Design Framework Research Articles

Co-Designing Digital Assistive Technologies for Autism Spectrum Disorder (ASD) Using Qualitative Approaches

ABSTRACT This study conducted a critical review to analyse qualitative studies related to the research, design, and implementation of digital assistive technologies for ASD, by evaluating their features in relation to conducting a co-design study for learners with ASD. This study identified 23 approaches used to study, design, configure, or develop digital assistive technologies for learners with ASD with studies focusing mostly on children, preschool, and adolescents. Qualitative approaches for co-design enabled collaboration from a wider community and the use of a multi-disciplinary approach; active involvement of learners with a user-centred approach; and the use of iterative or incremental design and development. Limitations and challenges revolved around restricted engagement to high-functioning learners; limited generalisability; implementation barriers in the real-world setting; lack of long-term evaluation or plan to assess effectiveness; and various implementation barriers. To engage people with moderate to severe ASD in co-design, researchers should scaffold their end-to-end design process using participatory design frameworks; embed various qualitative approaches within an iterative design, development, and testing process; and leverage tools that would enable structured customisation and personalisation of approach for participants.

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

Design and Assembly of Conductive Covalent Organic Frameworks for Proton Exchange Membrane Application

AbstractTo develop proton exchange membranes (PEMs) with robust structure stability and remarkable proton conductivity and explore their application in PEMs fuel cells have significant implications for realizing reduced carbon emission and environmental pollution. Covalent organic frameworks (COFs), as crystalline porous polymer material composed of organic monomers and connected by covalent bond, possess specific framework, inherent porosity, adjustable functional group and eminent thermal/chemical structure stability. Therefore, COFs display prominent superiorities in constructing rigid ordered proton transfer channels and improving fuel cell performance long‐term durability. In this review, the proton conduction properties of extrinsic proton‐conductive COFs (incorporating carriers into the pore), intrinsic proton‐conductive COFs (introducing conductive groups on the backbone) and combined extrinsic/intrinsic proton‐conductive COFs in the form of pressed pellets are discussed in detail. Meanwhile, proton‐conductive COFs related PEMs, including COFs‐related polymer‐based composite membranes, COFs‐based composite membranes and self‐supporting COFs membranes are also systematically summarized. In addition, the existing challenges are analyzed and future outlooks are addressed.

Efficient Neural Collaborative Search for Pickup and Delivery Problems.

In this paper, we introduce Neural Collaborative Search (NCS), a novel learning-based framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the collaboration between the latest prevalent neural construction and neural improvement models, establishing a collaborative framework where an improvement model iteratively refines solutions initiated by a construction model. Our NCS collaboratively trains the two models via reinforcement learning with an effective shared-critic mechanism. In addition, the construction model enhances the improvement model with high-quality initial solutions via curriculum learning, while the improvement model accelerates the convergence of the construction model through imitation learning. Besides the new framework design, we also propose the efficient Neural Neighborhood Search (N2S), an efficient improvement model employed within the NCS framework. N2S exploits a tailored Markov decision process formulation and two customized decoders for removing and then reinserting a pair of pickup-delivery nodes, thereby learning a ruin-repair search process for addressing the precedence constraints in PDPs efficiently. To balance the computation cost between encoders and decoders, N2S streamlines the existing encoder design through a light Synthesis Attention mechanism that allows the vanilla self-attention to synthesize various features regarding a route solution. Moreover, a diversity enhancement scheme is further leveraged to ameliorate the performance during the inference of N2S. Our NCS and N2S are both generic, and extensive experiments on two canonical PDP variants show that they can produce state-of-the-art results among existing neural methods. Remarkably, our NCS and N2S could surpass the well-known LKH3 solver especially on the more constrained PDP variant.

