scholarly journals Research and modeling of operational planning of cargo operations in the sea port

2020 ◽  
pp. 130-135
Author(s):  
Ю.Я. Настин
Keyword(s):  
Artificial Intelligence ◽  
Dynamic Programming ◽  
Operational Planning ◽  
Modeling Tools ◽  
Simultaneous Processing ◽  
Multi Stage ◽  
Wide Range ◽  
N Stage ◽  
Reduced Dimension ◽  
Np Problems

Статья продолжает прежние исследования автора в области построения модели оперативного планирования грузовых операций в морском порту. Затрагивается широкий круг вопросов (стратификация, семиотика, искусственный интеллект). Основное внимание уделено моделированию на верхней «математической» страте - объёмно-календарному планированию на основе многоэтапного нелинейного и динамического программирований (НП), эвристик и экстраполирования. Оптимальный план должен поступать на нижнюю страту, где рассматриваются каргопланы, грузовые технологические схемы, а средства моделирования – системы искусственного интеллекта. В основе моделей верхней страты - диспач-демередж, сталийное время, норма одновременной обработки. Предложены три группы моделей. Во-первых, n-этапные сепарабельные задачи НП; показан алгоритм решения двухэтапной задачи НП динамическим программированием с понижением размерности и множителем Лагранжа; обсуждаются проблемы решения при n>2. Во-вторых, экстраполяционные модели; они включают в себя в качестве «ядер циклов» модели из 1-й группы; обсуждаются способы применения эвристик. В-третьих, несепарабельные задачи НП, которые учитывают процедуры вхождения судов в норму одновременной обработки. Обозначено направление исследований и проектирования модели планирования. The article continues the author's previous research in the field of building a model for operational planning of cargo operations in a seaport. It covers a wide range of issues (stratification, semiotics, artificial intelligence). The main attention is paid to modeling on the upper "mathematical" stratum-volume-calendar planning based on multi-stage nonlinear and dynamic programming (NP), heuristics and extrapolation. The optimal plan should be sent to the lower stratum, where cargoplans, cargo technological schemes are considered, and modeling tools – artificial intelligence systems. The upper stratum models are based on dispatch-demurrage, steel time, and the rate of simultaneous processing. Three groups of models are proposed. First, n-stage separable NP problems; an algorithm for solving a two-stage NP problem by dynamic programming with reduced dimension and a Lagrange multiplier is shown; solution problems for n>2 are discussed. Second, extrapolation of the model; they include models from group 1 as "cycle cores"; ways to apply heuristics are discussed. Third, non-separable NP tasks that take into account the procedures for vessels entering the simultaneous processing norm. The direction of research and design of the planning model is indicated.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
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.

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

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.

183 Designing Research for Beef Cattle Production in Extensive Environments

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 98-99
Author(s):  
Timothy DelCurto ◽  
Sam Wyffels
Keyword(s):  
Beef Cattle ◽  
Statistical Inference ◽  
Multiple Regression ◽  
Feed Intake ◽  
Cattle Production ◽  
Modeling Tools ◽  
Activity Monitors ◽  
Multiple Observations ◽  
Wide Range ◽  
Beef Cattle Production

Abstract Designing research for beef cattle production in rangeland environments is an ongoing challenge for researchers worldwide. Specifically, creating study designs that mirror actual production environments yet have enough observations for statistical inference is a challenge that often hinders researchers in efforts to publish their observations. Numerous journals will accept “case study” or observational results that lack valid statistical inference. However, these journals are limited in number and often lack impact. Approaches are available to gain statistical inference by creating multiple observations within a common group of animals. Approaches to increasing statistical observations will be discussed in this presentation. Modeling animal behavior and performance on extensive rangeland landscapes is commonly practiced in wildlife ecology and, more recently, has been published in Animal Science journals. Additionally, new technology has made it possible to apply treatments (e.g., supplementation studies) to individual animals on extensive environments where large, diverse herds/flocks of cattle/sheep are managed as a single group. Use of individual animal identification (EID) and feed intake technology has opened a wide range of research possibilities for beef cattle production systems research in rangeland environments. Likewise, global positioning system (GPS) collars and activity monitors have created the opportunity to evaluate animal grazing behavior in remote and extensive landscapes. The use of multiple regression models to evaluate resource use in extensive environments will, in turn, help managers optimize beef cattle production and the sustainable use of forage/rangeland resources. Embracing new technologies such as GPS, activity monitors, EID tags, and feed intake monitors combined with multiple regression modeling tools will aid in designing and publishing beef cattle production research in extensive rangeland environments.

