Search Results (1,534)

Optimal drip irrigation management in shallow groundwater areas needs to clarify the effects of flow rate and layout designs on the soil moisture, salt distribution, cotton root length density, plant height, leaf area, and yield. In this study, a one-year field experiment was conducted from April to October 2018 in the fifth company of the 16th Regiment in Alar City, Xinjiang, to investigate the effects of various drip flow rates and layout designs of cotton growth. Two drip flow rates (2.8 and 5.6 L·h⁻¹) and two layout designs (one film, two drip tapes, and six rows; one film, three drip tapes, and six rows) were applied to explore the optimal combination, resulting in a total of four treatments that were irrigated three times in the whole growth period. Soil moisture, salt distribution, cotton root length density, plant height, and leaf area were measured. The main results were as follows: (1) Under the same layout designs, the soil moisture content was higher and the soil salinity was lower when the drip flow rate was 5.6 L·h⁻¹, and the cotton root length density, plant height, leaf area, and yield were significantly higher than that of 2.8 L·h⁻¹. (2) Under the same drip flow rate, the soil desalination rate, cotton growth indexes, and yield under the three-tapes treatment were significantly higher than the values of the two-tapes treatment. The actual yield of treatment D was 21.56%, 19.23%, and 11.71% higher than that of treatments A, B, and C, respectively. (3) The crop evapotranspiration of cotton during the two irrigation cycles showed an increasing trend, and the groundwater contribution showed a smaller and then increasing trend. Overall, the combination of three tapes and a drip flow rate of 5.6 L·h⁻¹ had the highest cotton yield and net income, which were 6211.36 kg·hm⁻² and 4820.21 kg·hm⁻² for the theoretical and actual yields. The results of this study can provide a reference for the management of limited irrigation leaching soil salinity and cotton cultivation in shallow groundwater areas. Full article

In view of the challenges faced by cotton dibbler in Xinjiang under high-speed operation, a novel crawler-type delayed hole-forming device has been designed to address the seed throwing and floating issues in high-speed cotton dibbling in Xinjiang, enhancing the duck bill’s performance. This mechanism increases the sowing speed to 6 km/h by extending the duck bill horizontally. Utilizing agronomic principles, the mechanism’s layout and key components were optimized for efficient hole-forming. DEM and multi-body dynamics simulations were employed to analyze the motion, focusing on the fixed the tilt angle of the duck bill (A), the depth of the duck bill hole-forming into the soil (B), and the angle of rotation of the moving duck bill (C) as factors affecting hole dimensions (longitudinal length of hole Y₁ and hole-forming depth of cotton seed hole Y₂). Quadratic regression test using RecurDyn-EDEM coupling identified optimal parameter settings for maximum hole-forming performance. When A was 2.4°, B was 42.4 mm, and C was 30.5°, the performance of the hole-forming was the best. Under the optimal parameter combination, the bench verification test was carried out. The error between the bench verification results and the simulation results is small, indicating that the model has high accuracy. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill (0.11 s). It meets the requirements of high-speed cotton planting in China and facilitating advancements high-speed planter technology. Full article

The COVID-19 pandemic has profoundly altered our perception of spatial experience, presenting new challenges and opportunities for sustainable development. This study adopts a transition design perspective to explore innovative strategies for enhancing spatial experiences in the post-pandemic era, underscoring the central role of sustainability in this process. By integrating expert interviews, stakeholder participation, and qualitative analysis, this research delves into the impacts of the pandemic on spatial design and identifies key challenges and opportunities. It proposes a series of innovative strategies, such as optimizing spatial layouts, incorporating smart technologies, augmenting with natural elements, and promoting modular design, aimed at creating safer, smarter, more comfortable, and sustainable spatial experiences. This study not only provides new insights into spatial experience design in the post-pandemic era but also underscores the potential of transition design in facilitating societal transformation and achieving sustainability goals, providing actionable insights and directions for future research. Full article

