Search Results (109)

Green roofs are effective passive strategies for enhancing building energy efficiency and indoor thermal comfort, particularly in response to climate change. This study presents an experimental and numerical assessment of an ultra-lightweight, soil-free green roof system for Mediterranean climates. In situ thermal monitoring was carried out on two identical test rooms in Rome (Italy), comparing the green roof to a traditional tiled roof under winter conditions. Results revealed a 45% reduction in thermal transmittance. These data were used to calibrate a dynamic TRNSYS 18 model and then applied to annual simulations of energy demand and indoor comfort across different roof configurations, including expanded polystyrene-insulated reference roofs. The model was calibrated in accordance with ASHRAE Guideline 14, achieving an MBE within ±10% and a CV(RMSE) within ±30% for hourly data, ensuring the simulation’s reliability. The green roof reduced cooling energy demand by up to 58.5% and heating demand by 11.6% relative to the uninsulated reference case. Compared to insulated roofs, it maintained similar winter performance while achieving summer operative temperature reductions up to 0.99 °C and PPD decreases up to 2.94%. By combining field measurements with calibrated simulations, this work provides evidence of the green roof’s effectiveness as a passive retrofit solution for Mediterranean buildings. Full article

Convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, driving the rapid development of hardware accelerators. However, memory efficiency remains a key challenge, as conventional accelerators adopt layer-by-layer processing, leading to frequent external memory accesses (EMAs) of intermediate feature data, which increase energy consumption and latency. While layer fusion has been proposed to enhance inter-layer feature reuse, existing approaches typically rely on fixed data management tailored to specific architectures, introducing on-chip memory overhead and requiring trade-offs with EMAs. Moreover, prevalent residual connections further weaken fusion benefits due to diverse data reuse distances. To address these challenges, we propose layer-centric tile fusion, which integrates residual data loading with feature merging by leveraging receptive field relationships among feature tiles. A reuse distance-aware caching strategy is introduced to support flexible storage for various data types. We also develop a modeling framework to analyze the trade-off between on-chip memory usage and EMA-induced energy-delay product (EDP). Experimental results demonstrate that our method achieves 5.04–43.44% EDP reduction and 20.28–58.33% memory usage reduction compared to state-of-the-art designs on ResNet-18 and SRGAN. Full article

Conventional health risk assessments do not adequately reflect short-term exposure characteristics following chemical accidents. We aimed to evaluate the efficacy of existing assessment methods and propose a more suitable risk assessment approach for short-term exposure to hazardous chemicals. We analyzed foundational studies used to derive reference concentration (RfC), reference dose (RfD), and minimal risk level (MRL) values and applied these health guidance values (HGVs) to a hypothetical chemical accident scenario. An analysis of the studies underlying each HGV revealed that, except for the RfC for formaldehyde and the RfD for toluene, all values were derived under research conditions comparable to their respective exposure durations. Given the differing toxicity mechanisms between acute and chronic exposures, MRLs that were aligned with the corresponding exposure durations supported more appropriate risk management decisions. The health risk assessment results showed that RfC/RfD-based hazard quotients (HQs) were consistently higher than MRL-based HQs across all age groups and both substances, indicating that RfC/RfD values tend to yield more conservative risk estimates. For formaldehyde, the use of RfC instead of MRL resulted in an additional 208 tiles (2.08 km²) being classified as areas of potential concern (HQ > 1) relative to the MRL-based evaluation. These findings highlighted that the selection of HGVs can significantly influence the spatial extent of areas of potential concern, potentially altering health risk determinations for large population groups. This study provides a scientific basis for improving exposure and risk assessment frameworks under short-term exposure conditions. It also serves as valuable foundational data for developing effective and rational risk management strategies during actual chemical accidents. To the best of our knowledge, this is the first study to apply MRLs to a short-term chemical accident scenario and directly compare them with traditional reference values. Full article

