Search Results (141)

Daoist robes in the Ming Dynasty literature underwent a marked transformation from exclusive religious vestments to widespread secular attire. Originally confined to Daoist priests and sacred rites, these garments began to appear in everyday work, entertainment, and ceremonies across social strata. Drawing on a hand-coded corpus of novels that yields robe related passages, and by analyzing textual references from Ming novels, Daoist canonical works, and visual artifacts, and applying clothing psychology and semiotic theory, this study elucidates how Daoist robes were re-coded as secular fashion symbols. For example, scholar-officials donned Daoist robes to convey moral prestige, laborers adopted them to signal upward mobility, and merchants donned them to impersonate the educated elite for commercial gain. By integrating close textual reading with cultural theory, the article advances a three-stage model, sacred uniform, ritual costume, and secular fashion, that clarifies the semantic flow of Daoist robes. In weddings and funerals, many commoners flaunted Daoist robes despite sumptuary laws, using them to assert honor and status. These adaptations reflect both the erosion of Daoist institutional authority and the dynamic process of identity construction through dress in late Ming society. Our interdisciplinary analysis highlights an East Asian perspective on the interaction of religion and fashion, offering historical insight into the interplay between religious symbolism and sociocultural identity formation. Full article

This study focuses on thermal comfort in residential buildings within the Iron Triangle area of South Australia, examining how indoor conditions influence residents’ comfort and adaptive behaviours. Conducted from June 2023 to February 2024 across 30 homes in Port Pirie, Port Augusta, and Whyalla, the research gathered data from 38 residents, who reported indoor comfort levels in living rooms and bedrooms. A total of 3540 responses were obtained. At the same time, the measurement of indoor conditions in the buildings was performed using a small HOBO MX1104 device. Using the Mean Thermal Sensation Vote (MTSV) concept, it was possible to determine the neutral operative temperature and temperature ranges for thermal comfort categories. According to the defined linear regression formula, the neutral temperature was 23.9 °C. In living rooms, it was slightly lower, at 23.7 °C, and in bedrooms, slightly higher, at 24.4 °C. For comparison, the neutral temperature was calculated based on the average Predicted Mean Vote (MPMV) and equal to 24.3 °C. Comparison of the regression curves showed that in terms of slope, the MPMV curve is steeper (slope 0.282) than the MTSV curve (slope 0.1726), and lies above it. Regarding the residents’ behaviour, a strong correlation was found between the operative temperature T_o and the degree of clothing I_cl in living rooms. Use of ceiling fans was also studied. A clear trend was also observed regarding window and door opening. The findings of the research can be used to inform the design and operation of residential buildings with a view to enhancing thermal comfort and energy efficiency. Full article

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article

Indoor environmental quality profoundly impacts student learning outcomes and teacher effectiveness, particularly in primary education, where children spend most of their developmental years. The study compares the New Zealand Ministry of Education’s Designing Quality Learning Spaces (DQLS) version 2.0 for primary school classrooms with international standards set by OECD countries to develop IAQ and thermal comfort best practices in New Zealand across six climate zones. The research evaluates indoor air quality (IAQ) and thermal comfort factors affecting students’ and teachers’ health and performance. Using Ladybug and Honeybee plugin tools in Grasshopper with Energy Plus, integrated into Rhino 7 software, the study employed advanced building optimisation methods, using multi-criteria optimisation and parametric modelling. This approach enabled a comprehensive analysis of building envelope parameters for historical classroom designs, the Avalon block (constructed between 1955 and 2000). Optimise window-to-wall ratios, ceiling heights, window placement, insulation values (R-values), clothing insulation (Clo), and window opening schedules. Our findings demonstrate that strategic modifications to the building envelope can significantly improve occupant comfort and energy performance. Specifically, increasing ceiling height by 0.8 m, raising windows by 0.3 m vertically, and reducing the window-to-wall ratio to 25% created optimal conditions across multiple performance criteria. These targeted adjustments improved adaptive thermal comfort, ventilation, carbon dioxide, and energy efficiency while maintaining local and international standards. The implications of the findings extend beyond the studied classrooms, offering evidence-based strategies for overall design and building performance guidelines in educational facilities. This research demonstrates the efficacy of applying computational design optimisation during early design phases, providing policymakers and architects with practical solutions that could inform future revisions of New Zealand’s school design standards and align them more closely with international best practices for educational environments. Full article

