Search Results (23,194)

As variable renewable energy penetration increases, resource adequacy becomes strongly seasonal, while annual accreditation can mask temporal reliability differences. This paper proposes a Sub-Annual Capacity Market and compares it with an Annual Capacity Market and an uncapped Energy-Only benchmark. Capacity credits are calculated using a marginal ELCC formulation based on Expected Energy Not Served and embedded into phase-specific clearing constraints. Using a Shanxi case study, we examine both deterministic and stochastic settings with 151 jointly perturbed load and renewable scenarios. Results show that ACM and SubACM can both approximate EO outcomes when parameters are well calibrated, but SubACM yields more stable economic performance under uncertainty, with 29% lower cost-deviation standard deviation and 67% fewer tail-risk scenarios, as confirmed by formal dispersion tests. The main benefit of sub-annual design is improved temporal alignment between capacity payments and physical reliability contribution, rather than guaranteed large average cost reductions. Full article

Infrared thermography is a non-contact tool for monitoring inflammatory processes in the diabetic foot, but quantitative bedside use remains challenging with low-cost thermal infrared cameras due to radiometric drift, non-uniformity (vignetting), geometric distortions, and visible–thermal parallax. This paper presents an end-to-end clinical and instrumental framework built around a cheap thermal camera to ensure reproducible acquisition and physically consistent temperature estimation. The approach combines a standardized mobile acquisition setup and measurement protocol, extraction of embedded radiometric data from raw images, radiometric inversion with atmospheric correction, vignette correction performed in the radiometric domain, and geometric calibration of both visible and infrared sensors using dedicated (thermal) calibration targets. Accurate visible–infrared registration is obtained from hybrid heated markers, enabling reliable overlay and downstream analysis. The full processing chain yields quantitative thermograms with radiometric errors below 0.15 °C and sub-pixel multimodal alignment, supporting the detection of clinically relevant plantar temperature asymmetries and paving the way for routine calibrated low-cost thermography in diabetic foot care. Full article

Risk stratification of impaired glycemic control remains a major challenge in biomedical data analysis due to heterogeneous metabolic, behavioral, and therapeutic factors observed in large-scale populations. This study proposes a calibrated and interpretable decision–support framework, termed Calibrated Multi-Task Stacking Ensemble (CMSE), for joint modeling of clinically related glycemic outcomes. The framework integrates demographic variables, lipid profiles, renal and inflammatory biomarkers, dietary and smoking indicators, and therapy-related features within a unified predictive architecture. Robust modeling is ensured through leakage-aware preprocessing, quantile-based Winsorization, out-of-fold stacking, and isotonic calibration of probabilistic outputs. The physiological coherence between short-term and long-term glycemic markers is investigated using an explicit intertask coupling mechanism based on the estimated average glucose (eAG) ratio. Model interpretability is supported using SHAP analysis, mutual information, distance correlation, and feature importance metrics. In the primary medication-free screening configuration, the framework is evaluated on the NHANES 2017–March 2020 dataset, achieving ROC-AUC of 0.865 for diabetes classification and R² values of 0.385 and 0.366 for plasma glucose and HbA1c prediction, respectively. These results indicate that CMSE provides a reliable and explainable approach for calibrated glycemic risk assessment and clinical decision support. Full article

Given the continuous expansion of global trade, coastal and estuarine environments have been increasingly modified by anthropogenic pressures associated with port development, particularly through inlet stabilization by jetties, which often causes unintended environmental changes. This study evaluates alterations in estuarine and coastal hydro-sedimentological dynamics resulting from the construction of jetties (1911–1915) in the Patos Lagoon estuary, Brazil. A calibrated and validated numerical model (TELEMAC-3D) was used to compare pre-jetties and present conditions. Results showed that the morphological changes induced by the jetties altered estuarine circulation and sediment retention mechanisms. The reduction in current velocities within the channel increased sediment trapping, decreasing sediment transport capacity towards the adjacent coast. In contrast, along the plume jet, flow acceleration enhanced offshore export of fine suspended sediments, shifting deposition from nearshore areas to deeper offshore zones. Under northeastern wind conditions, a higher potential for mud deposition near the western jetty was observed in the post-construction scenario, reflecting a change in local deposition trends. These human-induced modifications not only reorganize sediment pathways but also influence habitat distribution and deposition patterns, highlighting the importance of considering engineering structures in sustainable coastal and estuarine management strategies. Full article

