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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Camino de Santiago is one of the most recognised cultural pilgrimage routes in the world. Notably, its current influence transcends the strictly inspirational and religious aspects, constituting also a socio-economic, cultural, historical, heritage and tourism cornerstone. This paper analyses, from an innovative bibliometric perspective, the influence of the Camino de Santiago on the academic literature in the period from 1980 to the present. For this purpose, two data sources have been used comparatively: manuscripts indexed in Scopus and doctoral theses. The results reveal a growth of its academic relevance in the last decade, especially in indexed publications. The analysis also reflects the leading sources, authors, countries and institutions, as well as their patterns of scientific collaboration, thus being extremely useful to improve both our knowledge of the Camino and its management, preservation and dissemination. The conceptual structure of the academic field, which differs between theses and indexed papers, also confirms the Camino as a crossroads not only of people but also of academic disciplines and scientific perspectives. [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]

This paper attempts to provide insights into the current network configurations of the food-trade system and to study the short-term effects of one of the ongoing and lasting global crises, the Ukraine War, on the link intensity. Towards this end, this analysis (1) reveals the pattern of countries' network positions in two most traded subcategories of the cereal network: wheat and meslin, and maize or corn, and (2) discusses the characteristics of the global cereal networks over the 2021–2022 period. The results highlight several features of the trade networks: (1) the distribution of cereal trade is highly concentrated, with considerable dependency on a small number of exporters and a low import diversification, making the system rigid and prone to shocks; (2) a central role of several key developed countries that leave many developing countries outside the centre of the networks; (3) a high network heterogeneity which confirms the propensity to have hub nodes. Particular indicators show that the highest level of interconnectivity is specific to the cereals' export network, the densest networks are the maize or corn ones, and the greatest heterogeneity appears for the cereals export network. [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]

A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected the tourism economy, causing a severe global shock and effects on tourism mobility unevenly geographically distributed across regions. This paper detects changes in the global tourism flows network (GTN) in the period 2018-2020 due to COVID-19 and explains the GTN dynamics from a network science perspective. The analysis reveals the effect of underlying economic geography, as the pandemic introduced a new social distancing friction that induced centrifugal forces to the tourism network. The network’s topological pattern was described by small-world network characteristics before the pandemic outbreak, while during the pandemic there was a statistically significant reshape into more lattice-like characteristics accompanied by peripheral markets expansion of local hierarchy. The findings also demonstrate a reduction in the number of tourism flows and spatial connectivity, a simplification of the macroeconomic travel behavior, and a resilient performance of the main tourism hubs shaping and a distinctive core-periphery network structure. Overall, this paper contributes to a better understanding of the response of tourism mobility to the pandemic. [ABSTRACT FROM AUTHOR]

Background and Objectives: This paper aims to assess the role of laser therapy in periodontitis through an innovative approach involving computational prediction and advanced modeling performed through network analysis (Gaussian graphical models—GGMs) and structural equations (SEM). Materials and Methods: Forty patients, exhibiting periodontal pockets with a minimum depth of 5 mm, were randomly divided into two groups: a control group and a laser group. Four specific indicators were measured for each tooth, namely periodontal pocket depth (PPD), clinical attachment level (CAL), bleeding on probing (BOP), and plaque index (PI), and the mean of six measured values was recorded at five time markers (baseline, 6 months, 1 year, 2 years, and 4 years). The assessment algorithm included enrollment, measurements, and differential non-surgical periodontal treatment, according to the group allocation. Scaling, root planing, and chlorhexidine 1% were conducted for the control group, and scaling, root planing and erbium, chromium:yttrium-scandium-gallium-garnet (Er,CR:YSGG) laser therapy were conducted for the laser group. Results: The main results highlight that the addition of laser treatment to scaling and root planing led to notable clinical improvements, decreasing the PPD values, reducing the BOP scores, and increasing the CAL. Conclusions: Notable relationships between the specific indicators considered were highlighted by both the GGMs and by SEM, thus confirming their suitability as proxies for the success of periodontal treatment. [ABSTRACT FROM AUTHOR]

