Search Results (1,187)

Physiologically relevant in vitro intestinal models are essential for studying key physiological processes, including barrier function, drug screening and gut-microbiota interactions. However, conventional 2D culture systems often fail to recapitulate structural and functional complexity. Here, we aimed to validate a 3D bioelectronic transmembrane platform, previously used for monitoring human intestinal epithelium and vascular endothelium, for modeling the rat small intestinal barrier in vitro. The device integrates a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) scaffold supporting co-cultures of rat intestinal epithelial cells (IEC-6) and rat fibroblasts (208F), enabling real-time monitoring of barrier formation through electrical measurements using electrochemical impedance spectroscopy (EIS). Barrier formation was monitored over 21 days and exhibited a time-dependent increase in barrier resistance. The 3D platform was compared with traditional 2D insert-based cultures and ex vivo rat tissue using an Ethylene Glycol Tetraacetic Acid (EGTA)-induced calcium switch assay to evaluate barrier disruption and recovery. EGTA treatment and removal induced reversible barrier disruption in the 3D in vitro and ex vivo models, whereas 2D in vitro cultures showed limited recovery. These findings demonstrate that the 3D platform more faithfully recapitulates native tissue architecture and function, closely paralleling ex vivo responses. Our study highlights the importance of validating advanced 3D in vitro models and establishes this bioelectronic platform as a robust tool for drug screening, barrier studies, and preclinical gastrointestinal research. Full article

To establish the complex functional relationship between the stator bar end structure and the maximum electric field strength, and to optimize the anti-corona structure, an optimization model for the stator bar end based on the Seahorse Optimization algorithm—Radial Basis Function (SHO-RBF) neural network is proposed in this paper. The RBF neural network is employed to establish the complex relationship between the maximum electric field strength at the stator bar end and the anti-corona structure parameters. The SHO is introduced to find the optimal anti-corona structure at the stator bar end structure. A simulation model of the stator bar end is developed, and 30 sets of simulation data are collected for training and optimization purposes. The relationship between the stator bar end structure and the maximum electric field strength is established, and an optimized scheme comprising six groups of anti-corona structures is developed. The feasibility of the proposed design is validated through simulation calculations. Compared to manually adjusting parameters individually within the simulation model, this approach offers a significant advantage in terms of computational efficiency and speed. Full article

Soil stabilization is widely applied in transportation engineering to enhance the mechanical performance and serviceability of road subgrades, particularly in fine-grained soils susceptible to moisture-induced deterioration. Although Portland cement provides rapid strength development and high load-bearing capacity, its high energy consumption and associated CO₂ emissions have encouraged the exploration of lower-impact stabilization alternatives. This study presents a performance-based comparative evaluation of fine-grained soils stabilized with Portland cement and kaolin at dosages of 3%, 5%, and 7% by dry soil mass. The experimental program included soil characterization, Standard Proctor compaction testing, and unconfined compressive strength (UCS) testing conducted at curing ages of 0, 7, 14, 28, 90, and 180 days. Cement-treated soils exhibited faster early-age strength development and higher long-term UCS values, supporting applications requiring early load-bearing capacity. In contrast, kaolin-treated soils showed gradual and stable strength gains primarily associated with densification and particle rearrangement mechanisms. Overall, the results demonstrate that kaolin can serve as a viable low-impact stabilizer for low-volume and secondary road infrastructure. The findings support performance-based and sustainability-oriented material selection strategies for context-sensitive road subgrade design. Full article

