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Introduction: Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features. Objectives: In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics. Methods: A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs. Results: Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu, with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere. Conclusion: The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency. [ABSTRACT FROM AUTHOR]

Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences. In this article, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools is critical and, most of the time, required for behavioral studies, but they are normally overlooked because they are difficult to find or use. For example, to foster the use of concordance analysis, the reliability module can perform inference using only one repetition of the same analysis. More than just bureaucratic requirements, these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Additionally, EW-OS provides extended capabilities of temporal segmentation for categorical and spatial segmentation for morphological and kinematic data extracted from videos. Finally, this software was built to allow for continued collaborative development; is freely available through an open-source platform cooperative environment and is freely available through an open-source platform cooperative environment. [ABSTRACT FROM AUTHOR]

Purpose: Paraneoplastic syndromes (PNS) are a group of rare disorders triggered by an immune response to malignancy, characterized by diverse neurological, muscular, and systemic symptoms. This study aims to leverage machine learning to develop a predictive model for cancer diagnosis in patients with paraneoplastic autoantibodies. Methods: Demographic data included age and sex, and presenting symptoms were recorded. Laboratory data comprised serum or cerebrospinal fluid (CSF) paraneoplastic autoantibody panels. The study included participants who tested positive for at least one autoantibody. Naive Bayes model was used to predict cancer presence. Model performance was evaluated using sensitivity, specificity, likelihood ratios, predictive values, AUC-ROC, Brier score, and overall accuracy. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. A graphical user interface (GUI)-based application was developed to facilitate model use. Results: The study included 116 participants, with an average age of 57.1 years and a higher proportion of females (53.4%). The most common presenting symptom was "Motor" (40.5%), followed by "Cognitive" (17.2%) and "Bulbar" (15.5%) symptoms. Cancer was present in 23 participants (19.8%). The Naive Bayes model demonstrated high performance with a sensitivity of 85.71% and specificity of 100.00%. The AUC-ROC was 0.9795, indicating excellent diagnostic capability. Age and the presence or absence of specific autoantibodies were significant predictors of cancer. Conclusion: Machine learning models, such as the Naive Bayes classifier developed in this study, can accurately stratify cancer risk in patients with positive paraneoplastic autoantibodies. [ABSTRACT FROM AUTHOR]

Calcium aluminate cement (CAC) is an important hydraulic cementitious material. It is widely used in construction, metallurgy, chemical industry and other fields due to its high early strength. The factors affecting its strength are also very complex. The research focus of this paper is to establish a prediction model for the compressive strength of CAC paste, so as to assist scientific research and practical engineering to quickly predict the strength of CAC paste at different ages under different mix ratios and curing conditions. In this paper, 273 sets of data are trained and tested based on support vector regression (SVR), random forest regression (RFR), gradient boosting (GB) and extreme gradient boosting (XGB) algorithms. It is found that the prediction accuracy of GB model can reach 89%. Meanwhile, based on the GB model, the feature importance analysis, global interpretation and dependence analysis are carried out. It is found that the main factors affecting the strength of CAC are relative humidity, silica fume content and curing temperature. To obtain high-strength CAC paste, the recommended mix ratio and curing conditions are as follows: Al2O3 content is 67%, CaO content is 32%, silica fume replacement rate is 10%, water–cement ratio is 0.1, relative humidity is 90%, curing temperature is 5 °C and low-temperature treatment time is greater than 60 days. Finally, a graphical user interface is established to facilitate direct prediction of CAC paste under new mix ratio and curing conditions. [ABSTRACT FROM AUTHOR]

Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machine learning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions. [ABSTRACT FROM AUTHOR]

The present study introduces new regression formulae that address several challenges of current subadult stature estimation methods by 1) using a large, contemporary, cross-sectional sample of subadult skeletal remains; 2) generating regression models using both lengths and breadths; 3) utilizing both linear and nonlinear regression models to accommodate the nonlinear shape of long bone growth; and 4) providing usable prediction intervals for estimating stature. Eighteen long bone measurements, stature, and age were collected from computed tomography images for a sample of individuals (n = 990) between birth and 20 years from the United States. The bivariate relationship between long bone measurements and stature was modeled using linear and nonlinear methods on an 80% training sample and evaluated on a 20% testing sample. Equations were generated using pooled-sex samples. Goodness of fit was evaluated using Kolmogorov–Smirnov tests and mean absolute deviation (MAD). Accuracy and precision were quantified using percent testing accuracy and Bland–Altman plots. In total, 38 stature estimation equations were created and evaluated, all achieving testing accuracies greater than 90%. Nonlinear models generated better fits compared to linear counterparts and generally produced smaller MAD (3.65 – 15.90cm). Length models generally performed better than breadth models, and a mixture of linear and nonlinear methods resulted in highest testing accuracies. Model performance was not biased by sex, age, or measurement type. A freely available, online graphical user interface is provided for immediate use of the models by practitioners in forensic anthropology and will be expanded to include bioarchaeological contexts in the future. [ABSTRACT FROM AUTHOR]

