous coal, limited availability of coal with desired quality, environmental consideration in combining the low-quality coal with good quality imported coal, and locations-based cost issues are among the primary influencers [57]. However, coal imports are associated with certain risks and challenges that include fluctuations in global spot prices and foreign exchange rates, law and order issues in exporting countries, and many more [60]. In view of the growing inclination towards the import of coal from oversea sources, there have been several initiatives to enhancement in-house coal production to the outmost possible which are within the ICMO’s 12th Five-year plan. The strategies within the 12th Five-year plan include enhancement of exploration drilling capacity, introduction of new mines, and fast initiation of activities related to projects in-process [57]. However, the improvement and expansion of in-house domestic mines involve a substantial amount of investment resulting in a considerable increase in the price of coal [60]. At the same time, it has been also noticed that service providers have facilitated firms in enhancing their business processes though their technical efficiency, expertise, cost-saving techniques, and flexibility when they were deployed for operational activities. Likewise, the captive mines have realized competitive advantage through deployment of modernized equipment, adoption of new technologies, and utilization of experience and expertise of the service providers [60]. The presence of these third-party private agencies has also been noteworthy in the development and operation of coal mines through contract mining and related technical services.
In view of the significant participation of the third-party providers, the coal mining organization in India have started several interventions involving such organizations to delegate some of the key operational activities. This, in turn, is expected to diminish the ever increasing demand–supply gap through increase in operational efficiency, cost-reduction techniques, sustainability of operations and minimization of wastes [56]. Considering the above, outsourcing of operational activities has been introduced by ICMO to suffice the growing energy demand.
One of the primary objectives of ICMO is to enhance its internal exploration drilling operation to accomplish the expected target as mentioned in the 12th Five-year plan. Outsourcing of drilling operation by CMPDIL has increased from 0.07 lakh meter in 2007–2008 to 2.86 lakh meter in 2012–2013 over a period of five years that has further increased to 6.15 lakh meter in 2013–2014 showing a significant increase of 115%. For the year 2014–2015 the drilling operation has further increased to 8.28 lakh meter realized through departmental resources and outsourcing [57]. The estimated target for the year 2015–2016 has considerably increased to 15 lakh meter where the departmental capacity has been raised to 4 lakh meter and the rest through outsourcing [61]. According to the report, a total of 50 blocks involving 17.7 lakh meter of drilling was awarded since 2008–2009, however, drilling has been performed in only 24 blocks. The rest of the blocks are remaining non-functional because of reasons like local law and order problems and non-availability of forest clearance [61].
As reported, ECL, a coal producing subsidiary within ICMO, produced 141.73 lakh tons of coal and raised 587.91 lakh cubic meter of overburden (OB) from 27 outsourcing opencast patches in 2014–2015 that increased to 171.12 lakh tons of coal and 882.20 lakh cubic meter of OB in the year 2015–2016 from 31 outsourced opencast patches [62]. CCL, another subsidiary, has already outsourced several opencast mines and have been outsourcing a number of activities like OB removal, exploration, and monitoring of geological exploration. NCL has been planning to outsource OB removal along with departmental outsourcing which is within their 2015–2016 production program [63]. As reported, MCL has been also planning to outsource activities for the expansion of the opencast projects [64]. Further, it has been clearly stated by ICMO and MoC that they have already engaged an international agency for studying the mine operations in order to modernize the existing mines through the implementation of state-of-the-art technology [57].
Thus, the above-stated facts emphasize the importance of outsourcing as a strategic decision of the organization in view of the improved organizational performances. However, while conducting the site visits and interacting with the company executives, the researcher discovered several lacunas related to the absence of strategic perspectives in regard to the outsourcing decisions of operational activities. It was observed that the decision of outsourcing is often taken in an aggressive manner with an emphasis on short-term cost advantage rather than giving due consideration in realizing the significant contribution of such decisions over the long-term competitiveness of the organization. Management of the company ought to have recognized the need to develop a logical step-wise approach towards adopting outsourcing through a clear understanding of the contribution of each operational activity and its relation to organizational core competencies. Second, the need to analyse the appropriateness of the organization’s outsourcing decision in the context of organizational strategy and its effect on organizational performance is another area of concern. There is absence of any performance evaluation framework for identification of the attributes (drivers) and their relative rank order for assessing organizational performance as a consequence of an outsourcing decision. In view of the advantages and short-term as well as long-term challenges, it may be worthwhile to develop a framework that can provide guidance to the practitioners faced with the dilemma of retaining activities in-house, or enter into outsourcing in a transactional manner, or to maintain an alliance relationship with the service provider. Last but not the least, one of the primary challenges of the organization is how to maintain an outsourcing arrangement with the service providers so that current competitive position along with a sustained business performance is maintained over an extended period of time. So, the identification of critical success factors for developing and maintaining a sustainable outsourcing relationship between the service provider and the client may be another area of research.
In this reference, it may be noted that apart from few studies conducted by the management consulting firms like the Indian Chamber of Commerce and Deloitte Consulting, there has been very limited studies on outsourcing for the coal mining organization specific to the Indian context. The existing literature does not adequately focus on the outsourcing decision support based on a strategic perspective that may facilitate the mining managers in outsourcing decision-making for the organizational activities. Adequate studies with a focus on the identification of key drivers for the assessment of organizational performance as a consequence of an outsourcing decision are limited in the extant literature. Studies on organization-level outsourcing decision strategies in the context of the coal mining organization in India have been rarely considered. Further, the literature on a structured approach that takes into account the sustainable relationship management aspect of the coal mining organization and its service providers in regard to the long-term competitiveness of the organization is also limited. Thus, reviewing the existing literature and considering the above-mentioned gaps, a comprehensive outsourcing decision model has been formulated to address the salient issues emerged from the identified gaps as presented in Figure 2.
The graphical representation of the outsourcing decision model.
The study emphases on developing an outsourcing decision support to help management make a more informed decision on outsourcing of the operational activities. The decision support is in regard to the outsourcing decision of operational activities to be strategic rather than an aggressive one. Besides, defining the candidates for outsourcing, the decision support may contribute towards the identification of core, partial core, and non-core activities through a logical phased approach based on their contribution towards the organizational competencies. Thus, comprehending the contribution of each operational activity towards the organizational competencies allows the management to check for its strategic soundness and thus help in retaining the competitive position of the organization.
Outsourcing is considered to be one of the strategic options for organizations to improve their business performance. Determination of the unexplored attributes (drivers) and their comparative rank order for the assessment of organizational performance out of an outsourcing decision is another area of research to be taken into consideration [65]. The analysis comprising of both quantitative and qualitative attributes based on the cognition of the decision makers may contribute to a thorough understanding of a practical real-life problem and could help managers in long-term decision making. Understanding the effect of the unexplored attributes may also benefit management of the organization to develop policies in maintaining competitive advantage in the market.
The outsourcing decision problem, also known as the make-buy decision of an organization is well-considered as one of the strategic decisions of any organization. The extant literature on the make-buy decision has been predominantly classified into two categories. The first one is related to the financial issues based on Transaction Cost Theory (TCT) whereas the second rests on strategic issues. There are several methods and approaches conferred in the earlier studies addressing such make-buy decision problems. The present study emphasizes on an organizational decision support to assess the optimal outsourcing strategy among insourcing (in-house), outsourcing (involving external service provider), and strategic alliance (partnership) relationship for operational activities of an organization [66].
