Search Results (7,111)

Globally, salt stress is one of the most significant abiotic stresses limiting crop production in dry-land regions. Nowadays, growing crops in dry-land regions under saline irrigation is the main focus. Soil amendment with organic materials has shown the potential to mitigate the adverse effects of salinity on plants. This study aimed to examine the ameliorative impact of soil amendment (manure + sandy, compost + sandy, clay + sandy and sandy soil) on the growth, yield, physiological, and biochemical attributes of Hedysarum scoparium Fisch. et Mey (HS) and Avena sativa L. (OT) under fresh and saline water irrigation in dry-land regions. The results showed that salt stress negatively affected both plant species’ growth, physiological traits, yield, and chloride ions. In response to saline irrigation, plants of both species increased catalase (CAT) and ascorbate peroxidase (APX) activities as part of a self-defense mechanism to minimize damage. Salt stress also significantly raised levels of hydrogen peroxide (H₂O₂), malondialdehyde (MDA), and chloride ions (Cl). However, soil amendment treatments like manure + sandy and compost + sandy soil countered the negative effects of saline irrigation, significantly improving plant growth and yield compared with sandy soil. Thus, organic soil amendment is a promising strategy for sustainable crop production under saline irrigation in dry-land regions. This study provides valuable insights into enhancing agricultural production by fostering resilient halophytes and salt-tolerant plant species in challenging environments. Full article

Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firstly, the previous models for event temporal relation extraction mainly rely on a classification framework, which fails to output the crucial contextual words necessary for predicting the temporal relations between two event triggers. Secondly, the prior research that formulated natural language processing tasks as text generation problems usually trained the generative models by maximum likelihood estimation. However, this approach encounters potential difficulties when the optimization objective is misaligned with the task performance metrics. To resolve these limitations, we introduce a reinforcement learning-based generative framework for event temporal relation extraction. Specifically, to output the important contextual words from the input sentence for temporal relation identification, we introduce dependency path generation as an auxiliary task to complement event temporal relation extraction. This task is solved alongside temporal relation prediction to enhance model performance. To achieve this, we reformulate the event temporal relation extraction task as a text generation problem, aiming to generate both event temporal relation labels and dependency path words based on the input sentence. To bridge the gap between the optimization objective and task performance metrics, we employ the REINFORCE algorithm to optimize our generative model, designing a novel reward function to simultaneously capture the accuracy of temporal prediction and the quality of generation. Lastly, to mitigate the high variance issue encountered when using the REINFORCE algorithm in multi-task generative model training, we propose a baseline policy gradient algorithm to improve the stability and efficiency of the training process. Experimental results on two widely used datasets, MATRES and TB-DENSE, show that our approach exhibits competitive performance. Full article

High-redshift active galactic nuclei (AGN) provide key insights into early supermassive black hole growth and cosmic evolution. This study investigates the parsec-scale properties of 86 radio-loud quasars at z ≥ 3 using very long baseline interferometry (VLBI) observations. Our results show predominantly compact core and core-jet morphologies, with 35% having unresolved cores, 59% with core–jet structures, and only 6% with core–double jet morphology. Brightness temperatures are generally lower than expected for highly radiative sources. The jets’ proper motions are surprisingly slow compared to those of lower-redshift samples. We observe a high fraction of young and/or confined peak-spectrum sources, providing insights into early AGN evolution in dense environments during early cosmic epochs. The observed trends may reflect genuine evolutionary changes in AGN structure over cosmic time, or selection effects favoring more compact sources at higher redshifts. These results stress the complexity of high-redshift radio-loud AGN populations and emphasize the need for multi-wavelength, high-resolution observations to fully characterize their properties and evolution through cosmic history. Full article

To address the challenges of cross-city travel for different types of electric vehicles (EV) and to tackle the issue of rapid charging in regions with weak power grids, this paper presents a strategic approach for locating and sizing highway charging stations tailored to such grid limitations. Initially, considering the initial EV state of charge, a path-demand-based model for EV charging station location–allocation is proposed to optimize station numbers and enhance vehicle flow, which indicates the passing rate of vehicles. Subsequently, a capacity configuration model is formulated, integrating wind, photovoltaic, storage, and diesel generators to manage the stations’ load. This model introduces a new objective function, the annual comprehensive cost, encompassing installation, operation, maintenance, wind and solar curtailment, and diesel generation costs. Simulation examples on north-western cross-city highways validate the efficacy of this approach, showing that the proposed wind–solar storage fast-charging station site selection and capacity optimization model can effectively cater to diverse electric vehicle charging demands. Moreover, it achieves a 90% self-consistency rate during operation across various typical daily scenarios, ensuring a secure and economically viable operational performance. Full article