Although impulsive and hedonic purchasing behaviors may seem to contradict sustainability principles, there are unexplored opportunities through which social media platforms and influencers can redirect these impulses toward sustainable actions. Young consumers, increasingly concerned about the ecological impact of their choices, can be encouraged to adopt responsible and sustainable buying behaviors when these are promoted attractively, enjoyably, and emotionally satisfyingly through social media. This research investigates how social media communication influences hedonic and impulsive purchasing behavior in the Romanian clothing market. In the context where social media is one of the main sources of information and influence for consumers, the research analyzes several determining factors of the purchase decision. Price reductions and the use of credit cards are highlighted as elements that facilitate spontaneous and hedonic targeted purchases, while the attractiveness of clothing items and the need felt play an important role in terms of the desire to buy. In addition, sources of information (such as reviews) have a major impact on consumers’ perceptions and their purchase intentions. Additionally, the study investigates factors such as overall shopping experience and its influence on consumer loyalty. It is approached from two perspectives: attitudinal loyalty, reflected in the preference for brands promoted on social media, and behavioral loyalty, expressed through repeat purchases. The results show that social media acts as an accelerator for hedonic and impulsive buying behaviors, prompting consumers to react quickly to stimuli such as discount campaigns or personalized recommendations. The conclusions highlight the importance of adopting digital marketing strategies that capitalize on the consumers emotional need while also strengthening brand loyalty. These perspectives can guide companies in the clothing industry to adapt their promotion methods to the specifics of the Romanian market and the consumer behavior. Full article

Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article

The transition from a linear to a more circular economy has pressured companies from different sectors to implement circular business strategies and redesign their existing business models or even create new ones. The aim of this investigation is to identify the different circular business strategies adopted by Portuguese companies in the textile and clothing industry and evaluate their impact on the sustainability of the business. This article presents a framework of strategies to guide managers in addressing the challenges of moving from fast to more sustainable fashion. This exploratory research is based on a qualitative methodology, relying on semi-structured interviews with the managers of six companies in the textile and clothing sector in Portugal that have implemented circular practices. The primary data collection took place between 20 July and 30 September 2022. The results show that companies have supported their circular economy practices mainly through product life extension strategies (mostly based on durable product design) and resource use reduction strategies, with resource recovery being the most common. The use of personalized product design and clothing repair strategies is still largely unexplored by companies. The findings also suggest that companies have to adapt their way of production and market relationships with consumers in order to accommodate the practices of a circular economy in their businesses. In the future, a quantitative approach could also provide new insights, as well as longitudinal and cross-country comparison studies. Full article

The building energy performance gap (BEPG) remains a significant challenge, undermining the accuracy of energy simulations and complicating efforts to design energy-efficient buildings. This study addresses this issue by developing an adaptive occupant behaviour framework for office buildings, integrating agent-based modelling (ABM) with a building performance simulation (BPS) platform. Conventional BPS models often rely on deterministic assumptions and overlook the dynamic, stochastic nature of occupant interactions, such as window and blind operations. By incorporating occupant-driven behaviours, this research enhances the realism of energy predictions and provides insights into reducing the BEPG. Focusing on a multi-functional office building at the University of Nottingham, the study used empirical data to validate the model. The ABM framework simulated occupant behaviours influenced by factors like indoor and outdoor temperatures, solar radiation, clothing levels, and metabolic rates. Profiles generated by the ABM were integrated into the energy model, creating an Adjust model compared against a Base model with deterministic settings. Validation against measured boiler energy use showed that the Baseline model over-predicted consumption by roughly 45 %, whereas the behaviour-informed Adjust model cut the deviation to about 26 %, albeit under-predicting the total load. Statistical analyses revealed improvements in mean squared error (MSE) and root mean squared error (RMSE), although hourly energy predictions remained a challenge. Additionally, the Adjust model provided a more realistic representation of thermal comfort, reducing variability in the predicted mean vote (PMV) index from extreme values in the Base model to a more stable range in the Adjust model. However, the Adjust model also predicted higher indoor CO₂ concentrations, particularly in individual offices, due to reduced ventilation associated with occupant actions. This study demonstrates the potential of integrating ABM with BPS models to address modelling discrepancies by capturing detailed and dynamic occupant interactions, emphasising the importance of adaptive behaviours in improving prediction accuracy and occupant well-being. Full article

