Search Results (974)

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an anchor-free collaborative localization framework for UAV–UGV teams that fuses pairwise UWB ranges (including UAV–UAV, UAV–UGV, and UGV–UGV) with onboard VIO in a factor-graph backend via a two-stage robust scheme. First, we bound VIO drift using per-agent state covariance and reject UWB outliers with a Mahalanobis gate, preventing early-stage bias when VIO is still accurate. Then, during global optimization, we adaptively estimate the Fisher information of UWB factors from measurement–state residuals, enabling online self-tuning of measurement confidence under time-varying SNR. Real-world experiments with three UAVs and two UGVs over multi-level rooftops and forest–open areas (~1.6 km²) show that, compared to an outlier-only variant, the proposed method further reduces localization RMSE by about 24.6% and maximum error by about 31.2% for both UAVs and UGVs, maintaining strong performance during long trajectories dominated by VIO drift and NLOS ranges. The approach requires no fixed anchors or GNSS and is applicable to UAV–UGV teams for disaster response, cooperative mapping/inspection, and bandwidth-limited operations. Full article

Urbanization and climate change are exerting significant pressure on the living environments of traditional rural settlements. In western Sichuan, the persistently cold and humid winter further intensifies the risks for local residents. Linpan, a distinctive agricultural settlement form that has evolved over centuries, embodies climate-responsive construction wisdom shaped by long-term human–environment interaction. Within Linpan, residential units—composed of outdoor and indoor spaces—serve as the primary activity spaces for inhabitants. Their spatial configuration and construction practices directly regulate the thermal environment and consequently influence daily life. However, whether the winter thermal environment satisfies contemporary thermal comfort requirements, and which landscape and construction determinants can effectively enhance thermal adaptation, remains insufficiently understood. To address this gap, this study integrated meteorological field measurements, thermal comfort questionnaire surveys, and coupled numerical simulations to systematically investigate winter thermal conditions in both outdoor and indoor spaces of Linpan residential units. The optimization performance of key landscape determinants (vegetation configurations and ground materials) and construction determinants (building layouts and envelope materials) was evaluated. The results reveal climate-responsive passive design strategies based on actual inhabitants’ thermal adaptation, establishing a sustainable design framework for improving winter thermal comfort in traditional agricultural settlements. The findings provide scientific support for rural revitalization and contribute theoretical insights into climate-resilient preservation of vernacular dwellings under changing environmental conditions. Full article

Monocular camera localization in known environments is a critical task for applications like autonomous navigation, augmented reality, and robotic positioning, requiring precise spatial awareness. Unlike localization in unknown environments, which builds maps in real time, this leverages pre-existing data for higher accuracy. This review comprehensively analyzes monocular camera localization methods in known environments, categorizing them into 2D-2D feature matching, 2D-3D feature matching, and regression-based approaches. It consolidates foundational techniques and recent advancements, providing inter-class and intra-class performance comparisons on mainstream datasets. Key findings show that 2D-3D methods generally offer the highest accuracy, especially in structured outdoor environments, due to robust use of 3D spatial information. However, recent scene coordinate regression methods, such as ACE and ACE++, achieve comparable or superior performance in indoor scenes with more efficient pipelines. This review highlights challenges and proposes future directions: (1) synthetic data generation to meet deep learning demands, while addressing domain gaps; (2) improving generalization to unseen scenes and reducing retraining; (3) multi-sensor fusion for enhanced robustness; (4) exploring transformer-based and graph neural network architectures; (5) developing lightweight models for real-time performance on resource-constrained devices. This review aims to guide researchers and practitioners in method selection and identify key research directions. Full article

