Python Programming Research Articles

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ПОИСКА РЕШЕНИЙ ДИФФЕРЕНЦИАЛЬНЫХ УРАВНЕНИЙ

An approach to modeling chemical technological processes for calculating solutions of differential methods using artificial neural networks is considered. The capabilities of the Keras module of the Python programming language within these problems are described

Дифференциальная мера эмиссии солнечных нановспышек, определенная с помощью алгоритма SITES

The paper presents the results of a study of capabilities of the SITES algorithm for reconstructing the differential emission measure (DEM) of a source from its radiation in several parts of the electromagnetic spectrum in the context of observing solar nanoflares with the AIA/SDO instrument. The SITES method was implemented in the Python programming language and was first used to construct the DEM of nanoflares. For this purpose, we tested the efficiency of the algorithm on model single- and double-peak DEM at characteristic temperatures of solar nanoflares. The test results indicate that the SITES algorithm can be of limited applicability for studying the DEM of nanoflares in the single-peak approximation. The algorithm has a combination of high accuracy and high counting rate in the studied temperature range from 1 to 3 MK. The features of DEM nanoflares reconstructed by the SITES method were examined using our previously found sample of 58855 events observed in 2019 with the AIA/SDO instrument. The results confirm that the characteristic plasma temperature in nanoflares is 1–2 MK. The reconstructed DEM of nanoflares generally have one maximum within this range, but the temperature distribution we obtained for all flares forms two clusters with maxima at 1.2 and 1.7 MK. We interpret this as possible evidence for the existence of two types of solar nanoflares, but this result requires further confirmation.

Differential emission measure of solar nanoflares measured with the SITES algorithm

The paper presents the results of a study of capabilities of the SITES algorithm for reconstructing the differential emission measure (DEM) of a source from its radiation in several parts of the electromagnetic spectrum in the context of observing solar nanoflares with the AIA/SDO instrument. The SITES method was implemented in the Python programming language and was first used to construct the DEM of nanoflares. For this purpose, we tested the efficiency of the algorithm on model single- and double-peak DEM at characteristic temperatures of solar nanoflares. The test results indicate that the SITES algorithm can be of limited applicability for studying the DEM of nanoflares in the single-peak approximation. The algorithm has a combination of high accuracy and high counting rate in the studied temperature range from 1 to 3 MK. The features of DEM nanoflares reconstructed by the SITES method were examined using our previously found sample of 58855 events observed in 2019 with the AIA/SDO instrument. The results confirm that the characteristic plasma temperature in nanoflares is 1–2 MK. The reconstructed DEM of nanoflares generally have one maximum within this range, but the temperature distribution we obtained for all flares forms two clusters with maxima at 1.2 and 1.7 MK. We interpret this as possible evidence for the existence of two types of solar nanoflares, but this result requires further confirmation.

РАЗРАБОТКА ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ ДЛЯ АВТОМАТИЗАЦИИ РАСЧЕТА РАДИАТОРОВ ОХЛАЖДЕНИЯ РЭА

The development and implementation of software that automates the calculation of cooling radiators for electronic equipment in the Python programming language using the PyQt library for creating windows and widgets is considered. In the developed software, two approaches to calculating radiators were implemented: through determining the required area and through determining the required specific thermal resistance

Application of virtual reality technologies in the rehabilitation of patients with left and right hemisphere lesions in the acute period of ischemic stroke

