The cooling intensity dependent on landscape complexity of green infrastructure in the metropolitan area

The cooling effect of green infrastructure (GI) is becoming a hot topic on mitigating the urban heat island (UHI) effect. Alterations to the green space are a viable solution for reducing land surface temperature (LST), yet few studies provide specific guidance for landscape planning adapted to the different regions. This paper proposed and defined the landscape complexity and the threshold value of cooling effect (TVoE). Results find that: (1) GI provides a better cooling effect in the densely built-up area than the green belt; (2) GI with a simple form, aggregated configuration, and low patch density had a better cooling intensity; (3) In the densely built-up area, TVoE of the forest area is 4.5 ha, while in the green belt, TVoE of the forest and grassland area is 9 ha and 2.25 ha. These conclusions will help the planners to reduce LST effectively, and employ environmentally sustainable planning

Fast Analysis Method of Overhead Line Tree Barrier Based on Vectorized Power Line

[Introduction] With the continuous and rapid growth of the number of tree barrier inspections, visible light aerial photogrammetry has become the most important inspection method for hidden dangers of tree barriers in transmission lines of power supply bureaus in various cities. Since airborne laser radar cannot perform rapid iteration on a large number of point clouds, it is difficult to obtain high-precision point cloud power lines with visible light aerial photogrammetry. In the case of flourishing trees, it is difficult to comprehensively judge and analyze the relationship between tree tops, tree crowns and power lines, which results in problems such as misjudgment and omission of hidden dangers of tree barriers in the process of power line inspection. In order to solve this kind of problem, a solution of acquiring vectorized power line by laser radar is proposed. Through the intelligent analysis of vegetation information collected by multi-period visible light aerial photogrammetry, the solution of tree barrier is obtained. [Method] In view of the comprehensive situation that the image data collected by visible light aerial photogrammetry couldn't obtain high-precision point cloud power lines, it was difficult to simultaneously and comprehensively analyze the relationship between tree tops, tree crowns and power lines, and laser point cloud data couldn't be quickly iterated in large quantities, an overhead line tree barrier rapid analysis and processing system for multi-source spatio-temporal data of vectorized power lines was developed, which was suitable for the current conventional tree barrier inspection means, had comprehensive functions, saved resources, and was suitable for transmission and distribution networks. [Result] Through the three-dimensional real scene display of laser point cloud and visible light image data, the function of identifying and analyzing hidden dangers of tree barriers in power lines is realized. The vectorized power lines are complete and accurate. At the same time, combined with oblique photography technology, the data analysis of the hidden dangers of tree barriers results can identify 100% of the number of tree barrier hidden dangers. [Conclusion] The technical solution is accurate and effective, improves the efficiency and accuracy of the inspection of hidden dangers of tree barriers, and can provide guidance for practical application

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://github.com/RiemanGraph/.Comment: Accepted by IEEE ICDM 2023, Full paper, 10 page

M gene reassortment in H9N2 influenza virus promotes early infection and replication: contribution to rising virus prevalence in chickens in China

Segment reassortment and base mutagenesis of influenza A viruses are the primary routes to the rapid evolution of high fitness virus genotypes. We recently described a predominant G57 genotype of avian H9N2 viruses that caused country-wide outbreaks in chickens in China during 2010-2013 which led to the zoonotic emergence of H7N9 viruses. One of the key features of the G57 genotype is the substitution of the earlier BJ/94-like M gene with the G1-like M gene of quail origin. We report here on the functional significance of the G1-like M gene in H9N2 viruses in conferring increased infection severity and infectivity in primary chicken embryonic fibroblasts and chickens. H9N2 virus housing the G1-like M gene, in place of BJ/94-like M gene, showed early surge in viral mRNA and vRNA transcription that were associated with enhanced viral protein production, and with early elevated release of progeny virus comprising largely spherical rather than filamentous virions. Importantly, H9N2 virus with G1-like M gene conferred extrapulmonary virus spread in chickens. Five highly represented signature amino acid residues (37A, 95K, 224N and 242N in M1 protein, and 21G in M2 protein) encoded by the prevalent G1-like M gene were demonstrated as prime contributors to enhanced infectivity. Therefore, the genetic evolution of M gene in H9N2 virus increases reproductive virus fitness, indicating its contribution to rising virus prevalence in chickens in China. Importance We recently described the circulation of a dominant genotype (G57) of H9N2 viruses in country-wide outbreaks in chickens in China, which was responsible through reassortment for the emergence of H7N9 viruses that cause severe human infections. A key feature of the G57 genotype H9N2 virus is the presence of quail origin G1-like M gene which had replaced the earlier BJ/94-like M gene. We found that H9N2 virus with G1-like M gene, but not BJ/94-like M gene, showed early surge in progeny virus production, more severe pathology and extrapulmonary virus spread in chickens. Five highly represented amino acid residues in M1 and M2 proteins derived from G1-like M gene were shown to mediate enhanced virus infectivity. These observations enhance what we currently know about the roles of reassortment and mutations on virus fitness and have implications for assessing the potential of variant influenza viruses that can cause rising prevalence in chickens

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasoning capabilities. This survey focuses on evaluating and improving LLMs from a causal view in the following areas: understanding and improving the LLMs' reasoning capacity, addressing fairness and safety issues in LLMs, complementing LLMs with explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning capacities can in turn contribute to the field of causal inference by aiding causal relationship discovery and causal effect estimations. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and equitable artificial intelligence systems