Under the influence of mountainous terrain, the spatial synergy between tourism resources and tourism service facilities has emerged as a pivotal factor affecting the overall efficiency enhancement of regional tourism destinations. In order to explore the synergistic effect of the two, taking Qimen County as the study site, this study utilizes Point of Interest (POI) data of tourism resources and tourism service facilities. It constructs a fine-scale multidimensional spatial synergy methodology based on grid vectorization to conduct scenario-based comparative analyses of altitude and population density. The objective is to elucidate the effects of fine-scale tourism development synergy and propose enhancement strategies. The findings are as follows: (1) The vertical zonation of mountains has led to a widespread, decentralized distribution of natural tourism resources in mid-to-high-altitude areas, while humanistic tourism resources in low-altitude urbanized areas exhibit a granular, clustered distribution. These contrasting scenarios manifest a polarization, making it difficult to achieve supply–demand matching of the layout pattern of tourism service facilities along transportation routes. (2) The spatial gradient effect of the synergy between the two in mountainous counties is significant, with a higher synergy level in core towns and obvious misalignment in peripheral areas. (3) Altitude and population density are critical factors influencing the supply of tourism service facilities. Through scale aggregation guidance and cost–benefit mechanisms, the spatial distribution can be classified, stratified, and optimized to better serve resource development. This study provides valuable insights into understanding laws governing development and utilization within mountainous county areas for academic research purposes. Full article

Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an “n”-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang’an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing’s central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing’s central urban area. Full article

With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators. Full article

Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on Deep Reinforcement Learning (DRL) show potential for tackling urban driving situations, they suffer from slow convergence, especially in complex scenarios with high mobility. In this paper, we present a new approach based on the Soft Actor-Critic (SAC) algorithm to control the autonomous vehicle to enter roundabouts smoothly and safely and ensure it reaches its destination without delay. For this, we introduce a destination vector concatenated with extracted features using Convolutional Neural Networks (CNN). To evaluate the performance of our model, we conducted extensive experiments in the CARLA simulator and compared it with the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) models. Qualitative results reveal that our model converges rapidly and achieves a high success rate in scenarios with high traffic compared to the DQN and PPO models. Full article

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process. Full article

With the expansion of Chinese university campuses, electric bikes (E-bikes) have become the most sustainable and effective commuting option because they are a flexible and energy-saving travel mode. Consequently, campus E-bike charging piles have become one of the most essential public service facilities on campuses. However, since most Chinese campuses are closed and independent, the principles of urban public service facilities cannot be simply applied to the layout and use of campus charging facilities. Thus, this study focuses on Zijingang Campus at Zhejiang University, and proposes an optimization strategy for the spatial and temporal layout of E-bike charging piles on the campus. First, trip chain demand models are constructed to examine the travel patterns of E-bike users on campus and the demands for charging areas and time. Second, a space location model is constructed to locate the charging piles in areas with high demand. Finally, according to the charging times of different users, user charging time is integrated into the strategy. This study enhances the layout and utilization system of campus E-bike charging facilities by considering both temporal and spatial dimensions. Overall, this study contributes to the advancement of sustainable transportation infrastructure planning on a campus-wide scale, offering theoretical insights for the design and utilization of functional facilities in large-scale, semi-enclosed environments (e.g., university campuses). Full article

Urban terminal logistics and distribution enterprises face the problems of duplicated network layouts, high costs, and inefficient urban environments. Recently, collaborative distribution using unmanned aerial vehicles (UAVs) and ground vehicles has been considered as a means of reducing costs and enhancing efficiency, thus overcoming the issues created by the high-density layout of enterprises’ relatively independent networks. This essentially involves constructing an air–ground collaborative common distribution network. To optimize the economic cost and distribution time of network operations, we established a site selection planning model for air–ground cooperative urban co-distribution centers and designed a solution method based on gray wolf optimization with K-means clustering. Taking the Wangsheren area of Jinan City, China, as an example, 15 UAV co-distribution centers and 13 vehicle co-distribution centers were identified. Although the average distribution cost of the network rose by 35–50% compared to traditional terminal distribution, the time saving was 80–85%, greatly improving the high-value-added service capacity. The end-distribution efficiency and customer satisfaction were also enhanced, which fully verifies the feasibility, validity, and applicability of the proposed model. Our approach can be applied to landing sites and the planning and optimization of large-scale commercial operations using logistics UAVs in urban areas. Full article

