Search Results (1,883)

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

Barriers to cancer care, including transportation and Internet insecurity, are of special concern in low-resource communities. A patient-centered, telehealth-based, barrier-focused lay navigator program may mitigate such barriers. We share insights from a quality improvement project wherein we developed and delivered a lay navigator program in a low-resource community in the Mojave Desert. We identified 68 patients scheduled for lung cancer detection/management at our institution, 55 of whom completed a barrier assessment, enrolled in the program, and could be evaluated. Participants were predominantly older (76%), White (84%), had a cancer diagnosis at enrollment (69%), and lived in socioeconomically disadvantaged neighborhoods. Thirty-three (60%) patients had ≥1 barrier, the most common being transportation (31%), Internet (24%), and financial (24%) concerns. These barriers were more frequent among patients with a lung cancer diagnosis at enrollment. Crisis-focused and after-hours encounters were more frequently initiated by older and advanced cancer patients. Transportation and Internet concerns were significantly associated with missed appointment rates. While the scope of our findings is limited, the delivery of a telehealth-based, barrier-focused lay lung navigator program in this low-resource setting was feasible. Neighborhood context and barrier resource planning are important for the implementation of similar programs within our institution’s clinical practice network. Full article

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically sound risk assessment formula that is still reliable even in the absence of complete vulnerability data. This is particularly important for financial institutions operating in emerging markets, where regulators rarely provide centralized vulnerability assessments and where Basel-type frameworks are only partially implemented. The contribution of the paper is a practically verified Bayesian network model that integrates threat likelihoods, vulnerability likelihoods, and their impacts within a probabilistic structure. Using 500 stratified Monte Carlo scenarios calibrated to real fintech and banking institutions operating under EU and national supervision, we demonstrate that excluding vulnerability impact from the model does not significantly reduce the predictive performance. These findings advance the theory of risk assessment, simplify practical implementation, and enhance the scalability of risk modeling for both traditional banks and fintech institutions in emerging economies. Full article

This study develops a three-dimensional evaluation framework integrating policy instruments, policy themes, and policy subjects to analyze China’s ESG (Environmental, Social, and Governance) policies. Based on 82 central government policy documents issued between 2007 and 2024, it employs content analysis, Latent Dirichlet Allocation (LDA) topic modeling, and social network analysis. The findings reveal a structural imbalance in policy instruments, with overreliance on environmental instruments and insufficient application of supply side and demand side mechanisms. Four major policy themes are identified: environmental governance, corporate responsibility and disclosure, technological innovation, and financial development. These themes show evolving priorities aligned with national strategies. Social network analysis shows weak coordination among stakeholders, with only a few central agencies driving most policies. This research contributes a systematic and quantitative approach to ESG policy evaluation, offering insights into structural shortcomings and governance fragmentation. It provides actionable recommendations for optimizing instrument use, enhancing thematic design, and improving multi-agency collaboration in ESG policymaking. This study contributes to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), by evaluating China’s ESG policies and proposing a more balanced and pragmatic policy framework. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

The European Green Deal (EGD) aims at driving the green transition in the EU and positions cities as pivotal actors in achieving climate neutrality and environment protection. Despite ambitious policy commitments, significant implementation gaps persist at the local level impeding urban green transitions. This study assesses barriers to the EGD urban implementation by integrating several methods (scoping literature review, expert consultations, and computational network analysis) to identify structural conditions hampering change. Barriers are clustered into five domains and reviewed by experts to distill eight structural conditions perpetuating the status quo of urban development, hindering transformative change. The findings illustrate how the emerged structural conditions, ranked by their in-degree centrality, regard insufficient policy implementation; upgrade of consolidated built environments’ layout; short-term mindset; lack of knowledge and data sharing among stakeholders; silos in policymaking and development processes; competition among stakeholders over space use; limited social acceptance; and limited financial resources. Conversely, high-out-degree barriers—such as limited technical expertise in urban departments and GDP-oriented paradigms—emerge as system triggers where targeted interventions could catalyze change. This research provides actionable insights for policymakers by identifying leverage points which could promote urban green transitions and enhance the EGD local implementation for accelerating urban green transitions. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks (HGNNs) offer a solution by modeling multiple relationships between enterprises. However, current models struggle with financial risk graph data challenges, such as the oversimplification of internal financial features and the lack of dynamic imputation for missing external topological features. To address these issues, we propose HGNN-EBP, an enterprise bankruptcy prediction algorithm that integrates both internal and external features. The model constructs a multi-relational heterogeneous graph that combines structured financial data, unstructured textual information, and real-time industry data. A multi-scale graph convolution network captures diverse relationships, while a Transformer-based self-attention mechanism dynamically imputes missing external topological features. Finally, a multi-layer perceptron (MLP) predicts bankruptcy probability. Experimental results on a dataset of 32,459 Chinese enterprises demonstrate that HGNN-EBP outperforms traditional models, especially in handling relational diversity, missing features, and dynamic financial risk data. Full article

