Search Results (1,986)

Urban streets are critical public spaces that significantly influence the thermal comfort of city dwellers. However, the issue of summer thermal discomfort in severely cold regions has been largely overlooked. This study focuses on Youth Street in Shenyang, a city in a severely cold region, to explore the impact of various street spaces and vegetation layouts on the thermal environment and comfort using ENVI-met modeling and correlation analysis. The study varied the aspect ratio (AR) of the street, street tree species, and plant spacing across 60 scenarios and simulated thermal comfort over a 10-h period on a typical summer day. Results show that air temperature (Ta), mean radiant temperature (Tmrt) and sky view factor (SVF) are positively correlated with physiologically equivalent temperature (PET). Street trees effectively reduce Ta, increase RH and lower wind speed (WS), but plant spacing has minimal impact on WS. Higher AR values lead to greater improvements in pedestrian thermal comfort. Specifically, the highest heat mitigation rate (HMR) is observed at low AR (9.87% at AR = 0.5 and 9.94% at AR = 1.0), while it is lower at high AR (8.16% at AR = 2.0). Conversely, larger plant spacing of street trees diminishes the effectiveness of thermal comfort improvements. The improvement effect of plant spacing is more pronounced in street spaces with smaller AR. In these spaces, closer plant spacing significantly enhances thermal comfort by providing more shade and reducing Ta and Tmrt. However, in street spaces with higher AR, overly dense plant configurations can reduce WS and limit the cooling effect of ventilation, thereby diminishing overall heat mitigation ability. Conclusions suggest that urban planners should consider both street space and vegetation layouts to optimize thermal comfort. For urban main streets in severely cold regions, an AR of 1:1 with deciduous broadleaf trees and hedges planted at 6 m spacing is recommended. In high-AR streets, dense plant configurations should be avoided. This study provides valuable insights for improving the thermal comfort and sustainable design of urban street spaces, supporting new construction and development in similar climate environments. Full article

This study introduces an automated method for constructing residential functional modules from the perspective of user behavior. By integrating the design structure matrix (DSM) and Fuzzy C-Means Clustering Algorithm (FCM), this approach systematically explores architectural functional modules. The DSM is employed to statistically analyze the correlations between residential behaviors. These correlations are then processed using FCM to generate various module segmentation schemes. The optimal scheme is selected based on modularity calculations. The results demonstrate improved modularity compared to traditional room-based designs, offering a greater variety of combinations and hierarchical organization. This methodology provides architects with a novel approach to address space integration challenges. Full article

Mental health challenges among university students and staff are a pressing concern globally and in Aotearoa, New Zealand. Despite adopting frameworks like the Okanagan Charter to promote health and well-being, there is a lack of empirical research on how campus landscapes contribute to mental health promotion. This is a preliminary study based on a Ph.D. research project aiming to investigate the role of campus landscapes in supporting relaxation and internal recovery through everyday activities. We conducted a comparative multi-case study involving 66 participants from the University of Auckland, Lincoln University, and the University of Otago, exploring how they use and prefer campus landscapes for relaxation. Our findings indicate that ‘enjoying nature’ is the most preferred relaxation activity, with participants engaging both actively and passively with various spaces such as gardens, open lawns, and forested areas. Additionally, in campus settings, the proximity of relaxation spaces appears to be more important than design quality because of the limited time during working hours, which points to the importance of thoughtful campus planning. This study also found that university staff are often overlooked in discussions about healthy universities, despite their significant role in the campus setting. Overall, this study highlights the importance of biophilic design principles in creating health-promoting campus environments and offers initial insights for integrating natural elements into campus planning to enhance mental health and well-being. Full article

Underground cold storage gives rise to special challenges that require innovative solutions to ensure maximum energy efficiency. Conventional energy systems tend to be based on high energy use, so sustainable solutions are crucial. This study explores the novel idea of biomimetics and how it might be used in the planning and building of underground cold storage facilities as well as other infrastructure projects. Biomimetic strategies, inspired by termite mounds, gentoo penguin feathers, and beehive structures, are applied to minimize reliance on energy-intensive cooling systems. These natural models offer efficient thermal regulation, airflow optimization, and passive cooling mechanisms such as geothermal energy harvesting. The integration of naturally driven convection and ventilation ensures stable internal temperatures under varying conditions. Biomimicry was employed in Revit Architecture, coupled with structural optimization, to eliminate urban space’s limitations and further increase energy efficiency. The analytical work for this paper utilized a set of formulas that represent heat flow, thermal resistance, R-value, thermal transmittance, U-value, solar absorption, and G-value. The results pointed to very good insulation, with exterior walls having an R-value of 10.2 m²K/W and U-value of 0.98 W/m²K. Among the chosen 3-layer ETFE cushion with a U-value of 1.96 W/m²K, with a G-value of 0.50, showed good heat regulation and daylight management. Furthermore, bagasse-cement composites with a very low thermal conductivity of 0.10–0.30 W/m·K provided good insulation. This research proposes a scalable and sustainable approach in the design of underground cold storage by merging modelling based on Revit with thermal simulations. Biomimicry has been demonstrated to have the potential for changing subterranean infrastructure, conserving energy consumption, and creating eco-friendly construction practices. Full article

