Search Results (47,873)

This systematic review and meta-analysis investigates the impact of artificial intelligence (AI) tools, including ChatGPT 3.5 and GitHub Copilot, on learning outcomes in computer programming courses. A total of 35 controlled studies published between 2020 and 2024 were analysed to assess the effectiveness of AI-assisted learning. The results indicate that students using AI tools outperformed those without such aids. The meta-analysis findings revealed that AI-assisted learning significantly reduced task completion time (SMD = −0.69, 95% CI [−2.13, −0.74], I² = 95%, p = 0.34) and improved student performance scores (SMD = 0.86, 95% CI [0.36, 1.37], p = 0.0008, I² = 54%). However, AI tools did not provide a statistically significant advantage in learning success or ease of understanding (SMD = 0.16, 95% CI [−0.23, 0.55], p = 0.41, I² = 55%), with sensitivity analysis suggesting result variability. Student perceptions of AI tools were overwhelmingly positive, with a pooled estimate of 1.0 (95% CI [0.92, 1.00], I² = 0%). While AI tools enhance computer programming proficiency and efficiency, their effectiveness depends on factors such as tool functionality and course design. To maximise benefits and mitigate over-reliance, tailored pedagogical strategies are essential. This study underscores the transformative role of AI in computer programming education and provides evidence-based insights for optimising AI-assisted learning. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by symptoms that vary in how severe they are. ASD individuals often present with psychiatric comorbidities that significantly impact their well-being and quality of life (QoL), with possible impacts on their family. Aims: This preliminary study aims to assess the impact of internalizing and externalizing behaviors, not closely related to the core symptoms of autism, on the QoL of their caregivers. Method: A pilot study was conducted with a sample of 58 children diagnosed with ASD and their caregivers, using the Quality of Life in Autism (QoLA) questionnaires for caregivers and the Child Behavior Checklist (CBCL) to evaluate the children’s behavioral and emotional problems. Results and Conclusions: The results revealed significant differences in the mean age of the children (3.32 ± 0.88 vs. 8.47 ± 2.51 years) and caregivers (35.68 ± 7.57 vs. 40.42 ± 6.43 years), with a notable impact of specific behavioral issues, such as attention, aggression, and externalizing behaviors, on the QoL of caregivers. In younger children, caregivers’ QoL was positively correlated with their age, with sleep problems being the primary source of stress. In older children, a negative correlation was found between caregivers’ age and their QoL, with conduct and social problems in children having a negative effect on caregivers’ well-being. These findings highlight the importance of targeted interventions to mitigate the impact of these factors on the QoL of caregivers of ASD children. Full article

