Relevance Of Task Research Articles

Task-Driven Computing Offloading and Resource Allocation Scheme for Maritime Autonomous Surface Ships Under Cloud–Shore–Ship Collaboration Framework

Currently, Maritime Autonomous Surface Ships (MASS) have become one of the most attractive research areas in shipping and academic communities. Based on the ship-to-shore and ship-to-ship communication network, they can exploit diversified and distributed resources such as shore-based facilities and cloud computing centers to execute a variety of ship applications. Due to the increasing number of MASS and asymmetrical distribution of traffic flows, the transportation management must design an efficient cloud–shore–ship collaboration framework and smart resource allocation strategy to improve the performance of the traffic network and provide high-quality applications to the ships. Therefore, we design a cloud–shore–ship collaboration framework, which integrates ship networking and cloud/edge computing and design the respective task collaboration process. It can effectively support the collaborative interaction of distributed resources in the cloud, onshore, and onboard. Based on the global information of the framework, we propose an intelligent resource allocation method based on Q-learning by combining the relevance, QoS characteristics, and priority of ship tasks. Simulation experiments show that our proposed approach can effectively reduce task latency and system energy consumption while supporting the concurrency of scale tasks. Compared with other analogy methods, the proposed algorithm can reduce the task processing delay by at least 15.7% and the task processing energy consumption by 15.4%.

The relevance of mathematics textbook tasks to indigenous cultural context

This paper reports about a study, which examines the mathematics textbooks used by the schools in the Sámi area of Norway. The Sámi are Indigenous people of Norway and neighboring countries (Finland, Russia and Sweden). Handcraft and reindeer herding are prevalent activities in Sámi culture. Drawing on the theory of didactical phenomenology, the study examines the relevance of textbook tasks to the cultural background of Indigenous children. Document analysis was used to extract and analyze the data from the old and new textbooks. The results indicate that the tasks in the old textbook are hardly relevant to Sámi culture. Though the old textbook had tasks, which could be adopted, the topic containing such tasks is no longer available in the new textbook. The adoption of such tasks could have helped to achieve the goal set in the new curriculum about teaching Sámi history and culture to all Norwegian children. It could also have helped to achieve another goal, which is about interdisciplinarity, because the tasks are about timeline in Norwegian history. The study has implications. In addition to using the tasks in the already available studies about the Sámi, we need more studies about designing tasks which utilize the Sámi cultural context. Additionally, adopting tasks, which use Indigenous contexts similar to the Sámi context, should also be considered instead of a mere translation of the mandated textbook into the Indigenous language. Another option is, consistent with earlier studies, designing a separate textbook for the Indigenous children based on their culture. Keywords: mathematics curriculum, Indigenous culture, measuring, textbook.

Motor inhibition during voluntary gait initiation in young and older adults

Based on a novel approach, this study explores feasibility and relevance of an inhibition task for studying age-related differences in motor inhibition during gait initiation. When presented with a go-signal, young adults (YA, n = 24) and older adults (OA, n = 55) were required to promptly initiate gait. Participants completed 3 blocks of 12 gait trials. Each block contained 3 stop trials in which the go-signal was followed by a stop-signal that required the person to block gait initiation and remain standing. Stop-signals were presented randomly and with different delays. Data analyses focused on changes in the centre of pressure (COP) and success of motor inhibition. Compared to go-trials, stop trials resulted in a marked decrease of timing and amplitude of COP displacement. Overall success rate of motor inhibition was low (29% in YA vs. 19% in OA) and decreased with increasing COP displacement. Inhibitory success was associated with two strategies: a pro-active cautious COP displacement; and the inhibition of further COP displacement after a stop-signal. Results demonstrate age-related differences in adaptive behavior as well as boundaries beyond which neither old nor young persons were successful. This study yields important insights into motor inhibition during gait and essential input nto further studies.

