Search Results (310)

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

Earth–air heat exchangers (EAHXs) use the soil’s thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling with no or very little need for resources other than those used during the EAHX construction, an obvious advantage compared to the significant operational costs of refrigeration machines. Contrary to the streamlined process applied in conventional HVAC design (using refrigeration machines), EAHX design lacks straightforward and well-established rules; moreover, EAHXs struggle to achieve office room design cooling demands determined with conventional indoor thermal environment standards, hindering designers’ confidence and the wider adoption of EAHXs for standalone room cooling. This paper presents a graph-based method to assist in the design of a large-diameter EAHX. One year of post-occupancy monitoring data are used to evaluate this method and to investigate the performance of a large-diameter EAHX with up to 16,000 m³/h design airflow rate. Considering an adaptive standard for thermal comfort, peak EAHX cooling capacity of 28 kW (330 kWh/day, with just 50 kWh/day of fan electricity consumption) and office room load extraction of up to 22 kW (49 W/m²) provided evidence in support of standalone use of EAHX for room cooling. A fair fit between actual EAHX thermal performance and results obtained with the graph-based design method support the use of this method for large-diameter EAHX design. Full article

Outdoor sweeping is a tedious and labor-intensive task essential for maintaining the cleanliness of public spaces such as gardens and parks. Robots have been developed to address the limitations of traditional methods. Coverage Path Planning (CPP) is a critical function for these robots. However, existing CPP methods often perform poorly in large environments, where such robots are typically deployed. This paper proposes a novel CPP framework for outdoor sweeping robots operating in expansive outdoor areas, defined as environments exceeding 1000 square meters in size. The framework begins by decomposing the environment into smaller sub-regions. The sequence in which these sub-regions are visited is then optimized by formulating the problem as a Travelling Salesman Problem (TSP), aiming to minimize travel distance. Once the visiting sequence is determined, a boustrophedon-based CPP is applied within each sub-region. We analyzed two decomposition strategies, Voronoi-based and grid-based, and evaluated three TSP optimization techniques: local search, record-to-record travel, and simulated annealing. This results in six possible combinations. Simulation results demonstrated that Voronoi-based decomposition achieves higher area coverage (average coverage of 95.6%) than grid-based decomposition (average coverage 52.8%). For Voronoi-based methods, local search yielded the shortest computation time, while simulated annealing achieved the lowest travel distance. We have also conducted hardware experiments to validate the real-world applicability of the proposed framework for efficient CPP in outdoor sweeping robots. The robot hardware experiment achieved 84% coverage in a 19 m × 17 m environment. Full article

A temporal monitoring of monumental buildings in calcarenite, exposed outdoors in the considered Mediterranean environment of Southern Italy, was performed using XPS, the surface-specific technique. The methodology adopted to monitor the surfaces interacting with atmospheric agents and biotic/abiotic pollutants involved progressive sampling, extended to about five years, from the walls of a new building, specifically installed in the immediate vicinity of an ancient farmhouse in an advanced state of degradation. Taking the ancient building as the final temporal reference, the aim was to obtain adequate information on the degradation processes of calcarenitic stones, from the initial and evolving phases of the new building towards those representative of the old reference. A large set of XPS data was obtained by resolving, through curve-fitting, the acquired spectra into component peaks, identified as ‘indicator’ chemical groups, which trend as a function of time, supported by PCA, demonstrates a close compositional similarity between the samples of the new building analyzed after 52 months from its installation and those of the ancient building dating back to over a century ago. The results obtained can be considered in the diagnostic strategy of the ongoing PNRR programs dedicated to the care of historical monuments and ecosystem sustainability. Full article

Like other mega cities in China, Beijing is undergoing a large-scale urban renewal process. However, in the context of global warming and the goal of promoting human health and well-being, urban renewal should follow the principle of minimal intervention, draw inspiration from the condition of the climate and environment itself, and pursue the goal of common health and development between humans and non-human beings. This study takes the Panjiayuan Antique Market as the research object. Unlike previous studies that focused on the behavior patterns of vendors and buyers, this study focuses on the increase in users’ expectation on environmental thermal comfort when the Panjiayuan Antique Market transforms from a conventional commercial market into an urban public space. This study aimed to find a minimal intervention strategy suitable for urban public space renewal from the perspective of the microclimate, encouraging people to use outdoor public spaces more, thereby promoting physical and mental health, as well as social well-being. We used a mixed-methods approach comprising microclimate measurements, questionnaires (n = 254), and field measurements. Our results show that the mean radiant temperature (T_mrt) is the key factor that affects thermal comfort, and it is a comprehensive concept that is associated with other microclimate factors. Linking the quantitative sun-related factors, such as the solar position angle (SAA), the shadow area ratio (SAR), and direct sun hours (DSHs), we also found that the correlation between the T_mrt and physical spatial characteristics, such as the ratio of the visible sky (SVF), the aspect ratio (H/W), and orientation of the building layout, helped us to generate design strategies oriented by regulating microclimate, such as controlling thermal mass/radiant heating, solar radiation, and air convection. One of the significances of this study is its development of a design method that minimizes intervention in urban public spaces from the perspective of regulating the microclimate. In addition, this study proposes a new perspective of promoting people’s health and well-being by improving outdoor thermal comfort. Full article

