Search Results (486)

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

Historical cross-sectional approaches examining cognitive aging consistently reveal a pattern of steady decline on nonverbal problem-solving, speeded tasks, and maintenance on verbal tasks. However, as measures developed and broadened the factor structure to align with Cattell–Horn–Carroll (CHC) theory, and age ranges were extended from 75 to 90 years, a more nuanced approach to cognitive aging emerged. The present study, using the Wechsler Adult Intelligence Scale, Fifth Edition (WAIS-5), examined the cognitive aging process through a cross-sectional approach. WAIS-5 normative sample data (aligned with the 2022 U.S. census) were obtained from the test publisher. The sample included adult participants aged 20–24 through 85–90 (n = 1660), which were mapped into 11 age groups. Using post-stratification weighting to control for educational attainment, cognitive decline was observed throughout aging; verbal skills were maintained longer than other abilities, while processing speed declined steadily and rapidly from young adulthood to old age. Working memory was vulnerable to the aging process but demonstrated slower patterns of decline than the other vulnerable abilities. Fluid reasoning and visual spatial skills (although aligning with separate CHC broad abilities theoretically) were strikingly similar in their pattern of decline across a person’s lifespan. Results are highly consistent with the large body of cross-sectional research conducted during the previous generation by Salthouse and his colleagues, as well as other teams of researchers. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

Objective: The aim of this study was to determine the relationship between the components of physical fitness (PF), creativity and fluid intelligence, as well as to determine which components of PF are predictors of the analysed cognitive potential. Material and Methods: A total of 584 Spanish schoolchildren (6−11 years old; age = 8.62 ± 1.77 years) took part in this study. Creativity was assessed using the Torrance Tests of Creative Thinking (TTCT) and fluid intelligence through TEA-1. Moreover, PF components were evaluated using a 25 m sprint, handgrip strength, standing long jump and 20 m SRT. Results: Boys exhibited a better PF performance than girls (p range from = < 0.001 to 0.05), as well as higher creativity score (p < 0.001), the fluid intelligence score and QI score (p < 0.05, respectively). Moreover, PF components (CRF, strength and speed) were positively associated with creativity (p range from = < 0.001 to 0.001) and fluid intelligence (p range from = < 0.001 to 0.015). Regression analysis showed that the creativity model explained between 31.4% and 36.6% of the variance (R² = 0.314−0.366, p < 0.001), while the fluid intelligence model accounted for 25.5% to 33.1% of the variance (R² = 0.255−0.331, p < 0.001 to 0.001). Conclusions: A positive relationship was found between creativity, fluid intelligence, and PF components. Children with higher PF levels scored better in creativity, with notable differences between boys and girls. These findings highlight the educational value of incorporating structured physical activity into school settings to support both cognitive and physical development. Full article

As communication technologies continue to evolve, Reconfigurable Intelligent Surfaces (RISs) have become a crucial and highly potential technology for sixth-generation (6G) mobile communication systems. Their key competitive advantages lie in their cost-effectiveness, minimal power consumption, and simple deployment. To address the limitations of current communication paradigms, this study innovatively integrates RIS technology into vehicle-to-vehicle (V2V) communication systems. Current methodologies fail to comprehensively elucidate the transmission principles underlying RIS-assisted V2V fluid antenna system (FAS) communications. The current channel characteristic analysis techniques and modeling theories struggle to achieve a balance between computational accuracy and computational complexity. To overcome these problems, this study systematically constructed a multipath sub-channel model in RIS-assisted V2V communication. Combining detailed simulation with theoretical analysis, a reliable parametric channel statistical model was established. This progress successfully overcame the main obstacle of the traditional RIS channel modeling method, which was unable to coordinate accuracy and efficiency. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

Underwater vehicles serve as critical assets for global ocean exploration and naval capability enhancement. The marine environment exhibits intricate hydrodynamic phenomena that significantly threaten underwater vehicle navigation safety, particularly in four prevalent complex conditions: surface waves, oceanic currents, stratified fluids, and internal waves. This comprehensive review systematically examines the impacts of these four marine environments on underwater vehicles through critical analysis and synthesis of contemporary advances in theoretical frameworks, experimental methodologies, and numerical simulation approaches. The identified influences are categorized into five primary aspects: hydrodynamic characteristics, dynamic response patterns, load distribution mechanisms, navigation trajectory optimization, and stealth performance. Particular emphasis is placed on internal wave interactions, with rigorous analysis derived from experimental investigations and numerical modeling of internal wave dynamics and their coupling effects with underwater vehicles. In addition, this review points out and analyzes the shortcomings of the current research in various aspects and puts forward some thoughts and suggestions for future research directions that are worth further exploration, including enriching the research objects, upgrading the experimental techniques, and introducing artificial intelligence methods. Full article

