User-friendly Graphical Interface Research Articles

Efficient, Automatic, and Reproducible Patch Clamp Data Analysis with “Auto ANT”, a User-Friendly Interface for Batch Analysis of Patch Clamp Recordings

Patch-clamp recordings are vital for investigating the electrical properties of excitable cells, yet the analysis of these recordings often involves time-consuming manual procedures prone to variability. To address this challenge, we developed the Auto ANT (Automated Analysis and Tables) open-source software, an automated, user-friendly graphical interface for the extraction of firing properties and passive membrane properties from patch-clamp recordings. Thanks to the novel built-in automation feature, Auto ANT enables batch analysis of multiple files recorded with the same protocol in minutes. Our tool is designed to streamline data analysis, providing a fast, efficient, and reproducible alternative to manual methods. With a focus on accessibility, Auto ANT allows the users to perform precise comprehensive electrophysiological analyses without requiring programming expertise. By combining automation with a user-centric design, Auto ANT offers a valuable resource for researchers to accelerate data analysis while promoting consistency and reproducibility across different studies.

Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review

Enhancements in monitoring and computational technology have facilitated data accessibility and utilization. Machine learning, as an integral component of the realm of computational technology, is renowned for its universality and efficacy, rendering it pervasive across various domains. Geotechnical disaster early warning systems serve as a crucial safeguard for the preservation of human lives and assets. Machine learning exhibits the capacity to meet the exigencies of prompt and precise disaster prediction, prompting substantial interest in the nexus of these two domains in recent decades. This study accentuates the deployment of machine learning in addressing geotechnical engineering disaster prediction issues through an examination of four types of engineering-specialized research articles spanning the period 2009 to 2024. The study elucidates the evolution and significance of machine learning within the domain of geotechnical engineering disaster prediction, with an emphasis on data analytics and modeling. Addressing the lacunae in existing literature, a user-friendly front-end graphical interface, integrated with machine learning algorithms, is devised to better cater to the requisites of engineering professionals. Furthermore, this research delves into a critical analysis of the prevalent research limitations and puts forth prospective investigational avenues from an applied standpoint.

ANN-based ensemble model for predicting micro-EDM responses and machining variability

Micro-electrical discharge machining (µEDM) requires high precision, quality and efficiency, yet machinists rely heavily on expertise and heuristics due to the complexity of the process and the use of machining different materials. This article introduces the use of artificial neural networks (ANNs) and ensemble models to predict key µEDM responses, such as machining time (MT), tool wear rate (TWR), overcut (OC) and taper angle (TA). The model incorporates material properties like thermal conductivity, melting point and electrical resistivity, along with input factors, including capacitance, voltage, feed rate, tool speed, tool diameter and workpiece type. Experimental data, collected using an I-optimal design of experiments (DOE) technique, was used to train the ANN models and optimized through grid search. The model’s accuracy, evaluated across training, testing and validation sets, showed mean accuracies between 76.84% and 86.62%. Deployment results revealed strong alignment between experimental and predicted outcomes, with predictions within a four standard deviation (4σ) range. A user-friendly graphical interface (GUI) facilitates the prediction process, improving precision and decision-making in µEDM for various workpieces.

Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models

The creep rupture life of 5Cr-0.5Mo steels used in high-temperature applications is significantly influenced by factors such as minor alloying elements, hardness, austenite grain size, non-metallic inclusions, service temperature, and applied stress. The relationship of these variables with the creep rupture life is quite complex. In this study, the creep rupture life of 5Cr-0.5Mo steel was predicted using various machine learning (ML) models. To achieve higher accuracy, various ML techniques, including random forest (RF), gradient boosting (GB), linear regression (LR), artificial neural network (ANN), AdaBoost (AB), and extreme gradient boosting (XGB), were applied with careful optimization of hidden parameters. Among these, the ANN-based model demonstrated superior performance, yielding high accuracy with minimal prediction errors for the test dataset (RMSE = 0.069, MAE = 0.053, MAPE = 0.014, and R2 = 1). Additionally, we developed a user-friendly graphical user interface (GUI) for the ANN model, enabling users to predict and optimize creep rupture life. This tool helps materials scientists and industrialists prevent failures in high-temperature applications and design steel compositions with enhanced creep resistance.

Intelligent leaching of Zn and Mn from spent disposable batteries to avoid traditional optimizing experiments.

Intelligent leaching of Zn and Mn from spent disposable batteries to avoid traditional optimizing experiments.

RIDIMS: A novel tool for processing direct-infusion mass spectrometry data.

rIDIMS: A novel tool for processing direct-infusion mass spectrometry data.

