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This study examined wealth trends in a rural Kenyan community between 2011 and 2018. Understanding wealth trends is important for understanding health outcomes and overall well-being of vulnerable communities, and for informing economic and health policy that can improve specific wealth challenges. Information regarding household materials, assets, education, and mortality from 20,370 households in the Kombewa Health and Demographic Surveillance System was used. Three indices were developed and compared to quantify wealth: a principal component analysis of assets, a multiple correspondence analysis of assets, and the multidimensional poverty index. Wealth quintiles and levels of deprivation relating to socioeconomic status were then created and analyzed over time. Wealth measured by the asset-based indices, suggested an increase in wealth during the assessment period with the percentage of households in the wealthiest quintile increasing from 19 to 23%. The multidimensional poverty index, however, suggests no change in socioeconomic status over time. Among other factors, a lack of access to sanitation and improved water seems to be the main justification. Our results indicate that households are accumulating assets, but their increased accumulation is not translating to changes in living conditions known to improve health. Hence, while houses may be getting wealthier, they may not necessarily be getting healthier. [ABSTRACT FROM AUTHOR]

Understanding the controlling factor of groundwater quality could certainly enhance the promotion of the sustainable development of groundwater resources. In fact, during the last decades, the coastal aquifer of Mahdia–Ksour Essef that belongs to the semi-arid region of Tunisia has been marked by an increase of groundwater extraction for irrigation and water for drinking. Moreover, the water quality shows deterioration due to the septic tanks and intensive agricultural activities especially in the coastal side of the aquifer. This study aimed to (1) assess the water quality using Piper Diagram, Box Plot, ions scatter diagrams, (2) understand the processes of mineralization acquisition in the aquifer, and (3) identify the chemical processes and their spatial distribution in the study area. To solve these objectives, the multivariate statistical analysis such as multiple correspondence analysis and hydrochemical analysis was performed. Accordingly, two types of groundwater are identified through the hydrochemical analysis as Na-Cl and Ca-SO4. According to multiple correspondence analysis, three water groups are determined (G1, G2, and G3). Indeed, the groundwater quality is controlled by the water-rock interactions and affected by the seawater intrusion and agricultural activities. The generated results would be helpful to provide a managing model highlighting the threatened locations. It could be considered as a basis for future monitoring programs that aspire to protect water resources. [ABSTRACT FROM AUTHOR]

Tourist satisfaction is one of the key factors in determining the success of a tourist destination, since it affects behavioural intentions, such as the willingness to revisit and recommend the destination. Therefore, much research has addressed the measuring of tourist satisfaction, either eliciting positive or negative evaluations of a holiday or destination. The spread between expected positive and negative characteristics can trigger a tourist experience. And the gap between expectations and perceptions will produce a feeling of satisfaction or dissatisfaction (Expectancy-Disconfirmation Paradigm, EDP). Under these conditions, this paper adopts a dual approach, considering tourist satisfaction and dissatisfaction as two distinct constructs; it also proposes a practical methodology, termed 4Q, for measuring these constructs in the field of tourism research. Lastly, two distinct composite indicators relating to Tourist Satisfaction (TS) and Tourist Dissatisfaction (TD) are proposed, for comparing the performance of tourist destinations over space and time, and addressing marketing strategies regarding different tourist segments. [ABSTRACT FROM AUTHOR]

The fast and explosive growth of digital data in social media and World Wide Web has led to numerous opportunities and research activities in multimedia big data. Among them, disaster management applications have attracted a lot of attention in recent years due to its impacts on society and government. This study targets content analysis and mining for disaster management. Specifically, a multimedia big data framework based on the advanced deep learning techniques is proposed. First, a video dataset of natural disasters is collected from YouTube. Then, two separate deep networks including a temporal audio model and a spatio-temporal visual model are presented to analyze the audio-visual modalities in video clips effectively. Thereafter, the results of both models are integrated using the proposed fusion model based on the Multiple Correspondence Analysis (MCA) algorithm which considers the correlations between data modalities and final classes. The proposed multimodal framework is evaluated on the collected disaster dataset and compared with several state-of-the-art single modality and fusion techniques. The results demonstrate the effectiveness of both visual model and fusion model compared to the baseline approaches. Specifically, the accuracy of the final multi-class classification using the proposed MCA-based fusion reaches to 73% on this challenging dataset. [ABSTRACT FROM AUTHOR]

number of studies have concluded that when students have greater confidence about their math skills and are aware of its usefulness, they have a more positive perception of the subject. This article aims to examine whether this pseudo linear trend in the relationship between affective and instrumental dimensions is also true of the university context. Special attention is devoted to the articulation of these dimensions in structuring student perceptions of quantitative methods so as to identify the various forms that this interaction can assume. Our second aim is to understand how the perceptions of these subjects are constructed by students from degree courses in distinct scientific areas. Can we speak of group dynamics whereby socialization within each degree course triggers the sharing of similar perceptions? We concluded that a certain linear trend can also be identified in the university context in the relationship between the affective dimension and usefulness of quantitative methods i.e., that the highest levels in the perception of the usefulness of these subjects corresponded to the highest levels of self confidence and enjoyment of the subjects. However, in addition to this scenario there is another configuration in which negative feelings coexist with the recognition of the usefulness of quantitative methods subjects. Namely, lower levels of self confidence and enjoyment of these subjects can also be associated with high levels of perceived usefulness. We also concluded that there is evidence of what we designated a certain course culture in the perceptions about quantitative methods. Nevertheless, when our observation is extended to the scientific area the heterogeneity of the perceptions becomes evident. Another important finding is the rejection of the thesis, at least in the university context, that defends the lack of self-confidence in quantitative methods among females student. [ABSTRACT FROM AUTHOR]

Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique. [ABSTRACT FROM AUTHOR]

Since 1999, Italian Universities have been monitoring teaching using students opinions to improve the quality of the education process. The acceleration of the evaluation has been promoted by ministerial laws, that have obliged Universities to write reports containing the main results. In this paper, the aim is to find a method to classify the Universities based on teaching evaluation. [ABSTRACT FROM AUTHOR]

This paper presents three results: (1) It is shown that the explained variance criterion is inappropriate for multiple correspondence analysis (MCA). (2) There are two strategies to interpret a MCA configuration: factor analytical interpretation and cluster analytical interpretation. Goodness-of-fit measures for both interpretations are constructed, (3) These measures give (a) a more adequate picture of the model fit and (b) allow to differentiate whether a factor analytical interpretation or a cluster analytical interpretation is more adequate. [ABSTRACT FROM AUTHOR]