Search Results (1,821)

Background: Neurogenic bladder (NB) in children may lead to recurrent urinary tract infections (UTIs), renal deterioration, and a reduced quality of life. Clean intermittent catheterization (CIC) is the standard of care, but in some patients, CIC may be unfeasible due to anatomical, sensory, or compliance issues. Button cystostomy (BC) has emerged as a minimally invasive, bladder-preserving alternative. This study aimed to assess the feasibility, safety, and outcomes in the long-term of BC in pediatric NB patients. Methods: Retrospective analysis was conducted on children with NB who underwent endoscopic BC placement between January 2020 and December 2024 in a tertiary pediatric center. Demographic data, operative time, complications, and follow-up outcomes were collected. All procedures used an endoscopic approach with cystoscopic guidance for safe device placement. Results: Thirty-three patients (25 males; median age 7.96 years) underwent BC placement. Most had spinal dysraphism (63.6%). The mean operative time was 48.5 ± 6 min. During a mean follow-up of 2.1 ± 1.4 years, five patients (15.2%) had febrile UTIs and two had minor leakage. No major complications occurred. Four buttons were removed due to clinical improvement (N = 1), the fashioning of a continent derivation (N = 1) and implantation of a sacral neuromodulator (N = 2); two patients accepted CIC. Satisfaction was reported by 93.9% of families. Conclusions: BC is an effective, minimally invasive alternative for urinary drainage in children with NB, even when compared to continent diversion techniques such as the Mitrofanoff, due to its lower invasiveness, greater feasibility, and lower complication rate. Broader adoption may be warranted, but prospective studies are needed to confirm long-term outcomes. Full article

With the globalized deployment of cross-border vehicle location services and the trajectory data, which contain user identity information and geographically sensitive features, the variability in privacy regulations in different jurisdictions can further exacerbate the technical and compliance challenges of data privacy protection. Traditional static differential privacy mechanisms struggle to accommodate spatiotemporal heterogeneity in dynamic scenarios because of the use of a fixed privacy budget parameter, leading to wasted privacy budgets or insufficient protection of sensitive regions. This study proposes a reinforcement-learning-based dynamic noise optimization method (DNO-RL) that dynamically adjusts the Laplacian noise scale by real-time sensing of vehicle density, region sensitivity, and the remaining privacy budget via a deep Q-network (DQN), with the aim of providing context-adaptive differential privacy protection for cross-border vehicle location services. Simulation experiments of cross-border scenarios based on the T-Drive dataset showed that DNO-RL reduced the average localization error by 28.3% and saved 17.9% of the privacy budget compared with the local differential privacy under the same privacy budget. This study provides a new paradigm for the dynamic privacy–utility balancing of cross-border vehicular networking services. Full article

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article

Background: Fixed-dose combinations of rosuvastatin and ezetimibe are increasingly used in clinical practice, but real-world data on their effectiveness and safety in large populations remain limited. Methods: This prospective, single-group, open-label, non-interventional observational study was conducted in the Republic of Korea to evaluate the effectiveness and safety of Rovazet^® (a fixed-dose combination of rosuvastatin and ezetimibe). Patients were prospectively enrolled from 235 institutions (50 general hospitals and 185 private clinics) as part of routine clinical practice over a five-year period. Lipid profiles and medication compliance questionnaire results were collected at baseline, 12 weeks, and 24 weeks of treatment. Results: A total of 5527 patients with dyslipidemia, the majority were men (53.0%), and the mean age was 60.4 years. Rovazet^® significantly reduced low-density lipoprotein cholesterol (LDL-C) by 23.5% at 12 weeks (from 117.47 ± 50.65 mg/dL to 81.14 ± 38.20 mg/dL; p < 0.0001) and by 27.4% at 24 weeks (from 117.47 ± 50.65 mg/dL to 74.52 ± 33.36 mg/dL; p < 0.0001). Total cholesterol was significantly reduced by 17.7% at 12 weeks and by 19.8% at 24 weeks. Rovazet^® treatment reduced triglycerides by 4.1% at 12 weeks and by 7.2% at 24 weeks. High-density lipoprotein cholesterol increased by 4.5% at 12 weeks and by 7.9% at 24 weeks following Rovazet^® treatment. These changes in lipid profiles were consistent, regardless of cardiovascular risk profiles. By 24 weeks of treatment with Rovazet^®, 91.8% of patients had reached their target LDL-C goals. Adverse drug reactions were reported in 2.81% of patients, most of which were minor, indicating that Rovazet^® was well tolerated. Conclusions: Rovazet^® was effective in improving lipid profiles and well tolerated in Korean adults with dyslipidemia. Full article

