Search Results (1,756)

High-rise building facades offer an alternative site for installing photovoltaic panels, which are traditionally placed on rooftops. The unique spatial configuration of high-rise buildings, characterized by a small footprint area relative to their height, supports the application of vertical facades for this purpose. Photovoltaic panels installed in these areas not only generate electricity but also enhance the aesthetic dimension of the urban landscape. The proposed methodology uses the EnergyPlus software to simulate the electricity generation of photovoltaic panels mounted on the walls of high-rise buildings in the city of Kragujevac, Serbia. A technical, economic, and environmental analysis was conducted for two scenarios: (1) photovoltaic panels installed on two facade areas with the highest solar potential, and (2) photovoltaic panels installed on all four available facade areas. In Scenario 1, the annual reduction in electricity consumption, annual cost savings in electricity consumption, and investment payback period range from 13 to 38%, 11 to 31%, and 8.4 to 10.6 years, respectively. In Scenario 2, these values range from 23 to 58%, 18 to 47%, and 10.9 to 12.9 years, respectively. The results indicate that southeast and southwest facades consistently achieve higher levels of electricity generation, underscoring the importance of prioritizing high-performing orientations rather than maximizing overall surface coverage. The methodology is particularly efficient for analyzing the solar potential of numerous buildings with comparable shapes, which is a characteristic commonly found in Eastern European architecture from the late 20th century. The study demonstrates the applicability of the proposed methodology as a practical and adaptable tool for assessing early-stage solar potential and providing decision support in urban energy planning. The approach addresses the identified methodological gap by offering a low-cost, flexible framework for assessing solar potential across diverse urban contexts and building typologies, while significantly simplifying the modeling process. Full article

Semantic segmentation of building facade images has enabled a lot of intelligent support for architectural research and practice in the last decade. However, the classifiers for semantic segmentation usually predict facade elements (e.g., windows) as graphics in irregular shapes. The non-smooth edges and hard-to-define shapes impede the further use of the predicted graphics. This study proposes a method to regularize the predicted graphics following the prior knowledge of composition principles of building facades. Specifically, we define four types of boxes for each predicted graphic, namely minimum circumscribed box (MCB), maximum inscribed box (MIB), candidate box (CB), and best overlapping box (BOB). Based on these boxes, a three-stage process, consisting of denoising, BOB finding, and BOB stacking, was established to regularize the predicted graphics of facade elements into basic rectilinear polygons. To compare the proposed and existing methods of graphic regularization, an experiment was conducted based on the predicted graphics of facade elements obtained from four pixel-wise annotated building facade datasets, Irregular Facades (IRFs), CMP Facade Database, ECP Paris, and ICG Graz50. The results demonstrate that the graphics regularized by our method align more closely with real facade elements in shape and edge. Moreover, our method avoids the prevalent issue of correctness degradation observed in existing methods. Compared with the predicted graphics, the average IoU and F1-score of our method-regularized graphics respectively increase by 0.001–0.017 and 0.000–0.012 across the datasets, while those of previous method-regularized graphics decrease by 0.002–0.021 and 0.002–0.015. The regularized graphics contribute to improving the precision and depth of semantic segmentation-based applications of building facades. They are also expected to be useful for the exploration of data mining on urban images in the future. Full article

The composition, architecture, and plant traits of temperate steppe communities are intricately associated with environmental factors. However, most studies primarily focus on aboveground observations, often overlooking the critical role of belowground root systems. Here we conducted a field survey at a large-regional scale to investigate the composition of temperate steppe communities and plant root traits. Cluster analysis, correspondence analysis and Pearson correlation coefficient matrix method were employed to classify vegetation associations based on plant community composition and root traits. The principal driving and limiting factors shaping plant root communities were systematically investigated. The results showed that the temperate steppe was categorized into three community subtypes: meadow steppe, typical steppe, and desert steppe, comprising five plant groups and thirteen plant associations. The RLFS analysis, based on belowground architectural and functional traits, demonstrated a spatial gradient differentiation with three ecological adaptations: tufted herbs, rhizome herbs, and non-tufted or rhizome herbs. Key environmental driving factors for meadow steppe included precipitation, soil carbon, nitrogen, and phosphorus content, while the average growing-season temperature as a limiting factor. The environmental driving factors for the typical steppe were not apparent, and the limiting factor was water. For the desert steppe, the environmental driving factors were altitude and average growing-season temperature. These findings reveal notable spatial heterogeneity and a distinct distribution pattern in community composition and vegetation classification based on belowground root traits in the Inner Mongolia steppes. Full article

