Search Results (2,278)

Cross-age face analysis has emerged as an important topic in biometric recognition due to substantial facial appearance variations caused by aging. Nevertheless, most existing approaches primarily focus on face verification (1:1 matching) and frequently rely on explicit age annotations, which limit their applicability in large-scale retrieval scenarios. In this study, large-scale cross-age face retrieval (1:N matching) is investigated, and a Hybrid Metric Learning Framework (HMLF) is proposed to learn age-invariant and retrieval-oriented facial representations without requiring age labels. The proposed framework integrates Additive Angular Margin Loss (ArcFace) with supervised contrastive learning to enhance feature discriminability. Furthermore, a mixed triplet mining strategy is introduced to improve the effectiveness of hard sample selection. A memory bank-based InfoNCE formulation is incorporated to provide a large number of negative samples, and an uncertainty-based adaptive weighting scheme is designed to automatically balance multiple loss components during optimization. To better simulate realistic retrieval scenarios, an extended cross-age retrieval evaluation protocol is established. Extensive experimental results demonstrate that the proposed framework achieves superior retrieval performance across different backbone architectures. The results further provide systematic insights into the influence of backbone design, loss formulation, and optimization strategies on cross-age retrieval accuracy. Full article

This study examines the interplay between CEO overconfidence, strategic risk-taking, and financial performance within Indonesian digital banks. Grounded in Upper Echelons Theory and behavioral corporate finance, we investigate whether strategic risk-taking serves as an organizational pathway through which CEO overconfidence is more likely to be associated with specific financial outcomes. We analyzed a census-based, longitudinal dataset of seven Indonesian digital banks from 2014 to 2024. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we tested a moderated mediation framework incorporating CEO age and gender as contextual characteristics. The empirical results reveal a nuanced pattern: while CEO overconfidence is positively associated with strategic risk-taking, such risk-taking tends to correlate negatively with financial performance. Since these direct and indirect pathways operate in opposite directions, the total association between overconfidence and performance is not statistically significant. This structure suggests that strategic risk-taking represents a primary channel through which the potential downside of CEO overconfidence may be translated into financial outcomes. Furthermore, this negative association appears more pronounced under male leadership, while CEO age exhibits no significant moderating association. Overall, the findings suggest that while CEO overconfidence may align with strategic ambition, its financial implications appear contingent upon the specific risk posture through which it is expressed. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

The Eurasian lynx (Lynx lynx) is an apex predator and an important sentinel for trophically transmitted helminths acquired via predation on wild ungulates. On 2 March 2022, an adult male lynx that was road-killed in the Apuseni Mountains (Surducel hunting ground, Bihor County) was collected, frozen for biosafety, and a necropsy was performed. Taeniid cestodes were detected, with a total intestinal burden of nine adult specimens. Genetic analyses confirmed Taenia lynciscapreoli, and the obtained sequences were deposited in GenBank (PV843597, PV855065, PV844409). Phylogenetic inference based on cox1 assigned the Romanian isolate within the European cluster, distinct from the Chinese isolate, while showing genetic proximity to Taenia sp. (MW846305) that have been reported from a lynx in China. This study represents the first molecular identification of T. lynciscapreoli in the Eurasian lynx in Romania and, to our knowledge, the first record from Southeastern Europe. Full article

Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the privacy–utility trade-off in anonymization process. The proposed approach integrates FOX-based global exploration with RUN-based local search using a hybrid adaptive control strategy and intensive mutation search to improve solution diversity in highly constrained solution spaces. Results: The experimental study on the Adult and Bank Marketing datasets shows that PA-FRIM exhibits stable convergence behavior compared to competing methods. The results indicate that full privacy is achieved on the Adult dataset with a violation value of 0.00, and information loss is minimized with an NIL measure of 0.5686. From the analytical utility perspective, PA-FRIM ensures data usability, even in the constrained region, achieving classification accuracies of 89.61% on the Bank Marketing dataset and 84.90% on the Adult dataset. Conclusions: By using a multi-metric evaluation strategy, PA-FRIM provides a robust optimization framework that eliminates privacy violations while maintaining high analytical performance. Full article

