Search Results (10,759)

Modern applications are increasingly built around networking, collaboration, and automation. Drones, or Unmanned Aerial Vehicles (UAVs), are a key part of this shift. Many complex missions require multiple UAVs to work together as a team, which means deciding how to group them efficiently is a real optimization challenge. This paper introduces EPICEAg (Enhanced Preference-Inspired Co-Evolutionary Algorithm with goal vectors), a new algorithm for forming optimal UAV teams, called coalitions. EPICEAg builds on an existing algorithm called PICEAg but adds three important improvements: it uses k-medoids clustering through the Partitioning Around Medoids (PAM) algorithm for more reliable team leader selection, and applies two advanced sorting methods—shift-based density estimation and epsilon-ranking—to manage the complexity of the search. The algorithm optimizes seven goals at once: how well tasks are completed, how efficiently resources are used, how reliable the team and its communications are, how trustworthy the individual drones are, and how much energy they have left. Tests across several mission scenarios show that EPICEAg consistently performs better than PICEAg, NSGA-II, and MOPSO. Full article

The capacitance resistance model (CRM) is an analytical tool widely used to forecast reservoir performance in enhanced oil recovery (EOR) methods. By representing flow dynamics and the connectivity between injection and production wells through the parameter of interwell connectivity, CRM offers fast computational processing and minimal input data requirements. These advantages make CRM a practical alternative for rapid reservoir analysis, especially when full-scale numerical simulations are infeasible due to time and budget constraints. CRM, rooted in material balance and productivity equations, uses injection/production rates and bottom-hole pressure data to construct reservoir models through optimization techniques, which can then be combined with oil fractional flow models for predictive purposes. Initially designed for waterflooding operations, CRM has seen limited but promising applications in gas injection projects, where research remains incomplete. This study presents a new formulation of CRM tailored for immiscible gas injection, incorporating the pseudo-pressure concept coupled with a saturation profile. The pseudo-pressure concept is a mathematical transformation that linearizes gas flow equations by accounting for variations in gas compressibility and viscosity with pressure. The proposed CRM was evaluated using a PUNQ-S3 benchmark reservoir model in the CMG IMEX black oil simulator, involving two injectors and four producers. History- matching results for fluid production rates showed that the newly developed CRM achieved the lowest NRMSE, outperforming other CRM models across a wide range of reservoir properties. Sensitivity analyses were conducted to examine the effects of gas and oil PVT properties on the model’s performance. The newly developed CRM, incorporating the pseudo-pressure concept and saturation profiles, demonstrates superior performance in predicting fluid production rates, achieving an average NRMSE of 15.3% in a base case scenario, compared to other tested CRM models. Additionally, the sensitivity analysis on the effect of fluid properties shows that higher gas viscosity, lower gas formation volume factor, and increasing oil API gravity improve the CRM model’s performance, and under all tested conditions the newly developed CRM provides the most accurate production history match. This study not only establishes the new CRM as a robust and accurate predictive tool for immiscible gas injection but also provides a comprehensive discussion on reservoir parameter ranges and model limitations, advancing the applicability of CRM in EOR processes. Full article

Power factor correction in transmission networks with nonlinear loads cannot be addressed solely from the viewpoint of reactive compensation because harmonic distortion and resonance may compromise the expected technical benefits. In this context, this study proposes a resonance-aware and decision-oriented methodology that integrates nonlinear-load screening, weighted bus prioritization based on power factor degradation and harmonic severity, and tuned passive-filter design validated through impedance-frequency analysis and IEEE 519 compliance criteria. The methodology was implemented in DIgSILENT PowerFactory using the IEEE 14-bus test system, where nonlinear loads were allocated at buses 9 and 14 to emulate converter-dominated operating conditions. Under this scenario, the power factor decreased to 0.78271 and 0.85875, while total harmonic distortion increased to 22.01% and 20.07%, respectively. After the implementation of tuned passive filters, the power factor improved to 0.83023 at bus 9 and 0.90414 at bus 14, whereas total harmonic distortion was reduced to 4.61% and 5.22%, respectively, thus restoring compliance with IEEE 519. In addition, load currents decreased by approximately 16–19%. These results demonstrate that the proposed framework provides a technically consistent procedure for identifying critical buses, mitigating dominant harmonics, improving power factor, and avoiding adverse resonance conditions within a unified compensation workflow. Full article

