Search Results (190)

Background: Diabetes mellitus (DM) is a condition that may increase the severity of acute pancreatitis (AP) through chronic inflammation and disturbances in immune responses. However, the independent effect of DM on clinical outcomes in AP has not yet been fully elucidated. Methods: In this retrospective cohort study, 492 patients diagnosed with acute pancreatitis at the Gastroenterology Clinic of Ankara Bilkent City Hospital between January 2022 and March 2025 were included. Patients were divided into two groups based on the presence of diabetes, and outcomes were compared using statistical methods. Results: Of the total 492 patients (mean age 58.6 ± 17.2 years; 50.2% female) included, 98 (19.9%) had DM. Moderate-to-severe AP occurred in 67.3% of diabetic versus 37.8% of non-diabetic patients (p < 0.0001), and severe disease developed more frequently in the diabetic group (6.1% vs. 1.0%, p = 0.0057). Systemic complications were significantly more common in patients with diabetes (45.9% vs. 26.9%, p = 0.0004). Hospital mortality was higher among patients with diabetes (9.2% vs. 4.6%, p = 0.0344), and Kaplan–Meier analysis demonstrated numerically lower overall survival in patients with diabetes (log-rank p = 0.095), with early divergence in survival curves. Cox proportional hazards analysis confirmed diabetes as an independent predictor of in-hospital mortality (adjusted HR 2.64, 95% CI 1.17–5.97; p = 0.019). After adjustment for confounders, diabetes remained independently associated with the development of moderate/severe pancreatitis (adjusted OR 2.00, 95% CI 1.24–3.22; p = 0.004). Diabetes also independently predicted in-hospital mortality (adjusted OR 3.36, 95% CI 1.35–8.34; p = 0.009), along with APACHE II score. ROC analysis demonstrated that adding diabetes mellitus to the APACHE II score significantly improved mortality prediction compared with APACHE II alone (AUC 0.785 vs. 0.724). The retrospective and single-center design of this study may limit its generalizability and create potential selection bias. There were insufficient data on the type of diabetes, its duration, and glycemic control (e.g., HbA1c), and therefore, we could not assess these factors, all of which may influence risk estimates. Although the survival curves showed early divergence, the borderline log-rank significance (p = 0.095) highlights the limited statistical power to detect long-term survival differences in this cohort. Conclusions: DM is associated with substantially increased severity and in-hospital mortality in AP, primarily through an elevated risk of systemic organ failure. Incorporation of diabetes status into early severity stratification may improve prognostic accuracy and guide closer monitoring and timely interventions in this high-risk population. Full article

Abnormal electrolyte levels can lead to failures in lead-acid batteries. The capacitive method, as a non-invasive liquid level inspection technique, can be applied to the nondestructive detection of electrolyte level abnormalities in lead-acid batteries. However, due to the high viscosity of sulfuric acid in lead-acid batteries, residual liquid films are easily adhered to the tube walls during rapid liquid level drops, resulting in significant dynamic measurement errors in capacitive methods. To eliminate dynamic measurement errors caused by residual liquid film adhesion, this study proposes a hybrid deep learning model—Poly-LSTM. This model combines polynomial feature generation with a Long Short-Term Memory (LSTM) network. First, polynomial features are generated to explicitly capture the complex nonlinear and coupling effects in the sensor inputs. Subsequently, the LSTM network processes these features to model their temporal dependencies. Finally, the time information encoded by the LSTM is used to generate accurate liquid level predictions. Experimental results show that this method outperforms other comparative models in terms of liquid level estimation accuracy. At a rapid drop rate of 0.12 mm/s, the average absolute error (MAE) is 0.5319 mm, the root mean square error (RMSE) is 0.7180 mm, and the mean absolute percentage error (MAPE) is 0.1320%. Full article