This study introduces an experimental method for evaluating the reflectivity of flexible textile materials under controlled lighting conditions. The proposed methodology employs a light booth and four standard illuminants (D65, TL84, Incandescent light A, and Department store light CWF), as well as a fixed-position lux meter to assess the reflective properties of textile samples with different knitted macrostructures. Each sample is measured against a black background, and reflectance is quantified as a ratio between the light intensity measured with and without the sample in place. The approach is especially relevant for the textile industry, as it provides valuable insights into the development of sustainable reflective materials for protective clothing and wearable technologies. By offering a repeatable, low-cost measurement technique, this method advances textile metrology, contributing to the optimization of material selection based on reflectivity needs and ensuring reliability across different lighting environments. This research supports the creation of more efficient, sustainable, and adaptive textiles. Full article

Accurately assessing human thermal comfort plays a key role in improving indoor environmental quality and energy efficiency of buildings. Non-invasive thermal comfort recognition has shown great application potential compared with other methods. Based on thermal correlation analysis, human facial temperature recognition and body thermal adaptive action detection are both performed by one binocular infrared camera. The YOLOv5 algorithm is applied to extract facial temperatures of key regions, through which the random forest model is used for thermal comfort recognition. Meanwhile, the Mediapipe tool is used to detect probable thermal adaptive actions, based on which the corresponding thermal comfort level is also assessed. The two results are combined with PMV calculation for multivariate human thermal comfort prediction, and a weighted fusion strategy is designed. Seventeen subjects were invited to participate in experiments for data collection of facial temperatures and thermal adaptive actions in different thermal conditions. Prediction results show that, by using the experiment data, the overall accuracies of the proposed fusion strategy reach 82.86% (7-class thermal sensation voting, TSV) and 94.29% (3-class TSV), which are better than those of facial temperature-based thermal comfort prediction (7-class: 78.57%, 3-class: 90%) and PMV model (7-class: 20.71%, 3-class: 65%). If probable thermal adaptive actions are detected, the accuracy of the proposed fusion model is further improved to 86.8% (7-class) and 100% (3-class). Furthermore, by changing clothing thermal resistance and metabolic level of subjects in experiments, the influence on thermal comfort prediction is investigated. From the results, the proposed strategy still achieves better accuracy compared with other single methods, which shows good robustness and generalization performance in different applications. Full article

Point cloud filtering plays a crucial role in ground point extraction in urban environments. It can effectively distinguish ground points from object points, reduce data redundancy, and improve processing efficiency, providing accurate foundational data for urban 3D modeling, environmental monitoring, and intelligent management. However, current point cloud filtering algorithms have significant limitations in multi-scale structural complexity and sparse-to-dense balancing, hindering accurate extraction in complex urban environments. To address those issues, this paper proposes a point cloud filtering algorithm based on cloth simulation and progressive TIN densification (CAP). The algorithm first applies the cloth simulation filtering (CSF) algorithm to perform an initial filtering of the point cloud data and extract the initial ground points. It then constructs a TIN model based on the initial ground points, incorporating the concept of the progressive TIN densification (PTD) algorithm. Through point-by-point thresholding, the ground and object points are further refined and optimized. In the urban public point cloud datasets provided by ISPRS, the average total error is 5.90% after CAP algorithm filtering. For 12 sets of point cloud data in the North Rhine–Westphalia experimental sample area, the results show that the CAP algorithm achieves an average total error of 2.86%, which is 2.01% lower than the PTD algorithm and 0.60% lower than the CSF algorithm. The average Kappa coefficient is 94.04%, which is an improvement of 4.17% and 1.22% over the PTD and CSF algorithms, respectively. This study demonstrates that the CAP algorithm exhibits superior accuracy and adaptability for point cloud filtering tasks in urban environments, with significant application potential. Full article

