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Simple Summary: California was the second largest sheep-producing state in the US, with an inventory of 533,000 head of sheep and lambs in 2022. Mastitis is a health and welfare concern for ewes and lambs. Mastitis is one of the top reasons for the removal of ewes from the flock and antibiotic use in ewes. In this study, California sheep producers were surveyed on the management of ewes. Seventy-one surveys were completed. Most respondents had flocks of ≥100 sheep (54%; 38/71). Twenty-three percent (16/70) of the respondents reported having >5% udder abnormalities in a flock per lactation. Further analysis showed two clusters among participating farms. Larger flock sizes, the use of breeding ewes for meat or wool production or contract grazing, and more extensive management practices were associated with >5% udder abnormalities and ≥5% orphan lambs. A subset of the respondents (20) participated in serological testing for small ruminant lentivirus (SRLV) in sheep. A total of 1106 ewes or ewe lambs were sampled. The ewe-level seroprevalence was 14.1% (183/1106), and the flock-level seroprevalence was 75% (15/20) of the sampled farms. Our results describe flock demographics, management practices, and producer-reported udder health abnormalities in California ewes. Larger flock sizes and extensive management practices could be risk factors for udder abnormalities in ewes. (1) Background: Information is lacking on small ruminant lentivirus (SRLV) status, prevalence, risk factors, and control measures for mastitis in California ewes. The goal of this survey was to outline characteristics of the sheep industry in California related to udder health and mastitis management. (2) Methods: An online survey consisting of 48 questions was completed by respondents between April 2022 and February 2023. Descriptive analysis and chi-squared tests were conducted to evaluate associations between variables. A multiple correspondence analysis (MCA) of general management practices, udder health management, and flock demographics was performed to assess clustering. A subset of respondents (20) participated in SRLV serology testing. (3) Results: Seventy-one completed surveys were submitted. The MCA showed two clusters. Larger flock sizes, the use of breeding ewes for meat or wool production or contract grazing, and extensive management practices were more closely related to >5% udder abnormalities per lactation and ≥5% orphan lambs. The flock-level seroprevalence of SRLV was 75% (15/20), and ewe-level seroprevalence was 14.1% (183/1106). (4) Conclusions: The results of this study highlight areas that need further research, such as exploring differences in mastitis and SRLV incidences among management systems, the efficacy of mastitis treatments, and education on critical timepoints for mastitis diagnosis and control. [ABSTRACT FROM AUTHOR]

Although the number of econometric analyses related to the renewable energy sector in Azerbaijan is increasing, studies on nonparametric dimensionality reduction are rather sparse. Principal component analysis (PCA) and multiple correspondence analysis (MCA) were chosen to fill this apparent research gap. As a result, a large dataset including the renewable energy sector and selected key macroeconomic indicators was evaluated. The PCA procedure yielded four distinct principle components reflecting the main macroeconomic variables, renewable energy production, industry-energy relations and natural resource revenues. The PCA method offers the possibility to examine the precise correlations and the underlying patterns between the displayed clusters of variables. Meanwhile, the MCA-based cross-country assessment of Azerbaijan's wind, solar and hydropower has struck somewhat pessimistic notes, as the country lags behind neighbouring and other post-Soviet countries (e.g. Estonia, Iran, Latvia, Russia) in developing its green energy sector. These findings are of great interest to policymakers, businesses and academics who wish to gain deep insight into the Azerbaijani economy in terms of renewable energy production. The practical value of the present work also lies in the fact that it analyses a multidimensional and relatively longitudinal dataset (1990-2022), which is an example of a methodological application of two nonparametric approaches. [ABSTRACT FROM AUTHOR]

Food fraud poses a significant challenge within the global food supply chain, with apprehensions regarding safety, authenticity, and efficiency. This study conducts a brief review of the literature by utilizing the Web of Science database, analyzing 2331 outcomes pertaining to the subject of food fraud. The analysis results demonstrated a noteworthy surge in scientific publications after 2013, which was propelled by events such as the horsemeat scandal and the formation of the European Food Safety Authority. Utilizing Multiple Correspondence Analysis (MCA), the study identified significant clusters pertaining to food transformation, safety, traceability, and distinct meat sources. In addition, trending topics shifted towards a holistic approach to food safety and the implementation of technologies like Blockchain (BC), Internet of Things (IoT), Artificial Intelligence (AI), and Big Data (BD). These technologies offer enhanced traceability, authentication, automation, and decision-making capabilities. The present research offers valuable perspectives on the evolving landscape of food fraud research and the potential of nascent technologies to tackle these issues. [ABSTRACT FROM AUTHOR]

