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In this article, we provide details of the suite DensToolKit-v2, which consists of a set of cross-platform, optionally parallelized programs for analyzing the molecular electron density (ρ), as well as different fields and chemical indices derived from it. Notably, with this version, the user can compute the Non-Covalent Interaction index, the Density Overlap Regions Index, and fields related to single-spin-type molecular orbitals, such as the spin density. In addition, DensToolKit-v2 includes several programs for analyzing other less-known fields, such as the Density Matrix of order 1, the two-electron pair density function, and the Fourier transforms of these fields, that is, functionals in momentum space. A new sub-program to compute integrated properties of each of the fields released in the suite is included. A simple graphical user interface is released, which eases the visualization of ρ critical points topology. Most interestingly, this version includes a program that renders estimations of pKa's of carboxylic acids and pKb's of amines (primary, secondary, and tertiary) through refined relations between experimental data and the molecular electrostatic potential computed at isosurfaces of ρ. Details related to the speed of the programs and a few examples of how to use the program in workflows are discussed, and the source code is released through a git repository under the GPLv3 terms. [ABSTRACT FROM AUTHOR]

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser–Parr–Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope. [ABSTRACT FROM AUTHOR]

The Atomic, Molecular, and Optical Science (AMOS) Gateway is a comprehensive cyberinfrastructure for research and educational activities in computational AMO science. The B-Spline atomic R-Matrix (BSR) suite of programs is one of several computer programs currently available on the gateway. It is an excellent example of the gateway's potential to increase the scientific productivity of AMOS users. While the suite is available to be used in batch mode, its complexity does not make it well-suited to the approach taken in the gateway's default setup. The complexity originates from the need to execute many different computations and to construct generally complex workflows, requiring numerous input files that must be used in a specific sequence. The BSR graphical user interface described in this paper was developed to considerably simplify employing the BSR codes on the gateway, making BSR available to a large group of researchers and students interested in AMO science. [ABSTRACT FROM AUTHOR]

The structures of metal nanoparticles (NPs) significantly influence their catalytic reactivities. Recent in situ experimental observations of dramatic structural changes in NPs underscore the need to establish a dynamic structure–property relationship that accounts for the reconstruction of NPs in reactive environments. Here, we present the MOSP, a free and open-source graphical user interface (GUI) package designed to simulate the structure and reactivity of metal NPs under operando conditions. MOSP integrates two models: the multiscale structure reconstruction model predicting equilibrium metal NP structures under specific reaction conditions and the kinetic Monte Carlo model simulating the reaction dynamics. This combination allows for the exploration of the dynamic structure–property relationships of NPs. MOSP enhances user accessibility through its intuitive GUI, facilitating easy input, post-processing, and visualization of simulation data. This article is the release note of MOSP, focusing on its implementation and functionality. [ABSTRACT FROM AUTHOR]

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge. [ABSTRACT FROM AUTHOR]

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]

Accidental fires have shown that fires can be of a spreading nature, burn for longer durations until the fuel has been exhausted, or be uncontrolled until fire fighter intervention. These observations limit the use of nominal fire curves for performance assessment of structural members during fire. In this article, a novel graphical user interface (GUI)-based computer program is developed to capture the spreading nature of fire. The temperature inside the compartment is evaluated by superposition of the temperature rise due to heat flux from flames and heat transfer from smoke. The temperature of the member is evaluated by solving the equation of heat conduction in space and time domain by the finite difference method. The smoke temperature, resultant temperature after collation, and temperature inside the concrete member are validated and a reasonable agreement is observed. A demonstration describing the application of the developed computer program has been discussed, which concluded that different locations inside a compartment are subjected to different temperature-time curves during the fire. This developed computer program can be applied to any occupancy in which fuel-controlled traveling fire can occur to predict the temperature distribution inside the concrete compartment as well as inside the concrete member during the fire duration. Additional features will be added in future to computer programs to perform structural analysis during fire. [ABSTRACT FROM AUTHOR]

The grounding system in substations generally uses electrode rods, because electrodes can affect the effectiveness of fault current conduction, so the equipment will be safer. Considering the importance of the grounding system, the installed grounding system must be considered and maintained properly. One of them is the grounding found in Glugur. The main objective of this research is to comprehensively evaluate the substation grounding system by modeling the grounding system at the Glugur Substation using MATLAB graphical user interface (GUI). The grounding resistance follows a grid system with an area of 20×15=300 m² with specific resistance being clay using 100 rod electrodes. From the results of ground resistance simulation modeling using MATLAB GUI, it can be concluded as follows: for a certain resistance value, the number of electrodes for 100 Om is 3.55 O, for ground resistance with a constant depth and a varying number of 100 electrodes, it is 3.45 O, and for. The grounding resistance with a constant and varying number of 1,000 rods is obtained at 2.65 O. From these results, the modeling carried out is in accordance with the standards of electricity regulations in Indonesia. [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]

