Search Results (2,971)

Cataract surgery is the most common eye operation worldwide and is regarded as one of the safest procedures in medicine. Yet, despite its low complication rates, it generates a disproportionate share of litigation. The gap between excellent safety profiles and rising medico-legal claims is driven less by surgical outcomes than by patient expectations, often shaped by healthcare marketing and the promise of risk-free recovery. This narrative review explores the clinical and legal dimensions of cataract surgery, focusing on complications, perioperative risk factors, and medico-legal concepts of predictability and preventability. Particular emphasis is given to European frameworks, with the Italian Gelli-Bianco Law (Law No. 24/2017) providing a model of accountability that balances innovation and patient safety. Analysis shows that liability exposure spans all phases of surgery: preoperative (inadequate consent, poor documentation), intraoperative (posterior capsule rupture, zonular instability), and postoperative (endophthalmitis, poor follow-up). Practical strategies for risk reduction include advanced imaging such as macular OCT, rigorous adherence to updated guidelines, systematic video recording, and transparent perioperative communication. Patient-reported outcomes further highlight that satisfaction depends more on visual quality and dialogue than on spectacle independence. By translating legal principles into clinical strategies, this review offers surgeons actionable “surgical–legal pearls” to improve outcomes, strengthen patient trust, and reduce medico-legal vulnerability in high-volume cataract surgery. Full article

Accurate estimation of flow velocities in torrents and steep rivers is essential for flood risk assessment, sediment transport analysis, and the sustainable management of water resources. While deep learning models are increasingly applied to such tasks, their evaluation often depends on statistical metrics that may yield conflicting interpretations. The objective of this study is to clarify how different evaluation metrics influence the interpretation of hydrological deep learning models. We analyze two models of flow velocity prediction in a torrential creek in Taiwan. Although the models differ in architecture, the critical distinction lies in the datasets used: the first model was trained on May–June data, whereas the second model incorporated May–August data. Four performance metrics were examined—root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott’s index of agreement (d), and mean absolute percentage error (MAPE). Quantitatively, the first model attained RMSE = 0.0471 m/s, NSE = 0.519, and MAPE = 7.78%, whereas the second model produced RMSE = 0.0572 m/s, NSE = 0.678, and MAPE = 11.56%. The results reveal a paradox. The first model achieved lower RMSE and MAPE, indicating predictions closer to the observed values, but its NSE fell below the 0.65 threshold often cited by reviewers as grounds for rejection. In contrast, the second model exceeded this NSE threshold and would likely be considered acceptable, despite producing larger errors in absolute terms. This paradox highlights the novelty of the study: model evaluation outcomes can be driven more by data variability and the choice of metric than by model architecture. This underscores the risk of misinterpretation if a single metric is used in isolation. For sustainability-oriented hydrology, robust assessment requires reporting multiple metrics and interpreting them in a balanced manner to support disaster risk reduction, resilient water management, and climate adaptation. Full article

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

Objectives: This study aims to examine the impact of Diagnosis-Related Group (DRG) payment on medical costs, efficiency, and quality of healthcare services in public hospitals, providing policy recommendations for further health insurance payment reforms in China. Methods: Utilizing inpatient medical insurance settlement data from 2020 to 2023 in the selected city, we constructed a regression discontinuity design (RDD) and an interrupted time series (ITS) model to evaluate the causal effects of the DRG reform. The analysis includes 66,533 inpatient settlement records. Results: Following the reform, the average length of stay (LOS) decreased by 2 days (95% CI: −3.43 to −0.70, p < 0.01), total hospitalization expenditures dropped by 13% (95% CI: −0.26 to −0.00, p < 0.05), and expenditures from the medical insurance fund declined by 25% (95% CI: −0.39 to −0.12, p < 0.01). Additionally, examination and consultation fees were reduced by 23% (95% CI: −0.41 to −0.05, p < 0.05), although patients’ out-of-pocket burden increased by 8% (95% CI: 0.05 to 0.10, p < 0.01). In terms of healthcare quality, the 30-day readmission rate decreased by 1% (95% CI: −0.01 to −0.00, p < 0.01), and the mortality rate among low-risk patients declined by 4% (95% CI: −0.04 to −0.03, p < 0.01). We found no evidence of patient selection or denial of admission. Heterogeneity analysis revealed that the reduction in hospital stay was concentrated among enrollees under the Urban and Rural Resident Basic Medical Insurance and those treated in secondary hospitals. The policy’s effects peaked shortly after implementation but gradually attenuated over time. Conclusions: Our study offers hospital-level evidence indicating that the initial stage of DRG implementation achieved its preliminary goals of optimizing medical resource allocation and improving the efficiency of medical insurance fund utilization. However, the reform still faces several challenges. These findings may offer valuable references for developing countries pursuing reforms in primary healthcare and health insurance payment systems. Full article