Stainless-steel provides substantial advantages for structural uses, though its upfront cost is notably high. Consequently, it's vital to establish safe and economically viable design practices that enhance material utilization. Such development relies on a thorough understanding of the mechanical properties of structural components, particularly connections. This research advances the field by investigating the behavior of stainless-steel connections through the use of a four-parameter fitting technique and explainable artificial intelligence methods. Training was conducted on eight different machine learning algorithms, namely, Decision Tree, Random Forest, K-nearest neighbors, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, Adaptive Boosting, and Categorical Boosting. SHapley Additive Explanations was applied to interpret model predictions, highlighting features like spacing between bolts in tension and end-plate height as highly impactful on the initial rotational stiffness and plastic moment resistance. Results showed that Extreme Gradient Boosting achieved a coefficient of determination score of 0.99 for initial stiffness and plastic moment resistance, while Gradient Boosting model had similar performance with maximum moment resistance and ultimate rotation. A user-friendly graphical user interface (GUI) was also developed, allowing engineers to input parameters and get rapid moment–rotation predictions. This framework offers a data-driven, interpretable alternative to conventional methods, supporting future design recommendations for stainless-steel beam-to-column connections. [ABSTRACT FROM AUTHOR]

Objectives: This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques. Methods: 1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long. Results: Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings. Conclusions: Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation. [ABSTRACT FROM AUTHOR]

In this paper, a novel methodology is developed for the characterization of the capacity of rectangular-shaped concrete-filled steel tubes (CFSTs). In the scientific research field, of particular interest is the behavior of long CFST columns under eccentric compressive load. These conditions promote failure mechanisms involving global member buckling. The developed methodologies are based on machine learning techniques found on artificial neural networks (ANNs). Furthermore, optimization methodologies, employing the grey wolf optimization algorithm and the firefly algorithm, have been attempted. For the training and validation of the models, a database consisting of 1,641 experimental tests collected from literature sources has been prepared, containing long and short specimens as well as specimens with or without load eccentricity. As the vast majority of the available experimental tests involve short specimens, the database has been augmented with 216 3D finite element models (FEMs), featuring increased member slenderness values. The calibration of the FEMs has been performed against experimental tests. The performance of the developed models has been measured through a number of performance indices, and compared with available code procedures. They have been found to provide significant improvements, both for short and long CFST columns, with the ANN model optimized with the firefly algorithm outperforming the others. Furthermore, a graphical user interface (GUI) has been developed which can be readily used to estimate the axial load capacity of CFST columns through the optimal ANN model. The developed GUI is made available as a supplementary material. [ABSTRACT FROM AUTHOR]

Development of a software facility for joint operations with experimental data on thermonuclear fusion, including software for information interaction and software for analysis and display of experimental data, is presented. The architecture, functionality, and concepts of constructing a graphical user interface were determined; the technological approach to their implementation was justified, and technological solutions were selected and tested. The considered solutions support remote participation in scientific experiments and operations with their results. Conclusions based on the results of the work carried out are formulated, and plans for the further development of the software facility are presented. [ABSTRACT FROM AUTHOR]

The European Union (EU) Medical Device Regulation and In Vitro Medical Device Regulation have introduced more rigorous regulatory requirements for medical devices, including new rules for post-market surveillance. However, EU market vigilance is limited by the absence of harmonized reporting systems, languages and nomenclatures among Member States. Our aim was to develop a framework based on Natural Language Processing capable of automatically collecting publicly available Field Safety Notices (FSNs) reporting medical device problems by applying web scraping to EU authority websites, to attribute the most suitable device category based on the European Medical Device Nomenclature (EMDN), and to display processed FSNs in an aggregated way to allow multiple queries. 65,036 FSNs published up to 31/12/2023 were retrieved from 16 EU countries, of which 40,212 (61.83%) were successfully assigned the proper EMDN. The framework's performance was successfully tested, with accuracies ranging from 87.34% to 98.71% for EMDN level 1 and from 64.15% to 85.71% even for level 4. [ABSTRACT FROM AUTHOR]

Tilde is a new in-house developed solution that the GNS Science's GeoNet program has recently developed to provide storage and access to low sample rate datasets used to monitor tsunami, landslides, and volcanoes in Aotearoa New Zealand. It includes datasets covering sample rates of 15 s or longer. Time series data are stored and disseminated in JSON and CSV formats, and users can access these through an application programming interface (API) and through graphical user interfaces (GUIs). Tilde's GUIs were created to allow technical and non-technical users easy access to the available data. The introduction of the Tilde system as one of the GeoNet program delivery channels has represented a big step forward for GeoNet's volcano data holdings by providing a single point to access all low to medium-sample rate volcano-specific monitoring data. We designed the system and developed a domain model, an API, a graphical data discovery interface, and associated data tutorials. This work leverages the open-by-default data policy for data generated through the GeoNet program. This paper is intended to highlight how we made many of the key decisions that shaped the Tilde system, how they were impacted by our multi-hazard monitoring requirements, and how they have improved access to volcano monitoring data. We conclude with some open questions about the need to develop common standards to share analysis-ready time series data within different disciplines in volcanology and geophysics. [ABSTRACT FROM AUTHOR]

