Search Results (366)

Surface-deployed wireless sensor networks (WSNs) provide a flexible platform for ocean monitoring, but ocean-current-dominant marine forcing causes persistent topology evolution, backbone distortion, and route breakage. This paper proposes an event-driven self-healing routing and topology-maintenance mechanism for drift-prone surface WSNs. The design combines dual-threshold cluster-head handover, CH-HELP backbone repair, Node-HELP member reattachment, loop-free upstream reselection, and conditional global reclustering as a low-frequency corrective layer for long-term topology degradation. Unlike fixed-round reorganization, the proposed framework prioritizes local repair and triggers global refresh only when backbone quality persistently deteriorates. Simulations driven by Taiwan Strait current-dominant flow–wind data show that the full Proposed-Hybrid method reduces the CH-disconnection rate from 8.15% in DARCR to 5.15%, whereas the local-only configuration without conditional global reclustering yields 9.13%. Conditional global reclustering further suppresses late-stage topology degradation, reducing the final-third mean CH-disconnection rate from 16.32% to 8.51% and the late-stage 95th-percentile peak from 34.43% to 17.21%. DARCR remains competitive in some late-stage metrics because of its fixed-period global reorganization. Full article

Monitor-roof designs are widely used in buildings to enhance natural ventilation while protecting interior spaces from rain penetration. However, the ventilation performance of rain-shield monitor roofs can be significantly influenced by their geometric configuration and the interaction between wind-driven and buoyancy-driven airflow. In this study, computational fluid dynamics (CFD) simulations were conducted to investigate the ventilation performance of rain-shield monitor roofs under hybrid natural ventilation conditions. The effects of key geometric parameters, including the outlet height (Lz) and lateral spacing (Ly), were examined under different approaching wind conditions. The results indicate that ventilation performance is governed by the combined influence of wind-driven and buoyancy-driven mechanisms. Among the investigated configurations, an intermediate outlet height of approximately Lz ≈ 0.6 m generally provides favorable ventilation performance, while a lateral spacing Ly in the range of 0.6–0.8 m maintains effective airflow passage without excessive flow resistance. The findings provide quantitative guidance for the design of rain-shield monitor-roof ventilation systems in buildings operating under hybrid natural ventilation conditions. Full article

Wind speed prediction is an essential spatiotemporal forecasting task in wind energy systems, yet it remains challenging due to the nonlinear and dynamic characteristics of atmospheric processes. The evolution of wind is governed by physical laws, which can be effectively described using partial differential equations (PDEs). To improve forecasting reliability and accuracy, this paper proposes a novel network model, termed DynWindNet, which integrates equation-based dynamics with data-driven dynamics within a unified framework. Specifically, an interactive dual-branch architecture is designed, where a Physics–Data Coupling Module (PDCM) enables adaptive information exchange between the two dynamics via attention-based gating mechanisms. In addition, a frequency-aware enhancement module (FAEM) is introduced to refine the representations of the data-driven branch by selectively emphasizing informative frequency components. Experimental results on the ERA5 dataset demonstrate that DynWindNet consistently outperforms representative baseline methods across atmospheric pressure levels. Overall, the proposed framework provides an effective approach for integrating physics-guided evolution modeling with deep spatiotemporal representation learning in wind field forecasting. Full article

Urban thermal comfort in winter is an important but insufficiently quantified component of sustainable, climate-adapted urban design in cold-weather cities facing energy-intensive winter environmental challenges. This study uses high-resolution simulations to evaluate discomfort across a downtown district in Sapporo, Japan, based on the standard effective temperature (SET*) index and universal thermal climate index (UTCI). A total of 2438 sampling points were assessed under 69 hourly winter scenarios. Discomfort hotspots were found in east–west streets and wind-exposed corners, driven by limited solar access or intensified wind. SET* is a more sensitive indicator under cold conditions, particularly in shaded areas. Wind speed and mean radiant temperature distributions revealed the environmental drivers of discomfort. The influence of building height was confirmed via quantitative correlation analysis, which revealed significant negative relationships between adjacent building heights and SET* across all streets analyzed, especially in east–west street canyons, where correlation coefficients ranged from −0.80 to −0.52 in the representative street. These findings contribute to urban sustainability by providing a quantitative tool for identifying winter thermal vulnerability and supporting passive, climate-adapted public-space design. The proposed framework can help improve winter walkability, outdoor activity, and the environmental quality of downtown spaces in cold-region cities. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

