Search Results (136)

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

To realize the region determination and accurate location of cable partial discharge, this paper proposes a cable partial discharge double-end location technique based on frequency-domain reflectometry. The cable partial discharge double-end location technique based on frequency-domain reflectometry mainly includes the frequency band modulation technique and partial discharge location method. The frequency band modulation technique determines the effective frequency band range of the acquired cable transfer function through the frequency band range of the partial discharge signals measured at both ends, which ensures the reliability of the transfer function. The partial discharge location method constructs the cable partial discharge location function and the region determination function via spectral analysis of the cable transfer function obtained from the partial discharge signals, which realizes region determination and determines precise location of the cable partial discharge, respectively. Our simulation and experiment show that the cable partial discharge double-end location technique based on frequency-domain reflectometry can effectively determine the existence region of cable partial discharge and its accurate location (with a location error of less than 1%), showing good potential for practical application in engineering. Full article

In order to mitigate the reduction in soybean yield caused by soil salinization, a soybean gene, GmSNF4, which promotes plant tolerance to salt–alkali stress, was identified in this study. The STRING database was used to predict the interaction between GmSNF4 and GmPKS4. The GmPKS4 gene was experimentally shown to be involved in salt–alkali stress tolerance. Firstly, the yeast two-hybrid technique and bimolecular fluorescence complementation (BiFC) technique were used to confirm the interaction between GmSNF4 and GmPKS4: the AMPK-CBM-CBS1 conserved domain was thereby determined to be the region of the GmSNF4 protein involved in the interaction. Secondly, the GmSNF4 gene was induced by salt–alkali stress according to qRT-PCR analysis, and the GmSNF4 protein was localized in the nucleus and cytoplasm. Finally, analysis of GmSNF4’s role in resistance to salt–alkali stress in transgenic soybean plants showed that transgenic lines had better phenotypic, physiological, and stress-related gene expression than non-transgenic soybeans. Thus, GmSNF4 may play a significant role in plant salt–alkali stress tolerance. Full article

China’s accelerated urbanization has instigated construction land expansion and ecological land attrition, aggravating the carbon emission disequilibrium. Notably, the “land carbon emission elasticity coefficient” in urban agglomerations far exceeds international benchmarks, underscoring the contradiction between spatial expansion and low-carbon goals. Existing research predominantly centers on single-spatial-type or static-model analyses, lacking cross-scale mechanism exploration, policy heterogeneity consideration, and differentiated carbon metabolism assessment across functional spaces. This study takes Xiong’an New Area as a case, delineating the spatiotemporal evolution of land use and carbon emissions during 2017–2023. Construction land expanded by 26.8%, propelling an 11-fold escalation in carbon emissions, while emission intensity decreased by 11.4% due to energy efficiency improvements and renewable energy adoption. Cultivated land reduction (31.8%) caused a 73.4% decline in agricultural emissions, and ecological land network restructuring (65.3% forest expansion and wetland restoration) significantly enhanced carbon sequestration. This research validates a governance paradigm prioritizing “structural optimization” over “scale expansion”—synergizing construction land intensification with ecological restoration to decelerate emission growth and strengthen carbon sink systems. Full article

Rice blast disease, caused by the ascomycete fungus Magnaporthe oryzae (syn. Pyricularia oryzae), poses a severe threat to global rice production. Southern China, a major rice-growing region characterized by diverse agroecological conditions, faces substantial challenges from blast disease, yet our understanding of the genetic structure of M. oryzae populations in this region remains limited. Here, we analyzed 885 M. oryzae strains from 18 nurseries across four rice ecological regions in Southern China using a panel of genome-wide SNP markers. Phylogenetic and principal component analyses revealed three distinct clonal lineages: lineage I (58.19%), lineage II (21.36%), and lineage III (20.45%). Lineage I exhibited a broader geographic distribution compared to the other two lineages. Host-adapted divergence was observed across rice subspecies, with lineage III predominantly associated with japonica growing-regions, while lineages I and II mainly colonized indica rice-growing regions. Genetic diversity exhibited significant spatial heterogeneity, with the nucleotide diversity (π) ranging from 0.17 in South China to 0.32 in the Middle–Lower Yangtze River region, reflecting differential cropping systems. The predominantly negative Tajima’s D values across populations suggested recent expansion or selective sweeps, likely driven by host resistance pressures. High genetic differentiation between lineage I and other lineages contrasted with low divergence between lineages II and III, indicating distinct evolutionary trajectories. Furthermore, an uneven distribution of mating types among three genetic lineages was observed, suggesting limited sexual recombination within clonal lineages. The information obtained in this study may be beneficial in devising suitable strategies to control rice blast disease in Southern China. Full article

