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This article analyses high-consistency pulp refining using a disk refiner. During the experiment, the size of the gap between the rotor and stator cutters (0.5 to 1.5 mm), rotor speed (2,000 to 2,500 rpm), pulp consistency (10 to 20%), and freeness value (15 to 60 °SR) of the pulp were varied. The refining results were characterised by changes in 10 output parameters: morphological properties of cellulose fibres (average length, width, fibrillation index, water retention value, average kink angle, and coarseness) and the physical and mechanical characteristics of handsheets (breaking length, bursting strength, tearing resistance, and folding endurance). A total of 56 observations were made on the samples. Factor and cluster analysis methods were used to study the structure of correlations between the output parameters. More than 96% of the total dispersion of all output parameters was due to a change in two latent (hidden) factors: the first one was responsible for 79.6% of the dispersion and is presumably identified as the degree of external fibre fibrillation and the second one (16.6% of the dispersion) as fibre flexibility (including coarseness and average kink angle). [ABSTRACT FROM AUTHOR]

In this paper, we have collected the data about the 507 high-cited (citations ≥ 25) papers on Covid 19 published by India using Scopus database. Within small span of time many papers have received considerable number of citations. Further we have analyzed in terms of types of papers, major funding agencies, major collaborating countries, prolific authors, productive organizations and cluster analysis of author supplied keywords. Most of the papers were research articles. Department of Science and Technology, Govt. of India has funded considerable numbers of papers on Covid 19 and K. Dhama form Indian Veterinary Research Institute has contributed highest numbers papers and USA was major collaborating country and PGIMER-Chandigarh was most contributing organization. This study will be useful to the scientist/researchers to know the characteristics of high cited papers on Covid-19 from India. [ABSTRACT FROM AUTHOR]

With the advancement of society and the improvement of living standards, the significance of students' physical health has become increasingly prominent. However, currently, the assessment and analysis of students' physical health heavily depend on conventional statistical methods. Even with the application of data mining-related methodologies for analysis and evaluation, the exploitation and utilization of physical health big data remain relatively restricted. In this paper, an improved Canopy-K-means algorithm based on principal component analysis (PCA) is used to construct and analyze a portrait of students' physical fitness and health. The method combines data dimensionality reduction techniques and cluster analysis techniques, and its combined performance is the best compared to other algorithms in the ablation experiments for both male and female student data groups. In this paper, the algorithm was used to process the grouping of physical fitness test data of male and female students, realize the construction and analysis of students' physical fitness and health portrait, and give the exercise prescription for different categories of students. In this paper, the physical health test data of students of Yunnan Agricultural University in 2020–2022 were collected to carry out experiments, and the results found that there are differences in physical health status among students of different genders, grades, and majors in this university, and the physical health status of the students of Classes 2018 and 2019 is generally deficient; on different majors, the students of the Faculty of Agricultural Sciences are slightly superior to the Faculty of Science and Technology, and the students of the Faculty of Science and Technology are slightly superior to the students of the Faculty of Humanities and Social Sciences. Our study offers novel methods and ideas for the assessment and analysis of students' physical health, holding significant implications for schools and related departments in formulating scientific and effective physical education policies and health promotion strategies. [ABSTRACT FROM AUTHOR]

ObjectiveMethodsResultsConclusionTo dynamically track the publications on central serous chorioretinopathy (CSC) and depict the research status and hot spots to guide future research.Gather all papers published in this area between 2004 and 2024 in the WOSCC databases comprehensively, assess their trends, and characterize the contributions of various nations, authors, institutions, and journals. In addition, VOSviewer, CiteSpace, and R software are used to obtain the most popular keywords for the topic.A total of 2,203 papers were published across 1,863 institutions in 59 countries. Among these, 6,907 authors contributed to publications in 300 journals and generated a total of 35,638 citations. The number of publications continues to grow steadily. Notably, Jay Chhablani’s team/Lab stands out as the leading contributor with ownership of 84 publications. Through keyword network analysis and clustering techniques, risk factor-related clustering, imaging-related clustering, pathogenesis-related clustering, and treatment-related clustering were identified. Furthermore, keyword analysis has unveiled emerging frontier areas including pachychoroid disease, choroidal vasculature abnormalities, PDT therapy, and optical coherence tomography that have garnered increasing interest.This study presents a comprehensive review of central serous retinopathy research conducted in the past two decades, highlighting key trends and exploring emerging research frontiers within this field. As such, it provides valuable references and suggestions for researchers engaged in studying this topic. [ABSTRACT FROM AUTHOR]

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures. [ABSTRACT FROM AUTHOR]

