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Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

The determination of mode-I fracture toughness of brittle structural materials by means of the notched Brazilian disc configuration is studied. Advantage is taken of a recently introduced analytical solution and, also, of data provided by an experimental protocol with notched marble specimens under diametral compression using the loading device suggested by International Society for Rock Mechanics (ISRM) and also the three-dimensional digital image correlation (3D-DIC) technique.

The analytical solution highlighted the role of geometrical factors, like, for example, the width of the notch, which are usually disregarded. The data of the experimental protocol were comparatively considered with those concerning the response of the specific material under uniaxial tensile load.

This combined study provided interesting data concerning some open issues, as it is the exact crack initiation point and the level of the critical load causing crack initiation. It was definitely indicated that the crack initiation point is not a priori known (even for notched specimens) and, also, that the maximum recorded load does not correspond by default to the critical load responsible for the onset of catastrophic macroscopic fracture.

It was suggested that the load considered critical one for the determination of mode-I fracture toughness K_IC is erroneous. At a load equal to about 70% of the maximum one, a process zone is formed (zone of non-reversible phenomena) around the notch's crown, designating termination of the validity of any linear elastic solution used to determine the normalized stress intensity factors (SIFs). Moreover, at a load level equal to about 95% of the macroscopically observed fracture load, crack propagation has already begun. Therefore, the experimental procedure must be monitored with additional equipment, providing an overview of the displacement field developed during loading.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

This study aims to investigate the fabrication of solid ankle foot orthoses (SAFOs) using fused deposition modeling (FDM) printing technology. It emphasizes cost-effective 3D scanning with the Kinect sensor and conducts a comparative analysis of SAFO durability with varying thicknesses and materials, including polylactic acid (PLA) and carbon fiber-reinforced (PLA-C), to address research gaps from prior studies.

In this study, the methodology comprises key components: data capture using a cost-effective Microsoft Kinect® Xbox 360 scanner to obtain precise leg dimensions for SAFOs. SAFOs are designed using CAD tools with varying thicknesses (3, 4, and 5 mm) while maintaining consistent geometry, allowing controlled thickness impact investigation. Fabrication uses PLA and PLA-C materials via FDM 3D printing, providing insights into material suitability. Mechanical analysis uses dual finite element analysis to assess force–displacement curves and fracture behavior, which were validated through experimental testing.

The results indicate that the precision of the scanned leg dimensions, compared to actual anthropometric data, exhibits a deviation of less than 5%, confirming the accuracy of the cost-effective scanning approach. Additionally, the research identifies optimal thicknesses for SAFOs, recommending a 4 and 5 mm thickness for PLA-C-based SAFOs and an only 5 mm thickness for PLA-based SAFOs. This optimization enhances the overall performance and effectiveness of these orthotic solutions.

This study’s innovation lies in its holistic approach, combining low-cost 3D scanning, 3D printing and computational simulations to optimize SAFO materials and thickness. These findings advance the creation of cost-effective and efficient orthotic solutions.

The oil and gas industry form the main resource of economy in Iraq and constructing any project in such sectors requires a huge amount of expenses due to the unique requirements that oil and gas facilities required in such projects. Therefore, adopting an appropriate technological approach such as building information modeling (BIM) which is unfortunately not adopted yet in Iraq is essential to successfully deliver these projects. Thus, this paper aimed to introduce BIM to Iraq through Basra Oil Company (BOC) which is one of the biggest public oil and gas companies in Iraq.

The related literature of journals articles, conference proceedings and published reports have been reviewed. As a result, firstly: a hypothesis has been derived that is “If Basra Oil Company (BOC) adopts and applies BIM approach instead of the 2D approach currently used to manage its projects, the company can overcome several constraints in managing its projects that associated with such 2D traditional approach”; secondly: homogenous, consistence and reliable web-based questionnaire has been designed as its Cronbach’s alpha equal to 0.897 and 0.711 for BIM benefits and barriers, respectively. This questionnaire distributed to the BOC related professionals to test such hypothesis by investigating their readiness and accepting of BIM approach and to rank BIM barriers based on five-point Likert scale.

Based on the analysis using IBM SPSS Statistics 26 of 115 responses, almost 50% of the respondents had experience 11–15 years, while 22.6% had experience more than 15 years in oil and gas industry construction projects. Those participants were from diverse engineering majors that are: 4.3% Architectural Engineers, 31.3% Civil Engineers, 20% Mechanical Engineers, 22.6% Electrical Engineers and 21.7% from other engineering majors. The respondents’ departments demography was 16.5% of design department, 12.2% of construction department, 20.9% of Project Management Department, 12.2% of Maintenance department, 4.3% of HSE Department, 13% of Production Department and 20.9% of “Other Department.” The study resulted in 1: accepting BIM approach to be an alternative of current 2D-traditional approach used by the company to manage and construct its projects, since mean of collected data is (4.4332), Kruskal–Wallis H test significance values were 0.398 and 0.372; and ANOVA test significance values were 0.433 and 0.599 among Engineering Majors groups and Company’s Department groups, respectively. 2: Disclosed and sequenced BIM barriers in the company based on their criticality. 3: verifying reliably how BIM attributes are important to oil and gas construction projects in Iraq, 4: the company top management and company policies are the most critical potential factors to hinder or adopt and implement BIM in the company, 5: while cost is not seen a critical barrier to implement BIM in the oil and gas sector.

