Search Results (3,415)

Background: Long-term care facility (LTCF) residents are highly susceptible to healthcare-associated infections, and prevention is challenging given frailty, dementia, communal living, and resource constraints. Environmental surface and air contamination contribute to transmission. Novel no-touch automated disinfection technologies have been studied in hospitals, but evidence specific to LTCFs is scarce. This scoping review summarizes recent LTCF-focused interventions, their effectiveness, and implementation considerations. Methods: This scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. We searched PubMed, Medline, Embase, CINAHL, and Scopus for observational or experimental studies evaluating environmental disinfection in LTCFs/nursing homes, excluding body decolonization, non-LTCF settings, and reviews/protocols. Two reviewers independently screened and extracted data via Covidence. This review has been registered on OSF (Open Science Framework). Results: Of 1491 records, 7 studies met the inclusion criteria (6 from the USA, 1 from Australia): one cluster randomized trial, one interrupted time series studies, three prospective observational studies, and two pre–post designs. Interventions included physical methods (HVAC-integrated UV/UVGI, continuous UVGI) and chemical approaches (dry hydrogen peroxide, room fogging plus chlorine dioxide wipes, hydrogen peroxide wipes). Outcomes were heterogeneous (surface SARS-CoV-2 RNA, COVID-19 attack/case rates, airborne/surface microbial loads, and one clinical endpoint—acute respiratory illness). Several studies reported reductions in environmental or airborne bioburden; however, UV-based studies did not demonstrate statistically significant reductions in clinical infections. Certainty was limited by small numbers, non-randomized designs, and diverse outcome measures. Conclusions: No-touch automated disinfection methods appear promising as supplements to standard infection prevention control bundles for reducing environmental contamination in LTCFs. Nevertheless, consistent clinical benefits are unproven. Rigorous, LTCF-tailored, adequately powered trials with standardized clinical and environmental outcomes, plus implementation and cost-effectiveness evaluations, are needed. Full article

To address the problem of high-frequency roll disturbances generated during dynamic balancing in non-coaxial two-wheeled robots, this paper proposes a Rotation-Optimized Inertial–Visual SLAM system (ROIV-SLAM) for robust state estimation. The proposed approach adopts a decoupled architecture for translation and rotation estimation. In the front-end, an Extended Kalman Filter (EKF) is employed to fuse LiDAR, an inertial measurement unit (IMU), and wheel odometry to obtain an initial translation estimate. Meanwhile, a physical manifold constraint is constructed using the gravity vector and surface normals extracted from RGB-D point clouds, supporting stable rotation estimation under high-frequency disturbances through Lie-group-based optimization. In the back-end, a factor graph is established, and loop closure robustness is enhanced through vision–LiDAR scan matching. Experimental results indicate that ROIV-SLAM achieves improved trajectory consistency with respect to the optimized reference trajectory and more robust mapping performance compared with the evaluated baseline approaches in the tested scenarios. The results further suggest that introducing task-specific physical dynamic constraints and a decoupled estimation mechanism helps suppress high-frequency motion noise inherent to balancing robots, thereby improving the robustness of state estimation in complex environments. Full article

To address the issues of neglecting population heterogeneity and the difficulties in determining constraint parameters in residential load adjustable capacity forecasting, this paper proposes a data-driven forecasting method that considers profile heterogeneity. First, K-means++ is utilized to extract diverse user electricity consumption profiles. Second, to solve the problem of real response data scarcity, the difference-in-differences (DID) method is employed to empirically calibrate the true physical constraint boundaries of different clusters, and high-quality response samples are generated in batches based on an electricity cost minimization model. Finally, a Long Short-Term Memory (LSTM) time-series forecasting model is constructed to achieve the precise quantitative evaluation of adjustable capacity. Case studies demonstrate that after introducing user profile labels, the three accuracy metrics of the predictive model are improved by 16.29%, 24.52%, and 20.21%, respectively. Although the practical application of synthetic labels faces minor limitations caused by uncertain user behaviors, this scalable framework supports seamless incremental retraining using future empirical response data to realize continuous model evolution and persistent accuracy improvement, thereby providing technical support for load aggregators’ market bidding and the precise dispatch of power grid demand response. Full article