The focus is to identify the factors responsible for establishing and maintaining a sustainable relationship between the third-party service provider and the client organization. In this era of globalization, when service providers are value-adding partners, trust, commitment, and long-term orientation are the key elements in maintaining a buyer–supplier relationship. Literature has witnessed a relationship characterized by such aforementioned traits not only help to better serve the customer but also intensifies mutual benefits. Investigation of relevant antecedents pertaining to trust, commitment, and long-term orientation have seldom been used in light of an on-going outsourcing relationship and are areas of concern.
Outsourcing has been one of the noticeable business practices in view of its demonstrated capability in accomplishing competitive advantage to the organization. With the developing fame of outsourcing in the manufacturing and service sectors, mining sector have also started capitalizing the conceivable outcomes of outsourcing. Outsourcing has now turned into a key device for mining industries for building up corporate capability through its viable and effective methods of value improvement. In view of this business prospect, the coal mining organization of India has started outsourcing its operational activities to meet the escalating demand of coal across the country. However, along with several success stories, there are quite a few pieces of evidence that portray several difficulties encountered by the organization. Accordingly, to accomplish any firm’s business goals, it is prudent to consider it as a key aspect of corporate decision choices.
The present study provides a general introduction about outsourcing followed by its potential benefits and the shortcomings as witnessed in the existing literature. Besides giving an overview, the study provides a backdrop of the Indian outsourcing scenario considering the coal mining organization and outsourced activities by manufacturing and service industries in general. The study then discusses the significant contribution of the coal mining organization in reference to the growing importance of coal in the country. Subsequently, the relevance of outsourcing in view of the improved organizational performances for the Indian coal mining organization has been elucidated. Finally, the chapter ends with proposing an outsourcing decision model that may provide a comprehensive approach towards evaluating the appropriateness of such strategic decision as outsourcing in consistent with the organizational strategy for performance improvement for the coal mining organization in India.
The present study contributes in understanding a practical problem of a coal mining industry that may act as a guiding instrument to the mining managers in terms of decision making related to strategic sourcing. Developing an outsourcing decision support may assist the managers of the coal mining organization to determine the candidates for outsourcing, thus identifying the set of core activities that needs to be nurtured and protected for organizational excellence. The study provides elementary guidance to the management in investigating the appropriateness of outsourcing with the organizational performance through identification of key drivers. Further an effective outsourcing decision support tool may help managers to decide upon the optimal sourcing strategy among insourcing, outsourcing, and strategic alliance (partnership) for the organization’s operational activities. While the Indian coal mining organization is reliant on third party service providers for identification and exploration of new coal reserves, investigation of relevant antecedents pertaining to trust, commitment, and long-term orientation may facilitate management as client firm to develop and improve outsourcing relationship with the service providers. Determining the impact of the aforementioned factors may also help the mining executives to formulate relevant policies accordingly. However, there are few limitations of the present study. First, the study deals with the development of an outsourcing decision-making framework broadly considering four aspects as presented. However, there are several possibilities to investigate an extensive set of decision elements within the said framework. Second, the outsourcing decision model proposed in the present study is based on the insights gained through the interaction with a particular expert group from the Indian coal mining organization, but to make it applicable for other industries, the framework may be altered/improved upon by incorporating changes as required. For a public sector like the Indian coal mining organization, it was difficult to obtain the various components of cost related to on-going outsourcing projects (particularly when many such projects are in either planning or finalization stage), the present study could not incorporate the influence of cost criteria and the required cost analysis for the proposed outsourcing decision framework. Hence, the current research work may be extended in future by incorporating several dimensions of cost. The study takes into account the client perspective, while it may be relevant to take into consideration the viewpoints of service providers to get a more comprehensive view.
The oceans act as a natural sink for carbon dioxide and other greenhouse gases. However, anthropogenic activities have severely polluted the marine environment in the past few decades. Pollutants including plastic, oil, toxic chemicals, radioactive waste, and domestic and industrial sewage can be found in marine waters. Marine pollution is also caused by the discharge of sewage into rivers and excessive nutrients entering marine waters from agricultural fertilizers and pesticides [1]. These pollutants have adverse impacts on marine ecosystem including but not limited to sensitive coral reefs, mangroves, and aquaculture [2]. Therefore, in addition to reducing pollutant flow into oceans, it is essential to map and monitor marine pollutants to ensure a sustainable marine ecosystem.
Scientists and researchers have been working on detailed ocean monitoring for a sustainable blue economy. A variety of sensing systems are now available for ocean monitoring including autonomous underwater vehicles (AUVs), profiling floats, gliders, drifters, volunteer measurements from ships, and sensing nodes with cable networks [3]. These approaches to marine monitoring usually measure temperature, conductivity, pH, salinity, dissolved oxygen, fluorescence due to chlorophyll, turbidity, and color dissolved organic matter (CDOM). The most common approach for marine pollution measurements is to use conventional method of collecting in situ water samples using boats/ships from different depths of water with water samplers. The water samples are analyzed in the laboratory to determine the physical and chemical properties of the water. Such methods are accurate but time-consuming and geographically constrained and require trained professionals and laboratory analysis. However, real-time or near real-time measurements of marine pollutants and toxins across a range of spatial scales are necessary for monitoring and managing the environmental impacts and understanding the processes governing their spatial distribution [3].
To overcome these problems, remote sensing technology provides spatially synoptic and near real-time measurements that can be effectively used to detect, map, and track many pollutants such as oil and chemical spills, algal blooms, and high suspended solid concentrations. Aerial and satellite remote sensing has been demonstrated as an effective tool in detecting and mapping pollutant spills and for providing useful input data for oil spill models, to track pollutants through space and time [4, 5, 6]. An added advantage of remote sensing is that it provides information from remote areas. However, existing remote sensing technology still has some limitations, such as estimating pollutants over the vertical dimension of the water column.
The initial premise of watercolor remote sensing was to determine optical water quality variables such as chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient, and water-leaving radiance spectra [7]. The optical properties of water depend on many factors, e.g., suspended organic and inorganic particles and dissolved substances. There have been many successful applications of using remote sensing sensors for water color monitoring. The coastal zone color scanner (CZCS), having a spatial resolution of 825 m for six spectral bands from 443 to 750 nm, was the earliest satellite sensor designed and launched in 1978 specially to study ocean color. The sea-viewing wide field-of-view sensor (SeaWiFS) was the successor to CZCS with a spatial resolution of 1.1 km for eight spectral bands from 402 to 885 nm. Currently, many satellite sensors provide ocean color data for marine monitoring such as the moderate resolution imaging spectroradiometer (MODIS), the geostationary ocean color imager (GOCI), the visible infrared imager radiometer suite (VIIRS), the ocean and land color imager (OLCI), the Landsat operational land imager (OLI), and the Sentinel-2 multispectral instrument (MSI), all of which have suitable spectral and spatial resolutions capable of detecting marine pollutants and other water quality parameters (Table 2).
In order to track marine pollutants, prior understanding of marine dynamics is important, such as ocean current direction and magnitude, direction and speed of surface winds, sea surface temperature (SST), and sea surface salinity (SSS). Remote sensing now provides multiple satellite and airborne sensors to acquire information about marine dynamics over the vast marine regions. Apart from optical data, scanning radiometers and microwave sounders measure SST data, altimeters collect wave height data, and synthetic aperture radar (SAR) can measure the sea surface roughness patterns from which information on sea surface winds can be derived [31]. These datasets are of critical importance for detection and tracking of pollutants.
