Search Results (11,944)

Abstract: Buildings, as key factors influencing population distribution, have various functional attributes. Existing research mainly focuses on the relationship between land functions and population distribution at the macro scale, while neglecting the finer-grained, micro-scale impact of building functionality on population distribution. To address this issue, this study integrates multi-source geospatial and spatio-temporal big data and employs the XGBoost algorithm to classify buildings into five functional categories: residential, commercial, industrial, public service, and landscape. The proposed model innovatively incorporates texture, geometric, and temporal features of building images, as well as socio-economic characteristics extracted using the distance decay algorithm. The results yield the following conclusions: (1) The proposed method achieves an overall classification accuracy of 0.77, which is 0.12 higher than that of the random forest-based approach. (2) The introduction of time features and the distance decay method further improved the model performance, increasing the accuracy by 0.04 and 0.03, respectively. (3) The correlation between the building functions and population distribution varies significantly across different scales. At the district and county levels, residential, commercial, and industrial buildings show a strong correlation with population distribution, whereas this correlation is relatively weak at the street scale. This study advances the understanding of building functions and their role in shaping population distribution, providing a robust framework for urban planning and population modeling. Full article

With the global shift to electric vehicles, countries face unique challenges and opportunities shaped by their geographical and economic contexts. This paper presents a system that leverages smart transport technologies, the Internet of Things, and decision-making algorithms, such as PROMETHEE, to optimize charging stations and their positioning in diverse urban and rural settings. The system addresses key obstacles, including managing charging infrastructure, balancing energy consumption, and enhancing transport accessibility. By analyzing local conditions, the proposed solution incorporates innovative algorithms for electricity demand forecasting, charging station management, and integration with urban transport systems. This approach ensures a flexible, scalable, and sustainable electric vehicle management system that aligns with international standards and evolving technological trends. Full article

This research employs multi-source data including big data, remote sensing raster data, and statistical vector data. Through the superposition of tourism activity points of interest with remotely sensed inversion raster data like human carbon emissions, net primary productivity, and kilometer-grid GDP, the carbon emissions, carbon sinks, and economic output of tourism attractions are obtained. Data envelopment analysis and econometric models are utilized to assess the “carbon emissions–carbon sinks–economic output” coupling efficiency relationship and driving mechanism under the framework of the tourism carbon neutrality process. This research takes Gannan Tibetan Autonomous Prefecture in the Qinghai–Tibet Plateau region, which has had a severe response to global climate change and is particularly deficient in statistical and monitoring data, as an example. It is found that in Gannan Prefecture, which is at the primary stage of tourism development, with a high degree of dependence on the location and regional economic development level, the challenge of decoupling carbon emissions from the economy is significant. The carbon neutrality process in natural tourism attractions is marginally superior to that in cultural tourism attractions. However, even among natural tourism attractions, the number of spots achieving high carbon sink efficiency is extremely limited. There remains considerable scope for achieving carbon neutrality process through carbon sinks in the future. The location and vegetation conditions can exert a direct and positive influence on the improvement of carbon efficiency in tourist destinations. Establishing natural tourism attractions near cities is more conducive to facilitating carbon neutrality. This research highlights the advantages of remote sensing methods in specific sectors such as tourism where quality monitoring facilities and methods are lacking and provides a reference for evaluating the tourism carbon neutrality process and managing environmental sustainability on tourism attractions in similar regions and specific sectors worldwide. Full article

In the rapidly evolving digital healthcare market, ensuring both the activation of the market and the fulfillment of the product’s social role is essential. This study addresses the service quality of smart running applications by utilizing big data text mining techniques to bridge the gap between user experience and service quality in digital health applications. The research analyzed 264,330 app reviews through sentiment analysis and network analysis, focusing on key service dimensions such as system efficiency, functional fulfillment, system availability, and data privacy. The findings revealed that, while users highly value the functional benefits provided by these applications, there are significant concerns regarding system stability and data privacy. These insights underscore the importance of addressing technical and security issues to enhance user satisfaction and continuous application usage. This study demonstrates the potential of text mining methods in quantifying user experience, offering a robust framework for developing user-centered digital health services. The conclusions emphasize the need for continuous improvement in smart running applications to meet market demands and social expectations, contributing to the broader discourse on the integration of e-commerce and digital health. Full article

Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson’s feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600–700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with $R^2$ = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages. Full article

This study aims to reveal the characteristics of the digital transformation of classroom teaching in health education. The key links of the digital transformation process in classroom teaching were identified to determine an implementation pathway. Based on the results, theoretical and practical guidance for the implementation of digital transformation in health education was proposed. Full article

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS. Full article

Urban land-use types, a fundamental aspect of urban planning, land management, and the effective utilization of spatial resources, are exhibiting increasing complexity. Efficient and scientific identification of large-scale urban land-use types has become a major challenge in urban research. To address this, the present study adopts a functional structure-based perspective and integrates commercial AOI data, POI data, nighttime light data, and population distribution data to classify land use. Departing from existing data weighting algorithms, this research applies artificial intelligence techniques, utilizing the categorical information of AOI data as labels. Through supervised deep learning, urban land-use types are refined into nine major categories and 21 subcategories across cities of different scales and locations. Compared to SVM, RF, and MLP models, the XGBoost model achieved the highest accuracy in classifying urban construction land (weighted avg F1 score = 0.87). Furthermore, by comparing the AOI data with real-world test datasets, the accuracy and granularity of land-use classification were significantly enhanced. Finally, this AI model, combined with remote sensing imagery and transportation network data, was used to generate a land-use map for the target city, offering insights into the generalizability of AI models in urban land-use classification. Full article