Today, ‘social sustainability’ is a key feature of many organisations’ environmental, social, and governance strategies, as well as underpinning sustainable development goals. The term refers to the implementation of targets such as reduced societal inequalities, the promotion of social well-being, and the practice of positive community relations. Building a meaningful, accountable, and quantifiable evidence-base from which to translate these high-level concepts into tangible and achievable goals is, however, challenging. The complexities of measuring social capital—often described as a building block of social sustainability—have been documented. The challenge lies in measuring the person, group, or collective in interaction with the context under investigation, whether that be a climate goal, an institution, or a national policy. Social identity theory is a social psychological approach that articulates the processes through which an individual internalises the values, norms, and behaviours of their contexts. Levels of social identification—a concept capturing the state of internalisation—have been shown to be predictive of outcomes as diverse as communication and cognition, trust and citizenship, leadership and compliance, and health and well-being. Applying this perspective to the articulation and measurement of social sustainability provides an opportunity to build an empirical approach with which to reliably translate this high-level concept into achievable outcomes. Full article

Autonomous navigation in dynamic, multi-vessel maritime environments presents a formidable challenge, demanding strict adherence to the International Regulations for Preventing Collisions at Sea (COLREGs). Conventional approaches often struggle with the dual imperatives of global path optimality and local reactive safety, and they frequently rely on simplistic state representations that fail to capture complex spatio-temporal interactions among vessels. We introduce a novel hybrid reinforcement learning framework, D* Lite + Transformer-Enhanced Soft Actor-Critic (TE-SAC), to overcome these limitations. This hierarchical framework synergizes the strengths of global and local planning. An enhanced D* Lite algorithm generates efficient, long-horizon reference paths at the global level. At the local level, the TE-SAC agent performs COLREGs-compliant tactical maneuvering. The core innovation resides in TE-SAC’s synergistic state encoder, which uniquely combines a Graph Neural Network (GNN) to model the instantaneous spatial topology of vessel encounters with a Transformer encoder to capture long-range temporal dependencies and infer vessel intent. Comprehensive simulations demonstrate the framework’s superior performance, validating the strengths of both planning layers. At the local level, our TE-SAC agent exhibits remarkable tactical intelligence, achieving an exceptional 98.7% COLREGs compliance rate and reducing energy consumption by 15–20% through smoother, more decisive maneuvers. This high-quality local control, guided by the efficient global paths from the enhanced D* Lite algorithm, culminates in a 10–32 percentage point improvement in overall task success rates compared to state-of-the-art baselines. This work presents a robust, verifiable, and efficient framework. By demonstrating superior performance and compliance with rules in high-fidelity simulations, it lays a crucial foundation for advancing the practical application of intelligent autonomous navigation systems. Full article