Equine endurance exercise induces physiological changes that alter metabolism and molecular pathways to maintain balance after intense physical activity. However, the specific regulatory mechanisms remain under debate. Identifying differentially expressed genes (DEGs) and differential metabolites (DMs) associated with equine endurance is essential for elucidating these regulatory mechanisms. This study collected blood samples from six Yili horses before and after an 80 km race and conducted transcriptomics and metabolomics analyses, yielding 722 DEGs and 256 DMs. These DEGs were primarily enriched in pathways related to amino acid biosynthesis, cellular senescence, and lipid metabolism/atherosclerosis. The DMs were predominantly enriched in fatty acid biosynthesis and the biosynthesis of unsaturated fatty acids. The integrative transcriptomics and metabolomics analyses of DEGs and DMs highlight functional changes during the endurance race. The findings offer a holistic understanding of the regulatory mechanisms underlying equine endurance and a solid foundation for formulating training programs to optimize horse performance in endurance racing. Full article

Water environment evaluation is the basis of water resource planning and sustainable utilization. As a successful case of the coordinated progress of ecological protection and economic development, the Xin’an River Basin is a model for exploring the green development model. However, there are still some problems in the synergistic cooperation between the two provinces. Exploring the differences within the basin is a key entry point for solving the dilemma of synergistic governance in the Xin’an River Basin, optimizing the allocation of resources, and improving the overall effectiveness of governance. Based on the DPSIR model, 21 water environment–related indicators were selected, and the entropy weight–TOPSIS method and gray correlation model were used to evaluate the temporal and spatial status of water resources in each county of the Xin’an River Basin. The results show that (1) The relative proximity of the water environment in Xin’an River Basin fluctuated in “M” shape during the ten years of the study period, and the relative proximity reached the optimal solution of 0.576 in 2020. (2) From the five subsystems, the state layer and the corresponding layer are the most important factors influencing the overall water environment of the Xin’an River Basin. In the future, it is intended to improve the departmental collaboration mechanism. (3) The mean values of relative proximity in Qimen County, Jiande City, and Chun’an County during the study period were 0.448, 0.445, and 0.439, respectively, and the three areas reached a moderate level. The water environment in Huizhou District and Jixi County, on the other hand, is relatively poor, and the mean values of proximity are 0.337 and 0.371, respectively, at the alert level. The poor effect of synergistic development requires a multi–factor exploration of reasonable ecological compensation standards. We give relevant suggestions for this situation. Full article

This study developed a one-part alkali-activated slag/wood biomass fly ash (WBFA) binder (AAS) for preparing cemented paste backfill (CPB) as an alternative to traditional cement. Through multi-scale characterizations (XRD, FTIR, TGA, rheological testing, and MIP) and performance analyses, the regulation mechanisms of slag/WBFA ratios on hydration behavior, microstructure, and mechanical properties were systematically revealed. Results demonstrate that high slag proportions significantly enhance slurry rheology and mechanical strength, primarily through slag hydration generating dense gel networks of hydration products and promoting particle aggregation via reduced zeta potential. Although inert components in WBFA inhibit early hydration, the long-term reactivity of slag effectively counteracts these negative effects, achieving comparable 28-day compressive strength between slag/WBFA-based CPB (4.11 MPa) and cement-based CPB (4.16 MPa). Microstructural analyses indicate that the disordered gels in AAS systems exhibit silicon–oxygen bond polymerization degrees (950 cm⁻¹) comparable to cement, while WBFA regulates Ca/Si ratios to induce bridging site formation (900 cm⁻¹), significantly reducing porosity and enhancing structural compactness. This research provides theoretical support and process optimization strategies for developing low-cost, high-performance mine filling materials using industrial solid wastes, advancing sustainable green mining practices. Full article

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R² = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R² = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts. Full article