We define narrative bias as a tendency to interpret information as part of a larger story or pattern, regardless of whether the facts support the full narrative. Narrative bias in title and abstract means that results reported in the title and abstract of an article are done so in a way that could distort their interpretation and mislead readers who had not read the whole article. Narrative bias is often referred to as "spin." It is prevalent in abstracts of scientific papers and is impactful because abstracts are often the only part of an article read. We found no extant narrative bias instrument suitable for exploring both efficacy and safety statements in randomized trials and systematic reviews of pain. We constructed a 6-point instrument with clear instructions and tested it on randomised trials and systematic reviews of cannabinoids and cannabis-based medicines for pain, with updated searches to April 2021. The instrument detected moderate or severe narrative bias in the title and abstract of 24% (8 of 34) of randomised controlled trials and 17% (11 of 64) of systematic reviews; narrative bias for efficacy and safety occurred equally. There was no significant or meaningful association between narrative bias and study characteristics in correlation or cluster analyses. Bias was always in favour of the experimental cannabinoid or cannabis-based medicine. Put simply, reading title and abstract only could give an incorrect impression of efficacy or safety in about 1 in 5 papers reporting on these products. [ABSTRACT FROM AUTHOR]

This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future. [ABSTRACT FROM AUTHOR]

Background: The Pontifical Academy for Life (PAV) is an academic institution of the Holy See (Vatican), which aims to develop and promote Catholic teachings on questions of biomedical ethics. Palliative care (PC) experts from around the world professing different faiths were invited by the PAV to develop strategic recommendations for the global development of PC ("PAL-LIFE group"). Design: Thirteen experts in PC advocacy participated in an online Delphi process. In four iterative rounds, participants were asked to identify the most significant stakeholder groups and then propose for each, strategic recommendations to advance PC. Each round incorporated the feedback from previous rounds until consensus was achieved on the most important recommendations. In a last step, the ad hoc group was asked to rank the stakeholders' groups by order of importance on a 13-point scale and to propose suggestions for implementation. A cluster analysis provided a classification of the stakeholders in different levels of importance for PC development. Results: Thirteen stakeholder groups and 43 recommendations resulted from the first round, and, of those, 13 recommendations were chosen as the most important (1 for each stakeholder group). Five groups had higher scores. The recommendation chosen for these top 5 groups were as follows: (1) Policy makers: Ensure universal access to PC; (2) Academia: Offer mandatory PC courses to undergraduates; (3) Healthcare workers: PC professionals should receive adequate certification; (4) Hospitals and healthcare centers: Every healthcare center should ensure access to PC medicines; and (5) PC associations: National Associations should be effective advocates and work with their governments in the process of implementing international policy framework. A recommendation for each of the remaining eight groups is also presented. Discussion: This white paper represents a position statement of the PAV developed through a consensus process in regard to advocacy strategies for the advancement of PC in the world. [ABSTRACT FROM AUTHOR]

Paper spray-mass spectrometry (PS-MS) is a rapid, solvent-efficient, and high-throughput analytical method for analyzing complex samples. In this study, a PS-MS method was developed to obtain MS profiles of Aurantii Fructus Immaturus ( aka Zhishi in Chinese) in positive and negative ion modes. In combination with multivariate analyses, including principal component analysis and cluster analysis, the PS-MS profiles of 25 batches of Zhishi were discriminated in 25 batches of Citri Reticulatae Pericarpium Viride ( aka Qingpi in Chinese; an adulterant of Zhishi). Moreover, a rapid quantitative analysis of synephrine, a prescriptive quality control component of Zhishi listed in the Chinese Pharmacopoeia, was conducted with PS-MS using synephrine-d2 as an internal standard (IS). The linearity range was 1.68–16.8 μg/mL (R 2 = 0.9985), the limit of quantitation was 0.5 μg/mL. Relative standard deviations in the intra- and inter-day precision of the MS were 4.87 and 4.90%, respectively. Compared with HPLC results, there was no significant difference in the quantitation of synephrine. This study demonstrated that the PS-MS method is useful for the rapid discrimination and quality control of Zhishi samples. [ABSTRACT FROM AUTHOR]

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc. [ABSTRACT FROM AUTHOR]

