Python Language Research Articles

System interactive reader using eye-tracker technology in ebook reader

Interest in using ebooks by the academic community is very high. Still, there is a problem when readers are reading through screens, tend to read fast, only scan the necessary parts, and don't focus on paying attention to the content they read, so this reduces the quality of reading because readers don't study the overall meaning of the sentence. Hence, this research aims to build an interactive reader system by integrating eye tracker technology with a webcam which is expected to solve the problem of decreasing the quality of reading through the screen by helping readers stay focused on their reading and providing an interactive system that makes it easier for readers to control the computer while reading. This research adopts the waterfall method and is divided into six stages. The system is designed using class diagrams, use case diagrams, and activity diagrams. Also, the system is built using the Python language with the Django framework. Then, the interactive reader system was tested using black box testing and usability testing methods. Based on the test results, it is shown that the interactive reader system that was built can help improve the quality and concentration when reading activities take place.

Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis

Nowadays, improvements in diabetes detection that provide patients with vital information are needed. This is due to the fact that Diabetes mellitus has generated a worldwide epidemic that costs society and people. Also, patients tend to misread symptoms, and clinicians who collect insufficient data may produce erroneous outcomes. Therefore, this study aims to demonstrate that a programme that integrates expert advice such as decisions, recommendations, or solutions is an excellent method for reducing the incidence of diabetes. Specifically, this study intends to implement a fuzzy expert system that can detect and report the early stages of diabetes as a viable approach. Furthermore, since this programme is available to everyone, people may easily self-diagnose themselves if they have a blood glucose monitoring device. However, developing the fuzzy expert system for real-world situations, such as diabetes patients, using any programming tools is not straightforward. Therefore, this study will provide a comprehensive approach to constructing a fuzzy expert system using the popular programming language Python.

Software Implementation of the Epps-Pulley Criterion in Matlab Modeling Environment

Purpose. Modeling systems and programming platforms provide ample opportunities for the use of statistical tools in research activities. Since the normal distribution is one of the most common distribution laws, the criterion for checking the sample for normality is in high demand among statistical assessment tools, among which the Epps-Pulley test has the status as one of the most powerful tests to check the deviation of the distribution from the normal one. There are a number of implementations of this test in the R and Python languages. However, this test is not implemented in one of the most popular Matlab modeling software. Thus, the purpose of this study is to develop a software implementation of the Epps-Pulley criterion in the Matlab environment and verify the correctness of the performed calculations.Materials and Methods. We implemented the calculation of Epps-Pulley statistics by two methods – classical, using cycles, and matrix-vector, using linear algebra operations. The classical method requires calculating the intermediate values necessary to obtain the criterion statistics using two independent cycles, the second cycle being a double one, in which one cycle is nested into the other. The matrix-vector method requires fewer code by performing calculations using linear algebra operations on matrices and vectors. We obtained critical statistical values for the sample size from 8 to 1000 observations with two-dimensional linear interpolation of tabular values. We used an approximation by a beta function of the third kind for a sample of over 1000 elements.Results. An assessment of the computational efficiency of the methods showed that the cyclic approach is about three times higher than the matrix-vector approach in terms of consumed time, which is presumably due to the processing of insignificant elements in triangular matrices when performing component-by-component operations. The correctness of the software implementation of the Epps-Pulley criterion was tested on several examples, which confirmed the compliance of the calculated values of the criterion statistics, as well as the critical values of statistics, with known data. We carried out a criterion statistical evaluation based on the empirical values of the error of the first kind. We obtained the error values correspondence to the specified significance levels. We performed comparative estimates of the Epps-Pulley test with the Anders-Darling and Shapiro-Wilk tests in terms of the criterion empirical power and tabulated the evaluation results. We published the software implementation of the Epps-Pulley test on the MATLAB Central Internet resource and for free use.Conclusion. We developed software implementation of the Epps-Pulley criterion as a new research tool that was previously unavailable in the Matlab modeling environment. We used the time spent on calculations to make a reasonable choice of the calculation algorithm for the criterion statistics. We confirmed correctness of the calculation algorithms by a set of selective checks and statistical estimates that showed the compliance with well-known theoretical provisions.

