Search Results (1,265)

Chronic inflammatory bowel disease, ulcerative colitis (UC), currently lacks specific drugs for clinical treatment, and screening effective therapeutic agents from natural plants represents a critical research strategy. This study aimed to investigate the therapeutic potential of the flavonoid extract of Polygonum viviparum L. (TFPV) against UC. Liquid chromatography-mass spectrometry (LC-MS) was used to identify the chemical components of TFPV, while cell and animal models were employed to evaluate its anti-inflammatory effects on lipopolysaccharide (LPS)-induced inflammation. The mechanism of anti-inflammatory action was further investigated using a mouse model of UC induced by dextran sulfate sodium (DSS). The results revealed the identification of 32 bioactive components in TFPV, with major compounds such as kaempferol, luteolin, galangin, and quercetin. TFPV effectively mitigated inflammatory damage induced by LPS in IPEC-J2 cells and C57BL/6 mice. In the UC modeled by DSS, TFPV attenuated intestinal inflammation by reducing pro-inflammatory cytokines IL-1β, IL-6, and TNF-α; increasing the anti-inflammatory cytokine IL-10; up-regulating tight junction protein expression such as Claudin-1, Occludin, and ZO-1; and inhibiting the expression of PI3K, AKT, NF-κB, and IL-17 proteins. Analysis of mice fecal samples through 16S rRNA gene sequencing demonstrated that TFPV adjusted the equilibrium of gut microbiota by boosting the abundance of Dubosiella and diminishing that of Enterococcus, Romboutsia, and Enterobacter. Untargeted metabolomics analysis further revealed that TFPV reduced inosine and ADP levels while increasing dGMP levels by the regulation of purine metabolism, ultimately resulting in decreased uric acid levels and thereby alleviating intestinal inflammation. Additionally, TFPV safeguarded the intestinal mucosal barrier by enhancing the expression of tight junctions. In conclusion, TFPV alleviates UC by blocking the PI3K/AKT/NF-κB and IL-17 signaling pathways, lessening intestinal inflammation and injury, safeguarding intestinal barrier integrity, balancing gut microbiota, and lowering uric acid levels, suggesting its promise as a therapeutic agent for UC. Full article

Seawater intrusion into coastal river systems poses increasing challenges for freshwater availability and estuarine ecosystem integrity, especially under evolving climatic and anthropogenic pressures. This study presents a multidisciplinary investigation of marine intrusion dynamics within the Magra River estuary (Northwest Italy), integrating field monitoring, isotopic tracing (δ¹⁸O; δD), and multivariate statistical modeling. Over an 18-month period, 11 fixed stations were monitored across six seasonal campaigns, yielding a comprehensive dataset of water electrical conductivity (EC) and stable isotope measurements from fresh water to salty water. EC and oxygen isotopic ratios displayed strong spatial and temporal coherence (R² = 0.99), confirming their combined effectiveness in identifying intrusion patterns. The mass-balance model based on δ¹⁸O revealed that marine water fractions exceeded 50% in the lower estuary for up to eight months annually, reaching as far as 8.5 km inland during dry periods. Complementary δD measurements provided additional insight into water origin and fractionation processes, revealing a slight excess relative to the local meteoric water line (LMWL), indicative of evaporative enrichment during anomalously warm periods. Multivariate regression models (PLS, Ridge, LASSO, and Elastic Net) identified river discharge as the primary limiting factor of intrusion, while wind intensity emerged as a key promoting variable, particularly when aligned with the valley axis. Tidal effects were marginal under standard conditions, except during anomalous events such as tidal surges. The results demonstrate that marine intrusion is governed by complex and interacting environmental drivers. Combined isotopic and machine learning approaches can offer high-resolution insights for environmental monitoring, early-warning systems, and adaptive resource management under climate-change scenarios. Full article

