Design Approach Research Articles

Toward next generation mixed reality games: a research through design approach

Mixed reality (MR) games refer to games that integrate physical entities with digitally mediated contents. Currently, it entails game creators to integrate heterogeneous virtual and physical components, which is often time-consuming and labor-intensive, without the support of a coherent technology stack. The underlying technodiversity manifested by the research corpus suggests a complicated, multi-dimensional design space that goes beyond merely technical concerns. In this research, we adopted a research-through-design approach and proposed an MR game technology stack that facilitates flexible, low-code game development. As design grounding, we first surveyed 34 state-of-the-art studies, and results were synergized into three different spectra of technological affordances, respectively activity range, user interface and feedback control, to inform our next design process. We then went through an iterative prototyping phase and implemented an MR game development toolset. A co-design workshop was conducted, where we invited 15 participants to try the prototype tools and co-ideate the potential use scenarios for the proposed technology stack. First-hand user feedback was collected via questionnaires and semi-structured interviews. As a result, four conceptual game designs with three major design implications were generated, which conjointly reflect a broader understanding on MR gameful experience and contribute fresh insights to this emerging research domain.

Optimizing porous asphalt mix design for permeability and air voids using response surface methodology and artificial neural networks

Easy infiltration of grouting material in Porous Asphalt Mixture (PAM) is one of the crucial phases in the construction of semi-flexible pavement (SFP). The gradation and compaction primarily influence the pores and their arrangement in PAM, which affects the air voids and permeability of porous mixes and effectiveness of slurry infiltration. Complexity of gradation and compaction makes it very difficult to predict air voids and permeability in PAM. Statistical analysis of these parameters based on experimental data requires very high volume of samples which is missing in literature. Compaction effort has been kept constant in most of them which otherwise is important for stability. Also, machine learning models to predict mix design of PAM has not been explored. This study investigates the PAM using systematic experimentation and modelling approaches such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to establish new and low trial-based guidelines for establishing a relationship between air voids and permeability greater than 0.12 cm/sec, while ensuring the stability of the mix. Using a Simplex Lattice design approach, five distinct types of aggregates were utilized as input variables and twenty-six PAM mixes were generated, which were subjected to 5 compaction levels. A total of 260 mixes were tested and then models were prepared based on RSM and ANN, which considered two output variables: air voids and permeability. RSM and ANN models demonstrated strong agreement between predicted values and actual experiment outcomes. It is observed that optimal permeability in PAM is achieved by adjusting aggregate proportions based on compaction levels. Proportion limits for low compaction suggested 60–70 % coarse aggregates and up to 20 % fines. For higher compaction (25–75 blows), it is recommended to increase the coarse aggregates up to70–90 % and limit fines up to 10 %. All proposed limits satisfied minimum permeability criteria (> 0.12 cm/sec) while ensuring the stability of the mixes. The proposed modelling approach offers greater efficiency and accuracy and reduces trial batch time, minimizing the need for destructive experiments and material wastage.

Europium‐Based Metal–Organic Framework with N─H···π Interaction and Intramolecular Energy Transfer Mechanisms for Self‐Electrochemiluminescence

AbstractThis work proposes a europium‐based metal–organic framework ([Eu2(HNCP)4Cl4(H2O)2]n, named ZL‐1), featuring dinuclear Eu2(COO)2 clusters as connecting nodes, linked by benzimidazolyl tridentate ligand 2‐(4‐carboxyphenyl)‐imidazo[4,5‐f]‐1,10‐phenanthroline (HNCP) for efficient self‐electrochemiluminescence (self‐ECL). The cathodic ECL emission shows two peaks with a maximum intensity of 7500 a.u.at 0 ∼ −1.8 V in pH 9.0 phosphated buffered solution (PBS) without extra coreactant. The cleavage of N─H bond in benzimidazolyl group can occur under alkaline conditions to generate neutral radical ZL‐1•. After applying voltage, the ZL‐1 can be reduced twice to form ZL‐1Re1•− and ZL‐1Re2•−. Then, ZL‐1Re1•− and ZL‐1Re2•− react with ZL‐1•, accompanied by transferring energy from HNCP to central Eu3+ to produce excited ZL‐1Re1* and ZL‐1Re2* for ECL1 and ECL2 emissions. The local excitation in the HNCP unit is demonstrated with cyclic voltammetry (CV) and stepping pulse ECL. The stimulated and luminous species are confirmed by density functional theory calculations and ECL spectra. This design approach of self‐ECL materials, which coordinated functional ligands with lanthanide metal ions as 3D‐structured MOF, broadened the applications of ECL systems to loose conditions and facilitated mechanistic exploration of ECL processes.

