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Besides scientific impact, papers can also achieve a technological impact that remains less known in the scientific community. Cited papers in the patents are considered as the index to measure the technological impact. This study aimed to analyze the technological impact of Iranian publications using co-authorship and coword map, their evolution, the journals, and the subject areas of these publications. This applied research focuses on the quantitative study and visualization with a scientometric approach. The research population was all studies (4554 records) that were published during 2011-2020 in one of the Iranian institutions and had been cited by one of the international patents. The data collection tool was the SciVal database. CiteSpace and Excel spreadsheets were used to analyze the data. Of the 4,554 papers cited by the scholarly outputs that have been cited in patents e patents, most of them were published in collaboration with the USA (9%). Islamic Azad University and Tehran University of Medical Sciences (13% each) were the most active Iranian universities. The number of Iranian papers cited in patents had a downward trend from 686 in 2011 to 57 in 2020. RSC Advances journal was the first top journal to publish these papers. Of 27 subject areas, engineering (24.1%) was the first popular subject that patents cite in their publications. The cluster analysis of keywords identified 8 clusters, including "x-ray diffraction," "animal," "adult," "escherichia coli," "tissue engineering," "coronavirus infection," "neural network," and "methane." The technological impact of Iranian research has declined in recent years. It is suggested that research policymakers should consider scholarly outputs that have been cited in patents, which, in a way, shows the flow of knowledge to the industry and encourages researchers to produce such papers. [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]

The advancement of Augmented Reality (AR) technology has been remarkable, enabling the augmentation of user perception with timely information. This progress holds great promise in the field of interaction design. However, the mere advancement of technology is not enough to ensure widespread adoption. The user dimension has been somewhat overlooked in AR research due to a lack of attention to user motivations, needs, usability, and perceived value. The critical aspects of AR technology tend to be overshadowed by the technology itself. To ensure appropriate future assessments, it is necessary to thoroughly examine and categorize all the methods used for AR technology validation. By identifying and classifying these evaluation methods, researchers and practitioners will be better equipped to develop and validate new AR techniques and applications. Therefore, comprehensive and systematic evaluations are critical to the advancement and sustainability of AR technology. This paper presents a theoretical framework derived from a cluster analysis of the most efficient evaluation methods for AR extracted from 399 papers. Evaluation methods were clustered according to the application domains and the human–computer interaction aspects to be investigated. This framework should facilitate rapid development cycles prioritizing user requirements, ultimately leading to groundbreaking interaction methods accessible to a broader audience beyond research and development centers. [ABSTRACT FROM AUTHOR]

In recent years, the custom wardrobe market has been steadily developing. While meeting the functional needs of users, it is gradually shifting towards aesthetic preferences. Rapidly grasping users' preferences for the appearance of custom wardrobes is a key focus of current research. This study collected a large number of decorative surface images of custom wardrobes and objectively analyzed the design features based on color moments and Tamura texture feature data in computer image analysis methods. K-means cluster analysis was performed on the feature data. Collected images of the points closest to the cluster centers were further screened to select representative finish images, and finally a questionnaire survey was conducted at Nanjing Forestry University, with the help of semantic differential method and factor analysis. The characteristics of the samples were comprehensively summarized to infer design elements. The study found that warm-toned, medium-low saturation, and medium brightness surfaces were preferred by the panel. Different colors, contrasts, saturations, brightness, element features, and arrangements have significantly different effects on visual perception. These conclusions can provide a reference for subsequent custom wardrobe design. [ABSTRACT FROM AUTHOR]

