Design Process Research Articles

Chaotic systems based on higher-order oscillatory equations

This paper discusses the design process toward new lumped chaotic systems that originates in higher-order ordinary differential equations commonly used as description of ideal oscillators. In investigated third-order case, two chaotic oscillators were constructed. These systems are dual in the sense of vector field geometry local to fixed points. The existence of robust chaos was proved by both standard routines of numerical analysis and practical measurement. For the fourth-order oscillatory equation, the concept based on interaction between superinductor and supercapacitor was examined in detail. Since both “superelements” are active, the nonlinearity essential to the evolution of chaos is fully passive. It is demonstrated that complex motion is robust and does not represent long transient behavior or numerical artefact. The existence of chaos was verified using standard quantifiers of the flow, such as the largest Lyapunov exponents, recurrence plots, approximate entropy and sensitivity calculation. A good final agreement between theoretical assumptions and practical results will be concluded, on a visual comparison basis.

The Digital Twin Immersive Design Process and Its Potential Disruption to Healthcare Design through a User-Centered Approach

This applied research proposes a solution to the static government design process for Thai healthcare architecture to better serve the needs of its elderly society. In its place, a novel real-time design process, termed the Digital Twin Immersive Design Process (DT-IDP), repurposes aspects of digital twin and virtual reality technologies into a ‘unitary’ immersive design system. This system accesses ‘experiential’ user-centered data, helping enhance the design of Thai healthcare space beyond a standardized government response. This text builds a rationale for departing from the current design process by describing the formation and advantages of the DT-IDP process. To test its credibility, the DT-IDP process is used to build and compare two digital versions of an existing healthcare space. In these spaces, participants are immersed (elderly patients n = 30; nursing staff n = 5; government healthcare architects n = 5) to assess visitor experiences based on daylighting, artificial lighting, and views of nature. Following immersion, government healthcare architects are interviewed in-depth to evaluate the process’s efficacy and their willingness to adopt it. Results confirm the potential for this process to capture ‘user-centered’ insights, otherwise unobtainable without immersion. Consequently, healthcare architects express a unanimous preference for DT-IDP, acknowledging its unique capacity to bridge a market gap through an experiential component that could better assist them in creating a superior final product. Ultimately, they assert that demand for these features could have a disruptive impact to the current healthcare design process, helping to re-envisage the design of future Thai healthcare space.

Measuring Services and Inferring Causes

Today, designers are bringing both data and data practitioners into the design process in increasingly diverse ways. These sorts of collaborations were once orchestrated by overarching product teams without input from a design team, but now, design teams have become active collaborators. ‘Data thinking’ and quantitative reasoning can be applied in the design process itself, rather than simply to the products of the design process. This often involves partnering with analytics or data science teams throughout the design process, instead of at the end of the process when seeking to evaluate output.

Theoretical foundations and implications of augmented reality, virtual reality, and mixed reality for immersive learning in health professions education

BackgroundAugmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) are emerging technologies that can create immersive learning environments for health professions education. However, there is a lack of systematic reviews on how these technologies are used, what benefits they offer, and what instructional design models or theories guide their use.AimThis scoping review aims to provide a global overview of the usage and potential benefits of AR/VR/MR tools for education and training of students and professionals in the healthcare domain, and to investigate whether any instructional design models or theories have been applied when using these tools.MethodologyA systematic search was conducted in several electronic databases to identify peer-reviewed studies published between and including 2015 and 2020 that reported on the use of AR/VR/MR in health professions education. The selected studies were coded and analyzed according to various criteria, such as domains of healthcare, types of participants, types of study design and methodologies, rationales behind the use of AR/VR/MR, types of learning and behavioral outcomes, and findings of the studies. The (Morrison et al. John Wiley & Sons, 2010) model was used as a reference to map the instructional design aspects of the studies.ResultsA total of 184 studies were included in the review. The majority of studies focused on the use of VR, followed by AR and MR. The predominant domains of healthcare using these technologies were surgery and anatomy, and the most common types of participants were medical and nursing students. The most frequent types of study design and methodologies were usability studies and randomized controlled trials. The most typical rationales behind the use of AR/VR/MR were to overcome limitations of traditional methods, to provide immersive and realistic training, and to improve students’ motivations and engagements. The most standard types of learning and behavioral outcomes were cognitive and psychomotor skills. The majority of studies reported positive or partially positive effects of AR/VR/MR on learning outcomes. Only a few studies explicitly mentioned the use of instructional design models or theories to guide the design and implementation of AR/VR/MR interventions.Discussion and conclusionThe review revealed that AR/VR/MR are promising tools for enhancing health professions education, especially for training surgical and anatomical skills. However, there is a need for more rigorous and theory-based research to investigate the optimal design and integration of these technologies in the curriculum, and to explore their impact on other domains of healthcare and other types of learning outcomes, such as affective and collaborative skills. The review also suggested that the (Morrison et al. John Wiley & Sons, 2010) model can be a useful framework to inform the instructional design of AR/VR/MR interventions, as it covers various elements and factors that need to be considered in the design process.

