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We discuss spectral clustering from a variety of perspectives that include extending techniques to rectangular arrays, considering the problem of discrepancy minimization, and applying the methods to directed graphs. Near-optimal clusters can be obtained by singular value decomposition together with the weighted kk-means algorithm. In the case of rectangular arrays, this means enhancing the method of correspondence analysis with clustering, while in the case of edge-weighted graphs, a normalized Laplacian-based clustering. In the latter case, it is proved that a spectral gap between the (k−1)\left(k-1)st and kkth smallest positive eigenvalues of the normalized Laplacian matrix gives rise to a sudden decrease of the inner cluster variances when the number of clusters of the vertex representatives is 2k−1{2}^{k-1}, but only the first k−1k-1 eigenvectors are used in the representation. The ensemble of these eigenvectors constitute the so-called Fiedler-carpet.

A new type of functional food products-bread supplemented with yeast extract-was developed and optimized. In the next product development phase, samples of bread with yeast extract were tasted and evaluated by 536 randomly selected consumers, and their acceptability was analyzed.. The survey results showed that consumers in all groups had a high level of awareness about the importance of product labeling and the impact of food on health, while low requirements for special diets. Bread sensory characteristics were highly acceptable, while the final price of bread with yeast extract was 20% higher than regular bread. Correspondence analysis showed differentiation among categories of consumers and their responses. Despite the high importance of product price and lack of proper marketing, there was a high level of consumers' willingness to change their current bread, and buy this new type.

The present study aimed to analyse the associations between spatiotemporal characteristics of the setting and the origin of the ball in terms of the game complexes for junior female volleyball teams. Multinomial logistic regression and multiple correspondence analysis were applied to analyse 3.675 setting actions (Complex I = 1.593, Complex II = 2.082) in the final phase of the Greek Junior Championship. Results showed that the origin of the ball from the left lane of the court eases the setter for an accurate setting during CI while passing from the right lane incommodes the setter during CII. Regarding setting zone, for accurate setting, odds are increased by 3.2 for zone 4 during CI while decreased by 23.8 for zone 3 during CII. The junior setters’ distribution of setting is predictable and creates favourable conditions for the opponent to deal with it. The improvement of junior female setters’ ability to follow the team’s offensive tactic regardless of the ball's origin, to manipulate passes received from the right lane of the court during CII and the acceleration of setting tempo for the wing hitters could be training goals for coaches.

Although many firms operate on global digital platforms, small countries and firms also play an essential role at the national level, especially during crises and the slowdown in globalization. This research investigates trade patterns in digital services at the country and firm level and identifies challenges in this area in providing new information and tools to startup mentors and policymakers, who need more evidence for national authorities to develop mentorship and digital programmes. The study also contributes to transaction cost theory, explaining how it is possible to reduce transaction costs. The methodology involves using quantitative and experimental methods, logistic regression for firms and correspondence analysis for countries. The WTO dataset was used to visualise trade in services data and to interpret clusters of digitalised countries. Interestingly, Estonia stands apart from other post-socialist countries in terms of digital services exports, being among smaller countries and hosting the highest concentration of startups per capita. The firmlevel analysis revealed that firms trading in digital services differ from others – being smaller, more focused on exports and more often controlled by non-residents. The study encourages investments in small countries and small firms that trade successfully in digital services.

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods for CA do not scale to large, high-dimensional datasets. In this paper, we provide a novel interpretation of CA in terms of an information-theoretic quantity called the principal inertia components. We show that estimating the principal inertia components, which consists in solving a functional optimization problem over the space of finite variance functions of two random variable, is equivalent to performing CA. We then leverage this insight to design algorithms to perform CA at scale. Specifically, we demonstrate how the principal inertia components can be reliably approximated from data using deep neural networks. Finally, we show how the maximally correlated embeddings of pairs of random variables in CA further play a central role in several learning problems including multi-view and multi-modal learning methods and visualization of classification boundaries. [ABSTRACT FROM AUTHOR]

