Search Results (128)

Land urbanization enables a thorough perspective to explore the decoupling of land use environmental efficiency (LUEE) and energy use, thereby supporting the shift into low-carbon land use by emphasizing energy conservation and reducing carbon emissions. This paper first calculates LUEE from 2011 to 2021 by using the EBM-DEA model in China. The geographical detector model is used to examine the driving factors of land use environmental efficiency. The results show the following: (1) China’s LUEE is high in general but shows a clear pattern of spatial differentiation internally, with the highest values in the eastern region represented by Beijing, Jiangsu, and Zhejiang, while the central and western regions show lower LUEE because of their irrational industrial structure and lagging green development. (2) Energy consumption, economic development, industrial upgrading, population size, and urban expansion are the driving factors. Their explanatory power for the spatial stratification heterogeneity of land use environmental impacts varies. (3) Urban expansion has the greatest impact on the spatial differentiation of land use environmental effects, while energy consumption also shows significant explanatory strength. In contrast, economic development and population size exhibit relatively weaker explanatory effects. (4) The interaction of the two driving factors has a greater impact on LUEE than their individual effects, and the interaction is a two-factor enhancement. Finally, we make targeted recommendations to help improve land use environmental efficiency. Full article

Global resource depletion has intensified scrutiny on Sustainable Development Goal 12 (SDG12), where consumption-based material footprint serves as a critical sustainability metric. Despite its transformative potential, the paradoxical role of Information and Communication Technology (ICT) in resource conservation remains underexplored. This study adopts an extended STIRPAT model as the analytical framework. It employs the Method of Moments Quantile Regression to evaluate the non-linear effects of digitalization-related indicators and other influencing factors on material footprint. The analysis is conducted across different quantiles for G20 countries from 2000 to 2020. The results show that (1) ICT exhibits a substantial positive effect on consumption-based material footprint under all quantiles. This leads to an increase in the material footprint, hindering the G20’s progress toward achieving SDG12. (2) The impact of ICT varies notably, with a more pronounced adverse effect on SDG12 in countries with higher resource consumption. (3) ICT goods export trade, technological innovation, and globalization significantly mitigate ICT’s adverse impact on resource consumption. This study provides targeted recommendations for G20 countries on how to leverage ICT to achieve SDG12 more effectively. Full article

The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts monthly/annual average PDO index of up to 15 months/5 years in advance, achieving a correlation coefficient of 0.56/0.55. By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. The combination of MMD and WOA further minimizes prediction errors and extends the forecasting effective time to 15 months by capturing essential modes. The BWM model can be employed for future PDO prediction and the predicted PDO will remain in its cool phase in the next year both using the PDO index from NECI and derived from near-time satellite data. This proposed model offers an effective way to advance the prediction skill of climate variability on multiple time scales by utilizing all kinds of data available including satellite data, and provides a large-scale background to monitor marine heatwaves. Full article

Forest musk deer (Moschus berezovskii Flerov), a critically endangered ruminant species, faces extinction risks, with captive populations further threatened by prevalent digestive and immune disorders. This study utilized comparative metagenomic sequencing to assess intestinal microbiota structure and functional profiles between wild populations in Chongqing and Hunan and captive individuals. Wild populations exhibited a Pseudomonadota-dominated gut microbiota (significantly more abundant than in captive counterparts), enriched with lignin-degrading genera Novosphingobium and Acinetobacter. In contrast, the captive group demonstrated increased abundances of Bacillota/Bacteroidota, alongside abnormal proliferation of Escherichia and Clostridium. Both alpha and beta diversity analyses confirmed significant compositional divergences among the three groups, with wild populations maintaining higher diversity than captive populations. Notably, while substantial disparities in microbial abundance existed between wild populations (attributed to habitat vegetation differences), core microbial diversity and carbohydrate metabolic functions exhibited convergence. Functional analyses marked divergences in metabolic pathways: Captive microbiota showed enrichment in translation and glycan metabolism pathways, whereas wild populations displayed pronounced enrichment in immune regulation and environmental sensing pathways. These findings establish a theoretical foundation for optimizing wild population conservation strategies and developing science-based captive management protocols. Full article