This study investigates the hydrodynamics of the net system of the bottom-mounted aquaculture farms located in the Bohai Sea, addressing the growing demand for high-quality aquatic products and the limitations of coastal aquaculture. Based on the validation part, the established lumped-mass method integrated with the finite element method ABAQUS/AQUA was employed to evaluate the structural responses of the net system with three arrangement schemes under diverse environmental loads. The hydrodynamic loads on net twines are modeled with Morison formulae. With the motivation of investigating the trade-offs between volume expansions, load distributions, and structural reliabilities, Scheme 1 refers to the baseline design enclosing the basic aquaculture volume, while Scheme 2 targets to increase the aquaculture volume and utilization rate and Scheme 3 seeks to optimize the load distributions instead. The results demonstrate that Scheme 1 provides the optimal balance of structural safety and functional efficiency. Specifically, under survival conditions, Scheme 1 reduces peak bottom tension rope loads by 14% compared to Scheme 2 and limits maximum netting displacement to 4.0 m. It is 21.3% lower than Scheme 3, of which the displacement is 5.08 m. It has been confirmed that Scheme 1 effectively minimizes collision risks, whereas the other schemes exhibit severe collisions. Scheme 1 trades off maximum volume expansion for optimal load management, minimal deformation, and the highest overall structural reliability, making it the recommended design. These findings offer valuable insights for the design and optimization of net systems in offshore aquaculture structures serviced in comparable offshore regions. Full article

The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks. Full article

This paper presents a methodology for determining manufacturing tolerances of feature lines on automotive outer panels using visual sensory tests. Feature lines—narrow and long curved surfaces on automotive panels—play a critical role in the visual appeal of vehicles. However, achieving precise feature lines in mass production is challenging due to material spring-back during the stamping process. Conventional tolerance determination methods are unsuitable for these esthetic elements. To address this, we employed psychophysical sensory tests to find the visual difference thresholds for feature lines. By creating geometric models and conducting controlled sensory tests, we identified the minimum radius variations perceptible to the human eye. Thirty-four participants were tested using the method of constant stimuli, resulting in psychometric functions for feature lines with radii of 8, 10, and 12 mm. The findings suggest manufacturing tolerances of ±1.2 mm, ±1.3 mm, and ±1.5 mm, respectively. This approach provides a quantitative foundation for setting tolerances that balance visual quality with production feasibility. Full article

Developing classical three-dimensional consolidation theories considers the small-strain assumption. This small-strain assumption is inappropriate when studying the consolidation process of soft or very soft clay layers. Instead, this study derives a novel generalized mathematical model describing a three-dimensional finite-strain consolidation process and applies the deep Galerkin method to deduce its novel numerical solution. Developing this mathematical model uses the Reynolds transport theorem to describe mass and momentum balances for clay grain and pore water phases. The governing equation is the sum of the resulting mass and momentum balance equations. Next, the deep Galerkin method is applied to train a deep neural network to minimize the loss function defined by the governing equation and available initial and boundary conditions. The unknowns are the average velocity, effective stress, and pore water pressure. Predicting consolidation settlements is implemented by updating the problem domain using the resulting average velocity. Beneficial from the deep Galerkin method, two real-world examples demonstrate that the current mathematical model provides accurate predictions of consolidation settlements caused by the self-weight of two very soft clay layers. The deep Galerkin method helps resolve ill-posed problems by fitting a family of fields constrained by sampling/regularization rather than physics if the physics is under-determined. Full article

Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delayed results, and reliance on well-prepared tissue slides. Mass spectrometry imaging (MSI) offers a complementary approach by providing molecular-level information, but its high dimensionality and the scarcity of labeled data present unique challenges for traditional supervised learning. In this study, we present the first implementation of foundation models for MSI-based cancer classification using desorption electrospray ionization (DESI) data. We evaluate multiple architectures adapted from other domains, including a spectral classification model known as FACT, which leverages audio–language pretraining. Compared to conventional machine learning approaches, these foundation models achieved superior performance, with FACT achieving the highest cross-validated balanced accuracy ($93.27\%\pm 3.25\%$) and AUROC ($98.4\%\pm 0.7\%$). Ablation studies demonstrate that these models retain strong performance even under reduced data conditions, highlighting their potential for generalizable and scalable MSI-based cancer diagnostics. Future work will explore the integration of spatial and multi-modal data to enhance clinical utility. Full article

This study carried out a detailed investigation into the potential application of hydrothermally treated bituminous coal (hydrochar) as an injectant in blast furnace (BF) ironmaking. A tuyere model was constructed through simulation methods, and the influence of hydrochar injection on the thermal conditions within the BF hearth was also thoroughly analyzed. The results show that the gas flow velocity at the lower part of the tuyere of hydrochar injection increases, and the residual carbon mass fraction of the tuyere decreases. As the oxygen-enriched concentration increases, the CO concentration decreases. The CO concentration in the swirl zone after hydrochar injection is the highest, reaching 43.93%. The distributions of CO and CO₂ exhibit opposite tendencies. Following hydrochar injection, a marked rise in temperature is observed. At an oxygen enrichment level of 30%, the tuyere zone temperature associated with hydrochar injection peaks, surpassing 2700 K. The corresponding pulverized coal burnout rate is also the highest. Thus, the injection of hydrochar has a positive impact on the air flow and temperature field, which can effectively maintain the heat balance and is conducive to strengthening BF smelting. Full article