Spatio−Temporal Changes and Key Driving Factors of Urban Green Space Configuration on Land Surface Temperature

Changes in land cover by rapid urbanization have diminished the cooling effect of urban green spaces (UGS), exacerbating the upward trend of land surface temperature (LST). A thorough and precise understanding of the spatio-temporal characteristics of UGS and LST is essential for mitigating localized high temperatures in cities. This study identified the spatio-temporal changes in UGS configuration and LST in Shanghai from 2003 to 2022. The correlation between UGS configuration and LST was explored using spatial autocorrelation analysis and causal inference. The results show that (1) the high-temperature space had grown from 721 km2 in 2003 to 3059 km2 in 2022; (2) in suburbs, the largest area of UGS tended to decrease, while the number of patches tended to increase, indicating a distinct feature of suburbanization; (3) changes in the largest area of UGS had more significant spatial correlation, indicating that urban sprawl primarily impacts large UGSs; and (4) compared to the number and shape of UGS, changes in the largest area are the key factor influencing regional LST. These findings enrich the knowledge of the spatio−temporal relationship between the UGS configuration and its cooling effect in urbanization, offering valuable insights for building cooler cities

Identifying Spatial Priority of Ecological Restoration Dependent on Landscape Quality Trends in Metropolitan Areas

Ecological restoration has become an important tool for mitigating and adapting to environmental degradation caused by global urbanization. However, current research has focused on single indicators and qualitative analysis, meaning that ecological restoration has not been effectively and comprehensively addressed. This study constructed a spatial priority identification system for ecological restoration, with landscape area, landscape structure and landscape function as the core indicators. The system has wide adaptability. In this work, the spatial classification of ecological degradation was performed by overlay analysis. The results showed the following: (1) In the Shanghai metropolitan area, the landscape quality showed a trend of degradation, with built-up areas encroaching on forests and cropland. (2) Ecological degradation in the suburbs was more severe than that in the urban center. Forests had the highest landscape area indicator (LAI) stability. Significant degradation of landscape structure indicators (LSIs) occurred when built-up area and cropland were transformed into forests. (3) Different types of ecological restoration had significant spatial distribution patterns. Through this identification system, this study aimed to help planners/managers of ecological restoration to recognize the changing patterns of regional landscape quality and its relationship with land cover. It ultimately provides a basis for the formulation of regional ecological objectives and spatial strategies

Molybdenum and tungsten chalcogenides for lithium/sodium-ion batteries: Beyond MoS2

Molybdenum and tungsten chalcogenides have attracted tremendous attention in energy storage and conversion due to their outstanding physicochemical and electrochemical properties. There are intensive studies on molybdenum and tungsten chalcogenides for energy storage and conversion, however, there is no systematic review on the applications of WS2, MoSe2and WSe2as anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs), except MoS2. Considering the importance of these contents, it is extremely necessary to overview the recent development of novel layered WS2, MoSe2and WSe2beyond MoS2in energy storage. Here, we will systematically overview the recent progress of WS2, MoSe2and WSe2as anode materials in LIBs and SIBs. This review will also discuss the opportunities, and perspectives of these materials in the energy storage fields

Predicting Urban Expansion to Assess the Change of Landscape Character Types and Its Driving Factors in the Mountain City

The urban landscape is being affected by rapid urbanization, leading to a complexity of land features and a fragmentation of patches. However, many studies have focused on the prediction of land-use change with a lack of research on the landscape character types which have more integrated descriptions of land features. Hence, this study predicts and identifies landscape character types (LCTs) in different periods based on the PLUS model and the K-Medoids algorithm, taking the central city of Chongqing as an example, to reveal the differences in the influence of driving factors on LCTs. The results show that (1) the urban landscape characteristic types present a gradient change from the built-up area to the outward expansion. (2) The SHDI and LPI of landscape character types decreased significantly with the expansion of construction land. (3) Nighttime light, distance from water bodies, and distance from the motorways are the main factors affecting the change of landscape character types. This study predicts and identifies urban landscape character types and quantifies the impact of urban expansion on landscape character. It can be used to guide urban planning and help governments to make more informed decisions on sustainable urban development and ecological conservation

Identifying Spatial Priority of Ecological Restoration Dependent on Landscape Quality Trends in Metropolitan Areas

Ecological restoration has become an important tool for mitigating and adapting to environmental degradation caused by global urbanization. However, current research has focused on single indicators and qualitative analysis, meaning that ecological restoration has not been effectively and comprehensively addressed. This study constructed a spatial priority identification system for ecological restoration, with landscape area, landscape structure and landscape function as the core indicators. The system has wide adaptability. In this work, the spatial classification of ecological degradation was performed by overlay analysis. The results showed the following: (1) In the Shanghai metropolitan area, the landscape quality showed a trend of degradation, with built-up areas encroaching on forests and cropland. (2) Ecological degradation in the suburbs was more severe than that in the urban center. Forests had the highest landscape area indicator (LAI) stability. Significant degradation of landscape structure indicators (LSIs) occurred when built-up area and cropland were transformed into forests. (3) Different types of ecological restoration had significant spatial distribution patterns. Through this identification system, this study aimed to help planners/managers of ecological restoration to recognize the changing patterns of regional landscape quality and its relationship with land cover. It ultimately provides a basis for the formulation of regional ecological objectives and spatial strategies