Accurate wireless positioning has remained challenging under dynamic bandwidth conditions and outdoor multipath environments that are typical in Internet of Things (IoT) and autonomous aerial vehicle (AAV) applications. Conventional learning-based localization methods rely on bandwidth-specific channel state information (CSI) representations, which causes the trained models to be inapplicable or less adaptive when the signal bandwidth differs from that used during training. To overcome this limitation, a unified and neural network-oriented framework is proposed, which constructs bandwidth-adaptive power delay profile (PDP) representations for learning-based models. A PDP preprocessing scheme through adaptive zero-padding and oversampled IFFT of heterogeneous CSI is introduced to generate dimension-consistent and delay-aligned neural network inputs. To enhance robustness, a sub-band-sliced PDP representation is developed to enhance model robustness, where each bandwidth is divided into equal-width sub-bands whose PDPs are independently processed and organized as Transformer tokens. A dedicated Transformer is designed to get the location estimation from PDPs of multi-access points. Simulation results have demonstrated that the proposed preprocessing-PDP-plus-Transformer framework achieves superior cross-bandwidth generalization and localization accuracy, compared to analytical and learning-based baselines. Full article

Global outdoor furniture consumes large amounts of virgin wood and polyolefins, while multilayer beverage cartons and rice husks are often landfilled or burnt despite their polymeric and lignocellulosic value. This study aims to evaluate the feasibility of converting both waste streams into pilot-scale, wood-like boards for low-load urban furniture using an industrially relevant extrusion plus compression-moulding route, and to identify a balanced PolyAl–rice husk formulation. Hybrid composites based on recycled Tetra Pak PolyAl and ground rice husk were manufactured as full-thickness boards and characterised in terms of density, tensile and flexural behaviour, Shore D hardness, and moisture uptake. A preliminary UV screening was also performed using short-term narrow-band UVC irradiation at 254 nm, which should not be interpreted as outdoor weathering. Increasing rice husk content enhanced hardness and stiffness but increased water uptake, evidencing the expected stiffness–durability trade-off in lignocellulosic-filled systems. Overall, the intermediate 70PolyAl–30rice husk composition provided the most balanced performance for the targeted low-load applications, supporting an industrial symbiosis pathway that valorises two locally available residues into a potentially scalable product. Full article

Cities worldwide face profound morphological changes due to population growth and urban densification. Coupled with climate change, this exacerbates the Urban Heat Island (UHI) effect and degrades outdoor thermal comfort. This paper introduces a novel simulation framework for climate-resilient urban design, transitioning from static planning standards to dynamic performance optimization. This research utilizes a multi-tiered data acquisition strategy, beginning with a PRISMA-guided Systematic Literature Review of 133 articles to identify key UHI mitigation variables. A high-fidelity, multi-physics Computational Fluid Dynamics (CFD) model was developed using the ANSYS Fluent solver, discretized with a poly-hexacore mesh of over 78 million cells. The simulation environment integrates multiscale data, including 2.5D urban geometry from GIS platforms, high-resolution satellite information (e.g., Copernicus and LiDAR) for surface and soil properties, and EUMETSAT weather files for boundary conditions. The model explicitly resolves aerodynamic and thermodynamic exchanges using Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, with vegetation represented via porous-medium parameterization. The core novelty lies in the development of a parameterized library of “Architectural Elements” (AEs) that introduces standardized material properties, derived from Ansys Granta Selector, directly with GIS-based street designs. This allows for iterative “what-if” scenario analyses over critical 24 h periods to assess the synergistic impact of green infrastructure (GI) and advanced materials. Validation against real-world monitoring data from the Grow-Green project confirmed the model’s accuracy, with a maximum error of only 0.22%. The results demonstrate that interconnecting isolated green areas and utilizing local porous materials can reduce UHI spot temperatures by 2–4 °C while significantly lowering building energy consumption. Full article

