Search Results (507)

In Taiwan, some cases charge high diagnostic fees based merely on manual visual inspection or other simple checks, which has severely undermined public trust and delayed judicial resolutions, forcing courts to repeatedly appoint alternative evaluators and prolonging dispute timelines. Based on convenient sampling under a 95% confidence level with a 10% margin of error and a 10–90% category proportion, this study analyzes 83 leakage identification cases collected through convenience sampling, covering diverse building types, leakage causes, and detection techniques such as infrared imaging, borescopes, and moisture meters. A clustering-based pricing framework was applied to classify cases by inspection methods and leakage causes and to link them with cost intervals. After rigorous filtering, cost categorization, one-hot encoding, and normalization, the model revealed three distinct cost groups and achieved an overall classification accuracy of 86.75%, with particularly high precision in the medium-cost range. The findings confirm that advanced methods (e.g., borescopes, high-pressure cleaning) correspond to higher fees, while simpler approaches (e.g., infrared imaging) remain in lower cost brackets. This framework supports transparent and standardized fee estimation, addresses long-standing pricing controversies, and enhances consumer trust in leakage diagnostics. Full article

Background/Objectives: Publicly available open databases offer advantages in terms of accessibility and transparency. However, their application in intensive care research remains limited. Therefore, in this study, we examined whether simple nationwide comparisons of intensive care unit (ICU) practice patterns are feasible using an open database. Methods: A multicenter, cross-sectional study was conducted using data from the Bed Function Report. ICU wards reimbursed under ICU admission fee types were included and classified as high-acuity or standard ICUs. The ward-level procedure frequencies of procedures, including mechanical ventilation, were calculated. Comparisons were performed according to ICU admission fee type and geographic region. Quasi-Poisson regression models with offsets for annual ICU admissions were applied, accounting for overdispersion. Results: A total of 602 ICUs were included in the study. Non-metropolitan ICUs demonstrated higher procedural rates for mechanical ventilation compared with metropolitan ICUs (rate ratio [RR], 1.11; 95% confidence interval [CI], 1.02–1.21). Standard ICUs consistently had lower procedural rates for mechanical ventilation than high-acuity ICUs (RR, 0.74; 95% CI, 0.68–0.81). Group analyses indicated that regional differences in procedure frequencies were evident in standard ICUs, but not in high-acuity ICUs. Conclusions: This study demonstrated the feasibility of comparing ICU practice patterns across different regions and facility types in Japan using a nationwide open public database. This approach may serve as an initial step in a stepwise research framework that links open-database profiling to patient-level analysis using more detailed data sources. Full article

Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling. Full article

As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m. Full article

The disruptive effect of digital platforms is forcing media companies to rethink their business models, particularly when it comes to increasing revenues from the audience as a share of their total revenues. Audience engagement has become a key issue for media companies since, without it, there is no basis for the introduction of subscription fees, paywalls of any kind, or schemes for soliciting donations/support. Hungary is no exception in this regard; but Hungarian media companies must also contend with other challenges. In a captured media environment, independent media are struggling to survive and have essentially been relegated to the digital space. However, in the last decade, several projects have been launched in the digital market, and many of these projects have become financially sustainable. This sustainability owes largely to the fact that these media companies have been able to monetise their popularity: the awareness of Hungarian media consumers has increased, and more people are willing to pay for quality content. The present study examines the extent to which news media have been able to build communities around their organisations, as well as the special place these communities occupy for many consumers in Hungary’s illiberal democracy. The paper presents the various forms of community-building used by independent media, and it draws on in-depth interviews to examine how media company managers view the importance of these communities. Full article

This study investigates the potential effects of Mobile Money (MM) and Central Bank Digital Currencies (CBDCs) on the average transaction costs of remittances to Sub-Saharan Africa (SSA), with a focus on Nigeria. While much of the current literature highlights the theoretical benefits of CBDCs in reducing intermediation costs, empirical evidence remains limited. The analysis combines descriptive statistics and regression models to examine the role of MM in reducing remittance fees across SSA. In addition, the Synthetic Control Method (SCM) is applied to assess the post-launch impact of Nigeria’s CBDC, the eNaira, on inward remittance costs. Results show that MM adoption is associated with significant reductions in remittance costs, reinforcing its importance as a tool for financial inclusion and efficiency. In contrast, the eNaira is not yet associated with transaction fee reduction and has not displaced the bank-dominated remittance channels, which are the most expensive. These findings suggest that while CBDCs hold promise, their effectiveness in emerging markets depends on complementary digital infrastructure and policies that support competition and interoperability. This paper offers one of the first empirical assessments of a CBDC’s economic impact on remittance costs, moving beyond largely theoretical or technical discussions. Jointly analyzing MM and CBDCs provides novel insights into their interaction and highlights policy considerations for emerging markets piloting CBDCs or expanding MM infrastructure. Full article

