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Combining traditional and digital methods, this essay models a new toolbox for analyzing the knotty problem of how many letters in the State Papers were introduced to the archive through interception. Estimates by scholars vary widely. Using the State Papers Foreign metadata for the reign of Elizabeth I, we employ a method of "layered searching" to find letters involving a third party. By structuring the communications as a network, we weight edges between correspondents and the Tudor State to distinguish acts of interception from forwarded letters or spy reports. These methods suggest likely candidates for interception with a sliding scale of confidence, helping to identify two broad models of letter interception outside England, which we dub "military-diplomatic" and "catch-and-release." Overall, we estimate more than 5 percent of the collection to have been intercepted, highlighting a continuous network of intercepting officials from the Dutch Revolt to the imprisonment of Mary Stuart. [ABSTRACT FROM AUTHOR]

The aim of this paper is to foreground network analysis as a statistical lens through which higher education institutions can articulate their own process of striving for teaching excellence, and how it is constituted in their own contexts. The paper offers an approach to analysis that extends the frontiers of methodologies in 'measurement' of teaching excellence; one that responds to the shortcomings of the current methodologies, critiqued for being reductive, performative, alienating, and promoting closure and convergence in how they assess teaching excellence. We review epistemological and methodological shifts in conceptualising teaching excellence and measurement that are required to work with our methodology, as well as provide statistical details, for anyone who wishes to reproduce our profiled examples. We thus build in the paper a link between the theory of (teaching) excellence and practice (of measurement) and champion a theory-based approach to the methodology of educational metrics. [ABSTRACT FROM AUTHOR]

This paper explores the Polish Jew journal as a pivotal second-tier record for advancing Holocaust studies and Jewish genealogy. Traditionally underutilized in academic research, this periodical provides a unique repository of names and narratives of Holocaust victims, filling crucial gaps in primary record collections. The investigation centers on the journal's potential not only to contribute names to existing databases of Holocaust victims—many of whom are still unrecorded—but also to enhance genealogical methods through the integration of network analysis. By examining Polish Jew, this study illustrates how second-tier records can extend beyond mere supplements to primary data, acting instead as vital tools for reconstructing complex social and familial networks disrupted by the Holocaust. The paper proposes a methodological framework combining traditional genealogical research with modern network analysis techniques to deepen our understanding of Jewish community dynamics during and after World War II. This approach not only aids in identifying individual victims and survivors but also in visualizing the broader interactions within Jewish diaspora communities. This research underscores the significance of Polish Jew in the broader context of Holocaust remembrance. It offers a novel pathway for the future of Jewish genealogical research, advocating for the strategic use of second-tier records in scholarly investigations. [ABSTRACT FROM AUTHOR]

This paper delivers an in-depth bibliometric analysis of distributed acoustic sensing (DAS) research within the realm of geophysics, covering the period from 2012 to 2023 and drawing on data from the Web of Science. By employing bibliographic and structured network analysis methods, including the use of Bibliometrix and VOSviewer®, the study highlights the most influential scholars, leading institutions, and pivotal research contributions that have significantly shaped the field of DAS in geophysics. The research delves into key collaborative dynamics, unraveling them through co-authorship network analysis, and delves into thematic developments and trajectories via comprehensive co-citation and keyword co-occurrence network analyses. These analyses elucidate the most robust and prominent areas within DAS research. A critical insight gained from this study is the rise of 'photonic seismology' as an emerging interdisciplinary domain, exemplifying the fusion of photonic sensing techniques with seismic science. This paper also discusses certain limitations inherent in the study and concludes with implications for future research. [ABSTRACT FROM AUTHOR]

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics–by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)–using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts. [ABSTRACT FROM AUTHOR]

Renewable energy resources can potentially solve global warming issues that arise from the large amounts of CO2 emissions from currently used energy resources. Microbial fuel cells (MFC) can produce electrical energy from the environment, making them attractive for energy harvesting. The structures and mechanisms of MFC have been studied for a few decades, and the performance of MFC has constantly been improved using new materials in the devices or by interacting microbial species on the anode chamber of MFC. However, the ecological phenomena, especially inter‐species interactions, within the microbial community in MFC are still largely unknown. Analysis and control of the microbial community have high potential to improve the MFC performance. In this idea paper, I have provided ways to analyze the species interactions in the microbial communities of MFC: (a) empirical dynamic modeling for detecting species interactions in MFC, and (b) network analysis to explore the core microbe species in the MFC. The interaction analyses of the microbial communities in MFC devices can potentially contribute to improving the performance of MFC. [ABSTRACT FROM AUTHOR]

