Independent Tasks Research Articles

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

ReShader: View-Dependent Highlights for Single Image View-Synthesis

In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes.

IIM: an information interaction mechanism for aspect-based sentiment analysis

ABSTRACT Term polarity co-extraction is an aspect-based sentiment analysis task, which has been widely used in the fields of user opinions extraction. It consists of two subtasks: aspect term extraction and aspect sentiment classification. Most existing studies solve aforesaid subtasks as independent tasks or simply unify the two subtasks without making full use of the relationship between tasks to mine the interaction of text information, which leads to low performance for practical applications. Meanwhile, the learning framework for these studies has a label drift phenomenon (LDP) in the process of predictive learning, increasing the learning error rate. To address the above problems, this study unifies subtasks and proposes a Unified framework based on the information interaction mechanism framework, called IIM. Specifically, we design an Information Interaction Channel (IIC) to construct closer semantic features to extract preliminary term-polarity unified labels from the perspective of basic semantics. For label inconsistency between aspect terms, a Position-aware Module (SAM) is proposed to alleviate the Label Drift Phenomenon (LDP). Moreover, we introduce a syntax-attention graph neural network (Syn-AttGCN) to model the syntactic structure of text and strengthen the emotional connection between aspect terms. The experimental results show that IIM outperforms most baselines. Meanwhile, the SAM module has a certain slowing effect on LDP.

Scaling the Inference of Digital Pathology Deep Learning Models Using CPU-Based High-Performance Computing

Digital pathology whole-slide images (WSIs) are large-size gigapixel images and image analysis based on deep learning artificial intelligence (AI) technology often involves pixel-wise testing of a trained deep learning neural network (DLNN) on hundreds of WSI images, which is time consuming. We take advantage of High-Performance Computing (HPC) facilities to parallelize this procedure into multiple independent (and hence delightfully parallel) tasks. However, traditional software parallelization techniques and regular file formats can have significant scaling problems on HPC clusters. In this work, a useful computational strategy is designed to localize and extract relevant patches in WSI files and group them in HDF5 files well suited for parallel I/O. HPC's array job facilities are adapted for hierarchical scaling and parallelization of WSI pre-processing and testing of trained algorithms. Applying these techniques to testing a trained DLNN on the CAMELYON datasets with 399 WSIs reduced the theoretical processing time of 18 years on a single CPU or 30 days on a single GPU to less than 45 hours on an HPC cluster of 4,000 CPU cores. The efficiency-accuracy trade-off we demonstrated on this dataset further reinforced the importance of efficient computation techniques, without which the accuracy may be sacrificed. The framework developed here for testing DLNNs does not rely on any specific neural network architecture and HPC cluster setup and can be utilized for any large-scale image processing and big-data analysis.

Tashmiimu As’ilati at-Taqyiimi Lita’liimi al-Lughati al-Arabiyati a’la Asasi adz-Dzakaati al-Mutaaddidati

This research aims to design evaluation questions for Arabic language learning based on multiple intelligences. The research methodology employed is qualitative descriptive with a literature study approach. Data collection is conducted through documentation by analyzing relevant literature, references, and sources related to Arabic language learning and multiple intelligences theory. The data analysis results indicate that for linguistic intelligence, independent tasks can be used as the form of evaluation questions. In the context of kinesthetic intelligence, project-based tasks such as Arabic language dramas can be utilized. For musical intelligence, students can be asked to create children's songs using Arabic vocabulary. Regarding intrapersonal intelligence, group work methods such as the jigsaw method can be employed as the form of evaluation questions. In conclusion, by considering multiple intelligences in evaluating Arabic language learning, teachers can provide equal opportunities for all students to showcase their intelligence in Arabic language acquisition.

Optimal tasks assignment policy in multi-task multi-attempt missions

Motivated by real-world applications like unmanned reconnaissance aerial vehicles, this paper considers a multi-task multi-attempt mission system, where each task may be attempted multiple times and each attempt may be performed simultaneously by multiple components to enhance the task completion probability. Such an active redundancy, on the other hand, incurs high cost and high risk associated with the failures of the components. To balance the reward and the risk, this paper formulates and solves a new optimization problem, which determines the number of components performing each uncompleted task in each attempt, referred to as the task assignment policy (TAP), minimizing the expected mission cost (EMC). A recursive algorithm is proposed to evaluate the EMC (aggregating the expected operational cost, cost of lost components, and cost associated with uncompleted tasks) for the considered multi-task multi-attempt system under a given TAP. Based on the suggested EMC evaluation algorithm, the genetic algorithm is implemented to solve the optimal TAP problem. A detailed case study of an unmanned reconnaissance aerial vehicle system performing five independent surveillance tasks is conducted to examine the impacts of several model parameters on the EMC, task successful completion probabilities, and the optimal TAP solutions.

Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

SummaryA difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).

Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation

Multimodal information (e.g., visual, acoustic, and textual) has been widely used to enhance representation learning for micro-video recommendation. For integrating multimodal information into a joint representation of micro-video, multimodal fusion plays a vital role in the existing micro-video recommendation approaches. However, the static multimodal fusion used in previous studies is insufficient to model the various relationships among multimodal information of different micro-videos. In this article, we develop a novel meta-learning-based multimodal fusion framework called Meta Multimodal Fusion (MetaMMF), which dynamically assigns parameters to the multimodal fusion function for each micro-video during its representation learning. Specifically, MetaMMF regards the multimodal fusion of each micro-video as an independent task. Based on the meta information extracted from the multimodal features of the input task, MetaMMF parameterizes a neural network as the item-specific fusion function via a meta learner. We perform extensive experiments on three benchmark datasets, demonstrating the significant improvements over several state-of-the-art multimodal recommendation models, like MMGCN, LATTICE, and InvRL. Furthermore, we lighten our model by adopting canonical polyadic decomposition to improve the training efficiency, and validate its effectiveness through experimental results. Codes are available at https://github.com/hanliu95/MetaMMF .

71 Examining the Psychometric Validity of NeuroScreen to Assess Neurocognition in Hospitalized Psychosis Patients in Uganda

Objective:People with psychotic disorders often experience neurocognitive deficits, such as neurocognitive impairment (NCI), which can negatively affect their daily activities (e.g., performing independent tasks) and recovery. Because of this, the American Psychology Association advocates integrating neurocognitive testing into routine care for people living with psychotic disorders, especially those in their first episode, to inform treatment and improve clinical outcomes. However, in low-and-middle income countries (LMICs), such as Uganda where the current study took place, administering neurocognitive tests in healthcare settings presents numerous challenges. In Uganda there are few resources (e.g., trained clinical staff, and culturally relevant and normed tests) to routinely offer testing in healthcare settings. NeuroScreen is a brief, highly automated, tablet-based neurocognitive testing tool that can be administered by all levels of healthcare staff and has been translated into indigenous Ugandan languages. To examine the psychometric properties of NeuroScreen, we measured convergent and criterion validity of the NeuroScreen tests by comparing performance on them to performance on a traditional battery of neurocognitive tests widely used to assess neurocognition in people with psychotic disorders, the Matric Consensus Cognitive Battery (MCCB).Participants and Methods:Sixty-five patients admitted into Butabika Mental Referral Hospital in Uganda after experiencing a psychotic episode and forty-seven demographically similar control participants completed two neurocognitive test batteries: the MCCB and NeuroScreen. Both batteries include tests measuring the neurocognitive domains of executive functioning, working memory, verbal learning, and processing speed. Prior to completing each battery, patients were medically stabilized and could not exhibit any positive symptoms on the day of testing. On the day of testing, medication dosages were scheduled so that patients would not experience sedative effects while testing. To examine convergent validity, we examined correlations between overall performance on NeuroScreen and the MCCB, as well as tests that measured the same neurocognitive domains. To examine criterion validity, an ROC curve was computed to examine the sensitivity and specificity of NeuroScreen to detect NCI as defined by the MCCB.Results:There was a large correlation between overall performance on NeuroScreen and the MCCB battery of tests, r(110) = .65, p < .001. Correlations of various strengths were found among tests measuring the same neurocognitive domains in each battery: executive functioning [r(110) = .56 p <.001], processing speed [r(110) = .44, p <.001)], working memory [r(110) = .29, p<.01], and verbal learning [r(110) = .22, p < .01]. ROC analysis of the ability of NeuroScreen to detect MCCB defined NCI showed an area under curve of .798 and optimal sensitivity and specificity of 83% and 60%, respectively.Conclusions:Overall test performance between the NeuroScreen and MCCB test batteries was similar in this sample of Ugandans with and without a psychotic disorder, with the strongest correlations in tests of executive functioning and processing speed. ROC analysis provided criterion validity evidence of NeuroScreen to detect MCCB defined NCI. These results provide support for use of NeuroScreen to assess neurocognitive functioning among patients with psychotic disorders in Uganda, however more work needs to be to determine how well it can be implemented in this setting. Future directions include assessing cultural acceptability of NeuroScreen and generating normative data from a larger population of Ugandan test-takers.