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems. Full article

Cardiovascular complications are a hallmark of Post-Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (PASC), yet the mechanisms driving persistent cardiac dysfunction remain poorly understood. Emerging evidence implicates mitochondrial dysfunction in immune cells as a key contributor. This study investigated whether CD14⁺⁺ monocytes from long COVID patients exhibit bioenergetic impairment, mitochondrial DNA (mtDNA) damage, and defective oxidative stress adaptation, which may underlie cardiovascular symptoms in PASC. CD14⁺⁺ monocytes were isolated from 14 long COVID patients with cardiovascular symptoms (e.g., dyspnea, angina) and 10 age-matched controls with similar cardiovascular risk profiles. Mitochondrial function was assessed using a Seahorse Agilent Analyzer under basal conditions and after oxidative stress induction with buthionine sulfoximine (BSO). Mitochondrial membrane potential was measured via Tetramethylrhodamine Ethyl Ester (TMRE) assay, mtDNA integrity via qPCR, and reactive oxygen species (ROS) dynamics via Fluorescence-Activated Cell Sorting (FACS). Parallel experiments exposed healthy monocytes to SARS-CoV-2 spike protein to evaluate direct viral effects. CD14⁺⁺ monocytes from long COVID patients with cardiovascular symptoms (n = 14) exhibited profound mitochondrial dysfunction compared to age-matched controls (n = 10). Under oxidative stress induced by buthionine sulfoximine (BSO), long COVID monocytes failed to upregulate basal respiration (9.5 vs. 30.4 pmol/min in controls, p = 0.0043), showed a 65% reduction in maximal respiration (p = 0.4035, ns) and demonstrated a 70% loss of spare respiratory capacity (p = 0.4143, ns) with significantly impaired adaptation to BSO challenge (long COVID + BSO: 9.9 vs. control + BSO: 54 pmol/min, p = 0.0091). Proton leak, a protective mechanism against ROS overproduction, was blunted in long COVID monocytes (3-fold vs. 13-fold elevation in controls, p = 0.0294). Paradoxically, long COVID monocytes showed reduced ROS accumulation after BSO treatment (6% decrease vs. 1.2-fold increase in controls, p = 0.0015) and elevated mitochondrial membrane potential (157 vs. 113.7 TMRE fluorescence, p = 0.0179), which remained stable under oxidative stress. mtDNA analysis revealed severe depletion (80% reduction, p < 0.001) and region-specific damage, with 75% and 70% reductions in amplification efficiency for regions C and D (p < 0.05), respectively. In contrast, exposure of healthy monocytes to SARS-CoV-2 spike protein did not recapitulate these defects, with preserved basal respiration, ATP production, and spare respiratory capacity, though coupling efficiency under oxidative stress was reduced (p < 0.05). These findings suggest that mitochondrial dysfunction in long COVID syndrome arises from maladaptive host responses rather than direct viral toxicity, characterized by bioenergetic failure, impaired stress adaptation, and mitochondrial genomic instability. This study identifies persistent mitochondrial dysfunction in long COVID monocytes as a critical driver of cardiovascular complications in PASC. Key defects—bioenergetic failure, impaired stress adaptation and mtDNA damage—correlate with clinical symptoms like heart failure and exercise intolerance. The stable elevation of mitochondrial membrane potential and resistance to ROS induction suggest maladaptive remodeling of mitochondrial physiology. These findings position mitochondrial resilience as a therapeutic target, with potential strategies including antioxidants, mtDNA repair agents or metabolic modulators. The dissociation between spike protein exposure and mitochondrial dysfunction highlights the need to explore host-directed mechanisms in PASC pathophysiology. This work advances our understanding of long COVID cardiovascular sequelae and provides a foundation for biomarker development and targeted interventions to mitigate long-term morbidity. Full article

The aim of this work is to highlight the beneficial effects of bioactive peptides present in fermented whey (FW) and carotenoids from pumpkin (P) against the pro-oxidant effects of aflatoxin B1 and ochratoxin A at the neuronal level. For this purpose, SH-SY5Y human neuroblastoma differentiated cells were exposed to (A) mycotoxins, (B) the digesta of mycotoxin-contaminated bread formulated with P, or (C) bread enriched with FW + P. A proteomic approach using HPLC-MS/MS-QTOF was then employed to characterize the metabolic pathways affected by the presence of these components, as well as their ability to modulate the toxic effects exacerbated by mycotoxins. Gene ontology functional analysis revealed proteins primarily associated with nucleosome structure, such as the H3-H4 tetramer, H2A-H2B dimer, and HIRA, which were overexpressed in the presence of mycotoxins and, interestingly, downregulated with the addition of the functional ingredients. Additionally, important metabolic pathways associated with the RHO GTPase family, estrogen-dependent gene expression, and androgen receptor transcription stimulated by PKN1 activation were discovered. Network interaction analysis highlighted the modulation of cytoskeletal dynamics, cell migration, and stress responses. These findings provide novel insights into the neuroprotective potential of functional food components, supporting their use in mitigating mycotoxin-induced neuronal damage and opening new avenues for dietary-based neuroprotection strategies. Full article

Male-factor infertility accounts for nearly half of all infertility cases, and mounting evidence points to oxidative stress as a pivotal driver of sperm dysfunction, genetic instability, and epigenetic dysregulation. In particular, the oxidative DNA lesion 8-hydroxy-2′-deoxyguanosine (8-OHdG) has emerged as a central mediator at the interface of DNA damage and epigenetic regulation. We discuss how this lesion can disrupt key epigenetic mechanisms such as DNA methylation, histone modifications, and small non-coding RNAs, thereby influencing fertilization outcomes, embryo development, and offspring health. We propose that the interplay between oxidative DNA damage and epigenetic reprogramming is further exacerbated by aging in both the paternal and maternal germlines, creating a “perfect storm” that increases the risk of heritable (epi)mutations. The consequences of unresolved oxidative lesions can thus persist beyond fertilization, contributing to transgenerational health risks. Finally, we explore the promise and potential pitfalls of antioxidant therapy as a strategy to mitigate sperm oxidative damage. While antioxidant supplementation may hold significant therapeutic value for men with subfertility experiencing elevated oxidative stress, a careful, personalized approach is essential to avoid reductive stress and unintended epigenetic disruptions. Recognizing the dual role of oxidative stress in shaping both the genome and the epigenome underscores the need for integrating redox biology into reproductive medicine, with the aim of improving fertility treatments and safeguarding the health of future generations. Full article

