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Microgrids have emerged as a promising solution for enhancing energy sustainability and resilience in localized energy distribution systems. Efficient energy management and accurate load forecasting are one of the critical aspects for improving the operation of microgrids. Various approaches for energy prediction and load forecasting using statistical models are discussed in the literature. In this work, a novel energy management framework that incorporates machine learning (ML) techniques is presented for an accurate prediction of solar and wind energy generation. The anticipated approach also emphasizes time series-based load forecasting in microgrids with precise estimation of State of Charge (SoC) of battery. A unique feature of the proposed framework is that utilizes historical load data and employs time series analysis coupled with different ML models to forecast the load demand in a commercial microgrids scenario. In this work, Long Short-Term Memory (LSTM) and Linear Regression (LR) models are employed for an experimental analysis to study the proposed framework under three different cases, such as (i) prediction of energy generation, (ii) load demand forecasting and, (iii) prediction of SoC of battery. The results show that the Random Forest (RF) and LSTM models performs well for energy prediction and load forecasting respectively. On the other hand, the Artificial Neural Network (ANN) model exhibited superior accuracy in terms of SoC estimation. Further, in this work, a Graphical User Interface (GUI) is developed for evaluating the efficacy of the proposed energy management framework. Article Highlights: Design of ML based framework for energy management system for commercial micro grids. Precise estimation of major sources of microgrids such as wind, solar and SoC of battery. A comparative study on various ML models with ANN model. An evaluation study for the efficiency of the proposed energy management framework by using real-time data incorporated GUI. [ABSTRACT FROM AUTHOR]

This research introduces an innovative methodology for evaluating and predicting soundboard quality in the intricate craftsmanship of sape instruments. Despite the sape's profound cultural significance, the process of selecting soundboard wood has been inadequately explored, resulting in uncertainties within the crafting community. Addressing this research gap, this study integrates advanced machine learning techniques and devises a specialized Graphical User Interface (GUI) tailored for sape makers. The methodology encompasses a thorough acoustic analysis of three distinct hardwoods--adau, merbau, and tapang--employing machine learning classification through Support Vector Machine with a Gaussian kernel. The study culminates in the development of a userfriendly GUI for soundboard quality assessment. Results underscore the model's proficiency for achieving an optimized accuracy of 87.8% in classifying sape audio samples. The MATLAB App Designer-based GUI streamlines the evaluation process, offering a practical and accessible tool for craftsmen. This integrated approach, harmonizing traditional craftsmanship with cutting-edge technology, holds the potential to revolutionize sape instrument manufacturing, ensuring the preservation and progressive evolution of this rich cultural heritage. [ABSTRACT FROM AUTHOR]

Multivariate regression is a fundamental supervised chemometric method for developing the relationship between the independent variables and quantitative response, and it has been widely applied for data analysis in many research fields. In this study, we propose an effective method for the quantitative determination of target compounds in traditional Chinese medicine, specifically Mongolia, using excitation-emission matrix (EEM) spectra with partial overlap. The accuracy and reliability of the established model have been validated, demonstrating that the proposed method can realize the accurate quantitative analysis purpose. In order to facilitate the calculation easier, the authors have developed a friendly graphical user interface (GUI). The GUI offers the procedures for data imputation, model establishment, model optimization and results presentation. [ABSTRACT FROM AUTHOR]