Alternate Wetting and Drying (AWD) is a proven water-saving irrigation technique that reduces irrigation water use and methane emissions from rice cultivation. The emission reduction achievable through AWD irrigation practices represents a significant opportunity for credits generation, particularly for the major rice-producing countries. To capitalize on this opportunity, a scalable, reliable, and cost-effective information system for AWD irrigation monitoring, reporting, and verification (MRV) is urgently needed. However, most existing MRV systems depend on manual data collection or software systems driven by field-based observation. Satellite remote sensing, derived from different tools and techniques, has achieved considerable traction in agriculture monitoring. This study attempts to develop a remote sensing and Internet of Things (IoT)-based system for large-scale AWD irrigation detection and monitoring as a potential tool for the MRV system. IoT sensor-based water level measurement, L-band PALSAR-2 full polarimetric data, and intensive field survey data were integrated and analyzed. Three study sites in the Naogaon District of Bangladesh, one of the major rice-growing regions, were selected as the study area. The PALSAR-2 full-polarimetric data were collected, radiometrically and geometrically corrected, and converted into the backscattered coefficient (Sigma-naught) value. Using the full-polarimetric channel of VV, VH, HH, and HV, the Freeman–Durden three-component decomposition, surface scattering, double-bounce, and volume scattering were constructed to assess the irrigation water condition of the rice paddy field. IoT sensors data, field survey data, and three-component data on 8 different dates and a total of 704 fields during the rice growing period were subsequently analyzed and cross-calibrated. The results showed that surface scattering and double bounce are more sensitive to irrigation water status, while volume scattering primarily responds to plant height changes. By leveraging the backscatter characteristics of these three components, a Random Forest classifier was applied to classify AWD and non-AWD irrigated paddy fields. Classification accuracy achieve 94% in early crop growth stages and declined to 80% during dense canopy stages. These findings offer a reliable and scalable approach to documenting water regime management with direct applicability to carbon emissions reduction verification and carbon credits claims. Full article

Accurate forecasting of respiratory virus-associated hospitalization rates per 100,000 population is essential for healthcare capacity planning, yet remains challenging during the COVID-19 era due to abrupt distribution shifts and symptom overlap among influenza-like illnesses caused by multiple pathogens. We propose a two-stage deep learning framework that disentangles stable pre-pandemic seasonal dynamics from COVID-19-induced excess hospitalizations. A lightweight GRU is first trained on pre-pandemic surveillance data to model baseline influenza/RSV-driven seasonality, after which an excess model learns from the residual series and integrates multiple online search trends (flu, COVID-19, and fever) using a standard multi-head self-attention mechanism. While we use COVID-19-era data as a case study, the proposed baseline–excess decomposition is not disease-specific and is intended to generalize to future large-scale respiratory outbreaks or pandemics that induce abrupt regime shifts. Experiments on U.S. weekly respiratory hospitalization rate data curated from CDC surveillance networks (AME) show that the proposed approach achieves strong accuracy on a chronological COVID-era split (2020–2025), reaching $R^2=0.907$ with MAPE = 19.22%. Beyond point forecasts, we further evaluate an expanding-window rolling-origin protocol and report calibrated prediction intervals via split conformal prediction, supporting deployment-oriented uncertainty quantification. By decoupling baseline and excess components and fusing behavioral trend signals in a disciplined manner, this framework improves predictive performance under regime shift while providing interpretable excess estimates for timely situational awareness and healthcare resource planning. Full article

