Consumption based coping strategies.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"80749",title:"Analysis of the Nexus between Coping Strategies and Resilience to Food Insecurity Shocks: The Case of Rural Households in Boricha Woreda, Sidama National Regional State, Ethiopia",doi:"10.5772/intechopen.102613",slug:"analysis-of-the-nexus-between-coping-strategies-and-resilience-to-food-insecurity-shocks-the-case-of",body:'Food is the most basic need for survival, growth, and good health of human beings. Freedom from hunger is the most fundamental human right that can be attained if an individual is food secure [1]. However, a significant proportion of the world’s population still lives under the situation of food insecurity. As it is clear from the FAO et al. [2] report on the state of food and nutrition in the world, even the prospect itself is not sufficiently bright to the extent expected. Five years after the world committed to ending hunger, it has been learned that, the world is still off track to achieve this objective by 2030. Given the current pace, the world is making headway neither towards Sustainable Development Goal target 2.1, of ensuring access to safe, nutritious and sufficient food for all people all year round, nor towards target 2.2, of ending all forms of malnutrition [2].
Looking at the trends and projections of the state of global food insecurity may help to understand this claim. According to the same report, the number of undernourished people was 690 million in 2019 (60 million more than in 2014), and is expected to exceed 840 million in 2030. When it comes to Africa, the continent’s share of undernourishment prevalence for 2019 exceeds one-third of global undernourishment with about 250 million undernourished people. This figure represents about 19% of its overall population and is projected to be about26% in 2030 [2].
Various reports show that Ethiopia hosts a handful proportion of food insecure people. For instance, WFP and CSA [3] report the persistence of poverty and food insecurity despite the country’s efforts to counteract the situation. MOFED [4] reported a level of food poverty prevalence of 33.6% in 2014
Response mechanisms to food insecurity shocks varies based on the objectives of the agents responding to it as well as the level at which they are targeted. As active actors/agents/of their own, households employ various coping strategies (response mechanisms) in the event of shocks that challenge their food security. According to Maxwell and Caldwell [6], USAID [7], and Degefa [8], such strategies are not uniform and may also not be equally sustainable, as in some cases they may erode household’s capacity to withstand future food insecurity shocks. Although, effects of households’ coping mechanisms and resilience to future shocks have been widely discussed, mainly at the conceptual level, empirical statistical evidences on the nexus are quite limited.
For instance, though Carter et al. [9] provide elegant theoretical explanation on the linkage between shock-initiated coping mechanisms and a household’s resilience, the unavailability of data on coping strategies constrains them from including this variable in their estimation model. The study of Tran [10] fails to make the distinction between positive and negative coping at the empirical level and focuses only on the immediate positive effects to recover from shocks. However, a particular coping strategy, though resilience erosive, can contribute to smooth current consumption and/or recovery from shocks. Moreover, capturing resilience only through the recovery speed proxy is also too simplistic. Thus, there is an increasing understanding of resilience as an
Maxwell and Caldwell [6] identify four coping strategies that households employ when they face food shortages or do not have the resources to purchase food. They include taking action on the quality of food to eat, looking for options that help increase food supply, reducing the number of household members that they have to feed through such mechanisms like sending some of them to neighbors’ houses, and managing the deficit through mechanisms such as food rationing. Conceptually, these strategies are consumption-based ones having a lesser impact on the households’ capacity to cope with future food insecurity shocks.
Carter et al. [9] put the households’ actions to cope with shock-induced food security challenges in a certain rational decision-based logical order. As per this source, initially households choose to depend on the markets and other institutions that they have access to. To maintain their consumption standard without further asset depletion, households with financial market access or access to informal finance might borrow against future earnings. Resorting to insurance arrangements, seeking for and receiving disaster aid as well as working for long hours are also coping options that they can exercise before taking action against their productive assets. Households without access to such options may opt to sustain their consumption by drawing down on their assets: the decision which they argue can further increase the sensitivity of assets and weaken the future. Finally, households may cope by reducing consumption. This coping strategy can be the last option for those lacking other assets or options and may also be pursued by households who are reluctant to increase their future vulnerability due to depletion of the stock of assets. However, coping by reducing consumption is regarded unfavorably as it does have multiple costs, i.e., immediate hunger as well as the long-term effect on children’s growth and development [17].