In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Since the number of these crashes is increasing, it is important to investigate light vehicle crashes to gain insights into potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. Using text search options and manual exploration, a database of 1,623 light delivery-related crashes was examined with a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are fatigue, alcohol impairment, young drivers on low to moderate speed roadways, open country and moderate speed state/U.S. highways, and interstate-related crashes due to inattention. Policymakers can use the findings of the current study to perform data-driven policy development and promote safety for delivery-related travels. [ABSTRACT FROM AUTHOR]

The role of preprints in the scientific production and their part in citations have been growing over the past 10 years. In this paper we study preprint citations in several different aspects: the progression of preprint citations over time, their relative frequencies in relation to the IMRaD structure of articles, their distributions over time, per preprint database and per PLOS journal. We have processed the PLOS corpus that covers 7 journals and a total of about 240,000 articles up to January 2021, and produced a dataset of 8460 preprint citation contexts that cite 12 different preprint databases. Our results show that preprint citations are found with the highest frequency in the Method section of articles, though small variations exist with respect to journals. The PLOS Computational Biology journal stands out as it contains more than three times more preprint citations than any other PLOS journal. The relative parts of the different preprint databases are also examined. While ArXiv and bioRxiv are the most frequent citation sources, bioRxiv's disciplinary nature can be observed as it is the source of more than 70% of preprint citations in PLOS Biology, PLOS Genetics and PLOS Pathogens. We have also compared the lexical content of preprint citation contexts to the citation content to peer-reviewed publications. Finally, by performing a lexicometric analysis, we have shown that preprint citation contexts differ significantly from citation contexts of peer-reviewed publications. This confirms that authors make use of different lexical content when citing preprints compared to the rest of citations. [ABSTRACT FROM AUTHOR]

Survey response data to address attitudes, satisfaction and other variables of interest to social scientists often rely on a set of statements for which respondents choose one category among all possible ordinal categorical answers. For each respondent, scores are often calculated as the summation of individual values obtained from each response. However, summation scores may be less accurate in representing latent traits, as different profiles may result in identical integer score values among all possible response patterns. We propose a method for the calculation of weighted scores from a set of ordinal categorical variables based on the factor coordinates along the oriented axes obtained from Multiple Correspondence Analysis. Our simulation results and the application of the proposed methodology to real data suggest that the proposed score has the potential of better representing latent variables than the simple summation of values of categorical variables over all sets of responses. Our worked example illustrates that respondents with different profiles might be misrepresented by the summation scores, as they could have, in some situations, the same score assigned to them whereas the proposed weighted scores discriminate every single profile. [ABSTRACT FROM AUTHOR]

Textile and clothing (T&C) products contribute to a substantial proportion of the non-food product recalls in the European Union (EU) due to various levels of associated risks. Out of the listed 34 categories for product recalls in the EU's Rapid Exchange of Information System (RAPEX), the category 'clothing, textiles, and fashion items' was among the top 3 categories with the most recall cases during 2013–2019. Previous studies have attempted to highlight the issue of product recalls and their impacts from the perspective of a single company or selected companies, whereas limited attention is paid to understand the problem from a sector-specific perspective. However, considering the nature of product risks and the consistency in a higher number of recall cases, it is important to analyze the issue of product recalls in the T&C sector from a sector-specific perspective. In this context, the paper focuses on investigating the past recalls in the T&C sector reported RAPEX during 2005–2021 to understand the major trends in recall occurrence and associated hazards. Correspondence Analysis (CA) and Latent Dirichlet Allocation (LDA) were applied to analyze the qualitative and quantitative recall data. The results reveal that there is a geographical pattern for the product risk that leads to the recalls. The countries in eastern part of Europe tend to have proportionately high recalls in strangulation and choking-related issues, whereas chemical-related recalls are proportionately high in countries located in western part of Europe. Further, text-mining results indicate that design-related recall issues are more prevalent in children's clothing. [ABSTRACT FROM AUTHOR]

Correspondence Analysis (CA), a multivariate statistical technique, allows a visual representation of the association between categorical variables through a contingency table consisting of frequencies representing the existence of relationships. Despite being a widely used statistical technique, the classical CA is not able to demonstrate the uncertainty in real-life problems. To address this issue, a new Interval-valued Hesitant Fuzzy CA approach is proposed to represent the uncertainty caused by human doubt. Due to the nature of operations defined on Hesitant Fuzzy Sets, it is hard to integrate the fuzzy calculations directly into the classical CA. Thus, a new hesitant expected value method is proposed to reveal the independence between two categorical variables. As the output of the proposed approach, an interval-valued hesitant fuzzy correspondence map consisting of rectangles of different sizes representing the amount of the hesitancy is constructed. The applicability of the proposed approach is demonstrated by a simple but effective illustrative example. [ABSTRACT FROM AUTHOR]

