Search Results (3,020)

Despite the rapid financial expansion over the past two decades, South Africa’s economic growth has remained sluggish, raising concerns about the disconnect between financial sector development and overall economic performance. This study aims to investigate the relationship between financialization and economic growth in South Africa using three proxy variables, finance, insurance, real estate, and business services as a percentage of GDP; money supply (M3) as a percentage of GDP; and credit to the private sector as a percentage of GDP, alongside a composite financialization indicator. Using quarterly time-series data from 1994Q1 to 2025Q2, this study employs the autoregressive distributed lag (ARDL) approach to examine both short- and long-term dynamics and cointegration between financialization and economic growth. The empirical findings reveal that financialization exerts a positive and statistically significant influence on South Africa’s economic growth. Meanwhile, the estimation results reveal that financialization has a positive and highly significant impact on economic growth in South Africa, demonstrating the need for policies that promote and enhance its effects. Full article

Sustainable urban development depends not only on efficient design and construction but also on the long-term governance of built assets during their operational phase. However, Building Information Modeling (BIM) is still predominantly applied to design and delivery processes, with limited integration into structured maintenance and renewal planning. This study develops a BIM-enabled lifecycle governance methodology that integrates lifecycle cost modeling, service-life estimation, and time-based renewal scheduling into a unified digital asset environment. Rather than proposing a new theoretical model, the study focuses on the systematic integration and operationalization of these components into a reproducible and auditable workflow. The methodology is validated through an anonymized multi-asset industrial portfolio comprising buildings, technical infrastructure, and external works, modeled over a 30-year planning horizon using structured maintenance and renewal data. Comparative scenario analysis between reactive and planned lifecycle strategies evaluates expenditure distribution, capital concentration, and intervention synchronization. The results demonstrate that embedding structured lifecycle parameters within BIM improves the predictability of annual expenditures, reduces cost concentration in peak renewal years, and enhances transparency of long-term asset planning without significantly altering cumulative lifecycle costs. These outcomes support more structured financial planning and coordination of maintenance and renewal activities at the portfolio level. The study does not quantify environmental or social sustainability impacts; its contribution lies in providing a governance-oriented methodology that transforms BIM-based asset data into decision-support outputs for long-term lifecycle planning. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Background/Objectives: Neglect of children’s oral health is a major concern at international, national, and regional levels. Of all the health problems that can occur in childhood, dental ones are among the most common. Tooth decay, for example, is a chronic condition in children and can have long-term consequences, especially in otorhinolaryngology and pediatric diseases if not treated properly. Methods: The data collection method was questionnaire. Questionnaires were administered to parents regarding oral hygiene habits and access to dental services; data were collected in dental offices across Timiș County, encompassing urban, peri-urban, and rural settings. Children enrolled in the study underwent clinical dental examinations to assess their oral health status (dental caries, gingival diseases, developmental anomalies). Results: Parental education level was not significantly associated with the habit of annual dental check-ups (χ², p = 0.092); however, a directional trend was observed. Total monthly family income was significantly associated with the stated reason for not attending dental check-ups (one-way ANOVA, p = 0.043): families with lower incomes more frequently cited financial and logistical barriers, whereas higher-income families cited lack of time or perceived lack of necessity. Parental education level (p < 0.001) and family income (p < 0.001) were both significantly associated with daily tooth-brushing frequency. Conclusions: The efforts of specialists must be increased through coherent policies, adapted education, and real support for vulnerable groups. An informed child, with supported parents, is a child with a real chance at a healthy life. This is not just a professional opinion, but a collective responsibility. Full article