Remote sensors capture the response of the electromagnetic interaction with water (Figure 1). Absorption and scattering are inherent optical properties (IOP) of water; and variations in IOP change the reflectance of water which is captured by a remote sensing sensor, and this is known as the apparent optical properties (AOP) of water (Figure 2). Reflection, absorption, and transmittance of electromagnetic radiation are highly dependent on the concentrations, types, and presence of substances in water. Total absorption is the sum of absorption by phytoplankton (microalgae), non-algal pigments (NAP), color dissolved organic matter (CDOM), and absorption by water, whereas light scattering by water is mainly controlled by suspended sediments (SS) present in water. Hence, ocean color represents the responses in , green, and red region, and data can be used to estimate the concentrations of water constituents [7].
Interaction of light with the water surface. a is absorption (aph, absorption by phytoplankton; anap, absorption by non-algal pigments; aCDOM, absorption by color dissolved organic matter; and aw, absorption by water), b is backscattering (bb, backward scattering; bf, forward scattering), Rrs is remote sensing reflectance recorded by sensor, Ed is downwelling irradiance, Lu is upwelling radiance, and Lw is water-leaving radiance [
Reflectance (Rrs) by clear water (blue), water with chlorophyll content (green), water with CDOM (black), and sedimented water (orange) [
Generally, clear water has low reflectance in the visible spectrum and has no reflection in near infrared (NIR) region, as it is absorbed by clear water. However, high reflectance measurements in red (600–700 nm) and NIR region (750–1400 nm) show a strong correlation with SS concentrations. A high concentration of suspended sediments blocks the transmittance to and from lower depths and therefore increases reflectance from the water surface. Similarly, high concentrations of chlorophyll (a photosynthetic pigment in phytoplankton and macroalgae) in water cause high reflectance in the green region (500–600 nm) and high absorption in the blue and red regions due to photosynthetic activity (Figure 2).
A portion of absorbed incident energy by the earth’s features is also re-emitted in the thermal infrared region of the electromagnetic spectrum. Many satellite sensors such as MODIS, VIIRS, the advanced very high-resolution radiometer (AVHRR), and the sea and land surface temperature radiometer (SLSTR) measure the emitted thermal energy to determine sea surface temperature (SST). SST is an important parameter for understanding ocean water circulation. In case of large oil spills, these data can be effective for pinpointing the oil spilled areas, as they appear cooler than water surface due to their lower emissivity [31].
Fluorescence is another type of energy emitted by a substance when it comes to a lower energy level from a higher energy level. The emitted energy is in a longer wavelength than the excitation wavelength. Algae absorb visible light for the photosynthesis process and emit excessive energy in the form of fluorescence signal (681 nm, the fluorescence band) when chlorophyll molecule comes to the non-excitation state during the photosynthesis process. The fluorescence can be detected by optical sensors with fine spectral resolution in the far-red and NIR and has a potential source for monitoring changes in the photosynthesis process in plants. Furthermore, in laser fluorometry, laser light is used to excite molecules [33]. This technique is common to detect oil and chemical spills [31].
There are now several remote sensing platforms for monitoring water pollutants, and they can be categorized into two types: airborne and spaceborne.
An aircraft flies at relatively low altitudes (a few hundred meters to a few kilometers above the surface); therefore, the acquired data always have higher levels of detail. Airborne data are particularly useful for real-time monitoring of oil and chemical spills. Four common airborne sensors used for spill surveillance [34] are listed below:
Infrared/ultraviolet line scan (IR/UVLS)
Side-looking airborne radar (SLAR)
Microwave radiometer (MWR)
Laser fluorosensor (LF)
Airborne hyperspectral sensors with fine spatial resolution are able to capture detailed spectral variations. Therefore, they help to select the appropriate spectral region to study a specific water quality parameter, design satellite sensors, and improve already developed algorithms. Some airborne hyperspectral sensors particularly useful for studying coastal/river water quality are described in Table 1.
Sensor | Manufacturer | Number of bands | Spectral range (nm) | Spatial resolution (m) | Studied parameter |
---|---|---|---|---|---|
Airborne visible infrared imaging spectrometer (AVIRIS) | NASA Jet Propulsion Lab | 224 | 400–2500 | 17 | Bottom albedo, water absorption, backscattering coefficients [35], Chl-a, CDOM, TSS [36] |
HyMap | Earth Search Sciences Inc. | 128 | 400–2500 | 3–10 | Heavy metals [37] |
Portable remote imaging spectrometer (PRISM) | NASA Jet Propulsion Lab | — | 350–1050, SWIR band (1240 and 1640) | 0.3 | Sediment, CDOM, chlorophyll fluorescence [38] turbidity, Chl-a, dissolved organic carbon [39] |
Airborne prism experiment (APEX) | VITO (Belgium) | 313 | VIS and NIR (380–970), SWIR (970–2500) | 2–5 | Chlorophyll fluorescence, SS [40] |
Hyperspectral airborne sensors used in water quality assessment.
Spaceborne sensors can cover extensive and remote areas for water quality monitoring. Optical spaceborne sensors used for marine monitoring are mostly in sun-synchronous orbit; only GOCI, designed specifically for marine monitoring, is placed in geostationary orbit. The spatial coverage of these sensors ranges from tens to hundreds of kilometers, and the temporal frequency is from hourly to weekly monitoring.
Many algorithms have been developed to retrieve water quality information such as primary productivity, Chl-a variability, SS, total suspended solids (TSS), turbidity, total nitrogen, total phosphorus, CDOM, and SST. Table 2 shows the satellite sensors most used for the study of water quality parameters related to marine pollution. The major application areas of active spaceborne sensors include, but are not limited to, sea surface currents, oil spills, biogenic films (algal blooms), and river plumes (Table 5).
