Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Jorge F. Arinez ◽  
Qing Chang ◽  
Robert X. Gao ◽  
Chengying Xu ◽  
Jianjing Zhang
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Systems ◽  
Hierarchical Organization ◽  
System Level ◽  
Current Status ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Collaboration Process

Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

scholarly journals The Agro-Industrial Sugarcane System in Mexico: Current Status, Challenges and Opportunities

2014 ◽  
Vol 6 (4) ◽  
pp. 26 ◽  
Author(s):  
Héctor Emmanuel Sentíes-Herrera ◽  
Fernando Carlos Gómez-Merino ◽  
Apolonio Valdez-Balero ◽  
Hilda Victoria Silva-Rojas ◽  
Libia Iris Trejo-Téllez
Keyword(s):  
Value Chain ◽  
Political Influence ◽  
Cane Sugar ◽  
Policy Framework ◽  
Current Status ◽  
Market Penetration ◽  
High Fructose ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
High Processing

Sugarcane cultivation in Mexico occurs under a wide range of socioeconomic, environmental and agricultural conditions, with the last three harvests (2010/2011, 2011/2012 and 2012/2013) providing yields ranging from 36-125 t ha-1 (variation > 347%), with an average yield of 70.2 t ha-1, which is below the world average of 80 t ha-1. The total area allocated to sugarcane production in Mexico is close to 800 thousand hectares, and could rise to nearly 5 million hectares given adequate conditions for its cultivation. This activity generates approximately 1 million direct jobs, 2.2 million indirect jobs, and more than 2.5 billion dollars (0.4% of GDP) per year. Climate change and the rapid market penetration of high fructose corn syrup are among the greatest threats to this agribusiness, including severe disintegration of production processes in the field, industry, commerce, and consumption of cane sugar. Technology lags, low investment, high processing costs and shortcomings in production sales are issues the industry must address by leveraging their resources and coordinating processing links to be more efficient and competitive. Political influence has imposed a suboptimal policy framework to achieve the projected potential. To overcome current lags in the field and refineries within the country, significant innovations across the value-chain are underway, including a robust breeding program, digitalization of sugarcane fields and novel investments in research and development. The sugarcane value-chain has great potential for Mexico, and exploiting this potential is possible if technological, organizational and commercial management innovations currently in progress in fields and factories are applied.

scholarly journals Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

2021 ◽  
Vol 18 (16) ◽  
pp. 8578
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Niloofar Shirvanizadeh ◽  
Andrés Ortiz ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Artificial Intelligence ◽  
Text Mining ◽  
The United States ◽  
Social Consequences ◽  
Current Status ◽  
Automatic Process ◽  
Scientometric Analysis ◽  
Wide Range ◽  
Health Related ◽  
Current Lines

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

scholarly journals On the Use of Artificial Intelligence to Define Tank Transfer Functions

2021 ◽  
Author(s):  
Pál Schmitt ◽  
Charles Gillan ◽  
Ciaran Finnegan
Keyword(s):  
Artificial Intelligence ◽  
Transfer Functions ◽  
Third Order ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Wave Characteristics ◽  
Highly Nonlinear ◽  
Wide Range ◽  
First Results ◽  
Time Trace

Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.

scholarly journals Neutrophils and Macrophages as Targets for Development of Nanotherapeutics in Inflammatory Diseases

Pharmaceutics ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1222
Author(s):  
Yujie Su ◽  
Jin Gao ◽  
Puneet Kaur ◽  
Zhenjia Wang
Keyword(s):  
Tissue Injury ◽  
Inflammatory Diseases ◽  
Current Status ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Potential Strategy ◽  
Novel Concept ◽  
Pathogen Clearance ◽  
Inflammation Responses

Neutrophils and macrophages are major components of innate systems, playing central roles in inflammation responses to infections and tissue injury. If they are out of control, inflammation responses can cause the pathogenesis of a wide range of diseases, such as inflammatory disorders and autoimmune diseases. Precisely regulating the functions of neutrophils and macrophages in vivo is a potential strategy to develop immunotherapies to treat inflammatory diseases. Advances in nanotechnology have enabled us to design nanoparticles capable of targeting neutrophils or macrophages in vivo. This review discusses the current status of how nanoparticles specifically target neutrophils or macrophages and how they manipulate leukocyte functions to inhibit their activation for inflammation resolution or to restore their defense ability for pathogen clearance. Finally, we present a novel concept of hijacking leukocytes to deliver nanotherapeutics across the blood vessel barrier. This review highlights the challenges and opportunities in developing nanotherapeutics to target leukocytes for improved treatment of inflammatory diseases.

