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",isbn:"978-1-80356-279-7",printIsbn:"978-1-80356-278-0",pdfIsbn:"978-1-80356-280-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"f03f48f4d6fd8beacecaac19314be864",bookSignature:"Dr. Bruno Carpentieri",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11540.jpg",keywords:"Magnetic Confinement, Disruptions and Instabilities, Electromagnetic Forces, Plasma Transport, Eddy Current Models, Magnetohydrodynamic Equation, Boundary Element Method, Finite Element Method, ITER Experiments, Fluid and Transport, Materials Science, Engineering Applications",numberOfDownloads:15,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 11th 2022",dateEndSecondStepPublish:"April 19th 2022",dateEndThirdStepPublish:"June 18th 2022",dateEndFourthStepPublish:"September 6th 2022",dateEndFifthStepPublish:"November 5th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"An academic researcher in applied mathematics, working in the field of computational modeling in electromagnetics, fusion energy, and engineering research. He is an editorial board member of the Journal of Applied Mathematics, an editorial committee member of Mathematical Reviews (American Mathematical Society), and a reviewer for about thirty scientific journals in numerical analysis. He has co-authored fifty publications in peer-reviewed scientific journals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"92921",title:"Dr.",name:"Bruno",middleName:null,surname:"Carpentieri",slug:"bruno-carpentieri",fullName:"Bruno Carpentieri",profilePictureURL:"https://mts.intechopen.com/storage/users/92921/images/system/92921.png",biography:"Bruno Carpentieri obtained a laurea degree in Applied Mathematics in 1997 from Bari University, Italy. He obtained a Ph.D. in Computer Science from the Institut National Polytechnique de Toulouse (INPT), France. After some post-doctoral experiences, Dr. Carpentieri served as an assistant professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, the Netherlands, and as a reader in Applied Mathematics at Nottingham Trent University, United Kingdom. Since May 2017, he has been an associate professor of Applied Mathematics at the Faculty of Computer Science, Free University of Bozen-Bolzano, Italy. His research interests include applied mathematics, numerical linear algebra, and high-performance computing. Dr. Carpentieri has served on several scientific advisory boards in computational mathematics. He is an editorial board member of the Journal of Applied Mathematics, an editorial committee member of Mathematical Reviews (American Mathematical Society) and a reviewer for about thirty scientific journals in numerical analysis. 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It is a rare neoplasm that originates in the cells of the biliary epithelium. In the USA, it has a mean incidence of 1 case/100,000 inhabitants/year.
\nThey are classified into three groups, with respect to their location within the biliary tree: (1) intrahepatic, (2) the upper and middle thirds and (3) the distal part of the common bile duct.
\nIntrahepatic tumours are the least frequent. They behave like primary hepatic tumours. Perihilar, or Klatskin, tumours are the most frequent, accounting for 60–80% of the total.
\nTheir incidence increases with age, more cases appearing in patients aged between 50 and 70 years, with a clear predominance in men.
\nThe diseases most frequently associated with it are primary sclerosing cholangitis (PSC), choledochal cysts, chronic infections with the parasite
Presentation depends fundamentally on the location of the tumour, although more than 90% of cases exhibit obstructive jaundice as their main sign. Other associated symptoms are episodes of cholangitis (which occur frequently after manipulation of the biliary tract), weight loss, abdominal pain, rebel pruritus and altered liver function test values, with elevated serum levels of bilirubin, alkaline phosphatase (AP) and gamma-glutamyl transpeptidase (GGT).
\nTransaminases can be quite elevated. The levels of serum tumour markers type CEA and CA 19-9 are usually raised, although they are of little diagnostic use due to their lack of sensitivity and specificity for this purpose. There are currently no other markers available that are useful for diagnosing or following up these tumours.
\nAbdominal ultrasound (AU) produces results of varying quality, depending on the location of the tumour. Distal tumours can be confused with primary pancreatic or periampullary neoplasms. Hilar tumours are usually infiltrative, characteristically causing dilation of the intrahepatic bile duct and collapse of the extrahepatic duct.
\nComputerised axial tomography (CAT) can help identify the type and stage of an existing tumour.
\nMagnetic resonance cholangiography (MRC) is the current imaging technique of choice for studying the bile duct, providing information simultaneously about the bile duct and its locoregional extension.
\nEndoscopic ultrasound (EU) is especially useful in tumours located in the distal third of the duct, enabling the tumour to be sampled and its cytology determined.
\nEndoscopic retrograde cholangiopancreatography (ERCP) and percutaneous cholangiography (PC) are currently reserved for the palliative treatment of obstructive jaundice, through the placement of biliary prostheses.
\nThe classification of bile duct tumours is controversial and complex. Perihilar tumours are customarily divided into four types, depending on their extension and type of surgical resection, following the Bismuth-Corlette classification [1, 2, 3].
\nThe working group of the Memorial Sloan-Kettering Cancer Center in New York has proposed a new classification that is better correlated with the prognosis, but which is very complex and does not take into account the current concepts of resectability.
\nIn general, it is considered appropriate to separate perihilar tumours and those of the distal bile duct into separate groups.
\nResectability is determined by the absence of metastases, including retropancreatic nodes, non-invasion of the great vessels and the possibility of performing a surgical resection with free margins. Some groups recommend to do an exploratory laparoscopy prior to the indication of surgery, to rule out the presence of dissemination.
\nThe discovery of lymph node involvement below the hepatoduodenal ligament indicates inoperability.