Objectives: Despite the widespread adoption of clear aligner therapy (CAT), its effectiveness in managing rotations remains debated. This systematic review aims to evaluate rotational accuracy in adults and the influence of treatment variables—such as attachments, interproximal reduction (IPR), and staging. Methods: Following PRISMA guidelines, seven databases and two grey literature sources were searched up to July 2025. Eligible studies assessed rotational accuracy in patients treated exclusively with clear aligners, using 3D digital model superimposition. Primary outcomes included percent accuracy, lack of correction (LC), or mean absolute error (MAE). Risk of bias (RoB 2, ROBINS-I) and certainty of evidence (GRADE) were assessed. Results: Twelve studies (one RCT, eleven non-randomized) were included, showing wide heterogeneity in aligner systems, tooth types, outcome measures, and adjunctive strategies. Reported accuracy ranged from 36% to 85%, averaging around 65%. LC values varied from 0.7° to 4.5°, and mean MAE was about 2.3°. Incisors and molars showed higher predictability, whereas maxillary canines and premolars remained the least reliable. Attachments and IPR were widely used, but their effectiveness was inconsistent. Staging protocols were generally set at 2°/aligner and most studies adopted 7–14-day wear schedules. Nearly all investigations showed moderate-to-serious risk of bias, and certainty of evidence was rated low to moderate. Conclusions: CAT shows limited yet improving predictability in rotational movements, with performance strongly influenced by tooth morphology and staging. Attachments, IPR, and overcorrections may contribute but lack consistent validation. Given the low certainty and high risk of bias of current evidence, these findings should be interpreted cautiously. Well-designed RCTs with standardized protocols are required to develop reliable clinical guidelines. Full article

Background: Problematic internet use among adolescents is linked to poorer mental health, academic performance, and social functioning, yet evidence from Eastern Europe remains limited. Methods: We conducted a school-based cross-sectional study at a Romanian high school (Arad County) including 308 students aged 15–18 years (154 males, 154 females). Students completed a demographic/behavioral questionnaire and the 20-item Internet Addiction Test (IAT), a widely used measure of problematic internet use. The prespecified primary analysis was a multivariable linear regression of IAT score on sex, age group, residence, daily screen time, prior attempts to reduce use, and main internet purpose; supporting analyses included t-tests, ANOVA, and Pearson correlation (α = 0.05). Results: In bivariable comparisons, males, older adolescents (17–18 years), and urban residents reported higher IAT scores; screen time correlated with IAT (r = 0.460, p < 0.001), and prior reduction attempts were associated with higher scores (Cohen’s d = 0.80). In the adjusted model, male sex (β = 4.97), older age (β = 5.36), greater daily screen time (β = 1.67 per hour), prior attempts to reduce use (β = 4.13), and primarily using the internet for gaming (β = 5.71) remained significant predictors (all p ≤ 0.045); urban residence was not retained (p = 0.218). The model explained 43% of IAT variance (R² = 0.43). Conclusions: Demographic and behavioral factors independently predict adolescent problematic internet use, highlighting high-risk profiles (older males, heavy screen time, gaming focus, prior reduction attempts). These findings support school-based screening and targeted digital-health interventions in underrepresented contexts. Full article