Leveraging GPU in Homomorphic Encryption: Framework Design and Analysis of BFV Variants

Leveraging GPU in Homomorphic Encryption: Framework Design and Analysis of BFV Variants

Microstructural material design of pearlitic steel lamella for desired mechanical properties

Designing materials for a targeted mechanical property is a complex problem due to its structure and property relationship nature, traditionally relying on experimental methods. Computational approaches offer a promising alternative, but they are often computationally intensive. This limits their applicability for solving the goal-based structure design problem. This study focuses on the design of microstructure to achieve specific mechanical properties by combining generative adversarial networks, deep neural networks, and genetic algorithm optimization techniques. In this work to demonstrate the effectiveness of the proposed approach we select a widely used pearlitic steel lamella microstructure where the phase fraction of ferrite and cementite and the lamella distribution significantly influence the strength and ductility. A method for designing the pearlite microstructure features for the targeted mechanical properties, such as yield strength (σy), ultimate strength (σut), and fracture strain (εf) is developed by applying inverse microstructure design techniques to achieve the targeted mechanical properties, specifically in fully pearlitic steel materials. The findings hold great promise, particularly when coupled with additive manufacturing, in offering an efficient and versatile framework for structural materials design for targeted material applications.

A PINN framework for inverse physical design of metal-loaded electromagnetic devices

To solve the difficulty of inverse design of metal-loaded electromagnetic devices, we propose a physics-informed neural network (PINN) framework. With the emergence of PINNs, some scholars within the field of electromagnetism have utilized them to design dielectric-loaded electromagnetic devices. However, using PINN to design metal-loaded electromagnetic devices is still uncommon due to the difficulty of binarization of metal shapes. In light of this, we propose a PINN framework to address the inverse physical design of metal-loaded electromagnetic devices. In PINN, we embed mathematical-physics models into the network, which endows the training parameters of PINN with practical physical significance. The main innovation of this paper is to solve the binarization problem in the inverse design of metal-loaded devices within the PINN. To verify the feasibility of our method, we provide an example of designing a dual-layer metal diaphragm-loaded parallel plate waveguide device. The PINN presented with metal binarization in this paper offers a novel approach to the inverse physical design of metal-loaded electromagnetic devices.

Entangled Internet of Musical Things and People: A More-Than-Human Design Framework for Networked Musical Ecosystems

Entangled Internet of Musical Things and People: A More-Than-Human Design Framework for Networked Musical Ecosystems

The Impact of Criminal Financial Sanctions: A Multistate Analysis of Survey and Administrative Data

We estimate the impact of financial sanctions in the US criminal justice system, leveraging nine natural experiments in a regression discontinuity design framework across a diverse range of enforcement levels ($17–$6,000) and institutional environments. We leverage survey and administrative data to consider a variety of short- and long-term outcomes, including employment, recidivism, household expenditures, and other self-reported measures of well-being. We find robust evidence of precise null effects, including ruling out long-run impacts larger than −$391–$142 in annual earnings and −0.001–0.01 in annual convictions, with no corresponding payment increases despite salient and heterogeneous enforcement mechanisms. (JEL G51, H76, J31, K14, K41, K42)

Bridging the agricultural extension gap: Insights from the teaching-learning theories and models

This article explores various theories and models that inform the design of teaching-learning frameworks to bridge the agricultural extension gap. It identifies the complexities surrounding the agricultural extension gap, emphasizing the need for a multi-theoretical approach to effectively address the diverse needs of farmers. The theories reviewed include Multimedia Learning Theory, Adult Learning Theory, Social Learning Theory, Theory of Planned Behavior, Social Exchange Theory (SET), Resource-Based View (RBV), Actor-Network Theory (ANT), Information Systems Success Model, Contextual Integrity Theory, and Task-Technology Fit (TTF). The models discussed include the Diffusion of Innovations Model, the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT). By synthesizing these theories and models, the article provides a comprehensive understanding of how they can enhance agricultural extension services.