A mesoscopic lattice Boltzmann method based on the BGK model is proposed to solve a class of two-dimensional second-order nonlinear partial differential equations by incorporating an amending function. The model provides an efficient and stable framework for simulating initial value problems of second-order nonlinear partial differential equations and is adaptable to various nonlinear systems, including strongly nonlinear cases. The numerical characteristics and evolution patterns of these nonlinear equations are systematically investigated. A D2Q4 lattice model is employed, and the kinetic moment constraints for both local equilibrium and correction distribution functions are derived in the four velocity directions. Explicit analytical expressions for these distribution functions are presented. The model is verified to recover the target macroscopic equations in the continuous limit via Chapman–Enskog analysis. Numerical experiments using exact solutions are performed to assess the model’s accuracy and stability. The results show excellent agreement with exact solutions and demonstrate the model’s robustness in capturing nonlinear dynamics. Full article

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the soybean genome, and their physicochemical properties, domain compositions, phylogenetic relationships, gene structures, and expression patterns were comprehensively analyzed to explore their impact on soybean seed protein content. The results revealed significant gene expansion within the GmKTI family in soybean. The gene structures and conserved motifs of GmKTI members exhibited both regularity and diversity, with distinct expression patterns across different soybean tissues. Haplotype analysis identified 7 GmKTI genes significantly associated with seed storage protein content, and the combination of superior haplotypes was found to enhance seed storage protein content. This is crucial for the improvement of soybean varieties and the enhancement of storage protein content. Additionally, the GmKTI family demonstrated evolutionary conservation, with its functions likely linked to light induction, biotic stress, and growth development. This study characterizes the structure, expression, genomic haplotypes, and molecular features of the soybean KTI domain for the first time, providing a foundation for functional analyses of the GmKTI domain in soybean and other plants. Full article

Trans-species polymorphisms (TSPs) are fundamental to preserving ancient genetic diversity, yet the evolutionary forces driving their long-term maintenance remain largely unexplored. Here, we investigate genome-wide TSPs in two Cucumis species, cucumber and melon, using whole-genome sequencing data from over 1200 accessions. A total of 5149 TSPs were identified, which predominantly located in genic and promoter regions. Coalescent analysis indicated that both gene flow and balancing selection have contributed to the persistence of these ancestral alleles. Moreover, among the 99 genes with shared coding-region polymorphisms, two genes that cluster by alleles rather than by species provide evidence of long-term balancing selection. These genes are involved in the immune and stress response processes with pleiotropic effects. Our findings elucidate the complex evolutionary forces driving TSPs in Cucumis, providing mechanistic insights into the maintenance of intraspecific genetic diversity in plants across deep evolutionary timescales. Full article

In this study, based on high-resolution online monitoring data of CO₂ concentration in Nanning City in the spring of 2024, we analyzed the characteristics of diurnal and monthly changes of CO₂ concentration in Nanning City and explored the influencing factors through the background sieving method and Lagrangian Particle Dispersion Model (LPDM) traceability simulations combined with meteorological factor analysis. The results demonstrates that the diurnal variation of CO₂ concentration in Nanning City exhibits a bimodal pattern of peak in the afternoon and trough in the early morning, with a mean concentration of 460 ± 15 ppm. Transportation emissions were identified as the dominant source of this variation. The trend of monthly concentration changes was first increasing and then decreasing, with an increase in February–March and a decrease in April, indicating that it was affected by the combined effect of vegetation photosynthesis and urban human activities. The results of the background sieving method and traceability simulation analysis showed that the CO₂ concentration in Nanning City was more affected by local emission sources than sinks, and the industrial sources and transportation sources in the north–south direction had a significant effect on the CO₂ concentration. This research provides critical data support for formulating carbon reduction strategies and coordinated atmospheric environment management in subtropical cities. Full article