In active debris removal missions in low Earth orbit, the semi-major axis difference between a service platform and its target can be large. Analytical relative dynamics models used in formation-flying applications typically retain only the first-order expansion in the orbital element differences; at large separations, the discarded higher-order terms—particularly the power-law dependence on the semi-major axis—introduce systematic along-track drift that degrades the propagation accuracy. This paper derives the power-law truncation correction, a closed-form additive vector that exactly compensates the truncated semi-major-axis power-law remainder, together with a consistent Jacobian correction for the extended Kalman filter covariance prediction. The state dimension and state transition matrix structure remain unchanged. Propagation tests over semi-major axis differences of 36–146 km yield ten-revolution terminal position errors of 0.008–0.065 km for the corrected models, compared with tens to hundreds of kilometers for the uncorrected first-order models and 0.1–8 km for the second-order state transition tensor. In 500-run Monte Carlo angles-only filtering experiments, the corrected filter reduces the median terminal position error by one to nearly three orders of magnitude relative to the uncorrected model. A process noise sensitivity study confirms robustness to calibration uncertainty across two orders of magnitude at a lower computational cost and with simpler implementation than higher-order tensor methods. Full article

Context: In numerous low- and middle-income nations, malaria remains a significant issue due to the challenges associated with diagnosing it through thin blood smears. The appearance of images can vary significantly depending on the microscope type, magnification, lighting conditions, slide preparation methods, and staining techniques. Due to the delicate morphology of parasites, false negatives might adversely affect patient care. Objective: To achieve optimal outcomes from validation, it is essential to construct a robust and easily replicable process. This pipeline should integrate the optimal elements of classical machine learning and end-to-end deep learning, enhance reliability by pairwise ensembling, and select ensemble weights in a logical, data-driven manner. Method: To achieve our objective, we propose two tracks. The initial track encompasses real-time augmentation, convolution-based feature extraction, and the training of calibrated classical classifiers. The second module focuses on training many convolutional networks from inception to completion. Subsequently, we construct paired ensembles and employ a hybrid methodology to select convex weights for combining the findings. This method initially evaluates a set of candidate weights and then refines them to maximise validation accuracy. Results: The precision of the two-track architecture consistently improves, transitioning from conventional baselines to end-to-end models. Optimal and consistent enhancements are achieved through weighted ensembling. Utilising optimised fusion reduces the incidence of false negatives for subtle parasites and false positives caused by staining artefacts. This yields an accuracy of 96.35% on the reserved data and reduced variance across folds. Conclusions: The integration of augmentation, multiple modelling tracks, and optimal pairwise ensembling yields the highest accuracy in categorising malaria smears. It facilitates further enhancements by incorporating supplementary models, multi-class extensions, and operating-point calibration. Full article

Hydrologic models are instrumental in understanding the behavior of the Nile River Basin (NRB), yet their effectiveness is often limited by the basin’s complex hydrology and sparse observational records. This study applies a basin-scale hydrological modeling approach to simulate near-natural, pre-reservoir flow conditions in the NRB, while incorporating lake and wetland submodels. The basin was discretized into 34 sub-watersheds with an outlet at Aswan. The conceptual GR4J rainfall–runoff model was implemented within the Raven modeling framework, chosen for its parsimony and suitability for data-limited conditions. Multi-objective calibration used discharge data from the Global Runoff Data Centre (GRDC), supplemented by digitized historical records to improve spatial and temporal coverage. A stepwise calibration strategy was applied at 18 sites, focusing on pre-reservoir periods to capture natural flow dynamics. The calibrated model reproduces observed discharges with high skill. At the Aswan outlet, Nash–Sutcliffe Efficiency (NSE) values were 0.87 (calibration) and 0.80 (validation), with percent bias (PBIAS) values of 6.1% and 5.0%, respectively. Model performance was strongest in the Blue Nile, White Nile headwaters, and the Nile main stem. The model also successfully simulated the hydrological step-change observed in Lake Victoria during the 1960s, underscoring its robustness in simulating regional hydroclimate disruptions. This calibrated model enables reconstruction of historical Nile discharge and simulation of past hydrologic disturbances, including those driven by major volcanic eruptions over the past millennia. Full article