Developing a zero-proof beverage course: Insights from industry professionals, educators, and students

This study examined beverage-focused hospitality courses in U.S. undergraduate programs, revealing that most courses address alcoholic beverages, with only two offering nonalcoholic (zero-proof) content. Given the growing demand for nonalcoholic beverages, the study aimed to identify key beverages and topics missing from current curricula and create an optimal zero-proof beverages course. Input from food and beverage professionals and hospitality educators highlighted coffee, tea, water, functional beverages, fermented beverages, and zero-proof cocktails as critical. The course was developed using the Integrated Course Design Framework and reviewed by hospitality educators and students for improvement. A syllabus template is included.

Satisfying Consumer Preferences: The Packaging Design of Guizhou Red Sour Soup

Red sour soup (RSS) is a traditional food with rich cultural connotations and nutritional value, unique to the Kaili region of Guizhou Province, China. However, the existing packaging design lacks cultural characteristics and visual appeal, which greatly limits its market potential. This study proposes a systematic research framework for RSS packaging design by integrating NLP, Kansei engineering, and semiotics. First, we mined consumers’ dual requirements by analyzing online reviews from e-commerce and tourism platforms using NLP and LDA. Second, we used Kansei engineering to construct a mapping model between consumers’ requirements and design elements. Notably, in the semantic space, we innovatively introduced the concept of a semantic network, considering the relationships between Kansei words for the first time. Finally, we proposed integrating the four dimensions of semiotics into the four stages of design, guiding the application of batik culture in packaging design. Based on this framework, we created a set of RSS packaging designs that integrate modern design concepts and traditional Miao cultural elements. The new design showed significant attractiveness in testing, with 100% of subjects preferring it, fully demonstrating the effectiveness and consumer acceptance of our approach. This study provides new methods and ideas for packaging in the food industry, which has positive significance for the modernized marketing of traditional foods.

A digital design framework for the dimensional optimization of parallel robots based on kinematic and elasto-dynamic performance

The dimensional optimization based on kinematic and dynamic performance indices is proven significant for improving the operational performance of parallel robots. As the pose-dependent performances vary with the dimensional parameters, there are still many challenges in the optimal design of parallel robots, such as the possible conflict amongst different performance indices and computational expensiveness. By considering a 4-DOF high-speed parallel robot as an illustrative example, this paper presents a framework for the optimal design of parallel robots by integrating skeleton modeling, CAD-CAE integration and multi-objective optimization techniques. In this approach, the models for kinematic and elasto-dynamic performance evaluation are first developed using the CAD and CAE techniques, respectively. After evaluating the performance distributions over the task workspace, several reference poses representative of multiple neighboring sample poses are determined to formulate corresponding local performance indices by using the Hard C-Means (HCM) clustering analysis algorithm. This consideration leads to the determination of the optimal dimensions by investigating the Pareto-optimal solutions of a multi-objective optimization problem, allowing the conflict amongst different performance indices to be appropriately handled and the computational time to be saved significantly. The results of the case study show that the proposed approach can significantly enhance the elastic dynamic performance while ensuring that the kinematic performance is not excessively compromised, when compared to the performance of existing physical prototype of the robot. A software package has been developed using the proposed framework, providing a highly accessible way for designing various types of parallel/hybrid robots based on CAD and CAE techniques.

Real-World Efficacy of Explainable Artificial Intelligence using the SAGE Framework and Scenario-Based Design

ABSTRACT This paper demonstrates a design and evaluation approach for delivering real world efficacy of an explainable artificial intelligence (XAI) model. The first of its kind, it leverages three distinct but complementary frameworks to support a user-centric and context-sensitive, post-hoc explanation for fraud detection. Using the principles of scenario-based design, it amalgamates two independent real-world sources to establish a realistic card fraud prediction scenario. The SAGE (Settings, Audience, Goals and Ethics) framework is then used to identify key context-sensitive criteria for model selection and refinement. The application of SAGE reveals gaps in the current XAI model design and provides opportunities for further model development. The paper then employs a functionally-grounded evaluation method to assess its effectiveness. The resulting explanation represents real-world requirements more accurately than established models.

