Search Results (15,212)

This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from the Siret, Ialomița, and Danube rivers are analyzed, covering diverse hydroclimatic conditions. Distribution parameters are estimated using the method of moments and maximum likelihood estimation. Model performance is evaluated using RMSE and MAE, complemented by an analysis of extreme quantile behavior. The results show that both distributions fit the observed data well, with only minor differences in global error metrics. However, for high return periods (T > 100 years), Halphen-A exhibits smoother extrapolation and yields more stable extreme quantile estimates, particularly when estimated by MLE. Although Pearson III often achieves slightly lower metrics values, its upper tail is more constrained and sensitive to skewness and record length. The study concludes that classical goodness-of-fit measures alone are insufficient for selecting models for design floods and that Halphen-A provides a robust complementary alternative for extreme flood estimation. Full article

Background: The prognostic value of conventional high-risk factors and the benefits of adjuvant chemotherapy (ACT) in stage II colon cancer with deficient mismatch repair (dMMR) remain controversial. The function of ACT in stage II dMMR colon cancer and survival results were assessed in this research. Methods: 273 patients with stage II dMMR colon cancer who had curative resection between August 2010 and October 2023 underwent a retrospective analysis. Clinicopathologic variables, postoperative treatment strategies, and survival endpoints were systematically assessed. Independent prognostic factors were identified using a multivariable Cox proportional hazards regression model. For subgroup analyses, a propensity score–matched (PSM) approach was used to minimize intergroup imbalances. Overall survival (OS) and disease-free survival (DFS) were evaluated using the Kaplan–Meier approach. Results: 177 (64.8%) patients had at least one high-risk factor. With a median follow-up of 62.6 months, the estimated 5-year OS and DFS rates were 94.7% and 89.8%. Age ≥ 65 years and examination of fewer than 12 lymph nodes were independently associated with OS. For DFS, age ≥ 65 years, LNs < 12, and receipt of ACT were identified as independent prognostic factors. According to subgroup analyses, ACT was linked to better OS and DFS in patients with high-risk features or poorly differentiated histology. Results were similar after propensity score matching. Conclusion: Traditional high-risk features also exert prognostic impact on this population. ACT appeared to be associated with improved survival in selected high-risk patients, particularly those with poorly differentiated histology. Full article

Romerillo (Retrophyllum rospigliosii), a vulnerable conifer native to the cloud forests of Cajamarca, Peru, persists in small remnants at high altitudes in San Ignacio province, where its integration into agroforestry systems may support both conservation and sustainable production. This study aimed to model the growth of R. rospigliosii associated with coffee (Coffea arabica L.) using diameter and height as indicators. Field data were collected over 18 months in two experimental plots and the study analyzed 329 individuals selected from 600 initially planted, with monthly monitoring to evaluate early growth and survival dynamics. The data were analyzed with nonlinear mathematical models, including Schumacher, Chapman–Richards, and Weibull, with model selection based on goodness-of-fit and prediction statistics such as R², AIC, and BIC. Results showed that Schumacher provided the best performance for height (R² = 0.98, AIC = 27,978.54), while Weibull (R² = 0.80, AIC = 27,204.63) and Chapman–Richards (R² = 0.80, AIC = 27,207.97) also yielded consistent estimates. For diameter, Schumacher was the most accurate (R² = 0.92, AIC = 2627.87). Survival analysis revealed significant differences between plots (p = 0.011), with higher survival at 1820 m (87.8% at 18 months) compared to 1540 m (77.3%). These findings indicate that the Schumacher model is most suitable for growth estimation, while altitude plays a critical role in survival, underscoring its importance in establishing R. rospigliosii within coffee-based agroforestry systems. Full article

