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Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.

We investigate the sub-networks involved in education policy in Israel in recent years, using Mixed Methods Social Networks Analysis - drawn from combined analysis of qualitative and quantitative data. Our objective was to comprehensively explore the Israeli education policy network to deliver an understanding of its structure, actors, and relationships. Our research offers a descriptive, analytical, and interpretive account of the contemporary Israeli education policy sector, including the actors involved, central policy sub-networks and organizations, and their relationships. We analyze dependence relations within the sub-networks using Resource Dependency Theory and Policy Networks Typology. Our findings contribute to understanding the dynamics of Israeli education policy networks and their perceived influence on policy-making and enactment processes. To the international field of educational policy-related research, we introduce a novel category of policy network actors, previously unaddressed as a distinct type. We have termed this category 'Ethos networks' to characterize this group of actors and elaborate on its significance within the field. Additionally, we underline the importance of considering external societal and political factors in education policy-making.

Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace-based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self-reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self-efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self-reports collected before the start of the MOOC offers better understanding of trace-based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self-reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.

In 2014, the OECD-PISA's Governing Board approved the addition of a set of global competence measures to its Programme of Student Assessment. In our paper, we explore whether and how there are discursive shifts between the two framing papers (2016/2018) and what the outcomes are for policy-shaping. To this end, we employ Network Text Analysis to map shifting semantic configurations. We show that (i) the concept of 'global competence' is radically redefined through the simplification and polarisation of the semantic universe surrounding it, (ii) that the concept 'global' becomes a shifting signifier which enables the establishment of an equivalence between the two studied documents, and that (iii) in this process, concepts such as 'culture' are now erased in the 2018 text. Our findings show the dramatic change of approach between the two documents reinforces a narrative that is familiar to the OECD around knowledge economy proxies.

Purpose: Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis. Design/methodology/approach: This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory. Findings: The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations. Research limitations/implications: LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning. Practical implications: LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data. Originality/value: To the best of the authors' knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

Teach for Australia was announced by the Australian Government in 2008, at a corporate dinner sponsored by Swiss multinational investment bank UBS, hosting New York Schools Chancellor Joel Klein. Conceptualising Teach for Australia as a polycentric policy network anchored in venture philanthropy, this paper examines how networks mobilise major government reform, drawing upon both market and state. As an organisation founded, staffed and registered by a major global consultancy firm (Boston Consultancy Group), the paper critiques and questions representation, divisibility and immutability. By drawing upon network ethnography, following people and policy, the paper traces the beginnings of Teach for Australia through inaugural founders, focusing upon co-affiliations. Mapping a heterarchical and polycentric network of non-state and state actors, it highlights indivisible flows of people, money and policy. Thus, this paper seeks to indicate how policy networks embedded in venture philanthropy mobilise policy, whilst being reliant upon formal bureaucracies. This is governance with government. Teach for Australia highlights the nexus between private and public, and fuses together critical connections between market and state, bolstering a major venture philanthropic network that chiefly impacts public schools.

Since social media is increasingly pervasive in modern society, this bibliometric study aims to investigate its educational applications. Using the Scopus database, the bibliometric method analyses publications published between 2010 and 2022. The research indicates that student participation and ease of access are the two main benefits of using social media in the classroom. However, it also spreads misinformation and poses privacy and security risks. Articles that discussed how social media could be used in the classroom were found and organised using a bibliometric analysis based on their subject matter, year of publication, and authors. The research shows that between 2001 and 2020, there was a rise in the number of papers discussing the use of social media in the classroom. In addition, the top five countries in terms of annual publication output include the United States, the United Kingdom, Australia/India, and Canada. To further explore the connections between relevant articles, a co-citation network analysis was performed. Therefore, there must be strict rules and policies for using social media in education to address privacy and security concerns and the spread of false information.