BACKGROUND: Currently, neurocognitive rehabilitation with the integration of innovative virtual reality (VR) technologies is the standard component of early rehabilitation treatment programs for patients with acute focal brain damage. AIM: Based on the statistical methodology, this study aimed to evaluate the efficiency of neurocognitive rehabilitation with the application VR technologies in patients with left and right hemisphere lesions in the acute period of ischemic stroke. MATERIALS AND METHODS: The study examined 160 patients diagnosed with ischemic stroke exhibiting cognitive impairments. Patients were divided into two groups based on stroke localization: group 1 (n=80) had left hemisphere lesions, whereas group 2 (n=80) had left hemisphere lesions. Since day 5 of hospitalization, in addition to basic therapy, rehabilitation was performed using a hardware–software multimedia complex based on VR for remote-controlled rehabilitation «Devirta-Delphi». Rehabilitation efficiency (RE) was assessed by testing objectively quantitative measures of accuracy and coordination of movement performance before and after treatment using a Kinect Delphi system. Cognitive and psychoemotional measures were assessed by neuropsychological tests on days 2–4 of hospitalization and at the end of the VR therapy. Mathematical statistics were calculated using Python programming language and its libraries Pandas and SciPy. RESULTS: Higher RE values were obtained in patients with right-sided stroke. Irrespective of lesion lateralization, significant improvement was noted with respect to mnestic, executive function, and ideomotor praxis, regression of anxiety, depression, and general aggression. Group 1 also showed an improvement in speech function and a decrease in general asthenia, whereas group 2 showed an increase in attention and emotion recognition ability. Factors that negatively affected RE were identified, namely, apathy, anxiety, prestroke cognitive decline, semantic aphasia, and decreased attention in group 1 and semantic and visual–spatial impairments, prestroke decline, and recurrent stroke in group 2. CONCLUSION: The application of an immersive environment in the early rehabilitation of patients with cognitive impairment improves the performance of verbal and visual tests reflecting mnestic, regulatory, and neurodynamic executive functions in lesions with different lateralization.

A new mathematical technique and its Python program to assess wind potential

BackgroundA new approach based on the Newton–Gauss method is used to find the Weibull parameters.ResultsA Python program was developed to employ the Newton–Gauss method. It is implemented to find Weibull parameters and wind potential of Pakistan’s eight cities (Hyderabad, Khuzdar, Multan, Quetta, Bahawalpur, Islamabad, Lahore, and Peshawar). Wind speed data recorded at an interval of ten minutes for 2016 is used to implement a Python program to calculate wind potential. To compare the values of the parameters, five known methods, the empirical method, method of moments, energy pattern factor method, maximum likelihood method, and modified maximum likelihood method, were also used to model and determine the wind potential. The root mean square error, mean absolute error, coefficient of determination, and Akaike information criterion were calculated to compare values of wind parameters and average wind speed. The correlation between recorded and modeled Weibull pdf was almost 99% for each city.ConclusionsThe new method only caters to those wind speeds that contribute to the wind potential; therefore, the average value of the wind speed is the least in the case of the new method. The maximum wind potential was observed for Hyderabad.

BHOO-MI – BHOONIDHI META INTELLIGENCE

Presently satellite data products are supplied to the users in various formats and with required meta data associated to it. In general, this Meta data contains the latitude/longitude information of the area covered. The utilization of satellite imagery in diverse fields necessities efficient methods to add value to the core images. In this research work, we designed a comprehensive methodology to design a system which provides the intelligence to the basic data product by adding various geographic features over that area. Both natural and manmade geographic features were considered in study. The features include post offices, monuments, airports, museums, hospitals, educational institutes (NITs & IITs) etc. All these features are extracted and preserved as a centralized repository/database. Leveraging a combination of Python programming, web scraping techniques, and advanced geospatial libraries such as Geopandas and Shapely, our system undertakes a multi-step approach. Initially, the system reads the metadata of the satellite image to obtain corner latitude and longitude coordinates. Then the system identifies and extracts relevant feature data. These feature’s meta information is then compiled into a shapefile, providing a comprehensive overview of the features present within the satellite image. This shapefile serves as a valuable resource for further analysis and decision- making in applications such as environmental monitoring, urban planning, and disaster response. Keywords: GIS, Intelligent Satellite Image, Satellite-Image, Data-Preprocessing in Band Data, Point-Data, Geospatial libraries

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Automated Extraction of Structured Data from the Social Network Instagram

