Python Programming Research Articles

The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.

To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis. All consecutive patients with kidney stone(s) only undergoing RIRS in 16 centers were prospectively included (January 2022-August 2023). adult, renal stone(s) only, CT scan (within three months), mid-stream urine culture (within 10 days). concomitant ureteral stone, bilateral procedures. In case of symptomatic infection/asymptomatic bacteriuria, patients were given six days of antibiotics according to susceptibility profiles. All patients had antibiotics prophylaxis. Variables selected for the model: age, gender, age-adjusted Charlson Comorbidity Index, stone volume, indwelling preoperative bladder catheter, urine culture, single/multiple stones, indwelling preoperative stent/nephrostomy, ureteric access sheath, surgical time. Analysis was conducted using Python programming language, with Pandas library and machine learning models implemented using the Scikit-learn library. Machine learning algorithms tested: Decision Tree, Random Forest, Gradient Boosting. Overall performance was accurately estimated by K-Fold cross-validation with three folds. 1552 patients were included. There were 20 (1.3%) sepsis cases, 16 (1.0%) septic shock cases, and three more cases (0.2%) of sepsis-related deaths. Random Forest model showed the best performance (precision = 1.00; recall = 0.86; F1 score = 0.92; accuracy = 0.92). A web-based interface of the predictive model was built and is available at https://emabal.pythonanywhere.com/ CONCLUSIONS: Our model can predict post-RIRS sepsis with high accuracy and might facilitate patient selection for day-surgery procedures and identify patients at higher risk of sepsis who deserve extreme attention for prompt identification and treatment.

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector

IMPLEMENTASI METODE NAÏVE BAYES UNTUK PREDIKSI KELAYAKAN BANTUAN MODAL DAN KREDIT USAHA DI DESA SELAT ABIANSEMAL BADUNG

Small, Micro, and Medium Enterprises (MSMEs) are small-scale businesses carried out by individuals or business entities with a certain amount of net worth and sales proceeds. MSMEs in Abiansemal Strait Village, Badung, still use manual methods in analyzing data. Conditions like this are the basis for this study to create a system that can help make it easier for the Abiansemal Badung Strait Village if there is an assistance program from the government, both business capital assistance, and business credit applications to be given to MSME actors. Naïve Bayes' method of classifying MSMEs is built on concepts explored from the interview process, literature studies and system implementation. The design of this system was made using the Python programming language. The CRISP-DM model is a system development method used in the classification of MSMEs. The Feasibility Prediction Model for Capital Assistance and Business Credit using the Naïve Bayes Method can make it easier for users to find out whether the existence of a web-based classification and recommendation system can make it easier for users to carry out the MSME classification process easily and users can see the solutions recommended by referral MSMEs that have better business conditions directly.

Design and Implementation of a Community Waimai Delivery Assistant System Based on Path Optimization Algorithm

With the rapid development of the food delivery industry, how to efficiently complete delivery tasks in complex community environments has become an important challenge for food delivery platforms and delivery personnel. This project focuses on the problem of planning delivery routes for b0oth inside and outside residential areas, and designs and implements a delivery assistant system based on the Internet of Things and path optimization algorithms. The system utilizes the DFRobot IoT platform to achieve real-time communication between Python programs and mobile apps. Through various path optimization algorithms such as genetic algorithm and simulated annealing algorithm, the system automatically generates the optimal delivery path, significantly improving delivery efficiency and reducing delivery costs. The experimental results show that the system has good operability and reliability in practical applications, providing an effective solution for improving the intelligence level of food delivery

RadicalPy: A Tool for Spin Dynamics Simulations.