Underwater acoustic communication (UAC) is of great strategic importance for marine resource exploration and security collaboration. However, its open physical nature exposes communication links to severe eavesdropping and localization threats, while limited bandwidth and severe attenuation further exacerbate the difficulty of secure transmission. To address this, this study introduces the underwater acoustic reconfigurable intelligent surface (UARIS) to reconfigure acoustic propagation paths, leveraging its programmable reflection capability to enhance link quality and provide additional spatial degrees of freedom for location privacy protection. Accounting for the partial observability caused by the coarse observations of a mobile eavesdropping user (EU), noisy channel state information (CSI), and the practical constraint of UARIS discrete phase quantization, a utility maximization problem is formulated to jointly optimize the secrecy rate and location privacy. To tackle the strong non-convexity and coupled constraints in dynamic environments, a Gated Recurrent and Conformal-calibrated Soft Actor–Critic (GC-SAC) algorithm is proposed. Specifically, GC-SAC employs a gated recurrent unit (GRU) to capture the temporal statistical features of channel evolution. By integrating a risk prediction network with a conformal calibration mechanism, conservative estimation and robust regulation of multidimensional constraint risks are enhanced. Simulation results demonstrate that the GC-SAC algorithm achieves faster convergence and superior stability in dynamic underwater environments. Compared with representative baselines, the proposed algorithm exhibits significant advantages in secrecy rate and location privacy protection, validating its effectiveness for UARIS-assisted secure resource optimization in underwater scenarios. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

With the rapid growth of data center (DC) energy consumption and the large-scale integration of renewable energy, DCs increasingly face time-varying power upper-bound constraints jointly shaped by grid power supply capability, renewable energy fluctuations, and demand response mechanisms. Meanwhile, DC power consumption exhibits a typical information-load-driven characteristic. The computing tasks hosted by virtual machines affect server-side IT power consumption through resource utilization states such as CPU, memory, disk I/O, and network I/O, and are further coupled with non-IT auxiliary power consumption from cooling, power distribution, and networking equipment. In such cyber–physical operation scenarios, physical-layer sensing data and hypervisor-level virtualization monitoring data jointly provide the state basis for power estimation, power warning, and migration decisions. To address the mismatch between dynamic power upper bounds and time-varying information loads, this paper investigates the information load scheduling problem under constrained power loads and proposes a two-stage virtual machine (VM) migration optimization framework. In the VM selection stage, a Multi-Factor Balanced (MFB) algorithm is designed. By introducing a warning-line trend model based on the arctangent function, MFB comprehensively considers resource utilization, power load variation trends, and service level agreement (SLA) violation levels to dynamically identify candidate VMs for migration. In the VM placement stage, a Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) algorithm incorporating a Random Roulette Wheel (RRW) selection mechanism is proposed. By constructing normalized multi-dimensional equilibrium factors, MFEACO coordinates the trade-off among energy consumption, load balancing, and SLA violations. Simulation experiments are conducted on an improved CloudSim platform using real-world cluster trace data from Google and Alibaba. The results show that, while satisfying dynamic power constraints, the proposed MFB–MFEACO framework achieves a favorable comprehensive trade-off among energy consumption control, SLA violation suppression, and migration reduction. Compared with traditional heuristic methods and a power-constrained genetic algorithm baseline, the proposed framework demonstrates better dynamic adaptability and scheduling stability. Full article

Gas–solid bubbling fluidized beds involving Geldart Group B particles are fundamental to numerous industrial thermochemical processes, where bubble dynamics dictate the efficiency of heat and mass transfer. However, accurately predicting these complex hydrodynamic behaviors remains a challenge due to the non-linear coupling of phase interactions. This study presents a comprehensive validation of 2D and 3D Eulerian–Eulerian Two-Fluid Models (TFM) against an extensive experimental dataset. A ‘core-flow’ consistency principle is adopted, demonstrating that the 3D cylindrical simulation provides a physically equivalent representation of the central bubbling dynamics in the rectangular experimental bed. A key innovation of this work is a novel post-processing framework that bridges raw CFD datasets and quantitative bubbling metrics. Unlike traditional threshold-based segmentation or localized probe measurements, which are often limited by spatial resolution and noise sensitivity, the integrated use of Autodesk 3DS Max for volumetric reconstruction and customized MATLAB (R2024a) algorithms allows for the seamless processing of heterogeneous 2D and 3D data. This methodology significantly enhances the capability to track complex bubble coalescence and breakup events while improving batch-processing efficiency, providing a high-fidelity alternative for analyzing gas–-solid flow patterns in complex geometries. The results show that both experimental data and 2D simulations align with Werther’s correlation, yielding Mean Relative Errors (MRE) of 8.2% and 10.5%, respectively. In contrast, the 3D simulation tracks Darton’s prediction closely with a lower MRE of 7.4%, demonstrating superior concordance in volumetric bubble growth. The core innovation lies in the definition of a clear dimensional choice framework: 2D simulations are computationally sufficient and accurate for predicting macro-scale bubble heights and frequencies under pseudo-2D or narrow-bed constraints. However, 3D simulations are strictly necessary when evaluating unconstrained radial expansion, core-flow dynamics, and precise volumetric bubble diameters (d_v) where full multi-directional degrees of freedom dictate hydrodynamics. Full article