Accurate pest recognition is an important foundation for intelligent plant protection, precision pesticide application, and sustainable agricultural management. However, in real field environments, pest targets are often small in scale, severely occluded, and embedded in complex backgrounds, which limits the performance of existing supervised learning methods under low-annotation and cross-scenario conditions. To address these issues, a multimodal self-supervised pretraining framework is proposed for pest recognition, in which field pest images and environmental sensor data are integrated to construct pest representations with environmental awareness. In this framework, image features, including pest morphology, leaf texture, and damaged regions, are first extracted through a visual encoding branch, while temporal variation features of ecological factors, including temperature, humidity, illumination, soil moisture, rainfall, and wind speed, are modeled through an environmental encoding branch. On this basis, a cross-modal contrastive consistency module is designed to align visual and environmental representations, a temporal consistency self-supervised module is introduced to characterize the continuous evolutionary relationship between pest occurrence and environmental changes, and a multimodal collaborative representation fusion module is constructed to adaptively integrate information from different modalities. The experimental results show that the proposed method achieves favorable performance in the pest recognition task, with Accuracy, Precision, Recall, and F1-score reaching $94.37\%$, $93.96\%$, $93.42\%$, and $93.69\%$, respectively, outperforming ConvNeXtV2-T, ViT-B/16, Swin-T, SimCLR, MAE, and the conventional Image + Sensor fusion method. The ablation experiments further show that, after removing the cross-modal contrastive consistency module, the temporal consistency self-supervised module, and the multimodal collaborative fusion module, the F1-score decreases to $91.00\%$, $91.36\%$, and $90.49\%$, respectively, thereby demonstrating the contribution of each module. This study provides a viable multimodal self-supervised learning approach for AI-driven intelligent pest recognition, early warning, and precision control in agriculture. Full article

Inhomogeneous magnetic field distributions in high-frequency planar transformers frequently cause severe localized thermal hotspots and elevated leakage inductance. Traditional interleaved winding designs rely heavily on empirical trial-and-error, which becomes computationally prohibitive for multi-layer parallel structures due to the factorial “curse of dimensionality.” To address this bottleneck, this paper proposes a universal, data-driven optimization methodology. First, a quantitative one-dimensional prefix-sum model is established to correlate winding arrangements with spatial magnetomotive force (MMF) distributions, effectively simplifying the electromagnetic evaluation. Subsequently, a customized Genetic Algorithm (GA) framework, featuring physical-constraint-preserving operators such as Order Crossover (OX), is introduced to efficiently navigate the high-dimensional discrete search space. Using an extreme 26-layer complex parallel winding configuration (Np:Ns = 9:2) as a primary case study, the proposed GA method effectively bypasses over 1.5 million permutations, converging to the global optimum within 100 generations. The optimized structure achieves profound peak-shaving, drastically reducing both the peak MMF and total uncoupled magnetic energy area. This methodology provides a systematic, computationally lightweight EDA solution that fundamentally replaces empirical trial-and-error in the design of high-frequency magnetic components. Full article

This study presents the design and performance evaluation of a hybrid solar-driven system (SOLIVE) that integrates tubular daylighting and buoyancy-driven natural ventilation within a single architectural component for industrial and large-scale buildings. While solar tubes and solar chimneys have been widely studied as independent passive technologies, their combined use in a unified system capable of delivering both daylight and ventilation remains largely unexplored. The proposed system utilizes solar tubes not only for transmitting natural daylight but also as thermal drivers that induce airflow through the stack effect generated by solar heating along the tube surface. A mathematical framework combining photometric daylight modeling and buoyancy-driven airflow analysis was developed to evaluate the system performance. Numerical simulations were conducted for three representative solar reference days (Equinox, Summer Solstice, and Winter Solstice). The influence of the key design parameters, including illuminated surface area (5–15 m²), solar tube diameter (0.1–0.3 m), and ventilated space volume (20–60 m³), was systematically analyzed. The results show that, under the adopted modelling assumptions, the system provides peak illuminance between 376 and 502 lux and ventilation rates up to 20.5 air changes per hour (ACH). These values are discussed as indicative benchmarks with respect to ISO 8995-1 and ASHRAE 62.1, rather than as proof of full real-building compliance, since glare, illuminance uniformity, thermal comfort, occupancy, wind effects and HVAC integration were not fully modelled. These findings demonstrate the potential of the proposed system as an effective passive solution for improving indoor environmental quality and reducing building energy demand in sunny climates. Full article

