Design Process Model Research Articles

Co-designing postpartum contraceptive services with and for immigrant women in Sweden: lessons learned from the IMPROVE-it project

Background and aimImmigrant women in many high-income countries including Sweden, report unmet need of sexual and reproductive health and rights, and face worse pregnancy outcomes and higher risk of unintended pregnancies. Postpartum contraceptive services are often inadequate to meet their needs. Co-design has shown to reduce health inequities, yet little is known about using this method for postpartum contraceptive service development and even less in collaborating with immigrant populations. The aim of this paper is to describe the co-design process and the strategies that were developed to help develop tailored and acceptable postpartum contraceptive services for immigrant women in Sweden.MethodsThe paper describes a co-design process that took place during 2022–2023, including the cyclical digital consultations with Arabic and Somali speaking immigrant women, midwives and researchers, as well as the outputs from the process. The theoretical framework for the co-design process was the ‘Double Diamond’ Design Process Model. Data analysis included qualitative content analysis.ResultsThe co-design process led to the joint development of intervention materials and strategies to improve postpartum contraceptive services. Specifically, the process revealed ideas on how to improve contraceptive counseling within three pre-identified areas of change: improve physical access to contraceptive services; improved communication strategies using visual aids and information charts; and empowerment strategies that focus on reflective practice without assumptions about what a group of women might expect. We found that participants contributed actively to the process with ideas and suggestions, and that the co-design process facilitated positive reflections on ongoing counseling practices.ConclusionThe co-design process resulted in the successful and participative development of innovative tools and activities to improve contraceptive counseling services. This approach is original because it involves both immigrant women, often left behind, and midwives delivering the services. Whilst this interplay allowed for careful refinement of services and tools by using an iterative process, it also facilitated reflective midwifery practice.

PENGARUH KUALITAS PRODUK DAN KEPERCAYAAN PELANGGAN TERHADAP KEPUTUSAN PEMBELIAN MOTOR MELALUI KEPUASAN PELANGGAN PADA PT. SURYA GEMILANG MOTOR SURABAYA

The purpose of this research is to test and analyze the influence of product quality, customer trust on purchasing decisions through customer satisfaction at PT. Surya Gemilang Motor, West Surabaya. Sampling method using Purposive Sampling. The scope of this research is only on variables related to product quality, customer trust which directly or indirectly influence purchasing decisions through customer satisfaction. The approach of this research is a quantitative approach method. The data analysis technique for this research uses the Partial Least Square (PLS) method with the SmartPLS 4.0 application program which includes the design process of the Inner Structural Model, Outer Structural Model and Hypothesis Testing (Resampling Bootstrapping). The research results found that there was no direct effect of product quality on customer satisfaction at PT. Surya Gemilang Motor Surabaya. There is a direct influence of Customer Trust on Customer Satisfaction at PT. Surya Gemilang Motor, West Surabaya. There is a direct effect of product quality on purchasing decisions at PT. Surya Gemilang Motor, West Surabaya. There is a direct influence of Customer Trust on Purchasing Decisions at PT. Surya Gemilang Motor, West Surabaya. There is a direct effect of purchasing decisions on customer satisfaction at PT. Surya Gemilang Motor, West Surabaya. There is no effect of Product Quality indirectly on Purchase Decisions through Customer Satisfaction at PT. Surya Gemilang Motor, West Surabaya. There is an indirect effect of Customer Trust on Purchasing Decisions through Customer Satisfaction at PT. Surya Gemilang Motor.

Understanding the Integration of Building Energy Modeling into the Building Design Process: Insights from Two Collaborative Construction Projects

This research investigates the integration of building energy modeling (BEM) within collaborative construction projects to inform design decisions for achieving high-energy performance goals. The study aims to understand current practices, benefits, and challenges associated with this integration. Using an ethnographic case study approach focused on two high-energy performance social housing projects with integrated project delivery and integrated design processes, data were collected through direct observations, document analysis, and interviews with project team members. Design process modeling was utilized to dissect current practices, followed by a hybrid inductive and deductive thematic analysis to find challenges related to energy performance design in collaborative projects. Findings from this research revealed that BEM experts often operate in isolation, with late integration of energy models into design decisions. Compliance-centric BEM usage and challenges related to interoperability of design and BEM tools further compound the issue of seamless collaboration. However, the study highlights that early collaboration among project stakeholders emerges as a pivotal factor in informed design decisions, bridging the gap between energy modeling and design. This research provides valuable insights for practitioners seeking to optimize BEM in their design process, and offers support to policymakers aiming to enhance the role of BEM in projects.