Short-Answer Grading (SAG) is a time-consuming task for teachers that automated SAG models have long promised to make easier. However, there are three challenges for their broad-scale adoption: A technical challenge regarding the need for high-quality models, which is exacerbated for languages with fewer resources than English; a usability challenge in adapting high-quality research prototypes to the needs of non-expert users, and a trust challenge in communicating the abilities and limitations of the tools. We propose to meet the technical challenge for German with a robust Transformer-based SAG model. We address the usability challenge with an easy-to-use graphical user interface for the SAG model, and the trust challenge with a workflow that allows teachers to evaluate the model on their own data, to choose on the basis of this evaluation which model predictions to trust, and in consequence to stay in control of grading their students while saving grading effort. [ABSTRACT FROM AUTHOR]

Undersea mining encounters challenges due to the presence of seawater. An influx of seawater into stop in undersea can result in enormous disaster. Predicting strata settlement is a crucial measure to ensure the safety of undersea mining. This study proposed an intelligent model based on deep forest (DF) to evaluate the strata settlement during undersea mining. Initially, the strata displacement was monitored in the Xishan mining area of Sanshandao gold mine, China. Comprehensive datasets encompassing roof displacement and twelve influencing factors were compiled from 120 observations. Then, these datasets were statistically analyzed and used to train the DF model. The developed DF model achieved a training R2 of 0.971 and a testing R2 of 0.936. Compared with other machine learning models, the DF model has superior performance in the prediction of strata settlement. Moreover, a graphical user interface was designed to facilitate the application of the DF model. Finally, to validate model feasibility, displacement monitoring was conducted in the Xinli mining area of Sanshandao gold mine. Additional datasets were collected to validate the capability of the DF model. The results suggested that the DF model can be used to predict strata subsidence in undersea mining effectively. [ABSTRACT FROM AUTHOR]

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result. [ABSTRACT FROM AUTHOR]

The convergence of pioneering innovations like 5G and edge computing, alongside the rise of vehicle communication requirements, has introduced challenges in vehicular Ad-hoc networks (VANET) research. These challenges have driven the development of novel protocols, devices, and utilization scenarios to enhance vehicle communication via wireless Ad-hoc networks. Due to the complexities of real-world testing, several simulators have been created to evaluate these advancements. This research investigates cooperative vehicular communication in urban scenarios using a MATLAB-based simulation framework. The simulation utilizes two core functions such as UrbanCitySimu and VANET, to model vehicle mobility, communication, and collaborative routing strategies. The Ad-hoc protocol, Ad-hoc On- Demand Vector along with the clustering technique is implemented to measure key metrics such as distance travelled, energy usage, network lifespan, throughput, and bit error rate. These metrics are presented graphically to provide a comprehensive analysis of network behaviour under various road and weather conditions. The graphical user interface further aids in visualizing the simulated network, enhancing the understanding of cooperative communication dynamics for VANET. The findings reveal critical insights into the effectiveness of cooperative strategies, contributing to the advancement of intelligent vehicular communication networks. [ABSTRACT FROM AUTHOR]

In the circular economy era, this study addresses sustainable business management for high-investment and long-life cycle projects, where accurate and reliable assessments are crucial to ensuring successful outcomes. The objective is to elevate the reliability of assessments by introducing a novel decision-making method that, for the first time, integrates time-based satisfaction and risk factors simultaneously. We propose a 3-phase multi-criteria decision-making (MCDM) method, which combines fuzzy MCDM comprising fuzzy analytic hierarchy process and fuzzy technique for order preference by similarity to ideal situation (TOPSIS), Kano model, and failure mode and effects analysis (FMEA) techniques, to handle reliable assessments effectively. Our method is distinct in its incorporation of time-based satisfaction weights derived from Kano model, emphasising decision-makers' criteria preferences in short, medium, and long terms. Furthermore, we introduce risk-discounted weights by using FMEA to tune criteria scores. The method is validated via a numerical example case, assessing and selecting the most appropriate hydrogen storage method for lightweight vehicles. The results suggest that cryo-compressed hydrogen tank with 250–350 bar and at cryogenic temperature is the most suitable storage method. Health & safety with a weight of 0.5318 emerges as the most important main criterion, and permeation & leakage with a weight of 0.4008 is the most important sub-criterion. To bridge the gap between theoretical research and practical application, we transform the new method into a user-friendly web application with graphical user interface (GUI). End-users can conduct reliable assessments and foster sustainable business management through informed decision-making. [ABSTRACT FROM AUTHOR]

Beans are a legume that has historically been a cheap source of protein, in the daily human diet. Therefore, it is challenging that the quality of this legume be guaranteed. In this work, we propose a novelty system for evaluating the contrasting bean quality, combining an algorithm for detecting black variety bean kernels and a neural network model based on an interval type-2 fuzzy set approach for weight estimation. To perform the detection task, YOLOv5 was used, which achieved favorable results, with an accuracy of 0.998, recall of 0.997, mAP_0.5 of 0.995 and mAP_0.5:0.95 of 0.733. Meanwhile, a model based on artificial neural networks was developed to estimate the weight. However, since the current quality assessment system includes vagueness and uncertainty, to deal with this problem, the interval type-2 fuzzy set approach was used by converting the collected data to fuzzy numbers with trapezoidal membership to use them in the training and validation process. The model for weight estimation showed favorable results, with a variance explained by the model (R2) of 0.99, a mean squared error (MSE) of 0.02, mean absolute percentage error of 0.05, and a mean absolute error (MAE) of 0.12 g. Meanwhile, an MSE of 0.02 and MAE of 0.11 g were the results for data not included in the training and validation process. Finally, both models were incorporated into a graphical user interface to facilitate their use in a real quality assessment process. The experiments with the proposed system demonstrated its effectiveness in detecting and estimating weight dynamically and with acceptable accuracy. [ABSTRACT FROM AUTHOR]

