Search Results (1,843)

Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments. Full article

Lidar technology plays a pivotal role in lunar exploration, particularly in terrain mapping, 3D topographic surveying, and velocity measurement, which are crucial for guidance, navigation, and control. This paper reviews the current global research and applications of lidar technology in lunar missions, noting that existing efforts are primarily focused on 3D terrain mapping and velocity measurement. The paper also discusses the detailed system design and key results of the laser altimeter, laser ranging sensor, laser 3D imaging sensor, and laser velocity sensor used in the Chang’E lunar missions. By comparing and analyzing similar foreign technologies, this paper identifies future development directions for lunar laser payloads. The evolution towards multi-beam single-photon detection technology aims to enhance the point cloud density and detection efficiency. This manuscript advocates that China actively advance new technologies and conduct space application research in areas such as multi-beam single-photon 3D terrain mapping, lunar surface water ice measurement, and material composition analysis, to elevate the use of laser pay-loads in lunar and space exploration. Full article

With the continuous advancement of urbanization, rural areas are facing increasingly severe environmental pollution, excessive energy consumption, and high carbonization resulting from both daily living and production activities. This study, which is aligned with the low-carbon objectives of “carbon sequestration increase and emissions reduction”, explores the optimization strategies for ecological benefits through the combined application of rooftop photovoltaics and rooftop greening in rural residences. Three design approaches are proposed for integrating rooftop photovoltaics with green roofing: singular arrangement, distributed arrangement, and combined arrangement. Using PVsyst (7.4.7) software, this study simulates the effects of roof inclination, system output, and installation formats on the performance of photovoltaic systems, providing a comprehensive analysis of carbon reduction benefits in ecological rooftop construction. A rural area in East China was selected as a sample for adaptive exploration of ecological roof applications. The results of our research indicate that the optimal tilt angle for rooftop photovoltaic (PV) installations in the sample rural area is 17°. Based on simulations combining the region’s annual solar path and the solar parameters on the winter solstice, the minimum spacing for PV arrays is calculated to be 1.925 m. The carbon reduction benefits of the three arrangement methods are ranked, from highest to lowest, as follows: combined arrangement $14530.470{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > singular arrangement $11950.761{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > distributed arrangement $7444.819{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$. The integrated design of rooftop PV systems and green roofing not only meets the energy demands of buildings but also significantly reduces their carbon footprint, achieving the dual objectives of energy conservation and sustainable development. Therefore, the combined application of rooftop PV systems and green roofing in rural spaces can provide data support and strategic guidance for advancing green transformation and ecological civilization in East China, offering significant practical value for promoting low-carbon rural development. Full article

In this study, we examined the application of neurostructural principles to the design of public spaces on university campuses to optimize students’ learning efficiency, social interactions, and psychological well-being. Using Hainan University in China as a case study, a descriptive analysis was used to evaluate the case study design of the data. Data on students’ preferences for and satisfaction with public learning spaces (libraries, student centers, and open learning areas) were also collected through a questionnaire. The questionnaire was based on the four stages of the AIDA (Attention, Interest, Desire, and Action) model and covered basic information about the participants and their first impressions of the learning spaces, design element preferences, emotional and cognitive influences, and willingness to participate in improving the design of campus spaces. Data were analyzed using quantitative methods, including frequency analysis and score aggregation, to assess the students’ satisfaction with the existing design elements of the learning space and their suggestions for potential improvements. A random sample of students enrolled at Hainan University was used to ensure that the data were representative. The results of the study indicate that the rational allocation of natural light, the optimization of the acoustic environment, the adoption of soothing color schemes, and flexible spatial layouts are effective at relieving students’ psychological stress, enhancing their academic performance, and facilitating social interactions. Some of the existing designs are already in line with neurostructural principles, but there is still room for improvement, especially in terms of color schemes and spatial configurations. Students have positive attitudes towards participating in campus space improvement, with especially high interest in light optimization, spatial layout, and the use of natural materials. This study verifies the effectiveness of using neural structure principles in campus public spaces by establishing an empirical model, proves its positive effect on the quality of the campus environment and students’ well-being, and provides empirical evidence and theoretical support for future campus design. Full article

