Rapid Iteration Research Articles

Explor-A-Thora: A Novel Three-Dimensionally Printed Pleural Simulator.

For procedural education, the shift from the traditional apprenticeship model to simulation-based mastery has become increasingly accepted as the gold standard and has underscored the importance of high-fidelity, cost-effective training options. However, cost-effective pleural procedure simulators providing both realistic haptic feedback and ultrasound compatibility are lacking. We aimed to create a pleural procedure simulator with characteristics of human tissue, at low cost and with ultrasound compatibility. This work used design-based research principles and a collaborative rapid iteration approach in collaboration with the University of California, San Francisco, Makers Lab and design-based researchers at the University of California, Berkeley, which led to the creation of a three-dimensionally printed pleural procedure simulator. The needs assessment indicated significant discomfort with pleural procedures and a request for more accessible simulation opportunities. Iterative prototyping resulted in a three-dimensionally printed rib cage and a series of innovations in the fluid pocket and skin layers to provide realistic tactile feedback and ultrasound imaging compatibility. The final model costs significantly less than commercial simulators, with durable components and replaceable parts that can be reused multiple times. The development of a low-cost, high-fidelity pleural procedure simulator addresses the current limitations of commercially available pleural simulators. By integrating three-dimensional printing technology and easily accessible materials, we were able to produce a simulator that closely replicates the feel of human tissue, allows ultrasound use, and is adaptable for different patient anatomies and clinical scenarios. This novel simulator is a scalable solution to elevate the standard of procedural education and ultimately positively affect patient care.

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

Technology Focus: Reservoir Simulation (July 2024)

The early days of artificial intelligence (AI) and machine learning (ML) were filled with promise, but now we’re witnessing a wave of practical applications transforming reservoir engineering. The integration of AI/ML with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation. These advancements lead to more-accurate models and faster simulation turnaround times, allowing us to model various scenarios more efficiently. These techniques enable us to understand and simulate reservoir behavior with greater fidelity and focus on the underlying physics. This deeper understanding translates to more-confident decisions regarding reservoir management and development, with a clearer picture of potential risks. Physics-informed machine learning emerges as a significant step forward, improving our understanding and providing better models while boosting runtimes, convergence, and overall performance. Paper IPTC 23730 highlights its application in tackling computationally intensive tasks such as critical temperature prediction with high accuracy, leading to significant speed-ups in simulations, particularly for complex compositional models undergoing miscible gas injection. Paper IPTC 23935 presents a fundamentally different approach to reservoir simulation studies. Adaptive models use multiple smaller, specialized models instead of one giant model. These models can be developed and run independently, allowing for parallel workflows and significantly reduced turnaround times. Adaptive models foster a culture of rapid experimentation and iteration, aligning perfectly with the fail-fast approach—prioritizing the quick evaluation of ideas and discarding those that don’t show promise early on. Paper SPE 214855 provides a comprehensive overview of a complex problem—wellbore modeling. This paper offers practical insights directly relevant to my work. These advancements represent a significant leap forward, paving the way for more-powerful and -versatile reservoir models. As research and development continue, these techniques are poised to revolutionize how we manage and optimize oil and gas reservoirs. I hope you enjoy reading this selection of papers and find them enlightening. Recommended additional reading at OnePetro: www.onepetro.org. SPE 216722 Developing Consistent Relative Permeability and Capillary Pressure Models for Reservoir Simulation of Carbon Capture and Storage Projects by L.S. Lun, ExxonMobil, et al.

Machine learning assisted prediction for hydrogen production of advanced photovoltaic technologies

The photovoltaic (PV) water electrolysis method currently stands as the most promising approach for green hydrogen production. The rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen production. In this work, the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha region. All-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy production. Seven machine learning (ML) algorithms are used to forecast the performance in power generation and hydrogen production of a 100 ​MW photovoltaic hydrogen production and energy storage (PH-S) system throughout its operational life. The long short-term memory (LSTM) algorithm exhibits the best performance, achieving mean absolute error (MAE) of 0.0415, root mean square error (RMSE) of 0.0891, and coefficient of determination (R2) of 0.8402. In terms of cost-effectiveness, heterojunction with intrinsic thin layer (HJT) PV technology achieves the lowest levelized cost of electricity (LCOE) and hydrogen (LCOH) at 0.025 $/kWh and 6.95 $/kg, respectively. According to the sensitivity analysis, when the cost of proton exchange membrane electrolysis (PEMEC) reduced 50%, the LCOH for PH-S system decreased 21.40%. This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.