Digital technology‐based business model design and innovation to address grand challenges: A process model

AbstractResearch SummaryThe question of how a new venture designs and innovates on its business model (BM) to leverage digital technology to address multiple grand challenges has received little attention in the literature. We address this through a longitudinal case study of a new social enterprise in China. We develop an accessing‐embedding‐strengthening‐expanding process model of BM design and innovation by which a venture may increase both the scale and scope of its impact as a complementor in ecosystems addressing grand challenges. We identify the specific actions and contingencies underlying this process. Our findings contribute to the understanding of BM design and innovation based on digital technologies and propose that assessment of a BM's impact on grand challenges may be in terms of scope as well as scale.Managerial SummaryHow may founders build a BM that exploits a technology's potential to address diverse sustainability challenges? Our study derives insights from one for‐profit Chinese venture whose founders had the explicit objective of leveraging remote sensing data and analytics to address important and diverse social and environmental challenges. We identify the steps in its ecosystem‐based strategy (accessing, embedding, strengthening, and expanding) by which the founders targeted and entered successive application ecosystems as a complementor. This strategy has enabled them to achieve financial goals and also increase their contribution to sustainability objectives in terms of both scale (vis‐à‐vis one grand challenge) and scope (across multiple grand challenges). We also identify the specific questions that the founders had to answer in the process of executing this strategy.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Language Model-Assisted Machine Learning, Photoelectrochemical, and First-Principles Investigation of Compatible Solvents for a CH3NH3PbI3 Film in Water.

Machine learning and data-driven methods have attracted a significant amount of attention for the acceleration of the design of molecules and materials. In this study, a material design protocol based on multimode modeling that combines literature modeling, numerical data collection, textual descriptor design, genetic modeling, experimental validation, first-principles calculation, and theoretical efficiency calculation is proposed, with a case study on designing compatible complex solvent molecules for a halide perovskite film, which is notorious for optoelectronic deactivation under hostile conditions, especially in water. In the multimode modeling design process, the textual descriptors play the central role and store rich literature scientific knowledge, which starts from the construction of a high-dimension literature model based on scientific articles and is realized by a genetic algorithm for materials predictions. The prediction is substantiated by follow-up experiments and first-principles calculations, leading to the successful identification of effective molecular combinations delivering an unprecedented large aqueous photocurrent (increasing by 3 orders of magnitude compared with that of CH3NH3PbI3) and remarkable aqueous stability (improving from 36% to 89% after immersion in water) under the hostile condition. This study provides a practical route via multimode modeling for accelerating the design of molecule-modified and solution-processed materials in a real scenario.

Design and three-dimensional simulation of flat-knitted sports uppers based on loop structure

PurposeThe purpose of this paper is to solve the diverse and complex problems of flat-knitting sports upper process design, improve the design ability of upper organization, and realize three-dimensional simulation function.Design/methodology/approachFirstly, the matrix is used to establish the corresponding pattern diagram and organizational diagram model, and the relationship between the two is established by color coding as a bridge to completed the transformation of the flat-knitted sports upper process design model. Secondly, the spatial coordinates of the loop type value points are obtained through the establishment of loop mesh model, the index of two-dimensional and three-dimensional models of uppers and the establishment of spatial transformation relationship. Finally, using Visual Studio as a development tool, use the C# language to implement this series of processes.FindingsDigitizing the fabric into a matrix model, combined with matrix transformation, can quickly realize the design of the flat-knitting process. Taking the knitting diagram of the upper process as the starting point, the loop geometry model corresponding to the element information is established, and the three-dimensional simulation effect of the flat-knitted upper based on the loop structure is realized under the premise of ensuring that it can be knitted.Originality/valueThis paper proposes a design and modeling method for flat-knitted uppers. Taking the upper design process and 3D simulation effect as an example, the feasibility of the method is verified, which improves the efficiency of the development of the flat-knitted upper product and lays the foundation for the high-end customization of the flat-knitted upper.