This study examines the determinants of profitability for 21 Bulgarian commercial banks over the period from the first quarter of 2007 to the first quarter of 2025, using financial statement data. Bank profitability is measured by return on assets (ROA) and return on equity (ROE) and modeled within a panel autoregressive distributed lag (PMG-ARDL) framework. The empirical specification combines bank-specific and macroeconomic variables, allowing for the identification of both long-run equilibrium relationships and short-run bank-level dynamics. The long-term results indicate that the net interest margin (NIM), net fee and commission margin (NFM), government bond yields, the growth of the gross domestic product (GDP), and the loan-to-deposit ratio (LDR) positively affect profitability. On the other hand, higher unemployment, rising housing prices, increased loan loss impairments, and the ratio of cash holdings to total assets reduce profitability. The findings provide policy-relevant insights for bank management, regulators, and macroprudential authorities regarding efficiency, income diversification, and credit risk management. The findings facilitate a more comprehensive assessment of banking sector resilience and provide a foundation for the development and refinement of macroprudential and supervisory policy measures. Full article

Establishing protected areas and promoting ecotourism are widely recognized as important pathways toward sustainable development. This study examines the development of ecotourism in oceanic island national parks by applying a systems-thinking perspective to analyze the structural feedback relationships associated with sustainability. Using the driving force–state–response (DSR) indicator framework, we construct a qualitative causal loop model to articulate the interdependencies among ecological, economic, and governance variables within island national park systems. The identified causal relationships can be organized into three principal feedback structures: one reinforcing loop and two balancing loops. These feedback structures provide a theoretically grounded interpretation of how system components may interact within the proposed conceptual framework. Although this study does not include quantitative modeling or simulation, the structural configuration highlights relational patterns among variables that may serve as a basis for subsequent empirical and computational investigation. In addition, this study uses fee-based policy as an illustrative example within the conceptual model to demonstrate how policy interventions may interact with feedback mechanisms and potentially influence park sustainability. The proposed framework provides a foundation for future research that may extend the qualitative structure into more formalized modeling approaches under alternative policy scenarios. Full article

Background/Objectives: Robot-assisted gastrointestinal (GI) cancer surgery has expanded in Japan since national reimbursement in 2018, yet hospital profitability remains uncertain because of capital, maintenance, and consumable costs. We examined whether a program-level volume threshold for profitability exists under Japan’s fee schedule and quantified actionable improvement targets. Methods: We developed a hospital-perspective, model-based economic evaluation (index admission to 30 days; 2025 Japanese yen (JPY)) comparing robot-assisted surgery (RAS) with conventional laparoscopic surgery (CLS) under Japan’s fee schedule (one point = ¥10) for gastrectomy, colectomy, rectal resection, and pancreatoduodenectomy. Case-level contribution margin differentials (ΔCM) were defined as the revenue differential minus the consumables differential and additional operating room (OR) time costs, plus savings from reduced length of stay (LOS), and were aggregated to annual program profit (Π) after fixed costs and platform sharing. Primary outputs were allowable consumables, required cut (%), and isoprofit contours. Uncertainty was assessed using 50,000-iteration probabilistic sensitivity analysis (PSA), one-way sensitivity analysis (OWSA), and learning-curve scenarios in line with Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022. Results: In the base case, ΔCM was predominantly ≤0 for colon, rectum, and pancreatoduodenectomy; therefore, when the case-mix-weighted mean ΔCM was ≤0, increasing volume could not achieve breakeven and instead increased losses. Each 10 min reduction in OR time increased allowable consumables by ¥15,000, and each bed-day reduction increased it by ¥30,000. These required-cut and isoprofit maps provide actionable targets for cost negotiation, operational improvement, and platform sharing. Conclusions: Volume expansion alone rarely yields profitability; coordinated reductions in consumables, OR time, and LOS, together with platform sharing, are required. Full article

Understanding and preventing student dropout presents a decision-critical modeling problem involving heterogeneous variables, nonlinear relationships, and the need for transparent inference. This study addresses the prediction of undergraduate academic outcomes, including Graduation, Enrolled, and Dropout, by proposing a efficientand interpretable machine learning framework that explicitly balances predictive performance, feature efficiency, and algorithmic explainability. The empirical analysis relies on a dataset of 4424 student records across 17 undergraduate programs from the Polytechnic Institute of Portalegre, Portugal. In contrast to existing approaches that rely on high-dimensional input spaces and opaque predictive architectures, we develop a reduced-dimensional classification pipeline based on recursive feature elimination with Gradient Boosting and Random Forest models. Starting from a comprehensive set of demographic, academic, and financial indicators, only 20 informative predictors are retained for model construction, substantially reducing input complexity while preserving predictive capacity. Comparative evaluation across multiple learning algorithms identifies Gradient Boosting as the most effective model, achieving an AUC of 0.891. Beyond predictive accuracy, the proposed framework emphasizes model interpretability through the integration of SHapley Additive exPlanations (SHAP), enabling quantitative attribution of feature contributions at both global and instance levels. The analysis reveals that second-semester academic engagement variables—including the number of courses approved, evaluated, and enrolled—as well as tuition fee payment status and age at enrollment, are the dominant factors shaping student outcomes. Overall, the results demonstrate that strong classification performance can be achieved using a compact feature set while maintaining transparent and explainable model behavior. By combining mathematically grounded feature selection with principled model explanation, this study advances methodological understanding of how efficiency, interpretability, and predictive accuracy can be jointly optimized in applied machine learning, with implications for decision-support systems in educational analytics. Full article