Personalized heart rate management through data-driven dynamic exercise control

Maximizing healthy life expectancy is essential for enhancing well-being. Optimal exercise intensity is crucial in promoting health and ensuring safe rehabilitation. Since heart rate is related to exercise intensity, the required exercise intensity is achieved by controlling the heart rate. This study aims to control heart rate during exercise by dynamically adjusting the load on a bicycle ergometer using a proportional-integral (PI) control. The choice of PI parameters is very important because the PI parameters significantly affect the performance of heart rate control. Since the dynamic characteristics of heart rate relative to work rate vary widely from subject to subject, the PI parameters for each subject must be determined individually. In this study, PI parameters are optimized directly from exercise data using a data-driven design approach. Thus, the proposed method does not require excessive exercise of the subject to model heart rate dynamics. Using the proposed method, the heart rate can be controlled to follow a designed reference model so that the heart rate is safely increased to the desired value. The quantitative evaluation of the control results of fifteen healthy volunteers confirmed that the proposed method improved the control error of the target heart rate trajectory by approximately 40%, regardless of gender or age. In addition, it was shown that control parameters from the exercise experiment also indicate that females are more likely than males to have an elevated heart rate at the same load.

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Mechanobiological optimization of scaffolds for bone tissue engineering.

Synthetic bone graft scaffolds aim to generate new bone tissue and alleviate the limitations of autografts and allografts. To meet that aim, it is essential to have a design approach able to generate scaffold architectures that will promote bone formation. Here, we present a topology-varying design optimization method, the "mixed-topology" approach, that generates new designs from a set of starting structures. This approach was used with objective functions focusing on improving the scaffold's local mechanical microenvironments to mechanobiologically promote bone formation within the scaffold and constraints to ensure manufacturability and achieve desired macroscale properties. The results demonstrate that this approach can successfully generate scaffold designs with improved microenvironments, taking into account different combinations of relevant stimuli and constraints.

An evolutionary algorithm-based approach for efficient design of Wiener spline non-linear adaptive filter and its experimental validation

An evolutionary algorithm-based approach for efficient design of Wiener spline non-linear adaptive filter and its experimental validation

A novel proposed approach for optimal design of a 2-Dof manipulator arm

A novel proposed approach for optimal design of a 2-Dof manipulator arm

Effect of Different Flow Design Approaches on Undergraduates' Computational Thinking During Pair Programming

Effect of Different Flow Design Approaches on Undergraduates' Computational Thinking During Pair Programming

Computational synthesis of locomotive soft robots by topology optimization.

Locomotive soft robots (SoRos) have gained prominence due to their adaptability. Traditional locomotive SoRo design is based on limb structures inspired by biological organisms and requires human intervention. Evolutionary robotics, designed using evolutionary algorithms (EAs), have shown potential for automatic design. However, EA-based methods face the challenge of high computational cost when considering multiphysics in locomotion, including materials, actuations, and interactions with environments. Here, we present a design approach for pneumatic SoRos that integrates gradient-based topology optimization with multiphysics material point method (MPM) simulations. This approach starts with a simple initial shape (a cube with a central cavity). The topology optimization with MPM then automatically and iteratively designs the SoRo shape. We design two SoRos, one for walking and one for climbing. These SoRos are 3D printed and exhibit the same locomotion features as in the simulations. This study presents an efficient strategy for designing SoRos, demonstrating that a purely mathematical process can produce limb-like structures seen in biological organisms.

The role of hyetograph shape and designer subjectivity in the design of an urban drainage system

ABSTRACT Even though it has been established that a hyetograph's shape affects the results of hydrological simulations, common engineering practice does not always account for this fact. Instead, a single design storm is often considered sufficient for designing a urban drainage system. This study examines the impact that this design paradigm, combined with the uncertainty introduced by subjective choices made during the design process, has on the robustness of a designed system. To do so, we evaluated a set of individual designs created by engineering students using the same Chicago hyetograph as a design storm. We then created ensembles of hyetographs with the same precipitation volume and duration as the Chicago hyetograph and evaluated the designs' hydrological responses. The results showed that designs, which performed equally well for the initial design storm, triggered varying responses for the storms in the ensembles and, consequently, showed different levels of robustness, hinting at a need to adapt the current design approach.