The paper explores the extraction of structured information from the social network Instagram through a suitable application programming interface, namely the unofficial Instagram Private API. It focuses on creating a computer program that identifies which posts a user has tagged as "Likes" and then stores this information for profiling specific user profiles. The introduction of the paper highlights the general use of social media in modern society and the importance of personal data for these platforms. It specifies the aim of the study, which is to extract information from Instagram and then analyse it for user profiling. It then describes the evolution of the social network Instagram and key features such as different types of posts. This paper further focuses on the solution and implementation by using Python programming language to minimize the load on Instagram servers and reduce the risk of detection of automated processes. It describes the process of setting up new Instagram accounts, the obstacles in obtaining login credentials, and the need to simulate human behaviour to bypass the network's defence mechanisms. It then focuses on the actual retrieval of information such as the users followed, their posts and information about which posts the user has marked as favourites. It mentions that extracting data from closed profiles is difficult and elaborates on the technical challenges associated with this task. A significant part of this paper is a discussion of Instagram's defence mechanisms that respond to automated computer programs. It describes access denial, account blocking, and identity verification prompts such as CAPTCHA tests. Finally, the conclusion summarizes the results obtained, which indicate the acquisition of approximately 90,000 records for user profiling. It discusses the shortcomings of a fully automated solution due to Instagram's account creation conditions and defence mechanisms. It mentions the need for further research and highlights key gaps and challenges in this area. Overall, the study highlights the technical and security challenges in extracting information from Instagram and emphasises the need for further research and improvements in the technical procedures for extracting data from the platform.

Using an online platform in a didactic learning model that stimulates critical thinking skills of future informatics teachers

Critical thinking is a key competency for future computer science teachers, who must be prepared not only for the rapid technological and informational development of the economy and society but also for the actively evolving process of digitalization in education. The aim of the research presented in the article is to develop methods and tools for enhancing critical thinking skills among future informatics teachers. To this end, the author has created an online platform that provides students with access to a variety of educational resources, including lectures, laboratory work, and interactive tasks. By integrating constructivist, cognitive, and sociocultural approaches to learning, this model promotes deep understanding and analysis of information by students. The platform is physically hosted on the server of Termez State University, built using the Python programming language with the Django framework, and is already being used by second-year students majoring in “Mathematics and Informatics”, studying under the discipline “Informatics and Digital Technologies”.The online platform offers the necessary functionality for organizing independent study sessions and self-monitoring, allowing students to track their progress and develop the ability to critically evaluate information. The educational platform is a key component of the didactic model for developing critical thinking skills in future informatics teachers, the architecture of which is detailed in the article.The results of the study presented in the article can be utilized in the educational practices of higher educational institutions (and even schools) seeking to update and optimize their training processes in the context of digitalization.

Determination of the In-Plane Behavior of Masonry Walls Using the Truss Model

ABSTRACT The diverse characteristics of masonry structures make it difficult to determine their behavior. The development of numerical methodologies using various approaches and assumptions reflects the need for adaptable methods to accommodate these diverse structures. This study focuses on a novel simplified approach for analyzing masonry walls. The proposed method uses truss elements with a well-known theoretical background to elucidate the load-bearing capacity, damage formation and its causative factors, and failure mechanics of masonry piers within a plane. Five masonry wall samples from three different studies were used to validate the accuracy of the proposed approach. Pushover analyses were performed using software with nonlinear analysis capabilities, which was developed in the Python programming language. Consequently, the obtained results were compared with the literature data. This method provides initial expectations such as simplification and consistency, making it a potential alternative for the preliminary assessment of masonry structures.

