Search Results (3,359)

This paper offers an overview of a large study of language and cognitive development in deaf and hard of hearing children. Specifically, we investigated how acquiring a signed or spoken language (language modality) and when a child’s access to language begins (i.e., at birth or later in development) influence cognitive development. We conducted in-person behavioral assessments with 404 children 3–10 years old (280 deaf and hard of hearing; 124 typically hearing). The tasks measured a range of abilities along a continuum of how strongly they depend on language input, such as general vocabulary and number words (strongly dependent) vs. skills such as tracking sets of two to three objects and standardized ‘nonverbal’ picture-similarity tasks (relatively independent of language). Overall, the timing of children’s access to language predicted more variability in their performance than language modality. These findings help refine our theories about how language influences development and suggest how a STEAM pedagogical approach may ameliorate the impacts of later access to language. These results underscore children’s need for language early in development. That is, deaf and hard of hearing children must receive fully accessible language input as early as possible through sign language, accompanied by hearing technology aimed at improving access to spoken language, if desired. Full article

Polyimide (PI), owing to its high heat resistance and low density, is often employed as a substitute for metallic materials in high-temperature environments, such as aircraft engines, bearings, and gears. However, the relatively high friction coefficient of pure PI limits its application under harsh conditions. Therefore, this study synthesized a composite lubricant with binary fillers to improve this performance. This study employed the hydrothermal method to synthesize MoS₂/MXene composite lubricating fillers and systematically investigated the high-temperature tribological properties of PI composites reinforced with these fillers. The results demonstrated that the optimal PI composite containing 5% MoS₂/MXene exhibited a 14 °C increase in initial decomposition temperature compared to pure PI. Additionally, its thermal conductivity was enhanced by 36%, while the hardness (0.398 GPa) and elastic modulus (6.294 GPa) were elevated by 12.4% and 18.6%, respectively, relative to the pure PI. In terms of tribological behavior, all composite formulations displayed typical temperature-dependent friction characteristics. It is worth noting that MXene’s high hardness and thermal conductivity inhibited the occurrence of abrasive wear. At the same time, the substrate was strengthened, and thermal resistance was enhanced, thereby delaying the plastic deformation of the material at high temperatures. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested. Full article

This study examined the effect of executive function training targeting both updating and inhibition in children. The training included both single training (i.e., number 2-back training) and combined training (i.e., number 2-back and fish flanker training). Event-related potentials were also recorded. In Experiment 1, we employed both single-training and combined-training groups, which were contrasted with each other and with an active control group. In Experiment 2, the control group and the combined-training group were recruited to perform training tasks identical to those used in Experiment 1, and their EEG data were collected during the pretest and posttest stage. Experiment 1 found that the single group showed clear evidence for transfer to letter 2-back task compared with the active control group. The combined group showed significant transfer to the letter 2-back and arrow flanker task. Both groups found no transfer to fluid intelligence or shifting. Experiment 2 revealed that the participants who received updating and inhibition training showed a significant reduction in N2 amplitude and a significant increase in P300 amplitude after training in comparison to the active control group. Importantly, there was a significant positive correlation between reduced N2 amplitude and decreased response time in conflict effects. Additionally, there was a strong positive trend toward a relationship between behavioral performance improvement and an increase in P300 amplitude. From the perspective of the near-transfer effect, combined training is more effective than single training. Our results showed that the extent of transfer depends on the cognitive component overlap between the training and transfer tasks. Full article

Full-section interval casting technology was adopted for the integral immersed tube of the Chebei Immersed Tunnel. Field tests (Chebei Immersed Tunnel) were conducted to establish the time-dependent development of the concrete shrinkage strain of the full-section casting segments. And laboratory experiments were then carried out to investigate the influence of factors such as the reinforcement ratio and stress, expansive agent content and composition, fly ash content, and curing temperature and humidity on the expansive effect of calcium–magnesium composite expansive agents. Field tests revealed that casting segments exhibit initial expansion followed by shrinkage, reaching a final strain of 348 με (microstrain). Laboratory investigations demonstrated that reinforcement (20–30 MPa stress) in post-casting belts effectively restrains segments without compromising the performance of calcium–magnesium composite expansive agents. The optimal 5:3:2 ratio of CaO, MgO 90s, and MgO 200s agents controlled shrinkage strain within 80 με by combining CaO’s rapid early expansion with MgO’s sustained effect. Field validation confirmed the mix’s effectiveness in preventing cracking, with key findings: (1) fly ash content and curing conditions significantly influence expansive behavior, and (2) shrinkage development can be precisely regulated through agent composition adjustments. Full article