\nThe placement of a biliary prosthesis before surgery is controversial. While some groups consider that it ameliorates the jaundice, allows the collection of biopsies and facilitates access to the bile duct after surgery, others postulate that it makes complications, especially cholangitis, more likely to arise [4, 5, 6].
\nThe type of resection depends on the location of the tumour. In distal tumours, a cephalic duodenopancreatectomy is performed. This should be carried out in a reference centre to reduce the likelihood of perioperative morbidity (30%).
\nIn hilar lesions, the resection should be individualised with respect to the extent of the tumour. A liver resection should normally be carried out that includes segments IV and V, extended according to the size of the tumour mass in order to ensure disease-free resection margins.
\nLiver transplantation is not a feasible therapeutic option because of the high rate of recurrence that occurs during follow-up.
\nIn cases in which resection is not possible, it is advisable to drain the bile duct through catheters that are carefully placed through endoscopy or inserted percutaneously. Endoscopic drainage is considered easier and safer in distal tumours, while the percutaneous approach is more effective in proximal tumours. The most common short-term complication is cholangitis, although this can be prevented by giving antibiotics before placement. In the long term, the obstruction of the prosthesis, either by tumour progression, biliary detritus or food debris, prevents the flow of bile through the duct, and the jaundice reappears.
\nThere is currently not enough scientific evidence available to make firm recommendations about the use of adjuvant therapy after surgery. The Guidelines of the European Society of Medical Oncology (ESMO) suggest the use of chemotherapy and/or palliative radiotherapy after non-curative resections and of chemoradiotherapy after curative resections.
\nNeoadjuvant therapy is not usually recommended because of the deterioration that patients usually present at the time of diagnosis [7, 8, 9, 10].
\nThe prognosis of bile duct cancer is bad. Malignant tumours of the bile duct are usually diagnosed at advanced stages, with infiltration of neighbouring organs such as the liver, gallbladder, adjoining vessels and distant metastases, which often contraindicates surgery. Average survival is 12–24 months. Distal tumours have a better prognosis, with a 5-year survival of 15–25%.
\nScreening programmes have not proved to be effective for the early detection of these tumours. However, in carriers of PSC, it is advisable to undertake periodic surveillance using imaging techniques (e.g. MRC) and the determination of CA 19-9 serum levels, although none of these procedures is sufficiently specific [11, 12].
\nAgriculture is a major source of food in Sub-Saharan Africa (SSA), and a primary source of livelihood [1]. The sector employs more than half of the total labour force and accounts for roughly a third of the gross domestic product (GDP) [2, 3, 4]. The share of agriculture in GDP varies significantly by country ranging from below 3% in Botswana to over 50% in Chad [5]. Due to the fragmentation of land caused by population pressure in most rural areas, farm sizes are typically less than 2 hectares each [6]. As a result, smallholder farms are dominant across the subcontinent [7]. They make up 80% of the farms, which translates to approximately 33 million smallholder farms [8]. Although the widely accepted view is that, smallholder farmers produce the majority of the food, because they farm land very intensively resulting in high levels of productivity per unit of land [9, 10], their farms are often too small to provide a sustainable income at the household level, let alone food security [7]1. In addition, smallholder farmers are known to face several challenges associated with missing markets for credit, insurance, information including economies of scale in marketing and transportation [10]. Problematically, they are also reliant on non-drought tolerant crops and seed varieties2, non-mechanised farming systems and subsistence rain-fed farming3, factors which jointly contribute to the volatility of agriculture and the vulnerability of the smallholder farmers [13]. Having a full grasp of the character of risks that affect smallholder farmers is key to developing appropriate solutions to deal with risks. Similarly, it is important to understand how farmers respond to the solutions designed to ameliorate risk as this will help to establish the effectiveness and compatibility of the measures apropos the target market.
This chapter seeks to explore the gap between the assertion, that weather index insurance (WII) is a promising risk transfer mechanism for smallholder farmers in SSA and the realisation that, even where microfinance is made available, subscription rates among smallholder farmers rarely rise. The chapter pays attention to the risk response behaviour of smallholder farmers when presented with the option of purchasing WII that is bundled with microfinance. Weather index insurance is crucial because it potentially addresses welfare losses due to weather risk and complements existing informal risk management strategies [14]. The linkage between WII and credit has been discussed widely in theory but rarely investigated empirically, yet a lot of recommendations have been put forward by scholars for WII to be bundled with microfinance (see [15, 16, 17, 18, 19]). This is because, when smallholder farmers are believed to pose less risk to creditors when they have access to insurance [20]. In this sense, agriculture insurance can crowd-in credit, the lack of which has long been identified as a major, if not the main, constraint for smallholders in developing countries [21].
Access to agriculture insurance is crucial for smallholders because agriculture is generally prone to production failure due to the risk of catastrophic events such as those linked to extreme weather events [22]. Weather extremes have been repeatedly seen to have long-lasting impacts on farming livelihoods [23, 24, 25, 26]. Sub-Saharan Africa is especially vulnerable to weather-related risks because of the strong reliance on climate-sensitive rainfed agriculture [27]. While extreme weather shocks are not new to this region, the frequency and intensity of the events have increased over the past few decades. Based on the Human Cost of Disasters 2000–2019 Report, there has been a sharp increase in weather-related disasters4 over the past 20 years. Notably, disasters including extreme weather events rose from 4212 in the period 1980 to 1999, to 7348 in the period 2000 to 2019 [28]. Weather events figure large among the recorded disasters5.
