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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Background: A psychoneurological symptom cluster composed of cancer-related fatigue, emotional distress, sleep difficulties, and pain is very common among patients with cancer. Cognitive difficulties are also frequently associated with this cluster. Network analyses allow for an in-depth understanding of the relationships between symptoms in a cluster. This paper details the study protocol of a longitudinal assessment of the psychoneurological symptom cluster in two distinct cohorts: breast cancer and digestive cancer survivors, using network analyses. Methods: Over two years, the symptoms involved in the psychoneurological symptom cluster, along with other common symptoms (e.g., digestive symptoms, financial difficulties) and variables (i.e., self-compassion, coping strategies) will be assessed in two cohorts: breast cancer survivors (N = 240) and digestive cancer survivors (N = 240). Online questionnaires will be completed at baseline, then 6, 12 and 24 months later. Network analyses will be used to assess the configuration of the symptom cluster at each measurement time and in each cohort. Comparison of networks between two measurement times or between the two cohorts will also be done with network comparison tests. Discussion: This study will enable a better understanding of the relationships between common symptoms endured by patients with cancer. The results will be employed to develop more cost-effective interventions which, ultimately, will significantly improve the quality of life of patients with breast or digestive cancer. Trial registration: ClinicalTrials.gov (NCT05867966). Registered on the 27th of April 2023. url: https://classic.clinicaltrials.gov/ct2/show/NCT05867966. [ABSTRACT FROM AUTHOR]

Objective: Microtia is a congenital auricular malformation with a hypoplastic external ear that ranges in severity from a slightly smaller auricle to complete the absence of the auricle. The present study was conducted to identify and analyze the characteristics of microtia-related articles published from 2006 to 2020 by using bibliometric analyses. Method: Microtia-related studies published from 2006 to 2020 were retrieved from the Web of Science Core Collection database. Keywords, first author, citations, date of publication, and publication journal were extracted and quantitatively analyzed using Bibliographic Item Co-Occurrence Matrix Builder software and the Bibliometric (https://bibliometric.com/app). VOSviewer was used to visualize research and form a network map on keywords and citations. Results: A total of 1031 articles from 2006 to 2020 were included. The number of articles showed an overall trend of growth over time. The United States and China are the top 2 countries in terms of the number of microtia-related articles. From the analysis of keyword clustering, keywords could be mainly divided into 4 clusters in the field of microtia research: surgery, tissue engineering, epidemiology, and rehabilitation including hearing-related treatments, evaluation of effects, and quality of life after surgery. The top 10 most frequently cited papers from 2006 to 2020 were also extracted and analyzed. Conclusion: A bibliometric research of microtia-related articles from 2006 to 2020 was conducted. This study may be helpful to understand the current research status of microtia and find the research trends in this field, thus proposing future directions for microtia research. [ABSTRACT FROM AUTHOR]

Migrants' home gardens may be created from elements of both old and new landscapes. We assume that medicinal plant assemblages in migrants' gardens are shaped by plant diversity and availability, therapeutic needs, and heritagization processes. Which of the factors prevail: those related to biodiversity and ecology, epidemiology, or heritage and memory? In this paper we offer new knowledge on the garden landscapes of the Global South. The research was conducted in the Atlantic Forest in Argentina. We surveyed 120 home gardens: 60 of transborder Paraguayan migrants, and 60 of transcontinental Europeans who arrived in Misiones, Argentina before WW2 and their descendants. We compared the richness, composition, medicinal uses, and the relationships of garden plants (via plant networks) between these groups, taking into account everyday scales and the transnational scale. Paraguayans cultivated and protected 137 species, predominantly native, and people of European origin 119 spp., native and exotic in similar proportions. The similarity in plant composition (68%) and the consensus in the medicinal use of plants (62%) were high between the migrant groups. Plant network analysis revealed many overlaps in assemblages of plants, but certain particularities of each group remained because of cultural expressions and therapeutic needs. This high level of similarity suggests that plant diversity, both native and allochthonous, shared concepts of illness, and the flux of knowledge between these groups was more significant than heritagization practices in shaping home gardens' medicinal plant assemblages. People of Paraguayan and European origins do not make an active effort to convert their home gardens into heritage. Medicinal plants are connected to the lived emplacement--intimate daily practices--rather than to ethnic identity strategies. Nevertheless, the plant assemblages in gardens have been shaped by ecology, colonial legacy, nostalgia, and transfer of knowledge; therefore migrants' home gardens can be considered heritage in a broad sense. [ABSTRACT FROM AUTHOR]