The oxidative degradation of anthocyanins in red wine was investigated under controlled conditions using hydroxyl radicals generated in the presence of Cu (II) as a catalyst. A full factorial experimental design with 23 replicates was used to evaluate the effects of hydrogen peroxide concentration, catalyst dosage, and reaction temperature on anthocyanin degradation over a fixed time. Statistical analysis (ANOVA and multiple regression) showed that all three variables and the main interactions significantly affected anthocyanin loss, with temperature identified as the most influential factor. The combined effects were described by a first-order polynomial model. The activation energies for degradation ranged from 56.62 kJ/mol (cyanidin-3-O-glucoside) to 40.58 kJ/mol (peonidin-3-O-glucoside acetate). Increasing the temperature from 30 °C to 40 °C accelerated the degradation kinetics, almost doubled the rate constants and shortened the half-life of the pigments. At 40 °C, the half-lives ranged from 62.3 min to 154.0 min, depending on the anthocyanin structure. These results contribute to a deeper understanding of the stability of anthocyanins in red wine under oxidative stress and provide insights into the chemical behavior of derived pigments. The results are of practical importance for both oenology and viticulture and support efforts to improve the color stability of wine and extend the shelf life of grape-based products. Full article

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

Background/Objectives: Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability—critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. Methods: MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. Results: MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose–response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. Conclusions: MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics. Full article

Understanding the rheological properties of fresh cement slurries is essential to maintain optimal pumpability, achieve dependable zonal isolation, and preserve long-term well integrity in oil and gas cementing operations and the 3D printing cement and concrete industry. However, accurately and efficiently modeling the rheological behavior of cement slurries remains challenging due to the complex fluid properties of fresh cement slurries, which exhibit non-Newtonian and thixotropic behavior. Traditional numerical solvers typically require mesh generation and intensive computation, making them less practical for data-scarce, high-dimensional problems. In this study, a physics-informed neural network (PINN)-based framework is developed to solve the governing equations of steady-state cement slurry flow in a tilted channel. The slurry is modeled as a non-Newtonian fluid with viscosity dependent on both the shear rate and particle volume fraction. The PINN-based approach incorporates physical laws into the loss function, offering mesh-free solutions with strong generalization ability. The results show that PINNs accurately capture the trend of velocity and volume fraction profiles under varying material and flow parameters. Compared to conventional solvers, the PINN solution offers a more efficient and flexible alternative for modeling complex rheological behavior in data-limited scenarios. These findings demonstrate the potential of PINNs as a robust tool for cement slurry rheological modeling, particularly in scenarios where traditional solvers are impractical. Future work will focus on enhancing model precision through hybrid learning strategies that incorporate labeled data, potentially enabling real-time predictive modeling for field applications. Full article

This study presents a microfluidic chip platform designed using a multiscale 3D printing strategy for fabricating microfluidic chips with integrated, high-resolution, and customizable membrane structures. By combining two-photon polymerization (2PP) for submicron membrane fabrication with liquid crystal display printing for rapid production of larger components, this approach addresses key challenges in membrane integration, including sealing reliability and the use of transparent materials. Compared to fully 2PP-based fabrication, the multiscale method achieved a 56-fold reduction in production time, reducing total fabrication time to approximately 7.2 h per chip and offering a highly efficient solution for integrating complex structures into fluidic chips. The fabricated chips demonstrated excellent mechanical integrity. Burst pressure testing showed that all samples withstood internal pressures averaging 1.27 ± 0.099 MPa, with some reaching up to 1.4 MPa. Flow testing from ~35 $\mathsf{\mu }$L/min to ~345 $\mathsf{\mu }$L/min confirmed stable operation in 75 $\mathsf{\mu }$m square channels, with no leakage and minimal flow resistance up to ~175 $\mathsf{\mu }$L/min without deviation from the predicted behavior in the 75 $\mathsf{\mu }$m. Membrane-integrated chips exhibited outlet flow asymmetries greater than 10%, indicating active fluid transfer across the membrane and highlighting flow-dependent permeability. Overall, this multiscale 3D printing approach offers a scalable and versatile solution for microfluidic device manufacturing. The method’s ability to integrate precise membrane structures enable advanced functionalities such as diffusion-driven particle sorting and molecular filtration, supporting a wide range of biomedical, environmental, and industrial lab-on-a-chip applications. Full article

A physical and mathematical model and a methodology for studying the mechanical properties of a nonferromagnetic conductive panel under the action of a quasi-steady electromagnetic field are proposed. A two-dimensional thermomechanics problem is formulated for the considered panel of a rectangular cross-section. The initial relations for finding the determinant functions, namely, the components of the quasi-static stress tensor, are given. The thermomechanical problem was addressed using the author’s approach, which involves approximating the defining functions by cubic polynomials along the thickness direction. This approach enabled the transformation of the initial two-dimensional boundary value problems into one-dimensional formulations based on the integral characteristics of the defining functions. Using a finite integral transformation in the transverse coordinate, the expressions of all integral characteristics and determinant functions were obtained. On the basis of the proposed mathematical model, a computer analysis of all components of the quasi-static stress tensor and stress intensity in a tungsten panel depending on the dimensionless Fourier time, the Biot criterion, and the induction heating parameters was carried out. The thermomechanical behavior of the panel was analyzed using two modes of near-surface and in-depth induction heating of the panel by a homogeneous quasi-steady electromagnetic field. Full article

