Complex Task Research Articles

The Human-Centred Design of a Universal Module for Artificial Intelligence Literacy in Tertiary Education Institutions

Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.

The Role of Effective Project Management in Strengthening Competitiveness

Objective: The complexity, or complexity of our world is not only due to the increasing number of changes in our lives. We are faced with more and more difficult and complex tasks and goals every day, and it is very difficult to perform better and better in the face of these tasks and challenges. Project management is precisely about helping to turn complex tasks into well-executed processes that always end with a result. Project management is more than a set of methodologies: it is a dynamic discipline that organises complex tasks, mitigates risks and directs efforts towards a common goal. Its relevance can be demonstrated in many areas. We can measure the success of our project management system on a number of points, from efficiency to the quality of teamwork and management satisfaction. Defining criteria for success is also an important challenge for researchers today. This paper addresses similar issues, thus trying to shed light on the importance of project management, while also trying to highlight the barriers and obstacles. Project management can be a major competitive advantage for companies in the 21st century, but only if it is carried out professionally and well, i.e. if it is given the right framework and support. Method: After a secondary literature review, this paper presents the results of a primary research study covering two countries. The research instrument was a questionnaire that assessed the project perception of Hungarian and Slovakian enterprises and the transformation of this perception in a complex way, based on the perceptions of enterprises in both countries. In addition to classical basic statistical methods, the study drew conclusions with the help of analysis of variance. Results and conclusion: In this study, we set out to assess the project perceptions of Hungarian and Slovakian businesses based on the opinions of SMEs. The sample clearly shows that Slovak enterprises attach more importance to the project approach, as indicated by the mean scores for each statement. The results also show that there is a high heterogeneity in the perception of enterprises on project management and project approach, especially when grouped along different size categories. All these results also show significant differences by country and segment, which are presented in detail in the research part of the study. Research implications: Our research results provide a good basis for businesses and educational institutions on how to look at their projects and their management. The study aims to strengthen the ability to think in terms of projects and to think in terms of projects, drawing attention to the most important directions and paths. Originality/value: The main focus of the study is on how such knowledge is perceived by the stakeholders interviewed and whether the answers can be used to draw conclusions that are of interest and informative for the wider society.

Decoding imagined speech with delay differential analysis.

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.

Deep learning for automatic facial detection and recognition in Japanese macaques: illuminating social networks.

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research has offered new methodological perspectives through the automatisation of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identification done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching an accuracy of 82.2% and (ii) the creation of an individual recogniser for the Kōjima Island macaque population (YOLOv8n model), reaching an accuracy of 83%. We also created a Kōjima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

Developing Virtual Leadership in Digital Technology E-commerce

Virtual leadership is essential in the current age of remote work and digital technology, requiring leaders to adapt their management style to provide guidance and support to their followers. A contemplative approach focusing on building rapport and providing moral support can especially be effective in virtual settings, while an initiating structure style is better suited for complex tasks or highly competent teams. Managing a virtual workforce requires a unique set of abilities to collaborate, lead, and manage remotely. Keywords: planning, technology, virtual leadership

Актуальные вопросы внедрения психолого-педагогических программ и технологий в образовательную среду

<p style="text-align: justify;">The article analyzes the actual practice of developing psychological and pedagogical programs for the All-Russian Competition of the Best Psychological and Pedagogical Programs and Technologies in the Educational Environment — 2023. A comprehensive assessment is carried out of the completeness of the competitive programs’ fulfillment of the requirements for them in accordance with the regulations on the competition. Typical deficiencies and shortcomings that appear during the preparation and implementation of programs have been identified, and factors that have influenced the programs’ incomplete compliance with the specified requirements have been identified. Analytical materials of existing discrepancies are presented and proposals for the scientific and methodological development of new developments of psychological and pedagogical programs are presented. The implementation of this complex task was undertaken by the Federation of Psychologists of Education of Russia, which unites psychological groups from all regions of the Russian Federation. The accumulated experience of such work allows us to formulate requirements for the development and implementation of psychological and pedagogical programs and technologies in the educational environment.</p>

Acute effect of complexity in basketball on cognitive capacity.

Executive functions, notably inhibition, significantly influence decision-making and behavioral regulation in team sports. However, more research must be conducted on individual player characteristics such as experience and motor skills. This study assessed how accumulated practical experience moderates inhibition in response to varying task difficulty levels. Forty-four university students (age: 20.36 ± 3.13 years) participated in this study with two sessions: one followed standard 1 × 1 basketball rules ("Regular Practice"), while the other imposed motor, temporal, and spatial restrictions ("Restriction Practice"). Functional difficulty was controlled by grouping pairs with similar skill levels. Flanker and Go-Nogo tasks were used. Increasing complexity worsened cognitive performance (inhibition). "Restriction Practice" showed a significantly slower and less accurate performance in both tests than "Regular Practice" (p < 0.001). Experience positively impacted test speed and accuracy (p < 0.001). In sports, acute cognitive impacts are intrinsically linked to the task's complexity and the athlete's cognitive resources. In this sense, it is essential to adjust individually the cognitive demands of the tasks, considering each athlete's specific cognitive abilities and capacities.

