Design Process Research Articles

Fire resistance time prediction and optimization of cold-formed steel walls based on machine learning

Many full-scale experiments and numerical studies have been conducted to determine the fire performance of cold-formed steel (CFS) walls, but these studies are expensive and time consuming. This study proposes a machine learning (ML) based framework aiming at accurately predicting the fire resistance time (FRT) and optimizing the design of CFS walls under ISO 834 fire condition. To overcome the limitation of 32 experimental data points in the literature, a validated numerical method was used to generate 592 data points to expand the dataset of CFS walls, considering different wall configurations, various sheathing board types and thicknesses. The XGBoost (eXtreme Gradient Boosting) model was trained with numerical data and tested with experimental data. The hyperparameter tuning of the XGBoost model was implemented with Bayesian optimization, and it was found that the XGBoost model accurately predicted the FRT of CFS walls, with R2 and MAPE values being 0.933 and 8.46 %, respectively. The prediction process of the XGBoost model was interpreted by the SHAP (SHapley Additive exPlanations) method to determine the relative importance of input variables. The NSGA-II algorithm was adopted to optimize the FRT-cost dual-objective of CFS walls and the Pareto front including optimal solutions was emerging. The cost for each solution of Pareto front sets was lower than that in the real dataset at same FRT level. This phenomenon implied that the ML-based optimization framework successfully identified the cost-efficient design process. The proposed ML-based optimization framework offers a promising alternative for engineers to design CFS walls effectively with both mechanical and economic objectives.

Aerodynamic and Structural Design Procedures Supported by Fabrication and Flight Testing of a Small Unmanned Helicopter

In this work, typical design, production, and testing procedures for a small unmanned helicopter are explained and performed. In doing so, preliminary sizing of the helicopter and three main disciplines are conducted: aerodynamic analytical and numerical simulations, power calculations, and structure analysis assessment. First, a thorough survey is implemented to obtain the trends for the maximum take-off weight versus some design constraints such as rotor diameter, motor power, payload, and empty weight. Performance calculation results are obtained to figure out all aspects that correspond to the specified mission. The designed rotor geometry along with the aerodynamic characteristics and flight performance variables is then validated using the blade element theory and numerical simulations. Second, based on the power curves obtained for different flight regimes, an electric brushless motor is selected. The numerical simulations (Computational Fluid Dynamics) analysis is used to enhance the selection which implies that the motor power should be greater than 5.4 kW to overcome the drag forces. The motor power selection corresponds to a maximum rotor pitch angle of 15∘ and a maximum rotor speed of 1450 RPM. Then, the aerodynamic loads are used as an input for the structural analysis using one-way coupling of fluid–structure interaction (FSI) and consequently designing the internal structure of the blade. Eventually, the internal structure manufactured using carbon fiber-reinforced polymer (CFRP) by applying a combined technique between wet layup and compression molding. The blade is statically tested compared with numerical finite element model results. The fuselage structure along with hub and tail units is manufactured and assembled with the existing on-shelf components to examine the helicopter lift capability with different payloads up to 9 kg. The results show that the detailed design process is significant for manufacturing such blades and the helicopter is capable of lifting off the ground with various payloads depending on the rotor pitch angles (8∘, 12∘, and 15∘) at a constant rotor speed of 1450 RPM.

Designing Tourist Itineraries with Design Thinking and System Usability Scale: A Case Study of Uluwatu Island, Bali

Purpose: This research aims to design a travel itinerary media that integrates necessary elements from various sources and presents them in an attractive and informative format. The approach used is Design Thinking, which places the user at the center of the design process. Method: The research employs the Design Thinking approach, which involves five main stages: Empathy, Define, Ideate, Prototype, and Test. Data were collected through direct observation, questionnaires, and analysis of tourist behavior and preferences. The resulting prototype was tested by a representative group of users to assess the functionality and usability of the travel itinerary design. Result: The study culminated in the creation of a tourist itinerary media design, developed through the Design Thinking process. This design effectively integrates essential elements from diverse sources, ensuring it is both visually appealing and informative. Crucially, user testing with the System Usability Scale (SUS) produced an average score of 76.75. This score reflects a high level of user satisfaction and confirms the system's strong usability, highlighting the design's potential for enhancing the tourist experience. The positive reception underscores the study's impact in offering a user-friendly and engaging tool for tourism planning. Contribution: The main contribution of this study is the development of a more innovative tourist itinerary design that is in line with user needs. Through the Design Thinking approach, this research provides practical guidance for designing relevant and effective solutions in the tourism industry context.