Dynamic on-resistance (R_ON) of commercial GaN on Si normally off high-electron-mobility transistor (HEMT) devices is a very important parameter because it is responsible for conduction losses that limit the power conversion efficiency of high-power switching converters. Due to charge trapping effects, dynamic R_ON is always higher than in DC, a behavior known as current collapse. To study how short-time dynamics of charge trapping and release affects R_ON we use rectangular 0–5 V gate voltage pulses with durations in the 1 μs to 100 μs range. Measurements are first carried out for single pulses of increasing duration, and it is found that R_ON depends on both pulse duration and drain current I_D, being higher at shorter pulse durations and lower I_D. For a train of five pulses, R_ON decreases with pulse number, reaching a steady state after a time interval of 100 μs. The response to a five pulses train is compared to that of a square-wave signal to study the time evolution of R_ON toward a dynamic steady state. The DC R_ON is also measured, and it is a factor of ten smaller than dynamic R_ON at the same I_D. This confirms that a reduction in trapped charges takes place in DC as compared to the square-wave switching operation. Additional off-state stress tests at V_DS = 55 V reveal the presence of residual surface traps in the drain access region, leading to four times increase in R_ON in comparison to pristine devices. Finally, the dynamic R_ON is also measured by the double-pulse test (DPT) technique with inductive load, giving a good agreement with results from single-pulse measurements. Full article

This study investigates cement–bentonite slurries with hydration accelerators for borehole backfilling applications in infrastructure reconstruction projects. Two formulations with different accelerator dosages (5 and 10 kg/m³) were evaluated through combined experimental testing and Moving Particle Semi-implicit (MPS) numerical modeling to optimize material performance. The research focuses on time-dependent rheological evolution and its impact on construction performance, particularly bleeding resistance and workability retention. Experimental flow tests revealed that both formulations maintained similar initial flowability (240–245 mm spread diameter), but the higher accelerator dosage resulted in 33% flow reduction after 60 min compared to 12% for the lower dosage. Bleeding tests demonstrated significant improvement in phase stability, with bleeding rates reduced from 2.5% to 1.5% when accelerator content was doubled. The MPS framework successfully reproduced experimental behavior with prediction accuracies within 3%, enabling quantitative analysis of time-dependent rheological parameters through inverse analysis. The study revealed that yield stress evolution governs both flow characteristics and bleeding resistance, with increases several hundred percent over 60 min while plastic viscosity remained relatively constant. Critically, simulations incorporating time-dependent viscosity changes accurately predicted bleeding behavior, while constant-viscosity models overestimated bleeding rates by 60–130%. The higher accelerator formulation (10 kg/m³) provided an optimal balance between initial workability and long-term stability for typical borehole backfilling operations. This integrated experimental–numerical approach provides practical insights for material optimization in infrastructure reconstruction projects, particularly relevant for aging infrastructure requiring proper foundation treatment. The methodology offers construction practitioners a robust framework for material selection and performance prediction in borehole backfilling applications, contributing to improved construction quality and reduced project risks. Full article

This paper presents a Multimodal Hidden Markov Model (MHMM) framework specifically designed for real-time human proficiency assessment, integrating physiological (Heart Rate Variability (HRV)), behavioral (Task Completion Time (TCT)), and subjective (NASA Task Load Index (NASA-TLX)) data streams to infer latent human proficiency states in industrial settings. Using published empirical data from the surgical training literature, a comprehensive simulation study was conducted, with the MHMM (Trained) achieving 92.5% classification accuracy, significantly outperforming unimodal Hidden Markov Model (HMM) variants 61–63.9% and demonstrating competitive performance with advanced models such as Long Short-Term Memory (LSTM) networks 90%, and Conditional Random Field (CRF) 88.5%. The framework exhibited robustness across stress-test scenarios, including sensor noise, missing data, and imbalanced class distributions. A key advantage of the MHMM over black-box approaches is its interpretability by providing quantifiable transition probabilities that reveal learning rates, forgetting patterns, and contextual influences on proficiency dynamics. The model successfully captures context-dependent effects, including task complexity and cumulative fatigue, through dynamic transition matrices. When demonstrated through simulation, this framework establishes a foundation for developing adaptive operator-AI collaboration systems in Industry 5.0 environments. The MHMM’s combination of high accuracy, robustness, and interpretability makes it a promising candidate for future empirical validation in real-world industrial, healthcare, and training applications in which it is critical to understand and support human proficiency development. Full article