THE IMPACT OF CRISIS ON INTERECOSYSTEMIC INNOVATION: DYNAMICS OF ENEL’S HYDROGEN ECOSYSTEM

Crisis highlights conflicts but also helps disparate organisations rally around common objectives through forming ecosystemic relationships. To improve future crisis response, we study the influence of crisis-driven transitions on innovation in ecosystems. We illustrate the study with the dynamics of hydrogen innovation at Enel. Here, innovation caters to multiple value propositions; therefore, we define interecosystemic innovation as an innovation that addresses the value propositions of more than one ecosystem. The unexpected disruption caused by the energy crisis focuses attention on a single value proposition, thus fomenting the dynamic, temporal sequencing of interecosystemic innovation. This increases activity in the ecosystem core as well as interaction with the ecosystem periphery, allowing more rapid innovation iterations throughout the ecosystem. When operating in the intercept of ecosystems, firms need to consider the multiple value propositions of ecosystem participants to identify and mitigate tensions that may impede innovation.

Neuro-oncology anywhere: A decentralized approach to clinical trials in brain tumors and beyond.

TPS1647 Background: The majority of patients with primary and secondary central nervous system (CNS) malignancy develop cognitive impairment, fatigue, and mood-related symptoms. Radiotherapy has been shown to contribute to symptom burden often limiting quality of life and functional status. The development of interventions mitigating neurocognitive toxicity requires an accurate assessment of function and symptom burden. Formal neuropsychological evaluation is resource and time intensive, and often challenging to incorporate in routine clinical practice. There is a critical need to accurately assess cognition without causing undue burden to cancer patients while evaluating the efficacy of interventions that can be scaled and implemented at a population level. Methods: To adequately assess the neurocognitive function and related symptoms in patients with CNS malignancy we have designed an open-label decentralized clinical trial that will randomize patients who received intracranial radiation for primary or metastatic brain tumors in a 1:1 allocation to a therapy targeting radiation-related toxicity (metformin shipped to patient home) plus health promotion with a wearable device vs. health promotion with a wearable device alone. Participants will be stratified based on tumor type (glioblastoma vs. other glioma vs. brain metastases vs. meningioma) and age (<70 vs. 70+ years). Participants will be provided a wearable device for monitoring health status. Participants will undergo serial remote cognitive assessments with the Mayo Test Development Through Rapid Iteration, Validation and Expansion platform (Mayo Test Drive). Remote reported symptom collection via patient surveys and remote neurologic examination by telehealth will also be incorporated. Cognitive assessments and symptom surveys will be designed for completion on a smartphone, tablet, or computer device. Health data will be collected from the wearable device, including the number of steps per day and sleep quality/duration. Participants receiving metformin will utilize device apps to report medication adherence and adverse events. Study-related laboratory assessments will be completed remotely and transmitted electronically. This decentralized clinical trial will provide real-world cognitive and quality-of-life data for patients with primary and metastatic brain tumors while providing preliminary data on the effectiveness of device-based health promotion with or without metformin for treating radiation-related cognitive decline. Results gained from this study will guide future trials in neuro-oncology and other fields. This study is expected to open in March 2024.

