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The development of new advanced materials, such as photopolymerizable resins for use in stereolithography (SLA) and Ti6Al4V manufacture via selective laser melting (SLM) processes, have gained significant attention in recent years. Their accuracy, multi-material capability and application in novel fields, such as implantology, biomedical, aviation and energy industries, underscore the growing importance of these materials. The purpose of this study is oriented toward the application of new advanced materials in stent manufacturing realized by 3D printing technologies.

The methodology for designing personalized medical devices, implies computed tomography (CT) or magnetic resonance (MR) techniques. By realizing segmentation, reverse engineering and deriving a 3D model of a blood vessel, a subsequent stent design is achieved. The tessellation process and 3D printing methods can then be used to produce these parts. In this context, the SLA technology, in close correlation with the new types of developed resins, has brought significant evolution, as demonstrated through the analyses that are realized in the research presented in this study. This study undertakes a comprehensive approach, establishing experimentally the characteristics of two new types of photopolymerizable resins (both undoped and doped with micro-ceramic powders), remarking their great accuracy for 3D modeling in die-casting techniques, especially in the production process of customized stents.

A series of analyses were conducted, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, mapping and roughness tests. Additionally, the structural integrity and molecular bonding of these resins were assessed by Fourier-transform infrared spectroscopy–attenuated total reflectance analysis. The research also explored the possibilities of using metallic alloys for producing the stents, comparing the direct manufacturing methods of stents’ struts by SLM technology using Ti6Al4V with stent models made from photopolymerizable resins using SLA. Furthermore, computer-aided engineering (CAE) simulations for two different stent struts were carried out, providing insights into the potential of using these materials and methods for realizing the production of stents.

This study covers advancements in materials and additive manufacturing methods but also approaches the use of CAE analysis, introducing in this way novel elements to the domain of customized stent manufacturing. The emerging applications of these resins, along with metallic alloys and 3D printing technologies, have brought significant contributions to the biomedical domain, as emphasized in this study. This study concludes by highlighting the current challenges and future research directions in the use of photopolymerizable resins and biocompatible metallic alloys, while also emphasizing the integration of artificial intelligence in the design process of customized stents by taking into consideration the 3D printing technologies that are used for producing these stents.

“Can artificial intelligence produce architectural plan schemes?” discussion is the starting point of this study. The aim of this paper is to question whether this will be a new method in architectural design by producing plans with artificial intelligence interfaces working with human–computer interaction and to create a discussion environment.

The main research topic is the evaluation of architectural design decisions with the text-to-image generation AI algorithms method based on shape grammar rules. First, a sample space consisting of Palladio plans or plan diagrams was created. Plan diagram production experiments were made with different interfaces (Midjourney, Dall-e2, Stable Diffusion, Craiyon, Nightcafe), and alternative plan diagrams were recorded as outputs. The discussion of the outputs has been made over architectural design and space.

In the conceptual design phase of the architectural discipline and in the production of architectural plan scheme, AI algorithms are trending. This interaction imposes a new responsibility on architects. AI can create paradigm shifts in architectural processes with its tools with high data processing potential. On the other hand, in this study, it is emphasized that architecture is not just an act of producing visuals, but a functional act of producing visuals.

The technology is effective in producing architectural plans and directing them to artificial intelligence algorithms. With this study, multi-alternative architectural plan productions were tried with text-to-image bots with fast results. In this direction, a new method proposal has been developed for the conceptual design phase in architecture.

This study aims to address the aspects of product and process innovation strategies and their determining factors to understand their characteristics in clothing manufacturing and contribution for a successful and competitive clothing industry.

This general review is based on literature data of previous studies on innovation that transcend and cover the aspects of innovation applicable in the clothing industry. Although the scope of discussion is theoretically broad, it focusses on the context of innovation strategies in clothing manufacturing and the determinant factors indicating the acquisition and implementation of product and process-related innovation activities, simultaneously exploring and linking their implications for adopting, managing and integrating enterprise activities to the values of desired innovation novel models.

Based on theoretical background and pragmatic generalizations, product and process innovation strategies in clothing manufacturing firms tend to incline more towards computer-integrated technologies and concepts meant to promote product development, process optimization and organizational integration. Industry, technological and R&D factors tend to significantly determine innovation capability of a clothing firm.

This review generates integrated conceptual frameworks for product and process innovation strategies applicable in clothing firms and their determinant factors as prelude to empirical validation.

This research aims to argue that manual geometric modeling is blocking the building information modeling (BIM) promotion to small-size companies. Therefore, it is necessary to study a manner of automated modeling to reduce the dependence of BIM implementation on manpower. This paper aims to make a study into such a system to propose both its theory and prototype.

This research took a prototyping as the methodology, which consists of three steps: (1) proposing a theoretical framework supporting automated geometric modeling process; (2) developing a prototype system based on the framework; (3) conducting a testing for the prototype system on its performance.