The performance of fluid solidified soil (FSS) depends on the curing agents as well as, to a great extent, the soil type. Currently, most studies focus on a single type of soil, which limits the applicability of research findings to practical engineering scenarios involving diverse soil conditions. To address this issue, this study selects two representative soil types—clay (CL) and silt (ML)—and employs alkali-activated red mud–slag as curing agent to prepare FSS. Laboratory experiments were conducted to evaluate the influence of soil type on the engineering properties and durability of the specimens. Specifically, the effects of soil type on flowability and unconfined compressive strength were comparatively analyzed. Durability was assessed through shrinkage, water stability and wet–dry cycle tests. Furthermore, X-ray diffraction, Thermogravimetric, Fourier transform infrared spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller were utilized to characterize the microstructure and hydration products of the samples. The results indicate that an increasing proportion of ML leads to a decrease in overall flowability but a significant enhancement in late-age unconfined compressive strength. Meanwhile, the drying shrinkage of ML is gradually reduced, and both water stability and resistance to wet–dry cycles are correspondingly improved. Microstructural analyses reveal that the primary hydration product across all samples is C-(A)-S-H gel. Samples with higher ML content exhibit a denser structure and an increased volume of hydration products, which is consistent with the observed macroscopic performance trends. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

A novel hand gesture recognition framework based on surface electromyography (sEMG) is proposed for soldier operational scenarios under small-sample conditions. The framework integrates Empirical Mode Decomposition (EMD) for signal reconstruction, histogram-based features, and the Deep Forest (DF) classifier. Evaluations are conducted under two protocols: subject-wise evaluation and mixed-subject nested 8-fold cross-validation. Under subject-wise evaluation, the proposed EMD-HIST-DF method achieves 99.94% accuracy with 0.00027 ms per sample. Under mixed-subject nested 8-fold cross-validation, 98.41% accuracy is maintained with 0.00053 ms per sample. Ablation studies confirm the significant contribution of EMD-based signal enhancement in the mixed-subject setting (approximately 10.6 percentage points, p < 0.001). Parameter sensitivity analysis guides optimal parameter selection, and statistical tests confirm significant performance gains over baseline methods. Confusion matrices illustrate high per-class accuracy with minimal inter-class confusion. The framework shows potential as a promising solution for accurate, efficient, and sample-sparing gesture recognition in resource-constrained environments such as supernumerary robotic limb control. Full article

This study proposes a comprehensive methodology for the design and performance assessment of infiltration ponds integrated within hybrid grey–green urban drainage systems. The scope of the ponds is twofold: (i) increase infiltration of rainwater, and hence groundwater recharge, and (ii) decrease pluvial discharge downstream. The framework is applied to the Rome Technopole district, which serves as a pilot case for testing and demonstrating the procedure. Through 30-year continuous simulations performed with the EPA Storm Water Management Model and forced with a 5 min historical rainfall, the approach enables a performance-based evaluation that captures the full hydrological variability and the hydraulic performances of urban drainage systems. The methodology relies on physically based models for both the grey stormwater drainage network and the infiltration ponds, combined with a long-term simulation and functional analysis under transient conditions. The approach explicitly represents the main hydrological processes, including runoff generation, flow routing, storage dynamics, infiltration, and soil moisture variability, enabling a quantitative evaluation of peak-flow attenuation, infiltration efficiency, groundwater recharge volumes, seasonal variability, and wet–dry cycle behaviour. The latter is used to assess the long-term evolution of pond performance and its implications for maintenance activities, including clogging development and removal. Scenario analyses explore the influence of pond geometry and storage volumes, highlighting the trade-offs between hydrological efficiency, evaporation losses, and drawdown times. Beyond the specific application to the Rome Technopole developed in this study, we propose a generalizable, practitioner-oriented design procedure suited to contexts where infiltration-based solutions are desirable but regulatory guidance is fragmented. The proposed design workflow identifies critical parameters for both the hydraulic design and the operational management of infiltration ponds, enabling a statistical evaluation of their performance. The analysis of peak-flow reduction, infiltrated volumes, and the timing and frequency of wet–dry cycles provides a robust technical basis for the proper sizing, integration, and long-term assessment of infiltration ponds within urban drainage planning. Full article