Can government environmental policy harmonize environmental protection with economic output? We explore this issue from the perspective of forestry eco-efficiency, using China's National Ecological Civilization Pilot Zone Policy (NECP), an environmental policy promulgated by the government of China, as the subject of this study. The study introduces forestry eco-efficiency as an indicator to assess the balance between economic development in the forestry sector and environmental conservation. The indicator, grounded in sustainable development theory, employs a super-efficiency SBM model that includes undesirable outputs to evaluate efficiency. Additionally, we empirically analyze the impact of NECP on forestry eco-efficiency by using the difference-in-difference (DID) model with provincial panel data from 2011 to 2020. Ultimately, we analyze the effects of spatial spillover by employing the spatial Durbin model (SDM). Our study yields the following conclusions. (1) In this paper, through hotspot clustering analysis, forestry eco-efficiency in each province is categorized into three categories: effective, semi-effective and ineffective. Our findings suggest that China's average forestry eco-efficiency falls into the ineffective category, highlighting the need to optimize resource allocation within the sector. (2) NECP significantly enhances forestry eco-efficiency, with robust findings across various stability tests. Thus, implementing government environmental policies can have a multiplier effect on forestry, i.e., it can synergize its economic development with environmental protection. (3) In provinces with a strong ecological foundation, the NECP significantly enhances forestry eco-efficiency. However, in other provinces, the improvement is only moderate. Furthermore, while the NECP has a substantial positive impact in the eastern region, it has yet to show a discernible effect in other regions. (4) The positive impacts of NECP implementation on forestry eco-efficiency have spatial spillover effects due to demonstration effects and comparative advantages. [ABSTRACT FROM AUTHOR]

Purpose: This paper first aims to examine associations between factors involved in business-to-business complaints management and results (satisfaction and loyalty) and analyses three types of distributors based on their cultural profile (domestic, low context and high context). Second, the paper investigates whether the identified associations remain stable over time. Design/methodology/approach: Data from a sample of distributors for a manufacturing company were gathered during two periods of time. A factorial analysis of correspondences and a cluster analysis were carried out to visually represent the associations among clients, complaints and results in the associations among clients, complaints and results. The stability over time of these relationships was also analysed by calculating the correlations between the Euclidean distances on the two maps (one per year) and their mobility ratio. Findings: The authors found significant evidence that clients from different cultures are associated with varying profiles of complaint and different result types and that certain associations remain stable over time. Originality/value: While many studies have analysed complaint behaviour in business-to-consumer contexts, there is a lack of research from an international business-business relations point of view, leaving questions virtually unexplored. Second, the last phases of supply chain management, specifically complaints management, have been undeveloped, limiting the cultural factor to the general scope of negotiation. In this vein, this paper compares different complaint profiles and results, comparing culturally different customers/distributors. Third, research has mostly referred to a single period, while this paper investigates two different periods of time for the same company (and their distributors) to analyse the relevance of the stability (or not) over time of the associations identified. [ABSTRACT FROM AUTHOR]

Intergenerational education mobility is a key dimension of social mobility and explores the extent to which educational attainment is transmitted across generations within a society. The implications of low education mobility concern both equity (everyone should have the same opportunities) and efficiency (it would be good for the economy and society if the most gifted and deserving young people were to study and not the children of the already educated). The literature identifies several drivers that can influence the level of social mobility in general and education mobility specifically, including characteristics of educational systems, public spending, degree of urbanisation, informal frictions, and beliefs. This paper seeks to identify 'patterns of intergenerational education (im)mobility' through a cluster analysis that takes into account the level of intergenerational mobility in education and a number of variables concerning its possible drivers, considering data on 82 countries (with different levels of development). The advantage of cluster analysis lies in the possibility of identifying regularities, but avoiding reasoning 'on average', i.e., safeguarding the possibility that different social patterns may exist. The results also allow us to speculate on possible policies to increase school mobility, highlighting, among other things, the 'equalising' role played by public spending on education. [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]

Tourism has become a major driver of China's economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consumption in tourism. Specifically, firstly, this paper decomposes the tourism energy consumption factors in each province into six factors and identifies the driving forces of different factors on energy consumption. Secondly, K-means clustering method is used to classify different provinces into three categories using the latest dynamic influencing factors as clustering factors and provincial targeted suggestions are made according to the characteristics of different categories. This paper combines the LMDI model with cluster analysis to find targeted energy optimization strategies for the energy consumption of the Chinese tourism industry. [ABSTRACT FROM AUTHOR]

This paper delves into regional airport system economics in Central Europe, with a particular focus on Slovakia, Czechia, Poland, Hungary, and Croatia. This research aimed to identify key indicators that shape optimal business models for regional airport systems by analyzing data from 24 airports between 2016 and 2019. Through cluster analysis, airports were categorized based on performance metrics, economic indicators, and ownership structures. The findings reveal distinct groupings among regional airports and shed light on critical factors influencing their operational and financial dynamics. By offering insights into the relationships between airport system characteristics and business model effectiveness, this paper aimed to provide valuable guidance for stakeholders, policymakers, and airport management teams. It facilitates informed decision-making and strategic planning for sustainable aviation infrastructure development in the region. [ABSTRACT FROM AUTHOR]