A central issue within sport management is the extent to which the field should develop a distinctive theoretical knowledge base. This paper empirically investigates the connectedness within (intrafield) and between (interfield) management and sport management disciplines in one specific knowledge domain—institutional theory. We utilized a database of 188 sport-related institutional studies and conducted a citation network analysis of the aggregated reference lists from these articles. We argue that the fields of management and sport management act like "ships in the night." That is, as the field of sport management has become more distinctive, the field is becoming less connected with general management literature and contemporary theoretical discussions. Potential implications for sport management scholarship and understanding the nature of the field are discussed, along with how it may be possible (if desired) to bridge the gap between sport and management research. [ABSTRACT FROM AUTHOR]

Purpose: This study provides a bibliometric analysis of bank efficiency and competition over the past years (from 1993 to 2022) to (1) discover the past and current state of knowledge on bank competition and efficiency, (2) identify leading and authoritative journals and scholars who made significant contributions to the distribution of knowledge and impact, (3) identify nations that made a significant contribution and impact to the literature and (4) identify the structure of collaboration that exists between scholars in the areas of bank competition and efficiency and key thematic areas. Design/methodology/approach: A total number of 868 documents made up of articles, reviews, book chapters, book and conference papers from the Scopus database were gathered. This study used a bibliometric analytic approach. Findings: The number of documents on bank competitiveness and efficiency has increased significantly, as have their total publications, citations and national output. Additionally, the most esteemed and prestigious academic journals of eminent academics who have had a significant impact on the dissemination of knowledge on bank efficiency and competition literature champion papers on banking efficiency and competition. In terms of citation performance and collaborative efforts, the United States tops the developed countries, led by China, which is also the most productive. Additionally, single-country publications predominate in the literature, with China ranking first among the top five countries with corresponding authors. While the Lerner index, H-statistic, concentration index and market power were used to measure bank competitive behaviour, the data envelopment analysis approach predominates efficiency estimation techniques that are linked to cost, profit or revenue, scale, technical and productivity indexes. Originality/value: This study is one of the first to offer bibliometric evidence of both bank competition and efficiency. It also offers proof of the distribution of knowledge and intellectual structure of the concepts and concerns in bank competition and efficiency. [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]

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]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews. [ABSTRACT FROM AUTHOR]

Despite advances in myocardial reperfusion therapies, acute myocardial ischaemia/reperfusion injury and consequent ischaemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardio-protective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischaemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signalling networks activated in the ischaemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications, and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischaemia, putting it in the context of the human 'diseasome'. Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischaemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischaemia/reperfusion injury and ischaemic heart failure in the post-genomic era. [ABSTRACT FROM AUTHOR]

China has established a nationwide formal e‐waste disassembly system based on the extended producer responsibility principle. However, the system mainly focuses on material recycling while excluding the reuse of used electrical and electronic equipment (used EEE) due to the complexity of flows and transactions during the reuse stage. Recently, emerging online platforms have played an increasingly crucial role in the value chain of reuse and recycling, significantly improving the visibility of flows in these activities. This paper aims to depict the spatial structure of the used EEE and e‐waste flow networks using a multi‐scale analysis framework. Using spatial analysis tools in complex network analysis, we characterize cross‐city reuse flows in China. A clear regional pattern of a hierarchical reuse network of notable regional hubs in certain regions in China is revealed in this analysis. The role of regulation is demonstrated in the comparison of the spatial flows in reuse and recycling. In conclusion, it is proposed that reuse should be included and emphasized in the management of waste electrical and electronic equipment in China to maximize the value of circularity. [ABSTRACT FROM AUTHOR]

Hydrodynamic models are used to analyse water networks (water distribution, drainage, surface water, district heating, etc.). The non-linear nature of water flows necessitates the use of iterative solution methods in hydraulic modelling. This requires a relatively large computational effort. To reduce this effort, networks, network forcing and/or the flow in networks are often simplified and analysed using the Graph Theory. The simplification options depend on the network characteristics. There are many topological features to describe Graph-based networks. In this paper, these characteristics are summarised, applied on 7 urban drainage networks and discussed. As the topological features do not describe the networks in a uniform manner, a new type of topological characterisation of looped drainage networks (Network Linearisation Parameter, NLP) is proposed based on linearized hydraulics and bottlenecks identified in paths to outfalls. [ABSTRACT FROM AUTHOR]

