Search Results (22,978)

Background: Accurate and timely prediction of mortality in intensive care unit (ICU) patients, particularly those with COVID-19, remains clinically challenging due to complex immune responses. Proteomic cytokine profiling holds promise for refining mortality risk assessment. Methods: Serum samples from 89 ICU patients (55 discharged, 34 deceased) were analyzed using a multiplex 21-cytokine panel. Samples were stratified into three groups based on time from collection to outcome: ≤48 h (Group 1: Early), >48 h to ≤7 days (Group 2: Intermediate), and >7 days to ≤14 days (Group 3: Late). Cytokine levels, simple cytokine ratios, and previously unexplored complex ratios between pro- and anti-inflammatory cytokines were evaluated. Machine learning-based feature selection identified the most predictive ratios, with performance evaluated by area under the curve (AUC), sensitivity, and specificity. Results: Complex cytokine ratios demonstrated superior predictive accuracy compared to traditional severity markers (APACHE II, SAPS II, SOFA), individual cytokines, and simple ratios, effectively distinguishing discharged from deceased patients across all groups (AUC: 0.918–1.000; sensitivity: 0.826–1.000; specificity: 0.775–0.900). Conclusions: Multiplex cytokine profiling enhanced by computationally derived complex ratios may offer robust predictive capabilities for ICU mortality risk stratification, serving as a valuable tool for personalized prognosis in critical care. Full article

The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ (x = 0.03–0.15) ceramics were fabricated via the traditional solid reaction method. Characterization results revealed that each component exhibited a pure perovskite structure, and the average grain size significantly diminishes with increasing x. The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramics exhibited prominent relaxor ferroelectric behavior, whose characteristic narrow hysteresis loops effectively enhanced the energy storage performance of the material. Most importantly, the composition with x = 0.10 demonstrated exceptional energy storage properties at 150 kV/cm, achieving a high recoverable energy storage density (W_rec = 1.91 J/cm³) and excellent energy efficiency (η = 90.87%). Under the equivalent electric field, this composition also displayed a superior pulsed discharge performance, including a high current density (871 A/cm²), a high power density (67.3 MW/cm³), an ultrafast discharge time (t_0.9 = 109 ns), and a discharged energy density of 1.47 J/cm³. These results demonstrate that the (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramic system establishes a promising design paradigm for the creation and refinement of next-generation dielectrics for pulse power applications. Full article

Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis. Full article

Isoquercitrin, a monoglucoside form of quercetin, exhibits superior antioxidant, anti-inflammatory, and cardiovascular protective effects in comparison to its precursor, rutin. However, its natural abundance is limited. This study aimed to increase the functional value of Diospyros lotus leaf extract through enzymatic conversion of rutin to isoquercitrin using α-l-rhamnosidase and to evaluate the changes in biological activities after conversion. A sugar-free D. lotus leaf extract was prepared and subjected to enzymatic hydrolysis with α-l-rhamnosidase under optimized conditions (pH 5.5, 55 °C, and 0.6 U/mL). Isoquercitrin production was monitored via high-performance liquid chromatography. Antioxidant and anti-inflammatory activities were assessed using the 2,2-diphenyl-1-picrylhydrazyl radical scavenging and lipoxygenase (LOX) inhibition assays, respectively. The enzymatic reaction resulted in complete conversion of 30 mM rutin into isoquercitrin within 180 min, increasing isoquercitrin content from 9.8 to 39.8 mM. The enzyme-converted extract exhibited significantly enhanced antioxidant activity, with a 48% improvement in IC₅₀ value compared with the untreated extract. Similarly, LOX inhibition increased from 39.2% to 48.3% after enzymatic conversion. Both extracts showed higher inhibition than isoquercitrin alone, indicating synergistic effects of other phytochemicals present in the extract. This study is the first to demonstrate that α-l-rhamnosidase-mediated conversion of rutin to isoquercitrin in D. lotus leaf extract significantly improves its antioxidant and anti-inflammatory activities. The enzymatically enhanced extract shows potential as a functional food or therapeutic ingredient. Full article

This paper develops empirical relationships to estimate FAA/EASA and MIL-3013B rules-compliant take-off field performance for single and multi-engine aircraft. Recent experience with modern aircraft flight manuals revealed that popular empirical legacy methods are no longer accurate; improvements in tires and brakes lead to significantly shorter certified distances. This work relies upon a survey of current operational aircraft and extensive numerical simulations of generic configurations to support the development of a collection of new equations to estimate take-off performance for single and multi-engine aircraft under dry and wet conditions. These relationships are individually tailored for civilian and U.S. Military rules; they account for the superior capability of modern braking systems and the implications of minimum-control speed on the certified distance. Full article