(1) Background: Information is lacking on small ruminant lentivirus (SRLV) status, prevalence, risk factors, and control measures for mastitis in California ewes. The goal of this survey was to outline characteristics of the sheep industry in California related to udder health and mastitis management. (2) Methods: An online survey consisting of 48 questions was completed by respondents between April 2022 and February 2023. Descriptive analysis and chi-squared tests were conducted to evaluate associations between variables. A multiple correspondence analysis (MCA) of general management practices, udder health management, and flock demographics was performed to assess clustering. A subset of respondents (20) participated in SRLV serology testing. (3) Results: Seventy-one completed surveys were submitted. The MCA showed two clusters. Larger flock sizes, the use of breeding ewes for meat or wool production or contract grazing, and extensive management practices were more closely related to >5% udder abnormalities per lactation and ≥5% orphan lambs. The flock-level seroprevalence of SRLV was 75% (15/20), and ewe-level seroprevalence was 14.1% (183/1106). (4) Conclusions: The results of this study highlight areas that need further research, such as exploring differences in mastitis and SRLV incidences among management systems, the efficacy of mastitis treatments, and education on critical timepoints for mastitis diagnosis and control.

The evolving research field of social studies of economics (SSE) has shown that economics as a profession has gained influence on public debate and political power in many countries and in different social contexts. The channels of influence for economists are complex and widespread, i.e., there are multiple channels through which economists are able to influence public policy issues. In this article, we provide an in-depth analysis of a sample of the 85 most published authors of trade-related research in top economics journals to characterize the social field of elite economists and show how position in that field correlates with influence and power in and outside academia. More specifically, our data include information on their academic careers, their professional social networks, and their involvement in a variety of possible intermediaries between academic economics and policymaking (e.g., institutions such as think tanks or the media). We apply multiple correspondence analysis (MCA) and cluster analysis to show how different configurations of intra-academic capital are associated to policy-relevant extra-academic capital. The overall results of this analysis are threefold. First, the biographical analysis of elite trade economists reveals a remarkable degree of concentration of different forms of capital, which indicates a strong hierarchical order within the elite segment of trade economists. Second, and in line with this, despite the overall unequal distribution of capital within this elite sample, different forms of intra-academic and extra-academic capitals become visible. Third, while the cluster analysis reveals a main “average core” of (trade-)policy-oriented economists, the group of economists with very high capital is separated into two smaller clusters: The “ivory elite” cluster consists mainly of academic capital variables, while the “public econs” cluster is also composed of non-academic variables from the policy and media channels. So, even among elite trade economists, it is only as small number, who really is successfully gaining political and societal impact.

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease–disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease–disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies. [ABSTRACT FROM AUTHOR]

This article investigates methodological problems of measuring family wealth among secondary school students with home possessions indicators. It employs multiple correspondence analysis (MCA) in order to explore (1) the dimensionality of home possessions items, (2) the nature of relationship between these items, and (3) the substantive meaning of the first and other relevant dimensions. It also employs regression analysis in order to study potential explanatory factors of the second dimension of the MCA solutions that may be related to measurement problems of the instrument. Two sources of data are used: (1) open access data from the National Survey of Student Achievement (NSSA) in Lithuania that provide a series of national educational studies conducted from 2002 to 2016, and (2) data from the Programme for International Student Assessment (PISA) study conducted in Lithuania in 2018 that is used to corroborate findings produced using the first data source. Results of the data analysis show that the first dimension of MCA solutions obtained from home possessions items in both NSSA and PISA data sets reflect high vs. low socio-economic status (SES) of the students. However, there is at least one more important dimension, and it reveals multi-dimensional nature of the measurement instrument. Analysis of potential explanatory factors of the second dimension of the MCA solutions revealed that reading abilities and school-related affect is related to this dimension. Thus, the second dimension of MCA solutions might also reflect methodological problems related to the measurement of home possessions in adolescent surveys.