Aim: A well-known method for quantitatively evaluating scholarly work is bibliometric analysis. Best-cited papers raise awareness of the influential publications and patterns in the literature on a specific subject. The aim was to conduct bibliometric analysis to determine most cited articles on vesiculobullous oral lesions. This is the first study on citation analysis with respect to vesiculobullous oral lesions. Materials and Methods: A retrospective data search was explored on December 2022 using the Scopus database. The articles were evaluated, and fundamental data for bibliometric analysis was reviewed. Standard details about the author, linked organizations, publishing year, and place of origin were noted. Statistical analysis was performed using Chi-square analysis. VOSviewer software was used to determine the bibliometric network analysis for co-occurrence among coauthors and commonly used keywords. Results: A total of 344 articles published from 1971 to 2022 were included in the study. A total of 6680 citations and 19.41 citations per article were observed. The journal Archives of Dermatology received the most citation. There was a significant association between the number of citations and the journal type (open access vs. non-open access) (P < 0.05). Four to five highly related clusters with the help of VOSviewer software were found during co-occurrence network analysis. Conclusions: The top 10 articles on vesiculobullous oral lesions that received the most citations were listed in detail in the present study. This will be a valuable resource for academics, clinicians, and researchers in the fields of dermatology, general pathology, oral pathology, and oral medicine. [ABSTRACT FROM AUTHOR]

Is it possible to measure how critical soft skills like leadership or teamwork are from the viewpoint of graduate studies offerings? This paper provides a conceptual and methodological framework that introduces the concept of a bipartite network as a practical way to estimate the importance of soft skills as socio-emotional abilities trained in graduate studies. We examined 230 graduate programs offered by 49 higher education institutions in Colombia to estimate the empirical importance of soft skills from the viewpoint of graduate studies offerings. The results show that: (a) graduate programs in Colombia share 31 soft skills in their intended learning outcomes; (b) the centrality of these skills varies as a function of the graduate pro- gram, although this variation was not statistically significant; and (c) while most central soft skills tend to be those related to creativity (i.e. creation or generation of ideas or projects), leadership (to lead or teamwork), and analytical orientation (e.g. evaluating situations and solving problems), less central were those related to empathy (i.e. understanding others and acknowledgment of others), ethical thinking, and critical thinking, posing the question if too much emphasis on most visible skills might imply an unbalance in the opportunities to enhancing other soft skills such as ethical thinking.

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from "reasoning chain construction tasks." We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks.

Purpose: This paper aims to explore the epistemic connections between three instructional coaches and a first-year in-service teacher during remote planning and debrief meetings. Prior evidence suggests that remote instructional coaching leads to better teaching practices and identifies the instructional coaching moves used to prompt teacher reflection. Design/methodology/approach: In this study, the authors utilized quantitative ethnography and epistemic network analysis (ENA) approaches to explore the epistemic frames of three remote university-based instructional coaches as they supported a first-year in-service teacher. Findings: Quantitative ENA findings shed light on the network connections between instructional coaches and teachers, as well as the epistemic frames observed during planning and debrief meetings. Additionally, the authors provide qualitative findings that complement and reinforce the quantitative results. Research limitations/implications: All data collection occurred via Zoom, and the class was in a hybrid modality, with some students attending class in person and some attending remotely via Zoom. This unique context could have impacted the epistemic connections surrounding technology and logistics. Practical implications: This study provides a practical codebook for use in future studies that explores instructional coaching. Findings from this study can be used to inform instructional coaching decisions. Originality/value: The ENA findings helped deepen the authors' understanding of how instructional coaches can support a first-year in-service teacher during planning and debrief meetings in several ways. Additionally, this study presents a unique context given the COVID-19 pandemic and the remote model of instructional coaching.

Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords "Sentinel", "deep learning" (DL), and "LSTM" (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images' accuracy. [ABSTRACT FROM AUTHOR]

The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem's scale, complexity, and constraints, necessitating careful consideration of each method's strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources. [ABSTRACT FROM AUTHOR]