Design Method of Freeform Surface Optical Systems with Low Coupling Position Error Sensitivity.

Freeform off-axis reflective systems are significantly more difficult to align and assemble owing to their asymmetric surface shapes and system structures. In this study, a freeform surface system design method with low coupling position error sensitivity (FCPESM) was proposed. First, we established a mathematical model of a reflective system when it was perturbed by coupling position errors and used the clustering-microelement method to establish the coupling error sensitivity evaluation function. The evaluation function was then applied to the design process of a freeform surface off-axis three-mirror optical system. The results showed that the FCPESM optical design method can significantly relax the assembly tolerance requirements of optical systems on the basis of ensuring image performance. In this study, the reflective system was perturbed by tilt and decenter simultaneously, and the disturbance mechanism of position errors on optical systems was further improved. Through this research, freeform surface systems with both image performance and error sensitivity can be obtained, which makes freeform off-axis reflective systems with better engineering realizability.

Efficient and lightweight convolutional neural network architecture search methods for object classification

Determining the architecture of deep learning models is a complex task. Several automated search techniques have been proposed, but these methods typically require high-performance graphics processing units (GPUs), manual parameter adjustments, and specific training approaches. This study introduces an efficient, lightweight convolutional neural network architecture search approach tailored for object classification. It features an optimized search space design and a novel controller design.This study introduces a refined search space design incorporating optimizations in both spatial and operational aspects. The focus is on the synergistic integration of convolutional units, dimension reduction units, and the stacking of Convolutional Neural Network (CNN) architectures. To enhance the search space, ShuffleNet modules are integrated, reducing the number of parameters and training time. Additionally, BlurPool is implemented in the dimension reduction unit operation to achieve translational invariance, alleviate the gradient vanishing problem, and optimize unit compositions. Moreover, an innovative controller model, Stage LSTM, is proposed based on Long Short-Term Memory (LSTM) to generate lightweight architectural sequences. In conclusion, the refined search space design and the Stage LSTM controller model are synergistically combined to establish an efficient and lightweight architecture search technique termed Stage and Lightweight Network Architecture Search (SLNAS).The experimental results highlight the superior performance of the optimized search space design, primarily when implemented with the Stage LSTM controller model. This approach shows significantly improved accuracy and stability compared to random, traditional LSTM, and Genetic Algorithm (GA) controller models, with statistically significant differences. Notably, the Stage LSTM controller excels in accuracy while producing models with fewer parameters within the expanded architecture search space.The study adopts the Stage LSTM controller model due to its ability to approximate optimal sequence structures, particularly when combined with the optimized search space design, referred to as SLNAS. SLNAS's performance is evaluated through experiments and comparisons with other Neural Architecture Search (NAS) and object classification methods from different researchers. These experiments consider model parameters, hardware resources, model stability, and multiple datasets. The results show that SLNAS achieves a low error rate of 2.86 % on the CIFAR-10 dataset after just 0.2 days of architecture search, matching the performance of manually designed models but using only 2 % of the parameters. SLNAS consistently demonstrates robust performance across various image classification domains, with an approximate parameter count 700,000.To summarize, SLNAS emerges as a highly effective automated network architecture search method tailored for image classification. It streamlines the model design process, making it accessible to researchers without specialized knowledge in deep learning. Optimizing this method unlocks the full potential of deep learning across diverse research areas. Interested parties can publicly access the source code and pre-trained models through the following link: https://github.com/huanyu-chen/LNASG-and-SLNAS-model.