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Background/Objectives: Conventional ketogenic diets, although effective for weight loss, often contain high total and saturated fat intake, which leads to increased low-density lipoprotein cholesterol (LDL-C). Thus, the Healthy Ketogenic Diet (HKD) was developed to address these concerns. It emphasizes calorie restriction, limiting net carbohydrate intake to 50 g per day, prioritizing unsaturated fats, and reducing saturated fat intake. However, adherence to the HKD remains a challenge in urban, time-constrained environments. Therefore, this pilot randomized controlled trial aimed to investigate the effects of Healthy Ketogenic Diet Ready-To-Eat (HKD-RTE) meals (provided for the first month only) versus HKD alone on weight loss and metabolic parameters among overweight adults. Methods: Multi-ethnic Asian adults (n = 50) with a body mass index (BMI) ≥ 27.5 kg/m² were randomized into the HKD-RTE group (n = 24) and the HKD group (n = 26). Both groups followed the HKD for six months, with the HKD-RTE group receiving HKD-RTE meals during the first month. Five in-person workshops and mobile health coaching through the Nutritionist Buddy Keto app helped to facilitate dietary adherence. The primary outcome was the change in body weight at 6 months. Linear regression was performed on the change from baseline for each continuous outcome, adjusting for demographics and relevant covariates. Logistic regression was performed on binary weight loss ≥ 5%, adjusting for demographics and relevant covariates. Results: In the HKD group, participants’ adherence to the 50 g net carbohydrate target was 15 days, while that in the HKD-RTE group was 19 days over a period of 30 days. Participants’ adherence to calorie targets was 21 days in the HKD group and 23 days in the HKD-RTE. The average compliance with the HKD-RTE meals provided in the HKD-RTE group was 55%. The HKD-RTE group experienced a greater percentage weight loss at 1 month (−4.8 ± 3.0% vs. −1.8 ± 6.2%), although this was not statistically significant. This trend continued up to 6 months, with the HKD-RTE group showing a greater percentage weight reduction (−8.6 ± 6.8% vs. −3.9 ± 8.6%; p = 0.092). At 6 months, the HKD-RTE group had a greater reduction in total cholesterol (−0.54 ± 0.76 mmol/L vs. −0.05 ± 0.56 mmol/L; p = 0.283) and LDL-C (−0.43 ± 0.67 mmol/L vs. −0.03 ± 0.52 mmol/L; p = 0.374) compared to the HKD group. Additionally, the HKD-RTE group exhibited greater reductions in systolic blood pressure (−8.3 ± 9.7 mmHg vs. −5.3 ± 11.0 mmHg), diastolic blood pressure (−7.7 ± 8.8 mmHg vs. −2.0 ± 7.0 mmHg), and HbA1c (−0.3 ± 0.5% vs. −0.1 ± 0.4%) than the HKD group (not statistically significant for any). Conclusions: Both HKD-RTE and HKD led to weight loss and improved metabolic profiles. The HKD-RTE group tended to show more favorable outcomes. Short-term HKD-RTE meal provision may enhance initial weight loss, with sustained long-term effects. Full article

This research demonstrates the potential of plant waste cellulose as a remarkable biomaterial for multilayer tablet formulation. Rice husks (RC) and orange peels (OC) were used as cellulose sources and characterized for a comparison with commercial cellulose. The FTIR characterization shows minimal differences in their chemical components, making them equivalent for compression into tablets containing ibuprofen. TGA measurements indicate that the RC is slightly better for multilayer formulations due to its favorable degradation profile. This is corroborated by an XRD analysis that reveals its higher crystalline fraction (~55%). The use of a heat press at combined high pressures and temperatures allows the layer-by-layer tablet formulation of ibuprofen, taken as a model drug. Additionally, this study compares the release profile of three types of tablets compressed with cellulose: mixed (MIX), two-layer (BL), and three-layer (TL). The MIX tablet shows a profile like that of conventional ibuprofen tablets. Although both BL and TL tablets significantly reduce their release percentage in the first hours, the TL ones have proven to be better in the long run. In fact, formulations made of extracted cellulose sandwiching ibuprofen display a zero-order release profile and prolonged release since the drug release amounts to ~70% after 120 h. This makes the TL formulations ideal for maintaining the therapeutic effect of the drug and improving patients’ wellbeing and compliance while reducing adverse effects. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

Background: Obstructive sleep apnea (OSA) is a prevalent disorder in both pediatric and adult populations, characterized by substantial morbidity encompassing cardiovascular, neurocognitive, and metabolic impairments. Management strategies vary by age group and underlying etiology, with orthodontic and non-orthodontic interventions playing key roles. This narrative review synthesizes the current evidence on orthodontic and non-orthodontic therapies for OSA in pediatric and adult populations, emphasizing individualized, multidisciplinary care approaches and highlighting future research directions. Methods: A narrative review was conducted using PubMed, Scopus, and Google Scholar to identify studies on diagnosis and management of OSA in children and adults from 2000 to 2025. Results: In pediatric patients, treatments such as rapid maxillary expansion (RME), mandibular advancement devices (MADs), and adenotonsillectomy have shown promising outcomes in improving airway dimensions and reducing apnea–hypopnea index (AHI). For adults, comprehensive management includes positive airway pressure (PAP) therapy, oral appliances, maxillomandibular advancement (MMA) surgery, and emerging modalities such as hypoglossal nerve stimulation. Special attention is given to long-term treatment outcomes, adherence challenges, and multidisciplinary approaches. Conclusions: The findings highlight the need for individualized therapy based on anatomical, functional, and compliance-related factors. As the understanding of OSA pathophysiology evolves, orthodontic and adjunctive therapies continue to expand their role in achieving durable and patient-centered outcomes in sleep apnea management. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article