Behaviour, finite element modelling and design of flanged cruciform section steel columns

A study into the mechanical behaviour and design of flanged cruciform section steel members subjected to axial compression is presented herein. The mechanical behaviour of flanged cruciform section columns is first described, with particular emphasis on the newly developed approach for determining the elastic local buckling load for full flanged cruciform cross-sections. Existing experimental data on flanged cruciform section steel columns collected from the literature are then employed to validate numerical models developed within the finite element package ABAQUS. A comprehensive parametric study is subsequently conducted that encompasses a broad spectrum of cross-sectional geometries and global slenderness values. The mechanical behaviour and ultimate resistance of flanged cruciform section columns are shown to be dependent on not only the global slenderness, but also on the ratio of the elastic torsional to flexural buckling loads. The existing experimental data alongside the numerical parametric study results are employed to evaluate the resistance predictions provided in the current Eurocode 3 design codes, revealing a high degree of conservatism. Finally, a new design approach for flanged cruciform section columns, suitable for incorporation into future revisions of Eurocode 3, is proposed which provides significantly improved accuracy and consistency in resistance predictions compared with the current provisions. A reliability analysis of the proposed design approach is conducted in accordance with the EN 1990 procedure, resulting in a recommended partial safety factor γM1=1.0.

Mass minimization approach for the optimal preliminary design of CMC inner liners in rocket thrust chambers

In the past decade, the world has witnessed a new space race, driven by a growing commitment to reducing the environmental impact of space missions. This has led to the widespread adoption of liquid-propellant rocket engines, which offer several advantages over their solid-propellant counterparts. One key advantage is their reusability, which not only helps to reduce the generation of space debris but also makes space exploration cheaper. To further enhance the performance of liquid rocket engines, researchers have been exploring innovative cooling techniques and advanced materials. Among these materials, Ceramic Matrix Composites (CMCs) have shown great potential in reducing the overall engine weight when used instead of high-tech metal alloys, resulting in lower fuel consumption and emissions during launches. This paper focuses on the mass minimization of inner liners made of CMCs in rocket thrust chambers. At this aim, a computationally efficient preliminary design approach, based on an analytical one-dimensional thermo-mechanical model, is proposed. A case study of mass minimization of an inner liner of rocket thrust chamber is also presented and discussed, by considering five different CMC materials.

Optimization design method for multi-stress accelerated degradation test based on Tweedie exponential dispersion process

In the operational environment of electronic products, simultaneous multiple stresses contribute to product degradation, with their interactions often being intricate. To effectively analyze and optimize multi-stress accelerated degradation tests, we introduce a Multi-Stress Generalized Coupling accelerated degradation model based on the Tweedie exponential dispersion process, coupled with a multi-decision optimization approach for test design. The model integrates stress interactions elucidated through fuzzy correlation analysis, reflecting the accelerated degradation behavior consistent with the Tweedie exponential dispersion process. Addressing the complexities of multi-parameter estimation, we propose a parameter estimation method underpinned by the Sine Cosine Algorithm for multi-objective optimization within the proposed framework. Considering budgetary constraints, D-optimization and V-optimization strategies are deployed to finetune test scheme allocations, optimizing resource utilization. Validation through a three-stress accelerated degradation test on LED chips confirms the model's efficacy in reliability assessment. Our findings indicate the superior descriptive power of the Tweedie exponential dispersion model for complex degradation phenomena and the proposed model's closer approximation to stress conditions in real-world settings, with a methodological error maintained below 2 %. Furthermore, the test optimization method enhances the D-optimization and V-optimization objective values by 21.20 % and reduces them by 4.08 %, respectively, marking a substantial improvement over traditional schemes and endorsing the holistic advancement of multi-stress accelerated degradation testing methodologies.

Design and science hybrid approach for material experiences: reinterpreting Napolitan tuf

Design and science hybrid approach for material experiences: reinterpreting Napolitan tuf

Automated multifactorial design of experiment and Bayesian optimisation algorithm approaches to method development for the green analysis by supercritical fluid chromatography of a pharmaceutical ingredient

During drug development, chromatography is frequently used for purity and stability testing of both drug substance and drug product. Reversed phase liquid chromatography (RPLC) is one of the most widely used methodologies due to its wide scope of application. In the later stages of drug development, the specified impurities and degradation products that define the critical quality attribute of the final API, also known as Key Predictive Sample Set (KPSS), are usually well defined and controlled. At this point, a method review enables selecting the most appropriate technique which should be the one providing optimal robustness (ICH-Q14[1]), with the support of Quality by Design (QbD) approaches. Supercritical Fluid Chromatography (SFC) is a preferred technique for its proven diversity in selectivity. The adoption of a technique which presents the most favourable environmental impact, such as, but not limited to, SFC, is also becoming increasingly important as laboratories strive to reduce carbon footprint. Re-developing a method requires high resource-demands in terms of staff, materials, and time. Any step of the process that can be automated can facilitate this approach, speeding up the delivery of the method whilst preserving robustness.In this article we describe how an SFC method was developed for the purity profiling of a late-stage oncology candidate, taking advantage of the superior selectivity of SFC towards structurally similar analytes, owed to the high orthogonality with R2 as low as 0.014 towards the KPSS. We describe two approaches to automate the method development. Firstly, a multifactorial design of experiments (DoE) and secondly, an optimization via a Bayesian algorithm, which was completed in one night, highlighting the potential and limitations, with an insight into the robustness. Both methods achieved baseline separation with varying levels of automation embedded into the process and a large reduction of the resource demands when compared to traditional optimisation methods. Finally, we describe the beneficial environmental impact that implementing SFC methods can yield, with a calculated green score reduced to a value between 17 and 30 % compared to RPLC, depending on the number of runs per sequence.