Construction competitive bidding has been studied by many researchers; however, their focus was mainly on certain bidding aspects. Thus, despite their value, existing bidding models have limited applicability for various reasons including the noninclusion of some real-life factors that impact bidding-related decisions. As such, there is a need for identifying a comprehensive list of bidding factors that impact construction bidding–related decisions, studying their associations, and identifying understudied factors to direct future research efforts. This paper fills this knowledge gap. First, the authors conducted a systematic selection of 124 construction bidding–related articles published within the past 40 years. Second, the authors identified a list of 43 bidding factors from the contractor's perspective and divided them into four groups (project, bidding environment, economy, and company) based on their characteristics and relevance. Thereafter, the authors implemented cluster analysis and social network analysis (SNA) to quantitatively analyze the level of inclusion, co-occurrence, and interconnectivity among the bidding factors and identify the gaps in the literature. Results of cluster analysis revealed that previous studies focused mainly on company-related factors, while economy-related factors were the least studied. Also, SNA outcomes highlighted that experience in similar project and availability and costs of rental labor/equipment/material are the most studied factors in theoretical discussions, while number of competitors and size of the project are the most investigated factors in developed models. Consequently, this paper identified understudied bidding factors, including company reputation level in the market, bidding method, bidding stages, and contractor's risk attitude. Thus, this paper contributes to the body of knowledge by promoting a comprehensive understanding of bidding-related factors and providing a road map for incorporating critical understudied bidding-related factors. This would minimize redundancies and maximize the effectiveness of future bidding-related research. Ultimately, this should provide better decision-making for all associated stakeholders. [ABSTRACT FROM AUTHOR]

The main objective of the presented paper was the analysis of the current state of food security in the world, segmentation of 100 selected countries and determination of its main driving factors. The analysis used 27 indicators covering 5 basic areas: agricultural production, poverty, demography, economic development, and environmental indicators. The analysis was based on data from the FAO and World Bank for the most recent available period, which was year 2020. The data dimension was reduced with the application of factor analysis, and the main driving factors of food sufficiency were determined. The result was 6 factors: technological development, economic development, agricultural production, environmental factor, and physical quality of life and environment. To group similar countries in terms of selected indicators, a cluster analysis was performed, whereby countries were grouped by similarity into three clusters. The 1st cluster consisted of the most economically developed countries, where only 2.54% of the population suffers from malnutrition. The countries in this cluster were characterized by high levels of economic development, high caloric intake of the population, and high, life expectancy. On the other hand, they recorded negative development in demographic indicators such as fertility and birth rates. The 2nd cluster included the poorest areas of the African continent, which were most endangered by direct food insufficiency (23.74% of the population). In contrast to the first cluster, these countries were characterized by low levels of economic development, high prices, and low-calorie intakes of the population, as well as low life expectancy, while on the other hand, these countries had high fertility and birth rates. The third largest cluster consisted of countries with a medium threat of food insufficiency, where 6.37% of the population suffers from malnutrition. The countries in the third cluster excelled in terms of crop and livestock production volumes, but in contrast to this, they achieved lower levels of fat, protein, and calorie intake of population. [ABSTRACT FROM AUTHOR]

Increasing attention is being paid towards the influence of regional contexts on innovation activities within regional development studies. Some of the literature in economic geography tends to consider the various peripheral areas as being homogenous and partly characterized by their remote location, weak innovation inputs and lack of knowledge exchange. This paper questions this approach by examining the role of innovation activities in peripheral regions. We offer a detailed and multifaceted taxonomy of the Norwegian economic regions. From an empirical viewpoint, the adoption of cluster analysis and a broad set of innovation, economic and territorial indicators allowed us to provide a nuanced picture of the current fabric of Norwegian innovation and economic-production. With the benefit of insights from relevant strands of literature (e.g. regional development, innovation systems and multi-scalar innovation networks), the case of Norway presented in our paper contributes to the scholarly debate on the role of structural preconditions for the innovation of firms in diverse peripheral areas. [ABSTRACT FROM AUTHOR]

In citation networks, the content relativity of papers is a precondition of engendering citations, which is hard to model by a topological graph. A geometric graph is proposed to predict some features of the citation networks with exponentially growing papers, which addresses the precondition by using coordinates of nodes to model the research contents of papers, and geometric distances between nodes to diversities of research contents between papers. Citations between modeled papers are drawn according to a geometric rule, which addresses the precondition as well as some other factors engendering citations, namely academic influences of papers, aging of those influences, and incomplete copying of references. Instead of cumulative advantage of degree, the model illustrates that the scale-free property of modeled networks arises from the inhomogeneous academic influences of modeled papers. The model can also reproduce some other statistical features of citation networks, e.g. in- and out-assortativities, which show the model provides a suitable tool to understand some aspects of citation networks by geometry. [ABSTRACT FROM AUTHOR]

