Sequential Task Research Articles

Age-related differences in finger interdependence during complex hand movements.

The well-known decrease in finger dexterity during healthy aging leads to a significant reduction in quality of life. Still, the exact patterns of altered finger kinematics of older adults in daily life are fairly unexplored. Finger interdependence is the unintentional comovement of fingers that are not intended to move, and it is known to vary across the lifespan. Nevertheless, the magnitude and direction of age-related differences in finger interdependence are ambiguous across studies and tasks and have not been explored in the context of daily life finger movements. We investigated five different free and daily-life-inspired finger movements of the right, dominant hand as well as a sequential finger tapping task of the thumb against the other fingers, in 17 younger (22-37 yr) and 17 older (62-80 yr) adults using an exoskeleton data glove for data recording. Using inferential statistics, we found that the unintentional comovement of fingers generally decreases with age in all performed daily-life-inspired movements. Finger tapping, however, showed a trend towards higher finger interdependence for older compared with younger adults. Using machine learning, we predicted the age group of a person from finger interdependence features of single movement trials significantly better than chance level for the daily-life-inspired movements, but not for finger tapping. Taken together, we show that for specific tasks, decreased finger interdependence (i.e., less comovement) could potentially act as a marker of human aging that specifically characterizes older adults' complex finger movements in daily life.NEW & NOTEWORTHY Kinematic finger movement data were analyzed with regard to age-related differences. Extensive analyses of complex and daily-life-inspired movements reveal that the direction of age effects is not uniform but task-dependent: Although older adults generally show more finger interdependence than younger adults in a simple finger tapping task, this effect is reversed for daily-life-inspired movement tasks. For these tasks, finger interdependence indices offer potential new markers to predict the age group of an individual using machine learning approaches.

Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models.

Fuzzy clustering-based neural networks (FCNNs) based on information granulation techniques have been shown to be effective Takagi-Sugeno (TS)-type fuzzy models. However, the existing FCNNs could not cope well with sequential learning tasks. In this study, we introduce incremental FCNNs (IFCNNs), which could dynamically update themselves whenever new learning data (e.g., single datum or block data) are incorporated into the dataset. Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions. In the aforementioned partition, the dynamic FCM adaptively adjusts the position of its prototypes by using sequential data. Due to the time-sharing arrival of training data, compared with batch learning models, incremental learning methods may lose classification (prediction) accuracy. In order to tackle this challenge, we utilize quasi-fuzzy local models (QFLMs) based on modified Schmidt neural networks to replace the popular linear functions in TS-type fuzzy models to refine and enhance the ability to represent the behavior of fuzzy subspaces. Meanwhile, the recursive least square error (LSE) estimation is utilized to update the weights of QFLMs from one-by-one or block-by-block (fixed or varying block size) learning data. In addition, the L2 regularization is considered to ameliorate the deterioration of generalization abilities caused by potential overfitting when carrying out weight estimation. The proposed method leads to the construction of FCNNs in a new way, which can effectively deal with incremental data as well as deliver sound generalization capability. Extensive machine-learning datasets and a real-world application are employed to show the validity and performance of the presented methods. From the experimental results, we show that the proposal can maintain sound classification accuracy when effectively processing sequential data.

Joint Optimization of Sequential Task Offloading and Service Deployment in End-Edge-Cloud System for Energy Efficiency

Joint Optimization of Sequential Task Offloading and Service Deployment in End-Edge-Cloud System for Energy Efficiency

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling processes. The report discusses the importance of production scheduling in achieving operational efficiency and meeting customer demands. Various strategies, such as lean manufacturing, just-in- time (JIT) production, and advanced planning and scheduling (APS) systems, are analyzed for their effectiveness in improving production scheduling. Additionally, the report explores technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) that can enhance production scheduling capabilities. Case studies and examples are used to illustrate how these strategies and technologies have been implemented successfully in real-world manufacturing settings. The report concludes with recommendations for manufacturers looking to optimize their production scheduling processes.

