Search Results (2,510)

Reductive soil disinfestation (RSD) is a promising strategy for mitigating soil degradation and enhancing soil health. While soil organic carbon (SOC) is crucial for soil fertility and climate regulation, the mechanisms underlying its stabilization via plant lignin and microbial humus in the RSD process remain elusive. Using a microcosm experiment, we investigated SOC dynamics by quantifying plant-derived (lignin phenols) and microbial-derived (amino sugars) C during RSD at key stages: initial (2 h), anaerobic (14 and 28 days), and aerobic (35 days). Concurrently, soil properties, microbial PLFA, and enzymatic activity were analyzed to elucidate underlying mechanisms. Over the initial 14 days, plant-derived C increased sharply by 61% before declining, yet still showed a 22% increase by the end of the RSD (35 days), a trend mirrored by bacterial-derived C. In contrast, fungal-derived C initially accumulated rapidly with a significant increase of 43%, then stabilized, and its proportion (21.63%) surpassed that of bacterial-derived C (5.56%). Over time, plant- (25.01% to 19.76%) and bacterial-derived C (7.81% to 5.56%) contributions to decreases in SOC, while fungal-derived C (about 21%) remained stable after day 14. This pattern is likely attributable to the initial anaerobic conditions, which caused a massive die-off of fungi and aerobic bacteria that utilize lignin and necromass, resulting in significant accumulation of both plant- and microbial-derived C. Subsequently, the proliferation of anaerobic bacteria consumed these plant- and bacterial-derived C sources in the soil, leading to their eventual decline. Key drivers of plant-derived C included soil pH, living fungi/bacteria, and β-1,4-glucosidase activity, whereas microbial-derived C depended on total nitrogen and living fungi. Our findings demonstrate that early SOC accumulation under RSD is driven by combined plant lignin and microbial necromass inputs, while fungal necromass becomes pivotal for long-term SOC stabilization, shaped by both abiotic and biotic factors. Full article

Microbial contamination of aviation fuels is a persistent operational and safety challenge, compromising fuel quality and accelerating material degradation. The global transition toward sustainable aviation fuels (SAF) amplifies the need to reassess microbial risks across both conventional and alternative fuel systems. Here, we present the first systematic review and meta-analysis to synthesize evidence on microbial prevalence in jet fuel environments and to evaluate implications for SAF deployment. Of 2837 records screened, 37 studies fulfilled the inclusion criteria. Microorganisms were detected in up to 87% of jet fuel systems worldwide (95% CI: 76–100%); however, this pooled estimate was associated with substantial heterogeneity (I² = 96%) and should therefore be interpreted with caution as reflecting an overall trend rather than a precise global value. Taxonomic analysis identified consistently reported bacterial genera (Actinomycetes, Halomonas, Mycobacterium, Nocardioides, Rhodococcus, Stenotrophomonas) and fungal genera (Aspergillus, Alternaria, Amorphotheca, Byssochlamys, Candida, Fusarium, Saccharomyces, Schizosaccharomyces, Talaromyces, Trichocomaceae). Deteriorative organisms dominated (bacteria 57%; fungi 75%) relative to non-deteriorative taxa (12% and 32%, respectively). These findings highlight microbial spoilage as a pervasive and underrecognized threat to fuel integrity. Importantly, they suggest that risks currently documented in conventional systems are likely to extend to SAF, reinforcing the urgent need for proactive monitoring frameworks and bio-contamination mitigation strategies to ensure aviation fuel reliability. Full article

Under deep mining conditions, coal and rock masses are subjected to high in situ stress and strong mining-induced disturbances, leading to intensified stress unloading, concentration, and redistribution processes. The stability of surrounding rock is therefore closely related to mine safety. Direct, real-time, and continuous monitoring of in situ stress magnitude, orientation, and evolution is a critical requirement for deep underground engineering. To overcome the limitations of conventional stress monitoring methods under high-stress and strong-disturbance conditions, a novel in situ stress monitoring device was developed, and its performance was systematically verified through laboratory experiments. Typical unloading–reloading and biaxial unequal stress paths of deep surrounding rock were adopted. Tests were conducted on intact specimens and specimens with initial damage levels of 30%, 50%, and 70% to evaluate monitoring performance under different degradation conditions. The results show that the device can stably acquire strain signals throughout the entire loading–unloading process. The inverted monitoring stress exhibits high consistency with the loading system in terms of evolution trends and peak stress positions, with peak stress errors below 5% and correlation coefficients (R²) exceeding 0.95. Although more serious initial damage increases high-frequency fluctuations in the monitoring curves, the overall evolution pattern and unloading response remain stable. Combined acoustic emission results further confirm the reliability of the monitoring outcomes. These findings demonstrate that the proposed device enables accurate and dynamic in situ stress monitoring under deep mining conditions, providing a practical technical approach for surrounding rock stability analysis and disaster prevention. Full article

