Individual Tasks Research Articles

Changing the Formations of Unmanned Aerial Vehicles

The development of hierarchical structures of unmanned aerial vehicles (UAVs) increases the efficiency of unmanned aerial systems. The grouping of UAVs increases the region of recognition or force of assault. Achieving these requirements is possible through a UAV formation. The UAVs in the formation must be controlled and managed by a commander, but the commander cannot control individual UAVs. The UAVs within the formation have assigned specific individual tasks, so is possible to achieve the flight of the formation with minimum collisions between UAVs and maximized equipment utilization. This paper aims to present a method of formation control for multiple UAVs that allows dynamic changes in the constellations of UAVs. The article includes the results of tests and research conducted in real-world conditions involving a formation capable of adapting its configuration. The results are presented as an element of research for the autonomy swarm, which can be controlled by one pilot/operator. The control of a swarm consisting of many UAVs (several hundred) by one person is now a current problem. The article presents a fragment of research work on high-autonomy UAV swarms. Here is presented a field test that focuses on UAV constellation control.

Employing Evolutionary Computing and Hybrid Artificial Neural Networks to Improve Drug Discovery

An ultimate relevant translational scientific endeavour that contributes to human vulnerability and happiness might be the creation and advancement of medications. Fast drug discovery procedures necessitate using contemporary computational approaches to tackle pharma data's complexities and high complexity. By combining Evolutionary Computing with Hybrid Artificial Neural Networks (EC-HANNs), this research introduces a novel strategy for optimizing and speeding up the drug development process. To handle various drug-target relations, predict the efficacy of compounds, and discover more accurate potential medications, the proposed model employs EC to evolve the computational construction and hyperparameters of HANNs in real-time. This innovation makes the model a flexible framework. Through repeated refinement of EC- HANN designs, the EC Module uses Particle Swarm Optimization (PSO) to choose the best configuration for individual drug discovery tasks. The model's distinctive feature is that it dynamically adjusts the network configuration to match the details of each drug discovery activity by using PSO to optimize the HANN's architecture and hyperparameters. Regarding sequential biological data, the HANN Module employs Convolutional Neural Networks (CNNs) to glean features for pattern recognition over time. By extracting high-level spatial information from genetic data, CNN can better identify prospective medication candidates. When tested on several benchmark datasets, the proposed framework demonstrates superior performance over traditional neural networks across the board regarding prediction accuracy, convergence speed, and model durability. As a powerful tool, this hybrid approach can simplify drug discovery, leading to more efficient drug development.

A risk-based approach to mitigating the Gen(AI) challenge to assessment integrity

In the evolving landscape of higher education, emerging technologies such as Generative AI (GenAI) present both opportunities and challenges. The rapid increase in access to, and sophistication of, GenAI poses significant challenges for our institution and the wider sector (Bearman & Luckin 2020, Bobula 2024, Chan & Hu 2023). The most immediate of these challenges is the increased risk to the existing approaches used to assure that students achieve the stated outcomes of their learning experiences. Director of Higher Education Integrity Unit, TEQSA, Dr Helen Gniel argues ‘We are entering an age where institutions can no longer ensure the integrity of every single assessment, and it makes sense to think at that bigger level […] what are the mechanisms that you have in place to be confident that an individual has demonstrated the learning outcomes to receive that degree’(TEQSA information request: AI risk mitigation, webinar, 21 March 2024). While higher education institutions are exploring opportunities to realise the potential benefits of GenAI, our immediate focus is on mitigating these risks. This poster describes institutional Programmatic Assessment Security Project (PASP) at the University of Newcastle (UON), a pioneering four-stage initiative designed to address academic integrity risks associated with inappropriate uses of GenAI in summative assessment (Lodge 2024). Academic integrity risks in this context refers to the potential for students to engage in academic misconduct by using GenAI tools in ways that violate institutional policies or ethical standards for assessment. These risks pose a threat to the validity and reliability of summative assessments, potentially compromising the integrity of academic qualifications and the learning process itself. Led by the central teaching and learning support unit, in collaboration with the Artificial Intelligence Working Group (AIWG) a group of expert practitioners and academic stakeholders at UON, the PASP aims to secure assessments across a program of study, assuring student attainment of program learning outcomes. This innovative approach aligns with the sector’s focus on programmatic assessment and supports compliance with regulatory requirements, including TEQSA’s mid 2024 request for institutions to submit a detailed action plan to mitigate the risks that GenAI poses to the integrity of awards (TEQSA 2024). This poster showcases exemplary innovation in practice through the PASP, advancing research and practice in technology-enhanced learning (TEL). The PASP employs a risk-based approach, identifying seven risk factors for inappropriate student use of GenAI in assessment tasks. The first step in this project involved Course Coordinators completing an Assessment Reflection Survey, guiding them to reflect on individual assessment tasks in relation to these risk factors. The survey data were then used to compile an understanding of the varying levels of assessment risk across the institution’s programs. The PASP will deliver a defined series of secure assessments, delivered at key moments across a program of study to assure student attainment of program learning outcomes. This poster provides insight into UON’s contemporary and agile approach to TEL and assessment redesign, with potential for staff at other institutions to adopt similar responses.