The current paper aimed to investigate the network structure and centrality indexes of hypersensitive narcissism using the hypersensitive Narcissism Scale (HSN). Additionally, we aimed to explore its relationships with dark triad personality aspects. A globally diverse sample of "53,981" participants (47.9% non-United States responders) completed the HSN and Dark Triad Dirty Dozen scale (DTD). We estimated the network structure across genders to determine the core characteristics of hypersensitive narcissism. Additionally, bridge and central nodes (characteristics) were identified. All analyses were performed using R-Studio programming software. The network comparison test indicated significant differences in the network structures between males and females (Network-Invariance: 0.0489, P < 0.01; Global Strength Invariance: 0.101, P < 0.01). In the network of HSN for male participants, characteristics with the highest strength centrality were "Highly affected by criticism" (HSN.2, strength = 1.08) and "Self-absorbed in personal pursuits" (HSN.8, strength = 1.28). For female participants, "Self-absorbed in personal pursuits" (HSN.8, strength = 1.32) and "privately annoyed by others' needs" (HSN.10, strength = 1.21) were the highest central characteristics. The assessment of bridge strength indicated that nodes HSN.2 (Highly sensitive to criticism), scoring 0.42, and DTD.1 (Tendency to manipulate for gain, a component of Machiavellianism), scoring 0.428, showed the highest bridge strength values. The current study identified core characteristics of hypersensitive narcissism and its correlation with dark triad personality, revealing gender-specific patterns and bridging symptoms between the two constructs. These findings showed that focusing on these core characteristics may be advantageous in treating individuals exhibiting elevated levels of narcissism. [ABSTRACT FROM AUTHOR]

In many real-world networks, it is often observed that subgraphs or higher-order structures of certain configurations, e.g. triangles and by-fans, are overly abundant compared to standard randomly generated networks (Milo et al. 2002). However, statistical models accounting for this phenomenon are limited, especially when community structure is of interest. This limitation is coupled with a lack of community detection methods that leverage subgraphs or higher-order structures. In this paper, we propose a new community detection method that effectively uses higher-order structures in a network. Furthermore, for the community detection accuracy, under an edge-dependent network model that consists of both community and triangle structures, we develop a finite-sample error bound characterized by the expected triangle degree, which leads to the consistency of the proposed method. To the best of our knowledge, this is the first statistical error bound and consistency result for community detection of a single network considering a network model with dependent edges. We also show, in both simulation studies and a real-world data example, that our method unveils network communities that are otherwise invisible to methods that ignore higher-order structures. [ABSTRACT FROM AUTHOR]

This work aims to study the trajectory of the architect Frei Otto (FO) using an exploratory analysis of his projects, which pays special attention to the conceptual and chronological temporal evolution, the type of structures designed, and the relationship with his partners for his built and unbuilt projects. In the latter case, the projects developed, not requested, that are not part of his built works have been approached (i.e., not considered under competition). The main results indicate that FO's architectural trajectory, from a chronological point of view, was intense, which is generally reflected throughout the years. The 1950s–1970s were very productive. In addition to the large number of projects, there were innovative designs for quantity and structural innovation in Germany and other countries (e.g., France and Saudi Arabia). The analyses that were carried out made it possible to ascertain FO's professional and academic influence, which has been reflected in his projects. Likewise, his relationships with his collaborators have been long-lasting in some cases. Practical Applications: This work aimed to study the work of the architect and engineer Frei Otto (FO), of recognized prestige by the awards received and his colleagues. Some mention the scarce diffusion of his work. The interest of this work lies mainly in making known to professionals, academics, and the general public, in a short format, such as that used in scientific papers, the design of his structures, built and unbuilt. As Félix Candela mentions about FO's career, he is a typical and outstanding example of architectural innovation who concentrated his vocation and efforts on the study and research of tensile structures. His international projection and toward the citizen, give Frei Otto academic interest. This work is developed from different perspectives, which include chronological, type of structure, the location and function of the project, and the collaborators he worked with, with whom he shared his innovation and construction activities. Attention has been paid to a collection of unpublished or little-known projects by FO. [ABSTRACT FROM AUTHOR]

If progress is to be made toward improving geohazard management and emergency decision-making, then lessons need to be learned from past geohazard information. A geologic hazard report provides a useful and reliable source of information about the occurrence of an event, along with detailed information about the condition or factors of the geohazard. Analyzing such reports, however, can be a challenging process because these texts are often presented in unstructured long text formats, and contain rich specialized and detailed information. Automatically text classification is commonly used to mine disaster text data in open domains (e.g., news and microblogs). But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order. These deficiencies are most obviously exposed in long text fields. Therefore, this paper uses the bidirectional encoder representations from Transformers (BERT), to model long text. Then, utilizing a softmax layer to automatically extract text features and classify geohazards without manual features. The latent Dirichlet allocation (LDA) model is used to examine the interdependencies that exist between causal variables to visualize geohazards. The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards. Moreover, it can help users visualize causes, processes, and other geohazards and assist decision-makers in emergency responses. [ABSTRACT FROM AUTHOR]

This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research. [ABSTRACT FROM AUTHOR]

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. [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]

Purpose: This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application. Design/methodology/approach: Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis. Findings: As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one. Research limitations/implications: This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted. Originality/value: This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS. [ABSTRACT FROM AUTHOR]