Background/Objectives: Psychiatric inpatient care varies substantially in its clinical goals, resource demands, and financial implications. Acute units focus on short-term crisis stabilization, whereas chronic units provide prolonged supervision for patients with persistent functional impairment. Limited evidence exists from Eastern Europe on how these differing service models impact both hospital costs and clinical outcomes such as early rehospitalization. This study aimed to compare the economic and operational performance of Acute versus Chronic Psychiatry and to identify predictors of 30-day readmission following acute psychiatric hospitalization. Methods: This retrospective observational study analyzed routinely collected data from a Romanian public hospital. All adult admissions to Acute and Chronic Psychiatry recorded between 1 January 2024 and 31 December 2024 were included. Standardized financial indicators were derived from administrative data, while clinical variables and readmission outcomes were extracted from electronic medical records. Between-group comparisons of economic and operational indicators were performed using t-tests. Multivariable logistic regression was used to determine independent predictors of 30-day readmission in Acute Psychiatry, reporting adjusted odds ratios (aOR) with 95% confidence intervals (CI). Model performance was evaluated with area under the curve (AUC), Hosmer–Lemeshow tests, and Nagelkerke R². Results: Acute Psychiatry demonstrated significantly higher mean cost per bed-day (798.76 vs. 373.75 lei; p < 0.001), but a lower mean cost per patient due to shorter hospitalization (10.17 vs. 53.32 days). A total of 188 acute patients (13.7%) were readmitted within 30 days. No early readmissions occurred in Chronic Psychiatry, consistent with its long-stay care model. Independent predictors of readmission included psychotic disorder diagnosis (aOR = 1.62, 95% CI: 1.18–2.23), multiple prior admissions (aOR = 1.35, 95% CI: 1.18–1.54), shorter length of stay (LOS) (aOR = 0.88 per 5-day increase, p = 0.006), and absence of a post-discharge plan (aOR = 0.54, 95% CI: 0.39–0.76). Model discrimination was acceptable (AUC = 0.74). Conclusions: Acute and chronic psychiatric services differ markedly in cost structures and care pathways. Early rehospitalization is a clinically relevant outcome within acute psychiatric care and is influenced by both patient-level and continuity-of-care factors. Enhancing discharge coordination, expanding continuity-of-care strategies, and optimizing resource allocation toward community-based support may reduce early rehospitalizations while improving hospital cost-efficiency. Full article

The agricultural sector is increasingly confronted with numerous challenges, including declining prices for agricultural products, escalating production costs, intensified globalization, rapid industrialization, urban expansion and growing competition in global markets. To promote rural development and improve farmers’ livelihoods through diversified sources of income, agritourism has been identified as a viable alternative strategy. This study aims to determine the factors influencing farmers’ willingness to participate in agritourism in Mpumalanga Province, South Africa. Primary data were collected from November 2022 to June 2023 using a structured questionnaire and a simple random sampling technique to select 100 farmers. A logistics regression model was used to analyse data. The findings revealed that profitability, non-farm employment, the number of labourers, and access to information positively influence WTP. Age also positively influenced WTP, while marital status showed a negative but significant effect. The findings imply that farmers with stronger financial capacity, labour availability and access to information are more likely to consider agritourism as a diversification strategy. The study suggests strengthening extension services, improving farm profitability and enhancing access to information to increase readiness to engage in agritourism. Full article

Climate change has emerged as a defining global crisis, with the frequency and intensity of climate-induced disasters rising sharply and imposing disproportionate costs on developing economies and small island states. This article examines the role of climate-resilient infrastructure as a central pillar of climate-smart growth, integrating mitigation, adaptation, and long-term development objectives. It situates climate-resilient infrastructure within a planetary health setting, emphasizing the interdependence between human well-being, ecological systems, and infrastructure resilience. Climate-resilient infrastructure, not merely seen as an engineering solution but as a public good that generates significant positive externalities, reduces systemic macroeconomic risk and delivers welfare gains that exceed private financial returns. It discusses the cross-country heterogeneities in resilience outcomes, driven by differences in geographic exposure, economic capacity, institutional quality, and political economy constraints. Building on this, the study advances a welfare-based approach to infrastructure prioritization that incorporates service disruptions, distributional impacts, and fiscal risk, rather than asset values alone. It further outlines policy and financing strategies to bridge the gap between social and private returns, including public investment, concessional finance, blended instruments, and nature-based solutions. By embedding infrastructure within a planetary health lens, the paper argues that resilient systems are critical not only for safeguarding lives and livelihoods, but also for sustaining ecological stability, reducing health risks, and enabling inclusive, sustainable, and climate-smart economic growth. Full article

Rural communities face significant healthcare access barriers that contribute to persistent health disparities. Mobile health clinics (MHCs) have emerged as a promising strategy for expanding healthcare access, yet their effectiveness in rural settings remains understudied. The aim of this review was to examine the literature to determine what is known about access, health outcomes, and the cost-effectiveness of rural MHCs, specifically with regard to their impact on patient access and outcomes, return on investment (ROI)/financial, and program sustainability. We conducted a comprehensive search of peer-reviewed and grey literature sources. Systematic screening yielded 34 documents for full analysis. Thematic analysis was conducted across three domains: patient access, patient outcomes, and ROI/sustainability. All 34 documents provided data on patient access, with common themes including expanded service utilization, multi-service integration, overcoming geographic and transportation barriers, and improved healthcare affordability. Thirty-two documents addressed patient outcomes, reporting improvements in preventive care delivery, chronic disease management, and high patient satisfaction. Twenty-eight documents included ROI/sustainability information, with evidence suggesting cost-effectiveness particularly through emergency department visit avoidance and multi-service integration. Across the literature reviewed, the quality of evidence varied considerably, yet we concluded mobile health clinics demonstrate promise for expanding healthcare access and improving outcomes in rural populations. Key success factors include multi-service integration, diverse funding partnerships, technological integration, and strong community engagement. More rigorous research with longitudinal clinical outcome measures and robust economic analyses is needed. Full article