Service Design for Social Innovation: A New Path for the Development of Government Public Services Based on the TB4D Model

Public service is related to everyone’s life and future and directly feeds back on the development of society and the country. How to better shape and develop it is a critical issue for both individuals and groups. As the primary provider, the government plays a vital role in its creation, development, and transformation. In recent years, with the rapid development of human society, this field has been placed in the midst of new challenges. These are the difficulties that government departments urgently need to solve, and the traditional solutions have shown apparent inadequacies. Service design, as a powerful responder, is gradually emerging and playing an important role, influencing a series of dimensions from service innovation to social policies. The application of service design in government public services holds significant social value. However, related research remains in its infancy, needing a systematic theoretical framework. This paper focuses on government public service design and carries out systematic design research on it in order to realize the corresponding social and academic value. This paper established a theoretical framework for government public service design centered on the TB4D model, based on an in-depth study of the relevant literature, nearly 30 representative industry case studies, and three practical projects conducted by the author. The model integrates the TSLC operational mechanism model, the Behavior-Organization-Policy operational content model, the “3 + 1” development mode, and the method reference guide for government public service design based on the Double Diamond design process model. The letters and numbers in the model’s name sequentially represent these four components. Specifically, the TSLC operational mechanism model derives its name from the initials of four parts: trans-dimensional cognitive and thinking mode, systems thinking, learning interactive innovation process, and cultural regeneration and value shaping. The “3 + 1” development model denotes three forms of development centered on internal design capability and the form of joint development focused on external design capabilities. The TB4D model combines both theory and practice, yet to better illustrate its overall performance, this paper reverted to the practical domain. It quantitatively evaluated 20 complete projects, calculating the mean value and 95% confidence intervals to verify the model. The results demonstrate that the model is valid and possesses high industry representativeness, effectiveness, and universality. The organic operation of the model will provide systematic, innovative solutions to address the problems and challenges faced in the public service field, promoting the sustainable development of society. Since research in this area is still at an early stage, the theoretical model proposed here emphasizes its intrinsic principles and universal logic. It aims to provide foundational theoretical support for the development of this field and systematic guidance for relevant practices. As the industry evolves, the model’s content can be further refined and enriched to better reflect contemporary characteristics. Additionally, because this model serves as a foundational framework for the entire field, it can be further expanded to develop sub-models with local characteristics when applied to different regions.

Stairway to Impact? Tracing the Business Model Design Processes of Nascent Sustainable Ventures

Stairway to Impact? Tracing the Business Model Design Processes of Nascent Sustainable Ventures

GeoAI Reproducibility and Replicability: A Computational and Spatial Perspective

GeoAI has emerged as an exciting interdisciplinary research area that combines spatial theories and data with cutting-edge AI models to address geospatial problems in a novel, data-driven manner. Although GeoAI research has flourished in the GIScience literature, its reproducibility and replicability (R&R), fundamental principles that determine the reusability, reliability, and scientific rigor of research findings, have rarely been discussed. This article aims to provide an in-depth analysis of this topic from both computational and spatial perspectives. We first categorize the major goals for reproducing GeoAI research, namely, validation (repeatability), learning and adapting the method for solving a similar or new problem (reproducibility), and examining the generalizability of the research findings (replicability). Each of these goals requires different levels of understanding of GeoAI, as well as different methods to ensure its success. We then discuss the factors that might cause the lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of training data; (2) the uncertainty that resides in the GeoAI model design, training, deployment, and inference processes; and more important (3) the inherent spatial heterogeneity of geospatial data and processes. We use a deep learning-based image analysis task as an example to demonstrate the results’ uncertainty and spatial variance caused by different factors. The findings reiterate the importance of knowledge sharing, as well as the generation of a “replicability map” that incorporates spatial autocorrelation and spatial heterogeneity into consideration in quantifying the spatial replicability of GeoAI research.

