Complex Task Research Articles

Relationships Among Baseline Concussion Balance Test and Gaze Stability Test Scores in Division-I Collegiate Athletes.

Sports-related concussions are commonly occurring injuries as a result of sports and recreation that may cause alterations in brain functioning. It is important to be able to evaluate the impact of these injuries on function to manage the injury recovery and ensure recovery. Recent literature suggests the use of objective evaluation strategies in a multifaceted approach to evaluate and manage these injuries. It is important to understand the relationships between the assessments and how best to utilize each assessment. The purpose of this study was to investigate if relationships exist between measures of vestibular function at baseline in assessments that may be used following sports-related concussions. Additionally, a secondary purpose was to determine if self-reported symptoms were related to performance on the assessments. This study aimed to identify if these assessments measured independent functions of the vestibulo-ocular reflex or if some redundancy existed among the assessment strategies. A cross-sectional study design was used in a cohort of collegiate athletes ages 18-24. Participants completed demographics questionnaires, the Post-Concussion Symptom Scale, Gaze Stabilization Test, and Concussion Balance Test. Spearman rho correlations were used to examine the relationships between the measures. One hundred and thirty-five collegiate athletes (82 males and 53females) were included, representative of 3 sports (cheerleading, soccer, and football) with a mean age of 19.77 (1.42)years old. There were weak to moderate, significant relationships between measures of Gaze Stabilization Test and Concussion Balance Test errors (r = .20-.31, P = .001-.03). The direction of these relationships indicated that greater Concussion Balance Test errors were associated with greater Gaze Stabilization Test performance. These relationships may be attributed to the difficulty created by the foam conditions and the integration of more complex sensory tasks required to maintain balance during the more difficult conditions.

Data Complexity: A New Perspective for Analyzing the Difficulty of Defect Prediction Tasks

Defect prediction is crucial for software quality assurance and has been extensively researched over recent decades. However, prior studies rarely focus on data complexity in defect prediction tasks, and even less on understanding the difficulties of these tasks from the perspective of data complexity. In this article, we conduct an empirical study to estimate the hardness of over 33,000 instances, employing a set of measures to characterize the inherent difficulty of instances and the characteristics of defect datasets. Our findings indicate that: (1) instance hardness in both classes displays a right-skewed distribution, with the defective class exhibiting a more scattered distribution; (2) class overlap is the primary factor influencing instance hardness and can be characterized through feature, structural, and instance-level overlap; (3) no universal preprocessing technique is applicable to all datasets, and it may not consistently reduce data complexity, fortunately, dataset complexity measures can help identify suitable techniques for specific datasets; (4) integrating data complexity information into the learning process can enhance an algorithm’s learning capacity. In summary, this empirical study highlights the crucial role of data complexity in defect prediction tasks, and provides a novel perspective for advancing research in defect prediction techniques.

Effect of artificial gravity on neurocognitive performance during head-down tilt bedrest

This study evaluated the acute and chronic effects of intermittent and continuous Artificial Gravity (AG) on cognitive performance during 60 days of Head-down tilt bedrest (HDTBR), a well-established ground-based spaceflight analogue method. Participants were randomly assigned to three groups: intermittent AG, continuous AG, and HDTBR control group without AG exposure. Task performance and electrophysiological measures of attention and working memory were investigated during Simple and Complex tasks in the Visual and the Auditory modality. Compared to baseline, faster reaction time and better accuracy was present during HDTBR regarding the Complex tasks, however, the practice effect was diminished in the three HDTBR groups compared to an ambulatory control group. Brain potentials showed a modality-specific decrease, as P3a was decreased only in the Auditory, while P3b decreased in the Visual modality. No evidence for acute or chronic AG-related cognitive impairments during HDTBR was found.

Enhancing Perceived Value in Human-Computer Interaction: The Mediating Role of User Participation and the Moderating Role of Task Complexity

The study investigates the interplay of human-computer interaction (HCI) quality, user participation, and perceived value in technology interactions. The authors aim to elucidate the mediating role of user participation and the moderating effect of task complexity. Using structural equation modeling, we analyzed a diverse dataset of user feedback. The measurement and structural models are employed to determine the structural paths and predictive capability of the proposed model. Our findings indicate that user participation significantly mediates the impact of HCI quality on perceived value. Further, task complexity moderates these relationships, highlighting its role in enhancing or attenuating the perceived value of digital experiences. These findings provide insights for HCI design, emphasizing the need for user-centered systems that adapt to task complexity. Enhancing perceived value in technology interactions is essential, impacting user satisfaction, loyalty, and ultimately, the success of digital services.