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

Soil salinization and alkalinization critically constrain alfalfa (Medicago sativa L.) productivity, yet the regulatory mechanisms underlying its responses to salt–alkali stress are not fully understood. In this study, the alfalfa variety “Zhongmu No. 1” was used as experimental material. The seeds were subjected to salt stress (75 mM NaCl), alkali stress (15 mM NaHCO₃), and combined salt–alkali stress (50 mM NaCl + 5 mM NaHCO₃) in dishes, with ddH₂O serving as the control (CK). After 7 days of germination, the seedlings were transferred to a hydroponic system containing Hoagland nutrient solution supplemented with the corresponding treatments. Following 32 days of stress exposure, leaf and root tissue samples were collected for morphological and physiological measurements, as well as mRNA and miRNA sequencing analyses. Physiological assays revealed significant growth inhibition and increased electrolyte leakage under stress conditions. Transcriptome profiling identified over 5000 common differentially expressed genes (DEGs) in both leaves and roots under stress conditions, mainly enriched in pathways related to “iron ion binding”, “flavonoid biosynthesis”, “MAPK signaling”, and “alpha-Linolenic acid metabolism”. MiRNA sequencing detected 453 miRNAs, including 188 novel candidates, with several differentially expressed miRNAs (DEMs) exhibiting tissue- and stress-specific patterns. Integrated analysis revealed 147, 81, and 140 negatively correlated miRNA–mRNA pairs across three treatment groups, highlighting key regulatory modules in hormone signaling and metabolic pathways. Notably, in the ethylene and abscisic acid signaling pathways, ERF (XLOC_006645) and PP2C (MsG0180000476.01) were found to be regulated by miR5255 and miR172c, respectively, suggesting a post-transcriptional layer of hormonal control. DEM target genes enrichment pathway analyses also identified stress-specific regulation of “Fatty acid degradation”, “Galactose metabolism”, and “Fructose and mannose metabolism”. qRT-PCR validation confirmed the expression trends of selected DEGs and DEMs. Collectively, these findings reveal the complexity of miRNA–mRNA regulatory networks in alfalfa’s response to salt–alkali stress and provide candidate regulators for breeding stress-resilient cultivars. Full article

The identification and monitoring of pavement pathologies are critical for maintaining road infrastructure and ensuring transportation safety. As traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, in recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool for pavement condition assessment due to their mobility, efficiency, and ability to capture high-resolution imagery and multi-sensor data. This Systematic Literature Review aims to synthesize and evaluate existing research on the use of UAV for identifying pavement pathologies, such as cracks, potholes, rutting, and surface degradation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, publications were screened and selected across major academic databases such as Scopus and Web of Science. A total of 361 relevant articles published from 2020 to July 2025 were identified and analyzed using bibliometric overview. And a full-text synthesis and qualitative analysis was performed on a subset of 108 studies, which met the quality assessment criteria. The review categorizes the UAV systems, computer vision approaches, pathology types, and pavement materials examined in the studies. The findings indicate a growing trend in the use of UAV and computer vision techniques for pavement pathology detection, along with evolving preferences for UAV platforms, analytical approaches, and targeted pathology categories over time. This review highlights current gaps and outlines future research directions to advance UAV-based pavement pathology identification as a viable and reliable alternative to conventional inspection methods. Full article

During the energy storage fracturing process of tight sandstone reservoirs, the pre-injection of fracturing fluid is used to supplement the formation energy, and the physical properties of rocks change under hydration. To reveal the damage mechanism of hydration on tight sandstone, the tight sandstone surrounding the Daqing Changyuan in the northern part of the Songliao Basin was taken as the research object. Through indoor static hydration experiments, combined with scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), Nano-indentation experiments, and other methods, the evolution laws of rock micro-pore morphology, microfracture parameters, Young’s modulus, hardness, and other mechanical indicators under different hydration durations and soaking pressures were systematically explored. The research results show that the water–rock interaction of acidic slick water fracturing fluid significantly changes the mineral composition and microstructure of mudstone and sandstone, controls the development of induced fractures, and degrades the micro-mechanical properties of rocks, with significant lithological differences. In terms of mineral evolution, the soaking time causes the clay minerals in mudstone to increase by up to 12.0%, while pressure causes the carbonate minerals in sandstone to decrease by up to 23.3%. In terms of induced fracture development, the induced fracture widths of sandstone and mudstone under 30 MPa of pressure increase by 122.4% and 85.7%, respectively. The fracture width of mudstone shows a trend of “increasing first and then decreasing” with time, while that of sandstone decreases monotonically. In terms of micro-mechanical properties, after soaking for 168 h, the Young’s modulus of mudstone decreases by up to 66.9%, much higher than that of sandstone (29.5%), while the decrease in hardness of both is similar (58.3% and 59.8%); the mechanical parameters at the induced fractures are only 53.0% to 73.6% of those in the matrix area, confirming the influence of microstructural heterogeneity. This research provides a theoretical basis and data support for optimizing hydraulic fracturing parameters, evaluating wellbore stability, and predicting the long-term development performance in tight sandstone reservoirs. Full article