Background Purpose Unsupervised machine learning describes a collection of powerful techniques that seek to identify hidden patterns in unlabeled data. These techniques can be broadly categorized into dimension reduction, which transforms and combines the original set of measurements to simplify data, and cluster analysis, which seeks to group subjects based on some measure of similarity. Unsupervised machine learning can be used to explore alternative subtyping of disorders of gut‐brain interaction (DGBI) compared to the existing gastrointestinal symptom‐based definitions of Rome IV.This present review aims to familiarize the reader with fundamental concepts of unsupervised machine learning using accessible definitions and provide a critical summary of their application to the evaluation of DGBI subtyping. By considering the overlap between Rome IV clinical definitions and identified clusters, along with clinical and physiological insights, this paper speculates on the possible implications for DGBI. Also considered are algorithmic developments in the unsupervised machine learning community that may help leverage increasingly available omics data to explore biologically informed definitions. Unsupervised machine learning challenges the modern subtyping of DGBI and, with the necessary clinical validation, has the potential to enhance future iterations of the Rome criteria to identify more homogeneous, diagnosable, and treatable patient populations. [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]

Aim: To determine the prevalence and clustering of NANDA‐International nursing diagnoses in patients assisted by pre‐hospital emergency teams. Design: Retrospective descriptive study of electronic record review. Methods: Episodes recorded during 2019, including at least a nursing diagnosis, were recovered from the electronic health records of a Spanish public emergency agency (N = 28,847). Descriptive statistics were used to characterize the sample and determine prevalence. A two‐step cluster analysis was used to group nursing diagnoses. A comparison between clusters in sociodemographic and medical problems was performed. Data were accessed in November 2020. Results: Risk for falls (00155) (27.3%), Anxiety (00146) (23.2%), Acute pain (00132), Fear (00148) and Ineffective breathing pattern (00032) represented 96.1% of all recorded diagnoses. A six‐cluster solution (n = 26.788) was found. Five clusters had a single high‐prevalence diagnosis predominance: Risk for falls (00155) in cluster 1, Anxiety (00146) in cluster 2, Fear (00148) in cluster 3, Acute pain (00132) in cluster 4 and Ineffective breathing pattern (00032) in cluster 6. Cluster 5 had several high prevalence diagnoses which co‐occurred: Risk for unstable blood glucose level (00179), Ineffective coping (00069), Ineffective health management (00078), Impaired comfort (00214) and Impaired verbal communication (00051). Conclusion: Five nursing diagnoses accounted for almost the entire prevalence. The identified clusters showed that pre‐hospital patients present six patterns of nursing diagnoses. Five clusters were predominated by a predominant nursing diagnosis related to patient safety, coping, comfort, and activity/rest, respectively. The sixth cluster grouped several nursing diagnoses applicable to exacerbations of chronic diseases. Implications for the profession and/or patient care: Knowing the prevalence and clustering of nursing diagnoses allows a better understanding of the human responses of patients attended by pre‐hospital emergency teams and increases the evidence of individualized/standardized care plans in the pre‐hospital clinical setting. Impact: What problem did the study address? There are different models of pre‐hospital emergency care services.The use of standardized nursing languages in the pre‐hospital setting is not homogeneous.Studies on NANDA‐I nursing diagnoses in the pre‐hospital context are scarce, and those available are conducted on small samples. What were the main findings? This paper reports the study with the largest sample among the few published on NANDA‐I nursing diagnoses in the pre‐hospital care setting.Five nursing diagnoses represented 96.1% of all recorded. These diagnoses were related to patients' safety/protection and coping/stress tolerance.Patients attended by pre‐hospital care teams are grouped into six clusters based on the nursing diagnoses, and this classification is independent of the medical conditions the patient suffers. Where and on whom will the research have an impact? Knowing the prevalence of nursing diagnoses allows a better understanding of the human responses of patients treated in the pre‐hospital setting, increasing the evidence of individualized and standardized care plans for pre‐hospital care. Reporting method: STROBE checklist has been used as a reporting method. No Patient or Public Contribution: Only patients' records were reviewed without further involvement. [ABSTRACT FROM AUTHOR]

The measurement of the abundance of galaxy clusters in the Universe is a sensitive probe of cosmology, which depends on both the expansion history of the Universe and the growth of structure. Density fluctuations across the finite survey volume add noise to this measurement, this is often referred to as super-sample covariance (SSC). For an unbinned cluster analysis, such noise has not been included in the cluster likelihood, since the effect of SSC was small compared to the Poisson shot-noise for samples of a few hundred clusters. For upcoming large cluster surveys such as the Rubin LSST, which will deliver catalogues of tens of thousands of clusters, this effect will no longer be negligible. In this paper, we propose a new hybrid likelihood based on the Gauss-Poisson Compound model (GPC), by using infinitesimal mass bins and standard redshift bins. This likelihood has the advantages of an unbinned Poisson likelihood while successfully incorporating the effects of SSC. Using a simulated dark matter halo catalogue, we find that the hybrid likelihood, accounting for both Poisson noise and SSC, increases the dispersion of the parameter posteriors by 20 per cent when using 100 000 clusters compared to the standard unbinned likelihood, based on Poisson statistics only. [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]