Safe operation of cranes is essential in the construction industry to ensure continuous gains in productivity and control potential hazards to workers on construction sites. In this research, which was based on human-machine-environment (HME) system thinking, a structured crane-related accident database was developed to identify and prioritize the safety concerns of construction crane operators. In order to technically investigate risk factors from accident reports, an approach was proposed in this work integrating factor clustering and prioritization. Accordingly, the research methodology was designed to first collect crane-related accident cases and determine database variables, including accident types, contributing operational factors, and accident consequences. Second, an advanced multiple correspondence analysis (MCA) method coupled with the fuzzy logic was applied to distribute the variables into different clusters and visualize their associations. Third, once the variables were well clustered, failure mode and effect analysis (FMEA) was adopted to investigate the multiscaled structure of clustering and explore the priorities of failure modes based on current legislation, regulations, and industrial codes of practice. In summary, the proposed approach integrating clustering analysis and prioritization analysis contributes to determining essential safety concerns and their potential impacts during crane operation, generating implications for construction crane safety management and eliciting detailed managerial implementation strategies for prevention measures for crane accidents in the construction industry. [ABSTRACT FROM AUTHOR]

Consumers' demand for 'healthy' food is growing at a fast pace. Beverages are one of the fastest-growing segments in the healthy food market. Kombucha is a product with a rapid market growth and is considered a 'healthy beverage'. Kombucha drinkers (n = 296) in the contiguous United States (U.S.) were targeted to investigate differences in the various consumer segments regarding the healthy eater's behavior, namely—(1) healthy diet stage-of-change attitudes, (2) healthy eating criteria, and (3) label information usage. An online survey questionnaire was used as a data collection tool. The data were analyzed via chi-square tests and multiple correspondence analysis (MCA). The majority of kombucha consumers were health-conscious and kombucha fitted into their view of a healthy diet. The female, older, western U.S. residents, and higher-educated consumers were more likely to take action and maintain a healthier diet. Healthy eaters' idea of a healthy diet was very similar to that of the general population. Kombucha consumers focused mainly on sugar/calories and natural/organic ingredients. Additional investigations to expand the understanding of the healthy eater's behavior with actual kombucha samples are needed in the future. [ABSTRACT FROM AUTHOR]

To determine the weight of the fiscal factor in the investment decisions of multinational companies in Tunisia, we conducted a survey of foreign companies established on Tunisian territory. There are 137 of these companies. They come from various origins, mainly French, Italian, American and German. We used the Multiple Correspondence Analysis (MCA). [ABSTRACT FROM AUTHOR]

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]

The creation of art in peripheral spaces is a process of artistic colonization of rural and natural spaces in the world through site-specific artworks grouped in artistic sites, and initiated by a recurring triptych of stakeholders: artist, sponsor (elected official, institutional, artistic curator) and manager (local authority, artistic institution, protected space). Within peripheral spaces, such an artistic investment is often a costly process which responds to somewhat political priorities on the field. Artistic production therefore tends to create specific territories, between land privatization and supply of cultural public services. Several treatments in MCA (multiple correspondence analysis) and HAC (hierarchical ascending classification) are mobilized in this article to account for the diversity and the stakes of a database of more than 210 artistic sites described by about forty variables. The results of the CAH show that, on a global scale, the sites fall into three main categories: artwashing sculpture parks, territorial art of peripheral spaces and the eco-artistic fronts of project-works.

Consumers’ demand for ‘healthy’ food is growing at a fast pace. Beverages are one of the fastest-growing segments in the healthy food market. Kombucha is a product with a rapid market growth and is considered a ‘healthy beverage’. Kombucha drinkers (n = 296) in the contiguous United States (U.S.) were targeted to investigate differences in the various consumer segments regarding the healthy eater’s behavior, namely—(1) healthy diet stage-of-change attitudes, (2) healthy eating criteria, and (3) label information usage. An online survey questionnaire was used as a data collection tool. The data were analyzed via chi-square tests and multiple correspondence analysis (MCA). The majority of kombucha consumers were health-conscious and kombucha fitted into their view of a healthy diet. The female, older, western U.S. residents, and higher-educated consumers were more likely to take action and maintain a healthier diet. Healthy eaters’ idea of a healthy diet was very similar to that of the general population. Kombucha consumers focused mainly on sugar/calories and natural/organic ingredients. Additional investigations to expand the understanding of the healthy eater’s behavior with actual kombucha samples are needed in the future.

