GUI Application Research Articles

Wireless image transfer by various antennas using transmitter and receiver modules at 5.8 GHz

Abstract This research proposes an inexpensive technique for wireless image transfer for security and surveillance applications. The technique uses a 5.8 GHz transmitter and receiver module, along with external antennas in the real-time image transfer within a radius of 100 m. The transferred images are stored in a laptop using a Python code-based graphical user interface application. Different antennas, dipole, circular split-ring resonators, hexagonal split-ring resonators, and metamaterial antennas are utilized for comparison. The Blind/Referenceless Image Spatial Quality Evaluator method is used to assess the picture quality of transferred images to quantify image transfer performance when no ground truth or reference photos are supplied. According to the presented results, images transferred using metamaterial antennas have higher quality than those transferred with other types of antennas. For security considerations, such a system can communicate and store the images in real time.

Machine learning-based analytical approach for mechanical analysis of composite hydrogen storage tanks under internal pressure

This study is a practical exploration of the application of machine learning for the mechanical analysis of filament-wound thin-composite hydrogen storage tanks under internal pressure. Our innovative approach seamlessly integrates classical laminate theory, comprehensive parametric analysis, and machine learning to advance the state-of-the-art in composite tank design. This versatile framework holds promise for addressing challenges in other structurally complex systems. A comprehensive parametric study was then conducted to explore the influence of key design parameters, such as internal pressure, tank radius, order of layer, and layer orientation. Pearson's correlation analysis was used to determine the parameters that had the most significant impact on the mechanical behavior of the tank. In addition, five machine learning models, namely, Extreme Gradient Boosting, Adaptive Boosting, Artificial Neural Network, Support Vector Machine, and Random Forest, were examined to determine their predictive capability for the mechanical performance of these composite tanks. Key findings demonstrate XGBoost's superior predictive capabilities, achieving a remarkable 99% accuracy in predicting stress in the x-direction compared to Random Forest's 96%. XGBoost also outperformed Random Forest in terms of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) with values of 26 and 0.02, respectively, versus 207 and 0.16. These results underscore XGBoost's potential to revolutionize hydrogen storage tank design. The development of a user-friendly GUI application further enhances the practicality of this research, allowing engineers and composite designers to efficiently simulate the mechanical behavior of these tanks in real time by adjusting the cursors for variables such as the pressure, radius, layer order, and layer orientation. This tool, with its intuitive interface and elimination of manual calculations, promises significant enhancements in the design process, providing a platform for the rapid assessment and optimization of tank architectures, thereby improving the efficiency and reliability of the design process.

MR Image Fusion-Based Parotid Gland Tumor Detection.

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%.

Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils

Hydraulic conductivity and compaction parameters are the key considerations in selecting lateritic soils for engineering construction. Nevertheless, the complexity and high cost of the required tests have driven many contractors and field engineers to skip them, resulting in a succession of engineering structure failures. To overcome this limitation, this study developed machine learning-based standalone GUI application to predict lateritic soils’ hydraulic conductivity (K), maximum dry density (MDD) and optimum moisture content (OMC) from indices including specific gravity, liquid limit, plasticity index, linear shrinkage and fine content. To achieve this goal, the geotechnical properties of three hundred samples, collected using grid sampling method from thirty different lateritic deposits in southwestern Nigeria, were evaluated through laboratory tests. The test results were used to train predictive models using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR). The models’ performance was compared using coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). Based on these performance metrics, ANN demonstrated the best performance (R2 = 0.9835, 0.9797, 0.9999; RMSE = 7.938, 0.252, 2.09E-08; MAPE = 0.288, 1.114, 1.587; MAE = 5.432, 0.169, 1.1E-08) for MDD, OMC and K, respectively, followed by GPR and then ANFIS. Thus, the ANN models were selected and embedded in a standalone GUI application to enhance easy and quick prediction of lateritic soils’ MDD, OMC and K. The validity of the ANN-based standalone GUI application was demonstrated by comparing it favorably to notable regression-based models in the literature.