Equally alarming are the rising patterns of loss and damage in the agricultural industry that are strongly correlated to the increasing catastrophic events [29]. For example, it is estimated that more than 75% of recent economic losses caused by natural hazards in Sub-Saharan Africa are attributable to climate change-induced weather events [30]. Outcomes linked to economic loss include livelihood insecurity, poverty, food insecurity and poor nutrition – cyclical patterns which can be ameliorated through adaptation financing [31]. What smallholders need therefore is access to perfect financial markets (savings, credit and insurance) and economic incentives to (re)invest in agriculture [7]. Reducing the economic impact of severe weather events is thus a crucial step towards supporting agricultural growth, sustainable livelihoods, poverty alleviation as well as bolstering food security and nutrition [32]. Given the foregoing, the vulnerability of smallholder farmers in lower-income countries is acute in part because they repeatedly lack access to financial mechanisms to efficiently manage production uncertainties [33]. In the absence of effective insurance and credit markets, households remain vulnerable to the financial consequences of high-magnitude loss events.
While SSA is not a single unit and challenges vary spatially and temporally, agriculture, and especially crop production in this region is predominantly rainfed and as such reliant on unpredictable climatic events [24, 31, 34, 35]. Under the current variable climate conditions, SSA already experiences a major deficit in food production especially in semi-arid and subhumid regions and areas [1]. This means a further drop in soil moisture due to mounting climate extremes will have devastating effects on agricultural production and will worsen food insecurity [26]. SSA is vulnerable to climate change also because the economies of most countries in this region are dominated by subsistence agriculture, the productivity of which is grossly susceptible to changing weather patterns [36]. Furthermore, the sub-continent is prone to complex natural climatic phenomena such as the El Nino-Southern Oscillation (ENSO), the West African Monsoon and the Indian Ocean Dipole [37], which influence climate variability (inter-annual and intra-seasonal rainfall), trends (upward or downward) and the persistence thereof [38]. The natural climatic phenomena give rise to regional climatic patterns which are impacted to some degree by climate change [37]. Because of the regional climatic phenomena, SSA has a long history of rainfall fluctuations of varying lengths and intensities (ibid) and is prone to cyclical drought patterns which are a frequent event in the semi-arid countries of the sub-continent [1]. The droughts in SSA have in recent times become more frequent and protracted, ostensibly due to climate change [39].
Climate forecasts have shown warming of approximately 0.71°C over much of the African continent in the twentieth Century [1], and an increase of over 1°C in the twenty-first Century [40]. Rather, average near-surface temperatures across parts of the continent have risen by more than twice the global rate of temperature increase in the twenty-first Century [41]. According to WMO [40], the year 2019 was among the three warmest years on record for the continent. Recent decadal predictions encompassing a five-year period from 2020 to 2024, signify continued warming and decreasing rainfall markedly over North and Southern Africa (ibid). Further predictions by Woetzel et al. [42] suggest that, due to climate change, the number and intensity of extreme weather events in SSA are set to increase. This is consistent with findings submitted in the Human Cost of Disasters 2000–2019 Report, which revealed that 1192 extreme weather events were recorded in Africa over the last 20 years (see [28]).
An enquiry on climate change and its likely impacts on SSA cannot be achieved by examining long term weather changes alone [43], as most countries in SSA suffer from intersecting stressors that give rise to low resilience and limited adaptative capacity to climate-related shocks [1]. Incidentally, climate change acts to exacerbate pre-existing conditions and has thus been dubbed a threat multiplier [25]. As such, the effects of drought and other climate extremes in SSA are exacerbated by endemic poverty, complex governance and institutional dimensions; limited access to capital, including markets, infrastructure and technology; ecosystem degradation; and complex disasters and conflicts ([36], p. 435).
SSA accounts for more than half of the world’s extreme poor, amounting to approximately 400 million people, most of which are smallholder farmers [37]. Poverty is among the key reasons why a lot of smallholder farmers in SSA are continuously exposed to inter-annual variations and occasional shocks caused by weather which affect agricultural output beyond their manageable limits [30]. As a result, at the 25th session of the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) that took place in Madrid in December 2019, it was revealed that 7 out of the 10 most climate-vulnerable nations in the world are located in Africa [44]. The figure is consistent with findings published in the Human Cost of Disasters 2000–2019 Report, which pointed out that, the top 10 list of countries with the highest share of affected populations by extreme weather shocks over the last 20 years is dominated by Sub-Saharan African countries, which make up 6 out of the 10 countries on the list [28].
Risk is the aggregate of the likelihood or possibility of a shock event occurring, and the severity of loss or impact caused by the event [45]. Three aspects make up risk, namely, threat, uncertainty, and loss. Climate change poses significant risks for food systems and has thus emerged as one of the greatest challenges of the twenty-first Century. Climatic extremes affect the primary sources of farm income, such as crops and livestock, and can further destroy household assets such as farming equipment – investments accumulated over time that are needed to generate future income [46]. Loss and damage due to extreme weather events can push farming households into cycles of poverty. According to GlobalAgRisk [47], households that are just above the poverty line can be pushed instantly below the poverty line by a major weather event. In the absence of perfect financial markets, including savings, credit and insurance, smallholdings in SSA generally struggle to recover from loss and damage caused by extreme weather events [34].