Type 1 Diabetes in care homes: A practical guide on management.

The primary purpose of the original NAPCHD project was to develop a national Strategic Document of Diabetes Care for Care Homes which has now been completed and well received as a worthwhile, sustainable, and effective guidance for delivering quality diabetes care in the UK. A Working Group of NAPCHD was established to produce a Position Statement on type 1 diabetes in care homes since this area was recommended as a topic to further develop. There are currently limited data on the prevalence and clinical outcomes associated with type 1 diabetes in care homes and management policies have been non-existent in the UK. Communication among all key stakeholders involved in direct care of residents with type 1 diabetes is generally fragmented and lacks coordination. This is compounded by a slowly growing utilisation of diabetes technology and the absence of a standard/agreed community-based model of interdisciplinary collaboration. The Rationale and Objectives were defined prior to commencing the work and a work plan with individual tasks was initially set out. After multiple correspondences and Team calls over a period of 9 months, the Group successfully generated a first draft in October 2023. This draft was then finalised the following month and circulated among stakeholders for feedback. Nine chapters have been provided including minimum standards of diabetes care, insulin regimens, avoiding hospitalisation and discharge planning. A scheme for a community-based model of care for type 1 diabetes has been included. Eight key messages were developed. In addition, an Appendix has been created which includes key assessments such as nutritional assessment, detection of frailty, sick day rules and foot risk stratification (available online).

From problem-crumbling to innovation: The design of a comprehensive professional development training programme to enhance creative competence among TEFL educators

This paper explores creativity in English language teaching through a multiple-case study action research-based continuous professional development (CPD) training programme. It introduces a professional creativity framework for EFL teachers, fostering pedagogical innovation through a blended course delivery. The study, involving seven EFL (English as a Foreign language) teachers, employs a triangulation of methods and data sources, including action research, surveys, Think Aloud Protocol (TAP), reflective diaries, and semi-structured interviews. The research sample consists of primary and secondary EFL teachers. The CPD training integrates creativity into English lessons, enhancing professional creativity for EFL teachers. The findings shed light on the relationship between professional development and creative endeavours, highlighting the need for interventions to boost self-awareness and self-efficacy among TEFL teachers. The study underlines the significance of individual and group tasks, reflective practice, and technology-enabled communities of practice. Additionally, it offers a descriptive overview of diverse TEFL teacher creativity profiles, contributing to understanding this specific creativity domain and suggesting avenues for future research.

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Modeling and solving time-sensitive task allocation for USVs with mixed capabilities

Efficient task allocation for Unmanned Surface Vehicles (USVs) in maritime operations is crucial for optimal resource utilization and mission success. This paper introduces a task assignment model that accounts for the capabilities and time constraints associated with the heterogeneity of USVs. An Extended-Restriction Multiple Traveling Salesmen Problem (ER-MTSP) model is formulated, encapsulating the diverse capabilities of USVs and time restrictions. To solve this problem, a Fuzzy Enhanced Non-Dominated Sorting Genetic Algorithm (FENSGA-II) is developed, incorporating adaptive random testing initialization and customized hierarchical operators to generate high-quality Pareto frontiers. The fuzzy-linguistic satisfactory degree then re-evaluates the linguistic importance preferences of the objectives within the Pareto set, aiding in reasonable decision-making. Additionally, a two-phase refinement strategy balances individual task values and structure topology, enabling flexible task selection in emergency situations. Simulations and lake trials conducted across various problem variants demonstrate the effectiveness of the model. The results highlight the superiority of our approach compared to current combinatorial optimization methods, providing enhanced solutions across different problem variations.