Hyperspectral unmixing (HU) aims to estimate endmembers and their corresponding abundances, a task commonly referred to as blind hyperspectral unmixing (BLU). Nonnegative matrix factorization (NMF) provides a unified framework for their joint estimation. It is widely assumed that more accurate endmember estimation leads to improved abundance estimation, enabling simultaneous optimization of both variables. However, this paper shows that, in practical noisy scenarios, the relationship between endmembers and abundances in NMF-based multi-variable joint optimization problems (NMF-based JOPs) is inherently coupled and significantly more complex, making it difficult to improve both estimation accuracies simultaneously. Furthermore, we demonstrate that the hard abundance sum-to-one constraint (ASC), commonly imposed in NMF-based JOPs, is inconsistent with realistic noisy conditions. To address these limitations, we propose a novel two-stage framework for BLU that decouples the refinement of endmembers and abundances. In the first stage, a strongly convex minimum-volume simplex model is employed to ensure robust and stable endmember extraction. In the second stage, we introduce a novel formulation, L₁_SoftASC, which promotes abundance sparsity and physical interpretability while improving convexity and robustness in abundance estimation. Experimental results on both synthetic and real benchmark datasets demonstrate that the proposed two-stage approach consistently outperforms existing single-stage NMF-based JOP methods in terms of both endmember and abundance estimation accuracy, while providing BLU with greater flexibility in handling ASC. Full article

This paper addresses the challenge of ensuring physically feasible and reliable real-time control of hybrid microgrids in harsh desert environments. A physics-constrained multi-agent Deep Q-Network (MA-DQN) is proposed for energy management of a grid-interactive microgrid in the Moroccan Sahara. The method embeds operational constraints directly into learning through action filtering, penalty-aware rewards, and coordinated PCC control. The results show a reduction in operational cost from 1250 MAD to 1120 MAD (−10.4%) and CO₂ emissions from 318.9 kg to 272.5 kg (−14.6%), while maintaining voltage within ±10% limits and eliminating PCC oscillations. The framework delivers stable, reliable, and deployment-ready control. Full article

This paper presents a physics-informed Deep Q-Network (DQN) framework for optimizing the deployment of 100 vortex bladeless turbines (VBTs) in a Saharan microgrid. The proposed approach integrates wake interaction modeling, land-use constraints, techno-economic factors, and power quality (PQ) indicators at the point of common coupling. The novelty lies in coupling aerodynamic modeling with reinforcement learning and grid constraints. Results show that dense layouts (≤400 m²) yield up to 41% gains but degrade PQ (Pst > 1.0, THD > 5%). An optimal range of 500–800 m² achieves stable performance with moderate gains (6–9%) and acceptable PQ. Larger surfaces (>1000 m²) show limited benefits (<4%). The framework supports efficient and sustainable wind deployment in constrained environments. Full article

The tree-contact single-phase-to-ground fault (TSF) in 10 kV distribution networks has high transition resistance, weak fault currents, and nonlinear steady-state waveforms. As existing high-impedance fault models cannot accurately describe its complete physical evolution, this paper proposes a novel modeling and parameter identification algorithm for TSF. First, based on recorded data from full-scale experiments, the initiation and development processes of TSF are studied, revealing the main factors affecting fault electrical characteristics—such as moisture evaporation, pyrolysis carbonization, air gap breakdown, and tree body current dissipation. Then, a dynamic resistance series model for TSF is constructed, with parameters identified and calibrated using experimental data, objective functions, and physical constraints. Finally, a 10 kV TSF simulation model is built and verified. Furthermore, a cross-condition predictive validation is performed using different voltage and geometric boundaries. Results demonstrate that the proposed physics-constrained model can effectively reproduce the RMS fault current envelope with asymmetric moisture evaporation characteristics. It also accurately predicts steady-state nonlinear waveform features without parameter re-tuning, providing more physically consistent data support for future TSF identification studies. Full article

