Search Results (5,790)

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R²_cv) of 0.72 with a root mean square error of cross-validation (RMSE_cv) of 51.50 ppm. Full article

A key direction of the development of modern power systems is the application of a continuously increasing number of grid-forming power converters to provide various system services. One of the possible strategies for the implementation of grid-forming control is a control algorithm based on a virtual synchronous generator (VSG). However, at present, the problem of VSG operation under abnormal conditions associated with an increase in output current remains unsolved. Existing current saturation algorithms (CSAs) lead to the degradation of grid-forming properties during overcurrent limiting or reduce the possible range of current output. In this regard, this paper proposes to use the structure of modified current-controlled VSG (CC-VSG) instead of traditional voltage-controlled VSG. A current vector amplitude limiter is used to limit the output current in the CC-VSG structure. At the same time, the angle of the current reference vector continues to be regulated based on the emerging operating conditions due to the voltage feedback in the used VSG equations. The presented simulation results have shown that it was possible to achieve a wide operating range for the current phase from 0° to 180° in comparison with a traditional VSG algorithm. At the same time, the properties of the grid-forming inverter, such as power synchronization without phase-locked loop controller, voltage, and frequency control, are preserved. In addition, in order to avoid saturation of the voltage controller, it is proposed to use a simple algorithm of blocking and switching the reference signal from the setpoint to the current voltage level. Due to this structure, it was possible to prevent saturation of integrators in the control loops and to provide a guaranteed exit from the limiting mode. The results of adding this structure showed a five-second reduction in the overvoltage that occurs when it is absent. A comparison with conditional integration also showed that it prevented lock-up in the limiting mode. The results of experimental verification of the developed prototype of the inverter with CC-VSG control and CSA are also given, including a comparison with the serial model of the hybrid inverter. The results obtained showed that the developed algorithm excludes both the dead time and the load current loss when the external grid is disconnected. In addition, there is no tripping during overload, unlike a hybrid inverter. Full article

Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality and to identify critical periods for intervention. We conducted a systematic survey of greenhouse operations in a representative area of Yunnan Province, Southwest China, and adopted a space-for-time substitution design. Using open-field cultivation (OF) as the control, we sampled and analyzed soils from vegetable greenhouses with greenhouse lifespans of 2 years (G2), 5 years (G5), and 10 years (G10). The results showed that early-stage greenhouse operation (G2) significantly increased soil temperature (ST) by 8.38–19.93% and soil porosity (SP) by 16.21–56.26%, promoted nutrient accumulation and enhanced aggregate stability compared to OF. However, as the greenhouse lifespan increased, the soil aggregates gradually disintegrated, particle-size distribution shifted toward finer clay fractions, and pH changed from neutral to slightly alkaline, exacerbating nutrient imbalances. Compared with G2, G10 exhibited reductions in mean weight diameter (MWD) and soil organic matter (SOM) of 2.41–5.93% and 24.78–30.93%, respectively. Among greenhouses with different lifespans, G2 had the highest soil quality index (SQI), which declined significantly with extended operation; at depths of 0–20 cm and 20–40 cm, the SQI of G10 was 32.59% and 38.97% lower than that of G2, respectively (p < 0.05). Structural equation modeling (SEM) and random forest analysis indicated that the improvement in SQI during the early stage of greenhouse use was primarily attributed to the optimization of soil hydrothermal characteristics and pore structure. Notably, the 2–5 years was the critical stage of rapid decline in SQI, during which intensive water and fertilizer inputs reduced the explanatory power of soil nutrients for SQI. Under long-term continuous cropping, the reduction in MWD and SOM was the main reason for the decline in SQI. This study contributes to targeted soil management during the critical period for sustainable production of protected vegetables in southern China. Full article