Analysis of the features of the shielding system of polymer insulation structures of high voltage electrical equipment

In the article, the authors analysed the existing shielding systems of insulating structures of high-voltage devices together with electrodes under high potential. At the same time, the possibilities of using toroidal screens of various designs and the conditions of their installation in gas-filled equipment structures (gas-filled current and voltage measuring transformers, arresters, etc.) using polymer insulating materials are considered. Based on the analysis of literary sources, the most effective method of estimating the electric load parameters for determining the electric field strength distribution along the continuous insulating structure of gas-filled high-voltage devices with axial symmetry is proposed. This design is a support-insulating shell filled with SF6 as an internal insulating medium. Methodical materials are recommended that allow you to estimate the electrical load parameters (voltage and field strength) depending on the design features of the device and the shielding system used to level the electric field. Based on them, it is possible to evaluate the effectiveness of the calculation coefficients of the shielding system of gas-filled high-voltage equipment in the design process and during the improvement of individual design solutions. The results of the analytical evaluation by such methods are in good agreement with the data of experimental studies and statistical information obtained as a result of monitoring the work of insulating structures, taking into account real operating conditions and the influence of external factors. The introduction of the obtained materials allows you to take into account the influence of external factors and operational characteristics inherent in measuring transformers and overvoltage limiters. To explain the results of the analysis of methodical materials carried out in the article, as an example, the results of calculations of the support-insulation shell of the gas-filled current transformer of the TOG series, which was the most affected during operation, are given. in adverse conditions, given under such conditions as external pollution, humidity and their combination, as well as under conditions of overvoltage of various origins, etc. On the basis of the presented materials, it is possible to more accurately determine the effectiveness of various screenings. system, as well as how to predict the field strength distribution parameters under the influence of the shielding system, taking into account its design features. In addition, on the basis of the conclusions formed in the work, it is possible to plan and conduct an additional series of calculations and experimental tests taking into account the features of specific structures. Thus, the obtained results can be used to evaluate the external insulation parameters of both gas-filled measuring transformers and similar high-voltage equipment of distribution devices and transformer substations.

Informative design exploration of tower building geometry typology and solar potential using SOM-MLPNN with generalized form-description-based parametric modelling

To synchronize designers early in the design process and help them understand the building forms and performance metrics needed to meet both qualitative and quantitative requirements is crucial for sustainable building designs. While the prevalent design exploration technique, which integrates self-organizing mapping with a Supervised Learning-based Agent Model (SOM-MLPNN), offers insights into design geometry and solar potential, it still has limitations in the range of the design exploration space, the efficiency and accuracy of data approximation, and the uncertainty in dealing with performance metrics related to more complex environmental factors. Using the solar potential of tower buildings as an example, this study proposes a method based on SOM-MLPNN by integrating a Generalized Tower Generator (GTG) and form description, testing its usability for the inclusion of complex surrounding environments, and improving its design exploration breadth as well as the efficiency and accuracy of data approximations. The results demonstrate improved efficiency and accuracy in data approximation across an expansive design space, facilitating visual and interactive exploration of design-performance relationships. The proposed method emphasizes the critical interplay between building geometry and environment-related performance, providing designers with a refined toolkit for discerning the correlation between design and performance, thereby enhancing their design decision-making abilities.

Informative Design Exploration of Tower Building Geometry Typology and Solar Potential Using SOM-MLPNN with Generalized Form-Description-Based Parametric Modelling

To synchronize designers early in the design process and help them understand the building forms and performance metrics needed to meet both qualitative and quantitative requirements is crucial for sustainable building designs. While the prevalent design exploration technique, which integrates self-organizing mapping with a Supervised Learning-based Agent Model (SOM-MLPNN), offers insights into design geometry and solar potential, it still has limitations in the range of the design exploration space, the efficiency and accuracy of data approximation, and the uncertainty in dealing with performance metrics related to more complex environmental factors. Using the solar potential of tower buildings as an example, this study proposes a method based on SOM-MLPNN by integrating a Generalized Tower Generator (GTG) and form description, testing its usability for the inclusion of complex surrounding environments, and improving its design exploration breadth as well as the efficiency and accuracy of data approximations. The results demonstrate improved efficiency and accuracy in data approximation across an expansive design space, facilitating visual and interactive exploration of design-performance relationships. The proposed method emphasizes the critical interplay between building geometry and environment-related performance, providing designers with a refined toolkit for discerning the correlation between design and performance, thereby enhancing their design decision-making abilities.

