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A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions.

Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work.

Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution.

Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

The hospitality industry is one of Ghana's key economic contributors. It is an industry that has significant indigenous investment. The sector also brings in foreign exchange for Ghana. In 2019, it generated $325 m through tourist visits. This makes the hospitality industry critical for the attraction of foreign direct investments. The research was therefore aimed at examining the business environment of the hospitality industry for evidence of negative factors that can hamper its greater contribution to the attainment of Goal 8 of the 17 Sustainable Development Goals of the UN such as guest-bullying and the incivility in hospitality occupations.

A convenience sampling method was used to select 346 samples out of the accessible 3,500 targeted population from 38 hotels in the capital city of Ghana, Accra, comprising of junior to senior employees of various departments. The questionnaires were scripted from a paper-based to digital format supported by the Opine software installed on tablets and smartphones, to enable complete adherence to all coronavirus disease 2019 (COVID-19) safety protocols. The study used a regression to ascertain the relationships between the dependent variables and the independent variables.

The study found the “Level of Permissiveness for Guests” positively and significantly “encouraged” guests to bully staff, while “Management and Staff Laxity” negatively but significantly explained guest bullying behaviour.

The study makes the first attempt in context to shed light on workplace bullying which represents one of the main factors that can inhibit or erode any gains or attempts to foster the achievement of Goal 8 of the 17 Sustainable Development Goals of the UN which is to create “Decent Work and Economic Growth”.

This paper aims to examine the USA's policy toward Myanmar or Burma, analyzing the bilateral relationship between the two countries from the pre-colonial period to the present day. It highlights the implications of political shifts in the system of government in Myanmar for the bilateral relationship with the USA, particularly after the 1962 coup and the emergence of rising regional powers like China. The paper also shows how the economic role of Myanmar has increased, leading to more equal relations with the United States of America.

This paper employs a qualitative research design, analyzing data from both primary and secondary sources. The methodology includes a literature review and document analysis to gain a comprehensive understanding of the USA–Myanmar bilateral relationship. The research also analyzes political and economic developments in Myanmar and the region, placing the USA policy in a broader context.

The research finds that the USA has been an influential player in Myanmar's politics and economy. However, with Myanmar's integration into the Chinese Belt and Road Initiative and its accession to Association of Southeast Asian Nations (ASEAN), its economic role has become more significant, leading to a more balanced relationship with the USA. The paper also shows that the USA's policy toward Myanmar has undergone significant changes, particularly after the 2011 easing of sanctions by former President Barack Obama.

Although many works have traced the history of USA–Myanmar ties, this study provides a fresh perspective by setting past events against the backdrop of contemporary geopolitical upheavals, therefore highlighting the complex evolution of their bilateral dynamics. The paper contributes to the literature on the USA–Myanmar bilateral relationship by providing a comprehensive analysis of the relationship from a historical perspective. The research also adds to the discussion of the implications of political and economic developments in Myanmar for the bilateral relationship with the USA. The findings of this study have important implications for policymakers and scholars interested in the USA's role in Southeast Asia and its relations with rising regional powers like China.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research?

Twenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.

The adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.

On the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This study aims to use a comparative analysis to examine the channel of deferring cash commitments, which can be seen as a strategic solution to mitigate the impact of COVID-19 on Moldova's service sector.

This paper uses the Oaxaca–Blinder decomposition analysis. The World Bank's post-COVID-19 survey is used. The methodology takes into account heterogeneity among firms.

The results of the Oaxaca–Blinder decomposition analysis show that service firms use deferred cash commitments more than industrial firms, corporate governance and their pandemic-related strategies are also effective in the post-COVID Moldovan economy. The results are robust to different modeling alternatives.

COVID-19 can be considered a key source of uncertainty for firms, especially those operating in economies where financial frictions occasionally occur in a transition economy. Therefore, this study can shed new light on the impact of COVID-19 on financial strategies in a transition economy.

This study aims to explore the main drivers that family businesses possess to strengthen their resilience during the COVID-19 crisis.

This study followed a quantitative method analysis through a multiple regression analysis based on a sample of 570 Italian family firms.

The results showed that job quality and innovation significantly stimulate family firms' resilience during the COVID-19 crisis.

The study has several academic implications. Firstly, the study contributes to family firm research by extending the studies on factors that significantly influence the concept of resilience; secondly, the work contributes to crisis management, offering suggestions to help other firms exceed the COVID-19 crisis.

The present study clarifies the role of family firms' resilience, and it reveals how job quality and innovation play a meaningful role during the COVID-19 crisis.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.