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BackgroundSexual health is one of the principal components of human well-being. Traditional methods for observing human sexual behavior typically adopt manual intervention approaches (eg, interviews). However, the data obtained by such traditional approaches suffer from intrinsic bias and limited sample sizes. Sexual behavioral data that are more reflective of the actual situation can be collected by equipping sex toys with sensors. ObjectiveTo address the limitations of traditional human sexual behavior data observation methods, a novel cyberphysical system is proposed to capture natural human sexual behavior data in China at the nationwide level. MethodsA cyberphysical human sexual behavior acquisition system (SeBA) was designed and implemented. SeBA jointly utilizes state of the art information and communication technologies such as smart sex toys, smartphones, and mobile social networks. Smart sex toys enable objective collection of data on human sexual behavior, while the mobile social network provides the possibility of partnered sex in a cyberphysical manner. The objectives and function settings are discussed, and the overall framework of the system architecture is presented. ResultsOperation and privacy policies are proposed and the technical solution of SeBA is described. The effectiveness of SeBA was verified based on analysis of users’ human sexual behavior data collected from January 2016 to June 2017. A total of 103,424 solo sexual behaviors were recorded involving 13,047 users, and 61,007 partnered sexual behaviors from 7,140 users were observed. The proportions of males and females in the solo and partnered sex groups were fairly consistent with recent statistics on unmarried individuals in China. We also found that only a small portion of individuals provided information on at least one other attribute besides the required input of gender, such as age, height, location, job, sex preferences, purposes, and interests. ConclusionsTo the best of our knowledge, this is the first study to analyze objective human sexual behavior data at the nationwide level. Although the data are restricted to China, this study can provide insight for further research on human sexual behavior based on the huge amount of data available from wireless smart sex toys worldwide. It is anticipated that findings from such objective big data analyses can help deepen our understanding of sexual behavior, as well as improve sexual health and sexual wellness.

Graph neural networks (GNNs) have become a defacto paradigm for graph representation learning. Generally, GNNs are trained in an end-to-end manner with supervision, requiring considerable task-specific labeled data. To reduce the labeling burden, recent works leverage self-supervised tasks to pre-train an expressive GNN model on abundant unlabeled data and finetune the trained model on downstream datasets with only a few labels. However, existing GNN pre-training approaches only concentrate on a single view for graph self-supervised learning while ignoring the rich semantic information in graphs, leading to the lack of sample utilization efficiency during the pre-training process. To tackle such challenges, we propose a multi-view graph contrastive encoding for graphs during GNN pre-training, called MVRACE. The critical insight is that we construct node and graph-level views to capture local attribute information and global structure in a graph. Concretely, the node-level view utilizes graph centrality and encodes the $r$-ego network to capture the local-whole relationship in a graph. The graph-level view aims to encode graph pairs to explore different graph structures and empower the discrimination ability of the GNN encoder. In addition, we combine multi-views with a joint contrastive loss function to integrate node- and graph-semantic information simultaneously. Comprehensive experiments on multiple domain datasets demonstrate that our approach can significantly yield competitive performance compared to state-of-the-art methods.

The study of the land use optimization of urban agglomerations is of great significance to the rational utilization of land resources and the sustainable development of urban agglomerations. Previous studies have simply regarded the urban agglomeration as a whole area or each city as an isolated individual, without considering the regional coordinated development of urban agglomerations in land use optimization objectives. This study proposed the first attempt to couple Zipf's law and a multiobjective optimization model of land use in an urban agglomeration. Zipf's law was applied to quantify the coordinated development of urban agglomeration and use it as one of the objectives of land use optimization. The genetic algorithm was adopted to establish the optimization model, and different future development scenarios were designed for comparison. The Beijing–Tianjin–Hebei (BTH) urban agglomeration was selected as the study area, and the proposed model obtained effective results within an acceptable time. The Zipf's law–based optimization objective provided a more coordinated scale structure for urban growth and led the urban structure to develop toward the optimized state. There was a conflict, however, between the coordination and the other two objectives (compactness and suitability). Different stakeholders need to consider the trade-offs among these optimization objectives in urban planning. Finally, based on the optimization patterns of local areas, we suggest the following: Some large-scale cities (e.g., Beijing and Tianjin) should strictly control their new urban expansion or transfer part of their resources and population; Langfang and Xingtai should accelerate their development or take over part of the noncapital function of Beijing; and Zhangjiakou and Chengde should optimize their urban morphology and suitability. [ABSTRACT FROM AUTHOR]