Computer vision for high-throughput analysis of pickering emulsions.

The quanitative analysis of solid-particle stabilized emulsions, known as Pickering emulsions, is crucial for their application in food, cosmetics, and pharmaceuticals. However, size analysis of these emulsion droplets, with diameters ranging from 5 to 500 μm, is challenging due to their non-uniform spatial and polydisperse size-distribution. Here, we investigate the application of the circle-Hough transform (CHT), a well-established computer-vision technique characterised by its ability to detect circular features in noisy images, for the seldom explored quantitative assessment of droplet size from optical microscopy images. This is particularly relevant to images where emulsions are captured in a single 2D focal plane. To implement the CHT with optical images, we have developed an open-source software application ("Hough-Scan"), which incorporates a user-friendly graphical interface for ease of use, and a tiling algorithm allowing localised regions of circles to be processed in parallel and improving computational efficiency. Using Hough-Scan, we demonstrate that the CHT has superior precision, recall and accuracy for the identification of Pickering emulsion droplets and determination of their size, compared to both manual identification and established computer vision methods. Our study demonstrates that CHT implementation using Hough-Scan can significantly increase the ease of image analysis for a diverse range of Pickering emulsion systems of varying spatial and size distribution, as well as visual artefacts common to example microscopy images.

Comparisons of PK-Sim® and R Program for Physiologically Based Pharmacokinetic Model Development for Broiler Chickens and Laying Hens: Meloxicam as a Case Study.

Physiologically based pharmacokinetic (PBPK) models play a critical role in evaluating drug residue concentrations and estimating withdrawal intervals (WDIs) for food-producing animals. These models are facilitated by various programming software (e.g., R program) and predefined PBPK platforms, such as Open Systems Pharmacology (OSP) suite integrated by PK-Sim® and Mobi, which offers a user-friendly graphical interface. Both R and OSP are open-source software. However, there is a lack of comparative analyses of both platforms and their potential impact on PBPK models. This study aims to evaluate the influence of different platforms on PBPK workflow, parameters selection, and output results, which is exemplified via a case study for meloxicam in chickens in both platforms. Our findings indicate that while the choice of PBPK platforms affected the workflow and input parameters, the predictive performance of established models remained consistent across both platforms. Both platforms predicted meloxicam pharmacokinetics in plasma and tissues accurately across different exposure scenarios. The PBPK-estimated WDIs under various dosing regimens from both platforms were quite similar. Notable differences between OSP suite and R were primarily observed during sensitivity analysis and parameter identification processes, especially the time consumption. This study offers insight into software variances and their implications for translating PBPK modeling knowledge between users of two platforms. Also, it provides a PBPK model structure template implemented in both software platforms for food safety and risk assessment in poultry and a detailed tutorial on expanding the model structure in PK-Sim® and Mobi.

Practice and Research Optimization Environment in Python (PyPROE)

Practice and Research Optimization Environment in Python (PyPROE) is a GUI-based, integrated framework designed to improve the user experience in both learning and research on engineering design optimization. Traditional optimization programs require either coding or creating complex input files, and often involve a variety of applications in sequence to arrive at the solution, which presents a steep learning curve. PyPROE addresses these challenges by providing an intuitive, user-friendly Graphical User Interface (GUI) that integrates key steps in design optimization into a seamless workflow through a single application. This integration reduces the potential for user error, lowers the barriers to entry for learners, and allows students and researchers to focus on core concepts rather than software intricacies. PyPROE’s human-centered design simplifies the learning experience and enhances productivity by automating data transfers between function modules. This automation allows users to dedicate more time to solving engineering problems rather than dealing with disjointed tools. Benchmarking and user surveys demonstrate that PyPROE offers significant usability improvements, making complex engineering optimization accessible to a broader audience.

A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines

The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant’s root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.

Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach

Abstract Modelling the dynamics of cryogenic distillation columns is challenging due to their complex, nonlinear behaviour. This study introduces a novel identification approach using a hybrid Artificial Neural Network (ANN) optimized with Particle Swarm Optimization (PSO), applied to cryogenic distillation as a case study. The NARX-PSO-ANN model effectively captures the nonlinear dynamics of the distillation process by optimizing model parameters and avoiding local optima. The novelty of this work lies in integrating the NARX (Nonlinear Autoregressive with Exogenous Inputs) architecture with PSO, which enhances robustness and performance. To validate the model’s efficacy, realistic simulations of the cryogenic distillation column were conducted using Aspen Plus Dynamics, generated 2,000 data samples-1,400 training and 600 for validation. The NARX-PSO-ANN model was evaluated against established methods like BP-ANN and NARX-based BP-ANN, consistently outperforming them in identifying cryogenic distillation column dynamics and demonstrating superior effectiveness for complex separation processes. A user-friendly Python-based graphical user interface (GUI) was developed for real-time methane composition prediction, making the model accessible for practical applications. This innovative approach offers a reliable solution for optimizing complex, nonlinear systems in the process industry.

Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models

Abstract Background Tick borne diseases are re-emerging around the world, including India. Information about the occurrence of the tick vectors in different geographical locations is essential for controlling the diseases. Tick surveys have not been conducted in many parts of India and information on the current prevalence of tick vectors is not available in all states of India. Many studies have been carried out utilizing modelling methods to predict the distribution of tick species in other countries. The MaxEnt model is widely used for predicting tick species distribution using bioclimatic variables. Lyme disease vectors such as Ixodes sp., Amblyomma sp., and Dermacentor sp. are the most commonly predicted tick species. However, very few studies have been carried out to predict the distribution of tick species in India. Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. Rhipicephalus (Boophilus) microplus was predicted across India using the generalized linear model (GLM). Identifying the tick vectors in transmitting the infection through conventional survey and identification methods is cumbersome due to the less number of experienced persons available. Prediction of tick vectors of public health concern, including other tick species in different geographical regions of Tamil Nadu, India, is essential for the prevention and control of tick-borne disease in humans and domestic animals. The present study adopts the package ‘SSDM’ (stacked species distribution models) with R software containing ensemble species distribution models to predict the distribution of tick species using different available environmental and climatic data. Results The categorical variables such as land use and land cover (LULC), soil type, elevation, Bio1, Bio10, Bio15, Bio19 and Bio8 contributed more to modelling the distribution of tick species. MaxEnt, GLM, GBM and GAM are suitable models for predicting the tick species distribution in the present study. Among these models, MaxEnt is the most suitable model for predicting tick species distribution in Tamil Nadu, India. Conclusions Our results suggest that MaxEnt is a suitable model for predicting the distribution of tick species. Both environmental factors such as LULC, elevation and soil type and bioclimatic factors such as temperature and precipitation contribute significantly to predicting tick species distribution in domestic animals in Tamil Nadu. The SSDM package is very useful and user-friendly graphical user interface for modelling the distribution of tick species. However, the package can be further improved by using higher resolution raster variables in larger areas, which is not currently supported. The predicted elevation range of Ha. spinigera distribution could not be provided due to software limitations.

Performance Analysis of a Novel Seismic Data Acquisition System to Measure Peak Particle Velocity and Source Distance

Abstract This paper presents a performance analysis of a novel development of multistations seismic data acquisition system called MSTGS. This system involves 4.5 Hz triaxial geophone sensors — Vertical (Z), East-West (E), and North-South (N) — and a user-friendly graphical user interface (GUI) for monitoring peak particle velocity (PPV) in the ground and estimating the distance of a vibration source. The experiment was conducted by employing an active artificial vibration source at a certain location by assuming a homogeneous medium with the specific p-s waves velocity (0.2471 m/s). The distance of the vibration source was varying up to 15 meters, and each test was repeated three times. This data acquisition system has already been compared to the commercial high-end 5 Hz geophone instrument. Since this research only analyses parameters in the time domain, therefore neglecting the frequency domain. PPV was determined by the sum vector of peak amplitude in each component. Meanwhile, the distance between source and receiver was calculated by subtracting p-wave and s-wave first break arrival time. Results showed a PPV measurement accuracy of 94.88% at Station 1 and 95.2% at Station 2, with increased accuracy using the Multi-Station Triaxial Geophone of 97.24%. The accuracy in measuring the distance of the vibration source was 98.59%. Sensors at each station were able to measure PPV values with delay times of 71 ms (Z sensor), 83 ms (E sensor), and 67 ms (N sensor), which met the ITU-T standards. The data indicated a correlation between the vibration source distance and PPV values, with a correlation coefficient (R2) of 0.8535 at Station 1 and 0.8213 at Station 2. These findings validate the novel developed instrument’s capability, offering a reliable alternative to existing commercial solutions. This research contributes to the field of geophysical engineering by providing a cost-effective and accurate method for monitoring ground vibrations, which is crucial for various applications including seismic studies and infrastructure monitoring.