To the linkage between coping mechanisms and shocks, it is postulated that adverse events (shocks) may cause a decline in assets and incomes in the short-run and might have negative effects on household livelihoods in the longer-run [10]. However, the extent of the effects, depends on the nature of the shocks, the asset dynamics, as well as on the coping strategies employed. Carter et al. [9] opine that when a given shock happens, it will have both direct and indirect impact on households’ resilience to future shocks. Firstly, the shock itself brings direct harm to the quality of households’ asset. As households’ respond to shocks using their assets and resources, the indirect impact comes via such responses to a particular shock. The whole idea here is that the coping mechanisms used in response to food insecurity-related shocks at a given point can cause a decline in the household’s ability to cope with future shocks depending on the strategies employed in between two time periods.
The origin of the concept of resilience is linked to the field of ecology. According to Holling [18], in ecology, the term resilience is used as a measure of systems persistence and capacity to absorb changes and disturbances and still retain the same relationship with state variables. To a household’s food security, resilience has been conceptualized as the ability of the household to maintain its food security withstanding shocks and stresses, depending on the options available and its ability to handle risks [11]. Accordingly, resilience is a multifaceted capacity: absorptive, adaptive, and transformative. While explaining the linkage between the nature of coping strategies and resilience, Frankenberger et al. [19] sustain those certain strategies may have negative and permanent consequences to resilience. Positive coping strategies are those based on available skills and resources, to face, manage and recover from shocks and that do not compromise resilience. On the other hand, negative coping strategies, if employed, undermine future options making it more difficult to cope with the next shock or stress [20]. Hence, it can be argued that the resilience status of a household at a particular time point (resilience to future food insecurity shocks) is partly a reflection of the type of coping strategies previously employed. Figure 1 represents this conceptualization.
Conceptual representation of food insecurity shocks-coping strategies-resilience nexus.
The illustrative case is based on the data collected from one of the food-stressed
Based on insights from literature and the resulting framework presented in Figure 1, it was assumed that the coping strategies employed by households in response to food insecurity shocks that happened at time (T0), can have an influence on the resilience level at a time (T1) in a way that households with negative coping strategies at T0scoreless on resilience at T1. As the households’ coping mechanisms are the response actions to shocks, data can be captured usually
The selection of the illustrative study was based on a cross-sectional survey conducted by using structured questionnaires and key informants’ interviews. It involved 420 randomly selected households from three randomly selected
Household’s resilience estimation procedure.
At the household level, the resilience index was estimated using the Eq. (1) below, which was further transformed using the weighting mechanisms and applying the Bartlett method of component scoring. The Bartlett method was selected as it generally produces latent variable scores that are unbiased and univocal [24].
where:
The surveys to analyze the coping mechanisms measurements included two sets of questions: consumption-based (strategies employed in the last 7 days before the date of the survey) and non-consumption based (strategies used in the last 2 years preceding the survey date). The analysis of data on coping strategies was done descriptively using percentages. The linkage between households’ resilience and the previously employed coping mechanisms was examined using contingency table and chi-square tests as well as using the odds ratio. In the analysis, households were categorized into two groups: those who previously employed negative (resilience erosive) coping mechanisms and those who did not employ such coping strategies over the past 2 years. In the current study, such categorization was done based on insights from conceptual literature such as [7, 19]. Hence, based on these conceptual works, coping strategies such as selling of reproductive animals, oxen used for farming, and land, land rental, withdrawal of children from school, borrowing money at the high interest rate, and diversion of loans from MFIs were treated as resilience erosive or negative strategies. Accordingly, households who did use any of these coping strategies over the past 2 years were classified under the negative coping category. Based on Guyu and Muluneh [15] and considering the relative location of the surveyed households on the latent variables (resilience scores,) the study households were categorized into resilient and none- resilient groups.