Background: Acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), remain major causes of morbidity and mortality, yet no targeted pharmacological therapy is available. Excessive neutrophil and macrophage infiltration drives reactive oxygen species (ROS) production and cytokine release, leading to alveolar–capillary barrier disruption and fatal respiratory failure. Methods: We applied an integrative multi-omics strategy combining single-cell transcriptomics, peripheral blood proteomics, and lung tissue proteomics in a lipopolysaccharide (LPS, 10 mg/kg)-induced mouse ALI model to identify key signaling pathways. Harpagide, an iridoid glycoside identified from our natural compound screen, was evaluated in vivo (40 and 80 mg/kg) and in vitro (0.1–1 mg/mL). Histopathology, oxidative stress markers (SOD, GSH, and MDA), cytokine levels (IL-6 and IL-1β), and signaling proteins (HIF-1α, p-PI3K, p-AKT, Nrf2, and HO-1) were quantitatively assessed. Direct target engagement was probed using surface plasmon resonance (SPR), the cellular thermal shift assay (CETSA), and 100 ns molecular dynamics (MD) simulations. Results: Multi-omics profiling revealed robust activation of HIF-1, PI3K/AKT, and glutathione-metabolism pathways following the LPS challenge, with HIF-1α, VEGFA, and AKT as core regulators. Harpagide treatment significantly reduced lung injury scores by ~45% (p < 0.01), collagen deposition by ~50%, and ROS accumulation by >60% relative to LPS (n = 6). The pro-inflammatory cytokines IL-6 and IL-1β were reduced by 55–70% at the protein level (p < 0.01). Harpagide dose-dependently suppressed HIF-1α and p-AKT expression while enhancing Nrf2 and HO-1 levels (p < 0.05). SPR confirmed direct binding of Harpagide to HIF-1α (KD = 8.73 µM), and the CETSA demonstrated enhanced thermal stability of HIF-1α. MD simulations revealed a stable binding conformation within the inhibitory/C-TAD region after 50 ns. Conclusions: This study reveals convergent immune–microenvironmental regulatory mechanisms across cellular and tissue levels in ALI and demonstrates the protective effects of Harpagide through multi-pathway modulation. These findings offer new insights into the pathogenesis of ALI and support the development of “one-drug, multilayer co-regulation” strategies for systemic inflammatory diseases. Full article

Although many studies have examined reaction zones in groundwater–seawater mixing areas, little attention has been given to how subsurface processes drive changes in iron (Fe) precipitation over time and space. This gap has limited our understanding of the “iron curtain” phenomenon in coastal aquifers. To address this, this study developed a reactive transport model to investigate how porosity evolves during the oxidative precipitation of Fe(II) in porous media. The model incorporates the dynamic effects of tortuosity, diffusivity, and surface area as minerals accumulate. Validation experiments, conducted with syringe tests that simulated Fe precipitation during freshwater–saltwater mixing, showed that precipitates formed mainly near the inlets, reflecting the development of a geochemical barrier at the groundwater–seawater interface. Scanning electron microscopy confirmed that Fe precipitates coated the surfaces of spherical particles. Numerical simulations further revealed that high Fe(II) concentrations drove pore clogging near the inlet, creating a dense precipitation zone akin to the iron curtain in coastal aquifers. At 10 mmol/L Fe(II), local clogging was observed, while at 100 mmol/L Fe(II), outflow rates (i.e., discharge) were substantially reduced. Together, the experiments and simulations highlight how hydrogeochemical processes influence hydraulic properties during the oxidative precipitation of Fe(II) in mixing zones. Full article