The scientific literature increasingly supports the use of computational models to predict fracture across a wide range of applications, which, when calibrated with experimental data, can yield highly consistent results. Although the extended finite element method (XFEM) is widely used in commercial packages, phase field (PF) methods have emerged as a robust alternative. In this study, a cohesive zone model (CZM) was implemented using both approaches (a PF model with an implicit damage initiation criterion and a standard commercial XFEM solver with an explicit damage initiation criterion) to analyze their robustness and computational efficiency. First, a standardized fracture test of a compact tension (CT) specimen was simulated and compared with experimental data to validate both methods, achieving accurate predictions under plane strain conditions with a dominant mode I fracture behavior. Subsequently, the application of both fracture models was extended to flexible thoracic prostheses across two distinct chest wall reconstruction scenarios: a single-rib unilateral model and a multi-rib bilateral configuration. An extreme-case compressive displacement was assessed to identify critical regions susceptible to fracture initiation and to evaluate the structural limits of the proposed designs. The results showed that the PF approach required a higher computational time, but exhibited more stable convergence. In contrast, the XFEM-based solver required careful mesh calibration to ensure convergence under complex conditions. These results highlight the potential of the PF approach as a practical tool for identifying and improving critical regions of implants, overcoming the limitations of commercial XFEM implementations. Full article

This study presents an integrated design methodology for earthquake demolition rescue robots by combining UXMs, Kano, and QFD to improve design rationality and performance in extreme rescue scenarios. It addresses key gaps in existing approaches, particularly the lack of systematic experiential data acquisition, quantitative requirement analysis, and effective design translation. UXMs are applied to reconstruct critical task scenarios and identify high-load nodes and user experience variations. The Kano model is used to prioritise and classify user requirements, which are then translated into engineering characteristics through QFD. Based on this framework, a conceptual robot design is developed using the FBS model and evaluated through process-level simulation and usability assessment. The results demonstrate that the proposed method enables structured requirement transformation and supports traceable design decisions. Simulation indicates the consistency of task workflows and coordination among functional modules at the process level. A System Usability Scale score of 80.22 indicates a relatively high level of perceived usability at the conceptual evaluation stage. The proposed methodology provides a structured and traceable conceptual design framework for earthquake rescue robots. While the current validation is based on conceptual-level evaluation, the methodology offers a traceable design pathway that may be extended to other high-risk emergency equipment with further empirical testing. Full article

The application of machine learning (ML) models to accelerate the discovery of high-transfection-potency ionizable lipids has gained significant momentum in advancing lipid nanoparticle (LNP)-mediated RNA delivery. In the present study, we adopt a screening-oriented evaluation framework based on early-recognition ranking metrics tailored to high-throughput discovery. Model performance was assessed using the enrichment factor (EF), normalized discounted cumulative gain (NDCG), and HitRate at the top 10% of the ranked list, with uncertainty quantified via 1000 nonparametric bootstrap resamples. To assess robustness of conclusions, additional analyses were conducted at the top 1% and top 5% thresholds, reflecting increasingly stringent prioritization scenarios. Four predictive models—XGBoost, Random Forest, Elastic Net, and Quantile Regression Forest—were evaluated across three molecular feature representations, circular Morgan fingerprints, expert-crafted descriptors, and Grover graph embeddings, using a held-out test set. Across all models and thresholds, Morgan fingerprints consistently yielded superior early-recognition performance. The best-performing configuration—XGBoost with Morgan fingerprints—achieved EF@10% = 4.850 (95% CI [3.182, 6.818]), NDCG@10% = 0.628 (95% CI [0.234, 0.909]), and HitRate@10% = 0.493 (95% CI [0.318, 0.683]), corresponding to nearly fivefold enrichment over random selection and identification of highly potent lipids in approximately half of the prioritized candidates. Threshold-sensitivity analyses revealed that although stricter cutoffs (top 1% and top 5%) exhibit greater variability, the relative performance ordering of molecular representations remains stable. Bootstrap distributional comparisons further demonstrate that Morgan fingerprints provide not only higher but also more consistent screening performance than expert descriptors and Grover embeddings. Collectively, these results indicate that molecular representation—rather than model architecture—is the primary determinant of early-recognition performance in ionizable lipid discovery and that this conclusion is robust across multiple screening depths. Full article

