Central China Normal University

Revegetation is effective in improving soil quality in ecologically fragile areas. However, little is known about the impact of diverse phytomanagement strategies of tailings on soil quality and ecological security in erosion-prone areas. We investigated the water stability, soil aggregate nutrients, and the risk of heavy metal contamination of abandoned tailings under phytomanagement and in adjacent bare land on the Loess Plateau. The results showed that phytomanagement significantly enhanced soil aggregate stability, as demonstrated by higher contents of soil organic carbon (SOC), glomalin-related soil protein (GRSP), aromatic-C, and alkene-C in macro-aggregates. The pollution load index (PLI) and ecological risk index (RI) of soil heavy metals were lower in shrub/herbaceous mixed forests than in natural grasslands and planted forests. The risk of heavy metal contamination was higher in macro-aggregates (>0.25 mm) than in micro-aggregates (<0.25 mm) and was significantly and positively correlated with the SOC and GRSP contents of the aggregates. Our study demonstrates that soil aggregate quality is closely related to the fate of heavy metals. Diversified tailing revegetation measures can improve soil quality and ensure ecological security.

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching.

We study the dissociation effect of $$J/\Psi $$ J / Ψ in magnetized, rotating QGP matter at finite temperature and chemical potential using gauge/gravity duality. By incorporating angular velocity into the holographic magnetic catalysis model, we analyze the influence of temperature, chemical potential, magnetic field, and angular velocity on the properties of $$J/\Psi $$ J / Ψ meson. The results reveal that temperature, chemical potential, and rotation enhance the dissociation effect and increase the effective mass in the QGP phase. However, the magnetic field suppresses dissociation, and its effect on the effective mass is non-trivial. Additionally, we explore the interplay between magnetic field and rotation, identifying a critical angular velocity that determines the dominant effect. As a parallel study, we also examine the rotation effect in the holographic inverse magnetic catalysis model, although the magnetic field exhibits distinctly different behaviors in these two models, the impact of rotation on the dissociation effect of $$J/\Psi $$ J / Ψ is similar. Finally, we investigate the influence of electric field and demonstrate that it also speeds up the $$J/\Psi $$ J / Ψ dissociation.

This study uses global patent cooperation data from 1999 to 2020, employs social network analysis, and explores the changes in and driving forces of global innovation networks (GINs) in the past 20 years as well as the change in China's position in the global innovation system. The results show that on the whole, the network accessibility of GINs has continuously improved, showing scale‐free network characteristics. The important nodes in the network are mainly developed countries such as Europe and the United States, and the node polarization effect has weakened; the network has four agglomeration subgroups. The phenomenon of subgroups and factions is not obvious. Further factor identification shows that economic factors and technological information factors have the strongest correlation with the innovation network and that demographic factors have a weaker correlation with the innovation network. In addition, China's position in the innovation network has been increasing year by year, and the role of ‘transit stations’ has become increasingly prominent.

Due to the high affinity with water molecules, amide compounds are easily contaminated by moisture; therefore, the water interference effect cannot be totally excluded from the amide‐involved reactions. Thus, the perfect solution is to use the interference effect but not shield it in a real application. In this work, we introduced different contents of sodium acrylate (AAS) to scavenge water from the monomers of N ‐isopropylacrylamide (NIPAm) when copolymerized with TPA‐Vinyl‐4CN. Herein, water molecules play a role as nucleophilic reagents to attack highly active functional groups as –C=C–CN from TPA‐Vinyl‐4CN, leading to a blue emissive TPA‐Vinyl‐2CHO. From this study, we made a deep awareness of the interactions between three reaction partners of AAS and NIPAm as well as TPA‐Vinyl‐4CN. Our results clearly demonstrated the fact that water can be perfectly used and controlled by the water absorbent of AAS, developing a new approach to synthesizing multiple emission‐coloured polymers by using only one luminogen of TPA‐Vinyl‐4CN.

