Design Tasks Research Articles

Football Defensive Actions Development in Small-Sided and Conditioned Games With Offensive Goals

With the use of small-sided and conditioned games (SSCGs), non-linear pedagogy (NLP), which is based on the ecological dynamics approach, has arisen with the aim of fostering a holistic perspective and optimizing players’ tactical offensive and defensive behavior’s. Given the potential injury and health risks associated with insufficiently structured training, such as overtraining or inadequate tactical preparation, the implementation of evidence-based approaches like NLP is crucial. In this study, the indirect impacts of an NLP-based intervention program (task design based on tactical attack principles and assaulting team’s numerical advantage) were examined in decision-making (DM) and execution (Ex) in U14 football defense technical-tactical actions. Twelve training sessions, split over two phases—preintervention and intervention—involved eighteen players (aged U14). 600 defensive actions measured during competitive matches were examined using the Game Performance Evaluation Tool (GPET) tool. The results revealed significant gains in marking (to the player with the ball: DM, p = 0.001; Ex, p = 0.001; and to the player without the ball: DM, p = 0.039; Ex, p = 0.046), blocking (DM, p = 0.015), and help- coverage (Ex, p = 0.014) actions. The interception and tackle actions did not differ noticeably. According to the results of this study, the NLP approach provides a suitable theoretical framework for fostering the development of defensive tactical behavior in football. Not all acts, though, saw improvements. In order to build technical-tactical training processes based on the phases of the football game (offensive and defensive) and taking the level of opposition into consideration, trainers should design representative tasks.

Do psychological traits influence the perceived usefulness of rule recommendations in configuration tasks?

ABSTRACT In this paper, we describe an empirical evaluation of the user's perceived usefulness of recommendations for configuration tasks in a smart home scenario. Our results suggest that while overall recommendations help improve the performance in the task, the psychological traits of Self-efficacy and Need for Cognition play an important role in determining the perceived usefulness of the recommendations. These effects are somehow different from those reported in other studies where recommendations are provided for a choice task rather than a constructive task. Thus, our results offer evidence regarding the importance of incorporating the evaluation of personality traits into the design of configuration constructive tasks in order to make informed decisions on whether and how to provide users with recommendations. Additionally, they show that it is crucial to take into account the nature of the task and, where possible, individual competencies.

Practicing variation theory beyond learning study

PurposeThe purpose of this paper is to explore whether and how principles from variation theory can contribute to the planning of teaching and learning beyond learning study.Design/methodology/approachWe study whether and how principles from variation theory contributed to a group of teachers’ planning of teaching and learning about decimal numbers in Grades 4 to 7 working in Subject Didactic Groups – a collaborative arrangement suited to daily teaching. A theoretical thematic analysis approach was used when analyzing eight audio-recorded meetings and written documents.FindingsThe study shows that variation theory principles contributed to the teachers’ planning of teaching and learning. Two themes were identified: the theory contributed to the teachers being able to (1) specify what their students needed to learn and (2) design tasks that they anticipated would afford the opportunity to learn what was identified as being necessary to learn.Originality/valueThe paper demonstrates how variation theory can contribute to teachers’ planning of teaching and learning when used in a collaborative arrangement other than learning study. This leads into a discussion about variation theory being used separately from learning study and the benefits and limitations this other collaborative arrangement can have for gaining knowledge of what is to be learned and taught.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.

Dynamic Analysis of Planar Mechanism in Numerical Methods

In kinematic synthesis and analysis of planar mechanisms the designer must define a geometry and set of motions for a design task. Then it is logical to determine the forces or torques in order to create such motion in the system, which means finding out a convenient approach to solving for the forces and torques that result from our kinematic system in such a way as to provide the designed accelerations. This task is called dynamic force analysis (or kinetic analysis). Many numerical methods to determine the position, velocity, and acceleration of a planar mechanism are introduced and applied successfully, for example, using the transformation matrix. To solve fully kinetic problems for a planar mechanism, it is necessary to establish a simple and easy procedure that determines the forces or torques maintaining the motion in the system with the help of a computer. This paper will focus on how to determine the forces or torques acting on the planar mechanism by numerical method when acceleration and dynamic properties of the linkage are known. Some of the chosen examples shown here aim to demonstrate in detail the numerical solution for dynamic force analysis. The paper’s purpose is not to demonstrate novelty, but to propose a new approach that can be applied to machinery design projects for students. University students can consult the content of this paper to carry out kinematic and kinetic analysis of planar mechanisms using this numerical method, as opposed to the traditional vector drawing method that is widely used.

Structure-Based Drug Design with a Deep Hierarchical Generative Model.

Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.