Microbiological contamination in public buildings is closely linked to human presence, such as airborne bacteria, fungi, and particulate matter, which strongly influence indoor air quality (IAQ). This study examined the distribution of microorganisms in a museum building in relation to time of day, air-handling unit (AHU) type, and ventilation operating mode. Exhibition rooms without natural light relied entirely on a central heating, ventilation and air conditioning (HVAC) system. Microbiological contamination was assessed using Koch’s passive sedimentation method over a 24 h cycle for two AHUs (I and III) and selected rooms, while CO₂ levels were monitored as indicators of occupancy and ventilation demand in line with EN 16798-1:2019 and ASHRAE 62.1-2022. Although the demand-controlled ventilation system increased the outdoor air fraction from 40% to 70–100% during peak visitor density, localized increases in microbial contamination occurred. AHU I showed higher loads of Staphylococcus sp. and fungi, while AHU III exhibited pronounced fungal peaks influenced by elevated humidity from an open water reservoir. Psychrophilic bacteria reached 140–230 CFU·m⁻³, mesophilic bacteria 230–320 CFU·m⁻³, and fungi up to 740 CFU·m⁻³. Most CFU values remained below commonly referenced upper limits (<1000 CFU·m⁻³), but several peaks exceeded lower recommended thresholds, indicating a need for improvements. Enhanced filtration, humidity control, increased airflow during high occupancy, and reducing moisture sources in AHUs may mitigate microbial growth and improve IAQ in public buildings. Full article

Urban areas face rising risks from extreme heat due to climate change, intensifying thermal stress and exacerbating social inequalities. Urban climate refuges—cool, accessible indoor and outdoor public spaces that maintain their ordinary functions—are increasingly adopted as a local adaptation measure to protect vulnerable populations during heat events. This study aims to develop and test a SWOT–CAME analytical framework to evaluate and compare the maturity, equity, and implementation logic of urban climate refuge networks in three European cities with contrasting climates and governance traditions: Barcelona, Amsterdam, and Copenhagen. A qualitative multiple-case design is combined with a transparent indicator set (coverage, accessibility, and typology mix) derived from official municipal sources and planning documents. Results show differentiated pathways: Barcelona represents an institutionalized network model; Amsterdam illustrates an emerging coordinated public-health approach; and Copenhagen reflects an ecosystem-based orientation where green–blue infrastructure provides substantial passive cooling capacity but requires clearer heat-specific operational protocols. The discussion highlights the need for hybrid adaptation strategies that combine nature-based solutions with operational governance and targeted support for vulnerable groups. The paper concludes with a transferable framework for cities seeking to integrate climate refuges into resilience and climate-justice agendas. Full article

Climate change is altering the use of public open spaces in historical urban environments, compounded by urban heat island effects. Especially considering urban squares, rising temperatures increase health risks for outdoor users, particularly for vulnerable individuals (by, e.g., age and fragility). Rapid risk assessment under current and future climate scenarios can exploit integrated simulations to support the process, considering both real-world environments and Built Environment Typologies (BETs), which represent the recurring morphological, constructive, and material features of such urban squares. Simulation-based approaches can also support the assessment of mitigation strategies considering sustainability, reversibility, visual integration, and compatibility with the heritage. This work proposes a framework for simulation-based heat risk assessment of outdoor users under current and future (2050 and 2080) overheating scenarios and considers pre- and post-mitigation conditions of urban squares. Outdoor temperature conditions are simulated using ENVI-met, enabling the multiscale assessment of users’ heat stress and thresholds in exposure timings before critical dehydration. The approach is applied to two Italian historical urban squares in Bari and Naples, and to their associated BETs. The results highlight the framework’s capabilities in addressing the impact of climate scenarios and pre-/post-mitigation conditions, considering the local and global conditions of the urban squares. Moreover, the observed similarities between POSs and their corresponding BETs demonstrate that these archetypes can support preliminary risk assessments, providing decision makers with a rapid overview before adapting analyses and mitigation strategies to the specific characteristics of each urban square. Full article

