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The dual task experimental paradigm is used to probe the attentional requirements of postural control. However, findings of dual task postural studies have been inconsistent with many studies even reporting improvement in postural stability during dual tasking and thus raising questions about cognitive involvement in postural control. A U-shaped non-linear relationship has been hypothesized between cognitive task complexity and dual task postural stability suggesting that the inconsistent results might have arisen from the use of cognitive tasks of varying complexities. To systematically review experimental studies that compared the effect of simple and complex cognitive tasks on postural stability during dual tasking, we searched seven electronic databases for relevant studies published between 1980 to September 2020. 33 studies involving a total of 1068 participants met the review’s inclusion criteria, 17 of which were included in meta-analysis (healthy young adults: 15 studies, 281 participants; Stroke patients: 2 studies, 52 participants). Narrative synthesis of the findings in studies involving healthy old adults was carried out. Our result suggests that in healthy population, cognitive task complexity may not determine whether postural stability increases or decreases during dual tasking (effect of cognitive task complexity was not statistically significant; P > 0.1), and thus the U-shaped non-linear hypothesis is not supported. Rather, differential effect of dual tasking on postural stability was observed mainly based on the age of the participants and postural task challenge, implying that the involvement of cognitive resources or higher cortical functions in the control of postural stability may largely depends on these two factors.

The growing adoption of modular robotic systems presents new challenges in ensuring ease of assembly, deployment, and reconfiguration, especially for end-users with varying technical expertise. This study proposes and validates an Assembly Complexity Index (ACI) framework, combining subjective workload (NASA Task Load Index) and task complexity (Task Complexity Index) into a unified metric to quantify assembly difficulty. Twelve participants performed modular manipulator assembly tasks under supervised and unsupervised conditions, enabling evaluation of learning effects and assembly complexity dynamics. Statistical analyses, including Cronbach’s alpha, correlation studies, and paired t-tests, demonstrated the framework’s internal consistency, sensitivity to user learning, and ability to capture workload-performance trade-offs. Additionally, we propose an augmented reality (AR) and virtual reality (VR) integration workflow to further mitigate assembly complexity, offering real-time guidance and adaptive assistance. The proposed framework not only supports design iteration and operator training but also provides a human-centered evaluation methodology applicable to modular robotics deployment in Industry 4.0 environments. The AR/VR-assisted workflow presented here is proposed as a conceptual extension and will be validated in future work.

When using a tournament in multi-period settings, firms have discretion in selecting the tournament horizon. For example, firms can use a single tournament (a grand tournament) or a sequence of multiple tournaments, each with a shorter horizon than a grand tournament (a repeated tournament). Firms have also begun to use a combination of both in which a repeated tournament is embedded within a grand tournament (a hybrid tournament). Using an experiment, we investigate whether the effect of tournament horizon on performance depends on the dynamic complexity of the task, which reflects the potential for effort in one period to influence the link between effort and performance in future periods. When dynamic task complexity is low, we find that performance is greatest in the hybrid tournament, followed by the repeated and then the grand tournament. In contrast, when dynamic task complexity is high, we find that performance is greatest in the repeated tournament, followed by the grand and hybrid tournaments, with similar performance in the latter two tournaments. More generally, the results of our experiment suggest that the effect of tournament horizon on performance depends on dynamic task complexity. These results can help firms make better decisions when designing their tournaments by reinforcing the need to align the tournament horizon with the task.

Purpose In light of the repercussions of the COVID-19 pandemic, electronic auditing otherwise known as computer-assisted audit tools and techniques (CAATTs) has become inevitable to automate the auditing process worldwide. Accordingly, the purpose of this study is to examine the influence of technological, organizational and environmental (TOE) factors on public sector adoption of CAATTs in developing countries such as Jordan under the COVID-19 pandemic conditions. Design/methodology/approach This study used 136 usable responses from the managers of internal audit (IA) of the Jordanian public sector entities. The data collected were analyzed using partial least squares-structural equation modeling. The TOE framework has been used in this study to consider a wide set of TOE factors. Then, this study suggests a CAATTs adoption model that incorporates the related technology factors of the diffusion of innovation theory to environmental and organizational factors. Further, this study contributes to the TOE framework by addressing government regulations, audit bodies’ support and audit task complexity as environmental factors affecting CAATTs adoption in the context of the public sector. Findings The results revealed that for technological factors, only the compatibility affects CAATTs adoption by the IA departments. For organizational factors, organizational readiness, top management support, auditors’ information technology competency and entity size were found to be significant factors. From the environmental factors, both government regulation and audit task complexity influence the CAATTs adoption. Besides, entity size moderates the influence of top management support on the CAATTs adoption in the public sector. Practical implications The findings could highlight the significance of the CAATTs adoption in the public sector institutions (by internal auditors) post-COVID-19, taking into consideration the TOE framework’s factors. Also, the findings are significant for the decision-makers and regulators in declaring new legislation for the electronic IA profession in the Jordanian public sector. Social implications It turns out that the CAATTs adoption in the public sector can definitely enhance their ability to achieve the role of IA in preserving public funds and restricting corrupt practices within the public sector. Originality/value To the best of the authors’ knowledge, this study is one of the first studies that address the professional audit agency support and audit task complexity as environmental factors, as well as the entity size as an organizational factor, that affect CAATTs adoption in the IA department of the public sector.

