Accomplishment Of Tasks Research Articles

Applied the augmented reality technology combined with social stories strategies and computational thinking games to improve the social skills of children with ASD

ABSTRACT Autism Spectrum Disorder (ASD) involves social skills deficits, hindering effective communication and meaningful relationships. Prior research indicates that Augmented Reality (AR) games appeal to children with ASD for learning social skills but lack a structured framework and clear instructions for grasping social concepts. Therefore, this research aims to develop AR game content with a social story context and computational thinking specifically tailored for specific social situations. The goal is to assist children with ASD in mastering the developmental context and social structure of complex social situations through sequential and staged game activities. This helps children with ASD develop social skills and reciprocal behaviors. Four high-functioning children with ASD, aged 6 -9, participated in the experiment. Their behavioral performance during game interactions, learning, and social task accomplishments was observed using a multiple-baseline design across subjects. After three stages of testing and assessments, the results showed that incorporating AR effectively piqued the interest of children with ASD in learning. In particular, combining social stories and computational thinking game elements as instructional strategies helped children with ASD understand social tasks and contextual sequences. These intervention strategies also indirectly manifested in changes in their behavioral abilities and improvements in social reciprocity skills.

Daily strengths use and work performance: A self‐determination perspective

AbstractDrawing on self‐determination theory, this study examines how using personal strengths at work in the morning is associated with different types of performance throughout the workday. Momentary satisfaction of the needs for autonomy, relatedness and competence are proposed as mechanisms that differentially link strengths use to four different performance outcomes: task accomplishment, goal attainment, organizational citizenship behaviour and counterproductive work behaviour. We collected data from 216 Dutch employees in a large variety of sectors twice a day for one workweek (N = 1470 observations) using a smartphone application. Results of multilevel structural equation modelling showed that momentary satisfaction of the needs for autonomy and competence at noon mediated the positive relationship between morning strengths use and afternoon task accomplishment and goal attainment. Furthermore, satisfaction of the need for relatedness mediated the positive relationship between morning strengths use and afternoon organizational citizenship behaviour, but not the negative relationship between morning strengths use and counterproductive work behaviour. These findings deepen our understanding of strengths use theory and uncover novel insight on the temporal aspects of strengths use within a working day.

A strategy based on Wave Swarm for the formation task inspired by the Traveling Salesman Problem

Multi-robots can perform complex tasks such as exploration, foraging, and formation. Efficient communication between robots can contribute to the accomplishment of collective tasks through efficient message exchange. This paper proposes a strategy based on the message’s propagation technique, Wave Swarm, for the formation task inspired by the Traveling Salesman Problem. The Wave Swarm communication approach uses the concept of wave propagation for message exchange between neighbors, establishing a Father and Son relationship between robots. However, different pairs of Father and Son robots can impact the simulation time, the average distance traveled by each robot, and the number of messages exchanged during the formation task. Thus, given a random distribution of robots into a swarm, we model the problem of assigning one position and route for each robot to achieve its place in the formation as a Traveling Salesman Problem. The routes resulting from the TSP solution establish a new parental relationship between the robots in the swarm. We performed preliminary experiments to define the technique used in the TSP resolution. We tested reinforcement learning and the genetic algorithm using the TSPLIB95 library. Thus, we develop a strategy for formation tasks based on Wave Swarm and TSP solved with reinforcement learning. We adopted the leader–follower approach in an unknown environment to validate the proposal. The results show the behavior of different sizes of robot groups for various desired shapes. Experiments with the robot simulator CoppeliaSim (V-REP) validate the proposed strategy and highlight its efficiency and robustness while running the formation task.

Factors affecting the work passion of physicians during the COVID-19 pandemic: a qualitative study in Thailand

ABSTRACT Purpose This qualitative study explores the factors influencing physicians’ work passion during the COVID-19 pandemic in the Thai medical context. Methods Using an appreciative inquiry (AI) approach, we conducted qualitative interviews with 30 physicians from a public hospital in Thailand to investigate the factors that influence passion for their professional work (work passion). Results Our analysis revealed 12 codes influencing physician work passion, which were grouped into four themes: job factors (meaningful work, challenging tasks, task accomplishment, autonomy, work-life balance); organizational factors (fairness, positive environment); relationship factors (having a role model, psychological support, collaboration, recognition and respect); and individual factors (spiritual drive). Conclusion These findings contribute to developing knowledge about work passion among physicians and provide information to develop effective strategies to harness and manage work passion, thus improving clinical practice and healthcare outcomes. This qualitative study sheds light on the factors that influence physician work passion during the COVID-19 pandemic in Thailand, providing valuable information for future research and guiding the development of strategies to optimize work passion and its impact on clinical practice.

