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With growing internationalism, there is a shift in research patterns in developing countries, especially China and India, generating vital and contemporary research areas that are beginning to challenge the existing Western‐dominated research literature in social sciences. Yet, many of the new ideas within conceptual papers by the social sciences are not empirically validated, let alone operationalized. This is where the group concept mapping method can play a role in bridging the gap between phenomenal conceptualization and having an empirically valid model that can then be operationalized. The group concept mapping process involves five steps: create statements, sort statements, run multidimensional scaling (MDS) of sorted units, run cluster analysis, and label the clusters. This approach allows for the collective thoughts of a pre‐defined group to be collected and organized into a tangible output with academic rigor. This paper offers an overview of the group concept mapping methodology, discussing the processes of the method, how the method can be utilized fully within the business and broader social science context, and the strengths, weaknesses, and practical implications of group concept mapping. [ABSTRACT FROM AUTHOR]

The performance and stability of the electric network are improved through smart grid technologies. The smart grid strongly rely on digital communication technologies creates security issues that must be addressed to distribute power efficiently and safely. For billing, load balancing, and energy management, the customer‐side smart meter regularly transmits the consumption reading to the system operator. However, dishonest customers created cyberattacks by entering fictitious readings of their electrical consumption in order to steal electricity for financial benefit. The distributed monitoring meter‐based network concept is utilized in this paper to closely monitor the losses. A two‐stage clustering‐based technique has also been suggested for the purpose of detecting theft. The approach can be detect the customer ID having abnormality in consumption pattern. To verify the effectiveness of the suggested strategy, numerical experiments on two datasets of smart meters have been conducted and four different types of attack patterns have been constructed. Simulation results show that the proposed scheme is very effective for detecting various types of attack patterns. In addition, the paper helps to predict how much power and financial loss the company will suffer as a result of multiple attacks. [ABSTRACT FROM AUTHOR]

Despite the importance of academic counselling for student success, providing timely and personalized guidance can be challenging for higher education institutions. In this study, we investigate the impact of counselling instances supported by a learning analytics (LA) tool, called TrAC, which provides specific data about the curriculum and grades of each student. To evaluate the tool, we measured changes in students' performance ranking position over 3 years and compared the performance of students who received counselling with and without the tool. Our results show that using the tool is related to an improvement in cohort ranking. We further investigated the characteristics of counselled students using cluster analyses. The findings highlight the potential beneficial influence on academic outcomes arising from the provision of guidance to students regarding their course load decisions via TrAC‐mediated counselling. This study contributes to the field of LA by providing evidence of the impact of counselling supported by an LA tool in a real‐world setting over a long period of time. Our results suggest that incorporating LA into academic counselling practices can improve student success. Practitioner notesWhat is already known about this topic By analysing student performance, teaching strategies and resource impact, learning analytics (LA) empowers institutions to make informed changes in curriculum design, resource allocation and educational policies.Through insights into academic progress, engagement and behaviour, LA counselling tools enable the identification of at‐risk students and those needing additional support.In the related literature, there are areas for further exploration such as understanding the scalability and long‐term effects of interventions on student success and retention.What this paper adds Through rigorous data analysis, the paper establishes a connection between LA utilization and enhanced student performance, offering concrete evidence of the effectiveness of LA interventions.By examining various factors such as academic stage and course load, the research offers valuable insights into the contextual nuances that optimize the outcomes of LA tool‐based support.It adds to the growing body of evidence that supports the efficacy of data‐driven interventions in education, fostering a more informed and evidence‐based approach to student support and success.Implications for practice and policy Enhanced student support strategies: By tailoring counselling interventions to align with the identified effective conditions, educators can proactively address individual student needs, improving academic outcomes and retention rates.Informed decision making: The demonstrated positive impact highlights the potential of similar data‐driven initiatives to foster student success. Policymakers can consider incentivizing the adoption of such interventions at institutional levels.Future directions for research: By identifying contextual factors that influence the efficacy of LA interventions, it encourages further exploration into how other LA interventions can be optimized for specific conditions. This can guide the development of more precise and effective student support strategies in the future. [ABSTRACT FROM AUTHOR]

Background: Dignity‐conserved nursing has been widely studied by scholars all over the world; however, there is no clear direction in which this field is trending. Aim: To conduct a bibliometric analysis that systematically characterises publications on dignity research in the nursing field from 2011 to 2020. Design: Bibliometric and visual analysis of retrieved articles. Methods: The Web of Science Core Collection database was used to retrieve all articles which addressed dignity in nursing from 2011 to 2020. The WoSCC's own analysis tool, CiteSpace and VOSviewer, were used to obtain visual analysis results. Reporting follows the STROBE checklist. Results: A total of 1429 papers on dignity care are included in this study. We found that the number of papers on this topic increased steadily, and the United States topped the list with 366 articles in total. The institute with the most publications was King's College London, and the most widely published journal was Nursing Ethics. We were able to identify four major research topics, namely dignity in: (a) palliative care, (b) dementia and the elderly, (c) health care and (d) nursing ethics. Terminally ill patient, home, value, rehabilitation and psychological distress were the five keywords with the highest burst strength. Conclusions: The interest in dignity care research has been steadily increasing from 2011 and is reflected in the number of published papers. The United States and Western Europe are leading in this field, both having a high number of cutting‐edge researchers and high‐level scientific research institutions. In the domain of dignity care, several stable and high‐yield core author groups have been formed. While the existing research mainly focuses on four hot spots, psychological distress, advanced cancer, maternity care and content analysis may be the research frontiers. [ABSTRACT FROM AUTHOR]

Decarbonizing industrial sectors is a critical global challenge, involving the creation of new industrial spaces—'net zero industrial clusters'—co‐locating energy sectors and 'hard‐to‐abate' industries such as oil refining and steelmaking. This paper provides the first empirically grounded geographical investigation of these emerging spaces. It employs a place‐based research agenda to unpack how UK net zero industrial clusters (ICs) are imagined and emplaced in policy and industry discourses through place‐based naming, spatial configuring and mapping activities. By conducting document analysis, 33 in‐depth stakeholder interviews and five field trips to three UK case studies, we show how cluster imaginaries vary across cases and policy contexts in terms of constituents, focus and purpose. Ontological complexity is compounded by different rationales among stakeholders in configuring clusters and by contested cluster naming and boundary setting. This ambiguous, evolving spatiality raises important political and justice concerns over who and where is excluded in cluster building. These findings advance the geographies of low‐carbon transitions by showing: (1) ways that ICs' spatial embeddedness, which underlies cluster spatial configurations, helps increase industry actors' recognition of their economic, social and cultural ties with the places of their making, even if this risks path dependency; (2) how fluid cluster boundaries, reflected in cluster names and maps, emphasize the value of a network topology of scale to enable spatially inclusive, multi‐scalar climate mitigation. Finally, we argue that a place‐sensitive net zero policy mindset is vital for fulfilling ICs and the UK's decarbonization potential in a manner that is both fair and locally grounded. [ABSTRACT FROM AUTHOR]