Designing and Implementing Sustainable Professional Development Programs: Embodied Curriculum and Instruction for Kindergarten Teachers

Embodied design is a theory-to-practice and pedagogical framework and emphasizes the role of embodied and situated activity. Since embodiment has a powerful impact on young children’s learning, implementing embodied deign in kindergartens is essential and beneficial to their future mathematical learning and performance. Therefore, this qualitative study aimed to develop a professional learning community composed of eight kindergarten teachers and university teacher educators (researchers) and to co-construct a professional development model for curriculum design and instructional implementation of embodied design in mathematics. Accordingly, the main research objectives were to obtain a sustainable professional development model and summarize principles of embodied mathematics curriculum design and strategies for embodied instructional activities, which can be used both in practical settings (i.e., kindergarten and preschool) and early childhood teacher education programs in higher education. Data were gathered through participative observations, including PD meetings and classroom observations, in-depth and follow-up interviews, teachers’ lesson design and reflection notes, and children’s learning profiles, and then were analyzed qualitatively using a descriptive and explanatory approach with replication logic. Based on the data collection and analysis, two major sections of the findings and discussion were included in this report, which were as follows: first, a sustainable “task design professional development model” was generated for collaboratively future improvements of both in-service and pre-service teacher training programs in higher education; second, two principles of embodied mathematics curriculum design and two effective embodied teaching strategies were proposed to promote kindergarten teachers’ professional knowledge and capabilities and their young children’s mathematics learning capabilities.

Technical and tactical performance in professional female futsal players: a descriptive analysis depending on teams level and playing group

ABSTRACT Among the many factors that can affect women’s futsal performance, technical and tactical actions can obscure relevant game data, even affecting specialists in both the programming and design of training tasks. This study describes the technical and tactical actions in elite futsal teams, analysing performance measures according to the playing group and team ranking level. One hundred and twenty-eight games from an Elite League in the 2020/2021 season were observed. The variables analysed were grouped into three groups: “Goals and Rules”, “Attack” and “Defence”. The two-way mixed ANOVA analysis revealed significant effects in attacking variables such as Shots to the Post (STP) variable (F1.2 =19.13, p = .04, η2 = .90) and Throw-in For (TIF) variable (F1.2 =75.00, p = .01, η2 = .97), as well as in defensive Interception (INT) variable (F2.4 = 8.74, p = .03, η2 = .81) in the playing group condition. This study observed how consistently those teams positioned at the top developed a higher goal average compared to those teams further down the ranking. The study highlights the significance of the offensive part and the number of goals in the game system and how achieving good numbers in this area can influence. Coaches and athletes can use the intraclass correlation coefficient to demonstrate how specific technical and tactical actions impact performance.

Task-Based Teaching Mode in College English Teaching

With the acceleration of globalization, college English teaching faces the challenge of cultivating students' comprehensive language skills and cross-cultural communication abilities. Task-based language teaching (TBLT), as a student-centered teaching mode, promotes the use of language in real and meaningful contexts through the design of authentic tasks. This study aims to explore the application and effectiveness of task-based teaching modes in college English teaching. Through literature review, teaching practice, and effect evaluation, the study finds that TBLT significantly improves students' language proficiency, motivates learning, and enhances autonomous learning capabilities. This research provides new ideas and practical guidance for the improvement of college English teaching.

Social virtual reality: systematic review of virtual teamwork with head-mounted displays

PurposeSocial virtual reality (SVR) has emerged as a solution for the facilitation of remote and distributed teamwork, promising to overcome challenges faced in virtual 2D communication channels. The purpose of this study is to systematically review empirical articles dealing with the application of SVR with head-mounted displays (HMDs) in teams.Design/methodology/approachThis study reviewed 40 empirical papers on teamwork that address the application of SVR using HMD in teams. The authors present results from the extracted data and identify themes that indicate the current state of research in this field.FindingsBased on data obtained from the journal articles, the authors identified three themes underlying current research. They are features and applications of SVR technology, collaboration dynamics and team performance in SVR and educational and professional training and learning. The results show that SVR enhances team collaboration and collaborative learning.Practical implicationsSVR can aid in improving teamwork and team outcomes. In its current stage, SVR is complementary to 2D technology and not likely to replace traditional tools. Findings suggest that the technology is promising for collaboration and learning in organisations, especially in collaborative design tasks and simulations.Originality/valueTo the best of the authors’ knowledge, this paper is one of the first studies to look at research emerging from the nascent area of SVR and summarise the current state of research. This study provides managerial implications and identifies future research areas for SVR in teamwork and learning.