Eye tracking can provide quantitative indices of visual exploration and cognitive processing during radiographic image interpretation. This study examined eye-movement patterns and pupil dynamics and their associations with task performance while fifth-year dental students interpreted a single mixed-dentition panoramic radiograph under free-viewing conditions. Task performance was defined as the number of correctly identified pre-specified items (three radiographic findings plus two interpretive items: dental age estimation and the presence/absence of congenital anomalies). Eye-movement patterns were classified into four groups: clockwise (R, 29.6%), counterclockwise (L, 44.4%), saccadic (S, 16.7%), and concentrated (C, 9.3%). Clockwise scan paths were associated with higher task scores and more globally distributed fixations than other patterns (p < 0.001). Linear mixed-effects modeling suggested that task scores increased up to 120 s of viewing time, whereas longer viewing times were not associated with further improvements. Furthermore, ordinal logistic regression analysis revealed that higher task scores were significantly associated with a smaller mean pupil area across the entire viewing time, combined with a larger pupil area specifically during fixations, suggesting more selective allocation of cognitive resources. These findings indicate associations between global scan structure, time allocation, pupil dynamics, and task performance in this single-image setting. Generalization to overall diagnostic competence or other radiographs requires replication using multiple panoramic images and a broader range of verified findings. Full article

This paper tackles the growing uncertainty in distribution networks caused by distributed generation, load fluctuations, and frequent topological changes. It proposes a multi-source data fusion framework using enhanced selective convolution (SKNet) and graph attention networks (GAT). First, heterogeneous measurement data, including Phasor Measurement Unit (PMU) and Supervisory Control and Data Acquisition (SCADA) data, are processed through a unified normalization and outlier elimination technique to ensure data quality. Second, SKNet is utilized to extract spatiotemporal multi-scale features, improving the detection of both rapid disturbances and long-term trends. Third, the extracted features are fed into GAT to model node electrical couplings, while power flow residual constraints are embedded in the loss function to enforce the physical validity of the estimated states. This physics-informed design overcomes a key limitation of pure data-driven models and enables an end-to-end framework that integrates data-driven learning with physical mechanism constraints. Finally, comprehensive validation is performed on the improved IEEE 33-node and IEEE 123-node test systems. The test scenarios include Gaussian measurement noise, data outliers, missing measurements, and topological changes. The results show that the proposed method outperforms baseline models such as Multi-Scale Graph Attention Network (MS-GAT), Bidirectional Long Short-Term Memory (BiLSTM), and traditional weighted least squares (WLS). It achieves Root Mean Square Error (RMSE) reductions of up to 18% and Mean Absolute Error (MAE) reductions of up to 15%. The average inference latency is only 10–18 ms. Even under unknown topological changes, the estimation error increases by only 15–25%. These results demonstrate the superior accuracy, robustness, and real-time performance of the proposed method for intelligent distribution network state estimation. Full article

Quadrotor unmanned aerial vehicles (QUAVs) are widely used in civil and defense applications, yet reliable trajectory tracking remains challenging under external disturbances and limited sensing. Conventional backstepping–sliding mode controllers ensure robustness only by selecting discontinuous gains larger than the disturbance bound, which increases chattering and limits the use of smooth switching functions. This paper addresses these limitations by integrating explicit disturbance compensation into a backstepping–sliding framework through a super-twisting observer (STO). The STO reconstructs matched disturbances acting on the translational and rotational dynamics in real time, and the estimated signals are directly injected into the control law. This approach enables effective disturbance rejection beyond the nominal sliding gain while preserving robustness under smooth control actions. Simulation results under single- and multi-frequency perturbations demonstrate accurate disturbance reconstruction (FIT indices above 95%), improved tracking performance, and a significant reduction in chattering. The proposed strategy provides a robust control solution for QUAVs operating in uncertain environments. Full article

Background/Objectives: This study focused on the sub-national regional heterogeneity in childhood obesity prevalence across five countries in south-east Europe and the correlation between this heterogeneity and socioeconomic differences. Previous studies have mainly observed national or cross-national data but this study used a sub-national regional approach that may be beneficial in the further investigation of childhood obesity. Methods: Nationally representative samples of children from Croatia, Montenegro, North Macedonia, Serbia and Slovenia were selected using the COSI methodology and used to estimate regional childhood obesity prevalence values. The Sub-national Human Development Database provided data on the Sub-national Human Development Index (SHDI). The spatial autocorrelation analysis of childhood obesity prevalence in sub-national regions was performed and its association with sub-national human development was tested with an ordinary least squares regression model. Results: This study found statistically significant differences in childhood obesity prevalence across sub-national regions in Croatia, Slovenia and Serbia, while no such differences were observed in North Macedonia and Montenegro. There was moderate clustering in childhood obesity rates (Moran’s I = 0.337). The results indicated a significant negative association between SHDI and childhood obesity prevalence across the 48 regions (β = −66.63, p < 0.001). Conclusions: Future public health efforts should take into consideration regional differences in childhood obesity prevalence, and more targeted research is essential for understanding the mechanisms of resilience and vulnerability on a sub-national level. Full article

