Search results

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Understanding the stakeholders' role and interest are critical for implementing climate change policy. The dichotomy between adaptation and mitigation, which arguably creates uncertainty at the implementation level, for example, is shaped by the role of the actors involved. In this regard, this study aims to investigate the interests and role of stakeholders in climate change adaptation at the national and regional levels in Indonesia. The country, Indonesia, is selected because it produces the largest amount of greenhouse gas emissions, and the people are at the highest risk of the impacts of climate change in the world. Furthermore, this study discusses the challenges in climate change adaptation in Indonesia.

This research uses a literature review and interviews with potential stakeholders. Purposive sampling methods were applied to select stakeholders for interviews. Interviews with key stakeholders were conducted through email and Zoom. Questions were developed based on the roles and interests of stakeholders.

According to the stakeholders, there is a need to establish links between climate change adaptation and local policy at the national and regional levels. The results reveal no integration strategy or approach to support climate change adaptation. Although there has been some climate change adaptation, few people are widely regarded as authorities on climate change policy. This study also discusses some challenges and opportunities to engage key stakeholders in Indonesia.

The study offers an understanding of stakeholders based on key stakeholders' interests and role in climate change adaptation in Indonesia. The research findings in this study generate prospects for the government or decision-makers or other stakeholders to deliberately aspire for policy planning. In addition, to prepare climate change adaptation policies relating to the role of stakeholders or community-based approaches to climate change adaptation, stakeholders can conduct more detailed studies to achieve community resilience in term of climate change adaptation.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.

This study aims to provide a new vision for investigating the different human resources management (HRM) practices in hotels after the COVID-19 pandemic.

A quantitative research approach was adopted using confirmatory factor analysis and structural equation modeling to reveal updated priorities and relations as stated by 504 hospitality employees.

The research approach contributed to highlighting exciting findings, as training and development (T&D) had an insignificant effect on turnover intention (IT) or affective organizational commitment (AOC) following the COVID-19 pandemic; job security (JS) had the highest impact on AOC and compensation had the highest effect on TI.

Individual HRM practices have different strong impacts on employee outcomes. This paper provides human resource directors with realistic and practical implications to boost individuals' AOC and reduces their intention to quit their job in conditions of uncertainty and ambiguity.

This pioneering study reveals the influence of HRM practices in the hospitality HRM systems regarding employee attitudes and behaviors, especially after the COVID-19 pandemic. It provides novel theoretical and practical contributions.

The purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.

A literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.

The performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.

This study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

There are variations in religious social norms and legal environments around the world. In this paper, we aim to examine the interaction between variations in religious social norms and legal environments on real activities manipulations and expense misclassification using a global sample of 63 countries. Our inquiry is motivated by a paucity of research on the interaction between legal environment and religion on earnings management practices in an international setting.

This study draws on a global sample of 63 countries to examine the effect of variations in religious social norms and legal environments on the trade-off between expense misclassification and real activities earnings management practices. Firm-specific financial data come from Global Compustat. Religion data are obtained from World Values Surveys of the World Bank. We obtain legal environment scores from the International Country Risk Guide.

Findings suggest that the interaction between law and religion serves as constraints on both classification shifting and real activities manipulation around the world. We find that religion strengthens the weak legal environment and the strong legal environment strengthens the weak religious environment to decrease both real activities manipulation and classification shifting when law and religion interact in an international setting. Therefore, our results contradict Zang's (2012) earnings management trade-off evidence. Again, our results contradict Malikov et al.’s (2018) evidence that mandatory International Financial Reporting Standards (IFRS) adoption is associated with increased real activities manipulation.

The study is limited to 63 countries limiting the generalizability of the findings.

This study provides novel evidence and shows that there is a link between law and religion. The interaction between law and religion decreases expense misclassification and real activities manipulation. We contribute that the interaction between religion and law benefits firms and increases shareholder value as real activities manipulation decreases. Therefore, strengthening the legal environment will complement religion, IFRS and other monitoring mechanisms put in place to mitigate unethical expense misclassification and real activities earnings manipulation around the world.

To address the challenges encountered in disaster responses, optimize resource utilization, minimize environmental and social impact, and ensure transparency and accountability, it is essential to review humanitarian supply chains and incorporate sustainability considerations. Humanitarian organizations can enhance their ability to deliver timely and effective assistance to those in need by continuously improving supply chain practices. Consequently, this work explores the convergence of two fast-growing domains: sustainability and humanitarian supply chain management (HSCM).