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API

Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI.

Multi-Objective Synergetic Operation for Cascade Reservoirs in the Upper Yellow River

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River.

Applying reinforcement learning to learn best net to rip and re-route in global routing

Physical designers typically employ heuristics to solve challenging problems in global routing. However, these heuristic solutions are not adaptable to the ever-changing fabrication demands, and the experience and creativity of designers can limit their effectiveness. Reinforcement learning (RL) is an effective method to tackle sequential optimization problems due to its ability to adapt and learn through trial and error. Hence, RL can create policies that can handle complex tasks. This work presents an RL framework for global routing that incorporates a self-learning model called RL-Ripper. The primary function of RL-Ripper is to identify the best nets that need to be ripped and rerouted in order to decrease the number of total short violations. In this work, we show that the proposed RL-Ripper framework’s approach can reduce the number of short violations for ISPD 2018 Benchmarks when compared to the state-of-the-art global router CUGR. Moreover, RL-Ripper reduced the total number of short violations after the first iteration of detailed routing over the baseline while being on par with the wirelength, VIA, and runtime. The proposed framework’s major impact is providing a novel learning-based approach to global routing that can be replicated for newer technologies.

Pathways of multi-hazard post-disaster housing reconstruction among Ivatan Indigenous households

Most disaster-affected populations face the complex task of reconstructing their dwellings with minimal or no external support. However, not much is known about the self-initiated reconstruction pathways of households, especially among Indigenous groups needing to address the competing impacts of multiple hazards. We conducted a case study analysis based in Itbayat, Batanes in the Philippines to understand the housing reconstruction trajectories of the Ivatan Indigenous households after the 2019 earthquakes that redefined their typhoon-resilient construction practices. Using interviews and focus group discussions, factors affecting the reconstruction process were elicited. Then, using concept maps, we explored the linkages of these factors leading to the adoption of the emergent housing outputs. We found that the emergence of new housing typologies that displaced the vernacular architecture was influenced by the compounded urgency to reconstruct houses, perceptions of housing safety influenced by the seismic events, the nature of aid provided alongside households’ financial capacity, and the regulatory barriers affecting traditional resource extraction. To achieve structural housing safety, this study demonstrates the need for policies that enable and guide reconstruction in emergency contexts, as well as systems to channel aid to provide equitable opportunities to build back safer. Additionally, the role of local governance is shown to leverage the use of existing indigenous construction practices salient for rebuilding. This study builds upon the heightened imperatives in the disaster risk reduction practice and policy environments to focus on the multi-hazard realities affecting communities and the use of local and indigenous knowledge to reduce disaster impacts.

AUTOMATED HIGH-PRECISION ALIGNMENT OF GEOLOGICAL MAPS WITH WELL ORIENTATION

The 3D reflection seismic interpretation entails the creation of structure maps of subsurface horizons across extensive areas, often spanning tens of thousands of square kilometers within sedimentary basins. In order to be correlated with well measurements, maps made from 3D reflection seismic are routinely assigned the correct depth at well control points. However, integrating orientation information measured from wells remains a challenge due to the absence of upscaling guidelines. We address the scale-dependent nature of geological structure and well orientation information in subsurface mapping, particularly focusing on integrating kilometer-scale seismic interpretation with centimeter-scale well-measured dip and azimuth data. Our pioneering methodology streamlines this integration process through three essential steps: first, transforming well dip and azimuth into a gradient representation; second, smoothly extending gradient differences between the map and well within a user-defined radius of influence; and third, constructing an updated horizon map by Bicubic spline interpolation to ensure automated and high-precision alignment. Additionally, a structure-oriented interpolation technique is introduced to preserve faults and local structures during orientation correction. Application of the methodology to a 3D reflection seismic horizon demonstrates its effectiveness in automating the complex task of tying subsurface maps to well orientation information. This not only reduces the need for manual interventions but also introduces a new perspective in subsurface mapping. It provides a robust framework that enhances the accuracy and reliability of geological interpretation, offering significant advancements beyond previous efforts in the literature.

