Task Execution Research Articles

Actigraphy-assessed sleep duration and quality and executive function in a sample of typically developing preschoolers.

Sleep is essential for cognitive development and brain maturation during early childhood. Studies focused on preschool populations using objective measures of sleep are still scarce. The objective of this study was to examine the associations between objective measures of sleep duration and quality with executive functioning in a 133 non-clinical sample of Spanish participants (Mage = 60.33 months, SD = 9.04; ages 41-77 months; 51.1% girls). Sleep was assessed for five weeknights using actigraphy; Shape School task was applied to assess inhibition and cognitive flexibility; and Word Span task was used to assess working memory. The results revealed that relation between sleep and executive functioning was significant for inhibition and working memory. Preschoolers with higher sleep efficiency showed better results in executive functioning tasks after controlling for the effect of age. Additionally, age and sleep duration and quality variables were predictive of inhibition, working memory, and cognitive flexibility. These results suggest that the different dimensions of sleep could play an important role in the development of executive functions during preschool age. Therefore, improving sleep could lead to an improvement in children's executive functioning in both clinical and educational contexts.

Formation Control of a Multi-Unmanned Surface Vessel System: A Bibliometric Analysis

This study provides an overview of the literature on multi-unmanned surface vessel (multi-USV) systems, addressing the increasing attention on formation control of USVs due to their enhanced task execution ability, efficiency, and robustness in complex marine environments. Despite numerous studies on USVs covering fields, such as autonomous decision making, motion control, perception, and communication technologies, there is a significant lack of systematic literature review and bibliometric analysis specifically focused on a multi-USV system. This study aims to summarize advancements in multi-USV research, highlighting key aspects, including publication trends, influential scholars and papers, research hotspots, challenges, and future opportunities. By reviewing the current state of multi-USV research, this study contributes to the field as a beneficial reference for researchers, practitioners, and policymakers. It will not only highlight the progress made so far but also shed light on the gap that needs to be addressed to advance the field.

Tracking Vigilance Fluctuations in Real-Time: A Sliding-Window HRV-based Machine-Learning Approach.

Heart rate variability (HRV)-based machine learning models hold promise for real-world vigilance evaluation, yet their real-time applicability is limited by lengthy feature extraction times and reliance on subjective benchmarks. This study aimed to improve the objectivity and efficiency of HRV-based vigilance evaluation by associating HRV and behavior metrics through a sliding-window approach. Forty-four healthy adults underwent psychomotor vigilance tasks under both well-rested and sleep-deprived conditions, with simultaneous electrocardiogram recording. A sliding-window approach (30s length, 10s step) was used for HRV feature extraction and behavior assessment. Repeated-measures ANOVA was used to examine how HRV related to objective vigilance levels. Stability selection technique was applied for feature selection, and the vigilance ground truth-high (fastest 40%), intermediate (middle 20%), and low (slowest 40%)-were determined based on each participant's range of performance. Four machine-learning classifiers-k-nearest neighbours, support vector machine (SVM), AdaBoost, and random forest-were trained and tested using cross-validation. Fluctuated vigilance performance indicated pronounced state instability, particularly after sleep deprivation. Temporary decrements in performance were associated with a decrease in heart rate and an increase in time-domain heart rate variability. SVM achieved the best performance, with a cross-validated accuracy of 89% for binary classification of high versus low vigilance epochs. Overall accuracy dropped to 72% for three-class classification in leave-one-participant-out cross-validation, but SVM maintained a precision of 84% in identifying low-vigilance epochs. Sliding-window-based HRV metrics would effectively capture the fluctuations in vigilance during task execution, enabling more timely and accurate detection of performance decrement.

MODERN PRINCIPLES AND MEANS OF AUTOMATION IN ACCOUNTING, MANAGEMENT, AND MARKETING

Automation of auxiliary business processes (specifically accounting, management, and marketing) has long ceased to be an experiment and has become necessary. This necessity is a direct result of the impact of using automated devices and technical means, as well as computing technology based on mathematical methods, on the speed of decision-making and task execution, labor cost levels, resource utilization efficiency, the number of errors, and the quality of products or services. The article outlines the modern principles and tools of automation in accounting, management, and marketing. The research highlights important aspects of business process automation and identifies some key points. Based on the results obtained, a deep analysis of the interaction between humans and machines in automated accounting, management, and marketing systems can be conducted.

Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems

Emerging assistance systems are designed to enable operators to perform tasks better, faster, and with a lower workload. However, in line with the productivity paradox, the full potential of automation and digitalisation is not being realised. One reason for this is insufficient training. In this study, the statistically significant differences among three different training scenarios on performance, acceptance, workload, and technostress during the execution of immersive measurement tasks are demonstrated. A between-subjects design was applied and analysed using ANOVAs involving 52 participants (with a statistical overall power of 0.92). The ANOVAs were related to three levels of the independent variable: quality training, manipulated as minimal, personal, and optimised training. The results show that the quality of training significantly influences immersive assistance systems. Hence, this article deduces tangible design guidelines for training, with consideration of the system-level hardware, operational system, and immersive application. Surprisingly, an appropriate mix of training approaches, rather than detailed, personalised training, appears to be more effective than e-learning or ‘getting started’ tools for immersive systems. In contrast to most studies in the related work, our article is not about learning with AR applications but about training scenarios for the use of immersive systems.

A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model’s execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

Narrative production and executive functions in post-stroke agrammatic aphasia

ABSTRACT Background Narrative production has been widely characterized as providing an ecologically valid way to assess language skills in post-stroke aphasia. Although narrative tasks have been instrumental in delineating patterns of lexical and syntactic production in individuals with agrammatic aphasia, our knowledge of how narrative skills are affected in agrammatic aphasia is still limited. Aims The study’s aims were to (a) compare narrative performance between individuals with agrammatic aphasia and a language-unimpaired group, (b) investigate the contribution of lexical and syntactic skills in each group’s narrative organizational skills, and (c) explore the effects of executive functions on each group’s narrative performance. Methods & Procedures The study included 14 individuals with agrammatic aphasia and 14 age- and education-matched language unimpaired individuals as the control group. Both groups told the Cinderella story, and their narrative production was analyzed in terms of microstructure (lexical diversity, syntactic complexity, clause length) and macrostructure (story grammar, story structure complexity). Each group’s executive functions were evaluated through the One-Touch Stockings of Cambridge test that assesses working memory, and the Intra-Extra Dimensional Set Shift test that assesses cognitive flexibility. Outcomes & Results Regarding narrative microstructure, the individuals with agrammatism scored significantly lower than controls in syntactic complexity and clause length, but not in lexical diversity, but they performed lower than controls in both narrative macrostructural measurements. Also, the agrammatic group scored lower than controls in both executive function tasks. A series of linear regression models showed that microstructural skills significantly affected narrative macrostructural abilities in the cohort with agrammatic aphasia, while controls’ macrostructure was affected by both narrative microstructure and executive function skills. Conclusions The individuals with agrammatic aphasia exhibited impairment in both low-level language features of narrative production, such as syntactic complexity and narrative length, and global measures of narrative organization. Their macrostructural performance critically relied on their language resources, while controls’ narrative macrostructure seemed to also draw on domain-general cognitive skills. These findings suggest that both low-level language and high-level discourse organizational skills are vulnerable in narrative production in agrammatic aphasia, and that macrostructural skills are mainly related to the individuals’ microstructural skills.

A lexicographic optimization-based approach for efficient task allocation in industrial transportation multi-robot systems

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects.

From microscope to head-mounted display: integrating hand tracking into microsurgical augmented reality.