At present, the commonly used Bag of Words (BOW) expression ignores the semantic information of text and the problems of high dimension and high sparsity of feature extraction. This paper presents a multi-class text representation and classification algorithm. This project is based on the vector expression of keywords and takes the multi-category classification problem as the research object. Then, a hybrid Deep Location network (HDBN) is constructed by combining DBN with Boltzmann (DBM). Then, this paper does a lot of tests on the algorithm and proves the effectiveness of the algorithm. In addition, the 2D visual experiment is carried out with HDBN, and then the high-level text expression based on HDBN is obtained. The expression has strong cohesion and weak coupling. [ABSTRACT FROM AUTHOR]

This paper investigates object marking strategies in Circum-Baltic languages and beyond, using a sample of 103 predicates from 30 Western Eurasian languages from the BivalTyp database. The study aims to identify areal clusters in object marking and evaluate the relevance of the Circum-Baltic linguistic area in this context. It finds that while most Circum-Baltic languages dissolve into larger, genealogy-driven clusters, areal signals are present, particularly with Lithuanian merging with Slavic languages due to genitive-taking predicates. German deviates from the larger Germanic cluster, merging with Latvian and Hungarian without a specific marking strategy driving this alignment. The results suggest that the concept of a linguistic area is less effective for describing object marking relationships around the Baltic Sea, in contrast to the Balkan linguistic area, where cross-linguistic clusters of object marking strategies do align with the linguistic area. Additionally, the paper discusses large-scale trends, such as comitativity prominence and the comparison of observed object marking strategy distributions with the predicted Zipf distribution. [ABSTRACT FROM AUTHOR]

Supervised classification algorithms for processing epileptic EEG signals rely heavily on the label information of the data, and existing supervised methods cannot effectively solve the problem of analyzing unlabeled epileptic EEG signals. In the traditional unsupervised clustering algorithm, the number of clusters and the global parameters must be predetermined, and the algorithm's analytical results are combined with a huge number of subjective errors, which affects the detection accuracy. For this reason, this paper proposes an unsupervised multivariate feature adaptive clustering analysis algorithm based on epileptic EEG signals. First, CEEMDAN and CWT are introduced into the epileptic EEG signal after preprocessing for joint denoising to further improve the signal quality. Then, the multivariate feature set of the signal is extracted and constructed, which includes nonlinear, time, frequency, and time-frequency characteristics. To reveal the hidden structures and correlations in the high-dimensional feature data, t-SNE dimensionality reduction is introduced. Finally, the DBSCAN clustering algorithm is optimized using the SSA algorithm to achieve adaptive selection of cluster number and global parameters.It not only enhances the clustering performance and reliability of the clustering results, but also avoids subjective errors in the analysis results. It provides a pre-theoretical foundation for the successful development of future seizure prediction devices and has good application prospects in clinical diagnosis and daily monitoring of patients. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) is also implemented in agriculture, where larger agricultural holdings have a higher influence. We focused on them and situated our study to a country with a large average farm acreage -- the Czech Republic. The aim of the paper is to assess to what extent large agricultural holdings perceive socially responsible behaviour as being important and how it is manifested in their relationship to the stakeholders and the behaviour in economic, social and environmental areas using Carroll's pyramid and concept of 3 'P'. It is also examined if the higher perception of CSR influences the assets. Firms were grouped according to the perceived CSR by a cluster analysis. The highest economic responsibility in cluster 1 corresponded to the relatively high value of the assets, but the highest was in cluster 2 and 3 with important legal and ethical responsibilities. Cluster 4 had the lowest CSR, the same as the value of the assets, but further investigation is needed. The most important stakeholders were the owners and employees, but also the range of activities was provided for the locals. CSR and the importance of the stakeholders in large Czech agricultural holdings were highly perceived, especially on an economic level and in the social area. [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]

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent. [ABSTRACT FROM AUTHOR]

Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]