In this study, we introduce an Unmanned Aerial Vehicle (UAV) centric visual semantic simultaneous localization and mapping (SLAM) framework that integrates RGB–D cameras, inertial measurement units (IMUs), and a 5G–enabled remote interaction module. Our system addresses three critical limitations in existing approaches: (1) Distance constraints in remote operations; (2) Static map assumptions in dynamic environments; and (3) High–dimensional perception requirements for UAV–based applications. By combining YOLO–based object detection with epipolar–constraint-based dynamic feature removal, our method achieves real-time semantic mapping while rejecting motion artifacts. The framework further incorporates a dual–channel communication architecture to enable seamless human–in–the–loop control over UAV–Unmanned Ground Vehicle (UGV) teams in large–scale scenarios. Experimental validation across indoor and outdoor environments indicates that the system can achieve a detection rate of up to 75 frames per second (FPS) on an NVIDIA Jetson AGX Xavier using YOLO–FASTEST, ensuring the rapid identification of dynamic objects. In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. The dual-channel communication architecture (Web Real–Time Communication (WebRTC) for video and Message Queuing Telemetry Transport (MQTT) for telemetry) reduces bandwidth consumption by 65% compared to traditional TCP–based protocols. Moreover, our hybrid dynamic feature filtering can reject 89% of dynamic features in occluded scenarios, guaranteeing accurate mapping in complex environments. Our framework represents a significant advancement in enabling intelligent UAVs/UGVs to navigate and interact in complex, dynamic environments, offering real-time semantic understanding and accurate localization. Full article

Unbounded dynamic scene reconstruction is crucial for applications such as autonomous driving, robotics, and virtual reality. However, existing methods struggle to reconstruct dynamic scenes in unbounded outdoor environments due to challenges such as lighting variation, object motion, and sensor limitations, leading to inaccurate geometry and low rendering fidelity. In this paper, we proposed Gaussian-UDSR, a novel 3D Gaussian-based representation that efficiently reconstructs and renders high-quality, unbounded dynamic scenes in real time. Our approach fused LiDAR point clouds and Structure-from-Motion (SfM) point clouds obtained from an RGB camera, significantly improving depth estimation and geometric accuracy. To address dynamic appearance variations, we introduced a Gaussian color feature prediction network, which adaptively captures global and local feature information, enabling robust rendering under changing lighting conditions. Additionally, a pose-tracking mechanism ensured precise motion estimation for dynamic objects, enhancing realism and consistency. We evaluated Gaussian-UDSR on the Waymo and KITTI datasets, demonstrating state-of-the-art rendering quality with an 8.8% improvement in PSNR, a 75% reduction in LPIPS, and a fourfold speed improvement over existing methods. Our approach enables efficient, high-fidelity 3D reconstruction and fast real-time rendering of large-scale dynamic environments, while significantly reducing model storage overhead. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

A project to assess air pollution at the National Archeological Museum in Naples was carried out. The main goal of the project was to develop and test a reliable yet simple monitoring system to be adopted at the same time in several exposition rooms. Nitrogen dioxide, hydrogen chloride, nitrous acid, and sulphur dioxide were the chemical species addressed by the technique. Monitoring was simultaneously performed in five rooms, and pollutant concentrations were determined using two passive samplers. The sampling time was approximately one month per period. In addition to passive samplers, environmental data loggers were used to obtain temperature and relative humidity data. Results show high concentrations of nitrogen dioxide inside rooms, which were consistent with those found in outdoor environments and are close to the values calculated considering the air exchange rates, estimated through time gradients of ambient temperature. The minimum values were recorded in a basement room that had a low ventilation rate. The conversion of nitrogen dioxide to real surfaces produces nitric acid and nitrous acid. Large amounts of nitrous acid, up to 15 µg/m³, were found in exposition rooms, with maximum values in the basement room, where the air exchange rate is limited, and the surface-to-volume ratio is the highest among the monitored rooms. Data analysis demonstrated that the system could discriminate between nitrous acid and nitrogen dioxide. The results show that, for the first time, passive samplers can overcome the problem of mutual interference between nitrogen-containing species. Nitrates and nitrites found in the alkaline passive sampler were generally found not to be interfered by nitrogen dioxide. Nitric acid was also found in the gas phase, likely generated by dissociation of ammonium nitrate in particulate matter. Hydrogen chloride and sulphur dioxide were present at few µg/m³. Nitrous acid is the most relevant acidic species found indoors. The presence of pollutants was discussed in terms of the reliability of the analytical procedure and its significance for indoor air pollution. Full article