Lakes are sensitive indicators of climate change and exhibit distinct responses to climatic variability. Using in situ eddy covariance and meteorological observations from Nam Co (“large lake”) and a small lake (“small lake”) adjacent to Nam Co, we evaluate the performance of the FLake model in simulating lake processes. The model generally reproduces the seasonal variations in mixed-layer depth and surface water temperature, although diurnal amplitudes are underestimated. Simulated sensible and latent heat fluxes agree well with observations when appropriate lake depth and light extinction coefficients are applied, with RMSEs of ~1 °C, 8 W m⁻², and 22 W m⁻² for lake surface temperature, sensible heat flux, and latent heat flux, respectively. For the “large lake”, latent heat flux simulations differ markedly between land-based and lake-based forcing, primarily due to differences in wind speed and air temperature. Long-term simulations (1981–2024) suggest progressive warming of lake surface waters, strengthened thermal stratification, and increasing surface heat fluxes, with downward longwave and shortwave radiation and near-surface air temperature identified as the dominant climatic drivers. Full article

Morus macroura ‘Panzhihua No. 1’ is a dual-purpose cultivar valued for its edible leaves and fruits. Derived from an ancient mulberry tree, it is a unique local germplasm resource. During asexual propagation, M. macroura exhibits sexual variation closely associated with fruit and leaf yield. To explore the molecular mechanisms of sexual dimorphism and its effects on nutritional traits, we integrated transcriptomic and metabolomic analyses of male and female inflorescences and leaves. Key sex-biased genes were enriched in plant hormone signaling, flavonoid biosynthesis, and carbohydrate metabolism pathways. Female plants had elevated expression of ethylene-responsive transcription factor 1 (ERF1), EIN3-binding F-box proteins (EBF1/2), and Chalcone synthase (CHS) genes and higher levels of bioactive flavonoids, including isoquercitrin and kaempferol derivatives. In contrast, male plants had increased expression of gibberellin 20-oxidase (GA20ox) and DELLA genes and accumulated glycosides, which are beneficial for leaf development. These findings reveal how sex-linked metabolic networks shape mulberry tissue functional profiles, providing molecular targets for breeding. Full article

Gas Metal Arc Welding (GMAW) is widely adopted in automated manufacturing industries where the accurate prediction of thermal fields and welding-induced distortions is essential to ensure joint integrity of the parts; however, finite element modeling, as the most reliable non-destructive predictive approach, remains time-consuming and highly user-specialized. This work presents an automated and low computational cost thermo-mechanical finite element methodology implemented in Ansys Parametric Design Language (APDL) for the parametric analysis of GMAW T-joints, integrating automated geometry generation, meshing, heat source implementation, and thermo-mechanical modeling for different beam and weld seam dimensions under continuous or intermittent single-pass configurations. A volume element selection strategy is introduced to limit heat input calculations to the active weld pool region, achieving up to a 50% computational time reduction while maintaining high predictive accuracy, in contrast with conventional and partial selection methods. Overall script performance was validated through temperature and displacement comparisons between the numerical and experimental results of two T-joint configurations using SM490A structural steel specimens. The results demonstrate that the developed macro provides a useful tool for automated thermo-mechanical welding analysis, significantly reducing model preparation effort while enabling the evaluation of parametric T-joint geometries and welding conditions with a low computational cost focus. Full article

In recent years, microplastics (MPs) pollution in agricultural soils has increased markedly, largely due to the improper management of plastic mulch films used to improve crop growing conditions. In this context, the present study evaluated the use of biochar (BC) as a soil amendment for mulch-derived MPs-contaminated soils in a radish (Raphanus sativus L.) crop under greenhouse conditions. A pot experiment was established in soils contaminated with MPs (0.5% w/w) and amended with four BC rates (w/w): 0% (Control), 1% (BC1), 3% (BC3), and 5% (BC5). Soil physicochemical indicators were assessed, together with germination, leaf, and radish bulb growth parameters. The experiment was conducted under greenhouse conditions until the radishes reached commercial maturity. Most of the soil’s physicochemical indicators, such as hydrogen potential (pH), electrical conductivity (EC), water holding capacity (WHC), total organic carbon (TOC), organic matter (OM), total nitrogen (TN), ammonium (N–NH₄⁺) and nitrates (N–NO₃⁻), showed significant differences between treatments (p < 0.05), with the exception of the carbon-nitrogen ratio (C/N), which did not vary significantly (p ≥ 0.05). No significant differences were observed among treatments (p ≥ 0.05) for germination indicators. For leaf traits, dry biomass was significantly lower in BC1 than in the other treatments (p < 0.05). For radish bulb traits, fresh weight was significantly higher in BC3 (p < 0.05) compared with the other treatments. Similarly, total plant fresh weight showed significant differences among treatments, with BC3 exhibiting the highest value (p < 0.05). Overall, the BC3 treatment provided the greatest improvement in radish development in MPs-contaminated soil. However, further research involving different types of MPs, BCs, or other crop species is needed to more comprehensively assess the impact of BC on agricultural soils contaminated with MPs. Full article

