Design Tasks Research Articles

Emergent relations and competing behavior: Effects on interruptions, accuracy and reaction time.

This study investigated the impact of engaging in concurrent behaviors on the emergence of untrained stimulus-stimulus relations, specifically examining interruptions, accuracy, and reaction times during test. Six participants completed a computerized matching-to-sample task under three conditions: a baseline condition, a singing condition hypothesized to be incompatible with covert verbal behavior, and a foot tapping condition hypothesized to be compatible with covert verbal behavior. The tempo was kept consistent across both singing and foot tapping tasks. Results indicated a higher average number of interruptions in the foot tapping condition compared to the singing condition. Accuracy and reaction time results varied between participants, indicating individual differences in the impact of the concurrent behaviors. Some participants showed decreased accuracy and increased reaction times during the foot tapping condition, while others demonstrated consistent performance across both conditions. These findings highlight the potential role of response fluency in the execution of concurrent tasks and the influence of verbal mediation on complex behaviors. These insights have implications for the design of experimental tasks and the interpretation of results in studies involving complex behavior and concurrent tasks.

Priors for natural image statistics inform confidence in perceptual decisions.

Decision confidence plays a critical role in humans' ability to make adaptive decisions in a noisy perceptual world. Despite its importance, there is currently little consensus about the computations underlying confidence judgements in perceptual decisions. To better understand these mechanisms, we addressed the extent to which confidence is informed by a naturalistic prior distribution. Contrary to previous research, we did not require participants to internalise parameters of an arbitrary prior distribution. We instead used a novel psychophysical paradigm leveraging probability distributions of low-level image features in natural scenes, which are well-known to influence perception. Participants reported the subjective upright of naturalistic image patches, targets, and then reported their confidence in their orientation responses. We used computational modelling to relate the statistics of the low-level features in the targets to the average distribution of these features across many naturalistic images, a prior. Our results showed that participants' perceptual and importantly, their confidence judgments aligned with an internalised prior for image statistics. Overall, our study highlights the importance of naturalistic task designs that capitalise on existing, long-term priors to further understand the computational basis of confidence.

Assessment of large language models for use in generative design of model based spacecraft system architectures

This paper investigates using Large Language Models (LLMs) within Model Based Systems Engineering (MBSE) as the basis for generative design tools for spacecraft. This study has developed tooling for automatically generating system architecture, functions, modes, and components from an initial requirement set. Specifically, a Python tool was developed to couple the Capella MBSE tool to an LLM in order to facilitate a rapid generative design process. The approach was tested by application to three system design tasks: a European Space Agency Earth observation mission, a CubeSat payload design, and a masters’ degree level group design for an Earth observation spacecraft. For each, generated outputs were evaluated against those produced by technical designers. It was found that the generation of system modes and components was of good quality, providing high traceability and alignment against requirements, and also providing generated architectures that in some areas were more detailed than human-generated equivalents. Further development could provide spacecraft system engineers with an ‘AI design assistant’, as human input is still at the centre of the process and appears necessary to ensure a high-quality output.

The development of RBL-STEM learning tools to improve students' computational thinking skills in solving plant disease classification problems using convolutional neural network segmentation

This research aims to develop Research Based Learning (RBL) and Science, Technology, Engineering and Mathematics (STEM) based learning tools to improve students' computational thinking skills. The tools include a student task design (RTM), a student worksheet (LKM) and a learning outcome test (THB). Using Thiagarajan's 4D development model (define, design, develop, disseminate), the device was tested on students using a Convolutional Neural Network (CNN) approach for citrus plant disease classification using data from quadcopter drones. The validation results showed that the device was valid with an average score of 3.85 (96.42%). The practicality of the device is very high with an implementation score of 3.85 (96.36%) and a positive student response of 90.08%. The effectiveness of the device was demonstrated by 90% of students achieving classical completeness on the post-test and an increase in computational thinking skills from 0% (high) on the pre-test to 92% on the post-test. The paired sample t-test results also confirmed the statistically significant increase. This learning tool proved to be valid, practical and effective, contributing to technology-based learning innovation in modern agriculture.