Clothing image synthesis has emerged as a crucial technology in the fashion domain, enabling designers to rapidly transform creative concepts into realistic visual representations. However, the existing methods struggle to effectively integrate multiple guiding information sources, such as sketches and texture patches, limiting their ability to precisely control the generated content. This often results in issues such as semantic inconsistencies and the loss of fine-grained texture details, which significantly hinders the advancement of this technology. To address these issues, we propose the Fast Fourier Asymmetric Context Aggregation Network (FCAN), a novel image generation network designed to achieve controllable clothing image synthesis guided by design sketches and texture patches. In the FCAN, we introduce the Asymmetric Context Aggregation Mechanism (ACAM), which leverages multi-scale and multi-stage heterogeneous features to achieve efficient global visual context modeling, significantly enhancing the model’s ability to integrate guiding information. Complementing this, the FCAN also incorporates a Fast Fourier Channel Dual Residual Block (FF-CDRB), which utilizes the frequency-domain properties of Fast Fourier Convolution to enhance fine-grained content inference while maintaining computational efficiency. We evaluate the FCAN on the newly constructed SKFashion dataset and the publicly available VITON-HD and Fashion-Gen datasets. The experimental results demonstrate that the FCAN consistently generates high-quality clothing images aligned with the design intentions while outperforming the baseline methods across multiple performance metrics. Furthermore, the FCAN demonstrates superior robustness to varying texture conditions compared to the existing methods, highlighting its adaptability to diverse real-world scenarios. These findings underscore the potential of the FCAN to advance this technology by enabling controllable and high-quality image generation. Full article

This study conducted a within-subject study to assess sleeping environmental comfort, acceptance, and self-reported sleep quality in air-conditioned and mixed-mode ventilated bedrooms in a subtropical region during the summer. A wide thermal comfort temperature range of 22.2 °C to 28.2 °C was observed, with slightly warmer thermal sensation at higher temperatures but no significant differences in sleep quality or environmental comfort acceptance within this range. Subjects adapted to warmer sleeping conditions by choosing lighter clothing and bedding insulation. Energy simulations indicated a reduction in the percentage of nights requiring cooling from 65% to 23% by increasing the set-point temperature from 22 °C to 28 °C, resulting in a potential 95% savings in cooling energy. This study advocates for an economical and energy-efficient approach to enhance sleeping thermal comfort while reducing cooling energy usage. These findings offer valuable insights for improved residential building design and optimized cooling energy management practices, especially in light of intensified climate change and the imperative for behavioral changes to promote building sustainability. Full article

After the COVID-19 lockdown, the health authorities established strict protocols for ventilating indoor spaces and reducing contagion. Although the control of the disease allowed these measures to be relaxed, indoor air quality (IAQ) and natural ventilation (NV) are still essential. However, in certain climatic conditions, this can affect the thermal comfort of the occupants. This situation is relevant in educational buildings, where thermal discomfort can influence students’ academic performance, especially during critical periods such as exams. In this context, this article explores how different NV strategies, both during and after the pandemic, affect the thermal comfort of students at a university in a Mediterranean climate zone. The analyses revealed that, despite the low temperatures and strict ventilation protocols due to COVID-19, thermal comfort during winter was higher than in spring and summer. These results led to an investigation into which variables could explain this phenomenon, detecting that the choice of clothing was crucial to achieving adequate comfort conditions. Regarding IAQ, ventilation was sufficient, even excessive, in some cases, especially during mandatory measures. In conclusion, it would be beneficial to establish ventilation protocols adapted to each environment and to advise students on individual strategies to improve their thermal comfort. Full article