Design Problem Research Articles

Types of formative feedback provided by technology teachers during practical assessment tasks

Technology learners require formative feedback from their teachers to aid them in structuring and solving design problems. Without such feedback, learners often do not explore sub-problems and are inclined to reproduce known and existing solutions. Due to a paucity of literature on formative feedback in technology classrooms, this study investigated the formative feedback types that teachers provide to learners during the problem-structuring and problem-solving phases. A qualitative case study design was employed to investigate such feedback from five technology teachers who supported their learners in solving ill-structured design problems. The findings indicate that teachers’ formative feedback consists predominantly of low-level questions, while generative design questions and low-level comments were the least observed. A deeper understanding of how formative feedback unfolds in a technology classroom may help teachers guide learners through problem structuring and solving. Further research is needed to determine the influence of formative feedback on learners’ design outcomes.

Smart prison technology and challenges: a systematic literature reviews

<span lang="EN-US">The rapid rise of intelligent technology, particularly in government, is igniting a new phase of the industrial revolution around the world. As governmental entities, prisons oversee upholding social order and lowering current crime. The concept of the smart prison has not received much attention but is gaining traction. The goal of this research is to conduct a literature review to identify current prison technologies and to analyse the challenges associated with implementing smart prisons using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Nine credible publishers were looked up between October 2022 and December 2022. The initial search yielded 362 articles, of which 25 were included in the final phase. This research provides the current state of prison according to technology-organization-environment (TOE). Some challenges arise in the context of TOE, such as the high cost of smart technology, inadequate technology design, poor management, ineffective service, overcrowding, ageing facilities, increasing violence, disease spread, and ethical problems. This study also classifies smart prison technology based on the internet of things (IoT) architecture layer. By providing the first comprehensive review on smart prison technology, this study makes an essential contribution to the subject of prisons.</span>

NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

The design and discovery of new materials is fundamental to advancing scientific and technological innovation. The recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of ‘superior’ materials. However, the materials genome approach can be stymied by the vast complexity of design spaces, which often demand substantial computational resources and sophisticated data processing capabilities. To address these challenges, this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks (NSGAN). Capitalising on the synergies of genetic algorithms (GA) and generative adversarial networks (GANs), NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design problems. To validate the efficacy of the proposed framework, we applied the model to a comprehensive dataset of aluminium alloys. Additionally, an online tool was created as a supplementary resource, offering a brief introduction to this innovative method for the wider scientific community. This study explores the potential of a predictive and data-driven approach in material design, indicating a promising pathway for widespread applications in the field of materials science.

Enhancing synergistic performance through implicit incentive contracts: Addressing the “talent dilemma” in the public sector

AbstractBecause of the conflict between the traditional public sector values of egalitarianism and new public management, talent management (TM) in the public sector is more difficult to implement under resource constraints. Equity and efficiency in China's public sector are squeezed out of each other under egalitarianism, resulting in the “ talent dilemma” in which talent works more but does not get more. The “talent dilemma” leads to the fact that it is very attractive for talents to hide their abilities and reduce their efforts in the public sector. In this paper, from the perspective of the “talent dilemma”, we study the contract design problem of the coexistence of heterogeneous talent's adverse selection and moral hazard in the public sector. Specifically, an incentive model combining exclusive and inclusive TM factors under information asymmetry is constructed. The optimal incentive contract is designed to motivate heterogeneous talents to display true information about their abilities and to maintain a maximized level of effort. The results of the study show that the optimal incentive contract can realize the role of information screening and break the “talent dilemma”. These findings provide a theoretical basis for the reform of incentive mechanisms in the public sector.

Subdivision Schemes Based on Weighted Local Polynomial Regression: A New Technique for the Convergence Analysis

The generation of curves and surfaces from given data is a well-known problem in Computer-Aided Design that can be approached using subdivision schemes. They are powerful tools that allow obtaining new data from the initial one by means of simple calculations. However, in some applications, the collected data are given with noise and most of schemes are not adequate to process them. In this paper, we present new families of binary univariate linear subdivision schemes using weighted local polynomial regression which are suitable for noisy data. We study their properties, such as convergence, monotonicity, polynomial reproduction and approximation and denoising capabilities. We establish conditions that connect the weight function with two essential properties: noise elimination and approximation capability. We propose a multi-objective optimization problem for improving these two capabilities and conclude that only some weight functions are Pareto optimal. For the convergence study, we develop some new theoretical results. Finally, some examples are presented to confirm the proven properties.