Analisis Sentimen Ulasan Aplikasi PosPay untuk Meningkatkan Kepuasan Pengguna dengan Metode K-Nearest Neighbor (KNN)

PT Pos Indonesia has launched a digital pospay service. Users who have a positive experience are more likely to return to application. User perceptions analysis can be known from the Review sentiments. Review sentiments that are classified as positive and negative are really needed by developers to improve services (user satisfaction). The research aims to increase user satisfaction of the PosPay application based on the application's review data. The source of data is a review of the pospay application at Google play store. The method used quantitative study method that is K-Nearest Neighbor (K-NN) that classify objects based on learning data that are closest to the object. Research variable is the word from user commentary that associated with the pospay application services. Application review data in scrapping, preprocessing, splits data (train data and test data). Supervised learning (TF-IDF and K-NN) prepared with python programming provides data visualizing. The research results show that the sentiment of Pospay application users tends to be positive. K-NN classification model produces 91% accuracy, 90% precision and recall by 99%. The key word of positive sentiment is: easy, helpful, transaction. Keyword negative sentiment: balance, pay, login.

Micro-Groove Optimisation of High-Speed Inner Ring Micro-Grooved Pumping Seal for New Energy Electric Vehicles

Traditional oil seals are insufficient for the high-speed and bi-directional rotation of new energy electric vehicles. Therefore, we developed a Python program focusing on micro-groove pump seals and examined the unexplored non-contact oil–air biphasic internal end-face seals. Real gas effects were described using the virial and Lucas equations. We introduced an oil–air ratio to determine the equivalent density and viscosity of the two-phase fluid in the seal. Furthermore, we solved the compressible steady-state Reynolds equation using the finite difference method. Analysing the seal’s pumping mechanisms and the effects of operating parameters on sealing performance, we assessed 17 types of hydrodynamic grooves. The results demonstrate that inverse fir tree-like grooves perform well under typical sealing conditions. Under the conditions given in this study, the pumping rate of the optimal groove type compared to other groove types even reached 633.54%. In the oil–air biphasic state, the micro-groove pump seal exerts significant dynamic pressure on the sealing surface. Seal opening force increases with rotational velocity, oil–air ratio, and inlet pressure but decreases with temperature. The pumping rate first increases and then decreases with rotational velocity, increases with oil–air ratio and temperature, and then decreases with inlet pressure. Some special working points require consideration in sealing design. Our results provide insights into designing micro-grooved pumping seals for new energy electric vehicles.

Human activities and increased anthropogenic emissions: A remote sensing study in Cyprus

BackgroundThe effects of human activity on the environment are widespread and widely known. Human activities on most continents can be generalized as shelters and food chains due to the basic requirements of human life. Most of these activities require Land-use Land-cover (LULC) transformations, and their effects can be seen as changes such as increases in the global Land Surface Temperature (LST) and air pollutant concentrations. The present research aims to use remote sensing to monitor LULC transformations in Cyprus. MethodThis research uses Sentinel-3 data, Python programming, geographical information systems, and remote sensing to develop a moving average research method for a case study of Cyprus. Importantly, this work eliminates all political and ethnic boundaries to produce a unified analysis. ResultBased on the research outcomes, the highest mean LST and sulfur dioxide, nitrogen dioxide, and particulate matter (with a diameter of 10 μm or less) emissions occur in the Limassol, Famagusta, Nicosia, and Larnaca districts. These emissions are mainly attributable to artificial surfaces, agricultural areas, and forested and semi-natural areas. These trends may relate to electric power plants, a cement factory, an airport, and intensified agricultural activities in the research area.

Machine Learning model for the prediction of self-diffusion coefficients in liquids, compressed gases and supercritical fluids