The existing literature on the volatility forecasting less considered the co-movement among stock markets from the spatial dimension. This paper builds the hybrid convolutional neural networks (CNNs)-gated recurrent unit (GRU) model for volatility forecasting under high frequency financial data based on transaction information and the topological characteristics constructed through the complex network of multi-market symbol patterns. The hybrid neural network CNN-GRU combines the advantages of CNN automatically extracting features for the input indicators and GRU processing long and short-term serially dependent features, which can better improve the forecasting accuracy. The empirical results show that with the integration of topologi-cal characteristics as the indicators based on complex network, the deep learning model has a significant improvement of one-step and multi-step volatility forecasting accuracy for the China's and the US stock markets. The research in this paper provides a complete index system from the spatial dimension and a more accurate and robust volatility forecasting method under the high-frequency financial data. K E Y W O R D S complex network, financial volatility forecasting, hybrid deep learning model, topological characteristic

The rapid shrinkage and salinization of the Aral Sea over the last few decades has precipitated an environmental disaster, with widespread implications for people whose livelihoods depend on it. Although debated extensively, few viable strategies have yet been identified for reviving the Aral Sea. Here, we propose a hydro‐eco‐social framework to develop a viable, sustainable solution and explore its feasibility to resolve the Aral Sea problem. Based on eco‐environmental indicators, we contend that it is feasible to raise the Aral Sea by 40 m above the Baltic Sea level, while maintaining salinity levels tolerable for aquatic organisms, simultaneously reducing sandstorm risk by 58%. Basin‐wide water balance under climate change scenarios shows that this level can be supported through management interventions that reduce water usage by 22.0–23.2 km³/year and ensure sufficient recharge into the lake, without compromising socio‐economic opportunities. To implement this solution, we propose establishing a socio‐ecologically aligned water governance network for basin‐scale water management that has potential application for other, similarly declining, major lake systems.

Numerous studies have explored the link between how well youth recognize emotions and their internalizing problems, but a consensus remains elusive. This study used a three-level meta-analysis model to quantitatively synthesize the findings of existing studies to assess the relationship. A moderation analysis was also conducted to explore the sources of research heterogeneity. Through a systematic literature search, a total of 42 studies with 201 effect sizes were retrieved for the current meta-analysis, and 7579 participants were included. Emotion recognition was negatively correlated with internalizing problems. Children and adolescents with weaker emotion recognition skills were more likely to have internalizing problems. In addition, this meta-analysis found that publication year had a significant moderating effect. The correlation between emotion recognition and internalizing problems decreased over time. The degree of internalizing problems was also found to be a significant moderator. The correlation between emotion recognition and internalizing disorders was higher than the correlation between emotion recognition and internalizing symptoms. Deficits in emotion recognition might be relevant for the development and/or maintenance of internalizing problems in children and adolescents. The overall effect was small and future research should explore the clinical relevance of the association.

Renewable‐electricity‐powered electrochemical CO 2 reduction (CO 2 RR) is considered one of the most promising ways to convert exhaust CO 2 into value‐added chemicals and fuels. Among various CO 2 RR products, CO is of great significance since it can be directly used as feedstock to produce chemical products through the Fischer–Tropsch process. However, the CO 2 ‐to‐CO electrocatalytic process is often accompanied by a kinetically competing side reaction: H 2 evolution reaction (HER). Designing electrocatalysts with tunable electronic structures is an attractive strategy to enhance CO selectivity. In this work, a CeNCl‐CeO 2 heterojunction‐modified Ni catalyst is successfully synthesized with high CO 2 RR catalytic performance by the impregnation‐calcination method. Benefiting from the strong electron interaction between the CeNCl‐CeO 2 heterojunction and Ni nanoparticles (NPs), the catalytic performance is greatly improved. Maximal CO Faradaic efficiency (FE) is up to 90% at −0.8 V (vs RHE), plus good stability close to 12 h. Detailed electrochemical tests and density functional theory (DFT) calculation results reveal that the introduction of the CeNCl‐CeO 2 heterojunction tunes the electronic structure of Ni NPs. The positively charged Ni center leads to an enhanced local electronic structure, thus promoting the activation of CO 2 and the adsorption of * COOH.