Literature indicates that the response of households to food insecurity challenges include different coping strategies. These may involve the modification of consumption habits (consumption-based coping strategies) and/or use of the available resources (non-consumption-based strategies). For instance, Christiaensen and Boisvert [25] contend that when they anticipate food shortage people start to consider changing their consumption habits rather than waiting until food is completely exhausted. Though such change in the consumption habits is generally believed to be a short-term adjustments, it could go long as a normal habit even in the situation where non-consumption-based strategies too are activated. This is mainly true in the situation where a given community lives under long standing food stress in terms of availability and/or access. The point here is that though non-consumption-based strategies such as selling key productive assets are used, foods obtained through such actions could still be subject to consumption-based coping such as rationing. This can lead us to safely argue that the two sets of coping strategies, consumption and non-consumption based, should not be seen as completely isolated and mutually exclusive as they appear in the literature. Notwithstanding the complexity here, the analysis of the household’s coping strategies was done in light of the general assumption that households are rational decision-makers and thus, the first options are those with the least impact on livelihood or future food security.
Consumption-based coping strategies constitute short-term alteration of consumption patterns. Writers like Watts [26], Corbett [27], and Devereux [28] consider them as easily reversible strategies that do not jeopardize long-term prospects as they mostly do not require a commitment of domestic resources. The households’ responses summary (Table 1) indicates that 60.2% (253) of the households rely on less preferred foods at least once in a week. 45.5% (191) reported that the consumption of adults was restricted in favor of children. According to one of the elderly key informants “during food shortage, usually mothers take the burden of not having to eat giving priority to children and father
Coping strategy | Number/proportion of households employed | |
---|---|---|
Count | Percentage (%) | |
Relied on less preferred foods | 253 | 60.2 |
Borrowed food or relied on help from a friend/relative | 166 | 39.5 |
Purchased food on credit | 136 | 32.4 |
Relied on wild foods, hunt, or immature crops | 32 | 7.6 |
Consume seed stock held for next season | 57 | 13.6 |
Household members sent to eat else where | 51 | 12.1 |
Household members sent to beg | 11 | 2.6 |
Portion size at mealtimes limited | 181 | 43 |
Consumption by adults restricted in order for small children | 191 | 45.5 |
Priority given for working members of household at the expense of non-working members | 75 | 17.9 |
Meals eaten in a day reduced | 141 | 33.6 |
Entire days skipped without eating | 7 | 1.7 |
Consumption based coping strategies.
Complementary and non-consumption-based coping strategies (Table 2) included, selling reproductive animals at least once within the last 2 years period (42.6%), and renting (10%) or selling (2.1%) their lands (10%). About 20.7% (87) and 21% (88) of the households had removed their children from school and borrowed money at high-interest rates respectively. A total of 37.6% (158) households reported that they coped by selling small animals and about 19% (80) migrated to nearer areas in search of wage labor. Almost none, 1.9% (8), of the households had engaged in the coping behavior of diverting loans from Monetary Financial Institutions (MFIs) to consumption and only 4.8% (20) households had drawn on financial savings to respond to the food insecurity problem. This could be due to a lack of cash savings to draw from and/or limited access to MFIs both of which are common in the rural context. Nearly half, 51.7% (217), reported that they have appealed for food aid to overcome food insecurity within the last 2 years. One-third of the households, 33.3% (140), reported that they used selling firewood as a coping mechanism (see Figure 3).