Importance: Understanding trends in the use of hormonal therapy (HT) for prostate cancer (PCa) is crucial to optimize treatment strategies, particularly for older men with locally advanced and metastatic disease. Objective: To evaluate changes in the patterns of adjuvant and primary HT use over time in older U.S. men diagnosed with locally advanced and metastatic prostate cancer. Design, Setting, and Participants: This cohort study utilized SEER-Medicare data, which covers approximately 48% of the U.S. population and links cancer registry data with Medicare claims, including 149,515 men aged ≥66 years diagnosed with PCa between 2010 and 2019. We analyzed trends in the use of adjuvant HT for higher-risk and primary HT for lower-risk PCa. Multivariable logistic regression models were used to adjust for clinical and demographic factors. Main Outcomes and Measures: The primary outcome was the proportion of men receiving any form of HT within 6 months of PCa diagnosis. HT included injectable Gonadotropin-releasing hormone (GnRH) agonists and antagonists, orchiectomy, and anti-androgens agents. Results: The rate of adjuvant HT in higher-risk PCa patients increased significantly from 53.6% in 2010 to 68.1% in 2019 (p < 0.0001), with a steady rise in the last four years. In contrast, the rate of men with lower-risk disease receiving primary HT declined from 25% in 2010 to 16.9% in 2013, then peaked at 28.2% in 2015, and stabilized between 25% and 27.3% from 2017 to 2019. The overall HT usage increased from 33.5% in 2010 to 45.2% in 2019, showing a consistent increase over the years. These patterns persisted after adjusting for clinical and demographic factors. Conclusions and Relevance: The increasing use of adjuvant HT in higher-risk PCa patients aligns with evolving treatment guidelines, while the stable rate of primary HT in lower-risk patients represents persistent inappropriate use and highlights the need for further efforts to optimize treatment choices. While previous studies focused on men with intermediate-risk PCa receiving radiation therapy, our study broadens the scope to include men who did not undergo radiation therapy, providing a more inclusive view of HT trends. Future research should focus on refining strategies to reduce inappropriate primary HT use and improve adjuvant HT administration. Full article

Deep coalbed methane reservoirs must utilize hydraulic fracturing technology to create high-conductivity sand-filled fractures for economical development. However, the mechanism by which proppant embedment affects fracture width in coal rock is not yet clear. In this article, using the discrete element particle flow method, we have developed a numerical simulation model that can replicate the dynamic process of proppant embedment into the fracture surface. By tracking particle positions, we have accurately characterized the dynamic changes in fracture width and proppant embedment depth. The consistency between experimental measurements of average fracture width and numerical results demonstrates the reliability of our numerical model. Using this model, we analyzed the mechanisms by which different proppant particle sizes, number of layers, and closure stresses affect fracture width. The force among particles under different proppant embedment conditions and the induced stress field around the fracture were also studied. Numerical simulation results show that stress concentration formed by proppant embedment in the fracture surface leads to the generation of numerous induced micro-fractures. As the proppant grain size and closure stress increase, the stress concentration formed by proppant embedment in the fracture surface intensifies, and the variability in fracture width along the fracture length direction also increases. With more layers of proppant placement, the particles counteract some of the closure stress, thereby reducing the degree of proppant embedment around the fracture surface. Full article

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation lies in the need to predefine the number of clusters. The aim of this study was to develop and validate a non-parametric method for clustering daily water consumption profiles based on a modified DBSCAN algorithm. The proposed approach includes the automatic optimization of neighborhood radius and the minimum number of points required to form a cluster. The input data consisted of half-hourly water supply and electricity consumption values for the water supply system of Gomel (Republic of Belarus), supplemented with the time-of-day factor. As a result of the multidimensional clustering, two stable regimes were identified: a high-demand regime (6:30–22:30), covering about 46% of the data and accounting for more than half of the total water supply and electricity consumption, and a low-demand regime (0:30–6:00), representing about 21% of the data and forming around 15% of the resources. The remaining regimes reflect transitional states in morning and evening periods. The obtained results make it possible to define the temporal boundaries of the regimes and to use them for data labeling in the development of predictive water consumption models. Full article

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

Gout is a form of sterile inflammatory arthritis in which monosodium urate (MSU) crystals deposit and provoke a neutrophil-predominant response, primarily driven by activation of the NACHT, leucine-rich repeat, and pyrin domain-containing protein 3 (NLRP3) inflammasome. Here, we show that auranofin, a Food and Drug Administration (FDA)-approved anti-rheumatic agent, exerts anti-inflammatory effects in both in vitro and in vivo models of gout. Auranofin inhibited NLRP3 inflammasome activation in human THP-1 cells and murine macrophages, leading to reduced cleavage of caspase-1, interleukin-1β (IL-1β), and interleukin-18 (IL-18). In MSU crystal-induced mouse models, auranofin treatment reduced paw swelling, serum cytokine levels, and tissue inflammation. Notably, auranofin suppressed neutrophil migration and decreased expression of C-X-C motif chemokine ligand 1 (CXCL1) in inflamed foot tissue and air-pouch exudates. Mechanistically, auranofin disrupted the interleukin-33 (IL-33)/suppression of tumorigenicity 2 (ST2) axis, a key signaling pathway promoting neutrophil recruitment. Overexpression of IL-33 abolished the anti-inflammatory effects of auranofin, highlighting the central role of IL-33 in gout pathogenesis. Together, our findings suggest that auranofin alleviates MSU-induced inflammation by concurrently inhibiting NLRP3 inflammasome activation and IL-33-mediated neutrophil recruitment, supporting its potential as a dual-action therapeutic candidate for gout. Full article