Aiming at the problem of the through-core current sensor based on cable partial discharge detection having difficulty in compatibility with high sensitivity and wide detection frequency band, this paper proposes a design method for an ultra-high-frequency through-core current sensor. Firstly, the sensor circuit model was established, and the relationship between the sensor hardware parameters, and the bandwidth and sensitivity was derived. Subsequently, a multi-objective particle swarm optimization model was established. The sensitivity and bandwidth were taken as the objective functions, and the hardware parameters were regarded as the decision variables. Constraint conditions were set according to the cable size, self-integration working mode, etc. The optimal hardware parameters were obtained through solution and calculation. Finally, an ultra-high-frequency through-core current sensor was fabricated, and the bandwidth and sensitivity of the sensor at different frequencies were tested. The test results of cable partial discharge signals demonstrate that the designed sensor maintains a sensitivity of no less than 20.46 V/A within the 3 MHz to 200 MHz frequency range. This performance not only satisfies the fundamental sensitivity requirement of 5 V/A in the 3–30 MHz band for cable partial discharge detection but also resolves the inherent trade-off between sensitivity and detection bandwidth, exhibiting superior performance compared to conventional sensors. Full article

The Netherlands has established climate-adaptive strategies shaped by its long history of water-related climate events, such as the floods in 1421 and 1953. UNESCO World Heritage (WH) properties in The Netherlands reflect centuries of human intervention and natural processes to adapt and mitigate climate challenges, including spatial design and hydraulic engineering. The Dutch Climate Research Initiative also highlights cultural heritage as an integral component in preparing for the 2026 National Climate Adaptation Strategy. This article aims to unveil climate-adaptive World Heritage management strategies (CAWHMSs), using WH properties in The Netherlands as a case study. It collects textual data from Statements of Outstanding Universal Value, State of Conservation Reports by the State Parties and management plans. Through qualitative coding and keywords aggregation of the documents, the visualised results of a Sankey diagram and two semantic networks confirmed two CAWHMSs: conservation and developing WH properties as collaborative knowledge hubs. Conservation supports regulating urban climate and sustainable water management. As collaborative knowledge hubs, multidisciplinary sectors explore opportunities to align WH properties with broader sustainable development initiatives. They also deepen younger generations’ awareness of cultural and natural significance relevant to mitigating climate threats. The results emphasise WH as a contributor to climate adaptation. Cross-sectoral stakeholders can advance holistic climate adaptation efforts using CAWHMSs. Full article

Alfalfa (Medicago sativa L.) plays a crucial role in the revitalization of the dairy industry and grassland agriculture in China. However, regional differences in economic and environmental performance have not been adequately specified or quantified. This study compares alfalfa production in Wuhe County (Southern China) and Ar Horqin Banner (Northern China) by integrating cost–benefit analysis (CBA) with life cycle assessment (LCA). Field data from 22 enterprises were analyzed using one ton of alfalfa hay and a net profit of CNY 10,000 as functional units, over a three-year evaluation period (2017–2019). The assessment encompassed four impact categories: primary energy demand (PED), global warming potential (GWP), acidification potential (AP), and water use (WU). The northern case systems exhibited 67.45% higher production costs but 96.99% greater profitability per ton compared to the southern case, alongside 2.13 × 10⁻² greater environmental impact. Conversely, the southern case systems were less profitable and demonstrated an 18.6% higher environmental impact per CNY 10,000 net profit compared to the northern case. Regional environmental hotspots differed: fertilizer use dominated impact in the south, whereas irrigation and electricity consumption drove burdens in the north. To facilitate a sustainable transition, policymakers should implement region-specific support measures, such as ecological incentives and crop rotation schemes for the south, and water-saving technologies along with renewable energy integration for the north. Farmers and enterprises are encouraged to adopt precision input strategies and climate risk management tools, while researchers should focus on advancing adaptive breeding techniques and optimizing resource utilization. The development of a unified system that integrates economic and environmental metrics is crucial for enabling stakeholders to drive the sustainable transformation of alfalfa production. Full article