Citrus production in Mexico relies predominantly on manual labor and traditional harvesting tools, which are often associated with physical overload, awkward postures, and reduced productivity. This study presents an exploratory, perception-based field evaluation of the BLIMPER, an early-stage ergonomic harvesting prototype designed for selective Persian lime collection. A total of 93 citrus harvesters participated through snowball sampling. A structured 33-item questionnaire was administered, covering five perception dimensions and open-ended comments. The instrument was expert-validated and demonstrated good internal consistency (Cronbach’s α = 0.85). Data analysis included descriptive statistics, Welch’s t-test for gender-based comparisons, and Hedges’ g to estimate the magnitude of the difference between groups. A modified Kano model was applied to classify perceived tool attributes and identify priorities for design refinement. The results indicated that 83–85% of respondents valued material strength, 64–70% approved of the unloading system, and 67–75% perceived reduced fatigue in the shoulders and lower back. The findings should be interpreted as an initial ergonomic validation based on user perceptions under real working conditions, rather than as evidence of readiness for large-scale deployment. The BLIMPER prototype shows potential to improve comfort and posture, while highlighting design aspects—weight distribution, mobility, and material selection—that require further optimization overall. Full article

Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ($\sigma =0.5$ and $\sigma =1.0$) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with $k\in \{1,3,5,7\}$ using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods. Full article

Autism Spectrum Disorder (ASD) is estimated to affect about 1% of children globally. While there is currently no cure, early detection and targeted interventions can significantly enhance the well-being and daily functioning of children with ASD. This paper presents an intelligent, content-based recommender system designed to suggest personalized activities aligned with each child’s preferences and developmental needs. The proposed system integrates social stories, educational videos, and interactive exercises supported by machine learning techniques to foster communication, social interaction, emotional regulation, and cognitive development—while reducing the need for constant parental supervision. Unlike traditional content-based systems, our approach incorporates the child’s emotional state (mood) to provide more diverse and context-aware recommendations, avoiding the filter bubble effect and enhancing personalization and engagement. A key contribution of this work lies in its focus on personalized and interactive learning experiences, made possible through the combination of multiple assistive technologies. Additionally, the study addresses the problem of data scarcity by providing a publicly available dataset to facilitate further research in ASD-focused intelligent systems. Preliminary feedback from therapists and parents indicates that the system holds strong potential to substantially improve the educational, communicative, and emotional skills of children with ASD. These promising results motivate future large-scale empirical evaluations to validate its effectiveness and establish it as a valuable tool for ASD intervention and inclusive education. Full article

Traditional FRP durability forecasting predominantly treats performance evolution as a discrete “point-to-point” regression, inherently overlooking temporal coherence and stochastic uncertainty. This study proposes a novel probabilistic framework based on the CSDI diffusion model to reconstruct continuous FRP degradation curves. By formulating long-term forecasting as a conditional imputation task, the methodology generates physically consistent performance trajectories from sparse experimental observations. Results from a multi-factor database demonstrate that CSDI enables a paradigm shift to continuous sequence generation, achieving high predictive accuracy (RMSE = 0.332, R² = 0.86) and robust probabilistic calibration (CRPS = 0.170) at a 30% missing ratio. This approach establishes a reliable probabilistic risk envelope, providing a scientific tool for the life-cycle reliability assessment of FRP structures under small-sample constraints. Full article

