Design Process Research Articles

Computational transformations of early childhood education: Pathways toward child‐centred computing in two educational change projects

AbstractProgramming is becoming a key subject in early education globally, with surging problems of how computer science can become a subject for children of all ages and backgrounds. Problems of implementing new technologies in the old curricula have long been noted, and lately, concern over computer science education goals is often too narrow and skills‐based without concerns for the critical educational potential. This study follows the change process of two case preschool departments implementing programming with a floor robot across a seven‐month design process. Mixed analytical techniques based on activity theory are used to examine the tensions, conflicts and development of pedagogical alternatives. The studies show the strenuous change processes involving local transformations toward a child‐centred, inclusive early computer science education and development of projects centring making, children's exploration, tinkering and play as key parts of the learning process. It discusses how the considerable change processes enabled pedagogical solutions coherent with important concepts in computer programming and how computing education can build on child‐centred pedagogies through local adaptations and age‐appropriate designs. Practitioner notesWhat is already known about this topic Computer science and programming are becoming part of early education worldwide. Less is known about how to implement computing with ever‐younger age groups, especially for those under three. That technological change can be a strenuous change process building on societal, historical and educational tensions, requiring individual learning and organisational change. What this paper adds Two cases of change designing computing education for 1–2‐ and 3–5‐year‐olds. Design processes raising critical questions about current standard childhood programming materials being resolved to enable a child‐centred computing education. Challenges and potentials specific to early childhood in adopting computer science education. Implications for practice and/or policy Computing education challenges pedagogical notions, such as the role of failure in education. Computing education can be developed for very young children. However, there are considerable critical challenges to resolve in developing a child‐centred approach. Childhood fundamentals such as play‐based pedagogy can work synergistically with computer science activities, providing educational potential, also relevant for computer science education at large.

Design and Testing of a Small-Scale Composting Facility for Sheep Manure Utilizing Aeration and Thermal Treatment

Inner Mongolia has the largest sheep population among China’s provinces, resulting in the production of a substantial amount of sheep manure. If left untreated, this manure can contribute to environmental pollution. However, sheep manure serves a dual purpose: it can be both a pollutant and a valuable source of organic fertilizer. Consequently, there is an urgent need to address the environmental issues arising from manure accumulation and its unused status. In this paper, a viable solution is proposed: the conversion of manure into fertilizer through a composting unit incorporating high-temperature aerobic fermentation technology. This unit, tailored for small farms and individual farmers, integrates critical functions such as ventilation, heating, and turning. Additionally, it boasts excellent thermal insulation, enhancing composting efficiency and enabling precise control over fermentation conditions. This design mitigates heat loss and accelerates maturation, addressing common challenges in traditional composting. The design process encompassed both equipment construction and control systems, with a primary focus on compost fermentation and aeration heating. The components were carefully designed or selected based on theoretical analysis and subsequently validated using simulation software, including EDEM and Fluent. The control system seamlessly integrates a touch screen interface, PLC programming, and control circuits to manage air pumps and electric heaters in response to changes in temperature and oxygen concentration. Furthermore, it controls the motors during the recovery phase. A comprehensive performance evaluation was conducted, revealing notable improvements. Under artificially heated conditions, the maximum temperature of the compost increased by approximately 20 °C, the composting cycle was reduced by roughly 4 days, and the seed germination index (GI) rose by about 9% when compared to natural fermentation. Thus, this device significantly accelerates composting and improves fertilizer quality by increasing the decomposition rate.

Reverse design of load-bearing broadband metamaterial absorber assisted by deep learning

