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This paper aims to present a human-in-the-loop natural teaching paradigm based on scene-motion cross-modal perception, which facilitates the manipulation intelligence and robot teleoperation.

The proposed natural teaching paradigm is used to telemanipulate a life-size humanoid robot in response to a complicated working scenario. First, a vision sensor is used to project mission scenes onto virtual reality glasses for human-in-the-loop reactions. Second, motion capture system is established to retarget eye-body synergic movements to a skeletal model. Third, real-time data transfer is realized through publish-subscribe messaging mechanism in robot operating system. Next, joint angles are computed through a fast mapping algorithm and sent to a slave controller through a serial port. Finally, visualization terminals render it convenient to make comparisons between two motion systems.

Experimentation in various industrial mission scenes, such as approaching flanges, shows the numerous advantages brought by natural teaching, including being real-time, high accuracy, repeatability and dexterity.

The proposed paradigm realizes the natural cross-modal combination of perception information and enhances the working capacity and flexibility of industrial robots, paving a new way for effective robot teaching and autonomous learning.

The current study addresses how blockchain can deal with the challenges that the midstream liquefied natural gas (LNG) supply chain poses combined from a management standpoint. Such challenges are: the volume of transactions, communication hurdles and the lack of contemporary management tools. The paper proposes a comprehensive framework to assess the impact of blockchain implementation in the midstream LNG supply chain in order to tackle those barriers.

The basis of the research is the business process modelling (BPM), through which entities, roles, tasks, resources and transactions can be modelled and simulated. The modelling of the midstream LNG supply chain, via BPM, is based on guidelines of the Society of International Gas Tanker and Terminal Operators (SIGGTO) and common industry business models. A quantitative analysis is employed to support the motivation and the potential impact of blockchain implementation. The methodology is used to identify (1) inefficiencies related to large volume of transactions between stakeholders and (2) critical areas of an LNG shipping company, where blockchain can be implemented.

Process repeatability, numerous shared documentation forms, excessive paperwork and communication imbroglios are mapped from the modelling section. Up to 327 processes are repeated during a typical vessel voyage, and up to 122 shared documentation forms are exchanged. Excessive paperwork and communication imbroglios are tracked through, which correspond to 25 severe errors as detected. By implementing the methodology, stakeholders can quantify the possible impact of blockchain on the operational performance of each stakeholder's operations separately and the supply chain as a whole in terms of real-time monitoring, transparency and paperwork reduction, time and cost savings.

The research has certain limitations deriving from its conceptual nature. The business processes' modelling is based on standard procedures described in the guidelines by SIGGTO and may need further adjustment for specific use cases. A structured case study has not been realisable as corporate data for an LNG shipping company regarding processes and other commercial sensitive information are required.

Potential practitioners may exploit the proposed framework as a low cost and seamless tool to evaluate how blockchain could disrupt their operations. Thus, the blockchain implementation's improvements or weaknesses can be pinpointed, and enabling the interested stakeholder of the LNG supply chain with specific feedback, it can guide them towards informed decisions on their operations.

The research has a novel approach as it combines the creation of practical management framework, with a comprehensive visualization of the midstream LNG supply chain. Thus, the reader can identify in which parts of the midstream LNG supply chain can blockchain be implemented, and what impact it could have in terms of supply chain operations.

This paper aims to introduce a novel method for the analysis of open cell porous components fabricated by laser-based powder bed metal additive manufacturing (AM) for the purpose of quality control. This method uses photogrammetric analysis, the extraction of geometric information from an image through the use of algorithms. By applying this technique to porous AM components, a rapid, low-cost inspection of geometric properties such as material thickness and pore size is achieved. Such measurements take on greater importance, as the production of porous additive manufactured orthopaedic devices increases in number, causing other, slower and more expensive methods of analysis to become impractical.

Here the development of the photogrammetric method is discussed and compared to standard techniques including scanning electron microscopy, micro computed tomography scanning and the recently developed focus variation (FV) imaging. The system is also validated against test graticules and simple wire geometries of known size, prior to the more complex orthopaedic structures.

The photogrammetric method shows an ability to analyse the variability in build fidelity of AM porous structures for use in inspection purposes to compare component properties. While measured values for material thickness and pore size differed from those of other techniques, the new photogrammetric technique demonstrated a low deviation when repeating measurements, and was able to analyse components at a much faster rate and lower cost than the competing systems, with less requirement for specific expertise or training.

The advantages demonstrated by the image-based technique described indicate the system to be suitable for implementation as a means of in-line process control for quality and inspection applications, particularly for high-volume production where existing methods would be impractical.

Powder bed-based additive manufacturing (AM) is a promising family of technologies for industrial applications. The purpose of this study is to provide a new metrics based on the analysis of the compaction behavior for the evaluation of flowability of AM powders.

In this work, a novel qualification methodology based on a camera mounted onto a commercially available tap density meter allowed to assess the compaction behavior of a selection of AM materials, both polymers and metals. This methodology automatizes the reading of the powder height and obtains more information compared to ASTM B527. A novel property is introduced, the “tapping modulus,” which describes the packing speed of a powdered material and is related to a compression/vibration powder flow.

The compaction behavior was successfully correlated with the dynamic angle of repose for polymers, but interestingly not for metals, shedding more light to the different flow behavior of these materials.

Because of the chosen materials, the results may lack generalizability. For example, the application of this methodology outside of AM would be interesting.

This paper suggests a new methodology for assessing the flowing behavior of AM materials when subjected to compression. The device is inexpensive and easy to implement in a quality assurance environment, being thus interesting for industrial applications.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This study aims to investigate the behaviour of soft lattices, i.e. lattices capable of reaching large deformations, and the influence of the printing process on it. The authors focused on two cell topologies, the body-centred cubic (BCC) and the Kelvin, characterized by a bending-dominated behaviour relevant to the design of energy-absorbing applications.

The authors analysed the experimental and numerical behaviour of multiple BCC and Kelvin structures. The authors designed homogenous and graded arrays of different dimensions. The authors compared their technical feasibility with two three-dimensional-printed technologies, such as the fused filament fabrication and the selective laser sintering, choosing thermoplastic polyurethane as the base material.

The results demonstrate that multiple design aspects determine how the printing process influences the behaviour of soft lattices. Besides, a graded distribution of the material could contribute to fine-tuning this behaviour and mitigating the influence of the printing process.

Despite being less explored than their rigid counterpart, soft lattices are now becoming of great interest, especially when lightweight, wearable and customizable solutions are needed. This study contributes to filling this gap.

Only a few studies analyse design and printing issues of soft lattices due to the intrinsic complexity of printing flexible materials.