Effect of positive social comparative feedback on the resting state connectivity of dopaminergic neural pathways: A preliminary investigation

Positive social comparative feedback is hypothesized to generate a dopamine response in the brain, similar to reward, by enhancing expectancies to support motor skill learning. However, no studies have utilized neuroimaging to examine this hypothesized dopaminergic mechanism. Therefore, the aim of this preliminary study was to investigate the effect of positive social comparative feedback on dopaminergic neural pathways measured by resting state connectivity. Thirty individuals practiced an implicit, motor sequence learning task and were assigned to groups that differed in feedback type. One group received feedback about their actual response time to complete the task (RT ONLY), while the other group received feedback about their response time with positive social comparison (RT + POS). Magnetic resonance imaging was acquired at the beginning and end of repetitive motor practice with feedback to measure practice-dependent changes in resting state brain connectivity. While both groups showed improvements in task performance and increases in performance expectancies, ventral tegmental area and the left nucleus accumbens (mesolimbic dopamine pathway) resting state connectivity increased in the RT + POS group but not in the RT ONLY group. Instead, the RT ONLY group showed increased connectivity between ventral tegmental area and primary motor cortex. Positive social comparative feedback during practice of a motor sequence task may induce a dopaminergic response in the brain along the mesolimbic pathway. However, given that absence of effects on expectancies and motor learning, more robust and individualized approaches may be needed to provide beneficial psychological and behavioral effects.

Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm

A ground station uses heterogeneous unmanned aerial vehicles (UAVs) with varying observation resources (primarily airborne cameras) for oblique photography based on mapping task requirements to obtain access to geographic information. In this paper, a cooperative multi-task allocation model is proposed for heterogeneous UAVs based on area segmentation to approximate real-world mapping applications. Specifically, it considers area mapping task requirements, UAV kinematics constraints, resource constraints (maximum endurance and sensor capabilities), and task number ceiling constraints; and optimises the task sequence based on the objective function that considers UAVs coverage path planning characteristics. However, a general genetic algorithm (GA) could undermine the feasibility of the solution in the population, resulting in an undesirable optimisation effect when performing genetic operations on a large-scale task allocation problem with strong coupling constraints and an obscure encoding relation. An improved double-chromosome encoding GA with a conflict-mediation mechanism is proposed to perform genetic operations on the population while meeting these constraints. The proposed GA ensures the universal excellence of population evolution while significantly improving the population optimisation and convergence performance. The simulation results show that the proposed GA has greater global search ability, stability, and speed compared with existing algorithms.

Acoustic Analyses of Tone Productions in Sequencing Contexts Among Cantonese-Speaking Preschool Children With and Without Childhood Apraxia of Speech.

Pitch variations (tone productions) have been reported as a measure to differentiate Cantonese-speaking children with and without childhood apraxia of speech (CAS). This study aims to examine fundamental frequency (F0) changes within syllables and the effects of syllable structure, lexical status, and syllable positions on F0 in Cantonese-speaking preschool children with and without CAS. Six children with CAS, six children with non-CAS speech sound disorder plus language disorder (S&LD), 22 children with speech sound disorder only (SSD), and 63 children with typical speech-language development (TD) performed the tone sequencing task (TST). Growth curve analysis was employed to analyze and compare the F0 values within syllables with three Cantonese tones (high level, high rising, and low falling). The analysis considered the effects of syllable structure (vowel and consonant-vowel), lexical status (word and nonword), and syllable position (initial, medial, and final) on F0, as well as comparisons within and between groups. Within each group, the effects of syllable structure and position on F0 values were found with different patterns. Between-group comparisons showed that the CAS group had reduced F0 contrasts. The CAS group could be differentiated from the control groups based on interactions of F0 with syllable structure and position, but not lexical status. The dissimilarity of F0 values detected between the CAS and SSD/TD groups was more prominent than that observed between the CAS and S&LD groups. This study demonstrated that Cantonese-speaking children with CAS had difficulty in varying F0 within syllables as compared to those without CAS, suggesting pitch variation difficulty and language-specific impairment profiles in CAS. Future investigations of objective measures for identifying Cantonese speakers with CAS and cross-linguistic investigations using growth curve analysis and the TST are suggested.

Unveiling Emotional Stress: Analyzing EEG Signals With Deep Learning Insights

This study focuses on a comprehensive exploration of EEG-based emotion recognition, with a particular focus on leveraging recurrent neural network (RNN) architectures, including long short-term memory (LSTM) and gated recurrent unit (GRU). The investigation is rooted in a publicly available EEG Brainwave Dataset, meticulously curated to capture human responses to emotional stimuli. The dataset features signals obtained from frontal and temporal brain lobes, categorized into distinct emotional states encompassing positive, neutral, and negative emotions. The analysis spans four distinct scenarios, each representing different combinations of features and preprocessing strategies. Notably primary emphasis is on LSTM and GRU architectures due to their proven effectiveness in sequential data processing tasks. In addition to evaluating the performance of LSTM and GRU models a thorough examination of all the four cases to assess their efficacy in accurately predicting emotional states from EEG signals. The study also emphasis on the importance of meticulous feature selection and preprocessing in optimizing model accuracy and robustness, highlighting the intricate interplay between data representation, model architecture, and task-specific requirements. Ultimately, the research findings contribute to advanced understanding of EEG-based emotion recognition and pave the way for further research in this exciting interdisciplinary field. Key Words: Electroencephalography(EEG), Long short-term memory (LSTM), Gated recurrent unit (GRU), Feature extraction, Preprocessing techniques, Statistical metrics, Frequency domain analysis.