Developments in electric vehicle (EV) technology are pushing on-board chargers (OBCs) toward higher power density and efficiency, making high-frequency transformer loss prediction a critical design bottleneck. However, the accuracy of commonly used analytical winding-loss models varies strongly with frequency, conductor type (Litz/solid), window fill factor, and winding layout (e.g., interleaved), which can render single-model-based optimization unreliable. In this study, six analytical copper-loss models from the literature were independently reimplemented in a unified Python 3.11.5 workflow with a standardized interface to enable fair comparison under identical geometry and operating conditions. The models were benchmarked against 2D finite-element simulations on test scenarios with increasing physical complexity, including high fill-factor Litz windings and interleaved arrangements. The results confirm a regime-dependent behavior: no single model consistently outperforms others across the full design space, and model dispersion increases in geometrically stressed and higher-frequency regions. To manage this uncertainty, variance maps were generated and model disagreement was quantified using the coefficient of variation (CV). Finally, a reliability-oriented multi-objective optimization framework based on NSGA-II was developed, where a SmartTransformerRouter selects a reference loss estimate per candidate and CV is incorporated via constraints/penalties, with optional FEM triggering in high-uncertainty regions. Full article

Objective gait assessment is increasingly needed beyond specialized laboratories, and 3D markerless motion capture is emerging as a viable option; however, evidence regarding its applied repeatability and practical use for spatiotemporal gait outcomes in scalable clinical and field settings remains limited. This study evaluated the applied repeatability and practical comparability of OpenCap (camera-based; CM) versus a commonly accepted photoelectric walkway (OptoGait; OPT). Thirty-nine healthy adults completed three 10-m overground trials at self-selected speed. CM parameters were derived from OpenCap’s Advanced Overground Gait Analysis. Within-device reliability was good-to-excellent for gait speed, stride length, and cadence (ICC (3,1) = 0.734–0.920 OPT; 0.791–0.917 CM) and excellent when averaging three trials (ICC (3,3) = 0.892–0.972 OPT; 0.919–0.971 CM); double support showed lower reliability (ICC (3,1) = 0.527 OPT; 0.647 CM). Between devices, CM showed higher mean speed (+0.110 m/s), stride length (+0.127 m), and double support (+3.17% of the gait cycle), while cadence was very similar (−0.59 spm). Correlations were high for speed (r = 0.951), stride length (r = 0.864), and cadence (r = 0.983) but moderate for double support (r = 0.405); absolute-agreement ICCs were highest for cadence (0.980) and lowest for double support (0.271). OpenCap provides reliable within-session estimates for key spatiotemporal measures, but systematic bias indicates it should be used consistently as a standalone tool rather than interchangeably with OptoGait without device-specific correction or reference values. Full article