Objectives: Evaluating middle ear function is essential for interpreting screening results and prioritizing diagnostic referrals for infants with hearing impairments. Wideband Acoustic Immittance (WAI) technology offers a comprehensive approach by utilizing sound stimuli across various frequencies, providing a deeper understanding of ear physiology. However, current clinical practices often restrict WAI data analysis to peak information at specific frequencies, limiting its comprehensiveness. Design: In this study, we developed five machine learning models—feedforward neural network, convolutional neural network, kernel density estimation, random forest, and support vector machine—to extract features from wideband acoustic immittance data collected from newborns aged 2–6 months. These models were trained to predict and assess the normalcy of middle ear function in the samples. Results: The integrated machine learning models achieved an average accuracy exceeding 90% in the test set, with various classification performance metrics (accuracy, precision, recall, F1 score, MCC) surpassing 0.8. Furthermore, we developed a program based on ML models with an interactive GUI interface. The software is available for free download. Conclusions: This study showcases the capability to automatically diagnose middle ear function in infants based on WAI data. While not intended for diagnosing specific pathologies, the approach provides valuable insights to guide follow-up testing and clinical decision-making, supporting the early identification and management of auditory conditions in newborns. Full article

Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning. Full article

The primary objective of this research is to comprehensively investigate the equivalence of single-event effects (SEEs) in silicon carbide metal-oxide semiconductor field-effect transistors (SiC MOSFETs) that are induced by protons and heavy ions. The samples utilized in the experiments are the fourth-generation symmetric groove gate SiC MOSFETs. Proton irradiation experiments were meticulously executed at varying energies, namely 70 MeV, 100 MeV, and 200 MeV, while heavy-ion irradiation was carried out using 138 MeV Cl ions. During these experiments, the drain–source current (I_DS) and drain–source voltage (V_DS) were continuously and precisely monitored in real time. Experimental results demonstrate that single-event burnout (SEB) susceptibility correlates strongly with proton energy and applied drain–source bias. Notably, SiC MOSFETs exhibit a stronger tolerance to proton SEB compared to heavy-ion SEB. Proton irradiation results in a sudden elevation in I_DS, whereas heavy-ion irradiation leads to a gradual increase. In summary, the mechanism underlying proton-induced SEE is intricately related to the ionization of secondary particles. Future research endeavors should place a greater emphasis on comprehensively considering proton effects to establish a more complete and effective evaluation system for SiC MOSFET SEEs. Full article

Physical and psychological distress frequently challenges athletes throughout their careers. The perception of pain and coping strategies are often crucial factors in achieving victory. These factors not only reflect their commitment to daily training, but can also indicate their level of athletic performance. This study is a cross-sectional research using convenience and snowball sampling methods. It explores the relationship between mindfulness and athletes’ distress tolerance, revealing the mediating roles of cognitive reappraisal and mental toughness. A sample of 285 athletes was drawn from universities, youth training centers, and sports academies in Hunan, Hubei, and Sichuan provinces in China. To assess the proposed hypotheses, structural equation modeling was conducted using AMOS v23. The findings identified a significant positive correlation between mindfulness, cognitive reappraisal, and mental toughness. Additionally, both cognitive reappraisal and mental toughness were positively associated with distress tolerance. Further analysis demonstrated that cognitive reappraisal and mental toughness function as mediators in the mindfulness–distress tolerance relationship. These results indicate that athletes with higher mindfulness levels exhibit enhanced cognitive reappraisal skills, greater mental toughness, and improved distress tolerance. This means that athletes with higher mindfulness levels are more likely to detach from negative psychological states in a timely manner, utilizing emotional regulation skills such as cognitive reappraisal, and face training and competition with greater mental resilience. This can help athletes alleviate negative psychological states and, to some extent, reduce their experience of pain, enabling them to better cope with challenges. Therefore, athletes can actively engage in mindfulness practices combined with cognitive reappraisal strategies to achieve better psychological states, which can support their adherence to training and rehabilitation plans. Full article

