Search Results (3,943)

This study presents a method to more reasonably control the quality performance of activated rubber asphalt by microwave activation. Different activated rubber asphalt preparation process parameters (reaction temperature, stirring rate, and reaction time) were selected to explore the influence of different process parameters on the macroscopic properties of rubber asphalt, and a multi-indicator evaluation model was set up using the theoretical method of the RSR model to determine the optimal production process parameters. The results showed that reaction temperature had the strongest influence (gray correlation > 0.85) among production parameters, followed by stirring rate and reaction time. The optimal parameters identified were a reaction temperature of 220 °C, a stirring rate of 1000 rpm, and a reaction time of 120 min, under which the viscosity–temperature sensitivity decreased by approximately 18%, and the rutting factor (G*/sinδ) increased by over 20%, indicating significant improvements in rheological stability and high-temperature performance. The integrated evaluation approach provided reliable and practical guidance for producing high-performance activated rubber asphalt. Full article

Mycoplasmal pneumonia of sheep (MPS), caused by Mesomycoplasma (Mycoplasma) ovipneumoniae, profoundly impacts ovine productivity and survival. Although gut–lung microbiota interactions are increasingly recognized in respiratory diseases, whether similar crosstalk occurs between the lung and rumen microbiota in MPS-affected sheep remains unknown. To investigate alterations in the lung and rumen microbiota of sheep with MPS, the crosstalk between these microbial communities, and their impacts on growth phenotypes. From a cohort of 414 naturally infected six-month-old male Hu sheep, we selected 10 individuals with severe pulmonary pathology and 10 healthy controls for detailed phenotypic and microbiome analyses. Assessment of 359 phenotypic traits revealed that MPS significantly impairs feed efficiency and growth rate (p < 0.05). Through 16S rRNA gene sequencing, we found that MPS significantly altered the pulmonary microbiota community structure (p < 0.01), with a noticeable impact on the rumen microbiota composition (p = 0.059). Succinivibrionaceae_UCG-001 was significantly depleted in both the rumen and lungs of diseased sheep (p < 0.05) and strongly associated with reduced average daily feed intake (p < 0.05). In addition, pulmonary Pasteurella and ruminal Succinivibrionaceae_UCG-002 were significantly enriched in MPS-affected sheep, showed a strong positive correlation (p < 0.05), and were both negatively associated with feed efficiency (p < 0.05). Notably, Pasteurella multocida subsp. gallicida may act as a keystone species influencing feed efficiency. These findings point to a previously unrecognized rumen-lung microbial axis that may modulate host productivity in sheep affected by MPS. This work provides new insights into the pathogenesis of MPS and offers potential targets for therapeutic intervention and management. Full article

Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

In the medical field, magnesium (Mg) alloys have been widely used due to their excellent antibacterial properties and biodegradability. However, in the marine environment, the antibacterial effect may be greatly attenuated, and consequently, microorganisms in the ocean are likely to adhere to the surface of Mg alloys, resulting in biocorrosion damage, which is really troublesome in the maritime industry and can even be disastrous to the navy. Currently, there is a lack of research on the biocorrosion of Mg alloys that may find important applications in marine engineering. In this paper, the biocorrosion mechanism of the Mg alloy Mg-3Nd-2Gd-Zn-Zr caused by Chlorella vulgaris (C. vulgaris), a typical marine microalga, was studied. The results showed that the biomineralization process in the artificial seawater containing a low concentration of C. vulgaris cells was accelerated compared with that in the abiotic artificial seawater, leading to the deposition of CaCO₃ on the surface to inhibit the localized corrosion of the Mg alloy, whereas a high concentration of C. vulgaris cells produced a high content of organic acids at some sites through photosynthesis to significantly accelerate the surface film rupture at some sites and severe localized corrosion there, but meanwhile, it resulted in the formation of a more protective biomineralized film in the other areas to greatly alleviate the corrosion. The contradictory biocorrosion behaviors on the Mg-3Nd-2Gd-Zn-Zr alloy induced by C. vulgaris were finally explained by a mechanism proposed in the paper. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Natural enemies commonly probe larval bodies and frass with their antennae for prey hunting. However, the attractants to natural enemies emitted directly from hosts and host-associated tissues remained largely unknown. Here, we used two generalist noctuid species, Helicoverpa armigera (Hübner) and Spodoptera frugiperda (JE Smith), along with the larval endoparasitoid Microplitis mediator (Haliday) to address the question. Extracts of larval frass of both the noctuid species were strongly attractive to M. mediator females when hosts were fed either maize, cotton, soybean leaves, or an artificial diet without leaf tissues. By using a combination of electrophysiological measurements and behavioral tests, we found that the attractiveness of frass mainly relied on a volatile compound ethyl palmitate. The compound was likely to be a by-product of host digestion involving gut bacteria because an antibiotic supplement in diets reduced the production of the compound in frass and led to the decreased attractiveness of frass to the parasitoids. In contrast, extracts of the larval bodies of both the noctuid species appeared to be less attractive to the parasitoids than their respective fecal extracts, independently of types of food supplied to the larvae. Altogether, larval frass of the two noctuid species was likely to be more important than their bodies in attracting the endoparasitoid species, and the main attractant of frass was probably one of the common metabolites of digestion involving gut microbes, and its emission is likely to be independent of host plant species. Full article