Radical pairs (electron-hole pairs, polaron pairs) are transient reaction intermediates that are found and exploited in all areas of science, from the hard realm of physics in the form of organic semiconductors, spintronics, quantum computing, and solar cells to the soft domain of chemistry and biology under the guise of chemical reactions in solution, biomimetic systems, and quantum biology. Quantitative analysis of radical pair phenomena has historically been successful by a few select groups. With this in mind, we present an intuitive open-source framework in the Python programming language that provides classical, semiclassical, and quantum simulation methodologies. A radical pair kinetic rate equation solver, Monte Carlo-based spin dephasing rate estimations, and molecule database functionalities are implemented. We introduce the kine-quantum method, a new approach that amalgamates classical rate equations, semiclassical, and quantum techniques. This method resolves the prohibitively large memory requirement issues of quantum approaches while achieving higher accuracy, and it also offers wavelength-resolved simulations, producing time- and wavelength-resolved magnetic field effect simulations. Model examples illustrate the versatility and ease of use of the software, including the new approach applied to the magnetosensitive absorption and fluorescence of flavin adenine dinucleotide photochemistry, spin-spin interaction estimation from molecular dynamics simulations on radical pairs inside reverse micelles, radical pair anisotropy inside proteins, and triplet exciton pairs in anthracene crystals. The intuitive interface also allows this software to be used as a teaching or learning aid for those interested in the field of spin chemistry. Furthermore, the software aims to be modular and extensible, with the aim to standardize how spin dynamics simulations are performed.

CEO Optimism on Corporate Risk Management: Evidence from China

Based on impression management theory and upper echelons theory, this study constructs an integrated mediating model involv-ing external investors. It aims to explore the impact of CEO optimism, a positive psychological advantage, on corporate crisis management performance. The research uses machine learning based on LSTM to analyze content text, measuring CEO displayed optimism. It also uses a Python program to crawl objective financial data and third-party objective reports after corporate crises occur, evaluating investor confidence and corporate crisis management performance. This study reveals the positive psychological advantage factors of leaders that enhance corporate crisis management performance and potential mediating mechanisms. It deepens our understanding of how CEOs improve corporate crisis management performance, provides practical insights for en-terprises and managers, and offers reference value for future research.

A New SDM-Based Approach for Assessing Climate Change Effects on Plant–Pollinator Networks

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology.

ESTIMATION OF OIL SPILLAGE AND SALVAGE REVENUE IN KOKORI OIL FIELD USING NUMERICAL METHODS AND PYTHON ALGORITHM

The study aimed to estimate oil spillage in the Kokori Oil Field, discovered in 1958, using a linear model. The field has 23 oil wells and produced 383 million barrels of oil. A linear model was developed using Python programming, comparing it with the trapezoidal method and salvage revenue. Results showed a linear relationship between oil spill quantity, duration, and mitigation measures. This study provides a valuable model for estimating oil spillage in the Kokori oil field, emphasizing the importance of accurate estimation for environmental and economic purposes. The study presents a comprehensive model for estimating oil spillage in the Kokori oil field, emphasizing the significance of accurate estimation for environmental and economic purposes. The correlation coefficient value supports the model's sufficiency, and the calculated salvage revenue indicates a commendable projected value for the Kokori oil field at its end of use. Salvage revenue is the estimated value of an asset at the end of its useful life, which is crucial in determining the cost of goods sold and depreciation charge. It lowers the asset's cost, influences its usable life, resale value, and replacement cost. Salvage revenue is calculated by subtracting revenue generated without mitigation measures from revenue realized with mitigation measures.

Development and Validation of Asphalt Pavement Rutting Prediction Model with Transient Temperature Field Considered

This study developed a transient rutting analysis program based on the principles of heat transfer and the viscoelastic–viscoplastic theory of asphalt mixtures. Using the finite element model (FEM) model corresponding to the multi-layer pavement structure and the subprogram interface of ABAQUS software, along with Python programming, the program was designed to estimate rutting under transient temperature field conditions with climate conditions, traffic flow, and structure layer constitutive parameters of pavement considered. Subsequently, the program was used to validate the efficiency of the solution and analyze the influencing factors of internal temperature field and rutting depth within the asphalt pavement structure. Results showed that, based on meteorological data and recommended values of material thermal properties, the FEM of transient temperature field can accurately simulate the temperature field distribution of pavement structure. Based on loading information and dynamic modulus test, the transient temperature variable rutting with FEM proposed in this paper can simulate and calculate the rutting of pavement structure layer with some errors for different pavement conditions from 5% to 40%. Through parametric analysis of the influence factors of temperature field and rutting, the modified transient temperature field rutting prediction program can effectively simulate rutting of pavement structure, and the minor errors were less than 20%.