Abstract In response to the current challenges of narrow absorption bandwidth, weak load-bearing capacity, and low design efficiency in absorbing structures, this study focuses on the reverse design of load-bearing broadband metamaterial absorber. A parameterized model of load-bearing metamaterial absorber was developed by integrating the composite sandwich structure with the electromagnetic resonant layers. The resonant layer was constructed using the combination of Vicsek-fractal and circular rings, with resistive films employed to broaden the absorption bandwidth. A deep learning-based forward prediction model was established to accurately predict the absorbance of the metamaterial absorber. The SHapley Additive exPlanations (SHAP) framework was utilized to analyze the forward prediction network, revealing the influence of various design parameters on the absorbance at center frequencies across the L to K band spectrum. Additionally, the Group Teaching Optimization Algorithm (GTOA) was introduced into the design process, leading to the development of an automated reverse design method for metamaterial absorber that can achieve specific design objectives. Using the GTOA-based reverse design method, a metamaterial absorber capable of effectively absorbing vertically incident electromagnetic waves within the 3-20 GHz frequency range was designed. The designed absorbing structure was fabricated, and its absorption performance was measured using the arch method. The measurement results were found to be in good agreement with the simulation data. The absorbing mechanism of the designed metamaterial absorber was analyzed based on the calculation of equivalent electromagnetic parameters and the electromagnetic resonance observed at the resonant frequency. It was determined that the primary absorbing effect is induced by electric resonance triggered by electromagnetic waves. The proposed metamaterial absorber can be applied to radar stealth design for military targets such as naval vessels. The research methodology and approach demonstrate significant generalizability and engineering applicability.

Seismic Design of Underground Structures’ Vulnerability – Review.

This article examines the vulnerability of underground structures to seismic activity, with a particular focus on design and mitigation strategies. Underground buildings are essential to the development of modern infrastructure and must include tunnels, subway stations, and storage facilities. Because of its deep location, seismic design and earthquake performance are both more challenging. In this study of seismic susceptibility, topics such as ground motion characterization, soil-structure interaction, structural reaction, and failure mechanisms specific to underground structures are discussed. According to the paper, their susceptibility can be affected by geotechnical conditions, the kind of structures they have, and the construction methods that are used. The seismic stability design and analysis process involves measuring the dynamic response of subsurface structures to seismic loads. The goal is to keep earthquake and seismic forces from toppling structures in any way that is possible. Advanced analytical and numerical approaches are used in seismic stability design. Some examples of these methods are finite element analysis (FEA), discrete element modeling (DEM), and the boundary element method (BEM). In addition to that, it investigates contemporary methodologies for assessing seismic vulnerability as well as international design standards. In addition, seismic retrofitting and design advances for underground structures are analyzed and discussed in this paper. This document compiles and evaluates previous research to gain an understanding of the seismic susceptibility of subsurface structures and to promote more seismic engineering research on this extremely important topic.

Fast parameter optimization for high-fidelity crystal plasticity simulation using active learning

Crystal plasticity (CP) simulation is a powerful tool for studying and understanding the mechanical behavior of materials. A critical aspect of this method is the accurate determination of CP parameters, which ensures that the constitutive model accurately represents the real deformation behavior of a material, especially in high-fidelity simulations. However, identifying these parameters poses a significant challenge due to the high computational cost and the difficulty of finding optimal solutions within a vast and complex parameter space. To address these challenges, we propose a fast search strategy that leverages active learning (AL) and experimental data to accelerate the optimization of CP parameters. Using the Al-Cu eutectic materials as a case study, we introduced a quantitative index, C ecp, to measure the consistency between simulated and experimental stress-strain curves. We demonstrated that Gaussian process regression (GPR) serves as the most appropriate surrogate model for relating CP parameters and C ecp based on our dataset. After only six iterations guided by AL, the optimal CP parameters were successfully identified, resulting in a high-fidelity CP model for analyzing the mechanical behavior of Al-Cu eutectic materials. The machine learning-enhanced strategy is far superior to traditional methods in terms of both efficiency and accuracy. It advances our understanding of the macro-micro relationships of materials and accelerates the material design process.

SCOPE ON BIM AS A TOOL FOR DESIGNING ARCHITECTURAL PROJECTS FOR ARCHITECTS AND ENGINEERS. CASE OF ALGERIA

Building Information Modeling, often known as BIM, is considered an indispensable component of the design and construction process in many nations throughout the world. In order to analyze the practitioners' perceptions of BIM's influence on construction in connection to the many tools and software, surveys were carried out and analyzed with the sphinx tool. The surveys were also carried out to determine the architects' and engineers' capacities to integrate BIM into their design processes. Despite having limited understanding about BIM as a new method of work, the results indicated that both architects and engineers are concerned about improving design processes in order to provide a higher level of quality to the projects they are working on in Algerian context.