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person's long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

The collection of SuperDARN ionospheric echo data to make ionospheric convection maps is of great significance for Space Weather research. The ionospheric echoes for SuperDARN are generally mixed with scatter from the ground or sea surface, thus the clustering analysis of SuperDARN backscatter echoes is important. In this paper, the first implementation of deep clustering based on the graph automatic encoder network is efficiently realized for classifying the SuperDARN data. In addition, the model is compared with the traditional algorithm and machine learning clustering algorithms. Application of the model to sample data reveals that the deep clustering algorithm can capture the structural characteristics of the echoes and improve the accuracy of echo clustering and classifying. [ABSTRACT FROM AUTHOR]

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

The sharing economy (SE) in tourism (SET) refers to all tourist-oriented initiatives and services based on individuals' practices of sharing resources with others, including transport and accommodation, among others, through the use of online networks. While tourism is one of the most rapidly growing sectors of the SE, the question arises if everyone has equal access to the SET. The aim of this paper is to verify the age-related differences of those who participate in the SET. We hypothesize that the mean age of those who potentially participate in the SET is lower than for non-participants. For this purpose, we conducted a cluster analysis on a sample of N = 35,279 Polish inhabitants to classify them according to their potential participation in the SET. We found that 22% of Polish inhabitants are potential participants in the SET. Their mean age is around 36 years. Those who are excluded (or who have a high chance of being excluded) from the SET have a mean age higher than those who are potential SET participants. Their share in total Polish inhabitants is 28%. Our findings may have broader significance for SET providers in Central-Eastern European countries, who (will) face problems of an aging population. [ABSTRACT FROM AUTHOR]

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets' efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

Dynamic Pattern Synthesis (DPS) provides a new longitudinal method for evaluating the impacts of macroeconomic and public policy interventions. Situated within complexity theory and critical realism, it has evolved from the established methods of Cluster Analysis and Configurational Modelling approaches. Dynamic Pattern Synthesis identifies case convergence and divergence (using quantitative techniques), while remaining close to the qualitative uniqueness of each case. In this paper, the DPS approach is used to consider macroeconomic convergence across Sub-Saharan Africa during the Millennium Development Goals, and the possible impacts of IMF interventions in stimulating long-term macroeconomic outcomes. The findings reveal a high degree of economic instability experienced across the region and varying responses to an external shock. The importance of 'outliers' and inconsistency in country convergence are also observed. The DPS method highlights the importance of individual country experiences in relation to external shocks. When factoring in IMF interventions, the findings highlight multiple paths to a given policy outcome, rather than a single optimal economic strategy. This opens up the debate on policy issues associated with economic complexity, including how best to create an overall environment of stability that might promote convergence and reduce the instability that undermines planning and investment. [ABSTRACT FROM AUTHOR]

Extant evidence indicates that exposure to adverse childhood experiences (ACE) tend to cluster among children and adolescents. Considering that adolescents from African countries present higher risk of being exposed to multiple ACE compared to other countries, the identification of victimization profiles in this population is clearly warranted. The aim of this study was to determine meaningful clusters of individuals with similar experiences of ACE in a sample of Kenyan adolescents. Latent class analysis (LCA) was conducted to identify latent classes of exposure to ACE. In addition, the relationships between the latent classes and gender, parental education, living arrangements and diagnosis of post-traumatic stress disorder (PTSD) were estimated. A three-class solution was found to be the best description of ACE, and the classes were labelled "Low Risk", "Intermediate Risk", and "High Risk". Compared with the Low-Risk class, the High-Risk class was found to be significantly more likely to have a diagnosis of PTSD and being a female may be an antecedent risk factor for high exposure to ACE. The Intermediate Risk class was significantly less likely to have parents with high school or college education. This paper indicates that Kenyan adolescents present higher risk of being exposed to multiple ACE and that trauma research may turn its focus on the individual as the unit of analysis rather than traumatic events. [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]

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]

This paper investigates the use of English verb-argument constructions (VACs) in second language writing in light of usage-based constructionist approaches to language development. It employs a comprehensive list of VACs to analyze every sentence in 390 essays written by L2 learners of three levels, i.e., Low, Mid, and High, and examines the theoretical hypotheses that the repertoires of VACs expand along with L2 proficient profile and that individual VACs, albeit varying in their expansion patterns, compose a structured inventory based on constructional information. Results indicate that L2 learners of higher proficiency used significantly more types of VACs than those of lower proficiency. It is also found that significant expansions of individual VACs appeared at different L2 proficiency levels. For example, the use of [Verb + NP complement] construction significantly increased between Low and Mid, not between Mid and High, whereas the [Verb + Prepositional object] construction significantly expanded throughout the three proficiency levels. Finally, there were strong cluster effects in the expansion of VACs as small sets of VACs showed similar by-text co-occurrence patterns. [ABSTRACT FROM AUTHOR]