Artificial Intelligence Security: Threats and Countermeasures

2023 ◽  
Vol 55 (1) ◽  
pp. 1-36
Author(s):  
Yupeng Hu ◽  
Wenxin Kuang ◽  
Zheng Qin ◽  
Kenli Li ◽  
Jiliang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Autonomous Vehicles ◽  
Security Threats ◽  
Technological Advancement ◽  
Security Issues ◽  
Whole Process ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Future Challenges

In recent years, with rapid technological advancement in both computing hardware and algorithm, Artificial Intelligence (AI) has demonstrated significant advantage over human being in a wide range of fields, such as image recognition, education, autonomous vehicles, finance, and medical diagnosis. However, AI-based systems are generally vulnerable to various security threats throughout the whole process, ranging from the initial data collection and preparation to the training, inference, and final deployment. In an AI-based system, the data collection and pre-processing phase are vulnerable to sensor spoofing attacks and scaling attacks, respectively, while the training and inference phases of the model are subject to poisoning attacks and adversarial attacks, respectively. To address these severe security threats against the AI-based systems, in this article, we review the challenges and recent research advances for security issues in AI, so as to depict an overall blueprint for AI security. More specifically, we first take the lifecycle of an AI-based system as a guide to introduce the security threats that emerge at each stage, which is followed by a detailed summary for corresponding countermeasures. Finally, some of the future challenges and opportunities for the security issues in AI will also be discussed.

Current Status and Future Perspective for Research on Medicinal Plants with Anticancerous Activity and Minimum Cytotoxic Value

2019 ◽  
Vol 20 (12) ◽  
pp. 1227-1243
Author(s):  
Hina Qamar ◽  
Sumbul Rehman ◽  
D.K. Chauhan
Keyword(s):  
Side Effects ◽  
Medicinal Plants ◽  
Anticancer Agents ◽  
Drug Targets ◽  
Psidium Guajava ◽  
Current Status ◽  
Future Perspective ◽  
Wide Range

Cancer is the second leading cause of morbidity and mortality worldwide. Although chemotherapy and radiotherapy enhance the survival rate of cancerous patients but they have several acute toxic effects. Therefore, there is a need to search for new anticancer agents having better efficacy and lesser side effects. In this regard, herbal treatment is found to be a safe method for treating and preventing cancer. Here, an attempt has been made to screen some less explored medicinal plants like Ammania baccifera, Asclepias curassavica, Azadarichta indica, Butea monosperma, Croton tiglium, Hedera nepalensis, Jatropha curcas, Momordica charantia, Moringa oleifera, Psidium guajava, etc. having potent anticancer activity with minimum cytotoxic value (IC50 >3μM) and lesser or negligible toxicity. They are rich in active phytochemicals with a wide range of drug targets. In this study, these medicinal plants were evaluated for dose-dependent cytotoxicological studies via in vitro MTT assay and in vivo tumor models along with some more plants which are reported to have IC50 value in the range of 0.019-0.528 mg/ml. The findings indicate that these plants inhibit tumor growth by their antiproliferative, pro-apoptotic, anti-metastatic and anti-angiogenic molecular targets. They are widely used because of their easy availability, affordable price and having no or sometimes minimal side effects. This review provides a baseline for the discovery of anticancer drugs from medicinal plants having minimum cytotoxic value with minimal side effects and establishment of their analogues for the welfare of mankind.

The Power of Matrix Factorization: Methods for Deconvoluting Genetic Heterogeneous Data at Expression Level

2021 ◽  
Vol 15 (8) ◽  
pp. 841-853
Author(s):  
Yuan Liu ◽  
Zhining Wen ◽  
Menglong Li
Keyword(s):  
Artificial Intelligence ◽  
Matrix Factorization ◽  
In Silico ◽  
Genetic Data ◽  
Heterogeneous Data ◽  
Expression Level ◽  
Mathematical Methods ◽  
Challenges And Opportunities ◽  
Factorization Methods ◽  
Genetic Profiles

Background:: The utilization of genetic data to investigate biological problems has recently become a vital approach. However, it is undeniable that the heterogeneity of original samples at the biological level is usually ignored when utilizing genetic data. Different cell-constitutions of a sample could differentiate the expression profile, and set considerable biases for downstream research. Matrix factorization (MF) which originated as a set of mathematical methods, has contributed massively to deconvoluting genetic profiles in silico, especially at the expression level. Objective: With the development of artificial intelligence algorithms and machine learning, the number of computational methods for solving heterogeneous problems is also rapidly abundant. However, a structural view from the angle of using MF to deconvolute genetic data is quite limited. This study was conducted to review the usages of MF methods on heterogeneous problems of genetic data on expression level. Methods: MF methods involved in deconvolution were reviewed according to their individual strengths. The demonstration is presented separately into three sections: application scenarios, method categories and summarization for tools. Specifically, application scenarios defined deconvoluting problem with applying scenarios. Method categories summarized MF algorithms contributed to different scenarios. Summarization for tools listed functions and developed web-servers over the latest decade. Additionally, challenges and opportunities of relative fields are discussed. Results and Conclusion: Based on the investigation, this study aims to present a relatively global picture to assist researchers to achieve a quicker access of deconvoluting genetic data in silico, further to help researchers in selecting suitable MF methods based on the different scenarios.

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