Task design for highly educated newcomers: improvement and implementation through Lesson Study

PurposeThis case study aims to explore the outcomes of two Lesson Study (LS) cycles involving four teachers of English for Academic Purposes (EAP) in the Netherlands, focusing on task-based language pedagogy.Design/methodology/approachUsing LS, teachers engaged in a Plan-Observe-Analyse-Evaluate cycle to address the unique needs of highly educated newcomers. Initially, teachers received input on task-based pedagogy and redesigned materials for specific learner groups. One teacher taught while others observed, and student feedback was collected. Post-lesson, teachers and the More Knowledgeable Other (MKO) discussed improvements, repeating the process the following week. Qualitative analyses of audio recordings, classroom observations, task designs and student feedback identified critical moments in the LS cycle.FindingsFindings indicate that the task-based approach, task design and classroom implementation offered by LS helped to improve teachers’ professionalism: The LS cycle made teachers aware of task difficulty and clarity relative to student proficiency and interests, leading to adaptations for high, average and low-achieving students. It shifted teachers’ focus from “how to teach” to understanding student needs, revealing that low performance may stem from personal traits rather than proficiency. The process also underlined the additional care needed for vulnerable students facing challenges like long commutes from asylum seeker centres.Practical implicationsThis study highlights the transformative potential of the LS model in enhancing task design, teaching practices and teacher development through collaboration, reflection and student-centred approaches. Teachers should adapt tasks collaboratively to accommodate diverse student needs, focusing on accessibility, engagement and emotional well-being, particularly in mixed-ability and vulnerable student groups. Real-time adjustments based on classroom experiences, combined with understanding students’ emotional challenges, can foster better outcomes. Future research could explore the long-term impacts of adaptive teaching on student engagement, confidence and performance as well as the integration of emotional support into teaching strategies for vulnerable learners.Originality/valueThis study contributes to understanding the effectiveness of LS in task development and improvement as well as professional development. It highlights implications for teacher training and task design in EAP contexts.

Layer-based management of collaborative interior design in extended reality

ABSTRACT We present CIDER, a novel framework for the collaborative editing of 3D augmented scenes. The framework allows multiple users to manipulate the virtual elements added to the real environment independently and without unexpected changes, comparing the different editing proposals and finalising a collaborative result. CIDER leverages the use of ‘layers’ encapsulating the state of the environment. Private layers can be edited independently by the different subjects, and a global one can be collaboratively updated with ‘commit’ operations. In this paper, we describe in detail the system architecture and the implementation as a prototype for the HoloLens 2 headsets, as well as the motivations behind the interaction design. The system has been validated with a user study on a realistic interior design task. The study not only evaluated the general usability but also compared two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on collaborative behaviour. According to the users' comments, we performed improvements to the interface and further tested their effectiveness.

Automating alloy design and discovery with physics-aware multimodal multiagent AI

The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLMs) and the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multiobjective design tasks and opens avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability.

Optimization and bioapplication of deep learning algorithm in the prediction of mechanical properties of metal matrix composites

This study addresses the optimization and bioapplications of a deep learning algorithm for predicting the mechanical properties of metal matrix composites (MMCs), a critical task for efficient material design. And it is also beneficial for deploring more bioapplications of MMCs. Leveraging a comprehensive experimental dataset from multiple research institutions, we employ a Convolutional Neural Network (CNN) for feature extraction and the Recurrent Neural Network (RNN) for sequence analysis. The dataset encompasses mechanical properties such as tensile strength, elastic modulus, and yield strength for diverse MMCs with varying compositions and processing conditions. The research methodology involves rigorous data preprocessing, feature selection, model development, and performance evaluation using metrics like R2 score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), precision, and recall. Addressing the challenge of model robustness and generalizability, we utilize k-fold cross-validation for training and validation. Optimal hyperparameter settings are identified to enhance predictive accuracy. Our results reveal high predictive performance, with R2 scores ranging from 0.89 to 0.92 for different mechanical properties, thereby demonstrating the model’s efficacy in facilitating material design and optimization processes for MMCs.

How does collaborative task design shape collaborative knowledge construction and group-level regulation of learning? A study of secondary school students’ interactions in two varied tasks

Abstract Computer-supported collaborative learning (CSCL) offers a modern setting for learners to engage in meaningful (meta)cognitive and socioemotional interactions. However, the task design, not technology alone, significantly shapes students’ learning interactions. This study investigates how a conceptual physics task and a hands-on robotics task promote collaborative knowledge construction (CKC) and group-level cognitive, as well as emotion–motivation, regulation among secondary school students. Utilizing video recordings of students’ collaborative interactions and process-oriented methods, we examined the occurrence and temporal interplay of these processes from the two tasks. Transmodal network analysis complemented by qualitative case examples revealed significant differences in the nature of CKC and regulation of learning during the tasks. Cognitive processes and strong interconnections between cognitive regulation and negotiation were more typical for the conceptual physics task. The hands-on robotics task featured more frequent, but shorter, sequences of initial CKC phases and emphasized socioemotional interactions for sustained positive collaboration. This study highlights task design’s importance in collaborative learning processes and provides insights for optimizing CSCL environments for effective collaboration.