CrowdDA: Difficulty-aware crowdsourcing task optimization for cleaning web tables

Web tables are rich sources of structured data for collecting and analyzing data, but there exist various data quality problems such as missing or inconsistent values. Crowdsourcing provides a new solution that leverages the human cognitive ability to clean tables, but existing crowdsourcing-based solutions suffer from cleaning quality. For most crowd are not experts, the difficulty degree of cleaning tasks will seriously affect the cleaning result. To help people clean web tables effectively and efficiently, it is important to reduce the overall difficulty of tasks. In this paper, we introduce a difficulty-aware crowdsourcing task optimization system CrowdDA, which aims to recommend the best task execution order from easy to difficult for crowd and support various kinds of cleaning tasks for web tables. CrowdDA takes both latency and space constraints into account for task optimization and generates the task execution order that minimizes the overall difficulty of tasks under two constraints. Furthermore, CrowdDA adopts partition strategies for large tables to improve system efficiency, and introduces independent task sequence to tolerate crowd’s inconsistent answers for system robustness. The experiments based on the real-world datasets demonstrate the performance superiority of CrowdDA for improving the cleaning quality of web tables.

An efficient multi-scale learning method for image super-resolution networks

The image super-resolution (SR) operation holds multiple solutions with the one-to-many mapping from low-resolution (LR) to high-resolution (HR) space. However, the SR of different scales for the same image is usually regarded as independent tasks in the existing SR networks. Therefore, these networks are inflexible to effectively utilize feature learning experience and require much more computing time to recover HR images in higher resolutions. Recent arbitrary scale SR methods still cannot solve these problems. To efficiently and effectively recover HR images, this paper presents an efficient multi-scale learning method for image SR networks based on a novel self-generating (SG) mechanism. This method (briefly named SG-SR) utilizes the feature learning results of SR networks to generate upscale filters by using the novel SG upscale module, which is proposed to replace the traditional upscale module. For each scale factor, the SG upscale module provides the corresponding amount of the spatial weights to filter the LR tensor and then converts filtered tensors with the original tensor to corresponding HR images. The proposed method is evaluated through extensive experiments and compared with state-of-the-art (SOTA) methods on widely used benchmark datasets. The experimental results show that our method has superior performance compared with SOTA methods, and the SG upscale module can improve the performance of existing SR networks effectively. What is more, our module has a much less calculation cost than the other upscale modules.

Bidynamical all-optical reservoir computing for parallel task processing.

A bidynamical all-optical reservoir computing (RC) system for parallel task processing is proposed based on a unidirectional semiconductor optical amplifier optical fiber loop. The polarization dynamics and intensity dynamics are excited by the input signals injected into the reservoir via phase modulation and intensity modulation, respectively. Simultaneous computation of two independent tasks is implemented based on the dynamical responses in polarization and intensity of the optical fiber loop. To our knowledge, this is the first time that two kinds of dynamical responses of an all-optical RC system are used as independent task processing channels to implement parallel task processing. The proposed RC system can achieve good parallel task processing performance with low system cost.

Scheduling independent tasks on multiple cloud-assisted edge servers with energy constraint

In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first paper in the literature which optimizes the makespan of task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers.

Evaluating machine learning architectures for sound event detection for signals with variable signal-to-noise-ratios in the Beaufort Sea.

This paper explores the challenging polyphonic sound event detection problem using machine learning architectures applied to data recorded in the Beaufort Sea during the Canada Basin Acoustic Propagation Experiment. Four candidate architectures were investigated and evaluated on nine classes of signals broadcast from moored sources that were recorded on a vertical line array of hydrophones over the course of the yearlong experiment. These signals represent a high degree of variability with respect to time-frequency characteristics, changes in signal-to-noise ratio (SNR) associated with varying signal levels as well as fluctuating ambient sound levels, and variable distributions, which resulted in class imbalances. Within this context, binary relevance, which decomposes the multi-label learning task into a number of independent binary learning tasks, was examined as an alternative to the conventional multi-label classification (MLC) approach. Binary relevance has several advantages, including flexible, lightweight model configurations that support faster model inference. In the experiments presented, binary relevance outperformed conventional MLC approach on classes with the most imbalance and lowest SNR. A deeper investigation of model performance as a function of SNR showed that binary relevance significantly improved recall within the low SNR range for all classes studied.

A Bayesian Approach to Risk-Based Autonomy, with Applications to Contact-Based Drone Inspections

Enabling higher levels of autonomy while ensuring safety requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A simulation case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions.