Multiple correspondence analysis (MCA) was used in this study to statistically analyze the nature of the relationship between the polarity of extraction solvents and the type of phytochemicals extracted from mushroom caps using solvents with different polarity. The caps of three mushrooms (Enoki, Buna shimeji, and Bunapi shimeji) were extracted with four solvents of varying polarities (water, methanol, acetone, and ethyl acetate). The phytochemical content of the extracts was identified using UPLC-QTOF, followed by coding of the compounds and the subsequent MCA using SPSS® version 22. The MCA showed a positive correlation between the extraction solvents and the phytochemicals extracted from the mushrooms. A positive correlation of r = 0:322 was observed between the identified phytochemicals and the solvents used during the extraction process. The pattern of the phytochemicals clustering suggested that solvent polarity differentiated the groups of phytochemicals extractable from the mushrooms. It is, therefore, concluded that MCA is a valid statistical tool for the determination of the relationship between solvent polarity and the types of phytochemicals extracted using such solvents. [ABSTRACT FROM AUTHOR]

Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009-2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities. [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]

This study aims to clarify the relation between preparedness status and the local natural resource conservation of small and medium-sized enterprises (SMEs). SMEs are key actors in sustainable development and local disaster risk reduction. To ensure the quick recovery of business, SMEs are expected to formulate a Business Continuity Plan (BCP). However, the rate of formulation has thus far not been very high among SMEs. This study conducted a questionnaire survey in 2017 targeting SMEs in Sukagawa, Fukushima. We analyzed the 240 responses collected through a Multiple Correspondence Analysis and used Cramer's coefficient of association to determine the strength of association. Major findings are as follows: 1) Environment-related indices were associated with both BCP formulation and business size, and these associations indicate the possibility of connecting environmental actions or management to BCP formulation. 2) In the association between BCP formulation status and environmental actions, the environmental management system had a stronger association than business size (capital, number of employees). 3) The group interested in the conservation of regulating services related to local natural resources (reducing CO2 emission, water quality, aquatic plants and animals) had a higher rate of BCP formulation. 4) This group considers mutual help more important than self-help or public help. 5) Capital and number of employees have a strong association with both BCP formulation status and their interest in participating in local natural resource conservation. Activities with local society and stakeholders may collectively increase SMEs' awareness with neighbor enterprises or the local community. It would help both SMEs and local society to build a seamless attitude toward disaster risk reduction, which would contribute to local sustainability. [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]

• The Institutional Research Regime (IRR) literature increasingly focuses on actors and activities. • A natural resource focus predominates across time in the IRR literature. • We identify three groups of IRR studies: complex, bottom-up and top-down IRR studies. This article presents a review of over 100 studies relying on the Institutional Research Regime (IRR) framework. We base the review on a set of 13 elements that cover categories concerning research focus, research design and results. The article first presents the development of IRR studies over time by showing and discussing how different elements of the IRR framework are more or less popular in the literature, and how this popularity develops over time. For example, we have identified a trend in the IRR literature to focus more on actors and activities, while the resource focus predominates across time. Second, the article analyses how different elements of the IRR framework are combined in the existing literature and thus identifies three groups of IRR studies, that is, complex IRR studies, bottom-up IRR studies, and top-down IRR studies. [ABSTRACT FROM AUTHOR]

Abstract Simultaneous and continuous measurements of visibility, meteorological parameters and air pollutants were carried out at a suburban site in Ningbo from June 1, 2013 to May 31, 2015. The characteristics of visibility and their relationships with air pollutants and meteorological factors were investigated using multiple statistical methods. Daily visibility ranged from 0.6 to 34.1 km, with a mean value of 11.8 km. During the 2-year experiment, 43.4% of daily visibility was found to be less than 10.0 km and only 9.2% was greater than 20.0 km. Visibility was lower in winter with a frequency of 53.4% in the range of 0.0–5.0 km. Annual visibility had an obvious diurnal variation, with the lowest and highest visibility being 7.5 km at approximately 06:00 local time and 15.6 km at approximately 14:00 local time, respectively. Multiple correspondence analysis (MCA) indicated that the different ranges of visibility were significantly affected by different levels of pollutants and meteorological conditions. Based on the analyses, visibility was found to be an exponential function of PM 2.5 concentrations within a certain range of relative humidity. Thus, non-linear models combining multiple linear regressions with exponential regression were subsequently developed using the data collected from June 2014 to May 2015, and the data from June 2013 to May 2014 was used to evaluate the performance of the model. It was demonstrated that the derived models can quantitatively describe the relationships between visibility, air quality and meteorological parameters in Ningbo. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

Talking and literary reading are important activities for children, especially for children with autism spectrum disorder (ASD). We try to integrate the activities with NAO robots to excite their communication willingness. In this paper, a novel multimodal picture book recommendation framework that combines textual information and image information to calculate the similarity between the picture books and the conversation topics is proposed and evaluated using a testing dataset. In the proposed framework, an image neighbor discovery method to get more relative terms and an near-duplicated keyframes (NDK) friend detection method to get more relative NDKs are proposed. Finally, the booklist generated from the experiment is evaluated by six performance indicators and the experimental results demonstrate that our proposed framework achieves satisfactory and promising performance. With the help of the proposed recommendation framework, an autistic child can talk to the NAO robot in a relaxed and enjoyable environment. Please note that the proposed framework is not evaluated for its performance with the ASD children but for its performance at recommending books based on visual and textual features. Therefore, no tests were performed with either professionals nor diagnosed individuals. [ABSTRACT FROM AUTHOR]