Textile, apparel, and fashion (TAF) industries contribute significantly to global environmental pollution at every point of the supply chain. Clothing manufacturing and transportation produce a large volume of waste and high greenhouse gas emissions, often taking advantage of cheap labor in developing countries. As a result, stakeholders are becoming more aware of the effect of the textile, apparel, and fashion industries on the climate and human rights, thus pushing businesses to mitigate their environmental damage. This paper offers a systematic literature review of sustainability trends in the TAF industries in the last 20 years. Bibliometric tools are also used to support the content analysis of the papers. The findings reveal three primary research areas in the TAF context: consumers' behaviour towards sustainable clothing, circular economy initiatives, and sustainability challenges across the whole supply chain. As a result, this study highlights literature gaps and provides future research suggestions for each identified research cluster. In addition, drivers and barriers to implementing corporate social responsibility and circular economy practices are identified. Consequently, this study will help researchers and academicians work in this area to identify unexplored sub-fields, which reflect some potential investigation areas for expanding scientific literature on the topic. Finally, this study supports practitioners and managers in exploring the main research themes addressed in the scientific field, providing knowledge to improve and align business models with current sustainability trends. [ABSTRACT FROM AUTHOR]

Over the last decade, human resource (HR) analytics has been widely discussed in the landscape of human resource management due to its dynamic capacity to transform into a data-driven decision-making system for optimizing workforce management. The aim of this paper is to provide a comprehensive summary of the emerging trends and themes of HR analytics drawn from previous literature and offer valuable insight into academia, researchers, practitioners, and policymakers. This paper employs a bibliometric methodology while using RStudio, Biblioshiny, and VOSviewer tools to collect and analyze 102 articles from the Scopus database from January 2008 to September 2023. The findings of the paper reveal current state-of-the-art research in the HR analytics domain while exploring key themes and areas for further study. This study offers practical guidelines for policymakers and contributes to the existing knowledge domain of HR analytics. [ABSTRACT FROM AUTHOR]

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale "rural-urban" population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world. [ABSTRACT FROM AUTHOR]

In light of continuous advancements in science and technology, the global economy is experiencing rapid growth. However, this growth has been accompanied by significant depletion of natural resources and environmental degradation. Consequently, there is a burgeoning global emphasis on energy conservation, emissions reduction, and sustainable development. In this study, based on the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) databases from 1990 to 2022, a statistical analysis of energy conservation and emission reduction in alignment with Sustainable Development Goals (SDGs)-related publications was undertaken using biblimometric methods. The findings reveal that (1) In recent years, there has been a discernible increase in global research on this subject, especially since 2009, with a sustained trend of exceeding 100 publications per annum. China prominently contributing to this domain, the proportion reached 34.2%, reflecting a growing emphasis on eco-friendly development trends. (2) Due to the burgeoning significance of energy conservation and emission reduction, there has been a notable escalation in research efforts pertaining to "Energy and Fuels," "Environmental Science" and "Green and Sustainable Science and Technology" and other related subjects. (3) Regarding the keyword analysis, "renewable energy" as the most frequently encountered term, often paired with "CO2 emissions." This association underscores the pivotal role of renewable energy technologies in advancing green development initiatives and mitigating emissions. (4) China, United States and United Kingdom occupy central positions in terms of both paper publication volume and collaborative networks, collectively accounting for about 54.7%, and these countries are pivotal contributors to the scholarly discourse on sustainable development and environmental conservation. (5) From 1990 to 2022, the top 20 cited articles predominantly address diverse sub-goals of Sustainable Development Goal 7, with a common emphasis on enhancing energy efficiency, sustainability and renewable energy. These findings furnish valuable analytical insights for subsequent researchers investigating energy conservation and emission reduction as well as sustainable development endeavors. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry. [ABSTRACT FROM AUTHOR]

This paper reports on a scientometric analysis bolstered by human-in-the-loop, domain experts, to examine the field of metal-organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human-in-the-loop processes, can help develop a comprehensive view of a field's research trends, influential works, and specialized topics. A scientometric analysis was conducted, integrating natural language processing (NLP), topic modeling, and network analysis methods. The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals. MOF researcher feedback was incorporated into our method. The data sample included 65,209 MOF research articles. Python3 and software tool VOSviewer were used to perform the analysis. The findings demonstrate the value of including domain experts in research workflows, refinement, and interpretation of results. At each stage of the analysis, the MOF researchers contributed to interpreting the results and method refinements targeting our focus on MOF research. This study identified influential works and their themes. Our findings also underscore four main MOF research directions and applications. This study is limited by the sample (articles identified and referenced by the Cambridge Structural Database) that informed our analysis. Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research. Additionally, the results will help domain scientists target future research directions. To the best of our knowledge, the number of publications collected for analysis exceeds those of previous studies. This enabled us to explore a more extensive body of MOF research compared to previous studies. Another contribution of our work is the iterative engagement of domain scientists, who brought in-depth, expert interpretation to the data analysis, helping hone the study. [ABSTRACT FROM AUTHOR]