Although Greece is a leading olive oil producer, research on olive tourism (OT) remains limited, restricting the development of evidence-based policies and strategies. This study utilises primary data from 55 qualitative interviews conducted with OT visitors across Greece in 2023 to examine the sociodemographic characteristics, visiting behaviours, motivations, and expectations of domestic OT participants. These visitors are primarily mature, highly educated individuals with medium to high income levels. Their main motivations include acquiring specialised knowledge, cultivating a personal interest in olive-related culture, and seeking connections with local and familial heritage. They prefer experiences that highlight the sociocultural and culinary aspects of olives and olive oil, especially those that facilitate the practical application of new knowledge. Interactivity, experimentation, social engagement, and outdoor activities are highly valued. Comparative analysis with OT studies from Spain, Portugal, and Italy reveals similarities in visitor demographics but also identifies notable differences in motivations and expectations. By addressing a significant research gap, these findings offer policymakers, tourism operators, and producers strategic guidance for OT development in Greece, as well as transferable insights useful to other olive-producing countries. The study also demonstrates the potential for well-designed OT initiatives to promote sustainable rural development, preserve cultural and environmental heritage, extend the tourism season, and strengthen local economies. Full article

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have matured significantly in structured and urban scenarios, their extension to outdoor natural domains introduces severe challenges, including dynamic vegetation, illumination variations, a lack of distinctive features, and degraded GNSS availability. Recent advances in Deep Learning have brought promising developments to VSLAM- and VI-SLAM-based pipelines, ranging from learned feature extraction and matching to self-supervised monocular depth prediction and differentiable end-to-end SLAM frameworks. Furthermore, emerging methods for adaptive sensor fusion, leveraging attention mechanisms and reinforcement learning, open new opportunities to improve robustness by dynamically weighting the contributions of camera and IMU measurements. This review provides a comprehensive overview of Visual and Visual–Inertial SLAM for UGVs in unstructured environments, highlighting the challenges posed by natural contexts and the limitations of current pipelines. Classic VI-SLAM frameworks and recent Deep-Learning-based approaches were systematically reviewed. Special attention is given to field robotics applications in agriculture and forestry, where low-cost sensors and robustness against environmental variability are essential. Finally, open research directions are discussed, including self-supervised representation learning, adaptive sensor confidence models, and scalable low-cost alternatives. By identifying key gaps and opportunities, this work aims to guide future research toward resilient, adaptive, and economically viable VSLAM and VI-SLAM pipelines, tailored for UGV navigation in unstructured natural environments. Full article

Identifying floating residual feed is a critical technology in recirculating aquaculture systems, aiding water-quality control and the development of intelligent feeding models. However, existing research is largely based on ideal indoor environments and lacks adaptability to complex outdoor scenarios. Moreover, current methods for this task often suffer from high computational costs, poor real-time performance, and limited recognition accuracy. To address these issues, this study first validates in outdoor aquaculture tanks that instance segmentation is more suitable than individual detection for handling clustered and adhesive feed residues. We therefore propose LMFF–YOLO, a lightweight multi-scale fusion feed segmentation model based on YOLOv8n-seg. This model achieves the first collaborative optimization of lightweight architecture and segmentation accuracy specifically tailored for outdoor residual feed segmentation tasks. To enhance recognition capability, we construct a network using a Context-Fusion Diffusion Pyramid Network (CFDPN) and a novel Multi-scale Feature Fusion Module (MFFM) to improve multi-scale and contextual feature capture, supplemented by an efficient local attention mechanism at the backbone’s end for refined local feature extraction. To reduce computational costs and improve real-time performance, the original C2f module is replaced with a C2f-Reparameterization vision block, and a shared-convolution local-focus lightweight segmentation head is designed. Experimental results show that LMFF–YOLO achieves an mAP50 of 87.1% (2.6% higher than YOLOv8n-seg), enabling more precise estimation of residual feed quantity. Coupled with a 19.1% and 20.0% reduction in parameters and FLOPs, this model provides a practical solution for real-time monitoring, supporting feed waste reduction and intelligent feeding strategies. Full article