Introduction: The Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD). Methods: To better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment. Results: At baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node. Conclusion: NIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections. [ABSTRACT FROM AUTHOR]

Background: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. Results: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. Conclusions: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution. [ABSTRACT FROM AUTHOR]

Background: Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. Results: We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. Conclusions: DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution. [ABSTRACT FROM AUTHOR]

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents. [ABSTRACT FROM AUTHOR]

Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science's diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as "inclusive society" and "early childhood development", by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs. [ABSTRACT FROM AUTHOR]

Root traits have an important impact on plant growth, which reflects the adaptation of plants to nutrients. This paper describes the focus and progress of root traits research and provides references for future research. Based on the Web of Science online database, articles related to root traits from 2000–2021 were evaluated using bibliometric methods. The results showed that the study of root traits has been growing steadily in the last two decades. Wang Yan is the author with the highest number of published papers, the most citations and the highest H-index. The top three published countries are the USA, China and India. The top three institutions are the University of Western Australia, China Agricultural University and Nanjing Agricultural University. Plant and Soil, Frontiers in Plant Science and PLoS One have the highest number of published articles. In terms of co-occurrence of keyword analysis, growth, soil and plant diversity are highly correlated with root traits. It was found that cooperation between authors, institutions and countries are not close enough and that z global network of cooperation has not yet been formed. Therefore, worldwide cooperation should be strengthened to promote resource sharing and the openness of relevant mechanisms. [ABSTRACT FROM AUTHOR]

Why do some international rivalries last longer than others? Rivalry literature focuses on the dyadic interactions within rivalries, but most rivalries have been connected to other rivalries. This paper examines a rivalry's overlapping immediate strategic environment focusing on the countries directly connected via rivalries or alliances. Specifically, I consider how rivals are connected to third parties as common enemies, common friends, or mixed. I focus on three mechanisms to explain the third parties' influence on the focal rivalry. First, a rivalry is more likely to end when it has common allies (information flow) or common enemies (prioritization). Second, a rivalry with mixed third parties is more likely to endure (stable blocs). Analyses of rivalry duration show that the duration of minor-minor rivalries is more likely to be influenced by common allies and mixed third parties, while that of major-minor rivalries is more likely to be affected by common enemies. [ABSTRACT FROM AUTHOR]

Networks form the backbone of any healthcare system. Various databases were searched with relevant keywords, data were abstracted, and numerous papers were appraised for this synthesis. This compiled systematic review gives a comprehensive overview of various networks that are found in healthcare, with a special reference to the treatment, referral, and best-practice care of patients. Special support networks, such as Clinical decision support systems, Physician collaboration networks, Telemedicine networks, and Shared healthcare record access, are also described, as these support networks play a pivotal role in improving the quality of healthcare for patients. [ABSTRACT FROM AUTHOR]

Although humans have interacted with wildfires for millennia, a science-based approach to fire management has evolved in recent decades. This paper reviews the development of fire-management research, focusing on publications that use this term in their title, abstract, or keywords identified on the Scopus platform. This resulted in the identification of 5624 documents published between 1973 and 2021. Publication rates have particularly increased since 2010. The paper details the characteristics of this body of the literature, including the main authors, institutions, and countries. Furthermore, it considers the bibliographic networks, main research foci, and the publications' study areas. First, these analyses provide researchers interested in fire management an overview of the field and its most prominent sources, authors, and publications. Second, they invite reflection on the current state of fire-management research. In particular, the considerable disparities in spatial foci and countries of authorship suggest that the challenges of today's problems in fire management are more likely to be overcome with a more balanced global research effort. [ABSTRACT FROM AUTHOR]