In the absence of perfect financial markets, an array of behavioural responses often emerge to fill in the gaps created by market failures [48]. Despite having considerable experience dealing with weather extremes, smallholders are much less likely to plan for low probability, high consequence risks [49]. This is a result of a cognitive bias that causes people to ignore risks with low probability, except when the likelihood of occurrence is well-known [50]. This psychological phenomenon influences the willingness of poor households to spend their limited income to cover low probability risks. Be that as it may, from a risk perspective, behavioural responses to shocks consist of three types of choices, namely, risk mitigation, risk coping and risk transfer [51]. The behavioural responses are characterised as being
Smallholder farmers in SSA are more susceptible to weather fluctuations than farmers in developed countries, who for instance, can more easily alter crop varieties, irrigate their fields, or secure crop insurance [42]. In more developed countries financial markets exist which allow farmers to insure against shocks
Faced with no savings, credit or insurance, they typically manage risk by smoothing consumption through choosing low-risk activities or technologies, which generally yield low to average returns [56]. Smallholder farmers in SSA also smooth consumption through asset attrition. According to Carter and Lybbert [57], since the rural poor have limited access to financial markets, consumption smoothing typically involves amassing assets in good times to use as a fallback in bad times. For example, a study on the impacts of drought on rural households in Burkina Faso showed that a good number of households that sell livestock do so to offset consumption shortfalls due to negative income shocks. Similar findings were observed in studies carried out in the rural Districts of Buhera and Nyanga in Zimbabwe where farmers mentioned the sale of livestock as a means of buffering income losses caused by production uncertainties [58]. Wealth for the rural poor is usually not in the form of cash or savings [47], but productive assets such as livestock [48]. Thus, when a severe weather event occurs, livestock is often sold off, often at a loss [59], because the distressed sale of large numbers of livestock at the same time flood the market, significantly reducing their value [60]. In contrast, insurance has been seen to positively influence households’ behavioural responses to risk through enabling them to reduce the need to rely on costly coping strategies such as selling productive, as this undermines future productivity. Results from an index-based livestock insurance (IBLI) pilot in Marsabit District of northern Kenya, showed that insured households are less likely to sell livestock [61]. Nonetheless, because insurance is not readily available in most rural areas, and where available, demand for it is low, smallholders tend to rely more on on-farm risk mitigation strategies. Thus, to preserve assets, households may smooth consumption further by cutting back on meals and diverting children from school which undermines crucial investments in human capital, hampering current and future productivity (ibid). In terms of its functional objective, consumption smoothing involves creating a balance between spending and saving to achieve a higher overall standard of living and can for that reason be used as a welfare dimension to assess a household’s preparedness to deal with climate change risk [62].
In addition to consumption smoothing, smallholder farmers in SSA also smooth income when dealing with climate change risk [44]. Income smoothing refers to the different strategies and approaches used by households to control the impact of extreme volatility in household income [48]. It is most often achieved
In all, there is an overlap between different types of shocks and behavioural responses to shocks. As the discussion above attests, high-frequency low losses are usually managed at the farm level and mitigated in part through access to household investments [63]. In an ideal world, residual risk (low frequency, medium loss) that cannot be retained by the farmer is better of transferred to third parties, usually insurance companies [45], which is not always an option for smallholders in SSA. Where insurance is an option, smallholders often deem it too costly for their limited income. Nonetheless, transferring a portion of income risk to a third party enables the farmer to have enough money to invest in higher-risk/higher-yield production technologies, such as improved seeds and inputs [20]. When weather-related shocks strike, households that receive indemnity payments have more response options, which notionally should reduce their reliance on detrimental coping strategies [66]. Although smallholder farmers in SSA have developed numerous adaptation mechanisms to cope with weather fluctuations over time, evidence has repeatedly shown that their methods are not adequate to deal with climate change [36]. If climate change adaptation investments are not made (e.g. by governments, multinational corporations and donor communities), the adaptation mechanisms used by smallholder farmers in SSA will not keep up with climate change impacts [22]. The Paris Agreement underlined the global importance of adaptation and contains provisions related to adaptation finance that follow guidelines from the Cancun Adaptation Framework [67]. Paragraph 28 of the Cancun Adaptation Framework stressed the need to explore options for risk-sharing and risk insurance, including options for micro-finance to reduce the devastating impacts of disasters among vulnerable populations [68].
Risk-sharing or risk transfer is a risk management strategy that involves the contractual shifting of risk from one party to another [51]. Risk transfer is most often achieved through an insurance policy, where the insurance carrier assumes the defined risks for the policyholder in exchange for a fee, or insurance premium [69]. Agricultural insurance is one risk transfer tool that farmers can use to manage risks that cannot be mitigated at the farm level [30]. It offers a promising means of cushioning in times of climate change-induced loss and damage for smallholder farmers [35]. Globally, however, less than 20% of smallholder farmers have any form of agricultural insurance [22]. Although the estimated global agricultural insurance premium volume almost doubled in the period 2004–2007, it remained low in African countries where it roughly reached an average of 0.13% of the 2007 agricultural GDP [70]. As a result, some scholars claim that only about 1.3% of the smallholder farmers in SSA have agricultural insurance [71]. Raithatha and Priebe [22] set the figure at 3%, while a more recent study suggests that the figure is around 3.5% which at any rate is far below the rates in Asia (46.2%) and Latin America (15.8%) [72]. Despite the low uptake of agricultural insurance by smallholder farmers in SSA, agriculture insurance is firmly believed can reduce the economic impact of severe weather events and help stimulate economic development through supporting agricultural growth, poverty alleviation, and the development of rural finance [14]. Based on the functional objective of agriculture insurance, it is an income smoothing
There are various types of agriculture insurance, the main ones being, indemnity-based crop insurance (e.g., named peril crop insurance and multiple peril crop insurance) and index-based insurance (e.g., index-based livestock insurance, area yield index insurance and weather index insurance). This chapter looks specifically at weather index insurance (WII). Weather index insurance has been presented as an important risk transfer mechanism that can assist smallholder farmers to deal better with climate risk [22, 35, 73]. The underlying risk for a WII product is the behaviour of the specific weather variable that contributes to production losses [14]. WII focuses on weather-related shocks because rainfall and temperature patterns for instance pose a serious threat for farmers [20]. The pervasive nature of catastrophic weather events is especially well-suited for index products, which explicitly insure against covariate shocks (ibid). Unlike traditional insurance, index-based insurance compensates policyholders according to a pre-determined index value [69] that serves as a proxy for losses rather than upon the assessed losses for individual policyholders [51]. Thus, some of the advantages of WII are that it has low operational costs, fast claim settlement speed and low risk of moral hazard and adverse selection [35]. Low operational costs give WII a critical advantage over traditional insurance, yet the hedging effectiveness of weather index-based insurance tends to be diminished by the often imperfect correlation between the index and realised losses. This is caused for instance, by the non-insurable difference between the weather events happening at the farm site and those occurring at the reference weather station, which is referred to as geographical basis risk [74]. In light of this, some of the drawbacks of WII include high basis risk, high actuarial difficulty, and high set-up costs [75].