Australian teachers’ conceptualisations of wellbeing at work: A prototype analysis

Defining teacher wellbeing is a complex task, and the relative importance of its components remain unclear. Employing a three-phase prototype analysis, we investigated whether there was an ideal representation of wellbeing at work according to Australian teachers; and if so, which components were considered more important than others. Phase 1 (n = 138) generated wellbeing features. Phase 2 (n = 438) identified central and peripheral components while Phase 3 (n = 418) confirmed recognition of central components in a cognitive task. Analysing linguistic units and measures of reliability, findings revealed a prototypical organisation of the teacher wellbeing at work concept with central components including feeling safe, supported, valued, and trusted, and achieving work/life balance. The results of this study may help inform schools and policy makers design interventions and develop policies to foster thriving educational environments for all teachers.

Measures to Reduce the Discharge of tire Wear into the Environment

The environmental impact of tire wear emissions has become increasingly apparent, and efforts to reduce their impact on the environment are on the rise. To minimise the generation of tire wear, it is essential to consider the influencing factors. However, as it cannot be entirely prevented, measures to reduce immissions are also necessary. This paper summarises possible measures derived from the literature, stakeholder workshops, and the authors’ own conclusions, taking into account the different perspectives: tire, vehicle, road, sustainable mobility and emissions treatment. The presentation of the entry paths of tire wear into the environment and the hotspots of generation can be used to prioritise reduction measures. Measures should be implemented at a political level, technical solutions applied, and awareness raised among the general public. It is evident that reducing tire wear is a complex task that requires a transdisciplinary approach.

Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments.

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Direct-to-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and user-friendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

The factors of job crafting in emergency nurses: regression models versus qualitative comparative analysis

BackgroundJob crafting is defined as a series of proactive behaviors exhibited by employees in order to balance work resources and needs, which has a significant positive impact on the nurses. It is necessary to find the core factors that influence the job crafting, as emergency nurses deal with the most complex tasks, so as to improve their job satisfaction.ObjectivesTo investigate the core factors of job crafting among emergency nurses.MethodsA cross-sectional design was used in the study. A total of 255 nurses were recruited from two hospitals in Zhengzhou and Shenzhen, China in December 2021. 255 nurses completed an online questionnaire. Hierarchical regression models and fsQCA models were used to explore the factors influencing job crafting among emergency nurses and helped us to identify core factors.ResultsThe hierarchical regression model and the fsQCA model found that the occupational benefit, psychological empowerment, and research experience were the core factors affecting their job crafting. Job involvement was not significant in the regression model, but the QCA model indicated that it needs to be combined with other factors to impact on job crafting. The QCA model uncovered seven key conditional configurations that led to high and low job crafting among emergency nurses, explaining 80.0% of the results for high job crafting and 82.6% of the results for the low job crafting, respectively.ConclusionsThe results of this study provide valuable insights into the job crafting experienced by emergency nurses. Junior emergency nurses should be granted a high level of psychological empowerment without assigning them overly complex tasks, such as research tasks, as these challenges can stop their job crafting. Intermediate and senior emergency nurses, on the other hand, can be assigned research tasks coupled with high psychological empowerment to enhance their job crafting.

Designing Reward Functions Using Active Preference Learning for Reinforcement Learning in Autonomous Driving Navigation

This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional reward functions, which often fail to facilitate complex tasks in continuous state spaces. We propose the adoption of active preference learning to resolve these issues and to generate reward functions that align with human preferences. This approach leverages an individual’s subjective preferences to guide an agent’s learning process, enabling the creation of reward functions that reflect human desires. We utilize mutual information to generate informative queries and apply information gained to balance the agent’s uncertainty with the human’s response capacity, encouraging the agent to pose straightforward and informative questions. We further employ the No-U-Turn Sampler (NUTS) method to refine the belief model, which outperforms that constructed using the Metropolis algorithm. Subsequently, we retrain the agent using reward weights derived from active preference learning. As a result, our autonomous driving vehicle can navigate between random starting and ending points without dependence on high-precision maps or routing, relying solely on its forward vision. We validate our approach’s performance within the CARLA simulation environment. Our algorithm significantly improved the success rate of autonomous driving navigation tasks that originally failed due to artificially designed rewards, increasing it to approximately 60%. Experimental results show significant improvement over the baseline algorithm, providing a solid foundation for enhancing navigation capabilities in autonomous driving systems and advancing the field of autonomous driving intelligence.