The hotel industry has been facing fierce competition in recent years. It is important for hotels to conduct effective strategic planning for competitiveness improvement to achieve sustainable development. Prior studies on hotel strategic planning mainly use questionnaire data or hotel internal data, which have the problems of insufficient data or neglecting customer perspectives. The purpose of this study was to develop an integrated method for customer-oriented strategic planning for hotel competitiveness improvement based on text mining of online reviews. First, text mining of online reviews was conducted to extract customer-concerned service attributes and evaluate customer concern level and the performance of the service attributes through Latent Dirichlet Allocation (LDA) and sentiment analysis. Second, the competitive structures of the hotels were analyzed and the main competitors were identified from the competitive hotels through correspondence analysis. Third, SWOT analysis of the target hotel toward the main competitors was conducted, and the priorities of factors in each SWOT category were determined. An empirical study on a five-star hotel is given to illustrate the feasibility and effectiveness of the proposed method. The results indicate that the proposed method can help managers in strategic planning to obtain more specific strategies for hotel competitiveness improvement. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the causes of flight delays of a selected airline operating in Europe and identify potential risks and reasons for delays in air transport, which carry risk, especially in connection with passenger dissatisfaction and the resulting need to pay compensation for delayed flights, according to EU law, which brings significant financial costs for airlines. Data collected from the years 2013–2019 have been used for the purposes of this article, including data on the duration and causes of the delays and the characteristics of individual flights, such as the flight type, aircraft type, time of departure, aircraft capacity, and the load factor. Multidimensional statistics methods have been applied for data processing, namely tests of independence and correspondence analysis. Dependencies have been presented in graphical form using correspondence maps. The analysis shows that the total share of delayed flights of the company in question does not increase over the mentioned period of time. The analysis furthermore proved that higher capacity aircraft were rather prone to longer delays. The share of delayed charter flights declined slightly over the period under review, while the number of delays caused by airline operating reasons, aircraft clearance by supplier companies, technical maintenance and aircraft defects, operating procedures, and crew flight standards and airport restrictions is increasing. The analysis also shows that the delays propagated by the delays of the previous flights are becoming more frequent, with the exception of the year 2019. In the last pre-COVID year, air traffic control could boast about a positive trend regarding the number of produced delays over the years, contributing to greater air passenger satisfaction and airline service quality. Due to the gradual revival of air transport after the COVID pandemic lockdowns, the topic of flight delays, passenger customer satisfaction and financial costs for the resulting delays is once again an actual issue for air management and operation. [ABSTRACT FROM AUTHOR]

Social media platforms constitute an indispensable tool for social movements to mobilise public opinion to promote social change. To date, however, little is known about the extent to which activist and political claims formulated on social media echo what the general public thinks about gender equality. This is especially important given that social movements often use social media to develop their actions and to build long-standing support around particular claims. Our data collection is based on relevant actor groups and keywords surrounding the women's strike that took place in Switzerland in June 2019. We investigate which actor groups were involved in gender equality discussions online, what were the prominent and polarising ideologies, and what were the main framings of the debate. Findings indicate that organizational committees and their followers were the most active, followed by political actors. We also observed a polarisation effect on social media between left and right-wing oriented actors, which is more pronounced than trends drawn from opinion surveys. We further find that social media discussions were organised along a continuum, which ranges between calling for attention and discussing concrete policy measures. [ABSTRACT FROM AUTHOR]