Unmanned aerial vehicles (UAVs) may encounter actuator faults in diverse flight scenarios, requiring robust fault detection models that can adapt to varying data distributions. To address this challenge, this paper proposes an approach that integrates Domain-Adversarial Neural Networks (DANNs) with a Mixture of Experts (MoE) framework. By employing domain-adversarial learning, the method extracts domain-invariant features, mitigating distribution discrepancies between source and target domains. The MoE architecture dynamically selects specialized expert models based on task-specific data characteristics, improving adaptability to multimodal environments. This integration enhances fault detection accuracy and robustness while maintaining efficiency under constrained computational resources. To validate the proposed model, we conducted flight experiments, demonstrating its superior performance in actuator fault detection compared to conventional deep learning methods. The results highlight the potential of MoE-enhanced domain adaptation for real-time UAV fault detection in dynamic and uncertain environments. Full article

The durability of concrete is compromised by early-age cracking, which provides a pathway for harmful ions and water to penetrate the material. Early-age cracking, however, is most commonly caused by concrete shrinkage. This study investigates strategies for minimizing the shrinkage of concrete by optimizing aggregate gradation via the Tarantula Curve, reducing cement content, and incorporating lightweight fine aggregates (LWFA) as internal curing agents. The commercially adopted mix design was used as a reference, with the cementitious materials-to-aggregate (C/A) ratio reduced from 0.21 (reference) to 0.15 (proposed), incorporating 0–15% LWFA replacement levels. Workability (ASTM C143), mechanical performance (ASTM C39, ASTM C78), durability (AASHTO TP 119-21), and dimensional stability (ASTM C157) were evaluated through ASTM standard tests. The results highlight that optimizing the C/A ratio cannot only improve both compressive and flexural strengths in regular concrete but also mitigate the total shrinkage by 12.68%. The introduction of LWFA further reduced shrinkage, achieving a 19.72% shrinkage reduction compared to regular concrete. In addition, the sustainability of the developed mix designs is enhanced by the reduced cement usage. A Life Cycle Assessment (LCA) based on the TRACI method confirmed the sustainability advantages of cement reduction. The optimized mix designs resulted in a 30% decrease in CO₂ emissions, emphasizing the role of mix design in developing environmentally responsible concrete. Overall, lowering the cement amount and the addition of LWFA provide an optimal combination of shrinkage control, strength retention, and sustainability for applications. Full article

Grid-forming (GFM) inverters play a critical role in stabilizing future power grids. However, their synchronization is inherently coupled with frequency support, which poses a challenge to prevent overloading while maintaining synchronization. While existing literature has proposed strategies to mitigate the overload of GFM inverters during frequency excursions, these typically focus on limiting primary frequency regulation and overlook their inertial contribution, limiting their effectiveness. The present work addresses this gap by analyzing three overload mitigation strategies that dynamically adjust both primary frequency regulation and inertia. The main contribution of this work is formal analysis of the control structures, providing insight into the tuning process, dynamic behavior, and inherent trade-offs. The performance of these strategies is evaluated under grid frequency excursions and oscillations, focusing on their ability to limit overloads and ensure seamless recovery. Simulation results are validated through experimental testing. Full article

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system. Full article

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region. Full article

Soil salinization, a major challenge caused by climate change over the past century, critically affects cultivated land and consequently reduces agricultural production worldwide. Recently, plant growth-promoting rhizobacteria have been collected and utilized to enhance plant growth and mitigate the effects of salt stress in different plant species including rice. In our current study, the Serratia marcescens strain VIRS2 with remarkable salt tolerance was successfully isolated from the saline soil in the Mekong River Delta of Vietnam. This isolate exhibited diverse plant growth-promoting properties, especially the production of a high indole acetic acid level. Treatments under both in vitro and greenhouse conditions indicated that VIRS2 could enhance growth and salt tolerance in rice. The VIRS2-inoculated rice plants exhibited biochemical profile alterations including proline, malondialdehyde, and relative water contents. In addition, the expression of genes involved in the plant stress response pathways was upregulated in the VIRS2-inoculated rice under salt treatments. Importantly, the whole genome sequencing data of VIRS2 also showed the presence of different genes associated with plant growth-promotion and stress-tolerance mechanisms. These results indicated the potential of the VIRS2 isolate for enhancing growth and salt tolerance in rice as well as other important crops. Full article