In the context of the current information age, leveraging Earth observation (EO) technology and spatial analysis methods enables a more accurate understanding of the characteristics of small towns. This study conducted an in-depth analysis of the urban morphology of small towns in the Qinba Mountain Area of Southern Shaanxi by employing large-scale data analysis and innovative urban form measurement methods. The U-Net3+ model, based on deep learning technology, combined with the concave hull algorithm, was used to extract and precisely define the boundaries of 31,799 buildings and small towns. The morphological characteristics of the town core were measured, and the core areas of the small towns were defined using calculated tessellation cells. Hierarchical clustering methods were applied to analyze 12 characteristic indicators of 89 towns, and various metrics were calculated to determine the optimal number of clusters. The analysis identified eight distinct clusters based on the towns’ morphological differences. Significant morphological differences between the small towns in the Qinba Mountain Area were observed. The clustering results revealed that the towns exhibited diverse shapes and distributions, ranging from irregular and sparse to compact and dense forms, reflecting distinct layout patterns influenced by the unique context of each town. The use of the morphometric method, based on cellular and biological morphometry, provided a new perspective on the urban form and deepened the understanding of the spatial structure of the small towns from a micro perspective. These findings not only contribute to the development of quantitative morphological indicators for town development and planning but also demonstrate a novel, data-driven approach to conventional urban morphology studies. Full article

Industrial land is essential for supply-side structural reforms, particularly in the Chengdu–Chongqing area, Western China’s most densely populated and industrially robust region. This area, a pivotal hub linking Southwest China with South Asia and Southeast Asia, is critical for the national strategic layout and regional economic restructuring. Despite its substantial industrial foundation as an old industrial base, internal developmental stagnation has led to an irrational industrial land use structure. This paper analyzed land transaction data from the China Land Market Network (2010–2021) using methods such as kernel density estimation, the standard deviation ellipse method, and Global Moran’s I index. The analysis focuses on the spatiotemporal evolution of industrial land marketization and its driving factors in 44 cities within the Chengdu–Chongqing economic circle. The findings aim to enhance the strategic implementation of national policies and regional economic optimization, suggesting intensified development efforts in key cities and promoting integrated growth in potential areas like Suining and Ziyang to foster a conducive environment for high-quality regional development. Full article

Urban park green spaces (UPGS) are essential resources for improving the urban ecological environment and meeting residents’ recreational needs. However, during rapid urbanization, the layout of UPGS often exhibits spatial inequity, with significant differences in the resources enjoyed by resident groups with different socioeconomic attributes. Accurately assessing the spatial equity of the UPGS layout (the equal accessibility of UPGS) is crucial for optimizing resource allocation and promoting social equity. This study takes the main urban area of Nanjing as an example and utilizes location-based service (LBS) data and multi-source geographic data to conduct an in-depth characterization of residents’ socioeconomic attributes, recreational behaviors, and park green space layout at the street scale. By constructing indicators of resident heterogeneity and UPGS supply–demand matching degree, it reveals the differences in park green space accessibility among different social groups and locations and explores the correlation between resident heterogeneity and UPGS spatial equity. The study finds that the layout of UPGS in the main urban area of Nanjing exhibits significant spatial inequity, with generally poor accessibility to park green spaces in the central urban area and low-income communities. The higher degree of diversification of residents’ socioeconomic attributes leads to a lower level of UPGS spatial equity in their streets. The results of the big data analysis verify the significant impact of resident heterogeneity on the equity of park green space layout. This study reveals the spatial equity issues of UPGS layout from the perspective of resident heterogeneity, providing new ideas and evidence for optimizing the allocation of park green space resources. Future UPGS planning should pay more attention to the diversity of residents’ recreational needs, focus on improving the accessibility of park green spaces in central urban areas and low-income communities, and balance the interests and demands of different stakeholders through public participation mechanisms. Full article

Based on the theory of user needs and the product development mode and framework of mobile Internet interactive innovation, a new “reality → virtual → reality” interactive innovation product development mode is constructed. It draws on the unique characteristics, systematic technical system, and comprehensive scientific and technological layout of the Metaverse. On this basis, a framework for product development and design based on interactive innovation from the Metaverse perspective is innovatively proposed. In the Metaverse scenario, interactive innovation knowledge can be easily and effectively transformed into design knowledge, and all groups of users truly participate in the whole process of product design. Moreover, the development of interactive innovative products in the Metaverse scenario can be combined with artificial intelligence (AI) technology to further automate the statistical analysis of user needs and preferences so as to meet the dynamic needs of users and accurately develop products that fit user needs and enterprise standards. In addition, users, designers, and enterprises can make joint decisions on product design solutions and development forms, and the Metaverse technology can also optimize the products with continuous iteration and obtain the optimal solutions. An automotive case study illustrates the feasibility and effectiveness of the model for product development innovation and enterprise digital transformation. Full article

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article