Exploring Non-linear Dynamics: Constructing the Bifurcation Diagram of a Damped Driven Pendulum using Python

In this paper, we present a detailed analysis and construction of the bifurcation diagram for the damped-driven pendulum system. The bifurcation diagram in general presents the qualitative changes of the steady-state behavior for the pendulum. For this purpose, we implement the use of the Python programming language with the inclusion of scientific libraries. This nonlinear dynamical system is an example of a system that exhibits a chaotic regime, which is the sensitivity of its behavior to the initial conditions and the parameters of the system. We investigate the response of the system to a range of drive strengths γ applied. By changing the driving strength, the system reveals patterns of periodicity, quasi-periodic, and chaotic regimes. The critical values where it goes from regular motion to chaotic one are highlighted, offering further understanding of the mechanisms of the transitions. This study presents the use of the Python programming language for the modeling and visualization of non-dynamic systems and contributes to a deeper understanding of nonlinear oscillator dynamics.

Flipped Classroom Trial for Python Programming with Raspberry Pi Using Moodle

A flipped classroom is a pedagogical method that flips traditional lectures and homework. In the case of a flipped classroom, the traditional lecture is viewed at home before class and homework activities are completed in the classroom with the tutor present to guide students in their work. At present, the above-mentioned flipped classroom has become increasingly popular, and sometimes controversial, within higher education. In this paper, we describe the flipped classroom example for Python programming to control I/O devices with Raspberry Pi. First, the tutor uploads the lecture notes onto Moodle. Secondly, students read the lecture notes prior to the class. Students can then ask questions on the Moodle Forum if there are any questions about the class notes. Thirdly, the students attend the class. As part of the class activities, students form into 10 groups. Each group creates a Python programme based on topics related to the class note, one hour is permitted for this activity. This is an example of a group activity. Next, each group will demonstrate the Python programme they have created to the tutor and other students in a 5-minute presentation. Following this, the tutor and other students will provide comments on the demonstration. Students will then complete a self-reflection journal about the flipped classroom on Moodle after the class. The tutor evaluates each journal written by the students. In this paper, a description of the Python programmes aims to control 8 LEDs is given as an example of a flipped classroom. Finally, the tutor added a questionnaire about the flipped classroom onto Moodle at the end of the semester. Collated questionnaire responses show that students felt the flipped classroom was appealing and helped improve programming skill. Thus, the author concludes that the flipped classroom is engaging and an effective way of teaching.

Web Application Development for TODIM Method Automation and Alternatives Evaluation

The TODIM method (Tomada de Decisao Interativa Multicriterio), which in Portuguese means “Interactive and Multicriteria Decision Making”, is a process of evaluation alternatives, with various academic and professional uses. The current project focuses on the first approach, considering students, professors and investigators as the main beneficiaries and target audience. There is a shortage of software that automate the TODIM method, therefore it is proposed to develop a web application mainly using Python, HTML and JavaScript programming languages that can satisfy this necessity. To guarantee a uniformity in the results obtained by this program, samples of results obtained by this method by other researchers are taken as a basis and guide. To develop the application, the complete functioning of TODIM as an alternative evaluation approach must be understood. In a general view, TODIM uses pairwise comparisons between decision criteria while eliminating the inconsistencies that arise from such comparisons. Thus, the main idea is to measure the degree of dominance in each alternative over the others using the prospective value function. As result, it calculates the overall and partial degrees of dominance of each alternative to finally lead to a classification or “ranking” of the best alternatives.

Estimating the design parameters of a space-based radio surveillance system

Currently, small spacecraft with both optoelectronic and radar imaging equipment are becoming increasingly common. The purpose of the work is to improve the efficiency of preliminary assessment of achievable characteristics of radio surveillance payloads at the initial stages of design of small spacecraft. The paper describes the advantages of using radar imaging equipment, as well as the main trends in its development. The main areas of application of spacecraft with radar of different frequency ranges are given; the advantages of each frequency range are described. The methodology of determining the parameters of the trajectory signal when the platform moves in a circular orbit, taking into account the assumptions associated with estimating the size of the irradiation zone in azimuth is considered. Based on the considered methodology, a program is developed in Python programming language that makes it possible to estimate the length of synthesized aperture, synthesis time and azimuthal resolution of the target equipment on the basis of mass and dimensional characteristics of the antenna and spacecraft orbit parameters. A graphical user interface has been developed for the program which allows the user to interactively vary the initial data for calculation and evaluate the graphical representation of the result. The developed software system can be used for calculation of the main design parameters of small spacecraft with radar imaging equipment at the preliminary design stage.