Impact of Thunderstorm Activities on Tropospheric NOx Over Koshi Province of Nepal and Their Possible Role in Climate Change

Lightning, a natural atmospheric phenomenon, plays a significant role in altering atmospheric chemistry and air quality by generating nitrogen oxides (NOX). This research aims to explore the correlation between lightning stroke density and atmospheric NOX over Koshi Province of Nepal by using observational data. Over the course of three year (2018, 2019 and 2020), sessional (lightning pre-dominant; pre-monsoon and post-monsoon) measurements of lightning activity (lightning stroke density) were obtained from Global Lightning Dataset (GLD 360) and NOX data acquired from NASA’s Ozone Monitoring Instrument (OMI). The computation was conducted by using programming language PYTHON. Correlation analysis was performed to examine the relationship between lightning stroke density and NOX concentrations by considering lightning stroke density as independent variable and average NOX as dependent variable. Our analysis shows a notable positive correlation between lightning stroke density and average NOX over Koshi Province of Nepal. The values of correlation coefficient (R) was found 0.625, 0.630 and 0.511 in pre-monsoon of years 2018, 2019 and 20120 respectively and 0.654, 0.605 and 0.370 respectively in post-monsoon. Increased lightning activity corresponded to heightened NOX concentrations, especially during pre-monsoon and post-monsoon. These results indicate that lightning significantly contributes to NOX emissions in Koshi Province. Understanding the relationship between lightning and NOX is essential for study atmospheric chemistry and assessing the impact of lightning on atmosphere.

Python tools for structural tasks in chemistry.

In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.

Data Aggregation by Web Scraping using Python

The standard data examination are based on the root and effect relationship, formed a model tiny assessment, abstract and quantitative assessment, the level headedness approach of making extrapolation assessment. The Web Scraper's scheming morals and methodology are compared, it clarifies about the working of how the scrubber is planned. The strategy of it is distributed into three pieces: the web scrubber draws the ideal connections from web, and afterward the information is removed to get the information from the source joins lastly stowing that information into a CSV record. The Python language is carried out for the completing. Thusly, connecting every one of these with the ethical information of libraries and working skill, One can have a satisfactory Scraper in our grasp to produce the ideal outcome. Because of a gigantic local area and library assets for Python and the impeccableness of coding stylish of python language, it is generally proper one for Scraping wanted information from the ideal site.

IOT Based Light Automation System

Electronic &Telecommunication (E&TC) Department of JSPM’s Bhivrabai Sawant Institute of Technology & Research (BSIOTR) Wagholi, Pune. Light System involves controlling departmental lighting using the Internet of Things (IoT), allowing for a user-friendly automation system within the department. Instead of switching light manually ON or OFF, this system uses technology for doing it automatically. It enables automatic control of electronic appliances such as fans and lights. In this paper, we have implemented the operation of lights through the Blynk Application to control departmental light automation, allowing users to turn lights ON and OFF as needed. The system primarily utilizes the Raspberry Pi module, a crucial component of our smart light system implementation. It comprises both hardware and software components. The hardware includes Raspberry Pi 3b+ and a Relay Module, while the software utilizes the Python Language to issue commands for turning the lights ON and OFF. Raspberry Pi 3b+ module keeps all electrical Department appliances connected through a single interface for complete smart home system automation. With this setup, there is no need to use the conventional switches, and hence human effort required for the system is comparatively very less. Keywords: Department Light System , Internet of Things (IoT) , User-friendly automation, Raspberry Pi module, Relay Module, Blynk Application. .

Study on Venture Capital Investments and its ability to stimulate Innovation and spur Economic Growth in India