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

Jordanian EFL Students' Views on the Impact of Online Learning on Their Academic Achievement and Self-Motivation

The research aims to explore Jordanian EFL students' views on the impact of online learning on their academic achievement and self-motivation. The study involved 1776 EFL participants from Al-Zaytoonah, Isra, and Middle East Universities in the academic year 2023-2024, out of which 388 were chosen randomly for sampling. Data gathering was facilitated through a validated survey, distributed both digitally via Google Forms and in printed forms. Collected responses were organized in Excel for analysis. The analysis process involved evaluating the data, focusing on participant demographics, and using frequency and percentage metrics. Both academic performance and self-motivation dimensions were evaluated through mean values, standard deviations, and the MANOVA test. The initially used 5-point Likert scale was restructured into three categories: low, moderate, and high. The results indicate a middle-range influence on the investigated domains, with academic performance taking a slight lead. Key influencing factors identified were gender and GPA. The effect of online learning on student outcomes was diverse, positively impacting collaboration, preparedness, and overall performance but showing limitations in areas like time management and assignment submission. Even though students expressed contentment in collaborative areas, the research highlighted gaps in intrinsic motivation and perceived relevance of tasks.

Ecological assessment of unilateral spatial neglect in immersive virtual reality: A multiple-case study to assess the feasibility and relevance of a Baking Tray Task

ABSTRACT The assessment of unilateral spatial neglect (USN) primarily relies on paper-and-pencil tests, which do not fully represent daily life difficulties. To address this limitation, ecological tests, like the Baking Tray Test (BTT), have been developed. However, the original BTT identifies the presence of USN without providing information on its severity. In this aim, a new severity measure, the Centre of Mass (CoM), has been proposed, but its calculation in real environments poses challenges. Immersive virtual reality (VR) offers a promising solution for implementing a BTT in which measures are automatically calculated. This study aimed to assess the feasibility and relevance of an immersive VR BTT. Nineteen right brain-damaged patients with and without USN and 25 healthy participants were included. Group analyses showed an equivalence between the two BTT versions. Individual analyses revealed that all USN patients, except one, had pathological results in both versions. They also underlined pathological scores in patients without USN signs on paper-and-pencil tests. Finally, the CoM strongly correlated with paper-and-pencil tests and appeared to be a good indicator of USN severity. These findings support the relevance of implementing the BTT in an immersive VR version, suggesting its potential to enhance USN assessment.

Not daily, sometimes not ever - mixed methods exploration of the contemporary relevance of tasks contained in extended activities of daily living scales.

Assessment of functional status is an integral part of older adult healthcare and research. Therefore, it is essential that tools to capture function are contextually appropriate. Many tools designed to evaluate extended Activities of Daily Living (eADLs) were developed decades ago. Our aim was to explore which eADL tasks are commonly performed by older adults and map these tasks to popular eADL scales, seeking stakeholder perspectives to inform recommendations on scale content. Online survey with quantitative questions and free text followed-by semi-structured interviews. Older adults (≥60-years), carers and health, social care and voluntary/community professionals. We extracted eADL tasks from existing scales to develop a survey on frequency of performance, with space for additional comment. The survey was disseminated via Join Dementia Research and other clinical and professional networks. Online semi-structured interviews were undertaken with thematic analysis of verbatim transcripts. From 2244 online survey responses, 87% came from older people and two-thirds of respondents were female. Some eADL tasks were never performed by many participants. Tasks not part of existing tools included online banking and mobile phone use. From fifteen interviews, a recurring theme was that technological and societal changes have altered daily tasks. Commonly used eADL scales contain obsolete tasks and omit tasks older people consider part of their everyday lives. There is a need to work collaboratively to update eADL tools to reflect the priorities and activities of older people to ensure these remain valid measures for use in practice and research. To complement this abstract, a video abstract is available online. A more detailed video-based summary of the content is also available as supplemental material.

Enhancing molecular property prediction with auxiliary learning and task-specific adaptation

Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks. Despite their ability to encode structural and relational features of molecules, traditional fine-tuning of such pretrained GNNs on the target task can lead to poor generalization. To address this, we explore the adaptation of pretrained GNNs to the target task by jointly training them with multiple auxiliary tasks. This could enable the GNNs to learn both general and task-specific features, which may benefit the target task. However, a major challenge is to determine the relatedness of auxiliary tasks with the target task. To address this, we investigate multiple strategies to measure the relevance of auxiliary tasks and integrate such tasks by adaptively combining task gradients or by learning task weights via bi-level optimization. Additionally, we propose a novel gradient surgery-based approach, Rotation of Conflicting Gradients (RCGrad\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathop {\ exttt{RCGrad}}\\limits$$\\end{document}), that learns to align conflicting auxiliary task gradients through rotation. Our experiments with state-of-the-art pretrained GNNs demonstrate the efficacy of our proposed methods, with improvements of up to 7.7% over fine-tuning. This suggests that incorporating auxiliary tasks along with target task fine-tuning can be an effective way to improve the generalizability of pretrained GNNs for molecular property prediction.Scientific contributionWe introduce a novel framework for adapting pretrained GNNs to molecular tasks using auxiliary learning to address the critical issue of negative transfer. Leveraging novel gradient surgery techniques such as RCGrad\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathop {\ exttt{RCGrad}}\\limits$$\\end{document}, the proposed adaptation framework represents a significant departure from the dominant pretraining fine-tuning approach for molecular GNNs. Our contributions are significant for drug discovery research, especially for tasks with limited data, filling a notable gap in the efficient adaptation of pretrained models for molecular GNNs.

Uncovering the interplay between drawings, mental representations, and arithmetic problem-solving strategies in children and adults.

There is an ongoing debate in the scientific community regarding the nature and role of the mental representations involved in solving arithmetic word problems. In this study, we took a closer look at the interplay between mental representations, drawing production, and strategy choice. We used dual-strategy isomorphic word problems sharing the same mathematical structure, but differing in the entities they mentioned in their problem statement. Due to the non-mathematical knowledge attached to these entities, some problems were believed to lead to a specific (cardinal) encoding compatible with one solving strategy, whereas other problems were thought to foster a different (ordinal) encoding compatible with the other solving strategy. We asked 59 children and 52 adults to solve 12 of those arithmetic word problems and to make a diagram of each problem. We hypothesized that the diagrams of both groups would display prototypical features indicating either a cardinal representation or an ordinal representation, depending on the entities mentioned in the problem statement. Joint analysis of the drawing task and the problem-solving task showed that the cardinal and ordinal features of the diagrams are linked with the hypothesized semantic properties of the problems and, crucially, with the choice of one solving strategy over another. We showed that regardless of their experience, participants' strategy use depends on their problem representation, which is influenced by the non-mathematical information in the problem statement, as revealed in their diagrams. We discuss the relevance of drawing tasks for investigating mental representations and fostering mathematical development in school.

Maximum Strength and Power as Determinants of Match Skating Performance in Elite Youth Ice Hockey Players.

Keiner, M, Kierot, M, Stendahl, M, Brauner, T, and Suchomel, TJ. Maximum strength and power as determinants of match skating performance in elite youth ice hockey players. J Strength Cond Res 38(6): 1090-1094, 2024-Maximum strength has a strong influence on speed-strength performances such as sprints and jumps. Important for sports practice is whether these findings are also reflected in game performance. Therefore, the aim of this study was to explore the influence of maximum strength and power performance on linear on-ice skating performance in testing and during game play. A cross-sectional study was conducted, and 24 highly trained male youth ice hockey players participated. Jump performances (countermovement jump [CMJ], drop jumps), maximum strength (1 repetition maximum [1RM] squat and isometric trap bar pull [ITBP]), and on-ice linear sprints (15 m [LS15], 30 m [LS30], flying 15 m [FLY15]) were measured. Match performances (among others: peak skating speed) were collected of 4 regular league games using a local positioning system. Correlation coefficient and explained variance were calculated ( ρ ≤ 0.05). Correlations between maximum strength and jump with on-ice linear sprint performance showed 1-35% explained variance. Correlations between "off ice" test (CMJ, relative 1RM) and game data (peak skating speed) showed 22-30% explained variance, respectively, while ITBP and DJ missed significant level. Between linear sprint and game performance showed 15-59% explained variance. In this study, a clear influence of 1RM in squatting and CMJ performance on on-ice linear sprint as well as in-game peak skating speed was observed. These findings show that strength and jumping performance can be valuable tests within a comprehensive test battery and indicate the relevance of strength and jumping tasks within the regular exercise program to improve in-game skating performance.