We introduce FACA – Fully Automated Co-Alignment, an open-source software designed to fully automate the workflow for co-aligning point clouds derived from Unmanned Aerial Vehicle (UAV) images using photogrammetry. We developed FACA to efficiently evaluate fieldwork with Unmanned Aerial Vehicles (UAVs) on landslides and coastal dynamics. The software applies to any research requiring comparative precise rapid multi-temporal point cloud generation from UAV imagery. UAVs are an essential element in most contemporary applied geosciences research toolkits. Typical products of UAV flights are point clouds created with photogrammetry, to measure objects and their change if multi-temporal data exists. Ground Control Points (GCPs) are considered the best method to increase the precision and accuracy of point clouds, but placing and measuring them is not always feasible during fieldwork. Co-alignment leads to the local precise alignment of multiple point clouds without GCPs. FACA uses Agisoft Metashape Pro and the Python standard library. The GPLv3 licensed FACA source code focuses on extendability, modifiability, and readability. FACA works interchangeably from the command line or a custom graphical user interface. We distribute the software with both usage and installation instructions. Three multi-temporal test datasets are available. We demonstrate the utility and versatility of FACA v1 with a multi-year and -region dataset acquired along Germany's Baltic Sea coast. FACA is in continuous open development. [ABSTRACT FROM AUTHOR]

Background: Tissue clearing combined with light-sheet microscopy is gaining popularity among neuroscientists interested in unbiased assessment of their samples in 3D volume. However, the analysis of such data remains a challenge. ClearMap and CellFinder are tools for analyzing neuronal activity maps in an intact volume of cleared mouse brains. However, these tools lack a user interface, restricting accessibility primarily to scientists proficient in advanced Python programming. The application presented here aims to bridge this gap and make data analysis accessible to a wider scientific community. Results: We developed an easy-to-adopt graphical user interface for cell quantification and group analysis of whole cleared adult mouse brains. Fundamental statistical analysis, such as PCA and box plots, and additional visualization features allow for quick data evaluation and quality checks. Furthermore, we present a use case of ClearFinder GUI for cross-analyzing the same samples with two cell counting tools, highlighting the discrepancies in cell detection efficiency between them. Conclusions: Our easily accessible tool allows more researchers to implement the methodology, troubleshoot arising issues, and develop quality checks, benchmarking, and standardized analysis pipelines for cell detection and region annotation in whole volumes of cleared brains. [ABSTRACT FROM AUTHOR]

This work presents an optimization framework for the simultaneous design of low-carbon hydrogen and carbon dioxide capture, use, and storage supply chains. The proposed model is a large-scale multiperiod mixed-integer linear programming that maximizes the net present value. Our approach addresses technological hydrogen production and carbon capture pathways, multimodal transportation of materials, electricity generation and transmission, and carbon dioxide utilization via enhanced oil recovery. The novelty of this work is twofold: the design of a low-carbon hydrogen supply chain and its integration into an oil field development of infrastructure designed to produce incremental crude oil via carbon dioxide injection; thus, it can be captured, utilized, and stored. We consider crude oil production profiles, characteristics of oil reservoirs, and well drilling constraints to represent the CO 2 -EOR operation. We conduct a nationwide long-term planning case study considering the potential for enhanced oil recovery operations and the availability of biomass, solar, and wind resources in Colombia to produce low-carbon hydrogen and monetize carbon dioxide through incremental crude oil production. Moreover, a graphical user interface (GUI) was designed to guide the end user in setting up case studies and analyzing the results through a series of tabular structures. • Modelling framework for the simultaneous design of low-carbon hydrogen supply chains and CO 2 -EOR operations. • Prediction of CO 2 injection considering petrophysical properties of the oil field. • Low-carbon hydrogen production from biomass and renewable energy. • Nationwide study applied to the Colombian context. [ABSTRACT FROM AUTHOR]

Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data. [ABSTRACT FROM AUTHOR]