In statistical inference, adaptive reasoning is defined as logical thinking to determine what can be inferred from data or statistical results and whether the justifications led to valid conclusions. Accordingly, adaptive reasoning is a mathematical proficiency required in statistical inference. This study aims to discover the association between adaptive reasoning and the initial statistical competence of undergraduate students. For this purpose, we performed mixedmethods research conducted by sequential exploratory analysis. This study involved 66 participants selected from undergraduate students in the Statistical Inference course offered by the mathematics education department at one university in Indonesia. The qualitative result describes the characteristics of students' adaptive reasoning proficiency at each grade. The proportion of students from grade 1 to grade 4 is 4.55%, 21.21%, 48.48%, and 25.76%, respectively. The quantitative result based on the chi-squared statistics test shows a significant association between adaptive reasoning proficiency and initial statistical competence. The correspondence analysis solution depicts that a high level of statistical competence is strongly associated with a high grade of adaptive reasoning proficiency, and conversely. Generally, the results provide evidence that the mastery of initial statistical competence is an important aspect in developing students' adaptive reasoning proficiency. The study provides some recommendations that will benefit the lecturer to develop adaptive reasoning proficiency in the Statistical Inference courses. [ABSTRACT FROM AUTHOR]

This paper is concerned with simultaneously regrouping regions and sectors when analyzing the relative sectorial specialization of regions and the relative regional concentration of sectors. An automatic two-mode clustering algorithm is proposed with a view toward a concept of overall localization, corresponding to a discrepancy between an actual two-way contingency table (regions × sectors) and an hypothetical table reflecting independence between regions and sectors. This procedure identifies similar regions (respectively sectors) according to the relative sectorial (respectively regional) structure. This algorithm significantly reduces the size of the original table and obtain an optimal collapsed table with low level of information loss vis-à-vis the degree of overall localization. The properties and results of the algorithm are discussed through two applications, namely Argentina and Brazil. [ABSTRACT FROM AUTHOR]

Objective: Human papillomavirus (HPV) vaccination is crucial, particularly for preventing cervical cancer. This study aimed to assess the perceptions of HPV vaccination among healthcare university students in Okayama City, Japan, with the goal of promoting HPV vaccination in the future., Methods: The study enrolled 168 students (94 male students and 74 female students, median age: 20 (minimum: 18, maximum: 27)) from a healthcare university in Okayama, Japan. Data collected included sex, age, year, HPV vaccination status, knowledge about HPV vaccination, and cervical cancer screening status (for female students). Participants completed self-administered questionnaires on their perceptions of HPV vaccination. The responses were analyzed using text mining., Results: The HPV vaccination rate among female participants was 16.2% (12 out of 74). Of the respondents, 43.6% of male respondents and 52.7% of female respondents knew that HPV causes cervical cancer. Text mining revealed that among the female respondents, the most frequently used words were "think", followed by "vaccine", "side effects", "vaccination", and "frightening". Distinctive words among female respondents, especially those who haven't been vaccinated, include knowing the term "HPV," understanding that HPV causes cervical cancer, and for those who haven't had screenings, terms like "side effects" and " frightening" were common., Conclusion: Among female students at the healthcare university, the HPV vaccination rate was thought to be comparatively low. Among those who had not received the HPV vaccine, it was particularly noted that they knew the term "HPV," were aware that HPV caused cervical cancer, and for those who had not undergone screenings, providing accurate information about "side effects" and " frightening" seemed necessary., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Experimental Research on Human Subjects at Okayama Healthcare Professional University issued approval 0091. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Yokoyama et al.)

Abstract Background Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis. Methods We propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ. Results In the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts. Conclusion CA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.

This study quantitatively analyzed healthcare administration studies in Japan using text mining, focusing on articles published during 1994–2021 in the Journal of the Japan Society for Healthcare Administration (prior to 2008, the journal was called Hospital Administration). Both the co-occurrence network and the correspondence analysis (these are extracted words that refer to the two systems) demonstrate two major changes: (1) the introduction of the long-term care insurance system, which was enacted in 1997 and came into effect in 2000, and (2) the introduction of the late-stage medical care system for the elderly in 2008, both of which had a significant impact on the Japanese public health and welfare system. Co-occurrence network and correspondence analysis were conducted to understand changes in research interests. The analysis used two time periods following a change in the journal's name in 2008. To readily comprehend changing research trends, 10-year segments were considered, resulting in three time periods. The research features and trends during the aforementioned periods were examined using correspondence analysis. Configuration figures derived from this analysis plotted time transition (first dimension) against certain abstract/concrete situations (second dimension). The extracted words displayed in the configuration maps at the axes' intersection were patient, survey, and evaluation. They revealed no distinctive features compared with other words and were commonly used in article titles within this journal during each period. The following results were obtained from the correspondence analysis: first, changes in the geriatric care system of public medical insurance and the introduction of the long-term care insurance system in 2000 were expressed in the characteristics of the extracted words; second, in the 14 years after the journal's name changed, published studies frequently referred to the roles of doctors, nurses, and other healthcare professionals. A chi-squared test on these extracted words and the period classification confirmed a statistically significant relationship between them.