Background/Objectives: Blood donation is a critical component of healthcare systems worldwide, yet donor recruitment remains challenging. This study evaluates the knowledge, attitudes, motivations, and practices (KAP) of blood donation among the general population in Saudi Arabia to identify key barriers and propose targeted interventions. Methods: A cross-sectional study was conducted using a structured, validated questionnaire distributed over five months (December 2022 to April 2023) via social media and in-person recruitment at the Central Blood Bank in Riyadh. A total of 1150 participants aged 18–60 years residing in Saudi Arabia were included in the final analysis. Statistical analysis was performed using SPSS version 22, with p < 0.05 considered significant. Results: Participants demonstrated moderate knowledge (mean score 5.43 ± 1.81 out of 9), with significantly higher scores among males, individuals aged 21–30 years, and those holding a bachelor’s degree. Attitudes toward donation were highly positive (mean score 15.46 ± 2.74 out of 20) and correlated with age, gender, marital status, and occupation. Despite this positive outlook, only 34.96% of participants had donated blood previously, although 95.25% expressed willingness to do so. Primary motivators included mobile donation units (89.22%) and paid leave (89.22%), whereas 51.22% of respondents considered current media campaigns ineffective. Common barriers to donation included health concerns (25.30%), time constraints (12.87%), and fear of needles (7.74%). Conclusions: This study reveals a critical disparity between positive public attitudes and actual donation practices in Saudi Arabia. To enhance donor participation, we recommend implementing convenient donation strategies such as mobile blood drives, workplace incentives, and more effective, culturally tailored educational campaigns. Addressing these factors could help Saudi Arabia improve its voluntary donation rates and ensure a sustainable, safe blood supply. Full article

This paper examines the effect of a bonus cap on the compensation structure of top earners in the Dutch banking sector. Following concerns that performance-based pay may induce excessive risk-taking, regulators introduced caps on variable compensation. This paper analyzes how such regulation affects the composition of pay. The identification strategy exploits a unique institutional setting in which banks with their statutory seat in the Netherlands are subject to a stricter bonus cap than banks headquartered in other EU countries, while operating in the same market. This paper uses administrative microdata and a difference-in-differences approach to compare compensation outcomes across these groups before and after the introduction of the Dutch bonus cap in 2015. Consistent with the predictions of a principal–agent model of incentive contracting, the hourly variable wage decreases by 23 percent, while the hourly fixed wage component increases by 12 percent. The findings indicate that compensation regulation reshapes the composition of pay. Full article

This study investigates the impact of revenue diversification on the performance and stability of listed Tunisian banks over the period 2008–2023, with the objective of assessing whether diversification strategies enhance bank performance and promote financial stability in an emerging-market context. The analysis relies on a panel dataset of Tunisian listed banks and employs a two-stage least squares (2SLS) estimation approach to address potential endogeneity issues, using ownership structure as an instrumental variable. Bank performance is measured by Return on Assets (ROA) and Net Interest Margin (NIM), while financial stability is captured by the Z-score. The empirical results show that revenue diversification has a positive and significant effect on bank performance, as measured by ROA, and on financial stability. However, it exerts a negative and significant impact on NIM, indicating that although diversification improves overall performance and strengthens stability, it may weaken traditional intermediation income. This study contributes to the limited literature on banking in emerging markets by jointly examining performance and stability effects while addressing endogeneity concerns through robust econometric techniques, and by providing new evidence from the Tunisian banking sector, which has experienced significant political and economic disruptions during the study period. Full article

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

Background: Food bank users experience food insecurity, a social determinant of health linked to poorer physical and mental health. However, evidence on the health-related quality of life (HRQoL) of food bank users in Spain is scarce. Objectives: This study sought to assess HRQoL among users of the Navarra Food Bank and identify associated sociodemographic factors. Methods: We performed a cross-sectional study of heads of household using the Navarra Food Bank. A simple random sample of 350 participants was selected from a population of 2749 families. HRQoL was assessed by telephone using the EQ-5D-5L. We described the prevalence of problems in the five EQ-5D-5L dimensions, calculated the EQ-5D-5L utility index using the Spanish value set, and analyzed EuroQol Visual Analogue Scale (EQ-VAS) scores. Associations with sociodemographic characteristics were examined using multivariable general linear models. Results: Mean EQ-VAS was 73.56 (95% CI: 71.62–75.50), and mean EQ-5D-5L utility index was 0.815 (95% CI: 0.800–0.831). The most frequently reported problems were anxiety/depression (62.9%) and pain/discomfort (55.7%), while mobility (25.5%), usual activities (19.7%), and self-care (8.7%) were less commonly affected. Older age was significantly associated with both EQ-VAS and EQ-5D-5L utility index. Employment status and nationality were significantly associated with EQ-VAS, whereas sex was significantly associated with the EQ-5D-5L utility index. Conclusions: HRQoL was impaired among users of the Navarra Food Bank, with the greatest burden observed in the anxiety/depression and pain/discomfort dimensions. Older age and selected sociodemographic characteristics were associated with poorer HRQoL. Given the cross-sectional design, the findings should be interpreted as associative rather than causal. 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