AGEISM IN THE LABOR MARKET: LEARNING AND EDUCATION FOR OLDER ADULT JOB SEEKERS

Abstract Despite older workers’ value to an organization and no differences in job performance or productivity from younger workers (Rego et al., 2018), older job seekers encounter ageism in employment during their job search (Zaniboni et al., 2019). Ageism in employment and the workplace have detrimental effects on older job seekers, organizations, and society. Therefore, this conceptual paper explores the financial, emotional, psychological, and physical factors that should be considered when supporting older job seekers in navigating reeducation and learning to obtain employment. We collected and reviewed extant literature on this topic by consulting the following databases: EBSCOhost, PsycINFO, Google Scholar, and PubMed and using search terms such as “older adult,” “workplace,” “ageism,” “employment.” Ageist practices and stereotypes can occur at any stage of the employment process, including application, interview, hiring, training, job duties, benefits, downsizing, or termination. Older job seekers or employees may experience age discrimination in the workplace directly (e.g., if employers consider candidates cannot project the youthful image that the business desires, they may decline to offer the position) or indirectly (employers reject older applicants for a job because of their overqualification). We provide practical implications for older job seekers by exploring workplace learning strategies for older adults, including peer mentoring, intergenerational learning (Arthanat, 2019), proactive personality, and effective job search activity (Zacher, 2013). In addition, we discuss how older workers need to see the relevance of tasks to be individually motivated to learn and explore the need for personalized learning programs (Seo et al., 2019).

Leadership Skills in Startups: A Quantitative Study on the Requirement Profile

Startups are young companies designed to grow quickly. In a highly competitive environment, leadership is a prerequisite for startup survival and an investment criterion for venture capitalists. Although entrepreneurial leadership is considered to be distinct, there is little empirical evidence on the leadership requirements faced by founders and Chief Executive Officers in startups. Against this background, an online survey was conducted on the relevance of leadership tasks and skills. 188 leaders in German startups reported on their understanding of leadership and rated 18 skills on the Occupational Information Network (O*NET) scales. The data show that startup leaders prioritize vision, strategy, and decision making over human resources and team development. The need for strategic, interpersonal, and cognitive skills is consistently perceived as high. Requirements for business skills are moderate and vary widely. The results contribute to a leadership skills model for entrepreneurs and provide implications for entrepreneurship education and training.

Образовательная политика Российской Федерации постсоветского периода в отношении трудностей в обучении и равенства образовательных возможностей

<p>The article examines the educational policy of Russia of the post-Soviet period in relation to the problem of learning difficulties, disadvantaged students, equity in education. Changes in the relevance of tasks and the content of the proposed solutions are analyzed in legislation and strategic documents at different stages in the period from 1992 to 2022. The prospects of keeping the topic on the agenda of educational policy in the current conditions are discussed. <strong><em> </em></strong></p>

Exploring the social aspects of assessment practices in an engineering context

Assessment is a powerful mechanism to leverage student learning and develop skills and practices that prepare students for the world of work. By extension, assessment can also play a critical role in creating learning contexts that are inclusive, responsive, and transformative. This paper draws on findings from a student survey, lecturer interviews, and student focus groups, conducted in an engineering school in South Africa, that provide a rich picture of the interaction between lecturer intentions, assessment practices, student experiences, and how these influence student intentions, actions, and ultimately learning. The findings presented in this paper focus on the social themes that emerged, highlighting the social nature of assessment practices and the role that these play in student identity, confidence, and engagement. The importance of a purposeful and holistically aligned assessment strategy is highlighted through several social themes, including the fairness and relevance of assessment tasks, feedback and relationships between lecturers and students, group work, the formation of social networks and community, and opportunities for including students as partners in assessment practices. The study reveals that a more collaborative and collectivist approach to assessment is needed. This is a significant finding that provides valuable insights that can be used to transform assessment practices, enhance student success, and facilitate social justice.

Feasibility and relevance of an immersive virtual reality cancellation task assessing far space in unilateral spatial neglect.