A Model for Individual Creative Tasks for Students in TVET: Analysis and Illustrative Applications with Examples

A Model for Individual Creative Tasks for Students in TVET: Analysis and Illustrative Applications with Examples

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

EEG oscillations in recent suicide attempters: Assessing responses to positive and negative future imagination tasks

BackgroundOur goal is to uncover Electroencephalography (EEG) power spectrum patterns during imagination tasks in individuals who attempted suicide within the past 1–4 weeks, addressing gaps in understanding the neural correlates of future imagination in suicidal behavior. MethodsThis case-control study comprised a total of 60 participants, consisting of 47 females and 13 males. The sample comprised three groups: Attempted Suicide + Major Depressive Disorder (SA+MDD), Major Depressive Disorder (MDD), and Healthy Controls (HC). To assess participants' future imagination, a unique approach called the Positive and Negative Future Imagination Task was designed, which involved scenario visualization concurrent with EEG recording. Statistical analyses included ANOVA with post-hoc pairwise comparisons to compare EEG power spectrum between three groups. ResultsIt was observed that the SA+MDD group experienced significantly increased theta frequency band in the right frontocentral regions when compared to the MDD group during both positive and negative imagination tasks (P-value < 0.05). Furthermore, increased gamma activity was observed in the SA+MDD group compared to the HC group, predominantly in the right frontocentral areas during both imagination tasks. ConclusionThe results of this study indicate that individuals who have recently attempted suicide exhibit heightened neural activity in the frontocentral regions of the right hemisphere of the brain, specifically in theta and gamma band frequencies, when contemplating both positive and negative aspects of the future. These findings, in the context of behavioral tasks may indicate a decrease in the ability to envision a positive future and an increase in visualizing a negative future.

Teacher Training Dynamics: Positive and Negative Interdependence in Moroccan Training Centers

This study explores the dynamics of competition and collaboration among trainee teachers at the Centres Régionaux des Métiers de l’Éducation et de la Formation (CRMEF) in Morocco. Given the increasing precarity in teacher training centers and the heightened emphasis on effectiveness and academic excellence, the research examines how these factors shape trainees' learning environment and professional development. Using Social Interdependence Theory, the study assesses how varying levels of competition and collaboration impact trainees' attitudes, behaviors, and overall performance, offering insights into their educational experiences and outcomes. The study employed an exploratory sequential design, beginning with data from two focus group discussions to develop a survey, which was then administered to 157 participants from three regional academies. The findings reveal high levels of competition at the CRMEF, contributing to increased stress and reduced teamwork effectiveness. However, trainees demonstrate strong collaboration in group tasks while displaying competitive behaviors during individual tasks. The study suggests that team-based assessments could mitigate the negative impacts of competition and enhance collaborative efforts.

Modeling extreme events: Univariate and multivariate data-driven approaches

AbstractThis article summarizes the contribution of team genEVA to the EVA (2023) Conference Data Challenge. The challenge comprises four individual tasks, with two focused on univariate extremes and two related to multivariate extremes. In the first univariate assignment, we estimate a conditional extremal quantile using a quantile regression approach with neural networks. For the second, we develop a fine-tuning procedure for improved extremal quantile estimation with a given conservative loss function. In the first multivariate sub-challenge, we approximate the data-generating process with a copula model. In the remaining task, we use clustering to separate a high-dimensional problem into approximately independent components. Overall, competitive results were achieved for all challenges, and our approaches for the univariate tasks yielded the most accurate quantile estimates in the competition.

Reaching the cognitive-motor interface: effects of cognitive load on arm choice and motor performance after stroke.