The operating microscope plays a central role in middle and inner ear procedures that involve working within tightly confined spaces under limited exposure. Augmented reality (AR) may improve surgical guidance by combining preoperative computed tomography (CT) imaging that can provide precise anatomical information, with intraoperative microscope video feed. With current technology, the operator must manually interact with the AR interface using a computer. The latter poses a disruption in the surgical flow and is suboptimal for maintaining the sterility of the operating environment. The purpose of this study was to implement and evaluate free-hand interaction concepts leveraging hand tracking and gesture recognition as an attempt to reduce the disruption during surgery and improve human-computer interaction. An electromagnetically tracked surgical microscope was calibrated using a custom 3D printed calibration board. This allowed the augmentation of the microscope feed with segmented preoperative CT-derived virtual models. Ultraleap's Leap Motion Controller 2 was coupled to the microscope and used to implement hand-tracking capabilities. End-user feedback was gathered from a surgeon during development. Finally, users were asked to complete tasks that involved interacting with the virtual models, aligning them to physical targets, and adjusting the AR visualization. Following observations and user feedback, we upgraded the functionalities of the hand interaction system. User feedback showed the users' preference for the new interaction concepts that provided minimal disruption of the surgical workflow and more intuitive interaction with the virtual content. We integrated hand interaction concepts, typically used with head-mounted displays (HMDs), into a surgical stereo microscope system intended for AR in otologic microsurgery. The concepts presented in this study demonstrated a more favorable approach to human-computer interaction in a surgical context. They hold potential for a more efficient execution of surgical tasks under microscopic AR guidance.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle

Autonomous execution of tasks by unmanned aerial vehicles (UAVs) relies heavily on object detection. However, object detection in most images presents challenges such as complex backgrounds, small targets, and obstructions. Additionally, the limited computing speed and memory of the UAV processor affects the accuracy of conventional object detection algorithms. This paper proposes LUD-You Only Look Once (YOLO), a small and lightweight object detection algorithm for UAVs based on YOLOv8. The proposed algorithm introduces a new multiscale feature fusion mode that solves the degradation in feature propagation and interaction through the introduction of upsampling in the feature pyramid network and the progressive feature pyramid network. The application of the dynamic sparse attention mechanism in the Cf2 module achieves flexible computing allocation and content awareness. Furthermore, the proposed model is optimized to be sparse and lightweight, making it possible to deploy on UAV edge devices. Finally, the effectiveness and superiority of LUD-YOLO were verified on the VisDrone2019 and UAVDT datasets. The results of ablation and comparison experiments show that compared with the original algorithm, LUDY-N and LUDY-S have shown excellent performance in various evaluation indexes, indicating that the proposed improvement strategies make the model have better robustness and generalization. Moreover, compared with multiple other popular competitors, the proposed improvement strategies enable LUD-YOLO to have the best overall performance, providing an effective solution for UAVs object detection while balancing model size and detection accuracy.

PERFORMANCE ANALYSIS OF TASK SCHEDULING USING HYBRID GENETIC MODIFIED WHALE OPTIMIZATION ALGORITHM IN CLOUD COMPUTING

Cloud computing plays a vital role, which is used to access computing resources and information online. There are a lot of challenges in accessing cloud computing systems. One of the major challenges among these is resource Management which includes scheduling, allocation, and sharing. In this paper, the Hybrid Genetic Modified Whale optimization algorithm which is a combined Genetic and Modified Whale optimization algorithm to analyze the performance of the cloud computing system such as task completion time, execution cost, speedup, and efficiency with proper allocation and sharing of resources The performance of the proposed algorithm is compared with Genetic algorithm and Whale optimization algorithm. The main target of this Proposed system is to reduce the completion time of the task by increasing the speed. Cloud Sim environment tool kit is used for the testing of the proposed system.

PERFORMANCE ANALYSIS OF TASK SCHEDULING USING HYBRID GENETIC MODIFIED WHALE OPTIMIZATION ALGORITHM IN CLOUD COMPUTING

Cloud computing plays a vital role, which is used to access computing resources and information online. There are a lot of challenges in accessing cloud computing systems. One of the major challenges among these is resource Management which includes scheduling, allocation, and sharing. In this paper, the Hybrid Genetic Modified Whale optimization algorithm which is a combined Genetic and Modified Whale optimization algorithm to analyze the performance of the cloud computing system such as task completion time, execution cost, speedup, and efficiency with proper allocation and sharing of resources The performance of the proposed algorithm is compared with Genetic algorithm and Whale optimization algorithm. The main target of this Proposed system is to reduce the completion time of the task by increasing the speed. Cloud Sim environment tool kit is used for the testing of the proposed system.

Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

Frontal brain volume correlates of impaired executive function in schizophrenia

Cognitive impairments affect functional capacity in individuals with schizophrenia (SZH), but their neural basis remains unclear. The Wisconsin Card Sorting Test (WCST), and the Stroop Task (SCWT), are paradigmatic tests which have been used extensively for examining executive function in SZH. However, few studies have explored how deficits on these tasks link to brain volume differences commonly seen in SZH. Here, for the first time, we tested associations between FreeSurfer-derived frontal brain volumes and performance on both WCST and SCWT, in a well-matched sample of 57 SZH and 32 control subjects. We also explored whether these associations were dissociable from links to symptom severity in SZH. Results revealed correlations between volumes and task performance which were unique to SZH. In SZH only, volumes of right middle frontal regions correlated with both WCST and Stroop performance: correlation coefficients were significantly different to those present in the control group, highlighting their specificity to the patient group. In the Stroop task, superior frontal regions also showed associations with Stroop interference scores which were unique to SZH. These findings provide important detail around how deficits on these two paradigmatic executive function tasks link to brain structural differences in SZH. Results align with converging evidence suggesting that neuropathology within right middle frontal regions (BA9 and BA46) might be of particular import in SZH. No volumetric associations with symptom severity were found, supporting the notion that the structural abnormalities underpinning cognitive deficits in SZH differ from those associated with symptomatology.

Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation

Abstract Corn, a crucial food crop in Indonesia, faces significant challenges from pests such as Spodoptera spp., impacting agricultural productivity and industrial applications. This study aimed to study the diversity and attack percentage of Spodoptera spp. on corn plants, along with the employed control techniques. The research was conducted using a survey method through field observation and farmer interviews. The insect sampling was carried out by purposive sampling, while the sampling of plants using the diagonal sampling method was done in three areas that were representative of the West Java province, Indonesia (Sumedang Regency, Cirebon Regency, and Bandung Regency). The results showed that Spodoptera frugiperda or fall armyworm has emerged as a dominant pest in all three areas. The diversity index (H′) of S. frugiperda across the Cirebon Regency, Bandung Regency, and Sumedang Regency was consistently low (<1). Varying infestation rates across districts, with the Cirebon Regency showing lower rates and Bandung and Sumedang Regencies exhibiting medium-level infestation. Farmers in Bandung and Sumedang Regencies are adopting a more diverse pest management strategy, including the regular monitoring and manual collection of larvae. In addition, the survey shows a positive relationship between the knowledge, skills, and attitude of farmers, suggesting that acquiring more information and understanding agricultural techniques contribute to effective task execution and foster positive mindsets. This comprehensive investigation sheds light on the regional variations in Spodoptera spp. dynamics, providing valuable insights for sustainable pest management in the corn cultivation of the West Java Province, Indonesia.

Catalyzing industrial evolution: A dynamic maintenance framework for maintenance 4.0 optimization

The concept of Maintenance 4.0 represents a transformative shift in industrial maintenance practices, leveraging cutting-edge technologies within the broader framework of Industry 4.0. This paradigm aims to revolutionize the planning, execution, and monitoring of maintenance tasks by integrating artificial intelligence, machine learning, big data analytics, the Internet of Things (IoT), and predictive analytics. The resulting holistic approach enhances equipment longevity, minimizes downtime, optimizes maintenance schedules, and improves overall process efficiency and cost-effectiveness for organizations.This study focuses on developing a novel dynamic maintenance framework that expands upon the traditional moving horizon approach. The primary objective is to categorize individual proactive maintenance (PM) activities within the planning horizon into PM groups, thereby establishing a fixed system maintenance schedule. This innovation not only improves practical implementation in the industry but also contributes to theoretical advancements.The distinctive advantage of this methodology lies in its capacity to dynamically revise upkeep data, significantly reducing the likelihood of operational failure. By bridging the gap between theory and application, this research contributes to the ongoing evolution of Maintenance 4.0, offering a more proactive, intelligent, and data-driven approach to industrial maintenance.