Metasurfaces have shown great potential in achieving low-cost and low-complexity signal enhancement and redirection. Due to the low transmission power and high attenuation issues of current high-frequency communication technology, it is necessary to explore signal redirection technology based on metasurfaces. This paper presents an innovative metasurface for indoor signal enhancement and redirection, featuring thin thickness, high gain, and wide-angle deflection. The metasurface integrates the design principles of a Fabry–Perot cavity (FPC) theory with a Phase Gradient Partially Reflective Metasurface (PGPRM). Its unit is a fishnet structure with a substrate only 1/33 λ thin. Based on the precise phase control of the dual-layer PGPRM (with an inter-layer distance of 8 mm), the proposed metasurface can obtain phase coverage as small as 78° while achieving high-gain beam deflection as large as 47°. Simulation results show that within the band 8.6–9.2 GHz (6.7%), a single-layer metasurface can deflect the beam to 29° with a maximum gain of 16.9 dBi. In addition, it is also 360° polarization-insensitive in the xoy plane at 9 GHz with large-angle deflection characteristic retained. Moreover, cascading PGPRM can effectively improve the beam deflection angle. After analysis, the scheme with a double-layer spacing of 8 mm was ultimately selected. Simulation results show a double-layer metasurface can deflect the beam to 47° with a maximum gain of 16.4 dBi. This design provides an efficient and cost-effective solution for large-angle beam deflection with gain enhancement for indoor wireless communication. Full article

With the rapid pace of urbanization, the integrity and connectivity of ecosystems are under serious threat, making biodiversity conservation a top priority. We use the Xiongan New Area in China as a case study to explore the significance and application of constructing urban ecological networks in the development of new cities. This study systematically applied the categorization of green space systems using remote sensing technology; MSPA was used to identify key landscape patches; InVEST was employed to assess habitat quality; and potential ecological corridors were established using the minimum cumulative resistance model (MCR). Moreover, targeted recommendations for optimizing ecological green spaces were put forward. The findings demonstrate that the Xiongan New Area has significant potential and needs for ecological network construction, and it faces the issue of ecological network fragmentation. This research highlights the significance of developing ecological networks within urban planning and proposes optimization strategies tailored to these networks. The objective is to offer scientific guidance for the design and development of emerging cities, such as the Xiongan New Area, to facilitate the alignment and integration of ecological preservation efforts with urban expansion, ultimately achieving the sustainable development goal of harmonious coexistence between the environment and urban areas. Full article

The increasing need for autonomy in space exploration missions is becoming more and more relevant in the design of missions to small bodies. The long communication latencies and sensitivity of the system to unplanned environmental perturbations mean autonomous methods could be a key design block for this type of mission. In this work, a fully autonomous Guidance, Navigation, and Control (GNC) methodology is introduced. This methodology relies on published CNN-based techniques for surface recognition and pose estimation and also on existing MPC-based techniques for the design of a trajectory to perform a soft landing on an asteroid. Combining Hazard Detection and Avoidance (HDA) with relative navigation systems, a Global Safety Map (GSM) is built on the fly as images are acquired. These GSMs provide the GNC system with information about feasible landing spots and populate a longitude–latitude map with safe/hazardous labels that are later processed to find an optimal landing spot based on mission requirements and a distance-fromhazard metric. The methodology is exemplified using Bennu as the body of interest, and a GSM is built for an arbitrary reconnaissance orbit. Full article

Recurrent neural networks (RNNs) have produced significant results in many fields, such as natural language processing and speech recognition. Owing to their computational complexity and sequence dependencies, RNNs need to be deployed on customized hardware accelerators to satisfy performance and energy-efficiency constraints. However, designing hardware accelerators for RNNs is challenged by the vast design space and the reliance on ineffective optimization. An efficient automated design space exploration (DSE) strategy that can balance conflicting objectives is wanted. To address the low efficiency and insufficient universality of the resource allocation process employed for hardware accelerators, we propose an automated two-stage design space exploration (DSE) strategy for customized RNN accelerators. The strategy combines a genetic algorithm (GA) and a reinforcement learning (RL) algorithm, and it utilizes symmetrical exploration and exploitation to find the optimal solutions. In the first stage, the area of the hardware accelerator is taken as the optimization objective, and the GA is used for partial exploration purposes to narrow the design space while maintaining diversity. Then, the latency and power of the hardware accelerator are taken as the optimization objectives, and the RL algorithm is used in the second stage to find the corresponding Pareto solutions. To verify the effectiveness of the developed strategy, it is compared with other algorithms. We use three different network models as benchmarks: a vanilla RNN, LSTM, and a GRU. The results demonstrate that the strategy proposed in this paper can provide better solutions and can achieve latency, power, and area reductions of 9.35%, 5.34%, and 11.95%, respectively. The HV of GRMD is reduced by averages of 6.33%, 6.32%, and 0.67%, and the runtime is reduced by averages of 18.11%, 14.94%, and 10.28%, respectively. Additionally, given different weights, it can make reasonable trade-offs between multiple objectives. Full article