Using EPCG for Designing a Hexagon Tangram Puzzle

Designing and tuning a game is a complex creative process, with a variety of tools have been designed for assisting human designers in this process. But, many existing tools either entirely take the design away from humans, or they only assist in generating content - they cannot be used to create an entire game from scratch. This paper explores how Exhaustive Procedural Content Generation (EPCG) can be integrated as a co-creative partner in the design process. We describe how EPCG was used alongside human designers to create a novel Hexagon Tangram puzzle, from initial conception to puzzle curriculum. EPCG is used to answer design questions at each step of the design, which enable human designers to make more informed design choices.

Additively manufactured microwave sensor for glucose level detection in saliva.

In this paper, a novel realization of an ink-on-glass microwave sensor for biomedical applications is proposed. The Aerosol Jet Printing (AJP) technology is leveraged to implement a compact single-layer coplanar waveguide sensor featuring arc-shaped interdigital fingers that can accommodate a droplet of the Material-Under-Test (MUT). Such geometry provides a high sensitivity to even a very small deviation of MUT`s electrical properties when placed as a superstrate. An application towards the detection of trace amounts of glucose in saliva, which is a biomarker for diabetes, is showcased. The design and fabrication process of an exemplary sensor is discussed in detail. A circular geometry feature is introduced that helps a droplet to lie over the sensitive region due to wettability difference of glass substrate and silver ink. Sensor operating in K-band is developed providing a tradeoff between circuit size and droplet volume. The study is conducted for an artificial saliva requiring roughly a 0.5 µL droplet where changes in mixture content are proportional to relative changes of sensor`s transmission coefficient in a broad frequency range for occupied vs. empty states. The obtained results show that 10mg of glucose per 100ml of saliva can be easily distinguished in a frequency range of 20-30GHz, whereas a monotonical change is visible for frequencies 20-26GHz, which indicates the applicability of this sensor towards the detection of saliva-glucose levels and potential application in the detection of small amounts of other substances in liquids.

Effectiveness of an Inquiry-Based Science Program on Enhancing Science Process Skills and Knowledge Among Moroccan Preschool Children

<p style="text-align:justify">This study evaluates the effectiveness of a twelve-week Inquiry-Based Science (IBS) program on enhancing science process skills and scientific knowledge among preschool children in Morocco. Conducted in a quasi-experimental setting, it involved 105 children (M = 60.46 months, SD = 4.32), with 37 in the IBS group and 68 in the control group. The program utilized the 5Es instructional model and the Engineering Design Process (EDP) to engage children in active, hands-on learning experiences. Statistical analysis demonstrated that the IBS group achieved substantial improvements in both science process skills and scientific knowledge relative to the control group, with between-group effect sizes (Cohen’s d) ranging from 1.02 and 2.31. These findings highlight the significant impact of structured inquiry-based approaches in early childhood education. The study underscores the need for integrating such methods into the preschool curriculum to foster scientific understanding and skills from a young age, thereby better preparing Moroccan children for future academic and professional challenges. The results advocate for educational stakeholders to consider adopting inquiry-based learning frameworks to enhance the overall quality of early childhood education in Morocco.</p>

Conditional Font Generation With Content Pre‐Train and Style Filter

AbstractAutomatic font generation aims to streamline the design process by creating new fonts with minimal style references. This technology significantly reduces the manual labour and costs associated with traditional font design. Image‐to‐image translation has been the dominant approach, transforming font images from a source style to a target style using a few reference images. However, this framework struggles to fully decouple content from style, particularly when dealing with significant style shifts. Despite these limitations, image‐to‐image translation remains prevalent due to two main challenges faced by conditional generative models: (1) inability to handle unseen characters and (2) difficulty in providing precise content representations equivalent to the source font. Our approach tackles these issues by leveraging recent advancements in Chinese character representation research to pre‐train a robust content representation model. This model not only handles unseen characters but also generalizes to non‐existent ones, a capability absent in traditional image‐to‐image translation. We further propose a Transformer‐based Style Filter that not only accurately captures stylistic features from reference images but also handles any combination of them, fostering greater convenience for practical automated font generation applications. Additionally, we incorporate content loss with commonly used pixel‐ and perceptual‐level losses to refine the generated results from a comprehensive perspective. Extensive experiments validate the effectiveness of our method, particularly its ability to handle unseen characters, demonstrating significant performance gains over existing state‐of‐the‐art methods.