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by worker bees. It employs multi-stage perturbation intensities and adaptive step-size tuning to enable efficient posterior sampling. Focusing on Bayesian inference for parameter estimation in the soliton solutions of the two-dimensional complex Ginzburg–Landau equation, we design a dedicated inference framework to systematically compare the performance of BeeSwarm-MH with the classical Metropolis–Hastings algorithm. Experimental results demonstrate that BeeSwarm-MH achieves comparable estimation accuracy while significantly reducing the required number of iterations and total computation time for convergence. Moreover, it exhibits superior global search capabilities and adaptive features, offering a practical approach for efficient Bayesian inference in complex physical models. Full article

Catalytic upgrading of pyrolysis oils is an important step for producing replacement hydrocarbon-rich liquid biofuels from biomass and can help to advance pyrolysis technology. Catalysts play a pivotal role in influencing the selectivity of chemical reactions leading to the formation of main compounds in the final upgraded liquid products. The present work involved a systematic study of solvent-free catalytic reactions of cyclohexanone in the presence of hydrogen gas at 160 °C for 3 h in a batch reactor. Cyclohexanone can be produced from biomass through the selective hydrogenation of lignin-derived phenolics. Three types of catalysts comprising undoped NbOPO₄, 10 wt% NiO/NbOPO₄, and 30 wt% NiO/NbOPO₄ were studied. Undoped NbOPO₄ promoted both aldol condensation and the dehydration of cyclohexanol, producing fused ring aromatic hydrocarbons and hard char. With 30 wt% NiO/NbOPO₄, extensive competitive hydrogenation of cyclohexanone to cyclohexanol was observed, along with the formation of C₆ cyclic hydrocarbons. When compared to NbOPO₄ and 30 wt% NiO/NbOPO₄, the use of 10 wt% NiO/NbOPO₄ produced superior selectivity towards bi-cycloalkanones (i.e., C₁₂) at cyclohexanone conversion of 66.8 ± 1.82%. Overall, the 10 wt% NiO/NbOPO₄ catalyst exhibited the best performance towards the production of precursor compounds that can be further hydrodeoxygenated into energy-dense aviation fuel hydrocarbons. Hence, the presence and loading of NiO was able to tune the activity and selectivity of NbOPO₄, thereby influencing the final products obtained from the same cyclohexanone feedstock. This study underscores the potential of lignin-derived pyrolysis oils as important renewable feedstocks for producing replacement hydrocarbon solvents or feedstocks and high-density sustainable liquid hydrocarbon fuels via sequential and selective catalytic upgrading. Full article

This study evaluates the production of base oils for biolubricants using fatty acid methyl esters (FAMEs) derived from sunflower oil as the raw material. The production process involved the synthesis of oleochemical esters through a single-step alkaline transesterification reaction with a high-molecular-weight polyol, such as trimethylolpropane (TMP). To assess the effectiveness of the developed catalytic system in conducting the transesterification reactions and its impact on the properties of the final product, two types of alkaline catalysts were used. Specifically, the reactions were carried out using either Ca/TEA alkoxide or sodium methoxide as catalysts in various configurations and concentrations to determine the optimal catalyst concentration and reaction conditions. Sodium methoxide served as the commercial benchmark catalyst, while the Ca/TEA alkoxide was prepared in the laboratory. The optimal concentration of Ca/TEA was determined to be 3.0% wt. in the presence of iso-octane and 3.5% wt. under vacuum, while the corresponding concentrations of CH₃ONa for both cases were determined to be 2.0% wt. The synthesized biolubricant esters exhibit remarkable performance characteristics, such as high kinematic viscosities and low pour points—ranging from 33–48 cSt at 40 °C, 7.68–10.03 cSt at 100 °C, to −14 to −7 °C, respectively—which are comparable to or improved over those of mineral oils such as SN-150 or SN-500, with the Ca/TEA alkoxide-catalyzed systems showing superior oxidation stability and reduced byproduct formation. Full article