Errorful learning improves recognition memory for new vocabulary for people living with memory and dysexecutive impairment following brain injury

ABSTRACT A widely accepted view is that errorless learning is essential for supporting new learning in people with anterograde amnesia, but findings are mixed for those with a broader range of memory impairments. People at a chronic stage of recovery from brain injury (BI) with impaired memory and executive function (N = 26) were compared with adults in a comparison group without any known risks to brain function (N = 25). Learning techniques were compared using a “Generate-and-correct” and “Read-only” condition when learning novel word pairs. At test, both groups scored above chance and showed benefits of Generate-and-correct (errorful learning). Poor learners in the BI group were classified from “flat” learning slopes extracted from an independent word-pair learning task. Critically, poor learners showed no benefit, but also no decrement to learning, using the Generate-and-correct method. No group was harmed by errorful learning; all, except the poorest learners, benefitted from errorful learning. This study indicates, that in some rehabilitation settings, encouraging clients to guess the meaning of unfamiliar material (e.g., from cards, magazines, newspapers) and then correct their errors, could have benefits for recognition memory. Determining when and how errorful learning benefits learning is a key aim for future research.

Study on the Effect of Personality Type on the Language Learning Strategies of Non-English Major Students Through MBTI Test

The learner's personality is one of the presumed internal variables influencing the success of foreign language learning. The globalized period has altered the landscape of English language teaching (ELT) along with demographic, geographic, and structural changes to English, and it has highlighted the significance of preparing language learners for cross-cultural communication. This study aimed to investigate the effect of personality type on the language learning strategies of non-English major students using the Myers-Briggs Type Indicator (MBTI) test. The reliability of a questionnaire's questions about independent, dependent, and moderating variables serves as a foundation for its adaptation. Out of 300 questionnaires, 250 were considered for statistical analysis because 50 questionnaires were not filled. Data were analyzed with a help of statistical software named Smart PLS 3 (SEM). Empirical tests of our working hypothesis were carried out using a Structural Equation Model (SEM) with Partial Least Square (PLS). The results of the study indicated that there were significant differences in language learning strategies based on the participants' personality types. Extroverted students tended to prefer group work and interactive activities, while introverted students preferred independent study and individual tasks. Additionally, sensing students tended to focus on the details and practical applications of language learning, while intuitive students tended to focus on the big picture and abstract concepts. These findings have important implications for language educators and suggest that tailoring language learning strategies to the individual needs and preferences of students based on their personality type may lead to more effective language learning outcomes.

BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets

Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein–protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx’s robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.

Comparative Study for Path Planning Based on the Decomposition of a Co-Safe LTL Specification

Abstract This paper is presenting a comparison between two different methods for decomposing a co-safe LTL global specification that must be accomplished by a team of mobile agents by combining our previous work. Both decomposition algorithms are aiming to break the global mission into independent tasks such that synchronizations between members of the robotic team are not required and the sub-formulas obtained can be accomplished by a single agent. Once the split of the LTL formula is performed, trajectories are projected based on an allocation of tasks to robots, which requires two steps. First time, a cost matrix is computed, followed by the calculation of a binary allocation matrix. The latest will dictate which task, or tasks, will be executed by each mobile robot. The comparison is taking into consideration the variation of the size of the robotic team and the complexity of the global LTL mission, while proposed scenarios and simulations are highlighting the advantages and disadvantages of each decomposition method. The quantitative analysis is involving two criteria: a time related one and a trajectory related one.

Вплив чинників навчання та режиму дня на здоров’я школярів: результати наукових досліджень лабораторії соціальних детермінант здоров’я дітей за 2017-2022 роки

The aim of the study was to examine the effects of learning and behavioural factors on the health and adaptability of school-aged children. Methods. The article presents the research results on the influence of behavioural and learning factors on the health of school-aged children based on the findings of three scientific research projects in the Laboratory of Social Determinants of Children's Health during 2017-2022. QAPACE, Q-RAPH, GPAQ, and RCADS-P-25 questionnaires were used for data collection. Physical and mental parameters were measured to evaluate health, computed comprehensive indicators, and analyzed behavioural factors. Statistical analysis was performed using STATISTICA 8.0 and IBM SPSS STATISTICS 26. Results. Significant differences were found in the duration of both total and different types of physical activity (PA), sedentary behaviour, sleep duration and quality in groups of children with different levels of health before and during the COVID-19 pandemic. Moderate-to-vigorous PA, about 60 min/day, the most maintained a high level of children's health, confirming other scientists' findings. Optimal levels of daily and weekly different PA were calculated for school-age children by age group and gender under ordinary living conditions and quarantine measures. Our research has shown how children adapt to distance learning varies significantly depending on the format used. Synchronous e-learning was the most beneficial and recommended approach for promoting the well-being of school-age children, whereas assigning independent work tasks through messaging platforms was less favourable. Conclusions. A significant influence of the format of distance learning and daily routine factors on the health and adaptation of school-age children in everyday living and during quarantine measures was established. We found that disrupting children's daily routines and excessive sedentary behaviour can interfere with adaptation and increase the potential for maladaptive responses to stress. Therefore, following physical activity guidelines for school-aged children based on their physiological and psychological characteristics is crucial to promote overall health and well-being.