Detecting financial fraud is a critical aspect of modern intelligent financial systems. Despite the advances brought by deep learning in predictive accuracy, challenges persist—particularly in capturing complex, high-dimensional nonlinear features. This study introduces a novel hybrid quantum recurrent neural network for fraud detection (HQRNN-FD). The model utilizes variational quantum circuits (VQCs) incorporating angle encoding, data reuploading, and hierarchical entanglement to project transaction features into quantum state spaces, thereby facilitating quantum-enhanced feature extraction. For sequential analysis, the model integrates a recurrent neural network (RNN) with a self-attention mechanism to effectively capture temporal dependencies and uncover latent fraudulent patterns. To mitigate class imbalance, the synthetic minority over-sampling technique (SMOTE) is employed during preprocessing, enhancing both class representation and model generalizability. Experimental evaluations reveal that HQRNN-FD attains an accuracy of 0.972 on publicly available fraud detection datasets, outperforming conventional models by 2.4%. In addition, the framework exhibits robustness against quantum noise and improved predictive performance with increasing qubit numbers, validating its efficacy and scalability for imbalanced financial classification tasks. Full article

Accurate modeling of event sequences is valuable in domains like electronic health records, financial risk management, and social networks. Random time intervals in these sequences contain key dynamic information, and temporal point processes (TPPs) are widely used to analyze event triggering mechanisms and probability evolution patterns in asynchronous sequences. Neural TPPs (NTPPs) enhanced by deep learning improve modeling capabilities, but most suffer from limited interpretability due to predefined functional structures. This study proposes KANJDP (Kolmogorov–Arnold Neural Jump-Diffusion Process), a novel event sequence modeling method: it decomposes the intensity function via stochastic differential equations (SDEs), with each component parameterized by learnable spline functions. By analyzing each component’s contribution to event occurrence, KANJDP quantitatively reveals core event generation mechanisms. Experiments on real-world and synthetic datasets show that KANJDP achieves higher prediction accuracy with fewer trainable parameters. Full article

The growing concern about climate change and increased carbon emissions has promoted the electric vehicle market. Lithium-Ion Batteries (LIBs) are now the prevailing technology in electromobility, and large amounts will soon reach their end-of-life (EoL). Most counties have not designed sustainable reverse logistics networks to collect, remanufacture and recycle EoL electric vehicle batteries (EVBs). This study is focused on estimating the future EoL LIBs generation through dynamic material flow analysis using a three parameter Weibull distribution function under two scenarios for battery lifetime and then designing a reverse logistics network for the region of Attica (Greece), based on a generalizable modeling framework, to handle the discarded batteries up to 2040. The methodology considers three different battery handling strategies such as recycling, remanufacturing, and disposal. According to the estimated LIB waste generation in Attica, the designed network would annually manage between 5300 and 9600 tons of EoL EVBs by 2040. The optimal location for the collection and recycling centers considers fixed costs, processing costs, transportation costs, carbon emission tax and the number of EoL EVBs. The economic feasibility of the network is also examined through projected revenues from the sale of remanufactured batteries and recovered materials. The resulting discounted payback period ranges from 6.7 to 8.6 years, indicating strong financial viability. This research underscores the importance of circular economy principles and the management of EoL LIBs, which is a prerequisite for the sustainable promotion of the electric vehicle industry. Full article