Modeling complex systems that consist of different types of objects leads to multilayer networks, in which vertices are connected by both inter-layer and intra-layer edges. In this paper, we investigate multiplex networks, in which vertices in different layers are identified with each other, and the only inter-layer edges are those that connect a vertex with its copy in other layers. Let the third-order adjacency tensor A ∈ R N × N × L and the parameter γ ≥ 0 , which is associated with the ease of communication between layers, represent a multiplex network with N vertices and L layers. To measure the ease of communication in a multiplex network, we focus on the average inverse geodesic length, which we refer to as the multiplex global efficiency e A (γ) by means of the multiplex path length matrix P ∈ R N × N . This paper generalizes the approach proposed in [15] for single-layer networks. We describe an algorithm based on min-plus matrix multiplication to construct P, as well as variants P K that only take into account multiplex paths made up of at most K intra-layer edges. These matrices are applied to detect redundant edges and to determine non-decreasing lower bounds e A K (γ) for e A (γ) , for K = 1 , 2 , ⋯ , N - 2 . Finally, the sensitivity of e A K (γ) to changes of the entries of the adjacency tensor A is investigated to determine edges that should be strengthened to enhance the multiplex global efficiency the most. [ABSTRACT FROM AUTHOR]

This paper seeks to bridge the gap between archival text analysis and network analysis by applying network clustering methods to analyze the coverage of Bulgaria in 123 issues of the newspaper Osservatore Romano published between January and May 1877. Utilizing optical character recognition and generalized homogeneity blockmodeling, the study constructs networks of relevant keywords. Those including the sets Bulgaria and Russia are rather isomorphic and they largely overlap with those for Germany, Britain, and War. In structural terms, the blockmodel of the two networks exhibits a clear core-semiperiphery-periphery structure that reflects relations between concepts in the newpaper's coverage. The newspaper's lexical choices effectively delegitimised the Bulgarian national revival, highlighting the influence of the Holy See on the newspaper's editorial line. [ABSTRACT FROM AUTHOR]

Pile of urban solid waste is one of the worst consequences of rapid urbanization especially in developing countries and managing them is even bigger a problem here. Being an old city, Burdwan town of Purba Burdwan district of West Bengal is no exception. Due to the infrastructural drawback, inefficient planning and policy action, and negligence from the citizens and the municipal waste workers, in almost every ward of the Municipality, one can find waste dumping along the road and drainage sides. This not only hinders aesthetics but appears to be a breeding ground for all unhygienic diseases, air pollution, and water pollution which makes the children suffer most. In such a severe condition, this paper tries to seek the status of the municipal waste management practices, the reasons behind its current scenario and suggests some infrastructural boosts to optimize the waste-stagnated situation through GIS Network Analysis techniques for this study, both the primary and secondary data were used via different methods of questionnaire surveying and data provided by the Municipal authority respectively. We tried to capture the perceptions from all the stakeholders involved and finally, the conflicting words from all the parties have brought an outcome. [ABSTRACT FROM AUTHOR]