Entrepreneurship education (EE) plays a vital role in fostering an entrepreneurial culture and promoting the growth of the small- and medium-sized business sector in a nation. Research on EE has advanced extremely rapidly in the last two decades. These changes are evident not only in the quantity of published works but also in the evolving methods of academic communication, the rise of emerging nations as prominent contributors to EE research, and the shifting areas of focus in study themes. This current study aims to provide a comprehensive picture of these processes based on a big-data-centred bibliometric analysis of a corpus of 3787 articles that appeared in the Web of Science literature database. The bibliographic metadata was analysed using Biblioshiny, CitNetExplorer, and VOSviewer. To understand the most significant development trends, researchers employed the triangulation method, which included scientific mapping and epistemological analysis in addition to standard tools of bibliometric analysis. This has led to the identification of some study needs. First, improving EE methods in secondary school curricula; second, EE in adult education and the practicality of the findings for andragogy; third, EE in least developed countries and its unique challenges; fourth, combining EE with internet-based, innovative training and education approaches like gamification and simulations; fifth, EE's role and methodological development in societal economic integration; and sixth, women's unique EE requirements. On top of that, this study provides the basis for policymakers and practitioners to consider incorporating entrepreneurial education programmes, which can help to create a prosperous entrepreneurial ecosystem. [ABSTRACT FROM AUTHOR]

COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmaz's method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects "from" and "to" other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoin's different market dynamics and enrich our understanding of Bitcoin's safe haven, hedge, and diversifier properties within a diversified portfolio. [ABSTRACT FROM AUTHOR]

The challenging international landscape and exacerbated extreme weather conditions contribute to the instability of global grain trade, complicating its impact on food security. This complexity is particularly pronounced for varieties like rice, which are heavily affected by policy-driven trade restrictions. There is insufficient research on how a country's rice trade characteristics affect food security. A network analysis approach is adopted to intricately dissect the structural characteristics of rice trade. To explore causality with food insecurity, this paper chooses structural holes and centrality as representatives of trade network characteristics and regresses them on the food insecurity indicator. With cross-national data spanning over 30 years, the network analysis provides a clear portrayal of the dynamic changes in international rice trade. The overall resilience of the trade network has increased, but specific countries' vulnerability has also risen. Unlike the changing trends in features observed in grain and food trade networks, there is a notable intensification in the imbalance of power distribution in the rice trade network compared to over 30 years ago. The panel data regression results show that constraint, indicating the scarcity of structural holes or connections to stronger trading partners, significantly and positively influences a country's level of food insecurity. Based on these findings, the policy proposal for importing countries emphasizes creating strategic trade connections. By choosing appropriate trade partners that reduce constraint, food security can be enhanced, even without improvements in other conditions. [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]

Wireless Mesh Networks (WMNs) remain integral to various sectors due to their adaptability and scalability, providing robust connectivity where traditional wired networks are impractical. From facilitating smart city initiatives to enabling disaster recovery efforts and supporting industrial automation, WMNs play a pivotal role in modern networking applications. Their versatility extends to rural connectivity, underscoring their relevance across diverse scenarios. Recent research in WMNs has honed in on optimizing gateway placement and selection to bolster network performance and ensure efficient data transmission. This paper introduces a novel approach to maximize average throughput by strategically positioning gateways within the mesh topology. Inspired by Coulomb's law, which has been previously employed in network analysis, this approach aims to improve network performance by strategically positioning gateways for optimization. Comprehensive simulations and analyses carried out in this research demonstrate the effectiveness of the proposed method in enhancing both throughput and network efficiency. By leveraging physics-based models such as Coulomb's law, the study offers an objective means to optimize gateway placement--a critical component in WMN design. These findings provide invaluable insights for network designers and operators, guiding informed decision-making processes for gateway deployment across a spectrum of WMN deployments. As such, this research contributes significantly to the ongoing evolution of WMN optimization strategies, reaffirming the indispensable role of gateway placement in establishing resilient and efficient wireless communication infrastructures. [ABSTRACT FROM AUTHOR]