For several decades, a leading paradigm of how to quantitatively assess scientific research has been the analysis of the aggregated citation information in a set of scientific publications. Although the representation of this information as a citation network has already been coined in the 1960s, it needed the systematic indexing of scientific literature to allow for impact metrics that actually made use of this network as a whole, improving on the then prevailing metrics that were almost exclusively based on the number of direct citations. However, besides focusing on the assignment of credit, the paper citation network can also be studied in terms of the proliferation of scientific ideas. Here we introduce a simple measure based on the shortest-paths in the paper's in-component or, simply speaking, on the shape and size of the wake of a paper within the citation network. Applied to a citation network containing Physical Review publications from more than a century, our approach is able to detect seminal articles which have introduced concepts of obvious importance to the further development of physics. We observe a large fraction of papers co-authored by Nobel Prize laureates in physics among the top-ranked publications. [ABSTRACT FROM AUTHOR]

Human-AI conversation provides a natural, unobtrusive, yet under-explored way to investigate trust dynamics in human-AI teams (HATs). In this paper, we modeled dynamic trust evolution in conversations using a novel method, trajectory epistemic network analysis (T-ENA). T-ENA captures the multidimensional aspect of trust (i.e., analytic and affective), and trajectory analysis segments conversations to capture temporal changes of trust over time. Twenty-four participants performed a habitat maintenance task assisted by a conversational agent and verbalized their experiences and feelings after each task. T-ENA showed that agent reliability significantly affected people's conversations in the analytic process of trust, t (38.88) = 15.18 , p < 0.001 , Cohen ′ s d = 4.72 , such as discussing agents' errors. The trajectory analysis showed that trust dynamics manifested through conversation topic diversity and flow. These results showed trust dimensions and dynamics in conversation should be considered interdependently and suggested that an adaptive conversational strategy for managing trust in HATs. [ABSTRACT FROM AUTHOR]

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Countries are experiencing a surge in political, economic, and financial integration, consequently shaping international market linkages. Financial crises can rapidly spread between countries, emphasizing the need to monitor and assess stock market connections. This paper investigates the degree of financial market connectedness using daily stock returns from January 1997 to August 2017 for 13 countries, both developed and developing. The connectedness measure of Diebold and Yilmaz (2009-2012) is applied to examine the connectedness of stock market returns and the direction of spillovers for all stock markets. This study also analyzes the dynamic connectedness from the U.S. stock market to all other stock markets. The results indicate that the U.S. stock market is the most influential stock market to the others. The results of the dynamic analysis show that connectedness changes over time, specifically during turmoil periods. Most developed countries are transmitters of return spillover shocks while developing countries are recipients. [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]

Background: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. Results: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. Conclusions: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability. [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]

The nature and the complex intermingling of the difficulties encountered by Inuit youth calls for a close collaboration between health and social professionals from different fields, the families of youth and their communities. Promising practices within Indigenous communities are described as being collaborative, community-driven and culturally grounded. Ideally most professionals would be of Indigenous heritage, and families would have access to Indigenous healing approaches if desired. Despite the documented importance of collaboration and self-determination of services, very little is known regarding how collaboration takes place in Indigenous communities, and what the facilitators and barriers to collaboration may be. This paper presents results from a study that took place in Nunavik Quebec between 2014 and 2015 and is based on qualitative interviews conducted with 42 key informants including community workers, social workers, doctors, teachers, family members and more. Using a combination of network and qualitative analyses, we explore the principal characteristics of health and social networks in child and youth mental health in Nunavik, Québec. Our findings show that while collaboration is desired by all participants, at the time of the interviews, non-Inuit and institutional actors were more central in collaborations than informal and community ressources, even if they were considered essential elements in service delivery. Participants spoke of many ways to foster collaboration; however, our project shows that collaboration is often hindered by structural and colonial challenges. [ABSTRACT FROM AUTHOR]