The purpose of this study was to investigate the effect of different additives on the microbial composition, fermentation quality, and bacterial community structure of big-bale Leymus chinensis silage. An experiment was set up with four treatment groups: a control (C) group, Lacticaseibacillus rhamnosus (L) group, molasses (M) group, and L. rhamnosus + molasses (LM) group, with three replications per group, and L. chinensis silages were fermented for 20 and 40 days. The lactic acid, acetic acid, 1,2-propanediol, and propionic acid contents increased, and pH, butyric acid, 1-propanol, and ethanol contents decreased in the L, M, and LM groups compared to the C group. In the LM group, the number of lactic acid bacteria was the highest, while the pH was the lowest. Enterobacter and Paucibacter were the main dominant genera in the C group. The addition of L. rhamnosus and molasses increased the relative abundance of Lactobacillus, Weissella, and Enterococcus. Lactobacillus abundance correlated positively (p < 0.01) with Lactococcus, Enterococcus, and Weissella and correlated negatively with Enterobacter and Paucibacter. Conversely, Enterobacter and Paucibacter showed a strong positive correlation (p < 0.01, R = 0.55) during fermentation. Lactobacillus, Enterococcus, and Weissella were positively associated (p < 0.01) with acetic and lactic acid levels, while Enterobacter abundance was correlated positively (p < 0.05, R = 0.43) with 1,2-propanediol content. In summary, the addition of both L. rhamnosus and molasses improved the fermentation quality and bacterial community structure of big-bale L. chinensis silage. In addition to inhibiting harmful microorganisms, this combination improved the fermentation products of big-bale L. chinensis silage through microbial regulation. Full article

Long-range underwater gliders (LRUGs) have emerged as essential platforms for sustained and autonomous observation in deep and remote marine environments. This paper provides a comprehensive review of their developmental status, performance characteristics, and application progress. Emphasis is placed on two critical enabling technologies that fundamentally determine endurance: lightweight, pressure-resistant hull structures and high-efficiency buoyancy-driven propulsion systems. First, the role of carbon fiber composite pressure hulls in enhancing energy capacity and structural integrity is examined, with attention to material selection, fabrication methods, compressibility compatibility, and antifouling resistance. Second, the evolution of buoyancy control systems is analyzed, covering the transition to hybrid active–passive architectures, rapid-response actuators based on smart materials, thermohaline energy harvesting, and energy recovery mechanisms. Based on this analysis, the paper identifies four key technical challenges and proposes strategic research directions, including the development of ultralight, high-strength structural materials; integrated multi-mechanism antifouling technologies; energy-optimized coordinated buoyancy systems; and thermally adaptive glider platforms. Achieving a system architecture with ultra-long endurance, enhanced energy efficiency, and robust environmental adaptability is anticipated to be a foundational enabler for future long-duration missions and globally distributed underwater glider networks. Full article

Peatlands store substantial amounts of carbon (C) in the form of peat, but are increasingly threatened by drought and shrub encroachment under climate warming. However, how peat decomposition and its temperature sensitivity (Q₁₀) vary with depth and plant litter input under these stressors remains poorly understood. We incubated peat from two depths with different degrees of decomposition, either alone or incubated with Sphagnum divinum shoots or Betula ovalifolia leaves, under five temperature levels and two moisture conditions in growth chambers. We found that drought and Betula addition increased CO₂ emissions in both peat layers, while Sphagnum affected only shallow peat. Deep peat alone or with Betula exhibited higher Q₁₀ than pure shallow peat. Drought increased the Q₁₀ of both depths’ peat, but this effect disappeared with fresh litter addition. The CO₂ production rate showed a positive but marginal correlation with microbial biomass carbon, and it displayed a rather similar responsive trend to warming as the microbial metabolism quotient. These results indicate that both deep and dry peat are more sensitive to warming, highlighting the importance of keeping deep peat buried and waterlogged to conserve existing carbon storage. Additionally, they further emphasize the necessity of Sphagnum moss recovery following vascular plant encroachment in restoring carbon sink function in peatlands. Full article