Against the background of the rise in higher‐education participation rates, this paper examines the spatial redistribution of undergraduates across the United Kingdom resulting from moves to and from university. Drawing on the Graduate Outcomes Surveys of 2017/2018 and 2018/2019, address data coded to 53 subregions (SRs) are used to track those enrolled on degree courses by age 20 from domicile to university and workplace 15 months after graduation. The paper begins by examining how university‐ward migration serves to concentrate this group geographically and the way in which subsequent job‐related moves tend to reinforce this process. Each person is then classified on the basis of their migration trajectories between domicile and workplace, enabling a set of migration accounts to be produced for each SR. Applying cluster analysis to these accounts, a six‐way grouping of SRs is used to gauge change between their domicile and workplace populations in both overall numbers and qualitative characteristics, the latter being measured in terms of educational qualifications preuniversity and occupational status 15 months after graduation. These analyses demonstrate how the different types of SRs fare in these exchanges of students/graduates, with more subregions suffering the 'double whammy' of losing out in both quantitative and qualitative terms than gaining from this process, with challenging implications for central government's current 'levelling‐up' agenda. [ABSTRACT FROM AUTHOR]

Cluster analysis is often used to group consumers based on their hedonic responses to products. We give a motivating example in which conventional cluster analyses converge on a solution where consumers do not agree on which products they like. We show why this occurs. We state a goal: to group together consumers who have a shared opinion of which products are delightful and which products are not delightful, apart from consumers who have a different opinion. To meet this goal, we code consumers' hedonic responses in ways inspired by top‐k box analysis, then cluster consumers using b‐cluster analysis. For comparison, we cluster consumers using two conventional methods. We interpret each cluster by focusing on which product(s) the cluster accepts and whether a large proportion of cluster members are aligned in accepting these products. Solutions from b‐cluster analysis based on top‐k box‐inspired codings met our goal better than conventional approaches, indicating that these methods deserve further study. Practical Applications: Cluster analysis outcomes are profoundly shaped by a researcher's decisions related to response coding and clustering algorithm. This paper highlights the importance of determining the goal of the cluster analysis first, then selecting a response coding and clustering algorithm to best meet this goal. Our stated goal is one that is frequently of interest in sensory evaluation but is not well met by conventional clustering approaches. The novel approaches that we give in this paper meet the goal and are available using software that is freely available in the public domain. [ABSTRACT FROM AUTHOR]

In this paper, we study and analyze the community detection algorithms and the related evaluation metrics to explore the clustering characteristics of China's stock market. We build the stock market network by the constituent companies of the CSI 300 index and study the tightness between nodes. We first choose an appropriate adjacency matrix, which is obtained by the relation coefficient. Then, based on the singular value decomposition (SVD) of the adjacency matrix, we analyze the optimal network based on the directed spectral clustering on the ratio of eigenvectors and the spectral clustering followed by local refinement. The optimal network is chosen based on the best clustering effect according to the clustering effectiveness indexes Calinski–Harabasz and Davies–Bouldin. We explore the community structure in the stock market and study the influence of each community. Through the index out‐degree and in‐degree, we also obtain the most important companies in the market. Different from the traditional strategies that are always based on qualitative methods and undirected networks, this paper explores the characteristics of how to detect China's stock market. Specifically, we offer suitable strategies and measurements to evaluate the clustering results objectively and fairly. The qualities of community detection are measured too. Based on the above analysis, we further provide valuable insights by exploring the characteristics of the stock market. [ABSTRACT FROM AUTHOR]

The paper shows that visions of the future can be used as a predictor of cooperation and division between actors in their efforts to shape the institutional environment, specifically policy in socio‐technical systems. Accordingly, the paper suggests a new method to analyze visions: a virtual solution space in which visions can be grouped according to their similarity. The similarity of visions is calculated based on cluster analysis. Empirically, the paper focuses on the networks between industry associations in the heating transition in the German building sector. It shows that actors whose visions of future socio‐technical system developments overlap are more likely to cooperate with each other. It also suggests that the fragmentation of the residential heating system in Germany is reflected in a fragmented actor network. Furthermore, the authors show that shared technological interests can outweigh similar visions. These fragmented technological interests hinder meaningful cooperation. This is potentially one reason why a powerful low‐carbon heating coalition in Germany that could facilitate an accelerated deployment of low‐carbon heat systems by driving policy change has not emerged to date. The paper contributes to a better understanding of how niche actors in sustainability transitions use their agency and specifically strategize to bring about institutional change. In this respect, the authors discuss how differing levels of system‐fragmentation influence transition dynamics in general and institutional change dynamics in particular. [ABSTRACT FROM AUTHOR]

An important cause of stunting is limited consumption of complementary foods, in terms of both quantities and nutrients. Although existing studies show a positive association between fathers' engagement and children's diet, programmes designed to improve complementary feeding practices often only target mothers. In response to this, maternal behaviour change communication (BCC), paternal BCC and food voucher programmes were designed and implemented in Ethiopia using a clustered randomized controlled trial design. The paternal BCC programme included gender‐equal messages to increase fathers' participation in childcare, household labour and decision making. The research reported in this paper is an examination of the BCC programmes, characterizing the behavioural, normative and control beliefs of both mothers and fathers in BCC households compared to those in control households. In this study, a total of 40 participants were included, with 13 mother–father pairs in the BCC + food voucher group, and seven pairs in the control group. Each participant was interviewed separately. We found that BCC mothers showed more gender‐equal tendencies than the control mothers despite being more rural in location. By contrast, the beliefs of BCC and control fathers were similar overall, suggesting men are more resistant to gender‐equal BCC. More work is needed to develop and test effective methods for changing fathers' beliefs and practices. Key messages: Mothers in behaviour change communication (BCC) group mothers held more gender‐equal beliefs than control mothers, while BCC and control fathers shared similar views, suggesting a male resistance to gender equality.Mothers found fathers' involvement in childcare socially acceptable but often perceived them as inexperienced, which limited their participation.Control mothers' traditional view on household chores maintained the conventional labour division, influencing fathers' involvement.Fathers typically resisted maternal control of resources; however, they agreed that the more knowledgeable should lead decision‐making. BCC mothers showed greater confidence in making household decisions as effectively as fathers. [ABSTRACT FROM AUTHOR]