QPSK circuit optimization Based on Model-Based Design

In order to address the difficult problem of hardware implementation of complex communication algorithm, this paper takes digital phase modulation as an example, and uses model-based design approach to quickly complete the algorithm modeling, simulation and automatic generation of HDL code. Model-based design is a fast and effective design process for algorithm modeling, simulation, testing and automatic code generation. The simulation results show that the model-based design method cannot only quickly accomplish the modeling and testing of the algorithm, but also improve the design accuracy.

Grey preferences selection index with trimmed group preference for evaluating university dormitory renovation design.

Renovating old buildings is a strategic approach to resource optimization and sustainability. However, dormitory design decisions are often made by a limited panel of experts, which risks excluding student preferences-key to the design process. Expert assumptions about student needs can lead to biased outcomes, and attempts to gather student input may suffer from respondent bias and inconsistent engagement, introducing data noise. This paper presents the Grey Trimmed Preferences Selection Index (GTPSI) Multi-Criteria Decision Making (MCDM) method, an improved preference selection index MCDM method that integrates expert weights and refines student feedback by filtering outliers, using interval grey numbers to address uncertainty. Applied to dormitory renovation proposals, GTPSI provides a stable ranking framework, as confirmed by whitening sensitivity analysis, ensuring consistency across grey bounds. To further validate the selected alternative, results are cross-verified with TOPSIS and ELECTRE-III, reinforcing GTPSI's robustness in aligning renovation designs with actual student needs. The GTPSI method thus offers a comprehensive solution for integrating user-centered preferences into expert evaluations, promoting designs that better meet stakeholder expectations.

Unveiling interactions among architectural sketching, parametric design, and digital fabrication using linkography

ABSTRACT A design environment represents the context and ecosystem in which design activities are conducted. Today, architectural practice includes a range of design environments, including parametric design, CAM, and digital fabrication, in addition to sketch environments, which serve to ideate and manufacture the architectural entity. However, there is a lack of empirical studies that unveil interactions among these environments. Therefore, this article aims to answer the following question: What is the role of the design environments and the cognitive impact of the interactive dialog between the design environments on design productivity while considering the parametric design and digital fabrication as an integrated process? For this purpose, an empirical study comprising 11 architects with a minimum three years/projects of Grasshopper experience was conducted. Design tasks required a transition between sketch, parametric design and digital fabrication environments. Methodologically, data were obtained by concurrent and thinking-aloud methods, and the design process was analyzed using the linkograph method. The findings indicate that the synthesis process is more pronounced during the sketching phase and diminishes during the parametric design phase because of the cognitive gaps. On the other hand, the parametric design environment is observed as a supportive representational environment for recalling previous design actions.

Research on optimizing the propagation path search of individualized products with multi-source design changes

Abstract The individualization and diversification of customer requirements are gradually affecting the product design and manufacturing process, and the production mode of enterprises is evolving towards customer-oriented mass individualized customization. The change in customer requirements is inevitable in the process of individualized product design, which is the key factor leading to the change in product design. This paper establishes the standard of individualized product parts and components association relationship through correlation analysis, and constructs the individualized product parts and components association network model based on the complex network theory; on this basis, comprehensively considering the influence of parts nodes and association relationship in the complex network, it calculates the intensity of change propagation through the node importance degree and the probability of change propagation, and carries out the search for multi-source design change propagation paths through the improved ant colony algorithm, and evaluates the influence of the change of customer requirement by taking the change rate of the network as the evaluation index, to help enterprises to decide the request.