The Effect of STEAM Activities Based on Experiential Learning on Ninth Graders' Mental Motivation.

The impact of STEAM (Science, Technology, Engineering, Arts, and Math) on pupils' learning has been increasingly highlighted recently. This study aims to shed light on the effect of STEAM activities based on experiential learning on ninth graders' mental motivation and learning. The present research adopted a mixed methodology (quantitative and qualitative). The study sample consisted of 90 students divided into three groups. The tools utilized in conducting the study included California Measurement Mental Motivation, and semi-structured interviews with (10) participants. The tools' validity and reliability were verified. After data were analyzed, the findings showed statically significant differences between students' post average scores regarding mental motivation due to teaching method, and in favor of the experimental groups (face-to-face STEAM activities, online STEAM activities). This provides tangible proof for the need to include STEAM activities in school curricula to enhance learners' curiosity, problem-solving skills and self-confidence through learning, as well as their task accomplishment ability.

Institutional Collaborative Culture and Task Accomplishment in Universities

Institutional Collaborative Culture and Task Accomplishment in Universities

Working memory and processing speed abilities are related to habituation and change detection in school-aged children: An ERP study.

High cognitive performance is related to efficient brain processing while accomplishing complex cognitive tasks. This efficiency is observed through a rapid engagement of the brain regions and the cognitive processes required for task accomplishment. However, it is unclear if this efficiency is also present in basic sensory processes such as habituation and change detection. We recorded EEG with 85 healthy children (51 males) aged between 4 and 13 years old, while they listened to an auditory oddball paradigm. Cognitive functioning was evaluated using the Weschler Intelligence Scales for Children Fifth Edition and the Weschler Preschool & Primary School for Intelligence Fourth Edition. Auditory evoked potentials (AEPs) analyses and repeated measure analysis of covariance as well as regression models were performed. The analysis revealed that P1 and N1 repetition effects were observed across levels of cognitive functioning. Further, working memory abilities were related to repetition suppression on the auditory P2 component amplitude, while faster processing speed was related to repetition enhancement on the N2 component amplitude. Also, Late Discriminative Negativity (LDN) amplitude, a neural correlate of change detection, increased with working memory abilities. Our results confirm that efficient repetition suppression (i.e. greater reduction in amplitudes with greater levels of cognitive functioning) and more sensitive change detection (greater amplitude changes of the LDN) are related to the level of cognitive functioning in healthy children. More specifically, working memory and processing speed abilities are the cognitive domains related to efficient sensory habituation and change detection.

ENSING: Energy saving based data transmission in Internet of Drones for 3D connectivity in 6G networks

The Internet of Drones represents an emerging framework with a plethora of applications in the context of Beyond-5G networks. Drone technology uses are spreading from commercial, industrial up to military applications. Drones, a.k.a. Unmanned Aerial Vehicles (UAVs), are largely adopted in all those fields where men cannot reach or are unable to perform in a timely and efficient manner. Communications among UAVs are important to allow task coordination and network organization. However, as a drawback, UAVs are strongly affected by energy consumption that limits their flight and task accomplishment. In this paper, we address the energy consumption issue in UAV swarms and present a novel routing approach, namely ENSING, that aims to save energy consumption. Differently from previous approaches that simply reduce the number of control packets to save UAV battery level, ENSING approach selects the next-hop UAV forwarder according to energy constraints. In this way, only eligible UAVs with acceptable energy requirements can be selected in the packet forwarding, thus avoiding the involvement of those UAVs with limited battery level. Compared to other routing techniques, ENSING exhibits better performance in terms of packet delivery rate, throughput and latency.

Analysis of Odor-Tracking Performance of Silk Moth Using a Sensory-Motor Intervention System.