Lung diseases are among the leading causes of death worldwide. Nowadays, to detect lung diseases, a specialist uses auscultation to make a diagnosis. Newer auscultation devices based on stethoscopes allow these sounds to be recorded for later analysis. However, the diagnosis process is time-consuming and relies on medical expertise to generate an accurate diagnosis. For these reasons, automated and objective diagnostic systems are crucial for the early detection of lung diseases and preventing them from worsening. In this study, a computer-aided diagnostic system that integrates the Radial Short-Time Fourier Transform (RSTFT) with a Convolutional Neural Network (CNN) enhanced by attention mechanisms is presented. The RSTFT is employed to convert lung sound recordings from a public dataset into angular frequency representations, which are used as input to the CNN. The network automatically extracts discriminative features and classifies the recordings into five categories: Chronic Obstructive Pulmonary Disease (COPD), bronchiectasis, pneumonia, asthma, and healthy lungs. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, precision, recall, and F1-score. These findings indicate that the proposed RSTFT–CNN framework provides an effective and reliable solution for the automated diagnosis of lung diseases, offering valuable support for clinical decision-making and early intervention. Full article

This study investigates alkali-activated recycled pumice as a sustainable cement replacement for hydraulic concrete used in rigid pavements. Cement was replaced at 15%, 25%, and 50% by mass and activated using NaOH solutions at 1 N, 0.5 N, and 0.25 N, resulting in nine mixture variants. Mechanical performance was assessed through compressive strength at 7, 14, and 28 days, and flexural strength at 28 days. Durability was evaluated via natural carbonation depth at 210 and 1090 days. X-ray diffraction (XRD) identified aluminosilicate phases in the pumice, supporting its alkali-reactive potential. Mixtures with 15% pumice replacement achieved compressive strengths up to 20.99 MPa, comparable to the control mix (20.45 MPa), whereas 25% and 50% replacements produced moderate strength reductions. Flexural strength in 15% mixtures (7.38–7.44 MPa) was also comparable to the control (7.30 MPa), while higher replacement levels reduced flexural performance. Carbonation resistance improved for mixtures with an optimized alkaline-to-pumice ratio (APR, defined as NaOH concentration relative to pumice content) between 0.0167 and 0.02, indicating more balanced activation and reduced CO₂ ingress. Overall, alkali-activated recycled pumice enables partial cement replacement while maintaining mechanical performance and carbonation resistance at 15% substitution, supporting circular economy strategies and lowering the carbon footprint of rigid pavement concrete. Full article

DC–DC converters have become a key component in the structure of renewable energy systems, where an interface to increase and regulate the output voltage is required. This paper presents a modular non-isolated topology that achieves high voltage gain through interconnected switched-inductor cells. For the proposed converter, the design rules for sizing the energy storage elements for n number of cells are obtained, considering continuous, discontinuous, and boundary operation modes. Therefore, design equations are provided to support the precise selection of passive components according to voltage and power specifications. A nonlinear dynamic model is developed, and a model-based control scheme with inner current and outer voltage loops ensures robust regulation and fast transient response. Experimental validation on a 200 W prototype confirms theoretical predictions under steady-state and real-life dynamic conditions. Full article

Most adaptive signal control systems rely on traffic data detected by fixed-point detectors, which suffers from characteristics of inaccuracy and latency. This study proposes a hierarchical coordinated signal control framework for arterials with asymmetric traffic, integrating traditional detector data and partial CV data. The arterial traffic operation is firstly considered, based on the traditional Webster’s model. An efficiency optimization model is then developed for the high-volume main direction traffic flow of the mainline. At last, a bandwidth maximization model is presented for the minor direction traffic. The experimental results based on VISSIM simulation scenarios demonstrate that the proposed approach performs better than the Synchro and MULTIBAND models, especially when the penetration rate of CVs is greater than 30%. In addition, as the penetration rate increases, the impact on mainline traffic is significant while the effect on arterial traffic is slight. Full article