Satellite sensor | Launch date | Spectral bands (nm) | Spatial resolution (m) | Swath width (km) | Marine parameter accessed |
---|---|---|---|---|---|
Landsats 4 and 5 TM | 1 March 1984 | 5 (450–1750), 1 (2080–2350), and 1 (1040–1250) | 30–120 | 185 | Chl-a, SS, Secchi depth [8] |
Landsat 7 ETM+ | 15 April 1999 | 6 (450–1750), 1 Pan (520–900), 1 (2090–2350), and 1 (1040–1250) | 15–30–60 | 183 | Chl-a, SS, Secchi depth, turbidity [9] |
Terra Aster | 18 December 1999 | 3 (520–860), 6 (1600–2430), and 5 TIR (8125–11,650) | 15–30–90 | 60 | Chl-a [10] |
EO-1 ALI | November 2000 | (443–2350) | 30 | Turbidity [11], SS [12] | |
EO-1 Hyperion | 1 November 2000 | 242 (350–2570) | 30 | 7.5 | Chl-a, SS, CDOM [13, 14] |
PROBA CHRIS | 22 October 2001 | 19 (400–105) | 18–36 | 14 | Chl-a, phycocyanin [15] behenic macroalgae [16] |
HICO | 10 September 2009 | 128 (350–1080) | 100 | 45–50 | Chl-a, turbidity, CDOM [17], SS [18] |
Landsat 8 OLI/TIRS | 11 February 2013 | 1 cirrus cloud detection (1360–1380), 5 (430–880), 1 Pan (500–680), 2 (1570–2290), 2 TIRS (10,600–12,510) | 15–30–100 | 170 | Chl-a, SS, turbidity, TN, TP [19] |
Sentinel-2 MSI | 23 June 2015 | 8 (490–865), 1(443) coastal aerosol, and 3 (1375–2190) | 10–20–60 | 290 | Chl-a, CDOM, DOC [20], SS [21] |
Orb View 2 SeaWiFS | 1 August 1997 | 8 (402–885) | 1130 | 2806 | Chl-a [22] |
Terra, Aqua MODIS | 18 December 1999 | 2 (620–876), 5 (459–2155), 29 (405–877), and thermal | 250–500–100 | 2330 | Chl-a, CDOM SS [23], turbidity [24], TP [25] |
ENVISAT-1 MERIS | 1 March 2002 | 15 (390–1040) | 300–1200 | 1150 | Chl-a, SS [26, 27] |
GOCI | 26 June 2010 | 8 (412–865) | 500 | 2500 | Chl-a, SS, turbidity [28] |
Suomi NPP VIIRS | 28 October 2011 | 5 bands (640–1145), 16 bands (412–12,013), DNB (500–900) | 375–750 | 3060 | Chl-a [29] |
Sentinel-3 OLCI | 16 February 2016 | 21 (400–1020) | 300 | 1270 | Chl-a, SS, CDOM, and Secchi depth [30] |
Satellite sensors mostly used to retrieve marine water quality parameters.
Most algal species are nontoxic and are always present in coastal and open oceans. Planktons are the base of the marine food chain [22]. But, algae do not have to produce toxins to be harmful to the environment. The accelerated growth of algae produces a large amount of biomass which blocks sunlight and produces an anoxic or hypoxic condition (dissolved oxygen is depleted from the water column), which is hazardous to marine life. Algal blooms also affect coastal operations such as movement of ships, coastal tourism, and coastal sports (Figure 3). Algal blooms can persist from a few days to more than a month and spatially they may extend from a few meters to tens of kilometers.
Spread of green algae along the coast of Qingdao in 2008, when summer Olympics was planned in this coast (source: Corey Sheran/Flickr) (right) and algae visible in MODIS false color image (shortwave, NIR, and Red) (source: MODIS rapid response project at NASA/GSFC) (left).
The impact of algal blooms on marine life depends largely on the algal species involved. In situ field data collected using vessels are important for determining the algal species and level of toxicity during the bloom. However, field data are always limited for estimating the spatial extent as well as the dispersion. Detection of algal bloom by estimating the Chl-a concentrations using satellite imagery has been well-researched, as remote sensing has been used to observe ocean primary productivity since the launch of CZCS in 1978. High spatial and temporal resolutions are the main requirements of remote sensing data to study the variability in ocean and coastal Chl-a. By comparing a time series of satellite images, researchers can evaluate the spatial and temporal variations in Chl-a concentration during the bloom. This can also help to understand the dynamics of blooms. However, there are still certain conditions for using optical remote sensing to detect Chl-a, including (i) no or low cloud cover, (ii) the bloom should be near to the surface, and (iii) the bloom must cause the coloration of the water.
Optical remote sensing can observe the coloration of water due to algal pigments. In the open ocean, the color of water is mainly determined by phytoplankton; hence, it is relatively simple to develop algorithms using a bio-optical approach and remote sensing reflectance [22]. In the open ocean, Chl-a can be retrieved from the ratio of blue and green wavelengths as Chl-a absorption is sensitive to blue wavelength and reflectance peak occurs in the green wavelength region [22]. However, in coastal waters, the color of water also depends on organic matter such as NAP, CDOM, and inorganic solids, and consequently it is more complex to determine accurate Chl-a concentrations in coastal/turbid waters. Researchers have demonstrated that waters with increased Chl-a concentrations show a lower spectral response at short wavelengths especially in the blue wavelength regions [41]. This is due to increased absorption of red and blue wavelengths during photosynthetic process. Figure 4 shows the reflectance of water with increasing Chl-a concentrations. Thus, in coastal waters, the red/NIR ratio is more effective for retrieval of Chl-a due to the presence of suspended solids and the increased spectral response of Chl-a pigments at longer wavelengths [43].
Changing spectral response of water with different levels of chlorophyll concentration [
Narrow spectral bandwidth is a necessity for accurate retrieval of Chl-a concentrations [7]. The height of the spectral peak between 700 and 710 nm is used as a proxy for phytoplankton biomass [44]. Many researchers have used broad wavelength data (i.e., Landsat, HJ-1A/1B) as input to regression and neural network approaches for estimating Chl-a, achieving reasonable accuracy (70–90%) [9, 19, 45, 46]. Table 3 shows some studies and datasets used to study Chl-a in marine regions. Lim and Choi [19] found that green and NIR bands of OLI are highly correlated with Chl-a (R = 0.71) in Korean waters. Nazeer and Nichol [46] also used the red/blue ratio to retrieve Chl-a with high accuracy (R = 0.85). Gurlin et al. [43] calibrated three models for Chl-a concentrations from 0 to 100 mg m−3 using two bands (red and NIR) of MERIS and MODIS reflectance data. They found that a simple two-band model achieved a higher accuracy than a complex three-band model. Moses et al. [51] also calibrated a red-NIR algorithm for high Chl-a concentrations in productive turbid waters. Figure 5 shows Chl-a concentrations in highly turbid Pearl River Estuary and connecting rivers, derived using high-resolution MSI data with the method of Moses et al. [51].
Band combination | Sensor | Reference | |
---|---|---|---|
All bands (neural network and other machine learning methods) | GOCI | [28] | |
TM, SAR | [45] | ||
Multiple bands and their ratios (multiple regression) | OLI band (2–5) | [19] | |
OLCI | [30] | ||
TM | [8] | ||
HICO | [17] | ||
Blue (400–500 nm) and green (500–600 nm) ratio | In situ | [22] | |
Blue (400–500 nm) and red (600–700 nm) ratio | TM, ETM+, HJ-1A/1B CCD | [9, 46] | |
Green (500–600 nm) and red (600–700 nm) ratio | TM | [47] | |
In situ (0.70/0.56 μm) | [44] | ||
Red (600–700 nm) and NIR (700 μm–900 μm) ratio | MERIS, MODIS | [43] | |
HICO | [48] | ||
Using a single band | Green (500–600 nm) | Daedalus Airborne Thematic Mapper | [49] |
Red (600–700 nm) | AVHRR | [50] |
Methods used to retrieve Chl-a using remote sensing data in the river and marine waters.
Chl-a concentration observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
Recently, machine learning approaches taking advantages of reflectance in all bands have also been applied using Landsat [45, 52] and GOCI data [28]. Our work also shows the potential use of Landsat TM, ETM+, and OLI with a machine learning approach to estimate Chl-a in coastal waters (Figure 6). We have evaluated three machine learning models to estimate Chl-a in the coastal waters of Hong Kong, of which artificial neural networks (ANN) performed best resulting in higher R (0.91) and lower RMSE (1.4 μg/L) than models based on support vector regression (SVR) and random forest (RF) algorithms. Chlorophyll indices such as the cyanobacteria index [53], maximum chlorophyll Index [54], and maximum peak height algorithm [55] have been demonstrated the robustness for detecting algal blooms and surface scum in coastal waters. Lunetta et al. [56] described the potential of using cyanobacteria index to measure cyanobacteria cell counts in bloom situations using MERIS data. Nazeer et al. [57] used board waveband band data (Landsat TM, ETM+, and HJ-1A/1B CCD) along with meteorological data as inputs to an artificial neural network model to map phytoplankton cell counts during a bloom in the complex coastal waters of Hong Kong and validated the model in two lakes in the United States and Japan.