This article presents a reflection and a methodological proposal on the use of the typological paragon in mixed sequential designs. The paragon is defined as the average or individual profile of a particular cluster that is obtained through multivariate techniques (correspondences analysis and cluster analysis). Here we propose four applications of the typological paragon: first, as a descriptive element, which aims to account for the content; second, as an element of sampling; third, as a link or connecting element between databases with the aim of facilitating matching; and finally, as an element that allows the convergence of results. Two studies provide the empirical basis of this innovative procedure and show the practical implementation of the four uses of paragon. The first deals with the analysis of the social stratification of households in Argentina (Fachelli, 2009). The second focuses on the study and identification of employment trajectories and their mutual influence with social capital in Spain and Catalonia (Alcaide, 2013). The four uses of the paragon that derive from both investigations allow us to reflect on the potentialities and limitations of the proposed model, the typological paragon. [ABSTRACT FROM AUTHOR]

This study examined the multidimensional nature of the association of stunting, wasting, and underweight for children below 5 years of age in India using data from the National Family Health Survey (NFHS)-3 (2005-2006). Multiple correspondence analysis (MCA) was applied to examine the association of the indicators. Additionally, log-linear model was used to find out the model of best fit to examine the nutritional status of children. It was found that underweight is associated with both stunting and wasting, whereas there was no consistent pattern of association between stunting and wasting. The results also confirmed that children suffered from multiple anthropometric failures. The results showed that height-for-age, weight-for-height, and weight-for-age taken together give the model of best fit for analysis of nutritional status. The study concluded that the three indicators of nutritional status should be considered simultaneously to determine the percentage of undernourished children.

Multimedia concept detection is a challenging topic due to the well-known class imbalance issue, where the data instances are distributed unevenly across different classes. This problem becomes even more prominent when the minority class that contains an extremely small proportion of the data represents the concept of interest as has occurred in many real-world applications such as frauds in banking transactions and goal events in soccer videos. Traditional data mining approaches often have difficulty handling largely skewed data distributions. To address this issue, in this paper, an importance-factor (IF)-based multiple correspondence analysis (MCA) framework is proposed to deal with the imbalanced datasets. Specifically, a hierarchical information gain analysis method, which is inspired by the decision tree algorithm, is presented for critical feature selection and IF assignment. Then, the derived IF is incorporated with the MCA algorithm for effective concept detection and retrieval. The comparison results in video concept detection using the disaster dataset and the soccer dataset demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM PUBLISHER]

This paper examines the multidimensional poverty status and also detects the factors that influence multidimensional poverty of Bodo household of Udalguri district, Assam. The study is entirely based on primary data. Multistage, stratified, purposive and random sampling techniques are used to collect primary data of 660 Bodo household covering twenty-two villages of eleven blocks of the study area. Multiple Correspondence Analysis (MCA) method is applied for constructing household level Multidimensional Poverty Index (MPI). Again, step-wise logistic regression is used to identify the factors influencing the multidimensional poverty status of the study area. The MPI value for the study area predicts that the area is moderately poor and health and literacy are the most important influencing factors. [ABSTRACT FROM AUTHOR]

A cross-sectional study was conducted to investigate the risk factors associated with zoonotic tuberculosis in humans and its transmission to people living at the wildlife−livestock−human interface. A questionnaire was administered to collect information on food consumption habits, food handling practices, and knowledge of zoonotic TB. Sputum samples were also collected from 150 individuals that belonged to households of cattle farmers with or without a bTB infected herd. In addition, 30 milk samples and 99 nasal swabs were randomly collected from cattle in bTB infected herds for isolation of Mycobacterium bovis (M. bovis). The sputum samples were screened for TB using the GeneXpert test and this was followed by mycobacterial culture and speciation using molecular techniques. No M. bovis was isolated from TB positive sputum samples and only one sample was confirmed as Mycobacterium tuberculosis (M. tuberculosis). M. bovis was isolated from 6.6% (n = 2/30) milk samples and 9% (n = 9/99) of nasal swabs. Ownership of a bTB infected herd and consumption of milk were recognized as highly significant risk factors associated with a history of TB in the household using multiple correspondence analysis (MCA) and logistic regression. The findings from this study have confirmed the potential for zoonotic TB transmission via both unpasteurized milk and aerosol thus, the role of M. bovis in human TB remains a concern for vulnerable communities.