Analysis of the features of the shielding system of polymer insulation structures of high voltage electrical equipment

In the article, the authors analysed the existing shielding systems of insulating structures of high-voltage devices together with electrodes under high potential. At the same time, the possibilities of using toroidal screens of various designs and the conditions of their installation in gas-filled equipment structures (gas-filled current and voltage measuring transformers, arresters, etc.) using polymer insulating materials are considered. Based on the analysis of literary sources, the most effective method of estimating the electric load parameters for determining the electric field strength distribution along the continuous insulating structure of gas-filled high-voltage devices with axial symmetry is proposed. This design is a support-insulating shell filled with SF6 as an internal insulating medium. Methodical materials are recommended that allow you to estimate the electrical load parameters (voltage and field strength) depending on the design features of the device and the shielding system used to level the electric field. Based on them, it is possible to evaluate the effectiveness of the calculation coefficients of the shielding system of gas-filled high-voltage equipment in the design process and during the improvement of individual design solutions. The results of the analytical evaluation by such methods are in good agreement with the data of experimental studies and statistical information obtained as a result of monitoring the work of insulating structures, taking into account real operating conditions and the influence of external factors. The introduction of the obtained materials allows you to take into account the influence of external factors and operational characteristics inherent in measuring transformers and overvoltage limiters. To explain the results of the analysis of methodical materials carried out in the article, as an example, the results of calculations of the support-insulation shell of the gas-filled current transformer of the TOG series, which was the most affected during operation, are given. in adverse conditions, given under such conditions as external pollution, humidity and their combination, as well as under conditions of overvoltage of various origins, etc. On the basis of the presented materials, it is possible to more accurately determine the effectiveness of various screenings. system, as well as how to predict the field strength distribution parameters under the influence of the shielding system, taking into account its design features. In addition, on the basis of the conclusions formed in the work, it is possible to plan and conduct an additional series of calculations and experimental tests taking into account the features of specific structures. Thus, the obtained results can be used to evaluate the external insulation parameters of both gas-filled measuring transformers and similar high-voltage equipment of distribution devices and transformer substations.

Facilitators and Barriers to Medicaid Doula Benefit Implementation in California: Perspectives From Managed Care Plans and Risk-Bearing Organizations

IntroductionMedicaid coverage of doula services is increasing as a policy strategy to reduce maternal health inequities in the United States. However, early adopter states struggled to offer accessible, equitable Medicaid doula benefits when implementation began. California began covering doula services through its Medicaid program, Medi-Cal, in 2023. Managed care plans (MCPs) and risk-bearing organizations (RBOs) play an important role in ensuring pregnant and birthing people can access doula support through Medicaid benefits. Materials and MethodsBetween 2021 and 2022, we conducted 14 interviews with MCP and RBO staff (n = 20) representing a total of 14 MCPs and RBOs. Data were analyzed in two stages: 1) rapid assessment process and 2) using the Consolidated Framework for Implementation Research (CFIR) to identify specific facilitators and barriers to Medi-Cal doula benefit implementation. ResultsWe identified 10 facilitators and 16 barriers across the five CFIR domains. Results indicate a general lack of familiarity with doula care and highlight the importance of relationship building with doulas and collaboration among plans. ConclusionsIn California, these findings can help guide improvements to emerging implementation challenges and evaluation efforts. Our findings can also help other states in the planning and Medicaid doula benefit design process.

Adaptive fuzzy event-triggered control for a class of nonlinear time-delay multi-agent systems with dead zone and partial state constraints

This paper presents an adaptive fuzzy event-triggered control (ETC) method for a class of time-delay nonlinear multi-agent systems (MASs) with dead zone (DZ) and partial state constraints (PSCs). It should be pointed out that the states considered in this paper are unmeasurable and part of the system states are constrained by time-varying boundary. By utilizing fuzzy logic systems (FLSs) and backstepping design framework, a fuzzy state observer is constructed to estimate the unmeasured states, while the introduction of a time-varying barrier Lyapunov function (BLF) addresses the partial state constrained problem. Furthermore, during the design process of the state observer, compensation for the influence of the unknown DZ input on the system is achieved through utilization of known designed event-triggered input signal and adaptive parameter. Building upon this foundation, employing the Lyapunov stability theory and adaptive backstepping technique with ETC strategy, the developed distributed control mechanism demonstrates that all variables of the closed-loop system are bounded, the consensus error of the system can converge to a small region of origin and Zeno behavior can be ruled out. Finally, two simulation examples further illustrate the proposed control strategy and theorem.