Comparison of measured and predicted values from three machine learning models. (a) Chl-a concentration using artificial neural network, (b) Chl-a concentration using support vector regression, and (c) Chl-a concentration using random forest.
Synthetic aperture radar (SAR) data can also be used to detect large algal blooms in cloudy weather as algal blooms may appear as an area of low backscatter compared to surrounding water surfaces [50].
Turbidity is an optical property of water and is highly influenced by concentrations of suspended and dissolved organic and inorganic materials in water, including Chl-a, SS, and CDOM. SS is mainly responsible for the light scattering, whereas CDOM and Chl-a control the light absorption properties of water [58].
Turbidity and TSS are two important variables of marine systems studies because of their direct linkages with photosynthetically available radiation, which affects the growth of plankton and other algae [41]. Turbidity has also been used to measure fluvial SS concentrations in rivers and river plumes [59]. These fluvial SS loads are rich in nutrients and considered a cause of eutrophication. So, it is vital to have time series records of suspended sediment concentrations for better understanding of land-ocean interactions. High SS loads negatively affect aquaculture [59] and are hazardous to benthic invertebrates [60]. These parameters are also associated with the diffuse attenuation coefficient (penetration of light, in the blue-green region of the spectrum, through water column) and Secchi disk depth (a measure of water transparency) [41]. For all these reasons, turbidity and TSS concentrations are considered to be critical parameters in the study of marine systems.
Ocean color remote sensing techniques are widely used to monitor spatiotemporal variations in SS concentration and for mapping of water turbidity. Figure 7 shows the changes in ocean color due to high sediment loads in the Yangtze River Estuary [60] and the Pearl River Estuary [61]. It is suggested that an algorithm using single bands provides a good estimation of TSS concentrations if an appropriate band is used [62]. Moreover Novo et al. [63] and Curran et al. [64] have demonstrated that a single-band approach may be adopted when water reflectance in the single band has a linear relationship with TSS concentrations. However, coastal water often consists of a complex mixture of substances and results in large variations in reflectance. In this case, multiple spectral bands should be adopted for TSS retrieval [62, 65, 66]. These methods using band arithmetic can achieve high accuracy around 80% for retrieving TSS concentrations in complex waters [67, 68]. The peak of the reflectance curve shifts from the green region to the red region with increasing concentration of dissolved and suspended matter; and water starts reflecting significantly in NIR region [21] (Figure 8). For water with high TSS concentrations, the spectral region between 600 and 900 nm should be used. Several studies using Landsat TM, ETM+, and OLI show that the blue, green, red, and NIR bands are useful for the determination of TSS [8, 19, 68, 69, 70]. Literature also shows that TM, ETM+, OLI, and MODIS are the most frequently used sensors for developing algorithms to study seasonal TSS variability in coastal and estuarine areas, due to the large amount of archived remote sensing data [24, 71, 72]. The recently launched MSI sensor onboard Sentinel-2A and Sentinel-2B provide high spatial resolution of 10–20 m with a high temporal resolution of 5 days. The high spatial resolution (10 m) red and NIR bands are capable of routine monitoring of TSS concentration and turbidity in narrow bays, rivers, and inlets. Figure 9 shows the suspended matter concentrations, and Figure 10 shows turbidity in the Pearl River Estuary and connecting rivers using MSI data with algorithms of Nechad et al. [62] and Nechad et al. [73], respectively.
Terra-MODIS true color image, captured on 16 September 2000, shows the sediment plume of the Yangtze River Estuary (left). The Sentinel-2 true color image, captured on 31 December 2017, shows high sediment concentrations in the Pearl River Estuary (right).
Remote sensing reflectance (Rrs) spectra of water containing different suspended solid concentration (mg/L) [
High levels of suspended matter concentration were observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
High levels of turbidity were observed in the Pearl River Estuary and its connecting rivers on 31 December 2017.
Methods and algorithms for estimation of TSS and turbidity have been evolved from simple methods such as linear/nonlinear regression and principal component analysis (PCA) to relatively complex techniques such as genetic algorithms and ANN. Nazeer and Nichol [68] initially developed a regression model resulting in an RMSE of 2.60 mg/L. Later, Nazeer et al. [52] evaluated the potential of a machine learning model for estimating TSS in the complex coastal area of Hong Kong achieving an RMSE of 4.59 mg/L. Our work of machine learning models with Landsat TM, ETM+, and OLI data in the same area also shows promising results for estimation of TSS (Figure 11). In our work, ANN outperformed the other two machine learning approaches, SVR (support vector machine) and RF (random forest), resulting in the lowest RMSE of 2.8 mg/L. Table 4 includes some studies and methods used to study TSS in rivers, bays, estuaries, and relatively open coastal waters.
Comparison of measured and predicted values from three machine learning models. (a) TSS concentration using artificial neural network, (b) TSS concentration using support vector regression, and (c) TSS concentration using random forest.
Band combination | Sensor | Reference | |
---|---|---|---|
All bands (neural network and other machine learning methods) | GOCI | [28] | |
Landsat TM, ETM+, OLI, HJ-1 A/B CCD | [52] | ||
TM, SAR | [45] | ||
Multiple bands and their ratios (multiple regression) | Landsat OLI band (2–5) | [19] | |
Landsat ETM+ | [9] | ||
Multiple green (500–600 nm) and red (600–700 nm) ratio | Landsat TM, ETM+ | [68] | |
Green (500–600 nm) and red (600–700 nm) ratio | HJ-1A/1B CCD | [67] | |
Red (600–700 nm) and NIR (700–900 nm) ratio | MODIS | [65] | |
Single band algorithms | Green (500–600 nm) | SeaWiFS | [58] |
EO-ALI | [12] | ||
Red (600–700 nm) | Landsat TM, ETM+, HJ-1 | [47, 68] | |
AVHRR, SeaWiFS | [58] | ||
MODIS, MERIS, SeaWiFS | [24, 62, 65] | ||
HICO | [17] | ||
NIR (700–900 nm) | MODIS, MERIS, SeaWiFS | [62] |
Methods used to retrieve TSS using remote sensing data in marine waters.
Stormwater runoff is also a large source of marine pollution as runoffs and pollutants from the urban watershed enter into the coastal environment after rainstorms. Stormwater runoff and municipal wastewater plumes may sometimes be overlooked due to persistent cloud cover in optical imagery. These types of runoff are often detectable via SAR as they deposit surfactants on the sea surface, smoothing the small gravity waves and thus producing an area of low backscatter in comparison to the surrounding sea surface [74]. DiGiacomo et al. [74] used high-resolution SAR to monitor such plumes in the Southern California Bight. In their study, the dynamics of runoff plume was modeled using SAR images together with meteorological data as a function of cumulative event discharge, timing of the peak flow, and total storm precipitation. Holt et al. [75] used multi-platform SAR data along with MODIS and precipitation data to study a stormwater plume and its flow direction.