Gendered Artificial Intelligence in Marketing: Behavioral and Neural Insights Into Product Recommendations

ABSTRACTMarketing research consistently demonstrates that gender stereotypes influence the effectiveness of product recommendations. When artificial intelligence (AI) agents are designed with gendered features to enhance anthropomorphism, a follow‐up question is whether these agents' recommendations are also shaped by gender stereotypes. To investigate this, the current study employed a shopping task featuring product recommendations (utilitarian vs. hedonic), using both behavioral measures (purchase likelihood, personal interest, and tip amount) and event‐related potential components (P1, N1, P2, N2, P3, and late positive potential) to capture explicit and implicit responses to products recommended by male and female humans, virtual assistants, or robots. The findings revealed that gender stereotypes influenced responses at both levels but in distinct ways. Behaviorally, participants consistently favored female recommenders across all conditions. Additionally, female recommenders received more tips than males for hedonic products in the virtual assistant condition and utilitarian products in the robot condition. Implicitly, the N1 and N2 components reflected a classic gender stereotype from prior research: utilitarian products recommended by male humans elicited greater attention and received more inhibition control. We propose that task design and cultural factors may have contributed to the observed discrepancies between explicit (consumer behaviors) and implicit responses. These findings provide insights for mitigating the impact of gender difference when designing the anthropomorphic appearance of AI agents, which would help the development of more effective marketing strategies.

Comparative analysis of Voxel-based morphometry using T1 and T2-weighted magnetic resonance imaging to explore the relationship between brain structure and cognitive abilities.

Voxel-based morphometry (VBM) of T1-weighted (T1-w) magnetic resonance imaging (MRI) is primarily used to study the association of brain structure with cognitive functions. However, in theory, T2-weighted (T2-w) MRI could also be used in VBM studies because of its sensitivity to pathology and tissue changes. We aimed to compare the T1-w and T2-w images to study brain structures in association with cognitive abilities. VBM analysis was applied to T1-w and T2-w MRI data of 120 healthy participants aged 20 to 40. The MRI data was collected using a 3T machine, and it was analyzed with CAT12 to extract maps of Gray matter(GM). We used six cognitive tasks to assess cognitive abilities, including the balloon analog risk task (BART), block design, forward and backward digit span (FDST and BDST), and trail-making tasks A and B. Compared to T2-w, T1-w data showed more brain voxels in the BART, block design, FDST, TMT-A, and TMT-B tasks. However, T2-w imaging identified a greater number of voxels in the BDST. T1-w images identified more correlated brain regions with cognitive scores in the FDST, TMT-A, and B tasks than T2-w. In BART and Block design tasks, both methods revealed the same number of correlated regions, and T2-w just showed more regions than T1-w in the BDST. Findings revealed distinct patterns of sensitivity between T1-w and T2-w imaging in detecting brain regions associated with cognition. The two approaches demonstrated different strengths in identifying areas correlated with cognitive abilities. This study provides valuable guidance for selecting appropriate methods for identifying the optimal approach for detecting brain regions that exhibit the strongest correlations with cognitive abilities.

Efficient evaluation of the Jacobian in the damped least-squares method for optical design problems using algorithmic differentiation.

The fast evaluation of the Jacobian is an essential part of the optimization of optical systems using the damped least-squares algorithm. While finite differences provide an intuitive way to approximate derivatives, algorithmic differentiation is a technique to evaluate them exactly. However, applying algorithmic differentiation to a ray tracing routine for optical systems with many parameters is computationally expensive, where the main costs are caused by the determination of the ray-surface intersection. To overcome this disadvantage, we present a mathematical analysis of the ray-surface intersection and its efficient differentiation in both forward and reverse mode algorithmic differentiation. Futhermore the structure of the optimization variables and operands is exploited to derive a method that allows computation of the Jacobian in the same order of computational complexity as the primal ray trace. The method is successfully tested for a rotationally symmetric lens system and a freeform design task.