The deformability of elastomer surfaces offers the opportunity to capture multimodal physical information, which opens up possibilities for applications in intelligent interactive systems and flexible electronics. Accurately identifying the surface deformation of elastomers has been a key issue for understanding the contact state in a soft touch. However, effective monitoring of the deformed surface topography during soft‐touch processes is difficult due to the complexity of interfacial deformation. Herein, the optical detection method based on an ideal diffuse reflection model is proposed to monitor the deformation of the elastomer, enabling the acquisition of a spatially continuous deformation of the surface in a soft touch. This optical system with the parallel incident light can distinguish the elastomers’ surface deformation field by constructing the correlation between the reflected light irradiance and the surface deformation angle. This method allows for the perception of the contact area. Through the deep learning method, the recognition rate can reach more than 84.24% when there are slight differences in the contact of objects with different geometric shapes. The design offers valuable insights for contact state detection tasks and facilitates soft‐touch sensing functions through the high precision of optical signal acquisition.

The zero-degree calorimeter (ZDC) plays a crucial role toward determining the centrality in the Cooling-Storage-Ring External-target Experiment (CEE) at the Heavy Ion Research Facility in Lanzhou. A boosted decision tree (BDT) multi-classification algorithm was employed to classify the centrality of the collision events based on the raw features from ZDC such as the number of fired channels and deposited energy. The data from simulated \(^{238}\textrm{U}\) + \(^{238}\textrm{U}\) collisions at 500 \(\mathrm{MeV/u}\), generated by the IQMD event generator and subsequently modeled using the GEANT4 package, were employed to train and test the BDT model. The results showed the high accuracy of the multi-classification model adopted in ZDC for centrality determination, which is robust against variations in different factors of detector geometry and response. This study demonstrates the good performance of CEE-ZDC in determining the centrality in nucleus–nucleus collisions.

The absence of semitauonic decays of charmed hadrons makes the decay processes mediated by the quark-level c→dτ+ντ transition inadequate for probing a generic new physics (NP) with all kinds of Dirac structures. To fill in this gap, we consider in this paper the quasielastic neutrino scattering process ντ+n→τ−+Λc, and propose searching for NP through the polarizations of the τ lepton and the Λc baryon. In the framework of a general low-energy effective Lagrangian, we perform a comprehensive analysis of the (differential) cross sections and polarization vectors of the process both within the Standard Model and in various NP scenarios, and scrutinize possible NP signals. We also explore the influence on our findings due to the uncertainties and the different parametrizations of the Λc→N transition form factors, and show that they have become one of the major challenges to further constrain possible NP through the quasielastic scattering process.

The environmentally friendly, low toxic or nontoxic, and functional material to solve the needs of agricultural development are the key to current scientific research. Here, acrylic thiazole copolymer clusters (AcTPCs) were successfully synthesized by using common methacrylic acid as an induced source, combined with the antimicrobial monomers acrylic thiazole (AcT) and Zn ²⁺ , which are of great value for agricultural applications. Utilizing the “cluster effect,” under the synergistic effect of the fully exposed active ingredients, they not only have long‐lasting antimicrobial effects against bacteria and fungi, but also promote the growth of plant roots and stems and increase the chlorophyll content. Therefore, this study provides a novel, environmental‐friendly, and low‐toxic method to solve the microbe infection problem, which is expected to improve the agriculture.

FAPbI 3 perovskites have garnered considerable interest owing to their outstanding thermal stability, along with near‐theoretical bandgap and efficiency. However, their inherent phase instability presents a substantial challenge to the long‐term stability of devices. Herein, this issue through a dual‐strategy of self‐assembly 3D/0D quasi‐core–shell structure is tackled as an internal encapsulation layer, and in situ introduction of excess PbI 2 for surface and grain boundary defects passivating, therefore preventing moisture intrusion into FAPbI 3 perovskite films. By utilizing this method alone, not only enhances the stability of the FAPbI 3 film but also effectively passivates defects and minimizes non‐radiative recombination, ultimately yielding a champion device efficiency of 23.23%. Furthermore, the devices own better moisture resistance, exhibiting a T 80 lifetime exceeding 3500 h at 40% relative humidity (RH). Meanwhile, a 19.51% PCE of mini‐module (5 × 5 cm ² ) is demonstrated. This research offers valuable insights and directions for the advancement of stable and highly efficient FAPbI 3 perovskite solar cells.