Games provide an excellent testing ground for machine learning and artificial intelligence, offering diverse environments with strategic challenges and complex decision-making scenarios. This study seeks to design a self-learning artificial intelligent agent capable of playing the trick-taking stage of the popular card game Thousand, known for its complex bidding system and dynamic gameplay. Due to the game’s vast state space and strategic complexity, other artificial intelligence approaches, such as Monte Carlo Tree Search and Deep Counterfactual Regret Minimisation, are infeasible. To address these challenges, the enhanced version of the REINFORCE policy gradient algorithm is proposed. Introducing a score-related parameter $\beta$ designed to guide the learning process by prioritising valuable games, the proposed approach enhances policy updates and improves overall learning outcomes. Moreover, leveraging the off-policy experience replay, along with the importance weighting of behavioural policy, enhanced training stability and reduced model variance. The proposed algorithm was applied to the trick-taking stage of the popular game Thousand Schnapsen in a two-player setup. Four distinct neural network models were explored to evaluate the performance of the proposed approach. A custom test suite of selected deals and tournament evaluations was employed to assess effectiveness. Comparisons were made against two benchmark strategies: a random strategy agent and an alpha-beta pruning tree search with varying search depths. The proposed algorithm achieved win rates exceeding 65% against the random agent, nearly 60% against alpha-beta pruning at a search depth of 6, and 55% against alpha-beta pruning at the maximum possible depth. Full article

The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with heterogeneous static graphs. BERT transfers information via token embeddings, which are propagated through GNNs. Text-specific information defines a static heterogeneous graph. Static graphs represent specific relationships and do not have the flexibility to add new knowledge to the graph. To address this issue, we build a tied connection between BERT and GNN exclusively using token embeddings to define the graph and propagate the embeddings, which can force the BERT to redefine the GNN graph topology to improve accuracy. Thus, in this study, we re-examine the design spaces and test the limits of what this pure homogeneous graph using BERT embeddings can achieve. Homogeneous graphs offer structural simplicity and greater generalization capabilities, particularly when integrated with robust representations like those provided by BERT. To improve accuracy, the proposed approach also incorporates text augmentation and label propagation at test time. Experimental results show that the proposed method outperforms state-of-the-art methods across all datasets analyzed, with consistent accuracy improvements as more labeled examples are included. Full article

This paper investigates the spatial attributes of the color properties and brightness characteristics of sustainable architectural strategies including daylight, electrical lighting, and surface color in architecture, which could potentially impact users’ spatial experiences. Images of 48 spaces varying in surface color configurations, type of light source, and position of the lighting strategy were evaluated. The analyses included assessments of color palettes, descriptors based on saturation and brightness properties, and brightness distribution maps. The results indicate that lighting design and types of light source influence the saturation and brightness properties of the perceived hues evaluated in the same environment, leading to variations in color descriptors or adjectives. Furthermore, this study demonstrates that variations in brightness between bright and dark zones, the creation of focal points, and perceived spatial fragmentation depend on the reflectance of the colors applied in the surfaces, the position of the lighting, and the type of light source. This study does not aim to establish best practices for enhancing users’ emotions through architecture. Instead, it explores how variations in color and light influence perceptual descriptions that have been previously associated with emotional responses. This research recognizes the impact of sustainable strategies including surface colors under daylight and electrical lighting on users’ spatial experiences. Full article