Coping strategies | Number of households adopted | |
---|---|---|
Count | Percentage (%) | |
Sold reproductive animals | 179 | 42.6 |
Sold oxen used for farming | 98 | 23.3 |
Sold land | 9 | 2.1 |
Rented out land | 42 | 10 |
Removed children from school | 87 | 20.7 |
Borrowed money at high interest rate | 88 | 21 |
Sold small animals | 158 | 37.6 |
Migrated to nearer areas to wage labor | 80 | 19 |
Drawing on savings | 20 | 4.8 |
Selling fire wood | 140 | 33.3 |
Diverting loans from MFIs to consumption | 8 | 1.9 |
Appealed for aid | 217 | 51.7 |
Non-consumption based coping strategies used by households.
Household members taking fire woods collected form forests to market centers.
According to the key informants, they collect fire wood from the forest around Bilate River towards the border of Loka Abaya
As referred above, several authors such as Frankenberger et al. [19], Carter et al. [9], Tran [10], and USAID [7], pinpoint that the types of coping mechanisms employed by households in response to previously happened shocks can affect their resilience to future shocks.
Based on these conceptual backdrops, we have endeavored to understand how the previously used coping strategies of households relate to their resilience status. To this end, households were asked if they experienced one or more shocks challenging their food security situation in the last 2 years preceding the survey and the responses are summarized in Table 3. Most of the surveyed households, 79.3% (333), experienced one or more types of shocks that they believe affected their food security situation. Households have also identified a set of coping strategies employed in the past 2 years to cope with food insecurity problems/shocks (Table 2).
Variables | Response | Count | Percentage (%) |
---|---|---|---|
If shocks affecting ability to feed HHs occurred within the last 2 years | Yes | 333 | 79.3 |
No | 87 | 20.7 | |
Number of shocks experienced* | Only one | 99 | 29.7 (23.6% of total) |
More than one | 234 | 70.3 (55.7% of total) |
Previously experienced food security situation threatening shocks.
List of shocks include crop failure, household member death, livestock death, and illness.
When it comes to identifying negative coping strategies (erosive resilience), it seems that literatures lack perfect unanimity. With the argument that they undermine future options making it more difficult to cope with next shocks, Pasteur [20] considers strategies such as delaying medical treatment, exploiting natural resources, taking children out of school, eating less, eating less nutritious food, and eroding productive assets as resilience erosive coping strategies. However, some of the strategies considered as negative coping here are consumption-based (temporary adjustments on eating) that are considered by others as easily reversible. Specially, stage 2 and stage 3 coping strategies from the list identified by Watts [26] and Frankenberger [29] are generally treated as erosive coping mechanisms. Based on the literature and on study area’s context, selling reproductive animals, oxen, and land, or renting land, taking children from school, borrowing money at high-interest rates, and diversion of loans from MFIs to consumption were considered as negative (resilience erosive) coping in this illustrative case. Accordingly, households were classified into two coping categories (Table 4): those who used negative coping in the past 2 years and those who did not. As indicated in the table, 59.5% (250) of the households employed one or more negative (erosive) coping strategies in the last 2 years preceding the date of the survey.
Frequency | Percent | Valid percent | Cumulative percent | ||
Valid | Non resilience erosive (positive) coping | 170 | 40.5% | 40.5% | 40.5% |
Resilience erosive (negative) coping | 250 | 59.5% | 59.5% | 100.0 | |
Total | 420 | 100.0 | 100.0 |
Households by coping type.