Understanding the respective roles of thermal and athermal effects during electric current treatment is critical for advancing current-assisted processing of metallic materials. In this study, strain hardening in cold-rolled A6061 was effectively relieved using high-density pulsed electric current. By conducting comparative experiments under room-temperature and liquid-nitrogen conditions, the thermal and athermal contributions were quantitatively evaluated. The results indicate that thermal effects dominate over athermal effects in dislocation density reduction and strain-hardening relief. Nevertheless, the athermal effect, driven by electron wind force, is capable of promoting dislocation motion and annihilation. This work provides a practical framework for evaluating thermal and athermal contributions and offers new insights into microstructure control via electric current, with implications for the design of advanced structural materials. Full article

In this article, a data-driven adaptive neural network additional damping controller (DDANN-ADC) is proposed to regulate rotor-side converters of a DFIG-based power system to suppress sub-synchronous control interaction (SSCI). Firstly, a back propagation (BP) intermediate variable observer is designed to construct a dynamic model of DFIG-based wind farms based on real-time input–output measurement data. Subsequently, a modified cost function is developed for a BP online controller to generate a target control law, thereby contributing additional damping to the DFIG-based power system. The proposed DDANN-ADC can effectively utilize limited data generated during the control process to achieve online system identification and precise control of the system. Then, the stability of DFIG-based power system under the proposed DDANN-ADC is demonstrated with the Lyapunov function. Finally, simulation results reveal that the proposed DDANN-ADC methodology outperforms the traditional method with better adaptability and robustness under different operational conditions. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

With the acceleration of urbanization and the intensification of climate change, wind conditions have become a critical factor in architectural design. They not only affect a building’s wind resistance but also influence ventilation, pollutant dispersion, pedestrian comfort, and energy consumption. Traditional computational fluid dynamics (CFD) simulations are costly. Although the application of machine learning for CFD prediction has become a relatively mature technology, machine learning models still face challenges in actual architectural design workflows. Building upon recent advancements in the field, it proposes two core technologies: a method for predicting outdoor wind environments in buildings based on the Site-Specific Training for Design Tasks (SST-DT) strategy, and an automated machine learning workflow. These innovations improve upon existing wind environment analysis methods and systems, establishing a fully automated working framework that is easy for architects to learn and use. Within this framework, dataset acquisition and model training are performed automatically. Finally, this framework was validated across various prediction tasks with different objectives. It significantly lowers the barrier to entry for architects adopting machine learning, advances the performance-driven design paradigm, and facilitates the deep integration of machine learning technologies into architectural wind engineering. Full article

Grain-oriented (GO) silicon steel cores in low-voltage current transformers suffer magnetic degradation from residual stress and increased dislocation density during slitting and winding. This study addresses the gap in systematic optimization of secondary annealing on assembled toroidal cores using a 3² full-factorial design varying temperature (650, 850, 1050 °C) and holding time (60, 90, 120 min) on M4 grade cores. Results showed temperature is the dominant factor, while holding time exhibits a synergistic non-linear effect. The optimal condition (850 °C, 90 min) reduced specific losses from 0.85 W/kg to 0.43 W/kg (49% reduction). Mechanistic analysis confirmed this improvement is driven by complete primary recrystallization (equiaxed grains ~50–60 µm), dislocation annihilation (~10 HV hardness reduction), and reinforcement of the Goss texture ({110} <001>). SEM, EDS, and ICP-OES demonstrated that the Carlite coating remained dimensionally (1.67–1.83 µm) and chemically stable, with beneficial decarburization. Temperatures above 850 °C caused magnetic deterioration due to excessive grain growth. These results provide a validated, industrial framework for recovering magnetic efficiency in wound toroidal cores without compromising coating integrity. Full article