The issue of corporate mandatory standards in production improvement programmes

Purpose: This paper aims to explore and describe how companies manage the level of standardisation of improvement practices in a multisite context. It seeks to explain the managerial strategies applied to change the standardisation level in manufacturing companies with multiple production sites worldwide.Design/methodology/approach: This paper collects data through interviews, observations and company documents from a large multinational producing company and, specifically, from of the largest production sites in the company. The research design resembles a grounded theory approach by being reflexive and open to emerging themes. The standardisation strategy was analysed at a company that strived to increase the standardisation of problem-solving practices within about 20 production sites as part of their corporate lean programme.Findings: Several managerial tools were applied at the corporate level to increase the standardisation level of problem-solving practices, such as developing standards and a company-specific toolbox aligned with an in-house maturity model. In addition, deploying change leaders and global implementation targets enabled audits and progress. However, consequences at the production-site level became minor adaptations of standards, the design of training models as a "roll-out", and a resource-demanding implementation process.Originality/Value: This paper empirically demonstrates strategic tools that corporate management teams apply to influence the company's standardisation level of practices. The study describes the purpose and consequences of the design of the toolbox, maturity model, training model, and implementation targets, which aims to simplify the complex task of managing standardisation in a corporate group. By applying a knowledge-based view, four processes (i.e. adaptation, integration, upskilling, and learning) were identified to improve the management strategies in multisite contexts.

Modular, Vision-Based Control of Automated Charging Systems for Electric Vehicles

Contemporary vehicle fleets include a variety of both manually and automated operated vehicles. A significant shift involves the increasing use of electric powertrains. However, the current electric charging infrastructure predominantly relies on manual processes. There is an opportunity to automate the charging process for these cars, with the intent to enhance convenience, aid people with accessibility needs, to support MW charging where EV cables are heavy, and finally to enable autonomous driving. The charging standards are common but the mechanisms to access the charging ports are not i.e., lids and protectives. This could be a challenge in the automation process given the complexity of the manipulation task at hand. With the increasing variety of electric vehicle models, standardizing charging mechanisms becomes imperative to streamline the charging process and enable broader adoption. In summary, this paper presents a holistic approach to addressing the evolving needs of electric vehicle charging infrastructure, emphasizing the importance of automation in enabling efficient, accessible, and future-ready charging solutions.

Remediation of Learning Difficulty Utilizing School-Based Cognitive Behavioral Intervention Measured by EEG Theta-Alpha and Theta-Beta Ratio During Resting and Cognitive Task Performance Conditions.

Background. EEG is an effective tool due to its ability to capture and interpret the changes in brain activity under different situations. Quantitative EEG (qEEG) can be essential in evaluating and treating children's learning problems. Methods and procedure. Fifty school-going children with difficulty in learning were studied. Analysis of the difference between pre-intervention and postintervention EEG power ratio of frequency bands, including Theta to Beta and Theta to Alpha, while eyes-closed, eyes-open, hyperventilation, writing, and reading conditions and the values for relative powers were calculated. The study correlated postintervention theta/beta ratio (TBR) and theta/alpha ratio (TAR) values with behavioral measures. Results. The findings suggested that there was a significant difference in pre-intervention and postintervention relative TAR and TBR power values. A significant increase of TAR and TBR power values was observed in eyes-closed (resting), hyperventilation, writing, and reading task conditions, indicative of a state of arousal at FP1FP2, T3T4, and O1O2 scalp locations. During eye open conditions, the TAR and TBR were significantly low at all 3 scalp locations, indicating a relaxed, conscious, and aware state of mind. Postintervention TAR and TBR values were significantly correlated with behavioral measures during 5 task conditions on several scalp locations. Conclusion. These quantitative electroencephalogram findings in children with learning problems indicate that with the increased complexity of the cognitive tasks, TAR and TBR increase, while postintervention, children could attain a relaxed and conscious state of mind during eyes-open condition.

An autonomous blockchain-based workflow execution broker for e-science

AbstractScientific workflows are essential for many applications, enabling the configuration and execution of complex tasks across distributed resources. In this paper, we contribute an Ethereum blockchain-based scientific workflow execution manager, which distributes workflows to run on cluster computing providers that utilize the Slurm workload manager to execute them. We extended our blockchain-based autonomous resource broker called eBlocBroker, which is a DAO-based decentralized coordinator, by providing distributed workflow execution via blockchain. Through various tests, we demonstrate how our eBlockBroker autonomous organization, which is programmed as a smart contract, can manage scientific workflow submission, scheduling, and execution on cluster computing providers. The utilization of blockchain for distributed workflow execution is a new concept. We are motivated because our system has been developed with e-Science in mind where scientific workflows are widely utilized.