Artificial intelligence in healthcare: an Italian perspective on ethical and medico-legal implications.

Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors' decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine "with" machines and not "of" machines.

Individual and team profiling to support theory of mind in artificial social intelligence.

We describe an approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions. We show how this can be supported through the provision of quantifiable, machine-readable, a priori information about the human team members to an agent. We first show how our profiling approach can capture individual team member characteristic profiles that can be constructed from sparse data and provided to agents to support the development of artificial theory of mind. We then show how it captures features of team composition that may influence team performance. We document this through an experiment examining factors influencing the performance of ad-hoc teams executing a complex team coordination task when paired with an artificial social intelligence (ASI) teammate. We report the relationship between the individual and team characteristics and measures related to task performance and self-reported perceptions of the ASI. The results show that individual and emergent team profiles were able to characterize features of the team that predicted behavior and explain differences in perceptions of ASI. Further, the features of these profiles may interact differently when teams work with human versus ASI advisors. Most strikingly, our analyses showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures. We discuss these findings in the context of developing intelligent technologies capable of social cognition and engage in collaborative behaviors that improve team effectiveness.

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG.

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Learning-based methods for adaptive informative path planning

Adaptive informative path planning (AIPP) is important to many robotics applications, enabling mobile robots to efficiently collect useful data about initially unknown environments. In addition, learning-based methods are increasingly used in robotics to enhance adaptability, versatility, and robustness across diverse and complex tasks. Our survey explores research on applying robotic learning to AIPP, bridging the gap between these two research fields. We begin by providing a unified mathematical problem definition for general AIPP problems. Next, we establish two complementary taxonomies of current work from the perspectives of (i) learning algorithms and (ii) robotic applications. We explore synergies, recent trends, and highlight the benefits of learning-based methods in AIPP frameworks. Finally, we discuss key challenges and promising future directions to enable more generally applicable and robust robotic data-gathering systems through learning. We provide a comprehensive catalog of papers reviewed in our survey, including publicly available repositories, to facilitate future studies in the field.

Animating Hypothetical Trips to Communicate Space-Based Temporal Uncertainty on Digital Maps.

This paper explores a novel approach to communicating plausible space-based temporal variability of travel durations. Digital maps most often only convey single numerical values as the estimated duration for a path and this piece of information does not account for the multiple scenarios hidden behind this point estimate, nor for the temporal uncertainty along the route (e.g., the likelihood of being slowed down at an intersection). We explore conveying this uncertainty by animating hypothetical trips onto maps in the form of moving dots along one or more paths. We conducted a study with 16 participants and observed that they were able to correctly extract and infer simple information from our uncertainty visualizations but that identifying moving dots' changes in speed is a more complex task. We discuss design challenges and implications for future visualizations of space-based temporal uncertainty.

Decision-making concordance: A comparative analysis between a multidisciplinary tumor board and a large language model (ChatGPT) in breast cancer management.

