The pathways and conditions of effectively combating the COVID-19 pandemic in Asia

The rapid outbreak of COVID-19 pandemic urges to seek advanced solutions to combat the disease and minimize its impacts on humankind and society. We employ a causal mechanism approach and develop a crisp-set Qualitative Comparative Analysis (QCA) model to study successful pathways in curbing COVID-19 among 37 Asian countries. Structural variables of GDP per capita, governance, democracy, health access and quality alongside with two government response indexes from the real-time Oxford COVID-19 Government Response Tracker are included as causal conditions in our QCA model. We identified a total of seven successful pathways, covering countries in different development stages. Regardless of income levels, we found democracy is essential in effectively controlling the pandemic. High democratic scores are characterized in five over seven pathways. Extensive testing and comprehensive contact tracing strategies have proved to be effective in containing COVID-19, especially in developed and emerging countries. Surprisingly, we found limited impacts of stringent containment measures such as gathering restricted and social distancing. We traced back to the early days of COVID-19, and by performing hierarchical agglomerative cluster analysis, we learned that restrictive containment measures in the early days helped prevent the spread of the pandemic, especially in developing countries. In the context that democracy is in decline around the world, our empirical results suggest that democracy is still essential in controlling the disease. Also, countries in under-resourced settings can still effectively combat COVID-19 with appropriate and timely containment measures.

Early behavioral profiles elucidating vulnerability and resiliency to later ASD outcomes

Infant siblings of children with autism spectrum disorder (ASD) exhibit greater heterogeneity in behavioral presentation and outcomes relative to infants at low familial risk (LR), yet there is limited understanding of the diverse developmental profiles that characterize these infants. We applied a hierarchical agglomerative cluster analysis approach to parse developmental heterogeneity in 420 toddlers with heightened (HR) and low (LR) familial risk for ASD using measures of four dimensions of development: language, social, play, and restricted and repetitive behaviors (RRB). Results revealed a two-cluster solution. Comparisons of clusters revealed significantly lower language, social, and play performance, and higher levels of restricted and repetitive behaviors in Cluster 1 relative to Cluster 2. In Cluster 1, 25% of children were later diagnosed with ASD compared to 8% in Cluster 2. Comparisons within Cluster 1 between subgroups of toddlers having ASD+ versus ASD− 36-month outcomes revealed significantly lower functioning in the ASD+ subgroup across cognitive, motor, social, language, symbolic, and speech dimensions. Findings suggest profiles of early development associated with resiliency and vulnerability to later ASD diagnosis, with multidimensional developmental lags signaling vulnerability to ASD diagnosis.

Towards a dynamic behavioral profile: A diachronic study of polysemous sentir in Spanish

This study examines the diachronic evolution of the polysemy of the Spanish verbsentir(‘to feel’) by means of a corpus-based dynamic behavioral profile (BP) analysis. Methodologically, it presents the first application of the BP approach to historical data and proposes some methodological innovations not only within the current body of research in historical semantics but also with regard to previous applications of the BP approach. First, whereas the majority of existing studies in quantitative historical semantics are largely based on observed frequencies or percentages of collocational co-occurrence, our study leverages more complex historical data that are based on the similarities of vectors. Second, this study also provides an extension of the methodological apparatus of the BP approach by complementing the traditional hierarchical agglomerative cluster analysis (HAC) with a dynamic BP approach derived from multidimensional scaling maps (MDS). Theoretically, this methodology contributes to a comprehensive perspective on the process of Constructionalization and the nature of networks, which is illustrated on the basis of the development of the Discourse Marker (DM)lo siento(‘I’m sorry’).

Cluster Analysis of Haze Episodes Based on Topological Features

Severe haze episodes have periodically occurred in Southeast Asia, specifically taunting Malaysia with adverse effects. A technique called cluster analysis was used to analyze these occurrences. Traditional cluster analysis, in particular, hierarchical agglomerative cluster analysis (HACA), was applied directly to data sets. The data sets may contain hidden patterns that can be explored. In this paper, this underlying information was captured via persistent homology, a topological data analysis (TDA) tool, which extracts topological features including components, holes, and cavities in the data sets. In particular, an improved version of HACA was proposed by combining HACA and persistent homology. Additionally, a comparative study between traditional HACA and improved HACA was done using particulate matter data, which was the major pollutant found during haze episodes by the Klang, Petaling Jaya, and Shah Alam air quality monitoring stations. The effectiveness of these two clustering approaches was evaluated based on their ability to cluster the months according to the haze condition. The results showed that clustering based on topological features via the improved HACA approach was able to correctly group the months with severe haze compared to clustering them without such features, and these results were consistent for all three locations.