Novel view synthesis of remote sensing scenes from satellite images is a meaningful but challenging task. Due to the wide temporal span of image acquisition, satellite image collections often exhibit significant appearance variations, such as seasonal changes and shadow movements, as well as transient objects, making it difficult to reconstruct the original scene accurately. Previous work has noted that a large amount of image variation in satellite images is caused by changing light conditions. To address this, researchers have proposed incorporating the direction of solar rays into neural radiance fields (NeRF) to model the amount of sunlight reaching each point in the scene. However, this approach fails to effectively account for seasonal variations and suffers from a long training time and slow rendering speeds due to the need to evaluate numerous samples from the radiance field for each pixel. To achieve fast, efficient, and high-quality novel view synthesis for multi-temporal satellite scenes, we propose SatGS, a novel method that leverages 3D Gaussian points for scene reconstruction with an appearance-adaptive adjustment strategy. This strategy enables our model to adaptively adjust the seasonal appearance features and shadow regions of the rendered images based on the appearance characteristics of the training images and solar angles. Additionally, the impact of transient objects is mitigated through the use of visibility maps and uncertainty optimization. Experiments conducted on WorldView-3 images demonstrate that SatGS not only renders superior image quality compared to existing State-of-the-Art methods but also surpasses them in rendering speed, showcasing its potential for practical applications in remote sensing. Full article

This study addresses the critical limitation of existing Ultra-Short Baseline (USBL) calibration algorithms in handling transducer positional errors and inter-element phase errors. We propose a novel positioning-calibration model based on vector projection theorem. The model achieves two key innovations: it eliminates the influence of inter-element positional errors through its structural design, and, for the first time, incorporates inter-element phase errors from acoustic array measurements as observational parameters to establish joint estimation equations for system installation angle errors and inter-element phase errors. The estimation process is implemented using an unscented Kalman filter (UKF). Simulation results demonstrate that the UKF outperforms the Gauss–Newton method (GNM), achieving estimation errors for installation angles and phase errors within 0.05°. Comparative evaluations confirm the model’s superiority over conventional calibration methods in accurately estimating installation angles under transducer positional errors. Field experiments further validate the algorithm’s effectiveness in real-world marine environments, successfully estimating system installation angle errors and inter-element phase errors to enhance final target positioning accuracy. This approach provides a practical solution to persistent calibration challenges in USBL systems. Full article

During deep foundation pit construction, slight improper operations may lead to excessive deformation, resulting in engineering accidents. Therefore, how to accurately predict the deformation of the deep foundation pit is of significant importance. With advancements in artificial intelligence technology, machine learning has been utilized to learn and simulate complex nonlinear relationships among various factors influencing foundation pit deformation. Prediction accuracy is significantly improved, and the dynamic trend of foundation pit deformation is accurately grasped to curb the risk of safety accidents. This paper systematically reviews the current applications of machine learning in deep foundation pit deformation prediction. The fundamental principles of machine learning models, including neural networks, support vector machines, and Bayesian networks, are elaborated in the context of their application to deep foundation pit deformation prediction. The application effects of various machine learning models in predicting deep foundation pit supporting structure deformation, surrounding surface settlement, and assessing foundation pit risks are summarized. The limitations and future development prospects of current machine learning models for deformation prediction in deep foundation pit construction are discussed. The research results offer valuable insights for the application and advancement of machine learning in the deep foundation pit deformation prediction field. Full article

It is particularly important to develop efficient air–rail intermodal path planning methods for making full use of the advantages of air–rail intermodal networks and providing passengers with richer and more reasonable travel options. A Time-Expanded Graph (TEG) is used to model the timetable information of public transportation providing a theoretical basis for public transportation path planning. However, if the TEG includes a large amount of data such as train stations, airports, train and air schedules, the network scale will become very large, making path planning extremely time-consuming. This study proposes an XGBoost-based heuristic path planning algorithm (XGB-HPPA) for large scale air–rail intermodal networks, which use the XGBoost model to predict transfer stations before path planning, and quickly eliminate unreasonable transfer edges by adding a heuristic factor, reducing the network scale, thus accelerating the computation speed. Comparative results indicate that XGB-HPPA can markedly enhance computational speed within large-scale networks, while obtaining as many valid solutions as possible and approximating the optimal solution. Full article