Demonstrating a decision support process for landscape conservation design

AbstractDespite the recent increase in landscape conservation and the design processes agencies are undertaking, there remains an implementation gap due to an inability to evaluate general strategies and account for uncertainties faced by managers. We demonstrated how a decision support process (DSP), recently developed to inform landscape conservation design, can address uncertainties and complexities inherent in landscape conservation to facilitate long‐term, large‐scale conservation planning. We applied the DSP to landscape conservation efforts within conservation opportunity areas (COA) of states in the Gulf Coastal Plains and Ozarks region. We engaged state planners within the region to identify important landscape conservation uncertainties they face in planning. We developed, simulated, and modeled the impacts of conservation addressing 3 uncertainties identified by state wildlife managers and evaluated the impacts by examining the responses of state and local populations of 2 bird species of conservation concern, prairie warbler (Setophaga discolor) and wood thrush (Hylocichla mustelina). The responses of prairie warbler populations to conservation strategies indicated that both approaches of protecting quality habitat from land‐use change and restoring and enhancing lower quality and nonhabitat improved their viability at regional, statewide, and COA scales. However, we noted that the relative effectiveness of strategies varied among states in ways that suggest a state's process for delineating COAs and what threats they face could influence which conservation approach to consider. Our findings highlight the need for regional coordination and the use of decision‐support processes to guide effective conservation at landscape scales.

Explore the driving factors of designers’ AIGC usage behavior based on SOR framework

Despite the widespread recognition of artificial intelligence’s advantages, it cannot replace human independent thinking and creativity, especially in fields such as artistic design that require creativity. Previous studies often examined its development trends from the perspective of technical advantages or application processes. This study explores the attitudes and acceptance of creative industry practitioners towards Artificial Intelligence Generated Content (AIGC) from the perspective of user behavior modification. Utilizing the Stimulus-Organism-Response Model (SOR) as the theoretical background, this research integrates the Technology Acceptance Model, Theory of Planned Behavior, and Self-Efficacy to form the research framework. By employing a mixed-method approach combining quantitative and qualitative analyses, data from 226 designers were explored, and structural equation modeling was used to verify the correlations between endogenous factors. The results indicate that users’ facilitating conditions significantly influence self-efficacy, which in turn determines their intention to adopt AIGC. Additionally, semi-structured interviews revealed that factors hindering the widespread application of AIGC mainly encompass legal security, ethical risks, and fairness. This study extends the application scope of the Stimulus-Organism-Response Model (SOR), enriches the Technology Acceptance Model, and provides a new research framework for the application of AIGC in the creative industry, detailing the responsibilities, processes, and content of designers in the Artificial Intelligence Generated Design (AIGD) process.

Virtual rapid prototyping of materials with deep learning: spatiotemporal stress fields prediction in ceramics employing convolutional neural networks and transfer learning

ABSTRACT Additive manufacturing offers a solution for producing advanced ceramics with complex geometries by enabling precise control over geometry, microstructure, and composition. By leveraging deep learning, rapid prototyping and evaluation of printed ceramic parts become feasible. This study employs convolutional neural networks and transfer learning to predict spatiotemporal fields in ceramics, using synthetic datasets generated from X-ray computed tomography (micro-CT) and finite element analysis. The novel approach integrates spatiotemporal factors into deep learning models, enhancing the prediction of stress and damage evolution, and ultimately providing deeper insights into material behaviour and performance. Additionally, we introduce a target loss training strategy, which focuses on achieving a specific accuracy level, thus reducing training time while maintaining high precision. The proposed deep learning framework achieves accurate predictions of spatiotemporal stress fields, and captures fracture initiation and propagation behaviours. This method facilitates rapid prototyping and advances material design and evaluation processes while significantly reducing experimental efforts.