Teaching content model for designing digital systems of power grid relay protection and automation

It was found that the effective formation of conceptual-analytical and productive-synthetic levels of formation of professional knowledge and skills regarding the design of digital systems of power grid relay protection and automation is possible with the systematic development and use of a generalized model of the teaching content. Based on the study of information models of technical systems, a feature model was adopted as a basic invariant, which includes a set of features of the purpose, construction, principle of operation and parameters of the technical system. It was found that the design process of digital systems of power grid relay protection and automation can be conventionally divided into three stages, namely, the development of the design task, the direct implementation of the design, and the determination of the parameters of operation. Causal chains of knowledge that form the basis of problem solving at these three stages are determined. It was found that the methods of step-by-step decomposition and aggregation are key in building the content of teaching the design of digital systems of power grid relay protection and automation based on causal chains of knowledge. A generalized model of the teaching content design of digital systems of power grid relay protection and automation based on causal chains of knowledge is substantiated. According to the generalized model, the teaching content design of specific microprocessor l systems of power grid relay protection and automation has been developed. With the help of experimental verification, the effectiveness of the teaching content design of digital systems of relay protection and automation based on causal chains of knowledge has been proven. The results of the research can be used in the educational process of professional training of future engineers in the specialty 141 Electric power, electrical engineering and electromechanics.

Teaching content model for designing digital systems of power grid relay protection and automation

It was found that the effective formation of conceptual-analytical and productive-synthetic levels of formation of professional knowledge and skills regarding the design of digital systems of power grid relay protection and automation is possible with the systematic development and use of a generalized model of the teaching content. Based on the study of information models of technical systems, a feature model was adopted as a basic invariant, which includes a set of features of the purpose, construction, principle of operation and parameters of the technical system. It was found that the design process of digital systems of power grid relay protection and automation can be conventionally divided into three stages, namely, the development of the design task, the direct implementation of the design, and the determination of the parameters of operation. Causal chains of knowledge that form the basis of problem solving at these three stages are determined. It was found that the methods of step-by-step decomposition and aggregation are key in building the content of teaching the design of digital systems of power grid relay protection and automation based on causal chains of knowledge. A generalized model of the teaching content design of digital systems of power grid relay protection and automation based on causal chains of knowledge is substantiated. According to the generalized model, the teaching content design of specific microprocessor l systems of power grid relay protection and automation has been developed. With the help of experimental verification, the effectiveness of the teaching content design of digital systems of relay protection and automation based on causal chains of knowledge has been proven. The results of the research can be used in the educational process of professional training of future engineers in the specialty 141 Electric power, electrical engineering and electromechanics.

When engineering design meets STEAM education in hybrid learning environment: teachers’ innovation key through design heuristics

ABSTRACT Implementing STEAM education in classrooms can be enhanced by incorporating other methods to bring students flexibility during learning, such as the engineering design process (EDP) and hybrid learning. We developed design heuristics to support teachers in enhancing the quality of STEAM lessons, emphasizing EDP within hybrid learning to assist teachers in developing lesson plans. The design heuristics were implemented in three groups of online professional development programs within educational design research, with a hundred and eighty-three teachers submitting their lesson plans. We scored the lesson plans and conducted the Kruskal Wallis H test procedure. The result showed that teachers who participated in this study were likely to incorporate the key principles of design heuristics in their lesson planning practices. However, we found that there is a necessity for further exploration, especially in the variation of assessment strategies and the exploration of problems or context for STEAM learning. Therefore, the result emphasized the significance of future exploration that defines and formulates problems for STEAM activities and its implications on teachers’ lesson planning. This study harmonizes technological advancement and pedagogical practices within hybrid learning by integrating emerging technologies into lesson planning and evolving a design approach into the STEAM curriculum.

Design and research of grounding current monitoring device for converter transformer core and clamp

The effective monitoring of the grounding current of the core and clamp of the converter transformer can prevent partial overheating, gassing of oil, and other abnormal conditions caused by the grounding fault of the core and clamp, ensuring the safety and stability of the power system. At present, the main detection and diagnosis method is to use the clamp current meter to detect the effective value of the grounding current. However, due to the larger capacity, higher voltage level, more complex structure and electromagnetic field distribution of the converter transformer, there is no clear standard and detection diagnosis method. Firstly, this paper analyzes the causes of grounding current and related calculations. And the design of the monitoring system for the grounding current of the converter transformer core and clamp design of the hardware system for the grounding current of the converter transformer is studied and the design process of the main circuit is given. Secondly, aiming at the problem of amplitude measurement deviation and non-integer frequency of harmonic generated by the grounding current of the core and clamp, a harmonic current detection and analysis scheme based on four Blackman-harris algorithms and the specific design process are proposed. Third, a diagnostic method based on probabilistic neural network is proposed, and a software platform is built to display and diagnose the grounding current state according to it. Finally, a prototype was manufactured and experimental study was carried out to verify the correctness of the proposed scheme.

