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This paper aims to examine three of the major issues relating to the welfare of seafarers, including wages, social security benefits and onboard and ashore welfare facilities and services. It is impossible to research all countries here, so this paper selects Greece – which is one of today’s primary shipping countries as for shipowning, as well as for global supply and demand for seafarers – to conduct an empirical case study.

Based on the existing knowledge and scholarship, and primary data collected in several phases of fieldwork, this paper intends to critically examine three major issues relating to the welfare for Greek seafarers, namely, wages, social security benefits and onboard and ashore welfare facilities and services.

This paper finds that they face poor labour conditions, which are getting worse due to the depressed world and Greek economies and intense financial pressures on shipping companies. The entry into force of the Maritime Labour Convention 2006 has a significant impact on the world maritime industry.

This paper critically examines the three major issues relating to the welfare of Greek seafarers.

Such issues, which Greece is facing are also common in other countries, so the findings and suggestions revealed from this paper are of importance for the global shipping industry and other states.

This research aims to improve the performance of rail fastener defect inspection method for multi railways, to effectively ensure the safety of railway operation.

Firstly, a fastener region location method based on online learning strategy was proposed, which can locate fastener regions according to the prior knowledge of track image and template matching method. Online learning strategy is used to update the template library dynamically, so that the method not only can locate fastener regions in the track images of multi railways, but also can automatically collect and annotate fastener samples. Secondly, a fastener defect recognition method based on deep convolutional neural network was proposed. The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region. The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.

Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways. Specifically, fastener location module has achieved an average detection rate of 99.36%, and fastener defect recognition module has achieved an average precision of 96.82%.

The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways, which has high reliability and strong adaptability to multi railways.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

This paper explores the quality and flow of facade product information and the capabilities for avoiding the risk of facade fires early in the design process.

A qualitative case study using the process tracing method is conducted in two stages. First, a thematic analysis of reports and literature identified two categories for the problems that caused fast fire spread across the Grenfell Tower facade. This enabled classifying the identified problems into four stages of a facade life cycle: product design and manufacturing, procurement, facade design and construction. Second, the capabilities for avoiding the problems were explored by conducting in-depth interviews with 18 experts in nine countries, analyzing design processes and designers' expertise and examining the usability of three digital interfaces in providing required information for designing fire-safe facades.

The results show fundamental flaws in the quality of facade product information and usability of digital interfaces concerning fire safety. These flaws, fragmented design processes and overreliance on other specialists increase the risk of design defects that cause fast fire spread across facades.

The findings have implications for standardization of building product information, digitalization in industrialized construction and facade design management.

This research adds to the body of knowledge on sustainability in the built environment. It is the first study to highlight the fundamental problem of facade product information, which requires urgent attention in the rapid transition toward digital and industrialized construction.