Accurate crop yield forecasting is essential for ensuring food security, optimizing agricultural resource allocation, and supporting climate-resilient farming systems. Recent advances in deep learning have improved yield prediction accuracy; however, most existing models provide deterministic estimates without quantifying predictive uncertainty. This limitation restricts their reliability under climatic variability, missing data, and real-world decision-making scenarios where risk awareness is critical. This study utilizes two publicly available multi-crop datasets comprising historical yield records integrated with weather and soil attributes across multiple growing seasons. An attention-based Transformer framework is proposed, augmented with uncertainty quantification through Monte Carlo Dropout, Quantile Regression, and Bayesian Attention mechanisms. The proposed approach represents an integrated uncertainty-aware Transformer framework that combines temporal self-attention with complementary uncertainty estimation strategies. The contribution of this work lies in the systematic integration and comparative evaluation of multiple uncertainty quantification mechanisms within a unified deep learning framework for multi-crop yield forecasting. Experimental results demonstrate improved predictive accuracy and calibration compared to deterministic baselines. However, these findings are bounded by the scope of the datasets, which consist of coarse tabular climatic and soil variables, and should be interpreted accordingly. Full article

Adaptive traffic signal control (ATSC) remains a critical challenge for urban mobility. In this direction, deep reinforcement learning (DRL) has been widely investigated for ATSC, showing promising improvements in simulated environments. However, a noticeable gap remains between simulation-based results and practical implementations, due to reward formulations that do not address phase instability. Stochastic variations may trigger premature phase changes (“flickers”), affecting signal behavior and potentially limiting deployment in real scenarios. Although several works have examined delay, queues, and decentralized coordination, stability-focused variables remain comparatively less explored, particularly in single yet complex intersections. This study proposes a decentralized DRL model for ATSC with noise injection (ATSC-DRLNI) applied to a single intersection, introducing a stability-oriented reward function that integrates flickers, queue length, and advantage actor-critic (A2C) learning feedback. The model is evaluated in the Simulation of Urban MObility (SUMO) platform and compared against seven baseline methods, using real traffic data from a Mexican city for calibration and validation. Results suggest that penalizing flickers may contribute to more stable phase transitions, while reductions of up to 40% in queue length were observed in heavy-traffic scenarios. These findings indicate that incorporating stability-related variables into reward functions may help in implementing DRL-based ATSC studies. Full article

To address load–energy dynamic coupling in heterogeneous unmanned aerial vehicle (UAV) emergency rescue, this paper proposes an energy-coupled heterogeneous UAV task allocation (EC-HUTA) model that explicitly characterizes nonlinear interdependencies among payload, velocity, and power consumption, minimizing aggregate mission costs subject to physical and temporal constraints. To tackle the resulting high-dimensional, nonconvex problem, we introduce a multi-strategy improved stellar oscillation optimizer (MISOO), establishing a closed-loop synergistic system through three coupled stages: (i) evolutionary game-theoretic strategy competition via replicator dynamics for adaptive exploration–exploitation balance; (ii) intuitionistic fuzzy entropy (IFE)-driven dimension-wise parameter control, where IFE calibrates global exploration intensity while dimension-specific crossover probabilities accommodate heterogeneous convergence; and (iii) memory-driven differential escape mechanisms modulated by historical memory parameters to evade local optima. Cross-stage coupling through IFE ensures state information flows across the “strategy selection-refined search-dynamic escape” pipeline. Coupled with a dual-layer encoding scheme, this framework ensures efficient feasible search. Ablation studies validate each mechanism’s contribution. Evaluations on CEC2017 benchmarks demonstrate MISOO’s superior convergence against six metaheuristics. Large-scale earthquake rescue simulations confirm that EC-HUTA/MISOO strictly adheres to nonlinear energy constraints while enhancing task completion and temporal compliance. These results validate the framework’s efficacy for time-critical emergency resource allocation. Full article