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

To enhance the anti-overturning performance of poles and prevent tilting or collapse, a prefabricated foundation for distribution lines is developed. Field tests are conducted on five groups of foundations. Based on the test results, finite element analysis (FEA) is employed to investigate the influence of different factors—such as pole embedment depth, foundation locations, soil type, and soil parameters—on the anti-overturning performance of pole prefabricated foundations. The results indicate that under ultimate load conditions, the reaction force distribution at the base of the foundation approximates a triangular pattern, and the lateral earth pressure on the pole follows an approximately quadratic parabolic distribution along the depth. When the foundation size increases from 0.8 m to 0.9 m, the bearing capacity of the prefabricated foundation improves by 8%. Furthermore, when the load direction changes from 0° to 45°, the foundation’s bearing capacity increases by 14%. When the foundation is buried at a depth of 1.0 m, compared with the ground position, the ultimate overturning moment of the prefabricated foundation increases by 10%. Based on field test results, finite element simulation results, and limit equilibrium theory, a calculation method for the anti-overturning bearing capacity of prefabricated pole foundations is developed, which can provide a practical reference for the engineering design of distribution line poles and their prefabricated foundations. Full article

Hydrogen-rich water (HRW) has shown neuroprotective effects in acute brain injury, but its role in chronic radiation-induced brain injury (RIBI) remains unclear. This study investigated the long-term efficacy of HRW in mitigating cognitive impairment and neuronal damage caused by chronic RIBI. Fifty male Sprague Dawley rats were randomly divided into five groups: control, irradiation (IR), IR with memantine, IR with HRW, and IR with combined treatment. All but the control group received 20 Gy whole-brain X-ray irradiation, followed by daily interventions for 60 days. Behavioral assessments, histopathological analyses, oxidative stress measurements, ¹⁸F-FDG PET/CT imaging, transcriptomic sequencing, RT-qPCR, Western blot, and serum ELISA were performed. HRW significantly improved anxiety-like behavior, memory, and learning performance compared to the IR group. Histological results revealed that HRW reduced neuronal swelling, degeneration, and loss and enhanced dendritic spine density and neurogenesis. PET/CT imaging showed increased hippocampal glucose uptake in the IR group, which was alleviated by HRW treatment. Transcriptomic and molecular analyses indicated that HRW modulated key genes and proteins, including CD44, CD74, SPP1, and Wnt1, potentially through the MIF, Wnt, and SPP1 signaling pathways. Serum CD44 levels were also lower in treated rats, suggesting its potential as a biomarker for chronic RIBI. These findings demonstrate that HRW can alleviate chronic RIBI by preserving neuronal structure, reducing inflammation, and enhancing neuroplasticity, supporting its potential as a therapeutic strategy for radiation-induced cognitive impairment. Full article

Global warming has resulted in an increase in the frequency of extreme high-temperature events. High temperatures can increase cell membrane permeability, elevate levels of osmotic adjustment substances, reduce photosynthetic capacity, impair plant growth and development, and even result in plant death. Under high-temperature stress, plants mitigate damage through physiological and biochemical adjustments, heat signal transduction, the regulation of transcription factors, and the synthesis of heat shock proteins. However, different plants exhibit varying regulatory abilities and temperature tolerances. Investigating the heat-resistance and regulatory mechanisms of plants can facilitate the development of heat-resistant varieties for plant genetic breeding and landscaping applications. This paper presents a systematic review of plant physiological and biochemical responses, regulatory substances, signal transduction pathways, molecular mechanisms—including the regulation of heat shock transcription factors and heat shock proteins—and the role of plant hormones under high-temperature stress. The study constructed a molecular regulatory network encompassing Ca²⁺ signaling, plant hormone pathways, and heat shock transcription factors, and it systematically elucidated the mechanisms underlying the enhancement of plant thermotolerance, thereby providing a scientific foundation for the development of heat-resistant plant varieties. Full article

The detection of glucose concentration has a wide range of applications and plays a significant role in the fields of the food industry, medical health, and illness diagnostics. The utilization of sensor technology for glucose concentration detection is an effective approach. Glucose sensors utilizing nanomaterials, with high sensitivity, strong resistance to interference, and compact size, exhibit tremendous potential in glucose concentration detection. Traditional enzyme-based sensors exhibit superior selectivity and high sensitivity; however, they are deficient in terms of interference resistance capabilities. With the development of nanotechnology, the performance of glucose sensors has been significantly improved. This review discusses the research progress in non-enzymatic electrochemical glucose nanosensors, including noble metal-based glucose sensors and non-noble transition metal compound-based glucose sensors, as well as the applications of multimetallic materials in nanosensors. Additionally, the application of nanosensors based on fluorescence and colorimetric principles in the detection of glucose concentration is introduced in this review. Finally, a perspective on the challenges and prospects of nanosensors in the field of glucose detection is presented. Full article