A large oil spill from tankers causes not only significant economic loss but also destruction to the aquatic ecosystem. After the spill, oil undergoes several processes such as spreading, evaporation, dissolution, drifting, photolysis, biodegradation, and the formation of oil-in-water and water-in-oil emulsions [76].
Owing to the dynamic spreading nature of the spill, both remote and station-based sensors are essential for comprehensive and effective monitoring. Airborne survey of an oil spill can be carried out by side-looking airborne radar (SLAR), laser fluorosensor (LF), and ultraviolet and thermal infrared video cameras. Ultraviolet, microwave, thermal, and optical airborne sensors all exhibit the ability to detect oil spills [6]. Ultraviolet sensors are sensitive to oil thickness of 0.01–0.05 μm. Oil appears as a bright target in this region of the spectrum, and brightness increases with the thickness of the oil. Optical sensors can measure thicker oil (2–500 μm) and are able to detect oil dispersed in water, whereas thermal infrared sensors measure oil with a thickness of about 10–50 μm [34]. Airborne LF and microwave radiometers (MWR) are considered to be the most appropriate sensors for oil spill detection. SLAR, ultraviolet, and thermal video cameras were used to identify areas of thick oil during the Sea Empress oil spill in 1996. Oil also undergoes weathering and aging. Multispectral satellite images, taking advantage of fluorescence characteristics of oil, can detect spills and assess the levels of weathering of the oil [31].
Spaceborne synthetic aperture radar (SAR) is commonly used for ocean pollution monitoring, especially oil spills. Table 5 includes some SAR-equipped satellites used for oil spill detection. The advantage of SAR is the capability to take measurements during all day and all-weather conditions. Therefore, they are considered superior to optical sensors in this application [5]. The spreading trend of oil highly depends on wind direction and speed. An oil spill would break up and disperse if the wind speed is greater than 10 m/s [74]. DiGiacomo et al. [74] used ERS-2 SAR and RADARSAT-1 SAR images to map oil spills in the Southern California Bight. Shirvany et al. [77] evaluated the potential of different polarizations using RADARSAT-2 data for oil spill detection in the Gulf of Mexico. In another study, ENVISAT data was used effectively as an input to a hydrodynamic model to track the fate of oil after the Kerch Strait oil spill in 2007 [78]. Figure 12 shows an incident of large oil spill on the Galicia coast [79] and the Korean coast [80] for which spaceborne SAR data was used to access the coverage areas and the damage caused by the spills.
Satellite sensor | Operation |
---|---|
Sentinel-1A | 2014, operating |
Sentinel-1B | 2016, operating |
TerraSAR-X | 2007, operating |
ENVISAT advanced synthetic aperture radar (ASAR) | 2002, not operating |
RADARSAT-1 | 1995, not operating |
European remote sensing (ERS) satellites: ESR-2 | 1995, not operating |
Active spaceborne sensors mostly used in oil spill detection.
(a) ASAR wide-swath image of northwest coast of Spain, captured on 17 Nov 2002, at 10:45 UTC showing oil from the wrecked tanker approaching Spanish coast (source, ESA), (b) ASAR image of South Korea, captured on 11 Dec 2007, at 01:40 UTC, showing oil spill from 146,000 ton damaged crude oil tanker (source ESA).
With the increasing amount of marine plastic litter, its adverse chemical, biological, and ecological impacts on the marine ecosystem have raised the public concerns [81]. It is estimated that 4.8–12.7 million metric tons of plastic is dumped in the sea every year [82] due to increased use of plastic in industry and daily life [83, 84]. Although some surveys have been undertaken [85] to estimate the density and weight of floating plastic in the oceans globally, there is a lack of long-term and large-scale monitoring.
Some research has been conducted using remote sensing technology for the detection of floating marine plastic [86]. However, this research domain is still in its early stages. The reflectance from water captured by sensors is different from that of floating plastic objects. There are several reasons for this, (1) the physical properties of water are different from that of plastic, and they have significant distinct reflectance; (2) the transmitting ability of light through water is different from that through plastic; (3) the absorption of light by water is different from plastic [87]. Figure 13 shows different pathways of incident light after interacting with the surface (with and without marine plastic). Some studies have used hyperspectral remote sensing to study marine macroplastics [87] and microplastics [88]. Goddijn-Murphy et al. [87] considered the spectral signatures and geometric optics of plastic and seawater to develop a reflectance model for detecting macroplastics. The key is to determine the appropriate reflectance peak of plastic and consider its ratio with wavelength bands where water-leaving reflectance is low. Their model considers reflectivity of only one type of plastic litter in two dimensions. However, there are some constraints for detecting marine plastics in a real scenario since there have no standard shape, dimension, color, chemical composition, etc. Nevertheless, this study demonstrated the possibility of using remote sensing as a useful means for mapping and tracking of marine plastic.
Schematic of solar radiance interacting with (A) an open water body and (B) the same water body but with floating plastic. Ld is total downwelling radiance (solar beam + diffuse skylight), Lds is subsurface downwelling radiance, Lws is subsurface upwelling radiance, Lwr is radiance reflected directly off the water surface, Lwt is subsurface upwelling radiance transmitted through the water-air interface, Lpr is radiance reflected off the plastic, and Lpt is subsurface upwelling radiance transmitted through the plastic. Lw is total water-leaving radiance, Lwr + Lwt, and Lp is total plastic leaving radiance, Lpt + Lpr; subscript ‘0’ indicates all the variables in the absence of plastic and FOV is a field of view [
Detecting coastal litter near land surface is easier than in open ocean, as its reflectance and shape characteristics are not affected by its pitching and rolling on ocean waves. Moy et al. [89] used aerial imagery along with spatial analysis to categorize and map marine litter deposited along the coasts of the Hawaiian Islands. Very high-resolution aerial imagery allowed precise measurements of the quantity, location, type, and size of dumped litter (>0.05 m2) (Figure 14). In another study, Martin et al. [90] discussed the potential of combining images from unmanned aerial vehicles (UAV) and a machine learning approach, to detect and map marine litter. Machine learning algorithms are able to detect and classify objects when training samples with known training objects are provided. Their results showed that a UAV-based beach survey is 39 times faster than beach screening on foot and the large footprint of a UAV can cover entire coastlines and beaches including those in remote areas.
Distribution and density of marine litter along the coasts of the main Hawaiian Islands. Areas with 100 and more item densities are shown as hotspots of high marine litter [
Increased levels of marine pollution due to anthropogenic activities are adversely affecting marine sustainability of marine ecosystems. Reviewed literature suggested that aerial and spaceborne sensors provide holistic information for monitoring many of the major marine pollutants. These include oil and chemical spills, sewage, high suspended solids, and algal blooms. Solid waste deposited in coastal areas can also be mapped using similar geospatial technology. However, there are some technical limitations in assessing detailed information about pollutants. These limitations stem from their dynamic nature, limited information of specific spectral response of pollutants, substrate response in optically shallow waters, and complex physics of light interaction through the water column. Despite these limitations, remote sensing is still capable of providing useful information about pollution events in sensitive marine areas.