The earliest applications of WII in emerging economies in the Americas and Asia are said to have taken place respectively in Mexico in 2002, followed by India in 2003 [47]. In SSA, the first application of weather index is said to have taken place in Malawi in 2005 [76] followed by Ethiopia in 2006 [77]. Almost 2 decades later, however, index insurance markets are still very thin in most African countries. To lessen the limitations of WII, insurance providers have initiated changes to their products being guided by scholarly recommendations and emerging best practices. Some of the key recommendations submitted by the scholarly community include interlinking reliable weather data with location-specific crop and agronomic conditions using flexible geospatial crop modelling tools (see [78]), interlinking WII with subsidies (see [79]) and interlinking WII with microfinance (see [16]). The mixed results of many WII pilot projects to date, for example, as presented by the lack of widespread implementation of even those projects considered successful, followed by the consistently low adoption rate by smallholder farmers, warrant an investigation into the changes needed for the products to become more scalable and sustainable.
Smallholder farmers often do not qualify for credit provided by mainstream banks due to the lack of usable collateral (e.g. savings, reliable earnings, effective land titles and other tangible and intangible assets) to guarantee loan repayments [49, 80]. In addition, the large fluctuations in farm revenue generally make it less commercially attractive to lenders, thus hampering credit provision to the agriculture sector [16]. Credit constraints discourage farmers from investing in higher-risk/higher-yield production technologies, such as improved seeds and inputs [20], which would otherwise boost their capacity to withstand the negative impacts of extreme weather events. In some instances, however, if no collateral is present, lenders may require crop insurance to securitize the repayment of the loan [16]. Thus, crop insurance can facilitate credit. Microfinance Institutions (MFIs) specialise in the supply of credit to segments of the population that is typically unattended by mainstream banks. The promise of microfinance is centred on the awarding of microloans to the poorest of the poor without requiring collateral [81]. What makes microfinance different from traditional forms of credit is its focus on small loans and other low-cost financial services which the poor can use to generate income and become self-reliant [82, 83]. However, while insurance may in some instances unlock credit, bundling microfinance with insurance is far from being the panacea for the credit constraint problem [21]. This is why more insight into the impact of linking insurance and credit is needed, particularly since the adoption rate of WII in SSA has remained low even in cases where microfinance has been made available.
Studies have indicated an uptake of less than a fourth of the smallholder population [80], which shows clearly that demand for WII is low. Actual demand according to the preceding scholars varies from 2 to 40% or 50% maximum. In general, low demand is ascribed to several factors which include farmer budgetary constraints, lack of trust in financial institutions, poor understanding of the contract, and the often imperfect correlation between the index and realised losses (basis risk) [84]. Marr et al. [80] have gone on to group the reasons for low demand into 3 categories namely, (1) neoclassical (i.e., risk aversion, risk mitigation, basis risk and price), (2) behavioural (i.e., understanding, trust and education), and (3) pecuniary determinants capturing credit and liquidity constraints (i.e., wealth, liquidity, credit and income). To weigh in briefly on the effect of the given determinants of low demand, firstly, it has been noted already in this chapter that smallholders are generally risk averse, which causes them to depend more on traditional risk mitigation strategies. Secondly, because, their risk mitigation strategies are limited, they are among the most vulnerable populations to climate change. Thirdly, when presented with risk mitigation strategies such as insurance, smallholders are not always willing to pay for indemnity. Aside from being risk averse, they are poor and often credit and liquidity constrained. Inevitably, price is a crucial factor that the smallholders consider before signing up for an insurance policy. There are thus tensions between what must be charged to insure low-probability high-consequence events and the willingness of households to pay for insurance products designed to protect against losses caused by these events [33].
Fourthly, basis risk interacts with other factors such as price and is an important factor known to drive price beyond the reach of smallholders. To increase demand for WII, suppliers need to focus on minimising basis risk. Even as basis risk is an inherent problem for index insurance, it can be reduced through product design and application [47]. To increase demand for WII, suppliers need to additionally educate smallholders about the benefits of insurance, which should be followed up by cultivating relationships of trust [85]. There is a further need for suppliers to come up with innovative ways to make insurance more attractive to smallholders. This involves adapting financial services and products to match the risk profile of the market demographic [33], for example through bundling WII with microfinance.