In today's academic environment, the rapid growth of research publications calls for advanced methods to organize and understand the extensive collections of academic work. This study aims to systematically categorize a substantial number of research paper abstracts from Kazakhstani institutions, focusing on identifying key themes and potential interdisciplinary collaboration opportunities. The dataset includes 13,356 abstracts from the Scopus database, covering a wide range of academic fields. The methodology of this research goes beyond traditional hand-done analysis by using advanced text analysis tools to organize the text data efficiently. This initial phase is crucial for summarizing each abstract's core content. The next steps of the analysis use this organized data to find and group similar thematic areas, considering the complex and multi-dimensional nature of academic research topics. The results reveal a diverse array of research themes, highlighting the dynamic academic contributions from Kazakhstan. Significant areas such as environmental science, technological advancements, linguistics, and cultural studies are among the prominent clusters identified. These insights not only provide an overview of current research directions but also highlight the potential for cross-disciplinary partnerships. Moreover, the findings have important implications for decision-makers, scholars, and educational institutions by illuminating key research areas and collaborative possibilities. This thematic overview acts as a guide for shaping research policies, fostering academic connections, and efficiently distributing resources within the scholarly community. Ultimately, this study adds to the academic conversation by offering a way to navigate and utilize the wealth of information in scientific literature, promoting a more collaborative and integrated research environment. [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]

Purpose: This paper examines how different configurations of societal impact are pursued by purpose-driven organizations (PDOs) and how these configurations align with the application of varying supply chain design (SCD) practices. Design/methodology/approach: This multi-method study uses quantitative data from 1588 B Corps and qualitative data from 316 B Corps to examine how PDOs align SCD with the pursuit of diverse types of societal impact. The authors first conduct a cluster analysis to group organizations based on the impact they create. Second, qualitative content analysis connects impact with enabling SCD elements. Findings: The analysis of the five identified clusters provides detailed empirical insights on influencers, design decisions and building blocks adopted by PDOs to drive a range of societal impacts. Specifically, the nature of the impact pursued affects (1) whether a PDO will be more influenced by a need in the political environment or an opportunity in the industry environment, (2) the relative importance of the design of social flows versus material flows and (3) the need to develop new relational resources with beneficiaries versus leveraging existing capabilities to manage inter-firm processes. Originality/value: This study responds to calls to disaggregate different dimensions of societal impact and examines the relationship between SCD and a breadth of sustainability impacts for different stakeholders. In doing so, the authors identify four SCD pathways organizations can follow to achieve specific societal impacts. This study is also the first to employ a supply chain perspective in the study of certified B Corps. [ABSTRACT FROM AUTHOR]

With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques. [ABSTRACT FROM AUTHOR]

This paper uses cluster analysis to explore the results of the 2016 National Financial Well-Being survey. Our analysis identifies four groups, two of which have very similar levels of financial well-being but markedly different objective financial situations. These findings indicate there is a systematic bias between financial well-being and objective financial situation. Although it is understood that these two constructs do not evaluate exactly the same thing, the difference in objective financial situation between the two groups suggests that, for large subsets of the American population, these constructs may be more different than the existing literature seems to suggest. This underscores the importance of considering both objective and subjective measures when assessing an individual's overall financial situation. [ABSTRACT FROM AUTHOR]

Why you should read this article: • UK care homes are an important example of a complex environment where research is essential but faces multiple challenges in terms of rigour and methodology • This paper offers a variety of important methodological strategies to identify and address challenges that could impact findings and the ability to carry out research • It highlights the importance of taking a critical stance with all elements of the research process and illustrates the need to identify and mitigate challenges when conducting research in complex environments. Background: The evidence base for hydration practice in care homes is underdeveloped. High-quality research is therefore needed to determine what practices support older people with dementia in drinking sufficient fluid. However, methodological developments are needed to be able to do this. Aim: To highlight the methodological issues researchers encountered during a feasibility cluster, randomised controlled trial of ThinkDrink, a hydration care guide for people with dementia living in UK care homes. Discussion: This is a challenging area because of the complexity of recruitment, participation and data collection in care homes. Researchers must pay extra attention to rigour and quality in the design of their studies. There may be multiple challenges, so various strategies may be required. Conclusion: It is important that researchers continue to reflect on rigorous approaches to develop evidence in a crucial area of care, despite these challenges. Implications for practice: Researchers working in complex environments face a variety of challenges to complete methodologically rigorous research. It is important for researchers to be critical of research processes and data, to mitigate and overcome these challenges. [ABSTRACT FROM AUTHOR]