Developments in agriculture is reshaping the agribusiness landscape, altering farms’ bargaining power and strategic positioning within supply chains. These dynamics raise important questions about how financial strength influences farmers’ preferences for different components of business relationships with input suppliers. The primary objective of this study is to examine the relationship between a farm’s financial power and the importance it assigns to the behavioral dimension in such relationships. To address this objective, we employ a two-stage research design. In the first stage, qualitative interviews with farmers were conducted to identify the key attributes contributing to relationship value, encompassing economic, strategic, and behavioral dimensions. In the second stage, a quantitative survey was administered to 249 farmers, supplemented with financial data from the Farm Accountancy Data Network (FADN). The Maximum Difference Scaling (MaxDiff) method was applied to assess the relative importance of these attributes, followed by statistical analysis linking the observed preferences to a composite indicator of financial power. The results indicate that financially stronger farms place greater emphasis on economic factors while attaching less importance to behavioral aspects. Among less financially powerful farms, two distinct patterns emerge: one characterized by opportunistic, price-oriented behavior, and another reflecting a relational orientation that values trust, communication, and long-term cooperation alongside economic conditions. These findings contribute to a better understanding of business relationships in agribusiness by explaining how financial power shapes the trade-off between economic and behavioral components. Full article

Modeling risk evolution in financial networks presents both practical and theoretical challenges, particularly during periods of heightened systemic stress. This issue has gained urgency recently in China as it faces unprecedented financial strain, largely driven by structural shifts in the real estate sector and broader economic vulnerabilities. In this study, we combine Fractional-order Partial Differential Equations (FoPDEs) with network-based analysis methods, proposing a hybrid framework for capturing and modeling systemic financial risk, which is quantified using the $\Delta CoVaR$ algorithm. The FoPDEs model is formulated based on reaction–diffusion equations and discretized using the Caputo fractional derivative. Parameter estimation is conducted through a composite optimization strategy, and numerical simulations are carried out to investigate the underlying mechanisms and dynamic behavior encoded in the equations. For empirical evaluation, we utilize data from China’s financial and real estate sectors. The results demonstrate that our model achieves a Mean Relative Accuracy (MRA) of 95.5% for daily-frequency data, outperforming LSTM and XGBoost under the same conditions. For weekly-frequency data, the model attains an MRA of 91.7%, exceeding XGBoost’s performance of 90.25%. Further analysis of parameter dynamics and event studies reveals that the fractional-order parameter $\alpha$, which controls the memory effect of the model, tends to remain low when $\Delta CoVaR$ exhibits sudden surges. This suggests that the model assigns greater importance to past data during periods of financial shocks, capturing the persistence of risk dynamics more effectively. Full article

In socio-economically disadvantaged communities, the challenges faced by children with special needs are often overshadowed by more visible issues such as poverty, family instability, and substance abuse. Children, especially those with special needs, are particularly vulnerable in these settings as they are disproportionately impacted by intersecting adversities, including neglect, exploitation, and limited access to education and healthcare. These adversities create a vicious cycle, where disability exacerbates financial hardship, and in turn, economic deprivation negatively impacts early childhood development, further entrenching disability. Conventional models, which require physical presence and focus primarily on diagnosis and treatment within clinical settings, often fail to address the broader social, environmental, and contextual complexities of disability. We propose an Information Technology-based Exit Pathway as an innovative, scalable solution to disrupt this cycle. Anchored in the five pillars of the Community-Based Rehabilitation (CBR) matrix of Health, Education, Livelihood, Social, and Empowerment, the model envisions a multi-level digital platform that facilitates coordinated support across individual, familial, educational, community, regional, and national levels. By improving access to services, fostering inclusive networks, and enabling early intervention, the proposed approach aims to promote equity, social inclusion, and sustainable development for children with special needs in marginalized communities. Full article

The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article