New features in the dose estimation program RADDOSE‐3D are summarised. They include the facility to enter a diffraction intensity decay model which modifies the "Diffraction Weighted Dose" output from a "Fluence Weighted Dose" to a "Diffraction‐Decay Weighted Dose", a description of RADDOSE‐ED for use in electron diffraction experiments, where dose is historically quoted in electrons/Å2 rather than in gray (Gy), and finally the development of a RADDOSE‐3D GUI, enabling easy access to all the options available in the program. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to develop a deep learning model based on a convolutional neural network (CNN) architecture to predict Invasive Ductal Carcinoma (IDC), a form of breast cancer. The model also includes a user-friendly graphical interface for healthcare professionals to utilize the tool conveniently. The results of the study showed a high accuracy rate, suggesting the potential of this model in improving early breast cancer detection and personalized treatment strategies. Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to facilitate the use of the model by healthcare professionals. Methods: The CNN model was trained and tested using a dataset of high-resolution microscopic images derived from 162 whole-mount slide images of breast cancer specimens. These images were meticulously scanned at 40× magnification using a state-of-the-art digital slide scanner to capture detailed information. Each image was then divided into 277,524 patches of 50 × 50 pixels, resulting in a diverse dataset containing 198,738 IDC-negative and 78,786 IDC-positive patches. Results: The model achieved an accuracy of 98.24% in distinguishing between benign and malignant cases, demonstrating its effectiveness in cancer detection. Conclusions: This study suggests that the developed CNN model has promising potential for clinical applications in breast cancer diagnosis and personalized treatment strategies. Our study further emphasizes the importance of accurate and reliable cancer detection methods for timely diagnosis and treatment. This study establishes a foundation for utilizing deep learning models in future cancer treatment research by demonstrating their effectiveness in analyzing large and complex datasets. This approach opens exciting avenues for further research and potentially improves our understanding of cancer and its treatment. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is widely used for field data acquisition and monitoring in different domains. To make this type of network functional, efficient routing protocols must be implemented. Nevertheless, WSNs have an energy constraint due to limited batteries. Many clustered protocols are proposed to overcome it. However, the implementation of these protocols would be difficult to understand without a simulation tool, as some problems may arise during their development. Testing real-world applications requires a lot of effort and cost because they often use many nodes in large networks. Therefore, the simulation tool is the most relative way to evaluate these protocols. This paper presents graphical-based cluster protocols simulation interface for WSN (GCPS-WSN), a new interface to simulate some clustered protocols in WSNs. GCPS-WSN allows the user to evaluate the performance of certain low energy adaptive clustering hierarchy (LEACH) enhanced protocols to choose the most appropriate one for his system. The user can simulate protocols without any knowledge of software programming. [ABSTRACT FROM AUTHOR]

A graphical user interface is a fundamental part of any software application as it enables users to interact with an application’s functionality, allowing interaction between computer software and hardware. The hybrid nature is a feature of GUIs. Therefore, it can qualify for all three forms of intellectual property protection: patent, copyright, and trademark. Each of these three forms has its pros and cons in terms of protecting graphical user interfaces. However, industrial design rights eventually seem to be the best system for protecting graphical user interfaces. Therefore, this study mainly focuses on the industrial design protection for the GUIs as well as the industrial design protection of GUIs in WTO member states, and in some cases, a comparison has been made with the Iranian legal system. A review of different legal systems indicates that in cases where the user interface is to be supported in more than one country, and at the international level, some points such as the scope of protection, filing routes and priority rights should be considered, and suggestions have been presented for modification of the protection system of GUIs as well as synchronization of protections. Furthermore, there are legal issues with respect to cloud computing and graphical user interfaces that need to be considered, including the protection of the intellectual property of both the user and the provider.

Automated testing of GUI elements on electronic screens by machine vision is widely used in smart manufacturing production lines. However, in the complex environment of factories, interference factors such as light reflection, platform vibration, and placement angle make it difficult for testing robots to recognize and locate GUI elements on the equipment's screen to be tested. In addition, the recognition algorithms based on screen GUI elements currently have problems such as slow detection speed and large model size, which limit the deployment of detection models on test robots. To address these problems, this paper proposes a lightweight model that can overcome the interference of the factory environment based on the YOLOv5 algorithm for recognizing GUI elements. First, a reinforced position and channel attention module (PCA) is proposed to enhance the extraction and fusion ability of weak feature elements affected by interference; then, a three-way feature pyramid network (TWFPN) module is constructed at the intermediate feature layer of the neck structure to provide more accurate localization information. Meanwhile, the loss function of the original algorithm is replaced with SIoU to improve the convergence speed. Finally, a lightweight structure combining the Ghost module and PConv is introduced into the model to reduce the model parameters and improve the detection speed. The method was evaluated on the constructed feature-enhanced GUI (FE_GUI) dataset. The final model shows a 2.7% improvement in average accuracy (mAP@0.5), a 30% reduction in model weight, and an increase in detection speed to 85 FPS compared to the original YOLOv5. The results demonstrate that the method solves the issues of element recognition and model deployment for a vision test system and surpasses existing models in terms of recognition accuracy and detection speed. [ABSTRACT FROM AUTHOR]

Molecular docking is by far the most preferred approach in structure‐based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI‐based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co‐crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK. [ABSTRACT FROM AUTHOR]