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Simple Summary: Heretofore, a series of research has targeted enhancing feed efficiency and identifying dependable feed efficiency biomarkers, yet less research has focused on improving RFI in Chuanzang black pigs. Therefore, our study facilitates a deeper understanding of the differences in serum and urine metabolites of Chuanzang black pigs with different residual feed intakes in seeking to identify differential metabolites with residual feed intake. Our study used a nontargeted metabolomics approach to analyze pig metabolite markers by differences in Chuanzang black pigs' serum and urine metabolites, providing new insights into the biological differences in residual feed intake selection in Chuanzang black pigs. We believe this paper would be of great interest to breeders of animals. This study was conducted to evaluate associations of blood variables and urine variables with different residual feed intakes (RFIs) in growing Chuanzang black (CB) pigs. A total of 228 growing CB boars from 99 days were used. The same basal diet was offered ad libitum and individual feed intake and body weight were measured over a period of 181 d. The CB pigs were categorized based on their residual feed intake values, with six individuals each from the high and low ends selected and divided into two groups: the low residual feed intake group (LS) and the high residual feed intake group (HS). Serum and urine samples were collected at the end of the experiment for determination of metabolomics profiling. Results showed that there were significantly different metabolites in serum and urine of different RFI groups (fold-change, FC > 2.0 or FC < 0.5, and p < 0.05), and 21 metabolites were identified in serum and 61 in urine. Cluster analysis showed that 20 metabolites were up-regulated and one metabolite was down-regulated in serum; 44 metabolites were up-regulated and 17 metabolites were down-regulated in urine. Kyoto Encyclopedia of Genes and Genomes analysis showed that the differential metabolites of serum were enriched in linoleic acid metabolism, and the differential metabolites of urine were enriched in steroid hormone biosynthesis, taurine and hypotaurine metabolism, and primary bile acid biosynthesis. The correlations between serum metabolites and urine metabolites indicated a significant positive correlation between all fatty acyls in serum metabolites and L-glutamate in urine. However, no compelling genetic or blood biomarkers have been found to explain the differences in RFI, suggesting multiple approaches to effective feed use in pigs. This study provides new insights into the subsequent assessment of RFI by metabolomics profiling, as well as the development of novel feed additives for the factors that will facilitate future research directions in CB pigs. [ABSTRACT FROM AUTHOR]

This paper applied the prevalent acoustic emission (AE) technology to identify the damage modes and critical conditions for FRP/steel-concrete composite beams during the failure process. AE signals generated by the structural damages were classified efficiently by using a novel self-adaptive real-time clustering (SARTC) method; damage modes corresponding to each clustering category were recognized and analyzed, and the dominant damage type at different stages was obtained by comparing the AE activities and feature values. By conducting the AE intensity analysis, the dynamic evolutionary mechanisms and critical conditions of composite beams were identified; the increase in intensity value from 0.2 to 0.3 reflects the process from critical yielding to major fracture. By establishing the non-linear fitting model between local response and cumulative AE energy, the instantaneous status at arbitrary local position of the composite beam can be inverted and predicted quantitatively by independent AE testing. [ABSTRACT FROM AUTHOR]

This paper examines whether nanotechnology projects funded under the European Union (EU) Framework Programmes (FPs) are a possible trigger for path upgrading (i.e., infusion of new technologies in existing traditional sectors) in less-advanced regions. First, the adoption of cluster analysis and a set of key indicators (i.e., technological intensity, scientific excellence, human capital, and research and development expenditure) allowed us to distinguish between 79 more-advanced and 127 less-advanced EU regions. Subsequently, through social network analysis and nonparametric testing we were able to demonstrate how the less-advanced EU regions (average degree centrality: 40.5) play a marginal role compared with the more-advanced ones (average degree centrality: 98.5) in the nanotechnology network created within Horizon 2020—i.e., the EU programming cycle implemented in the 2014–2020 period. Despite this, we observed that a few less-advanced regions (33 out of 127) were able to score higher than the EU median in terms of participation in the targeted nanotechnology network, thus benefiting from relevant knowledge flows potentially leading to re-industrialization processes. The adoption of qualitative comparative analysis allowed us to determine which combinations of key innovation, scientific and socioeconomic factors could facilitate such beneficial interregional interactions and related knowledge exchange in these types of regions (i.e., primarily what we defined as "relative innovativeness," excellence in nanotechnology research and a comparatively high level of gross domestic product per capita). Our empirical results provided some clear policy implications. For instance, the necessity to I) remove the barriers impeding a more balanced participation to promote a widespread renewal of traditional industries in less-advanced regions and II) implement coordinated EU and domestic actions designed to encourage the involvement of the great majority of the less-advanced regions, which remain marginal in the periodically launched FPs. [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]