Decentralized Chatting Application Using Blockchain Technology

Abstract: The emergence of blockchain technology has spurred innovation in various domains, including decentralized communication systems. In this context, a decentralized chat application leveraging blockchain, Remix Ethereum IDE, Ganache blockchain, and MetaMask offers a novel approach to secure and privacy-centric messaging. The application harnesses blockchain's immutable ledger to store chat messages, ensuring data integrity and censorship resistance. Remix Ethereum IDE provides developers with a robust environment for crafting smart contracts, the backbone of the chat application's functionality. Developers can design and deploy smart contracts defining chatroom logic, message encryption, and user authentication. Ganache blockchain serves as a local testing environment, enabling developers to validate smart contracts and simulate blockchain interactions in a controlled setting. This facilitates rapid iteration and debugging during the development phase. MetaMask acts as the gateway for users to access the decentralized chat application. Through MetaMask, users authenticate securely using their Ethereum wallets, ensuring pseudonymous identity verification and enabling seamless interaction with the Ethereum blockchain. Together, these technologies form the foundation of a decentralized chat application that prioritizes privacy, security, and user control. By leveraging blockchain and associated tools, the application offers a compelling alternative to centralized messaging platforms, empowering users with sovereignty over their data and communication.

Rancangan Aplikasi Presensi Karyawan CV. Pramana Mukti Jaya menggunakan Metode Extreme programming (XP) Berbasis Web dengan Integrasi Teknologi Leaflet.JS

Employee attendance management is an integral part of company operations. This study aims to develop a web-based employee attendance application at CV Pramana Mukti Jaya, integrated with Leaflet.js technology. This application enables employees to report their attendance efficiently and accurately through a responsive web interface. The development method used is Extreme Programming (XP) to ensure rapid iterations and active stakeholder participation. During the analysis phase, both functional and non-functional requirements of the application were identified. The application allows CV Pramana Mukti Jaya employees to record attendance by inputting time and location information directly through a web form. With the integration of Leaflet.js technology, the application provides an interactive map that facilitates employees in selecting accurate attendance locations. The application testing was conducted to verify the accuracy and ease of use of the Leaflet.js map interactions. The result of this research is an effective web-based employee attendance application at CV Pramana Mukti Jaya. The integration of Leaflet.js technology provides a significant visual dimension in employee attendance reporting. This application is expected to help the company manage employee attendance more efficiently and accurately while introducing the benefits of implementing Leaflet.js technology in web-based applications.

Discussion on the Management Strategy of Imaging Medical Equipment under the New Situation

In the realm of healthcare, particularly in the management of medical imaging equipment, unprecedented challenges and opportunities are encountered. This is especially pronounced in the niche domain of gastrointestinal endoscopy, where innovative management strategies are pivotal in the face of evolving circumstances. With the rapid iteration of technology, the escalating scarcity of medical resources, alongside the rising costs of management and maintenance, coupled with the ongoing flux of regulations and standards, these factors collectively constitute the intricate landscape confronting current management of medical imaging equipment. Addressing the pace of technological advancement, employing proactive technological foresight and flexible updating strategies are paramount. This entails not only technological upgrades of the equipment itself but also continuous training and education for operators to ensure their alignment with technological advancements. Given the scarcity of medical resources, enhancing the utilization and efficiency of existing equipment becomes imperative. This may involve optimizing equipment allocation and scheduling, reducing idle time, while also exploring the application of emerging technologies such as telemedicine to expand service coverage. This article will delve into how, amidst new circumstances, effective management strategies can optimize the utilization and maintenance of gastrointestinal endoscopy to cope with challenges such as the rapid pace of technological evolution, scarcity of medical resources, increasing management and maintenance costs, as well as changes in regulations and standards.

Design Approaches and Electromechanical Modeling of Conformable Piezoelectric‐Based Ultrasound Systems

AbstractPainless, needleless delivery of drugs through the skin can be realized through aphenomenon called sonophoresis by applying an ultrasound field to the biological tissue. Development of wearable embodiments of such systems demands comprehensive characterization of both the physical mechanism of sonophoresisas well as wearability parameters. Here, we present a framework for analyzing disk‐type piezoelectric transducers in a polymeric substrate to create acoustic cavitation in a fluid coupling medium for sonophoresis applications. The device design and operating parameters such as the working frequency, applied voltage range, acoustic pressure distribution, and transducer spacing were determine dusing a finite element methods (FEM),and verified with experimental measurements. The influence of the surrounding water and tank reflections on the acoustic pressure field, and the interaction between the elements in the array structure were also studied.Finally, the impact of skin and the substrate geometry on the acoustic pressure fields was characterized to simulate the invivo use‐case of the system. These analytical models can be used to guide critical parameters for device design such as the separation distance of the piezoelectric transducer from the skin boundary. We envision that this tool boxwill support rapid design iteration for realization of wearable ultrasound systems.