Background/Objectives: University students frequently engage in risky alcohol consumption, making them a priority population for targeted interventions. Motivational interventions (MIs) have been widely implemented to address this issue, but evidence of their effectiveness remains heterogeneous. This study aimed to evaluate the efficacy of MIs in reducing alcohol consumption and related harm among university students through a systematic review and meta-analysis. Methods: A systematic search was conducted in PubMed, Scopus, and BVS Library, including randomized controlled trials (RCTs) published up to April 2025. The PRISMA and RoB-2 tools guided reporting and risk of bias assessment. Random-effects models were applied to pool effect sizes for changes in alcohol consumption patterns and related consequences. Results: Fifteen RCTs were included. MIs significantly reduced daily alcohol intake (−0.55 drinks/day; 95% CI: −0.78 to −0.32), with additional reductions in weekly consumption and binge drinking episodes, though these were not statistically significant. Positive effects were also observed in reducing alcohol-related consequences and blood alcohol concentration levels. Short, single-session formats (45–90 min) showed consistent efficacy across studies, with effects sustained at 2–3 months and, in some cases, up to one year post-intervention. Conclusions: MIs are effective, brief, and adaptable strategies for reducing harmful alcohol use and associated negative outcomes among university students. Their simplicity, feasibility, and sustained effects make them valuable tools for university health programs. Future research should focus on optimizing intervention components and evaluating their effectiveness in diverse cultural and socioeconomic contexts. Full article

To address the challenges of accurately predicting and controlling the annular equivalent circulating density (ECD) in ultra-deepwater gas hydrate-bearing formations of the Qiongdongnan Basin, where joint production of hydrates and shallow gas through dual horizontal wells faces a narrow safe pressure window and hydrate decomposition effects, this study develops an ECD prediction model that incorporates riser drilling operations. The model couples four sub-models, including the static equivalent density of drilling fluid, annular pressure loss, wellbore temperature–pressure field, and hydrate decomposition rate, and is solved iteratively using MatlabR2024a. The results show that hydrate cuttings begin to decompose in the upper section of the riser (at a depth of approximately 600 m), causing a reduction of about 2 °C in wellhead temperature, a decrease of 0.15 MPa in bottomhole pressure, and an 8 kg/m³ reduction in ECD at the toe of the horizontal section. Furthermore, sensitivity analysis indicates that increasing the rate of penetration (ROP), drilling fluid density, and flow rate significantly elevates annular ECD. When ROP exceeds 28 m/h, the initial drilling fluid density is greater than 1064 kg/m³, or the drilling fluid flow rate is higher than 21 L/s, the risk of formation loss becomes considerable. The model was validated against field data from China’s first hydrate trial production, achieving a prediction accuracy of 93%. This study provides theoretical support and engineering guidance for safe drilling and hydraulic parameter optimization in ultra-deepwater hydrate-bearing formations. Full article

In recent years, the integration of flexible resources into active distribution networks (ADNs) has been significantly enhanced. By coordinating a variety of such resources, the economic efficiency, operational security, and overall stability of ADNs can be improved. In this study, a bi-level planning model is proposed for active distribution networks. The upper-level model aims to minimize the annual comprehensive cost, while the lower-level model focuses on reducing network losses. To solve the upper-level problem, a hybrid whale optimization algorithm (HWOA) is developed. The algorithm integrates adaptive mutation based on Gaussian–Cauchy distributions, a nonlinear cosine-based control strategy, and a dual-population co-evolution mechanism. These enhancements allow HWOA to achieve faster convergence, higher accuracy, and stronger global search capabilities, thereby reducing the risk of falling into local optima. The lower-level problem is addressed using the interior point method due to its nonlinear and continuous nature. The proposed model and algorithm are validated through simulations on the IEEE 33-bus system. The results show that DG consumption increases by 88.77 MWh, network losses decrease by 6.8 MWh, and the total system cost is reduced by CNY 3.62 million over the entire project lifecycle. These improvements contribute to both the economic and operational performance of the ADN. Compared with the polar fox optimization algorithm (PFA), HWOA improves algorithmic efficiency by 18.92%, lowers network loss costs by 6.22%, and reduces the total system costs by 0.71%, demonstrating its superior effectiveness in solving complex bi-level optimization problems in active distribution networks. These findings not only demonstrate the technical efficiency of the proposed method but also contribute to the long-term goals of sustainable energy systems by improving renewable energy utilization, reducing operational losses, and supporting carbon reduction targets in active distribution networks. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article