Effortless Student Attendance: A Smart Human-Computer Interactive System Using Real Time Facial Recognition

Objective: Recording attendance is a critical process in academic institutions due to its significant impact on student performance and engagement. Current methods for recording attendance are often time-consuming and labour-intensive for lecturers and administrative staff, necessitating the development of more efficient and flexible solutions. While various automated attendance systems have been proposed, they often encounter challenges related to cost, implementation complexity, or reliability, hindering widespread adoption in educational settings. Method: This paper introduces a novel approach to automating attendance registration using face recognition technology. Our method integrates multiple feature extraction algorithms within a user-friendly graphical interface, specifically designed in English to enhance usability. By using existing security cameras commonly found in academic institutions, our approach addresses both cost and time inefficiencies. The attendance registration process involves capturing a video of the classroom, which is then processed to identify and log student attendance in a CSV file. A significant aspect of our study is using a comprehensive dataset comprising 2,170 images collected from 31 students at Mustansiriyah University. This extensive dataset enhances the robustness and reliability of our system, providing a diverse range of facial expressions, angles, and lighting conditions that improve the accuracy and generalizability of our model. Result: The system demonstrated accuracy of up to 100%, with deep learning algorithms outperforming machine learning methods. Conclusion: These promising results suggest that face recognition technology can effectively streamline and automate attendance tracking, offering a viable solution for educational institutions seeking to improve operational efficiency and accuracy

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging.

Low-cost prototype for real-time analysis using liquid crystal optical sensors in water quality assessment

In the food production sector, quickly identifying potential hazards is crucial due to the resilience of many pathogens, which could lead to wasted production results and, more severely, epidemic outbreaks. E. coli monitoring is essential; however, traditional quality control methods in fish farming are often slow and intrusive, thus promoting an increase in fish stress and mortality rates. This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy (POM), constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels. The sensors are based on the immune complexation reactions between E. coli specific antibodies and the disruption of liquid crystal (LC) alignment, which are measured with the POM technique. The prototype yielded a sensitivity of 1.01%±0.17%/log10 (CFU/mL) for E. coli. In this paper, tests using sunlight as the prototype’s light source were also performed, and a user-friendly graphical user interface was designed.

Interactive Voice-Controlled Assistant

Abstract: This project focuses on the development of "Nova," an intelligent virtual assistant designed to enhance productivity and streamline user interactions through voice commands. Built with Python and PyQt5, Nova integrates natural language processing, speech recognition, and automation to execute various tasks, such as managing applications, performing web searches, filling forms, and interacting with hardware components like cameras. It also features multilingual support, allowing users to interact in both English and Hindi. The assistant is implemented with a user-friendly graphical interface and runs seamlessly to provide real-time assistance.

OpenWFS—a library for conducting and simulating wavefront shaping experiments

Abstract Wavefront shaping (WFS) is a technique for controlling the propagation of light. With applications ranging from microscopy to free-space telecommunication, this research field is expanding rapidly. As the field advances, it stands out that many breakthroughs are driven by the development of better software that incorporates increasingly advanced physical models and algorithms. Typical WFS software involves a complex combination of low-level hardware control, signal processing, calibration, troubleshooting, simulation, and the WFS algorithm itself. This complexity makes it hard to compare different algorithms and to extend existing software with new hardware or algorithms. Moreover, the complexity of the software can be a significant barrier for end users of microscopes to adopt WFS. OpenWFS addresses these challenges by providing a modular Python library that separates hardware control from the WFS algorithm itself. Using these elements, a WFS algorithm can be written in a minimal amount of code, with OpenWFS taking care of low-level hardware control, synchronization, and troubleshooting. Algorithms can be used on different hardware or in a completely simulated environment without changing the code. Moreover, we provide full integration with the Micro-Manager microscope control software, enabling WFS experiments to be executed from a user-friendly graphical user interface.

MorphoHeart: A quantitative tool for integrated 3D morphometric analyses of heart and ECM during embryonic development.

Heart development involves the complex structural remodelling of a linear heart tube into an asymmetrically looped and ballooned organ. Previous studies have associated regional expansion of extracellular matrix (ECM) space with tissue morphogenesis during development. We have developed morphoHeart, a 3D tissue segmentation and morphometry software with a user-friendly graphical interface (GUI) that delivers the first integrated 3D visualisation and multiparametric analysis of both heart and ECM morphology in live embryos. morphoHeart reveals that the ECM undergoes regional dynamic expansion and reduction during cardiac development, concomitant with chamber-specific morphological maturation. We use morphoHeart to demonstrate that regionalised ECM expansion driven by the ECM crosslinker Hapln1a promotes atrial lumen expansion during heart development. Finally, morphoHeart's GUI expands its use beyond that of cardiac tissue, allowing its segmentation and morphometric analysis tools to be applied to z-stack images of any fluorescently labelled tissue.

MetaboLabPy-An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis.

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.