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The purpose of this paper is to demonstrate the effect of δ-ferrite on the susceptibility to hydrogen embrittlement of type 304 stainless steel in hydrogen gas environment.

The mechanical properties of as-received and solution-treated specimens were investigated by the test of tensile and fatigue crack growth (FCG) in 5 MPa argon and hydrogen.

The presence of δ-ferrite reduced the relative elongation and the relative reduction area (H₂/Ar) of 304 stainless steel, indicating that δ-ferrite increased the susceptibility of hydrogen embrittlement in 304 stainless steel. Moreover, δ-ferrite promoted the fatigue crack initiation and propagation at the interface between δ-ferrite and austenite. The FCG tests were used to investigate the effect of δ-ferrite on the FCG rate in hydrogen gas environment, and it was found that δ-ferrite accelerated the FCG rate, which was attributed to rapid diffusion and accumulation of hydrogen around the fatigue crack tip through δ-ferrite in high-pressure hydrogen gas environment.

The dependence of the susceptibility to hydrogen embrittlement on δ-ferrite was first investigated in type 304 steel in hydrogen environment with high pressures, which provided the basis for the design and development of a high strength, hydrogen embrittle-resistant austenitic stainless steel.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

This study clarifies the history and significance of cryptocurrencies. It explores the underlying decentralisation and trustlessness concepts that set these digital assets apart from conventional fiat currencies. It clarifies how blockchain technology functions as the core component of decentralised money. The mechanics of mining, its function in creating and validating Bitcoin transactions, and the emergence of substitute consensus mechanisms to solve environmental issues are all covered in this study. An in-depth analysis of blockchain technology covers its advantages, such as immutability and transparency, as well as its architecture and consensus processes. This study continues with a focus on the future by examining the development of decentralised finance (DeFi) and showcasing numerous DeFi applications, including yield farming, lending protocols, and decentralised exchanges (DEXs). As a result of the development of cryptocurrencies and blockchain technology, DeFi has become possible, ushering in a new era of financial independence and inclusivity. This study emphasises the significance of striking a balance between innovation and suitable regulatory measures as the globe embraces this revolution in order to enable the proper integration of DeFi into the global financial environment. The revolutionary potential of DeFi, particularly in increasing financial inclusion and empowerment for marginalised groups globally, is one of the major themes discussed. To negotiate legal frameworks while maintaining DeFi's decentralised nature, this study looks at the regulatory problems that come with this potential.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.