The identification of malaria infection using microscope images of blood smears is considered as a 'gold standard'. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using 'cvui' library to interact with the OpenVINO's inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported. [ABSTRACT FROM AUTHOR]

An aquaculture automation system (AcAS) is a user-friendly single-window unit. This allows end users to monitor and control the entire system easily through a built-in, customizable graphical user interface. AcAS was designed for simplicity, making it easy to configure and use. This system was integrated with highly efficient industrial-grade environmental sensors (pH, conductivity, oxidation-reduction potential, and dissolved oxygen) to ensure precise and error-free results in harsh environments. It can also store user and system data in an built-in memory device. It is equipped with built-in Wi-Fi, LoRa/ZigBee, and 4G/5G modules for data transfer, making it compatible with modern communication technologies. The program was programmed to be farmer-friendly and helped farmers maintain optimal shrimp growth conditions by monitoring various parameters. AcAS takes corrective measures as required, and provides updates to farmers through a graphical display unit. Farmers can also configure devices to receive alerts for important field parameters or alarm conditions. Therefore, AcAS enhances the efficiency and sustainability of aquaculture farming by enabling precise control of farming conditions and proactive management of aquaculture. [ABSTRACT FROM AUTHOR]

The Hard X-Ray collimation system was installed at the T-15MD tokamak (major radius of R = 1.48 m, minor radius of a = 0.67 m, elongation of k = 1.7–1.9, triangularity of δ = 0.3–0.4, toroidal magnetic field of BT = 2 T, plasma current of up to Ip = 2 MA, and discharge duration of up to 30 s) to provide the possibility of measuring the spatial and temporal evolution of runaway electron beams. The diagnostic is based on LaBr3(Ce) scintillation detectors, witch provides energy spectrum measurements for energies form 0.3 to 10 MeV. Monte Carlo simulation of the shielding efficiency in the GEANT4 environment showed that the collimator provides 2° field of view (FOV). Horizontal and vertical FOV movement along the plasma cross-section is arranged by a rotating base and screw support elements. An algorithm for processing the signals from the scintillation detectors with a graphical user interface and data visualization is used for reconstruction of the spectra. The results of testing the detector in a laboratory setup and in preliminary experiments carried out with the T-15MD tokamak in which runaway electrons beams were generated are presented. [ABSTRACT FROM AUTHOR]

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]

This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 × 1, 1 × 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods—decision tree (DT), random forest (RF), AdaBoost, and XGBoost—were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly. [ABSTRACT FROM AUTHOR]

This study aims to make a unique contribution to the existing body of knowledge about rock strength and deformation parameters and crack stress thresholds through intelligent and statistical approaches applied to a database comprising various rock types (i.e., sedimentary, igneous, and metamorphic rocks). The database contains physical–mechanical and ultrasonic parameters. Six distinct machine learning (ML) algorithms— artificial neural network (ANN), random forest (RF), decision tree (DT), K-nearest neighbor (KNN), support vector regression (SVR), and bagging regressor (BR)— along with the conventional linear regression techniques, were employed to develop predictive models. These models estimate uniaxial compressive strength ( σ c ) and Tangent Young's modulus ( E t ) based on bulk density (ρ ) and P-wave ultrasonic velocity ( V p ). Furthermore, they predict crack stress thresholds (i.e., crack closure stress σ cc , crack initiation stress σ ci , and crack damage stress σ cd ) as a function of σ c , E t , ρ , V p , axial strain at failure ( ε 1 f ), and lateral strain at failure ( ε 3 f ). Various performance indices were utilized to evaluate and compare the performance of these models. The results indicated that the RF method outperformed other ML-based and linear regression-based approaches in predicting the output parameters. Additionally, the multiple parametric sensitivity analysis (MPSA) was carried out to determine the significance of input parameters in predicting the output variables. This analysis revealed that V p and ρ have the highest and lowest impact on predicting σ c and E t , respectively. On the other hand, σ c was identified as the most influential parameter in predicting σ ci and σ cd , while parameters ε 3 f and V p showed the least impact on the foregoing outputs, respectively. This is while ε 1 f and ρ were, respectively, found as the most important and least important factors in predicting σ cc . Finally, to facilitate easy access to the prediction results and enhance the practicality of the proposed RF model, a graphical user interface (GUI) was developed, which enables the practical application of the most performing developed prediction model. Highlights: A comprehensive database including different rock types was prepared to evaluate the rock strength parameters and crack stress thresholds. The used six robust machine learning algorithms effectively unveiled the latent complex nonlinear relationship between the parameters. The random forest algorithm outperformed the traditional regression and machine learning algorithms in predicting the output variables. P-wave velocity, uniaxial compressive strength, and axial strain at the failure point were identified as the most influential parameters in predicting outputs. The developed user-friendly graphical user interface brings the power of the random forest model to practitioners without the need for complex analyses and destructive laboratory tests. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is a method of electrical neuromodulation used to treat a variety of neuropsychiatric conditions including essential tremor, Parkinson's disease, epilepsy, and obsessive–compulsive disorder. The procedure requires precise placement of electrodes such that the electrical contacts lie within or in close proximity to specific target nuclei and tracts located deep within the brain. DBS electrode trajectory planning has become increasingly dependent on direct targeting with the need for precise visualization of targets. MRI is the primary tool for direct visualization, and this has led to the development of numerous sequences to aid in visualization of different targets. Synthetic inversion recovery images, specified by an inversion time parameter, can be generated from T1 relaxation maps, and this represents a promising method for modifying the contrast of deep brain structures to accentuate target areas using a single acquisition. However, there is currently no accessible method for dynamically adjusting the inversion time parameter and observing the effects in real-time in order to choose the optimal value. In this work, we examine three different approaches to implementing an application for real-time optimal synthetic inversion recovery image selection and evaluate them based on their ability to display continually-updated synthetic inversion recovery images as the user modifies the inversion time parameter. These methods include continuously computing the inversion recovery equation at each voxel in the image volume, limiting the computation only to the voxels of the orthogonal slices currently displayed on screen, or using a series of lookup tables with precomputed solutions to the inversion recovery equation. We find the latter implementation provides for the quickest display updates both when modifying the inversion time and when scrolling through the image. We introduce a publicly available cross-platform application built around this conclusion. We also briefly discuss other details of the implementations and considerations for extensions to other use cases. [ABSTRACT FROM AUTHOR]