This paper explores a stochastic model of noisy observations with a sparse true signal structure. Such models arise in a wide range of applications, including signal processing, anomaly detection, and performance monitoring in telecommunication networks. As a motivating example, we consider round-trip time (RTT) data, which characterize the transit time of network packets, where rare, anomalously large values correspond to localized network congestion or failures. The focus is on the asymptotic properties of the mean-square risk associated with thresholding procedures. Upper bounds are obtained for the mean-square risk when using the theoretically optimal threshold. In addition, a central limit theorem and a strong law of large numbers are established for the empirical risk estimate. The results provide a theoretical basis for assessing the effectiveness of thresholding methods in localizing rare anomalous components in noisy data. Full article

Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications. Full article

Real-time precipitation monitoring through satellite remote sensing represents a critical technological frontier for operational hydrology in data-scarce mountainous regions. Following a comprehensive evaluation of reanalysis precipitation products in the downstream Yarlung Zangpo watershed, this investigation advances understanding by systematically assessing eight satellite-based precipitation retrieval algorithms against ground truth observations from 18 meteorological stations (2014–2022). Multi-temporal performance analysis employed statistical metrics including correlation analysis, root mean square error, mean absolute error, and bias assessment to characterize algorithm reliability across annual, monthly, and seasonal scales. Representative monthly spatial analysis (January, April, July) and comprehensive 12 month × 18 station heatmap visualization revealed pronounced seasonal performance variations and elevation-dependent error patterns. Satellite retrieval algorithms demonstrated systematic underestimation tendencies, with observational precipitation averaging 2358 mm/yr, substantially exceeding remote sensing estimates across six of eight products. IMERG_EarlyRun and IMERG_LateRun achieved optimal performance with annual correlation coefficients of 0.41/0.37 and minimal bias (relative bias: −3.0%/1.4%), substantially outperforming other products. Unexpectedly, IMERG_FinalRun exhibited severe deterioration (correlation: 0.37, relative bias: −73.8%) compared to Early/Late Run products despite comprehensive gauge adjustment, indicating critical limitations of statistical correction procedures in data-sparse mountainous environments. Temporal analysis revealed substantial year-to-year performance variability across all products, with algorithm accuracy strongly modulated by annual precipitation characteristics and underlying meteorological conditions. Station-level assessment demonstrated that 100% of stations showed underestimation for IMERG_FinalRun versus balanced patterns for IMERG_EarlyRun/LateRun (53% underestimation, 47% overestimation), confirming systematic gauge-adjustment failures. Supplementary terrain–precipitation analysis indicated GSMaP_MVK_G shows superior spatial pattern representation, while IMERG_LateRun excels in capturing temporal variations, suggesting multi-product integration strategies for comprehensive monitoring. Comparative assessment with previous reanalysis evaluation establishes that satellite products offer superior real-time availability but exhibit greater temporal variability compared to model-based approaches’ consistent performance. IMERG_EarlyRun and IMERG_LateRun are recommended for operational real-time applications, GSMaP_MVK_G for terrain-sensitive spatial analysis, and reanalysis products for seasonal assessment, while IMERG_FinalRun and FY2 require substantial improvement before deployment in high-altitude watershed management systems. Full article

Background/Objectives: Associations between renal function, as measured by the estimated glomerular filtration rate (eGFR) or its decline (dGFR), and clinical parameters in patients with rheumatoid arthritis (RA) were evaluated using a retrospective case–control series dataset. Methods: Patients with RA who followed up for one or more consecutive years were recruited for the study. For calculating the eGFR, cystatin C (CysC) was adopted. The moment when CysC was measured was set as the baseline. The association between the eGFR and baseline clinical parameters, including disease activity in RA as measured by the simplified disease activity index (SDAI), was statistically evaluated. The association between the mean annual decline in the eGFR from the baseline and clinical parameters was also statistically assessed. Results: A total of 513 patients were enrolled; with a mean age of 70.9; a mean follow-up length of 52.5 months; a mean BMI of 22.9; a 68.7 eGFR; and a mean annual dGFR of 2.74. Significant parameters that correlated with the eGFR were age; rheumatoid factor titer; C-reactive protein; the presence of hypertension; chronic heart failure; chronic obstructive pulmonary disease; type 2 diabetes mellitus; methotrexate administration; and polypharmacy at baseline. An annual dGFR was correlated with the follow-up length, and the mean SDAI score multiplied by the yearly length of the follow-up was significantly correlated. Conclusions: Many factors confound the determination of the eGFR in RA patients. The disease activity score and length of time are the key factors for declining eGFRs. Full article