Biodiversity serves as the fundamental underpinning for ecosystem functions and services. As a result of human‐induced global change, there is a growing awareness of the substantial alterations in terrestrial above‐ground biodiversity, particularly within alpine regions. However, it remains uncertain whether below‐ground biodiversity will exhibit similar responses, both in terms of magnitude and manner, to anthropogenic global changes as above‐ground biodiversity. Here, we conducted a meta‐analysis to assess the impacts of warming, nutrient addition and grazing on plant and soil microbial biodiversity in alpine grasslands on the Qinghai–Tibetan Plateau, which are known to be climate‐sensitive and vulnerable. The analysis included 819 experimental observations from 152 studies, focussing on species richness, Shannon diversity and Pielou's evenness. We found that plant biodiversity exhibited greater sensitivity to climate warming and anthropogenic activities compared with soil microbial biodiversity. Specifically, plant richness and Shannon diversity were reduced by warming and nutrient addition, while plant evenness was increased by grazing. However, only microbial richness was increased by grazing and microbial evenness was increased by warming slightly. The responses of biodiversity to climate warming and anthropogenic activities were modulated by multiple factors. Specifically, the negative effects of warming on plant biodiversity were more pronounced in long‐term experiments under warmer or drier environmental conditions. The negative effects of nitrogen addition on biodiversity were enhanced by the intensity and duration of nitrogen treatment. Appropriate intensity and frequency of grazing were beneficial to sustaining plant biodiversity. Soil microbial biodiversity was weakly regulated, where bacterial Shannon diversity was more sensitive to nutrient addition, while fungal species richness was sensitive to grazing. Synthesis . Our findings reveal a mismatch between above‐ground plant and below‐ground microbial biodiversity in response to climate warming and anthropogenic activities in alpine grasslands, with plant biodiversity being more sensitive. In the context of future global change, plant biodiversity may be at greater risk than soil microbial biodiversity. In addition, biodiversity responses of different experimental and environmental conditions should be distinguished, and more attention is needed on biodiversity conservation in alpine steppe, or areas with warmer and drier environmental conditions, high‐intensity fertilization or heavy grazing.

The diversity of functional traits of macroinvertebrates in freshwater ecosystems is one of the current research hotspots in the field of biodiversity. Understanding the functional trait composition of macroinvertebrates and their factors can make the study of watershed beta diversity and river ecological security patterns more effective. Based on two field surveys conducted in September 2020 and July 2021 in the Qingyijiang (QYJ) River, the longest tributary on the south bank of the lower Yangtze River in China, we explored the functional trait compositions of macroinvertebrates and relationships between functional traits and water environment factors using functional diversity indices, beta diversity decomposition, R‐mode linked to Q‐mode, and fourth‐corner analysis methods. Our results showed that water environmental factors affected the distribution of macroinvertebrates traits, and the degree of effect was reflected by the correlation between water environmental factors and the abundance of species traits. Physical factors like flow velocity and water depth were significantly correlated with biological traits of species such as body size and swimming ability. In counterparts, some nutrient indicators significantly affected species ecological traits, such as total phosphorus, ammonia nitrogen, and chemical oxygen demand, which affected species habits and functional feeding group traits. At the same time, we also found a high turnover rate between communities in the river, reflecting the strong dispersal ability of the species. This will be an important guide for the conservation of macroinvertebrates diversity in the river. Local governments can take measures such as conducting quarterly monitoring of macroinvertebrates species; restoring riparian vegetation; limiting hardening of riverbanks and dredging of rivers; controlling wastewater discharges from residential areas on both sides of the river; controlling the use of pesticides in agricultural lands, and so on. These will become the key directions for the conservation of macroinvertebrates diversity.