This paper investigates the contribution of the B Corp movement in Europe to achieving Sustainable Development Goals (SDGs) compared with non‐B Corps. B Corp certification aims to identify organisations that utilise business as 'a force for good', but there is limited empirical evidence on how effectively certified organisations translate their prosocial claims into measurable SDG contributions. We address this through cluster analysis, comparing 313 B Corp‐certified and 1506 non‐certified European companies by way of the SDG Action Manager tool. Our findings reveal three clusters of B Corp‐certified organisations with varying contribution levels across all SDGs, indicating that the certification does not consistently identify organisations with significant SDG impacts. Non‐certified companies, meanwhile, do not exhibit distinct patterns. These results raise doubts about whether the B Corp category identifies a distinctive prototype member that can define clear prosocial norms and practices. The paper contributes to understanding B Corp organisations' narratives by providing insights into their empirical performance compared with non‐certified organisations. It also offers potential explanations for these discrepancies and proposes strategies to strengthen B Corp certification bodies in alignment with the SDGs, while acknowledging the presence of intra‐category variations. [ABSTRACT FROM AUTHOR]

The sustainable development goals are aimed at making humankind aware of the importance of establishing common guidelines relating to three fundamental purposes: eradicating poverty, protecting the planet, and improving people's lives. Sustainable development is linked to financial and business risk, financing is necessary to achieve the SDGs. In this context, the aim of this paper is to analyze the homogeneity in SDG achievement, based on People, Planet, Prosperity, Peace, and Partnership for year 2021. To that end, a cluster analysis is applied, distinguishing between two samples of countries defined by their level of wealth. Furthermore, a synthetic indicator is used to produce a ranking of countries according to their achievement of the SDGs; contingency tables are then created and the χ2 test is used to identify which country risk factors are associated with SDG achievement. The results indicate that the major powers perform similarly in successfully achieving the 5Ps. Also, the χ2 test confirms that a good economic and financial position which allows access to international financial markets at a reasonable cost is linked to optimal SDG achievement in high‐income areas. However, Economic assessment, Access to international capital markets and Debt indicators are not associated with better SDG achievement in the poorest countries. [ABSTRACT FROM AUTHOR]

Rationale: Patient‐reported outcomes (PROs) are increasingly used in the context of clinical care, but evaluation of patients' perspectives of PRO‐based applications in routine care remains limited. Aims and Objectives: This paper investigates patients' acceptability of a personalized web‐based decision report for total knee or hip replacement and identifies opportunities to refine the report. Method: This qualitative evaluation was embedded in a pragmatic cluster randomized trial of the report. We interviewed 25 patients with knee and hip osteoarthritis about their experiences using the personalized decision report in the context of a surgical consultation. The web‐based report contained current descriptive PRO scores of pain, function and general physical health; tailored predicted postoperative PRO scores (i.e., personalized likely outcomes based on actual knee or hip replacement outcomes of similar patients in a national registry); and information about alternative nonoperative treatments. Two trained researchers analysed the interview data qualitatively using a combination of inductive and deductive coding. Results: We identified three major categories for evaluation: content of report, presentation of data in report and engagement with report. Patients generally liked the report overall but specifically valued different pages of the report based on where they were in the surgical decision‐making process. Patients identified areas of confusion in data presentation related to graph orientation, terminology and interpretation of T‐scores. Patients also highlighted support needs to meaningfully engage with the information in the report. Conclusion: Our findings highlight areas of opportunity to further refine this personalized web‐based decision report and similar patient‐facing PRO applications for routine clinical care. Specific examples include additional tailoring of reports via filterable web‐based dashboards and scalable educational supports to facilitate more independent patient understanding and use. [ABSTRACT FROM AUTHOR]

Psychology & Marketing (P&M), an internationally reputed journal, publishes original, peer‐reviewed, empirical research on the application of psychological theories and techniques to marketing. The aim of this essay is to provide a bibliometric overview of the leading trends in the special issues of P&M over its history (1984–2020). Using bibliometric techniques, we analyze the impact of the special issues via their most cited papers, most productive authors, affiliated institutions and countries, as well as the best guest editors who contributed to the selection of the most cited special issue articles. Using network analysis VOSviewer software, we also group the special issues into four clusters to identify common themes. Further, we develop graphical visualization of coauthorships, bibliographic coupling, and cocitations. Results show that the most productive contributors are from American institutions and that P&M remains well connected to other leading journals in the marketing and psychology discipline, such as the Journal of Marketing Research, the Journal of Personality and Social Psychology, the Journal of Consumer Research, the Journal of Marketing, and the Journal of Business Research. [ABSTRACT FROM AUTHOR]

A large amount of data generated in industrial processes exhibit multi‐modal, nonlinear, time‐domain correlation, and other characteristics. This poses great difficulty for the traditional principal component analysis (PCA) method since it requires that the input data need to conform to the Gaussian distribution. However, the data may have autocorrelation, that is, the data at the current moment will be affected by the past data. To this end, this paper proposes an enhanced dynamic principal component analysis (DPCA) method based on hierarchical clustering analysis. On the basis of the DPCA algorithm, the idea of data classification and enhanced training is used to strengthen the training of the dimensionality reduction matrix. Then, calibration, on‐line monitoring, and fault diagnosis of process data can be conducted. Finally, this paper demonstrates that the performance of the proposed method is greatly improved compared with PCA and DPCA through the Tennessee Eastman process system. [ABSTRACT FROM AUTHOR]

Genetic research's growing importance in understanding pulmonary arterial hypertension (PAH) and developing effective treatments prompted the RAPID‐PAH study. This study sought feedback from stakeholders who participated in two genomic studies to enhance genetic study delivery and clinical integration. Stakeholders from nine UK PH centres, representing various roles, ages, genders, and mutation statuses, took part in 53 semi‐structured interviews and focus groups. Transcripts were thematically coded using inductive analysis. Clustering analysis was conducted to identify patient groups that shared attitudes. In this paper, we focus on patients', carers', and relatives' perspectives. The key interview themes revealed insights into participants' attitudes toward genetic research and testing more generally, expertise and knowledge of the disease itself, motivations and barriers to participating in genetic research, awareness of and interest in consent procedures and the use of personal and genetic data, as well as the process of communicating individual genetic results. Factors influencing genetic research participation included altruistic motives, personal diagnostic experiences, and family‐related hopes. Clustering analysis produced distinct clusters based on the presence of barriers and motivators for research participation; however, hardly any patients shared identical sets of attitudes, emphasising the need for personalised approaches to recruitment. Most patients reported poor engagement with study‐related materials. Patients who received individual genetic results expressed satisfaction with the process, whereas those who did not were disappointed with the lack of feedback. Reflecting on patient perspectives, we offer recommendations to improve the genetic study delivery process. Enhancing genetic research integration into clinical practice requires tailored engagement, clear communication, and support from healthcare stakeholders. [ABSTRACT FROM AUTHOR]