Validating the rapid validity testing concept across regions

Empirical studies in innovation management often draw on data collected in particular regions, mostly in Western economies, and imply universal applicability of the findings across regions. Given the relevance of innovation for economic progress and structural and cultural differences among regions, there is surprisingly little knowledge available on whether the knowledge developed from Western firms can be transferred to emerging economies. This dearth of knowledge is especially notable for the early phases of innovation processes. Rapid iterations between knowledge creation and learning are critical at the front end of innovation (FEI), a phase that, in turn, is crucial for innovation success. In a recently published article, Peña Häufler and co‐authors developed the concept of rapid validity testing (RVT) and found support for its positive relation with innovation performance. Since the authors draw on a sample of Western firms, we explore whether the concept of RVT and its performance relevance remain valid across regional contexts. The data for this study stem from a sample of 1625 respondents from 232 firms in Europe and Latin America. Our findings advance our understanding of innovation management practices in different contexts, informing future research on the approach and relevance of assessing the effect of different contexts on central measures for improving innovative capabilities.

Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses.

Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor in air quality applications employing LCSs. Three-dimensional printing, specifically fused deposition modeling (FDM), is a standard for prototyping and small-scale custom plastics production because of its low cost and ability for rapid iteration. However, little information exists about whether FDM-printed thermoplastics affect measurements of trace atmospheric gasses. This study investigates how five different FDM-printed thermoplastics (ABS, PETG, PLA, PC, and PVDF) affect the concentration of five common atmospheric trace gasses (CO, CO2, NO, NO2, and VOCs). The laboratory results show that the thermoplastics, except for PVDF, exhibit VOC off-gassing. The results also indicate no to limited interaction between all of the thermoplastics and CO and CO2 and a small interaction between all of the thermoplastics and NO and NO2.

Using Embedded Assessment to Evaluate Science Center Theatre

While some people claim museum theatre is a powerful tool that can affect visitors’ attitudes and inspire learning, others continue to question if its role is education, entertainment, or some combination of the two. In light of these mixed perceptions of museum theatre, there is ongoing demand for evidence that these shows promote meaningful learning. One method for measuring outcome achievement in these nontraditional theatre settings is embedded assessment (i.e., unobtrusive instruments integrated into the experience being evaluated). This article presents a case study of using embedded assessment to measure the stability of audience understanding of three key concepts from a science show. During Lunch in Space, the project team developed a strategy where the audience took on the role of food scientists and completed a paper and pencil “menu” by selecting good foods to send into space. Results suggest that this is a stable strategy for measuring short-term learning from this type of program. Three key insights learned for embedded assessment with museum theatre include: 1) creating a role for the audience; 2) refining the assessment through rapid iteration; 3) and designing assessments with a universal design approach, simple, easy-to-understand instructions, limited text, and clear visuals.

Nanotechnology Used in Drug Delivery System.

This paper mainly focuses on exploring the connection between nanotechnology and drug delivery systems, and how these two can be combined to promote a better response. Nanomaterials are one of the most popular topics nowadays, and drug delivery systems with the help of nanomaterials will have many benefits and drawbacks. Through research, it is hopeful to address these issues to build up a safer drug delivery system. With the development of the biopharmaceutical industry and the rapid iteration of innovative drugs, the problem of insufficient utilization of drug components in the human body has always existed. Nanocarriers help drug molecules enter the body and be transported to the targeted sites in an accurate manner at the right time, improving the efficiency of drug utilization in the human body. To load drugs onto drug carriers, a series of emerging nanotechnology systems are needed. Nano drug delivery systems can increase drug solubility, alter drug distribution within the body, and improve drug targeting, thereby improving treatment efficacy and reducing the incidence of adverse reactions.