Objectives: To investigate the reliability and validity of the Singer Reflux Symptom Score (sRSS), a new patient-reported outcome questionnaire documenting the severity of reflux symptoms in singing voice is proposed. Methods: Amateur and professional singers consulting the European Reflux Clinic for laryngopharyngeal reflux disease (LPRD) symptoms and findings were prospectively recruited from January 2022 to February 2023. The diagnosis was based on a Reflux Symptom Score (RSS) > 13 and Reflux Sign Assessment (RSA) > 14. A control group of asymptomatic singer subjects was recruited from the University of Mons. The sRSS was rated within a 7-day period to assess test–retest reliability. Internal consistency was measured using Cronbach’s α in patients and controls. A correlation analysis was performed between sRSS and Singing Voice Handicap Index (sVHI) to evaluate convergent validity. Responsiveness to change was evaluated through pre- to post-treatment sRSS changes. The sRSS threshold for suggesting a significant impact of LPRD on singing voice was determined by receiver operating characteristic (ROC) analysis. Results: Thirty-three singers with suspected LPRD (51.5% female; mean age: 51.8 ± 17.2 years) were consecutively recruited. Difficulty reaching high notes and vocal fatigue were the most prevalent LPRD-related singing complaints. The sRSS demonstrated high internal consistency (Cronbach-α = 0.832), test–retest reliability, and external validity (correlation with sVHI: r = 0.654; p = 0.015). Singers with suspected LPRD reported a significant higher sRSS compared to 68 controls. sRSS item and total scores significantly reduced from pre-treatment to 3 months post-treatment except for the abnormal voice breathiness item. ROC analysis revealed superior diagnostic accuracy for sRSS (AUC = 0.971) compared to sRSS-quality of life (AUC = 0.926), with an optimal cutoff at sRSS > 38.5 (sensitivity: 90.3%; specificity: 85.0%). Conclusions: The sRSS is a reliable and valid singer-reported outcome questionnaire for documenting singing symptoms associated with LPRD leading to personalized management of Singers. Future large-cohort studies are needed to evaluate its specificity for LPRD compared to other vocal fold disorders in singers. Full article

Infection control and bleeding management in deep wounds remain urgent and unmet clinical challenges that demand innovative, multifunctional, and sustainable solutions. Unlike previously reported sodium alginate and silk fibroin-based gel formulations, the present work introduces a dual-functional system combining antimicrobial and haemostatic activity in the form of conformable aerogel beads. This dual-functional formulation is designed to absorb exudate, promote clotting, and provide localized antimicrobial action, all essential for accelerating wound repair in high-risk scenarios within a single biocompatible system. Aerogel beads were obtained by supercritical drying of a silk fibroin–sodium alginate blend, resulting in highly porous, spherical structures measuring 3–4 mm in diameter. The formulations demonstrated efficient ciprofloxacin encapsulation (42.75–49.05%) and sustained drug release for up to 12 h. Fluid absorption reached up to four times their weight in simulated wound fluid and was accompanied by significantly enhanced blood clotting, outperforming a commercial haemostatic dressing. These findings highlight the potential of silk-based aerogel beads as a multifunctional wound healing platform that combines localized antimicrobial delivery, efficient fluid and exudate management, biodegradability, and superior haemostatic performance in a single formulation. This work also shows for the first time how the prilling encapsulation technique with supercritical drying is able to successfully produce silk fibroin and sodium alginate composite aerogel beads. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

Ion-conductive-hydrogel strain sensors demonstrate broad application prospects in the fields of flexible sensing and bioelectric signal monitoring due to their excellent skin conformability and efficient signal transmission characteristics. However, traditional preparation methods face significant challenges in enhancing adhesion strength, conductivity, and mechanical stability. To address this issue, this study employed a freeze–thaw cycling method, using polyvinyl alcohol (PVA) as the matrix material, tannic acid (TA) as the adhesion reinforcement material, and lithium chloride (LiCl) as the conductive medium, successfully developing an ion-conductive hydrogel with superior comprehensive performance. Experimental data confirm that the PVA-TA-0.5/LiCl-1 hydrogel achieves optimal levels of adhesion strength (2.32 kPa on pigskin) and conductivity (0.64 S/m), while also exhibiting good tensile strength (0.1 MPa). Therefore, this hydrogel shows great potential for use in strain sensors, demonstrating excellent sensitivity (GF = 1.15), reliable operational stability, as the ΔR/R₀ signal remains virtually unchanged after 2500 cycles of stretching, and outstanding strain sensing and electromyographic signal acquisition capabilities, fully highlighting its practical value in the fields of flexible sensing and bioelectric monitoring. Full article

Basalt-fiber-reinforced polymer (BFRP) composites, utilizing a natural high-performance inorganic fiber, exhibit excellent weathering resistance, including tolerance to high and low temperatures, salt fog, and acid/alkali corrosion. They also possess superior mechanical properties such as high strength and modulus, making them widely applicable in aerospace and shipbuilding. This study experimentally investigated the mechanical properties of BFRP plates under various strain rates (10⁻⁴ s⁻¹ to 10³ s⁻¹) and directions using an electronic universal testing machine and a split Hopkinson pressure bar (SHPB).The results demonstrate significant strain rate dependency and pronounced anisotropy. Based on experimental data, relationships linking the strength of BFRP composites in different directions to strain rate were established. These relationships effectively predict mechanical properties within the tested strain rate range, providing reliable data for numerical simulations and valuable support for structural design and engineering applications. The developed strain rate relationships were successfully validated through finite element simulations of low-velocity impact. Full article