The authors conducted a systematic literature review of peer-reviewed articles to identify the prominent research trends and themes from the two domains' interactions. The extant literature is represented under the theory, context, characteristics, and research method (TCCM) framework. The authors have utilized a stakeholder theory perspective to identify coordination and collaboration among the various stakeholders.

This study's review findings reveal five future research directions formulating this study's central themes: the role of environmental sustainability, coordination, and collaboration in building effective HSCs; the role of humanitarian aid for the responsive HSC; the influence of big data predictive analytics on the HSC performance; development and empirical validation of sustainable HSC performance framework; the role of HSC stakeholders in building effective and efficient HSCs.

There is no existing academic literature review available on sustainable HSCM. This review fills this void by fostering discussion about sustainable humanitarian supply chains where the authors notably propose the TCCM framework in the context of sustainable HSCM, followed by a stakeholder network.

The paper examines the impact of market power on wages within the context of a developing country, focusing on Brazil.

With access to matched employer–employee data from Brazil, we first characterized the evolution of the local labor market concentration (Municipality Herfindahl–Hirschman Index [HHI]). Then, we built a fixed-effect model with instrumental variables to verify the association between the local labor market concentration and wages. Finally, a difference-in-difference (DiD) was implemented to verify whether a merger transaction impacted the workers’ earnings in the Brazilian banking sector.

The paper’s findings suggest that there may be a negative relationship between market power and workers’ earnings.

This research conducted an in-depth investigation of the labor market power in a developing country. As far as we know, our work is the first to evaluate the extension of local concentration in Brazilian formal labor markets and to illustrate its evolution over the last decades. Additionally, when going through the effects of market concentration on wages, we use a new identification strategy that explores changes in the HHI that are caused by national trends in an industry as a source of exogenous variation. Finally, the last part of the paper assesses the effects of antitrust policy on the labor market, a kind of investigation that is still scarce.

This paper seeks to understand how a new elite, known as the cork aristocracy, emerged in the Bordeaux wine field, France, between 1850 and 1929 as wine merchants replaced aristocrats. Classic class and status perspectives, and their distinctive social closure dynamics, are mobilized to illuminate the individual and organizational transformations that affected elite wineries grouped in an emerging classification of the Bordeaux best wines. We build on a wealth of archives and historical ethnography techniques to surface complex status and organizational dynamics that reveal how financiers and industrialists intermediated this transition and how organizations are deeply interwoven into social change.

This study is part of a series aimed at improving the city's environment, as fully restoring the past soundscape is hardly feasible. The initial study aims to uncover the city's sound characteristics, including iconic sounds that have shaped the city's environment for decades, contributing to its status as Indonesia's second most popular tourist destination. This stage is critical for informing policymaking to carefully manage and enhance the urban acoustic environment in alignment with the preserved culture.

The city's sound profile was examined using standard urban sound taxonomies. The study used quantitative methods, including (1) sound pressure level (SPL) measurements and sound recordings, (2) in situ surveys and (3) memory-based surveys. The first set of data were compared to current standards and standard urban sound taxonomies, while the second set was analysed to determine the median rating score for determining the soundscape dimensions. The third data set was used to identify the specific acoustic aspects inherent in Yogyakarta.

Yogyakarta's acoustic environment was bustling, with traffic noise and human activities dominating the soundscape, surpassing the standard levels. Many sounds not classified in standard urban sound taxonomies were present, showing the diverse nature of urban sound classification, particularly in a cultural and traditional city like Yogyakarta. The memory-based survey unveils Yogyakarta's two most remarkable soundmarks, “gamelan” and “andong”, which support the findings of prior studies. The in situ survey rated the city's acoustic environment as eventful, pleasurable and generally appropriate, emphasising the presence of cultural sounds unique to Yogyakarta, even though they are not fully audible in the current environment.

The standard sound taxonomies used in urban areas need to be adjusted to include the unique sounds produced by cultural and traditional activities in developing countries. The ordinates and subordinates of the taxonomies also need to be updated. When cultural and daily activities are massively seen in a particular city, the sounds they produce can be recalled exclusively as the city's signature. It is urgent to implement policies to safeguard the few remaining soundmarks before they disappear entirely.