Search Results (6,387)

Open-channel irrigation systems often face constraints due to water supply uncertainty and insufficient gate control precision. This study proposes an integrated framework for canal water allocation and gate control that combines interval-based uncertainty analysis with intelligent optimization to address these challenges. First, we predict the inflow process using an Auto-Regressive Integrated Moving Average (ARIMA) model and quantify the range of water supply uncertainty through Maximum Likelihood Estimation (MLE). Based on these results, we formulate a bi-objective optimization model to minimize both main canal flow fluctuations and canal network seepage losses. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to obtain Pareto-optimal water allocation schemes under uncertain inflow conditions. This study also designs a Fuzzy Proportional–Integral–Derivative (Fuzzy PID) controller. We adaptively tune its parameters using the Particle Swarm Optimization (PSO) algorithm, which enhances the dynamic response and operational stability of open-channel gate control. We apply this framework to the Chahayang irrigation district. The results show that total canal seepage decreases by 1.21 × 10⁷ m³, accounting for 3.9% of the district’s annual water supply, and the irrigation cycle is shortened from 45 days to 40.54 days, improving efficiency by 9.91%. Compared with conventional PID control, the PSO-optimized Fuzzy PID controller reduces overshoot by 4.84%, and shortens regulation time by 39.51%. These findings indicate that the proposed method can significantly improve irrigation water allocation efficiency and gate control performance under uncertain and variable water supply conditions. Full article

Continuous bladder volume monitoring in a wearable format can improve outcomes for patients with bladder dysfunction, heart failure, and other conditions requiring precise fluid management. Bioimpedance-based methods offer a promising, noninvasive solution; however, the influence of patient-specific anatomy, particularly waist circumference and subcutaneous fat thickness, remains poorly characterized. In this study, we use in silico finite element modeling to quantify how these anatomical factors affect two key bioimpedance metrics: voltage change ($\Delta V$) and voltage change ratio (VCR). Comprehensive simulations were performed across 15 virtual anatomies, generating a reference dataset for guiding future analog front-end and algorithm designs. We further compared generalized volume estimation models against conventional patient-specific void regression approaches. With appropriate input scaling, the generalized models achieved performance within 10% of patient-specific calibrations and, in some cases, surpassed them. Certain configurations reduced mean average error (MAE) by more than 20% relative to individualized models, potentially enabling a streamlined setup without the need for laborious ground-truth acquisition such as voided volume collection. These results demonstrate that incorporating simple anatomical scaling can yield robust, generalizable bladder volume estimation models suitable for wearable systems across diverse patient populations. Full article

Background/Objectives: Adherence to the Mediterranean Diet (MD) is shaped by its multidimensional nature, encompassing nutritional, cultural, and environmental dimensions. However, systematic reviews indicate a notable decline in MD adherence across Mediterranean countries over the past decade. This study aimed to objectively assess the affordability of the MD under Turkish conditions using nationally representative data for a typical four-person household. Methods: A Turkish Mediterranean Diet Food Basket (MDFB) was developed for a reference household and its affordability evaluated through a four-step analytical framework: (i) construction of the MD food basket, (ii) collection of price data and estimation of average monthly cost, (iii) verification of nutritional adequacy, and (iv) assessment of affordability by comparing the basket cost with household income indicators Results: Based on the regional equivalised median income in the TR62 region (21,331 TRY/month), the monthly cost of the MDFB (TRY 20,930) represented 98% of household income. Using the national median income for couples with children (27,918 TRY/month), this share decreased to 75%. Both estimates substantially exceed the national average share of food expenditure (18.1%). Among the lowest-income households, the MDFB cost corresponded to 214% of income, indicating economic inaccessibility. For middle- and high-income groups, the ratios were 91.9% and 37.3%, respectively. Conclusions: Despite its recognized health benefits, the MD remains economically unattainable for most households in Türkiye, underscoring persistent socioeconomic disparities in diet quality and accessibility. Full article