Background/Objectives: Neutrophil-to-lymphocyte ratio (NLR) may predict outcomes after organ transplantation. This study evaluated the peri-transplant prognostic value of NLR in lung transplantation (LTx). Methods: This retrospective study included 282 LTx recipients (2012–2020). NLR measured on PODs 1, 3, 7, and 28 predicted 6-month mortality. Generalized estimating equations analyzed serial trends. Multivariable regression and ROC analysis identified predictors for a composite model, assessing discrimination and calibration. Results: Among 282 recipients (mean age, 54.2 years; male, 65.2%; idiopathic pulmonary fibrosis, 54.3%), 24.1% died within 6 months, most commonly from infection. Median NLR increased sharply after LTx (pre-LTx, 5.4; POD 1, 23.1; POD 3, 31.2), then decreased (POD 7, 18.8; POD 28, 8.7). Non-survivors had significantly higher preoperative and postoperative NLRs, particularly on POD 28. POD 28 NLR independently predicted 6-month mortality (multivariable analysis: OR, 1.05 per unit; 95% CI, 1.02–1.07; p < 0.001), alongside age and donor lung PaO₂/FiO₂ (P/F) ratio. Notably, a composite model combining these variables demonstrated significantly superior discrimination (area under the curve [AUC], 0.742; p = 0.001) compared to the NLR-only model (AUC, 0.698; p < 0.05). GEE demonstrated significantly steeper post-transplant NLR decline among survivors than non-survivors after adjusting for age (p = 0.02). Patients with NLR > 9.20 at POD 28 (area under the curve, 0.698; 95% CI, 0.615–0.782; sensitivity, 71.4%; specificity, 59.8%)—showed significantly lower survival on Kaplan–Meier analysis (p < 0.001, log-rank). Conclusions: Persistent NLR elevation on POD 28 independently predicts early mortality post-LTx and may support routine post-transplant risk stratification. Full article

Retrofitting existing buildings is widely recognized as a critical strategy for achieving global decarbonization goals. As a part of this effort, several tools have been developed for building retrofit analysis, each offering distinct advantages and limitations. However, the current approaches and tools still lack the capability to generate well-calibrated detailed building energy models that can evaluate both individual and combined energy efficiency measures. Moreover, no existing analysis tool can identify the most cost-optimal combination of retrofit measures through a comprehensive optimization search using different objectives. To address these shortcomings, this paper describes a new Simplified and Automated Building Energy Retrofit (SABER) analysis approach and tool. The SABER tool is a Python-based interactive platform designed to assist users by automatically creating detailed energy models of existing buildings. It incorporates a novel automatic calibration algorithm that adjusts operational schedules using building energy signature characteristics, ensuring accurate model performance. In addition, SABER can assess various building energy efficiency measures using a sequential search technique to determine the most cost-effective retrofit packages. This paper describes the key functionalities of SABER and demonstrates its capabilities through two residential building case studies. By integrating several key features into a unified framework, SABER represents a significant step toward the next generation of building energy retrofit analysis tools that can effectively assist the industry’s transition to a sustainable future. Full article

In the European Union, mandatory labeling of food and feed products is required when authorized genetically modified organisms (GMOs) exceed 0.9% per ingredient, necessitating reliable analytical methods for official control laboratories. Event-specific PCR assays validated according to ISO/IEC 17025 are the reference approach for GMO detection, identification, and quantification. The growing use of digital PCR (dPCR) has encouraged the adaptation of real-time PCR methods to dPCR-based strategies, as dPCR enables absolute quantification without calibration standards, shows reduced sensitivity to inhibitors, and allows for the design of a multiplex assay. In this study, an in-house validation of a duplex dPCR assay targeting the maize GM event NK603 and the HMG reference gene was performed on three platforms: Bio-Rad QX200™ (Pleasanton, CA, USA), Qiagen QIAcuity (Venlo, The Netherlands), and Thermo Fisher QuantStudio Absolute Q (Waltham, MA, USA). All validation parameters met the Joint Research Centre (JRC) acceptance criteria. In particular, this assay demonstrated high specificity, sensitivity (limit of quantification or LOQ < 35 copies per reaction), precision, and trueness (RSDr and bias <25%). The data indicate that the duplex dPCR assay can be used for routine GMO analysis and future collaborative validation studies. Full article