The households’ resilience position (status) was determined based on their relative resilience scores and using the criteria of [15]. Based on relative resilience score (index) achieved by households, Guyu and Muluneh [15] classify four resilience categories: Vulnerable (resilience index (RI) < 0.100). Moderately Resilient (0.100 ≤ RI < 0.250), Resilient (0.250 ≤ RI < 0.500) and Highly Resilient (RI ≥ 0.500). Using the resilience scores estimated through the Bartlett method in PCA and applying these cutoff schemes, households are categorized into four categories (Table 5). A very significant proportion of the surveyed households (61%) was not resilient (or vulnerable to food insecurity shocks) and only 39% was resilient at different levels. With these pieces of information on the nature of previously employed coping and resilience status, now the discussion turns to examine the relationship between the nature of coping mechanisms and the relative resilience position (status) of the households. Our analysis proceeds with the proposition that the nature of previously used coping strategies can affect the predictive resilience of households (estimated at time T1) in the form that those with prior negative coping strategies scoreless on resilience. Contingency Table and chi-square test statistic, and the odds ratio were employed to analyze and test this proposed relationship of the two variables. Table 6 presents cross-tabulation of previously employed coping types and households’ resilience levels. About 59.5% (250) of the households used one or more types of erosive resilience (negative) coping strategies within the last 2 years. From this group, only 19.6% (49) was found to be resilient (scoring relatively high on resilience index) at time T1 (time of the survey). Most households, 80.4% (201), that adapted one or more negative coping strategies were found to be non-resilient. On the other hand, out of the total households who did not previously use negative coping strategies, 67.6% (115) was found to be resilient at time T1 (scoring relatively high on resilience index) against 32.4% (55) scoring relatively low on resilience (non-resilient).
Measurement | Households by resilience category | Total | |||
---|---|---|---|---|---|
Non resilient | Moderately resilient | Resilient | Highly resilient | ||
Count | 256 | 22 | 18 | 124 | 420 |
Percent | 61% | 5.2% | 4.3% | 29.5% | 100 |
Distribution of household resilience status.
Coping type | Total | ||||
---|---|---|---|---|---|
Non-resilience erosive (positive coping) | Resilience erosive (negative coping) | ||||
Resilience level | Resilient | Count | 115 | 49 | 164 |
% within resilience level | 70.1% | 29.9% | 100.0% | ||
% within coping type | 67.6% | 19.6% | 39.0% | ||
% of Total | 27.4% | 11.7% | 39.0% | ||
Non-resilient | Count | 55 | 201 | 256 | |
% within resilience level | 21.5% | 78.5% | 100.0% | ||
% within coping type | 32.4% | 80.4% | 61.0% | ||
% of Total | 13.1% | 47.9% | 61.0% | ||
Total | Count | 170 | 250 | 420 | |
% within resilience level | 40.5% | 59.5% | 100.0% | ||
% within coping type | 100.0% | 100.0% | 100.0% | ||
% of Total | 40.5% | 59.5% | 100.0% |
Cross-tabulation of households’ resilience level and previously used coping strategy.
The Chi-Square test was run as a way of checking if the observed frequency (or percentage) differences in the contingency table (Table 6) were statistically significant. In statistical terms, it tests the implicit null hypothesis that there is no relationship between types/nature of previously employed coping strategies and the resilience status of the households. That is, it tests the hypothesis that the household’s resilience score (status) at time T1 is independent of types of coping methods employed by a household in response to shocks that occurred before time T1. The result of the Chi-Square test (Table 7) revealed high significance for
Value | Df | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | Exact Sig. (1-sided) | |
---|---|---|---|---|---|
Pearson Chi-Square | 98.149a | 1 | 0.000 | 0.000 | 0.000 |
N of valid cases | 420 |
Tests of association between resilience status and coping type.
0 cells (0.0%) have expected count less than 5. The minimum expected count is 66.38.
Computed only for a 2 × 2 table.
Value | Approx. sig. | Exact sig. | ||
---|---|---|---|---|
Nominal by Nominal | Phi | 0.483 | 0.000 | 0.000 |
N of valid cases | 420 |
Test of the strength of association (resilience level and coping type).
Not assuming the null hypothesis.
Using the asymptotic standard error assuming the null hypothesis.
Both association (Chi-Square) and strength of association (Phi test) tests highlighted the existence of meaningful relationships between the two variables under consideration. To further check the strength of association between the two variables the odds ratio was used as a supplement to the Phi test. The odds ratio here refers to the ratio of the odds that a household will be resilient to future shocks with no prior use of negative coping strategies to the odds that a household will be resilient through it previously used some kind of negative coping strategies. Based on frequencies in Table 6, the odds ratio was computed as:
This ratio shows that the number of households who are resilient with no prior use of negative coping is as twice as those who are non-resilient though they did not employ negative (erosive) coping before. It is also possible to be resilient or non-resilient to future shocks without prior negative (erosive) coping. However, it is more likely to be resilient than non-resilient given the initial state (previous experience in terms of coping type) is that of no negative (erosive) coping strategy.