Active and hyperspectral airborne sensors are often considered superior to spaceborne sensors for monitoring coastal and estuarine pollutants due to their real-time and detailed monitoring capability. Spaceborne sensors are more reliable for large-scale ocean, but with the recent development of sensor technology, especially hyperspectral and active sensors with high temporal resolution, the applications of spaceborne sensors in coastal regions are also increasing. Presently, monitoring of marine waters is offered through numerous satellite sensors such as MODIS, VIIRS, AVHRR, OLCI, GOCI, Landsat, and Sentinel-2 with spectral and spatial resolutions able to measure marine pollutants and other marine parameters. Active satellite sensors such as SAR, altimeters, scanning radiometers, and microwave sounders, which are mostly used in physical oceanography, also possess the potential for detection of marine pollution under specific meteorological conditions and provide useful data to track and model the impact of these pollutants.
Heavy metal pollution in coastal and estuarine region is another major concern of marine managers and researchers. Studies have attempted to use airborne hyperspectral data for this task, but satellite remote sensing is not yet able to detect these loads directly. However, the core factors causing these pollutants such as river plumes, sewerage, and industrial waste entering into these sensitive systems can be monitored using satellite remote sensing. If the point source of heavy metals is traced by remote sensing, policies and management practices can be applied according to the specific pollutants, and their mobilization and transfer of heavy metal to sensitive coastal environments can be avoided. Multiple approaches have proven reliable for this task.
In addition, recent developments in software and computation power have led to the increased use of data captured by remote sensing systems. Computer systems can now store and analyze large datasets. Therefore, marine protection agencies and government can utilize the full potential of remote sensing data in geographic information systems (GIS) and decision support systems (DSS) to manage marine resources and pollution. Collaboration between the research community and government is of utmost importance for using the full potential of this data in marine pollution management. Different applications of remote sensing such as detection of floating marine plastic litter and the use of active remote sensing for detecting algal blooms are still in the research. With the advancement of remote sensing sensors, sophisticated methods will be developed in the future for monitoring marine pollution.
Authors would like to acknowledge the General Research Fund (project id: 15246916), the Hong Kong Ph.D. Fellowship Scheme from the Research Grants Council of Hong Kong. The authors would also like to acknowledge US Geological Survey for providing Landsat (TM, ETM+, and OLI) image archive, the Copernicus Open Access Hub for providing Sentinel-2 data, and the Hong Kong Environmental Protection Department for providing station-based coastal water quality data for developing numerical models.
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The main part of this chapter presents the classification of collaborative XR systems, the concept of the major application architectures and the consistency models of shared virtual game environments. The next subchapter briefly deals with the sharing of property ownership. The mentioned concepts and examples used in the chapter are implemented in many works and projects using a collaborative environment (also in gamified form) developed in the laboratory LIRKIS, the home laboratory of the authors. The knowledge presented in this chapter may provide tips and inspiration for some other game projects, and practical and useful notes on the advantages or disadvantages of some systems will be interesting and useful.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Branislav Sobota, Marián Hudák and Emília Pietriková"},{id:"82031",title:"Serious Games Development and Impact for Business Education",slug:"serious-games-development-and-impact-for-business-education",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.103085",abstract:"Learning methodologies and experiences have changed over the recent years thanks to the incorporation of digital technology, among many of these technologies are Serious Games, that has a better opportunity to be used during recent pandemic times, the process of designing and incorporating games technologies is not easy and there are very few available development tools, this paper focus on basic guidelines and a practical experience. Review the process of developing a serious game and address some of the challenges of making a serious game from scratch. The objective is also to understand the challenges of developing and implementing gaming mechanics in Serious Games and the impact and results of the experience of using it with students’ samples from Latin America and Europe. The results of this study is that gamming has a very positive impact in the learning process of higher educations students, whom value the use of this technologies in their education, however in general these technologies are not being use in higher education, there is more simulation type implementations, especially in marketing and logistic areas. There is a need to train teachers and create pedagogical departments that will enhance and develop this experiential learning tools.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Dario Liberona, Aravind Kumaresan, Lionel Valenzuela and Giovanny Tarazona"},{id:"81629",title:"Leveraging on Data Sciences: Review of Architectural Practice and Education in Nigeria",slug:"leveraging-on-data-sciences-review-of-architectural-practice-and-education-in-nigeria",totalDownloads:23,totalDimensionsCites:0,doi:"10.5772/intechopen.103097",abstract:"Big data sciences demand the significant role of the architect. Particularly, facilitate the birth of an antifragile construction industry and more robust data sciences community of professionals. Skilled community necessary to build sustainable liveable cities with emerging creator’s economy. Liveability, well-being, and sense of belonging in the city are connected. Conversely, dismissive attitude by decision-makers towards architectural practice and education, even among architects, in recognizing architecture as data-driven and source of data deserve rethink. Here the chapter demonstrates architects as data scientists and the symbiotic relationship that exist between architecture and 3D computer graphics while highlighting emerging data sciences opportunities and threats. The chapter adopted principally reviews of scholarly literatures, draws from authors’ 20-years personal experiences, and industry leaders’ views. The language is accessible yet academically concise. The chapter concluded with recommendations, including highlights of big data technologies potential transformation of 3D computer graphics. The implications are policy, design, and education.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"John Allison and Anita Alaere Bala"},{id:"81217",title:"Commercial-off-the-Shelf (COTS) Games: Exploring the Applications of Games for Instruction and Assessment",slug:"commercial-off-the-shelf-cots-games-exploring-the-applications-of-games-for-instruction-and-assessme",totalDownloads:40,totalDimensionsCites:0,doi:"10.5772/intechopen.103965",abstract:"Despite the growing interest in utilizing commercial off-the-shelf (COTS) games for instructional and assessment purposes there is a lack of research evidence regarding COTS games for these applications. This chapter considers the application of COTS games for instruction and assessment and provides preliminary evidence comparing COTS game scores to traditional multiple-choice assessments. In a series of four studies, we collected data and compared results from the performance in a COTS game to scores on a traditional multiple-choice assessment written for the purposes of each study. Each assessment was written to evaluate the same content presented in the game for each respective study. Three of the four studies demonstrated a significant correlation between the COTS game and the traditional multiple choice assessment scores. The non-significant value in Study 4 was likely due to a small sample size (n < 100). The results of these studies support our hypothesis and demonstrate that COTS games may be a useful educational tool for training or assessment purposes. We recommend that future research focuses on specific applications of COTS games to explore further opportunities for utilizing COTS in education and assessment.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Diana R. Sanchez, Amanda Rueda, Leila Jimeno Jimènez and Mahsa Norouzi Nargesi"},{id:"80731",title:"The Use of Serious Games for Learning Cardiopulmonary Resuscitation Procedures: A Systematic Mapping of the Literature",slug:"the-use-of-serious-games-for-learning-cardiopulmonary-resuscitation-procedures-a-systematic-mapping-",totalDownloads:70,totalDimensionsCites:0,doi:"10.5772/intechopen.102399",abstract:"This article presents a systematic mapping, with an analysis of 35 selected works according to established criteria, seeking to connect the points and find relevant information for the following research areas: basic life support, cardiopulmonary resuscitation, serious games, and games for healthcare. Among the main results found, we can mention the representativeness of works by regions and their most productive years, the most common platforms, noting a focus on VR technologies, in addition to identifying the preference for the Unity 3D tool for implementations. It was also possible to show that serious games can be very effective in teaching CPR.