The neurobehavioural effects of cannabidiol in alcohol use disorder: Study protocol for a double-blind, randomised, cross over, placebo-controlled trial

Current treatments for alcohol use disorders (AUD) have limited efficacy. Recently, Cannabidiol (CBD) has been examined in a multitude of clinical settings. Preclinical and clinical results suggest that CBD might be particularly well suited for the treatment of AUD and may reduce alcohol cue and stress-induced craving and alcohol seeking. This study aims to investigate this new pharmacotherapy with a particular focus on neurobiological and physiological indicators of craving. Methods: In this double-blind, within-subject, randomised, placebo-controlled, cross-over study, non-treatment seekers will be randomly allocated to three days of four 200 mg CBD gel capsules (800 mg/day) or placebo, with an 18-day washout period. Cognitive, clinical, and neuroimaging assessments will be completed during these three days. The CBD and placebo assessments will be compared. The primary outcomes are i) BOLD signal as a proxy for regional activity during a cue reactivity and a fear response task measured with functional magnetic resonance imaging (fMRI), ii) heart rate variability and skin conductance levels as a proxy for psychophysiological responses to alcohol stimuli. The secondary outcomes are: i) neurometabolite levels (γ-Aminobutyric acid, ethanol, glutathione, and glutamate + glutamine (combined signal)) using magnetic resonance spectroscopy (MRS); ii) functional connectivity using resting state fMRI (rsfMRI); iii) executive functioning task results; iv) clinical outcomes such as craving, anxiety, and sleep. Discussion: This study will improve the understanding of the mechanisms of action of CBD and provide early signals of efficacy regarding the therapeutic potential of CBD in the treatment of alcohol use disorder.ClinicalTrials.gov Identifier: NCT05387148.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Altered brain function during movement programming is linked with motor deficits after stroke: a high temporal resolution study.

Stroke leads to motor deficits, requiring rehabilitation therapy that targets mechanisms underlying movement generation. Cortical activity during the planning and execution of motor tasks can be studied using EEG, particularly via the Event Related Desynchronization (ERD). ERD is altered by stroke in a manner that varies with extent of motor deficits. Despite this consensus in the literature, defining precisely the temporality of these alterations during movement preparation and performance may be helpful to better understand motor system pathophysiology and might also inform development of novel therapies that benefit from temporal resolution. Patients with chronic hemiparetic post-stroke (n = 27; age 59 ± 14 years) and age-matched healthy right-handed control subjects (n = 23; 59 ± 12 years) were included. They performed a shoulder rotation task following the onset of a stimulus. Cortical activity was recorded using a 256-electrode EEG cap. ERD was calculated in the beta frequency band (15-30 Hz) in ipsilesional sensorimotor cortex, contralateral to movement. The ERD was compared over time between stroke and control subjects using permutation tests. The correlation between upper extremity motor deficits (assessed by the Fugl-Meyer scale) and ERD over time was studied in stroke patients using Spearman and permutation tests. Patients with stroke showed on average less beta ERD amplitude than control subjects in the time window of -350 to 50 ms relative to movement onset (t(46) = 2.8, p = 0.007, Cohen's d = 0.31, 95% CI [0.22: 1.40]). Beta-ERD values correlated negatively with the Fugl-Meyer score during the time window -200 to 400 ms relative to movement onset (Spearman's r = -0.54, p = 0.003, 95% CI [-0.77 to -0.18]). Our results provide new insights into the precise temporal changes of ERD after hemiparetic stroke and the associations they have with motor deficits. After stroke, the average amplitude of cortical activity is reduced as compared to age-matched controls, and the extent of this decrease is correlated with the severity of motor deficits; both were true during motor programming and during motor performance. Understanding how stroke affects the temporal dynamics of cortical preparation and execution of movement paves the way for more precise restorative therapies. Studying the temporal dynamics of the EEG also strengthens the promising interest of ERD as a biomarker of post-stroke motor function.