The paradoxical findings in consumer choice research have captivated the interest of many scholars from diverse fields and transformed the understanding of consumer decision‐making. This paper is the first attempt to systematically review, synthesize, and integrate the paradoxical findings in consumer choice research. One of the first paradoxical findings was the violations of value maximation models of rational choice. The second important paradox in consumer choice research is the puzzling role of variety in consumer choices. We focus on these two critical paradoxes discovered in consumer choice literature over the last five decades and refer to them as the "paradox of rational choice" and the "paradox of variety." We conducted a co‐citation cluster analysis to help identify the most significant studies in the domain. The cluster analysis helped eliminate the subjective bias in article selection. We also used inferential analysis to widen the scope of clusters to other relevant and especially recent studies. A review of 233 papers published between 1972 and 2023 reveals the four themes covering the two paradoxes of consumer choice. The first theme covers the first paradox: the paradox of rational choice. It presents research on preference reversals and other violations of value maximization rational choice models due to contextual factors and choice environment. The following three themes cover the second paradox of consumer choice: the paradox of variety. The second theme focuses on the benefits of variety and the natural desire of consumers to seek more variety. Paradoxically, the third theme highlights when and why variety will likely cause harmful psychological consequences for consumers. Finally, the last theme reconciles the opposing ideas of themes two and three by identifying the boundary conditions and suggesting ways to resolve this paradox. We also provide research directions that scholars can consider for future research related to each of these themes. [ABSTRACT FROM AUTHOR]

The American eel (Anguilla rostrata) has long been regarded as a panmictic fish and has been confirmed as such in the northern part of its range. In this paper, we tested for the first time whether panmixia extends to the tropical range of the species. To do so, we first assembled a reference genome (975 Mbp, 19 chromosomes) combining long (PacBio and Nanopore and short (Illumina paired‐end) reads technologies to support both this study and future research. To test for population structure, we estimated genotype likelihoods from low‐coverage whole‐genome sequencing of 460 American eels, collected at 21 sampling sites (in seven geographic regions) ranging from Canada to Trinidad and Tobago. We estimated genetic distance between regions, performed ADMIXTURE‐like clustering analysis and multivariate analysis, and found no evidence of population structure, thus confirming that panmixia extends to the tropical range of the species. In addition, two genomic regions with putative inversions were observed, both geographically widespread and present at similar frequencies in all regions. We discuss the implications of lack of genetic population structure for the species. Our results are key for the future genomic research in the American eel and the implementation of conservation measures throughout its geographic range. Additionally, our results can be applied to fisheries management and aquaculture of the species. [ABSTRACT FROM AUTHOR]

The paper deals with the exterior design of townhouses in an urban block of the eastern part of Bhaktapur in Kathmandu Valley. The primary purpose is to provide an analysis method to determine the typical combination among exterior design components of Nepalese townhouses. By using multiple correspondence analysis and cluster analysis, 118 samples are grouped into 13 clusters. Secondly, all the clustered samples were put onto the original plots on the map to see the characteristics of their locations. Such a process would help a designer/planner reflect on how they should compose a proper facade in reconstruction works of townhouses after the earthquake in 2015. [ABSTRACT FROM AUTHOR]

Supply chain clusters achieve viability by exploiting geographical concentration to gain efficiencies in matching supply and demand. Although the benefits of operating in these environments are understood, little is known about how supply chain clusters form and adapt. From a systems theory perspective, causal events lead to this formation and adaptation and facilitate sustained operation in modern business settings. This paper extends this understanding by theorizing key antecedents responsible for supply chain cluster formation and adaptation, conceptualizing individual firms as agents. Through a characterization of agents as either passive or active, we construct propositions and a conceptual model. The modelling, supported by causal loop diagrams, details the interactions between agents that lead to cluster formation and adaptations to contextual changes in supply chain clusters. These findings can guide policy design that is more effective in enabling the emergence and sustained existence of supply chain clusters through facilitated causal interactions. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease. [ABSTRACT FROM AUTHOR]

Social sciences are increasingly recognized as significant for building a sustainable world since the social perspective can assist researchers in other fields in navigating public controversy and designing more responsible interaction mechanisms between the natural and social systems. However, the question arises: to what extent do natural sciences rely on social science research in their studies? Examining life science publications from seven PLoS journals, this paper attempts to characterize the volume and trajectory of citations from life sciences to social sciences. We explore three core questions: To what extent do life sciences cite social sciences? What actors in the life sciences are citing social sciences? Which actors in the social sciences are being cited? Our analysis estimates social sciences influence 15%–19% of life science publications, contributing to 1.1%–1.5% of references in 2018. Social science citers are found across peripheral and central topics of life science disciplines. Cited social science publications exhibit various levels of interdisciplinarity and achieve the greatest citation impact among peers. Citations to social sciences are prevalent in both theoretically and methodologically oriented sections. We show empirically the increasing impact of social sciences on the development of the life sciences. [ABSTRACT FROM AUTHOR]

Religious identities play an important role in shaping migrants' experiences. Understanding the spatiotemporal distribution of religions enhances our knowledge of both religious expansion and religious cultures. This paper aims to leverage spatiotemporal analysis to characterise the topology of the spread of multiple religions in the Taipei metropolitan area from 1660 to 2020, including Folk Taoism (853 temples/altars, 55.39%), Christianity (306 churches, 19.87%) and Buddhism (332 temples, 21.56%). Three major factors affecting religious distribution are considered: the year of establishment of religious units (churches, temples or altars), the reclamation year of religious locations, and the economic index of religious sites. The results show that Folk Taoism and Buddhism have a long history of development in early reclamation regions with a hopping expansion pattern. However, Christianity expanded later in the later reclaimed and early reclaimed areas. The Christian religious units have highly concentrated spatial patterns and are located in the higher‐income districts, centralised in the core regions of the Taipei metropolis. Modern forms of highly institutionalised religion, such as Buddhism and Christianity, are more prevalent in the relatively higher‐income, later‐cultivated areas, which provide richer social resources for development. Folk Taoism, as a traditional religion, is maintained and developed by local communities. This macroscopic analysis helps to (1) identify the exact chronology of different growth waves and explain them in a historical context, and (2) elucidate the spatial distribution differences between institutionalised religions and folk religion. Our research shows the evolution of religious landscapes from an undeveloped city to a developed city, offering valuable insights for geographical and religious studies, and enhancing understanding of diffused and institutionalised religious expansion. [ABSTRACT FROM AUTHOR]