Slow and Steady or Fast and Furious: An Analysis of Completion Duration in open.uom.lk

The open.uom.lk platform is an open learning platform developed by the University of Moratuwa, Sri Lanka, offering free asynchronous online IT related courses. The platform was developed as an initiative to meet the growing human capital requirement of the IT industry in Sri Lanka, one of the only sectors showing strong growth even during the country’s recent economic crisis. In the two years since the release of its flagship course Trainee Full-Stack Developer, there have been over 270,000 enrolled learners. The course consists of six subjects covering Python programming, web design, and a professional practice module that includes a capstone project. Despite the massive number of enrollments, as in many other MOOCs, completion rates remain low. For instance, for the two beginner courses in programming and web design, into which all participants are enrolled automatically, completion rates stand at 7-10%. Completion rates for subsequent subjects are higher given that enrollment is conditional on completing prerequisites. From a planning perspective, it is not just estimates of how many participants complete the programs but also the time they take to do so that are integral for determining the future growth of the IT industry. As such, this study aims to develop a model for course completion using survival analysis which has the advantage of being able to model censored data (e.g. when duration is unknown due to non-completion) and provide forecasts of completion rates at different points in time. The analysis uses student activity completion reports and demographic information of participants including employment and educational background. The findings suggest that the course interaction variables, particularly the speed of completing early activities, are more important for predicting course completion than the demographic variables, though education and employment status are also significantly correlated with completion. The survival model could then be used for purposes of predicting both the completion probability as well as the timeframe within which the completion may be expected.

A Python program to merge Sanger sequences: an update.

Gene cloning is an important step in investigating gene structure and function. To verify gene sequence, Sanger sequencing is used, which may produce several overlapping sequencing files that need to be merged before alignment to the target gene sequence is performed. Previously, we reported the Python program to Merge Sanger sequences (https://peerj.com/articles/11354/), which ran in command line and relied heavily on EMBOSS suite. In this updated version of the program, we have made several remarkable improvements. It provides a graphical user interface (GUI) written with tkinter, which is convenient and stable. It does not require users to rename the input sequences before performing merging. With regard to the implementation, the updated version utilizes Python function (Align.PairwiseAligner) to align adjacent sequences, which is more flexible (can adjust program parameter i.e., the number of first-time consecutive matching bases). The new version of the program makes merging Sanger sequences much more convenient and facilitates gene study.

Exploring Mean Cordial Labeling Possible Combination Position of Vertices in Graphs: A Computational Approach

Objectives: We have developed the Python module of mean cordial labeling for the different types of graphs, like Path, Cycle, and Subdivision of Star graphs. We aim to identify the number of combination positions of vertices that satisfy the mean cordial labeling rules. We have found mathematical equations that describe the labeling behavior in these graphs. Methods: In this paper, a Python program was developed to find the mean cordial labeling of the Path, Cycle, and Subdivision of the Star graph. We have obtained the number of combination positions of vertex obeying the mean cordial labeling condition using this Python module. We have drawn the graph between the number of vertices and the number of combination positions of the vertex. Findings: We have found that the different graph types like Path, Cycle, and Subdivision of Star graph satisfy the mean cordial labeling condition. The result indicates that the number of combination positions of the Path and cycle graph fulfill the power equation Y=a*xb and the Subdivision of the Star graph meets the exponential growth equation Y=a*bx. Novelty: In this paper, a new method has been developed to find the number of combination position of varies graphs using the Python module. This computational approach will be useful to analyze the biological networking and protein-protein interaction study. Keywords: Mean Cordial, Path, Cycle, Subdivision of Star graph, Computational graph theory

Sentiment Analysis of the MK Decision Trial of the Result of the 2024 President and Vice President General Election on Social Media X Using the Support Vector Machine Method