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

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.

scholarly journals The Study of Internet Use and Academic Achievement of Elementary Students in Bangkok

2016 ◽  
Vol 6 (2) ◽  
pp. 71
Author(s):  
Aouyporn Suphasawat ◽  
Sirichai Hongsanguansri ◽  
Patcharin Seree ◽  
Ouaychai Rotjananirunkit
Keyword(s):  
Academic Achievement ◽  
Social Media ◽  
Math Achievement ◽  
Achievement Test ◽  
Internet Usage ◽  
School Students ◽  
Wide Range Achievement Test ◽  
Multi Stage ◽  
Wide Range ◽  
Negative Effect

<p>The purpose of this study is to investigate the relationship between internet usage behavior and academic achievement among elementary school students from grade 4-6 in Bangkok. The researcher employed Multi-stage Sampling to recruit 297 samples. The data was gathered via the following tests: 1) Intelligence tests, namely Colored Progressive Matrices (CPM) for students aged 5-11 year old or Standard Progressive Matrices (SPM) for 12 year old and above, and 2) Academic achievement test, namely Wide Range Achievement Test Thai Edition: WRAT-Thai. The findings revealed that time spent on the internet is negatively correlated to student’s reading achievement (r = -.24, p &lt; .001), spelling achievement (r = -.26, p &lt; .001), and math achievement (r = -.20, p = .001). More surprisingly, academic related internet usage was also found to be negatively correlated to math achievement (r = -.20, p &lt; 0.05). Meanwhile, internet usage for social media has a correlation with academic achievement in math and reading, (r = -.20, p = .001) and (r = -.13, p &lt; .05), respectively. Moreover, internet usage for entertainment was found to have a negative correlation with academic achievement in reading, spelling and math, (r = -.25, p &lt; .001), (r = -.27, p &lt; .001) and (r = -.21, p &lt; .001), respectively. Internet usage for online business, however, yielded no correlation to academic achievement. The study concluded that daily internet usage does have an effect on academic achievement in math. Moreover, when used for entertainment and social media, internet usage can pose a negative effect on academic achievement in reading and writing.</p>

scholarly journals Prediction Models for the Estimation of Soil Moisture Content

2011 ◽  
Author(s):  
Swathi Gorthi ◽  
Huifang Dou
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Modeling Tools ◽  
Wide Range

This paper provides a survey on different kinds of prediction models developed for the estimation of soil moisture content of an area, using empirical information including meteorological and remotely sensed data. The different models employed extend over a wide range of machine learning techniques starting from Basic Linear Regression models through models based on Bayesian framework, Decision tree learning and Recursive partitioning, to the modern non-linear statistical data modeling tools like Artificial Neural Networks. The fundamental mathematical backgrounds, pros and cons, prediction results and efficiencies of all the models are discussed.

scholarly journals Penerapan Algoritma Dynamic Programing pada Pergerakan Lawan dalam Permainan Police and Thief

2019 ◽  
Vol 2 (2) ◽  
pp. 114
Author(s):  
Insidini Fawwaz ◽  
Agus Winarta
Keyword(s):  
Artificial Intelligence ◽  
Dynamic Programming ◽  
Search Algorithm ◽  
Dynamic Programming Algorithm ◽  
Minimum Cost ◽  
Programming Algorithm ◽  
Shortest Route ◽  
Basic Meaning ◽  
Dynamic Programing ◽  
Approximate Distance

<p class="8AbstrakBahasaIndonesia"><em>Games have the basic meaning of games, games in this case refer to the notion of intellectual agility. In its application, a Game certainly requires an AI (Artificial Intelligence), and the AI used in the construction of this police and thief game is the dynamic programming algorithm. This algorithm is a search algorithm to find the shortest route with the minimum cost, algorithm dynamic programming searches for the shortest route by adding the actual distance to the approximate distance so that it makes it optimum and complete. Police and thief is a game about a character who will try to run from </em><em>police.</em><em> The genre of this game is arcade, built with microsoft visual studio 2008, the AI used is the </em><em>Dynamic Programming</em> <em>algorithm which is used to search the path to attack players. The results of this test are police in this game managed to find the closest path determined by the </em><em>Dynamic Programming</em> <em>algorithm to attack players</em></p>

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

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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