Protein crops can represent a sustainable answer to growing demand for high quality, protein-rich food in Europe. To better understand the state of scientific studies on protein crops, a systematic review of field trials results to collect existing knowledge and agronomic practices on protein crops in European countries was conducted using published data from the literature (1985−2017). A total of 42 publications was identified. The following seven protein crops were considered: quinoa, amaranth, pea, faba bean, lupin, chickpea, and lentil. Observations within the studies were related to one or more of eight wide categories of agronomic managements: deficit irrigation (n = 130), salinity (n = 6), tillage (n = 211), fertilizers (n = 146), sowing density (n = 32), sowing date (n = 92), weed control (n = 71), and multiple interventions (n = 129). In 86% of the studies, measures of variability for yield mean values are missing. Through a multiple correspondence analysis (MCA) based on protein crops, European environments, and agronomic management factors, we provide a state of art of studies carried out in Europe on protein crops over the 32-year period; this study will allow us to understand the aspects that can still be developed in the topic. Most investigated studies refer to southern Europe and showed some trends: (i) faba bean, pea, and lupin provide highest seed yields; (ii) sowing date, sowing density, fertilization, and deficit irrigation are the agronomic practices that most influence crop yield; (iii) studies conducted in Central Europe show highest seed yields. The output from this study can be used to guide policies for sustainable crop management.

Research has acknowledged the relevance of accident information for prevention and learning and the need for companies to develop a reporting and learning culture. Few studies have approached this issue by comparing the different learning strategies used by companies. The aim of this study is to explore how companies use accident information and to develop strategies for learning from accidents, which cover all the learning cycle phases by: (a) identifying learning patterns across company and activity sectors, (b) checking for potential differences among certified and non-certified companies. Seventeen case studies were conducted with organizations operating in different sectors in Portugal. Data was collected from extensive, semi-structured interviews with key stakeholders and an analysis of relevant documentation. All data was subjected to a descriptive analysis, followed by multivariate analysis using Multiple Correspondence Analysis (MCA). The main MCA results showed two dimensions corresponding to the technical and social learning and four patterns were found, each corresponding to different levels of learning practices ranging from the minimal practices used to a higher degree of learning, combining practices of a technical and social nature. Additionally, the results revealed that companies in the same activity sector may have very different practices, independently of OHSAS certification. The results allow us to conclude that organizations with good safety practices tend to follow the complete learning cycle. Overall, these organizations have established procedures to report accidents and to collect information on them but there are organizations that still do not maximize their means of learning from work accidents. [ABSTRACT FROM AUTHOR]

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume. The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume. This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible. [ABSTRACT FROM AUTHOR]

Studies conducted from the 2000s are characterized as having “multinational contexts” and “an expansion of the cultural domains” and as using “big data and various methodologies.” However, very few studies have been conducted in Asia, especially that examine hierarchical taste revealed across the culture and everyday life as Bourdieu (1984) suggested. Thus, this study attempted to identify how the cultural topography of Korean society is drawn by analyzing the preference for cultural activities, music genres, television programs, food, beverages, and alcoholic drinks. For this, the study used data collected from the Survey on the Cultural Capital of Korean Society (2011) conducted for a research project called The Social Landscape of Cultural Production and Consumption: An Approach of Economic Sociology for Cultural Capital. The data were analyzed by multiple correspondence analysis (MCA), an advanced method of correspondence analysis used by Bourdieu in his Distinction (1984) to examine the correlation between class and taste. As a result, the cultural taste and lifestyle of Koreans were divided into six types according to class and age. Among the age groups, the 20s and the 50s did not correspond to the class variables and were identified as having unique generational cultural taste. [ABSTRACT FROM AUTHOR]

The monetary approach is not sufficient to understand the multiple dimension of poverty. The purpose of this research is therefore to measure and analyze multidimensional poverty reduced to a single non-monetary dimension and according to the characteristics of household heads. Based on the non-monetary variables provided by the most recent country survey (QUIBB 2006), we use Multidimensional Correspondence Analysis techniques to construct a Composite Poverty Indicator. The results of the non-poverty index suggest that the poorest are large families and households in rural areas. The deprivation is also more serious in households whose heads are male, aged between 51 and 99 and less educated. The findings are the same for the monetary approach at the poverty line, leading to the conclusion that both types of poverty are quite strongly and positively correlated. Finally, we propose some recommendations for socioeconomic policies. [ABSTRACT FROM PUBLISHER]