Background/Objectives: To evaluate short-term postoperative outcomes in octogenarians undergoing robotic pancreaticoduodenectomy. Methods: In compliance with the PRISMA statement standards, a systematic review and random-effects meta-analysis was conducted. All studies reporting short-term postoperative outcomes in patients aged ≥ 80 undergoing robotic pancreaticoduodenectomy were included and analyzed. Results: A total of 321 octogenarians from five studies were included. The mean operative time was 459.7 min (95% CI 398.6–520.8) and the estimated intraoperative blood loss was 216.1 mL (95% CI 147.4–284.8). Conversion to open occurred in 3.8% (95% CI 0.0–7.7). The risk of postoperative mortality was 4.5% (95% CI 1.7–7.2) and Clavien-Dindo grade ≥ III (major) complications occurred in 28.0% (95% CI 22.9–33.1). The risk of grade B or C postoperative pancreatic fistula was 10% (95% CI 6.5–13.5). The hospital stay was 14.9 days (95% CI 10.2–19.5). The risk of reoperation and readmission were 8.0% (95% CI 4.4–11.7) and 25.6% (95% CI 16.9–34.3), respectively. Compared to patients aged <80, the risk of major complications was higher (OR: 1.81, p = 0.010) and hospital stay was longer (MD: 5.19 days, p = 0.030) in octogenarians. Compared to the open approach, robotic approach was associated with longer operative time (MD: 137.08 min, p = 0.0009), less intraoperative blood loss (MD: −246.00 mL, p = 0.010), and lower major complications (OR: 0.62, p = 0.020). Conclusions: Subject to selection and confounding bias, robotic pancreaticoduodenectomy may be safe with acceptable postoperative mortality and morbidity in highly selected octogenarians with good performance status. The results of the current study can be used for hypothesis synthesis and power analysis in future comparative studies. Full article

Background: Physical inactivity remains a major public health challenge, particularly for underserved populations lacking exercise facility access. AI-powered smartphone applications with real-time human pose estimation offer scalable solutions, but they lack rigorous clinical validation. Objective: This study validates the clinical efficacy of a 16-week on-device AI-driven resistance training program using MediaPipe pose estimation technology in young adults with limited facility access. Primary outcomes included muscular strength (1RM squat), body composition, functional movement (FMS), and cardiorespiratory fitness (VO₂max). Methods: A single-group pre–post study enrolled 216 participants (mean age 23.77 ± 4.02 years; 69.2% male), with 146 (67.6%) completing the protocol. Participants performed three 30 min weekly sessions of seven compound exercises delivered via a smartphone app providing real-time pose analysis (97.2% key point accuracy, 28.6 ms inference), multimodal feedback, and personalized progression using self-selected equipment. Results: Significant improvements across all domains: muscular strength (+4.39 kg 1RM squat, p < 0.001, d = 1.148), body fat (−2.92%, p < 0.001, d = −1.373), skeletal muscle mass (+2.19 kg, p < 0.001, d = 1.433), FMS (+0.29 points, p = 0.001, d = 0.285), and VO₂max (+1.82 mL/kg/min, p < 0.001, d = 0.917). Pose classification accuracy reached 95.8% vs. physiotherapist assessment (ICC = 0.94). Conclusions: This study provides the first clinical evidence that on-device AI pose estimation enables facility-independent resistance training with outcomes comparable to traditional programs. Unlike cloud-based systems, our lightweight model (28.6 ms inference) supports real-time mobile deployment, advancing accessible precision exercise medicine. Limitations include a single-arm design and gender imbalance, warranting future RCTs with diverse cohorts. Full article

Climate-smart agricultural practices (CSAPs) are promoted as pathways for improving productivity and resilience among smallholder farmers; however, empirical evidence on their welfare effects remains limited in South Africa. This study examines the impact of CSAP adoption on household welfare among smallholder maize farmers in KwaZulu-Natal Province. A cross-sectional survey of 300 households was conducted using a multistage sampling approach. Welfare outcomes was measured using multidimensional indicators including the Household Dietary Diversity Score (HDDS), the Household Food Insecurity Access Scale (HFIAS), the Coping Strategy Index (CSI), and the Foster–Greer–Thorbecke (FGT) poverty index. An Endogenous Switching Regression (ESR) model was employed to correct for selection bias and to generate counterfactuals that estimate what adopters’ welfare would have been in the absence of CSAP uptake. Results show that access to extension, group membership, and training significantly increased the likelihood of CSAP adoption. ESR outcomes indicate that adopters had higher dietary diversity, lower food insecurity, and reduced reliance on severe coping strategies. Counterfactual analysis reveals that adopters would have experienced significantly poorer welfare outcomes had they not adopted CSAPs. The findings demonstrate that CSAP adoption yields measurable welfare benefits and improves household resilience. The study recommends targeted investments in extension support, farmer organizations, and institutional arrangements to accelerate the adoption of CSAP and enhance household welfare. Full article

