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This study aims to produce textile-like surfaces using fused deposition modelling (FDM) 3D printers and create a garment collection.

Experiments were conducted using different types of materials in FDM 3D printers until the sufficient flexibility was achieved to create textile-like structures. During the research, properties of polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) were observed. Geometrical patterns were printed and each of them gave a different result depending on the pattern. Based on the information obtained from the experiments, a garment collection with four total looks was designed inspired by Vivaldi’s “Four Seasons”.

Among the materials used, TPU, a flexible filament, yielded the best results. Because of the rigid properties of PLA and ABS, chain-like structures were printed to create relatively flexible surfaces, but the results were still not successful enough to create a clothing material. Therefore, TPU was preferred for the garment material selection.

In this study, combinations of 3D printed flexible structures and different types of fabrics were used to create a garment collection. It was concluded that, with the right material selection, 3D printing can be used as an alternative method to create a new aesthetic language in fashion design.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The human-following task is a fundamental function in human–robot collaboration. It requires a robot to recognize and locate a target person, plan a path and avoid obstacles. To enhance the applicability of the human-following task in various scenarios, it should not rely on a prior map. This paper aims to introduce a human-following method that meets these requirements.

For the identification and localization of the target person (ILTP), this paper proposes an approach that integrates data from a camera, a light detection and ranging (LiDAR) and a ultra-wideband (UWB) anchor. For path planning and obstacle avoidance, a modified timed-elastic-bands (TEB) algorithm is introduced.

Compared to the UWB-only method, where only UWB is used to locate the target person, the proposed ILTP method in this paper reduces the localization error by 41.82%. Experimental results demonstrate the effectiveness of the ILTP and the modified TEB method under various challenging conditions. Such as crowded environments, multiple obstacles, the target person being occluded and the target person moving out of the robot’s field of view. The complete experimental videos are available for viewing on https://youtu.be/ZKbrNE1sePM.

This paper offers a novel solution for human-following tasks. The proposed ILTP method can recognize the target person among multiple individuals, determine whether the target person is lost and publish the target person’s position at a frequency of 20 Hz. The modified TEB algorithm does not rely on a prior map. It can plan paths and avoid obstacles effectively.

The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

This study aims to explore the level of adoption of Construction 4.0 (C4.0) technologies and of Lean Construction (LC) ambidexterity capability in China’s construction industry. By incorporating C4.0 and LC ambidexterity into a sociotechnical systems (STS) framework, this research provides practical insights for construction practitioners and companies seeking C4.0 and LC.

The study adopts the questionnaire survey method. Data was obtained from Chinese construction professionals working on projects using C4.0 technologies. The data were analysed using mean score ranking and three nonparametric techniques, including the Wilcoxon signed-rank test and the Spearman rank correlation.

According to the ranking analysis, building information modelling and offsite construction have the highest adoption level, while additive manufacturing and blockchain rank lowest. Digital technology is more widely used in China’s construction industry than in physical technology. Many C4.0 technologies are significantly correlated with LC ambidexterity. Based on the STS perspective, we propose a holistic approach that incorporates C4.0 technologies and LC ambidexterity.

Conceptually, the study incorporates C4.0 and LC ambidexterity into the STS framework. Empirically, this study fills the gap in the current literature by exploring the level of C4.0 technology adoption in the Chinese construction industry, as well as the level of LC ambidexterity capabilities. Construction companies can use this framework to make strategic decisions regarding investments in LC practices and the adoption of C4.0 technologies, such as investing in staff skill training and new technology deployment to adapt to the ever-changing environment.

One of the most cited literature in SHRM is Schuler and Jackson’s (1987) behavioural model. This model proposes that organisational performance is dependent on the extent to which HRM practices can be effectively connected to competitive strategy and desired employee behaviours. Importantly, this model recognises the salient role of employee behaviour in performance outcomes and, moreover that different competitive strategies imply both promulgation and reinforcement of different sets of employee skills and behaviours. Surprisingly, despite its significant influence on SHRM, studies rarely examine this model in its entirety. Motivated by the need to better understand this model’s arguments in contemporary settings, our study uses a multi-actor design to explore the connections between competitive strategies (cost reduction and differentiation), employee behaviours, and HRM practices in service environments.

Adopting a multi-level, multi-actor survey design, our exploratory deductive study assesses the utility of strategic HRM’s (SHRM) behavioural model. Drawing on data from a sample of service organisations and using univariate analyses, we compare operationalised HRM practices and employee behaviours across different strategy types.

Results lend provisional support for the behavioural model, particularly in the case of a differentiation strategy where notable differences in HRM practices and employee behaviours were observed. Findings suggest growing levels of memetic and competitive isomorphism may be occurring, with this likely attributable to the increased incidence of idea generation and information sharing about best practices occurring amongst practitioners, as well as a growing nuance in operating markets, managerial preferences, employee expectations, stakeholder objectives, and the like.

Our study suggests refinements to the behavioural model are needed. Some support for the model’s key tenets is found, but these appear context specific. Thus, the merit in developing a priori typologies linking strategy type to HRM practices and employee behaviours where organisations operate in environments which are particularised and tumultuous appears debatable.

This study highlights the behavioural model’s nuance to modern service organisations and, by doing so, practitioners are provided with a behavioural pathway for achieving competitive advantage through their HRM practices. Findings also suggest that increasingly competitive environments might be encouraging practitioners to engage in isomorphic behaviours.

The use of a comparative research design allowed our study to contribute much needed empiricism to the largely conceptually informed stylised typologies depicting the linkages between different competitive strategies, implied employee role behaviours and HRM practices, thereby supporting the need for model refinement.