Support Vector Machine (SVM) is a method of machine learning often used in classification and regression issues, especially in the classification of commentary reviews on social media such as Twitter. The Constitutional Court (MK) has the authority to resolve disputes resulting from the general election, including the 2024 presidential election. As an institution that maintains fairness and transparency in the democratic process, the Constitutional Court's decisions are often at the center of public attention and debate, especially on social media. In the 2024 general election, various allegations of fraud led to protests from several parties who felt aggrieved. The final and binding Constitutional Court's decision is expected to resolve the conflict that arises, but it often does not satisfy all parties, causing political and social tensions. This conflict can be reflected through public opinion expressed on social media, such as Twitter, where various responses and sentiments to the decision are essential analysis materials. This Research uses the Support Vector Machine (SVM) algorithm with a dataset of 1383 review comments divided by an 80:20 ratio for training and testing. The system was implemented using the Python programming language, with evaluations showing the highest accuracy at 61.00%, precision at 61.00%, and recall at 62.00%. This study aims to analyze public sentiment regarding the Constitutional Court's decision using the SVM method and identify the tendency of public opinion as positive, negative, or neutral. Through this study, it is expected that a deeper understanding of the public's perception of the Constitutional Court's decision is obtained. In addition, this Research is likely to contribute to developing sentiment analysis methods in the future and provide a basis for recommendations for the Constitutional Court in handling election result disputes better.

Opinion divergence, investor sentiment, and stock liquidity: Evidence from social media

ABSTRACT This paper developed a mediating effect model of retail opinion divergence, investor sentiment, and stock liquidity to investigate how investor comments and bullish-bearish-polling activities on social media affect investors’ attitudes and behaviours, thus, the stock market. The paper first used the Python programming language to scrape the bullish-bearish-polling outcomes for each stock and developed an investor opinion divergence index. Next, the study collected online investor comments, from which an online investor sentiment index was developed through machine-learning-based ways. The analysis results show that both investor sentiment and retail opinion divergence significantly impact stock liquidity, and investor sentiment plays a mediating role in it.

Automation of vegetable production analysis in the Russian Federation using a modern information system

The presented study is aimed at solving the problems of information support for making effective management decisions in the field of vegetable growing in Russia based on the use of technologies and tools that automate complex algorithms of statistical analysis, which expands the range of users of up-to-date, reliable and complete information on the factors that shape the volumes and efficiency of production in the vegetable growing industries. The information system proposed by the authors, developed on the basis of the PyCharm2023.1 programming environment using the Python programming language, allows the user to obtain statistical characteristics of the phenomenon under study, apply methods of correlation and regression analysis, ANOVA to identify cause-and-effect relationships, modeling processes and phenomena in order to predict their development. The web application was developed taking into account officially published information on the conditions and results of vegetable production, and its operation has been tested. At the same time, the information system has the potential for development and can be adapted to any data set.

Comparison of Microleakage of Monolithic Zirconia after Surface Treatment and Thermal Cycles Using Data Analysis Software

Objective: The aim of this study was to investigate the microleakage of monolithic zirconia after different surface treatments using computer software. Materials and Methods: Three different monolithic zirconia ML (Multilayered), STML (Super Translucent Multilayered), UTML (Ultra Translucent Multilayered) were prepared as discs with a diameter of 15 mm and a thickness of 1.2 mm. Four different surface treatments (Hydrofluoric acid, Tribochemical silica coating, Hydrofluoric acid application + Tribochemical silica coating, Milling + Tribochemical silica coating + Hydrofluoric acid application) were applied to the prepared samples according to their groups (n=8). Samples, Group C: Control group, Group HF: Hydrofluoric acid application, Group T: Tribochemical silica coating, Group HF+T: Hydrofluoric acid application + Tribochemical silica coating, Group F+HF+T: Milling + Tribochemical silica coating + Hydrofluoric acid application, then adhesive system was applied to all specimens and repaired with resin cement. The specimens were thermocycled for one year aging and then immersed in basic fuchsin solution to evaluate microleakage. The specimens were separated with a micro-cut device and evaluated under a stereomicroscope. The dimensions of the images were measured in Python program and the permeability and surface treatments of the zirconia samples were compared. Statistical analysis was performed by two-way ANOVA (p<0.05). Results: UTML F+HF+T showed the lowest microleakage (12.15 ± 1.69), while ML C showed the highest microleakage (73.93 ± 1.59). Among the zirconia specimens, the highest adaptation was obtained in the UTML zirconia (37.59 ± 23.58). Conclusion: According to the data obtained, milling + tribochemical silica coating + acid application surface treatments are recommended for the repair of monolithic zirconia restorations. The sintering temperature and Yttrium Oxide (Y2O3) content of the monolithic zirconia used are effective factors in microleakage after repair.