With the development of intelligent transportation systems, platooning can reduce vehicle aerodynamic drag by decreasing spacing between vehicles, improving transportation efficiency and reducing emissions. However, it is difficult for existing models to enable dynamic adjustment and real-time feedback. Therefore, this study proposes a digital twin system for real-time drag coefficient prediction using stacking ensemble learning. First, 2000 datasets of pressure distributions and drag coefficients under varying spacings were obtained through simulations. Then, an online prediction model for the aerodynamic performance of platooning vehicles was then constructed, realizing real-time drag coefficient prediction, and verifying the model performance using computational fluid dynamics data. The results indicate that the model proposed achieves 98.56% prediction accuracy, significantly higher than that of the traditional BP model (75.78%), and effectively captures the nonlinear relationship between vehicle spacing and drag coefficient. The influence mechanism of vehicle spacing on the aerodynamic performance of platooning vehicles revealed in this study enables high-precision real-time prediction under dynamic parameters. Full article

The control of ventilation systems is often performed by automatic algorithms which often do not consider the future evolution of the system in its control politics. Digital twins allow system forecasting for a more sophisticated control. This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). This predictive model was created to forecast the temperature and humidity evolution of a ventilated room to be implemented in a digital twin for better unsupervised control strategies. To replicate the full range of annual conditions, a series of CFD simulations were configured and executed based on seasonal data collected by sensors positioned inside and outside the room. These simulations generated a dataset used to develop the predictive model, which was based on a Deep Neural Network (DNN) with fully connected layers. The model’s performance was evaluated, yielding final average absolute errors of 0.34 degrees Kelvin for temperature and 2.2 percentage points for relative humidity. The presented results highlight the potential of this methodology to create AI-driven digital twins for the control of room ventilation. Full article

Computational fluid dynamics became an essential tool for analyzing complex fluid behavior, with applications ranging from aerospace engineering to renewable energy systems. Recent advancements in artificial intelligence further enhanced computational fluid dynamics capabilities, improving computational efficiency and predictive accuracy. However, despite its widespread adoption, the integration of artificial intelligence in computational fluid dynamics for thermal energy storage remained an underexplored research area. This study presented a bibliometric analysis of the existing literature on artificial intelligence applications in computational fluid dynamics, with a specific focus on thermal energy storage systems. By comparing two research domains—artificial intelligence in computational fluid dynamics and artificial intelligence in computational fluid dynamics applied to thermal energy storage—this paper identified a significant gap in the latter, as reflected in the low number of publications, limited collaboration networks, and weak citation relationships. While artificial intelligence-driven computational fluid dynamics research expanded across multiple disciplines, its application in thermal energy storage is still in its early stages, highlighting the need for further investigations. The results indicated a growing interest in artificial intelligence-enhanced computational fluid dynamics models for thermal energy storage optimization, particularly in areas such as heat transfer, phase change materials, and system efficiency improvements. The results also included an analysis of leading contributors to this field, along with emerging countries’ contributions. A study of the key publication sources with a high impact in this domain was also included. Full article

Math learning is a key educational goal, and one marked by substantial individual differences even in the earliest grades. Although considerable research has examined the extent to which domain-general processes, such as executive functions and fluid intelligence, contribute to this variability, there is a notable gap in understanding how they may interact to predict early math learning. In particular, prior work had not examined potential moderating effects whereby the relation between executive functions and math outcomes depends on a child’s fluid intelligence, and vice versa. The current study addressed this gap by examining the math skills in Russian first-graders (N = 160) as a function of fluid intelligence (measured with Raven’s matrices) and various components of executive functions. Consistent with prior research, the results revealed the main effects of Raven’s scores, verbal working memory, and the control component of executive function (a composite of inhibition and cognitive flexibility scores) on math growth. Importantly, extending previous research, the study found that both memory and control components of executive function interacted with fluid intelligence. Specifically, executive function had a stronger positive effect on math learning for children with lower levels of fluid intelligence. The implications for intervention research and educational practice are discussed. Full article