Unilateral spatial neglect (USN) is a highly prevalent neuropsychological syndrome. However, its assessment in clinical practice, mainly based on paper-and-pencil tests, encounters limits as only near space, called peripersonal, is assessed. However, USN is a multicomponent syndrome that can also affect far space, called extrapersonal. This space is not assessed in current clinical assessment although it can be more impacted than peripersonal space. Immersive virtual reality (VR) allows developing tasks in far space to assess this heterogeneity. This study aimed to test the feasibility and the relevance of an immersive VR task to assess far space. A cancellation task, the Bells test, was used in its original paper-and-pencil version and was also adapted into a far immersive VR version. Ten patients with left USN and sixteen age-matched healthy participants were included. A single-case method was performed to investigate the performance of each patient. Although five patients showed very similar results between both versions, the five others exhibited a dissociation with a more severe impairment in the VR version. Three of these five patients significantly differed from the healthy participants only on the VR version. As USN in far space is not brought to light by paper-and-pencil tests, immersive VR appears as a promising tool to detect USN affecting this space.

Comparative advantages in the digital era–A Heckscher-Ohlin-Vanek approach

This paper revisits the Heckscher-Ohlin-Vanek (HOV) theorem and investigates its fit for digital tasks and ICT capital, which both represent endowment factors that are expected to shape the digital transformation. We use a theory-consistent methodology for calculating the measured net factor content of trade (Trefler and Zhu, . The structure of factor content predictions) and apply it to a unique dataset on digital and non-digital tasks performed in detailed occupations, as well as recent data on ICT capital stocks. Equipped with these data we provide new evidence on the factor-based trade patterns for 25 EU countries and use it to test the HOV theorem. Overall, the performance of the sign test and the rank test is good if not impressive. In 83% of the cases countries are net exporters of those factors with which they are abundantly endowed, with a higher score achieved for digital tasks than for ICT capital. The relevance of digital tasks and ICT capital are corroborated by a gravity model which indicate that higher endowments with these factors foster bilateral exports.

A multi-task learning model for fast prediction of mechanical behavior of UD-CFRP composites under transverse tension

The weaker transverse mechanical response of unidirectional carbon fiber reinforced polymer (UD-CFRP) composites has raised a lot of attention and research. The single-task modeling strategies based on deep learning (DL) have achieved impressive results in terms of surrogating the finite element method to accelerate computation, but they do not sufficiently consider the data correlation between different tasks. In this study, a multi-task learning (MTL) model based on DL is developed for fast and accurate prediction of the transverse mechanical response of UD-CFRP, which includes crack morphology and stress–strain response. Different from traditional single-task learning (STL) models, MTL models can take into account the relevance of different tasks to achieve one model to predict multiple tasks simultaneously. The prediction results of the RVE model confirmed that the developed MTL model has better prediction accuracy and generalization compared to the STL model. Moreover, the model can perform inference for two subtasks simultaneously in milliseconds. The developed MTL model takes the image of the initial structure as input without any prior assumptions and simplifications. Therefore, it has good potential to be extended and transferred to other multitask scenarios in the field of composites.

Changes in Frequency, Relevance, and Importance of Pharmacist-Reported Job-Related Tasks from 2012-2022

Changes in Frequency, Relevance, and Importance of Pharmacist-Reported Job-Related Tasks from 2012-2022

Acoustic analysis in stuttering: a machine-learning study.

Stuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS). We assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine - SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering. Fifty-three PWS (20 children, 33 younger adults) and 71 age-/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN). Acoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings. Acoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).

Automatic Fluency Assessment Method for Spontaneous Speech without Reference Text

The automatic fluency assessment of spontaneous speech without reference text is a challenging task that heavily depends on the accuracy of automatic speech recognition (ASR). Considering this scenario, it is necessary to explore an assessment method that combines ASR. This is mainly due to the fact that in addition to acoustic features being essential for assessment, the text features output by ASR may also contain potentially fluency information. However, most existing studies on automatic fluency assessment of spontaneous speech are based solely on audio features, without utilizing textual information, which may lead to a limited understanding of fluency features. To address this, we propose a multimodal automatic speech fluency assessment method that combines ASR output. Specifically, we first explore the relevance of the fluency assessment task to the ASR task and fine-tune the Wav2Vec2.0 model using multi-task learning to jointly optimize the ASR task and fluency assessment task, resulting in both the fluency assessment results and the ASR output. Then, the text features and audio features obtained from the fine-tuned model are fed into the multimodal fluency assessment model, using attention mechanisms to obtain more reliable assessment results. Finally, experiments on the PSCPSF and Speechocean762 dataset suggest that our proposed method performs well in different assessment scenarios.