An analysis is the process of browsing and searching for specific information from an entire dataset. The simplest analysis that can be performed on the data is visual analysis. However, it does not provide absolute certainty as to correctness and quality. A more advanced way of selecting required data is computer-based analysis. Analytical operations are performed on the data entered into the computer. The user defines the query, and the program performs calculations and displays the answer on the monitor screen. The aim of this publication is to conduct network analyses with the use of spatial databases. Besides focusing on the analysis as the leading research method, the paper also adopts this method to analyze the literature on the subject. In addition, the paper points to the complementary roles of the raster model and the vector model, emphasizing their coexistence. The paper shows a variety of applications of GIS analyses, from simple buffers around selected areas, through selection, and the intersection of layers, to network analyses. The high degree of advancement of GIS tools allows to build advanced models in which analyses that go beyond the original application of the collected databases can be run. [ABSTRACT FROM AUTHOR]

Over the past years, psychopathology has frequently been represented as a complex system, where psychiatric symptoms are causally interconnected in a network architecture. The network theory of psychopathology has led to more than 300 novel publications, academic courses, methodology for estimating novel models, and freely available software. However, despite the success of this novel research avenue, all relevant information has mostly been published in English. This paper translates the network theory of psychopathology and its model, together with its relevance for research and clinical practice of psychology and psychiatry, to the Spanish language. To serve the dissemination of this theory, this paper serves as an introductory paper for Spanish scholars, for example, as a starting point to learn more about the approach or for academic courses. The main idea of the network theory of psychopathology is that the activation of one clinical symptom in the network leads to the activation of a neighboring symptom. If symptoms are strongly connected with each other, for example, excessive worry and insomnia, they are more likely to be in the same state, meaning that if a person faces a stressful life event such as losing one's job, the activation of the symptom excessive worry will increase the probability they will also suffer from insomnia. In this way, a whole symptom activation pattern develops from which mental disorders emerge. Mental disorders are conceived as stable states of strongly connected symptom networks, allowing for a common explanatory model for multiple mental disorders, thereby providing a comprehensive model of psychopathology. Traditional representations of mental disorders conceptualize symptoms as merely passive indicators of latent, underlying mental disorders which act as common causes for patients' symptomatology. The network theory of psychopathology flips the explanatory and statistical model: instead of focusing on one underlying cause or underlying causes, it proposes to study the direct interactions between these symptoms. This imposes two important implications for the conceptualization of mental disorders. First, symptoms are no longer statistically exchangeable since every symptom can have a different role in the onset and development of psychopathology. Some symptoms can be more important than others in keeping the whole system "stuck" in a disordered state. Second, comorbidity is conceptualized as clustering symptoms which are connected to each other via certain "bridge symptoms". Bridge symptoms are symptoms which are attributed to two (or more) mental disorders, such as Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD). If a person suffers from symptoms of MDD, such as loss of motivation and depressed mood, this can lead to the activation of bridge symptoms such as fatigue and concentration problems, which by themselves lead to the activation of GAD symptoms such as irritability and excessive worry. [ABSTRACT FROM AUTHOR]

The concept of force chains transmitting stress through granular materials is well established; however identification of individual force chains and the associated quantitative analysis is non-trivial. This paper proposes two algorithms to (1) find the network of percolating contacts that control the response of loaded granular media, and (2) decompose this network into the individual force chains that comprise it. The new framework is demonstrated considering data from discrete element method simulations of a ribbed interface moving against a granular sample. The subset of contacts in the material that transfers load across the sample, namely the percolating contact network ( G perc ), is found using the maximum flow algorithm. The resulting network is fully-connected and its maximum flow value corresponds to the force percolating the system in the direction normal to the ribbed wall. G perc re-orientates in response to the ribbed interface movement and transmits 85– 95 % of the stress, with only 40– 65 % of the contacts in the sample. Then, G perc is split into individual force chains using a novel implementation of the widest path problem. Results show that denser materials with increased force-chain centrality promote a higher density of force chains, which results in a higher macro-scale strength during interface shearing. The contribution of force chains in the network is revealed to be highly centralized, composed by a small set of strong and long-lived force chains, plus a large set of weak and short-lived force chains. [ABSTRACT FROM AUTHOR]

