Design Framework Research Articles

Reverse design for mixture proportions of recycled brick aggregate concrete using machine learning-based meta-heuristic algorithm: A multi-objective driven study

Construction and Demolition Wastes (CDW) have a significant impact on global waste streams. Brick waste stands out as a prominent type of CDW, and numerous studies have explored its recycling for the creation of environmentally-friendly concrete. Reverse design of recycled brick aggregate concrete (RBAC) mixture proportion is presented in this paper with a focus on four key objectives, that is: compressive strength, cost, and environmental elements (i.e., energy consumption and carbon emission). Based on compiled experimental datasets of 374 samples, the back propagation neural network (BP), random forest (RF), and four meta-heuristic algorithm optimization models were constructed to achieve the desired compressive strength objective. In all machine learning (ML) methods, the compressive strength of RBAC can be predicted with high accuracy, with the SSA-BP (optimized back propagation neural network model using the sparrow search algorithm) model achieving superior results (i.e., NSE=0.91, RPD=3.2). The SSA-BP is therefore used as the objective function for compressive strength. The economic objective is primarily influenced by material costs, and the objective functions of energy consumption and carbon emission are determined by various aspects of production, transportation, and their mixing processes. In order to obtain the optimal RBAC design, the Non-Dominated Sorting Genetic Algorithm (NSGA-III) was implemented considering imperative constraints. Results indicate that cement amount and recycled brick aggregate (RBA)-to-natural aggregate proportion have a positive impact on the compressive strength. The suggested design framework allows for the creation of RBAC composite designs with varying levels of RBA substitution rates and strength targets, providing valuable guidance for tackling the CDW challenge and optimizing RBA usage.

Collaborative sound design for automotive applications - the sound for electric vehicle as an emblematic use case

Within the automotive industry, the Electric Vehicle (EV) represents an incredible challenge and an extensive field of research for sound design developments and applications. In fact, conceiving the external sound of a silent and dynamic object moving in urban or rural environments meets several important constraints: functionality, usability, aesthetics, identity, and of course, sustainability. Then, the multidisciplinary nature of this topic argues for a collaborative approach, in order to co-understand the problem and co-conceive the solutions. But, whereas design has long been developing relevant research works and efficient methods for co-design, sound design still lacks references and tools for co-sound design. In a long-standing collaboration with a French car manufacturer, two recent - and still in-progress - projects have allowed to implement, adapt and test in real life a collaborative sound design framework that was previously developed in a research context, and has since been instantiated in various other applications. This framework - based on a set of tangible intermediary objects and a digital sound lexicon expliciting sound properties - will be presented. Its implementation will be detailed in a comparative approach. Its strengths and weaknesses will be developed in the light of some results obtained within both respective projects.

Selective ligand recognition and activation of somatostatin receptors SSTR1 and SSTR3

Somatostatin receptors (SSTRs) exert critical biological functions such as negatively regulating hormone release and cell proliferation, making them popular targets for developing therapeutics to treat endocrine disorders, especially neuroendocrine tumors. Although several panagonists mimicking the endogenous ligand somatostatin are available, the development of more effective and safer somatostatinergic therapies is limited due to a lack of molecular understanding of the ligand recognition and regulation of divergent SSTR subtypes. Here, we report four cryoelectron microscopy structures of Gi-coupled SSTR1 and SSTR3 activated by distinct agonists, including the FDA-approved panagonist pasireotide as well as their selective small molecule agonists L-797591 and L-796778. Our structures reveal a conserved recognition pattern of pasireotide in SSTRs attributed to the binding with a conserved extended binding pocket, distinct from SST14, octreotide, and lanreotide. Together with mutagenesis analyses, our structures further reveal the dynamic feature of ligand binding pockets in SSTR1 and SSTR3 to accommodate divergent agonists, the key determinants of ligand selectivity lying across the orthosteric pocket of different SSTR subtypes, as well as the molecular mechanism underlying diversity and conservation of receptor activation. Our work provides a framework for rational design of subtype-selective SSTR ligands and may facilitate drug development efforts targeting SSTRs with improved therapeutic efficacy and reduced side effects.