ReMKiT1D - A framework for building reactive multi-fluid models of the tokamak scrape-off layer with coupled electron kinetics in 1D

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treatment of the electron kinetic equation. As such, the framework allows for flexibility in fluid models of the Scrape-Off Layer while allowing the easy addition of kinetic electron effects. We focus on presenting both the high-level design decisions that allow for model flexibility, as well as the most important implementation aspects. A significant number of verification and performance tests are presented, as well as a step-by-step walkthrough of a simple example for setting up models using the Python interface. Program summaryProgram title: ReMKiT1DCPC Library link to program files:https://doi.org/10.17632/j47ym66xzj.1Developer's repository link:https://github.com/ukaea/ReMKiT1D and https://github.com/ukaea/ReMKiT1D-PythonLicensing provisions: GPLv3Programming language: Fortran, PythonSupplementary material:https://doi.org/10.14468/fdq7-z869Nature of problem: The flexible generation and modification of 1D models pertaining to multi-fluid simulations of the tokamak Scrape-Off Layer (SOL) with electron kinetics and reaction support. This would then allow both for rapid iteration on reduced models as well as the evaluation of kinetic electron effects in equilibria and during transients, following the formalism previously developed for SOL-KiT [1]. The framework was not only envisioned as the successor to SOL-KiT, but a tool that would allow users to construct their own models coupled with electron kinetics capabilities.Solution method: The framework is written heavily utilizing Object-Oriented design principles, in particular using an extended version of the puppeteer pattern as presented by Rouson et al. [2], as well as the heavy use of the strategy pattern/dependency injection. The Fortran code is MPI parallel and utilizes the PETSc library for implicit time-stepping. MPI parallelization is extended to distribution function Legendre harmonics, allowing for improved strong scaling. Initialization of the Fortran framework is done using JSON configuration files generated by an accompanying Python interface, and data analysis is standardized using widely used data formats such as HDF5.Additional comments including restrictions and unusual features: The present manuscript focuses on the design and high-level implementation of the framework, as well as the demonstration of the workflow and various verification and performance benchmarking tests. Some details are avoided for the sake of brevity at various points, and these are meant to be available as part of the general code documentation or tutorials offered on the main repositories.

ParaStell: parametric modeling and neutronics support for stellarator fusion power plants

The three-dimensional variation inherent to stellarator geometries and fusion sources motivates three-dimensional modeling to obtain accurate results from computational modeling in support of design and analysis of first wall, blanket, and shield (FWBS) systems. Manually constructing stellarator fusion power plant geometries in computer-aided design (CAD) and defining the corresponding fusion source can be cumbersome and challenging. The open-source parametric modeling toolset ParaStell has been developed to automate construction of such geometries in low-fidelity. Low-fidelity modeling is useful during the conceptual phase of engineering design as a means of rapidly exploring the design space of a given device. The modeling capability of ParaStell includes in-vessel components and magnets, for any given stellarator configuration, using a parametric definition and plasma equilibrium data. Furthermore, the toolset automates the generation of detailed, tetrahedral neutron source definitions and DAGMC geometries for use in neutronics modeling. ParaStell assists rapid design iteration, parametric study, and design optimization of stellarator fusion cores. As a demonstration of the design iteration capability, the effect of the three-dimensional parameter space on tritium breeding and magnet shielding is investigated, using the WISTELL-D configuration as a design basis. Blanket and shield thicknesses are varied in three dimensions, using the space available between the plasma edge and magnet coils as a constraint. The corresponding effects on tritium breeding ratio and magnet heating are tallied using the open-source Monte Carlo particle transport code OpenMC. The inclusion of additional and higher-fidelity modeling capabilities is planned for ParaStell’s future, as well as its implementation in machine-driven optimization.