In the current context of the global energy landscape, China is facing a growing challenge in oil and gas exploration and development. It is difficult to evaluate the log data because of the lithological composition of igneous rocks, which displays an unparalleled degree of complexity and unpredictability. Against this backdrop, this study deploys advanced multifractal detrended fluctuation analysis (MF-DFA) to comprehensively analyze key parameters within igneous rock logging data, including natural gamma-ray logging, resistivity logging, compensated neutron logging, and acoustic logging. The results unequivocally demonstrate that these logging data possess distinct multifractal characteristics. This multifractality serves as a powerful tool to elucidate the inherent complexity, heterogeneity, and structural and property variations in igneous rocks caused by diverse geological processes and environmental changes during their formation and evolution, which is crucial for understanding the subsurface reservoir behavior. Subsequently, through a series of rearrangement sequences and the replacement sequence on the original logging data, we identify that the probability density function and long-range correlation are the fundamental sources of the observed multifractality. These findings contribute to a deeper theoretical understanding of the data-generating mechanisms within igneous rock formations. Finally, multifractal detrended cross-correlation analysis (MF-DCCA) is employed to explore the cross-correlations among different types of igneous rock logging data. We uncover correlations among different igneous rocks’ logging data. These parameters exhibit different properties. There are negative long-range correlations between natural gamma-ray logging and resistivity logging, natural gamma-ray logging and compensated neutron logging in basalt, and resistivity logging and compensated neutron logging in diabase. The logging data on other igneous rocks have long-range correlations. These correlation results are of great significance as they provide solid data support for the formulation of oil and gas exploration and development plans. Full article

Hippophae rhamnoides subsp. turkestanica is mainly distributed in the mountains, valleys, and desert edges of Central Asia. It plays a vital role in maintaining ecological stability in arid and semiarid areas. In this study, the MaxEnt model was used to simulate the habitat suitability of H. rhamnoides subsp. turkestanica, and the key environmental factors affecting its distribution were identified. Additionally, we explored habitat sensitivity to climate change, and provided essential information for the conservation and management of this important subspecies in arid and semiarid regions. Under four different climate scenarios (SSP126, SSP245, SSP370, and SSP585) in 2040, 2060, 2080, and 2100, the prediction of habitat suitability and changes in species distribution centroids in the future were simulated. The results revealed that suitable habitats for H. rhamnoides subsp. turkestanica are primarily located in Tajikistan, Kyrgyzstan, China, Pakistan, and Afghanistan. Altitude (Alt), isothermality (bio3), and slope (Slo) emerged as the main environmental factors. Projections suggest a significant expansion in the total area of suitable habitat under future climate scenarios. By 2100, the suitable habitat areas under the SSP126, SSP245, SSP370, and SSP585 scenarios will reach 10,526,800 km², 12,930,200 km², 15,449,900 km² and 14,504,800 km², respectively. In addition, a slight northwestward shift was observed in the distribution centroid. These findings provide important insights for conservation efforts aimed at protecting H. rhamnoides subsp. turkestanica and supporting its biodiversity. By understanding the factors affecting habitat suitability and predicting changes in climate scenarios, this study provides valuable guidance for developing long-term conservation strategies. Full article

Landslides frequently occur in the Xinjiang Uygur Autonomous Region of China due to its complex geological environment, posing serious risks to human safety and economic stability. Existing studies widely use machine learning models for landslide susceptibility prediction. However, they often fail to capture the threshold and interaction effects among environmental factors, limiting their ability to accurately identify high-risk zones. To address this gap, this study employed a gradient boosting decision tree (GBDT) model to identify critical thresholds and interaction effects among disaster-causing factors, while mapping the spatial distribution of landslide susceptibility based on 20 covariates. The performance of this model was compared with that of a support vector machine and deep neural network models. Results showed that the GBDT model achieved superior performance, with the highest AUC and recall values among the tested models. After applying clustering algorithms for non-landslide sample selection, the GBDT model maintained a high recall value of 0.963, demonstrating its robustness against imbalanced datasets. The GBDT model identified that 8.86% of Xinjiang’s total area exhibits extremely high or high landslide susceptibility, mainly concentrated in the Tianshan and Altai mountain ranges. Lithology, precipitation, profile curvature, the Modified Normalized Difference Water Index (MNDWI), and vertical deformation were identified as the primary contributing factors. Threshold effects were observed in the relationships between these factors and landslide susceptibility. The probability of landslide occurrence increased sharply when precipitation exceeded 2500 mm, vertical deformation was greater than 0 mm a⁻¹, or the MNDWI values were extreme (<−0.4, >0.2). Additionally, this study confirmed bivariate interaction effects. Most interactions between factors exhibited positive effects, suggesting that combining two factors enhances classification performance compared with using each factor independently. This finding highlights the intricate and interdependent nature of these factors in landslide susceptibility. These findings emphasize the necessity of incorporating threshold and interaction effects in landslide susceptibility assessments, offering practical insights for disaster prevention and mitigation. Full article