The ratio here shows that the number of resilient households experiencing previous negative coping is about four times less than the number of non-resilient households.
Given the two pieces of information (odds ratios presented above) and referring to the first equation, the odds ratio of interest here (the odds that a household will be resilient to future shocks with no prior use of negative coping strategies to the odds that a household will be resilient through it previously used some kind of negative coping strategies) can be computed as follows:
The odds ratio indicates that households who did not previously use negative coping strategies were 8.57 times more likely to be resilient to future shocks. So, the clear implication of this finding is that the type of coping mechanisms used in response to given food insecurity-related shocks at a particular point in time can have an impact on households’ ability to respond to the upcoming shocks. This finding is in line with the Chi-Square test result above and the extant theoretical literature discussed in the chapter.
Depending on the initial state of the households, some of the coping strategies can lead to the poverty trap and erode the ability to cope with similar problems in the future. If left uncontrolled, even the coping mechanisms with no immediate individual impact, like selling firewood, may not be environmentally sustainable. This is especially true in the case of the study area as the source of firewood collection is, mostly, the single leftover of the ancient forest, which is confined to marginal areas around the Billate River. Additionally, the coping mechanisms utilized currently by the households can have important implications on their capacity to cope with future shocks, depending on their resource base. Hence, well-targeted interventions that go beyond saving lives (humanitarian emergency) and focusing on livelihood assets protection and capacity building to future shocks is the recommended policy option.
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Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.",book:{id:"8141",slug:"social-media-and-machine-learning",title:"Social Media and Machine Learning",fullTitle:"Social Media and Machine Learning"},signatures:"Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, Mohamed Ali Mahjoub and Catherine Cleder",authors:[{id:"247090",title:"Ph.D. Student",name:"Leila",middleName:null,surname:"Kerkeni",slug:"leila-kerkeni",fullName:"Leila Kerkeni"}]},{id:"63164",title:"Introduction to Kalman Filter and Its Applications",slug:"introduction-to-kalman-filter-and-its-applications",totalDownloads:10954,totalCrossrefCites:25,totalDimensionsCites:64,abstract:"We provide a tutorial-like description of Kalman filter and extended Kalman filter. This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. 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Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.",book:{id:"5416",slug:"cloud-computing-architecture-and-applications",title:"Cloud Computing",fullTitle:"Cloud Computing - Architecture and Applications"},signatures:"Shanrong Zhao, Kirk Watrous, Chi Zhang and Baohong Zhang",authors:[{id:"176364",title:"Dr.",name:"Shanrong",middleName:null,surname:"Zhao",slug:"shanrong-zhao",fullName:"Shanrong Zhao"}]}],onlineFirstChaptersFilter:{topicId:"9",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82351",title:"Building an Online Ecosystem for English Teaching and Learning in the Times of Covid-19 Pandemic and Beyond",slug:"building-an-online-ecosystem-for-english-teaching-and-learning-in-the-times-of-covid-19-pandemic-and",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105651",abstract:"Online education is a revolutionary trend of educational technology today, particularly, since the Covid-19 pandemic, online classes have become the cornerstone of modern education. 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Data deluge brings substantial challenges in the collection and management of massive amounts of unstructured data towards decision making. Likewise, unprecedented production of information exceeds the ability of authoritative bodies to create regulations and policies that can keep up with these transformations in the nature of work. We explicate the impact of well-timed policies (fiscal and monetary), prediction of long-term structural changes in the industrial sector, industrial strategy formulation practices, and examine the economic studies and analysis of sustainable development in these areas.",book:{id:"11198",title:"Digital Transformation",coverURL:"https://cdn.intechopen.com/books/images_new/11198.jpg"},signatures:"Kinda R. Dahlan, Ahmed A. 