The production of stone sculptures in the form of statues, steles, and pagodas was arguably one of the most prominent forms of Buddhist devotion in early medieval China. The abundant Buddhist imagery and devotional inscriptions adorning their surfaces provide rich information on their creation, commission, consecration, and worship, which is essential for comprehending the local community's religious landscape. This paper employs a network analysis approach to visualize and analyze the spatial arrangement of Buddhist images on the surfaces of pagodas from Nannieshui 南涅水, Shanxi 山西 Province. Network analysis reveals that Nannieshui pagodas were commissioned and worshipped differently from other kinds of stone sculpture, as shown by its emphasis on the spatial and sequential order of Buddhist images depicted on the exterior of these pagodas. [ABSTRACT FROM AUTHOR]

Summary: The 'One Hundred Important Questions Facing Plant Science Research' project aimed to capture a global snapshot of the current issues and future questions facing plant science. This revisiting builds on the original 2011 paper. Over 600 questions were collected from anyone interested in plants, which were reduced to a final list of 100 by four teams of global panellists. There was remarkable consensus on the most important topics between the global subpanels. We present the top 100 most important questions facing plant science in 2022, ranging from how plants can contribute to tackling climate change, to plant‐defence priming and epigenome plasticity. We also provide explanations of why each question is important. We demonstrate how focussing on climate change, community and protecting plant life has become increasingly important for plant science over the past 11 years. This revisiting illustrates the collaborative and international need for long‐term funding of plant science research, alongside the broad community‐driven efforts to actively ameliorate and halt climate change, while adapting to its consequences. [ABSTRACT FROM AUTHOR]

Background: Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques such as single-cell transcriptomics continue to drive the need for clustering or classification techniques to focus on portions of datasets of interest. While many libraries and packages exist that implement various algorithms, there remains the need for clustering packages that are easy to use, integrated with visualization of the results, and integrated with other commonly used tools for biological data analysis. clusterMaker2 has added several new algorithms, including two entirely new categories of analyses: node ranking and dimensionality reduction. Furthermore, many of the new algorithms have been implemented using the Cytoscape jobs API, which provides a mechanism for executing remote jobs from within Cytoscape. Together, these advances facilitate meaningful analyses of modern biological datasets despite their ever-increasing size and complexity. Results: The use of clusterMaker2 is exemplified by reanalyzing the yeast heat shock expression experiment that was included in our original paper; however, here we explored this dataset in significantly more detail. Combining this dataset with the yeast protein–protein interaction network from STRING, we were able to perform a variety of analyses and visualizations from within clusterMaker2, including Leiden clustering to break the entire network into smaller clusters, hierarchical clustering to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations between our hierarchical visualization and the UMAP plot, fuzzy clustering, and cluster ranking. Using these techniques, we were able to explore the highest-ranking cluster and determine that it represents a strong contender for proteins working together in response to heat shock. We found a series of clusters that, when re-explored as fuzzy clusters, provide a better presentation of mitochondrial processes. Conclusions: clusterMaker2 represents a significant advance over the previously published version, and most importantly, provides an easy-to-use tool to perform clustering and to visualize clusters within the Cytoscape network context. The new algorithms should be welcome to the large population of Cytoscape users, particularly the new dimensionality reduction and fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

In recent years, knowledge management (KM) has consistently attained considerably growing research attention. Consequently, several literature reviews have been performed addressing different topic areas of KM. This paper seeks to present a comprehensive bibliometric and network analysis on KM to understand its development from the perspective of academic communities. Subsequently, it seeks to identify the structure of associations between prior and current themes, predict emerging trends and offer a longitudinal perspective on KM research. This study used web of science database and the initial sample was trimmed down by considering only the articles contributing to KM literature, and further 8,721 KM papers published in the last 30 years were systematically evaluated. The descriptive statistics and science mapping methods employing co-citation analysis were performed with VOSviewer software. In the descriptive analysis, we have analysed publication trends over time, geographical localization of the contributing institutions, journals, most prolific authors, top-performing institutions and most cited articles. Science mapping analysis is based on co-word analysis and co-citations analysis, namely articles' co-citations and authors' co-citations. The main findings of this paper will help researchers and academicians to develop knowledge in a specific sub-field by analysing the research outcomes of the papers included in the body of literature. [ABSTRACT FROM AUTHOR]