A knowledge graph-aided decision guidance method for product conceptual design

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making.

Extending the Finite Area Method for enhanced simulation of deformable membranes and its application to extrusion blow moulding

This paper investigates the mechanics of the Extrusion Blow Moulding (EBM) process, focusing on the clamp and inflation phases. The simulation framework employed in this study has been developed using OpenFOAM ® and extends the available Finite Area Method with a vertex-centred approach. This cutting-edge developments integrated a corotational formulation with a quasi-static approximation. The computational code and algorithm are verified and validated through comparisons between analytical solutions, experimental measurements and simulation results, ensuring the accuracy and reliability of the proposed approach. The implemented code is verified by checking fundamental principles of hyperelastic materials behaviour, demonstrated through equibiaxial deformation of spherical balloons. The use of neo-Hookean and Mooney–Rivlin material models provides crucial insights into material responses under different inflation conditions. The studies begin employing a conceptual mould within the EBM process that allows examining the effects of mould clamping on parison deformation. Then the study extends to analysing a real bottle manufactured via EBM, employing Stereolithography (STL) digitization and thickness mapping to quantify the thickness distribution. The simulation results closely replicates experimental observations, capturing the inherent non-linearity of these process stages, including large deformations and displacements, hyperelasticity and parison-mould contacts. This interdisciplinary research enhances understanding of the EBM process phases and analogous processes, providing valuable insights for plastic manufacturing. It improves the design process for meeting specific requirements and offers guidelines to avoid potential failures. The findings underscore the importance of such studies in advancing sustainable and efficient plastic manufacturing practices, with applications ranging from inflatable structures to EBM processes.

Inverse design of functionally graded porous structures with target dynamic responses

Although functionally graded porous structures (FGPS) have been studied comprehensively due to their excellent mechanical and functional properties, the design process of FGPS with target stress response still dependent on continuous trial and error, which is not only time-consuming but also heavily relies on experience and intuition. This study proposes a novel inverse design framework of 2D FGPS with targeted dynamic stress-strain responses via a combination of conditional diffusion model and a residual neural network. Unlike traditional binary pixel representations, this approach utilizes nuclei position maps and color mapping technique to represent the structure configurations and cell wall thicknesses to improve the efficiency and performance of the model. Firstly, various functionally graded porous structures were constructed by employing Voronoi diagram techniques, and finite element simulations were conducted to calculate the nonlinear responses subjected to dynamic loadings. A dataset comprising 2100 FGPS and corresponding nonlinear stress-strain responses was established. Then, an inverse design framework is formulated by integrating a generator that uses a diffusion model to synthesize structures conditioned on specific target responses, with a predictor that employs a residual neural network to assess the responses of these structures. Finally, to demonstrate the effectiveness of the approach, the performance of the predictor and generator was thoroughly investigated. Structures with various target stress-strain responses were generated through the proposed method, and validated by finite element analysis. A detailed impact experiment was also carried out to verify the effectiveness of the proposed methods. The result shows that the framework can effectively generate new structures with objective nonlinear responses. This work offers a fast and efficient way to design FGPS that meet specific performance objectives.

Paper-pop-up design as three-dimensional skillin a higher education context

This applied research developed within the framework of a Portuguese Higher Education Institution. Its purpose is to assess academic practice in the context of a curricular unit in a graphic design degree course. This practice has been developed within three complementary perspectives: (a) the pedagogical concept and the proposed design exercise; (b) the selection of paper processes and materials used; (c) the design and structural definition of the graphic product. The purpose of this research is to describe the activity criteria from each perspective, exposing realization guidelines presented to students in designing a specific graphic product, from ideation, design process, and estimation of a feasible production. All this from the analysis of the proposed activity and evaluation of the prototype that represents Paper-pop-up design.

Truly Human: Leveraging AI to Better Serve Our Children

To keep AI grounded in best practices for teaching children, it’s essential to prioritize ethical considerations, continually evaluate and refine algorithms to ensure they align with educational goals, and involve educators and child development experts in the design and implementation process.