Bundling index-insurance with credit is a practice that is widely debated in literature but mainly at a theoretical level [80]. There are several benefits that come with combining microcredit with insurance, some of which have already been discussed in this chapter. Since both insurance and credit are recognised as important tools for smoothening and enhancing income [16], it is believed that when bundled together, they can enhance on-farm efforts (e.g., through increased input, improved seed varieties and investments in and specialised and diversified farming) to mitigate climate risks. Meyer et al. [21] are of the view that neither credit nor insurance markets can exist independently in low-collateral environments. This makes perfect sense considering that insurance can ensure the success of credit by promoting lending to smallholders in credit constrained environments where farmers have weak collateral to offer, and systemic risks are the main cause of loan defaults. While credit on the other hand can ensure the success of insurance by enhancing household income and protecting farmers against the financial risk of crop failure. Linking the two contracts thus seems beneficial for farmer productivity, food systems resilience and incidentally, the growth of rural financial markets. A potential downside of this practice, however, which cannot be overlooked by this chapter is that, if a loss occurs which is not covered by the insurance because the index was not correlated to the realised loss and an indemnity payment was not triggered, the farmer may not be able to repay their loan. On its own, index insurance can harm farmers by extracting insurance payments while providing little or no actual risk coverage [20]. When combined with credit, the farmer may be worse off than if their loan were not insured because they have to pay the insurance premium as well as repay the loan [21]. This shows that while insurance could unlock credit and produce desired results such as higher investments, it could also produce undesired results such as higher default rates (ibid).
A few empirical studies have been carried out to understand the credit insurance linkage in different parts of SSA, and the results have been conflicted. Among these, a study by Giné and Yang [19] sought to test whether reducing risk through WII induces greater demand for credit among smallholder farmers in Malawi. Half the farmers were randomly selected to be offered credit to purchase high-yielding hybrid maize and groundnut seeds for planting. The other half were offered a similar credit package but were also required to purchase (at actuarially fair rates) a weather insurance policy that partially or fully forgave the loan in the event of poor rainfall. The uptake of credit was 33% for farmers offered a loan without insurance and 17.6% for farmers offered a loan bundled with weather insurance. The findings suggest that smallholders do not always value insurance as the demand for credit fell when bundled with insurance. An explanation for the behavioural response given by the authors is that farmers understood that they were implicitly insured by the limited liability inherent in the loan contract so that going for a loan bundled with insurance (for which an insurance premium was charged) would effectively increase the interest rate on the loan. On the other hand, the overall poor uptake rate could be taken to mean that smallholders generally do not trust financial institutions [86].
In a study carried out by Karlan et al. [87]. A randomised control trial was conducted to investigate whether price risk affected the demand for credit by smallholders in Eastern Ghana. Farmers were offered loans with an indemnity component that forgave 50% of the loan if crop prices dropped below a threshold price. A control group was offered a standard loan product at the same interest rate. Loan uptake was high among all farmers. The indemnity component had little impact on the uptake or other outcomes of interest. The indemnity product had incorporated insurance into the loan rather than as an add on, to avoid potential choice overload problems that arise sometimes when too many choices cause stagnation in decision making. Yet, findings showed a high take-up rate of credit despite indemnity, which made it difficult for the authors to assess heterogeneity in behavioural response. What is apparent from the findings is that insurance made no difference to the demand for credit. This again implying that smallholders do not always value insurance. To explain the outcome, the authors suggested among other reasons that, the farmers perhaps did not understand the contract.
In a different study carried out by Mishra et al. [88] in Northern Ghana, results also found no evidence that insurance has a significant impact on increasing the uptake of credit. The study investigated whether coupling agricultural loans with micro-level and meso-level drought index insurance can stimulate the demand and supply of credit and increase technology adoption. Based on empirical findings, if at all, bundling loans with insurance increased the likelihood of loan applications for female farmers. Gallenstein et al. [84] published a paper on the same population in Northern Ghana. The authors investigated the willingness to pay for drought index insurance backed loans and found out that insurance lowered overall demand for loans. In fact, adding an insurance policy to an agricultural loan reduced the demand for credit as 75.3% of the population were willing to pay the market interest rate for the uninsured loan. What is also apparent from the findings of this study is that smallholders do not always value insurance. In this case, insurance had a bearing on demand for credit, albeit in a negative way.
Different results were observed, however, in a study carried out in Machakos County, Kenya by Ndegwa et al. [89]. The authors sought to investigate the causal effect of bundling WII with credit on uptake of agricultural technology among smallholders. 1170 sample households were randomly assigned to one of three research groups, namely, control, risk contingent credit and traditional credit. Based on the findings the average credit uptake rate was 33% with the uptake of bundled credit being significantly higher than that of traditional credit. In this case, insurance was seen to influence the uptake of credit. By and large, the study observed that risk rationing was among the key reasons responsible for the negative credit uptake among smallholders.