In this work, we introduce a new four-parameter distribution, called the integrated linear–Weibull (ILW) model, constructed by embedding a dynamic linear component within the Weibull framework. The ILW distribution is capable of capturing a wide variety of symmetric and asymmetric density shapes and accommodates diverse failure-rate behaviors. We derive several of its key mathematical and statistical properties, including moments, extropy, cumulative residual entropy, order statistics, and their asymptotic forms. The mean residual life function and its reciprocal relationship with the failure rate are also obtained. An algorithm for generating random samples from the ILW distribution is provided, and model identifiability is examined numerically through the Kullback–Leibler divergence. Parameter estimation is carried out via maximum likelihood, and a simulation study is conducted to assess the accuracy of the estimators; the results show improvements in estimator performance as sample size increases. Finally, three real datasets involving failure-time observations and one describing hydrological and epidemiological data are analyzed to demonstrate the practical usefulness of the ILW model. In these applications, the proposed model exhibits competitive or superior performance relative to several existing lifetime distributions based on standard model selection criteria and goodness-of-fit measures. Full article

Generalized censoring, combined with a power-based distribution, improves inferential efficiency by capturing more detailed failure-time information in complex testing scenarios. Conventional censoring schemes may discard substantial failure-time information, leading to inefficiencies in parameter estimation and reliability prediction. To address this limitation, we develop a comprehensive inferential framework for the alpha-power Weibull (APW) distribution under a generalized progressive hybrid Type-II censoring scheme, a flexible design that unifies classical, hybrid, and progressive censoring while guaranteeing test completion within preassigned limits. Both maximum likelihood and Bayesian estimation procedures are derived for the model parameters, reliability function, and hazard rate. Associated uncertainty quantification is provided through asymptotic confidence intervals (normal and log-normal approximations) and Bayesian credible intervals obtained via Markov chain Monte Carlo (MCMC) methods with independent gamma priors. In addition, we propose optimal censoring designs based on trace, determinant, and quantile-variance criteria to maximize inferential efficiency at the design stage. Extensive Monte Carlo simulations, assessed using four precision measures, demonstrate that the Bayesian MCMC estimators consistently outperform their frequentist counterparts in terms of bias, mean squared error, robustness, and interval coverage across a wide range of censoring levels and prior settings. Finally, the proposed methodology is validated using real-life datasets from engineering (electronic devices), clinical (organ transplant), and physical (rare metals) studies, demonstrating the APW model’s superior goodness-of-fit, reliability prediction, and inferential stability. Overall, this study demonstrates that combining generalized censoring with the APW distribution substantially enhances inferential efficiency and predictive performance, offering a robust and versatile tool for complex life-testing experiments across multiple scientific domains. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