The lithofacies analysis of logging data is an essential step in reservoir evaluation. Multiresolution graph-based clustering (MRGC) is a commonly used methodology that provides information on the best number of clusters and cluster fitting results for geological understanding. However, the cluster fusion approach of MRGC often leads to an overemphasis of the boundary constraints among clusters. MRGC neglects the global cluster distribution relationship, which limits its practical application effectiveness. This paper proposes a new methodology, named kernel multiresolution graph-based clustering (KMRGC), to improve the merging part of clustering in MRGC, and it can give more weight to the spatial relationship characteristics among clusters. The clustering performance of K-means, Gaussian Mixture Model(GMM), fuzzy c-means(FCM), Density-Based Spatial Clustering of Applications with Noise(DBSCN), spectral clustering, MRGC and KMRGC algorithm was evaluated on a publicly available training set and noisy dataset, and the best results in terms of the adjusted Rand coefficients and normalized mutual information(NMI) coefficients on most of the datasets were obtained using KMRGC algorithm. Finally, KMRGC was used for logging data lithofacies clustering in cased wells, and the clustering effect of KMRGC algorithm was much better than that of the K-means, GMM, FCM, DBSCN, spectral clustering and MRGC algorithms, and the accuracy and stability were better. [ABSTRACT FROM AUTHOR]

Background: This paper discusses the stages of health system resilience (HSR) and the factors underlying differences in HSR during the covid-19 pandemic, especially the vaccination challenge. We studied the relationship between vaccination strategies and macro-context backgrounds in 21 Latin American countries. Our objective was to capture the impact of those aspects in the SR and identify potential improvements for future crises and for vaccination programs in general. Methods: The study uses mixed-methods research to provide insights into how the countries' backgrounds and vaccination strategies impact the HSR. Particularly, we used explanatory sequential mixed methods, which entails a quantitative-qualitative two-phase sequence. The quantitative phase was conducted using cluster and variance analysis, in which the HSR was measured using as a proxy the covid-19 vaccination outcomes in three cut-offs of reaching 25%, 50%, and 75% of population coverage. This approach allows us to discriminate covid-19 vaccination progress by stages and contrast it to the qualitative stage, in which we performed a country-case analysis of the background conditions and the changes in vaccination strategies that occurred during the corresponding dates. Results: The paper provides a rich comparative case analysis of countries, classifying them by early, prompt, and delayed performers. The results show that differences in vaccination performance are due to flexibility in adapting strategies, cooperation, and the ability to design multilevel solutions that consider the needs of various actors in the health ecosystem. These differences vary depending on the vaccination stage, which suggests the importance of acknowledging learning, diffusion, and feedback processes at the regional level. Conclusions: We identified the importance of societal well-being as an ideal country antecedent for high and sustained levels of performance in covid-19 vaccination. Whereas in other countries where the set-up and beginning phases were rough, the value of the operational decisions and the learning on the move regarding their own and their peers' trajectories were crucial and were reflected in performance improvement. A contribution of this study is that the above-mentioned analysis was done using vaccination coverage cut-off points that allow a performance view that takes into consideration the stages of the vaccination progress and the learning process that goes with it. As well as framing this into the HSR shock cycles that allow to differentiate the stages of resilience on which countries must act. [ABSTRACT FROM AUTHOR]

Introduction: Hepatocellular carcinoma (HCC) is a common malignant tumor, so we need a convenient and objective way to diagnose and treat HCC. We discuss the current situation, progress, hotspots, and existing problems of Albumin-Bilirubin (ALBI) in HCC, which can provide new ideas for the prevention, diagnosis, and treatment of HCC. Methods: We adopt Excel 2019 software and visual analysis tools based on Web of Science database search. This manuscript uses VOSviewer, Co-Occurrence13.3 (COOC13.3) software to conduct overall trend analysis, synonym merging, frequency of countries, journals, institutions, funds, dissimilarity matrices, co-occurrence matrices, bimodal matrices, coupling matrices, cluster analysis of topic evolution time zone graphs. Results: A total of 610 papers were included, and the number of papers output showed an overall upward trend. ALBI has been valued by the industry in HCC and plays an important role in diagnosing and treating HCC, even better than the classic Child-Pugh (C-P) grade. At the same time, hot spots in the treatment of HCC and other applications of ALBI were discovered. Conclusion: ALBI score is a convenient and objective liver function evaluation index, which plays an important role in the prediction of patient survival rate and prognosis. Promoting the ALBI score in HCC can help doctors judge the patient's condition and improve the diagnosis and precise treatment effect. [ABSTRACT FROM AUTHOR]