Purpose – One core element of interactive information retrieval (IIR) experiments is the assignment of search tasks. The purpose of this paper is to provide an analytical review of current practice in developing those search tasks to test, observe or control task complexity and difficulty. Design/methodology/approach – Over 100 prior studies of IIR were examined in terms of how each defined task complexity and/or difficulty (or related concepts) and subsequently interpreted those concepts in the development of the assigned search tasks. Findings – Search task complexity is found to include three dimensions: multiplicity of subtasks or steps, multiplicity of facets, and indeterminability. Search task difficulty is based on an interaction between the search task and the attributes of the searcher or the attributes of the search situation. The paper highlights the anomalies in our use of these two concepts, concluding with suggestions for future methodological research related to search task complexity and difficulty. Originality/value – By analyzing and synthesizing current practices, this paper provides guidance for future experiments in IIR that involve these two constructs.

Recent second language (L2) writing research informed by task-based theories of second language acquisition has enthusiastically adopted task complexity frameworks to describe the specific cognitive demands of a given writing task and the effect of those cognitive demands on written L2 production. However, missing from many studies on the effects of task complexity on L2 written production is a discussion of genre as a potential source of task complexity. This paper examines the potential of genre as a resource-dispersing form of task complexity that is unique to writing. The article summarizes the predictions of task-based theories of second language acquisition particularly the predictions of the Cognition Hypothesis and its intersection with Kellogg’s widely-cited model of working memory in writing. It then argues that formal genre-specific knowledge constitutes a resource-dispersing form of task complexity that is distinct from general L2 proficiency and general writing proficiency.

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.

Researchers have tried to induce a deeper approach to learning by means of student-centred learning environments. Findings did not always confirm the positive hypotheses. This has given rise to the question as to what the discouraging or encouraging factors are for inducing a deep approach to learning. The aim of this research study is to determine whether perceived workload and task complexity are discouraging or encouraging factors. In addition, these relationships will be investigated under different induced conditions which offer the potential to deepen our understanding of the nature of the investigated relationships. Participants were 128 second year Bachelor level students in educational sciences. After an introduction with the theory, students were given four tasks with various workloads and task complexities after which they filled out questionnaires on learning approaches, perceived workload and perceived task complexity. For every task, correlations and multiple stepwise regressions were calculated. The information from the interviews was used to support and illustrate the results of quantitative analyses. In general, results show no significant relationship between perceived workload and students' approaches to learning. For perceived task complexity, it was found that a perceived lack of information is a discouraging factor for inducing a deep learning approach. A lack of information consistently increases students' surface approaches to learning regardless of the induced workload and task complexity.

Prompt Tuning is a lightweight fine-tuning method that demonstrates efficient task adaptation and parameter efficiency for pre-trained language models (PLMs). Prompt Tuning highlights an important contribution to the advancement of NLP technology. The purpose of this paper is to explore the basic principles and methods of Prompt Tuning and to analyze the design features of discrete and continuous Prompts and their application performance in different scenarios. It is found that discrete Prompt performs well in interpretability and simple task adaptation, while continuous Prompt is more advantageous in complex tasks and cross-domain generalization; meanwhile, Prompt Tuning significantly improves the model performance in less sample scenarios, and the parameter efficiency is several times higher than that of traditional fine-tuning. However, the dependence of Prompt Tuning on Prompt design and the limitation of generalization to specific tasks still need to be further optimized. This paper hopes to provide an important reference for future theoretical research and practical applications of Prompt Tuning.

The paper presents a comparative analysis of engineering tools that are widely utilized in major industries. With the rapidly changing world, new tools emerge and advance, enabling their use for various tasks. Modeling and analysis of structures with complex and simple appearances are an essential part of the engineering world, and hence, choosing the right tool for a certain task can be beneficial in terms of quality and the precision of the end results. This reviews several engineering tools and defines their major advantages. Such tools as SolidWorks, Ansys, Abaqus, Inventor, and Fusion 360 were selected for the research. It is noted in the work that these tools are commonly used in the engineering world for various tasks. It was also found that each engineering tool has its own unique features that differentiate it from others.