【标题】Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic
【参考中译】探索人工智能在管理农业供应链风险中的作用以应对新冠肺炎疫情的影响
【类型】 期刊
【关键词】 Artificial intelligence; Structural equation modeling; Supply chain risk mitigation; Agriculture supply chain (ASC)
【参考中译】 人工智能；结构方程建模；供应链风险缓解；农业供应链(ASC)
【作者】 Kirti Nayal; Rakesh Raut; Pragati Priyadarshinee; Balkrishna Eknath Narkhede; Yigit Kazancoglu; Vaibhav Narwane
【摘要】 Purpose - In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI's influence on supply chain risk mitigation (SCRM). Design/methodology/approach - This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses. Findings - This study's findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence. Originality/value - This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.
【参考中译】 目的-在印度，人工智能(AI)在供应链管理(SCM)中的应用仍处于起步阶段。因此，本文旨在研究影响人工智能应用的因素，并验证人工智能对供应链风险缓解的影响。设计/方法/方法-本研究探讨了基于技术、组织和环境(TOE)框架的因素和其他三个因素(包括供应链集成(SCI)、信息共享(IS)和过程因素(PF))对人工智能采用的影响。调查的数据来自297名来自印度农产工业的受访者，并使用结构方程模型(SEM)来检验所提出的假设。研究结果表明，流程因素、信息共享和供应链整合(SCI)对人工智能的采用起着至关重要的作用，而人工智能对供应链管理有积极的影响。技术、组织和环境因素与人工智能之间存在不显著的负相关关系。原创性/价值-这项研究为研究人员、院士、政策制定者、创新项目处理者、技术服务提供商和经理提供了见解，以更好地了解人工智能采用的作用以及人工智能在降低因新冠肺炎疫情等中断造成的供应链风险方面的重要性。
【来源】 The International Journal of Logistics Management 2022, vol.33, no.3
【入库时间】 2023/2/27
【标题】A High-Performance Rotational Energy Harvester Integrated with Artificial Intelligence-Powered Triboelectric Sensors for Wireless Environmental Monitoring System
【参考中译】一种集成人工智能摩擦电传感器的高性能旋转能量采集器无线环境监测系统
【关键词】 Artificial intelligence; Circular halbach array magnets; High-performance rotational energy harvesters self-powered sensors
【参考中译】 人工智能；圆形Halbach阵列磁体；高性能旋转能量收集器
【作者】 Kumar Shrestha; Pukar Maharjan; Trilochan Bhatta; Sudeep Sharma; Muhammad Toyabur Rahman; Sanghyun Lee; Md Salauddin; SM Sohel Rana; Jae Y. Park
【摘要】 The prevailing energy harvester utilizes a convectional magnet that limits the output power due to the imperfect coupling of the flux linkage and the leakage of the magnetic fluxes away from the coil. Herein, a circular Halbach array magnet comprising the arc magnets is proposed as a high-performance rotational energy harvester for preventing flux leakage by concentrating the magnetic flux in a particular path. The Halbach magnet is made up of eight individual arc magnet segments that are kept apart by 1 mm to induce a fourfold increase in magnetic flux density over a conventional magnet. The proposed rotational energy harvester can deliver an exceptional 603.2 W m~(-3) power density, which is attributed to a three times increase in the power density. The harvested power is utilized to charge a 30 mAh battery for driving a complete IoT system for the development of self-powered wireless environmental monitoring systems. Furthermore, an intelligent system is designed using cutting-edge artificial intelligence (AI) technology which accounts for Mxene/P(VDF-TRFE)-based triboelectric sensor output data and considers different weather parameters to accord a high accuracy of 99% in wind speed prediction.
【参考中译】 目前流行的能量收集器使用对流磁铁，由于磁链的不完美耦合和线圈外的磁通泄漏，限制了输出功率。在这里，提出了一种由弧形磁铁组成的圆形Halbach阵列磁体，作为一种高性能的旋转能量收集器，通过将磁通集中在特定路径上来防止漏磁。Halbach磁体由八个单独的弧形磁体部分组成，间隔1 mm，使磁通量密度比传统磁体增加四倍。提出的旋转能量采集器可以提供603.2 W m~(-3)的特殊功率密度，这归因于功率密度提高了3倍。收集到的电力被用来为一个30毫安的电池充电，以驱动一个完整的物联网系统，用于开发自供电的无线环境监测系统。在此基础上，利用先进的人工智能(AI)技术，对基于MXene/P(VDF-TrFE)的摩擦电传感器的输出数据进行了处理，并考虑了不同的天气参数，从而使风速预测的准确率达到了99%。
【来源】 Advanced Engineering Materials 2022, vol.24, no.10
【标题】Maximizing margins and optimizing operational conditions for residue fluid catalytic cracking with an artificial intelligence hybrid reaction model
【参考中译】基于人工智能混合反应模型的渣油催化裂化边际最大化及操作条件优化
【关键词】 AI; Deactivation; Lump model; Machine learning; Optimization; RFCC
【参考中译】 人工智能；停用；集总模型；机器学习；优化；RFCC
【作者】 Eiji Kawai; Hideki Sato; Kazuya Furuichi; Takatsuka Toru; Toshio Yoshioka
【摘要】 Because of the recent declining demand for gasoline, the key to making refineries competitive is to maximize the yields of propylene and aromatics by converting heavier feedstock into basic petrochemicals through the residue fluid catalytic cracking (RFCC) process. This study presents an artificial intelligence (AI) hybrid reaction model to optimize the catalyst make-up rate and maximize the product yield in a real-time operation by (1) developing a catalyst activity evaluation method, (2) integrating the catalyst to oil (Cat/Oil) ratio to evaluate the reaction performance, and (3) incorporating the yield prediction model into the latest digital technologies. To this end, the catalyst deactivation function, which uses a deep neural network of the basic machine learning method, was added to the past RFCC reaction model. Under actual operational conditions, this study shows that the AI hybrid reaction model using the catalyst deactivation function can minimize catalyst loss and produce an accurate yield prediction as a production planning support tool.
【参考中译】 由于最近对汽油的需求下降，使炼油厂具有竞争力的关键是通过渣油催化裂化(RFCC)工艺将更重的原料转化为基础石化产品，从而最大限度地提高丙烯和芳烃的产量。本研究提出了一种人工智能(AI)混合反应模型，通过(1)开发一种催化剂活性评价方法，(2)结合催化剂与油(Cat/Oil)的比例来评价反应性能，以及(3)将产率预测模型融入最新的数字技术中，从而在实时操作中优化催化剂的补充率并最大化产品收率。为此，在过去的重油催化裂化反应模型中加入了催化剂失活函数，该函数采用了深度神经网络的基本机器学习方法。在实际操作条件下，本研究表明，使用催化剂失活函数的AI混合反应模型可以最大限度地减少催化剂损失，并产生准确的产率预测，作为生产计划的支持工具。
【来源】 Journal of Advanced Manufacturing and Processing 2022, vol.4, no.3