Objectives: This study examines how different benefit payment modes under China’s long-term care insurance (LTCI) program influence healthcare utilization and medical expenditures among middle-aged and older adults. Specifically, it compares the effects of in-kind benefits and mixed benefits on healthcare service use and financial burden. Methods: This study uses data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, focusing on middle-aged and older adults with functional limitations. Exploiting the staggered implementation of LTCI pilot programs across 14 cities, a difference-in-differences (DID) approach is employed to estimate the causal effects of different benefit payment modes on healthcare utilization and expenditures. Heterogeneity analyses are conducted to explore differences between rural and urban populations. Results: The results indicate that the in-kind benefit mode significantly reduces inpatient visits, total medical costs, and out-of-pocket expenditures. By contrast, the mixed benefit mode shows only a modest reduction observed mainly in outpatient visits. Heterogeneity analysis further reveals that in-kind benefits are particularly effective in reducing healthcare utilization and medical expenditures among rural residents, while urban residents experience higher reductions in out-of-pocket spending. Conclusions: These findings highlight the importance of benefit design in shaping the effectiveness of LTCI policies. Prioritizing service-based benefits may improve healthcare system efficiency and reduce financial burdens among older adults. The results provide policy-relevant insights for optimizing LTCI benefit design in China and other aging societies. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) poses a substantial public health challenge in China owing to its increasing prevalence and substantial economic burden. In response, the diagnosis–intervention packet (DIP) payment reform was implemented to control healthcare costs and enhance service efficiency. Methods: To evaluate the effect of the DIP reform on medical costs, hospitalization days, and individual out-of-pocket payments for COPD inpatients in M City, a pilot city in central China, we conducted an interrupted time series (ITS) analysis using monthly reimbursement records from January 2020 to December 2023. The study included 84,410 hospitalized patients from a city-wide database of 3,241,233 inpatient records with COPD who met the inclusion criteria. The analysis focused on the total healthcare costs, length of stay, and individual out-of-pocket costs. Results: The DIP reform resulted in a 3.7% reduction (95% CI: 0.9% to 6.5%) in the total hospitalization costs in the first month post-reform, with a sustained monthly decline of 0.8% (95% CI: 0.5% to 1.1%). The length of stay decreased from 9.53 (95% CI: 9.31 to 9.75) to 8.74 days (95% CI: 8.62 to 8.86). Conversely, the proportion of out-of-pocket payments relative to total costs increased. Conclusions: While the DIP reform effectively reduced hospitalization costs and days, it led to an increase in individual out-of-pocket payments. Future research should focus on optimizing payment rules, enhancing the supervision of medical services, and refining health insurance policies to achieve the reform’s objectives better and alleviate the financial burden on patients. Full article

Rural hospitals are essential access points for healthcare delivery in the United States, yet they continue to experience disproportionate rates of closure and service disruption that threaten community health, economic stability, and equity. This rapid systematic review synthesizes recent peer-reviewed evidence examining rural hospital closures and service disruptions, with emphasis on financial, policy, workforce, and performance-related factors and their downstream impacts. Guided by PRISMA methodology, four databases were searched for U.S.-based studies published between January 2024 and June 2025. Following screening and consensus-based review, 59 articles met inclusion criteria. Across studies, financial vulnerability, characterized by revenue instability, low patient volumes, unfavorable payer mix, and reliance on non-operating revenue, emerged as a dominant precursor to closure and service reductions. Policy context, particularly Medicaid expansion status, telehealth and broadband infrastructure, and reimbursement adequacy, strongly shaped hospital sustainability. Closures and service disruptions were consistently associated with increased travel distances, reduced access to maternal, surgical, mental health, and chronic care services, higher prices at surviving hospitals, and increased strain on remaining providers. Workforce shortages further compounded these challenges. Collectively, findings demonstrate that rural hospital closures reflect interconnected structural weaknesses rather than isolated organizational failure. Coordinated policy action, targeted financial stabilization, workforce development, and technology-enabled care models are necessary to mitigate continued erosion of rural healthcare access. Full article