I have three more than you, you have three less than me? Levels of flexibility in dealing with additive situations

Assessment and intervention in the early years should ideally be based on evidence-based models describing the structure and development of students’ skills. Mathematical word problems have been identified as a challenge for mathematics learners for a long time and in many countries. We investigate flexibility in dealing with additive situations as a construct that develops during grades 1 through 3 and contributes to the development of students’ word problem solving skills. We introduce the construct based on prior research on the difficulty of different situation structures entailed in word problems. We use data from three prior empirical studies with N = 383 German grade 2 and 3 students to develop a model of discrete levels of students’ flexibility in dealing with additive situations. We use this model to investigate how the learners in our sample distribute across the different levels. Moreover, we apply it to describe students’ development over several weeks in one study comprising three measurements. We derive conclusions about the construct in terms of determinants of task complexity, and about students’ development and then provide an outlook on potential uses of the model in research and practice.

Methodology of Organizing and Conducting Distance Olympiads in Korean as a Second Foreign Language in the Process of Professional Training of Future Foreign Language Teachers

In this article, the author presents the methodology of organizing and conducting distance Olympiads in Korean as a second foreign language in the process of professional training of foreign language teachers.The purpose of the study is to examine specifics of second foreign language distance Olympiads organization in the context of professional training, main objectives of the Korean language Olympiad (increasing motivation, interest in learning Korean, exposure to Korean culture, etc.), and to analyze the main requirements for distance Olympiad tasks of the first round (tests), and tasks of the second round, which are aimed to identify the level of communication abilities. Interview, testing and observation are used as the main methods of research.Results. The methodology proposed by the author was tested in groups of 3–4 year students of Sarsen Amanzholov East Kazakhstan University (educational program 6B01703 foreign language: two foreign languages). The article presents analysis of the distance Olympiad’s results, examples of tasks for each round and the main criteria for evaluating the tasks. The requirements to the second foreign language Olympiad tasks implied: possibility of mass coverage of students with Olympiad tasks; compliance of the tasks with the curriculum; time limit; relying on the students’ experience, including their native language skills and abilities; increasing tasks complexity; variety of tasks forms and content, the tasks contents and novelty; problem nature tasks to stimulate students thinking, language guess; using authentic material for the Olympiad tasks; the use of local history and extralinguistic material. The general complexity of the tasks, and difficulties associated with the nature of the linguistic material were also taken into account.Conclusion. According to the results of the experimental work, the author comes to the following conclusion: the leading principle of organizing distance Olympiads assumes that the university faculty will be focused on the timely identification of the future teachers’ interest to the Korean language, their involvement in preparation for the Olympiad, thus forming their interest in Korean culture and language. Olympiad assignments should be creative so that students learn to find their own ways of solving problems and develop self-education skills. Participation in the Olympiad increases students’ learning motivation, broadens their horizons, and develops their ability to express their opinions. The variety of requirements to the work of participants makes the assessment of each member of the jury quite subjective, but the collective nature of the evaluation procedure ensures correctness and objectivity of the final decision, allows participants to familiarize with the results of their work and consider appeals after each round of the Olympiad participants.

Preparing a microemulsion-loaded hydrogel for cleaning wall paintings and coins

Removing unwanted materials, such as organic coatings and soil, from the cultural relic surface is a complex and significant task in the field of cultural heritage conservation. Microemulsion-loaded gel can effectively and safely remove those organic coatings and soil. Here, we employed a simple solvent exchange strategy to prepare a microemulsion-loaded polyvinyl alcohol/polyethyleneimine (PVA/PEI) hydrogel. First, PVA and PEI were dissolved into DMSO to form a gel. Then, the gel was immersed into a microemulsion composed of water, ethyl acetate, propylene carbonate, sodium dodecyl sulfate, and 1-pentanol to exchange DMSO. Microemulsion-loaded PVA/PEI hydrogel can be synthesized by completely substituting DMSO. To investigate the microstructure, rheological properties, and mechanical properties of the gel, scanning electron microscopy, a rheometer, and a universal testing machine were used, respectively. Fourier transform infrared (FT-IR) analysis was conducted to explore the synthesis mechanism and confirm the successful loading of microemulsion within the microemulsion-loaded PVA/PEI hydrogel. Furthermore, FT-IR, a depth-of-field microscope, and a glossmeter were utilized to evaluate the cleaning efficiency of the microemulsion-loaded PVA/PEI hydrogel for removing animal glue and soil from the surfaces of cultural relics. Moreover, an X-ray fluorescence spectrometer was used to analyze the element component of the ancient coin. The application results showed that the microemulsion-loaded PVA/PEI hydrogel can effectively remove animal glue from an ancient wall painting surface. Moreover, it is capable of removing soil from an ancient coin surface as well, which helped to confirm the age of the coin. This offers a novel method to prepare microemulsion-loaded hydrogel and demonstrates great potential in the cleaning for cultural heritage.