The COVID-19 pandemic highlighted the role of urban parks as green spaces in mitigating social isolation and supporting public mental health. Research in this area is limited due to the lack of large-scale datasets. Moreover, timely studies are indeed necessary under pandemic conditions. This study employs quantitative methods to analyze the temporal and spatial changes in social interaction in 160 urban parks before, during, and after the COVID-19 pandemic, and assesses their correlation with the built environment. Social media data from the Dianping platform were collected for this purpose. A two-step analytical approach was employed: first, machine learning-based keyword analysis identified review data related to social interaction, leading to the construction of two indicators: social interaction intensity and social interaction recovery rate. Second, we applied regression models to explore the correlation between the two indicators in urban parks and 18 characteristics of the built environment. The built environment characteristics associated with social interaction intensity varied across different periods, with seven factors, including natural landscapes, perceptual experience, building density, and road intersections, showing significant correlations with the recovery of social interaction capabilities in the post-pandemic era. Based on these findings, it is recommended that urban planners consider integrating more flexible design element, such as adding greenery and enriching the audio-visual experience for visitors. Furthermore, enhancing the quality and accessibility of park amenities can foster social interaction, thereby contributing to public health resilience in future crises. This research recommends that urban park design should not only support communities’ immediate needs but also prepare for unforeseen challenges. Full article

The Multi-demand Multidimensional Knapsack Problem (MDMKP) is a challenging combinatorial task due to its capacity and demand constraints. Local search operators play a key role in metaheuristics when navigating such complex solution spaces, yet their impact on MDMKP performance has received limited attention. In this work, we investigate four local operator configurations—Add, Drop, Swap, and All Operator—within the Whale Optimization Algorithm framework. Our approach integrates these operators to broaden search coverage and refine candidate solutions. This design aims to enhance solution quality by balancing exploration and exploitation across multiple dimensions of the MDMKP. Experimental results on benchmark instances with different sizes ($n=100,250,$ and 500) show that the All Operator configuration consistently achieves better maximum and average values. In large-scale instances (n = 500), the “All Operator” configuration achieves an average maximum value of 107,967, which is approximately 1.4% higher than the 106,490 achieved by the “Add Operator” and about 0.2% higher than the 107,771 obtained by the “Swap Operator”, while significantly outperforming the “Drop Operator” (average maximum of 99,164). Statistical tests confirm its advantage over the other configurations, suggesting that combining multiple local operators can significantly strengthen performance in high-dimensional and constraint-heavy settings like the MDMKP. Full article

Coal mine scene image recognition plays an important role in safety monitoring and equipment detection. However, traditional methods often depend on manually designed neural network architectures. These models struggle to handle the complex backgrounds, low illumination, and diverse objects commonly found in coal mine environments. Manual designs are not only inefficient but also restrict the exploration of optimal architectures, resulting to subpar performance. To address these challenges, we propose using a neural architecture search (NAS) to automate the design of neural networks. Traditional NAS methods are known to be computationally expensive. To improve this, we enhance the process by incorporating Particle Swarm Optimization (PSO), a scalable algorithm that effectively balances global and local searches. To further enhance PSO’s efficiency, we integrate the lifespan mechanism, which prevents premature convergence and enables a more comprehensive exploration of the search space. Our proposed method establishes a flexible search space that includes various types of convolutional layers, activation functions, pooling operations, and network depths, enabling a comprehensive optimization process. Extensive experiments show that the Lifespan-PSO NAS method outperforms traditional manually designed networks and standard PSO-based NAS approaches, offering significant improvements in both recognition accuracy (improved by 10%) and computational efficiency (resource usage reduced by 30%). This makes it a highly effective solution for real-world coal mine image recognition tasks via a PSO-optimized approach in terms of performance and efficiency. Full article

With their compact design and versatility, CubeSats have emerged as critical platforms for advancing space exploration and communication technologies. However, achieving reliable and efficient communication in the dynamic and constrained environment of low Earth orbit (LEO) remains a significant challenge. Beam-steering antenna systems offer a promising solution to address these limitations, enabling adaptive communication links with improved gain and coverage. This review article provides a comprehensive analysis of the state-of-the-art in CubeSat communication, concentrating on the latest developments in beam-steering antennas. By synthesizing the findings from recent studies, the key challenges are highlighted, including power constraints, miniaturization, and integration with CubeSat platforms. Furthermore, this paper investigates cutting-edge techniques, such as phased array systems, metasurface-based designs, and reconfigurable antennas, which pave the way for enhanced performance. This study can serve as a resource for researchers and engineers, offering insights into current trends and future opportunities for advancing CubeSat communications through innovative antenna systems. Full article