Purpose: This study explores the alignment of higher education with labour market demands in Uzbekistan, focusing on the gap between skills offered by public Higher Educational Institutions (HEIs) and those required by employers. Methodology/Approach: The methodology employed in this research includes a survey assessing employers' and alumni's perceptions of skill importance and performance, utilising Importance-Performance Analysis (IPA) and hierarchical cluster analysis to categorise skills and identify perception gaps. Findings: The findings reveal a pronounced misalignment in the perceived importance and actual performance of skills, especially in the areas of digital literacy and entrepreneurship. This misalignment is further accentuated by cultural factors, generational differences, and the evolving Uzbek economy. Research Limitation/implication: Amidst a backdrop of educational reforms and increasing competition from private and foreign universities, this research employs a survey-based importance-performance analysis and cluster analysis to gauge employer and alumni perceptions of 65 skills. Originality/Value of paper: This misalignment is further accentuated by cultural factors, generational differences, and the evolving Uzbek economy. Category: Research paper. [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]

This paper demonstrates several sufficient frameworks for the multi-cluster flocking behavior of the fractional Cucker–Smale (CS) model. For this, we first employ the Caputo fractional derivative instead of the usual derivative to propose the fractional CS model with the memory effect. Then, using mathematical tools based on fractional calculus, we present suitable sufficient conditions in terms of properly separated initial data close to the multi-cluster, and well-prepared system parameters for the multi-cluster flocking of the fractional system to emerge. Finally, we offer several numerical simulations and compare them with the analytical results. [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]

Introduction: Mild cognitive impairment (MCI) is a stage between health and dementia, with various symptoms including memory, language, and visuospatial impairment. Chiropractic, a manual therapy that seeks to improve the function of the body and spine, has been shown to affect sensorimotor processing, multimodal sensory processing, and mental processing tasks. Methods: In this paper, the effect of chiropractic intervention on Electroencephalogram (EEG) signals in patients with mild cognitive impairment was investigated. EEG signals from two groups of patients with mild cognitive impairment (n = 13 people in each group) were recorded pre- and post-control and chiropractic intervention. A comparison of relative power was done with the support vector machine (SVM) method and non-parametric cluster-based permutation test showing the two groups could be separately identified with high accuracy. Results: The highest accuracy was obtained in beta2 (25–35 Hz) and theta (4–8 Hz) bands. A comparison of different brain areas with the SVM method showed that the intervention had a greater effect on frontal areas. Also, interhemispheric coherence in all regions increased significantly after the intervention. The results of the Wilcoxon test showed that intrahemispheric coherence changes in frontal-occipital, frontal-temporal and right temporal-occipital regions were significantly different in two groups. Discussion: Comparison of the results obtained from chiropractic intervention and previous studies shows that chiropractic intervention can have a positive effect on MCI disease and using this method may slow down the progression of mild cognitive impairment to Alzheimer’s disease. [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 paper subscribes to the broad strand of literature that examines the interplay between banking activity and climate risk, by particularly focusing on identifying and classifying European Union countries into similar, homogenous groups based on their intrinsic pattern related to climate vulnerability and readiness to cope with the negative effects of natural disasters. By applying an unsupervised learning clustering algorithm on a novel input dataset comprising six proxy indicators for the physical risk associated with climate challenges, we reveal the climate profile of the EU countries. A direct implication of our findings consists of ascertaining which banking systems are more exposed to environmental risks arising from physical sources in the home country they headquarter or in the host countries envisaged for the conduct of transnational financial activity. Results indicate that the least vulnerable EU countries to physical risks, being at the same time best performers in the process of climate risk adaptation, prevention, and management are Denmark, Luxembourg, Germany, Sweden, and Finland. Hence, their banking systems are less exposed to the adverse consequences of the physical risks. In contrast, banks operating in Bulgaria, Croatia, Poland, and Romania are the most exposed to the ripple effects of these risks, due to countries' increased vulnerability to climate risk and to the low degree of performance in implementing climate policies. [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]