While positive psychology has been in development for more than 20 years, positive psychologists still face the challenge of finding optimal measurements of well-being. The PERMA framework provides a new understanding of well-being based on years of research. However, whether it adds value in terms of being distinct from the already established measures of Subjective Well-Being (SWB) has recently been under debate. Certain characteristics of PERMA make it unsuited for traditional factor analysis and data analysis techniques. In this paper, we argue that a network approach is more suitable for understanding the interrelated nature of PERMA components. We then show how the components of PERMA and SWB are distinct using exploratory graphical analysis. Our results show that while PERMA predicts SWB, PERMA provides us a more nuanced way to understand the interrelated antecedents of well-being, which we refer to as the web of well-being. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores international trade of the Chinese manufacturing industries through the lenses of network analysis (NA) to visualise the world trade network of the Chinese economy, describe its topology and better explain the international organisation of Chinese manufacturing industries. Design/methodology/approach: The authors built a dataset of 40,550 Chinese companies and their 107,026 subsidiaries in 118 countries from Orbis-BVD and used a NA to investigate the connection between China and other countries. In particular, the authors studied the connections between Chinese companies and their subsidiaries in order to build a network of Chinese industries. Findings: The authors found that the network of Chinese companies is ramified but not wide and it can be divided into two clusters. Moreover, the relations between China and other peripheral countries are strongly mediated by a few leading locations (e.g. Hong Kong and the USA). Originality/value: This paper contributes to the literature in several ways. First, the authors provide empirical evidence on the magnitude and ramifications of Chinese enterprises in the world. The existing studies generally focus on applying NA to sectoral insights (Mao and Yang, 2012; Shaikh et al., 2016; Zheng et al., 2016; Wanzenbö ck, 2018; Krichene et al., 2019), whereas in this work the authors take a comprehensive view of the entire Chinese manufacturing system. Second, this paper complements the existing literature identifying the difference between cluster levels in Chinese manufacturing (Wu and Jiang, 2011) by proposing a cluster centralisation method to analyse the international network of Chinese firms rather than just the national network. Finally, the results also shed light on the international trade relationship between China, Hong Kong and the USA. [ABSTRACT FROM AUTHOR]

Peer attention is a significant feature in the horizontal cross-loading of EU countries when there are many uncertainties in the decision-making. Taking the case of the 28-EU countries' interactive media attention during China's second Belt and Road Initiative (BRI) forum in April 2019, this paper explores the two questions of who pays attention to whose action to the BRI, and what are the determinants of the peer attentions? The measurements from the network analysis identify the positions and roles of EU countries in the attention network. The gravity model further examines the determinants on different levels of peer attention flows. The findings suggest that the peer attention network the EU countries formulate is a core-periphery structure where big powers and forerunners construct the core and connect other big power and peripheral countries from the nearby to the far-reaching. Substantively, this paper contributes to the literature on the horizontal interaction of countries in response to China's engagement. Methodologically, the network analysis is an innovative method to study how a country receives the influence imposed by a single country, by a cluster of countries and the peer attention network as a whole. [ABSTRACT FROM AUTHOR]

The idea of predicting links in multiplex networks has gained increasing interest in recent years. In this paper, we propose a comprehensive framework which benefits from the structural information of auxiliary layers to predict links on a target layer of multiplex networks. Specifically, we assume that the likelihood of the existence of a link between two nodes is determined by the contributions from both the nodes' neighbors on the target layer and their counterparts' neighbors on a manually network generated by auxiliary layers. The final likelihood matrix is acquired by an iterative algorithm. In addition, we show advantages of our methods for predicting links on sparse and dense networks as well as on networks with assortative and disassortative structural layers. The effectiveness of the proposed methods are evaluated through extensive experiments on real-world multiplex networks. [ABSTRACT FROM AUTHOR]

The constitutive organisations of governance systems tend to multiply and diversify over time. In parallel, a tendency toward homophily favours the creation of clusters of homogeneous organisations. Yet, few systems drift to the point of disconnection or dislocation. Several remain sufficiently cohesive to allow adaptation and other complex properties to emerge. To maintain equilibrium between order and chaos, some organisations must create bridges between otherwise homogeneous groups. This paper argues that hybrid organisations are ideally suited for this role. By their nature, hybrids share characteristics with different types of organisations in global governance, allowing them to overcome strict homophily and create bridges across clusters. Hybrids benefit from acting as brokers and in doing so, they facilitate the exchange of material and ideational resources across the governance system. Even if it is not their intention, they contribute to holding governance systems together and counterbalance the effect of homophily. We illustrate this argument by examining the space governance system and the hybrid nature, bridging activities, and brokerage role of the European Space Agency. [ABSTRACT FROM AUTHOR]

This article delves into the analysis and identification of influential individuals within social networks, a fundamental issue in the realm of social network science and complex network analysis. Within social networks, the impact of individuals on information propagation, shaping beliefs, and influencing the behavior of others is of paramount importance. Most of the current methods are based on the assumption that the entire graph is visible. However, this assumption does not hold for many real-world graphs. This study is conducted to extend current maximization methods, with link prediction techniques to pseudo-visibility graphs. The proposed method in this paper leverages graph models and influence algorithms to identify and select influential individuals within a social network. It begins by employing graph models to predict connections within the network and subsequently employs influence algorithms to opt for the most suitable individuals for exerting influence within the network. The primary advantage of this approach lies in its capability to enhance communication and influence within social networks with minimal observation. This method aids in a better understanding of the network's structure and the behaviors of its members, ultimately facilitating more effective information dissemination. In this study, we implemented five concepts for influence maximization in the context of the proposed method. Our results indicate that the algorithms perform best when considering lower diffusion coefficients. This suggests that focusing on lower diffusion coefficients allows the algorithm to demonstrate its superiority. Additionally, our findings reveal a notable consistency in results across various graph removal scenarios, highlighting a key advantage of our proposed approach on real-world graphs. [ABSTRACT FROM AUTHOR]

