Rural Development and Restructuring in Central China’s Rural Areas: A Case Study of Eco-Urban Agglomeration around Poyang Lake, China

This study aims to provide a scientific reference for rural reconstruction and revitalization in the areas covered by Eco-Urban Agglomeration Around Poyang Lake. Rural development and restructuring is a comprehensive process involving multiple elements and a long-time sequence. Accordingly, scientific knowledge concerning the evolution and characteristics of the spatial and temporal patterns of rural development and reconstruction is crucial for successively facilitating rural revitalization and ensuring the sustainable development of rural areas. In this study, a framework of rural development and restructuring was constructed for areas around Poyang Lake Eco-Urban Agglomeration based on the data regarding population, land, and industrial elements in the rural regional system, as well as the data of counties covered by Poyang Lake Eco-Urban Agglomeration. For this purpose, the entropy value and other research methods were used to analyze the level of rural development and the degree of rural reconstruction, as well as to identify the characteristics of rural reconstruction types. The study results revealed the following: (1) Rural Comprehensive Development Level has increased from 0.218 to 0.347, and the geographical development gap of the countryside has narrowed; however, the development level of each region and each factor continues to remain uneven. (2) The results demonstrated a wave-like advancement in the Rural Comprehensive Restructuring Degree, with a decreased Rural Population Restructuring Degree, an increased Rural Industry Restructuring Degree, and a decreased Rural Land Restructuring Degree. (3) Rural restructuring in the study area can be divided into six zones according to the level of rural development and the degree of rural restructuring, with Type I and Type III being the main types. Based on the above results, this research proposes optimizations for different rural development and reconstruction type zones

Deep Deformable Artistic Font Style Transfer

The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts

Graph Autoencoder with Preserving Node Attribute Similarity

The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks

Manipulation of Crystallization Kinetics for Perovskite Photovoltaics Prepared Using Two-Step Method

Two-step fabricated perovskite solar cells have attracted considerable attention because of their good reproducibility and controllable crystallization during production. Optimizing the quality of perovskite films plays a decisive role in realizing superb performance via a two-step method. Many breakthroughs have been achieved to obtain high-quality film from the perspective of manipulating crystallization kinetics in the two-step preparation process, which promotes the rapid development of perovskite photovoltaics. Therefore, focusing on the crystallization process in the two-step preparation process can provide a reliable basis for optimizing the performance of two-step devices. In this review, recent progress on regulating the crystallization process for two-step PSCs is systematically reviewed. Firstly, a specific description and discussion are provided on the crystallization process of perovskite in different two-step methods, including spin-coating, immersion and evaporation. Next, to obtain high-quality perovskite film via these two-step methods, current strategies of additive engineering, composition engineering, and solvent engineering for regulating the crystallization process for two-step perovskite are classified and investigated. Lastly, the challenges which hindering the performance of the two-step perovskite photovoltaics and an outlook toward further developments are proposed

Manipulation of Crystallization Kinetics for Perovskite Photovoltaics Prepared Using Two-Step Method

Two-step fabricated perovskite solar cells have attracted considerable attention because of their good reproducibility and controllable crystallization during production. Optimizing the quality of perovskite films plays a decisive role in realizing superb performance via a two-step method. Many breakthroughs have been achieved to obtain high-quality film from the perspective of manipulating crystallization kinetics in the two-step preparation process, which promotes the rapid development of perovskite photovoltaics. Therefore, focusing on the crystallization process in the two-step preparation process can provide a reliable basis for optimizing the performance of two-step devices. In this review, recent progress on regulating the crystallization process for two-step PSCs is systematically reviewed. Firstly, a specific description and discussion are provided on the crystallization process of perovskite in different two-step methods, including spin-coating, immersion and evaporation. Next, to obtain high-quality perovskite film via these two-step methods, current strategies of additive engineering, composition engineering, and solvent engineering for regulating the crystallization process for two-step perovskite are classified and investigated. Lastly, the challenges which hindering the performance of the two-step perovskite photovoltaics and an outlook toward further developments are proposed

Thiol‐Functionalized Conjugated Metal–Organic Frameworks for Stable and Efficient Perovskite Photovoltaics

Metal-organic frameworks (MOFs) have been investigated recently in perovskite photovoltaics owing to their potential to boost optoelectronic performance and device stability. However, the impact of variations in the MOF side chain on perovskite characteristics and the mechanism of MOF/perovskite film formation remains unclear. In this study, three nanoscale thiol-functionalized UiO-66-type Zr-based MOFs (UiO-66-(SH)2 , UiO-66-MSA, and UiO-66-DMSA) are systematically employed and examined in perovskite solar cells (PSCs). Among these MOFs, UiO-66-(SH)2 , with its rigid organic ligands, exhibited a strong interaction with perovskite materials with more efficient suppression of perovskite vacancy defects. More importantly, A detailed and in-depth discussion is provided on the formation mechanism of UiO-66-(SH)2 -assisted perovskite film upon in situ GIWAXS performed during the annealing process. The incorporation of UiO-66-(SH)2 additives substantially facilitates the conversion of PbI2 into the perovskite phase, prolongs the duration of stage I, and induces a delayed phase transformation pathway. Consequently, the UiO-66-(SH)2 -assisted device demonstrates reduced defect density and superior optoelectronic properties with optimized power conversion efficiency of 24.09% and enhanced long-term stability under ambient environment and continuous light illumination conditions. This study acts as a helpful design guide for desired MOF/perovskite structures, enabling further advancements in MOF/perovskite optoelectronic devices