Structural Optimization of Trusses in Building Information Modeling (BIM) Projects Using Visual Programming, Evolutionary Algorithms, and Life Cycle Assessment (LCA) Tools

The optimal structural design is imperative in order to minimize material consumption and reduce the environmental impacts of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as the genetic algorithm (GA). However, the integration of AI-based optimization, together with visual programming (VP), in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps, including the following: (i) generation of parametric trusses in Dynamo VP; (ii) performing finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. This methodology provides a new interoperable framework with minimal interference with existing supply-chain processes, and it will be flexible to technology literacy and allow architectural, engineering and construction (AEC) professionals to employ VP, global optimization, and FEM in BIM-based projects by leveraging open-sourced software and tools, together with commonly used design software. The feasibility of the proposed workflow was tested on benchmark problems and compared with the open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction. Despite the success of this study in developing a workable, user-friendly, and interoperable framework for the utilization of VP, GA, FEM, and BIM for structural optimization, the results obtained could be improved by (i) increasing the callback function speed between Dynamo and RSA through specialized application programming interface (API); and (ii) fine-tuning the GA parameters or utilizing other advanced global optimization and supervised learning techniques for the optimization.

Distributed l2−l∞ filtering over wireless sensors based on adaptive event triggering

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [Formula: see text] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [Formula: see text] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.

Curved Domains in Magnetics: A Virtual Element Method Approach for the T.E.A.M. 25 Benchmark Problem

In this paper, we are interested in solving optimal shape design problems. A critical challenge within this framework is generating the mesh of the computational domain at each optimisation step according to the information provided by the minimising functional. To enhance efficiency, we propose a strategy based on the Finite Element Method (FEM) and the Virtual Element Method (VEM). Specifically, we exploit the flexibility of the VEM in dealing with generally shaped polygons, including those with hanging nodes, to update the mesh solely in regions where the shape varies. In the remaining parts of the domain, we employ the FEM, known for its robustness and applicability in such scenarios. We numerically validate the proposed approach on the T.E.A.M. 25 benchmark problem and compare the results obtained with this procedure with those proposed in the literature based solely on the FEM. Moreover, since the T.E.A.M. 25 benchmark problem is also characterised by curved shapes, we utilise the VEM to accurately incorporate these “exact” curves into the discrete solution itself.

Understanding Graphic Design Process in Visual Communication

From a chocolate wrapper design to a huge advertising hoarding, graphic design play a significant role in our daily life, which takes into consideration three important aspect of acceptance of a design - an aesthetic appeal, functional and semiotic approach. Graphic design encompasses the diverse set of design solution in visual form -from designing a logo, poster, signage to advertising design, web based design and many more that utilizes various visual elements, combining art and technology to communicate a message visually. Since it a coherent creative process of visual communication, the content of communication need to be streamed between the clients and the end user that takes into consideration certain aspect of structural stages or design process in order to get the best service and design solution to a problem. The paper highlights on the structure of the design process involve in a graphic design in bringing about a solution to a design problem within the practice of visual communication.

Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective

Practitioners and academics have long appreciated the benefits of covariate balancing when they conduct randomized experiments. For web-facing firms running online A/B tests, however, it still remains challenging in balancing covariate information when experimental subjects arrive sequentially. In this paper, we study an online experimental design problem, which we refer to as the online blocking problem. In this problem, experimental subjects with heterogeneous covariate information arrive sequentially and must be immediately assigned into either the control or the treated group. The objective is to minimize the total discrepancy, which is defined as the minimum weight perfect matching between the two groups. To solve this problem, we propose a randomized design of experiment, which we refer to as the pigeonhole design. The pigeonhole design first partitions the covariate space into smaller spaces, which we refer to as pigeonholes, and then, when the experimental subjects arrive at each pigeonhole, balances the number of control and treated subjects for each pigeonhole. We analyze the theoretical performance of the pigeonhole design and show its effectiveness by comparing against two well-known benchmark designs: the matched-pair design and the completely randomized design. We identify scenarios when the pigeonhole design demonstrates more benefits over the benchmark design. To conclude, we conduct extensive simulations using Yahoo! data to show a 10.2% reduction in variance if we use the pigeonhole design to estimate the average treatment effect. This paper was accepted by George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02184 .

Exploring the Influence of Requirement Representation on Idea Generation

Abstract The objective of this research is to understand how different representations of requirements influence idea generation in terms of quantity, addressment, novelty, and variety of conceptual sketches. Requirements are testable statements of need, desires, and wishes of the stakeholders that are used by engineers to frame the problem. Project success is highly dependent on well-defined requirements documents. An experimental study was conducted with 52 fourth-year mechanical engineering undergraduate students. Two design problems are used with three different representations: a problem statement with embedded requirements, a problem statement and a traditional requirement list, and a problem statement with contextualized scrum stories. Each student addressed both design problems with two different representations of requirements. It was found that the use of contextualized scrum story representations significantly affected the novelty of solution fragments and addressment of requirements, while no significant change in variety and quantity was seen. Finally, it was found that quantity is not directly related to the number of requirements addressed in the sketches.