Aerosol transport flux LiDAR was used to observe heavy pollution events in Luohe City during January 2022 and was combined with monitoring data of ground meteorological parameters and conventional pollutants to analyze the vertical optical properties of aerosols, transport sources, and causes of heavy pollution. Two pollution events (January 2nd–5th and 13th–20th, 2022) were effectively monitored and divided into four pollution phases according to PM_2.5 concentrations and relative humidity (RH). The results showed that all ground PM_2.5/PM₁₀ values were above 0.5 throughout the pollution, indicating a predominance of fine particulate matter. Analysis of the vertical distribution of aerosol flux LiDAR data showed that the inversion layer was distributed below 1 km; the vertical profile of extinction coefficient showed that all the pollution events were dominated by local emissions, while the contribution of regional transmission during the January 2nd to 5th was also quite prominent; kriging interpolation results showed that this pollution covered the most central and eastern regions of China during January 2022. The flux LiDAR monitoring results showed that there were three main transmission channels of PM_2.5: east (Zhoukou, Lu–Wan–Yu–Su junction), northeast (Lu–Yu junction), and southeast (YRD). The analysis of the clustered backward trajectories, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models showed that the potential transmission sources of PM_2.5 were mainly in junction zones of Lu–Wan–Yu–Su as well as Shaanxi Province, with PSCF values above 0.7 and CWT values above 70 μg/m³. This study could provide a scientific basis for the prevention and control of local pollution. Full article

Epigallocatechin-3-O-gallate (EGCG) is an important ingredient that indicates tea quality and has healthcare functions. Magnesium nutrition can improve the quality and yield of tea plants, but its regulatory role in the biosynthesis of EGCG in tea plants has not been clarified. Herein, we performed a comprehensive analysis of the metabolomics and transcriptomics of the shoots of ‘Huangdan’ at five magnesium concentrations: L1-L5 (0, 0.15, 0.45, 0.6, and 0.9 mmol/L mg²⁺, respectively). The results showed that the EGCG content of tea shoots treated with low magnesium concentrations was higher compared to those treated with high magnesium concentrations. The contents of related metabolites such as p-coumaric acid and cyanide in the EGCG synthesis pathway increased in the L4 and L5 treatment groups, while those of dihydroquercetin, dinnamic acid, and epicatechin increased significantly in the L2 and L3 treatment groups. Under the influence of magnesium treatment, the biosynthesis of EGCG was affected by a series of structural genes: CsPAL (HD.01G0005520), HD.02G0024350), Cs4CL (HD.15G0008250, HD.13G0010220), CsDFR (HD.04G0026220), CsANS(HD.12G0016700) with CsaroDE (HD.03G0002480)-positive regulation, and CsPAL (HD.13G0009900, HD.06G0008610), CsC4H (HD.06G0017130), Cs4CL (HD.02G0027390, HD.04G0003270), CsCHS (HD.10G0022640), CsCHI (HD.01G0011100), CsF3′H (HD.15G0015490), CsF3′5′H (HD.13G0004300), CsANS (HD.07G0023630), and Csaro B (HD.01G0028400) with CsSCPL (HD.01G0041070)-negative regulation. Transcription factors MYB 44 and WRKY 17 may play a key role in EGCG biosynthesis, which is significantly induced by magnesium nutrition in tea tree shoots. This study elucidates the effect of magnesium nutrition on EGCG biosynthesis in tea plants and provides key candidate transcription factors to provide a reference for further research on high-EGCG tea varieties to improve tea quality. Full article

Soybean is a vital crop globally and a key source of food, feed, and biofuel. With advancements in high-throughput technologies, soybeans have become a key target for genetic improvement. This comprehensive review explores advances in multi-omics, artificial intelligence, and economic sustainability to enhance soybean resilience and productivity. Genomics revolution, including marker-assisted selection (MAS), genomic selection (GS), genome-wide association studies (GWAS), QTL mapping, GBS, and CRISPR-Cas9, metagenomics, and metabolomics have boosted the growth and development by creating stress-resilient soybean varieties. The artificial intelligence (AI) and machine learning approaches are improving genetic trait discovery associated with nutritional quality, stresses, and adaptation of soybeans. Additionally, AI-driven technologies like IoT-based disease detection and deep learning are revolutionizing soybean monitoring, early disease identification, yield prediction, disease prevention, and precision farming. Additionally, the economic viability and environmental sustainability of soybean-derived biofuels are critically evaluated, focusing on trade-offs and policy implications. Finally, the potential impact of climate change on soybean growth and productivity is explored through predictive modeling and adaptive strategies. Thus, this study highlights the transformative potential of multidisciplinary approaches in advancing soybean resilience and global utility. Full article