Complexity science perspectives like the network approach to psychopathology have emerged as a prominent methodological toolkit to generate novel hypotheses on complex etiologies surrounding various mental health problems and inform intervention targets. Such approach may be pivotal in advancing early intervention of suicidality among the younger generation (10–35 year-olds), the increasing burden of which needs to be reversed within a limited window of opportunity to avoid massive long-term repercussions. However, the network approach currently lends limited insight into the potential extent of proposed intervention targets' effectiveness, particularly for target outcomes in comorbid conditions. This paper proposes an in silico (i.e., computer-simulated) intervention approach that maps symptoms' complex interactions onto dynamic processes and analyzes their evolution. The proposed methodology is applied to investigate potential effects of changes in 1968 community-dwelling individuals' distress symptoms on their suicidal ideation. Analyses on specific subgroups were conducted. Results were also compared with centrality indices employed in typical network analyses. Findings concur with symptom networks' centrality indices in suggesting that timely deactivating hopelessness among distressed individuals may be instrumental in preventing distress to develop into suicidal ideation. Additionally, however, they depict nuances beyond those provided by centrality indices, e.g., among young adults, reducing nervousness and tension may have similar effectiveness as deactivating hopeless in reducing suicidal ideation. Caution is warranted when generalizing findings here to the general population. The proposed methodology may help facilitate timely agenda-setting in population mental health measures, and may also be augmented for future co-creation projects. • An in silico intervention approach to extend network analyses' findings is proposed. • Theory-driven framework emulates changes in and temporal dynamics between symptoms. • Potential effects of distress symptom intervention on suicidal ideation are explored. • Nuances across subgroups and beyond those shown by centrality indices are observed. • The proposed approach is aimed at facilitating timely mental health policy decisions [ABSTRACT FROM AUTHOR]

We construct scientific research cooperation networks in the field of public management to provide empirical support for exploring the trend in cooperation in the public administration domain. Based on the SSCI database, the co-authored papers in the field of public administration from 1921 to 2022 are selected as data sources. Ucient software is used to visualize the cooperation networks of countries, cities, institutions, and authors in public administration research, and to explore the spatial structure and driving factors of cooperation networks at different levels. The country-level cooperation in public administration research is closely related to geographical location and is affected by regional agreements to some extent. London and Washington are located at the center of the global public administration cooperation network, and the city-level cooperation network is affected by south-north differentiation and the east-west gap in global economic development and thus exhibits significant non-equilibrium. The institutions in the United Kingdom, America, and Canada are the main forces of international cooperation in the field of public administration and accordingly occupy a dominant position in cooperation networks. The authors' collaboration network in the public administration research shows strong centrality. [ABSTRACT FROM AUTHOR]

This paper contributes to debates about the globalisation of higher education by providing a pioneering geographical exploration of Chinese–foreign cooperation in running transnational higher education, or TNHE, programs. Departing from widespread neoliberal and postcolonial critiques of TNHE, which tend to emphasise liberal market forces and Anglo‐American hegemony in the circulation of academic knowledge, our study examines how the Chinese state's developmental targets, strategic policies, and political–ideological considerations have shaped the evolutionary trajectory, geographical distribution, and cross‐border connections of China's TNHE programs. We demonstrate that the place‐based development of TNHE is mediated by governments' interventionist policies and embedded in existing higher education regimes, especially in the context of East Asian developmental states. By maintaining a higher education system dominated by public universities and by being the ultimate examination and approval authority, the Chinese government both determines the status of TNHE in the national higher education system, constrains the typologies and sources of knowledge flows, and shapes the national landscape of TNHE development. The territorial geographies of TNHE thus reflect complicated interactions between the state and the market, the global and the local, and economic and political/cultural forces. [ABSTRACT FROM AUTHOR]

Innovations in precision agriculture continue to increase in both number and significance. Contemporaneously, given their expanding scope and application, the growing pool of technologies is increasingly complex. Although some recent attempts have been made, a knowledge gap remains in respect to the evolution of precision agricultural innovation. Synthesized, such knowledge should inform the untapped opportunities for innovation. This paper narrows this gap by conducting a network analysis of patent applications in areas concerning precision agriculture. Spanning the years from 1960 to 2021, the analysis shows an annual average growth in patent applications of 15.1%. During the last decade, this growth is almost entirely attributable to China and the USA. Priority innovation themes have been dynamic. Its phases can be characterized as 'motorized mechanization' (1960–1999), 'mechanical automation' (2000–2009), 'digital mechanization' (2010–2019), and 'digital automation' (2020–2021). The significance of information communication technology has grown in association with other innovation clusters, but a lag time has persisted in introducing innovations based on such technologies. Opportunities are ripe for speeding transference through economic and other incentives. [ABSTRACT FROM AUTHOR]