GLARE laminate experiences multiple damage modes during bending failure, including fiber fracture, matrix cracking, interlayer delamination, and fracture of aluminum alloy. In most cases, two or more failure modes occur together, which makes the damage process complex and unpredictable. This paper proposes a combination of experimental testing and numerical simulation to investigate the influence of fiber lay‐up direction and center open‐hole on the bending failure behavior of GLARE laminate. Simultaneously, Acoustic Emission (AE) technology was used to monitor the bending process of GLARE laminates in real‐time, and a numerical simulation model for the three‐point bending of GLARE laminates was established. The experimental results showed that the bending strength of intact specimens decreased significantly with the increase of the fiber off‐axis angle, whereas the strength retention of open‐hole specimens increased significantly with the increase of the fiber off‐axis angle. Based on the K‐means++ clustering algorithm, the peak frequencies corresponding to different damage modes of GLARE laminates were identified as follows: aluminum alloy damage [1–70 kHz], matrix cracking [93–210 kHz], interlaminar delamination [211–304 kHz], and fiber breakage [304–516 kHz]. The simulation results agreed well with the experimental results, showing that the damage in intact specimens initiated at the laminate edges and propagated sequentially along the thickness direction, while the damage in open‐hole specimens initiated at the hole edges and then propagated towards the laminate edges. Highlights: The present study investigated the effects of fiber lay‐up direction and central open‐hole on the bending performance and failure behavior of GLARE laminate.In‐situ monitoring of the GLARE laminate during three‐point bending was conducted using AE technology.A model for simulating the progressive damage of GLARE laminate was established and its accuracy was verified. [ABSTRACT FROM AUTHOR]

Highlights The objective of this paper is to analyze the mechanical properties and damage mechanisms of carbon fiber‐reinforced polyamide thermoplastic composite laminates. Four specimens with different ply orientations were designed for open‐hole tensile experiments, and interlaminar toughness experiments including double cantilever beam and end‐notched flexural were carried out. The experimental process was monitored synchronously using acoustic emission, and the strain field changes of the tensile specimens were captured using digital image correlation technology. The unsupervised clustering of the peak frequencies of the acoustic emission signals based on the K‐means++ algorithm was employed to ascertain the peak frequency ranges corresponding to the various damage modes. Typical signals from different specimens were selected, and the gray wolf algorithm was used to optimize the variational modal parameters to decompose the signals. The waveform characteristics, frequency components, and Hilbert spectra of each damage mode were given. The correlation analysis of the intrinsic mode function (IMF) components of the same damage in different specimens demonstrated that the IMF components exhibited high similarity. By analyzing the time series changes in the energy of each damage mode in different specimens, the contribution of different damage modes to the evolution of laminated plate damage was evaluated. The mechanical properties of CF/PA6 laminates were investigated based on open‐hole tensile specimens and pre‐cracked delamination specimens. Unsupervised clustering of AE peak frequencies using K‐means++ to establish the relationship between peak frequencies and damage patterns. AE counts and cumulative energy were used to assess damage evolution. By identifying a single damage signal and providing a more intuitive treatment of the damage energy evolution. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge posed by the size of certain objects that do not conform to the constraints of microscope‐coupled Raman spectrometers, making sample collection impossible due to their inherent value or nature. Specifically, materials like hydroxyapatite‐based substances used in artistic and ornamental carvings, such as bone or ivory, fall within this problematic category. The focus of this study is the enhancement of analytical capabilities in the context of large objects using a Raman microscope system. The study details the innovation involving a remote probe integrated with fiber optics, elaborating on the design and performance aspects, and emphasizing the appropriateness of chosen components in the analysis of ivory artifacts belonging to private collectors. In order to assess the robustness of our discriminative approaches, an archaeological bone and the exposed dentine in a human tooth were also evaluated and compared. Results showed that using an 805‐nm longpass dichroic mirror successfully directed the near‐infrared laser onto the samples and significantly suppressed the Rayleigh scattering contribution to the spectrum. Regarding the preprocessing methods to spectra evaluation essayed, the most promising approach was the use of principal component analysis for dimension reduction followed by k‐means cluster analysis. By leveraging the complementary strengths of PCA and k‐means clustering, the robustness and interpretability of clustering analyses are enhanced. [ABSTRACT FROM AUTHOR]

Over the last decade, supply chain finance (SCF) has gained popularity and increasing attention among academicians and stakeholders in the context of financial flows in the supply chain. However, some research gaps still exist that need to be explored to improve the sustainability of supply chains. Specifically, there is a critical research need to look at the conceptual background of SCF and its potential applicability in various phases of supply chains. Therefore, this article aims to bridge this gap by conducting a comprehensive State-of-the-Art literature review based on 367 papers published from 2006 to 2020. Furthermore, this article is one of the first attempts to present current and past studies in the domain of SCF in a holistic manner. The analysis highlights the most influential authors, keywords, organisations, leading publications and clusters in existing research areas. This article also sets out a proposed research framework based on the triangulation approach perspective, that is, financial perspective, buyer perspective and supply chain-oriented perspective. The most important and unique contribution of the article is the identification of new and emerging research areas where the application of SCF is still in the nascent stage. These findings can guide stakeholders at every stage of the value chain to appropriately use techniques that model policies to better inform investment and operational decisions in line with Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

In recent decades, there has been a significant increase in the number of newly registered motorcycles worldwide. However, there is not only an increase in the number of motorcycles in traffic but also an increase in the number of conflicts between motorcyclists and the surrounding environment. A relatively significant research gap can be identified in the relationship between spatial factors and motorcycle accident rates. This paper analyses the spatiotemporal patterns of motorcycle accidents and studies their underlying factors. The KDE+ method (an extension of the kernel density estimation method) is used to identify concentrations of motorcycle accident key hotspots. To study the underlying traffic accident determinants, a two‐step cluster analysis is used. The analysis is based on the database of motorcycle accidents in the Czech Republic from 1 January 2016 to 31 December 2020. The paper achieves a few main findings. By applying the KDE+ method, the most dangerous sections of the road network in the Czech Republic were identified, where a significant accumulation of motorcycle accidents occur. Motorcycle accidents are highly seasonal. Motorcycle accidents tend to accumulate in the afternoon, especially during the summer months. Concerning the frequency of accidents and the collective risk index, urban traffic, that is the traffic density, is an important cause of motorcycle accidents, along with the winter period with rather unfavourable weather conditions, and especially the directional conditions—curves and intersections—are among the hazardous sections. [ABSTRACT FROM AUTHOR]

This paper combines reference point techniques and econometric analyses to provide the profile of non‐life insurers that simultaneously optimize the strategic growth, profitability, and risk goals. The econometric analyses provide the relevant relations among the variables. Non‐life insurers from 33 Organisation for Economic Co‐operation and Development countries over a six‐year period are analyzed. A cluster analysis allows forming groups of countries according to the non‐life insurance penetration ratio. Several scenarios, which are characterized by the maturity of the market and the crisis/non‐crisis situation, are studied. The results indicate that the highest level of profitability (growth) is linked to scenarios with a medium (low) level of maturity and booming times. They also show that the lowest level of risk that is representative of good performance is associated with scenarios where markets have a high level of maturity and crisis times. We find that a higher recommendable size is associated with more mature markets. The results also indicate that reinsurance utilization is linked to a crisis time. We additionally find that the recommendable level of capitalization differs significantly among scenarios. [ABSTRACT FROM AUTHOR]