Geospatial cellular automata (Geo-CA) models have encountered challenges in computing efficiency and scalability when conducting large-scale land use change simulation applications. Parallel computing has proven to be effective to address these challenges. However, parallelization strategies for existing parallel Geo-CA models are always optimized for specific models and lack applicability to others. Besides, most parallel Geo-CA models focus on parallelizing land use change simulations, ignoring land use transition rule mining. Moreover, there is a lack of effective parallel strategies for demand-constrained land use change simulation on distributed heterogeneous architectures. This study proposes a parallel framework on hybrid parallel computing architectures applicable for raster-based Geo-CA models to enhance their computing efficiency and scalability while maintaining simulation accuracy. The framework provides parallelization strategies for both the land use transition rule mining for multiple land use types and the demand-constrained land use change simulation on distributed heterogeneous architectures. The framework was employed to parallelize two contemporary Geo-CA models, i.e., PLUS and MCCA. Experiments showed that the parallelized models achieved significant improvements in computing efficiency and scalability, confirming the effectiveness of the proposed framework for large-scale land use change simulation studies. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA. [ABSTRACT FROM AUTHOR]

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks: book rating recommendation, user rating recommendation, and the book summary recommendation; it also explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers' attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks. [ABSTRACT FROM AUTHOR]

The reverse k nearest neighbors (RkNN) query is a prominent yet time-consuming spatial query used in facility siting, influential domain analysis, potential customer analysis, etc. Its aim is to identify all points that consider the query point as one of their k closest points. However, when k is relatively large (e.g. k = 1000), existing RkNN techniques often struggle to provide acceptable response times (within a few seconds). To address this issue, we propose a verification approach called conic section discriminance (CSD). This method serves to determine whether points belong to the RkNN set. With CSD, only a small fraction of candidates require costly k nearest neighbors (kNN) queries for verification, while the rest can be rapidly verified with O(1) complexity. Furthermore, we propose a Voronoi-based candidate generation approach to curtail the candidate set size. By leveraging the VoR-tree structure, we integrate these two approaches to form a novel RkNN algorithm named CSD-RkNN. A comprehensive set of experiments is conducted to compare CSD-RkNN with Slice as the state-of-the-art RkNN algorithm, and VR-RkNN as the original RkNN algorithm on VoR-tree. The results indicate that CSD-RkNN consistently outperforms the other two algorithms, especially when k is relatively large. [ABSTRACT FROM AUTHOR]

Community discovery is one of the most popular issues in analyzing and understanding a network. Previous research suggests that the discovery can be enhanced by assigning weights to the edges of the network. This paper proposes a novel edge weighting method, which balances both local and global weighting based on the idea of shared neighbor ranging between users and the interpersonal significance of the social network community. We assume that users belonging to the same community have similar relationship network structures. By controlling the measure of "neighborhood", this method can adequately adapt to real-world networks. Therefore, the famous similarity calculation method-SimRank-can be regarded as a special case of our method. According to the practical significance of social networks, we propose a new evaluation method that uses the communication rate to measure its divided demerit to better express users' interaction relations than the ordinary modularity Q. Furthermore, the fast Newman algorithm is extended to weighted networks. In addition, we use four real networks in the largest Chinese micro-blog website Sina. The results of experiments demonstrate that the proposed method easily meets the balancing requirements and is more robust to different kinds of networks. The experimental results also indicate that the proposed algorithm outperforms several conventional weighting methods.