Natural ventilation is a common cooling strategy in open dairy barns, but its efficiency largely depends on external wind directions and speeds. Misalignment between external airflow and fan jets often led to non-uniform air distribution, reduced local cooling efficiency, and an elevated risk of heat stress in cows. However, few studies have systematically examined the combined effects of wind directions and speeds on airflow and heat dissipation. Most research isolates natural or mechanical ventilation effects, neglecting their interaction. Accurate computational fluid dynamics (CFD) modeling of the coupling between outdoor and indoor airflow is crucial for designing and evaluating mixed ventilation systems in dairy barns. To address this gap, this study systematically analyzed the effects of external wind directions (0°, 45°, 90°, 135°, 180°) and speeds (1, 3, 5, 7, 10 m s⁻¹) on fan jet distribution and convective heat transfer around dairy cows using the open-source CFD platform OpenFOAM. By evaluating body surface airflow and regional convective heat transfer coefficients (CHTCs), this study quantitatively linked barn-scale airflow to animal heat dissipation. Results showed that both wind directions and speeds markedly influenced airflow and heat exchange. Under 0° wind direction, dorsal airflow reached 6.2 m s⁻¹ and CHTCs increased nearly linearly with wind speeds, indicating strong synergy between the fan jet and external wind. Crosswinds (90° wind direction) enhanced abdominal airflow (approximately 5.2 m s⁻¹), whereas oblique and opposing winds (135–180°) caused stagnation and reduced convection. The dorsal-to-abdominal CHTCs ratio ($R_{d/a}$) increased to about 1.6 under axial winds but decreased to 1.1 under cross-flow, reflecting reduced thermal asymmetry. Overall, combining axial and lateral airflow paths improves ventilation uniformity in naturally or mechanically ventilated dairy barns. The findings provide theoretical and technical support for optimizing ventilation design, contributing to energy efficiency, animal welfare, productivity, and the sustainable development of dairy farming under changing climatic conditions. Full article

The adoption of novel technologies such as Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) operations remains limited. As a result, their full potential is not yet realized. Although UAVs have been deployed on an ad hoc basis, typically under manual control by dedicated operators, assisted and fully autonomous configurations remain largely unexplored. In this study, three SAR frameworks are systematically evaluated within a unified benchmarking framework: conventional ground missions, UAV-assisted missions, and fully autonomous UAV operations. As the key performance indicator, the target localization time was quantified and used as the means of comparison amongst frameworks. The conventional and assisted frameworks were experimentally tested through physical hardware in a controlled outdoor setting, wherein simulated callouts occurred via rescue teams. The autonomous swarm framework was simulated in the form of a multi-agent Reinforcement Learning (RL) method via the use of the Proximal Policy Optimization (PPO) algorithm. This enabled the optimization of the decentralized cooperative actions that could occur for efficient exploration of a partially observed three-dimensional environment. Our results demonstrated that the autonomous swarm significantly outperformed the conventional and assisted approaches in terms of speed and coverage. Finally, a detailed depiction of the framework’s integration into an operational system is provided. Full article

The heating load of residential buildings is closely related to the local microclimate. However, there is a lack of quantitative indicators for assessing the impact of the outdoor microclimate on building heating loads in Lhasa residential buildings. This study established an analytical relationship between surface temperature and building heating load through theoretical derivation. Simulations of the outdoor microclimate and building surface temperatures were conducted using Phoenics2019 and Ladybug1.8.0 tools. Statistical models were developed to correlate outdoor microclimate parameters with the surface temperatures of both transparent and opaque building envelopes. Ultimately, these individual models were integrated to form a comprehensive framework for directly calculating heating loads from microclimate data. The model validation results indicate that the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) is 12.87%, which meets the ASHRAE Guideline 14 international standard requirement of ≤30% for hourly data. The Normalized Mean Bias Error (NMBE) is –9.76%, also satisfying the ASHRAE Guideline 14 criterion of ±10% for hourly data. These results suggest that the model exhibits a minor underestimation, which is acceptable from an engineering perspective. The proposed model can provide a quantitative reference to a certain extent for the comprehensive evaluation of outdoor microclimate environmental performance in residential buildings in Lhasa. Full article