This study investigated the feasibility of incorporating Chenopodium album L. (CAL) into ruminant feed ingredients through evaluating the effects of harvest time and substitution levels on in vitro rumen fermentation. In the first phase, a sole-substrate experiment was conducted using CAL harvested from June to August, analyzing its chemical composition and total saponin content. The impact of harvest time on fermentation parameters was assessed with CAL as the sole substrate. The second phase involved a mixed-substrate experiment using an early-fattening Hanwoo diet (30% rice straw and 70% concentrate), where increasing proportions of CAL (control: 0%, T1: 5%, T2: 10%, T3: 15%, and T4: 20%) replaced rice straw. Seasonal variations in CAL composition influenced the fermentation characteristics. CAL harvested in July exhibited higher fermentability, with total volatile fatty acids (TVFAs) reaching 103.87 mM at 72 h. In contrast, CAL harvested in August showed lower fermentability and digestibility. However, August-harvested CAL was selected for the subsequent experiment, as it provided a more practical balance of sufficient biomass yield and a higher saponin concentration, aligned with the study’s methane mitigation objectives, while also exhibiting a fiber composition comparable to that of rice straw. We hypothesized that the saponins in CAL contribute to methane reductions. Supplementation with 15% of CAL significantly reduced methane production per gram of digested substrate (p < 0.05), likely due to differences in crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and saponin content. However, despite having the lowest fiber content, T4 (20% CAL) exhibited the lowest in vitro dry matter digestibility (IVDMD), suggesting that factors such as saponins, CAL’s chemical composition, or microbial shifts may have hindered digestibility. Ammonia–nitrogen production increased from 0 to 3 h, but it continuously decreased between 3 and 9 h due to microbial growth and nitrogen assimilation, as microbes incorporate ammonia into their biomass (p < 0.05). Fermentation characteristics further revealed that the acetate-to-propionate (A/P) ratio decreased with increasing CAL levels, with T4 showing the lowest ratio (1.55 at 72 h), confirming a shift toward propionate-based fermentation. Notably, T2 (10% CAL) showed an optimized fermentation efficiency, producing the highest TVFA concentration at 24 h (98.28 mM). These findings highlight the potential for using CAL as a functional feed ingredient, with moderate substitution levels (10–15%) enhancing fermentation efficiency while reducing methane production. Full article

This paper extends the multipoint flux approximation (MPFA-O) method to model coupled pressure and saturation dynamics in subsurface reservoirs with heterogeneous anisotropic permeability and non-K-orthogonal grids. The MPFA method is widely used for reservoir simulation to address the limitations of the two-point flux approximation (TPFA), particularly in scenarios involving full-tensor permeability and strong anisotropy. However, the MPFA-O method is known to suffer from spurious oscillations and numerical instability, especially in high-anisotropy scenarios. Existing stability-enhancing techniques, such as optimal quadrature schemes and flux-splitting methods, mitigate these issues but are computationally expensive and do not always ensure monotonicity or oscillation-free solutions. Building upon prior advancements in the MPFA-O method for pressure equations, this work incorporates the saturation equation to enable the simulation of a coupled multiphase flow in porous media. A unified framework is developed to address stability challenges associated with the tight coupling of pressure and saturation fields while ensuring local conservation and accuracy in the presence of full-tensor permeability. The proposed method introduces stability-enhancing modifications, including a local rotation transformation, to mitigate spurious oscillations and preserve physical principles such as monotonicity and the maximum principle. Numerical experiments on heterogeneous, anisotropic domains with non-K-orthogonal grids validate the robustness and accuracy of the extended MPFA-O method. The results demonstrate improved stability and performance in capturing the complex interactions between pressure and saturation fields, offering a significant advancement in subsurface reservoir modeling. This work provides a reliable and efficient tool for simulating coupled flow and transport processes, with applications in CO₂ storage, hydrogen storage, geothermal energy, and hydrocarbon recovery. Full article