Enhancing our understanding of the specific characteristics that disuse-induced models should possess, based on the immobilized joint, could significantly enhance the effectiveness of research in this field. Objective: Our objective was to quantify the decrease in skeletal muscle mass and strength in humans subjected to upper limb disuse-induced models. Methods: PubMed, Scopus, Web of Science, Embase, LILACS, SPORTDiscus, CINAHL, and Epistemonikos databases were searched from inception to November 2025. Randomized controlled trials, cross-over clinical trials, or quasi-experimental studies performed in healthy adults ≥18 years old, subjected to an induced-disuse model to investigate the effects on skeletal muscle mass or strength were included. Results: Forty-five studies were included. Significant differences in skeletal muscle mass, equivalent to a small effect size (SMD = −0.453; 95% CI = −0.698 to −0.208; p < 0.001) and a total loss of 3.44% (estimated average daily decline = 0.16%) were observed after 21 days of immobilization. Skeletal muscle mass loss was heterogeneous between the arm (5.03%), forearm (1.56%), and hand (4.67%) muscles. Significant differences in strength, equivalent to a large effect size (SMD = −1.36; 95% CI = −1.69 to −1.02; p < 0.001) and a total loss of 18.06% (estimated average daily decline = 1.55%), were observed after 18 days of immobilization. Strength decreases were heterogeneous between the arm (16.67%), forearm (21.42%), and hand (10.46%) muscles. Conclusion: Based on evidence of very low certainty, upper limb disuse-induced models appear to induce a nonlinear loss of skeletal muscle mass and a suggested substantially more severe and rapid loss of strength in healthy young adults, with effects varying heterogeneously across different muscle groups. Despite data limitations, these estimates provide a basis for designing experimental countermeasures, though caution is warranted due to the heterogeneity of the findings. Full article

In disciplines such as epidemiology, economics, and public health, inference and estimation of heterogeneous treatment effects (HTE) are critical. This approach helps reveal differences in treatment effect estimates between subgroups, which supports personalized decision-making processes. While a variety of meta-learners (e.g., S-, T-, X-learners) have been proposed for estimating HTE, there is a lack of consensus on their relative strengths and weaknesses under different data conditions. To address this gap and provide actionable guidance for applied researchers, this study conducts a comprehensive simulation-based comparison of these methods. We first introduce the causal inference framework and review the underlying principles of the methods used to estimate these effects. We then simulate different data generating processes (DGPs) and compare the performance of S-, T-, X-, DR-, and R-learners with the causal forest, highlighting the potential of meta-learners for HTE estimation. Our evaluation reveals that each learner excels under distinct conditions: the S-learner yields the least bias and is most robust when the conditional average treatment effect (CATE) is approximately zero; the T-learner provides accurate estimates when the response functions differ significantly between the treatment and control groups, resulting in a complex CATE structure, and the X-learner can accurately estimate the HTE in imbalanced data.Additionally, by integrating Z-bias—a bias that may arise when adjusting the covariate only affects the treatment variable—with a specific sensitivity analysis, this study demonstrates its effectiveness in reducing the bias of causal effect estimates. Finally, through an empirical analysis of the Trends in International Mathematics and Science Study (TIMSS) 2019 data, we illustrate how to implement these insights in practice, showcasing a workflow for HTE assessment within the meta-learner framework. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

Predicting the Remaining Useful Life (RUL) of engine oil is critical for proactive maintenance and fleet reliability. However, irregular and noisy single-point sampling presents challenges for conventional prognostic models. To address this, a hierarchical physics-informed transfer learning (TL) framework is proposed that reconstructs nonlinear degradation trajectories directly from non-time-series data. The method uniquely integrates Arrhenius-type oxidation kinetics and thermochemical laws within a multi-level TL architecture, coupling fleet-level generalization with engine-specific adaptation. Unlike conventional approaches, this framework embeds physical priors directly into the transfer process, ensuring thermodynamically consistent predictions across different equipment. An integrated uncertainty quantification module provides calibrated confidence intervals for RUL estimation. Validation was conducted on 1760 oil samples from dump trucks, dozers, shovels, and wheel loaders operating under real mining conditions. The framework achieved an average R² of 0.979 and RMSE of 10.185. This represents a 69% reduction in prediction error and a 75% narrowing of confidence intervals for RUL estimates compared to baseline models. TL outperformed the asset-specific model, reducing RMSE by up to 3 times across all equipment. Overall, this work introduces a new direction for physics-informed transfer learning, enabling accurate and uncertainty-aware RUL prediction from uncontrolled industrial data and bridging the gap between idealized degradation studies and real-world maintenance practices. Full article