MXene, an emerging class of two-dimensional materials, has garnered significant attention in electrochemical energy storage applications due to its high specific surface area, tunable surface functional groups, excellent electrical conductivity, and mechanical stability. However, their practical application in energy storage devices remains challenged by issues such as the stacking of their layered structure, surface degradation, and limited ion diffusion properties. Functionalization has emerged as a key strategy to enhance the performance of MXene materials. By modulating surface functional groups, doping with various elements, and integrating with other materials, researchers have significantly improved the electrical conductivity, chemical stability, ion transport properties, and mechanical strength of MXenes. This review provides a comprehensive overview of MXene materials, categorizing them and highlighting their advantages in electrochemical energy storage applications. It also examines recent advancements in MXene preparation and optimized synthesis strategies. In-depth discussions are presented on the functionalization of MXenes and their applications in energy storage devices, including supercapacitors, lithium-ion batteries, and sodium-ion batteries. Finally, the review concludes with a summary of the practical applications of MXenes and explores future research directions, aiming to guide further developments in the energy storage field. Full article

Agricultural mechanization plays a critical role in addressing labor shortages and promoting sustainable agricultural production in developing countries. However, few studies have explored the mechanisms by which mechanization affects agricultural output through various channels, especially identifying the key drivers of this association. Taking winter wheat as an example, this study investigates how and to what extent agricultural mechanization affects agricultural output using county-level panel data from China during 1998–2016. The results show that mechanization has a significant positive impact on winter wheat output. Heterogeneity analysis suggests that this positive impact is more pronounced in the plains with better transport conditions. Further decomposition of winter wheat output growth shows that mechanization drives winter wheat output growth through both expansion in sown area and increase in yield, with the former being the dominant pathway. These findings enrich the understanding of how mechanization increases agricultural output and provide policy guidance for leveraging its potential to promote agricultural development in developing countries. Full article

Fruits are an important source of a healthy diet due to their essential nutrients for daily intake. Melon is known as a significant fruit crop of the Cucurbitaceae family based on its various dietary benefits, but its shelf life needs to be maintained for long-term usage. 1-Methylcyclopropene (1-MCP) is a cyclopropene-derived synthetic plant growth regulator (PGR) that is used for significantly delaying the ripening process and maintaining the shelf life of climacteric fruits during storage. In this study, freshly harvested melon fruits were fumigated with various concentrations (1.0 µL·L⁻¹, 2.0 µL·L⁻¹, and 3.0 µL·L⁻¹) of 1-MCP treatment for 12 h (h) and stored at low temperature (8 ± 1 °C) for 30 days (d). The obtained results showed that 1-MCP fumigation coupled with low-temperature treatment maintains the postharvest shelf life of melon fruit. It was noticed that the increase in color hue (a* (red/green), b* (blue/yellow), L* (lightness)) was slowed down and the external fresh color was effectively maintained. At the same time, the firmness, soluble solids, titratable acids (TAs), and vitamin C (VC) content seemed to be maintained at a high level; weight loss and cell permeability were reduced; respiratory intensity and ethylene emission were inhibited; and the accumulation of superoxide anions and malondialdehyde (MDA) was also reduced. In addition, an upsurge in the activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) was noticed in melon fruits under the combined treatment of 1-MCP and low-temperature storage as compared with the control group (CK, without treatment), indicating that 1-MCP treatment can effectively enhance the antioxidant metabolism of melon fruits during storage. Overall, we can recommend that the 3.0 µL·L⁻¹ concentration of 1-MCP had the best effect on maintaining the internal and external quality of sweet melon fruit during storage. Full article