Paradigm shift in mechanical system design: toward automated and collaborative design with digital twin web

AbstractAnalyzing multi-vendor mechanical system designs requires a significant amount of manual work, resulting in a design paradigm where analysis is conducted only after the design is locked and components are selected. This leads to a suboptimal design with compatibility issues, over-dimensioned components, inferior performance, poor energy efficiency, and a lack of collaboration between OEMs (original equipment manufacturers) and system integrators. To overcome these issues, this paper proposes Co-Des (collaborative design) framework for automated and collaborative multi-vendor system design. The framework relies on standardized digital twin documents (DTD) of system designs, components, and analyses. The discoverability and distribution of these DTDs are enabled with digital twin web (DTW). Co-Des framework allows for finding suitable components for the design task by automatically running selected analyses employing component digital twins. In addition, OEMs can provide customized components for system integrators using the initial system design defined in the system design DTD. The use of the Co-Des framework was demonstrated with a windmill powertrain design use case, and the applicability of the automated assembly analysis for component selection was verified with performance measurements. The adoption of the proposed framework will lead to a paradigm shift from manual and siloed work relying on the exchange of PDFs to a more automated and collaborative design of mechanical systems. The adoption rate is defined by the willingness of system integrators to publish their initial system designs and OEMs their components as public digital twins.

Digital Game Design for Older Adults: A Conceptual Framework

The digital revolution, particularly digital games, can potentially improve older adults' lives. However, the commercialised digital games on the market are not designed specifically to help this demographic cope with the adverse effects of ageing. There are no specific guidelines or frameworks that consider the andragogy principles. We aimed to address our research question: How can older adults’ game experience influence design consideration? and discusses a framework that could aid the design and development of digital games focusing on older adults and their gaming experience. Mixed-method approach is used in this study, consisting of four stages in the research design. This article introduces and discusses the purpose, design, play and experience (PDPE) framework which has informed the development of a game prototype that was used to validate the framework by investigating older adults’ experiences.

A fast forward prediction framework for energy material design based on machine learning method

A fast forward prediction framework for energy material design based on machine learning method

Quad-Band Rectifier Circuit Design for IoT Applications

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4, 3.5, 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies.

Sliding mode control design using a generalized reduced-order fractional model for chemical processes

This article presents a comprehensive study that evaluates the potential advantages of adopting a unified, systematic, and organized general fractional model within the Sliding Mode Control (SMC) design framework. The exploration extends to the model's applicability in a variety of disciplines. Although prior research has utilized SMC and fractional-order systems independently, no previous work has established a mathematical framework that integrates a generic fractional-order SMC model. The study addresses this gap and highlights potential implications for control design and applications in a variety of fields, particularly for chemical processes.

Quantum Machine Learning for Performance Optimization of RIS-Assisted Communications: Framework Design and Application to Energy Efficiency Maximization of Systems With RSMA

Quantum Machine Learning for Performance Optimization of RIS-Assisted Communications: Framework Design and Application to Energy Efficiency Maximization of Systems With RSMA

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.

High and temperature-stable electrostrain in lead-free environmentally friendly BiFeO3-BaTiO3 piezoelectric ceramics

Currently, environmental pollution and industrial effluents are depleting soil, water, and aquatic life and pose serious concerns to climate change. Environmentally friendly piezoelectric materials are considered potential agents for industrial applications, as they can generate clean energy by applying mechanical forces. High and temperature-insensitive piezoelectric strain is an ever-increasing demand in lead-free ceramics. In this work, a lead-free La-doped BiFeO3-BaTiO3 composition is designed at the crossover boundary of normal-relaxor ferroelectrics. The properties are optimized as a function of sintering temperature (Ts) from 960-1000 °C. An enhanced and thermally stable converse piezoelectric constant (d33*) of 760±23 pm/V over a wide range of 25-150 °C is achieved at an optimized TS of 980 °C. This high electrostrain response is mainly attributed to the low energy barrier for domain switching due to multiple-point crystal structure morphotropic phase boundary. Additionally, the high Curie temperature (TC) of 355 °C with excellent piezoelectric strain performance of our work are promising results for high-temperature piezoelectric actuators. The framework of chemical composition design and phase engineering of this work could inspire further development of lead-free ceramics for industrial applications.

