Search Results (371)

Extreme weather conditions impose significant challenges on distribution network inspection because UAV flight safety, energy consumption, vehicle mobility, and task coverage are strongly coupled under wind disturbances. To improve inspection efficiency and operational robustness, this paper proposes a weather-aware asynchronous vehicle–UAV cooperative scheduling method based on bi-level MODDPG–NSGA-II optimization. First, a dynamic wind field model and a wind-sensitive UAV energy model are established to describe the effects of background wind, vertical wind shear, and local gust disturbances on UAV motion and state-of-charge evolution. Then, an asynchronous vehicle–UAV collaboration mechanism is developed, allowing the vehicle to move toward downstream parking sites after UAV deployment while UAVs perform inspection and cross-site recovery under rendezvous and energy safety constraints. On this basis, a bi-level optimization framework is constructed, in which NSGA-II searches global coordination parameters and MODDPG learns adaptive multi-UAV scheduling policies in continuous decision spaces. Controlled wind-factor experiments show that, with the task scale fixed at 52 inspection tasks, the proposed method maintains 100% task coverage under 0–10 m/s wind conditions. As the reference wind speed increases from 0 m/s to 10 m/s, the mission completion time increases from 40.97 min to 70.24 min, while the minimum residual SOC decreases from 50.32% to 13.82%, which remains above the predefined safety threshold. Repeated stochastic trials and statistical significance analysis further indicate that the proposed method achieves shorter mission time and more stable task coverage than representative baselines under the same experimental conditions. The scope of this study is simulation-level validation; real-world flight tests and hardware-in-the-loop verification will be further investigated in future work. 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

In the severely cold regions of northern China, large-scale clean heating retrofits in rural areas face critical problems, including substandard indoor thermal environments, excessive energy consumption, and prohibitive operating costs. To address these challenges, this study focuses on rural residences in Hohhot as the research subject. Field measurements were conducted throughout the heating season in a typical rural house in Hohhot, a representative city with severe cold weather, to collect indoor/outdoor thermal parameters and real-time operational data of an air-source heat pump (ASHP). A dynamic simulation platform was established using TRNSYS 18. The optimization scheme integrates passive envelope retrofitting (ground insulation improvement and energy-efficient windows) with the active optimized control of the ASHP system. Indoor thermal comfort was evaluated using the Predicted Mean Vote (PMV) index. The results show that the ASHP exhibits excellent heating effectiveness and economic viability, making it the preferred technology for rural residences in Hohhot and similar regions. After implementing the active–passive scheme, the proportion of time with comfortable indoor conditions in rural houses surges from 34.1% to 84.1%, while during the severe cold period, this proportion increases from 16.97% to 61%. The indoor thermal comfort index shifts from its previous state to the baseline comfort range of −1.0 to 0. The total heating energy consumption decreased from 18,646 kWh to 15,861 kWh, and the seasonal operating cost dropped from 3207 to 2579.3 RMB, achieving an overall reduction of 19.6% in both energy and costs. The proposed active–passive synergistic optimization scheme simultaneously improves the indoor thermal environment and reduces heating energy consumption, overcoming the limitations of single-measure retrofits. This study fills the research gap on the quantitative evaluation of active–passive synergy for rural clean heating in severely cold regions, providing a theoretical basis and technical support for clean heating retrofits in Hohhot and Inner Mongolia, facilitating low-carbon and efficient rural clean heating in northern China. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements. Full article