Thermomechanical Nonlinear Vibration of Axially Loaded Functionally Graded Material Cylindrical Panels Including Porosity

For the first time, the combined influences of axial compressive load, porosity, geometric imperfection, elastic edge restraint, and elevated temperature on the nonlinear free vibration of functionally graded material (FGM) cylindrical panels are investigated in this paper. Unlike previous studies, the structural model considered in this paper includes practical situations and conditions arising in the design process and application of FGM cylindrical panels. The properties of material constituents are assumed to be temperature-dependent, and the effective properties of porous FGM are determined using a modified rule of mixture. Governing equations in terms of deflection and stress function are established based on first-order shear deformation theory, taking into account von Kármán–Donnell nonlinearity and initial geometric imperfection. Analytical solutions are assumed to satisfy simply supported boundary conditions, and the Galerkin method is applied to derive a time differential equation containing both quadratic and cubic nonlinear terms. This differential equation is numerically integrated employing the fourth-order Runge–Kutta scheme to determine the frequencies of nonlinear free vibration. Parametric studies are carried out to assess numerous effects on both linear and nonlinear frequencies of porous FGM cylindrical panels. The study reveals that natural frequencies are strongly decreased and that frequency nonlinearity is more significant due to axial compressive loads.

Application of machine learning for ‘what if?’ stress analysis

Analysis of stresses in components of even modestly complex geometries often require the use of finite element analysis (FEA). For testing a large number of design options quickly, FEA can be time consuming and provides more accuracy than required. In this project, a machine learning-based system is developed to provide quick and approximate solutions to stress analysis problems in parametrised compressor disc geometries. Simple mechanics problems were completed preliminarily to test the practicality of machine learning approaches for this application. This included applying instance selection by the [Formula: see text]-medoids algorithm to a 2D FEA problem. A parametrised compressor disc geometry was designed and defined by eight dimensions. Stress fields were produced from two superimposed loading schemes: the rotational body force and the force exerted on the disc by the blades 495,338 samples of training data were collected from 4374 FEA simulations. Four networks were trained to predict stresses caused by each loading scheme in order to produce stress fields. The best network was of the structure 9/20/20/2 and used normalised training data. For the geometry tested, it predicted stresses with a root-mean-square error of 1.51%. The code took 0.7 s to run in total, from start-up to completion of a stress plot. The importance of the inputs in the training data set were scored with a feature selection algorithm to aid further optimisation of the system. The low computation time makes this system suitable for the early stages in a design process.

The Feasibility of AgileNudge+ Software to Facilitate Positive Behavioral Change: Mixed Methods Design

Background In today’s digital age, web-based apps have become integral to daily life, driving transformative shifts in human behavior. “AgileNudge+” (Indiana University Center for Health Innovation and Implementation Science) is a web-based solution to simplify the process of positive behavior change using nudging as an intervention. By integrating knowledge from behavioral economics with technology, AgileNudge+ organizes multiple steps, simplifies complex tasks, minimizes errors by enhancing user engagement, and provides resources for creating and testing nudge interventions. Objective This paper aimed to outline the design process, methodologies, and usefulness of “AgileNudge+” for the development of evidence-based nudges. It used a mixed methods approach to evaluate the software’s interface usability and usefulness for creating and testing nudge interventions. Methods AgileNudge+ was developed through iterative processes integrating principles from behavioral economics and user-centered design. The content of AgileNudge+ operationalizes an Agile science–based process to efficiently design, embed, and disseminate evidence-based nudges that encourage positive behavior change without limiting choice. Using a mixed methods approach, we tested AgileNudge+ software’s ability to organize and simplify the nudge intervention process, allowing a diverse range of scholars with limited knowledge of Agile science to use nudges. Usability testing assessed the tool’s usefulness and interface with a sample of 18 health care professionals, each asked to interact with the software and create a nudge intervention to solve a problem within their professional project’s sphere. Results The study was funded in August 2022, with data collection occurring from June 2023 to July 2024. As of July 2024, we have enrolled 18 participants. Quantitative results found a mean usefulness rating of AgileNudge+ of 3.83 (95% CI 3.00-4.66). Qualitative results highlighted ways to modify the language used in AgileNudge+ to be more comprehensible to a diverse user base and promoted modifications to the software that facilitate real-time assistance and prioritize time efficiency in user interactions. Feedback further supported the positive impact of gamification on participant motivation when using the software. Conclusions AgileNudge+ is an effective assistive tool for simplifying the positive behavior change process using nudge interventions, with tailored content and interactions to meet users’ needs and demands. Building onto the current design, future iterations of AgileNudge+ will use artificial intelligence to process large volumes of data while reducing the time and mental energy required to scan for existing cognitive biases and nudge prototypes. The software is also being upgraded to build on current gamification efforts, encouraging more sustained motivation by increasing the temporal resolution of the digital interface. These modifications stay true to the agility and user-centered aspects of AgileNudge+, emphasizing the novelty of the constantly evolving software design process.