Remote sensing image retrieval (RSIR) plays a crucial role in remote sensing applications, focusing on retrieving a collection of items that closely match a specified query image. Due to the advantages of low storage cost and fast search speed, deep hashing has been one of the most active research problems in remote sensing image retrieval. However, remote sensing images contain many content-irrelevant backgrounds or noises, and they often lack the ability to capture essential fine-grained features. In addition, existing hash learning often relies on random sampling or semi-hard negative mining strategies to form training batches, which could be overwhelmed by some redundant pairs that slow down the model convergence and compromise the retrieval performance. To solve these problems effectively, a novel Deep Multi-similarity Hashing with Spatial-enhanced Learning, termed DMsH-SL, is proposed to learn compact yet discriminative binary descriptors for remote sensing image retrieval. Specifically, to suppress interfering information and accurately localize the target location, by introducing a spatial enhancement learning mechanism, the spatial group-enhanced hierarchical network is firstly designed to learn the spatial distribution of different semantic sub-features, capturing the noise-robust semantic embedding representation. Furthermore, to fully explore the similarity relationships of data points in the embedding space, the multi-similarity loss is proposed to construct informative and representative training batches, which is based on pairwise mining and weighting to compute the self-similarity and relative similarity of the image pairs, effectively mitigating the effects of redundant and unbalanced pairs. Experimental results on three benchmark datasets validate the superior performance of our approach. Full article

The exploration and advancement of feminism are vital for addressing critical issues such as social progress, equitable education, and healthcare access. This paper comprehensively examines the feminist movement’s impact on architecture over the past century, identifying key trends and significant areas to establish an academic foundation for feminist architecture. A literature review on feminism in urban planning, architecture, landscape design, and urban safety highlights the current research focus on feminist architectural development. Furthermore, this paper traces the evolution of feminist architecture through both purpose-driven and process-oriented approaches, exploring the interplay between feminist and modern architectural practices. It specifically examines the development of feminist architecture within the Chinese context from two perspectives: the influence of feminist thought on architecture and the evolution of gendered spaces within the “Residence and Courtyard” model. By comparing the internal logic of feminist architectural development in China and the West, this study investigates how geopolitical culture and regional differences shape the future trajectory of this field. Unlike traditional feminist architectural research, which often emphasizes women’s practices within specific feminist ideologies or focuses on visual culture and psychological interpretations of gendered spaces, this paper redefines the scope of feminist architectural studies through a comparative analysis of historical and contemporary contexts, and Eastern and Western perspectives, employing a systematic genealogical approach. Full article

The city, like a piece of architecture, is a structure in space, but one of gigantic scale, something perceived only over a long period. A space is termed a place when it acquires an identity. The entirety of urban personality, urban communication, urban conduct, and urban design constitute the urban identity. This research delves into divided urban identities and examines how urban and architectural design influence the fragmentation of the cityscape. It explores the connection between urban environments’ physical attributes and the divide of social, cultural, and political identities within cities. This study uses a multidisciplinary method to acquire thorough knowledge by combining architectural studies, urban planning theories, and social-cultural perspectives. The case study of reference is the city of Matera, in southern Italy, which has a unique history of a slow shaping of its urban and productive landscape throughout centuries due to heavy environmental constraints and resource availability and which has suffered forced evacuation and major discontinuities in the past century. Here, the opposing traits of the historical Sassi district and the new town are examined, focusing on their resulting separated urban identities. The study also looks at how divided cities may be reconciled and integrated, emphasizing the relevance of a holistic urban approach for the framing of complex issues. The research proposes methods and best practices for developing inclusive urban settings that promote cohesion and shared identities through the analysis of successful cases of urban regeneration, adaptive reuse of spaces, and participatory design processes. The findings of this research contribute to both academic and practical knowledge by deepening the understanding of the relationship between urban design, architecture, and divided urban identities. It emphasizes the value of comprehensive approaches to urban planning that take into account the social, cultural, and historical settings of cities to foster inclusivity, rapprochement, and the development of common urban identities. Full article

This paper examines the essential role of urban green spaces in fostering healthy living from a landscape architecture perspective. Health goes beyond the mere absence of disease to include physical, mental, and social wellbeing, all of which are greatly enhanced by accessible green spaces. By synthesising existing literature, this paper shows that urban green spaces have strong positive associations with health outcomes, especially in urban settings where environmental stressors are pronounced. The paper stresses the importance of designing attractive and accessible green spaces that encourage physical activity, mental wellbeing, and social interaction, addressing public health issues such as obesity and mental health disorders. In addition to physical and mental health benefits, the paper explores the potential of local food production through edible green infrastructure, such as community gardens, which can significantly improve diet and nutrition. Additionally, the study discusses disparities in the access to quality green spaces, particularly between the Global North and South, and advocates for equitable design strategies that serve diverse populations. Integrating evidence-based approaches into landscape architecture, the paper argues for the establishment of urban green spaces as essential elements of public health infrastructure. Finally, the paper calls for future research and policy efforts to maximise the health benefits of urban green spaces and improve the quality of life in urban environments. Full article

Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods for jointly embedding concepts and instances achieve promising performance, they still face two issues: (1) They fail to explicitly reconstruct the hierarchical tree structure of concepts in the embedding space; (2) They ignore disjoint concept pairs and overlapping concept pairs derived from concepts. In this paper, we propose a novel concept representation approach, called Hyper Spherical Cone Concept Embedding (HCCE), to explicitly model the hierarchical tree structure of concepts in the embedding space. Specifically, HCCE represents each concept as a hyperspherical cone and each instance as a vector, maintaining the anisotropy of concept embeddings. We propose two variant methods to explore the impact of embedding concepts and instances in the same or different spaces. Moreover, we design score functions for disjoint concept pairs and overlapping concept pairs, using relative position relations to incorporate them seamlessly into our geometric models. Experimental results on three benchmark datasets show that HCCE outperforms most existing state-of-the-art methods on concept-related triples and achieves competitive results on instance-related triples. The visualization of embedding results intuitively shows the hierarchical tree structure of concepts in the embedding space. Full article

The arrangement and design of urban streets have a profound impact on the thermal conditions within cities, including the mitigation of excessive street land surface temperatures (LSTs). However, previous research has mainly addressed the linear relationships between the physical spatial elements of streets and LST. There has been limited exploration of potential nonlinear relationships and the influence of population density variations. This study explores multi-dimensional street composition indicators obtained from street-view imagery and applies generalized additive models (GAMs) and geographically weighted regression (GWR) to evaluate the indicators’ impact on LST in areas with various population densities. The results indicate the following: (1) The six indicators—green space index (GSI), tree canopy index (TCI), sky open index (SOI), spatial enclosure index (SEI), road width index (RWI), and street walking index (SWI)—all have significant nonlinear effects on summer daytime LST. (2) Among all categories, the GSI negatively affects LST. Moreover, the TCI’s impact on LST shifts from negative to positive as its value increases. The SOI and SWI positively affect LST in all categories. The SEI’s effect on LST changes from negative to positive in the total and high-population (HP) categories, and it remains negative in the low-population (LP) category. The RWI positively affects LST in the total category, shifts from negative to positive in the LP category, and remains negative in the HP category. (3) The influence ranking is GSI > SEI > SWI > SOI > TCI > RWI, with GSI being the most significant factor. These findings provide key insights for mitigating street LSTs through design interventions, contributing to sustainable urban development. Full article

In view of global climate and environmental challenges, exploring sustainable urban vegetation management and development is crucial. This study aims to investigate the design strategies of urban road green space plants under the guidance of the dual theories of carbon sequestration and cooling eco-efficiency and aesthetics. In this study, Yangling, a representative small- and medium-sized city, was selected as the study area, and road green space plants were identified as the research objects. The assimilation method was employed to ascertain the carbon sequestration and oxygen release, as well as the cooling and humidification capacities of the plants. The aesthetic quality of the plants was evaluated using the Scenic Beauty Estimation and Landscape Character Assessment. Finally, we propose design strategies for landscapes with higher aesthetic and carbon sequestration and cooling benefits. The results demonstrate a clear nonlinear positive correlation. The carbon sequestration and cooling benefits of plants and the aesthetic quality, with correlation coefficients of 0.864 and 0.922, respectively. Across the same sample points, the rankings of standardized values for carbon sequestration, cooling benefits, and aesthetic quality vary minimally. This indicates that eco-efficient plants with harmonious colors and elegant forms can boost the aesthetic appeal and ecological function in road green spaces. Furthermore, the Sophora japonica Linn., Ligustrum lucidum Ait., Koelreuteria paniculata Laxm., Prunus serrulata Lindl., Prunus cerasifera Ehrhar f., Ligustrum sinense Lour., Photinia × fraseri Dress, Ligustrum × vicaryi Rehder, Sabina chinensis (L.) Ant. cv. Kaizuca, and Ophiopogon japonicus (L. f.) Ker Gawl. are proved to be ecologically dominant plants. They can be employed as the principal selected species for plant design. This study summarizes applicable design strategies for three types of green spaces: avenue greenbelts, traffic separation zones, and roadside greenbelts. The nonlinear regression model developed here provides a reference for scientifically assessing and optimizing urban planting designs. Full article