The city of Biskra is ranked the second city in Algeria with the highest traffic accident rate. There are multiple causes of accidents that derive from the diversity of traffic. In addition, its geographical location as a crossroads between the northeast and the south of Algeria is a major factor. Biskra is also considered a commercial and industrial city that receives daily large flows of travellers and merchandise. Traffic accidents are a complex problems that is dealt with in several approaches. In this research, from a geographical point of view, we will contribute to improve the trajectory of intervention and evacuation. Therefore, this research paper aims to create a geodatabase including the road network with associated entities to carry out spatiotemporal analyses and evaluate the service area of health facilities in terms of the nearest path. This paper uses GIS tools for evacuation to the public hospital employing a Network Analyst. This study may serve as a decision-making aid for the local authorities of this city in terms of evacuation and intervention in the event of a road accident. It provides real-time information on the location of the accident and the nearest path along with an estimated time. The outputs of these analyses can determine a map of accessibility, which allows for training in order to improve the real-time evacuation. As a result, this approach will lead to the reduction of evacuation time by less than 10 minutes, and thus leading to a decrease in the number of fatalities. [ABSTRACT FROM AUTHOR]

At present, mainstream research on complex network evolution involves measuring the developing characteristics of network structure at the macro level. Few studies have focused on the internal mechanism of network structure change and the prediction of network structure evolution at the micro‐level. However, for some special complex network time series, the traditional analysis and research methods at the macro level are invalid, and the micro‐level analysis method needs to be adopted. Based on the evolution characteristics of nodes and edges in complex networks, this paper constructs a time series network analysis and prediction algorithm to explore the structural evolution trends of complex network time series and make a prediction. Based on the enterprise supply–demand relationship data, this paper constructs the enterprise supply–demand network time series. Then, we use the method proposed in this paper to analyse and predict the complex network time series and verify the rationality and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Aims and objectives: This paper presents an exploratory account of an innovative methodology to record and evaluate fundamental care. Fundamental care is defined as the care required by everyone for survival, health and welfare. Background: Fundamental care has been informed by the development and testing of the Fundamentals of Care Framework, which describes how fundamental care is complex and multidimensional, and consists of three interrelated dimensions and 38 elements. This accords with a broader re‐examination of care provision as part of a complex adaptive system in which existing linear models of cause and effect are inadequate to describe the totality of activity. Design: Informed by graph theory and complexity science, this paper presents a novel methodological innovation. It uses the Fundamentals of Care Framework to create a Matrix to quantify the relationships between different elements within the Framework. Methods: We use a Matrix methodology to process care recipient narratives to generate three outputs: a heat map, a summary table and a network analysis. Conclusions: The three outputs serve to quantify and evaluate fundamental care in a multidimensional manner. They capture different perspectives (care recipients and their families, direct care providers and care managers) to improve care outcomes. The future aim is to advance this exploration into digitalising and operationalising the Matrix in a user‐friendly manner for it to become a real‐time mechanism to evaluate and potentially predict patterns of fundamental care. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the new CapekDraCor corpus and the DraCor project with its research-oriented concept of a programmable corpora focused on quantitative analyses within the framework of computational literary studies. This digital platform extends the possibilities of large-scale drama analysis with a focus on the dramatic character(s). The basic operationalisation is the interaction within a dramatic configuration, i.e., the scenic co-presence of two speakers, from which network data are automatically extracted, both global networks of interactions of dramas and data characterising individual actors, i.e., literary characters. The paper demonstrates the CapekDraCor corpus, a new contribution to the extensive DraCor database, and presents the way the data are processed with respect to their specific multi-layered structure. The corpus contains all the plays written by Karel and Josef Čapek and the data are processed in a standardized format based on XML and general TEI guidelines for processing drama with a defined basic drama tagset. CapekDraCor also uses the newly created EZdrama format for data processing, which works as an intermediate step from.txt to.xml file as a lightweight YAML-like markup language. A file in this format can be automatically converted into a DraCor-ready XML file with a TEI header. The advantage of the programmable corpora concept is the possibility to use suitably structured data for drama research outside the DraCor platform and with other methods or tools for textual analysis. Simultaneously, this approach moves the researcher from the technical requirements of the analysis to operationalised computational analysis based on research questions and pre-prepared and flexible tools. DraCor is a unique open infrastructure (both in terms of data and tools) for the analysis of European drama, currently comprising 15 corpora in 10 different languages with a total of about 3,000 plays from a wide range of periods. [ABSTRACT FROM AUTHOR]