Accurate segmentation of kidney microstructures in whole slide images (WSIs) is essential for the diagnosis and monitoring of renal diseases. In this study, an end-to-end instance segmentation pipeline was developed for the detection of glomeruli and blood vessels in hematoxylin and eosin (H&E) stained kidney tissue. A tiling-based strategy was employed using Slicing Aided Hyper Inference (SAHI) to manage the resolution and scale of WSIs and the performance of two segmentation models, YOLOv11 and YOLOv12, was comparatively evaluated. The influence of tile overlap ratios on segmentation quality and inference efficiency was assessed, with configurations identified that balance object continuity and computational cost. To address object fragmentation at tile boundaries, an Enhanced Syncretic Mask Merging algorithm was introduced, incorporating morphological and spatial constraints. The algorithm’s hyperparameters were optimized using Particle Swarm Optimization (PSO), with vessel and glomerulus-specific performance targets. The optimization process revealed key parameters affecting segmentation quality, particularly for vessel structures with fine, elongated morphology. When compared with a baseline without postprocessing, improvements in segmentation precision were observed, notably a 48% average increase for glomeruli and up to 17% for blood vessels. The proposed framework demonstrates a balance between accuracy and efficiency, supporting scalable histopathology analysis and contributing to the Vasculature Common Coordinate Framework (VCCF) and Human Reference Atlas (HRA). Full article

Addressing the technical demands for the rapid, precise detection of earthquake-triggered landslides in loess tablelands, this study proposes and validates an innovative methodology integrating enhanced deep learning architectures with large-tile processing strategies, featuring two core advances: (1) a critical enhancement of YOLOv8’s shallow layers via a higher-resolution P2 detection head to boost small-target capture capabilities, and (2) the development of a large-tile segmentation–tile mosaicking workflow to overcome the technical bottlenecks in large-scale high-resolution image processing, ensuring both timeliness and accuracy in loess landslide detection. This study utilized 20 km² of high-precision UAV imagery acquired after the 2023 Gansu Jishishan Ms 6.2 earthquake as foundational data, applying our methodology to achieve the rapid detection and precise segmentation of landslides in the study area. Validation was conducted through a comparative analysis of high-accuracy 3D models and field investigations. (1) The model achieved simultaneous convergence of all four loss functions within a 500-epoch progressive training strategy, with mAP50(M) = 0.747 and mAP50-95(M) = 0.46, thus validating the superior detection and segmentation capabilities for the Jishishan earthquake-triggered loess landslides. (2) The enhanced algorithm detected 417 landslides with 94.1% recognition accuracy. Landslide areas ranged from 7 × 10⁻⁴ km² to 0.217 km² (aggregate area: 1.3 km²), indicating small-scale landslide dominance. (3) Morphological characterization and the spatial distribution analysis revealed near-vertical scarps, diverse morphological configurations, and high spatial density clustering in loess tableland landslides. Full article

Enteroviruses of the Picornaviridae family are transmitted primarily by the fecal–oral route. Transmission may occur following hand contact with contaminated fomites and subsequent ingestion of virus conveyed to the mouth by the contaminated hand. The persistence of these viruses on fomites likely plays a role in this transmission scenario. Six echoviruses (1, 2, 3, 5, 6, and 7) that cause frequently reported clinical cases in the United States were studied, along with poliovirus type 1 vaccine strain LSc-2ab. The infectivity half-lives of the enteroviruses deposited on vinyl tile coupons in a 10% fecal solution ranged from 1.7 to 12.6 h. The echovirus serotypes most commonly associated with reported infections persisted longer on the vinyl tiles than the less commonly reported types. This increased persistence on surfaces may favor the transmission of these echoviruses through the fecal–oral route. These results inform the future selection of appropriate model enteroviruses for challenging newly formulated and eco-friendly disinfectants or other strategies in infection prevention and control for enteroviruses. Full article

This study examines the architectural ceramic corpus—comprising azulejos (tiles) and alicatados (tiling mosaics)—preserved in the Alhambra Museum, with the aim of elucidating its historical, artistic, and technical significance. Through a systematic methodology combining visual analysis, documentary research, and typological classification, a representative selection of ceramic artefacts was assessed. This article explores the artistic characteristics and technological principles of pieces produced using painted, relief, metallic lustre, incrustación, alicatado, cuerda seca, and arista techniques and reconstructs the historical trajectory of these decorative practices, tracing their origins in the pre-Islamic world to their adaptation within the Alhambra Palatine City. This diachronic perspective contextualises the innovations observed in the citadel, where production strategies reflect both inherited traditions and local adaptations across different historical phases. The findings highlight the richness and diversity of the Nasrid (mediaeval era) and Christian (modern era) ceramic legacy in the Alhambra and contribute to a more nuanced understanding of manufacturing processes and conservation challenges associated with these architectural elements. This preliminary characterisation establishes a basis for future material analysis and supports broader initiatives in documentation and heritage management. Full article

Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous shape and color. Currently, manual flower counting is the only available method, but it is time-consuming and prone to errors. In this study, a novel vision-based method for automatic flower counting specifically designed for hazelnut plants (Corylus avellana) exploiting a commercial high-resolution imaging system and an image-tiling strategy to enhance small-object detection is proposed. The method is designed to be fast and scalable, requiring less than 8 s per plant for processing, in contrast to 30–60 min typically required for manual counting by human operators. A dataset of 2000 labeled frames was used to train and evaluate multiple female hazelnut flower detection models. To improve the detection of small, low-contrast flowers, a modified YOLO11x architecture was introduced by adding a P2 layer, improving the preservation of fine-grained spatial information and resulting in a precision of 0.98 and a Mean Average Precision (mAP@50-95) of 0.89. The proposed method has been validated on images collected from hazelnut groves and compared with manual counting by four experienced operators in the field, demonstrating its ability to detect small, low-contrast flowers despite occlusions and varying lighting conditions. A regression-based bias correction was applied to compensate for systematic counting deviations, further improving accuracy and reducing the mean absolute percentage error to 27.44%, a value comparable to the variability observed in manual counting. The results indicate that the system can provide a scalable and efficient alternative to traditional female flower manual counting methods, offering an automated solution tailored to the unique challenges of hazelnut yield estimation. Full article

Lightweight, high-temperature thermal insulation materials play a critical role in aerospace applications, where extreme temperature conditions necessitate lightweight, high-performance solutions. This paper explores advancements in lightweight, high-temperature insulation materials specifically designed for aerospace environments, focusing on innovative flexible ceramic fiber felts, thermal insulation tiles, nano-insulation materials (aerogels), and multilayer insulations (MLIs). These materials exhibit superior thermal resistance, low density, and durability under dynamic and harsh conditions. Key developments include the integration of nanostructures to enhance thermal conductivity control and improve mechanical stability. This paper also highlights applications in spacecraft thermal protection systems, providing insights into the challenges of future material design strategies. These advancements underscore the growing potential of thermal insulations to improve energy efficiency, safety, and performance in aerospace missions. Full article

Stimulated Raman Scattering (SRS) nanoscopy imaging offers unprecedented insights into tissue molecular architecture but often requires stitching multiple high-resolution tiles to capture large fields of view. This process is time-consuming and frequently introduces vignetting artifacts—grid-like intensity fluctuations that degrade image quality and hinder downstream quantitative analyses and processing such as super-resolution deconvolution. We present VIgnetted Stitched-Tile Adjustment using Morphological Adaptive Processing (VISTAmap), a simple tool that corrects these shading artifacts directly on the final stitched image. VISTAmap automatically detects the tile grid configuration by analyzing intensity frequency variations and then applies sequential morphological operations to homogenize the image. In contrast to conventional approaches that require increased tile overlap or pre-acquisition background sampling, VISTAmap offers a pragmatic, post-processing solution without the need for separate individual tile images. This work addresses pressing concerns by delivering a robust, efficient strategy for enhancing mosaic image uniformity in modern nanoscopy, where the smallest details make tremendous impacts. Full article

The paper presents an efficient light field image synthesis method based on single-viewpoint images, which can directly generate high-quality light field images from single-viewpoint input images. The proposed method integrates light field image encoding with the tiled rendering technique of 3DGS. In the construction of the rendering pipeline, a viewpoint constraint strategy is adopted to optimize rendering quality, and a sub-pixel rendering strategy is implemented to improve rendering efficiency. Experimental results demonstrate that 8K light field images with 96 viewpoints can be generated in real time from end to end. The research presented in the paper provides a new approach for the real-time generation of high-resolution light field images, advancing the application of light field display technology in low-cost environments. Full article