Machine Learning (ML) models were developed in this work to predict self-diffusion coefficients (D11) of dense fluids using four training algorithms: Gradient Boosting, k-Nearest Neighbors, Decision Tree, and Random Forest. A database of 7931 experimental points from 223 substances, at different pressures and temperatures, was used to train the models. From an initial set of 34 input features (variables/properties), the eight most important ones, ranked by decreasing relevance, were: density, acentric factor, temperature, critical temperature, critical volume, number of NH and/or OH bonds, pressure, and number of rotatable bonds. The best performance was achieved by models using the first 5 and 8 input features (ML5-D11 and ML8-D11) using the Gradient Boosting algorithm, for which the average absolute relative deviations (AARDglobal) were 9.06 % and 7.14 %, for the test set. The performance of the ML5-D11 and ML8-D11 models was compared with four phenomenological models – the predictive Zhu et al. equation, the 2-parameters Dymond-Hildebrand-Batschinski correlation, and the 1-parameter and 4-parameters Lennard-Jones correlations (LJ1 and LJ4) – which showed AARDglobal of 104.05 %, 82.94 %, 16.92 % and 7.97 %, respectively, for the same test set. Despite the good results of LJ4 equation, it is worth noting it embodies 4 substance-specific parameters that must be fitted to experimental data in advance. The new ML5-D11 and ML8-D11 models are purely predictive and can be applied to polar/nonpolar, spherical/non-spherical, and even hydrogen-bonding molecules in liquids, compressed gases or supercritical fluids. The ML5-D11 and ML8-D11 are provided for use as a Python program.

Normalised similarity assessment to inform grouping of advanced multi-component nanomaterials by means of an Asymmetric Sigmoid function

This manuscript presents a procedure for similarity assessment as a basis for grouping of multi component nanomaterials (MCNMs). This methodology is an adaptation of the approach by Zabeo et al. (2022), which includes an impactful change: the calculated similarities are normalised in the [0,1] domain by means of asymmetric Logistic scaling to simplify comparisons among properties' distances. This novel approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. It can be applied to assess groups of MCNMs as well as mixed groups of multi and single component nanomaterials as well as chemicals. To facilitate the application of the proposed methodology, a software script was developed by using the Python programming language, which is currently undergoing migration to a user-friendly web-based tool. The presented approach was tested against a real industrial case study provided by the Andalusian Innovation Centre for Sustainable Solution (CIAC): SiO2-ZnO hybrid nanocomposite used in building coatings, which is designed to facilitate photocatalytic removal of NOx gases from the atmosphere. The results of applying the methodology in the case study demonstrated that ZnO is dissimilar from the other candidates mainly due to its different dissolution profiles.

TESTING THE ELONGATION OF 3-TUPLES RELATING TO POLYNOMIALS AND POLYGONAL NUMBERS WITH PECULIAR PROCLAMATION

In this paper, special 2-tuples with elements stand well-known polynomials and polygonal numbers with different sides that exclusively meet the property that the summation or difference of two elements provides a square can be stretched to 3-tuples but not 4-tuples with the same characteristics are displayed. Also, the similar characteristics of all such 3-tuples are checked with numerical values by Python programs. KEYWORDS: Diophantine m-tuples, Congruence relations, polygonal numbers, polynomials

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions.

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic.

Calculation of heat protective parameters of clothing packages by method simulation modeling (Monte Carlo method)

The heat-protective properties of clothing have been studied taking into account the influence of deterministic factors, and the influence of random factors on the heat-protective properties of clothing has not been studied. In this regard, to obtain objective values of indicators of the heat-protective properties of clothing, taking into account the nature of the distribution of factors for a batch of products that are random. Knowing the laws of distribution of random factors effectively, it is possible to carry out calculations of the thermal insulation properties of clothing material packages using the simulation method. The work has developed a method for calculating the thermophysical parameters of clothing packages based on simulations of random parameters distributed according to a normal law. For the calculation, an algorithm and calculation program were compiled in the Python programming language. The thermophysical parameters of the clothing package were calculated, and the influence of wind flow speed on them was analyzed. When calculating the wind flow speed, which have a significant impact on the values of the effective thermal conductivity coefficient of the packages and the values of the total coefficient of the packages, they are taken taking into account the Beaufort table and the recommendations of the Ministry of Emergency Situations of Kazakhstan. The developed program for calculating the thermophysical characteristics of a package of clothing materials allows to change the model parameters during the calculation process: introduce new layers of materials into the package, change the thermophysical and linear characteristics of individual layers of the package, change external conditions.