Summary This paper analyzes the consequences of an alternative weighting scheme for the Multidimensional Poverty Index (MPI), using a data-driven approach, as opposed to the currently employed equal weighting scheme. This weighting scheme has been under strong scrutiny since the MPI's inception, given the sensitivity of country rankings to different weights and indicator choices. Therefore, the current study employs Multiple Correspondence Analysis (MCA) for weighting of the indicators and investigates its impact on the scores and relative ranking of 28 countries. The results show that equal weighting of the three dimensions cannot be statistically justified. Moreover, the statistical weights differ systematically across countries, implying differences in deprivations across regions, although household poverty rankings are highly correlated between normative and statistical weights. Given the high correlation between all indicators employed within the MPI, a large overlap is found in the information, implying that there might not actually be so much multidimensionality within the dimensions of the MPI. [ABSTRACT FROM AUTHOR]

É recorrente concluir-se que níveis mais elevados de autoconfiança dos alunos, relativamente às suas capacidades para interagir com a matemática, associados a uma maior utilidade percebida desta concorrem para os alunos terem representações positivas da disciplina. Um dos objectivos deste artigo é averiguar se no contexto universitário se mantém esta tendência linear na relação entre as duas dimensões - afectiva e instrumental - estruturantes das representações das disciplinas de métodos quantitativos. Um segundo objectivo consiste em perceber como é que os alunos de diferentes cursos (de áreas científicas distintas) geram as suas representações relativamente a essas disciplinas. Foi aplicado um inquérito por questionário a uma amostra de alunos numa instituição universitária. Para a definição dos padrões de representações, foi usada a Análise de Correspondências Múltiplas e, para a identificação de factores explicativos dos perfis dos alunos, foi usada a Regressão Categorial. Os resultados evidenciam que o espaço das representações dos métodos quantitativos em contexto universitário é um espaço marcado pela heterogeneidade, mas cuja complexidade ultrapassa essa tendência linear na relação entre a dimensão da utilidade e a dimensão afectiva. Outra das conclusões é a não sustentabilidade da tese que aponta (pelo menos em contexto não universitário) para a feminização da falta de autoestima e da autoconfiança para lidar com disciplinas dessa área científica. Conclui-se, ainda, que parece existir uma certa cultura de curso, na medida em que os cursos tendem a reflectir um padrão predominante de representações dos métodos quantitativos.It is often concluded that higher levels of self-confidence in pupils in relation to their ability to interact with mathematics, associated to the perception of the latter's greater utility contributed for pupils making positive representations of this discipline. One of the objectives of this article is to inspect if in the university context this linear trend is maintained in the relation between the two dimensions – affective and instrumental – which structure the representations of the disciplines of quantitative methods. A second objective consists in observing how students from different courses (from different scientific areas) create their representations about these disciplines. A questionnaire was applied to a sample of students from a university institution. The definition of patterns of representations was done through the use of Multiple Correspondence Analysis (MCA), and Categorical Regression (Catreg) was employed to identify explaining factors in the students' profile. The results show that the space of the representations of quantitative methods in a university context is marked by heterogeneity, with a complexity that goes beyond this linear trend in the relation between the utility dimension and the affective dimension. Another conclusion is about the untenability of the thesis that points (at least in a non-university context) to the feminization of the lack of self-esteem and self-confidence to deal with disciplines from this scientific area. A further conclusion is that a course culture seems to exist, insofar as the courses tend to reflect a predominant pattern of representations of the quantitative methods.

News web videos uploaded by general users usually include lots of post-processing effects (editing, inserted logo, etc.), which bring noise and affect the similarity comparison for news web video event mining. In this paper, a framework based on the concept of Near-Duplicate Segments (NDSs) which effectively integrates spatial and temporal information is proposed. After each video being divided into segments, those segments from different videos but sharing similar visual content are clustered into groups. Each group is named as an NDS, which infers the latent content relations among videos. The spatial-temporal local features are extracted and used to represent each video segment, which could effectively capture the main content of news web videos and omit the noise such as the disturbance/influence from video editing. Finally, the visual information is integrated with the textual information. The experiment demonstrates that our proposed framework is more effective than several existing methods with a significant improvement. [ABSTRACT FROM AUTHOR]