This paper explores the applications and impacts of artificial intelligence (AI) in building envelopes and interior space design. The relevant literature was searched using databases such as Science Direct, Web of Science, Scopus, and CNKI, and 89 studies were selected for analysis based on the PRISMA protocol. This paper first analyzes the role of AI in transforming architectural design methods, particularly its different roles in the auxiliary, collaborative, and leading design processes. It then discusses AI’s applications in the energy-efficient renovation of building envelopes, smart façade design for cold climate buildings, and thermal imaging detection. Furthermore, this paper summarizes AI-based interior space environment design methods, covering the current state of research, applications, impacts, and challenges both domestically and internationally. Finally, this paper looks ahead to the broad prospects for AI technology in the architecture and interior design fields while addressing the challenges related to the integration of personalized design and environmental sustainability concepts. Full article

Automatic navigation, as one of the modern technologies in farming automation, enables unmanned driving and operation of agricultural vehicles. In this research, the ZPTM (Zigzag Path Tracking Method) was proposed to reduce the complexity of path planning by using a point cloud consisting of a series of anchor points with spatial information, which are obtained from orthophotos taken by UAVs (Unmanned Aerial Vehicles) to represent the curved path in the zigzag. A local straight path was created by linking two adjacent anchor points, forming the local target path to be tracked, which simplified the navigation algorithm for zigzag path tracking. A nonlinear feedback function was established, using both lateral and heading errors as inputs for determining the desired heading angle of agricultural vehicles, which were guided along the local target path with minimal errors. A GUI (Graphic User Interface) was designed on the navigation terminal to visualize and monitor the working process of agricultural vehicles in automatic navigation, displaying interactive controls and components, including representations of the zigzag path and the agricultural vehicle using affine transformation. A high-clearance sprayer equipped with an automatic navigation system was utilized as the test platform to evaluate the proposed ZPTM. Zigzag navigation tests were conducted to explore the impact of path tracking parameters, including path curvature, moving speed, and spacing between anchor points, on zigzag navigation performance. Based on these tests, a regression model was established to optimize these parameters for achieving accurate and smooth movement. Field test results showed that the maximum error, average error, and RMS (Root Mean Square) error in the zigzag navigation were 3.30 cm, 2.04 cm, and 2.27 cm, respectively. These results indicate that the point cloud path-based ZPTM in this research demonstrates adequate stability, accuracy, and applicability in zigzag navigation. Full article

Source number detection and Direction-of-Arrival (DOA) estimation are usually addressed in two stages, leading to high computational load. This paper proposes a simple solution to efficiently estimate the source number and DOAs using deep neural network (DNN) and clustering, named DNN-C. By observing that sources in space are usually few, DNN-C uses a simple fully connected DNN to obtain a spatial spectrum. Then, the K2-means clustering is specially designed to extract the source information from the obtained spatial spectrum. In particular, to enable the proposed DNN-C with the ability to detect the mixed sources, we first develop a new strategy for training data generation, and provide a guideline for data balance setting. We then explore the prior knowledge of array signal processing and spatial spectrum to obtain a peak vector and propose to add a virtual peak into the peak vector, and thus transform the task of source detection as a binary clustering problem of noise and sources. Overall, DNN-C provides a lightweight solution to implement source number detection and DOA estimation simultaneously and efficiently. Its testing time is about 2 times less than the classical solution (i.e., minimum descriptive length and multiple signal classification, shortened as MDL-MUSIC) when the grid step is 1° Importantly, it is robust to nonuniform noise by nature and can identify the absence of sources. The effectiveness of DNN-C is verified by simulation results. Furthermore, the DNN-C model trained by simulated data shows its generalization to real data measured by a circular array of eight sensors. Full article

The eastern subterranean termite, Reticulitermes flavipes, is challenged by the significant energy expenditures of tunnel construction for resource discovery. Subterranean termites use idiothetic mechanisms to explore large spaces, while the use of resource-specific cues for localized search is disputed. Here, termite response to wood mulch, termite workers, extracts of wood mulch, and CO₂ alone were tested using a bioassay design that distinguished between attraction and arrestment. Termites showed significant attraction to wood mulch with workers or to wood mulch alone. They did not respond to workers alone at the initial dose tested, but were attracted to workers at higher densities. Termites did not respond to water or the acetone extracts of wood mulch, but did show a partial response to hexane extract compared to intact wood mulch. More significantly, when CO₂ was removed from the emissions of wood mulch and workers using soda lime, attraction was eliminated. Furthermore, termites showed a quadratic response to CO₂ concentration that peaked at ca. 14,000 ppm. The response to CO₂ alone predicted by the model matched termite response to mulch + workers when compared at the level of CO₂ they emitted. The results suggest that CO₂ is both necessary and sufficient to explain the attraction response of R. flavipes to mulch and workers we observed. It is argued that orientation to food cues complements the previously demonstrated idiothetic program to maximize the efficiency of resource location. Full article