e13648 Background: Advances in breast cancer management have led to more tailored therapies, increasing the complexity of decision-making. Establishing multidisciplinary tumor boards (MTB) is crucial for optimizing patient care, challenging healthcare teams intellectually. The integration of expertise, deep understanding, and clinical judgment is crucial. Artificial intelligence (AI), particularly through rapid developments and Large Language Models (LLM) offers opportunities to revolutionize healthcare by processing complex ideas and integrating medical evidence into decisions. This study evaluates ChatGPT-4 concordance with a Breast MTB. Methods: This study included 124 patients discussed at our Tumor Board from March 2023 to January 2024, treatment decisions from ChatGPT-4 were compared with those made by the MTB. Results: The median age was 52 years (24-85), with 98% female and 51% premenopausal. In 36% of the patients, the diagnosis followed a screening, while 64% were diagnosed due to symptoms like a palpable nodule or skin changes. 33% of patients meet the NCCN criteria for a family history of cancer. The distribution of clinical stages at diagnosis was 0 (15%), I (35%), II (31%), III (10%), and IV (8%). Most cases were left-sided (52%), followed by right-sided (43%), and bilateral in 5%. The phenotype was Luminal A-like (35%), Luminal B-like HER2 positive (23%), Luminal B-like HER2 negative (15%), and triple negative (10%). The overall concordance between the LLM and MTB rate achieved was 80.65% in patient profiles, including ductal carcinoma in situ. Concordance rates for treatment option indications were high: surgery (96%), radiotherapy (98%), chemotherapy (97%) and endocrine therapy (100%). For additional evaluations (PET/CT or oncogeriatric assessment), the concordance rate was 99%, for indicating genomic signatures was 96%, and for referrals for genetic evaluation, it was 93%. The lowest concordance was observed in type of anti HER2 therapy at 68%. Overall management concordance for patients in stage IV was 50%. Conclusions: AI is seeking ways to integrate into clinical routines by identifying individualized and personalized therapy option for our patients. We explore the concordance and feasibility of employing the LLM ChatGPT-4 in executing one of the most complex tasks in patient care, achieving high rates of concordance. Currently, AI is not capable of replacing medical professionals, but the use of LLM such as ChatGPT as an assistance and support tool for clinicians is likely to expand and evolve. Its capabilities could make it a valuable support tool for therapeutic management in the near future. All authors confirm that this abstract was not thought of or written by ChatGPT.

Machine learning trial to detect sex differences in simple sticker arts of 1606 preschool children.

Previous studies suggested that drawings made by preschool boys and girls show distinguishable differences. However, children's drawings on their own are too complexly determined and inherently ambiguous to be a reliable indicator. In the present study, we attempted to develop a machine learning algorithm for classification of sex of the subjects using children's artworks. We studied three types of simple sticker artworks from 1606 Japanese preschool children aged 51-83 months (803 boys and 803 girls). Those artworks were processed into digitalized data. Simulated data based on the original data were also generated. Logistic regression approach was applied to each dataset to make a classifier, and run on each dataset in a stratified ten-fold cross-validation with hyperparameter tuning. A probability score was calculated in each sample and utilized for sex classification. Prediction performance was evaluated using accuracy, recall, and precision scores, as well as learning curves. Two models created from the original and simulated data showed comparably low metrics. The distributions of probability scores in the samples from boys and girls mostly overlapped and were indistinguishable. Learning curves of the models showed an extremely under-fitted pattern. Our machine learning algorithm was unable to distinguish simple sticker arts created by boys and girls. More complex tasks will enable to develop an accurate classifier.

Adaptive differential steering strategy for distributed driving unmanned ground vehicle with variable configurations based on modified localized modelling sliding mode control

When performing complex tasks such as position transfer and material transportation, the distributed driving unmanned platform with variable configurations needs to address the challenge of multi-wheel cooperative torque distribution control to achieve high-performance differential steering and enhance vehicle dynamics. The configuration change will impact the dynamic performance of the unmanned platform, posing a challenge to the performance of the existing control strategy based on mathematical model development. In order to address the aforementioned issues, this paper analyzes the impact of changes in vehicle configuration on steering gain and proposes a hierarchical adaptive differential steering strategy based on variable vehicle configurations. Firstly, the response characteristics of the yaw angle relative to the active yaw moment under the influence of changes in wheelbase and tread are analyzed. Based on this analysis, two structural modes, maneuverable and balanced, are selected. Secondly, a localized-modelling sliding mode control method with an extended state observer is proposed to estimate the desired yaw moment in the upper controller, considering the motor's execution delay. Then, the lower controller optimizes the torque of each wheel in real-time using the whale optimization algorithm. It aims to optimize tire energy dissipation and tire load rate while ensuring driving stability and achieving differential steering. Finally, through co-simulation and experiments on a scaled prototype, the reliability of the dynamics theory and the superiority of the control algorithm are validated. This optimization has led to significant improvements in the tire dissipation energy index and tire load rate index.