Understanding 3D seismic data visualization with C++, OpenGL and GLSL

Seismic data visualization in 3D space is a valuable interpretation tool. Several open-source visualization tools are available. However, little explanation is provided about the inner working of visualization process. The current work discusses a “hello world” equivalent source code for 3D seismic data visualization using Graphical Processing Units (GPUs) with OpenGL and the OpenGL Shading Language (GLSL) programming languages. Rendering is the core process generating 2D image that we see on the screen from the 3D data structures being visualized. Texture mapping-based rendering commonly applied to seismic data starts with creating an OpenGL object called texture. The texture is then mapped over a rectangular object to display a seismic line. The work presented here is performed using OpenGL, GLSL, C++ and Qt toolkit. Here Qt provides the application GUI framework, C++ is used for data I/O, filtering, and sorting, and OpenGL and GLSL are used for 3D rendering. This paper describes the data flow through the application, and two implementations of vertex and fragment GLSL shaders. Visualization is critical for seismic data processing and interpretation. The low-level details of the process presented here will hopefully help readers in obtaining better understanding of the visualization concept and inner working principle and facilitate further development.

Rethinking AI code generation: a one-shot correction approach based on user feedback

Code generation has become an integral feature of modern IDEs, gathering significant attention. Notable approaches like GitHub Copilot and TabNine have been proposed to tackle this task. However, these tools may shift code writing tasks towards code reviewing, which involves modification from users. Despite the advantages of user feedback, their responses remain transient and lack persistence across interaction sessions. This is attributed to the inherent characteristics of generative AI models, which require explicit re-training for new data integration. Additionally, the non-deterministic and unpredictable nature of AI-powered models limits thorough examination of their unforeseen behaviors. We propose a methodology named One-shot Correction to mitigate these issues in natural language to code translation models with no additional re-training. We utilize decomposition techniques to break down code translation into sub-problems. The final code is constructed using code snippets of each query chunk, extracted from user feedback or selectively generated from a generative model. Our evaluation indicates comparable or improved performance compared to other models. Moreover, the methodology offers straightforward and interpretable approaches, which enable in-depth examination of unexpected results and facilitate insights for potential enhancements. We also illustrate that user feedback can substantially improve code translation models without re-training. Ultimately, we develop a preliminary GUI application to demonstrate the utility of our methodology in simplifying customization and assessment of suggested code for users.

PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification.

Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.

REDQT: a method for automated mobile application GUI testing based on deep reinforcement learning algorithms

REDQT: a method for automated mobile application GUI testing based on deep reinforcement learning algorithms

Non-Destructive Measurement on Kwini Mango Fruit using Capacitive Sensing Technique at 250kHz to 2MHz for Ripeness Determination

This study aims to evaluate the characteristics and quality of Kwini mango fruit using non-destructive capacitive sensing methods. Unlike other fruits, identifying ripe Kwini mangoes can be challenging due to their green appearance and unfamiliar characteristics to consumers. Despite the growing interest in non-destructive fruit quality assessment techniques, there has not been significant prior exploration of capacitive sensing in the context of Kwini mangoes. Therefore, in this experiment, a parallel plate was used to measure the capacitance of Kwini mango fruit at frequencies ranging from 250 kHz to 2 MHz. Results showed that the moisture content and pH level decreased within 15 days of investigation. This technique was able to predict Kwini maturity with significant precision by applying the capacitance formula. Because of its high R2 value and sensitivity in linear regression, 500kHz was determined to be the most efficient operating frequency for Kwini mango fruits among the frequencies evaluated. In this experiment, the relationship between capacitance and Brix also yielded encouraging findings. The study also includes the development of a GUI application for determining the sweetness and maturity of Kwini mangoes. Overall, this research demonstrates the potential of capacitive sensing technology as a non-destructive approach to measuring Kwini mango fruit ripeness.

Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming

Python is popular in artificial intelligence (AI) and machine learning (ML) due to its versatility, adaptability, rich libraries, and active community. The existing Python interoperability in Java was investigated using socket programming on a non-graphical user interface (GUI). Python's data analysis library modules such as numpy, pandas, and scipy, together with visualization library modules such as Matplotlib and Seaborn, and Scikit-learn for machine-learning, aim to integrate into Java graphical user interface (GUI) applications such as Java applets, Java Swing, and Java FX. The substantial method used in the integration process is TCP socket programming, which makes instruction and data transfers to provide interoperability between Python and Java GUIs. This empirical research integrates Python data analysis and visualization graphs into Java applications and does not require any additional libraries or third-party libraries. The experimentation confirmed the advantages and challenges of this integration with a concrete solution. The intended audience for this research extends to software developers, data analysts, and scientists, recognizing Python's broad applicability to artificial intelligence (AI) and machine learning (ML). The integration of data analysis and visualization and machine-learning functionalities within the Java GUI. It emphasizes the self-sufficiency of the integration process and suggests future research directions, including comparative analysis with Java's native capabilities, interactive data visualization using libraries like Altair, Bokeh, Plotly, and Pygal, performance and security considerations, and no-code and low-code implementations.

CREATION OF WEB BROWSER USING PyQt5

A web browser is a software application for retrieving, presenting, and traversing information resources on the World Wide Web. The information resource is identified by a Uniform Resource Identifier (URI) and may be a web page, image, video, or other piece of content. In these project our major goal is to create a web browser using only PYQt5. The PyQt5 is one of the GUI Toolkit of applications of python programming language. PyQt5 is cross-platform GUI toolkit. One can develop an interactive desktop application with so much ease because of the tools and simplicity provided by this library. A GUI application consists of Front-end and Back-end. PyQt5 has provided a tool called ‘Qt Designer’ to design the front-end by drag and drop method so that development can become faster and one can give more time on back-end stuff. It shows an sample browser like Google with some extra features like home , reload, back ,forward and moreover it is easy for creating a browser with PyQt5,by using this library the most useful thing for engineers to code reducibility..

SEGUL: Ultrafast, memory-efficient and mobile-friendly software for manipulating and summarizing phylogenomic datasets.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop an ultrafast and memory-efficient software, SEGUL, that performs common phylogenomic dataset manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line interface (CLI) and graphical user interface (GUI) applications, and as a library for Rust, R and Python, with possible support of other languages. The CLI and library versions run native on Windows, Linux and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS, iPadOS and Android operating systems. SEGUL leverages the high performance of the Rust programming language to offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL's efficiency reduces economic barriers by allowing analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

Integrating TextBlob and VADER for Dynamic Sentiment Analysis: A GUI-Based Approach with Emotion Visualization and Confidence Assessment

Abstract: This research presents a Graphical User Interface (GUI) application developed in Python using the Tkinter library for sentiment analysis. The GUI integrates two distinct sentiment analysis libraries, TextBlob and VADER, to provide a comprehensive analysis of text sentiment. Each library's sentiment analysis results are displayed along with confidence scores, allowing users to gauge the reliability of the analysis. Additionally, the GUI features a visual indicator in the form of a lamp that dynamically changes color based on the detected emotion (positive, negative, or neutral) inferred from the sentiment analysis results. Moreover, the GUI includes a combined sentiment score, calculated by averaging the sentiment scores obtained from both TextBlob and VADER, enhancing the overall accuracy and reliability of the sentiment analysis. This GUI provides users with an intuitive and interactive tool for analyzing text sentiment, with the ability to visualize emotions and assess sentiment confidence.

Auto-phylo v2 and auto-phylo-pipeliner: building advanced, flexible, and reusable pipelines for phylogenetic inferences, estimation of variability levels and identification of positively selected amino acid sites.