Airway diseases cause significant challenges while diagnosing it accurately as well as timely. In current years, the combination of deep learning techniques with Explainable Artificial Intelligence (XAI), has emerged as a promising possibility to enhance the understanding as well as interpretability of complex medical data which are associated with these conditions. The study explores the usage of advanced convolutional neural networks (CNNs) on large-scale of Chest X-ray or CT scan image datasets and their application in the domain of medical imaging to detect as well as classify airway diseases such as pneumoconiosis, pulmonary embolism, Covid-19, tuberculosis, and Lung cancer. The visualization of the images have been enhanced by applying sharpening techniques and are later subsequently used to train various deep learning models which include InceptionResNetV2, MobileNet, Xception, ResNet152, EfficientNetV2M, and DenseNet169. Further, various parameters have been used for the evaluation of the performance of these models in determining the most optimal choice. During training the model, it has been found that the highest accuracy, precision, recall, and F1 score has been computed by InceptionResNetV2 with 99.15%, 98% and 99% (both) respectively on a loss of 0.0275. Besides this, a graphical user interface (GUI) has been also developed to improvise the interaction between the AI based model and end users. The GUI is based on the theory of XAI which after predicting the diseases provides in detail explanation of treatment as well as preventive measures of the disease. The motto of the research is to escalate the knowledge of deep learning techniques for various lung based issues as well as to enlighten with the significance of XAI in promoting interpretability as well as transparency. The findings of the research implies towards the advancement of decision support systems at various medical sectors to promote effective as well as personalized interventions in the realm of pulmonology. [ABSTRACT FROM AUTHOR]

Considering the assembly as a multi-span, continuous beam-column, a system of "three moment equations" can be derived to compute side force components as well as all reactive forces, deflection, bending moment, and slope. Solving high-order nonlinear equations involves programming and debugging, which can be time-consuming. Moreover, many methods for solving such equations may not necessarily yield correct results. However, the Python open-source library "sympy" simplifies this process by eliminating the need for complex programming and debugging. It only requires the necessary equation information to quickly solve the high-order nonlinear equations. Additionally, C# provides the capability to rapidly create graphical user interfaces, enhancing the solution's usability. By utilizing Python.NET to call Python code, it reduces the programming workload and effectively addresses the problem of solving a system of three moment equations. [ABSTRACT FROM AUTHOR]

Enzymes are biological catalysts that accelerate chemical reactions by reducing their activation energy. Enzymes specific environmental conditions to function optimally. Additive molecules and compounds, such as organic solvents, ionic liquids, and deep eutectic solvents, have crucial effects on enzyme behavior by changing activity and stability. However, finding and testing different additives is an expensive process that requires specialists, laboratory equipment, and chemical compounds. Machine learning, which has been present in all fields of science and technology in recent years, is one of the ways to find a suitable additive for our desired enzyme without doing a time-consuming experimental process. In this manuscript, we introduce ADDZYME, a machine learning-based software, to predict the effect of additives on enzyme activity. ADDZYME utilizes an ensemble of extremely randomized trees models alongside physicochemical descriptors to make a prediction. To ease usage, ADDZYME is accompanied by a graphical user interface. ADDZYME is freely available on www.github.com/miladrayka/addzyme for further experiments. [ABSTRACT FROM AUTHOR]