FPGA-based reconfigurable systems play a vital role in many critical domains by virtue of their unique advantages. They can effectively adapt to dynamically changing application scenarios, while featuring high parallelism and low power consumption. As a result, they have been widely adopted in key sectors such as aerospace, nuclear industry, and weapon equipment, where high performance and stability are of utmost importance. However, these systems face significant challenges. The continuous and drastic reduction in chip process size has led to increasingly complex and delicate internal circuit structures and physical characteristics. Meanwhile, the operating environments are often harsh and unpredictable. Additionally, the adoption of untrusted third-party foundries to reduce development costs further compounds these issues. Collectively, these factors make such systems highly susceptible to reliability threats, including environmental radiation, aging degradation, and malicious hardware attacks. These problems severely impact the stable operation and functionality of the systems. Therefore, ensuring the highly reliable operation of reconfigurable systems has become a critical issue that urgently needs to be addressed. There is a pressing need to summarize their technical characteristics, research status, and development trends comprehensively and in depth. In response, this paper conducts relevant research. By systematically reviewing 183 domestic and international research papers published between 2012 and 2024, it first provides a detailed analysis of the root causes of reliability issues in reconfigurable systems, thoroughly exploring their underlying mechanisms. Second, it focuses on the key technologies for achieving high reliability, encompassing four types of fault-tolerant design technologies, three types of aging mitigation technologies, and two types of hardware attack defense technologies. The paper comprehensively summarizes relevant research findings and the latest advancements in this field, offering a wealth of references for related research. Finally, it conducts a detailed comparative analysis and summary of the research hotspots in the field of high-reliability reconfigurable systems. It objectively evaluates the achievements and shortcomings of current research efforts and delves into the development trends of key technologies for high-reliability reconfigurable systems, providing clear directions for future research and practical applications. Full article

The durability of coral concrete in marine tidal zones is a critical concern due to the coupling effects of impact loads and aggressive ion erosion. This study investigates the dynamic mechanical degradation of Sisal Fiber-Reinforced Coral Aggregate Concrete (SFCAC) under a semi-submerged environment, focusing on the interplay between fiber bridging and corrosion evolution. Split Hopkinson Pressure Bar (SHPB) tests were conducted on specimens with varying fiber dosages (0–6 kg/m³) and erosion durations (0–120 days). Quantitative results indicate that while the addition of sisal fibers had a limited effect on increasing the peak impact-compression strength, it significantly modified the failure characteristics. The dynamic compressive strength exhibited a non-linear trend, peaking at 30 days due to pore filling. However, after 120 days, the strength of the Plain Coral Concrete (SF0) deteriorated to 70.84 MPa, while the 6 kg/m³ fiber-reinforced group (SF6) maintained a higher residual strength of 77.63 MPa. Crucially, although the 6 kg/m³ specimens still suffered crushing failure under high strain rates, the fibers effectively mitigated catastrophic shattering by holding the fragments together, exhibiting superior post-peak energy absorption compared to the pulverized plain matrix. Microscopic analysis (SEM) revealed that although the hydrophilic nature of sisal fibers accelerated ion transport (leading to Friedel’s salt and gypsum formation), their physical bridging effect counteracted the corrosion-induced brittleness. Collectively, these findings provide a theoretical basis for the durability design of SFCAC structures in severe marine splash zones and offer new insights into utilizing sustainable, locally sourced materials for island engineering. Full article

Channel length modulation (CLM) in indium gallium zinc oxide (IGZO) thin film transistors (TFTs) reduces the output resistance (r_o) in the saturation regime. It also degrades current driving accuracy for active matrix organic light emitting diode (AMOLED) backplanes. For top gate, self-aligned devices with nominal channel lengths of 5–15 μm, transmission line method (TLM) analysis yields an effective channel length reduction (ΔL) of about 1.8 μm. This result is consistent with lateral hydrogen redistribution from the self-aligned source/drain (S/D) process. At L = 5 μm, the conventional TFT exhibits r_o = 13.5 ± 2.5 MΩ and an Early voltage (V_A) = 56.1 ± 10.4 V (n = 5). We propose a source connected bottom gate (SCBG) structure that electrostatically stabilizes the pinch-off region and suppresses CLM. The SCBG TFT increases r_o to 475 ± 52 MΩ and V_A to 1159 ± 173 V at L = 5 μm (n = 5), while maintaining normal transfer characteristics. Two-dimensional device simulations reproduce the trend and show that the drain-bias-induced pinch-off shift is reduced, with d(ΔL)/dV_DS decreasing from 0.027 to 0.012 μm/V (about 55%). These results indicate that the SCBG approach is effective for enhancing current saturation in short channel IGZO TFTs for high-resolution AMOLED applications. Full article