What decides naming and shaming behavior of states? In the present paper I examine state recommendations in the Universal Periodic Review and show that naming and shaming is inherently a social phenomenon and network analysis can contribute to the better understanding of this broader social context. Using original qualitative data collected from Geneva, Switzerland, I develop a theory of the interdependent nature of state shaming, mainly through the work of non-governmental organizations. I argue that states are more likely to name and shame other states if many other countries have also named and shamed the same states. As non-governmental organizations contribute to revealing information about reviewer states' interests to one another, states believe their costs are lower when states shame together than when they shame alone. The empirical analyses of Exponential Random Graph Models of the Universal Periodic Review shaming network from 2012 to 2016 find evidence of bandwagon effects even after controlling for the effects of other network structures, dyadic geopolitical relations, and state level attributes. This paper helps us better understand the decision of state-to-state shaming by incorporating social relations and networks. ¿Qué hace que un Estado decida usar el recurso de citar por el nombre a otros estados con el propósito de avergonzarlos? En el presente documento examinamos las recomendaciones estatales en el Examen Periódico Universal y mostramos que citar por el nombre a otros estados con el propósito de avergonzarlos es, intrínsecamente, un fenómeno social y que el análisis de redes puede contribuir a comprender mejor este contexto social más amplio. Utilizando datos cualitativos originales recogidos en Ginebra (Suiza), desarrollamos una teoría sobre la naturaleza interdependiente del avergonzamiento por parte del Estado, principalmente a través de la labor de las organizaciones no gubernamentales. Sostenemos que es más probable que los Estados citen por el nombre a otros Estados con propósito de avergonzarlos si algunos otros países también han citado y avergonzado a los mismos Estados. Como las organizaciones no gubernamentales contribuyen a revelar información sobre los intereses de los Estados revisores entre sí, los Estados creen que sus costes son más reducidos cuando los Estados se avergüenzan juntos que cuando se avergüenzan solos. Los análisis empíricos de los modelos gráficos aleatorios exponenciales de la red de avergonzamiento del Examen Periódico Universal de 2012 a 2016 encuentran pruebas de efectos de arrastre, incluso después de controlar los efectos de otras estructuras de red, de relaciones geopolíticas diádicas y de atributos a nivel estatal. Este artículo nos ayuda a comprender mejor la decisión de un Estado de avergonzar a otro incorporando las relaciones y redes sociales. Pourquoi les États en dénoncent-ils d'autres ? Dans cet article, j'analyse les recommandations des États dans l'Examen périodique universel, puis je montre que la dénonciation est un phénomène social par nature et que l'analyse des mécanismes peut permettre de mieux comprendre ce contexte social dans son ensemble. À l'aide de données qualitatives originales issues de Genève, je théorise l'interdépendance naturelle de la dénonciation des États, principalement grâce au travail des organisations non gouvernementales. Selon moi, les États en dénoncent plus facilement d'autres quand ils sont déjà nombreux à l'avoir fait. Comme les intérêts qui unissent les États examinateurs sont mis au jour par des ONG, les États sont convaincus qu'ils courent moins de risques si la dénonciation est collective. En analysant empiriquement des modèles de graphes aléatoires exponentiels des mécanismes de dénonciation de l'Examen périodique universel entre 2012 et 2016, on observe que les États prennent souvent « le train en marche », et ce, même après avoir neutralisé les effets d'autres structures de mécanisme, relations géopolitiques dyadiques et attributs nationaux. En intégrant les relations sociales et les mécanismes, cet article facilite notre compréhension des décisions de dénonciation interétatique. [ABSTRACT FROM AUTHOR]

Urban Park green space is an essential carrier and form of urban recreation space, playing an important role in improving the ecological environment and enhancing the image of the city. With the process of urbanization and the continuous improvement of residents’ living standards, urban park construction has become increasingly important to meet people’s growing needs for a better life. However, the evaluation indicators for urban park green space in China mostly consider macroscopic levels, such as the area, number and per capita green area of parks. This cannot fully reflect the level of park green space services. Accessibility can reflect the convenience of residents to reach the park, provide guidance for the reasonable layout of parks and have various evaluation methods relying on geographic information technology. Therefore, it is necessary to quantitatively study the layout and accessibility of park green space. Taking the central urban area of Nanjing as the research object, this paper summarizes existing research and focuses on evaluating the accessibility of park green space while analyzing the layout of parks from multiple perspectives, combined with the existing pressure appraisal of park green space services. Relevant data on Nanjing, including overall planning and statistical yearbooks, were collected and analyzed using geographic information system (GIS) tools, network analysis and Thiessen polygon theory to analyze the service pressure, demand and accessibility of park green space in the central urban area under different transportation methods. Based on the results, targeted optimization strategies are proposed. [ABSTRACT FROM AUTHOR]