Animals can adaptively behave in different environmental conditions by converting environmental information obtained from their sensory organs into actions. This sensory-motor integration enables the accomplishment of various tasks and is essential for animal survival. This sensory-motor integration also plays an important role in localization to females, relying on sex pheromones floating in space. In this study, we focused on the localization behavior of the adult male silk moth, Bombyx mori. We investigated sensory-motor integration against time delay using odor plume tracking performance as an index when we set a certain time delay for the sensory and motor responses. Given that it is difficult to directly intervene in the sensory and motor functions of the silk moth, we constructed an intervention system based on a mobile behavior measurement system controlled by them. Using this intervention system, not only can timing the detection of the odor in the environment and timing the presentation of the odor to the silk moth be manipulated, but timing the reflection of the movement of the silk moth can also be manipulated. We analyzed the extent to which the localization strategy of the silk moth could tolerate sensory delays by setting a delay to the odor presentation. We also evaluated behavioral compensation by odor sensory feedback by setting a delay to the motor. The results of the localization experiment have shown that the localization success rate did not decrease when there was a motor delay. However, when there was a sensory delay, the success rate decreased depending on the time delay. Analysis of the change in behavior after detection of the odor stimulus has shown that the movement was more linear when we set a motor delay. However, the movement was accompanied by a large rotational movement when there was a delay in the sensory input. This result has suggested that behavior is compensated for the delay in motor function by feedback control of odor sensation, but not when accompanied by sensory delay. To compensate for this, the silk moth may acquire appropriate information from the environment by making large body movements.

Preliminary data on micro errors as a sensitive measure of everyday functioning in older adults

AbstractBackgroundMild difficulties with daily tasks are associated with cognitive abilities and future cognitive decline but are difficult to measure objectively. Performance‐based tests with high task demands (e.g., multitasking, prospective memory) have been fruitful in capturing mild functional difficulties, however measuring certain aspects like overt errors or completion time often shows floor effects and thereby fail to discriminate between individuals with subtle functional decline. We propose a different approach that involves coding micro‐error defined as inefficient but not overtly erroneous actions (e.g., mis‐reaching toward the wrong object) during relatively simple, performance‐based tests, such as the Naturalistic Action Test (NAT). We present preliminary findings on the validity of micro‐errors as sensitive measures of cognition in participants with healthy cognition, mild cognitive impairment (MCI), and mild dementia.Method27 older adults (Healthy, MCI, Dementia) completed the NAT, which requires participants to make a breakfast and a lunch using objects presented on a table. Standard cognitive tests (memory, language, etc.) were administered. NAT scores reflecting micro‐errors, total steps accomplished, and completion time were examined across participant groups and included in correlation analyses.ResultWorse performance on the NAT was associated with disease severity (micro errors Mean (SD) ‐ Healthy: M = 3.05 (.79); MCI: M = 8.80 (1.28); Dem: M = 9.33 (4.84); completion time(sec) ‐ Healthy: M = 211.84 (10.70); MCI: M = 352.00 (110.50); Dem: M = 363.00 (36.67); total steps accomplished‐ Healthy: M = 32.79 (0.12); MCI: M = 31.80 (0.73); Dem: M = 30.67 (2.33)). Among those with healthy cognition and MCI, greater micro errors were associated with lower accomplishment scores (r = ‐.422, p = .040) and longer completion times (r = .388, p = .061). Micro‐errors, but not accomplishment scores or completion time, were significantly associated with cognitive tests, who made more micro‐errors obtained a lower overall cognitive composite (r = ‐.434, p = .034) and a lower memory score (r = ‐.438, p = .032).ConclusionSubtle errors and inefficient actions (micro‐errors) during highly familiar everyday tasks are valid indicators of cognitive difficulties in older adults without dementia. Micro‐errors may be more sensitive than time to completion and task accomplishment and may be obtained from 5‐10 minutes of task performance. Micro‐errors have potential to identify risk of decline and as sensitive functional outcomes in clinical trials.

Action Planning for Packing Long Linear Elastic Objects Into Compact Boxes With Bimanual Robotic Manipulation

In this article, we propose a new action planning approach to automatically pack long linear elastic objects into common-size boxes with a bimanual robotic system. For that, we developed a hybrid geometric model to handle large-scale occlusions combining an online vision-based method and an offline reference template. Then, a reference point generator is introduced to automatically plan the reference poses for the predesigned action primitives. Finally, an action planner integrates these components enabling the execution of high-level behaviors and the accomplishment of packing manipulation tasks. To validate the proposed approach, we conducted a detailed experimental study with multiple types and lengths of objects and packing boxes.

