Search Results (14,349)

In composite membranes, non-woven substrates are often included to offer higher mechanical strength. The use of non-wovens is currently limited in electrochemical applications, apart from lab-made electrospun non-woven membranes. In this manuscript, three commercial non-wovens are compared to test their potential use in acid-based electrochemical applications, for instance redox flow batteries, and are also compared to a woven fabric substrate. The three non-wovens are found to have variable suitability in terms of the stability of solvents used in further membrane processing. However, all are deemed limiting due to their relatively high area resistance (0.37–1.47 ohm.cm²). In comparison, free-standing and selective commercial ion exchange membranes have area resistances around 0.08–0.27 ohm.cm². More open substrate backings such as a woven structure are recommended instead to allow for lower resistance of the resulting composites. Full article

Background: Chemotherapy is a cornerstone of cancer treatment; however, resistance to first-line chemotherapeutic agents remains a major challenge. ROS1, one of fifty-eight receptor tyrosine kinases, has been implicated in various cancer subtypes, including glioblastoma, non-small-cell lung cancer, and cholangiocarcinoma. Notably, the Gly2032Arg mutation in the ROS1 protein has been linked to resistance against the kinase inhibitor crizotinib. Objectives: Given the challenge, we conducted a comprehensive in silico study to identify new drug candidates. Methods: The study starts with modeling the Gly2032Arg-mutated ROS1 protein, followed by structure-based screening of the PubChem database. Results: Out of 1760 molecules screened, we selected the top 4 molecules (PubChem CID: 67463531, 72544946, 139431449, and 139431487) with structural features similar to crizotinib, a high docking score, and drug likeness. To further validate the effectiveness of the identified compounds, we assessed their binding affinity using the Molecular Mechanics with Generalized Born Surface Area (MM-GBSA) scoring method. To underpin the behavior and stability of protein–ligand complexes, 500 ns molecular dynamics (MD) simulations were conducted, and parameters including RMSD, RMSF, and H-bond dynamics were studied and compared. Density functional theory (DFT) at the B3LYP/6-31G* level was performed to elucidate molecular features of the identified compounds. Conclusions: Overall, this study sheds light on a new series of compounds effective against mutated targets, thereby offering a new horizon in this area. Full article

The deep sea is often depicted as a barren environment. Using the abyssal plain as a baseline system characterized by high pressure, extreme nutrient limitation, and slow growth rates, this review contrasts these conditions with specialized habitats that serve as oases of life such as whale falls, cold seeps, and hydrothermal vents. These environments retain the high-pressure characteristic of deep-sea habitats, but other unique environmental factors select for organisms with distinct life-history strategies and growth rates. This review examines the environmental constraints, organism physiological adaptations, and life-history strategies that define each habitat. Through synthesizing these factors, we identify patterns that influence not only growth and succession, but broader ecosystem vulnerability and resilience, defined here as the capacity of these communities to recover from disturbance. By evaluating how biological traits contribute to resilience across the four habitats in response to specific environmental constraints, this comparative framework identifies trade-offs between growth specialization and habitat stability. Understanding these environmental factors is critical in evaluating the resilience of these habitats to growing anthropogenic disturbances and determining future directions of study. This review concludes that while hydrostatic pressure and temperature impose fundamental metabolic constraints, nutrient availability and habitat stability are the primary determinants of organismal growth rates and life-history strategies. In the context of each ecosystem, both these variables can play a large role in the ability and time to recover from disturbance and may be good indicators of resilience at both a community and an organismal level. Consequently, slow-growing, long-lived fauna may possess far lower intrinsic resilience to anthropogenic disturbance compared to rapidly growing organisms with shorter life histories. Varying resilience of these habitats may necessitate habitat-specific strategies for assessment and protection. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