This paper addresses the important issue of the proper management and protection of subterranean monuments. It concerns the analysis and decoding of the microclimate that is created in heritage structures, which are structures located beneath the soil or carved into rock. The aim of this study is to understand the hygrothermal processes occurring in the mass of underground structural elements, such as evaporation, condensation, water content, and heat fluxes, based on the principles of building physics. The methodology used is the following: a systematic literature review on the topic, an overview of the factors affecting the microclimate, the assessment methodology, and the simulation tools used to decode and evaluate microclimate in subterranean heritage structures; a discussion of the current gaps; and finally, a proposal for future directions for research. A review of the literature reveals that researchers worldwide have employed similar methodologies to approach this complex issue. Recordings and analyses of the microclimate inside underground monuments lead to decision-making and the formulation of actions for optimal preservation. Due to the large number of parameters involved in microclimate analysis, computer software for numerical simulation has been used in many cases. Following the review of the relevant literature in the field of study, a critical discussion concludes by proposing directions for future research on this important topic. Basic results of this research identify current gaps, problems, and limitations. These include technical and practical issues or gaps concerning lack of data for material properties and weather conditions. Another significant limitation arises from the complexity of physical interactions, as well as from the human factor, which involves the proper use of the simulation program and the correct interpretation of the calculation results. This study demonstrates that the microclimate of subterranean heritage structures is the result of complex interactions between climate, geology, architectural design, material properties, and human use. Across different geographical and cultural contexts, subterranean monuments exhibit distinct microclimatic behaviors. The comparative analysis of case studies highlights that while subterranean environments generally benefit from thermal stability, they remain highly vulnerable to moisture dynamics, ventilation changes, and external climatic coupling. Hence, there is a necessity for context-specific approaches rather than generalized conservation solutions. Decoding subterranean microclimates requires a multidisciplinary framework that combines environmental monitoring, material indicators, architectural analysis, and numerical modeling. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

Conservation management of large, multi-species landscapes requires integrating heterogeneous data streams—such as satellite imagery, GPS telemetry, camera traps, bioacoustic sensors, weather stations, and field reports—into a unified model capable of simulating ecosystem dynamics and generating actionable recommendations. This paper proposes a tiered, energy-aware AI architecture for constructing ecosystem digital twins that enables prescriptive, rather than merely descriptive or predictive, landscape-scale conservation management. The framework classifies conservation tasks across three computational tiers: classical machine learning for continuous environmental monitoring and species distribution prediction, deep learning for perception-oriented tasks such as computer vision and bioacoustic analysis, and foundation models for cross-domain synthesis and stakeholder interaction. We apply this architecture to a comprehensive digital twin of the Greater Yellowstone Ecosystem, anchored in the ongoing conservation crisis of the Sublette Pronghorn Herd—a population that crashed from 43,000 to 24,000 animals in a single winter due to compounding severe weather and a Mycoplasma bovis outbreak. We formalize a coupled change model linking population dynamics, forage condition, corridor permeability, winter severity, and disease pressure, and demonstrate how a prescriptive recommendations engine can generate goal-conditioned management actions for the herd’s 165-mile “Path of the Pronghorn” migration corridor. A comparative energy footprint analysis, grounded in hardware-level energy measurements using Intel RAPL instrumentation and the CodeCarbon framework, estimates that the tiered architecture reduces computational energy consumption by approximately 34% relative to a deep-learning-everywhere baseline and by over three orders of magnitude relative to a foundation-model-centric baseline. The architecture provides a replicable blueprint for resource-constrained conservation organizations seeking to deploy AI-powered ecosystem management at landscape scale. Full article

CubeSats are used as a platform in modern space missions due to their standardized form factor, reduced development cost, and shortened launch timelines. Earth observation, space weather monitoring and even re-entry applications make use of the CubeSat standard. Despite their advantages, CubeSats are constrained by limited onboard resources, with electrical power availability being one of the most critical bottlenecks. This work presents a dynamic, hybrid offline/online task scheduling and power management algorithm for a re-entry CubeSat, combining pre-computed schedules with real-time adaptation to changing flight conditions. The algorithm employs a heuristic-based approach, ranking tasks by parameters including priority, execution delay, duration, and power consumption. It adapts to varying flight conditions and system failures. In critical battery State of Charge (SoC) scenarios, only high-priority tasks above a defined threshold are executed, conserving power. A simulation suite was developed to evaluate performance under realistic mission profiles and stress tests with high loads and numerous tasks. The metrics included average and maximum task delay and average power consumption. The results show that appropriate heuristic weight selection can yield significant improvements in reliability and efficiency. The proposed algorithm offers a flexible, scalable solution for CubeSat power management, which is capable of maintaining operational reliability under dynamic conditions. Full article