As the significance of knowledge convergence continues to grow, universities are making efforts to develop methods that promote multidisciplinary learning. To address this educational challenge, our paper applies network theory and text mining techniques to analyze university curricula and introduces a graphical syllabus rendering method. Visualizing the course curriculum provides a macro and structured perspective for individuals seeking alternative educational pathways within the existing system. By visualizing the relationships among courses, students can explore different combinations of courses with comprehensive search support. To illustrate our approach, we conduct a detailed demonstration using the syllabus database of Yonsei University. Through the application of our methods, we create visual course networks that reveal the underlying structure of the university curriculum. Our results yield insights into the interconnectedness of courses across various academic majors at Yonsei University. We present both macro visualizations, covering 18 academic majors, and visualizations for a few selected majors. Our analysis using Yonsei University's database not only showcases the value of our methodology but also serves as a practical example of how our approach can facilitate multidisciplinary learning. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the factors influencing the international beef market's trade flows by applying the gravity model. We focus on the effects of trade networks, non-tariff measures, and natural disasters on the beef trade. This is the first study to incorporate network analysis eigenvector scores into the gravity approach with a view to examining the impact of trade network improvements on trade. Eigenvector centrality scores reveal the prominent role played by the European and Southern Common Market countries and show a well-connected beef network. The results of our gravity model show that beef trade increases more when an importer improves its position in the trade network, shedding light on the importance of strategic network engagement. We also show that Sanitary and Phytosanitary measures are more trade-restrictive than tariffs, a finding which emphasises the need for trade policy to pay attention to such measures. Meteorological (hydrological) catastrophes hurt beef imports (exports); this highlights the importance of implementing safeguarding measures on beef farms. [ABSTRACT FROM AUTHOR]

The rapid spread of inaccurate information through social media has become a major concern in recent years. This paper presents a mathematical model for analyzing the impact of inaccurate information on the spread of accurate information in a network. We aim to evaluate the effectiveness of three different strategies of control: media literacy programs and fact-checking and a combination of both. The model is analyzed using the maximum principle of Pontryagin to derive the optimal control strategies for minimizing the spread of inaccurate information while maximizing the spread of accurate information. A numerical simulation is presented to illustrate the effectiveness of the control strategies, and statistical analysis is performed to compare the impact of the different control strategies on the spread of information. The results demonstrate that the combination of media literacy programs and fact-checking is the most effective strategy for increasing the spread of accurate information and reducing the spread of inaccurate information. These findings have important implications for the design of effective strategies to combat the spread of misinformation and promote the spread of accurate information in a network. [ABSTRACT FROM AUTHOR]

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

Research Question: Which are the trends and the impact in the research area covering corporate governance in sensitive industries? Motivation: The research enhances prior literature on corporate governance under specific risk, uncertainty and socio-economic conditions. The referenced topics and timeframe highlight the focus on sensitive industries as a challenge, considering the mixed effects of crises conditions. Idea: This paper creates a comprehensive overview on the corporate governance in sensitive industries research landscape, considered under universalist, social constructivist, and presentation perspectives. Data: Information on publication trend, papers’ main characteristics, academic collaboration, and factors with significant influence on times cited are collected for a sample of 926 scientific publications from the Web of Science database. Tools: The network visualization for co-authorship and co-occurrence of keywords is based on using VOSviewer software, while statistical and econometric analysis of the database is performed using SPSS Statistics V.23. Findings: The results emphasize that most of the papers are published in leading journals, while 2022 is the year that stands out with the highest number of publications. From the perspective of the business field, the research focus is on Banking, Energy, Logistic, Pharmaceutical, and Tourism industries. Muchmore, statistical analysis demonstrates that there is a significant difference between the types of publications. Regression analysis shows that times cited are positively and statistically significant correlated with cited reference count and article age. Contribution: This study may be of great interest to academic and business environments. It summarizes information for a specific topic, whose evolution managers and practitioners are interested in. Furthermore, it fills the gap in examining the research impact based on several characteristics that exert an influence on times cited, contributing to the body of knowledge regarding the classification of research features under the universalist, social constructivist, and presentation perspectives.

This paper empirically explores the propagation mechanisms through which exogenous shocks are diffused and amplified throughout the input-output network structure of regional economies in the European Union. To do so, it develops weighted and directed input-output network models across the European Union (EU) NUTS-2 regions and computes measures of network typology capturing connectivity, centrality, and clustering. Network analysis facilitates a clustering of the EU countries into groups of common topological features, favoring a mix of tailored policies per each group. Spatial regression analysis reveals that the intensity of the sectors’ clustering is positively associated with regional resilience, while the sectorial clustering of regions negatively affects their resilience to exogenous shocks, revealing a competitive relationship between network topology and functionality in the configuration of regional resilience. The overall approach provides a methodological framework for an instrumented policy making and empirically highlights the importance of innovation and knowledge-based policies for enhancing regional economic resilience. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate optimal control to a mathematical model that describes agreedisagree opinions during polls in a multi-zone framework. We first present the model and recall its different compartments and we present some properties of the multi-zone model. We formulate the optimal control problem by supplementing our model with a functional objective. Optimal control strategies are proposed to reduce the number of disagreeing people in a targeted zone and the cost of interventions. We prove the existence of solutions to the control problem, and we employ the Pontryagin’s maximum principle to find the necessary conditions for the existence of the optimal controls and Runge- Kutta forward-backward sweep numerical approximation method is used to solve the optimal control system, and perform numerical simulations using various initial conditions and parameters to investigate several scenarios. [ABSTRACT FROM AUTHOR]