Complex Event Processing (CEP) is essential for real-time analytics in domains such as industrial IoT, cybersecurity, and financial monitoring, yet CEP adoption is still hindered by the difficulty of authoring temporal rules and by rigid redeployment workflows. This paper presents PatternStudio, a neuro-symbolic CEP framework that translates natural language specifications into validated event-processing patterns and executes them on a deterministic Apache Flink-based runtime without interrupting service. The generative layer is constrained to produce a typed intermediate representation, while the symbolic layer enforces validation and runtime execution guarantees. We evaluate the prototype as a single-node system-characterization study on commodity hardware representative of edge and near-edge gateways rather than microcontroller-class devices. Under this setting, PatternStudio reaches 47,910 events per second at 250 active rules while maintaining a bounded memory footprint between 1.6 GB and 1.9 GB during the reported runs. Beyond 500 active rules, throughput degradation is driven primarily by CPU saturation and alert amplification, which also explains the sharp increase in tail latency. Additional measurements with parallelism 4, a static baseline, and a two-stage NL-to-IR evaluation further show that the architecture remains functional under partitioned execution, incurs moderate dynamic-orchestration overhead, preserves rule structure reliably under natural-language authoring, and supports interchangeable LLM backends at the semantic front end. Full article

Dementia caregiving represents a major public health challenge, with spousal caregivers assuming the greatest burden. Spouses, themselves typically older adults, provide high intensity, long-term, and largely unpaid care across all stages of cognitive decline. Despite their central role in dementia care, the health consequences experienced by spousal caregivers remain insufficiently characterized in the literature and inadequately addressed in clinical and public health practice. This structured narrative review synthesizes current evidence on the multidimensional impact of dementia caregiving on the physical, psychological, cognitive, social, and financial health of spousal caregivers. It further contextualizes these consequences within the trajectory of dementia progression, and identifies interventions, support systems, and policy considerations necessary to mitigate caregiver burden. Spousal caregivers experience disproportionate burden due to continuous, escalating responsibilities that often mirror the progressive deterioration of their partners. Emotional burdens, including uncertainty during pre-diagnostic stages, role strain, conflict, loss of intimacy, and anticipatory grief. Physically, spouses endure musculoskeletal strain, sleep disruption, poor nutrition, and heightened frailty risk. Psychologically, spousal caregivers exhibit elevated rates of depression, anxiety, loneliness, and stress-related disorders. Socially, caregivers experience substantial isolation, stigma, and erosion of social networks. Financial hardship, including early retirement, reduced employment, and uncompensated care hours, further exacerbate stress. Evidence suggests that chronic caregiving stress contributes to biological changes such as immune dysregulation, inflammation, acceleration, aging, and potential cognitive decline in caregivers themselves. Caregiver burden influences patient outcomes as evidenced by increased emergency department use, falls, and earlier institutionalization in persons with dementia whose caregiver is subjected to a high burden. Current care models rarely include routine, caregiver assessment or structured guidance following diagnosis, resulting in substantial unmet needs. Effective mitigation requires integrated, stage-sensitive interventions, including psychosocial support, caregiver education, respite services, culturally tailored programs, and digital health tools, alongside broader policy reforms to reduce financial and structural barriers. Full article

Customer churn refers to the termination of a customer’s business relationship with a bank, representing a direct loss of future revenue. Product downsell manifests as a reduction in the number of financial products held or a downgrade in service tier, often signaling early customer disengagement. Accurately identifying customers at risk of these two behaviors has become a cornerstone of profitable growth in the competitive retail banking industry as downsell frequently serves as a precursor to total churn. However, the existing research typically treats these highly correlated behaviors as independent prediction tasks, overlooking their intrinsic link and failing to address the critical challenges of class imbalance and regulatory demands for model interpretability. To tackle these problems, we propose an enhanced multi-task learning network (EMTL-Net), a deep learning framework specifically designed to capture the nuanced interplay between churn and downsell behaviors. EMTL-Net introduces an explicit feature interaction module to enhance the modeling of high-order feature relationships and utilizes a shared representation layer to extract universal customer risk patterns, enabling the joint prediction of churn and downsell. Furthermore, we employ Focal Loss as the training objective to dynamically adjust sample weights, effectively mitigating the class imbalance problem. Critically, to meet financial compliance requirements, we implement a SHAP-based interpretation mechanism that is compatible with multi-task outputs, providing preliminary insights into feature importance. Formal validation of interpretability claims remains an important direction for future research. The experimental results on a publicly available pedagogical bank customer benchmark dataset demonstrate that EMTL-Net achieves excellent performance on both tasks. For churn prediction, the model achieves an AUC of 0.8259, an accuracy of 0.8361, and an F1-score of 0.6235, significantly outperforming the existing baseline models. For downsell prediction (noting that the downsell label is rule-derived from the number of products held), the model achieves an AUC of 0.8932, an accuracy of 0.8571, and an F1-score of 0.7504. Ablation studies confirm the critical contributions of the explicit feature interaction module, Focal Loss, and the residual structure to model performance. Crucially, the interpretability analysis corroborates business intuition by identifying customer age, account balance, and product holdings as dominant churn drivers—a consistency that reinforces the model’s credibility and practical utility in high-stakes financial environments. Full article