On the polysemy of the Polish complete path construction: A corpus-based exploratory study

In this article, we present a corpus-based analysis of two major types of the Polish Complete Path (CP) construction in which a source-PP, headed by od+GEN, is immediately followed by a goal-PP, headed by do+GEN or po+ACC, as in od jesieni 1920 do jesieni 1921 ‘from autumn 1920 to autumn 1921’ and od kreskówek po rysunki techniczne ‘from cartoons to technical drawings’. The aim of the study is to shed some light on the polysemous structure of the CP construction on the basis of its usage patterns. To this end, we used a random sample of over 500 instances of both construction types retrieved from the National Corpus of Polish. The data were annotated for a number formal and semantic features and subsequently explored using hierarchical agglomerative cluster analysis. When interpreting the results of several analyses performed on different sets of variables, we gave special attention to three levels of semantic granularity encoded in the data, concluding that, on the whole, all analyses point towards a distinction between the spatial, temporal and abstract meanings of the construction under investigation.

A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.

TTO characteristics and university entrepreneurship: a cluster analysis

Purpose The purpose of this paper is to analyze potentially significant differences in a series of relevant characteristics of universities’ technology transfer offices (TTOs). To this end, TTOs have been classified by the function of their resources assigned to the enhancement of university entrepreneurship. The factors analyzed are the number of academic spin-offs created with the support of TTOs as well as the TTOs’ age, experience, professionalization and relational capital. Design/methodology/approach The authors have performed a hierarchical agglomerative cluster analysis to identify the groups of TTOs with homogeneous behavior and features. This multivariate technique allows determining whether it is possible to identify some differentiated conglomerates of TTOs. Findings The results of the cluster analysis allow concluding that the number of academic spin-offs created with the support of TTOs, the age and degree of professionalization of these TTOs, the experiences of their employees in matters related to entrepreneurship and their relationships with market actors explain the different levels of commitment of TTOs toward the enhancement of university entrepreneurship. In contrast with the expected results, the relationship between TTOs and academic actors does not seem to explain such differences. Originality/value This research contributes to the identification of the particular design characteristics that TTOs should exhibit to promote the entrepreneurial performance of universities, offering important recommendations to academic institutions regarding the efficient design of TTOs to manage university ambidexterity and to build TTOs’ entrepreneurial identity.

Pattern Recognition of Melaleuca cajuputi Powell Essential Oils Fingerprint in Terengganu, Malaysia Using Chemometric Approach

Cajuputi essential oil is extracted from the leaves of Melaleuca cajuputi Powell. This study is performed to spatially classify the variation of Melaleuca cajuputi essential oil fingerprint based on different sampling location using chemometric technique along Terengganu coastal area. Discriminant Analysis (DA) successfully discriminate 10 fingerprint of essential oil into three different groups with three significant peaks in FTIR analysis. Hierarchical agglomerative cluster analysis (HACA) successfully grouped the 10 sampling stations into three groups (cluster A, B and C).Classification criteria is based on the intensity movement of functional group either bending or stretching of the essential oil compound Multiple linear regression (MLR) was used to develop an equation model that explains the prediction of species fingerprint in each cluster by different locations.

Control limit detection for source apportionment in Perlis River Basin, Malaysia

This study presents the application of selected environmetric in the Perlis River Basin. The results show PCA extracted nine principal components (PCs) with eigenvalues greater than one, which equates to about 77.15% of the total variance in the water-quality data set. The absolute principal component scores (APCS)-MLR model discovered BOD and COD as the main parameters, which indicates the measure of the agricultural pollution in the Perlis River Basin, the hierarchical agglomerative cluster analysis (HACA) shows 11 monitoring stations assembled into two clusters in accordance with similarities in the concentration of BOD and COD, which are grouped in P4. The X ̅ control chart shows that the mean concentration of BOD and COD in P4 is in the control process. The capability ratio (Cp) was applied to measure the risk of the concentration in terms of the river pollution in a subsequent period of time using the limit NWQS.