Background/Objectives: Durable apical support after hysterectomy is crucial to prevent subsequent vaginal vault prolapse. Abdominal sacrocolpopexy remains the gold standard but carries risks of visceral injury and wound morbidity. The vaginal natural orifice transluminal endoscopic surgery (vNOTES) approach provides a scarless, minimally invasive alternative, but data on vNOTES-assisted sacrocolpopexy (vNOTES-SC) performed concurrently with hysterectomy remain limited. Methods: A retrospective cohort of 30 women with stage II uterine prolapse underwent concomitant hysterectomy and vNOTES-assisted sacrocolpopexy between January 2023 and January 2024. Anatomical outcomes were evaluated using the Pelvic Organ Prolapse Quantification (POP-Q) system preoperatively and at 12 months postoperatively. The primary endpoint was anatomical success (C ≤ −1 cm); the secondary endpoint used the IUGA criterion (C < −TVL/2). Complications were graded using the Clavien–Dindo classification. Statistical analyses included Wilcoxon signed-rank tests, effect-size estimation, ROC analysis, logistic regression, and Spearman correlation. Results: Mean operative time was 100.2 ± 11.7 min, mean blood loss 155.3 ± 74.8 mL, and mean hospital stay 1.5 ± 0.7 days. Significant improvements were seen in Aa, Ba, C, and Bp points (p < 0.001). Anatomical success (C ≤ −1 cm) was achieved in 73.3% and clinical success in 93.3% of patients. Two patients exhibited anatomical recurrence (6.7%), whereas one patient reported symptomatic recurrence (3.3%). Using the IUGA definition, anatomical success increased to 83.3%. The difference between strict success (C ≤ −1 cm) and IUGA success (C < −TVL/2) reflects definitional sensitivity, particularly in post-hysterectomy vaginal length. All complications were minor (Grade I–II). ROC analysis showed age as a weak predictor (AUC = 0.67). Effect sizes were large for apical and anterior compartments (Cohen’s d = 1.84 for C-point). Conclusions: Concomitant hysterectomy with vNOTES-assisted sacrocolpopexy is a feasible, safe, and effective scarless approach for apical support restoration. The procedure provides significant anatomical correction and rapid recovery with low morbidity. Patients had symptomatic stage II prolapse with risk factors for early failure after native-tissue repair, supporting the selection of sacrocolpopexy for durable apical support. Larger prospective trials are needed to confirm long-term efficacy and functional outcomes. Full article

Background: Hip fractures in older adults are sentinel events linked to high mortality and functional decline. Few studies have quantified long-term survival probabilities, standardized mortality ratios (SMRs), and risks of new nursing home admission alongside patient-related predictors. Methods: We retrospectively analyzed 355 patients aged ≥ 60 years who underwent hip fracture surgery at a general hospital in Japan (2020–2024). Primary outcomes were mortality and new nursing home admission. Survival probabilities and remaining life expectancy were estimated, and SMRs were calculated using age- and sex-matched national data. Cox regression identified independent predictors. Results: Mean age was 84 years; 76% were female. Mortality probabilities at 1, 2, and 3 years were 23%, 41%, and 60%, respectively; SMRs consistently exceeded 9. Median remaining life expectancy was 260 days. New nursing home admissions occurred in 42%, with cumulative probabilities of 16%, 27%, and 35% at 1, 2, and 3 years, respectively, showing a rapid rise within 9 months. Independent predictors of mortality were delayed surgery, higher Charlson Comorbidity Index, and low Geriatric Nutritional Risk Index. Older age and failure to regain ambulatory ability at 3 months predicted institutionalization. Conclusions: Older adults with hip fractures face persistently high mortality and institutionalization risks, comparable to advanced malignancies or neurodegenerative diseases. Surgical timing, comorbidities, nutrition, and functional recovery critically influence prognosis and should guide perioperative care and discharge planning. Full article

Uncertainty and variability in soil properties strongly impact slope stability under extreme rainfall. This study applies a probabilistic hydro-mechanical slope stability assessment to unsaturated volcanic hillslopes in southern Guam, covering a range of slope angles and subjected to four major 2023 typhoons. The slope scenarios analyzed include bare slopes, vegetated slopes with root water uptake, and vetiver with both uptake and root reinforcement. Laboratory-derived variability in effective cohesion, friction angle, and unit weight was incorporated via Latin hypercube sampling. Gentler slopes (≤40°) remained stable with a probability of failure (PoF) = 0%. For steep slopes (45–60°), vetiver root reinforcement improved the mean factor of safety by up to 12–15% and reduced variability in outcomes to less than 2%. Probabilistic predictions advanced failure timing compared to deterministic estimates, with differences more pronounced on steeper slopes. By integrating soil variability and vegetation effects within probabilistic frameworks, this approach provides a more accurate and comprehensive assessment of tropical slope failure risks, thereby informing more effective and resilient slope management strategies. Full article