Energy performance and wind exposure of windward, lateral, and leeward high-rise photovoltaic facades

The growing demand for sustainable energy solutions leads to the integration of photovoltaic/thermal (PV/T) modules into building facades. This study evaluates and compares the energy potential, wind load, and environmental benefits of PV/T modules installed on different facades of high-rise buildings. Using advanced computational techniques, including Computational Fluid Dynamics (CFD), the wind interaction with PV/T modules on windward, lateral, and leeward facades is modeled and analyzed. Each computational process requires over two weeks to complete due to the complexity of the simulations. The results demonstrate that the leeward facade is more effective for electricity generation, while the windward and sideward facades perform better for thermal energy storage. Maximum energy outputs of 217 kW for thermal energy and 73 kW for electrical energy are recorded, depending on wind conditions. Additionally, energetic and exergetic efficiencies reach 55.2% and 17.7%, respectively. The study also finds that the leeward facade has the greatest impact on reducing CO2 emissions. These findings provide valuable insights into designing optimal PV/T facades for high-rise structures, expanding the range of structural designs and offering opportunities for implementing mitigation measures to enhance system durability and efficiency. The novelty of this research lies in the detailed comparative analysis of PV/T performance across multiple facades, offering a practical framework for future sustainable building designs.

Techno-economic analysis and optimization of a hybrid solar-wind-biomass-battery framework for the electrification of a remote area: A case study

An off-grid hybrid energy framework on the basis of wind turbines and photovoltaic panels as the primary source of energy and a biogas generator and energy storage unit as a back-up system, was considered for clean energy generation in a rural area in Semnan, Iran. This study was aimed at designing an optimized hybrid renewable framework with minimal costs and CO2 emissions and highest reliability, utilizing comprehensive modeling based on two energy management strategies. The decision-making variables in this work were the type and number of wind turbines, number of storage units, capacity of solar panels, and the capacity of biogas generator. Then variations in turbine model, inflation rate and biofuel prices and their effect on the optimal design of the hybrid framework was investigated in a range of uncertainties (0, 1, and 2.5 %). Additionally, the powerplant emissions penalty cost rate was also considered. Eventually, the optimal hybrid frameworks were economically and environmentally compared with a stand-alone diesel generator. Optimization results confirmed the superiority of the proposed hybrid wind-solar-biomass-battery system in comparison with other investigated systems. The cost of energy for the optimal design including biogas generator (22 kW), photovoltaic panels (30.7 kW), a 10 kW wind turbine, 11 batteries and an inverter (15.1 kW), while considering the inflation rate of 10 % and the uncertainty rate of 0 %, is equal to 0.201 $/kWh. Additionally, the selected optimal design emitted 97 % less CO2 annually, in comparison with the similarly-sized diesel-based energy system.

Design and synthesis of ladder-type covalent organic frameworks

Design and synthesis of ladder-type covalent organic frameworks

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Creating a comprehensive framework for Design, construction and management of healthy buildings

While healthy buildings are investigated in academic research, practice, and policy, they often fail to address the needs and gaps due to insufficient knowledge transfer between them. Moreover, there is no general definition of healthy buildings that could act as a reference for different stakeholders of the building sector. Therefore, this paper presents a novel framework for healthy buildings and a definition for compiling existing knowledge and addressing the needs and gaps. The holistic approach of the framework was developed through an extensive literature review and tested with 12 building case studies in the major European climate zones. The framework outlines five different dimensions (improving mental and physical health, designed for human needs, sustainably built and managed, resilient and adaptive, empowering people), including 24 indicators and sub-indicators. The validation of the framework revealed that it is possible to assess different building projects against different indicators under each dimension. However, challenges remain due to issues such as lack of time, cost of data collection, or lack of documentation. The developed framework provides a balanced approach that promotes both individual well-being and broader sustainability goals. It provides necessary guidance to inform practitioners and policy makers as well as other building sector stakeholders to integrate healthy buildings thinking into their project development, assessment, and decision making.