The outdoor climate is expected to undergo significant and extreme changes. These changes may lead to increased building requirements depending on their location. This is critical, as human beings tend to spend a large part of their time inside buildings. Accordingly, it is crucial to take future conditions into account to ensure an adequate indoor climate, simultaneously meeting the current drive for decarbonization of the built environment. One avenue is opting for thermally efficient building products and technologies with a lower carbon footprint to guarantee a comfortable indoor climate while minimizing energy consumption. This study focuses on the Northeast region of Brazil, specifically its nine states, given the usage of specific passive thermal strategies in new buildings that have high compensatory energy consumption. This is achieved through developing computational thermal models of a housing unit in a multi-family building, commonly constructed in several cities in this region. This thermal model was employed to analyze indoor thermal comfort, energy consumption, and carbon footprint. To account for future climate projections, the analysis includes scenarios based on Representative Concentration Pathways 4.5 and 8.5. The efficiency of certain sustainable passive rehabilitation is demonstrated in this region, highlighting the importance of adopting passive and efficient thermal measures appropriate to the region’s climate. Full article

This study evaluated outdoor thermal comfort in commercial and residential mixed-use blocks in hot and humid climates. A subjective survey questionnaire examined thermal environment metrics, individual data, and 141 pedestrian responses. The findings indicated that the average air temperature (31.8 °C) and relative humidity (65.8%) of the four mixed-use blocks were considerably high. The thermal environment differed between each block owing to the influence of block texture and building form. In addition, subjective sensation scores differed among the blocks, aligning with subjective preferences, though subjective acceptability remained largely within a “neutral” range across all blocks. The relationship between thermal environment and subjective perception was intricate, as their patterns of variation were not merely characterized by simple positive or negative correlations but were influenced by a multitude of factors. Multiple linear regression analysis indicated that air temperature, relative humidity, and mean radiant temperature were crucial factors affecting subjective acceptability, all demonstrating statistical significance at p-value < 0.05. Furthermore, this study examined the effect of morphological features on thermal comfort, identifying texture density, street height-to-width ratio (D/H), and orientation strategy as significant factors. The research provides valuable insights into outdoor thermal comfort in mixed-use blocks and provides recommendations for enhancing thermal environment management. Full article

The ongoing and projected future climate change impacts the heating, cooling, and air-conditioning sectors both directly and indirectly. The consideration of heating, cooling, and energy degree-days is a consistent, robust, and widely used approach for quantitatively estimating the energy demand of closed environments based on outdoor thermal conditions. Hence, the spatial distribution and the long-term changes in this demand depend on on the quantity of final users for such services; it is essential to consider demographic data in the assessment. The paper presents a comprehensive analysis of the population-weighted degree-days for the near past and the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) scenario-driven future over Southeast Europe for all four ‘Tier 1’ Shared Socioeconomic Pathways (SSPs) based on the methodology of the United Kingdom Meteorological Office and performed using large NEX-GDDP CMIP6 ensemble of global circulation models (GCMs) and up to date population dynamics data from the NASA’s SEDAC. As an expression of regional warming tendencies, the study reveals an overall reduction in heating and an increase in cooling degree-days, confirming the leading role of the climate. We also provide evidences for the influence of the population factor, which significantly alters the region’s degree-day climatology in both space and time. The resulting overall picture on country-wide and regional level is complex; in some cases, the population dynamics is projected to outbalance the thermal-induced changes. Full article

LiDAR-based place recognition (LPR) is crucial for the navigation and localization of autonomous vehicles and mobile robots in large-scale outdoor environments and plays a critical role in loop closure detection for simultaneous localization and mapping (SLAM). Existing LPR methods, which utilize 2D bird’s-eye view (BEV) projections of 3D point clouds, achieve competitive performance in efficiency and recognition accuracy. However, these methods often struggle with capturing global contextual information and maintaining robustness to viewpoint variations. To address these challenges, we propose R2SCAT-LPR, a novel, transformer-based model that leverages self-attention and cross-attention mechanisms to extract rotation-robust place feature descriptors from BEV images. R2SCAT-LPR consists of three core modules: (1) R2MPFE, which employs weight-shared cascaded multi-head self-attention (MHSA) to extract multi-level spatial contextual patch features from both the original BEV image and its randomly rotated counterpart; (2) DSCA, which integrates dual-branch self-attention and multi-head cross-attention (MHCA) to capture intrinsic correspondences between multi-level patch features before and after rotation, enhancing the extraction of rotation-robust local features; and (3) a combined NetVLAD module, which aggregates patch features from both the original feature space and the rotated interaction space into a compact and viewpoint-robust global descriptor. Extensive experiments conducted on the KITTI and NCLT datasets validate the effectiveness of the proposed model, demonstrating its robustness to rotation variations and its generalization ability across diverse scenes and LiDAR sensors types. Furthermore, we evaluate the generalization performance and computational efficiency of R2SCAT-LPR on our self-constructed OffRoad-LPR dataset for off-road autonomous driving, verifying its deployability on resource-constrained platforms. Full article