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]

This paper presents a classification method for earthquake ground motion records utilizing the results of K-means cluster analysis. The moment magnitude and Joyner–Boore distance are utilized as the primary parameters for clustering the earthquake ground motion records. The classification boundaries are established through an examination of moment magnitude ranges, Joyner–Boore distance ranges, and spectral characteristics within each cluster. In this study, a comprehensive dataset comprising 7627 horizontal earthquake acceleration records was meticulously curated for analysis. The data were subjected to separate clustering and grouping procedures, allowing for insightful comparisons between the resultant clusters. Significant disparities in spectral characteristics across the classification groups were demonstrated. These differences become particularly pronounced when a moment magnitude threshold of 6 and a Joyner–Boore distance threshold of 140 km are employed to categorize the ground motion records. The approach underscores the substantial impact of classification based on earthquake ground motion spectral characteristics, while also mitigating the potential instabilities inherent in cluster analysis results. A refined and quantitatively robust framework for understanding and categorizing earthquake ground motions is provided, offering valuable insights for seismic data analysis and contributing to more accurate and reliable assessments of seismic activity. [ABSTRACT FROM AUTHOR]

This study aims to identify a grouping of academic researchers based on how they perform academic engagement knowledge transfer (AEKT) activities. Cluster analysis was performed to classify 512 researchers from Spanish public universities according to AEKT activities. The analysis identified three profiles of researchers: (1) Non-transferring researchers in AEKT, (2) Transfer researcher in certain forms of AEKT, and (3) Global transferring researchers in AEKT. A portrait of each type of transfer researcher based on demographic, institutional, and labor drivers was made. The most intensive researchers in AEKT are the most senior, and have a high level of international collaboration. It could be understood that these factors have a continuous effect. In addition, there are highly gendered and fielded differences. This study has practical implications for policymakers in helping them develop policies that promote knowledge transfer productivity. It also has implications for universities as it can help establish specific strategies in line with their mission and encourage their researchers to perform these AEKT activities. This study also has implications for company managers in that AEKT positively impacts companies by improving their competitive positions. This study reflects the behavior and characteristics of academic researchers in AEKT activities. The main contribution of this paper is to show how the degree of access or belonging to these factors profiles the researcher, making him/her belong to one of the established groups or clusters. [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]

Background: Mild cognitive impairment (MCI) is commonly defined as a transitional subclinical state between normal aging and dementia. A growing body of research indicates that health behaviors may play a protective role against cognitive decline and could potentially slow down the progression from MCI to dementia. The aim of this study is to conduct a bibliometric analysis of literature focusing on health behaviors and MCI to summarize the factors and evidence regarding the influence of health behaviors on MCI. Methods: The study performed a bibliometric analysis by retrieving publications from the Science Citation Index and Social Sciences Citation Index subdatabases within the Web of Science Core Collection. Utilizing VOSviewer and CiteSpace software, a total of 2,843 eligible articles underwent co-citation, cokeywords, and clustering analyses. This methodology aimed to investigate the current status, trends, major research questions, and potential future directions within the research domain. Results: The bibliometric analysis indicates that research on healthy behaviors in individuals with MCI originated in 2002 and experienced rapid growth in 2014, reflecting the increasing global interest in this area. The United States emerged as the primary contributor, accounting for more than one-third of the total scientific output with 982 articles. Journals that published the most articles on MCI-related health behaviors included "Journal of Alzheimer's Disease," "Neurobiology of Aging," "Frontiers in Aging Neuroscience," and other geriatricsrelated journals. High-impact papers identified by VOSviewer predominantly cover concepts related to MCI, such as diagnostic criteria, assessment, and multifactorial interventions. Co-occurrence keyword analysis highlights five research hotspots in health behavior associated with MCI: exercise, diet, risk factors and preventive measures for dementia, cognitive decline-related biomarkers, and clinical trials. Conclusion: This study provides a comprehensive review of literature on health behavior in individuals with MCI, emphasizing influential documents and journals. It outlines research trends and key focal points, offering valuable insights for researchers to comprehend significant contributions and steer future studies. [ABSTRACT FROM AUTHOR]