Investigation of capacitive deionization process parameters on salt adsorption capacity via experimental design approach

Process parameters of Capacitive Deionization (CDI) was investigated and optimized in this study. The effect of adsorption period (7–13 min), flow rate (5–20 ml min−1), and NaCl concentration (2–22 mM) on salt adsorption capacity was examined via Box-Behnken experimental design approach. A quadratic regression model (R2 = 0.9987) was developed, revealing significant individual and binary effects between process parameters and salt adsorption capacity. The ANOVA results demonstrated its high significance with the p-value<0.0001. Optimal conditions varied with salt concentration, indicating significant interactions between the parameters. Maximum salt adsorption capacity occurred at 5 ml min−1 flow rate and 13 min adsorption period for 22 mM NaCl, whereas for 12 mM and 2 mM NaCl concentrations, maximum adsorption capacity was obtained at the flow rate of 20 ml min−1 and duration of 13 min. This study provided deep insights into CDI modelling and optimization, crucial for water treatment advancements.

Research on computationally generative design of woven braced structure based on fiber composite material in architectural design industry

ABSTRACT With the intervention of computer algorithm-assisted design in the realm of contemporary architectural design, architectural spatial forms and corresponding design methods have improved significantly. However, the limitations of material strength and plasticity impose certain constraints on the architectural design of the new century. Based on this occurrence, this paper studies the development of a computational generative design system for woven braced structure composed of fiber-reinforced materials. Through experimental data of material performance, the research firstly argues feasibility of employing fiber composite materials to braced structure. The conclusion is that the structure designed and generated could produce a support of 1 ton per ㎡. Moreover, based on the ant colony algorithm and the algorithm principle of cellular automata on the Java platform, the study designs a generative system with iterative computing of force as the major axis to achieve the self-organization and adaptability of the braced structure. Above all, this paper proposes a design strategy engaging with computational generative design approach, as well as a design attempt for the material and form of architecture.

First iteration of a User-Centered Design process to develop an in-bed leg press.

Physical inactivity is prevalent among hospitalized patients and the daytime spent lying should be utilised for exercise. However, implementing new interventions in hospitals' complex daily routines is difficult and requires a participatory approach. Exploring clinical settings and clinicians' perspectives regarding exercise in hospitalized patients to gauge the potential of further development of an in-bed training device and to formulate development goals. A User-Centered Design approach was employed, consisting of work shadowing and focus groups with physiotherapists and nurses. Content and network analyses of the focus group data were performed. Personas were then developed and used to create clinical scenarios. Some clinicians perceived in-bed exercise counterproductive, while others recognized potential for unsupervised training. The most important design characteristics appeared to be a small size, low weight and simplicity of use to facilitate storage, transportation, and administration, respectively. The scenarios revealed that the device's use could increase the physical activity time by 1.4%, although it would also increase the working time of clinicians. The study highlighted the difficulties in developing a usable training device but encouraged the authors to pursue their efforts under the strict condition of following the formulated development goals.

Multiple Self-Avatar Effect: Effects of Using Diverse Self-Avatars on Memory Acquisition and Retention of Sign-Language Gestures.

This study proposes a new learning method that employs multiple embodied self-avatars during learning, to use the potential benefit of virtual reality (VR) for effective learning and training. In this study, by taking advantage of the benefit of virtual reality (VR), we propose a new learning method that employs multiple embodied self-avatars during learning. Based on the multiple-context effect, which posits that learning in diverse situations can prevent forgetting and enhance memory retention, we conducted a between-participants study under two conditions: the varied avatar condition, in which participants learned sign languages with different self-avatars in six iterations, and the constant avatar condition, in which the same self-avatar was used consistently. We employed sign language as a learning material that naturally draws attention to self-avatars and is suitable for investigating the effects of varying self-avatars. Initially, the varied avatar condition performed worse than the constant avatar condition. However, in a test conducted after one week in the real world, the varied avatar condition showed significantly less forgetting and better retention than the constant avatar condition. Furthermore, our results suggested a positive correlation between the degree of embodiment toward the avatars and the effectiveness of the proposed method. This study presents an innovative design approach for the use of self-avatars in VR-based education.