Objective: This paper examines the impact of Venture Capital Investments (VCI) on Innovation and Economic Growth in India. Venture Capital has emerged as a critical source of funding for startups, enabling them to develop innovative ideas, scale their operations, and contribute to economic growth. India, with its vibrant entrepreneurial ecosystem, has witnessed a significant increase in venture capital investments over the past decade. Research Design and Methods used: This paper gathers key information from the market, varying from various reports and relevant data published by private players like E&Y, Bain & Company, Hurun, Venture Intelligence, Indian Venture Capital Association (IVCA); besides, governmental reports of SIDBI, SEBI, Economic Surveys, MSME Annual Reports and ‘Intellectual Property India’ report published by the Controller General of Patents, Design, Trademarks and Geographical Indications, Ministry of Commerce, Government of India. This is qualitative research. For the purpose of this research, the data pertains to last ten years. Using Python language, data analysis has been done by finding Correlation and Regression Analysis. The model has been run on Jupyter Notebook for Python. Findings: There is, visible upsurge in the VC investments in the last few years, much credited to the pro-active approach of the Government in creating the right ambience and the infrastructure. The flagship programs of the Government, like, Startup Mission and Make in India have yielded positive results. Pandemic did its own damage, but, post Covid-19 pandemic, there has been a considerable upward shift in the VC investments as they venture into new terrains, seeding new technologies and nurturing Startups. AS a result of this, there has been a significant jump in the number of Unicorns. There has been a quantum jump in number of VCs that have invested in India and also the deal size is consistently on the rise. Implications and Recommendations: By studying the existing research and data available, it is observed that there is asymmetry between the literature and the industry. The venture capital industry is inward cautious and there is dearth of exact deal data because of the secrecy and privacy issue, coupled with demographic and geographical shortcoming to collect the data. The role played by IVCA and other private players is, undoubtedly, worth a praise. It is recommended that further research should alienate with industry and be more methodical. Contribution and Value Added: This study will benefit, immensely, to both researchers, intending to do further research in this area, and, also to the industry in building a nexus with academia. This research has taken one step forward in building the bridge between the two. This paper fills the gap by identifying the relationship between key variables and apply statistical tools like Correlation and Regression analysis to assess the trends and contribution of venture capital investments in terms of key variables innovation and economic growth. The outcomes of this study will offer a comprehensive understanding of the fundamental elements of venture capital investments (VCI) and their influence on both innovation and the growth of the Indian economy.

Optimizing Fuel Injection Timing for Multiple Injection Using Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel Engine

<div>Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [<span>1</span>]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NO<sub>x</sub>–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to demonstrate the use of RL algorithms while maintaining a constant indicated mean effective pressure. A change in fuel quantity has a larger influence on the indicated mean effective pressure than a change in fuel injection timing. The focus of this work was to present a novel methodology of using the 3D combustion data from analysis software in the form of a functional mock-up unit (FMU) and showcasing the implementation of a RL algorithm in Python language to interact with the FMU to reduce the NO and soot emissions by suggesting changes to the main injection timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms identified the operating injection strategy, i.e., main injection timing for a pilot–main and pilot–main–post-injection strategy, reducing NO emissions from 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection strategies.</div>

Prediction of grain boundary strength and mechanical responses of high-entropy carbides reinforced by hybrid graphene and SiC nanowire

The excellent performance of high entropy ceramics (HECs) has attracted extreme attention in the most advanced functional applications and engineering structures, yet the poor toughness limits the application of HECs in many fields. As a consequence, advanced technology and new doping phases are urgently demanded to enhance the comprehensive mechanical properties of HEC composites. The microstructure model of 0.2 wt% graphene (G) and SiC nanowires (SiCnw) doped in (HfNbTaTiZr)C is established in Abaqus with Python language. The effects of HEC-HEC interface strength and HEC-G interface strength on crack propagation and mechanical properties of HEC composites were investigated. The improvement of mechanical properties of (HfNbTaTiZr)C with different content of SiCnw was predicted respectively. The composites obtain the optimal combination of mechanical properties at the content of 1.5 wt% SiCnw. The accuracy of the simulation is verified by the experimental data of mechanical properties changing trend and toughening mechanism, providing theoretical support for toughening HEC composites.

Bayesian analysis suggests independent development of sensitization to different fungal allergens