Real-Time Phased Array Ultrasonic Monitoring of Debonding Growth Under Cycling Loading in CFRP

Carbon Fibre Reinforced Polymer (CFRP) composites are widely employed in diverse industries due to their specific stiffness and strength, and corrosion resistance. However, over time, service-induced damage can initiate a failure that can threaten the operation’s safety and reliability, especially in critical industries like aerospace applications. The challenge lies in identifying and monitoring the defects before they escalate into serious issues. Non-Destructive Testing (NDT) methods play a pivotal role in this scenario, aiming to detect and evaluate defects without causing harm to the material. Detecting flaws in composite materials such as CFRP remains a complex task. Traditional NDT methods often struggle to effectively pinpoint and assess defects within these intricate structures. However, recent advancements in ultrasonic testing, especially Phased Array Ultrasonic Testing (PAUT), show promise in examining the internal composition of CFRP composites with higher accuracy and reliability. This study aims to demonstrate the applicability of PAUT for real-time monitoring of debond growth under dynamic loading. The loading was applied to the test specimens up to one million cycles, and 64 elements 2 MHz linear phased array probe was attached to the specimen. The acquired signals were analyzed and could also be recorded for offline processing or archiving. To validate the credibility of the findings, the performance of PAUT was compared to Acoustic Emission (AE), a well-established method for detecting and analysing high-frequency signals emitted by materials undergoing deformation or damage. Encouragingly, both methods exhibited similar trends in capturing the growth of debond. Furthermore, to verify the final defect lengths, post-test ultrasonic C-Scans utilizing the through transmission technique were conducted, complemented by visual inspections. These assessments served to confirm the accuracy and reliability of the results obtained through Phased Array Ultrasonic Testing. Real-time monitoring of composite structures using phased array technology comprises a valuable tool for real- time assessment of debonding in the composite. As industries continue to rely on Carbon Fibre Reinforced Polymer composites for multifaceted applications, the utilization of advanced Non-Destructive Testing methodologies like Phased Array Ultrasonic Testing emerges as a cornerstone in guaranteeing their continued integrity and performance.

An anti-infection and biodegradable TFRD-loaded porous scaffold promotes bone regeneration in segmental bone defects -experimental studies.

Addressing segmental bone defects remains a complex task in orthopedics, and recent advancements have led to the development of novel drugs to enhance the bone regeneration. However, long-term oral administration can lead to malnutrition and poor patient compliance. Scaffolds loaded with medication are extensively employed to facilitate the restoration of bone defects. Inspired by the local use of total flavonoids of Rhizoma Drynariae (TFRD) in the treatment of fracture, a novel 3D-printed HA/CMCS/PDA/TFRD scaffold with anti-infection, biodegradable and induced angiogenesis was designed, and to explore its preclinical value in segmental bone defect of tibia. The scaffold exhibited good degradation and drug release performance. In vitro, the scaffold extract promoted osteogenesis by enhancing bone-related gene/protein expression and mineral deposition in BMSCs. It also stimulated endothelial cell migration and promoted angiogenesis through the upregulation of specific genes and proteins associated with cell migration and tube formation. This may be attributed to the activation of the PI3k/AKT/HIF-1α pathway, facilitating the processes of osteogenesis and angiogenesis. Furthermore, the HA/CMCS/PDA/TFRD scaffold was demonstrated to alleviate infection, enhance angiogenesis, promote bone regeneration, and increase the maximum failure force of new formed bone in a rat model of segmental bone defects. Porous scaffolds loaded with TFRD can reduce infection, be biodegradable, and induce angiogenesis, presenting a novel approach for addressing tibial segmental bone defects.

Can Digital Mindfulness Stimulate the Effects of EFL Learners’ Cognitive Awareness on Cognitive Load in Computer Assisted Writing Classrooms?

This study investigates the impact of digital mindfulness (DMFN) on the cognitive load (CL) experienced by English as a Foreign Language (EFL) learners in computer-assisted writing classrooms, with cognitive awareness (CA) as a mediator and perceived task complexity (PTC) as a moderator. Employing a time-lagged survey-based methodology, EFL learners’ perceptions are assessed at multiple time points to establish causal relationships. The findings reveal that DMFN has a significant direct and indirect effect on CL through the mediation of CA. Moreover, PTC is found to moderate the relationship, demonstrating that the effectiveness of DMFN in reducing CL is more pronounced when learners perceive writing tasks as highly complex. This study suggests that the incorporation of DMFN practices can enhance EFL learners’ CA, subsequently alleviating CL, especially when learners perceive writing tasks as challenging. The implications of this research highlight the potential benefits of DMFN in language learning environments, offering a promising avenue for educators to optimize writing instruction in computer-assisted classrooms.