Instance segmentation accurately extracts the position and outline of ships, serving as the foundation for maritime safety tasks such as multi-object tracking, sensor fusion, and collision warning. This study focuses on single-frame segmentation and aims to address the challenge of multi-scale ship occlusion in congested ports, providing reliable observational data through high-precision recognition to ensure navigation safety. Existing methods suffer from performance degradation in complex maritime environments due to factors such as multi-scale distribution, low resolution of distant targets, and frequent occlusions. Among these, ship occlusion is particularly problematic as it leads to feature confusion between adjacent instances and inaccurate boundary segmentation. To address these challenges, we propose a novel instance segmentation algorithm (MAF-ORDNet) based on Multi-branch Adaptive Feature Fusion and Occluded Region Decoupling. Firstly, a multi-branch adaptive feature fusion module is designed to capture contextual information through different receptive fields and dynamically fuse multi-scale features, thereby restoring occluded semantics and enhancing robustness. Secondly, an occlusion region decoupling module is constructed to accurately localize occluded regions and enhance contour responses via adaptive sampling, achieving refined boundary processing. In addition, we constructed and annotated the Occlusion ShipSeg dataset, which contains 1969 real occlusion images, 2150 simulated occlusion images, and 1132 images under adverse weather conditions, totaling 17,352 fine instance annotations. Experimental results show that, compared with PatchDCT, YOLOv11s, and Mask2Former, our method improves AP by 2.7%, 3.2%, and 2.4%, respectively, while maintaining a comparable inference speed to YOLOv8s. These results confirm that MAF-ORDNet achieves a favorable balance between accuracy and efficiency in multi-scale occluded ship segmentation tasks. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

To enhance the energy efficiency and environmental sustainability of solar greenhouses, precise microclimate control is essential. Composite thermal blankets critically influence heating demand and carbon footprint, yet conventional heat transfer models often neglect their internal structural characteristics, limiting simulation accuracy and optimization. Accordingly, a heat transfer model for composite thermal blankets was developed based on the law of energy conservation. The model discretizes the internal structure and integrates radiation, convection, conduction, and latent heat from condensation. It uniquely incorporates dynamic environmental factors and blanket properties including layered composition, porosity, and moisture content. Accuracy was validated through numerical simulations and field experiments in both traditional brick-wall and prefabricated flexible-wall solar greenhouses under various weather conditions. Validation showed strong agreement: for the brick-wall greenhouse, mean absolute error (MAE) was 1.21 °C, root mean square error (RMSE) 1.27 °C, and R² 0.97; for the flexible-wall greenhouse, MAE was 0.56 °C, RMSE 1.08 °C, and R² 0.85. These indicators confirm that the model reliably quantifies the impact of thermal insulation blanket material and structure on thermal performance, providing a basis for design optimization and a reduction in supplemental heating demand and carbon emissions. Further analysis examined the porosity and moisture effects on spray-bonded cotton, PE foam, and needle-punched felt. Under low moisture, higher porosity reduced thermal conductivity by up to 27.4%, 57.6%, and 52.4%, respectively. However, under high moisture, conductivity increased with porosity in materials with interconnected pores (spray-bonded cotton and Needle-punched felt) due to continuous water channels, while closed-cell PE foam conductivity continued decreasing. All materials showed linearly increasing conductivity with moisture content, with higher-porosity materials exhibiting greater sensitivity. For example, at porosities of 0.95, 0.95, and 0.85, moisture content rising from 0 to 0.225 increased conductivity by 264%, 209.6%, and 196.7%. This model provides a robust theoretical foundation for the scientific selection, structural optimization, and performance evaluation of composite thermal blankets in greenhouse applications. Full article