The global shift towards cleaner energy sources is driving the adoption of hydrogen as an environmentally friendly alternative to fossil fuels. Among the forms currently available, Liquid Hydrogen (LH₂) offers high energy density and efficient storage, making it suitable for large-scale transport by rail. However, the flammability of hydrogen poses serious safety concerns, especially when transported through confined spaces such as railway tunnels. In case of an accidental LH₂ release from a freight train, the rapid accumulation and potential ignition of hydrogen could cause catastrophic consequences, especially if freight and passenger trains are present simultaneously in the same tunnel tube. In this study, a three-dimensional computational fluid dynamics model was developed to simulate the dispersion and explosion of LH₂ following an accidental leak from a freight train’s cryo-container in a single-tube double-track railway tunnel, when a passenger train queues behind it on the same track. The overpressure results were analyzed using probit functions to estimate the fatality probabilities for the passenger train’s occupants. The analysis suggests that a significant number of fatalities could be expected among the passengers. However, shorter users’ evacuation times from the passenger train’s wagons and/or longer distances between the two types of trains might reduce the number of potential fatalities. The findings, by providing additional insight into the risks associated with LH₂ transport in railway tunnels, indicate the need for risk mitigation measures and/or traffic management strategies. Full article

In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally demanding. Machine learning (ML) offers an alternative, but its application to direct reconstruction of full pressure traces remains limited. This study evaluates three strategies for reconstructing cylinder pressure under unmeasured operating conditions, establishing a machine learning-assisted framework that generates the complete pressure–crank angle (P–CA) trace. The framework treats crank angle and operating conditions as inputs and predicts either pressure directly or apparent heat release rate (HRR) as an intermediate variable, which is then integrated to reconstruct pressure. In all approaches, discrete pointwise predictions are combined to form the full P–CA curve. Direct pressure prediction achieves high accuracy for overall traces but underestimates HRR-related combustion features. Training on HRR improves combustion representation but introduces baseline shifts in reconstructed pressure. A hybrid approach, combining non-combustion pressure prediction with combustion-phase HRR-based reconstruction delivers the most robust and physically consistent results. These findings demonstrate that ML can efficiently reconstruct in-cylinder pressure at unmeasured conditions, reducing experimental requirements while supporting combustion diagnostics, calibration, and digital twin applications. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

Molecular dynamics simulations were conducted using the attachment energy (AE) model to investigate the growth morphology of lead 2,4,6-trinitrororesorcinate (LTNR, lead styphnate) under vacuum and different solvents. The adsorption energy of LTNR on (001), (110), (011), (020), (111), (200), and (201) crystal planes were calculated. Meanwhile, the crystal morphology in solvents of ethanol, toluene, dichloromethane, acetone, dimethyl sulfoxide (DMSO), and water at 298 K was predicted by calculating the interaction energies between the solvents and crystal planes. The calculated results show that the morphology of LTNR crystals in different solvents is significantly different. In toluene, LTNR crystal morphologies are flat, while in pure solvents of ethanol, acetone, and DMSO, the number of crystal planes increases, and the crystal thickness is larger. In the water, LTNR tends to form tabular crystals, which is similar to the experimental results. Both radial distribution function (RDF) and mean squared displacement (MSD) analyses reveal that hydrogen bonding dominates the interactions between LTNR and solvent molecules. Solvent molecules with higher diffusion coefficients exhibit increased desorption tendencies from crystal surfaces, which may reduce their inhibitory effects on specific crystallographic planes. However, no direct correlation exists between solvent diffusion coefficients and crystal plane growth rates, suggesting that surface attachment kinetics or interfacial energy barriers play a more critical role in crystal growth. Full article