The double sampling plan (DSP) is a generalized version of the single sampling plan (SSP) that provides several advantages, such as reduced sample size, increased discriminatory power, and better communication between producers and consumers. This study proposes a DSP for truncated life tests (TLT-DSP) using a two-parameter Lindley distribution. The proposed TLT-DSP's parameters are determined by a mathematical model designed to minimize the average sample number while fulfilling two constraints related to predefined quality levels and tolerated risks. Performance measures of the sampling plans are investigated to evaluate and compare their efficiency and effectiveness. Our results demonstrate that the proposed DSP is more efficient than the traditional SSP, especially in cases where the lot's quality is excellent or poor, and provides necessary protection to both parties involved. Additionally, two examples are presented, discussed and illustrated through a graphical user interface designed to validate the practicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to facilitate the use of the model by healthcare professionals. Methods: The CNN model was trained and tested using a dataset of high-resolution microscopic images derived from 162 whole-mount slide images of breast cancer specimens. These images were meticulously scanned at 40× magnification using a state-of-the-art digital slide scanner to capture detailed information. Each image was then divided into 277,524 patches of 50 × 50 pixels, resulting in a diverse dataset containing 198,738 IDC-negative and 78,786 IDC-positive patches. Results: The model achieved an accuracy of 98.24% in distinguishing between benign and malignant cases, demonstrating its effectiveness in cancer detection. Conclusions: This study suggests that the developed CNN model has promising potential for clinical applications in breast cancer diagnosis and personalized treatment strategies. Our study further emphasizes the importance of accurate and reliable cancer detection methods for timely diagnosis and treatment. This study establishes a foundation for utilizing deep learning models in future cancer treatment research by demonstrating their effectiveness in analyzing large and complex datasets. This approach opens exciting avenues for further research and potentially improves our understanding of cancer and its treatment.

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications. After undergoing friction stir processing (FSP), the refined grain structure leads to a notable decrease in corrosion rate. However, a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking. The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters (rotational speed, traverse speed, and shoulder diameter) for WE43 alloy. The Taguchi L27 design of experiments was used for the experimental analysis. In addition, synthetic data was generated using particle swarm optimization for virtual sample generation (VSG). The application of VSG has led to an increase in the prediction accuracy of machine learning models. A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate. The shoulder diameter had a significant impact in comparison to the traverse speed. A graphical user interface (GUI) has been created to predict the corrosion rate using the identified factors. This study focuses on the WE43 alloy, but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Hardware and software monitoring complex is designed to study the relationship between changes in the patterns of the magnetic field intensity distribution with power equipment failures. In experimental studies with predetermined technical faults artificially introduced into the object the complex will provide useful and reliable results by registering changes in the magnetic field induction in the spatial, temporal and frequency domains. The choice and flexible adjust-ment of data collection tools by the researcher will ensure the use of the most appropriate method for measuring, con-verting and displaying information about the investigated faults. The article presents the main operating modes and graphical user interfaces of a personal computer and a mobile data acquisition system that jointly convert, store, ana-lyze and display measurement information received from various magnetic sensors. The generalized functional specification of the complex software, the design of the graphical user interface (GUI) as parts of the real-time operating system (RTOS) of the mobile data acquisition system (DAQ) and PC software will also be used in the development of problem-oriented computer diagnostic systems for electric power equipment. Ref. 12, fig. 3.

Abstract Persistent free radicals (PFRs) are emerging substances with diverse impacts in biochar applications, necessitating accurate prediction of their content and types prior to their optimal use and minimal adverse effects. This prediction task is challenging due to the nonlinearity and intricate variable relationships of biochar. Herein, we employed data-driven techniques to compile a dataset from peer-reviewed publications, aiming to systematically predict the PFRs by developing supervised machine learning models. Notably, extreme gradient boosting (XGBoost) model exhibited the best predictive performance for both regression and classification tasks in predicting the PFRs, achieving a test R2 value of 0.95 for PFR content prediction, along with an Area Under the Receiver Operating Curve (AUROC) of 0.92 for PFR type prediction, respectively. Based on XGBoost model, a graphical user interface (GUI) was developed to access PFRs predictions. Analysis of feature importance revealed that the biochar properties, such as metal/non-metal doping, pyrolysis temperature, carbon content, and specific surface area were identified as the four most significant factors influencing PFRs contents. Regarding the types of PFRs in biochar, specific surface area, pyrolysis temperature, carbon content, and feedstock were top-ranked influencing factors. These findings provide valuable guidance for accurately predicting both the contents and types of PFRs in biochar, and also hold significant potential for highly efficient utilization of biochar across various applications. Graphical Abstract