Hydrocephalus is a complex neurological condition marked by abnormal cerebrospinal fluid (CSF) accumulation, often leading to elevated intracranial pressure and structural brain damage. Despite advances in surgical treatment, diagnostic precision and prognosis remain challenging, especially in idiopathic normal pressure hydrocephalus (iNPH). This narrative review aims to synthesize the current knowledge regarding molecular and neuroimaging biomarkers that hold diagnostic, prognostic, and therapeutic significance in hydrocephalus. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. The inclusion criteria encompassed peer-reviewed studies involving congenital or acquired hydrocephalus and reporting on mechanistic, diagnostic, or monitoring biomarkers. Both established and emerging biomarkers were included, and preclinical findings were considered when translational relevance was apparent. The review highlights a broad spectrum of molecular markers including aquaporins, vascular endothelial growth factor, neurofilaments, glial fibrillary acidic protein, matrix metalloproteinases, and neuroinflammatory markers. The genetic markers associated with ciliogenesis also show promise in subtyping disease. Parallel to molecular advances, neuroimaging techniques, ranging from classic markers like Evans’ index to advanced modalities such as diffusion tensor imaging (DTI), arterial spin labeling (ASL), and glymphatic MRI, provide functional perspectives on hydrocephalus diagnosis and management, while artificial intelligence may further enhance diagnostic algorithms. Molecular and imaging markers could not only increase diagnostic confidence, but also provide information on disease causes and progression. As research progresses, merging various methodologies may result in more accurate diagnoses. Full article

Diagnosing Alzheimer’s disease (AD) remains a significant challenge due to its multifactorial nature and the limitations of traditional diagnostic methods, such as clinical assessments and neuroimaging, which often lack the specificity and sensitivity required for early detection. The urgent need for innovative diagnostic tools is further underscored by the potential of early intervention to improve treatment outcomes and slow disease progression. Recent advancements in biosensing technologies offer promising solutions for precise and non-invasive AD detection. Electrochemical and optical biosensors, in particular, provide high sensitivity, specificity, and real-time detection capabilities, making them valuable for identifying key biomarkers, including amyloid-β (Aβ) peptides and tau proteins. Additionally, integrating these biosensors with nanomaterials enhances their performance, stability, and detection limits, enabling improved diagnostic accuracy. Beyond nanomaterial-based sensors, emerging innovations in microfluidics, surface plasmon resonance (SPR), and artificial intelligence-assisted biosensing further contribute to the development of next-generation AD diagnostics. This review provides a comprehensive analysis of the latest advancements in biosensing technologies for AD, highlighting their mechanisms, advantages, and future perspectives in detecting biomarkers from biological fluids. Full article

To enhance the oxidative stability of aging diesel fuel stored in nuclear power emergency systems, we propose a novel hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation. Unlike previous studies that address treatment efficiency, flow optimization, or simulation separately, our method achieves real-time, simulation-informed optimization by embedding CFD-based performance evaluation directly into the GA fitness function. The SSN is employed to construct a comprehensive superstructure of feasible conditioning paths, which are dynamically explored and optimized by the GA under flow and boundary constraints. The CFD model, implemented via Ansys Fluent, accurately simulates the antioxidant mixing process in the tank and provides feedback on concentration uniformity at key monitoring points. The results demonstrate that the proposed framework reduces the conditioning time by 5.38% and significantly enhances the additive distribution uniformity. This work offers a generalizable approach for intelligent diesel upgrading in high-reliability energy systems and contributes a structured pathway for integrating data-driven optimization with physical process simulation. Full article

This study was performed based on the previous work of this research group to promote the practical engineering application of trailing edge flaps. Specifically, the established “intelligent blade” simulation platform was used for simulation calculations, bringing about the achievement of a significant load reduction effect in which the standard deviation of the blade root pitching moment decreased by 12.4% under the influence of the trailing edge flap. Then, the dynamic conditions of the wind turbine and trailing edge flap under active control, obtained from the “intelligent blade” simulation platform, were input into CFD for further high-fidelity simulations. Additionally, a simulation method that allows for the real-time observation of the flow field was optimized with CFD as a flow field visualizer. This approach assisted in analyzing how the trailing edge flap affects the flow characteristics around the blade. The results reveal that the deflection of the trailing edge flap generated new vortex structures. These new vortex structures interacted with the pre-existing vortex structures. Moreover, the vortex structures produced by flap deflection supplemented the energy dissipation caused by flow separation on the leeward surface of the blade, contributing to the weakening of flow separation on the leeward side of the blade, affecting the pressure exerted by the fluid on the blade surface, and ultimately lowering the blade’s load. Full article