Первая азиатская картографическая конференция AsiaCarto 2024

The author briefly reviews the first regional cartography conference in Asia, AsiaCarto 2024, held in Hong Kong, PRC, in December 2024. The theme of the conference, “Cartography and Geospatial AI”, promotes research on using advanced technologies in cartography and geoinformatics. The summary of the conference`s scientific program describes the main activities and outcomes of the event. Totally, 125 papers were presented from more than 30 countries, including the Russian Federation. Research and application of innovative methods, promising approaches to cartographic design as well as understanding of space were most topical at the conference. The papers covered implementing advanced technology to solve the tasks of map design, urban planning and navigation, disaster prevention and management, improvement of technical and methodological aspects of cognitive mapping, and others. The success of AsiaCarto 2024 shows the importance of arranging additional international platforms for discussion and joint projects in all continents under the auspices of the International Cartographic Association. In the long term, AsiaCarto is planned to become a permanent series of biennial conferences to be held in different Asian countries. The aim is to develop cartography and geoinformation technologies in the Asia-Pacific region

Structured mathematical modelling in an authentic scientific-engineering context

Abstract This study suggests the framing of structured mathematical modelling (MM) in an authentic scientific-engineering context. Taking a task design view, structure is maintained through inherent scaffolding for the dual purpose of preserving the authenticity of the extra-mathematical context and making the adaptations required to allow incorporation of the task into the duration of a regular lesson and within the full capacity of the curriculum. Participants were 23 secondary school novice mathematics teachers and two experts who possess extensive experience in MM task design. Research tools included a reflective diary filled in by the novice teachers throughout the design process, and a task design evaluation form that was filled in by the two expert designers. Both tools aimed at identifying MM task characteristics and the alignment of the designed structured MM tasks with the MM cycle phases. Findings show the presence of all the unique characteristics that underly a quality designed MM task, with relatively low presence of openness and high presence of accessibility and authenticity from the novice teachers’ point of view. The alignment with the MM cycle was further manifest in the designed tasks, with the validation phase as being less evident compared to the other MM phases. Theoretical and practical contributions are discussed in the paper, indicating the sequentially-oriented process by which the structured MM tasks scaffold students through the MM cycle.

Evaluation of Intelligent Auxiliary Information Presentation Mode for Collaborative Flight Formation

Collaborative flight formations represent a promising operational model, but the integration of multi-source information in manned interfaces often results in cognitive overload and reduced situation awareness. This study evaluates the effectiveness of intelligent auxiliary information presentation modes in enhancing personnel capabilities. Using a simulation-based collaborative flight formation system, four presentation modes with varying levels of information and dynamics were experimentally tested and evaluated across subjective dimensions (cognitive workload, situation awareness, and interface design) and objective dimensions (design task flow information load and operational task flow information load). The results indicate that Level 3 and Level 4 modes significantly reduced mental workload and improved practical operational ability compared to the original mode. Level 3 achieved the highest interface evaluation scores, while Level 4 demonstrated the lowest design task flow information load. Both modes significantly enhanced situation awareness. Altogether, Level 3 and Level 4 resulted in the most significant improvements in personnel capabilities. These findings provide valuable insights for optimizing interface design and improving situation awareness in collaborative flight formation tasks.

Is AI image generation a creative or optimize tool in product design process?—The usage of AI image generation tools for vehicle shape as an example

This study explores the shape thinking processes and decision-making factors of designers when using AI image generation tools for conceptualizing the shapes of two-wheeled vehicles through four design tasks. Eight designers were invited to create hand-drawn sketches based on a specific aesthetic direction (technological geometry), followed by a shape divergence exercise using two AI graphics tools, Stable Diffusion and Vizcom, to generate images from text prompts. After selecting the designs closest to their original concepts and their favorite designs, the designers used an iPad to explore different shape directions (technological biology) for partial shape modifications. Finally, retrospective interviews were conducted to understand whether there were differences in designers’ thinking process regarding the use of various AI tools for shape conceptualization, as well as their focal points regarding design modification and shape thinking at different stages of the process. The research findings indicate that current AI tools are more suitable for shape divergence. If designers wish to achieve shape convergence, they need to be more familiar with the various settings of AI image generation tools and understand which prompts significantly influence specific shape characteristics. Designers’ perceptions of shape modification primarily revolve around: 1. Outline contours, 2. Parting lines, 3. Variations in surface curvature, and 4. The resulting features (light and shadow effects). Furthermore, it is recommended that future AI image generation tools, if developed as professional assistive tools for product design, should provide two modes—shape divergence and convergence—focusing on both the main shape and details. Additionally, it is suggested that developing AI-3D technologies should address the four key aspects of shape manipulation presented in this study, offering adjustments for overall appearance and detailing the contour lines of parts, including the manipulation of surface curvature and shape positioning.