Site Dependency of Anodal Transcranial Direct-Current Stimulation on Reaction Time and Transfer of Learning during a Sequential Visual Isometric Pinch Task.

Considering the advantages of brain stimulation techniques in detecting the role of different areas of the brain in human sensorimotor behaviors, we used anodal transcranial direct-current stimulation (a-tDCS) over three different brain sites of the frontoparietal cortex (FPC) in healthy participants to elucidate the role of these three brain areas of the FPC on reaction time (RT) during a sequential visual isometric pinch task (SVIPT). We also aimed to assess if the stimulation of these cortical sites affects the transfer of learning during SVIPT. A total of 48 right-handed healthy participants were randomly assigned to one of the four a-tDCS groups: (1) left primary motor cortex (M1), (2) left dorsolateral prefrontal cortex (DLPFC), (3) left posterior parietal cortex (PPC), and (4) sham. A-tDCS (0.3 mA, 20 min) was applied concurrently with the SVIPT, in which the participants precisely controlled their forces to reach seven different target forces from 10 to 40% of the maximum voluntary contraction (MVC) presented on a computer screen with the right dominant hand. Four test blocks were randomly performed at the baseline and 15 min after the intervention, including sequence and random blocks with either hand. Our results showed significant elongations in the ratio of RTs between the M1 and sham groups in the sequence blocks of both the right-trained and left-untrained hands. No significant differences were found between the DLPFC and sham groups and the PPC and sham groups in RT measurements within the SVIPT. Our findings suggest that RT improvement within implicit learning of an SVIPT is not mediated by single-session a-tDCS over M1, DLPFC, or PPC. Further research is needed to understand the optimal characteristics of tDCS and stimulation sites to modulate reaction time in a precision control task such as an SVIPT.

Optimization of Berth-Tug Co-Scheduling in Container Terminals under Dual-Carbon Contexts

In order to address the dynamic changes in vessel preferences for berth lines caused by the deployment of shore-based power equipment in major ports and the collaborative scheduling problem of berthing and towing assistance, this paper quantifies the environmental costs of pollutants from the main engines of tugs and auxiliary engines of container ships using an environmental tax. Additionally, considering the economic costs such as vessel delay and shore power cable connection, a two-layer mixed-integer linear programming model is constructed using the task sequence mapping method. This model integrates the allocation of continuous berths at container terminals with coordinated towing scheduling for shore power selection. A solution approach is designed by combining the commercial solver (CPLEX) and the immune particle swarm optimization algorithm (IAPSO). The proposed scheme is validated using the example of the Nansha Phase IV Terminal at the Port of Guangzhou. The results show that compared to the traditional first-come-first-served and adjacent scheduling schemes, the collaborative scheduling scheme proposed in this paper reduces the total cost by 21.73%. By effectively utilizing berth resources and shore power equipment while densely arranging collaborative tasks and appropriately increasing the number of tugs, the port can convert the economic cost of leasing a small number of tugs (increased by 10.63%) into environmental benefits (decreased by 33.88%). This approach provides a reference for addressing nearshore pollution emissions in ports.

MEC-enabled resource allocation in Internet of Vehicles

Mobile edge computing (MEC), a promising technology, is widely used in the context of the Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile devices. Leveraging its advantageous features, MEC facilitates lower latency and more efficient computing services for intelligent vehicles compared to preceding technologies. The tasks inherent to IoV exhibit varying sensitivities to latency, necessitating a nuanced approach to processing. Therefore, this study aims to develop a sophisticated solution in the form of a joint task sequencing and resource allocation (JTSARA) algorithm based on deep reinforcement learning. The primary objective is to adeptly manage both computing and communication resources within the MEC environment, thereby ensuring the fulfillment of performance requirements of tasks. Initially, tasks are categorized into distinct levels according to their latency tolerance. Subsequent to this classification, a method is formulated to optimize task sequencing, improving the efficiency of computational processing. Furthermore, we tackle the intricate challenge of resource allocation by transforming it into a Markov decision process, employing deep reinforcement learning for resolution. The feasibility and effectiveness of the proposed scheme are demonstrated through comprehensive simulation experiments, affirming its potential for enhancing the performance of IoV systems.

Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot collaborative disassembly: A case of retired electric vehicle battery

The burgeoning electric vehicle (EV) industry has precipitated a commensurate surge in the consumption of EV batteries, which are currently labor-intensive and inefficient for the recycling and disassembly of EV batteries. However, it is a potential trend to enhance the efficacy and safety of the disassembly of EV batteries based on human-robot collaboration (HRC) method. Because of the uncertainty of retired EV battery disassembly and the inefficiency of the existing disassembling sequence, it is difficult to be fully accomplish through HRC disassembly. The collaborative disassembly of EV batteries by humans and robots can be conceptualized as agents engaging with and learning from the environment, and modeled as a multi-agent Markov game process. This paper aims to address the challenge of HRC in the disassembly of EV batteries by recognizing the dual attributes of partial observability and non-smoothness in the suitable disassembly scenario. A partially observable multi-agent reinforcement learning environment is constructed, incorporating the structural aspects of the EV battery and the disassembly task. The framework is extended to the QMIX-HRC algorithm on the QMIX architecture (as a value-based multi-agent deep reinforcement learning algorithm), specifically designed to tackle the sequence problem in human-robot collaborative disassembly of EV batteries. The optimization results would yield a task sequence to offer maximal global co-benefit during the exploration iteration, facilitating a reduction in labor costs and an enhancement of co-efficiency. The viability of the QMIX-HRC disassembly strategy would be verified through the eventual disassembly sequence of a simulated battery pack through a real human-robot collaborative disassembly station.

0071 Does Additional Training on a Motor Sequence Task Improve Sleep-Dependent Memory Consolidation for Older Adults?

0071 Does Additional Training on a Motor Sequence Task Improve Sleep-Dependent Memory Consolidation for Older Adults?

The limitations of automatically generated curricula for continual learning.

In many applications, artificial neural networks are best trained for a task by following a curriculum, in which simpler concepts are learned before more complex ones. This curriculum can be hand-crafted by the engineer or optimised like other hyperparameters, by evaluating many curricula. However, this is computationally intensive and the hyperparameters are unlikely to generalise to new datasets. An attractive alternative, demonstrated in influential prior works, is that the network could choose its own curriculum by monitoring its learning. This would be particularly beneficial for continual learning, in which the network must learn from an environment that is changing over time, relevant both to practical applications and in the modelling of human development. In this paper we test the generality of this approach using a proof-of-principle model, training a network on two sequential tasks under static and continual conditions, and investigating both the benefits of a curriculum and the handicap induced by continuous learning. Additionally, we test a variety of prior task-switching metrics, and find that in some cases even in this simple scenario the a network is often unable to choose the optimal curriculum, as the benefits are sometimes only apparent with hindsight, at the end of training. We discuss the implications of the results for network engineering and models of human development.

Subitizing endures in sequential rather than simultaneous comparison tasks.

Subitizing is the ability to appraise a number of small quantities (up to four) rapidly and precisely. This system, however, can be impaired by distractors presented along with targets to be enumerated. To better understand whether this limitation arises in perceptual circuits or in the response selection stage, we investigated whether subitizing can endure in simultaneous comparison tasks. Participants were asked to compare the number of dots in two sets on the left and right sides of the screen, presented either simultaneously or sequentially. For comparing within the numerosity range (6-32 dots), both the error rate and reaction time increased steadily as the ratio between the two numbers compared approached "1." Namely, a phenomenon labeled the ratio effect was revealed. For comparison with small numbers (<5), the sequential comparison task was errorless despite the ratio, suggesting the feature of subitizing. Individual efficiency (measured by the inverse efficiency score [IES]) did not correlate between number ranges in sequential comparison, suggesting that distinct mechanisms were involved. However, we found that in simultaneous tasks, error rate and efficiency showed an increase as the ratios of the two numbers compared approached "1." This is similar to the ratio effect revealed in the comparison for moderate numbers. Individual efficiency within these two ranges correlated, indicating that the enumeration within these two ranges was based on a single mechanism. These results suggest that subitizing cannot process sets in parallel, and numerosity takes the job whenever subitizing fails.

Personalized recommendations for learning activities in online environments: a modular rule-based approach

Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.

EOTE-FSC: An efficient offloaded task execution for fog enabled smart cities.