The vast amount of genome sequence data that is available, and that is predicted to drastically increase in the near future, can only be efficiently dealt with by building automated pipelines. Indeed, the Earth Biogenome Project will produce high-quality reference genome sequences for all 1.8 million named living eukaryote species, providing unprecedented insight into the evolution of genes and gene families, and thus on biological issues. Here, new modules for gene annotation, further BLAST search algorithms, further multiple sequence alignment methods, the adding of reference sequences, further tree rooting methods, the estimation of rates of synonymous and nonsynonymous substitutions, and the identification of positively selected amino acid sites, have been added to auto-phylo (version 2), a recently developed software to address biological problems using phylogenetic inferences. Additionally, we present auto-phylo-pipeliner, a graphical user interface application that further facilitates the creation and running of auto-phylo pipelines. Inferences on S-RNase specificity, are critical for both cross-based breeding and for the establishment of pollination requirements. Therefore, as a test case, we develop an auto-phylo pipeline to identify amino acid sites under positive selection, that are, in principle, those determining S-RNase specificity, starting from both non-annotated Prunus genomes and sequences available in public databases.

Abstract 7430: Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets

Abstract Background: Single-cell atlases provide a high-resolution and tissue-specific depiction of cellular state at the transcriptomic level. Newer technologies like spatial transcriptomics, MALDI, and long-read sequencing introduce orthogonal modalities to single-cell data, making the analyses of large atlases even more complex. RShiny package in R is used to develop user-friendly GUI applications that could enable users to use analysis tools in an interactive manner, thus minimizing the requirement of prior programming experience. Methods: Publicly available scRNA-seq datasets were collected for a wide range of prostate-related pathologies. The datasets were evaluated for their quality based on cell count and availability of the appropriate markers in each compartment. Finally, 11 datasets out of 16 datasets were selected for building integrated datasets. These datasets were filtered for cell types with <10% mitochondrial RNA, and cells with <500 genes were removed from the datasets. The datasets were then clustered to evaluate the effects of cell cycle, dissociation-induced stress, and the presence of doublets. The quality-controlled datasets were batch-corrected using the Harmony package and the harmonized principal components were used in Seurat for clustering the integrated data. Tools used for the analysis of single-cell datasets, beginning from raw counts, have been developed in R with a focus on modularity. These tools are packaged into a simple and intuitive RShiny application which follows the workflow shown in Figure 1a. Results: The atlas obtained include samples from healthy prostate, benign prostate hyperplasia, primary prostate tumor, ICC/IDC, castration-resistant prostate cancer (CRPC), and Neuroendocrine prostate cancer (NEPC). The dataset includes 72 patients and ~320k cells with a median number of ~1600 genes expressed. We were able to distinctly identify Luminal, Hillock, Club, Basal, Neuroendocrine, Endothelial, Fibroblast, Smooth Muscle, Myeloid, T, B, NK, Mast, and Plasma cells in the integrated along with some unknown cellular subtypes in the epithelial compartment. The integrated data was manually annotated using cell type-specific markers. The single-cell atlas obtained was weaved with the workflow tools into an RShiny app. Conclusions: Besides being a useful reference for PCa, the atlas would find extensive use in understanding the transcriptomic landscape of PCa biology. The integrated dataset will also prove crucial in developing bulk RNA sequencing deconvolution and single-cell imputation algorithms. Finally, we aim to provide a user-friendly interface to the prostate cancer community for using the single-cell PCa atlas and analysis tools with minimal computational requirements. Citation Format: Shivang Sharma, Eugene Shenderov. Development and demonstration of a user-friendly application to explore single-cell atlas for prostate cancer (PCa) and analyze newly generated single-cell and spatial PCa datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7430.