A generalized kinematic scheme of the installation of quadcopter motors is presented and its main advantages are described. In accordance with the scheme, a mathematical model of the kinematics of the quadcopter was developed. The model was implemented in the MatLab software environment. The presented mathematical expressions are used to calculate kinematic characteristics, such as the values of the thrust of the motors and the reverse effect of the motors on the body of the quadcopter. The comparison of the obtained data with the experimental characteristics showed a 5% deviation of the magnitude of the dependence of the thrust on the average value of the voltage on the motors and a 30% deviation of the magnitude of the dependence of the motor action on the thrust magnitude.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with a user-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.

This study proposes a machine learning-assisted method for assessing steel reinforcement corrosion, utilising data on chloride ion concentration (CIC) and chloride ion concentration threshold (CCT) from eco-friendly coral aggregate concrete (EFCAC). A total of 2185 data points were collected to establish the EFCAC-CIC database. A multi-objective slime mould optimised support vector regression (MOSMA-SVR) model for EFCAC-CIC was developed. The significance of each feature to EFCAC-CIC was analysed. Subsequently, a graphical user interface (GUI) was developed based on the MOSMA-SVR model. Finally, the GUI and EFCAC-CCT were used to assess the corrosion behaviour of reinforcement. Results indicate that the MOSMA-SVR model provides predictions that are closer to the actual values, with smaller mean errors and standard deviations. The performance indicators, including coefficient of determination, mean absolute error, mean absolute percentage error, mean square error, root mean square error, and a20-index, of the MOSMA-SVR model are 0.987, 0.0124, 0.0332, 2.91e − 4, 0.0171 and 0.9991, respectively, and they are superior to those of the other tested models. The water type and the water–binder ratio are identified as the two most critical factors. Furthermore, by integrating the GUI and feature importance analysis results, chloride salt-resistant EFCAC can be designed. When this is combined with the GUI and EFCAC-CCT, the corrosion of rebar in EFCAC can be assessed in real time. [ABSTRACT FROM AUTHOR]

Hypertension, often termed "the silent killer", is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model's ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management. [ABSTRACT FROM AUTHOR]

Psychological disorders have increased globally at an alarming rate. Among these disorders, depression stands out as one of the leading and most prevalent conditions that have affected more than 280 million people. However, it remains widely undiagnosed and untreated due to lack of sensitive and reliable diagnostic tools. This underscores the imperative for the development of a sensitive and accurate diagnostic tool facilitating the early diagnosis of depression symptoms to mitigate the impending mental illness epidemic. To address this need, we developed an artificial intelligence (AI)-assisted tool utilizing facial expression-based emotion recognition and electroencephalogram (EEG) analysis for the detection of depression symptoms along with their severity level assessment. Our approach yielded successful detection of depression symptoms with an accuracy of 93.58%, a sensitivity of 92.70%, a specificity of 93.40%, and an f1-score of 93.68% through facial emotion recognition task. Additionally, severity level detection employing EEG biomarkers achieved an accuracy of 99.75%, a sensitivity of 99.75%, a specificity of 99.92%, and an f1-score of 99.75%. Consequently, a graphical user interface (GUI) tool was developed that seamlessly integrated the AI with facial image and EEG data inputs, enabling efficient recognition of depression from both real-time and pre-recorded data. The resulting AI assistant demonstrates high sensitivity, precision, and accuracy in the early detection of depression, establishing its potential as a reliable diagnostic tool. The application of our tool may be extended to clinicians, therapists, and hospitals for the identification of depression at its early stage. [ABSTRACT FROM AUTHOR]