Pupil size change is a widely adopted, sensitive indicator for sensory and cognitive processes. However, the interpretation of these changes is complicated by the influence of multiple low-level effects, such as brightness or contrast changes, posing challenges to applying pupillometry outside of extremely controlled settings. Building on and extending previous models, we here introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit to model pupil size changes to dynamically changing visual inputs using a convolution approach. Open-DPSM incorporates three key steps: (1) Modeling pupillary responses to both luminance and contrast changes; (2) Weighing of the distinct contributions of visual events across the visual field on pupil size change; and (3) Incorporating gaze-contingent visual event extraction and modeling. These steps improve the prediction of pupil size changes beyond the here-evaluated benchmarks. Open-DPSM provides Python functions, as well as a graphical user interface (GUI), enabling the extension of its applications to versatile scenarios and adaptations to individualized needs. By obtaining a predicted pupil trace using video and eye-tracking data, users can mitigate the effects of low-level features by subtracting the predicted trace or assess the efficacy of the low-level feature manipulations a priori by comparing estimated traces across conditions. [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]

Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts. Highlights: An efficient hybrid ensemble model based on HBA is proposed for predicting tunnel overbreak caused by blasting. An explicit high-accuracy prediction equation for tunnel overbreak is constructed using GEP technology. The model performance is comprehensively evaluated through various metrics and non-parametric statistical tests. The constructed model demonstrates excellent generalization capabilities and interpretability. [ABSTRACT FROM AUTHOR]

One challenge that current de novo drug design models face is a disparity between the user's expectations and the actual output of the model in practical applications. Tailoring models to better align with chemists' implicit knowledge, expectation and preferences is key to overcoming this obstacle effectively. While interest in preference-based and human-in-the-loop machine learning in chemistry is continuously increasing, no tool currently exists that enables the collection of standardized and chemistry-specific feedback. Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists' detailed feedback on molecular structures. The GUI enables chemists to explore and evaluate molecules, offering a user-friendly interface for annotating preferences and specifying desired or undesired structural features. By providing chemists the opportunity to give detailed feedback, allows researchers to capture more efficiently the chemist's implicit knowledge and preferences. This knowledge is crucial to align the chemist's idea with the de novo design agents. The GUI aims to enhance this collaboration between the human and the "machine" by providing an intuitive platform where chemists can interactively provide feedback on molecular structures, aiding in preference learning and refining de novo design strategies. Metis integrates with the existing de novo framework REINVENT, creating a closed-loop system where human expertise can continuously inform and refine the generative models. Scientific contribution We introduce a novel Graphical User Interface, that allows chemists/researchers to give detailed feedback on substructures and properties of small molecules. This tool can be used to learn the preferences of chemists in order to align de novo drug design models with the chemist's ideas. The GUI can be customized to fit different needs and projects and enables direct integration into de novo REINVENT runs. We believe that Metis can facilitate the discussion and development of novel ways to integrate human feedback that goes beyond binary decisions of liking or disliking a molecule. [ABSTRACT FROM AUTHOR]

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]." [ABSTRACT FROM AUTHOR]

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength () of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength. [ABSTRACT FROM AUTHOR]

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications. [ABSTRACT FROM AUTHOR]

In order to realize the gravity standard using the gravity data of a specified group of stations, the gravity reference network is crucial for national gravity measurements. Dependable gravity values are required in the modern height system and for different tasks in geophysics. In particular, it can be utilized as an initial data point for airborne gravity measurements or other fields. There are some programs for processing gravity data like GrafLaB, GRAVSOFT, but its main focus are the spherical harmonic computation and gravity field modeling, and can not process the observations of relative measurements. Even though there are pyGABEUR-ITB and GRAVNET, but above mentioned software don't have the graphical user interface (GUI) and also don't give the detail mathematical error correction. Therefore, the GravNetAdj software package, which is based on the MATLAB 2018a platform by using the appdesigner, is developed using an up-to-date robust method for parameter estimation. Firstly, this paper reviews the fundamental theory of gravity network adjustment, covering the relative gravity data processing, the methods of network adjustment, and precision evaluation. Then, a case study for the local gravity reference network in China is conducted, which is used to prove the reliability and availability of the presented software. [ABSTRACT FROM AUTHOR]

This paper presents a framework designed to collect, archive, and share time series data coming from sensor networks at Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (Italy), which we have developed and called Time Series Database management System (TSDSystem). The framework proposes a flexible database model for the standardization of sensor networks data and implements an optimized technology for storage and retrieval of acquired time series data. It is designed for the implementation of multiparametric databases and then suitable for development in volcanological observatories worldwide. The proposed framework provides a web service to perform writing and reading data via a standard web communication protocol, which easily enables interaction with other instruments or automatic systems. All results provided by the TSDSystem web service are represented using common data formats in the context of online services. In particular, the station networks metadata representation follows a schema inspired by the International Federation of Digital Seismograph Networks, widely known in seismology. A web GUI (graphical user interface) is provided to test and document the web service. Additionally, basic built-in web applications are supplied with the web GUI to perform joint and synchronized time series data visualization as well as representation of stations on a geographical map. The web GUI also offers administration tools for data access policy management, creation of monitoring dashboards and data publication through web pages. The framework implements an authorization system that can be used to restrict both writing or reading operations. The TSDSystem can also be a useful tool for engineering surveillance systems. The implementing code of the framework is available with an open source license on a public repository together with a user manual. [ABSTRACT FROM AUTHOR]