The aim of this paper is to clarify the difference of spatial closeness between natural enemies and Frankliniella intonsa, and to provide a scientific basis for rational protection and utilization of natural enemies. This paper applied the block‐sample square difference analysis method, Gray correlation degree method, the aggregation intensity index method and ρ index method to study the difference in the closeness of the spatial relationship between F. intonsa and natural enemies when the number of F. intonsa and their natural enemies was the highest and the minimum area occupied by F. intonsa individuals and colonies in five tea plantations in Hefei, Anhui province in 2021 and four tea plantations in 2020. The results were as follows: 1) Tetragnatha squamataKarsch, Xysticus ephippiafus, Erigonidium graminicolum and Theridion Octomaculatum were the top four natural enemies closely related to F. intonsa in 2021. The top three natural enemies closely related to F. intonsa in 2020 were Tetragnatha squamata Karsch, Xysticus ephippiafus and Theridion Octomaculatum. Two years ago, Tetragnatha squamata Karsch and Xysticus ephippiafus were the same. 2) Cluster size did not change the distribution patterns of natural enemies and F. intonsa. 3) The aggregation of F. intonsa was caused by their own causes or some environmental factors, and the aggregation of natural enemies was caused by environmental factors. 4) In 2021, the minimum area occupied by individuals and groups of F. intonsa in tea gardens was 2m2, and in 2020, it was 8 m2. [ABSTRACT FROM AUTHOR]

This paper analyses the cross‐country impacts of the 2008 global financial crisis and the subsequent recovery process, with a specific focus on small economies. Key growth volatility variables highlight the critical exposure of small economies to the transmission of exogenous shocks owing to their high degrees of trade openness and inherent output and export specialisation, notably in financial services and tourism. These factors also constrain the mitigation of exogenous shocks giving rise to greater growth volatility. The paper demonstrates systematic asymmetries between countries with respect to the impact of the crisis and its persistence according to their size and patterns of sectoral specialisation. Small tourism‐dependent economies and nonsovereign entities were particularly adversely affected although an offshore financial sector partly mitigated the impacts. The robustness of the findings is examined further in the appendix with regard to truncation problems arising from the use of international datasets. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This work proposes an adaptation of the method developed in previous papers to determine the onset and demise of the rainy season (ONR and DER) dates in the areas of the South American monsoon system (SAMS) based on the pentad antisymmetric outgoing long‐wave radiation (AOLR). In those papers, the sign change of the mean AOLR in the central Amazon Basin (CAM) and western central Brazil (WCB) from positive to negative defined the ONR, from negative to positive, the DER dates. Since the monsoon convection presents a northwest–southeast oriented progression, the antisymmetric area to the WCB was selected subjectively. Thus, here we propose to use the Ward hierarchical clustering method to select areas in the SAMS and in the northern tropical America (NTA) for the regionalized AOLR calculation. The significant (at the 95% confidence level) negative correlations with the largest magnitude among the clusters in the SAMS and NTA and the outgoing long‐wave radiation (OLR) and precipitation annual cycles in each group define the pairs to calculate the AOLR. Then, the AOLR time series is calculated and subjected to a 5‐pentad running mean filter. This method keeps the climatological features of the convection annual cycle such that the closer (farther) the pair is to the equator the longer (shorter) the rainy season. The ONR and DER dates found with this new method are remarkably close to those found previously. Therefore, the new method proposed here highlights regional aspects of rainy season and can easily be automatized for its routine application at the operational climate monitoring centres, for instance at INPE. This is the most important advantage of the method and might be relevant to the SAMS rainy season monitoring. [ABSTRACT FROM AUTHOR]

Since the eruption of the Covid‐19 pandemic, in response to the global health emergency, governments have focused on designing policies aimed at the development of more innovative products and services. Effective collaboration, communication, and Open Innovation (OI) between government organizations, education and research institutions, and the marketplace have been fundamental to the success of each country's response during the crisis period. Using a comprehensive data set from OECD on innovation policies implemented by governments before and during the Covid‐19 crisis, this paper analyses the extent to which these innovation policies promote OI and how these policy decisions evolve to support an effective response to the pandemic. Through a cluster analysis, we identify four possible government innovation policy strategies (centralizers; conservative OI promoters; collaborative supporters; open collaborators) and analyze how these strategies evolve before and during Covid‐19. Our findings confirm that even though there is an increased use of innovation policies promoting OI during the crisis, there is little evidence of consistency between the policy strategy used pre‐Covid and during the crisis for each country. However, there is an increased use of four types of innovation policy instruments, i.e., those entailing formal consultation with stakeholders and experts; fellowships and postgraduate loans and scholarships; networking and collaborative platforms; and dedicated support to research infrastructures. Although the paper limits the scope of the analysis to the early government reactions in selected OECD countries, it captures an important moment in time (i.e., reaction to a severe shock), which opens avenues for future studies. [ABSTRACT FROM AUTHOR]

Summary Clustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non‐linearity, making it difficult to identify meaningful patterns and relationships among the data. Despite the increasingly common application of cluster analysis for longitudinal data, many existing methods are still less known to researchers, and limited guidance is provided in choosing between methods and software packages. In this paper, we review several commonly used methods for clustering longitudinal data. These methods are broadly classified into three categories, namely, model‐based approaches, algorithm‐based approaches and functional clustering approaches. We perform a comparison among these methods and their corresponding R software packages using real‐life datasets and simulated datasets under various conditions. Findings from the analyses and recommendations for using these approaches in practice are discussed. [ABSTRACT FROM AUTHOR]

Summary Peter J. Rousseeuw is a statistician known mainly for his work on robust statistics and cluster analysis. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the partitioning around medoids clustering method and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a full professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association. In the course of his career, Peter published three books and over 200 papers, together receiving over 100 000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. He is the creator and sole sponsor of the Rousseeuw Prize for Statistics, which was first handed out by the King of Belgium in 2022. [ABSTRACT FROM AUTHOR]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

Background and Rationale Challenges and Ways Forward Dental care systems have the potential to influence population oral health and patterns of socioeconomic inequalities. Therefore, understanding the impact of the ways in which countries fund, provide, and organize their dental care services is key in the analysis of determinants of oral health. In this commentary we offer a synopsis of recent typologies of healthcare systems, based on a rapid review, and highlight that none of them fit dental care services given the separation of dental care from general healthcare provision in many countries. The paper also summarizes evidence on dental care systems as determinants of population oral health and argues why a new typology of dental care systems is needed.We argue that a typology must consider institutional arrangements, structures, and processes behind the provision of dental care, and that specific dimensions/variables that inform the typology should result from a process of discussion and consensus. Some methodological considerations for developing typologies are also discussed, including the challenges in the collection and analysis of data followed by an advanced cluster analysis. Despite their limitations, typologies have evolved into an essential tool for comparing the similarities and differences of healthcare systems across countries. Therefore, a dental specific typology for health systems will be useful for researchers, policymakers, and dental professionals to characterize the provision of dentalcare services in different countries. This will also enable examining their potential role as determinants of population oral health and inequalities. [ABSTRACT FROM AUTHOR]