Size and Dispatch Co-Optimisation of a Grid-Connected Agrivoltaic System

Agrivoltaic systems that leverage the opportunity of integrating solar photovoltaic (PV) systems into land used for agriculture, have been shown to provide an effective platform for a mutually beneficial cooperation between energy and food. However, the mainstream literature has failed to investigate the systematic design and dispatch considerations that must be made to ensure the robust and profit-maximising operation of a grid-connected agrivoltaic system from an energy perspective subject to meeting onsite load demands, such as irrigation pumps, centre pivot systems, and cow shed pumps. This necessitates formulating a coordinated, system-level strategic design and dispatch problem that considers the localised energy system and its individual components. Accordingly, this paper introduces a novel agrivoltaic system energy planning optimisation method with an integrated dispatch scheduling framework. The proposed method enables the consideration of augmenting value streams, such as temporal energy arbitrage with the grid, especially regarding the presence of behind-the-meter stationary battery storage devices and electric agricultural vehicles’ batteries. Furthermore, the proposed method has a general crop type-independent structure. This allows for greater adaptability of the method to different types of agrivoltaic systems. The effectiveness of the proposed method in improving the economic feasibility of grid-connected agrivoltaic systems is demonstrated based on simulation results obtained from its application to a conceptual agrivoltaic system backed by stationary and mobile battery storage systems, proposed for implementation in a rural location in Aotearoa New Zealand.

Leveraging the concept of information entropy to improve a multi-fidelity design framework for early-stage design exploration of complex vessels

Early-stage design exploration is crucial since most of the major design decision are locked-in and only small design modifications are possible at later stages. To assess the performance of the various design candidates while performing design exploration, there are available methods and tools of various fidelities. These methods can be combined to form a multi-fidelity (MF) framework that guarantees accuracy through the high-fidelity model and achieves faster computational speeds through low-fidelity models. The present study proposes the adoption of information-theoretic entropy to improve a MF design framework based on Gaussian Processes (GPs). Entropy quantifies the uncertainty associated with the prediction of the design space. We propose using this uncertainty metric both as a criterion to determine whether further designs should be sampled to construct a reliable approximation of the design space and as a criterion to establish in which optimization step the optimization of the covariance matrix for the MF-GPs should be performed. The approach was tested to benchmark analytical functions and to a ship design problem of an AXEfrigate. The approach holds potential in practical applications, as it aids in the determination of whether additional resources should be allocated for high-fidelity analysis to support early-stage exploration.

Adaptive event-triggered optimal [formula omitted] tracking control for uncertain nonlinear systems: Comparative analysis applied to autonomic tractor-trailer system

In this paper, an event-triggered tracking optimization control method is proposed for nonlinear uncertain systems with H∞ discounted cost. First, the linearization method using the infinite series expansion theorem is given. And the tracking control design problem is solved by appropriate state augment. Secondly, the zero sum differential game strategy is introduced to address the control design problem of uncertain systems with event-triggered inputs. The actor-critic-disturbance networks framework is used to approximate the optimization of event- triggered controller strategy, optimization costs, and the disturbance strategy, respectively. Due to the consideration of discrete time and continuous time dynamics, we put the impulsive system method to use and choose appropriate Lyapunov functions to prove stability and boundedness, at the same time, there will be no infinite triggering situation. Finally, the effectiveness of the proposed scheme is demonstrated by an example of a practical tractor system with n trailers through simulation compared with another typical design method.

Design strategies for aging adaptation design of public spaces under the community elderly care model

As the aging population intensifies, the community elderly care model has gradually become an important model for elderly care. The research takes the community elderly care model as the starting point, the public spaces is used as the key research objective. Firstly, the relevant theoretical research and conceptual definition of aging adaptation design are elaborated. Secondly, the excellent practical cases of the construction and renovation of elderly care communities both domestically and internationally are analyzed. The problems in the aging adaptation design of public spaces in China are analyzed. Finally, based on the psychological, physiological, and social needs of the elderly, a design strategy for public spaces that are suitable for aging under the community elderly care model is proposed, namely, “layout consultation spaces at different levels, form leisure spaces that are conducive to communication, and construct kitchen spaces with composite infiltration”. Through reasonable design and planning, the aging adaptation design strategy of public spaces under the community elderly care model can create a livable and enjoyable leisure environment. Elderly people feel the care and support from the community, improve their quality of life, and enhance social well-bei

