scholarly journals C2H5NO Isomers: from Acetamide to1,2-Oxazetidine and Beyond

2021 ◽  
Author(s):  
John Simmie
Keyword(s):  
Machine Learning ◽  
Interstellar Medium ◽  
Catalytic Reaction ◽  
Chemical Bonding ◽  
Energy Barrier ◽  
Current Debate ◽  
Molecular Formula ◽  
Water Addition ◽  
Water Ice ◽  
The Subject

This work documents the properties of a number of isomers of molecular formula C2H5NO from the most stable, acetamide, through 1,2-oxazetidine and including even higher energy species largely of a dipolar nature. Only two of the isomers have been detected in emissions from the interstellar medium (ISM); possible further candidates are identifi�ed and the likelihood of their being detectable are considered. In general hardly any of these compounds have featured in the existing chemical literature so this work represents an important contribution extending the canon of chemical bonding which can contribute to machine-learning | providing a more exacting test of AI applications. The presence of acetamide, CH3C(O)NH2, is the subject of current debate with no clear and obvious paths to its formation; it is shown that a 1,3[H]-transfer from (E,Z ) ethanimidic acid, CH3C(OH){{NH, is feasible in spite of an energy barrier of 130 kJ/mol. It is speculated that the imidic acid can itself be formed from abundant precursors, H2O and CH3C{{{N, in an acid-induced, water addition, auto-catalytic reaction on water-ice grains.

C2H5NO Isomers: from Acetamide to1,2-Oxazetidine and Beyond

2021 ◽  
Author(s):  
John Simmie
Keyword(s):  
Machine Learning ◽  
Interstellar Medium ◽  
Catalytic Reaction ◽  
Chemical Bonding ◽  
Energy Barrier ◽  
Current Debate ◽  
Molecular Formula ◽  
Water Addition ◽  
Water Ice ◽  
The Subject

This work documents the properties of a number of isomers of molecular formula C2H5NO from the most stable, acetamide, through 1,2-oxazetidine and including even higher energy species largely of a dipolar nature. Only two of the isomers have been detected in emissions from the interstellar medium (ISM); possible further candidates are identifi�ed and the likelihood of their being detectable are considered. In general hardly any of these compounds have featured in the existing chemical literature so this work represents an important contribution extending the canon of chemical bonding which can contribute to machine-learning | providing a more exacting test of AI applications. The presence of acetamide, CH3C(O)NH2, is the subject of current debate with no clear and obvious paths to its formation; it is shown that a 1,3[H]-transfer from (E,Z ) ethanimidic acid, CH3C(OH){{NH, is feasible in spite of an energy barrier of 130 kJ/mol. It is speculated that the imidic acid can itself be formed from abundant precursors, H2O and CH3C{{{N, in an acid-induced, water addition, auto-catalytic reaction on water-ice grains.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

scholarly journals Fortællingen i fokus. Vartovbogen 1981

1982 ◽  
Vol 34 (1) ◽  
pp. 86-89
Author(s):  
Viggo Mortensen
Keyword(s):  
Current Debate ◽  
Behavioural Science ◽  
The World ◽  
The Subject ◽  
Church Politics ◽  
The Many

Focus on the NarrativeThe Vartov Book. Edited by Asta Poulsen, Jørgen Kristensen and E. Jakob Petersen. 168 pp. Published Kirkeligt Samfunds Forlag. Reviewed by Viggo Mortensen. byThis review is also largely in the form o f an informative article showing how Grundvig’s authorship is still an inspiration, not only within the traditional Grundtvig circles but also in the current debate on Church politics and cultural affairs.The editors o f The Vartov Book have elected to centre their essays on a view expressed in a theological dissertation that was presented at Århus University in 1981, in which major areas o f Grundtvig’s theology are interpreted on the basis that “Christianity at heart is a narrative” (Sv. Bjerg: The Basic Christian Narrative, Århus 1981, cf. A . Pontoppidan Thyssen in Danish Theological Journal 1/1982).Amongst the many essays in The Vartov Book that in various ways point to the value o f narrating reality - before, that is, philosophy, politics, behavioural science and statistics had alienated us from the world - Viggo Mortensen singles out Peter Balslev-Clausen’s treatment o f Grundtvig’s poem, “Thou faded leaves, thou cool wind” (I falmede blade, du kølige vind). This poem, now included in the supplement to the Danish Hymnbook - “46 Hymns”, is seen in an authoritative analysis as an element in Grundtvig’s wrestling with the basic conditions o f existence. In fact it is these basic conditions that are the subject o f all the essays.

