Search Results (4,168)

This study examines the peer effects of bank digital transformation facilitated by shareholder networks and explores the underlying mechanisms. A time-varying network is constructed based on common shareholder connections among banks, and a corresponding measure is developed to quantify peer effects in digital transformation. Using the Peking University digital transformation index together with ownership and financial data from CSMAR, an empirical analysis is performed on a panel of 114 Chinese commercial banks from 2010 to 2021 to evaluate these effects. Fixed-effects estimations indicate that bank digital transformation is significantly affected by peer effects transmitted through common shareholder connections, with a one-unit increase in peers’ digitalization index associated with a 0.151-unit rise in the focal bank’s index. These findings remain robust and economically meaningful across alternative specifications, including system GMM, IV/2SLS designs, and different ownership thresholds. Further analyses indicate that the peer effects operate through mechanisms such as intensified competition, enhanced information sharing, and pooled resources. However, such peer influence also exacerbates disparities in digital progress across the industry, reflecting a Matthew Effect in which leading banks consolidate their advantages. Heterogeneity analysis reveals that the peer effects are more pronounced among banks with larger workforces, more diversified operations, and higher ownership concentration. The findings of this study provide insights into how financial institutions can leverage technological innovations through network-based channels, offering practical implications for promoting industry-wide transformation in the digital economy era. Full article

Major Depressive Disorder (MDD) is a high-risk mental illness that severely affects individuals across all age groups. However, existing research lacks comprehensive analysis and utilization of brain topological features, making it challenging to reduce redundant connectivity while preserving depression-related biomarkers. This study proposes a brain network analysis and recognition algorithm based on class-specific correlation feature selection. Leveraging electroencephalogram monitoring as a more objective MDD detection tool, this study employs tensor sparse representation to reduce the dimensionality of functional brain network time-series data, extracting the most representative functional connectivity matrices. To mitigate the impact of redundant connections, a feature selection algorithm combining topologically aware maximum class-specific dynamic correlation and minimum redundancy is integrated, identifying an optimal feature subset that best distinguishes MDD patients from healthy controls. The selected features are then ranked by relevance and fed into a hybrid CNN-BiLSTM classifier. Experimental results demonstrate classification accuracies of 95.96% and 94.90% on the MODMA and PRED + CT datasets, respectively, significantly outperforming conventional methods. This study not only improves the accuracy of MDD identification but also enhances the clinical interpretability of feature selection results, offering novel perspectives for pathological MDD research and clinical diagnosis. Full article

Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection. Full article

Background/Objectives: Gestational Diabetes Mellitus (GDM) is a global health issue with immediate and long-term maternal–fetal complications. Current diagnostic approaches, such as the Oral Glucose Tolerance Test (OGTT), have limitations in accessibility, sensitivity, and timing. This study aimed to identify key nodes and structural interactions associated with GDM using graph theory and network analysis to improve early predictive strategies. Methods: A literature review inspired by PRISMA guidelines (2004–2025) identified 44 clinically relevant factors. A directed graph was constructed using Python (version 3.10.12), and centrality metrics (closeness, betweenness, eigenvector), k-core decomposition, and a Minimum Dominating Set (MDS) were computed. The MDS, derived using an integer linear programming model, was used to determine the smallest subset of nodes with systemic dominance across the network. Results: The MDS included 20 nodes, with seven showing a high out-degree (≥4), notably Apo A1, vitamin D, vitamin D deficiency, and sedentary lifestyle. Vitamin D exhibited 15 outgoing edges, connecting directly to protective factors like HDL and inversely to risk factors such as smoking and obesity. Sedentary behavior also showed high structural influence. Closeness centrality highlighted triglycerides, insulin resistance, uric acid, fasting plasma glucose, and HDL as nodes with strong predictive potential, based on their high closeness and multiple incoming connections. Conclusions: Vitamin D and sedentary behavior emerged as structurally dominant nodes in the GDM network. Alongside metabolically relevant nodes with high closeness centrality, these findings support the utility of graph-based network analysis for early detection and targeted clinical interventions in maternal health. Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

MiRNA expression profiles exhibit notable alterations in numerous diseases, particularly viral infections. Consequently, miRNAs may be regarded as both therapeutic targets and markers for the development of complications. MiRNAs can significantly influence the modulation of immune responses, offering an extra layer of regulation during viral infections. In this study, miRNAs associated with viral infections were analyzed using an in silico approach. Computer modeling predicted a number of miRNAs capable of influencing the functionality of specific components of the immune system. As a result, 242 miRNAs common to the three types of infections were identified. A network of miRNA-gene regulatory interactions, encompassing 502 nodes (224 miRNAs and 278 genes) and 2236 interactions, was developed. Within this network, subnetworks were identified that are involved in the operation of specific connections in the immune response to viruses. For each step of the immune response, the miRNAs involved in governing these processes were examined. These predicted miRNAs are of particular interest for further analysis aimed at establishing the relationship between their differential expression and disease symptom severity. The obtained data lay the foundation for identifying the most promising molecules as predictive biomarkers and the subsequent development of a diagnostic system. Full article