The limitation of this study is the excluding of decision makers of BOC, thus more profound future studies need to be conducted where top management and decision makers are involved, particularly the present study demonstrated that support of company top management is the most critical factor which can help the company to adopt (BIM).

The study concludes that BIM approach is valuable for managing projects in oil and gas sector in Iraq and identify the originality in output by using the research method. This noble study provides a leverage for enhanced research to adopt and implement building information modeling (BIM) in Iraq as the study originally demonstrates benefits and identifies the critical barriers in BIM implementation to push the boundaries toward adopt Digitalization and reduce CO2 emission in Iraqi oil and gas sector. The study can be used as evidence and platform to encourage professionals and practitioners to present more sophisticated tools of BIM in the oil and gas industry, especially for facility and operation management. These findings achieved via oil and gas experts, and it is first time to achieve such findings from a case study in Iraqi oil and gas sector.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.

This paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.

A series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.

The proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

This study aims to develop a taxonomy of requirements for mobile BIM technologies (MBT), clarify the relating terms and concepts, and identify the interactions between MBT features and the construction management functions on sites.

A positivist approach with elements of interpretivism is adopted to allow to capture what is perceived as “reality” in relation to individuals’ interpretation and experience in the use and implementation of MBT. This is achieved by using a mixed qualitative-quantitative approach that can capture the various understandings of MBT. The research methods included a longitudinal case study over 12 months, two project workshops, expert interviews and an industry survey that together helped to investigate MBT at project, enterprise and industry levels.

The MBT requirements taxonomy included requirements relating to both project and organisation. Project requirements addressed MBT functionalities for sites and information management, while organisation requirements focused on the integration of MBT solutions with the enterprise from information technology, legal and security perspectives. A detailed matrix showing the interactions between five key MBT features and seven construction management functions was also developed.

The two constructs developed by this study can help researchers to structure their investigation of key uses of MBT applications and their benefits. It can be used by researchers aiming to investigate integrated approaches to the digitalisation of construction sites, such as those enabled by Digital Twins. The interaction matrix can aid researchers in evaluating the intersections between the MBT functionalities and the site construction management functions (e.g. theoretical analysis of interactions from Lean Construction, benefit evaluation perspective). More broadly, the two constructs can support research and practice investigating the development of data-driven approaches on construction sites.

The developed MBT taxonomy can guide construction organisations in selecting suitable MBT for Field BIM for their projects. It can also act as a baseline against which varying MBT solutions can be compared.

Constructs such as taxonomies for MBTs; an understanding of MBT capabilities and use within the industry; and a lack of delineation between related terms, such as Mobile BIM, Field BIM, Site BIM, Cloud BIM and Mobile Apps, were lacking in the literature. This study contributed to addressing this gap.

The coronavirus (COVID-19) pandemic has led to a surge in demand for health-care facilities, medicines, vaccines and other health-care items. The purpose of this study is to investigate different facets of integrated Green Lean Six Sigma and Industry 4.0 approach in the context of COVID-19 for better healthcare management. Integrating Green Lean Six Sigma (GLSS) and Industry 4.0 (I4.0) has the potential to meet the modern demand of health-care units and also leads to improving the quality of inpatient care with better safety, hygiene and real-time diagnoses. A systematic review has been conducted to determine the tools/techniques, challenges, application areas and potential benefits for the adoption of an integrated GLSS-I4.0 approach within health-care facilities from the perspective of COVID management. Further, a conceptual framework of integrated GLSS-I4.0 has been proposed for better COVID management.

To conduct the literature review, the authors used the preferred reporting items for systematic reviews and meta-analysis and covers relevant papers from the arrival of COVID-19. Based on the systematic understanding of the different facets of the integrated GLSS-I4.0 approach and through insights of experts (academicians and health-care personnel), a conceptual framework is proposed to combat COVID-19 for better detection, prevention and cure.

The systematic review presented here provides different avenues to comprehend the different facets of the integrated GLSS-I4.0 approach in different areas of COVID health-care management. In this study, the proposed framework reveals that the Internet of Things, big data and artificial intelligence are the major constituents of I4.0 technologies that lead to better COVID management. Moreover, integration of I4.0 with GLSS aids during different stages of the COVID management, right from diagnosis, manufacture of items and inpatient and outpatient care of the affected person.

This study provides a significant knowledge database to the practitioners by understanding different tools and techniques of an integrated approach for better COVID management. Moreover, the proposed framework aids to grab day-to-day information from the affected people and ensures reduced hospital stay with better space utilization and the creation of a healthy environment around the patient. This inclusive implementation of the proposed framework will enhance knowledge base in medical areas and provides different novel prospects to combat other medical urgencies.

To the best of the authors’ knowledge, this study is the first of its kind to review different facets of the integrated GLSS-I4.0 approach with a view of the COVID health-care perspective and provides a conceptual framework.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.