Solving the Team Allocation Problem in Crowdsourcing via Group Multirole Assignment

In mass crowdsourcing, a platform is used for task allocation. Large complex tasks may be assigned to teams directly. To ensure the rapid accomplishment of tasks, we need to assign a task to multiple teams, while a team is composed of at least one worker. On the other hand, in order to ensure the enthusiasm and income of workers, the platform allows one worker to participate in a limited number of teams. Thereby, a team may undertake multiple but limited tasks. It is obvious that task allocation needs to avoid each worker being overloaded as well as prevent information leakage, which is caused by a task assigned to different teams at the same time. Therefore, different teams being assigned to a certain task cannot include the same workers. Such a scenario forms a many-to-many (M2M) assignment problem [or group multirole assignment (GMRA)] under high-order cardinality (HC) constraints, while the platform wants to choose appropriate and excellent teams for all the tasks in a specific time window. In order to solve this problem, this article studies and formalizes the above problem by introducing HC and conflicting agents on roles (CAR) constraints to GMRA. The main contributions of this article include: 1) the first formalization of the team allocation problem (TAP) in crowdsourcing through extending GMRA and the creation of a composition matrix to express HC constraints of agents and conflict avoiding constraints; 2) theoretical proofs of the theorems of the formalized problem, such as a necessary and sufficient condition (NSC), which confines the solution space of the problem; and 3) a practice solution to the proposed problem based on the IBM ILOG CPLEX optimization package (CPLEX). All the proposed approaches are verified by simulation experiments, which demonstrates that the proposed approaches are efficient, feasible, and practicable.

“Baby Steps” for Baby Breaths–How Incremental Changes Changed Our Performance

Introduction:Neonatal resuscitations are challenging to any mixed ED with rotating medical staff. Covid-19 decimated nursing numbers and reduced training to a standstill. New doctors and nurses find pediatric resuscitations (simulations and in real cases) challenging as there are complex algorithms/calculations as well as preparing and operating systems such as a Drager ResuscitaireR.Training rotating or new staff for rare complex resuscitations can be time and resource consuming with little yield.We describe our experience of applying incremental measures after almost every simulation to improve team performance and knowledge.Method:This is an ongoing audit of simulations and cases for neonatal / infant resuscitations using our pediatric bay and Drager ResuscitaireR system. Our main aim was to improve:1. Nursing preparation time / competence for:- Pediatric / neonatal drug doses- Drager ResuscitaireR system setup2. Medical staff competence for:- Neonatal resuscitation algorithms- Drager ResuscitaireR usage (PPV/Ventilator setup)Two ED consultants ran “in-situ” simulations and recorded gaps/errors (including feedback in debrief). Any measures deemed fixable were implemented ASAP. Improvement was made if error was not repeated in subsequent two independent simulations.Results:Audits of five real cases and fifteen simulations revealed gaps (e.g. dose miscalculations, equipment unfamiliarity) which were corrected by simple measures after each discovery. These include: 1. Neonatal resuscitation checklist with steps to setup the Resuscitaire2. Weight-based resuscitation cards / pre-made packs of equipment instead of manual calculations3. Position markers for "ideal" ResuscitaireR ventilator settings4. Step by step ResuscitaireR numbered markers on machine We found improvements in knowledge gaps, task accomplishment rates, staff satisfaction, appreciation of deficits and in-situ simulation uptake. More gaps are found and resolved at every simulation.Conclusion:A Human Factors approach with incremental adjustments and simple improvements with each simulation led to better team task accomplishment in complex preparation and resuscitation.