Traumatic brain injury (TBI) results in a cascade of neuropathological events, which can significantly disrupt synaptic integrity. This review explores the acute, subacute and chronic phases of synaptic dysfunction and loss in trauma which commence post-TBI, and their contribution to the subsequent neurological sequelae. Central to these disruptions is the loss of dendritic spines and impaired synaptic plasticity, which compromise neuronal connectivity and signal transmission. During the acute phase of TBI, mechanical injury triggers presynaptic glutamate secretion and Ca²⁺ ion-mediated excitotoxic injury, accompanied by cerebral edema, mitochondrial dysfunction and the loss of the mushroom-shaped architecture of the dendritic spines. The subacute phase is marked by continued glutamate excitotoxicity and GABAergic disruption, along with neuroinflammatory pathology and autophagy. In the chronic phase, long-term structural remodeling and reduced synaptic densities are evident. These chronic alterations underlie persistent cognitive and memory deficits, mood disturbances and the development of post-traumatic epilepsy. Understanding the phase-specific progression of TBI-related synaptic dysfunction is essential for targeted interventions. Novel therapeutic strategies primarily focus on how to effectively counter acute excitotoxicity and neuroinflammatory cascades. Future approaches may benefit from boosting synaptic repair and modulating neurotransmitter systems in a phase-specific manner, thereby mitigating the long-term impact of TBI on neuronal function. Full article

This paper investigates the application of image segmentation techniques in endodontics, focusing on improving diagnostic accuracy and achieving faster segmentation by delineating specific dental regions such as teeth and root canals. Deep learning architectures, notably 3D U-Net and GANs, have advanced the image segmentation process for dental structures, supporting more precise dental procedures. However, challenges like the demand for extensive labeled datasets and ensuring model generalizability remain. Two semi-automatic segmentation workflows, Grow From Seeds (GFS) and Watershed (WS), were developed to provide quicker acquisition of ground truth training data for deep learning models using 3D Slicer software version 5.8.1. These workflows were evaluated against a manual segmentation benchmark and a recent dental segmentation automated tool on three separate datasets. The evaluations were performed by the overall shapes of a maxillary central incisor and a maxillary second molar and by the region of the root canal of both teeth. Results from Kruskal–Wallis and Nemenyi tests indicated that the semi-automated workflows, more often than not, were not statistically different from the manual benchmark based on dice coefficient similarity, while the automated method consistently provided significantly different 3D models from their manual counterparts. The study also explores the benefits of labor reduction and time savings achieved by the semi-automated methods. Full article

High parasitic capacitance from poly-insulator-poly capacitors in complementary metal oxide semiconductor (CMOS) processes presents a major bottleneck to achieving high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) in imaging systems. This study proposes a Phase-Adjustable SAR ADC that addresses this limitation through a reconfigurable architecture. The design utilizes a phase-adjustable logic unit to switch between a conventional SAR mode for high-speed operation and a noise-shaping (NS) SAR mode for high-resolution conversion, actively suppressing in-band quantization noise. An improved SAR logic unit facilitates the insertion of an adjustable phase while concurrently achieving an 86% area reduction in the core logic block. A prototype was fabricated and measured in a 0.35-µm CMOS process. In conventional mode, the ADC achieved a 7.69-bit effective number of bits at 2 MS/s. By activating the noise-shaping circuitry, performance was significantly enhanced to an 11.06-bit resolution, corresponding to a signal-to-noise-and-distortion ratio (SNDR) of 68.3 dB, at a 125 kS/s sampling rate. The results demonstrate that the proposed architecture effectively leverages the trade-off between speed and accuracy, providing a practical method for realizing high-performance ADCs despite the inherent limitations of non-ideal passive components. Full article

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

High-resolution remote sensing imagery is crucial for ship extraction in ocean-related applications. Existing object detection and semantic segmentation methods for ship extraction have limitations: the former cannot precisely obtain ship shapes, while the latter struggles with small targets and complex backgrounds. This study addresses these issues by constructing two datasets, DIOR_SHIP and LEVIR_SHIP, using the SAM model and morphological operations. A novel MambaSegNet is then designed based on the advanced Mamba architecture. It is an encoder–decoder network with MambaLayer and ResMambaBlock for effective multi-scale feature processing. The experiments conducted with seven mainstream models show that the IOU of MambaSegNet is 0.8208, the Accuracy is 0.9176, the Precision is 0.9276, the Recall is 0.9076, and the F1-score is 0.9176. Compared with other models, it acquired the best performance. This research offers a valuable dataset and a novel model for ship extraction, with potential cross-domain application prospects. Full article

Architectural heritage, as a highly symbolized medium of cultural expression, plays a vital role in transmitting collective memory and shaping intercultural tourism experiences. Yet, how visitors from diverse cultural backgrounds perceive and emotionally respond to Chinese architectural symbols remains insufficiently understood. This study addresses this gap by integrating architectural semiotics with cross-cultural psychology to examine perceptual differences across three visitor groups—Mainland China and Hong Kong/Macau/Taiwan (C), East and Southeast Asia (A), and Europe/North America (UA)—at eleven representative Chinese heritage sites. Drawing on 235 in-depth interviews and 1500 online reviews, a mixed-methods design was employed, combining semantic network analysis, grounded theory coding, and affective clustering. The findings reveal that memory structures and cultural contexts shape symbolic perception, that cultural dimensions and affective orientations drive divergent emotional responses, and that interpretive pathways of architectural symbols vary systematically across groups. Specifically, Group C emphasizes collective memory and identity, and Group A engages through structural analogies and regional resonance, while Group UA favors aesthetic form and immersive experiences. These insights inform culturally adaptive strategies for heritage presentation, including memory-anchored curation, comparative cross-regional interpretation, and immersive digital storytelling. By advancing a micro-level model of “architectural symbol–perceptual theme–emotional response–perceptual mechanism,” this research not only enriches theoretical debates on cross-cultural heritage perception but also offers practical guidance for inclusive and resonant heritage interpretation in a global tourism context. Full article