Persistent free radicals (PFRs) are emerging substances with diverse impacts in biochar applications, necessitating accurate prediction of their content and types prior to their optimal use and minimal adverse effects. This prediction task is challenging due to the nonlinearity and intricate variable relationships of biochar. Herein, we employed data-driven techniques to compile a dataset from peer-reviewed publications, aiming to systematically predict the PFRs by developing supervised machine learning models. Notably, extreme gradient boosting (XGBoost) model exhibited the best predictive performance for both regression and classification tasks in predicting the PFRs, achieving a test R2 value of 0.95 for PFR content prediction, along with an Area Under the Receiver Operating Curve (AUROC) of 0.92 for PFR type prediction, respectively. Based on XGBoost model, a graphical user interface (GUI) was developed to access PFRs predictions. Analysis of feature importance revealed that the biochar properties, such as metal/non-metal doping, pyrolysis temperature, carbon content, and specific surface area were identified as the four most significant factors influencing PFRs contents. Regarding the types of PFRs in biochar, specific surface area, pyrolysis temperature, carbon content, and feedstock were top-ranked influencing factors. These findings provide valuable guidance for accurately predicting both the contents and types of PFRs in biochar, and also hold significant potential for highly efficient utilization of biochar across various applications. Highlights: • Recognizing dual nature of PFRs, a machine-learning framework predicts them in biochar. • XGBoost excels, achieving an R2 (0.95) for PFR content and an AUROC (0.92) for PFR type. • Important factors of PFR: doping, pyrolysis temp, carbon, and surface area. • GUI enhances accessibility, enabling PFR predictions before biochar preparation. [ABSTRACT FROM AUTHOR]

• FSP was performed on WE 43 Mg alloy to assess the corrosion behavior using ML. • Synthetic data was generated using PSO for virtual sample generation. • Five different machine-learning models were compared using the original dataset. • A sensitivity analysis was performed using Shapley Additive Explanations. • A graphical user interface (GUI) has been created to predict the corrosion rate. The corrosion rate is a crucial factor that impacts the longevity of materials in different applications. After undergoing friction stir processing (FSP), the refined grain structure leads to a notable decrease in corrosion rate. However, a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking. The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters (rotational speed, traverse speed, and shoulder diameter) for WE43 alloy. The Taguchi L 27 design of experiments was used for the experimental analysis. In addition, synthetic data was generated using particle swarm optimization for virtual sample generation (VSG). The application of VSG has led to an increase in the prediction accuracy of machine learning models. A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate. The shoulder diameter had a significant impact in comparison to the traverse speed. A graphical user interface (GUI) has been created to predict the corrosion rate using the identified factors. This study focuses on the WE43 alloy, but its findings can also be used to predict the corrosion rate of other magnesium alloys. [ABSTRACT FROM AUTHOR]

Heatmaps are preferred visualization modes for biologists to display high-dimensional information from high-throughput omics data. Many software including website services and R packages are available to generate various types of customized heatmaps. Here, we describe oppHeatmap (omics pilot platform of heatmap), a new tool constructed to render different kinds of heatmaps through MATLAB. oppHeatmap is available for plotting ordinary heatmaps, hierarchical clustering, TreeMaps, microplates graph, sample correlation (full heatmap and upper and lower triangle parts), gene correlation (between columns or tables), and polar heatmaps. oppHeatmap can support the modification of borders, fonts, and colors to customize the final plots. oppHeatmap can not only read data from Microsoft Excel to generate specific heatmaps but also make Excel heatmaps by coloring each cell in Excel. The graphs can be stored in SVG (supported vector graph) format and modified by other SVG recognition software. oppHeatmap is designed by MATLAB AppDesigner with GUI (graphical user interface) operation. The program for oppHeatmap was available at https://github.com/HangZhouSheep/oppHeatmap.are diagramed by All kinds of heatmaps oppHeatmap. The main function of oppHeatmap is to enable visualization of two types of heatmaps and a total of 11 graphs. The first type is a heatmap with a polar or rectangular coordinate system, including ordinary heatmap, microplates graph, polar heatmap, and Excel heatmap. The last one involves writing the heatmap in Excel and others in MATLAB. The second type is with rearranged rows and columns, which includes hierarchical clustering, TreeMaps, and sample/gene correlation between columns or rows. All plots support row standardization for better color contrast. Eleven base functions implement heatmap plotting in the command line environment of MATLAB. GUI designed by AppDesigner supports the interactive construction of heatmaps. [ABSTRACT FROM AUTHOR]

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames. [ABSTRACT FROM AUTHOR]

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron. [ABSTRACT FROM AUTHOR]