For freight and servicing movements entering a city, parking provides the access enabling service to customers. This will invariably occur on the street in parking spots or in modern buildings in off-street facilities. In light of changing urban planning priorities and as a result, declining on-street loading zone spaces, this paper explores the provision and challenges of off-street loading docks to support freight and servicing task activity in major urban centres. While it may not be fully appreciated, provisions to adequately accommodate a city's generated freight task is critical to urban planner's broader objectives. As a non-discretionary transport task, freight vehicles will continue to enter cities. If good off-street loading dock facilities are not provided, vehicles will seek out legitimate or illegal on-street parking, and urban planner's place making objectives are likely to be compromised. The paper first considers the planning approaches that govern the provision of loading docks. This paper focuses on Sydney but draws on comparisons primarily to the Borough of City of London. Based on recent observations in Sydney, it then considers various stakeholder perspectives towards loading dock provision and use. Modelling approaches to forecast better requirements that could assist to overcome the planning issues faced are then outlined. The paper concludes with actions taken to address the challenges. • Lack of tools for planning loading docks leads to conflict between developers and government. • Poor provision of loading docks in new developments can compromise urban planning objectives. • Regression and clustering analysis techniques allow the capacity of loading docks and their levels of service to be estimated. [ABSTRACT FROM AUTHOR]

The large amount of scientific papers provides a convenient way for users to know the latest research progress of a specific research topic. However, the large volume and the diverse research themes hiding among these papers usually hinder users from conveniently locating the specific papers that they are interested in. To tackle this problem, we propose a novel unsupervised cascade learning scheme that aims to extract a ‘cluster-theme keywords’ structure from the related papers of a research topic so as to help users locate their research interests quickly. Our approach first selects some representative papers for a research topic. It then clusters these selected papers into several small clusters with the help of a domain ontology. It finally extracts some theme keywords for each cluster. Our approach not only greatly reduces the time-consuming and labour-intensive paper-seeking process for users, but also comprehensively displays the diverse themes of a research topic. We conducted extensive experiments to evaluate our proposed approach. The experimental results demonstrate the effectiveness of this approach, which produces promising results. [ABSTRACT FROM PUBLISHER]

Groups of sensor nodes are called as sensor networks. They transmit sensed data to base station in cooperation with one another. An efficient use of power is important for increased network lifespan. Wireless sensor networks are tiny, light in weight, battery powered sensor nodes. Various parameters like clustering overhead, lifetime of network, energy hole, FND (first node dies) LND (last end dies) are optimized in WSN with the help of various proposed hybrid unequal clustering protocol. Another issue for data gathering in WSN is the hot spot or energy hole that are formed near sink node are also addressed. Various new emerging strategies laid stress on the best optimal usage of constrain resources like energy, memory and various other processing abilities. This paper addresses a review of different research works done in this area. [ABSTRACT FROM AUTHOR]

Background This analysis estimates the association between smoking-related knowledge and smoking behaviour in a Chinese context. To identify the specific knowledge most directly related to smoking status, we used a novel latent variable analysis approach to adjust for the high correlations between different measures of knowledge about tobacco smoking. Method Data are from the Global Adult Tobacco China Survey, a nationally representative sample of 13 354 household-dwelling individuals 15 years of age or older. Multinomial logistic regressions estimated the association between smoking status (ie, never smoked, current smoker or past smoker) and four smoking-related beliefs: whether or not smoking causes lung cancer, heart attack and stroke, and whether or not low-tar cigarettes are less harmful. A latent variable approach reassessed these associations while taking into account the general level of knowledge about smoking. Results After demographic variables and general knowledge about smoking had been controlled for, the belief that low-tar cigarettes are not less harmful was more prevalent in persons who had never smoked than in current smokers (OR=1.3 (95% CI 1.0 to 1.7) in men and OR=2.8 (95% CI 1.3 to 5.9) in women); this association was even stronger when past smokers and current smokers were compared (OR=2.1 (95% CI 1.5 to 3.0) in men and OR=5.0 (95% CI 1.3 to 20.1) in women). Conclusions Compared with those who have never smoked and those who have ceased smoking, current smokers in China are more likely to believe that low-tar cigarettes are less harmful than regular cigarettes. [ABSTRACT FROM AUTHOR]