A two-dimensional shallow flow model is developed by integrating a positivity-preserving, well-balanced central-upwind scheme with a block-structured quadtree AMR grid implemented in the Afivo open-source framework. A non-negative water depth reconstruction ensures second-order spatial accuracy and the robust treatment of wetting and drying, while coarse-grid fluxes at refinement boundaries are obtained by summing the corresponding fine-grid fluxes, thereby guaranteeing strict mass conservation between refinement levels. Mesh refinement is driven by gradients in water surface elevation, which focus resolution on regions of rapid flow variation, thereby improving both accuracy and computational efficiency. Model validation through benchmark problems and the Malpasset dam-break event show close agreement with analytical solutions, laboratory measurements, and previous numerical simulations, while achieving substantial reductions in computational cost. Full article

Background/Objectives: The principal function of pharmaceutical blister packaging is to provide protection for the drug product. Moisture is regarded as a critical factor in the physical and chemical aging of drug products. The present work proposes a modeling framework to predict the performance of tablet blister materials based on the moisture uptake profile of the drug product as well as degradation characteristics of the drug substance, while the consumption of water due to degradation is included. Methods: The model incorporates three kinetic superimposed processes that define moisture uptake and drug stability. The processes of permeation, sorption and degradation are each described with a rate constant. Based on a mass balance, these rate processes are interconnected and the relative humidity in the blister cavity is predicted. Results: In a case study, the model was applied to demonstrate the feasibility of predicting the stability of blistered tablets. By establishing a correlation between the moisture uptake of the tablet and the drug stability demonstrated in the model, it was feasible to predict the drug content over shelf life. Conclusions: Modeling of the drug stability of blister-packed products enables a rational packaging which offers novel possibilities for reducing material in order to avoid overpackaging of pharmaceutical products. As some of the commonly used barrier materials are considered to not be sustainable, this model can be used to consider a rationally justified reduction or even abandonment of the barrier materials. Full article

Due to the regulated nature and purity standards of the bioprocess and biotechnology industries, the sector has seen comparatively less sustainable practices than other chemical industries have. The achievement of sustainability in microbial fermenter design requires that quantitative tools with links between process parameters and end-environmental outcomes are employed. This review begins with environmentally friendly metrics such as process mass intensity, water and energy intensity, and related indicators that act as a template for resource usage and waste generation assessment. The objective of this paper is to highlight the primary focus on computational fluid dynamics (CFD) applied to bioprocesses in aerated stirred bioreactors using Escherichia coli (E. coli). Second, the objective of this paper is to explore state-of-the-art CFD models and methods documented in the existing literature, providing a fundamental foundation for researchers to incorporate CFD modelling into biotechnological process development, while making these concepts accessible to non-specialists and addressing the research gap of linking CFD outputs with sustainability metrics and life cycle assessment techniques. Impeller rotational models such as sliding mesh are an accurate and commonly used method of modelling the rotation of stirring. Multiple different turbulence models are applied for the purpose of stirred bioreactors, with the family of k-ε models being the most used. Multiphase models such as Euler-Euler models in combination with population balance models and gas dispersion models to model bubble size distribution and bubble characteristics are typically used. Full article

Based on the energy equation of the hemispherical resonator, this study analyzes the influence of ion beam balancing on the frequency split of the hemispherical resonator. Firstly, the formula for mass defects and the resonant frequency of the resonator is obtained through the energy equation. All mass defects of the resonator are equivalent to fourth harmonic wall thickness defects. The quantitative relationship between harmonic wall thickness defects and the resonant frequency and the geometric relationship between the heavy axis of the resonator and the distribution of mass defects are determined. Secondly, the balancing function is introduced into the resonant frequency equation of the hemispherical resonator, and a mathematical model is established for the resonant frequency, defect wall thickness, and balancing depth of the hemispherical resonator. By introducing relevant errors, the impact of balancing errors on the frequency characteristics of the hemispherical resonator is calculated. Finally, an ion beam balancing experiment is designed to verify the effectiveness of the theory. The results show that the frequency split can be better than 0.001 Hz after balancing, effectively improving the hemispherical resonator’s performance. Full article