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors' collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies' investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies. [ABSTRACT FROM AUTHOR]

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Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to investigate the recent literature on auditory experiences within automotive environments and discusses potential future research directions in this area. Forty-six papers obtained through the PRISMA protocol were selected from literature published over the past 15 years. The collected literature was categorized based on engine type, and a comparative analysis of research trends in the automotive industry was conducted, explicitly focusing on internal combustion vehicles (ICVs) and electric vehicles (EVs). A network analysis was performed utilizing the keywords of the papers to identify the predominant research topics. The analysis revealed research topics actively studied in existing ICV research but not covered in EV and newly emerging research topics in the EV field. The study proposes future research topics related to auditory experience design. It aims to provide insight into the design of auditory experiences in automobiles, particularly as the automotive paradigm expands to include electric and autonomous vehicles.

With the emergence of modern food supply chains, there has been a noticeable decline in consumer trust and an increase in information asymmetry. Short food supply chains, including farmers' markets, offer potential solutions to these issues. Currently, farmers' markets are primarily found in the United States and the European Union, and their impact on sustainability has gained significant attention. However, the relevance of this traditional approach within modern supply chains remains largely unexplored. Thus, this study aims to examine the existing literature on farmers' markets using bibliometric techniques applied to 1765 documents sourced from the Scopus and Web of Science databases spanning from 1955 to 2022. The paper tracks the research trends associated with farmers' markets by identifying the stages of evolution of key topics, articles, journals, author citations, and co-citation networks. The findings demonstrate an increasing trend in publication of papers on this subject, highlight five interconnected areas of market research, and provide a foundation for future research and policy making by outlining the main and specific research avenues to explore.

The longstanding illicit trafficking of archaeological artefacts has persistently presented a global issue, posing a substantial threat to cultural heritage. This paper introduces an innovative automated system that utilises Natural Language Processing (NLP), Machine Learning (ML), and Social Network Analysis (SNA) to construct a Knowledge Graph for antiquities. The objective is to offer insights into the provenance of artefacts and identify potential instances of illicit trafficking. The paper delineates a comprehensive methodology, from the ontology to the Knowledge Graph. The methodology comprises four distinct phases: the initial phase involves tailoring existing ontologies to match project-specific needs; the second phase centres on selecting appropriate technologies, and scraping and text-mining tools are designed to extract pertinent data from textual sources; the third phase centres in the creation of a robust and accurate Knowledge Graph that captures artefact provenance. The paper suggests employing NLP models, specifically utilising Named Entity Recognition (NER) techniques. These models automatically extract relevant information from the unstructured provenance texts, organising them as events to which both objects and actors participated with their locations and dates. The final phase is concerned with defining and building the Knowledge Graph. The authors explore a property graph model that distinctively represents nodes and relationships, each augmented by associated properties. Employing an SNA approach, the model is projected in multiple network levels of ownership histories (actor-object network) or actor relationships (actor-actor network). This approach reveals patterns within the antiquities market. When integrated with the authors’ recommended strategies such as crowdsourced ontology definition, collaboration with reputable organisations for quality sources, and the application of transfer learning techniques, the suggested approach holds promising implications for the protection of cultural heritage.

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]

Aquatic ecosystems, especially freshwater ecosystems, are facing increasing anthropogenic disturbances, highlighting the urgency for accurate ecosystem assessment for informed decision-making and effective management. While ecosystem health and integrity assessments have been widely applied, they often focus on ecosystem elements, overlooking their interconnections and dynamic characteristics. Establishing an integrated ecosystem assessment framework is vital to apply a comprehensive perspective in evaluations. In the current paper, a systematic review of aquatic ecosystem assessment methods is done, encompassing health and integrity assessment, network analysis, and stability and regime shift assessment. These approaches consider not only the individual elements but also the complex interconnections and dynamic characteristics within the ecosystem, which have been rarely studied due to limitations in field data availability. A case study of Poyang Lake is further presented for practical demonstration on the specific operation of the combined assessments. The integration of aquatic ecosystem assessment with ecological modeling is strongly advocated because it not only helps overcome the limitations of field data for assessing historical or current conditions, but it also enables prediction of future developments. [ABSTRACT FROM AUTHOR]