In recent years, the interaction of a human operator with teleoperated robotic systems has been much improved. One of the factors influencing this improvement is the addition of force feedback to complement the visual feedback provided by traditional graphical user interfaces. However, the users of these systems performing tasks in isolated and safe environments are often inexperienced and occasional users. In addition, there is no common framework to assess the usability of these systems, due to the heterogeneity of applications and tasks, and therefore, there is a need for new usability assessment methods that are not domain specific. This study addresses this issue by proposing a measure of usability that includes five variables: user efficiency, user effectiveness, mental workload, perceived usefulness, and perceived ease of use. The empirical analysis shows that the integration of haptic feedback improves the usability of these systems for non-expert users, even though the differences are not statistically significant; further, the results suggest that mental workload is higher when haptic feedback is added. The analysis also reveals significant differences between participants depending on gender. [ABSTRACT FROM AUTHOR]

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]

The purpose of this research is to propose a new method for identifying diabetic retinopathy using retinal fundus images. Currently, identifying diabetic retinopathy from computerized fundus images is a challenging task in medical image processing and requires new strategies to be developed. The manual analysis of the retinal fundus is time-consuming and requires a significant amount of skill. To assist clinicians, this research develops a graphical user interface that integrates imaging algorithms to assess whether the patient's fundus image is affected by diabetic retinopathy. The diagnosis is made using a deep neural network, specifically the Resnet152-V2, which has been shown to have 100% accuracy in all evaluation criteria including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the graphical user interface and the patient's information is stored in a local database. This proposed method can also be used by ophthalmologists as a backup option to support in disease detection, reducing the necessary processing time. [ABSTRACT FROM AUTHOR]

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable strength prediction reduces costs and time in design and prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Machine Learning (ML) models to enhance the prediction of CS, analyzing 1030 experimental CS data ranging from 2.33 to 82.60 MPa from previous research databases. The ML models included both non-ensemble and ensemble types. The non-ensemble models were regression-based, evolutionary, neural network, and fuzzy-inference-system. Meanwhile, the ensemble models consisted of adaptive boosting, random forest, and gradient boosting. There were eight input parameters: cement, blast-furnace-slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with the CS as the output. Comprehensive performance evaluations include visual and quantitative methods and k-fold cross-validation to assess the study's reliability and accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to understand better how each input variable affects CS. The findings showed that the Categorical-Gradient-Boosting (CatBoost) model was the most accurate prediction during the testing stage. It had the highest determination-coefficient (R2) of 0.966 and the lowest Root-Mean-Square-Error (RMSE) of 3.06 MPa. The SHAP analysis showed that the age of the concrete was the most critical factor in the predictive accuracy. Finally, a Graphical User Interface (GUI) was offered for designers to predict concrete CS quickly and economically instead of costly computational or experimental tests. [ABSTRACT FROM AUTHOR]

Nowadays, digital medical images have become an essential source for the grand success of e-health technology. At the same time, the massive storage also plays a vital role. One of cloud storage's main objectives is the affordable and easily accessible storage of vast amounts of multi-structured data. The cloud paradigm gives an illusion of infinite storage of data. The future of Cyber-Physical Systems (CPS) relies upon technologies like cloud computing to thrive. However, the major lacuna is data security. This paper deals with the Confidentiality Integrity Availability (CIA) aspects required for cloud-based medical image repositories. Since it is for the medical image, the Region of Interest (RoI) is separated, and the integrity check is applied for RoI. A two-tier security for the medical image has been proposed, including an additional security layer for RoI. A 3-D Lorenz chaotic attractor has been used to generate the key where the keyspace is widely increased. Deoxyribonucleic Acid (DNA) based image diffusion in different stages of cryptosystem offered an average entropy of 7.98042 and a correlation of 0.002864 for RoI only and for ciphered medical image an average entropy of 7.99724 and a correlation of − 0.00063. Text encryption is performed over metadata to ensure the privacy of client authentication. Encrypted metadata and 320 bits have been generated for the RoI part embedded in an image's Non-Region of Interest (NRoI) part in the random pixel indexes obtained using a 1D Tent map. This proposed approach also gives a Graphical User Interface developed using Python 3.8 to support non-technical persons or medical practitioners. The proposed security framework provides a complete CIA triad for medical image repositories in the cloud. [ABSTRACT FROM AUTHOR]

Laboratories are considered to play a unique role in circuits teaching. Laboratories can be traditional, with physical components and desks, or virtual with graphical simulators. Applying these facilities in teaching, students can make experiments or measurements by exploring electric circuits' features. However, an intriguing research question is whether physical components or graphical simulators are more appropriate to build knowledge, enhance skills and improve attitudes. Thus, the aim of this article is: 1) to perform a review in order to explore the characteristics of the studies that compare the tangible and graphical user interfaces and 2) to apply a meta-analysis for the effects of the interfaces under study. The meta-analysis included 88 studies with pre/post-tests designs with 2798 participants, which were emerged from: a) 4 databases, b) forward snowballing method. The review showed that the majority of researchers have focused on the knowledge gaining, while a few researchers have examined skills and attitudes. The meta-analysis showed that the combination of user interfaces (tangible/graphical) appears to be the most beneficial for students in the domain of electric circuits teaching. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. Finally, this article concludes with a discussion of the possible solutions to the observed limitations and identifies areas for further research. Such a Wi-Fi channel perturbation pattern analysis is believed to be an efficient, economical, and privacy-friendly approach to be potentially utilized in mutual human interaction recognition for indoor activity monitoring, surveillance system, smart health monitoring systems, and independent assisted living. [ABSTRACT FROM AUTHOR]