The CAD hypothesis holds that there is mapping between the three moral emotions (contempt, anger and disgust) and the three moral codes of community, autonomy and divinity. Different from previous designs to establish correlations between emotions and eliciting situations which instantiate moral codes, this paper takes a narratological approach to the CAD hypothesis by examining the relationships between the three moral emotions and moral judgment relating to the three moral codes in the context of eliciting situations. First, similarity data pertaining to eliciting situations were collected by using the Order k/n-1 with fixed K method. Second, the participants were instructed to write down both their responses and justifications of their responses to the eliciting situations. A narratological analysis of the justifications of responses show that they vary along three variables: narrator, character, and basis (mostly in the form of moral judgment). The descriptive statistics of participants’ responses and of their justifications show that more than a half of responses are in the categories of anger (24.8%), disgust (20.7), and contempt (7.7%) and that about 60% of justifications contain a component of moral judgment based on the three moral codes of autonomy (30.03%), divinity (18.1), and community (11.82%). Correspondence analyses among eliciting situations, emotional responses and the three variables of justifications, together with results from the Multidimensional Scaling analysis of the similarity data, show that the CAD hypothesis is largely supported if mappings are set between the emotions in question and moral judgment concerning the eliciting situations (the basis variable of justification) and that the hypothesis is conditioned by the variable of character.

As a consequence of the application advanced maintenance practices, the theoretical probability of failures occurring is relatively low. However, observations of low levels of market intelligence and maintenance management have been reported. This comprehensive study investigates the determinants of maintenance practices in companies utilising hydraulic machinery, drawing on empirical evidence from a longitudinal questionnaire-based survey across the West-Balkan countries. This research identifies critical predictors of technical and sustainable maintenance performance metrics by employing the CA-AHC (Correspondence Analysis with Agglomerative Hierarchical Clustering) method combined with non-parametric machine learning models. Key findings highlight the significant roles of the number of maintenance personnel employed; equipment size, determined on the basis of nominal power consumption; machinery age; and maintenance activities associated with fluid cleanliness in influencing hydraulic machine maintenance outcomes. These insights challenge current perceptions and introduce novel considerations with respect to aspects such as equipment size, maintenance skills and activities with the aim of preserving peak performance. However, the study acknowledges the variability resulting from differing operational conditions, and calls for further research for broader validation. As large-scale heterogeneous datasets are becoming mainstream, this research underscores the importance of using multidimensional data analysis techniques to better understand operational outcomes.

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Correspondence analysis (CA) is a statistical method for depicting the relationship between two categorical variables, and usually places an emphasis on graphical representations. In this study, we discuss a CA formulation based on canonical correlation analysis (CCA). In CCA-based formulation, the correlations within and between row/column categories in a reduced dimensional space can be expressed by canonical variables. However, in existing CCA-based formulations, only orthogonal rotation is permitted. Herein, we propose an alternative CCA-based formulation that permits oblique rotation. In the proposed formulation, the CA loss function can be defined as maximizing the generalized coefficient of determination, which is a measure of proximity between two variables. Simulation studies and real data examples are presented in order to demonstrate the benefits of the proposed formulation. [ABSTRACT FROM AUTHOR]