Human West Nile virus (WNV) infections can have severe neurological health effects, especially among those over 50 years of age. As changes in weather patterns lead to more frequent and intense droughts, there is a public health need for improved understanding of drought associated WNV risks. While multiple studies have reported an association between drought conditions and human WNV cases, this information has not yet been synthesized systematically across studies. Our review aims to evaluate the existing evidence of an association between drought and human WNV cases while considering the impacts of different study regions, methodological approaches, drought metrics, and WNV case definitions. We conducted a systematic literature search of peer-reviewed epidemiological studies that examined a potential association between drought and human WNV cases. Our inclusion criteria targeted studies that employed measures of drought beyond precipitation and reported effect estimates along with measures of error. The literature search and screening process resulted in the inclusion of nine papers with study periods spanning from 1999 to 2018. The included peer-reviewed publications employed a wide variety of study designs and methods, such as linear mixed-effects models, generalized linear models using simultaneous autoregression, generalized additive models, Bayesian model averaging, and a case-crossover design using conditional logistic regression models. We summarize the key findings and provide study quality evaluations for each of the nine included studies. Studies that analyzed drought indices averaged over a seasonal period of three to four months reported positive associations between drought and WNV. However, studies that analyzed drought indicator variables averaged over weekly periods of time had less consistent results. We discuss potential mechanisms underlying the observed associations between drought and human WNV cases. Full article

The rapid rise in the popularity of cryptocurrencies has drawn increasing attention from investors, entrepreneurs, and the public in recent years. However, this rapid growth comes with risk: many coins fail early and become what are known as “dead coins”, defined by a lack of recorded activity for more than a year. This study applies deep learning techniques to estimate the short-term risk of a cryptocurrency’s death. Specifically, three Recurrent Neural Network architectures, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), were trained on 18-month time series of daily closing prices and trading volumes using a stratified five-fold cross-validation framework. The models’ predictive performances were compared across input windows ranging from 10 to 180 days. Using the previous 180 days of data as input, GRU achieved the highest point accuracy of 0.7134, whereas BiLSTM exhibited the best performance when evaluated across input sequence lengths varying from 10 to 180 days, reaching an average accuracy of 0.676. These findings show the ability of recurrent architectures to anticipate short-term failure risks in cryptocurrency markets. Theoretically, the study contributes to financial risk modeling by extending time series classification methods to cryptocurrency failure prediction. Practically, it provides investors and analysts with a data-driven early-warning tool to manage portfolio risk and reduce potential losses. Full article

Two-legged hopping is a well-established model for assessing leg stiffness; however, in existing studies, it is unclear whether the trial segment selection affects the results. This study aimed to assess if the selected hopping segment alters the value and individual variability (%CVind) of leg stiffness and kinetic performance metrics. Elite women athletes (42, volleyball, basketball, handball) and 14 non-athletic women performed barefoot two-legged hopping (130 bpm) on a force-plate (Kistler, 9286AA, sampling at 1000 Hz). Leg stiffness was estimated from the Fz registration (resonant frequency method). Four cumulative range segments (1–10, 1–20, 1–30, and 1–40 hops) and three segments of 10-hop subranges (11–20, 21–30, and 31–40) were analyzed (repeated measures one-way Anova, p ≤ 0.05, SPSS v30.0). The hopping segment did not significantly alter the leg stiffness value (segment average 30.6 to 31.2 kN/m) or its %CVind (segment average ≈ 3%). The kinetic performance metrics depicted a solid foundation for the extracted leg stiffness value, with %CVind not exceeding 6.2%. The results indicate a data collection of just 15 hops, in continuance reduced to a 10 hops segment (after excluding the first five to avoid neuromuscular adaptation) as a robust reference choice. Full article