A Bayes–Prony oscillating modal identification and hierarchical control strategy for low-frequency oscillation (LFO) of a ship microgrid (SM) is presented in this paper. The modal probabilistic estimation of the proposed algorithm replaces point estimation of the traditional Prony method and improves the robustness of modal identification. The hierarchical control strategy first performs modal identification by means of the batch least squares Prony (BLS-Prony) algorithm. The modal identification results are calibrated by the explanatory variance score (EVS), and the control process is transferred to recursive least squares Prony (RLS-Prony) real-time detection. The third layer of decision making transfers to Bayesian Prony (Bayes–Prony) identification in case of a loss of modality or failure of identification. The designed Bayes–Prony algorithm identifies the oscillatory modal of signals with a signal-to-noise ratio (SNR) equal to 2 dB. Compared to BLS-Prony and RLS-Prony, Bayes–Prony reduces the SNR convergence domain of the signal by 30 dB as the last layer of hierarchical control. Therefore, the third-layer decision commands are used as a scheduling reference for damping control in SM power plants. The proposed algorithms and strategies maximize the saving of computational resources while ensuring that the modal identification is effective. Finally, the correctness of the proposed algorithm and strategy is verified by the LFO waveforms of the experimental platform. Full article

Intelligent Transportation Systems (ITSs), particularly autonomous driving, face critical challenges when sensor modalities fail due to adverse conditions or hardware malfunctions, causing severe perception degradation that threatens system-wide reliability. We present a unified geometry-aware cross-modal translation framework that synthesizes missing sensor data while maintaining temporal consistency and quantifying uncertainty. Our pipeline enforces 92.7% frame-to-frame stability via an optical-flow-guided spatio-temporal module with smoothness regularization, preserves fine-grained 3D geometry through pyramid-level multi-scale alignment constrained by the Chamfer distance, surface normals, and edge consistency, and ultimately delivers dropout-tolerant perception by adaptively fusing multi-modal cues according to pixel-wise uncertainty estimates. Extensive evaluation on KITTI-360, nuScenes, and a newly collected Real-World Sensor Failure dataset demonstrates state-of-the-art performance: 35% reduction in Chamfer distance, 5% improvement in BEV (bird’s eye view) segmentation mIoU (mean Intersection over Union) (79.3%), and robust operation maintaining mIoU under complete sensor loss for 45+ s. The framework achieves real-time performance at 17 fps with 57% fewer parameters than competing methods, enabling deployment-ready sensor-agnostic perception for safety-critical autonomous driving applications. Full article

To address the edge blurring issue of drone inspection images of mountainous transmission lines caused by non-uniform haze interference, as well as the low operational efficiency of traditional dehazing algorithms due to increased network complexity, this paper proposes a high-frequency guided dual-branch attention multi-scale hierarchical dehazing network for transmission line scenarios. The network adopts a core architecture of multi-block hierarchical processing combined with a multi-scale integration scheme, with each layer based on an asymmetric encoder–decoder with residual channels as the basic framework. A Mix structure module is embedded in the encoder to construct a dual-branch attention mechanism: the low-frequency global perception branch cascades channel attention and pixel attention to model global features; the high-frequency local enhancement branch adopts a multi-directional edge feature extraction method to capture edge information, which is well-adapted to the structural characteristics of transmission line conductors and towers. Additionally, a fog density estimation branch based on the dark channel mean is added to dynamically adjust the weights of the dual branches according to haze concentration, solving the problem of attention failure caused by attenuation of high-frequency signals in dense haze regions. At the decoder end, depthwise separable convolution is used to construct lightweight residual modules, which reduce running time while maintaining feature expression capability. At the output stage, an inter-block feature fusion module is introduced to eliminate cross-block artifacts caused by multi-block processing through multi-strategy collaborative optimization. Experimental results on the public datasets NH-HAZE20, NH-HAZE21, O-HAZE, and the self-built foggy transmission line dataset show that, compared with classic and cutting-edge algorithms, the proposed algorithm significantly outperforms others in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM); its running time is 19% shorter than that of DMPHN. Subjectively, the restored images have continuous and complete edges and high color fidelity, which can meet the practical needs of subsequent fault detection in transmission line inspection. Full article