A Novel Design Optimization Framework to Sustain Remanufacturability

The ever-increasing global carbon emissions have urged the need for environmentally conscious/sustainable product design, for which the design for remanufacturing (DfRem) is one potential approach. DfRem targets at designing products that have multiple life cycles, thus significantly reducing raw material usage, energy consumption, and carbon emissions. In this paper, we develop a three-stage framework that consists of (1) systematic design space exploration and a multi-objective optimization formulation to minimize the likelihood of failure causes (such as fatigue and wear) and environmental footprint, (2) topology optimization to further reduce material usage without significantly affecting the load-carrying capability of the product, and (3) post-topology optimization design verification to ensure the proposed design satisfies all design constraints. The environmental impact can be assessed at varying comprehensiveness levels (e.g., design and manufacturing phase, use phase) and in terms of carbon or GHG emission, energy use, and waste generation. Because the novel design framework predominantly adjusted the geometry, we focused on mass-based change and energy savings due to sustained remanufacturability. The multi-objective optimization formulation in the first step results in a Pareto optimal set of possible design solutions that the designer can use for the second step. We demonstrate the utility of this framework through a case study of an engine cylinder head subjected to thermo-mechanical loads, where we find that about 5% of the product mass can be conserved with only about a 3% increase in surface area that has a fatigue life less than 10,000 cycles.

Optimal Sin Taxation and Market Power

We study how market power impacts the efficiency and redistributive properties of sin taxation, with an empirical application to sugar-sweetened beverage taxation. We estimate an equilibrium model of the UK drinks market, which we embed in a tax design framework to solve for optimal sugar-sweetened beverage tax policy. Positive price-cost margins for drinks create inefficiencies that lower the optimal rate compared with a perfectly competitive setting. Since profits mainly accrue to the rich, this is partially mitigated under social preferences for equity. Overall, ignoring market power when setting tax policy leads to welfare gains 40 percent below those at the optimum. (JEL D62, H21, H23, H25, L13, L25, L66)

Towards a conceptual design framework for bee botanic gardens: integrating perceptions on urban biodiversity into landscape design processes

Botanic gardens, valued for their live plant collections, have evolved into scientific institutions with roles in conservation, education, research, and public recreation. This study repositions botanic gardens as essential urban habitats enhancing biodiversity, focusing particularly on their support for both European honeybees and Western Australian native bees. This research addresses the intersection of urbanisation and bee conservation by developing a conceptual design for a bee-friendly botanic garden in Yanchep, Western Australia. It employs semi-structured interviews and research through design (RtD) methods to collaborate with local beekeepers, landscape architects, and ecologists. A systematic design framework for a bee-friendly botanic garden in Yanchep, Western Australia was proposed, encompassing the entire design process, from scope to detailed considerations, and aiming to be applicable across other parts of Western Australia and Australia. The research highlights three key findings: the necessity of interdisciplinary collaboration, the importance of defining the garden type and functions early in the design process, and the need for diverse plant selection and nesting options to support native bees.

A comprehensive control paradigm for prescribed performance attainment in complex nonlinear systems

This paper presents a comprehensive control framework tailored for achieving Prescribed Performance Control (PPC) in the context of complex nonlinear systems, addressing multifaceted challenges prevalent in control design. The focus is on a control scenario characterized by the simultaneous presence of nonlinearity, external disturbances, time delay, and unknown control direction—issues that pose considerable obstacles for existing solutions. To surmount these challenges, our proposed approach integrates well-established techniques, including neural networks, Nussbaum-type gains, and adaptive control strategies within a unified control design framework. The specific technical challenge addressed in this work involves the effective management of these intricate complexities in Single-Input Single-Output (SISO) systems. Our contributions extend the theoretical foundations, presenting an ideal PPC control design and introducing two adaptive neural network-based control methods capable of accommodating both known and unknown control directions. Utilizing Lyapunov-Krasovskii functionals, we showcase a unique integration that surpasses a mere combination of individual techniques. This work advances the theoretical underpinnings of control engineering tailored for real-world scenarios. The proposed controller’s efficacy is validated through rigorous simulations and compared with recent results and benchmark PPC controllers, establishing its superiority in addressing the intricacies of complex control scenarios.