Algorithm for Automated Generation of a Training Sample for Solving the Problem of Determining Semantic Similarity between a Pair of Keywords using Machine Learning Methods

2021 ◽  
Vol 12 (6) ◽  
pp. 283-294
Author(s):  
K. V. Lunev ◽  
Keyword(s):  
Machine Learning ◽  
Semantic Similarity ◽  
Training Sample ◽  
Subject Area ◽  
Training Dataset ◽  
Training Set ◽  
Automated Generation ◽  
Machine Learning Methods ◽  
Subjective Value ◽  
The Subject

Currently, machine learning is an effective approach to solving many problems of information-analytical systems. To use such approaches, a training set of examples is required. Collecting a training dataset is usually a time-consuming process. Its implementation requires the participation of several experts in the subject area for which the training set is collected. Moreover, for some tasks, including the task of determining the semantic similarity of keyword pairs, it is difficult even to correctly draw up instructions for experts to adequately evaluate the test examples. The reason for such difficulties is that semantic similarity is a subjective value and strongly depends on the scope, context, person, and task. The article presents the results of research on the search for models, algorithms and software tools for the automated formation of objects of the training sample in the problem of determining the semantic similarity of a pair of words. In addition, models built on an automated training sample allow us to solve not only the problem of determining semantic similarity, but also an arbitrary problem of classifying edges of a graph. The methods used in this paper are based on graph theory algorithms.

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

scholarly journals Computational studies into urea formation in the interstellar medium

2020 ◽  
Vol 497 (4) ◽  
pp. 5413-5420
Author(s):  
Eren C S Slate ◽  
Rory Barker ◽  
Ryan T Euesden ◽  
Max R Revels ◽  
Anthony J H M Meijer
Keyword(s):  
Interstellar Medium ◽  
Computational Methods ◽  
Radical Reaction ◽  
Closed Shell ◽  
Computational Studies ◽  
Water Ice ◽  
Charged Species ◽  
Ice Shell ◽  
Partial Water

ABSTRACT Formation routes, involving closed shell, radical, and charged species for urea, have been studied using computational methods to probe their feasibility in the interstellar medium. All reactions involving closed shell species were found to have prohibitive barriers. The radical–radical reaction possesses a barrier of only 4 kJ mol−1, which could be surmountable. A charged species based route was also investigated. A barrier of only 8 kJ mol−1 was found in that case, when a partial water ice shell was included.

scholarly journals The Relevance of Cataract as a Risk Factor for Age-Related Macular Degeneration: A Machine Learning Approach

2019 ◽  
Vol 9 (24) ◽  
pp. 5550
Author(s):  
Antonieta Martínez-Velasco ◽  
Lourdes Martínez-Villaseñor ◽  
Luis Miralles-Pechuán ◽  
Andric C. Perez-Ortiz ◽  
Juan C. Zenteno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factor ◽  
Macular Degeneration ◽  
Significant Risk ◽  
Age Related Macular Degeneration ◽  
Supervised Machine Learning ◽  
Current Debate ◽  
Learning Approach ◽  
Age Related ◽  
Machine Learning Approach

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico.

scholarly journals Thermal desorption of water ice in the interstellar medium

2001 ◽  
Vol 327 (4) ◽  
pp. 1165-1172 ◽  
Author(s):  
H. J. Fraser ◽  
M. P. Collings ◽  
M. R. S. McCoustra ◽  
D. A. Williams
Keyword(s):  
Interstellar Medium ◽  
Thermal Desorption ◽  
Water Ice
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