Precise quantification of fine motor behaviour is essential for understanding neural circuit function and for evaluating therapeutic interventions in neurological disorders. While markerless pose estimation frameworks such as DeepLabCut (DLC) have transformed behavioural phenotyping, the choice of convolutional neural network (CNN) backbone has a critical impact on tracking performance, particularly in tasks involving small distal joints and frequent occlusions. In this study, we present the first systematic comparison of nine CNN architectures implemented in DLC for lateral-view analysis of skilled reaching movements in the Montoya Staircase test, a gold-standard assay for forelimb dexterity in rodent models of stroke and neurodegenerative disease. Using a dataset comprising both control and primary motor cortex (M1)–lesioned mice, we evaluated model performance across six key dimensions: spatial accuracy (RMSE, PCK@5 px), mean average precision (mAP), robustness to occlusions, inference speed, and GPU memory usage. Our results demonstrate that multi-scale DLCRNet architectures substantially outperform conventional backbones. DLCRNet_ms5 achieved the highest overall accuracy, while DLCRNet_stride16_ms5 provided the most favourable balance between precision and computational efficiency. These findings provide practical methodological guidance for neuroscience laboratories and highlight the importance of CNN architecture selection for the reliable quantification of fine motor behaviour in preclinical research. Full article

Methane is the second most important contributor to global warming, and monitoring super-emitters from space is critical for climate mitigation. Despite the advancements in hyperspectral remote sensing, comparing methane observations across diverse imaging spectrometers remains a challenging task. Different retrieval algorithms, plume segmentation techniques and uncertainty treatments make it very hard to perform fair comparisons between different products. To overcome these difficulties, this study presents HyGAS (Hyperspectral Gas Analysis Suite), a unified, open-source framework for sensor-agnostic methane retrieval and flux estimation. Starting from the established clutter-matched-filter (CMF) formalism and a physical calibration in concentration–path-length units (ppm·m), we propagate both instrument noise and surface-driven background variability consistently from methane enhancement to Integrated Mass Enhancement (IME) and flux. The framework further includes a spectrally matched background-selection strategy, scale-aware segmentation with fixed physical criteria across resolutions, and emission-rate estimation via an IME–U_eff$U_{eff}$ approach informed by Large Eddy Simulation (LES). We demonstrate the framework on near-simultaneous observations of landfills and gas infrastructure in Argentina, Turkmenistan, and Pakistan, spanning Level-1 radiance workflows (PRISMA, EnMAP, Tanager-1) and Level-2 methane products (EMIT, GHGSat). The standardised chain enables systematic inter-comparison of methane enhancement products and reduces methodological bias, supporting robust multi-mission assessment and future global monitoring. Full article

The long-term creep performance of geosynthetics is crucial for the safe design of reinforced-soil structures. Previous studies have not sufficiently clarified the long-term creep behavior of high-density polyethylene (HDPE) geogrids or the influence of different failure criteria. Therefore, further research is needed to validate creep reduction factors’ (RF_CR) estimation and the applicability of the stepped isothermal method (SIM). In this study, the creep behavior of HDPE geogrids was examined using both conventional creep tests and SIM, conducted in accordance with ISO 13431 and ASTM D6992. Master curves were generated under load levels representing 40–60% of the ultimate tensile strength. The SIM results matched with the conventional tests in the early stage but exhibited higher creep strains beyond 1000 h, primarily due to the thermal sensitivity of HDPE. RF_CR values were determined using two design criteria, namely, 20% creep strain and creep rupture. For a 100-year design life, the RF_CR values based on a 20% creep strain were determined to be 3.04 and 2.43 based on the combined data and block-shift analysis, respectively, whereas the rupture criterion yielded a lower value of 2.30. These findings demonstrate that the 20% strain limit provides a more conservative and reliable criterion for estimating the long-term design strength. This study confirms the applicability of SIM for accelerated creep evaluation and provides practical guidance for the selection of RF_CR values in reinforced-soil design. Full article