Background: In modern healthcare, decision-making processes are becoming increasingly data-driven, aiming to optimize patient care and resource allocation. Coronary artery bypass grafting (CABG) is a critical procedure for high-risk patients, and the use of intra-aortic balloon pump (IABP) assistance can significantly impact patient outcomes. Objective: To identify preoperative factors that predict the need for IABP assistance in high-risk CABG patients and to develop a predictive model that can enhance clinical decision-making. Subjects and Methods: The study involved fifty high-risk coronary artery bypass graft patients, with 24 cases necessitating intra-aortic balloon pump assistance and the rest not requiring it. Seven preoperative variables, including age, weight, ejection fraction, number of grafts, creatinine level, diabetic status, and blood pressure, were considered as potential predictors. SPSS was adopted to conduct a multi-variable regression analysis and derive the mathematical equations for the predictive model. A graphical user interface (GUI) was developed to utilize these equations for predicting the need for IABP support. Results: The findings revealed a strong correlation among several preoperative factors and the likelihood of requiring IABP assistance. By employing the predictions from the model to guide preoperative intra-aortic balloon insertion, the potential for optimizing hemodynamic support during surgery was maximized. Conclusions: This research emphasizes the importance of enhancing risk assessment techniques to empower medical specialists to tailor treatment plans to each patient's specific needs. Early identification of individuals at risk of requiring IABP can significantly enhance surgical outcomes and patient safety. Incorporating the predictive model into clinical practice allows for more individualized and effective care during high-risk CABG surgeries. This modern strategy has the potential to revolutionize cardiac outcomes, improve patient results, and extend the use of intra-aortic balloon support.

Wireless Sensor Networks (WSNs) serve as the backbone for the Internet of Things (IoT) and smart cities, enabling the gathering of essential data and vital information. The widespread deployment of sensors created new scenarios that presented new domains for various applications. Due to their numerous advantages, WSN’s simulators are still gaining considerable attention as the primary method for testing and evaluating new protocols and approaches for WSN. The scientific community has developed various simulators, some tailored explicitly for WSNs and others for general use. In addition, an old but renovated direction has been enormously grown recently, representing the researcher’s attention to building its own simulator. However, many researchers struggle to select the most appropriate tool for performance analysis, which requires extensive research into available options. Choosing a suitable simulator to meet the necessary simulation criteria necessitates exhaustive research into the available options. The published studies on WSN’s simulators have limitations, such as the limited number of simulators under examination, ignoring the essential functions of the simulators, and inadequate performance criteria for precise comparison. To get beyond these restrictions in previous studies and offer a fresh study that compares the simulation tools comprehensively, an analysis of thirty-three different simulators was conducted based on a new taxonomy. Furthermore, this study examines the advantages and constraints of each simulator regarding many specific academic areas in WSNs. Moreover, the study presented a detailed comparison among the performance analysis tools according to many famous performance metrics.

Determining the magnetic field profile is very important for studying flux surfaces, elongation, plasma boundaries, and real-time control in tokamaks. In this research, a system for calculating and displaying the shape, position, and boundary of plasma for Damavand Tokamak control room has been developed. This system determines the magnetic field profile and flux surfaces in the time interval between two consecutive shots. This software is implemented based on measurements with the probes' sensors and electromagnetic flux loops. It also solves mathematical equations based on the physics of the problem. In order to calculate the magnetic field profile and the plasma shape, after preparing and collecting the data from the data acquisition system, a developed code is used to pre-process the data, such as noise reduction, removing unnecessary data, etc. Then, using another code, the cross-section parameters such as shape, position, and plasma boundary are calculated and displayed in the tokamak control room. All the codes of this system are written in MATLAB software and as a graphical user interface procedure. This is so the tokamak operator has easy access to the results after every shot. This system is currently installed and operated in the Damavand tokamak control room.

The behavior of concrete columns reinforced with fiber reinforced polymer (FRP) bars is different from conventional reinforced concrete columns due to the mechanical properties of FRP bars. This study develops a novel machine learning (ML) model, namely gradient boosting regression tree (GBRT), for efficiently predicting the axial load-bearing capacity (ALC) of concrete columns reinforced with FRP bars. A data base containing 283 experimental results is collected to develop the ML model. Seven code-based and empirical-based equations are also included in comparison with the developed ML models. Moreover, we also propose a multiple linear regression (MLR)-based formula for calculating the ALC of the FRP-concrete column. The performance results of GBRT model are compared with those of published formulas and the proposed MLR-based formula. Statistical properties including , , and are calculated to evaluate the accuracy of those predictive models. The comparisons demonstrate that GBRT outperforms other models with very high values and small . Moreover, the influence of input parameters on the predicted ALC isevaluated. Finally, an efficient graphical user interface tool is developed to simplify the practical design process of FRP-concrete columns.

