316L(N) Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods

A model that describes creep behavior is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) steel—i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature.

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Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽

10.3390/info12070275 ◽

2021 ◽

Vol 12 (7) ◽

pp. 275

Author(s):

Peter Cihon ◽

Jonas Schuett ◽

Seth D. Baum

Keyword(s):

Artificial Intelligence ◽

Corporate Governance ◽

Law Enforcement ◽

Nonprofit Organizations ◽

Public Interest ◽

Department Of Defense ◽

The Public ◽

Wide Range ◽

The Media ◽

The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

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The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.898.763 ◽

2014 ◽

Vol 898 ◽

pp. 763-766

Author(s):

Zhi Hao Li

Keyword(s):

Artificial Intelligence ◽

Environmental Changes ◽

Vision System ◽

Mineral Exploration ◽

Intelligent Robot ◽

Application Range ◽

Intelligent Robots ◽

External Data ◽

Wide Range ◽

Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

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APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

International Journal of Creative Future and Heritage (TENIAT) ◽

10.47252/teniat.v3i1.251 ◽

2015 ◽

Vol 3 (1) ◽

Author(s):

Tse Guan Tan ◽

Jason Teo

Keyword(s):

Artificial Intelligence ◽

Content Analysis ◽

Video Games ◽

Analysis Method ◽

Engineering Economics ◽

Game Ai ◽

Content Analysis Method ◽

Wide Range ◽

Computer Based ◽

Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

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Being an adult child of an elderly person living in a nursing home: a phenomenological approach to the Turkish case

Quality in Ageing and Older Adults ◽

10.1108/qaoa-10-2017-0040 ◽

2018 ◽

Vol 19 (2) ◽

pp. 117-125 ◽

Cited By ~ 1

Author(s):

Pelin Önder Erol ◽

Elif Gün

Keyword(s):

Nursing Home ◽

Adult Children ◽

Lived Experiences ◽

Filial Piety ◽

Elderly Person ◽

Adult Child ◽

Phenomenological Approach ◽

Content Type ◽

Elderly Parents ◽

Wide Range

Purpose A long-established cultural norm of filial piety may cause ambivalent feelings for adult children who are considered the primary caregivers for their elderly parents in Turkish culture, and whose parents have been placed into nursing homes. The purpose of this paper is to provide an insight to the lived experiences of adult children of elderly people living in a nursing home in Turkey. Design/methodology/approach Drawing upon dramaturgical theory and phenomenological methodology, the authors conducted interviews with ten adult children whose elderly parents had been admitted to a nursing home in Izmir, Turkey. Multi-stage purposeful random sampling was used as the sampling scheme. Thematic analysis was performed to interpret the data. Findings Three themes emerged from the data: adult children’s coping strategies, the ways in which the adult children rationalize their decisions, and the ways in which the adult children manage the placement process. The interviews revealed that the adult children often feel like social outcasts and experience a wide range of difficulties, including social pressures, their own inner dilemmas, and negotiations with their elderly parents. Originality/value An exploration for the lived experiences of adult children relating to the nursing home placement of their elderly parents contributes an insight about the well-established cultural norms that produce feelings of ambivalence.

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Micro-Scale Flow Simulations and Permeability Estimation of Cleat Networks in Coal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.846.42 ◽

2016 ◽

Vol 846 ◽

pp. 42-47

Author(s):

J. Busse ◽

S. Galindo Torres ◽

Alexander Scheuermann ◽

L. Li ◽

D. Bringemeier

Keyword(s):

Gas Flow ◽

Structural Information ◽

Porous Matrix ◽

Groundwater Levels ◽

Micro Scale ◽

Wide Range ◽

Boltzmann Method ◽

The Impact ◽

Flow Experiments

Coal mining raises a number of environmental and operational challenges, including the impact of changing groundwater levels and flow patterns on adjacent aquifer and surface water systems. Therefore it is of paramount importance to fully understand the flow of water and gases in the geological system on all scales. Flow in coal seams takes place on a wide range of scales from large faults and fractures to the micro-structure of a porous matrix intersected by a characteristic cleat network. On the micro-scale these cleats provide the principal source of permeability for fluid and gas flow. Description of the behaviour of the flow within the network is challenging due to the variations in number, sizing, orientation, aperture and connectivity at a given site. This paper presents a methodology to simulate flow and investigate the permeability of fractured media. A profound characterization of the geometry of the cleat network in micrometer resolution can be derived by CT-scans. The structural information is fed into a Lattice Boltzmann Method (LBM) based model that allows the implementation of virtual flow experiments. With the application of suitable hydraulic boundary conditions the full permeability tensor can be calculated in 3D.