Background/Objective: Spinal cord injury (SCI) is a life-altering condition representing a major cause of long-term disability and substantial health burden worldwide. In the Middle East, including Saudi Arabia, rapid urbanization and evolving injury patterns may have influenced SCI trends; however, national data remain limited. This study aimed to examine age-standardized trends in SCI incidence, prevalence, and years lived with disability (YLDs) in Saudi Arabia from 1990 to 2021, comparing transport-related and non-transport unintentional injuries, and describing age- and sex-specific SCI patterns in 2021. Methods: Using data from the Global Burden of Diseases (GBD) 2021 study, we conducted a population-based trend analysis for Saudi Arabia from 1990 to 2021, stratified by age, sex, and injury cause. Outcomes included age-standardized incidence, prevalence, and YLD rates per 100,000 population, along with percentage changes, average annual percentage changes, and rate ratios with 95% uncertainty intervals (UIs). Results: Between 1990 and 2021, age-standardized SCI showed a point estimate increase in incidence (25.0%; 95% UI: −28.3 to 116.8) and prevalence (24.3%; 95% UI: 0.8 to 53.4), while YLDs showed a modest rise (1.4%; 95% UI: −44.5 to 83.9). Males experienced greater increases in incidence (31.9%) and prevalence (32.3%) than females. Non-transport unintentional injuries surpassed transport-related causes, accounting for nearly 75% of SCI-related YLDs in 2021. The highest burden occurred among young adult males (highest incidence) and older adults (peak prevalence). Conclusions: The burden of SCI in Saudi Arabia has increased over the past three decades, with a shift toward non-transport unintentional injuries. Because wide uncertainty intervals limit definitive conclusions on trend direction, strengthening injury prevention, rehabilitation, and surveillance programs is crucial to mitigate this growing burden. Full article

Upstream processes strongly influence downstream analysis in sequential data-processing workflows, particularly in machine learning, where data quality directly affects model performance. Conventional statistical imputations often fail to capture nonlinear dependencies, while deep learning approaches typically lack uncertainty quantification. We introduce a hybrid imputation model that integrates a deep learning autoencoder with Convolutional Neural Network (CNN) layers and a Transformer-based contextual modeling architecture to address systematic variation across heterogeneous data sources. Performing multiple imputations in the autoencoder–transformer latent space and averaging representations provides implicit batch correction that suppresses context-specific remains without explicit batch identifiers. We performed experiments on datasets in which 10% of missing data was artificially introduced by completely random missing data (MCAR) and non-random missing data (MNAR) mechanisms. They demonstrated practical performance, jointly ranking first among the imputation methods evaluated. This imputation technique reduced the root mean square error (RMSE) by 50% compared to denoising autoencoders (DAE) and by 46% compared to iterative imputation (MICE). Performance was comparable for adversarial models (GAIN) and attention-based models (MIDA), and both provided interpretable uncertainty estimates (CV = 0.08–0.15). Validation on datasets from multiple sources confirmed the robustness of the technique: notably, on a forensic dataset from multiple laboratories, our imputation technique achieved a practical improvement over GAIN (0.146 vs. 0.189 RMSE), highlighting its effectiveness in mitigating batch effects. Full article

Traffic became a major issue in large and crowded metropolitan cities and might cause people to waste in the order of days within a year. It is notable that traffic speed estimation problems were addressed in three main horizons: short term, medium term, and long term. In this paper, we both introduce a novel network feeding strategy improving short- and medium-term traffic forecasting and define the aforementioned horizons by evaluating the prediction results up to 6 h. We combined the advantages of both distant and recent historical data by developing two different Recurrent Neural Network (RNN)-based methods, H-LSTM and H-GRU, that employ Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The proposed Historical Average Long Short-Term Memory (H-LSTM) model demonstrates superior performance compared to traditional methods, as it is capable of integrating both the typical long-term traffic patterns observed in a specific location and the daily fluctuations, such as accidents, unanticipated events, weather conditions, and human activities on particular days. We achieve up to 20% improvement, especially for rush hours, compared to the traditional approach, i.e., exploiting only recent historical data. H-LSTM could make predictions with an average of ±7.5 km/h error margin up to 6 h for a given location. Full article