As a key component for maintaining the efficient and stable operation of flexible DC transmission systems, the arm reactor often suffers from uneven loss distribution and localized overheating in its windings due to the superimposed AC and DC currents, which adversely affects its operational lifespan. Furthermore, arm reactors are frequently deployed in offshore environments for long-distance, high-capacity power transmission, imposing additional requirements on energy utilization efficiency and seismic resistance. To address these challenges, this study proposes an optimization design method for arm reactors based on a triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise,” aiming to achieve “low loss–low temperature rise–low weight.” First, an equivalent electromagnetic model of the arm reactor under combined AC and DC operating conditions is established to analytically calculate the self- and mutual-inductance-distribution characteristics between winding layers and the loss distribution across windings. The calculated losses are then applied as heat sources in a fluid–thermal coupling method to compute the temperature field of the arm reactor. Next, leveraging a Kriging surrogate model to capture the relationship between the winding temperature rise in the bridge-arm reactor and the loss density, encapsulation width, encapsulation height, and air duct width, the revised analytical expression reduces the temperature rise error from 43.74% to 11.47% compared with the traditional empirical formula. Finally, the triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise” is proposed to balance interlayer current distribution, suppress total loss generation, and limit localized hotspot formation. A prototype constructed based on the optimized design demonstrates a 44.51% reduction in total loss, a 39.66% decrease in hotspot temperature rise, and a 24.83% reduction in mass while maintaining rated inductance, validating the effectiveness of the proposed design algorithm. Full article

The reaction system of nanocrystalline iron carburization and carbon deposit formation as an example of a parallel chemical reaction was studied. The main measurement procedure was the Chemical Potential Programmed Reaction method, according to which the course of a chemical reaction in this particular case was controlled by the methane–hydrogen mixtures of precisely selected variable composition. The measurements were performed in a tubular differential flow reactor with thermogravimetric measurement and analysis of the gas phase composition at a temperature of 650 °C under atmospheric pressure. In the current research, by measuring the mass of the solid sample at changing carburizing potential and after balancing the reacting system, the reaction rates of parallel iron carburization and carbon deposit formation were precisely determined using the model of the reaction of a nanocrystalline substance with the gas phase in states close to chemical equilibrium. The reaction rate constants for those reactions were estimated as well based on model equations. Full article

Background: Adnexal masses are common in women across different age and hormonal states: pregnancy, premenopause, and postmenopause. Ovarian carcinoma, a malignancy arising in the adnexa, poses significant health risks. While malignancy risk increases with age and postmenopausal status, current methods for stratifying borderline cases remain inadequate, potentially leading to over- or undertreatment that may affect fertility or survival. Methods: This retrospective study was conducted with 318 adult women who were diagnosed with adnexal masses and underwent surgery at a university hospital between 2020 and November 2023. Patient data were retrieved from the hospital’s electronic medical record system. Routinely measured preoperative serologic parameters—carbohydrate antigen (CA)125, CA19-9, CA15-3, carcinoembryonic antigen (CEA), Alpha-fetoprotein (AFP), Lactate dehydrogenase (LDH), and fibrinogen-to-albumin ratio (FAR) levels—were analyzed alongside final histopathological results. No procedures outside routine clinical practice were performed. Diagnostic performance of each marker was evaluated using receiver operating curve (ROC) analysis. Results: A total of 318 patients with adnexal masses were analyzed. The FAR levels were significantly elevated in malignant compared to borderline and benign groups (p < 0.001), and FAR alone showed 47% sensitivity and 91% specificity for borderline tumors, whereas CA125 showed 70% sensitivity and 85% specificity. Multivariate models combining FAR, CA125, and CA15-3 achieved the highest diagnostic accuracy, with superior AUCs compared to single biomarkers. Conclusions: FAR is a simple, accessible inflammatory marker that complements CA125 by enhancing specificity. Combination of multiple markers with the highest sensitivity and specificity, together with FAR, may reduce the risk of both false negatives, offering a more balanced and accurate diagnostic tool for preoperative stratification of borderline tumor cases. Full article