In this paper, a simple and efficient high‐performance liquid chromatography method was established to analyze the chemical composition fingerprint of Centellae Herba (CHA) and determine five triterpenoid active components in CHA, namely, asiaticoside B (AB), madecassoside, asiaticoside, madecasic acid, and asiaticoic acid. The separation of the compounds was carried out on an XD‐C18 column (250 mm × 4.6 mm, 5 µm), the wavelength of the ultraviolet detector was set to 205 nm, and the mobile phase was composed of acetonitrile −0.05% phosphoric acid, 2 mmol/L β‐cyclodextrin aqueous solution, and gradient elution at the flow rate of 1.0 mL/min. A general chromatographic fingerprint consisting of 20 characteristic peaks of 18 batches of CHA samples was established, and the samples were classified and evaluated by similarity analysis, hierarchical cluster analysis, and principal component analysis combined with multivariate econometric analysis. In quantitative analysis, all calibration curves showed good linear regression in the test range (r > 0.999), the average recovery was 96.12%–104.63%, and the relative standard deviations of repeatability and stability were less than 2.00%. The results show that the method is accurate and effective and can be used for the comprehensive quality control of CHA. [ABSTRACT FROM AUTHOR]

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human‐supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process. [ABSTRACT FROM AUTHOR]

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production. [ABSTRACT FROM AUTHOR]

The main goal of the paper is to provide a statistical categorization of small and micro knowledge‐intensive business service (KIBS) companies, based on their knowledge management (KM) attitude. Since knowledge is the main production factor and output of these companies, it is essential to achieve a better understanding of how they manage this resource. A questionnaire‐based survey was conducted on a sample of Polish small and micro KIBS operating in various service sectors. A cluster analysis of the data was performed, to categorize the sample according to the KM attitude of the companies. Three main groups of companies were identified, varying in terms of their levels of "knowledge needs", "intensity of use" of KM practices and "perceived barriers to KM implementation". This classification is shown to characterize attitudes towards KM to a higher level of statistical significance than do structural characteristics. The survey was based on a single country sample. On the one hand, this provides consistency to the analysis. On the other hand, further insights can be obtained by a multi‐national study. In addition, cluster analysis is exploratory in nature. The results provide useful insights for policy makers (to formulate policies for facilitating KM implementation in small KIBS) and managers (to reflect on the KM attitudes of their company). The statistical categorization of small and micro KIBS in terms of their KM attitude has been very rarely undertaken. Even the most recent investigations of KM issues used samples from large companies. [ABSTRACT FROM AUTHOR]

Overconsumption poses severe ethical problems and causes harm to the environment. Today many individuals change their consumption practices and actively limit their overall consumption due to ethical and environmental concerns. This article focuses on voluntary simplicity, a sustainable lifestyle phenomenon that comprises environment‐friendly consumption practices as an innate characteristic. This paper offers a fresh and nuanced understanding of voluntary simplicity through the lens of social cognitive theory (SCT) and by examining textual content generated by voluntary simplifiers on Facebook. Five thousand three hundred thirty‐six Facebook posts and comments were scrapped and examined using sentiment analysis, associative networks and cluster analysis. Results further our understanding of this lifestyle in the three interconnected domains of SCT (i.e., personal, behavioural and environmental). Insights obtained from this study can help develop a more complete conceptualization of voluntary simplicity and consumers who consider adopting this lifestyle choice. [ABSTRACT FROM AUTHOR]

The current educational disruption caused by the COVID‐19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID‐19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID‐19, Corona, e‐learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID‐19, online learning, schools, distance learning, e‐learning, students, and education were clustered among each other. The number of daily COVID‐19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools. Lay Description: What is already known about this topic: The current educational disruption caused by COVID‐19 pandemic has led to a plethora investment and the use of educational technologies for Emergency Remote Learning.There are wide mixed perceptions about online learning for Emergency Remote Learning during the current pandemic.There is little to no research conducted to examine public perception about online learning for Emergency Remote Learning during COVID‐19.The research paper's significant contribution is to investigate the opinions and perceptions of people around the world on COVID‐19 and Online Learning related issues. What this paper adds: Twitter was selected for this study due to its wide application and the ease of collecting tweets.Machine learning techniques, including world clouds, sentiment analysis, cluster analysis, correlations analysis, and trends analysis, were applied to identify useful insights..The study revealed topics like 'covid', 'learning', 'online', 'distance', 'students', 'schools' and 'pandemic' as the most mentioned words worldwide with nearly half of the public sentiments being positive (48.9%) and neutral (36%).Reopening of schools has received lots of mixed reactions and online learning appears to be a sustainable option during these unprecedented times. Implications for practice and/or policy: Theoretically, this study corresponds to literature relating to COVID‐19, online learning, and Emergency Remote Learning by exploring word frequency, discussed topic, public sentiments, relationships between themes, correlation, and trend analysis.This study recommends that educational institutions, government agencies and other stakeholders should work together to ensure an efficient and effective online learning policies, implementation, and its utilization for Emergency Remote Learning (ERL) as we fight the pandemic.Also, schools need to consider providing further support for educators and students by delivering workshop trainings on the importance and effective use of the online learning system for Emergency Remote Learning (ERL).Further, counselling services are strongly recommended to be made available online for educators and students to increase their teaching and learning morale. [ABSTRACT FROM AUTHOR]

In 1965, Price foresaw the day when a citation-based taxonomy of science and technology would be delineated and correspondingly used for science policy. A taxonomy needs to be comprehensive and accurate if it is to be useful for policy making, especially now that policy makers are utilizing citation-based indicators to evaluate people, institutions and laboratories. Determining the accuracy of a taxonomy, however, remains a challenge. Previous work on the accuracy of partition solutions is sparse, and the results of those studies, although useful, have not been definitive. In this study we compare the accuracies of topic-level taxonomies based on the clustering of documents using direct citation, bibliographic coupling, and co-citation. Using a set of new gold standards-articles with at least 100 references-we find that direct citation is better at concentrating references than either bibliographic coupling or co-citation. Using the assumption that higher concentrations of references denote more accurate clusters, direct citation thus provides a more accurate representation of the taxonomy of scientific and technical knowledge than either bibliographic coupling or co-citation. We also find that discipline-level taxonomies based on journal schema are highly inaccurate compared to topic-level taxonomies, and recommend against their use. [ABSTRACT FROM AUTHOR]

Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering, mixture models, and then several related topics of which any cluster analysis practitioner should be aware. Even then, this review cannot do justice to the chosen topics. There is a lot of literature, and often it is somewhat ad hoc. That is generally the nature of cluster analysis—each application requires a bespoke analysis. Nonetheless, clustering has proven itself to be incredibly useful as an exploratory data analysis tool in biology, advertising, recommender systems, and genomics. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification [ABSTRACT FROM AUTHOR]