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability. [ABSTRACT FROM AUTHOR]

Fuzzy clustering algorithms are one of the most important techniques for analysing and extracting information from data when working with datasets containing overlapping clusters. Fuzzy clustering provides a more precise representation of complex data structures compared to conventional crisp (hard) clustering approaches. It accomplishes this by allowing data points to be assigned to multiple clusters with different degrees of membership. This paper provides an extensive review of various fuzzy clustering algorithms, such as Fuzzy C - Means (FCM) and its variations including Gustafson - Kessel (GK), Noise Clustering (NC), Possibilistic C - Means (PCM), Possibilistic Fuzzy C - Means (PFCM), Credibilistic Fuzzy C - Means (CFCM), and Kernel Fuzzy C - Means (KFCM). Their underlying mathematical theories, pseudocodes and applicability to different types of data are reviewed. Furthermore, the time complexity of these algorithms are analysed and a detailed comparison is presented to clarify their performance and scalability. By examining both theoretical aspects and empirical results, this study aims to provide a comprehensive knowledge of the trade-offs between computational efficiency and clustering accuracy. This analysis is intended to serve as a resource for researchers and practitioners in selecting appropriate fuzzy clustering techniques for their specific applications. [ABSTRACT FROM AUTHOR]

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Background: Numerous studies have cast light on the relationship between the gastric microbiota and gastric carcinogenesis. In this study, we conducted a bibliometric analysis of the relevant literature in the field of gastric cancer and the gastric microbiota and clarified its research status, hotspots, and development trends. Materials and methods: Publications were retrieved from the Web of Science Core Collection on 18 July 2023. CiteSpace 6.2.R4, VOSviewer 1.6.19.0, and Biblioshiny were used for the co-occurrence and cooperation analyses of countries, institutions, authors, references, and keywords. A keyword cluster analysis and an emergence analysis were performed, and relevant knowledge maps were drawn. Results: The number of published papers in this field totaled 215 and showed an increasing trend. The analysis of funding suggested that the input in this field is increasing steadily. China had the highest number of publications, while the United States had the highest betweenness centrality. Baylor College of Medicine published the most articles cumulatively. Both Ferreira RM and Cooker OO had the highest citation frequency. The journal Helicobacter showed the most interest in this field, while Gut provided a substantial research foundation. A total of 280 keywords were obtained using CiteSpace, which were primarily focused on the eradication and pathogenic mechanisms of Helicobacter pylori, as well as the application of the gastric microbiota in the evaluation and treatment of gastric cancer. The burst analysis suggested that in the future, research may focus on the application of gastric microorganisms, particularly Fusobacterium nucleatum, in the diagnosis and treatment of gastric cancer, along with their pathogenic mechanisms. Conclusion: Current studies have been tracking the eradication of Helicobacter pylori and its pathogenic mechanisms, as well as changes in the gastric microbiota during gastric carcinogenesis. Future research may focus on the clinical application and pathogenesis of stomach microorganisms through bacteria such as Fusobacterium nucleatum. [ABSTRACT FROM AUTHOR]

With large-scale grid-connected renewable energy, new power systems require more flexible and reliable energy storage power sources. Pumped storage stations play an important role in peak shaving, valley filling, and promoting renewable energy consumption. This paper presents the reasonable energy-abandonment operation of a combined power generation system (CPGS), in which a pumped storage station is the core control power, with an ultra-high proportion of renewable energy. Firstly, based on the seasonal characteristics of wind, solar, and load demand, typical days are selected through improved clustering analysis algorithms. Then, a daily optimal scheduling model for combined power generation systems (CPGS) is developed with the goals of economy, low-carbon, and stable operation. Finally, the correlation between the energy-abandonment rate and pumped storage station peak shaving and system optimization operation indicators is obtained by a reasonable energy-abandonment calculation method considering source-grid-load coordination. Taking the operation data of an energy base in the western region of China as an example, when the penetration rate of renewable energy is 60–70% in the future, the operating cost on the power side is greatly affected by the construction of the source side. When the system operates at a planned reasonable energy-abandonment rate of 2%, electricity regulation, load tracking, and daily operating costs all show better performance. [ABSTRACT FROM AUTHOR]