Home Automation Lighting System using Bluetooth

The main objective of this project is to develop a home automation system using Arduino board with Bluetooth being remotely controlled by any Android OS smart phone. As technology is advancing so houses are also getting smarter. Modern houses are gradually shifting from conventional switches to centralized control system, involving remote controlled switches. presently, conventional wall switches located in different parts of the house makes it difficult for the user to go near them to operate. Even more it becomes more difficult for the elderly or physically handicapped people to do so. Remote controlled home automation system provides a most modern solution with smart phones. In order to achieve this, a Bluetooth module is interfaced to the Arduino board at the receiver end while on the transmitter end, a GUI application on the cell phone sends ON/OFF commands to the receiver where loads are connected. Wireless technologies are becoming more popular around the world and the consumers appreciate this wireless lifestyle which gives them relief from the well known“ cable chaos” that tends to grow under their desk. Now with the embedded Bluetooth technology, digital devices form a network in which the appliances and devices can communicate with each other. Today, home automation is one of the major applications of Bluetooth technology. Operating over an unlicensed, globally available frequency of 2.4GHz, it can link digital devices within a range of 10m to 100m at the speed of up to 3Mbps depending on the Bluetooth device class. With this capability of Bluetooth; we propose a home automation system based on Bluetooth technology.

Fasta2Structure: a user-friendly tool for converting multiple aligned FASTA files to STRUCTURE format

BackgroundThe STRUCTURE software has gained popularity as a tool for population structure and genetic analysis. Nevertheless, formatting data to meet STRUCTURE's specific requirements can be daunting and susceptible to errors, especially when handling multilocus data. This article highlights the creation of a graphical user interface (GUI) application tailored to streamline the process of converting multiple sequence alignments into a single, cohesive file that is compatible with the STRUCTURE software.ResultsThe application has been developed utilizing Tkinter for the GUI and Biopython for handling FASTA files. This program processes the files, pinpoints variable sites, and converts the sequences into a binary format. Subsequently, the sequences are concatenated and presented within the graphical interface's text area, enabling users to review and confirm the results. Furthermore, the program stores the concatenated results in a file, delivering a ready-to-use input for the STRUCTURE software.ConclusionThis application offers an efficient and dependable solution for transforming multiple aligned FASTA files into a concatenated binary format file, which is compatible with the STRUCTURE software. With its user-friendly graphical interface and error-reduction approach, this tool proves invaluable for researchers engaged in population structure and genetic analysis.

EksPy: a new Python framework for developing graphical user interface based PyQt5

This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python.

Following the Writer’s Path to the Dynamically Coalescing Reactive Chains Design Pattern

Two recent studies addressed the problem of reducing transitional turbulence in applications developed in C# on .NET. The first study investigated this problem in desktop and Web GUI applications and the second in virtual and augmented reality applications using the Unity3D game engine. The studies used similar solution approaches, but both were somewhat embedded in the details of their applications and implementation platforms. This paper examines these two families of applications and seeks to extract the common aspects of their problem definitions and solution approaches and codify the problem-solution pair as a new software design pattern. To do so, the paper adopts Wellhausen and Fiesser’s writer’s path methodology and follows it systematically to discover and write the pattern, recording the reasoning at each step. To evaluate the pattern, the paper applies it to an arbitrary C#/.NET GUI application. The resulting design pattern is named Dynamically Coalescing Reactive Chains (DCRC). It enables the approach to transitional turbulence reduction to be reused across a range of related applications, languages, and user interface technologies. The detailed example of the writer’s path can assist future pattern writers in navigating through the complications and subtleties of the pattern-writing process.

PEMANFAATAN CHATBOT MENGGUNAKAN NATURAL LANGUAGE PROCESSING UNTUK PEMBELAJARAN DASAR-DASAR GUI TKINTER PADA BAHASA PEMROGRAMAN PYTHON

Along with the significant development of technology today, websites have become an alternative media as a means of promotion and information dissemination. The website has many facilities that can make things easier for users, one of which is a chatbot. Chatbots or what are commonly known as virtual assistants are able to understand and respond to user requests with the right answers. The use of the Python programming language is quite popular among novice programmers, its simple syntax and clear and logical code means that this language is often used for creating games, websites, prototyping and also for handling big data. One of the modules for creating Graphical User Interface (GUI) applications in Python is Tkinter. In this research, a chatbot will be created which aims to make it easier for users to find solutions to the problems they face when using the Python programming language in the Tkinter GUI module using Natural Language Processing in an interactive way.