This manuscript aims to map and characterize, through knowledge routes, the evolution of scientific studies on coronavirus based on papers developed by researchers affiliated with research institutions in Brazil. Inspired by the KDD processes, the publication's data were obtained from Web of Science until 2021. Then the citation network was built and, using SPLC algorithm, the knowledge routes were designed. The results showed the high concentration of publications after 2020, demonstrating the concern and commitment of Brazilian researchers in investigations on this topic. The results also showed priority areas such as medical, followed by social that approach measures to contain the virus spread. It also identified two knowledge routes with different characteristics, reinforcing the scope of knowledge related to the topic. Thus, this paper contributes to the literature by presenting the evolution of scientific knowledge, reiterating the quality and production capacity of researchers affiliated to Brazilian institutions. It also contributes by presenting the most researched topics and pointing out the profiles of the researchers involved. Finally, it complements the current literature by ratifying the use of network analysis as a tool for representing and investigating knowledge, in addition to mapping and analyzing the evolution of knowledge. [ABSTRACT FROM AUTHOR]

The modeling and forecasting of dynamically varying covariances has received a great deal of attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and the DCC. In this paper, we advance a new method based on network analysis and a new targeting approach for both the above models with the aim of better estimating covariance matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market and assuming that those relationships remain unaltered at least in the long run. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature. Furthermore, we find and analyze all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large. [ABSTRACT FROM AUTHOR]

Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), other attributes that model diverse real-life system characteristics are present. Among the most common attributes studied in the literature are vehicle capacity and route duration constraints. The influence of these restrictions on the overall structure of the problem and the performance of local search algorithms used to solve it has yet to be well known. This paper aims to explain the impact of constraints present in different variants of VRP through the alterations of the structure of the underlying search space that they cause. We focus on Local Optima Network Analysis (LONA) for multiple Traveling Salesman Problem (m-TSP) and VRP with vehicle capacity (CVRP), route duration (DVRP), and both (DCVRP) constraints. We present results that indicate that measures obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the problem's constraints. Additionally, we use the LONA measures to explain the difficulty of VRP instances for solving by local search algorithms. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which maps the set of nodes into a coarsened graph, is crucial for graph-level tasks. We argue that a well-defined graph pooling operation should avoid the information loss of the local node features and global graph structure. In this paper, we propose a hierarchical graph pooling method based on the multihead attention mechanism, namely GMAPS, which compresses both node features and graph structure into the coarsened graph. Specifically, a multihead attention mechanism is adopted to arrange nodes into a coarsened graph based on their features and structural dependencies between nodes. In addition, to enhance the expressiveness of the cluster representations, a self-supervised mechanism is introduced to maximize the mutual information between the cluster representations and the global representation of the hierarchical graph. Our experimental results show that the proposed GMAPS obtains significant and consistent performance improvements compared with state-of-the-art baselines on six benchmarks from the biological and social domains of graph classification and reconstruction tasks. [ABSTRACT FROM AUTHOR]

Therefore, this paper aims to investigate the gender differences and influencing factors of cognitive function in stable schizophrenia patients, and to explore the cognitive characteristics of male and female patients by network analysis. However, since our study focused on schizophrenia patients, their cognitive function was impaired, integrity of their cognitive network was reduced,[49] alertness was decreased,[50] and they had to redistribute attention.[51] As a result, the negative correlation between Delayed Memory and Immediate Memory was possible. On the other hand, from this study, we know the core cognitive function of male and female schizophrenia patients, so that targeted cognition training can be carried out to maximize the recovery of patients' social functions. [Extracted from the article]

Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (≥4). Network indices (i.e., shortest path and bridge expected influence [1-step & 2-step]) were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are vital in activating other risk factors and adverse outcomes. Results: Network analyses estimated a mutually reinforcing web of childhood and prenatal risk factors, with each risk connected to at least two other risks. Bridge influence indices suggested that childhood physical and sexual abuse and multiple ACEs were highly interconnected to others risks. Overall, risky health behaviours during pregnancy (i.e., smoking & illicit drug use) were identified as 'active' risk factors capable of affecting (directly and indirectly) other risk factors and contributing to the persistent activation of the global risk network. These risks may be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity. We took a network approach to assessing links between ACEs and birth outcomes. ACEs, other prenatal risk factors, and birth outcomes had complex inter-connections Health behaviours in pregnancy were indicated as optimal intervention targets. [ABSTRACT FROM AUTHOR]