A vexing characteristic of motor disability after stroke is that many individuals fail to use their affected arm effectively despite having the capacity to do so, a phenomenon termed arm nonuse. Based on the hypothesis that nonuse is influenced by the competing cognitive demands of many daily activities, we examined the effects of cognitive load on arm choice and motor performance in individuals with stroke using a novel virtual reality paradigm that mimics the demands of real-life visual search, object selection, and reaching to targets. Twenty individuals with single left or right hemispheric chronic stroke (11 left cerebrovascular accident; 9 right cerebrovascular accident) and 10 age-matched neurotypical participants completed the Virtual Reality Arm Choice task, in which they reached for target objects in an array under varied cognitive demand. To manipulate cognitive demand, we varied the semantic similarity of objects in the reaching space and the presence or absence of a secondary task. The results showed reduced use of the paretic arm under increased demand. Under cognitive load, participants with stroke also showed slower reach initiation, slower movements, increased reach curvature, and increased performance differences between the paretic and non-paretic arms. The arm choice of neurotypical individuals was also modulated under cognitive load. These data indicate that cognitive factors influence arm choice and motor performance in naturalistic reaching tasks in individuals with chronic stroke. Performance decrements under cognitive load may in turn influence reduced paretic arm use during daily activities.

EEG Evidence of Acute Stress Enhancing Inhibition Control by Increasing Attention.

Research about the impact of acute stress on inhibitory control remains a contentious topic, with no consensus reached thus far. This study aims to investigate the effects of acute stress on an individual's inhibitory control abilities and to elucidate the underlying neural mechanisms by analyzing resting state electroencephalogram (EEG) data. We recruited 32 male college students through participant recruitment information to undergo within-subject experiments under stress and non-stress conditions. Physiological indicators (cortisol and heart rate), self-report questionnaires, and behavioral data from the Stroop task were collected before, during, and after the experiment. Additionally, a five-minute eyes closed resting state EEG data collection was conducted during the Stroop task before. (1) Acute stress led to a reduction in the conflict effect during the participants' Stroop task in individuals. (2) Stress resulted in an increase in the power of the beta in the resting state EEG. (3) Acute stress caused an increase in the duration of class D and an increase in the transition probabilities from classes C and B to class D in the microstates of the resting state EEG. (4) Acute stress leads to an increase in beta power values in individuals' resting state EEGs, which is significantly negatively correlated with the reduction of the conflict effect in the Stroop task under stress. Acute stress can enhance individuals' attentional level, thereby promoting inhibitory control performance.

EEG synchronization patterns during a Go/No-Go task in individuals with aphasia in subacute and chronic phases of stroke

Stroke and subsequent neuroregenerative processes cause changes in neural organization of attentional functions. In this study, we attempted to identify differences in neural synchronization patterns during a visual Go/No-Go task in people with post-stroke aphasia in both subacute and chronic stroke phases. To identify neuronal underpinnings of the behavioral differences we investigated pairwise connectivity patterns using corrected imaginary phase locking value and graph-theoretic measures (efficiency, modularity and clustering coefficient) at global and local level in subacute (n = 13) and chronic stroke phases (n = 14) during a Go/No-Go task. We observed significantly lower phase synchronization in the Subacute Group in the alpha band in the connections spanning frontal and central areas of both hemispheres alongside lower local efficiency and clustering coefficient in the left frontal region. Additionally, we observed higher modularity in the beta band in the unaffected right parietal region in the Subacute Group which may denote inhibition of motor and attention functions. Those mechanisms could serve to align cognitive abilities between the damaged and healthy hemispheres, harmonizing the activity of the neuronal networks of both hemispheres disrupted by the effects of the stroke. Our findings have potential implications for rehabilitation therapies, which should take into account the pattern of connectivity changes during different phases of reovery.

Methodology for organizing an individual-personal approach for studying the theme "Modeling and formalization" at informatics lessons

The article discusses the possibilities of implementing an individual-personal approach in informatics lessons. The main technique for implementing such an approach is to integrate the material of various school disciplines that are studied at the same time, and to give individual tasks to students at informatics lessons taking into account their interests and preferences. Examples are given that can be implemented when studying the theme "Modeling and formalization" of the informatics course, in integrating the material studied in informatics with the material of biology, physics, chemistry and geography lessons. The considered method of implementing an individual-personal approach can be used both in studying other themes of the informatics course in secondary school and in integrating informatics with other school subjects.

Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes

Abstract Manufacturing processes undergo continuous changes in order to meet various requirements, such as process/product changes and variations in tool/workpiece conditions, leading to mixed, heterogenous, or anomalous data. As a result, a quality prediction model trained from previous data may not perform well when new tasks emerge. In order to achieve in-time and accurate product quality prediction, it is crucial to develop a predictive method that adapt to variations in the manufacturing system, capable of learning from new tasks without forgetting previous ones and detecting unknown tasks. This study proposes a deep learning method integrated with continual learning for in-situ quality prediction that is capable of learning from new tasks without forgetting previous ones. To demonstrate this idea, deep CNNs are designed to analyze in-process sensor data, which consist of shared layers to capture the common underlying features across all tasks, and task-specific layers that capture specific characteristics of each individual task. In order to identify the task to which incoming product belongs, a task prediction approach based on task relevancy using filter subspace distance is proposed. When new data come in, the model first identifies the task, followed by predicting the quality of the current product. The proposed method is demonstrated on two case studies, including quality prediction of the workpiece using acoustic emissions during the laser-induced plasma micro-machining process, and quality prediction of the product through thermal images during the hot stamping process.

Investigation of comparative Nonword repetition performance in multiple sclerosis: Group differences, subtype variations, and disability effects

This study investigated Nonword Repetition (NWR) tasks in individuals with Multiple Sclerosis (MS) compared to healthy controls (HC), focusing on phonological working memory (WMP). Significant differences were found in NWR acurracy (NWRacc) score between MS subgroups and HC (H = 48.2, p < 0.001). NWRacc decreased as the number of syllables increased in both groups, indicating increased cognitive load. All MS subtypes showed lower NWRacc compared to HC across varying syllable lengths (Mann Whitney U Test: two syllables U = 64.5, p < 0.001; three syllables U = 183, p < 0.001; four syllables U = 248, p < 0.001; five syllables U = 283.5, p < 0.001). However, no significant differences were found within MS subtypes based on syllable length. NWRacc did not differ between mild and severe MS groups. Overall, the NWR test effectively assessed WMP in MS, highlighting its utility in diagnosing and addressing language-cognitive challenges in individuals with MS. This underscores the importance of tailored intervention strategies to mitigate these challenges.

Upaya Meningkatkan Hasil Belajar Pendidikan Agama Katolik dan Budi Pekerti dengan Metode Pembelajaran Problem Based Learning Berbantuan Media Video pada Kelas X Tari di SMK Negeri 1 Kasihan

This study aims to improve the learning of Catholic Religious Education in class X Tari at SMK Negeri 1 Kasihan, which was initially dominated by the less interactive lecture method, affecting the motivation and learning outcomes of students. To overcome this, the Problem Based Learning (PBL) method assisted by video media is applied, which involves active students in solving problems relevant to everyday life. The research was conducted in two cycles, with PBL steps such as problem orientation, individual and group investigation, and presentation and evaluation of results. The results showed significant improvements in the cognitive, skill and affective aspects of learners. The cognitive average score of the group task increased by 11.2%, from 79.7 in cycle I to 90.8 in cycle II, while the individual task increased by 12%, from 76.8 to 89. In terms of skills, the average score increased by 13%, from 76 to 88. The affective aspect also experienced a significant increase of 23.2%, from 63.4 to 88. The application of PBL assisted by video media proved effective in improving students' learning outcomes, both in terms of critical thinking, responsibility, and understanding and application of Catholic teachings. This study recommends the use of PBL method and video media as an effective learning strategy to improve the quality of learning Catholic Religious Education and Cultivation.

A Task-Guided, Implicitly-Searched and Meta-Initialized Deep Model for Image Fusion.

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. However, most existing methods just consider image fusion as an individual task, thus ignoring its underlying relationship with these downstream vision problems. Furthermore, designing proper fusion architectures often requires huge engineering labor. It also lacks mechanisms to improve the flexibility and generalization ability of current fusion approaches. To mitigate these issues, we establish a Task-guided, Implicit-searched and Meta-initialized (TIM) deep model to address the image fusion problem in a challenging real-world scenario. Specifically, we first propose a constrained strategy to incorporate information from downstream tasks to guide the unsupervised learning process of image fusion. Within this framework, we then design an implicit search scheme to automatically discover compact architectures for our fusion model with high efficiency. In addition, a pretext meta initialization technique is introduced to leverage divergence fusion data to support fast adaptation for different kinds of image fusion tasks. Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e.g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.