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Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management 被引量:1
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作者 MOHAGHEGH Shahab D. 《Petroleum Exploration and Development》 2020年第2期225-228,共4页
Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of e... Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models. 展开更多
关键词 and reservoir management Contribution of artificial intelligence and machine learning to reservoir engineering Subsurface analytics reservoir modeling
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Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study
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作者 Rocco de Filippis Abdullah Al Foysal 《Advances in Bioscience and Biotechnology》 CAS 2024年第7期406-431,共26页
Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy... Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy of pharmacological interventions and rehabilitation strategies to improve patient outcomes and quality of life. Utilizing a randomized controlled trial with multiple treatment arms, participants will receive pharmacotherapy, polypharmacotherapy, rehabilitation interventions, or combination treatments. Outcome measures will be assessed using standardized scales, including the Hamilton Depression Scale, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), and Mania Scale. Preliminary data suggest improvements in symptom severity and functional outcomes with combination treatments. This research aims to inform clinical practice, guide treatment decisions, and ultimately enhance the quality of care for individuals living with bipolar disorder. Findings will be disseminated through peer-reviewed journals and scientific conferences to advance knowledge in this field. 展开更多
关键词 Bipolar Disorder (BD) Pharmacotherapy (PT) Rehabilitation Interventions (RI) Hamilton Depression Scale (HAM-D) Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Mania Scale (MS) machine learning (ML) and Artificial Intelligence (AI).
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Artificial Intelligence Trends and Ethics: Issues and Alternatives for Investors
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作者 Yoser Gadhoum 《Intelligent Control and Automation》 2022年第1期1-15,共15页
Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society’s economic and technological transformation. For industrial value chains and internatio... Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society’s economic and technological transformation. For industrial value chains and international businesses, it means that a structural change is necessary since these machines can learn and apply new information in making forecasts, processing, and interacting with people. Artificial intelligence (AI) is a science that uses powerful enough techniques, strategies, and mathematical modelling to tackle complex actual problems. Because of its inevitable progress further into the future, there have been considerable safety and ethical concerns. Creating an environment that is AI friendly for the people and vice versa might be a solution for humans and machines to discover a common set of values. In this context, the goal of this study is to investigate the emerging trends of AI (the benefits that it brings to the society), the moral challenges that come from ethical algorithms, learned or pre-set ideals, as well as address the ethical issues and malpractices of AI and AI security. This paper will address the consequences of AI in relation to investors and financial services. The article will examine the challenges and possible alternatives for resolving the potential unethical issues in finance and will propose the necessity of new AI governance mechanisms to protect the efficiency of the capital markets as well as the role of financial authority in the regulation and monitoring of the huge expansion of AI in finance. 展开更多
关键词 Artificial Intelligence machine learning Banking Sector Ethical AI
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Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains 被引量:1
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作者 Petar Radanliev David De Roure +5 位作者 Kevin Page Jason R.C.Nurse Rafael Mantilla Montalvo Omar Santos La’Treall Maddox Pete Burnap 《Cybersecurity》 CSCD 2020年第1期155-175,共21页
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r... Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks. 展开更多
关键词 Industry 4.0 Supply chain design Transformational design roadmap IIoT supply chain model Decision support for information management artificial intelligence and machine learning(AI/ML) dynamic self-adapting system cognition engine predictive cyber risk analytics
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Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains
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作者 Petar Radanliev David De Roure +5 位作者 Kevin Page Jason R.C.Nurse Rafael Mantilla Montalvo Omar Santos La’Treall Maddox Pete Burnap 《Cybersecurity》 2018年第1期767-787,共21页
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r... Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks. 展开更多
关键词 Industry 4.0 Supply chain design Transformational design roadmap IIoT supply chain model Decision support for information management artificial intelligence and machine learning(AI/ML) dynamic self-adapting system cognition engine predictive cyber risk analytics
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