Integrating Unmanned Aerial Vehicles (UAVs) into urban airspace requires a risk-aware approach to strategic flight planning and trajectory optimization, particularly for beyond-visual-line-of-sight operations. Existing regulatory frameworks impose strict restrictions and lack dynamic, trajectory-based risk assessments. This study presents a methodology to compute efficient UAV flight paths that comply with a predefined Target Level of Safety (TLS) for ground risk. An A* algorithm with an adaptive, risk-weighted cost function optimizes trajectories by balancing flight efficiency and ground risk exposure. The risk model incorporates key urban factors, including population exposure, road-traffic density and flow, sheltering effects, UAV-specific parameters, and wind conditions. The approach is validated through a large-scale simulation study using synthetic urban environments, systematically analyzing TLS compliance and the impact of UAV parameters on optimal trajectories. In a real-world case study using open urban GIS data, the method achieved a 72.2% reduction in induced ground risk compared to the direct path, while increasing the detour factor only to $1.06$ and maintaining full TLS compliance, demonstrating its practical relevance for strategic, risk-aware UAV flight planning. Full article

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities. Full article

Type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD) are not merely coexisting epidemics but co-evolving manifestations of a shared cardiometabolic continuum. Despite advances in glycemic, lipid, and blood pressure control, residual cardiovascular risk remains high, underscoring the limitations of siloed approaches. In this perspective, we argue for reframing T2D and CVD as interconnected conditions driven by inflammation, adipose tissue dysfunction, and organ crosstalk. Beyond metformin, which remains foundational, several glucose-lowering drug classes are now evaluated not only for glycemic control but also for their cardiovascular and renal impact. Landmark trials and recent meta-analyses confirm that sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists improve cardiorenal outcomes. More recently, tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor agonist, has shown unprecedented efficacy in weight and glucose management, with potential to further transform cardiometabolic risk reduction. Yet enthusiasm for these therapies must be tempered by heterogeneity of response, treatment costs, and inequitable access. Integrated care models, supported by multidisciplinary teams, digital health tools, and value-based reimbursement, are essential to close the gap between trial efficacy and real-world outcomes. Attention to sex, age, ethnicity, and comorbidity profiles is critical to ensure equity, as is the adaptation of strategies to low- and middle-income countries where the burden of cardiometabolic disease is rapidly rising. Ultimately, advancing cardiometabolic medicine requires not only novel therapies but also a unifying framework that integrates biology, behavior, economics, and health systems to deliver the right treatment to the right patient at the right time. Full article

In order to study the generation, management, and disposal status of waste mineral oil in China’s railway transport industry, this article takes the Beijing Railway Bureau and its subordinate Tangshan Locomotive Depot as the research objects and comprehensively applies the survey, case study, and statistical analysis methods to analyze the source of the generation of railway waste mineral oil, the distribution of the disposal enterprises and locomotive depots, the management mode, and the economic and environmental benefits of recycling waste engine oil. The results show that waste oil mainly originates from locomotive overhaul and maintenance. There is significant regional imbalance in the generation and disposal capacity of railway waste oil. The Beijing Railway Bureau can achieve the timely disposal of waste mineral oil and reduce transport risks. Waste mineral oil management integrates generation, storage, transfer, and disposal. If cooperation is initiated with waste oil disposal enterprises, the use of recycled oil can save up to RMB 178,600/year and reduce carbon emission by 76.42 tCO₂/year for this locomotive depot. In view of the current situation, the railway industry should improve the management and disposal deficiencies and explore the new model of waste oil reduction, reuse, and recycling. Full article

Ice disasters in the Yellow River’s Inner Mongolia reach exhibit sudden onset and high destructiveness, driven by climatic and channel constraints. The Shisifen Bend, within this reach, is particularly prone to initial ice jamming during freeze-up periods annually. This susceptibility arises from channel narrowing, increased upstream ice influx, and complex river morphology. To address persistent ice flood risks and mitigation challenges at Shisifen Bend, this study developed a coupled ice-transport numerical model. Utilizing MIKE21’s hydrodynamic and particle tracking modules alongside measured bathymetric and depth data, the model simulates ice movement under three distinct flow conditions: 2000, 2500, and 3000 m³/s. Analysis of ice trajectories and distribution patterns under varying flow conditions reveals key transport mechanisms for both ice and water. These findings provide critical insights for enhancing ice flood prevention and disaster reduction strategies along the Inner Mongolia Yellow River during freeze-up period. Full article