BackgroundFungi are known for their ability to cause allergies, but data on individual sensitization to them are insufficient. The purpose of the study was to carry out a comprehensive analysis of the fungal allergens’ sensitization profile in the Ukrainian population and to determine both population and individual sensitivity to these allergens. MethodsWe utilized a set of ALEX allergy test data from 20,033 inhabitants of 17 regions of Ukraine from 1 to 89 years conducted in 2020–2022. A complex of programs in the Python language was developed and Bayesian network analysis was applied to determine the sensitivity combinations in individual patients to various fungal components. ResultsSensitivity to Alt a 1 dominated and was observed in 79.39% of patients, and 62.17% of them were sensitive solely to Alt a 1. Exclusive sensitivity to Mala s 6 was second in individual patient profiles with a frequency of 4.06%. Combined sensitivity to Alt a 1 – Asp f 3 was third with a share of 3.28%. Pen ch and Cla h extracts stimulated the production of the lowest median sIgE levels. The highest median sIgE levels were for Alt a 1, Mala s 11 and Asp f 6, respectively. Median sIgE levels increased in adults compared to children for all components of Aspergillus fumigatus, as well as for Mala s 5 and Mala s 11. In the rest of the cases, they decreased in adults compared to children. The sensitization rates to fungi in general and specifically to Alternaria were lower in the western parts of Ukraine, especially in the Carpathian region, situated within the Broad-leaved Forest zone. The results of Bayesian modeling revealed that in the case of Alt a 1, the simultaneous absence of sensitivity to Cla h 8, Mala s 11, Mala s 5 and Mala s 6 molecules could condition the presence of sensitization to the major Alternaria allergen with a probability of 92.42%. In all other cases, there was a high probability of absence of sensitivity to particular allergen against the background of absence of sensitivity to other ones, which may indicate the independent development of sensitization to different fungal allergens. ConclusionsSensitivity to Alt a 1 dominated in the studied population with a lower rate in the western regions. The highest median sIgE levels were induced by Alt a 1, Mala s 11 and Asp f 6. Bayesian Analysis suggest a high probability of the independent development of sensitization to different fungal allergens. The idea that sensitization to one allergen may be protective against sensitization to another one(s) requires further clinical study.

Enhancing Arabic Sign Language Recognition using Deep Learning

The present time, Sign language is very important for people who suffer from hearing loss or who cannot speak. Normal humans tend to disregard the significance of signal language, which is a mere supply of communique to mute and deaf societies. So, this study proposes a developed model for sign Language Recognition for Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then set the algorithm by developing programming on Open-CV, using Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, and each one of them included 1500 images incorporating hand gestures in at-variance environments. The data set was divided into a training section with a percentage of 70 %, a section for testing with a percentage of 20 %, and a section for validation with a percentage of 10 %. The results show that the suggested model achieved an accuracy rate of 94.8%, and it has proven its effectiveness and success, especially after being tried and tested by several users and obtaining their comments and feedback.

Consumer Sentiment is Extracted from the e-Commerce Website Evaluates Dataset using an Assembly Model

Globe is getting more digitalized in the modern-day. E-commerce is gaining traction in today's digitalized world since it enables customers to purchase products without leaving their homes. As more customers rely on online purchases, the value of a review grows. For a product to be selected, a consumer has to examine thousands to grasp the product. However, in this age of burgeoning machine learning (ML), sorting by hundreds of assessments will be much easier if the model were employed to polarize and learn from those evaluations. In this study, the Python language is used to apply the classification algorithm for the obtained data. The accomplishment of a voting classifier consisting of other three classifiers which are SVC, XGB, and XtraTree demonstrates greater precision than other previous research done. The dataset used here is Amazon product Review and work is implemented on Python 3.1.

Monitoring Forest Dynamics and Conducting Restoration Assessment Using Multi-Source Earth Observation Data in Northern Andes, Colombia

Examining the efficacy of current assessment methodologies for forest conservation and restoration initiatives to align with global and national agendas to combat deforestation and facilitate restoration efforts is necessary to identify efficient and robust approaches. The objective of this study is to understand forest dynamics (1996–2021) and assess restoration implementations at the Urra’s supplying basin hydroelectric reservoir in Colombia. The processing approach integrates optical and radar Earth Observation (EO) data from Sentinel-2 and Landsat for forest mapping and multi-temporal forest change assessment (1996–2021), and a Sentinel-1 backscatter time-series analysis is conducted to assess the state of forest restoration implementations. The processing chain was scaled in a cloud-based environment using the Nebari and SEPPO software and the Python language. The results demonstrate an overall substantial decrease in forested areas in the 1996–2000 period (37,763 ha). An accuracy assessment of multi-temporal forest change maps showed a high precision in detecting deforestation events, while improvements are necessary for accurately representing non-forested areas. The forest restoration assessment suggests that the majority of the 270 evaluated plots are in the intermediate growth state (82.96%) compared to the reference data. This study underscores the need for robust and continuous monitoring systems that integrate ground truth data with EO techniques for enhanced accuracy and effectiveness in forest restoration and conservation endeavors.