A multimodal approach for fostering knowledge transfer in engineering design activity: an eye-tracking and fNIRS study

Knowledge transfer in engineering design activity involves effectively applying prior practical experience in novel contexts. While related works fostering knowledge transfer focus on educational practices and experience generalisation, recent advancements enable us to analyze its complex cognitive processes using neuroimaging technology. This paper proposes a multimodal approach for facilitating knowledge transfer in engineering design, utilising functional near-infrared spectroscopy (fNIRS) and eye-tracking technologies based on neurocognitive profiling. We conducted knowledge transfer experiments using mixed and extended design tasks. Meanwhile, The analysis focused on gaze duration, gaze counts, brain region activation levels, functional connectivity, and effective connectivity of the participants. Finally, we employed stepwise linear regression to identify significant and potential associated factors of knowledge transfer. By combining eye-tracking predictors and brain network predictors, a multivariate linear discriminant analysis (LDA) significantly differentiated between participants in near-transferred and far-transferred states, achieving an accuracy of 91.16% using leave-one-out cross-validation. The ‘basic-supplementary-target task’ method effectively improved the knowledge transfer performance by 27.6%. Our findings demonstrate the efficacy of the proposed multimodal approach in facilitating knowledge transfer in engineering design activities.

Automated architectural spatial composition via multi-agent deep reinforcement learning for building renovation

This paper focuses on utilizing deep reinforcement learning technology to explore how to achieve automated architectural space composition under established built environment conditions to address the extensive demands of old building renovation. A reinforcement learning platform has been develop0065d, centered around the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The paper leverages functional blocks as agents within Grasshopper to simulate existing architectural structures and their interactions, establishing a task-based reward system to guide the design process. Furthermore, data exchange between algorithmic and modeling software is facilitated through UDP communication. Training results using typical frame structure spaces indicate a significant increase in cumulative rewards around the 650,000th training step, effectively achieving the given design tasks. The paper demonstrates the potential of automated architectural space composition methods, based on deep reinforcement learning, to enhance design efficiency, optimize spatial arrangements, and promote human-machine collaborative design in building renovations.

Performance During a Task That Simulates Passive Sonar Operator Duties Under Conditions of Varying Workloads.

It is critical to develop and implement lab-based computer experiments that simulate real-world tasks in order to characterize operational requirements and challenges or identify potential solutions. Achieving a high degree of laboratory control, operational generalizability, and ease-of-use for a task is challenging, often leading to the development of tasks that can satisfy some facets but not all. This can result in insufficient solutions that leave real-world stakeholders with unsolved problems. This issue is addressed using a customized passive sonar simulator application that provides extensive researcher control over the design and manipulation of a sonar task; a visual appearance and cognitive demand similar to a true submarine-based sonar task; and a convenient and short training routine for sonar novices. The task requires participants to watch for multiple signal sources of varying appearance and salience and subsequently classify these signals into their respective categories. The current study investigated the effects of stimulus signal strength and signal density on sonar task performance-including metrics of classification accuracy, classification confidence, and response times-finding an interaction between signal density and signal strength that resulted in greater performance errors with high signal density at the weakest signal strength. The lab-based sonar application provides new possibilities for research, not limited to signal intensity and signal density but also through the manipulation of parameters such as the number of unique targets, target appearance, and task duration. This application may illuminate the operational demands that each of these factors may have on operator behavior within the dynamic tasks.

Evaluating the Geospatial Thinking of Pre-Service Geography Teachers Through an Orienteering Design Task: Analysis Using the Many-Faceted Rasch Model

Measurement is significant in geospatial thinking research. This study evaluated the geospatial thinking of pre-service geography teachers through an orienteering design tasks and subjective evaluation methods. Moreover, it analyzed their level of geospatial thinking, difficulty of evaluation indicators and rater severity with the Many-Faceted Rasch Model. The results showed the geospatial thinking of participating pre-service geography teachers was at a moderate level. The estimation results of the model were well fitted. The evaluation indicators had a good degree of differentiation. Therefore, using the MFRM helps interpret the results of a geospatial thinking assessment in a more comprehensive way.

Analytical solution of conductor tensile force in asymmetrical spans used in overhead power lines and substations with influence of tension insulators