Background: Somatic symptoms and related factors in patients with chronic heart failure have been extensively researched. However, more insight into the complex interconnections among these constructs is needed, as most studies focus on them independently from each other. Aims: The aim of this study is to gain a comprehensive understanding of how somatic symptoms and related factors are interconnected among patients with chronic heart failure. Methods: A total of 379 patients were enrolled. Network analysis was used to explore the interconnections among the somatic symptoms and related risk factors. Results: The four core symptoms of chronic heart failure were daytime dyspnea, dyspnea when lying down, fatigue and difficulty sleeping. Within the network, the edge weights of depression–anxiety, subjective social support–objective social support, and subjective social support–social support availability were more significant than others. Among physiological, psychological and environmental factors, the edge weights of NYHA–dyspnea, depression–difficulty sleeping, and social support availability–dyspnea when lying down were more significant than others. Depression and anxiety had the highest centrality, indicating stronger and closer connections with other nodes. Conclusions: Psychological and environmental factors stood out in the network, suggesting the potential value of interventions targeting these factors to improve overall health. Summary statement: What is already known about this topic? Limited studies have explored core somatic symptoms in patients with chronic heart failure using network analysis.There is a lack of a holistic perspective when studying symptoms in patients with chronic heart failure and their association with various factors. What this paper adds? This study identifies daytime dyspnea, dyspnea when lying down, fatigue and difficulty sleeping as the four core symptoms of chronic heart failure.Targeting these core symptoms in treatments and management can potentially lead to improvements in patients' overall health.The network analysis highlights the importance of psychological and environmental factors, suggesting that interventions targeting these factors may contribute to overall health improvement. The implications of this paper: It provides evidence of core symptoms of chronic heart failure.It provides evidence of the complex interconnections among symptoms and related factors in chronic heart failure.It provides implications for future interventions and strategies in managing chronic heart failure. [ABSTRACT FROM AUTHOR]

This paper discusses the increasing severity of droughts due to climate change. It emphasises the complexity of defining drought and the diverse perspectives among stakeholders. Lots of stakeholders with unclear responsibilities are involved, which can lead to uncertainty and indecisiveness in addressing the issue. To tackle this, the present paper proposes a methodology to dissect drought systems and reveal the intricate relationships between their components. This approach combines a comprehensive definition of drought with the "Water Resilience Assessment Framework" and an "Actor-Relational Approach", visualised through network analysis. The methodology was applied to a case study situated in the Leie Basin of Flanders, Belgium. By employing this strategy, policymakers and mediators can gain a deeper understanding of drought, identify its root causes, and prioritise necessary changes for more effective drought and water management. [ABSTRACT FROM AUTHOR]

It is a matter of debate whether the discipline independence in discipline formation narrows its interdisciplinarity. It is also less well understood how disruptive works emerge in investigative practice rather than a theory‐driven approach. Aggregation‐induced emission (AIE) is an atypical photophysical phenomenon, in which the whole (aggregate) is brighter than the sum of its parts (single molecule). Through measuring and computing the cognitive extent and evolution of research on AIE, including topics, epistemic‐social collaborative networks, interdisciplinarity, emergent concepts, core concept networks and knowledge flow, this study shows that a cross‐research scales concept and its practice can establish new bridges in the sciences and promote disruptive work. Focusing on mesoscale entities, scientists from many different branches of science are involved in theoretical research on mechanisms, as well as developing different AIE systems for applications. The data analysis in this study provides details showing how non‐reductionist concepts based on new scientific discoveries cross traditional disciplinary boundaries and aggregate interdisciplinary research. The emergence and evolution of the AIE field implies that scientists may be motivated to embrace nonreductionist ideas at different research scales, leading to a more permeable field boundary. [ABSTRACT FROM AUTHOR]

A conducive business environment is crucial for establishing a sustainable market economy. This paper aims to quantitatively analyze the evolution of China's business environment policy, offering valuable insights for future optimization. Focusing on 1335 national-level business environment policies from 2006 to 2023, this study employed a social network analysis (SNA) and the latent Dirichlet allocation (LDA) model to investigate policy changes in terms of the issuing rhythm, collaborating departments, and policy attention. The findings reveal: (1) China has established a mature policy system for its business environment, with both an increasing number and improving impact of related policies. (2) A governance network of the business environment has gradually formed, with the participation of differentiated departments and the domination of one central unit, ensuring efficient collaboration and clear instruction. (3) The attention of the business environment policy has shifted from attracting foreign investment and developing the industrial chain to simplifying bureaucratic approval procedures, smoothing trade circulation, and ensuring legal reliability. This paper's results serve as an empirical contribution to the field of business environment policy research. [ABSTRACT FROM AUTHOR]