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Highlights: [•] Typology of diverse technical management routes (TMRs) and selection of cases for study. [•] This method takes into account the links among farmer technical options within TMRs. [•] Our comprehensive “tool kit” combines multidimensional analysis and association rules. [•] The method is applied to a sample which covers five main types of TMRs in vineyards. [•] Other researchers can adapt our model for new situations and for any crop. [Copyright &y& Elsevier]

Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval -- Information filtering, Retrieval models General Terms: Algorithms, Experimentation [ABSTRACT FROM AUTHOR]

Abstract: Some of the main research questions in the assessment ecosystem services include how to integrate ecological and social information into the analysis and how to make it spatially explicit. We mapped six ecosystem services delivered by forests in the Sierra Nevada Mountains (south-east Spain) from the supply- to the demand-sides, taking into account the influence of protected areas on the capacity of supply services. Semi-structured interviews and geographical information system sources were used to map the supply-side, whereas 205 face-to-face questionnaires were distributed to assess and map the demand-side. Our results show the existence of consistent ecosystem service bundles in terms of both the supply- and demand-sides, particularly between erosion control–recreational hunting and between mushroom harvesting–nature tourism. We found a spatial scale mismatch for the erosion control, with its supply at the local scale and its demand at the regional–national scales, with implications at the institutional scale at which it should be managed. Consequently, mapping both the supply- and demand-sides is essential for environmental decision making because it can indicate where management interventions should be focused, either by defining high-priority areas for protection or defining the institutional scale at which these services should be managed. [Copyright &y& Elsevier]

The performance of ranking the retrieval results based on context and/or content information is usually limited because it depends heavily on the quality of the extracted information. Therefore, re-ranking approaches have been developed to improve the retrieval performance, but how to re-rank the retrieval results remains a challenge. In this paper, the inclusive and exclusive relationships between semantic concepts are utilized to re-rank the retrieval results. The inclusive relationship refers to the high co-occurrence relation between a target semantic concept and a reference semantic concept; while the exclusive relationship refers to the low or none co-occurrence relation. Moreover, multiple reference concepts provide more information from different aspects than only one reference concept does. Multiple Correspondence Analysis (MCA) is applied in this paper to capture the correlation in terms of the impact weights between attributes and the co-occurrence classes, and the average value of all the impact weights is utilized to re-rank the retrieved results for the target semantic concept. Experimental results on the MediaMill Challenge problem and the TRECVID 2011 video collections demonstrate that our proposed framework can enhance the retrieval results for a number of target semantic concepts. [ABSTRACT FROM AUTHOR]

This paper explains the key role played by windowing in the preliminary analysis of multifactor and multivariate (MFMV) databases. The explanation is based on the general case of a database featuring quantitative or qualitative measurement variables and a hyperparallelepipedic structure in which the directions correspond to the factors. In order to maintain the MFMV aspects of this data structure, the windowing approach as described in this paper does not reduce the information as much as most of the basic non-windowing summarizing procedures using the standard statistical indicators. First, the data in each cell of the hyperparallelepiped are transformed into membership values that can be averaged over factors, such as time or individuals. Then, these membership values may be potentially investigated into with several graphic techniques; for this paper, multiple correspondence analysis (MCA) was chosen. The presentation fall into two parts. First, a didactic example based on a simulated data set describes the approach in comparison with more traditional approaches, and then a real data set, with multidimensional signals recorded for 34 subjects in 15 experimental overtaking situations, is used to demonstrate the power of the “space windowing/MCA” pair on a large real database. Next, the discussion section weighs out the pros and the cons of using space windowing to perform a preliminary analysis of a large MFMV database in studies of human component systems. [ABSTRACT FROM PUBLISHER]

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]

This article explains the main role that space windowing plays in preliminary knowledge extraction from multifactor and multivariate databases coming from complex system empirical studies. The explanation is based on the general case of a database with a hyperparallelepipedic structure in which the directions correspond to the factors and where the measurement variables may be quantitative or qualitative, temporal or nontemporal, and objective or subjective. First, the data in each cell of the hyperparallelepiped is transformed into membership values that can be averaged over factors, such as time or individual. Then, several graphic techniques can be exploited to investigate membership values. This article mainly focuses on the use of multiple correspondence analysis (MCA). A didactic example with several factors and several kinds of variables—nontemporal vs. temporal where each one may be either quantitative or qualitative—is used to illustrate the widespread use of the pair “space windowing/MCA.” The discussion presents the advantages and disadvantages of using space windowing to perform a preliminary analysis of a multifactor multivariate system study. [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]