With the rapid development of autonomous collision avoidance algorithms on ships, the technical demand for the testing and verification of autonomous collision avoidance algorithms is increasing; however, the current testing of autonomous collision avoidance algorithms is mainly based on the virtual simulation of the computer. To realize the testing and verification of the autonomous collision avoidance algorithm in the real ship scene, a method of virtual reality fusion testing in open water is proposed and real ship testing is carried out. Firstly, an autonomous ship collision avoidance test and evaluation system is established to research the test method of ship encounters in open water. Starting from the convention on the international regulations for preventing collisions at sea (COLREG), the main scenario elements of ship collision avoidance are analyzed. Based on the parametric modeling method of ship collision avoidance scenarios, a standard test scenario library for ship collision avoidance in open waters is established. Then, based on the demand for a ship collision avoidance function test, the evaluation index system of ship collision avoidance is constructed. Subsequently, for the uncertainty of the initial state of the real ship test at sea, the virtual–real space mapping method to realize the correspondence of the standard scenario in the real world is proposed. A standardized testing process to improve testing efficiency is established. Finally, the method of conducting virtual simulation and virtual reality fusion tests for various scenarios are verified, respectively. The test results show that the test method can effectively support the testing of autonomous collision avoidance algorithms for ships in open waters and provide a practical basis for improving the pertinence and practicability of ship collision avoidance testing. Full article

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions. Full article

This research aims to assess consumer attitudes towards purchasing pre-loved luxury fashion items and explore how these attitudes influence their intention to buy such products. Luxury goods consumption is evolving into a multifaceted proposition where customers actively take on new responsibilities. In addition to being purchasers and users, they occasionally turn into luxury brand product dealers. Luxury fashion, which includes more expensive materials, apparel, and frequently new and limited-edition items, is unquestionably stylish. Luxury brands could draw clients and the attention of many audiences, becoming quite prominent, even though luxury fashion only makes up a small portion of the economy compared to other significant businesses. Using a convenience sampling technique, data were collected from 282 individuals in Cairo. The analysis was conducted through SPSS software v2023. Our findings show that consumers’ concerns about the environment have a big influence on their perceived value (PI) of used luxury fashion items, both directly and indirectly through the mediation of their desire for sustainability. Nonetheless, attitude strength has a moderating effect on this association. It is interesting to note that the relationship between environmental concern and sustainability is weakened under the influence of attitude strength. Furthermore, our findings indicate that modest levels of attitude strength make it easy to change how customers’ environmental concerns affect their previously owned luxury fashion items. High-end stores can also fight off counterfeit marketplaces by providing authentication services to consumers of pre-loved luxury clothing. This study emphasizes the role of consumer attitude as a mediator in shaping purchase intentions for pre-loved luxury fashion. However, its focus on one region and cross-sectional data collection presents limitations. Future studies should explore other markets and use longitudinal data for a deeper understanding. This research contributes to the existing literature by offering insights for consumers, marketers, and sellers promoting pre-loved luxury fashion. Full article

Background: Female reproductive aging remains irreversible. More evidence is needed on how polyunsaturated fatty acids (PUFAs) affect the female reproductive lifespan. Objectives: To identify and validate specific PUFAs that can influence the timing of menarche and menopause in women. Methods: We utilized a two-sample Mendelian randomization (MR) framework to evaluate the causal relationships between various PUFAs and female reproductive longevity, defined by age at menarche (AAM) and age at natural menopause (ANM). Our analyses leveraged summary statistics from four genome-wide association studies (GWASs) on the plasma concentrations of 10 plasma PUFAs, including 8866 to 121,633 European individuals and 1361 East Asian individuals. Large-scale GWASs for reproductive traits provided the genetic data of AAM and ANM from over 202,323 European females and 43,861 East Asian females. Causal effects were estimated by beta coefficients, representing, for each increase in the standard deviation (SD) of plasma PUFA concentration, the yearly increase in AAM or ANM. Replications, meta-analyses, and cross-ancestry effects were assessed to validate the inference. Conclusions: Higher plasma DHA was identified to be associated with delayed natural menopause without affecting menarche, offering a potential intervention target for extending reproductive longevity. Full article

To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process. Additionally, inspired by blind navigation principles, we propose an active collision mechanism that relies on continuous collisions to identify obstacles and adjust the next movement direction, thereby improving the efficiency of escape path planning. We also implement an obstacle memory mechanism to prevent exploration into erroneous areas during sampling, significantly increasing the success rate of escape and reducing the path length. We validate the proposed algorithm in a dedicated MATLAB environment, comparing its performance with existing RRT, RRT*, and APF-RRT* algorithms. Experimental results indicate that the improved algorithm achieves significant enhancements in both planning speed and path length compared to the other methods. Full article

Water pollution presents one of the biggest challenges in the world today, as the degradation of water quality of rivers in many instances is increasing so fast and poses a big danger to all forms of life, eventually causing many aquatic species and other species that depend on them to be endangered. Hence, with the development of Internet of Things (IoT) and Wireless Sensor Networks (WSNs), there arises a need to monitor river waters for a timely response in protecting the rivers, which is the aim of this paper. With respect to this project, we searched a little bit for some existing IoT technologies and other related work. In this paper, we propose a practical low-cost solution based on Long Range (LoRa) technology to obtain real-time observations of, with certain sensors, such water parameters as temperature, pH, conductivity and turbidity. Data gathered at a sensor node are transmitted via LoRa modulation to a gateway for processing and local storage on a Message Queuing Telemetry Transport (MQTT) server, visualization on a Node-RED interface, or transmission to the cloud. The prototype system created is employed in the actual field and demonstrates that the water quality monitoring in the river can be carried out effectively within a small scale of the area of roughly 20 km² depending on the location of the study site. Full article