Effects of urban green belts on the air temperature, humidity and air quality

As urbanization increases, designing green space that offers ecological benefits is an increasingly important goal of urban planning. As a linear green space in an urban environment, green belts lower air temperature, increase relative humidity, and improve air quality. To quantify the ecological effects of urban green belts and to identify a critical width for effective urban green belts, we analysed the width of urban green belts in terms of their effects on air temperature (T), relative humidity (RH), concentration of negative air ions (NAI) and bacteria rate (BR). The air T, RH and NAI from 8:00 to 18:00 and BR at 9:00 over seven days were investigated on six widths of green belts (0–10 m, 10–20 m, 20–30 m, 30–40 m, 40–50 m and over 50 m) along the west Fourth Ring Road of Beijing in April, July, October and December 2009. We found that (1) the T-RH benefits increased with the width of the green belts, and the 6 m belt had the smallest effect on T-RH, followed by the 16 m and 27 m belts, whereas the effect was obvious with the 34 m belt and conspicuous and stable with the 42 m belt (approximately 80% green coverage) (P < 0.05); (2) the critical width reference value of urban green belts for an obvious effect on the increase in NAI concentration was approximately 42 m (approximately 80% green coverage) (P < 0.05) and the NAI concentration increased with the width of green belts even in July; and (3) the positive effect on the decrease in the BR was greater than the negative effect, the BR decreased with the green belt width and the changes in the brs were stable with the 34 m belt. The results of this study may help urban planners and designers achieve urban green space designs that optimize ecological effects and cultural benefits

Rhizosphere microbe populations but not root traits induced by drought in Populus euphratica males

Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. U1803231). Publisher Copyright: © 2023, Higher Education Press.How sex-related root traits and soil microbes and their interactions respond to drought remains unclear. Here, we investigated how fine root traits and the composition of rhizosphere microbial communities in Populus euphratica females and males respond to drought in concert in 17-year-old plantations. Females increased specific root length (SRL) in response to drought. However, males showed no changes in their roots but significant increases in arbuscular mycorrhizal hyphal biomass and population of Gram-negative bacteria in the rhizosphere. Also, fungal symbiotroph communities associated with root systems in males differed from those in females under drought. We further demonstrated that the Gram-positive to Gram-negative bacteria ratios positively correlated with the SRL, while fungi to bacteria ratios were negatively correlated. Meanwhile, the relative abundance of symbiotrophs was negatively correlated with the SRL, while saprotroph abundance was positively correlated. Nevertheless, the relative abundance of symbiotrophs was positively correlated with the root carbon content (RCC). These findings indicate that microbial responses to drought depend highly upon the sex of the plant and microbial group and are related to root trait adjustments to drought. This discovery also highlights the role of plant-microbial interactions in the ecosystems of P. euphratica forest plantations.Peer reviewe

Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model

The potential of blockchain technology in advancing sustainable energy: a study on the mediating role of specialization in the growth of Chinese hidden champions

Innovation is a crucial factor for hidden champions to gain a competitive edge and foster organizational growth. This study focuses on Chinese-listed hidden champion firms between 2010 and 2019 and examines the impact of innovation heterogeneity on their growth. The study explores the impact of different types of innovation, including overall innovation, product innovation, and process innovation, and their interaction effects on the growth of hidden champions. The study also investigates the mediating role of the degree of specialization, which refers to the extent to which a firm focuses on a particular area of expertise. Furthermore, the potential of blockchain technology in advancing a sustainable energy future is becoming increasingly apparent. By enabling the creation of decentralized energy markets, blockchain can facilitate the integration of renewable energy sources, such as solar and wind power, into the grid. This can help to reduce carbon emissions and promote the transition to a low-carbon economy. The results of the study indicate that innovation, including overall innovation, product innovation, and process innovation, is significant for hidden champions’ growth. Moreover, the study reveals that product innovation and process innovation have complementary effects on the growth of hidden champions. The degree of specialization also plays a mediating role between different types of innovation and hidden champions’ growth. The results of this study provide empirical evidence to improve the growth of hidden champions from the perspective of enterprise innovation. By focusing on different types of innovation and understanding their complementary effects, firms can develop a more comprehensive innovation strategy that can enhance their growth potential. Moreover, the mediating role of degree of specialization highlights the importance of aligning innovation efforts with a firm’s core competencies to achieve sustained growth. This study contributes to the literature on hidden champions by shedding light on their innovation strategies and their impact on firm growth

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks. However, existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph, and overlook critical issues in effectiveness and generalization ability. In this paper, we advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set without explicit graph structures, i.e., graph-free data. Our idea is to implicitly encode topology structure information into the node attributes in the synthesized graph-free data, whose topology is reduced to an identity matrix. Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data. Through training trajectory meta-matching, SFGC aligns the long-term GNN learning behaviors between the large-scale graph and the condensed small-scale graph-free data, ensuring comprehensive and compact transfer of informative knowledge to the graph-free data. Afterward, the underlying condensed graph-free data would be dynamically evaluated with the graph neural feature score, which is a closed-form metric for ensuring the excellent expressiveness of the condensed graph-free data. Extensive experiments verify the superiority of SFGC across different condensation ratios.Comment: Accepted by NeurIPS 202