Background: Although many individuals with alcohol dependence (AD) are recognized in the German healthcare system, only a few utilize addiction-specific treatment services. Those who enter treatment are not well characterized regarding their prospective pathways through the highly fragmented German healthcare system. This paper aims to (1) identify typical care pathways of patients with AD and their adherence to treatment guidelines and (2) explore the characteristics of these patients using routine data from different healthcare sectors. Methods: We linked routinely collected register data of individuals with a documented alcohol-related diagnosis in the federal state of Bremen, Germany, in 2016/2017 and their addiction-specific health care: two statutory health insurance funds (outpatient pharmacotherapy for relapse prevention and inpatient episodes due to AD with and without qualified withdrawal treatment (QWT)), the German Pension Insurance (rehabilitation treatment) and a group of communal hospitals (outpatient addiction care). Individual care pathways of five different daily states of utilized addiction-specific treatment following an index inpatient admission due to AD were analyzed using state sequence analysis and cluster analysis. The follow-up time was 307 days (10 months). Individuals of the clustered pathways were compared concerning current treatment recommendations (1: QWT followed by postacute treatment; 2: time between QWT and rehabilitation). Patients' characteristics not considered during the cluster analysis (sex, age, nationality, comorbidity, and outpatient addiction care) were then compared using a multinomial logistic regression. Results: The analysis of 518 individual sequences resulted in the identification of four pathway clusters differing in their utilization of acute and postacute treatment. Most did not utilize subsequent addiction-specific treatment after their index inpatient episode (n = 276) or had several inpatient episodes or QWT without postacute treatment (n = 205). Two small clusters contained pathways either starting rehabilitation (n = 26) or pharmacotherapy after the index episode (n = 11). Overall, only 9.3% utilized postacute treatment as recommended. Conclusions: A concern besides the generally low utilization of addiction-specific treatment is the implementation of postacute treatments for individuals after QWT. [ABSTRACT FROM AUTHOR]

Epimedium is an essential genus of homologous plants used as medicine and food, which has various pharmacological effects and great utilization value in the market. Currently, research into the quality of Epimedium focuses mainly on species and region identification. However, the harvesting period also influences the quality of Epimedium; this aspect requires further study. In this paper, a systematic method is developed based on high-performance liquid chromatography (HPLC) fingerprint analysis, chemometrics, and network pharmacology methods to compare the quality of Epimedium from two harvest periods. A method is developed using liquid chromatography–mass spectrometry (LC-MS) to identify fingerprint peaks. Chemical composition analysis is performed using cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA). Ultimately, network pharmacology analysis is employed to forecast the potential effects of compounds associated with standard chromatographic peaks. The fingerprint peaks were identified through LC-MS and corroborated with references. Subsequently, chemometric analysis revealed a distinct division between the HPLC fingerprints of samples from the two harvest periods. This was accomplished by employing cluster analysis and OPLS-DA. Additionally, utilizing the variable impact projection (VIP) obtained from OPLS-DA, a total of seven compounds exhibiting quality discrepancies were identified. Lastly, the network pharmacology analysis indicated that compounds corresponding to typical chromatographic peaks demonstrate therapeutic effects on osteoporosis. Moreover, primary bioactive compounds are identified through degree value screening. This approach is powerful for the quality assessment of Epimedium from different harvesting periods and can also be extensively applied for the quality control of other herbal materials. [ABSTRACT FROM AUTHOR]

In order to secure supply chains (SCs), researchers and policy makers need to be abreast of developments in supply chain risk management (SCRM). This study selected frequently cited papers of WoS from the last 10 years, performing quantitative visualization analysis to establish the current trends within the field. Further attention was paid to those studies that focused on the impact of COVID-19. This study used a keyword timeline and clustering analysis map to establish the main research directions between 2012 to 2018, as well as the perspectives from which SC optimization was studied from 2018 to 2021. The key journals and research institutions for SCRM are established, as well as the key categories that the published literature falls under. Cluster analysis shows which areas in the published literature have the most references. Finally, the study establishes the direction of current trends within SCRM, as well as its understudied areas. [ABSTRACT FROM AUTHOR]

The focus of this paper is to analyze the trends and locations of accidents in the Greater Melbourne Area (GMA) during a 15-year period (2006–2020). The places where accidents were most prevalent were discovered and the reasons which are contributing to the high accident rates in those areas are determined. Analyzing the patterns over time and variations in the frequency of accidents helped to identify areas that have improved or deteriorated in terms of road safety. A Tweedie model was developed to examine the intricate interaction between the accident frequency and its potential contributing factors such as socio-demographics, road transport infrastructure, and built environment. Ultimately, a clustering analysis was performed to elucidate the dispersion of road accident risk ratings among different local government areas (LGAs), offering useful insights into road safety initiatives and prioritization. • The study linked built environment to accidents with Twidee and Space-time Cube. • Accidents down overall (20 yrs), but hotspots move outward. • Melbourne suburbs form five risk clusters based on accident frequency. [ABSTRACT FROM AUTHOR]