Analisis Faktor Ergonomi Terhadap Kinerja Pekerja Las (Studi Kasus Pekerja Las di Daerah Surakarta)

Human resources are one of the factors that influence the progress of the business being undertaken. The Surakarta area has an increasing number of MSMEs every year. One of the MSMEs that is growing is a welding workshop. There are several things that affect employee performance, one of which is ergonomic conditions. Of course, this condition can be described in the task or work factor that is assigned. This study aims to analyze ergonomic factors on the performance of welding workers in the Surakarta area. The research method used is descriptive qualitative. The number of respondents or samples in this study was 50 welding workers in the Surakarta area. The data analysis method used is Structural Equation Modeling (SEM). The work environment around the welding workshop actually affects employee performance. One of them is ergonomics. The ergonomic aspect of performance is influenced by several factors, two of which are work organization and workload. This study uses work organization and workload variables as indicators of measuring ergonomic factors on the performance of welding workers in the welding workshop. Based on the results of the study, it can be seen that the work organization variable as an ergonomic indicator has a positive influence on the performance of workers in the welding workshop. This can be seen from the p values ​​of 0.000 less than the alpha value of 5%. There are three aspects that can optimize employee performance in terms of work organization, namely effective communication between fellow workers, good human resource management and optimal work time task design. Meanwhile, the performance load does not affect the performance of welding workers in the welding Keywords: Ergonomics, Surakarta, Welding, Workers

Modeling and Evaluation of Attention Mechanism Neural Network Based on Industrial Time Series Data

Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause safety accidents. Therefore, this paper proposes an improved transformer model based on a self-attention mechanism for modeling industrial processes. Then, an evaluation mechanism based on root mean square error (RMSE) and Kullback–Leibler divergence (KLD) metrics is designed to obtain more appropriate model parameters. The Variational Auto-Encoder (VAE) network is used to compute the associated KLD. Finally, a real nonlinear dynamic process in the petrochemical industry is modeled and evaluated using the proposed methodology to predict the time series data of the process. This study demonstrates the validity of the proposed transformer model and illustrates the versatility of using an integrated modeling, evaluation, and prediction scheme for nonlinear dynamic processes in process industries. The scheme is of great importance for the field of industrial soft measurements as well as for deep learning-based time series prediction. In addition, the issue of a suitable time domain for the prediction is discussed.

Arithmetic is not arithmetic: Paradigm matters for arithmetic effects.

Research on arithmetic uses different experimental paradigms. So far, it is unclear whether these different paradigms lead to the same effects or comparable effect sizes. Therefore, this study explores how different experimental paradigms influence mental arithmetic performance, focusing on understanding the potential differences and similarities in cognitive processes between paradigms. Six paradigms were systematically compared: decision paradigms (verification, forced-choice, delayed forced-choice) and production paradigms (written production, verbal-keyboard production, and simple verbal production). The results show consistent arithmetic effects related to operation (addition vs. subtraction) and task difficulty (with or without carry/borrow) across all paradigms, particularly in reaction time measures. However, accuracy varied between paradigms, with verbal-keyboard production and simple verbal production paradigms showing higher effect sizes for accuracy measures. These findings underscore the importance of considering each paradigm's specific demands and characteristics in arithmetic research, suggesting that paradigm selection can influence the observed outcomes. Our study provides critical methodological insights that can guide future research in the design and interpretation of arithmetic tasks, enhancing the reliability and ecological validity of findings in numerical cognition.

Praxeology Analysis of The Arabic Language Textbook for Madrasah Ibtidaiyah

This study aims to analyze the Arabic language textbook for Grade 1 of Madrasah Ibtidaiyah, published by the Directorate of KSKK, Ministry of Religious Affairs of the Republic of Indonesia, and to identify the characteristics of Arabic language task design from the perspective of didactic anthropology theory, specifically praxeology. The research method employed in this study is descriptive qualitative, utilizing a literature research design. Data collection techniques include both primary and secondary sources. Primary sources consist of the Arabic language textbook for Grade 1 published by the Directorate of KSKK, while secondary sources encompass relevant books and journals. Data analysis employs qualitative methods, which include data collection, data reduction, data presentation, and conclusion drawing. The results indicate that the first part of the task design (T1) could not be analyzed using praxeology, as it did not incorporate specific techniques. Furthermore, the types of tasks (T2-T6) have not effectively constructed the theory of students' ability to communicate in Arabic, as the overall task presentations rely heavily on teacher direction. Additionally, obstacles to student learning were identified, including ontogenic and didactic challenges.