The situation of the COVID-19 pandemic has had enormous social and economic impacts and has significantly affected the modal split. Many cities worldwide have adopted various blocking policies that affect how people travel. Micromobility systems, such as scooters and bicycle sharing, were among the transport systems affected by COVID-19. Electric scooters and shared bicycles provide comfortable and fast first-/last-mile connections for short-distance rides. The shared nature of these modes, together with the spread COVID-19, has contributed to the declining use of these services. The quantification of the impact of COVID-19 on shared services was demonstrated by this research through various mathematical methods. Satisfaction with the use of alternative modes of transport during the pandemic was determined based on the evaluation of a questionnaire survey. Independence tests of qualitative features and statistically significant associations that were demonstrated with a correspondence analysis were used for comparison. The main conclusion of the research was to point out the reasons for the preference for alternative modes of transport and to highlight the impacts on health and fears of contracting COVID-19 when using micromobility services. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to analyse the way both hands are placed in relation to the steering wheel. The study is based on a 2D-tracking task, in which the stimulus is a steering wheel angle signal recorded while driving on 7 road geometries in real situations at 30 km/h. Hand movements are measured using a 3D-motion capture system. Data analysis is based on the segmentation of the hand angular position using 8 fuzzy angle windows (labelled as approximately 0°, approximately 45°, etc.), when the hand rests on the steering wheel, one window, else. To obtain information about the coordination aspect, 9 × 9 bivariate windows are considered for each time sample (recorded at 25 Hz); then, the average membership values are computed for each individual driving situation. The individual membership value sets related to the road geometries are studied via Correspondence Analysis, allowing to displaying both the inter- and intra-individual differences and the most discriminating bivariate windows. The methodology with 9 × 9 windows is more complex but implies less information loss than alternative ones e.g. when the two hands are separately analysed, which constitutes 9 + 9 windows and contains no information about the coordination aspect. [ABSTRACT FROM AUTHOR]

This systematic review aims to give an overview of the diversity of research areas related to human biometeorology in Brazil. The main focus of this paper addresses research trends, represented by published papers with national and international authorship, main contributions and shortcomings, as well as challenges and prospects of research in this area of study. An extensive literature search was conducted in the Scopus, Web of Science, and Science Direct databases so as to identify relevant publication output up to July 2021 related to the research area. The screening resulted in 96 studies chosen for full-text reading. Overall, results indicated a reduced amount of articles on the subject matter published internationally, with noticeable gaps in research in some regions of the country, such as the Amazon region and in the Brazilian Midwest region. Research gaps in relevant areas have been identified with limited output in the climate dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers. Such gaps should further encourage researchers to engage in research focused on those areas. [ABSTRACT FROM AUTHOR]

A defining characteristic of mixed methods research, integration receives considerable attention in the field, yet evidence suggests that the process of integration may be challenging in practice. Crossover analyses, in which methods typically used with one data type are applied to a different data type, can deepen integration. Using an example from the field of education, this article demonstrates the iterative application of multiple correspondence analysis as a crossover analysis through a four-stage integration process. Contributions to the field of mixed methods include (a) the utility and potential of multiple correspondence analysis to surface new dimensions of integrated findings, (b) considerations for rigor in the application of crossover methods, and (c) considerations for making sense of diffractive or dynamic findings. [ABSTRACT FROM AUTHOR]

This study aims to use LMS log data to suggest a way to understand CoI constructs. Students' interactions in Moodle components were weighted for indicators of cognitive, teaching and social presences. Traces reflecting students' online interactions were obtained from the Moodle LMS and analyzed through learning analytics techniques. The data is examined with the Euclidean Distance Model, and Correspondence Analysis methods to evaluate the levels of interactions and presences. The results indicated that, cognitive presence is at the center of the CoI constructs, and student-content interaction, is found is more prominent than other interactions in terms of its relation to cognitive presence. Social presence scores were mostly related with student-student and student-teacher interaction scores. In addition, teaching presence scores were found in parallel with student-system interaction scores. [ABSTRACT FROM AUTHOR]

This work faced the construction of an index composed by the factors that influence the student's academic performance. To fulfill this objective, the information from students was collected by means of a questionnaire and later, the exploratory analysis of data was made, where the relevant characteristics of the sample were detected. To study the relations between the factors involved, a multiple correspondence analysis was made and the index was constructed by means of a factorial analysis. The index showed that the student's high performance is mainly associated to data related to academic achievement and personal relationships, owed to their factorial charges of 0.787 y 0.708 respectively. The compound index can be applied in other institutions, serving as basis for decision making and reforms in the educative sector, in order to obtain a lower desertion and a higher academic performance, among other aspects of the educational problems.