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Creativity is an important skill for design teams to reach new and useful solutions. Designers often use one or more of creativity and innovation techniques (CITs) to achieve the desired creative potential during new product development (NPD). The selection of adequate CITs requires considerable expertise, given the multiple application contexts and the extensive number of techniques available. The purpose of this study is to present a creativity support system able to manage this amount of information and provide valuable knowledge to improve NPD.

This study presents a knowledge-based system prototype using artificial intelligence (AI) to support knowledge management on the selection of CITs for design. CITs assertion is modelled through a double inference process using five categories, correlating over 500 different entry scenarios to 24 implemented CITs. The techniques are classified according to: design stage, innovation focus, team relationship, execution method and difficult of use. Prototype outputs explanations on the inference process and chosen techniques information.

To demonstrate the system scope, two opposite design cases are presented. The system was validated by experts in knowledge management and mechanical engineering design. The validation process demonstrates relevance of the approach and improvement directions for future developments.

Though literature contains toolkits and taxonomy for CITs, no work applies AI to identify design scenarios, select best CITs and instruct about their use. Validators reported to know less than half of the available techniques, showing a clear knowledge gap among design experts.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

This paper proposes a dexel‐based virtual prototyping system, which builds a virtual prototype with dexels or rectangular strips of solid. The approach resembles the physical fabrication process of most powder‐based rapid prototyping (RP) systems. It simulates an RP process to create a virtual prototype. Colour virtual prototypes may also be fabricated relatively easily. Thus, the designer can perform design validation and accuracy analysis easily in a virtual environment as if using a physical prototype. In addition to numeric quantification of the RP process, the system provides vivid visualisations of the prototype for studying its characteristics. Furthermore, the prototype may be superimposed on the product model, and the areas with dimensional errors beyond design limits may be clearly highlighted for subsequent improvement. The designer may thus analyse and compare the surface texture point‐by‐point of the prototype with the product design.

The integration and automation of the whole design and implementation process have become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage, often manifest through a number of significant areas, from design representation, cognition and translation to process fragmentation and loss of design integrity. Whilst building information modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offer significant potential for enhancing the design experience to mitigate this challenge.

The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage, where the core drivers and indicators of BIM and generative design are identified and mapped into a generative BIM (G-BIM) framework and subsequently embedded into a G-BIM prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure architecture, construction and engineering (AEC)-based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts.

The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM's parametric change management features).

This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works.

Originality and value rest with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems, where, for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.

This study aims to investigate a systematic approach to the production and use of additively manufactured injection mould inserts in product development (PD) processes. For this purpose, an evaluation of the additive tooling design method (ATDM) is performed.

The evaluation of the ATDM is conducted within student workshops, where students develop products and validate them using AT-prototypes. The evaluation process includes the analysis of work results as well as the use of questionnaires and participant observation.

This study shows that the ATDM can be successfully used to assist in producing and using AT mould inserts to produce valid AT prototypes. As a reference for the implementation of AT in industrial PD, extracts from the work of the student project groups and suitable process parameters for prototype production are presented.

This paper presents the application and evaluation of a method to support AT in PD that has not yet been scientifically evaluated.

This research proposes a virtual reality (VR) platform for construction detailing that provides experiential learning in a zero-risk environment. It builds on integrating VR technology as a medium and building information modeling (BIM) as a repository of information and a learning tool.

This work discusses the proposed environment curricular unit prototype design, implementation and validation. The validation of the VR environment was conducted in three phases, namely, piloting, testing (system usability and immersion) and learning gain validation, each of which has its aim and outcomes and has been assessed both qualitatively and quantitatively.

After considering the feedback, the VR environment prototype is then validated on the level of learning outcomes, providing the evidence that it would enhance students' engagement, motivation and achievement accordingly. The results indicated 30% learning progress after experiencing the VR environment vs. 13.8% for paper-based studying.

In reference to building construction education, construction site visits provide students with real-life practical experience which are considered an extension for classroom. Nevertheless, it is challenging to integrate construction site visits regularly during the academic semester with respect to the class specific needs. The research at hand adopts integrating VR and BIM in AEC (Architecture, Engineering and Construction) education by proposing a system that can work as a mainstream complementary construction detailing learning method for architecture students. The proposed VR system facilitates a virtual construction site that meets the learning needs where students can explore and build in a real scale environment.

This study aims to incorporate innovative technology (i.e. blockchain) into the design of an online travel agency (B-OTA). The authors accelerated the design of the prototype system through expert interviews to understand the convenience of the blockchain travel and accommodation platform for users. The authors further validated the prototype system design and improved reference capability through usability testing.

Rapid prototyping of B-OTA was carried out through the Delphi method and literature compilation. Design validation of usability testing was conducted using modified systematic evaluation methodology. Prototype system suggestions and improvements were collected through feedback from test subjects.

This study will be helpful for the development of B-OTAs and related technologies. Through usability testing, the authors verified that creating a familiar operating environment for users can reduce users’ fear of unfamiliar technology. The results of this research provide future B-OTA researchers and practitioners with design guidelines.

Beyond the blockchain-related technologies used, few studies have reported experiences and interface design of B-OTAs. This study states that, as many human–computer interaction-related papers have pointed out, the basic prerequisite for successful interface design is a clearer definition of users’ needs during operation. User experience and user interface design should provide a good user experience.