Theoretically framing views of people who smoke in understanding what might work to support smoking cessation in coastal communities: adapting the TIDieR checklist to qualitative analysis for complex intervention development

IntroductionPeople living in coastal communities have some of the worst health outcomes in the UK, driven in part by high smoking rates. Deprived coastal communities include socially disadvantaged groups that struggle to access traditional stop smoking services. The study aimed to seek the views of people who smoke living in coastal communities, to assess the optimal smoking cessation intervention for this population. In addition, the Template for Intervention Description Replication (TIDieR) checklist was adapted as an analytical framework for qualitative data to inform intervention design.MethodsCurrent or recent ex-smokers (n = 25) were recruited to participate in qualitative interviews from a range of community locations in a deprived English seaside town. A thematic analysis of the interview data was undertaken adapting the TIDieR framework. This analysis was triangulated with relevant literature and notes from stakeholder meetings and observations to map onto the TIDieR checklist to describe the optimal intervention.ResultsBarriers to quitting smoking in the target population included low motivation to quit, high anxiety/boredom, normalisation of smoking and widespread illicit tobacco use. There was broad support for combining behavioural support, e-cigarettes and financial incentives, with a strong preference for the intervention to be delivered opportunistically and locally within (non-healthcare) community settings, in a non-pressurising manner, ideally by a community worker specially trained to give stop smoking support.ConclusionsAn intensive community-based smoking cessation intervention was acceptable to the target population. Adapting the TIDieR checklist as a deductive qualitative analytical framework offered a systematic approach to intervention development. Combined with other intervention development activities, this ensured that the intervention design process was transparent and the proposed intervention was well defined. It is recommended that prior to intervention development researchers speak to members of the target population who may give valuable insight into the optimal intervention.

From design to implementation: Lessons from planning the first marine protected area network in Canada

The social-ecological outcomes of marine protected areas (MPAs) can be amplified when sites are designed in networks that function cooperatively and synergistically. From 2015–2024, seventeen First Nations, the Province of British Columbia, and the Government of Canada collaboratively developed a Network Action Plan to establish an MPA Network for the Northern Shelf Bioregion (NSB). The NSB is a diverse and complex social-ecological region in British Columbia that spans an area of 102,000 km2 from Northern Vancouver Island to the Alaska border. Here, we describe the MPA Network planning process and the transition from Network planning to implementation. We discuss key elements of success that enabled the Network Action Plan’s collaborative development and lessons learned to inform and improve regional MPA Network implementation and global MPA Network planning and implementation. All phases of the planning process, including governance, development, engagement, design, and analysis, were guided by the following principles: collaborative governance, multiple ways of knowing, participatory engagement, and adaptation to new information. The Network Action Plan’s dynamic design process resulted in innovative approaches and tools to protect conservation objectives throughout the NSB. As the NSB MPA Network process transitions from planning toward implementation, collaborative governance, network monitoring, and ongoing participatory engagement processes will play a central role in advancing the goals and objectives articulated in the Network Action Plan and ensuring equitable social-ecological outcomes. Overall, the NSB MPA Network planning process provides a model for collaborative design of MPA Networks and can inform co-governance and co-management processes throughout the world.

Know Your Tools: Neuroscience and Service Design

The early stages of service design projects are defined by empathy, awareness and a deep analysis of the context in which designers engage. Therefore, understanding our cognitive capacities (‘neuro-related tools’) enhances the ability to detect environmental cues, enriching the service design process, especially within innovative and creative explorations, to envision desirable futures.

Does Artificial Intelligence Change How We Design User Interfaces? A Case Study from Representatives in the Medical Device Industry

Intravascular ultrasound (IVUS) is an imaging technique that allows interventional cardiologists (ICs) to capture and visually display images of blood vessels during Percutaneous Coronary Interventions (PCI). IVUS images allow ICs to: (i) evaluate the type and characteristics of lesions present, (ii) assess the appropriate intervention, (iii) determine the size of the stent or balloon to be used if they were needed, and (iv) examine if the intervention was successful. The size of the stent or balloon is determined by identifying, and then measuring, the diameters of vessel or lumen borders at the treatment locations. Identification of vessel borders is a manual time-consuming process that is rigorous to accomplish. Alternatively, a machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed. This industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.