THEORY OF "ATMOSPHERIC AESTHETICS" IN THE ENVIRONMENTAL DESIGN OF TRADITIONAL CHINESE VILLAGES

Purpose of this study is to identify ways to apply the theory of atmospheric aesthetics in the design of traditional rural landscapes to enhance the visual and experiential appeal of rural areas. Today, the theory of "atmospheric aesthetics" is recognized as a foundation for revitalizing rural environments within the ecological design framework and is widely discussed in academic circles in China and around the World. Methodology. The research utilized literature analysis, inductive reasoning, case studies, and factor analysis of environmental design. Results. Based on the analysis of the practical application of the theory of "atmospheric aesthetics" in rural design, techniques to improve the aesthetic perception of traditional villages are demonstrated. The main advantages of using this theory in the design of traditional villages have been identified, such as: enhanced aesthetic experience; promotion of cultural heritage; improved quality of life; resolution of environmental crises. The practical application of the theory of "atmospheric aesthetics" helps create a unique artistic atmosphere and emotional resonance, making rural landscapes more attractive and strengthening the sense of identity and belonging among residents. Emphasis on preserving traditional cultural elements, such as ancient architecture and streets, contributes to the continuity of cultural heritage and the sustainable development of rural areas. Scientific novelty. Theoretical and practical approaches to rural environment design based on the theory of "atmospheric aesthetics" have been further developed. Extensive evaluations of the effectiveness of using this theory in rural design were conducted during field studies and surveys in various locations. This innovative theory enriched methods of studying environmental aesthetics, offering scientific support for rural revitalization strategies and innovative designs. Practical significance. The practical significance of the obtained results lies in their potential application by designers in the design of traditional Chinese village environments, where an aesthetically comfortable and attractive environment for both local residents and tourists is created based on the identification of emotional and sensory qualities of the surroundings.

Automated de novo design of architectured materials: Leveraging eXplainable Artificial Intelligence (XAI) for inspiration from stochastic microstructure outliers

Engineered architectured Materials, such as metamaterials with periodic patterns, achieve superior properties compared with their stochastic counterparts, such as the random microstructures found in natural materials. The primary research question focuses on the feasibility of learning advantageous microstructural features from stochastic microstructure samples to facilitate the generative design of periodic microstructures, resulting in unprecedented properties. Instead of relying on brainstorming-based, ad hoc design inspiration approaches, we propose an eXplainable Artificial Intelligence (XAI)-based framework to automatically learn critical features from the exceptional outliers (with respect to properties) in stochastic microstructure samples, enabling the generation of novel periodic microstructure patterns with superior properties. This framework is demonstrated on three benchmark cases: designing 2D cellular metamaterials to maximize stiffness in all directions, to maximize the Poisson’s ratio in all directions, and to minimize the thermal expansion ratio. The effectiveness of the design framework is validated by comparing its novel microstructure designs with known stochastic and periodic microstructure designs in terms of the properties of interest.

Directed design of amorphous metal-organic frameworks for efficient and selective removal of Cu(II) from wastewater

Cu(II) is an essential trace element for the human body, but excessive concentrations in water or the human body can cause irreparable harm to ecosystems and human health. In this study, an amorphous metal-organic frameworks adsorbent (DKTA-MOF) was designed and synthesized using a hydrothermal method for the efficient and selective removal of Cu(II) from water. Various factors affecting the adsorption performance of DKTA-MOF were investigated, including solution pH, reaction time, initial concentration of Cu(II), temperature, coexisting ions, and the number of adsorption-desorption cycles. The experimental results indicate that the maximum adsorption capacity of DKTA-MOF is 160.5 mg/g at 318 K, and the optimal adsorption pH is 5. The adsorption process of Cu(II) by DKTA-MOF follows the pseudo-second-order (PSO) kinetic and the Langmuir isotherm models, indicating that the process mainly involves single-layer chemisorption. The adsorption process is spontaneous, driven by both electrostatic attraction and the sharing of lone pair electrons. DKTA-MOF demonstrates highly efficient and selective removal of Cu(II) in the presence of coexisting ions and maintains a high adsorption capacity after six cycles. Furthermore, the strategies and methods adopted in this design provide new perspectives for the removal of metal ions in wastewater.