Previous researches into automated geometric modeling only respectively focused on a specific procedure for a particular engineering domain. No general model was abstracted to support generic geometric modeling. This paper, taking higher level of abstraction, proposed such a model that can describe general geometric modeling process to serve generic automated geometric modeling systems.

This paper focused on only geometric modeling, skipping non-geometric information of BIM. A complete BIM model consists of geometric and non-geometric data. Therefore, the method of combination of them is on the research agenda.

The model proposed by this paper provide a mechanism to translate engineering geometric objects into textual representations, being able to act as the kernel of generic automated geometric modeling systems, which are expected to boost BIM promotion in industry.

This paper aims to investigate the role of organizational and technological enablers and their arrangement and alignment with the external environment to facilitate supply chain integration (SCI), which consequently improves operational performance.

The paper uses a structural equation modeling approach and the data from 307 manufacturing firms from the International Manufacturing Strategy Survey version VI for hypotheses testing.

The findings of the study reveal that (1) the alignment and particular arrangement of the sociotechnical organizational factors enable the SCI of a firm, (2) suitable organizational arrangements help in leveraging SCI under environmental pressures, and (3) SCI leverages the relationship between sociotechnical organizational factors and operational performance of the firm.

This paper informs managers that SCI leverages the operational performance of firms under heightened environmental pressures. Developing suitable manufacturing technologies infrastructure followed by organizational practices aligned with the manufacturing technologies make it easier to realize SCI.

This study explores the interaction of technological, organizational, and environmental factors as driving and enabling factors that help achieve SCI. Firms that develop an open and collaborative environment and use communication and integrative technologies to complement their work practices better cope with external pressures. These modern forms of working and the use of technologies facilitate SCI and leverage it effectively to positively impact firm performance.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This paper aims to fulfil a need to identify assembly interfaces from existing products based on their Assembly Process Planning (APP). It proposes a tool to identify assembly interfaces responsible for reused components integration. It is integrated into a design for mixed model final assembly line approach by focusing on the identification of assembly interfaces as a generic tool. It aims to answer the problem of interfaces’ identification from the APP.

A tool is developed to identify assembly interfaces responsible for reused component integration. It is based on the use of a rule-based algorithm that analyses an APP and then submits the results to prohibition lists to check their relevance. The tool is then tested using a case study. Finally, the resulting list is subjected to a visual validation step to validate whether the identified interface is a real interface.

The results of this study are a tool named ICARRE which identify assembly interfaces using three steps. The tool has been validated by a case study from the helicopter industry.

As some interfaces are not contained in the same assembly operations and therefore, may not have been identified by the rule-based algorithm. More research should be done by testing and improving the algorithm with other case studies.

The paper includes implications for new product development teams to address the difficulties of integrating reused components into different products.

This paper presents a tool for identifying interfaces when sources of knowledge do not allow the use of current methods.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

The purpose of this research study is to empirically investigate the Figma prototyping technology adoption factors among entrepreneurship and innovation libraries for providing support to startups by developing and evolving the prototype solutions in collaboration with health libraries.

This study uses the technology adoption model (TAM) as a framework and the partial least squares structural equation modeling (PLS-SEM) method of structural equation modeling (SEM) using SmartPLS 3.2.9 software version to investigate the prototyping adoption factors among entrepreneurship and innovation libraries for rural health innovations. A total of 40 libraries, spread over 16 entrepreneurship and innovation libraries, participated in this survey, including participants from Europe (35%), Asia (15%) and USA (50%).

The findings show that previous experience, social impact, brand image and system quality have a significant positive impact on entrepreneurship and innovation libraries' perceived usefulness (PU) of prototyping technology. Perceived ease of use of prototype technology is positively influenced by usability, training materials and documentation, experience and self-efficacy. Together, perceived usefulness and perceived ease of use have a significant influence on behavioural intention. Behavioural intention is positively impacted by minimal investment and shallow learning curve. Technology adoption is furthered by behavioural intention. The control variables, for instance location, gender and work experience (as librarian), were found not having any impact on Figma technology adoption.

Through strategic partnerships with other libraries (including health libraries), policymakers, and technology providers, the adoption of prototype technology can be further accelerated. The important ramifications for policymakers, technology providers, public and entrepreneurship and innovation libraries to create a self-reliant innovation ecosystem to foster rural health innovation based on entrepreneurship are also listed in the article.

This research is distinctive since it integrates several areas of study, including entre, advances in rural healthcare and libraries. A novel idea that hasn't been thoroughly investigated is the collaboration between entrepreneurship and innovation libraries and health libraries for supporting businesses. This study offers insights into the factors that drive technology adoption and offers practical advice for policymakers and technology providers. It also advances understanding of the adoption of Figma prototyping technology among libraries for rural health innovation. Overall, this study provides a novel viewpoint on the nexus between different disciplines, showing the opportunity for cooperation and innovation in favour of rural health.