Beyond Technical Specs: Using AHP to Prioritize User Needs in Turkish Basketball Arena Designs

Basketball arenas are crucial spaces that must meet specific standards to ensure the sustainability of the sport and the quality of play. While Post-Occupancy Evaluation (POE) assesses these standards, it often assumes that all performance indicators are equally important, potentially leading to a gap between design intent and actual user experience. This study aims to bridge this gap by using the Analytic Hierarchy Process (AHP) to prioritize evaluation criteria for Turkish professional basketball arenas designed for national and international matches, regional/local competitions, and training programs. Existing studies and reports indicate that basketball arenas in Turkey often fall short in meeting necessary physical requirements and exhibit performance deficiencies. To address this gap, this study identifies factors determining arena performance, categorized as technical, functional, and behavioral based on Preiser's framework. These criteria were further categorized according to different spaces within the arena, including general use areas, administrative spaces, and athlete-specific areas. A panel of expert sports facility design professionals provided judgments about the relative importance of these criteria using pairwise comparisons. The AHP method was then employed to calculate priority weights for each criterion. The results reveal the relative importance of different criteria for each space. For example, in general use areas, "furniture suitability/sufficiency" and "provision of ergonomic conditions for the disabled" ranked highest. In administrative spaces, "fire protection" and "accessibility" were deemed most important, while in athlete-specific areas, "visual and auditory privacy" and "security" took precedence. This study demonstrates the potential of AHP for developing a systematic and reliable framework for qualitative evaluations that measure user satisfaction. AHP-based evaluation models offer a valuable tool for architects, facility managers, and decision-makers to assess architectural design quality, prioritize design criteria, and support user-centered design processes.

Implementasi Metode Waterfall untuk Membuat Antarmuka pada Aplikasi Koperasi Simpan Pinjam Berbasis Mobile

Current technological developments have penetrated various sectors of human daily activities, almost every human activity is supported by technology that functions to facilitate the completion of work, starting from things that seem trivial to quite complicated work. Therefore, Banjar Tri Parartha residents who have a business unit in the form of a savings and loan cooperative with funds from member contributions, run it by carrying out cooperative principles to help the economic movement of its citizens based on the principle of family. At this time the process of recording, calculating to the reporting process is still done conventionally, so that cooperative administrators are quite busy every time they make an accountability report that is carried out every year. By running the analysis and design process in the application creation stage using the waterfall method. So in the interface design it is adjusted to what is needed by the cooperative. It is hoped that this research can be a reference in making applications as the next stage. By using the application design starting from the recording stage, processing calculations and presenting reports, it is hoped that all stakeholders will find it easier to monitor and obtain cooperative financial information, so that the transparency of cooperative funds can provide certainty for its members.

Towards end-to-end structure determination from x-ray diffraction data using deep learning

Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures.

Tour and Travel Services Company Website Design with Enhanced Awareness of Transportation Safety

<p><em>Transportation safety is a critical aspect of the tour and travel industry. With increased focus on passenger safety and comfort, tour and travel companies must adopt information technology to enhance safety aspects. This article discusses the design and development of a tour and travel company website that is more safety-conscious, integrating important features such as NIK usage, passenger tracking, real-time notifications, and customer awareness education regarding safety. The design process employs the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) methodology. In summary, the new features added to the booking system have greatly improved user satisfaction and operational efficiency for the company. The safety-conscious website has positively impacted passenger safety and comfort. Technologies like NIK integration, real-time tracking, and automatic notifications have made the service more reliable and user-friendly. However, there's still a need for better education on safety requirements for drivers and vehicles, as many users admit to lacking understanding in this area. Continued efforts to educate users transparently can enhance trust and safety in transportation services.</em></p>

A user-centred future for agricultural digital innovation: demonstrating the value of design thinking in an animal health context