Life expectancy (LE) varies widely across Organization of Islamic Cooperation (OIC) countries, reflecting differences in economic, social, environmental, and health-system conditions. This review aimed to synthesize quantitative evidence on determinants of LE at birth in OIC member countries. The study was conducted in accordance with the PRISMA guidelines, and a systematic search of electronic databases was performed up to September 2025. After screening 5312 records and assessing full texts, studies were appraised using the Joanna Briggs Institute checklists, with an inclusion threshold of ≥80%. A total of 54 studies, mainly ecological, time-series, and panel analyses using national-level data, were included. Higher gross domestic product per capita, education, employment, and health expenditure were consistently associated with longer LE. In contrast, poverty, income inequality, air pollution, and carbon dioxide emissions were associated with shorter LE. Clear differences were observed across World Bank income groups, with LE being lowest in low-income OIC countries and highest in high-income Gulf Cooperation Council states, where gains were driven more by health-system resources than by income growth. Improving LE in OIC countries requires integrated economic, social, environmental, and health-system policies. Full article

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders. Full article

The stability of Indonesia’s banking sector is closely linked to the effectiveness of capital regulations, particularly as a country that aligns its policies with Basel III standards. Ensuring that banks have adequate capital buffers is crucial for mitigating systemic risk. However, the interaction between regulatory requirements and actual banking behavior in developing countries remains poorly understood. This study aims to evaluate the impact of Indonesia’s capital requirement instruments, including the countercyclical capital buffer (CCyB), the capital conservation buffer (CCB), and the capital surcharge, on credit performance and financial stability across various bank categories. Using a quantitative approach, the analysis utilizes panel data from commercial banks, state-owned banks and regional development banks over several periods, using the panel regression method and Difference-in-Differences (DID) to assess how changes in buffer levels affect credit growth, Non-Performing Loans (NPLs), and the Capital Adequacy Ratio (CAR). The results show that capital buffers have a statistically significant effect on lending behavior: a 1% increase in buffer levels is associated with a measurable decrease in credit expansion across several bank groups, while CCBs exhibit a stronger stabilizing effect than CCyBs. Although these instruments do not eliminate financial uncertainty, they contribute to more prudent risk-taking. This study also revealed that the CCyB rate increases when the financial cycle is in an expansionary phase. Conversely, if the economy slows (as during the pandemic), the CCyB rate can be lowered back to 0% to encourage bank intermediation, thus shaping the bank’s responses to regulation. Several implications of implementing a capital buffer in Indonesia include the benefits of resilience and bank behavior during credit expansion. Overall, this study concludes that aligning regulatory frameworks with real-world banking behavior is crucial for enhancing financial stability in developing countries, such as Indonesia. Full article

This study provides a comparative evaluation of three state-of-the-art large language models (LLMs), namely OpenAI’s (San Francisco, CA, USA) GPT-4.0, Google’s (Google LLC, Mountain View, CA, USA) Gemini 2.0 Flash, and Meta’s (Meta Platforms, Menlo Park, CA, USA) LLaMA-4-Scout-17B-16E, in a decision-oriented framework in which the models generate structured outputs based only on historical closing-price data. The evaluation covers 150 stocks sampled from three countries (India, the United States, and South Africa) across ten economic sectors, including Information Technology, Banking, and Pharmaceuticals. Unlike many prior studies that combine numerical and textual inputs, this study relies solely on three years of numerical time series data and examines model responses in terms of decision labels such as buy, sell, or hold. The LLMs were provided with historical closing-price sequences and prompted with three types of finance-related questions: (a) whether to buy a stock, (b) whether to sell or hold a stock, and (c) in a pairwise comparison, which stock to buy or hold. These prompts were evaluated across two investment horizons: 1 month and 3 months. Model outputs were compared against realized market outcomes during the corresponding test periods. Performance was assessed across four key dimensions: country, sector, annualized volatility, and question type. The models were not given any supplementary financial information or instructions on specific analytical methods. The results indicate that GPT-4.0 achieves the highest average accuracy (56%), followed by LLaMA-4-Scout-17B-16E (48%) and Gemini 2.0 Flash (39%). Overall performance remains moderate and varies across market conditions, with relatively higher accuracy observed in high-volatility regimes (51%). This work evaluates how LLMs behave when presented with structured numerical price sequences in a controlled decision-labeling setting and contributes to the broader discussion on the potential and limitations of LLMs for numerical decision tasks in finance. Full article