The article describes the problem of communication of microcontrollers of the AVR family with other Bluetooth devices using special modules. A comprehensive solution is proposed, including a modified interaction protocol, a library for Arduino that implements interaction on the microcontroller side, and a mobile application for creating interfaces that allows a user to interact with Arduino via Bluetooth. The development environment was chosen to be Android Studio together with the SDK tools (API 34). The Kotlin language and software tools from Android Jetpack, including Jetpack Compose, are used for programming, which allows you to create more flexible, efficient and attractive user interfaces for Android applications. A modification of the SPP protocol is used for data exchange between devices. To implement user interaction with the application on the Arduino side, the BluetoothMe library has been developed, which, using the IBluetoothAdapter interface, encapsulates work with the Bluetooth adapter and provides convenient functions for sending and receiving data. This approach allows the user to use any Arduino-compatible Bluetooth module. The development is aimed primarily at amateurs of robotics and microelectronics, but it will be useful for professionals as well. The proposed software allows developers to create more complex and powerful electronic devices in less time. This makes it possible to expand the boundaries of technical progress and ensures the creation of new, more efficient and powerful devices that can perform various tasks. Ref. 11, pic. 5, tabl. 2

Purpose: This study aims to automate the Monte Carlo (MC) workflow utilized for radiotherapy dosimetry, focusing on an Elekta LINAC delivery system. It addresses the challenge of integrating MC simulations into routine clinical practice, making this accurate yet complex method more accessible and efficient for radiotherapy dosimetry. Methods and Materials: We developed a user-friendly software featuring a graphical user interface (GUI) that integrates EGSnrc for MC simulations. The software streamlines the process from retrieving Digital Imaging and Communications in Medicine (DICOM) data to executing dose calculations and comparing dose distributions. To validate our proposed tool, we compared its computed doses for IMRT and VMAT plans from the Pinnacle TPS for an Elekta Versa HD linear accelerator against MC simulation results. This comparison utilized our in-house software and GUI as the tool, covering various treatment sites and prescriptions. Results: The automated MC workflow demonstrated high accuracy in dose calculations and streamlined integration with clinical workflows. The comparison between the MC-simulated and TPS-calculated doses revealed excellent agreement, highlighting the reliability of MC for independent dose verification in complex treatment scenarios. Conclusions: The automated MC workflow developed represents a substantial improvement in the practicality and efficiency of MC simulations in radiotherapy. This advancement not only simplifies the dosimetry process but also ensures high accuracy, establishing it as a valuable tool for routine patient-specific quality assurance and the development of specialized treatment procedures.

Craft beer production has grown extensively worldwide. The variety of products and grains that can be used in production make this artisanal product unique. In this study, we propose a system dynamics model that allows for the evaluation of different production scenarios in which figs are used as the main ingredient. This research is inspired by the real case of small fig producers in Valle del Mayo in Navojoa, Sonora, Mexico, who, in 2020, took on the challenge of creating a processing factory for fig-derived products. This paper presents the development and application of a system dynamics approach to model the entire supply chain of overripe figs, i.e., figs that cannot be marketed in prime quality but can still be used in the production of derivative products. The method used for its development encompasses the following stages: (1) defining the craft beer supply chain variables; (2) elaborating on causal diagrams; (3) producing model stock and flow diagrams; (4) model validation; (5) sensitivity analysis and scenario evaluations; and (6) building a graphical user interface (GUI). The proposed model allows managers to assess several production policies under various assumptions of capacity and beer demand, demonstrating its value as an effective tool for strategic decision making. [ABSTRACT FROM AUTHOR]

Cryo‐electron microscopy (cryo‐EM) has witnessed radical progress in the past decade, driven by developments in hardware and software. While current software packages include processing pipelines that simplify the image‐processing workflow, they do not prioritize the in‐depth analysis of crucial metadata, limiting troubleshooting for challenging data sets. The widely used RELION software package lacks a graphical native representation of the underlying metadata. Here, two web‐based tools are introduced: relion_live.py, which offers real‐time feedback on data collection, aiding swift decision‐making during data acquisition, and relion_analyse.py, a graphical interface to represent RELION projects by plotting essential metadata including interactive data filtration and analysis. A useful script for estimating ice thickness and data quality during movie pre‐processing is also presented. These tools empower researchers to analyse data efficiently and allow informed decisions during data collection and processing. [ABSTRACT FROM AUTHOR]

Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6–17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results. [ABSTRACT FROM AUTHOR]