本研究旨在在线旅行社 (B-OTA) 的设计中融入创新技术（即区块链）。通过专家访谈了解了区块链旅行的方便性和用户打造的住宿平台, 从而加快了原型系统设计。我们进一步通过可用性测试验证原型系统设计并提高其参考能力。

进行了 B-OTA 的快速原型设计通过德尔菲法和文献汇编。设计验证可用性测试是使用修改后的系统评估进行的方法。通过收集受试者的建议对原型系统进行改进。

本研究将有助于B-OTAs及相关领域的发展技术。通过可用性测试, 我们验证了创建一个熟悉的用户的操作环境可以减少用户对陌生技术的恐惧。我们的研究结果为未来的 B-OTA 研究人员和从业者提供了设计相关的指南。

除了使用的区块链相关技术之外, 很少有研究报告了 B-OTA 的经验和界面设计。这项研究指出, 正如许多 HCI 相关论文所指出的, 成功的界面设计基本的先决条件是在使用过程中更清晰地定义用户需求。用户体验和用户界面设计应该提供良好的用户体验。

The purpose of this paper is to outline the application of user centred design (UCD) within a research project to support the design, development and evaluation of a brain computer interface (BCI) with associated home-based services and remote therapy station for people with acquired brain injury (ABI).

A multi- stakeholder UCD approach was adopted to include people living with ABI, their caregivers and therapists providing rehabilitation. A three-phased iterative approach was implemented: Phase 1 was to gather user requirements, Phase 2 an iterative design phase with end user (EU) groups and therapists and finally the verification and implementation phase. The final phase had two strands of a home-based BCI evaluation with target EUs and their caregivers, alongside this, therapists evaluated the final therapist station that supports the use of the BCI at home. Ethical governance, inline with Ulster University, was awarded.

UCD enabled the co-creation and validation of a home-based BCI system for social inclusion and rehabilitation.

This was the first BCI project to adopt UCD to design and validation a novel home-based BCI system and migrate this from the lab to home. It highlights the importance of UCD to bridge the gap between the technical developers and those whom the technology is aimed at. This complex design process is essential to increase usability and reduce device abandonment.

The goal of this research is to demonstrate how web‐based applications improve the effectiveness of a very important human resource (HR) function. It proposes a framework of a web‐based training needs assessment system for HR professionals to effectively assess employee needs for competency‐based training.

A conceptual framework of a survey‐based training needs assessment using the Internet was developed. A use case detailing a walkthrough of a prototype system developed under this proposed framework was created to illustrate how this system works. A two‐phased operation test of the prototype system was then conducted to assess how well human resource (HR) managers would accept and adopt this technology to develop organizational competencies. The first phase involved demonstrating the use case to a group of experienced HR managers and polling their opinion toward the system. The second phase involved one‐on‐one interviews with three select HR managers who were asked to experience the prototype system hands‐on.

HR managers from both phases of validation demonstrated positive acceptance of both the needs assessment model and the process improvement generated from the web‐based prototype system.

The qualitative validation test was conducted to a small sample of Taiwan's HR managers. Caution is advised when generalizing the positive results to other regions or countries with more advanced IT applications in HR practices.

This study contributes to the HR practice in several ways. First, it qualitatively confirms that HR professionals accept a competence‐based approach to build company's training curriculum. Second, it observes a lack of effective tools to help HR professionals in the task of training needs assessment. Third, it shows that HR professionals do recognize the power of web technology in helping them become more efficient.

Formal competency‐based training programs are rarely implemented in the real world because the process required is both tedious and time‐consuming. This paper adopts Internet technologies in a conceptual model for effective competency‐based training needs assessment, and presents an efficient web‐based tool to assist HR professionals in the needed analysis.

This research aims to aid product development managers to estimate the expected cost associated with the development of cost-intensive physical prototypes considering transitions associated with pertinent states of quality of the prototype and corresponding decision policies under the Markovian setting.

The authors evolve two types of optimization-based mathematical models under both deterministic and randomized policies. Under the deterministic policy, the product development managers take certain decisions such as “Do nothing,” “Overhaul,” or “Replace” corresponding to different quality states of prototype such as “Good as new,” “Functional with minor deterioration,” “Functional with major deterioration” and “Non-functional.” Under the randomized policy, the product development managers ascertain the probability distribution associated with these decisions corresponding to various states of quality. In both types of mathematical models, i.e. related to deterministic and randomized settings, minimization of the expected cost of the prototype remains the objective function.

Employing an illustrative case of the operator cabin from the construction equipment domain, the authors ascertain that randomized policy provides us with better decision interventions such that the expected cost of the prototype remains lower than that associated with the deterministic policy. The authors also ascertain the steady-state probabilities associated with a prototype remaining in a particular quality state. These findings have implications for product development budget, time to market, product quality, etc.

The authors’ work contributes toward the development of optimization-driven mathematical models that can encapsulate the nuances related to the uncertainty of transition of quality states of a prototype, decision policies at each quality state of the prototype while considering such facets for all constituent subsystems of the prototype. As opposed to a typical prescriptive study, their study captures the inherent uncertainties associated with states of quality in the context of prototype testing, etc.