Smart cities provide ease in lifestyle to their community members with the help of Information and Communication Technology (ICT). It provides better water, waste and energy management, enhances the security and safety of its citizens and offers better health facilities. Most of these applications are based on IoT-based sensor networks, that are deployed in different areas of applications according to their demand. Due to limited processing capabilities, sensor nodes cannot process multiple tasks simultaneously and need to offload some of their tasks to remotely placed cloud servers, which may cause delays. To reduce the delay, computing nodes are placed in different vicinitys acting as fog-computing nodes are used, to execute the offloaded tasks. It has been observed that the offloaded tasks are not uniformly received by fog computing nodes and some fog nodes may receive more tasks as some may receive less number of tasks. This may cause an increase in overall task execution time. Furthermore, these tasks comprise different priority levels and must be executed before their deadline. In this work, an Efficient Offloaded Task Execution for Fog enabled Smart cities (EOTE - FSC) is proposed. EOTE - FSC proposes a load balancing mechanism by modifying the greedy algorithm to efficiently distribute the offloaded tasks to its attached fog nodes to reduce the overall task execution time. This results in the successful execution of most of the tasks within their deadline. In addition, EOTE - FSC modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. The load balancing results of EOTE - FSC are compared with state-of-the-art well-known Round Robin, Greedy, Round Robin with longest job first, and Round Robin with shortest job first algorithms. However, fog computing results of EOTE - FSC are compared with the First Come First Serve algorithm. The results show that the EOTE - FSC effectively offloaded the tasks on fog nodes and the maximum load on the fog computing nodes is reduced up to 29%, 27.3%, 23%, and 24.4% as compared to Round Robin, Greedy, Round Robin with LJF and Round Robin with SJF algorithms respectively. However, task execution in the proposed EOTE - FSC executes a maximum number of offloaded high-priority tasks as compared to the FCFS algorithm within the same computing capacity of fog nodes.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

Changing risk-taking: the effects of tasks and incentives on the variability of risk-taking

The capability to vary risk-taking is an important aspect of performance in organizations where behavioral adjustments are required to suit changing objectives. Incentive schemes are one way to influence risk-taking. Yet, evidence indicates incentives do not have their intended effects and may encourage excessive risk-taking. To examine this issue, we draw on compensation activation theory that proposes individual motives are activated by specific features of compensation schemes and expressed in behaviors. We extend compensation activation theory by focusing on (1) responses to a sequence of tasks designed to activate risk-taking and (2) the effects of incentive schemes on these relationships. We conducted a laboratory experiment with 173 participants who were allocated randomly to one of three bonus schemes. The linear scheme has a linear relationship between returns from risk-taking and rewards. The bonus cap scheme operates similarly up to a point where no further rewards are paid. The outcome-adjusted scheme, with a two-year hypothetical time frame, requires realized gains for the first year of investment and no losses in next year. Results support our hypotheses that these incentive schemes have differential effects on the strength and direction of relationships between risk-taking across a sequence of tasks. The linear scheme strengthens the relationships between risk-taking across sequential tasks. Conversely, the bonus cap scheme weakens the relationships between risk-taking across sequential tasks. The outcome-adjusted scheme creates variability by decreasing risk-taking when the connections between risk-taking and rewards are less salient and increasing risk-taking when connections between risk-taking and rewards are more salient. We contribute to the literature concerning compensation activation and incentives by deepening our understanding of the roles played by tasks and incentives in activation processes and by explaining the variability of risk-taking in terms of changes in connections between behavior and rewards.

Investigating effect of standardized total body skin examination using sequence-networks

This study employs sequence-network analysis to investigate the influence of instructing a standardized procedure on total-body skin examination (TBSE) performance. A between-subjects study was conducted with thirty-one participants comprising medical students and attending physicians. Among these participants, fifteen were randomly assigned to the uninstructed group and sixteen to the instructed group. The participants' gaze and field of view were recorded using eye tracking glasses while performing TBSE on a male and a female standardized patient. The recordings were then transcribed to depict the examination process. The instructed group missed significantly fewer body parts (p = 0.045) and had higher time efficiency (p = 0.007) while examining the female patient, but no significant difference was observed for the male patient examination. Furthermore, the examination sequences of the instructed group contained lesser variability than the uninstructed group. Hence, implementing a standard optimal procedure to perform TBSE could minimize the likelihood of missing body parts, increase examination efficiency, and improve performance consistency. This study demonstrated the potential of sequence-network analysis to study human performance in sequential tasks.