To mitigate the threats posed by habitat fragmentation due to rapid urbanization on bird diversity, this study introduces an innovative framework for analyzing the synergistic effects of habitat quality (HQ), ecological network connectivity (ENC), and bird richness (BR) in the Pearl River Delta National Forest Urban Agglomeration (PRDNFUA). The framework, based on a stratified ecological network perspective that distinguishes between urban agglomeration and urban core areas, incorporates different types of ecological corridors (interactive corridors and self-corridors), providing a novel approach for effectively quantifying and spatially visualizing the temporal and spatial evolution of the “HQ–ENC–BR” synergy. By integrating geographic detectors through ternary plot analysis combined with a zonation model, this study identified the synergetic effects of HQ and ENC on BR observed during 2015–2020 and proposed strategies for optimizing “HQ–ENC–BR” synergy. The results indicate that between 2015 and 2020, (1) the Pearl River Estuary and coastal areas are hotspots for bird distribution and also represent gaps in ecological network protection. (2) The positive synergistic effects between ecological network structure (HQ, ENC) and function (BR) have gradually strengthened and are stronger than the effects of individual factors; this synergy is especially significant in urban agglomerations and interactive corridors and is particularly pronounced in the northern cities. (3) The area overlap between the optimized ecological network and bird richness hotspots will increase by approximately 78.2%. The proposed ecological network optimization strategies are scientifically sound and offer valuable suggestions for improving bird diversity patterns in the PRDNFUA. These findings also provide empirical support for the United Nations Sustainable Development Goals (SDG 11: Sustainable Cities and Communities; SDG 15: Life on Land). Full article

Economic implications of livestock diseases extend far beyond direct treatment costs and affect productivity, trade, and public health. Despite the growing recognition of animal health economics, a comprehensive analysis of its research landscape has been lacking. Therefore, this study employs bibliometric techniques to systematically analyze research on the economics of animal health between 2000 and 2024 using data extracted from the Web of Science Core Collection. A total of 1070 peer-reviewed publications were analyzed to map publication trends, influential authors, research themes, and international collaborations. The results showed that after 2014, the research output increased steadily to a peak in 2018, thus illustrating the increased global interest in economic evaluations of livestock diseases. The USA, UK, and the Netherlands emerged as key contributors, whereas low-income regions showed low research output, indicating an equity gap for animal health economics studies. The most frequently used keywords were “economics”, “cost–benefit analysis”, “economic impact”, “foot-and-mouth disease”, and “vaccination”, with increasing focus on zoonotic diseases. Coauthorship network analysis demonstrated that the institutions are well connected in Europe and North America, but research from developing countries has remained mostly fragmented. However, notable research gaps were discovered: advanced modelling approaches were underutilized, and the translation of economic research into policy was limited. This work highlights the increasing interdisciplinary nature of animal health economics, while emphasizing the need for broader species coverage, stronger international collaboration, and deeper methodological innovation. These insights provide a foundation for guiding future research priorities and shaping evidence-based policies in animal health economics. Full article

The issue of modeling the personal innovativeness of project team members is determined in this study. Findings from prior research on social capital associated with innovations and innovative activities reveal that social capital factors such as trust, social networks and connections, and social values determine a person’s attitude to innovations. Different connections involved in bridging (external) and bonding (internal) social capital can create conflict between project team members in different ways. To stimulate innovation in a conflict environment, a specially configured conflict management system is required that is capable of regulating the strength and intensity of the relationship between project team members. This paper analyzes the relationship between three constructs—innovativeness, social capital, and conflict. The existence of these latent constructs, which are formed by observable indicators of employees, is proven using confirmatory factor analysis (CFA). The construct of innovativeness depends on indicators such as creativity, risk propensity, and strategicity. Social capital includes observable indicators such as trust, social networks and connections, and social norms and values. Conflict consists of observable indicators of conflict between tasks, processes, and relationships. Using structural equation modeling (SEM), the causal relationship between social capital and innovativeness is substantiated with the mediating role of conflict in project groups between its participants—innovators and adaptors. The developed sociodynamic model for measuring conflict between innovators and adapters examines the required values of the controlled parameters of intra-group and inter-group connections between innovators and adapters in order to achieve equilibrium conflict dynamics, resulting in cooperation between them. This study was conducted using data from a survey of employees of a research organization. All model constructs were tested on a sample of employees as a whole, as well as for groups of innovators and adaptors separately. Full article