Hot Powder Bed Compaction: A Rapid Fabrication Approach for Thermoplastic Composite Components

Traditionally thermoplastic composite prepreg fabrication methods often rely on molds or costly tooling, limiting the agility and efficiency of the fabrication process. A compelling need for a high-performance thermoplastic composite prepreg fabrication approach that eliminates the dependence on molds, simplifies the fabrication process and accelerates prototyping, enabling rapid iterations in product development. The proposed hot powder bed compaction (HPBC) technique eliminates the need for specialized molds and still provides fully consolidated components. The HPBC process involved a three-step sequence comprising powder densification, heating, and consolidation stages. These stages help effectively control time–temperature-pressure parameters to achieve good fusion between the prepreg sheets. This paper showcased the HPBC process on a hinge bracket made from glass fiber-reinforced nylon prepreg sheets to illustrate its performance. Experimental mechanical characterization was carried out on treated HPBC coupons and compared with the molding process. Notably, the experimental flexural test results indicated a flexural modulus of 13.79 GPa and flexural strength of 295 MPa for HPBC-treated specimens, placing them in a comparable range with molded components. Microscopy analysis revealed the complete fusion interface of the prepreg sheets, and DSC analysis shows increases in crystalline fraction from 31.5 % to 44.1 % in PA6. Hence, the HPBC process presents a versatile and effective approach for fabricating high-performance, complex-shaped, void-free, components using thermoplastic composite prepreg.

Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

The rapid development of deep learning has significant implications for the advancement of computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in predicting boundary layer flows and poor adaptability to geometric shapes. Although graph neural network models for unstructured grid-based unsteady flow prediction have better geometric adaptability, these models suffer from error accumulation in long-term predictions of unsteady flows. More importantly, fully data-driven models often require extensive training time, greatly limiting the rapid update and iteration speed of deep learning models when facing more complex unsteady flows. Therefore, this paper aims to balance the demands for training overhead and prediction accuracy by integrating physical constraints based on the finite volume method into the loss function of the graph neural network. Additionally, it incorporates a twice-message aggregation mechanism inspired by the extended stencil method to enhance the unsteady flow prediction accuracy and geometric shape generalization ability of the graph neural network model on unstructured grids. We focus particularly on the model's predictive accuracy within the boundary layer. Compared to fully data-driven methods, our model achieves better predictive accuracy and geometric shape generalization ability in a shorter training time.

Research on Product Conceptual Design Scheme Configurations from a Designer–User Conflict Perspective

In the context of rapid product iteration, design conflicts arise from discrepancies in designers’ understanding of user needs, influenced by subjective preferences, behavioural stances, and other factors. This paper proposes a product conceptual design approach based on the design conflict perspective. First, user comments and design documents are collected. Natural language processing (NLP) methods, including cleaning, filtering, lexical segmentation, feature clustering, and sentiment analysis, are employed to identify design themes. The intuitionistic fuzzy sets (IFSs) and term frequency–inverse document frequency (TF-IDF) algorithms are then utilized to obtain evaluation matrices for the products from both users and designers. Subsequently, design conflicts between users and designers are calculated, and an optimal configuration for product conceptual design is determined through regression analysis and planning methods. Finally, the proposed method is validated using a mobile phone as a product example, and suggestions for product improvement are presented. The results indicate that considering design conflicts as a factor in product design and synthesizing designer and user product concepts enhance the accuracy and reliability of product conceptual design generation. The findings of this study offer new insights into the conceptual design configuration for product iteration.

Product innovation design process combined Kano and TRIZ with AD: Case study.

In the era of rapid product iteration, companies need simple and effective methods to guide the entire process of product innovation design and enhance their product innovation capabilities. Most research focused on improving one or several steps in the product design process. Although some scholars have proposed methods that guided the entire process, they combined more than three different theories, which increased the difficulty of theoretical learning and the complexity of practical implementation. This paper proposed a product innovation design process composed of three theoretical methods: Kano, Axiomatic Design (AD), and Theory of the Solution of Inventive Problems (TRIZ). This new process guided the entire product design process with fewer theoretical methods, reducing the difficulty of learning and implementation. The paper demonstrated the effectiveness of this method through the design practice of a portable two-wheeled self-balancing vehicle. Additionally, the discussion section explored the method's potential from the design management perspective.