Looking at both sides, outcomes of positive workplace relational systems: A phenomenological study

Relational systems are like a gear wheel in organizational processes that keeps employees connected and moving. These relationships are developed and maintained within the work environment for task accomplishment. But these relational systems are originating other outcomes that directly and indirectly affect the organizational process. The present study aimed to explore the outcomes of positive workplace relational systems. Under qualitative phenomenological approach followed a classical content analysis technique employed to analyze the data collected from 20 Bank employees through semi-structured interviews. The data analysis revealed that outcomes of positive relational systems varied depending on the duration and quality of relationships. Organizational culture identified as an important factor that influences the quality of relationships and their outcomes. The data analysis revealed that positive workplace relational systems along with positive behavioral outcomes also develop negative outcomes among employees such as perception of organizational politics. This study argued that outcomes of positive workplace relational systems depend on the perception and need satisfaction of participants. The result highlighted the important areas for individuals and organizations to consider in the context of relationship management, developing interpersonal skills in employees, and encouraging relationship generosity. It will help to enhance the well-being of employees and improve organizational performance.

Case Study: COVID-19 Brain Fog or Auditory Processing Disorder?

Case Study: COVID-19 Brain Fog or Auditory Processing Disorder?

Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of research can enhance the transfer of knowledge from simulations to real-world environments and can increase the responsiveness and safety of UAV systems.

Hybrid Recurrent Neural Network Architecture-Based Intention Recognition for Human-Robot Collaboration.

Human-robot-collaboration requires robot to proactively and intelligently recognize the intention of human operator. Despite deep learning approaches have achieved certain results in performing feature learning and long-term temporal dependencies modeling, the motion prediction is still not desirable enough, which unavoidably compromises the accomplishment of tasks. Therefore, a hybrid recurrent neural network architecture is proposed for intention recognition to conduct the assembly tasks cooperatively. Specifically, the improved LSTM (ILSTM) and improved Bi-LSTM (IBi-LSTM) networks are first explored with state activation function and gate activation function to improve the network performance. The employment of the IBi-LSTM unit in the first layers of the hybrid architecture helps to learn the features effectively and fully from complex sequential data, and the LSTM-based cell in the last layer contributes to capturing the forward dependency. This hybrid network architecture can improve the prediction performance of intention recognition effectively. One experimental platform with the UR5 collaborative robot and human motion capture device is set up to test the performance of the proposed method. One filter, that is, the quartile-based amplitude limiting algorithm in sliding window, is designed to deal with the abnormal data of the spatiotemporal data, and thus, to improve the accuracy of network training and testing. The experimental results show that the hybrid network can predict the motion of human operator more precisely in collaborative workspace, compared with some representative deep learning methods.

A matching game-based crowdsourcing framework for last-mile delivery: Ground-vehicles and Unmanned-Aerial Vehicles

This paper tackles the problem of allocating time-constrained last-mile delivery tasks to crowdsourced vehicles and Unmanned Aerial Vehicles (UAVs). With the increasing development of E-commerce, the demand for home deliveries is rapidly increasing. However, last-mile delivery remains the costliest part endured by shipping companies, particularly for time-critical deliveries. To mitigate this high cost, recently, several researchers and companies, such as Amazon and Mercedes-Benz, have adopted (1) crowdsourced and (2) UAVs-based delivery solutions. Nevertheless, crowdsourcing delivery tasks, where workers with vehicles are being recruited to deliver parcels, suffers from long delivery time and delays for time-critical tasks during peak traffic hours. Although UAVs are perceived currently as a trending solution for fast and timely delivery of time-critical delivery tasks, yet they are constrained by the flying time and the package weight. These limitations have a direct impact on the cost of delivery and the task accomplishment rate. In this paper, a hybrid framework that crowd-sources vehicles’ drivers and UAVs for delivery tasks is proposed. It aims at improving the delivery time and the allocation percentage mainly at peak hours, by the proposed, (1) task filtering, (2) worker quality estimation, and (3) task allocation and scheduling. The filtering of possible tasks, that each type of worker can accomplish is done based on the availability of the workers and their ability to reach the destination. The worker quality, with regard to the delivery task, is estimated using the Bayesian belief function that is utilized in the allocation. The allocation of tasks and workers is performed using the Gale–Shapley matching game to consider the preferences of both workers and tasks. The simulation results show that the hybrid framework outperforms the non-hybrid crowdsourcing model in terms of task allocation percentage, delivery time, and payoff.

Augmented reality-assisted gesture-based teleoperated system for robot motion planning

Purpose The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization. Design/methodology/approach Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. Findings The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks. Social implications The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one. Originality/value The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.