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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.

Recently, interest and necessity for cloud-based hospital information systems (HISs) have emerged as an appropriate alternative for revitalizing medical information exchange between hospitals, analyzing “big data” medical information and developing the use of new medical technologies. The purpose of this paper is to investigate factors that affect the switching of information systems in existing on-premise environments into cloud-based HISs.

A research model was developed using the push–pull–mooring model based on migration theory. The research model was analyzed using confirmatory factor analysis and path analysis using partial least squares structural equation modeling.

The results of this study showed that low compatibility, perceived value, low cost and inertia influenced the intention to switch to cloud-based HISs; low flexibility and low compatibility influenced dissatisfaction; and low cost, ease of maintenance and ease of managing indicators influenced perceived value.

This study is expected to be used as the basis for developing a research model in subsequent studies to analyze the transition to new innovative technologies. Also, in practice, it is expected to contribute to the activation of cloud computing environments in hospitals.

This chapter introduces Learning Health Systems (LHS) and the impact of data science on such systems. It also examines the necessary properties of data used in LHS and identifies patients who may benefit from a transition to palliative care. Finally, it examines the way LHS can be used to identify racial and social disparities in access to palliative care.

The inward displacement perpendicular to the body surface produced by compression garment is an important index to evaluate pressure comfort and optimal design of tight clothing products. The purpose of this study is to explore the pressure distribution state at waist position of elastic legwear and then to solve the common problem of excessive pressure or easy slippage for waist of elastic legwear.

In this paper, the authors obtained the waist cross-section model of human body using CT scanning and mimics modeling and then simulated the pressure and displacement distribution after wearing sample four elastic legwear using finite element method. The dressing process of elastic legwear was divided into six periods (instantaneous, 1, 2, 4, 8 and 12 h) in this study, and the finite element software ANSYS was used to simulate the displacement and deformation of the waist cross section. The authors finally obtained the functional relationship between pressure/displacement ratio and angle using curve fitting.

In this paper, the authors obtained the functional relationship between pressure/displacement ratio and angle using curve fitting. Comparison found that the “pressure/displacement–angle” function curve showed an almost consistent trend at any time. That was to say, when the human body was in the state of clothing pressure, the corresponding displacement value of the human body can be calculated by the curve equation under the premise of known pressure value.

This study solves the difficult problem which hard to measure displacement values by conventional methods due to the small deformation of the human body after dressing the compression garment. Conclusions also provide a theoretical reference for evaluating pressure comfort and optimizing clothing structure for the elastic legwear, and this method is also applicable to other types of compression garment.

Digital healthcare once again emerges due to pandemic (Covid-19). Digital healthcare can be minimising the issue of accessibility, availability, accuracy and affordability of healthcare service during a pandemic. Digital healthcare playsa significant role to provide healthcare equity during the pandemic. This article presents the current trends and scenario of digital healthcare with a focus on health equity. The main objective of this chapter is to review the four aces of health equity in the digital healthcare literature. The scope and challenges faced by the policymakers to implementation of digital healthcare to improve health equity. This chapter considers the hybrid literature review based on the bibliometric and the systematic literature based on the various theme, sub-theme, concept and context-related health equity through digital healthcare. This study provides the previous and current research trends and preposition for the future researcher, healthcare professional, policymakers and digital healthcare innovators to invent the tool which leads the health equity through the digital healthcare in the healthcare.

Medical devices are undergoing rapid changes because of the increasing affordability of advanced technologies like additive manufacturing (AM) and three-dimensional scanning. New avenues are available for providing solutions and comfort that were not previously conceivable. The purpose of this paper is to provide a comprehensive review of the research on developing prostheses using AM to understand the opportunities and challenges in the domain. Various studies on prosthesis development using AM are investigated to explore the scope of integration of AM in prostheses development.

A review of key publications from the past two decades was conducted. Integration of AM and prostheses development is reviewed from the technologies, materials and functionality point of view to identify challenges, opportunities and future scope.

AM in prostheses provides superior physical and cognitive ergonomics and reduced cost and delivery time. Patient-specific, lightweight solutions for complex designs improve comfort, functionality and clinical outcomes. Compared to existing procedures and methodologies, using AM technologies in prosthetics could benefit a large population.

This paper helps investigate the impact of AM and related technology in the field of prosthetics and can also be viewed as a collection of relevant medical research and findings.

This study aims to cover the overall gamut of rapid prototyping processes and biomaterials used for the fabrication of occlusal splints in a comprehensive manner and elucidate the characteristics of the materials, which are essential in determining their clinical efficacy when exposed to oral surroundings.

A collective analysis of published articles covering the use of rapid prototyping technologies in the fabrication of occlusal splints, including manufacturing workflow description and essential properties (mechanical- and thermal-based) evaluation of biocompatible splinting materials, was performed.

Without advances in rapid prototyping processes and materials engineering, occlusal splints would tend to underperform clinically due to biomechanical limitations.

Three-dimensional printing can improve the process capabilities for commercial customization of biomechanically efficient occlusal splints.

Rapid technological advancement in dentistry with the extensive utilization of rapid prototyping processes, intra-oral scanners and novel biomaterial seems to be the potential breakthrough in the fabrication of customized occlusal splints which have endorsed occlusal splint therapy (OST) as a cornerstone of orthodontic treatment.

Pores and shrink holes are unavoidable defects in the die-casting mass production process which may significantly influence the strength, fatigue and fracture behaviour as well as the life span of structures, especially if they are subjected to high static and dynamic loads. Such defects should be considered during the design process or after production, where the defects could be detected with the help of computed tomography (CT) measurements. However, this is usually not done in today's mass production environments. This paper deals with the stress analysis of die-cast structural parts with pores found from CT measurements or that are artificially placed within a structure.

In this paper the authors illustrate two general methodologies to take into account the porosity of die-cast components in the stress analysis. The detailed geometry of a die-cast part including all discontinuities such as pores and shrink holes can be included via STL data provided by CT measurements. The first approach is a combination of the finite element method (FEM) and the finite cell method (FCM), which extends the FEM if the real geometry cuts finite elements. The FCM is only applied in regions with pores. This procedure has the advantage that all simulations with different pore distributions, real or artificial, can be calculated without changing the base finite element mesh. The second approach includes the pore information as STL data into the original CAD model and creates a new adapted finite element mesh for the simulation. Both methods are compared and evaluated for an industrial problem.

The STL data of defects which the authors received from CT measurements could not be directly applied without repairing them. Therefore, for FEM applications an appropriate repair procedure is proposed. The first approach, which combines the FEM with the FCM, the authors have realized within the commercial software tool Abaqus. This combination performs well, which is demonstrated for test examples, and is also applied for a complex industrial project. The developed in-house code still has some limitations which restrict broader application in industry. The second pure FEM-based approach works well without limitations but requires increasing computational effort if many different pore distributions are to be investigated.

A new simulation approach which combines the FEM with the FCM has been developed and implemented into the commercial Abaqus FEM software. This approach the authors have applied to simulate a real engineering die-cast structure with pores. This approach could become a preferred way to consider pores in practical applications, where the porosity can be derived either from CT measurements or are artificially adopted for design purposes. The authors have also shown how pores can be considered in the standard FEM analysis as well.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.