Efficient and lightweight convolutional neural network architecture search methods for object classification

Determining the architecture of deep learning models is a complex task. Several automated search techniques have been proposed, but these methods typically require high-performance graphics processing units (GPUs), manual parameter adjustments, and specific training approaches. This study introduces an efficient, lightweight convolutional neural network architecture search approach tailored for object classification. It features an optimized search space design and a novel controller design.This study introduces a refined search space design incorporating optimizations in both spatial and operational aspects. The focus is on the synergistic integration of convolutional units, dimension reduction units, and the stacking of Convolutional Neural Network (CNN) architectures. To enhance the search space, ShuffleNet modules are integrated, reducing the number of parameters and training time. Additionally, BlurPool is implemented in the dimension reduction unit operation to achieve translational invariance, alleviate the gradient vanishing problem, and optimize unit compositions. Moreover, an innovative controller model, Stage LSTM, is proposed based on Long Short-Term Memory (LSTM) to generate lightweight architectural sequences. In conclusion, the refined search space design and the Stage LSTM controller model are synergistically combined to establish an efficient and lightweight architecture search technique termed Stage and Lightweight Network Architecture Search (SLNAS).The experimental results highlight the superior performance of the optimized search space design, primarily when implemented with the Stage LSTM controller model. This approach shows significantly improved accuracy and stability compared to random, traditional LSTM, and Genetic Algorithm (GA) controller models, with statistically significant differences. Notably, the Stage LSTM controller excels in accuracy while producing models with fewer parameters within the expanded architecture search space.The study adopts the Stage LSTM controller model due to its ability to approximate optimal sequence structures, particularly when combined with the optimized search space design, referred to as SLNAS. SLNAS's performance is evaluated through experiments and comparisons with other Neural Architecture Search (NAS) and object classification methods from different researchers. These experiments consider model parameters, hardware resources, model stability, and multiple datasets. The results show that SLNAS achieves a low error rate of 2.86 % on the CIFAR-10 dataset after just 0.2 days of architecture search, matching the performance of manually designed models but using only 2 % of the parameters. SLNAS consistently demonstrates robust performance across various image classification domains, with an approximate parameter count 700,000.To summarize, SLNAS emerges as a highly effective automated network architecture search method tailored for image classification. It streamlines the model design process, making it accessible to researchers without specialized knowledge in deep learning. Optimizing this method unlocks the full potential of deep learning across diverse research areas. Interested parties can publicly access the source code and pre-trained models through the following link: https://github.com/huanyu-chen/LNASG-and-SLNAS-model.

Designing an Effective Governance Model for Data Collaboratives

Overview: Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives. Practitioner Takeaways: Practitioners can develop a detailed understanding of the governance factors that influence the successful creation of data collaboratives. The governance design process model offers practitioners a tangible roadmap and concrete activities to help them revamp an existing data collaborative or create a new one. The list of reflective questions can guide practitioners’ initial design efforts and help them assess the robustness of their data collaboratives’ current governance models.

Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Canter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

Multitask Learning Deep Neural Networks Enable Embedded Design of Active Metamaterials.

In this study, we propose and implement a deep neural network framework based on multitask learning aimed at simplifying the forward modeling and inverse design process of photonic devices integrating active metasurfaces. We demonstrate and validate our approach by constructing a continuously tunable bandpass filter that is effective in the midwave infrared region. The key to this filter is the combination of a metasurface and Fabry-Perot (F-P) cavity structure of the tunable phase-change material Ge2Sb2Se4Te (GSST) and the precise control of the crystallinity of the GSST by a silicon-based heater. With the help of a deep learning framework, we are able to independently model the crystallinity and geometric parameters of the filter to maximize the use of GSST tuning for bandpass filtering. Our model discusses the self-attention mechanism and the effect of noise and compares several existing popular algorithms, and the results show that a multitask deep learning strategy can better assist the on-demand reverse design of photonic structures with phase change materials. This opens up new possibilities for personalization and functional extension of optical devices.