DNASCANNER v2: A Web-Based Tool to Analyze the Characteristic Properties of Nucleotide Sequences.

Throughout the process of evolution, DNA undergoes the accumulation of distinct mutations, which can often result in highly organized patterns that serve various essential biological functions. These patterns encompass various genomic elements and provide valuable insights into the regulatory and functional aspects of DNA. The physicochemical, mechanical, thermodynamic, and structural properties of DNA sequences play a crucial role in the formation of specific patterns. These properties contribute to the three-dimensional structure of DNA and influence their interactions with proteins, regulatory elements, and other molecules. In this study, we introduce DNASCANNER v2, an advanced version of our previously published algorithm DNASCANNER for analyzing DNA properties. The current tool is built using the FLASK framework in Python language. Featuring a user-friendly interface tailored for nonspecialized researchers, it offers an extensive analysis of 158 DNA properties, including mono/di/trinucleotide frequencies, structural, physicochemical, thermodynamics, and mechanical properties of DNA sequences. The tool provides downloadable results and offers interactive plots for easy interpretation and comparison between different features. We also demonstrate the utility of DNASCANNER v2 in analyzing splice-site junctions, casposon insertion sequences, and transposon insertion sites (TIS) within the bacterial and human genomes, respectively. We also developed a deep learning module for the prediction of potential TIS in a given nucleotide sequence. In the future, we aim to optimize the performance of this prediction model through extensive training on larger data sets.

Calculation and prediction of divertor detachment via impurity seeding by using 1D model

Abstract Achieving the divertor detachment helps to alleviate excessive heat load and sputtering problems on the target plates, thereby prolonging the lifetime of divertor components for fusion devices. In order to provide a rapid but relatively reliable prediction of plasma parameters along the flux tube for future devices design, a one-dimensional (1D) modeling code for the impurity seeded detached divertor operational point has been developed based on the Python language, which is a fluid model based on the previous work.[1] The experimental observation of the onset of divertor detachment by neon (Ne) and argon (Ar) seeding in EAST is well reproduced by using the 1D modeling code. The comparison of the 1D modeling and 2D simulation by SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows good consistency. The predictions of the radiative power loss and requirement of the corresponding impurity concentration for achieving divertor detachment via different impurity seeding for the high heating power scenarios on EAST and CFETR Phase II by the 1D model are also presented. Based on the predictions, the optimized parameter space for divertor detachment operation on EAST and CFETR has also been determined. Such a simple but reliable 1D model can provide a reasonable parameter inputs for a detailed and accurate analysis by 2D or 3D modeling tools through rapid parameter scanning.

Research on the Application of Computer-based Psychotherapy in Clinical Guidance

Based on the B/S architecture combined with MVC programming ideas, this paper uses Python language to design and implement a computer psychotherapy system. Numerous experimental results show that its effect is not significantly different from traditional therapies, and it has broad prospects for development.

Research on Dynamic Gesture Recognition and Control System based on Machine Vision

Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. During the experiment's algorithm verification phase, we test and analyze the algorithm using the Python language, compilation environment, and data set. In the control experiment, we evaluated the system's ability to control the office application's start, play, next, previous, and exit functions. We achieve 96.3% accuracy on the test set. Experimental results show that the system can recognize a wide range of hand gestures and accurately control the presentation.

Semi-automatic Knowledge Graph Construction Based on Deep Learning

The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of < entity, relationship, entity > or < entity, attribute, attribute value > to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.

Performance Analysis of Universities Based on Combination Weighting Method and Grey Correlation Analysis

The performance evaluation of universities is of great significance for improving their educational level and allocating funding resources. This article constructs a performance evaluation model for universities based on the combination weighting method and grey correlation analysis. The model first uses principal component analysis and average mutual information method to select indicator features and select indicators with high importance; On this basis, the grey correlation analysis method is used to obtain the relationship between input indicators and output indicators; Finally, develop a visual performance analysis platform for universities using Python language and validate it with examples to conduct research. The research results indicate that the model and platform can effectively and intuitively display evaluation results, and can assist education management departments in identifying shortcomings and formulating targeted improvement strategies based on evaluation results.