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. Full article

Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak containment. This study proposes a veterinary-aware digital traceability framework that embeds herd health data, milk-quality testing, and inspection outcomes directly into batch-level EPCIS event records. By representing veterinary public health controls as structured, machine-actionable traceability elements, the framework enables automatic logging of mandatory control points, systematic compliance verification, and rule-based risk state transitions within standard EPCIS infrastructures. Using regulation-consistent dairy simulations modeling delayed Listeria detection during maturation, we evaluate the operational impact of event-level causal traceability within the proposed architecture. Compared with conventional time-window recall strategies, provenance-based trace-forward queries reduced recall scope under the evaluated synthetic scenarios. Integrating structured veterinary controls into EPCIS-based traceability systems supports automated regulatory evidence generation and more targeted recall decisions, contributing to improved auditability and reduced food waste in dairy supply chains. Full article

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

England’s population is living longer, a sign of progress and better health, but adult social care (ASC) services must adapt to support a growing number of older residents, who may need help to remain independent, safe, and well. Kent County Council (KCC), in South East England, projects a 28% and 53% increase in its residents aged 65+ and 85+, respectively, over the next decade. This study aimed to inform the development of KCC’s ASC Prevention Framework using a System Dynamics Modelling (SDM) approach to evaluate the impact of preventive interventions on ASC demand and expenditure. Using linked local health and social care data and the Johns Hopkins ACG® tool, the 1.3 million adult population was stratified into Patient Needs Groups. Analyses showed that higher ASC costs were associated with being older females, living alone, deprivation, and frailty-related indicators such as dementia, history of falls, etc. Around 28% of older adults aged 65+ accounted for 80% of ASC costs within that cohort, and related scenario testing projected a 48% rise in ASC costs over 10 years without interventions, moderated to 33% with targeted prevention. These findings demonstrate the value of integrated data and modelling to inform strategic, prevention-focused ASC planning. Full article

Railway switch machines are key devices in railway signal systems and have a critical impact on train operation safety. However, in real operating conditions, fault samples are scarce because field data collection is cumbersome and often constrained by safety requirements, which limits the diagnostic accuracy and generalization capability of traditional fault diagnosis methods in few-shot scenarios. To address the challenge posed by insufficient accuracy in railway switch machine state recognition using sensors under few-shot conditions, we propose a regularized supervised contrastive meta-learning (RSCML) fault diagnosis method for switch machines. First, the tri-axial vibration signals acquired from the throwing rod and the reducer are transformed into axis-wise STFT spectrograms and organized as a unified three-channel time-frequency representation for subsequent cross-channel feature learning. Second, channel expansion and attention enhancement are employed to obtain more informative feature representations among similar fault types under limited samples. Finally, the feature extractor is integrated into the regularized supervised contrastive ANIL framework, while multi-loss optimization and stability regularization jointly constrain the meta-learning training process. Experimental results show that the proposed method achieves a maximum accuracy of 99.73% on 3-way and 5-way few-shot tasks, together with an F1-score of up to 99.72%. In the cross-category generalization experiment, it achieves a 93.08% accuracy and a 92.84% F1-score, indicating improved robustness when the fault categories at test time differ from those used during meta-training. The proposed method shows superior classification performance and stronger generalization to unseen fault categories under the current dataset setting, which suggests promising potential for switch machine fault diagnosis under limited sample conditions. Full article