Insufficient physical activity (IPA) among university students remains an important public health concern associated with adverse health outcomes. Although barriers to physical activity (PA) are widely documented, less is known about how these barriers cluster within different university contexts and student subgroups. This study examined hierarchical configurations of perceived barriers associated with IPA in a multicountry university sample. A cross-sectional analytical study was conducted with 686 undergraduate students (60.8% women; mean age = 22.4 ± 5.1 years) from Chile, Mexico, Spain, and Italy. Perceived barriers were assessed using the BBAQ-21 and self-reported PA using the IPAQ–Short Form. CART and Exhaustive CHAID decision-tree models were applied to identify subgroup configurations based on cumulative barrier burden. Country-based subsamples and self-reported post-pandemic PA emerged as the principal segmentation variables. The Mexican subsample showed the highest barrier burden. Students reporting increased PA generally clustered within lower-barrier configurations, whereas stable or reduced PA tended to coincide with greater perceived barrier burden. Perceived barriers formed differentiated and context-dependent configurations associated with PA patterns. These findings provide exploratory insight into how barriers cluster within university populations and support more context-aware interpretation of PA-related constraints within higher education settings. Full article

Haze commonly degrades visible-spectrum remote sensing (RS) images by reducing contrast and distorting colors. Existing RS dehazing methods still face two limitations. Prior-driven methods rely on handcrafted assumptions that may become unreliable in complex wide-area scenes without explicit sky regions. Learning-based methods require paired training data, yet real aligned hazy/haze-free RS image pairs are difficult to collect, which limits their real-world generalization. To address these limitations, we propose a method called Remote Sensing Image Dehazing via RGB-Space Physical Constraints (RDPC). The new method revisits the atmospheric scattering model (ASM) from the perspective of RS imaging and builds the restoration process on several physical properties of hazy image formation. For atmospheric light estimation, the RGB-space line-convergence behavior of local regions with similar reflectance and slight depth variations is exploited, allowing atmospheric light to be estimated without explicit sky areas. For transmission estimation, the geometric relation between observed pixels and atmospheric light is used in RGB space, where local perpendicularity provides physically plausible haze-removal guidance and global compensation helps avoid excessive darkening and color degradation. The estimated transmission and albedo guidance are further refined by enforcing ASM consistency and variation sparsity through joint optimization. Experiments on synthetic and real-world RS image dehazing benchmarks demonstrate that RDPC achieves competitive performance against representative prior-based and learning-based methods, including Image Dehazing and Exposure (IDE), Iterative Predictor-Critic (IPC), Curvature-to-Plane Prior (C2P), Adaptive Structure-Texture Awareness (ASTA), Asymmetric U-Net (AU-Net), Efficient Multi-scale Prior Fusion (EMPF), and Lightweight Feature Dehazing (LFD), in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), neural image assessment (NIMA), and processing time. Full article

High spatial resolution wind speed data is very important for wind farm planning, design, operation and maintenance. But due to cost, site and other factors, it is impossible to build a large number of anemometer towers to obtain high spatial resolution measured data. Therefore, this paper proposes a method for generating wind speed data in renewable energy bases based on physics-informed neural networks, which incorporates fluid mechanics control equations such as the Navier−Stokes equation as physical constraints into the model training process. The model’s input includes the wind speed data and the wind direction data of the anemometer towers as input, as well as the geographical difference data between the input anemometer towers and the output point, enabling to learn the mapping relationship between geographical differences and wind speed differences at different locations, achieving the goal of generating high spatial resolution wind speed data. Using normalized root mean absolute error (NMAE) to measure the model error, the average wind speed error and the average wind direction error of the proposed wind speed data generation method on different test sets are 8.28% and 10.50%, which is lower than that of BP neural network and graph convolutional neural network, and can provide more refined data support for wind turbine layout planning and wind farm power prediction of renewable energy bases. Full article

Aiming at the multi-objective solution problem of the deployment optimization of the hybrid communication network based on PLC, wireless and dual-mode collaborative networking, this paper proposes an algorithm design based on comprehensive performance optimization with business benefits as the orientation. Firstly, according to the non-ideal channel conditions and the low latency service requirements, the cross-layer modeling of the physical layer and MAC layer is adopted. Then, a dynamic weighting mechanism based on different service levels is defined, and a hybrid communication network adaptive access model considering the constraints of business benefits, network performance, and networking costs is designed. The hybrid communication network deployment and access algorithm design based on K-mean clustering and the improved NSGA-II are realized. Finally, the algorithm performance simulation and comparative analysis are carried out. The simulation results show that the proposed algorithm design can effectively balance the two objectives of network benefits and deployment costs under various network constraints and provide diversified deployment strategies in a targeted manner. Full article