At 300 km/h, an end-to-end vision delay of 100 ms corresponds to 8.3 m of unobserved travel; therefore, real-time anomaly monitoring must balance sensitivity with strict tail-latency constraints at the edge. We propose a hybrid cache–retrieval inference architecture for visual anomaly detection in high-speed motorsport that exploits lap-to-lap spatiotemporal redundancy while reserving local similarity retrieval for genuinely uncertain events. The system combines a hierarchical visual encoder (a lightweight backbone with selective refinement via a Nested U-Net for texture-level cues) and an uncertainty-driven router that selects between two memory pathways: (i) a static cache of precomputed scene embeddings for track/background context and (ii) local similarity retrieval over historical telemetry–vision patterns to ground ambiguous frames, improve interpretability, and stabilize decisions under high uncertainty. Routing is governed by an entropy signal computed from prediction and embedding uncertainty: low-entropy frames follow a cache-first path, whereas high-entropy frames trigger retrieval and refinement to preserve decision stability without sacrificing latency. On a high-fidelity closed-circuit benchmark with synchronized onboard video and telemetry and controlled anomaly injections (tire degradation, suspension chatter, and illumination shifts), the proposed approach reduces mean end-to-end latency to 21.7 ms versus 48.6 ms for a retrieval-only baseline (55.3% reduction) while achieving Macro-F1 = 0.89 at safety-oriented operating points. The framework is designed for passive monitoring and decision support, producing advisory outputs without actuating ECU control strategies. Full article

The Yangtze Estuary is confronting unprecedented challenges due to intensifying human activities and a rising incidence of extreme weather events, which collectively threaten its essential functions in flood control, water supply, ecological sustainability, and navigation. In this study, the functions of the Yangtze Estuary under evolving hydrological and sediment conditions are comprehensively investigated and evaluated based on long-term measurement and statistical data. Using the comprehensive indicator evaluation method (CIEM) and the analytic hierarchy process (AHP), a River Functional Assessment Model for the Yangtze Estuary (RFAM-YE) is developed. The model incorporates four sub-models: flood control, water supply, ecological protection, and navigation demand. The southern branch of the Yangtze Estuary is selected as a case study for evaluation. The results show that in recent years, the flood control capacity, waterway stability, and ecological conditions in the southern branch have improved. However, the ongoing overall erosion trend poses a threat to water supply security and the integrity of biological habitats. The assessment results are generally consistent with actual engineering conditions, demonstrating the validity and applicability of the proposed model. Finally, recommendations for the protection and restoration of the Yangtze Estuary are proposed based on the evaluation. This study provides both theoretical and practical references for the management and conservation of the Yangtze Estuary. Full article

The catalyst in methanol oxidation plays a pivotal role in direct fuel cell reaction. The aim of this work is to study the influence of polypyrrole polymer (PPy) added in the carbon Vulcan support for the methanol oxidation reaction. The catalytic active phase synthesized was nickel-based materials, which have been demonstrated to exhibit remarkable chemical stability in alkaline solutions. The metallic-active phase was supported at the PPy-carbon Vulcan matrix. PPy is a conductor polymer and the research of electric conduction in synergy with a carbon Vulcan and a Ni catalyst is scarcely reported. The morphology characterization of composite catalytic material was carried out by XRD, XPS, and TEM techniques. In turn, the catalytic activity of the composite is characterized by means of cyclic voltammetry (CV). Electrochemical impedance spectroscopy (EIS) showed the influence of PPy on the charge transfer resistance (Rch. t.). The results indicate that a decrease in the Rch. t. was associated with an increase in methanol oxidation; therefore, higher amounts of charge transfer is produced. Furthermore, the DEMS technique corroborates the EIS results, confirming elevated conversion toward oxidation products. In turn, the selectivity of the composite-catalytic support on the methanol oxidation was elucidated using in situ Raman spectroscopy. Full article