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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:"82300",title:"An Effective Method for Secure Data Delivery in IoT",slug:"an-effective-method-for-secure-data-delivery-in-iot",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104663",abstract:"The Internet of Things (IoT) has become very popular recently due to its important features that contribute to many aspects of our lives such as health and transportation. It consists of a vast number of different projects such as sensors, tags, actuators, and mobile devices, which can communicate and collaborate without human interactions. These devices carry small memory and low-energy battery, which affects their performance and lead to many issues. In this work, we are going to focus on the efficiency and security issues. We will propose a secure and efficient routing protocol for data delivery in order to improve its performance. The proposed technique will be evaluated in an implemented platform with appropriate case study. The expected outcome of this study will be a reference design and its practical implementation to support efficiency and security in IoT.",book:{id:"11197",title:"Internet of Things - New Trends, Challenges and Hurdles",coverURL:"https://cdn.intechopen.com/books/images_new/11197.jpg"},signatures:"Mnar Alnaghes, Nickolas Falkner and Hong Shen"},{id:"82267",title:"Methods for Speech Signal Structuring and Extracting Features",slug:"methods-for-speech-signal-structuring-and-extracting-features",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.104634",abstract:"The preliminary stage of the biometric identification is speech signal structuring and extracting features. For calculation of the fundamental tone are considered and in number investigated the following methods – autocorrelation function (ACF) method, average magnitude difference function (AMDF) method, simplified inverse filter transformation (SIFT) method, method on a basis a wavelet analysis, method based on the cepstral analysis, harmonic product spectrum (HPS) method. For speech signal extracting features are considered and in number investigated the following methods – the digital bandpass filters bank; spectral analysis; homomorphic processing; linear predictive coding. This methods make it possible to extract linear prediction coefficients (LPC), reflection coefficients (RC), linear prediction cepstral coefficients (LPCC), log area ratio (LAR) coefficients, mel-frequency cepstral coefficients (MFCC), barkfrequency cepstral coefficients (BFCC), perceptual linear prediction coefficients (PLPC), perceptual reflection coefficients (PRC), perceptual linear prediction cepstral coefficients (PLPCC), perceptual log area ratio (PLAR) coefficients, reconsidered perceptual linear prediction coefficients (RPLPC), reconsidered perceptual reflection coefficients (RPRC), reconsidered perceptual linear prediction cepstral coefficients (RPLPCC), reconsidered perceptual log area ratio (RPLAR) coefficients. The largest probability of identification (equal 0.98) and the smallest number of coefficients (4 coefficients) are provided by coding of a vocal of the speech sound from the TIMIT based on PRC.",book:{id:"10992",title:"Speech Recognition - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/10992.jpg"},signatures:"Eugene Fedorov, Tetyana Utkina and Tetiana Neskorodieva"},{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.104944",abstract:"Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. 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\r\n\tThe environment is subject to severe anthropic effects. Among them are those associated with pollution, resource extraction and overexploitation, loss of biodiversity, soil degradation, disorderly land occupation and planning, and many others. These anthropic effects could potentially be caused by any inadequate management of the environment. However, ecosystems have a resilience that makes them react to disturbances which mitigate the negative effects. It is critical to understand how ecosystems, natural and anthropized, including urban environments, respond to actions that have a negative influence and how they are managed. It is also important to establish when the limits marked by the resilience and the breaking point are achieved and when no return is possible. The main focus for the chapters is to cover the subjects such as understanding how the environment resilience works, the mechanisms involved, and how to manage them in order to improve our interactions with the environment and promote the use of adequate management practices such as those outlined in the United Nations’ Sustainable Development Goals.
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",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",keywords:"Human activity, Pollutants, Reduced risks, Population growth, Waste disposal, Remediation, Clean environment"},{id:"41",title:"Water Science",scope:"