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1 – 5 of 5Xiaoxian Yang, Zhifeng Wang, Qi Wang, Ke Wei, Kaiqi Zhang and Jiangang Shi
This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports…
Abstract
Purpose
This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.
Design/methodology/approach
This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.
Findings
To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.
Originality/value
This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.
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Zhifeng Chen, Yixiao Liu, Yuanyuan Hu and Longyao Zhang
Greenhouse gas (GHG) emission has a detrimental impact on climate change. There is an increasing trend for firms to use disclosure to signal stakeholders about its environmental…
Abstract
Greenhouse gas (GHG) emission has a detrimental impact on climate change. There is an increasing trend for firms to use disclosure to signal stakeholders about its environmental responsibilities and performance in dealing with climate change. China is one of the countries producing the most carbon emissions. Over the last decade, Chinese state-owned enterprises (SOEs) are becoming important players in international trade. However, the existing literature provides limited evidence on how Chinese SOEs influence GHG disclosure. Through the lens of stakeholder–agency theory, this chapter studies the top 300 listed firms to examine the relationship between Chinese SOEs and the likelihood of GHG disclosure. The result suggests a negative relationship between Chinese SOEs and the likelihood of GHG disclosure. This could be explained as a consequence of the managers' political self-interests, economic and policy-oriented decision-making process and the power differentials between the government and SOE managers. This research extends the GHG literature to Chinese SOEs context, providing direct evidence on how state ownership impacts on GHG disclosure.
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Abstract
Purpose
As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.
Design/methodology/approach
In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.
Findings
With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.
Originality/value
This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.
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Chinedu Onyeme and Kapila Liyanage
This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on…
Abstract
Purpose
This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.
Design/methodology/approach
The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.
Findings
The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.
Originality/value
The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).
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Wei Li, Yuxin Huang, Leilei Ji, Lingling Ma and Ramesh Agarwal
The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.
Abstract
Purpose
The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.
Design/methodology/approach
This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.
Findings
The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.
Research limitations/implications
Large computational cost.
Practical implications
Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.
Originality/value
Simplify the setting of boundary conditions in the transient calculation.
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