In another study, Pelka et al. [74] analysed the influence of weather variations on the repayment performance of credit among smallholder farmers in Madagascar. The farmers studied primarily grow rice in monoculture. The weather risk for rice cultivation in the central highlands of Madagascar is the excessive amount of rain in the harvest period (between the end of February to April), which reduces rice yields and, thus, leads to revenue losses for farmers. Findings demonstrated a high correlation between precipitation and credit risk, where credit risk is defined as whether or not a borrower can pay back all loan instalments by the due date. Thus, findings revealed in particular that, the credit risk of loans granted to smallholders increased in the harvesting period due to the excessive amount of precipitation. Based on the analysis given by the authors, credit risk would reduce significantly if the farmers had weather index insurance policies. This assumption is based on the hypothesis that, “the effect of weather events on the repayment performance of loans equals the effect of the returns of weather index-based insurance on the repayment performance of loans” Pelka et al. [74]. As such, the authors surmise that weather index-based insurance might have the potential to mitigate a portion of the risk in agricultural lending. In this study, the authors do not seem to argue for the bundling of credit and WII, but instead, propose that WII would be instrumental in mitigating credit risk in cases where lending is involved which would work only where the weather index is perfectly correlated to the realised loss. To avoid issues of credit risk, the weather index insurance programs in Malawi often bundle credit with mandatory weather index insurance [78]. However, while making insurance mandatory is good in that it assures worried lenders, the downside is that it may discourage farmers from seeking loans [21] as seen in experiments carried out in Malawi and Ghana earlier cited in this section.
A review of the literature showed mostly mixed results regarding the impact of bundling WII with microfinance among smallholders in SSA. The literature confirmed the premise that, even where microfinance has been made available, the demand for WII has remained consistently low across the sub-continent. Thus, a wide gap still exists between the expectations of what WII can achieve for smallholder farmers in dealing with climate change risk, and the reality that is on the ground, which is that current demand varies from 2 to 40% (50% at the most). While WII may not provide complete protection against losses, it can improve the financial protection coverage needed for smallholders to effectively deal with the financial consequences of high-magnitude climatic loss events. In this way, WII can play an instrumental role in creating an enabling environment for rural financial services including banking and microfinance. For WII to work, it must complement existing risk management strategies, to ensure all round cover against climate change risks.
The chapter focused mainly on demand side dynamics paying considerable attention to the risk behavioural responses of the smallholders. It is crucial to understand how farmers respond to solutions designed to mitigate against risk as this will help to establish the effectiveness and compatibility of the measures apropos the target market. Based on the reviewed studies, the determinants of low demand for WII are many, ranging from risk aversion, liquidity and credit constraints, lack of trust in financial institutions, poor understanding of the indemnity contract to risk rationing. To improve demand for WII, suppliers need to design products to match the needs of target markets. A needs-based approach or deficit model recognises all needs, including underlying needs as valid claims. And so, insurance providers must be fully cognizant of community needs in their entirety for them to package WII more attractively. This would entail tackling more than just weather risk. Some insurance providers are already doing this. For example, research has shown that the uptake of WII is higher in Ethiopia when insurance is channelled through group-based informal insurance schemes
Demographical information and behavioural economics make generalisations about populations which can help insurance providers to know what the customers are looking for and how their product meets customer needs [90]. Since WII takes on an anti-poverty approach, insurance providers should go beyond tactical strategies, and understand and view things from the perspectives of smallholder farmers. Thus, customer empathy is a requirement for the design of WII packages that meet the underlying needs of customers, while factoring in customer feelings about the products being offered. A typical market research seeks to understand the obvious characteristics of population (e.g., age, sex, income, employment, level of education, farm size, cropping activities). While a person-centered market research would seek to understand further information such as, how cultural beliefs and attitudes/religious views/willingness to adopt change/willingness to pay for change/value placed on change (in monetary terms) influence technological preferences. Other information that could be sought by insurance providers include, household structure/headship and gender practices, to ascertain who does what? who has what? and who decides what? at the household level. SSA is home to more than 500 million women who account for about half of the continent’s population [91]7. Based on data from 45 countries in SSA for the periods 1980–2015, until the ages of 15–19, there are more boys than girls in the rural sector and fewer boys than girls in the urban sector, which changes dramatically between the ages 20–24 [92]. This suggests that in a lot of countries in SSA, there are more women than men who live and work in rural areas from the age of 20 onwards. This is consistent with reports which state that, in SSA, women are responsible for much of the food production in rural areas [93]. According to WorldBank [91], the share of labour varies across countries, ranging from 24% in Niger to 56% in Uganda, but remains consistently well below the commonly cited 60–80%. Despite their contribution to agriculture, women in male headed households have very little say in decision-making compared to women who head their own homes (female headed households) [94]. The point is, if women are a major demographic in rural areas, gender differences and practices are important factors that should be incorporated into the design and application of WII in SSA. In a study on Northern Ghana carried out by Gallenstein et al. [84], bundling loans with microinsurance was seen to increase the likelihood of loan applications for female farmers more than men. An analysis into such behavioural patterns could help WII providers to package their products in a gender sensitive manner so as to appeal to the needs of both male and female smallholders, which will potentially increase demand.
The more information is understood about the characteristics and preferences of the target population, and the more inclusive the insurance product or package is, the more likely it is to influence demand in a positive way. For as long as WII suppliers do not genuinely put the people first, combining WII with other innovations will not increase demand. Bundling WII with key farm inputs such agricultural inputs (i.e., fertilisers, seeds, loans, etc.), key agricultural institutions (e.g., Agri-banks, input suppliers, farmers’ organisations, etc.) has not boosted demand for WII in Malawi [27]. From the lessons learned in this chapter, a ‘One Size Fit All’ WII design does not work well in SSA – what worked in Machakos County, Kenya did not work in Eastern and Northern Ghana.
This publication was made possible (in part) by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.
The author declares no conflict of interest.
I would like to acknowledge my academic supervisor Professor Mark New whose comments enabled me to improve this chapter.