Introduction. Designing electrical substations involves analyzing the horizontal tensile force in flexible tension conductors under varying temperatures. These temperature changes affect the conductor’s length and forces. Problem. Existing methods for calculating horizontal tensile force in conductors often focus on symmetric spans or require complex finite element modeling (FEM), which is impractical for routine substation design. Asymmetric spans with tension insulators present a more complex challenge that current solutions do not adequately address. Purpose. Universal analytical solution and algorithm for calculating the horizontal tensile forces in conductors in asymmetric spans with tension insulators used in power substations or short overhead power line spans. The solution is designed to be easily implementable in software without requiring complex tools or extensive FEM. Methodology. The methodology involves deriving an analytical solution based on the catenary curve formed by the conductor between attachment points at different heights. The analysis includes calculating the conductor’s length for a given tensile force and using a state change equation to determine forces under new temperature conditions. Validation is performed using FEM calculations. Results. The proposed solution was validated against FEM models with varying height differences (5 m and 15 m) and conductor temperatures (–30 °C, –5 °C, +80 °C). The results showed a minimal error (less than 0.15 %) between the analytical solution and FEM results, demonstrating high accuracy. Originality. This paper presents a novel analytical solution to the problem of calculating tensile forces in asymmetric spans with tension insulators. Unlike existing methods, our solution is straightforward and easily implementable in any programming language. Practical value. The solution is practical for routine design tasks in electrical substations or short overhead power lines. Especially in power substations, accurate tensile forces are needed not only for mechanical design and sag calculations but also for calculating the dynamic effects of short-circuit currents. References 23, tables 4, figures 3.

Coupling of saccade plans to endogenous attention during urgent choices.

The neural mechanisms that willfully direct attention to specific locations in space are closely related to those for generating targeting eye movements (saccades). However, the degree to which the voluntary deployment of attention to a location necessarily activates a corresponding saccade plan remains unclear. One problem is that attention and saccades are both automatically driven by salient sensory events; another is that the underlying processes unfold within tens of milliseconds only. Here, we use an urgent task design to resolve the evolution of a visuomotor choice on a moment-by-moment basis while independently controlling the endogenous (goal-driven) and exogenous (salience-driven) contributions to performance. Human participants saw a peripheral cue and, depending on its color, either looked at it (prosaccade) or looked at a diametrically opposite, uninformative non-cue (antisaccade). By varying the luminance of the stimuli, the exogenous contributions could be cleanly dissociated from the endogenous process guiding the choice over time. According to the measured timecourses, generating a correct antisaccade requires about 30 ms more processing time than generating a correct prosaccade based on the same perceptual signal. The results indicate that saccade plans elaborated during fixation are biased toward the location where attention is endogenously deployed, but the coupling is weak and can be willfully overridden very rapidly.

Vagus nerve stimulation during training fails to improve learning in healthy rats

Learning new skills requires neuroplasticity. Vagus nerve stimulation (VNS) during sensory and motor events can increase neuroplasticity in networks related to these events and might therefore serve to facilitate learning on sensory and motor tasks. We tested if VNS could broadly improve learning on a wide variety of tasks across different skill domains in healthy, female adult rats. VNS was paired with presentation of stimuli or on successful trials during training, strategies known to facilitate plasticity and improve recovery in models of neurological disorders. VNS failed to improve either rate of learning or performance for any of the tested tasks, which included skilled forelimb motor control, speech sound discrimination, and paired-associates learning. These results contrast recent findings from multiple labs which found VNS pairing during training produced learning enhancements across motor, auditory, and cognitive domains. We speculate that these contrasting results may be explained by key differences in task designs, training timelines and animal handling approaches, and that while VNS may be able to facilitate rapid and early learning processes in healthy subjects, it does not broadly enhance learning for difficult tasks.

Artistic sense of interior design and space planning based on human machine intelligent interaction

The rapid development of artificial intelligence technology is gradually penetrating into multiple fields such as interior design and spatial planning. The aim of this study is to integrate artificial intelligence with interior design, enhance design artistry and user experience, and address the interactive needs of interior space design choices. A set of indoor space design recognition system has been designed by introducing artificial intelligence networks and attention mechanisms. This study first optimizes the CenterNet algorithm based on attention mechanism and feature fusion to improve its accuracy in identifying complex components. Afterwards, the long short-term memory network and convolutional neural network are trained to complete the task of spatial layout feature recognition and design. The performance test results showed that after testing 100 images, the software could recognize indoor design space images and create corresponding vector format space maps in about 5 minutes, providing them to the 3D modeling interface to generate 3D scenes. Compared to the approximately 25 minutes required by manual methods, the design efficiency has been significantly improved. The research and design method has a fast convergence speed and low loss during the retraining process. In simulation testing, its mAP value reached 91.0%, higher than similar models. It performs better in detecting walls, doors and windows, bay windows, double doors, and two-way doors. Moreover, it has outstanding ability when facing structures such as short walls and door corners, and can recognize and create vector format spatial maps within 5 minutes, which is accurate and efficient. The system designed in this project has optimized the interaction between designers and clients in interior design, accurately capturing user intentions and assisting designers in improving work efficiency.

Digital engineering implementation in nuclear demonstration and nonproliferation projects at Idaho National Laboratory

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

Quantum Molecular Docking with a Quantum-Inspired Algorithm.

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.