The automotive industry faces radical technological change, driven by the adoption of electrification, automation, and digitalization. As a leading industrial hub with key OEMs and suppliers, such as Volkswagen, Southeast Lower Saxony is disproportionately impacted by this structural transformation. As a consequence of these trends, the region’s automotive base faces economic uncertainties, local regulatory lag, and technological disruptions. In this study a scenario planning methodology is conducted, to identify three potential mobility futures for 2035: a Best-Case scenario, where innovation and favorable policies enable a stable growth environment for the local automotive industry; a Trend scenario, marked by incremental yet uneven progress, while maintaining the current status quo; and a Worst-Case scenario, defined by economic stagnation and regulatory impediments, leading to a slow degradation of the regional automotive industry. The scenarios are then evaluated based upon their impact and probability of occurrence, while individual impact factors were also prepared and categorized to support future decision-making on a topical basis. This study offers an overview of potential scenarios for the Southeast Lower Saxon automotive industry, supporting the strategic decision-making. Full article

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

Background: Osteoarthritis (OA) is a degenerative joint disease characterized by extracellular matrix (ECM) breakdown, inflammation, and pain-associated functional impairment. Current pharmacological treatments primarily provide symptomatic relief without preventing cartilage degeneration. Kaempferia parviflora extract (KPE), rich in polymethoxyflavonoids, has been reported to have anti-inflammatory properties; however, its in vivo effects on cartilage homeostasis in OA remain incompletely defined. Methods: A monosodium iodoacetate (MIA)–induced rat model of knee OA was used to evaluate the therapeutic effects of KPE. Following OA induction, rats received oral KPE at low, medium, or high doses for 19 days. Pain-associated functional impairment was assessed by static weight-bearing analysis. Cartilage integrity was evaluated histologically, serum inflammatory and cartilage degradation biomarkers were quantified, and expression of matrix-degrading enzymes and their endogenous inhibitor, tissue inhibitor of metalloproteinase-1 (TIMP-1), was analyzed in articular cartilage. Results: MIA injection induced marked joint dysfunction, including an approximately 50% reduction in weight bearing on the affected limb. While KPE did not significantly reduce acute knee swelling, all KPE doses significantly improved weight-bearing imbalance compared with MIA controls. Histological analysis demonstrated preservation of cartilage structure and proteoglycan content in KPE-treated groups. Serum CTX-II levels were significantly reduced across all KPE doses, indicating attenuation of collagen degradation. Systemic inflammatory markers showed differential modulation: significant reductions in serum CRP and COX-2 at medium and high doses, while PGE2 showed a consistent downward trend that did not reach statistical significance. In articular cartilage, KPE treatment restored TIMP-1 expression, whereas modulation of individual MMPs was modest and variable. Conclusions: KPE alleviates OA-associated functional impairment and cartilage degeneration in an experimental OA model. The therapeutic effects are associated with reinforcement of TIMP-1–mediated matrix homeostasis and modulation of inflammatory pathways, supporting the potential of KPE as a natural adjunct candidate for OA management. Full article

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved water-quality in the middle and lower reaches of the Yangtze River (MLRYR) basin. However, its ecological benefits for key biotic components, particularly AV communities, remain unclear. To address this knowledge gap, this study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km²) within the MLRYR basin from 1989 to 2023. A U-Net deep learning model was employed to extract EA data (2016–2023), and a vegetation and bloom extraction algorithm was applied to map different AV groups (1989–2023). Results indicate that by 2023, 88% (22/25) of the lakes had completed EA removal. Over the 34-year period, floating/emergent aquatic vegetation (FEAV) exhibited fluctuating trends, while submerged aquatic vegetation (SAV) demonstrated a significant decline, particularly during the EA demolition phase (2016–2023), when its area sharply decreased from 804.8 km² to 247.3 km²—a reduction of 69.3%. Spatial comparative analysis further confirmed that SAV degradation was substantially more severe in EA removal areas than in EA retention areas. This study demonstrates that EA demolition, while beneficial for improving water quality, exerts significant short-term negative impacts on AV. These findings highlight the urgent need for lake governance policies to shift from single-objective management toward integrated strategies that equally prioritize water-quality improvement and ecological restoration. Future efforts should enhance targeted restoration in EA removal areas through active vegetation recovery and habitat reconstruction, thereby preventing catastrophic regime shifts to phytoplankton-dominated turbid-water states in lake ecosystems. Full article

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article