The food industry generates large volumes of nutrient-rich by-products that remain underutilised despite their considerable biochemical potential. These materials originate predominantly from the fruit and vegetable, dairy, meat, and fish and seafood sectors and represent a substantial opportunity for sustainable valorisation. Fermentation has emerged as a powerful platform for converting such by-products into high-value ingredients, including bioactive compounds, functional metabolites, enzymes, antimicrobials, and nutritionally enriched fractions. This review synthesises recent advances in microbial fermentation strategies—spanning lactic acid bacteria, filamentous fungi, yeasts, and mixed microbial consortia—and highlights their capacity to enhance the bioavailability, stability, and functionality of recovered compounds across diverse substrate streams. Key technological enablers, including substrate pre-treatments, precision fermentation, omics-guided strain selection and improvement, and bioprocess optimisation, are examined within the broader framework of circular bioeconomy integration. Despite significant scientific progress, major challenges remain, particularly related to substrate heterogeneity, process scalability, regulatory alignment, safety assessment, and consumer acceptance. The review identifies critical research gaps and future directions, emphasising the need for standardised analytical frameworks, harmonised compositional databases, AI-driven fermentation control, integrated biorefinery concepts, and pilot-scale validation. Overall, the evidence indicates that integrated fermentation-based approaches—especially those combining complementary by-product streams, tailored microbial consortia, and system-level process integration—represent the most promising pathway toward the scalable, sustainable, and economically viable valorisation of food industry by-products. Full article

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

Non-invasive electrocardiogram (ECG) and respiratory signals are easy to acquire via low-cost sensors, making them promising alternatives for sleep staging. However, existing methods using these signals often yield insufficient accuracy. To address this challenge, we incrementally optimized the sleep staging model by designing a structured experimental workflow: we first preprocessed respiratory and ECG signals, then extracted fused features using an enhanced feature selection technique, which not only reduces redundant features, but also significantly improves the class discriminability of features. The resulting fused features serve as a reliable feature subset for the classifier. In the meantime, we proposed a hybrid optimization algorithm that integrates the snake optimization algorithm (SO) and ant colony optimization algorithm (ACO) for automated hyperparameter optimization of support vector machines (SVMs). Experiments were conducted using two PSG-derived public datasets, the Sleep Heart Health Study (SHHS) and MIT-BIH Polysomnography Database (MIT-BPD), to evaluate the classification performance of multimodal features compared with single-modal features. Results demonstrate that the bimodal staging using SHHS multimodal signals significantly outperformed single-modal ECG-based methods, and the overall accuracy of the SHHS dataset was improved by 12%. The SVM model optimized using the hybrid Snake–ACO algorithm achieved an average accuracy of 89.6% for wake versus sleep classification on the SHHS dataset, representing a 5.1% improvement over traditional grid search methods. Under the subject-independent partitioning experiment, the wake versus sleep classification task maintained good stability with only a 1.8% reduction in accuracy. This study provides novel insights for non-invasive sleep monitoring and clinical decision support. Full article

Background/Objectives: Gentamicin (GEN) is an aminoglycoside antibiotic used in veterinary medicine to treat infections caused mainly by Gram-negative bacteria. GEN is a mixture of pharmacologically active components, known as isoforms. The objective was to develop and validate a sensitive, accurate, and precise Ultra-Performance Liquid Chromatography with triple quadrupole mass detector (UPLC-MS/MS) method to quantify the different GEN isoforms in pig plasma and feces using streptomycin as an internal standard. Methods: Solid-phase extraction (SPE) was carried out. A high-strength silica (50 × 2.1 mm, 1.8 µm) column was used for chromatographic separation and a mobile phase of 0.26% HFBA in water (A) and acetonitrile (B) was delivered in a gradient with a flow rate of 0.5 mL/min. The column temperature was 40 °C and the sample injection volume was 30 µL. Results: The method showed good selectivity and specificity, with no interfering peaks. Calibration curves were linear in the range from 0.05 to 0.3 µg/mL for all isoforms in both matrices. Within- and between-run precision and accuracy were satisfactory for the lower limit of quantification (LLOQ), with coefficients of variation (CV) ≤ 13.4% and deviations ≤ 116.5% in plasma and CV ≤ 12.3% with deviations ≤ 101.7% in feces. No carry-over was observed, and analyte stability was confirmed under different storage conditions. Conclusions: The method development fulfilled all validation criteria established by the European Medicine Agency Guideline (EMA/CHMP/ICH/172948/2019). Moreover, the applicability of the method in clinical practice was demonstrated by the quantification of GEN in plasma and feces samples from pigs. Full article