Dynamic content layout optimization for news website front pages

Purpose The home page design of a digital news website is a key factor in determining its attractiveness to readers. This study aims to propose an approach to manage the frequent adjustment of the dynamic layout of the news content on the website home page in a real-time environment to increase its attractiveness to readers. Design/methodology/approach This paper shows that this news website layout design problem can be modeled as an optimization problem based on the information of news contents that change within a multiple-period planning horizon similar to the dynamic facility layout problem. A hybrid genetic algorithm-based approach integrated with local search heuristic methods is also proposed to improve the solution. Findings This paper finds that the DPLP model is effective in modeling the changing layout of a digital news website. The problem can solved in a timely manner using the proposed hybrid genetic algorithm. Research limitations/implications This paper was based on hypothetical data and on the assumption of equal section size. Actual data would help fine-tune the application of the dynamic facility layout model. As well the algorithm could be enhanced for unequal size sections. Practical implications The model should help online newspapers apply sophisticated algorithms to optimize the layout of news websites dynamically in a timely manner. Social implications News websites are increasingly the desired medium to consume news. So it has an important role in educating society. Thus optimizing and improving the process will help in this regard. Originality/value To the best of the authors’ knowledge, this paper is the first one to apply the DPLP model to the digital newspaper website dynamic design problem.

Multiband multiple‐input multiple‐output antenna design with high‐resolution circularly polarized wireless communication

SummaryA design problem of multiband multi‐input, multi‐output (MIMO) antenna arrays for maximal side lobe level lessening with constraint of a fixed beam width is dealt with. Multiband MIMO antenna is the most promising solution in a communication system, due to its maximal speed, higher data rate, and short‐range communication. Conversely, MIMO faces several complications including designing a proper antenna for a specific bandwidth and lesser antenna efficacy. In this manuscript, a high‐resolution CP multiband MIMO (HS‐CPMB‐MIMO) antenna design for wireless communication is proposed. MIMO antenna plays a vital part in the present wireless technology at MSA. A multiband microstrip line‐fed two‐port hybrid CPMIMO antenna is intended and fabricated. The proposed microstrip feed antenna contains a tapering structure, and the inverted L‐shaped slots in the radiating element are opened on both sides to present notches in sub‐6‐GHz frequencies. The inverted L‐shaped slots is positioned in the middle of asymmetric Z‐shaped radiating elements for enhancing the isolation among the two ports. The invention of a multiband MIMO antenna with four CP bands has an efficient ECC value because of a large L‐shaped slot in the ground as well as a protruding Z‐shape ground stub embedded with circular rings to have highly isolated cells. Finally, the proposed HS‐CPMB‐MIMO antenna design is implemented by utilizing simulation software CST Microwave Studio, and it is evaluated with the performance metrics. The simulation outcomes of the proposed HS‐CPMB‐MIMO antenna provide 36.34%, 57.36%, and 25.33% higher BW, 24.13%, 15.96%, and 21.22% higher gain compared with existing methods.

Integral reinforcement learning–based adaptive fuzzy fault tolerant control of hypersonic flight vehicle

The fault tolerant control (FTC) of hypersonic flight vehicle (HFV) with actuator fault is proposed in this paper. The fault model considered in this paper is a general model which HFV may encounter in practice. For designing the FTC, the nonlinear model of HFV is represented by T-S fuzzy models, and policy iteration (PI) strategy is utilized to solve the design problem of T-S controller for the built T-S model of HFV without actuator fault. Then, based on the normal T-S controller, an adaptive fuzzy FTC controller is proposed, in which the feedback gain matrices can improve themselves according to the special fault. The stability of the proposed adaptive fuzzy FTC is proved by Lyapunov theory, and an integral reinforcement learning (IRL)–based solving algorithm is proposed. Simulations on three different kinds of actuator faults are proposed, and the simulation results show that, under three different faults, the designed adaptive FTC can ensure the reliable flight of HFV.

Research on wind resistance principles and design of super high-rise buildings

Due to the high and flexible characteristics of super high-rise buildings, the structure is very sensitive to wind loads, and wind vibration effects and comfort are issues that cannot be ignored in the structural design of super high-rise buildings. The paper mainly introduces the problems in wind resistance design of super high-rise buildings. Including wind load values, wind vibration effects, and vibration control methods and applications. The result show that the wind induced vibration effect and comfort of the structure are often determined through wind tunnel tests. For super high-rise buildings with complex structural forms. There are two methods for controlling wind-induced vibration in high-rise buildings: Optimizing structural form through architectural methods; Take structural control measures. At the same time, with the cost of high-performance materials and engineering technologies, it is a challenge to find building materials and technologies that can meet wind resistance requirements while keeping costs in check. However, as computational technology continues to evolve, the wind-resistant design of tall buildings will benefit from more accurate wind-tunnel simulations and computational modelling, which will help to better predict and address wind-resistance challenges. Scientists and engineers will continue to research novel materials and structural designs to improve the wind resistance of tall buildings and reduce costs.

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.