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\n\nIn recent years, supply chains in the manufacturing industry have become more and more complicated, and many cases of supply chain disruptions due to natural disasters have been confirmed. It is necessary for manufacturers to build a system that can help them alleviate losses and shorten recovery periods due to supply chain disruptions. Supplier diversification, as well as supplier evaluation and selection, are discussed as risk aversion measures in many papers. However, even if the procurement source has been evaluated enough, there are problems, such as opportunity loss during recovery periods and soaring procurement costs during normal periods. In this chapter, to help Japanese manufacturers to alleviate opportunity loss under component procurement disruption situations and keep cost competitiveness in normal periods, decision-making models of supply chain structure assessment, supplier selection, procurement allocation, and trading contracts are designed and verified.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Kotomichi Matsuno, Jiahua Weng, Noriyuki Hosokawa and Takahiro Ohno"},{id:"1085055",title:"Performance Measurement Using Deterministic and Stochastic Multiplicative Directional Distance Functions",slug:null,totalDownloads:3,totalDimensionsCites:null,doi:"10.5992/intechopen.1000179",abstract:'Performance measurement is essential for fostering continuous improvement of the production and operation management in a firm or organization. We consider a deterministic scenario based on a flexible structure of production technology and establish a multiplicative relationship between the generalized multiplicative directional distance function (GMDDF) and geometric distance function (GDF). We also introduce a stochastic multiplicative directional distance function (SMDDF). Based on a stochastic scenario, the SMDDF can be estimated by the method of convex nonparametric least squares. As an illustrative application, we investigate the productive performance of Japanese life insurance companies using a panel dataset spanning 2016 to 2020.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Yu Zhao"},{id:"1085559",title:"Assessment of Medical Equipment Maintenance Management",slug:null,totalDownloads:2,totalDimensionsCites:null,doi:"10.5992/intechopen.1000210",abstract:'Today's modern hospital is highly dependent on different types of medical equipment to help diagnose, monitor, and treat patients. Medical equipment maintenance is important to reduce costs, reduce patient dissatisfaction, treat the patient in a timely manner, and reduce mortality and risks during patient care. Good maintenance management is important to have well-planned and implemented programs through which hospitals can minimize medical device failures or other problems with the operation of medical equipment. Medical equipment plays an important role in the hospital system; therefore, the acquisition, maintenance, and replacement of medical equipment are key factors in hospitals for the implementation of the health service. Thus, in order to ensure the quality of medical devices for the provision of medical care, it is imperative to evaluate the safety of using hospital maintenance management. In order to achieve these goals, hospitals must develop checklists that identify the state of performance of medical equipment maintenance. It is essential for clinical managers and engineers not only to increase the capacity of the hospital but also to predict the risks of sudden failure. Given the lack of unique and comprehensive maintenance management checklists, the current goal is to design and develop medical equipment maintenance management checklists.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Călin Corciovă, Robert Fuior, Doru Andriţoi and Cătălina Luca"},{id:"81687",title:"Managing Foodservice Quality in the Foodservice Industry",slug:"managing-foodservice-quality-in-the-foodservice-industry",totalDownloads:2,totalDimensionsCites:null,doi:"10.5772/intechopen.104800",abstract:"Quality has become a value that enables businesses to survive and continue existing. Henceforth, food industries need to entrench quality into their business performance. Foodservice quality is characterized as a service that bears on its ability to satisfy stated or implied needs and service free of defects. Foodservice businesses are an integral part of social life, both biologically and socially, biologically as satisfying the nutrition requirements of the society and socially in terms of addressing socialization and esthetics-pleasure values. Therefore, by adopting quality approaches, food industry businesses may encourage customers’ preferences for those businesses that diligently offer these services. Managing food service quality is a complex and challenging task requiring commitment, discipline, and emergent effort from everyone involved in food production processes. The task also requires the necessary management and administration techniques to continuously improve all processes (including quality control from raw material to finished product). Food industries need to be organizationally structured, establish policies and quality programs, measure customer satisfaction, use more quality tools and methodologies, embrace knowledge, apply techniques, and food safety programs to manage food quality. This chapter aims to describe the ISO 22000 system—widely used for quality management in the food industry.",book:{id:"11170",title:"Quality Control",coverURL:"https://cdn.intechopen.com/books/images_new/11170.jpg"},signatures:"Lindiwe Julia Ncube"},{id:"1084630",title:"Analysing the Possibility of Failure of Cascade Dam System and a Case Study from Brazil",slug:null,totalDownloads:2,totalDimensionsCites:null,doi:"10.5992/intechopen.1000202",abstract:'A cascade dam system poses more hazards for downstream life and structures, when compared with a single dam located on a river. Therefore, there is a need to develop differentiated procedures to classify and regulate these dams. In the state of Mato Grosso (MT), Brazil, it is common to find multipurpose dams, which can be considered as a cascade, when a dam failure causes adverse effects in downstream dams. The objective of the study is to analyse the possibility of dam failure located in the cascade system operated by the municipality of Várzea Grande, MT by the Associated Potential Damage (APD) classification used throughout the country. In order to do this, the specification namely “Simplified Methodology to Define the Classification Flood Zone of Associated Potential Damage of a Dam” developed by the National Laboratory for Civil Engineering in Portugal (LNEC in Portuguese) was utilised. This specification was adapted by the National Water and Sanitation Agency (ANA in Portuguese) in Brazil. In the case study, there are three dams (Dam 1, Dam 2 and Dam 3) in the cascade system. Dam 1 can cause overtopping problem for Dam 2 and Dam 3. According to APD classification, dams considered for the study are categorised as “high dam”.
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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{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"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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