Enhancing biological system engineering realisation through advanced feature transfer and extenics: a bionic design approach

During the process of bionic design, the discrepancies between biological systems and engineering systems often result in designers being unable to handle the knowledge involved in biological systems effectively, thereby leading to a low utilisation rate of biological knowledge. This study proposes a model for the transformation of biological systems to engineering systems on the basis of feature transfer. First, the basic system is determined according to user requirements, followed by the search for suitable biological systems and the performance of similarity analysis. Second, the element model of extenics is used to represent biological systems, and the extension analysis method is used to extend biological systems. Then, on the basis of substance-field theory in TRIZ combined with the extension transformation method, three approaches for transforming biological systems into engineering systems are proposed: no processing, forward processing, and reverse processing. After transformation, the features of the engineering system are extracted and incorporated into the basic system to complete the structural design. The container bag outlet folding device is taken as an engineering case to validate the effectiveness of the proposed model.

Human-computer interaction interface design of flight simulator based on situation awareness.

To meet the comprehensive requirements of drivers for Human-Computer Interaction (HCI) during operation and to enhance situation awareness (SA), this paper proposes a design and evaluation method for flight simulator HCI interfaces grounded in SA. Firstly, the widely adopted Endsley's SA model, which comprehensively covers processes relevant to individual drivers was selected as the foundational framework. The study concentrates on flight, cruise, and landing scenarios within the simulator, analyzing and restructuring the information architecture of the HCI interface. Secondly, a self-assessment scale was developed based on the three-level model of information processing for SA to measure drivers' SA cognitive levels. The core scenarios of the simulation training were analyzed to establish a design-making process. Thirdly, invite participants were invited to engage in simulated driving sessions on the flight simulator, where their SA cognitive levels were assessed. An analysis of detail features was performed on samples with high SA cognitive levels. Subsequently, drivers' requirements for the control layout features of the HCI interface design were gathered through surveys. The design detail features of the HCI interface associated with high SA cognitive levels were identified and synthesized into four distinct dimensions to guide the detailed design process. Finally, participants engaged in simulated driving using the finalized interface design, and their SA cognitive levels were reassessed. The results demonstrated that the HCI interface design derived from this method significantly improved SA cognitive levels compared to the original design.

Thought Experiments in Design Ethics

How is the designer to approach questions of responsibility, obligation, or right and wrong in relation to their role in creating, sustaining and altering the complex worlds which we inhabit together? Every design decision stands as the first teetering domino at the head of an infinitely complex fractal chain reaction of consequences, the full repercussions of which are ultimately unforeseeable. The moment of ethical crisis – being faced with a range of possible options and lacking a fully adequate knowledge of which path is best to choose – is an unavoidable component of the design process. Ethical theories, principles, codes and rules always lag one step behind the frontier of the new, the territory in which design operates. Recognising that ethical crisis is an integral reality of the design process, how can designers be best supported and equipped for this challenge? This paper explores one path towards this aim, presenting the case for the use of philosophical thought experiments as appropriate and useful devices for developing capacities for ethical thinking and for nurturing a transformed ethical mindset within practising designers. Thought experiments do not directly guide or provide knowledge or answers as to the ‘right’ thing to do in any given situation. Rather, the argument is presented here that thought experiments can stimulate productive conditions in which designers can learn not to ‘know ethics’ but to ‘think ethics’. By playing these mind games, the designer can exercise and strengthen their mental capacities for responding to ethical encounters within the complexity of real-world design practice.