ABSTRACT Purpose Much of the literature investigating farmer engagement with digital technology has focused on farmer-centred adoption factors. However, more recent research has called for new user-centred approaches to technology design to promote more responsive development and ultimately more successful uptake. Methodology Responding to these calls, this study draws on Stanford’s design thinking framework and principles to design a digital innovation for use in an animal health context. Findings The study details how a co-design workshop was used to incorporate end-user needs, values, and requirements for a digital animal health innovation. Additionally, it outlines how co-design approaches can be useful to identify and address innovation design issues early on in the design process. Practical implications This study highlights the importance of end-user engagement for the design, development, and implementation of digital innovations for the agricultural sector and the importance this has for successful technology development and user uptake. Theoretical implications This study contributes to the literature on innovation development for agriculture and to the theory on farmer engagement. Originality/value This study is one of few that applies co-design and design thinking methodologies to the creation of a digital animal health diagnostic tool.

THE ROLE OF BIG DATA ANALYSIS IN FASHION DESIGN

Today, as the contemporary, economic and cultural landscape becomes increasingly volatile, the dynamics of the fashion industry are becoming less predictable. The fast-changing fashion industry is constantly generating data. Big data is a powerful tool that offers new and exciting opportunities for fashion designers. It is used for different purposes such as understanding consumer behaviour, predicting trends and creating more personalized products. Design processes that were previously driven by intuition and instinct have become more fluid, data and goal-oriented with the opportunities offered by big data. This article aims to reveal the purposes of using big data by investigating fashion design models based on big data in the fashion industry in recent years. For this purpose, the current models of big data in fashion design, its limitations and development direction are discussed. While many of the areas of use of big data in the fashion industry (trend analysis, demographic, geographical analysis, age and gender, etc.) have been intensively researched, the number of purely design-oriented research is only recently increasing. This study is unique in that it focuses on the design phenomenon and applications of big data in fashion. In this context, there is no academic study in the national literature. It is expected to contribute to the discussion on the effects of big data and big data-based digital technologies in fashion design.

High fidelity adaptive mirror simulations with reduced order models

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate’s prototype of the adaptive mirror of the Giant Magellan Telescope.

Information Modeling Implementation in PAO “Rosneft” as Challenge of Digitization in Construction Industry

Key goals of construction industry modernization and quality improvement are the introduction of information modeling which is an effective tool for increasing the enterprise competitiveness through the quality of design and construction, resource optimization and efficiency of interaction between process participants throughout the entire life cycle.The article presents information modeling (3D design) in PAO “Rosneft” with respect to the tasks of ensuring adaptation to design procedure in the company and the transition to full-scale 3D design. The choice of software is justified, real examples of completed pilot projects using information modeling are described, and information modeling implementation are presented. The experience in information modeling implementation helps companies to choose one or another software, and become familiar with the process at PAO “Rosneft”. As a result of the digital transformation of design processes, a model of the life cycle management system of capital construction project is developed, the basis of which is its information model. In addition, the main problems that require solutions for the successful information modeling implementation are identified, i.e., shortcomings of domestic software, shortage or absence of highly qualified personnel, unwillingness of customers to work with technical information, efficiency decrease of the organization due to the introduction of a new approach to project implementation into existing processes, resistance from employees, customers and contractors of construction, and installation works.The information modeling introduction in the PAO “Rosneft” allow to achieve goals for im[1]port substitution of technologies and prepare a worthy response to global geopolitical challenges.

Study on finite element simulation and experiment based on the design of zero-point clamping system

The zero-point positioning system (Z-PCS) is more suitable for the increasingly common production modes of multiple types, small batches, and changing conditions than to conventional fixtures. Currently, the Z-PCS cannot provide sufficient clamping force, and the design of the positioning structure is prone to over-positioning. The working principles of each functional module in the system are unclear, making it difficult to promote product design optimization and troubleshooting. This article aims to design a high-performance Z-PCS, revealing the structural composition and working principle of the system, including the clamping structure, positioning structure, and air circuitry arrangement. During the design process, the finite element numerical calculations were adopted to verify the mechanical properties of each key load-bearing component. Finally, the designed product underwent positioning accuracy and clamping force testing. The results indicate that the Z-PCS designed in this article can provide a clamping force of at least 73.7 kN and control the repetitive positioning error below 0.002 mm. Micro-deformation grooves can be adaptively added to compensate for the over-positioning error. Heat-treated martensitic-type stainless steel is an ideal material for constructing the body of a high-performance Z-PCS.