Sustainability requires a combination of meaningful co-production of locally relevant solutions, synthesis of insights gained across regions, and increased cooperation between science, policy and practice. The Programme for Ecosystem Change and Society (PECS) has been coordinating Place-Based Social-Ecological Sustainability Research (PBSESR) across the globe and emphasizes the need for regional scientific nodes from diverse biocultural regions to inform sustainability science and action. In this paper, we assess the strengths of the PBSESR communities in Latin America and the Caribbean (LAC). We provide an overview of PBSESR literature associated with this region and highlight the achievements of two prominent regional networks: The Social-Ecological Systems and Sustainability Research Network from Mexico (SocioEcoS) and the South American Institute for Resilience and Sustainability Studies from Uruguay (SARAS Institute). Finally, we identify the potential in these nodes to constitute a regional PECS node in Latin America and discuss the capacity needed to ensure such function. The results of the literature review show that while still loosely interconnected across the region, networks play key roles in connecting otherwise cloistered teams and we illustrate how the SocioEcoS network (focusing on transdisciplinary co-production of knowledge towards sustainability) and the SARAS Institute (focusing on innovative approaches for looking at complex social-ecological problems, rooted in slow science and arts) operate as key connectors in the region. We conclude that these organizations combined can embody a Latin American node for PECS, and would thereby not only contribute to regional but also global capacities to advance the sustainability agenda. [ABSTRACT FROM AUTHOR]

The principal goal of this study is to analyze the evolution of sensor research and technologies from 1990 to 2020 to clarify outlook and future directions. This paper applies network analysis to a large dataset of publications concerning sensor research covering a 30-year period. Results show that the evolution of sensors is based on growing scientific interactions within networks, between different research fields that generate co-evolutionary pathways directed to develop general-purpose and/or specialized technologies, such as wireless sensors, biosensors, fiber-optic, and optical sensors, having manifold applications in industries. These results show new directions of sensor research that can drive R&D investments toward promising technological trajectories of sensors, exhibiting a high potential of growth to support scientific, technological, industrial, and socioeconomic development. [ABSTRACT FROM AUTHOR]

Which role do city‐regions play in European innovation network formation? We study the evolution of innovation collaborative networks in European city‐regions outlining two opposite models: in the exclusive network model city‐regions establish a closed network of innovators among themselves; in the inclusive network models city‐regions build a network of innovators which includes the peripheral regions. Employing a temporal exponential random graph model on 248 regions for the period 2000–2016, we find that the two models coexist. We conclude that in the EU the city‐regions act as both engines of generation of innovation and integrators of innovation actors. [ABSTRACT FROM AUTHOR]

Objective/Context: This paper explores the relationship between corporate networks and business influence to figure out the conditions of state capture. Methodology: We analyze corporate networks through interlocking directorates in three Central American countries - Panama, Costa Rica, and El Salvador - using network analysis, and we pinpoint the relationship between the structure of the corporate network and two corporate political actions: contributions to presidential campaigns and revolving doors. Conclusions: Results show corporate networks were fragmented in the three Central American countries, except for a well-connected business cluster in Panama. The organization of business elites based on a single cohesive business cluster could facilitate coordination to finance political parties, which gives them more strength to demand government posts. On the other side, the absence of connections between business elites makes it difficult to reach agreements and weaken business influence. Findings suggest that networking among some (not all) business elites can be a source that precedes state capture, as it organizes access to the state through collective mobilization of resources and coordinated action planning. Originality: This paper is the first study on networks of interlocking directorates in Central American economies and combines original data on contributions to electoral campaigns and revolving doors. Therefore, it can be a benchmark for future studies on business power. In addition, this study introduces the analysis of interlocking directorates in the literature on state capture. [ABSTRACT FROM AUTHOR]