Development of the design and synthesis of metal-organic frameworks (MOFs) - from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions.

Owing to the exceptional porous properties of metal-organic frameworks (MOFs), there has recently been a surge of interest, evidenced by a plethora of research into their design, synthesis, properties, and applications. This expanding research landscape has driven significant advancements in the precise regulation of MOF design and synthesis. Initially dominated by large-scale synthesis approaches, this field has evolved towards more targeted functional modifications. Recently, the integration of computational science, particularly through artificial intelligence predictions, has ushered in a new era of innovation, enabling more precise and efficient MOF design and synthesis methodologies. The objective of this review is to provide readers with an extensive overview of the development process of MOF design and synthesis, and to present visions for future developments.

Advancing Sustainability in Urban Planning by Measuring and Matching the Supply and Demand of Urban Green Space Ecosystem Services

As global cities rapidly develop, the realization of sustainable urban development has become a pressing global concern. As a critical component of the urban built environment, urban green spaces are essential for maintaining the sustainability of urban ecosystems by offering a variety of ecological benefits. However, there are some problems in the design and construction of urban green framework, which often disregard the real needs of residents and hinder the sustainable development of urban areas. Therefore, urban planning that takes into account the supply and demand relationship of urban green space is considered to be a key technology that should help to improve residents’ happiness and promote urban sustainable development. This paper takes Zunyi City as an example; selects six indicators; combines UAV images, population, economic, and environmental data to evaluate ecosystem services valuation existing green space in the city; and analyzes its social and ecological needs. Affected by land utilization and development density, the supply value showed low spatial density characteristics in the central urban area and high spatial distribution characteristics in the surrounding urban area. The correlation between demand value and population density is the largest, and the distribution pattern is similar to that of population density. The supply-demand ratio in the study area is −0.54, indicating a severe imbalance between supply and demand. The correlation analysis between the comprehensive supply and demand ratio and each influencing factor shows that the correlation with population density is the largest. The combination of supply and demand maps addresses a major problem in planning, namely, ensuring that communities have priority in providing urban green space based on society’s need for ecosystem services. In addition, these maps will help develop the best green space planting plans to meet the specific needs of different communities. Taken together, the findings can help city planners and decision makers improve the layout of urban green areas, increase ecosystem service potential, and improve urban environmental quality and sustainability.

An Optimization Method for Design Solutions to Active Reflective Surface Control Systems Based on Axiomatic Design and Multi-Criteria Decision Making

The design of an Active Reflective Surface Control System (ARCS) is a complex engineering task involving multidimensional and multi-criteria constraints. This paper proposes a novel methodological approach for ARCS design and optimization by integrating Axiomatic Design (AD) and Multi-Criteria Decision Making (MCDM) techniques. Initially, a structured design plan is formulated within the axiomatic design framework. Subsequently, four MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Entropy Weight Method (EWM), Multi-Criteria Optimization and Compromise Solution (VIKOR), and the integrated TOPSIS–Grey Relational Analysis (GRA) approach—are used to evaluate and compare the alternative solutions. Additionally, fuzzy information axioms are used to calculate the total information content for each alternative to identify the optimal design. A case study is conducted, selecting the optimal actuator for a 5 m diameter scaled model of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), followed by digital control experiments on the chosen actuator. Based on the optimal design scheme, an ARCS prototype is constructed, which accelerates project completion and substantially reduces trial-and-error costs.