In this paper, we develop a quantitative review of the evolution of the concept of environmental strategy. We show how it has formed the backbone of the development of firm strategy and the natural environment as a research tradition and how consensus regarding it has evolved in the academic community during the stages of its historical evolution. We us co‐word analyses to address changes in the structure of its definitional landscape and how it has evolved through the analysis of centrality of its core and periphery keywords. Furthermore, we develop, by the first time, a cluster analysis to identify the main definitional factors behind definitions along the two periods analyzed. Finally, four propositions for future development and a consensual definition of environmental strategy are proposed. [ABSTRACT FROM AUTHOR]

Background: Valuable safeguards against fast‐spreading conjectures about learning in times of fear and uncertainty are evidence‐based approaches to the assessment of the impact of sudden and unforeseen disruptions on learning practices. The present research focused on physics learning in such times because conceptual and computational literacy in physics is critical to the development of a scientifically and technologically literate society. Objectives: The present research aimed (a) to offer an objective assessment of whether performance differences in a physics course of the general education curriculum existed between the face‐to‐face medium (familiar mode of instruction) and the online medium (unfamiliar mode of instruction) for both male and female students, and then (b) to develop a response to the evidence collected to ensure a quality education for all parties involved. The research intended to fill two critical gaps in the extant literature: mixed findings concerning students' performance in the face‐to‐face and online mediums as well as scarce coverage of specific domains of knowledge that are critical to STEM learners. Methods: Students' performance was examined as a function of the type of assessment (formative and summative), instructional mode (online and face‐to‐face), and gender. An understudied student population of STEM students of Middle Eastern descent without prior formal exposure to online instruction was targeted. Results and Conclusions: In both formative and summative assessments, male students performed better online than face‐to‐face, whereas the performance of female students was either higher online or equivalent between instructional mediums. The evidence collected suggested that consideration be given to remedies that foster academic success in the face‐to‐face instructional medium, particularly for male students. Takeaways: An evidence‐based approach to learning dismantled emotion‐driven expectations regarding the impact of the online medium on physics learning, and encouraged new perspectives about instruction. Lay Description: What is already known?: Ongoing debates on the impact of the pandemic on students' learning shape decisions regarding future reliance on the online medium for instruction. What this paper adds: The extant literature is mixed concerning performance in the face‐to‐face and online mediums, including particular domains of knowledge critical to STEM learning.This study focused on physics learning in an understudied population of STEM students who were unaccustomed to online instruction.Performance was higher online than face‐to‐face, but gender differences emerged. Implications for practice: Lessons learned focused on the flexibility of online learning and on the need to develop materials to enhance learning in males enrolled in face‐to‐face or hybrid/blended classes. [ABSTRACT FROM AUTHOR]

Parental substance misuse is a significant child welfare issue and associated with increased risk of child maltreatment. The aim of the present study was to understand what social care outcomes children who live with parental substance misuse have, and to assess factors associated with those social care outcomes over a two‐year period. The paper reports on a retrospective longitudinal study of 299 children all living with parental substance misuse and referred to one local authority in England. Data were collected from children's social work case files about procedural social care outcomes and factors which may be associated with those outcomes. Using cluster analysis, a new typology of children's longitudinal trajectories through the children's social care system was developed, consisting of five distinct types. Analysis indicated that some children received too little intervention from children's social care despite ongoing concerns, while other children were potentially unnecessarily caught up in the social care system. Factors associated with children having the poorest outcomes were: caregiver instability resulting from substance misuse, parenting capacity and household instability. The study's findings indicate that some children who live with parental substance misuse are at significant risk of harm, but others are not and may be better supported through non‐statutory services such as early help. [ABSTRACT FROM AUTHOR]

With global estimates showing an increasing trend in flooding and its adverse effects on communities and population health, resilience is presented as a concept with potential to help integrate disaster risk management, sustainable development, and climate change adaptation concerns. Resilience research and practice have conceptual and empirical challenges of how to understand, characterize and measure resilience, particularly at community level. Using a multidimensional framework, this paper takes a systems approach to understanding, characterizing, and measuring community flood resilience. Through cluster analysis, bivariate methods and multivariable‐adjusted binary logistic regression modeling, we developed a context and hazard specific construct of community flood resilience and investigated its predictor variables. The factors defining the community flood resilience construct captured the community needs to withstand disasters through purpose‐built infrastructure, early warning systems for preparedness and utilization of local human capacity for adaptation. These results strengthen the previous arguments for utilizing a comprehensive multidimensional framework for resilience analysis. Access to services for improved health and psychosocial well‐being was significantly associated with the three‐item measure of being more flood resilient. Additionally, a strong sense of place and resistance to relocation were presented as key elements of resilience, maintaining community system function, and preserving livelihoods. The study further found that these key factors would not be adequate to guarantee community flood resilience outside the transformative capacity of a well‐resourced village civil protection committee that can prepare and mobilize stakeholders in response to flood emergencies. Our results suggest that, in the context where policymakers seek to strengthen resilience of communities without relocating them, a focus on public health and on strengthening and utilizing local capacities as adaptation, are key in disaster risk management policymaking and implementation. For the international research community, this study demonstrated the importance of utilizing context and hazard specific measures for defining, characterizing, and measuring resilience to inform policy. [ABSTRACT FROM AUTHOR]

Essential oils (EOs) are commercially important products, sources of compounds with antioxidant and antimicrobial activities considered indispensable for several fields, such as the food industry, cosmetics, perfumes, pharmaceuticals, sanitary and agricultural industries. In this context, this systematic review and meta‐analysis, a novel approach will be presented using chemometric tools to verify and recognize patterns of antioxidant, antibacterial, and antifungal activities of EOs according to their geographic, botanical, chemical, and microbiological distribution. Scientific papers were selected following the Preferred Reporting Items for Systematic Review and Meta‐Analyses statement flow diagram, and the data were evaluated by the self‐organizing map and hierarchical cluster analysis. Overall, this novel approach allowed us to draw an overview of antioxidants and antimicrobials activities of EOs reported in 2019, through 585 articles evaluated, obtaining a dataset with more than 10,000 data, distributed in more than 80 countries, 290 plant genera, 150 chemical compounds, 30 genera of bacteria, and 10 genera of fungi. The networks for geographic, botanical, chemical, and microbiological distribution indicated that Brazil, Asia, the botanical genus Thymus, species Thymus vulgaris L. "thyme," the Lamiaceae family, limonene, and the oxygenated monoterpene class were the most representative in the dataset, while the species Escherichia coli and Candida albicans were the most used to assess the antimicrobial activity of EOs. This work can be seen as a guide for the processing of metadata using a novel approach with non‐conventional statistical methods. However, this preliminary approach with EOs can be extended to other sources or areas of food science. [ABSTRACT FROM AUTHOR]