Computational fluid dynamics and machine learning (ML) models are employed to investigate the relationships between scaffold topology and key flow parameters, including permeability (k), average wall shear stress ($WS{S}_a$), and the 25th and 75th percentiles of WSS. Statistical analysis showed that $WS{S}_a$ values are consistent with those found in common scaffold architectures, while percentile-based WSS properties provided insight into shear environments relevant for bone and cartilage differentiation. No significant effect of pore shape was observed on k and $WS{S}_a$. Correlation analysis revealed that k was positively associated with topological features of the scaffold, whereas $WSS$ metrics were negatively correlated with these properties. ML models trained on six topological and flow inputs achieved a performance of $R^2$ above 0.9 for predicting k and $WS{S}_a$, demonstrating strong predictive capability based on the topology. Their performance decreased for $WS{S}_{25\%}$ and $WS{S}_{75\%}$, reflecting the difficulty in capturing more specific shear events. These findings highlight the potential of ML to guide scaffold design by linking topology to flow conditions critical for osteogenesis. Full article

Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to the typically small and densely distributed characteristics of this disease, its identification poses considerable challenges. In this study, we introduce YOLO-MSPM, a lightweight and accurate detection framework, designed on the YOLOv11 architecture to efficiently identify cotton Verticillium wilt. In order to achieve a lightweight model, MobileNetV4 is introduced into the backbone network. Moreover, a single-head self-attention (SHSA) mechanism is integrated into the C2PSA block, allowing the network to emphasize critical areas of the feature maps and thus enhance its ability to represent features effectively. Furthermore, the PC3k2 module combines pinwheel-shaped convolution (PConv) with C3k2, and the mobile inverted bottleneck convolution (MBConv) module is incorporated into the detection head of YOLOv11. Such adjustments improve multi-scale information integration, enhance small-target recognition, and effectively reduce computation costs. According to the evaluation, YOLO-MSPM achieves precision (0.933), recall (0.920), mAP50 (0.970), and mAP50-95 (0.797), each exceeding the corresponding performance of YOLOv11n. In terms of model lightweighting, the YOLO-MSPM model has 1.773 M parameters, which is a 31.332% reduction compared to YOLOv11n. Its GFLOPs and model size are 5.4 and 4.0 MB, respectively, representing reductions of 14.286% and 27.273%. The study delivers a lightweight yet accurate solution to support the identification and monitoring of cotton Verticillium wilt in environments with limited resources. Full article

The growing need for sustainable materials in architecture has sparked significant interest in natural-fiber-based composites. Among these, coconut coir, a by-product of the coconut industry, has emerged as a promising raw material owing to its abundance, renewability, and excellent mechanical properties. The promise of coir-based composites in architecture is highlighted in this review, which also looks at their problems, advantages for the environment, manufacturing processes, and mechanical, thermal, and acoustic performances. The fibrous shape of the coir provides efficient thermal and acoustic insulation, while its high lignin concentration guarantees stiffness, biological resistance, and dimensional stability. Fiber-matrix adhesion and durability have improved owing to advancements in treatment and environmentally friendly binders, opening up the use of cement, polymers, and hybrid composites. In terms of the environment, coir composites promote a biophilic design, reduce embodied carbon, and decrease landfill waste. Moisture sensitivity, inconsistent fiber quality, and production scaling are obstacles; however, advancements in hybridization, grading, and nanotechnology hold promise. This review provides comprehensive, architecture-focused review that integrates material science, fabrication techniques, and real-world architectural applications of coir-based composites. Coir-based composites have the potential to be long-lasting, sustainable substitutes for conventional materials in climate-resilient architectural design if they are further investigated and included in green certification programs and the circular economy. Full article

This article examines the entangled relationship between hagiographic memory, liturgical space, and sacred landscape in the rock-cut monastic settlements of Cappadocia. Drawing on archeological, iconographic, and acoustic analyses, this article argues that the morphology of these sanctuaries—shaped by volcanic tuff and carved over centuries—was not only functional but performed a theological and mnemonic function. The spatial arrangement of apses, naves, and funerary chambers encoded rituals and commemorative acts, while painted iconographies mediated doctrinal meaning and cosmic orientation. Furthermore, this study situates Cappadocia within broader traditions of monastic hagiography, tracing how carved architecture preserved saintly narratives, communal memory, and devotional performance. By engaging with recent debates in heritage theory, the article also contends that these monastic landscapes continue to act as living archives, sustaining religious and cultural identities beyond their historical moment. The study thus contributes to emerging interdisciplinary discussions on sacred space, material devotion, and the performativity of memory in medieval Christian monasticism. Full article