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low computational efficiency in current research. First, a set of safety evaluation rules is constructed by considering factors such as terrain slope, roughness, illumination, and rock abundance. Second, a distributed path-planning strategy based on a safety-map tile pyramid (DPPS-STP) is proposed, using a weighted A* algorithm with hash table-based open and closed lists (OC-WHT-A*) on a Spark cluster for efficient and safer path planning. Additionally, high-resolution digital orthophoto maps (DOM) are utilized for small crater detection, enabling more refined path planning built upon the overall mission-planning result. The method was validated in four lunar regions with distinct characteristics. The results show that DPPS-STP, which considers multiple environmental factors, effectively reduces the number of hazardous nodes and avoids crater obstacles. For long-distance tasks, it achieves an average speedup of up to 11.5 times compared to the single-machine OC-WHT-A*, significantly improving computational efficiency. Full article

Liver cancer is a major global health challenge, significantly contributing to mortality rates. The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liver shape variability, proximity to other organs, low contrast between tumors and healthy tissues, and unclear lesion boundaries. To address these challenges, we propose the Deep Residual Dual-Attention Network (DRDA-Net), a novel model for end-to-end liver and tumor segmentation. DRDA-Net integrates a Residual UNet architecture with dual-attention mechanisms, multi-scale tile and patch extraction, and an Ensemble method. The dual-attention mechanisms enhance focus on key regions, addressing variations in size, shape, and intensity, while the multi-scale approach captures fine details and broader contexts. Additionally, we introduce a unique pre-processing pipeline employing a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. Evaluated on the 3DIRCADb dataset, DRDA-Net achieved Dice scores of 97.03% and 75.4% for liver and tumor segmentation, respectively, outperforming state-of-the-art methods. These results demonstrate the model’s effectiveness in overcoming segmentation challenges and highlight its potential to improve liver cancer diagnostics and treatment planning. Full article

The construction sector is one of the industries with the highest environmental impact due to resource consumption and waste generation. Material waste exacerbates these impacts by increasing carbon emissions and energy consumption. This study introduces an innovative approach by integrating Life Cycle Assessment (LCA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate material waste and environmental impacts simultaneously. By analyzing scenarios of material use in the design and construction phases, this study explores their effects on material efficiency and environmental performance while addressing a notable research gap. Existing studies on the integration of LCA and TOPSIS in evaluating material waste and its environmental impacts remain limited. This research not only demonstrates the applicability of these methods but also contributes to filling this gap. Material waste and efficiency were assessed through Building Information Modeling (BIM), while BIM-LCA integration was used to evaluate environmental impacts. The findings were examined in two stages: LCA and TOPSIS. The TOPSIS analysis considered two scenarios—material waste and environmental impacts. In the first scenario, cast-in-place concrete (5000 psi) and stone and ceramic tiles emerged as priorities. In the second scenario, where carbon emissions and environmental impacts were emphasized, cast-in-place concrete (5000 psi), laminated timber, and stone tiles were identified as critical materials. The results reveal that reducing material waste significantly enhances environmental performance, lowers costs, and promotes sustainability. These findings provide practical insights for developing sustainable strategies in diverse cultural and geographical contexts, particularly for residential projects. The integration of LCA and TOPSIS offers a robust decision-making framework, enabling targeted actions to minimize environmental footprints across all life cycle stages. This study contributes to the literature by providing actionable recommendations for optimizing resource use and improving sustainability in construction practices. Full article

Located in the heart of Porto, Portugal, the S. Bento train station is renowned worldwide for its architectural splendour and historical significance. Inaugurated in 1916, this UNESCO World Heritage Site presents stunning ceramic tile panels and architecture influenced by contemporary French design. This study presents a comprehensive historical analysis of the conservation state of S. Bento station, detailing observed anomalies, their origins, probable causes, and the maintenance and rehabilitation techniques employed over the years. Moreover, it explores the relationship between conservation practices and tourist perceptions of the station, focusing on how rehabilitation efforts influence user satisfaction. This analysis was carried out through a comprehensive sentiment analysis of over 4000 tourist reviews between 2011 and 2023, and data from the station management entity, providing insights into the effectiveness of these interventions. The research contributes to the broader discussion on heritage conservation, offering recommendations for future maintenance strategies that integrate user expectations and sentiment. Full article