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Ingrid Nery Mendes, Maicon de Araújo Nogueira, Filipe Valente Mendes, Otavio Noura Teixeira and Viviane Almeida dos Santos"},{id:"80515",title:"View Synthesis Tool for VR Immersive Video",slug:"view-synthesis-tool-for-vr-immersive-video",totalDownloads:142,totalDimensionsCites:0,doi:"10.5772/intechopen.102382",abstract:"This chapter addresses the view synthesis of natural scenes in virtual reality (VR) using depth image-based rendering (DIBR). This method reaches photorealistic results as it directly warps photos to obtain the output, avoiding the need to photograph every possible viewpoint or to make a 3D reconstruction of a scene followed by a ray-tracing rendering. An overview of the DIBR approach and frequently encountered challenges (disocclusion and ghosting artifacts, multi-view blending, handling of non-Lambertian objects) are described. Such technology finds applications in VR immersive displays and holography. Finally, a comprehensive manual of the Reference View Synthesis software (RVS), an open-source tool tested on open datasets and recognized by the MPEG-I standardization activities (where “I” refers to “immersive”) is described for hands-on practicing.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Sarah Fachada, Daniele Bonatto, Mehrdad Teratani and Gauthier Lafruit"}],onlineFirstChaptersTotal:8},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261",scope:"Modern physiology requires a comprehensive understanding of the integration of tissues and organs throughout the mammalian body, including the cooperation between structure and function at the cellular and molecular levels governed by gene and protein expression. While a daunting task, learning is facilitated by identifying common and effective signaling pathways mediated by a variety of factors employed by nature to preserve and sustain homeostatic life. \r\nAs a leading example, the cellular interaction between intracellular concentration of Ca+2 increases, and changes in plasma membrane potential is integral for coordinating blood flow, governing the exocytosis of neurotransmitters, and modulating gene expression and cell effector secretory functions. 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His primary area of interest is physiology and pathophysiology of the gastrointestinal (GI) tract, with the major focus on the mechanism of GI mucosal defense, protection, and ulcer healing. He was a postdoctoral NIH fellow at the University of California and the Gastroenterology VA Medical Center, Irvine, Long Beach, CA, USA, and at the Gastroenterology Clinics Erlangen-Nuremberg and Munster in Germany. He has published 290 original articles in some of the most prestigious scientific journals and seven book chapters on the pathophysiology of the GI tract, gastroprotection, ulcer healing, drug therapy of peptic ulcers, hormonal regulation of the gut, and inflammatory bowel disease.",institutionString:null,institution:{name:"Jagiellonian University",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"10",title:"Animal Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",isOpenForSubmission:!0,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. 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From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). W inner of the Bimbo PanAmerican Nutrition, Food Science and Technology Award 2006 and 2012, South AmericaHuman Nutrition, Professional Category. 2006 award in pharmacology, Bernardo\r\nHoussay, in recognition of his meritorious works of research. Angel Catalá belongto the Editorial Board of Journal of lipids, International Review of Biophysical ChemistryFrontiers in Membrane Physiology and Biophysics, World Journal oExperimental Medicine and Biochemistry Research International, W orld Journal oBiological Chemistry, Oxidative Medicine and Cellular Longevity, Diabetes and thePancreas, International Journal of Chronic Diseases & Therapy, International Journal oNutrition, Co-Editor of The Open Biology Journal.",institutionString:null,institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}},editorTwo:null,editorThree:null},{id:"12",title:"Human Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",isOpenForSubmission:!0,editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. He is a physiologist working in the field of skeletal muscle. He was awarded his sports science diploma in 1995 by the University of Tsukuba and began his scientific work at the Department of Physiology, Aichi Human Service Center, focusing on the molecular mechanism of congenital muscular dystrophy and normal muscle regeneration. His interest later turned to the molecular mechanism and attenuating strategy of sarcopenia (age-related muscle atrophy). His opinion is to attenuate sarcopenia by improving autophagic defects using nutrient- and pharmaceutical-based treatments.",institutionString:null,institution:{name:"Tokyo Institute of Technology",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"331519",title:"Dr.",name:"Kotomi",middleName:null,surname:"Sakai",slug:"kotomi-sakai",fullName:"Kotomi Sakai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000031QtFXQA0/Profile_Picture_1637053227318",biography:"Senior researcher Kotomi Sakai, Ph.D., MPH, works at the Research Organization of Science and Technology in Ritsumeikan University. She is a researcher in the geriatric rehabilitation and public health field. She received Ph.D. from Nihon University and MPH from St.Luke’s International University. Her main research interest is sarcopenia in older adults, especially its association with nutritional status. Additionally, to understand how to maintain and improve physical function in older adults, to conduct studies about the mechanism of sarcopenia and determine when possible interventions are needed.",institutionString:null,institution:{name:"Ritsumeikan University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"13",title:"Plant Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",isOpenForSubmission:!0,editor:{id:"332229",title:"Prof.",name:"Jen-Tsung",middleName:null,surname:"Chen",slug:"jen-tsung-chen",fullName:"Jen-Tsung Chen",profilePictureURL:"https://mts.intechopen.com/storage/users/332229/images/system/332229.png",biography:"Dr. Jen-Tsung Chen is currently a professor at the National University of Kaohsiung, Taiwan. He teaches cell biology, genomics, proteomics, medicinal plant biotechnology, and plant tissue culture. Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. He has published more than ninety scientific papers and serves as an editorial board member for Plant Methods, Biomolecules, and International Journal of Molecular Sciences.",institutionString:"National University of Kaohsiung",institution:{name:"National University of Kaohsiung",institutionURL:null,country:{name:"Taiwan"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:16,paginationItems:[{id:"82135",title:"Carotenoids in Cassava (Manihot esculenta Crantz)",doi:"10.5772/intechopen.105210",signatures:"Lovina I. Udoh, Josephine U. Agogbua, Eberechi R. Keyagha and Itorobong I. 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Buchholz and Erik J. Behringer",hash:"e373a3d1123dbd45fddf75d90e3e7c38",volumeInSeries:1,fullTitle:"Calcium and Signal Transduction",editors:[{id:"89438",title:"Dr.",name:"John N.",middleName:null,surname:"Buchholz",slug:"john-n.-buchholz",fullName:"John N. Buchholz",profilePictureURL:"https://mts.intechopen.com/storage/users/89438/images/6463_n.jpg",biography:"Full Professor and Vice Chair, Division of Pharmacology, Loma Linda University, School of Medicine. He received his B.S. Degree in Biology at La Sierra University, Riverside California (1980) and a PhD in Pharmacology from Loma Linda University School of Medicine (1988). Post-Doctoral Fellow at University of California, Irvine, College of Medicine 1989-1992 with a focus on autonomic nerve function in blood vessels and the impact of aging on the function of these nerves and overall blood vessel function. Twenty years of research funding and served on NIH R01 review panels, Editor-In-Chief of Edorium Journal of Aging Research. Serves as a peer reviewer for biomedical journals. Military Reserve Officer serving with the 100 Support Command, 100 Troop Command, 40 Infantry Division, CA National Guard.",institutionString:null,institution:{name:"Loma Linda University",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"6925",title:"Endoplasmic Reticulum",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6925.jpg",slug:"endoplasmic-reticulum",publishedDate:"April 17th 2019",editedByType:"Edited by",bookSignature:"Angel Català",hash:"a9e90d2dbdbc46128dfe7dac9f87c6b4",volumeInSeries:2,fullTitle:"Endoplasmic Reticulum",editors:[{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. 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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:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{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:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/75406",hash:"",query:{},params:{id:"75406"},fullPath:"/chapters/75406",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)}()