Background: Health system approaches to improve hypertension control require an effective referral network. A national referral strategy exists in Kenya; however, a number of barriers to referral completion persist. This paper is a baseline assessment of a hypertension referral network for a cluster-randomized trial to improve hypertension control and reduce cardiovascular disease risk. Methods: We used sociometric network analysis to understand the relationships between providers within a network of nine geographic clusters in western Kenya, including primary, secondary, and tertiary care facilities. We conducted a survey which asked providers to nominate individuals and facilities to which they refer patients with controlled and uncontrolled hypertension. Degree centrality measures were used to identify providers in prominent positions, while mixed-effect regression models were used to determine provider characteristics related to the likelihood of receiving referrals. We calculated core-periphery correlation scores (CP) for each cluster (ideal CP score = 1.0). Results: We surveyed 152 providers (physicians, nurses, medical officers, and clinical officers), range 10–36 per cluster. Median number of hypertensive patients seen per month was 40 (range 1–600). While 97% of providers reported referring patients up to a more specialized health facility, only 55% reported referring down to lower level facilities. Individuals were more likely to receive a referral if they had higher level of training, worked at a higher level facility, were male, or had more job experience. CP scores for provider networks range from 0.335 to 0.693, while the CP scores for the facility networks range from 0.707 to 0.949. Conclusions: This analysis highlights several points of weakness in this referral network including cluster variability, poor provider linkages, and the lack of down referrals. Facility networks were stronger than provider networks. These shortcomings represent opportunities to focus interventions to improve referral networks for hypertension. Trial registration: Trial Registered on ClinicalTrials.gov NCT03543787, June 1, 2018. [ABSTRACT FROM AUTHOR]

BACKGROUND The model proposed by Rothbart is one of the most frequently used models to describe children's temperament. However, the structure of temperamental traits in children is not unambiguous. We examine this structure in children from 3 to 10 years of age using two different measures in a less often studied cultural context (i.e., central Europe), and using a recently developed and still less common approach to study the internal structure of scales (i.e., network psychometrics). PARTICIPANTS AND PROCEDURE This paper examines the structure of temperamental dimensions in children using two different measures - the Children's Behavior Questionnaire (CBQ) and the Temperament in Middle Childhood Questionnaire (TMCQ) - in two studies conducted on children aged 4-7 years (N = 178; CBQ) and aged 7-10 years (N = 189; TMCQ). We verified the structural validity of these measures with a bootstrapped exploratory graph analysis, which represents the network psychometric approach. RESULTS Network psychometric analysis supported differentiation of three factors of temperamental traits in both groups of children. In addition, the construct validity of these instruments was supported through correlations of the temperament factors measured by the CBQ and the TMCQ with Emotionality, Activity, Sociability, and Shyness scales measured with the EAS Temperament Survey; and through confirmation of expected gender differences in the CBQ and TMCQ factors. CONCLUSIONS Our findings support a three-factor solution of temperamental traits in children representing Negative Affectivity, Effortful Control, and Surgency. [ABSTRACT FROM AUTHOR]

We evaluate the environment, society, and corporate governance rating (ESG rating) contribution from a new perspective; the highest ESG rating mitigates the impact of unexpected change in the implied volatility on the systemic stock market risk. For this purpose, we use exchange-traded funds (ETF) classified by their ESG rating into quartiles to estimate the synchronization as a proxy by systemic risk. Then, for each ETF quartile, we study the effect of the implied volatility over the synchronization. Our study is the first to model sustainable ETFs' synchronization by combining econometric modeling and network methods, including 100 ETFs representing 80% of the global ETF market size between 2013 and 2021. First, we find that a higher ESG rating mitigates the effect of implied volatility over ETF synchronization. Surprisingly, the effect is the opposite in the case of ETFs with lower ESG ratings, where an increase in the volatility expectation increases the synchronization. Our study depicts the effect of sustainable ETFs on lessening the systemic risk due to returns synchronization, this being a novel contribution of this asset class. Finally, this paper offers extensions to deepen the contribution of other asset classes of ETFs in terms of their synchronization behavior and impact on risk management and financial performance. [ABSTRACT FROM AUTHOR]

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed. [ABSTRACT FROM AUTHOR]