Current multimodal imaging–based source localization (SoL) methods often rely on synchronously recorded data, and many neural network–driven approaches require large training datasets, conditions rarely met in clinical neuroimaging. To address these limitations, we introduce MieSoL (Multimodal Mutual Information Extraction and Source Localization), a unified framework that fuses EEG and MRI, whether acquired synchronously or asynchronously, to achieve robust cross-modal information extraction and high-accuracy SoL. Targeting neuroimaging applications, MieSoL combines Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), leveraging their complementary strengths—MRI’s high spatial resolution and EEG’s superior temporal resolution. MieSoL addresses key limitations of existing SoL methods, including poor localization accuracy and an unreliable estimation of the true source number. The framework combines two existing components—Unified Left Eigenvectors (ULeV) and Efficient High-Resolution sLORETA (EHR-sLORETA)—but integrates them in a novel way: ULeV is adapted to extract a noise-resistant shared latent representation across modalities, enabling cross-modal denoising and an improved estimation of the true source number (TSN), while EHR-sLORETA subsequently performs anatomically constrained high-resolution inverse mapping on the purified subspace. While EHR-sLORETA already demonstrates superior localization precision relative to sLORETA, replacing conventional PCA/ICA preprocessing with ULeV provides substantial advantages, particularly when data are scarce or asynchronously recorded. Unlike PCA/ICA approaches, which perform denoising and source selection separately and are limited in capturing shared information, ULeV jointly processes EEG and MRI to perform denoising, dimension reduction, and mutual-information-based feature extraction in a unified step. This coupling directly addresses longstanding challenges in multimodal SoL, including inconsistent noise levels, temporal misalignment, and the inefficiency of traditional PCA-based preprocessing. Consequently, on synthetic datasets, MieSoL achieves 40% improvement in Average Correlation Coefficient (ACC) and 56% reduction in Average Error Estimation (AEE) compared to conventional techniques. Clinical validation involving 26 epilepsy patients further demonstrates the method’s robustness, with automated results aligning closely with expert epileptologist assessments. Overall, MieSoL offers a principled and interpretable multimodal fusion paradigm that enhances the fidelity of EEG source localization, holding significant promise for both clinical and cognitive neuroscience applications. Full article

Multi-UAV search missions for prioritized targets under constrained communications suffer from weak communication-decision integration, limited global perception synchronization, and delayed mission response. This paper formulates multi-UAV collaboration search as a multi-objective optimization problem to balance communication overhead and search performance. A Cooperative Hierarchical Target Search under Constrained Communications (CHTS-CC) algorithm is proposed to address the problem. The algorithm incorporates a Cluster-Consistent Information Fusion with Event Trigger (CCIF-ET) method, which enables intra-cluster information fusion. When clusters connect, a single merge that applies joint weighting by cluster scale and uncertainty reduces communication overhead. Furthermore, a Dynamic Preemptive Task Allocation (DPTA) mechanism reallocates UAV resources based on target priority and estimated time of arrival (ETA), enhancing responsiveness to high-priority targets. Simulation results show that when all UAVs and communication links operate normally, CCIF-ET reduces total confirmation time by 8.73% compared to the uncoordinated baseline and maintains a 24.43% advantage during single-UAV failures. In scenarios with obstacles, failures, and dynamic targets, CHTS-CC reduced mission completion steps by 34.78%, 32.35%, and 55.45% compared to the non-allocation baseline. The average detection time for high-priority targets decreased by 28.48%, 29.41%, and 58.82%, respectively, demonstrating the effectiveness of the proposed algorithm. Full article