The World Health Organisation advocates Digital Health Technologies (DHTs) for advancing population health, yet concerns about inequitable outcomes persist. Differences in access and use of DHTs across different demographic groups can contribute to inequities. Academics and policy makers have acknowledged this issue and called for inclusive digital health strategies. This systematic review synthesizes literature on these strategies and assesses facilitators and barriers to their implementation. We searched four large databases for qualitative studies using terms relevant to digital technology, health inequities, and socio-demographic factors associated with digital exclusion summarised by the CLEARS framework (Culture, Limiting conditions, Education, Age, Residence, Socioeconomic status). Following the PRISMA guidelines, 10,401 articles were screened independently by two reviewers, with ten articles meeting our inclusion criteria. Strategies were grouped into either outreach programmes or co-design approaches. Narrative synthesis of these strategies highlighted three key themes: firstly, using user-friendly designs, which included software and website interfaces that were easy to navigate and compatible with existing devices, culturally appropriate content, and engaging features. Secondly, providing supportive infrastructure to users, which included devices, free connectivity, and non-digital options to help access healthcare. Thirdly, providing educational support from family, friends, or professionals to help individuals develop their digital literacy skills to support the use of DHTs. Recommendations for advancing digital health equity include adopting a collaborative working approach to meet users' needs, and using effective advertising to raise awareness of the available support. Further research is needed to assess the feasibility and impact of these recommendations in practice. [ABSTRACT FROM AUTHOR]

Children in malaria-endemic regions can experience repeated Plasmodium infections over short periods of time. Effects of re-infection on multiple co-existing CD4+ T cell subsets remain unresolved. Here, we examine antigen-experienced CD4+ T cells during re-infection in mice, using scRNA-seq/TCR-seq and spatial transcriptomics. TCR transgenic TEM cells initiate rapid Th1/Tr1 recall responses prior to proliferating, while GC Tfh counterparts are refractory, with TCM/Tfh-like cells exhibiting modest non-proliferative responses. Th1-recall is a partial facsimile of primary Th1-responses, with no upregulated effector-associated genes being unique to recall. Polyclonal, TCR-diverse, CD4+ T cells exhibit similar recall dynamics, with individual clones giving rise to multiple effectors including highly proliferative Th1/Tr1 cells, as well as GC Tfh and Tfh-like cells lacking proliferative capacity. Thus, we show substantial diversity in recall responses mounted by multiple co-existing CD4+ T cell subsets in the spleen, and present graphical user interfaces for studying gene expression dynamics and clonal relationships during re-infection. CD4+ T cells are known to be important in Plasmodium infection. Here the authors use mouse models to track antigen-experienced TCR transgenic and polyclonal CD4+ T cells during Plasmodium re-infection, and show different T cell phenotypes and varied responses in different areas of the spleen. [ABSTRACT FROM AUTHOR]

Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra's agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers' decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations. [ABSTRACT FROM AUTHOR]

Brain–Computer Interface is tested as a successful method in improving human cognitive functions such as attention and memory. Attention plays a significant role in areas ranging from a person's day-to-day life to educational domain and professional activities. When attention is evaluated using camera-based techniques, the users may suffer privacy issues. Using Brain–Computer Interface (BCI) to obtain a measure of attention will be useful in this regard. The paper proposes a Human Attention Recognition System (HARS) in which EEG signal acquisition is used to obtain the attention of the individual, Renyi's entropy-based mutual information method is used for feature selection and a deep learning-based classifier is used to classify the signals. HADS is not camera-based: therefore, faces of the subjects are not revealed. EEG signals were collected using the Ultracortex Mark III dry electrodes and were visualised using OpenBCI GUI (Graphical User Interface). The experiment is validated using the publicly available Confused Student EEG dataset from Kaggle, giving an accuracy of 99.21%. The results indicate that the proposed method can identify attention levels accurately and can be effectively used in educational systems, biofeedback and medical research. [ABSTRACT FROM AUTHOR]

Bacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role. [ABSTRACT FROM AUTHOR]

Cyber-physical systems are complex environments that combine physical devices (i.e., sensors and actuators) with a software controller. The ubiquity of these systems and dangers associated with their failure require the implementation of mechanisms to monitor, verify and guarantee their correct behaviour. This paper presents ParetoLib 2.0, a Python tool for offline monitoring and specification mining of cyber-physical systems. ParetoLib 2.0 uses signal temporal logic (STL) as the formalism for specifying properties on time series. ParetoLib 2.0 builds upon other tools for evaluating and mining STL expressions, and extends them with new functionalities. ParetoLib 2.0 implements a set of new quantitative operators for trace analysis in STL, a novel mining algorithm and an original graphical user interface. Additionally, the performance is optimised with respect to previous releases of the tool via data-type annotations and multi core support. ParetoLib 2.0 allows the offline verification of STL properties as well as the specification mining of parametric STL templates. Thanks to the implementation of the new quantitative operators for STL, the tool outperforms the expressiveness and capabilities of similar runtime monitors. [ABSTRACT FROM AUTHOR]