Vision-based navigation systems (VINS) are increasingly utilised as an alternative to GNSS for UAVs operating in urban environments, but they suffer from performance degradation under visual fault conditions like illumination variation, rapid motion, texture-less environments, and weather effects. While hybrid architecture incorporating Kalman filters and machine learning (ML) improves performance, they often lack evidence of providing contingency for non-Gaussian error distributions, limiting operational safety. To address these shortcomings, an enhanced hybrid VINS architecture is proposed, featuring a Bayesian GRU-based error correction network (B-GRU) to provide a contingency while compensating model errors. To the best of the authors’ knowledge, this is the first attempt to estimate uncertainty using a B-GRU compensator while addressing data uncertainty for VINS applications. The system architecture integrates an Error-State Kalman Filter (ESKF) and the B-GRU, compensating for position errors with uncertainty prediction. The proposed approach is validated using datasets from MATLAB incorporated in an Unreal Engine simulated environment, replicating the complex fault conditions. The ML model is trained on various visual failure modes to adapt the variability in the signal patterns during flights with simulated datasets and tested across varied flight paths and lighting scenarios. The results demonstrate that the fusion strategy effectively corrects erroneous measurements arising from corrupted sensor data and imperfect models and achieves an improvement of 78.06% compared to SOTA hybrid VIO on the horizontal axis while capturing complex flight dynamics in an unseen environment. A comparative analysis demonstrates the effectiveness of B-GRU in mitigating failure modes with a predictive error boundary, achieving a 72% improvement in performance compared to the architecture that integrates GRU-based error compensation. This approach shows a step forward in enhancing positioning accuracy and contingency in challenging urban environments. Full article

Hurricane Florence (2018) proved to be a damaging tropical cyclone that formed off the coast of the Cabo Verde Islands. On 12 UTC 14 September 2018, Florence made landfall as a weakened category 1 Hurricane in Wrightsville Beach, NC. In the midst of landfall, Florence’s ground speed stalled considerably to near zero. Because of this stall, Florence continued to accumulate feet of rain along the coastline, and the inundation of seawater became extreme. Due to the impacts of Florence, the Weather Research and Forecasting Model (WRF-ARW) was used to simulate the tropical cyclone and provide insight into the thermodynamics and dynamics that played a significant role at the time of landfall. After the control case, several sensitivity experiments were conducted. The historical sensitivity experiments utilize the thermodynamic and moisture fields of ERA5 reanalysis data from 1968 and 1998, respectively, to modify the thermodynamic and moisture fields in the initial conditions of the WRF–ARW control case. In addition, to study the potential future climate impacts of Florence, the NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research dataset was utilized. The same approach was taken as the historical versions of Florence for sensitivity experiments for future climate, i.e., thermodynamic and moisture fields for both 2038 and 2068 under the RCP6.0 and RCP8.5 climate scenarios, respectively. Results suggest a corresponding intensity shift with minor track deflections. Based on these modifications, synoptic and mesoscale dynamics will be studied to provide insight into how Florence-like hurricanes may change based on certain climate scenarios. Full article

To investigate the thermal environments of three large-span plastic tunnels with different orientations and shapes (two east–west-oriented asymmetrical tunnels, WE15-5 and WE13-7, and one north–south-oriented symmetrical tunnel, NS10-10) under summer high-temperature and winter low-temperature conditions, we continuously monitored the air and soil temperature and conducted a comparative analysis of both under typical weather conditions. Computational fluid dynamics (CFD) simulations were used to further analyze the temperature and airflow fields. The results showed that, in summer, NS10-10 exhibited a superior ventilation and cooling performance with the most uniform temperature distribution, making it more suitable for summer crop cultivation. In winter, WE13-7 demonstrated optimal insulation and heat retention, with the highest minimum air temperatures and best daylighting capacity. CFD model validation showed a good agreement with the measured data (RMSE: 0.73–0.85 °C). These findings provide structural optimization recommendations for large-span plastic tunnels in different seasons. Full article