Evaluation and instantiation of a generic process model for early-stage sustainable product-service system design within three manufacturing companies

Although product-service systems (PSS) have been in focus in literature and industry for some years, no generically agreed or applied PSS design process exists thus far, in contrast to the neighbouring fields of product development or service design, respectively. This paper presents the evaluation and instantiation of a recently proposed generic process model (GPM) for early-stage sustainable PSS design in capital goods manufacturing companies. The model was applied in three case studies with an aim to quantitatively and qualitatively evaluate the GPM concerning eight criteria, and study its instantiation process actions to a company-specific model while simultaneously identifying and proposing solutions for challenges to instantiation. Based on observation and feedback received from the companies, the GPM shows promise for widespread applicability, aided by proposed guidelines for instantiating the GPM to company-specific contexts, as also described in this paper. These guidelines aim to facilitate the translation of the GPM into pragmatic models for practitioners in the field of PSS design, thus increasing its chances of successful implementation as well as impact.

Innovative design of multi-contradiction systems based on the function-structure model

Innovative design of complex systems faces multi-contradiction (MC). The existing methods to solve MC are mainly based on the General Theory of Powerful Thinking (OTSM) which is independent of the classical Engineering Design Method (ED) to form the process model, they cannot inherit the accumulated knowledge and advantages of ED. This paper proposes a product innovation design process model for MC based on the function-structure model. It starts with an initial scenario of the system to build the function-structure model and MC network based on the root contradiction analysis. The network structure entropy and function importance decision methods are introduced for the trimming target confirming. The key contradiction-node is identified and solved by strategies of trimming based on internal and external resources of the system. The function-structure model in ED and trimming in TRIZ are combined to form an innovative design process for MC systems. The proposed method is evaluated in a case study of an innovative design of the girder bridge erector.

A survey on graph neural networks for intrusion detection systems: Methods, trends and challenges

Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the increasing sophistication of cyber attack methods, traditional detection approaches are encountering more challenges. In recent years, graph neural networks (GNNs) have garnered significant attention in the field of intrusion detection due to their unique ability to capture the relationships within the graph structure of data communications. In this review, we propose a novel taxonomy that categorizes advanced research into three distinct areas: tasks related to graph construction, network design, and GNN models deployment. We detail a generalized design process for GNN-based intrusion detection models, discussing the challenges encountered at each stage. Building upon these discussions, we conduct a systematic survey of existing works. Ultimately, we delve into a thorough exploration of the future research directions and the pending issues in this domain. By adopting a problem-oriented taxonomy and conducting a targeted survey, this review aims to provide scholars with a clear, systematic framework for deepening their understanding and further exploration of the field.

Artificial intelligence in analogue circuit design

Analog circuit design plays an integral part in modern society; however, traditional approaches often fall short in terms of efficiency and effectiveness. This paper explores the application and challenges presented by artificial intelligence (AI) technologies in analog circuit design. The objective of this paper was to evaluate the capacity of artificial intelligence technologies such as machine learning, deep learning and reinforcement learning to advance circuit modeling, optimization and layout design processes. A comprehensive literature review and analysis were performed. The study finds that while AI can make significant contributions to circuit design, several challenges must first be overcome. These include data scarcity for training AI models and the opaque nature of AI decisions which prevent their widespread acceptance. Finally, future research directions such as creating synthetic data or developing more interpretable AI models were identified as potential future research avenues.

A Comparative Analysis of Student Perceptions of Recommendations for Engagement in Design Processes

Abstract Engineering designers are tasked with complex problems necessitating the use and development of various supports for navigating complexity. Prescriptive design process models are one such tool. However, little research has explored how engineering designers perceive these models' recommendations for engagement in design work. In this exploratory study, we analyzed data from individual semi-structured interviews with 18 mechanical engineering students to identify participant perceptions of design process models. As many design process model visualizations lack explicit attention to some social and contextual dimensions, we sought to compare perceptions among two models drawn from engineering texts and one model that was developed with the intent to emphasize social and contextual dimensions. We identified perceptions of the recommendations from the design process models related to starting and moving through a design process, gathering information, prototyping, evaluating or testing, and what they should consider. Participant perceptions across the three process models suggest different design process models make perceptions of certain recommendations more salient than others. However, participant perceptions also varied for the same process model. We suggest several implications for design education and training based on participant perceptions of the process models, particularly the importance of leveraging multiple design process models. The comprehensive descriptions of participant perceptions provide a foundation for further investigations bridging designers' perceptions to intent, behavior, and, ultimately, design outcomes.