The topic proposal concerns the axes of climate operation and modification, the consequences and/or benefits of the flow of the economy, as well as the risks to social security, amidst the evolution of human interventions, which the Anthropocene highlights. Atmospheric data demonstrates the interaction of gaseous pollutants and aerosols, with the contribution of different emission and pollution sources to its chemical composition. At the same time, satellite remote sensing of precipitation and the water cycle reveal an imbalance in components and effects, in an environment of rapid rates of commercial production and human mobility in the developed world. How does mobility prevent the full observation and modeling of the elements involved (in atmospheric and hydrological data)? What is the role of multi-sensor technologies for detecting gases and what are their applications in decontamination? With sources from bibliographic reviews, data were collected from the detection of point sources of gases and dynamic analyses of the extent of the water surface, in order to highlight the descriptive characteristics of the meteorological phenomena and their activity. The scientific approach to analyzing the individual data is based on the techno-scientific Actor-Network Theory, in order to test their connection and contribution to the overall problematic result. The aim of this study is to build an interdisciplinary analysis with documentation of vulnerabilities in the expression of weather phenomena, of the present geological time. The ambition of the study is to propose principles of regulation and precaution, related to the sustainable development of geo-resources and ways to reduce vulnerability. Full article

Urbanization has posed significant challenges to cities globally, including urban sprawl, traffic congestion, reduced livability, and poor walkability. In Doha, Qatar’s capital, these issues are particularly pronounced in the West Bay Central Business District (CBD). Transit-Oriented Development (TOD) is widely recognized as a key strategy to advance sustainable urbanism and mitigate such challenges. This study employs the Integrated Modification Methodology (IMM) to systematically assess the urban design and spatial configuration of West Bay through observational analysis. The research aims to reassess the urban form and enhance transit integration through a multi-stage, iterative process, focusing on critical determinants such as compactness, complexity, and connectivity. The analysis is structured around five essential design dimensions: (i) walkability, (ii) ground-level land use balance, (iii) mixed-use and public spaces, (iv) inter-modality and transport hubs, and (v) the public transportation network. Findings reveal key urban design deficiencies, including limited intermodal connectivity, insufficient green open spaces, and a lack of diverse land use around the metro station. To address these gaps, the study proposes a set of context-sensitive policy and design guidelines to support TOD-based regeneration. This research contributes directly to SDG 11: Sustainable Cities and Communities, and supports SDG 9 and SDG 13 through its emphasis on infrastructure integration and climate-responsive planning. The findings offer practical insights for urban planners, developers, and policymakers engaged in sustainable urban transformation. Full article

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

With the rapid advance of digital technologies, the service industry has become a key driver of sustainable economic growth and the restructuring of international trade. Drawing on value-added trade flows for five pivotal service industries—construction, air transportation, postal telecommunications, financial intermediation, and education—over 2013–2021, this study examines the spatial evolution of the global service value chain (GSVC). Using social network analysis combined with a Temporal Exponential Random Graph Model (TERGM), we assess the dynamics of the GSVC’ core–periphery structure and identify heterogeneous determinants shaping their spatial networks. The findings are as follows: (1) Exports across the five industries display an “East rising, West declining” pattern, with markedly heterogeneous magnitudes of change. (2) The construction industry is Europe-centered; air transportation exhibits a U.S.–China bipolar structure; postal telecommunications show the most pronounced “East rising, West declining” shift, forming four poles (United States, United Kingdom, Germany, China); financial intermediation contracts to a five-pole core (China, United States, United Kingdom, Switzerland, Germany); and education becomes increasingly multipolar. (3) The GSVC core–periphery system undergoes substantial reconfiguration, with some peripheral economies moving toward the core; the core expands in air transportation, while postal telecommunications exhibit strong regionalization. (4) Digital technology, foreign direct investment, and manufacturing structure promote network evolution, whereas income similarity may dampen it; the effects of economic freedom and labor-force size on spatial network restructuring differ significantly by industry. These results underscore the complex interplay of structural, institutional, and geographic drivers in reshaping GSVC networks and carry implications for fostering sustainable services trade, enhancing interregional connectivity, narrowing global development gaps, and advancing an inclusive digital transformation. Full article

Analyzing the spatial layout equilibrium of highway transportation networks is essential for optimizing transportation networks, enhancing system efficiency and sustainability. To promote the equitable distribution and management of highway traffic resources, this study introduces a framework for assessing the spatial layout equilibrium of highway networks based on community structure. A new algorithm, named the C-Louvain algorithm, is introduced in this paper to address improving the stability of detection results in unconnected networks. The method first constructs a spatial node-based network, then detects the community structure of the highway network using the C-Louvain algorithm, and identifies key communities of the community structure network through a depth-first search. Network spatial layout imbalance is quantitatively assessed through supply–demand equilibrium analysis based on the Gini coefficient. This methodology is applied to the regional highway network in Shenyang, China. Results indicate that the C-Louvain method is optimal, excelling in accuracy, volatility, and efficiency compared to the classic FN, Leiden, and Louvain algorithms, providing a valuable contribution to the literature on graph clustering and data mining. There are significant differences in the number of communities within different connected components, which reflects the heterogeneity of the network’s structure. By this method, the imbalanced area in the highway transportation network layout is quickly found, and the equitable distribution of traffic resources is quantitatively evaluated. The research results can provide a theoretical basis for managers to make scientific investment decisions for road network construction. Full article