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions. Full article

(1) Background: Xinjiang smoked horsemeat sausage is a popular food; however, bio-genic amine (BA) production is a concern for food safety. (2) Methods: the present study selected the three most toxic BAs for food safety (histamine, tyramine, and putrescine) and determined the bacteria that produce them. (3) Results: After 24 h of incubation, fifteen isolated strains, especially Enterobacter ludwigii MT705841 and Enterobacter bugandensis MT705832 produced putrescine (485.52 μg/mL and 408.95 μg/mL, respectively, p < 0.05); eight isolated strains, especially Proteus vulgaris MT705833 and Bacillus subtilis MT705839 produced histamine (63.86 μg/mL and 30.40 μg/mL, respectively, p < 0.05); and 14 isolated strains, especially Staphylococcus saprophyticus MT705831 and Proteus penneri MT705835 produced tyramine (482.26 μg/mL and 497.76 μg/mL, respectively, p > 0.05). Artemisia oil moderately inhibited P. vulgaris MT705833 and B. subtilis MT705839 after 48 h of in vitro incubation, decreasing histamine production by 44.83% and 47.92% for these two bacteria after 24 h and 20 h of incubation, respectively. Cinnamon oil strongly inhibited putrescine production by E. bugandensis MT705832 and E. ludwigii MT705841, decreasing production by 96.63% and 92.03% for these two bacteria after 24 h of incubation, respectively. Grapeseed oil slightly inhibited P. penneri MT705835 tyramine production (only after 4 h of incubation) and had an unstable inhibitory effect on Citrobacter freundii MT705836 tyramine production. (4) Conclusions: the results of this study suggest that cinnamon oil can be an effective food additive for the prevention of BA production in Xinjiang smoked sausages. Full article

Current evaluations of carbon emission efficiency in China’s provincial power industry often neglect the impact of carbon transfers from inter-regional power transmission, leading to biased assessments that hinder the sustainable development of the energy transition. To address this, we propose an advanced efficiency evaluation model that incorporates a multi-regional input–output (MRIO) framework, refining CO₂ emission calculations and correcting parameter deviations in the slack-based measure (SBM) model. This model improves both the precision and fairness of carbon emission efficiency assessments. We apply the MRIO-SBM model to evaluate carbon emission efficiency in the power industry across 30 provinces in China for 2012, 2015, and 2017, revealing the impact of carbon transfers on efficiency. The results indicate that incorporating MRIO improves the precision of SBM evaluations. Significant regional disparities are observed: eastern coastal regions achieve higher efficiencies, while northeastern and western regions typically exhibit lower values, ranging from 0.5 to 0.7. Efficiency evaluations must account for carbon transfer dynamics, as these transfers can lead to overestimations of efficiency by up to 19% in electricity-importing regions and underestimations of approximately 10% in electricity-exporting regions. Furthermore, the findings emphasize the need to foster low-carbon cross-regional collaboration to promote sustainable development in the power industry. Full article

This study explores the corrosion behavior of pure copper in simulated oilfield-produced water and evaluates the inhibitory effect of cetylpyridinium chloride (CPC) on microbiologically influenced corrosion (MIC). Weight loss tests, potentiodynamic polarization, and pitting analyses revealed that sulfate-reducing bacteria (SRB) activity significantly accelerated corrosion, with the maximum pit depth reaching 7.54 µm in the absence of CPC—approximately 1.83 times greater than under abiotic conditions. The introduction of CPC substantially reduced corrosion rates and pit depths, with maximum pit depths decreasing to 2.97 µm, 1.11 µm, and 1.02 µm at 10, 50, and 80 mg/L CPC, respectively. CPC inhibited SRB biofilm formation, metabolic activity, and corrosion product accumulation, achieving an inhibition efficiency of up to 89% at 80 mg/L. These findings highlight CPC’s dual role as a biocide and a corrosion inhibitor, offering a promising approach to controlling MIC in oilfields and similar industrial environments. Full article