Apart from in hydrosilylation, platinum has traditionally played a limited role in homogeneous catalysis due to its high thermodynamic stability and lower intrinsic reactivity compared to other group 10 metals. However, the emergence of N-heterocyclic carbene (NHC) ligands has substantially broadened the catalytic profile of transition metals by enabling access to new mechanistic pathways and enhancing robustness under demanding conditions. This review summarizes advances in Pt–NHC catalysis reported between 2010 and 2025. These transformations encompass hydrosilylation of amides and CO₂, hydroboration and diboration, hydroamination, alkyne hydration, hydrogenation, selective alkyne dimerization, Suzuki–Miyaura coupling, arene C–H borylation, and cycloisomerization reactions, in which NHC ligands enhance bond activation, control regio- and stereoselectivity, and stabilize reactive Pt intermediates, including chiral architectures, enabling high enantioselectivity. Full article

This study evaluates the potential of fine-grained waste gneiss as a soil improver, with particular emphasis on its chemical and grain composition and its effects on plant growth. The experimental material consisted of mixtures of fine-grained waste gneiss with varying proportions (from 0.38% to 7.5% in the pot) supplemented with varying proportions of dolomite (from 0.14% to 0.22% in the pot). Pot experiments were conducted for 57 days under controlled conditions using selected crops. Plant response was assessed based on growth rate, green mass production, and dry mass. For most tested variants, the results demonstrated a dose-dependent enhancement in plant productivity associated with gneiss supplementation. Compared to the control, experiments containing higher proportions of fine-grained gneiss resulted in an increase in green and dry mass from 8.14% to 78.73% and by 12.5% to 96.88%, respectively. Additionally, strong positive correlations between gneiss content and yield parameters (Pearson’s r > 0.8) were observed. In contrast, the dolomite fraction mainly conceptually affected soil chemical properties, including calcium and magnesium availability and pH stabilization. Overall, the findings suggest that fine-grained waste gneiss acts as a growth-promoting soil conditioner, as evidenced by the marked improvement in plant biomass. The findings confirm the high potential of waste gneiss as a functional soil improver, supporting sustainable resource management and aligning with the principles of the circular economy. Full article

Severe permeability reduction caused by drilling-fluid contamination has significantly impaired injectivity and deliverability in the K gas storage reservoir. This study aims to restore reservoir performance through the optimization and application of a composite acid system. A series of laboratory evaluations combined with core-flow experiments, continuous core scanning, and NMR T₂ analysis were conducted to assess acid performance and elucidate damage-removal mechanisms and pore–throat evolution. The results show that the optimized composite acid exhibits favorable compatibility, effective corrosion and precipitation control, a strong clay-stabilization capacity, and high permeability restoration. Core-scale experiments and NMR analyses indicate that the acid selectively removes near-wellbore and deep plugging while restoring pore–throat connectivity without inducing excessive dissolution or framework damage. Field application further confirms the laboratory findings, demonstrating substantial improvements in gas injection and production performance, along with enhanced reservoir energy retention and recovery. Overall, the proposed composite acid system provides an effective and practical solution for mitigating formation damage and improving the long-term injectivity and deliverability of gas storage reservoirs. Full article

Systematic sampling design is an ordered observation scheme that is popular in data collection because it has the property of uniform coverage and is operationally simple. This scheme is, however, susceptible to extreme observations, which may severely compromise the accuracy of traditional location estimators. To overcome this weakness, this research proposes a robust location retrieval or estimation method that regulates the impact of unusual observations in the ordered selection framework. In the suggested strategy, a set of twenty influence-adjusted estimators is built with a variety of re-descending weighting functions, which is then extended with another family of five generalized ones. Large-scale derivations of mean squared error (MSE) and percentage relative efficiency (PRE) are used to explore the theoretical properties of the proposed estimators. Significant improvements in stability and efficiency over existing methods are demonstrated by extensive empirical analyses, both with real data (e.g., mtcars and Trees) and on a wide variety of synthetic problems containing embedded outliers. The findings suggest that location retrieval based on influence-controlled processes is much more robust when using an ordered observation scheme and can be an efficient and scalable tool implemented in the modern data-intensive and computationally demanding environment. Full article