Application of computer technologies for calculation of gears at studying of a course " Details of machines"

The computer program GearKURT has been created to calculate mechanical gears. The program allows you to calculate gears: - closed cylindrical spur gear - closed cylindrical helical gear - open cylindrical spur gear - Novikov's gear - closed bevel spur gear - closed bevel gear with indirect teeth - open bevel gear - worm-gear. The computer program has a dialog interface written in the object-oriented programming language Delphi and compiled into an exe-file. The program allows you to choose the necessary material and method of heat treatment for the manufacture of gears, to calculate the optimal geometric dimensions and transmission parameters, to determine the design of gears. The program provides all the necessary reference materials in the form of tables and graphs, which must be used to select the coefficients and other values necessary for calculations. The program provides access to the theoretical material of the course "Machine Parts" and the ability to save the results of calculations in a separate file. Recommendations for using this program are given.

Evaluating the GO Programming Language with Design Patterns

<p>GO is a new object-oriented programming language developed at Google by Rob Pike, Ken Thompson, and others. GO has the potential to become a major programming language. GO deserves an evaluation. Design patterns document reoccurring problems and their solutions. The problems presented are programming language independent. Their solutions, however, are dependent on features programming languages provide. In this thesis we use design patterns to evaluate GO. We discuss GO features that help or hinder implementing design patterns, and present a pattern catalogue of all 23 Gang-of-Four design patterns with GO specific solutions. Furthermore, we present GoHotDraw, a GO port of the pattern dense drawing application framework JHotDraw. We discuss design and implementation differences between the two frameworks with regards to GO.</p>

Evaluating the GO Programming Language with Design Patterns

<p>GO is a new object-oriented programming language developed at Google by Rob Pike, Ken Thompson, and others. GO has the potential to become a major programming language. GO deserves an evaluation. Design patterns document reoccurring problems and their solutions. The problems presented are programming language independent. Their solutions, however, are dependent on features programming languages provide. In this thesis we use design patterns to evaluate GO. We discuss GO features that help or hinder implementing design patterns, and present a pattern catalogue of all 23 Gang-of-Four design patterns with GO specific solutions. Furthermore, we present GoHotDraw, a GO port of the pattern dense drawing application framework JHotDraw. We discuss design and implementation differences between the two frameworks with regards to GO.</p>

Python the game changer in the field of Machine Learning, Data Science and IoT: A Review

Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.

Python: A Quintessential approach towards Data Science

Python is an interpreted object-oriented programming language that is sustainably procuring vogue in the field of data science and analytics by fabricating complex software applications. Establishing a righteous nexus between developers and data scientists. Python has undoubtedly become paramount for data scientists mindful of cosmic and robust standard libraries which are used for analyzing and visualizing the data. Data scientists have to deal with the exceedingly large amount of data alias as big data. With elementary usage and a vast set of python libraries, Python has doubtlessly become an admired option to handle big data. Python has developed and evolved analytical tools which can help data scientist in developing machine learning models, web services, data mining, data classification, exploratory data analysis, etc. In this paper, we will scrutinize various tools which are used by python programmers for efficient data analytics, their scope with contrast to other programming languages.

A New Modern Object-Oriented Programming Languages

Industries evolve. Our thinking changes as well. Programming languages need evolvement too. “The thing is that ideas for new features with its ways of thinking will be flourished, and so perfectly designed those languages won’t be perfect anymore.” Where did logical programming go? “Notice that you can use this paradigm and just provide a set of constraints for a website and expect the website to develop automatically based on them.” It is possible to implement that. Likewise, new paradigms will sooner or later be born. It can’t be that we’ve explored everything. “Technologies are born likewise the old way of thinking, which represents by the previous programming languages might not be adequate. This project is an open-source modern object-oriented programming language that aims to bridge the gap between modern expressive programming paradigms like python and strictly typed rigid languages like Java and C#. Our goal is to provide the usefulness of an object-oriented programming language while holding the simplicity of an expressive programming language without having to sacrifice performance.

IMAGES CONVERTER OF GEOLOGIC- LITHOLOGICAL PROFILES

The article is devoted to the program "Converter", which allows you to translate the geologic-lithological profile of a mineral field into a digital format in the form of a two-dimensional array. The object-oriented programming language Python was used to write the program. The NumPy, OpenCV, and MatPlotlib libraries are actively used. The implementation of this program is based on image segmentation and finding the prevailing colors in the OpenCV library. Image segmentation is a preliminary step in image processing. The obtained values allow you to find out the density distribution in the area under consideration. The program "Converter" has a good graphical representation of the results obtained using the MatPlotlib library. The program writes the final converted result as a two-dimensional array to a text file along the desired path. Thus, the matrix is easy to read for further use in conjunction with other programs. The purpose of this work was to create a program that converts the geologic-lithological profile of the field into a digital format in the form of a two-dimensional array, for further use of this matrix as the distribution density of the oil field. The "Converter" program converts any geologic-lithological profile into a two-dimensional array in a matter of minutes.

Parsing Expression Grammar and Packrat Parsing – A Review

Bryan Ford presented Parsing Expression Grammars (PEGs) as an alternative to specify rules for programming language, along with a Packrat parser, based on an idea of memoization. The idea proposed by B. Ford guarantees parsing of grammar written using PEGs in linear time in spite of backtracking. The primary aim of the paper is to highlight the details of PEGs followed by various challenges existing for a better understanding of the readers. From the entire overview presented, it has been observed that PEGs address the issue of undesired ambiguity in grammar for computer-oriented programming language, by not allowing any ambiguity in rules itself at the first place. However, the guarantee of linear time execution comes with a cost of large heap consumption, making it infeasible to implement for large inputs. Optimizing the resources required for memoization, may allow us to utilize the benefits offered by PEGs.