This study models the auditor–client relationship as a strategic game shaped by two-sided information asymmetry and examines how this structure influences key audit outcomes, namely audit delay and audit fees, in Türkiye. Using a game-theoretic framework complemented by empirical analysis, the study analyzes independent audit reports dated 31 December 2024 for 201 Borsa Istanbul firms audited by Big Four auditors. Two ordinary least squares models are estimated: one for audit delay and one for the logarithm of audit fees. The findings indicate that firm size and effort-related cost proxies play a central role in explaining audit fees, reflecting scale-related audit complexity. Financial risk, while not significantly associated with audit fees, is found to be negatively related to audit delay, suggesting that riskier firms may accelerate the reporting process through stronger monitoring, earlier planning, or tighter regulatory scrutiny. Audit opinion, by contrast, does not exhibit a statistically meaningful association with reporting delay, likely due to limited variation within the sample. Overall, the results partially support the risk–effort–cost mechanism proposed by the game-theoretic framework and highlight how institutional features of the Turkish audit market shape the relationship between risk and reporting timeliness. The study contributes to the literature by framing the audit process as a strategic decision environment and by providing updated evidence from an emerging market context. Full article

Student dropout remains a critical challenge for higher education institutions, with significant implications for resource allocation, academic planning, and institutional sustainability. This study applies machine learning techniques to predict student non-continuation and attrition to support data-driven retention strategies in higher education. By framing the problem as a multi-class classification task (Dropout, Enrolled, Graduate), the proposed framework enables early and differentiated intervention planning. Using a publicly available higher education student dataset (4424 records, 34 features, multi-class outcome), a structured analytical pipeline was implemented, incorporating Winsorisation for outlier mitigation, SMOTE for class imbalance handling, and targeted feature engineering. Model performance was assessed using a 5-fold nested cross-validation framework. Four classifiers, Extra Trees, Random Forest, Gradient Boosting, and Logistic Regression, were trained on an optimised subset of 28 features. Among these, the Extra Trees model achieved the strongest performance, attaining a mean AUC of 0.96 (±0.0053) and an accuracy of 87.4% (±0.012). Model interpretability was enhanced through SHAP analysis, which identified cumulative approved academic units and tuition fee payment status as the most influential predictors of student outcomes. The findings underscore the value of early predictive analytics for informing proactive institutional interventions, particularly in academic monitoring and financial support to strengthen student retention frameworks. Full article

This study explores strategies for dynamic pricing and flexible refund fee setting in railway line services, aiming to optimize ticket sales revenue by integrating refund mechanisms into the revenue management framework. By introducing a consistent concept of opportunity cost applicable to both passengers and railway operators, we propose an integrated approach that combines dynamic pricing with flexible refund fees grounded in the demand-driven opportunity cost of seat resources. A dynamic programming model is constructed to quantify the opportunity cost of seat resources. To address the computational challenges arising from the model’s scale, state and time dimension compression methods are applied to develop an approximate linear programming model with fewer constraints. The proposed model is solved using a turning point search algorithm and a constraint generation algorithm. Numerical experiments and ticket sales simulations are conducted to verify the feasibility of the proposed methods and to explore the application effects of different pricing strategy combinations. The results demonstrate that the integration of dynamic pricing and flexible refund fees can significantly enhance ticket sales revenue, particularly in scenarios of supply shortfall. Full article

Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions. Full article

In this study, we applied dynamical system theory to analyze evolutionary trends of the higher education system, consisting of three indicators: university scale development, government financial investment, and student fee standards. The higher education system has significant uncertainty over a long period of evolution, especially with the development scale and speed of higher education largely depending on government financial investment and changes in student fee standards. We adopted the theory and methods of dynamical system theory to analyze the evolutionary trend of the higher education system, composed of these three indicators. Using the principles and methods of differential dynamics, we put forward a three-dimensional dynamic system model, involving variables such as higher education scale expansion, standard of tuition, and government financial investment. Based on the qualitative theory of ordinary differential equations, we have obtained the stability conditions for the equilibrium of the dynamic model and the conclusion of the asymptotic of the asymmetric solution of the three-dimensional dynamical system. The general integral expression of the system was obtained under specific conditions. The general integral can explain the global structure of the system in some aspects, and can compensate for the shortcomings of the local structure of equilibrium points. Full article