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In the era of aesthetic economy, users pay more and more attention to the aesthetic quality and emotional experience of products. The study discusses the intelligent design method of product model integrating computational aesthetics, combines the advantages of variational self-encoder and generative adversarial network, constructs a generative model based on VAE-GAN, and quantitatively analyzes the product images generated by the VAE-GAN network to explore the effect of the products it generates. Then, take the car form generated by the model as an example, establish the visual presentation quantitative index system of the product model form layout, and evaluate the visual presentation effect of the generated car model after applying gray correlation analysis to assign the indexes. The VAE-GAN model has a better generative effect, and its SSIM value is greater than 0.8 in most of the training phases. Among them, the product model generated after 2000 steps of training is the most excellent, with the highest evaluation score. The product model is the most outstanding, with the highest evaluation score of 1.92. The overall visual presentation effect of the product forms generated by the model is good, and the comprehensive evaluation scores are above 0.6. The VAE-GAN model can be used for product generation, and the visual presentation evaluation method that integrates computational aesthetics can realize the aesthetic evaluation of product forms, which can help designers make design creations and program decisions.

Low cost and high performance are key elements for the industrialization of organic solar cells (OSCs). In view of this, developing highly‐efficient organic photovoltaic molecules with simple chemical structure is one of the most effective countermeasures. Herein, three simple‐structure terpolymers PTQ12‐5, PTQ12‐10, and PTQ12‐15 are designed and synthesized, by embedding difluoro‐substituted bithiophene (2T‐2F) unit into the backbone of low‐cost and high‐efficiency polymer PTQ10. The synthesis of three terpolymers contains only two‐step synthetic routes and over 85% synthetic yields from cheap raw compounds, thus exhibiting distinctly low‐cost characteristic. Remarkably, PTQ12‐5 shows significantly higher absorbance coefficient and hole mobility than PTQ10. Consequently, PTQ12‐5‐based OSCs shows more efficient exciton dissociation and charge transfer, suppressed carrier recombination, tighter molecular π‐π stacking, and faster and more balanced charge transport, thus demonstrates a significantly improved efficiency of 18.77% than that of PTQ10‐based device (18.03%). Moreover, an outstanding efficiency of 19.36% is achieved in PTQ12‐5‐based ternary device by further modulating the energy level alignment and blending features of photoactive molecules, which is the highest efficiency of OSCs based on low‐cost polymers to date. This work has significant implications for guiding the design of low‐cost and high‐performance organic photovoltaic materials. [ABSTRACT FROM AUTHOR]

Community detection has been one of the key research directions in network analysis. Most of the current network community detection algorithms mainly use the structural information of the network to adopt a greedy algorithm to maximize a certain indicator, which cannot fully consider the node feature information, edge weight, and network community relationship asymmetry. To address this situation, this paper proposes an autoencoder-like modularity nonnegative matrix factorization (AMNMF) community detection algorithm. The algorithm expands the depth of non-negative matrix factorization by using an encoder-like structure, and introduces modularity and graph regularizer into the objective function optimization process of non-negative matrix factorization to fully mine the node and community structure information in the network. The problem of community relationship imbalance is solved by adding orthogonal constraints to the middle layer of the encoder. Experiments on multiple real networks show that: AMNMF is an effective NMF extension algorithm that uses node feature information and network structure information. Compared with the best results of baseline algorithms, it achieves an improvement of about 15% to 122%, and can accurately and effectively complete the community detection task. [ABSTRACT FROM AUTHOR]

Myanmar is among the regions with the most frequent activities of International Non-Government Organizations (INGOs). Analyzing the spatio-temporal patterns of these activities holds crucial importance for optimizing organizational coordination and enhancing governmental oversight. This study focuses on the spatio-temporal evolution characteristics and influencing factors of INGO activities in Myanmar from 2010 to 2021, utilizing spatial autocorrelation and regression analysis. The results show that the number of INGOs in Myanmar has shown a gradual slowdown in growth trends, with the number of activities exhibiting a wave-like pattern, primarily driven by spontaneous activities of INGOs. The spatial distribution of INGO activities in Myanmar is concentrated in the southern plains, with the core located in Yangon, Naypyitaw, and Loilen. Furthermore, there is significant spatial polarization in the hotspot area of INGO acticities. The hotspots followed an evolutionary path from "South Myanmar" to "North Myanmar" and then back to "South Myanmar". INGO activities in Myanmar are more focused on the local economic level, urbanization level, medical level, education level, and total population size, providing the necessary support and services for the local society and making up for the "government malfunction" and "market malfunction". [ABSTRACT FROM AUTHOR]

To overcome the limitations of long-term prediction of PM2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM2.5 sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM2.5 time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R2 values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction. [ABSTRACT FROM AUTHOR]

The relationship between people and land is becoming increasingly strained, and the optimal allocation of land resources and the redevelopment of stock has become the main path for urban construction and development. In this context, how to stimulate the potential value of urban landfills through landscape regeneration has attracted much attention. Based on the CNKI (China National Knowledge Infrastructure) database and the Web of Science core collection database, the bibliometric method was used to analyze a total of 1436 Chinese and English studies on urban landfill landscape regeneration using the scientometric literature analysis software CiteSpace 6.2.R3. From this, metrological and visual mapping analyses were undertaken, and literature co-citations were taken to reveal current trends in the research for urban landfill landscape regeneration. The results show that most of the existing studies focus on landfill pollution control (779 articles) and landscape regeneration design (517 articles). Further analysis of the trends shown in publications and research disciplines shows that the future development direction of landfill landscape regeneration will be led by the ecological restoration of the site and the organic combination of site pollution control and landscape planning and design to achieve sustainable landscape reshaping of landfills. This informs recommendations for the regeneration of landfill landscapes. [ABSTRACT FROM AUTHOR]

Coastal areas are usually considered as pioneering areas for economic development and reform due to their unique geographical locations and ecological conditions. Correspondingly, rapid urbanization in coastal urban agglomerations has resulted in population concentration and land use/cover change (LUCC), leading to the decline of habitat quality and biodiversity. However, few studies have quantitatively explored the impacts of urban agglomeration expansion in coastal zones on habitat quality. Taking the Guangdong-Hong Kong-Macao-Great Bay Area (GBA) as a case study, we applied the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the habitat quality based on land use data obtained from remote sensing images from 1980 to 2020 and developed a geographically weighted regression model to quantitatively analyze the response of habitat quality to urbanization. The results showed that (1) LUCC in the GBA was dramatic from 1980 to 2020, dominated by the shift from various land use types to construction land, which led to increased fragmentation; (2) the overall habitat quality showed a decreasing trend, characterized by low values in the central part and high values in the surrounding area; (3) population and construction land such urbanization elements had a more significantly negative effect on habitat quality changes, while the relationships among slope, road distance, and habitat quality changes were complex. Based on above analysis, this paper suggests that future land management in the GBA should develop in the direction of intensification, refinement, and regional integration. [ABSTRACT FROM AUTHOR]