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Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

Scientific Reports ◽

10.1038/s41598-020-75899-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Ali Rohani ◽

Jennifer A. Kashatus ◽

Dane T. Sessions ◽

Salma Sharmin ◽

David F. Kashatus

Keyword(s):

Single Cell ◽

High Throughput ◽

Data Science ◽

Structural Information ◽

Morphological Changes ◽

Mitochondrial Morphology ◽

Single Cell Level ◽

Mitochondrial Network ◽

Cell Level ◽

Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

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Three-Dimensional Interaction Effects in an Internally Multicracked Pressurized Thick-Walled Cylinder— Part I: Radial Crack Arrays

Journal of Pressure Vessel Technology ◽

10.1115/1.2842200 ◽

1996 ◽

Vol 118 (3) ◽

pp. 357-363 ◽

Cited By ~ 13

Author(s):

M. Perl ◽

C. Levy ◽

J. Pierola

Keyword(s):

Radial Crack ◽

Crack Depth ◽

Three Dimensional ◽

Internal Surface ◽

Interaction Effects ◽

Life Assessment ◽

Fatigue Life Assessment ◽

Dimensional Interaction ◽

Least Squares Fit ◽

Wide Range

Under certain conditions, numerous internal surface cracks develop in pressurized thick-walled cylinders, both in the radial and longitudinal directions. For fatigue life assessment of such vessels, the 3-D interaction effects among these cracks on the prevailing stress intensity factors (SIFs) need evaluation. In Part I of this paper, radial crack arrays are considered exclusively. The mode I SIF distribution for a wide range of semi-circular and semi-elliptical cracks are evaluated. The 3-D analysis is performed via the finite element method with the submodeling technique, employing singular elements along the crack front. SIFs are evaluated for arrays of up to n = 180 cracks; for a wide range of crack depth to wall thickness ratios, a/t, from 0.05 to 0.6; and, for various ellipticities of the crack, i.e., the ratio of crack depth to semicrack length, a/c, from 0.2 to 2. Using a least-squares fit, two simple expressions for the most critical (n = 2) SIFs are obtained for sparse and dense crack arrays. The formulas, which are functions of a/t and a/c, are of very good engineering accuracy. The results clearly indicate that the SIFs are considerably affected by the interaction among the cracks in the array as well as the three-dimensionality of the problem. In Part II of this paper, the interaction effects between longitudinal coplanar cracks will be analyzed.

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Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Cellular Physiology and Biochemistry ◽

10.33594/000000336 ◽

2020 ◽

Vol 55 (S3) ◽

pp. 14-45

Keyword(s):

Artificial Intelligence ◽

Molecular Dynamics ◽

Ion Channels ◽

Ion Channel ◽

Computational Methods ◽

Homology Modelling ◽

Channel Structure ◽

Learning Approaches ◽

Qsar Analysis ◽

Wide Range

Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

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Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

10.26434/chemrxiv.13019993.v1 ◽

2020 ◽

Author(s):

Amol Thakkar ◽

Veronika Chadimova ◽

Esben Jannik Bjerrum ◽

Ola Engkvist ◽

Jean-Louis Reymond

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Virtual Screening ◽

Generative Models ◽

Synthetic Route ◽

Synthetic Accessibility ◽

Wide Range ◽

Computer Aided ◽

Synthesis Planning ◽

Retrosynthetic Analysis

<p>Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity. </p>

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Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

10.2196/preprints.26646 ◽

2020 ◽

Author(s):

Oliver Maassen ◽

Sebastian Fritsch ◽

Julia Gantner ◽

Saskia Deffge ◽

Julian Kunze ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Practice ◽

Patient Care ◽

Biomedical Research ◽

Positive Attitude ◽

Patient Data ◽

University Hospitals ◽

Web Based ◽

Wide Range ◽

Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

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