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability. Due to those characteristics, it is necessary to develop a family of computer-aided supporting models for rapid prototyping and analysis of the new designs of sensors of various shapes and configurations, which this paper presents by use of simulation tools. For a prototyping of the sensor lay out, an algorithm is elaborated, based on an application created in LabVIEW 2022 software, which generates two spreadsheets formatted by the requirements of Autodesk Inventor 2014 and COMSOL Multiphysics 5.6 software, based on data entered by the user. As a result, a tailored-in-shape CCPS layout is prepared. A parametric model of the sensor is prepared in Autodesk Inventor software, which automatically changes its geometric dimensions after changing data in an MS Excel spreadsheet. Then, the generated layout is analyzed to obtain electromechanical characteristics for defined CCPS geometry and materials used in the COMSOL Multiphysics software. Another application is devoted to purely mechanical analysis. The graphical user interface (GUI) add-on based on the Abaqus 2018 software engine is prepared for advanced mechanical analysis simulations of sensor materials in selected loading scenarios. The GUI is used for entering material libraries and the selection of loading conditions and a type of specimen, while the results of the numerical analysis are delivered through Abaqus. The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. For example, using a rigid epoxy resin for an insulating layer shows a negligible difference in the level of strain compared to the structure during a simulated tensile test, specifically in the tested layer thickness range of up to 0.3 mm. However, during bending tests, an approx. 17% change in principal strain level can be observed through the top to bottom edge of the epoxy resin layer. The adopted methodology for carrying out simulation studies assumes the parallel use of a set of various computer-aided tools. This approach allows for taking advantage of individual software environments, which allows for expanding the scope of analyses and using the developed models and applications in further research activities. [ABSTRACT FROM AUTHOR]

To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level. Furthermore, the pre-trained networks achieved higher accuracies on defect classification compared to the self-built network, with ResNet-50 displaying higher accuracy. The inspection system consistently obtained fast and accurate surface classifications because it imposed OK classification on models trained with images from both illumination modes. The obtained surface information was then successfully sent to a server to be forwarded to a graphical user interface for visualization. The developed system showed considerable robustness, demonstrating its potential as an efficient tool for industrial quality control. [ABSTRACT FROM AUTHOR]

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators (R 2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R 2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and t w, l w and f ′c are the key parameters affecting the shear capacity of the flanged shear wall. [ABSTRACT FROM AUTHOR]

AbstractFibre-reinforced polymer (FRP) composites are increasingly favoured for strengthening existing structures due to their numerous structural benefits. Nevertheless, the performance of such technology is strongly affected by the behaviour of the epoxy resin adhesive layer, which is largely dependent on its curing conditions. This study introduces a deep learning (DL) framework that leverages eXtreme Gradient Boosting (XGBoost) and genetic programming (GP) to comprehensively study the influence of curing scenarios on the vitreous transition of the adhesive. An experimental dataset comprising 160 data points was used to develop predictive models. The XGBoost models exhibited high predictive accuracy for both the onset vitreous transition temperature and the peak tan δ vitreous transition temperature, achieving R2 values of 0.982 and 0.993 for the unseen test set, respectively. While the GP models exhibited lower predictive accuracy with R2 values of 0.834 and 0.842, they provided explicit equations that enhance the interpretability of the DL model and facilitate practical application. To make these insights accessible to engineers without programming expertise, a web-based graphical user interface software was developed, incorporating all DL models. Additionally, a feature influence assessment was conducted, providing visual representations of the impact of each feature on the output results, thus enhancing interpretability for engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Linux-based operating system, namely, DicomOS, tailored for medical imaging and enhanced interoperability, addressing user-friendly functionality and the main critical needs in radiology workflows. Traditional operating systems in clinical settings face limitations, such as fragmented software ecosystems and platform-specific restrictions, which disrupt collaborative workflows and hinder diagnostic efficiency. Built on Ubuntu 22.04 LTS, DicomOS integrates essential DICOM functionalities directly into the OS, providing a unified, cohesive platform for image visualization, annotation, and sharing. Methods include custom configurations and the development of graphical user interfaces (GUIs) and command-line tools, making them accessible to medical professionals and developers. Key applications such as ITK-SNAP and 3D Slicer are seamlessly integrated alongside specialized GUIs that enhance usability without requiring extensive technical expertise. As preliminary work, DicomOS demonstrates the potential to simplify medical imaging workflows, reduce cognitive load, and promote efficient data sharing across diverse clinical settings. However, further evaluations, including structured clinical tests and broader deployment with a distributable ISO image, must validate its effectiveness and scalability in real-world scenarios. The results indicate that DicomOS provides a versatile and adaptable solution, supporting radiologists in routine tasks while facilitating customization for advanced users. As an open-source platform, DicomOS has the potential to evolve alongside medical imaging needs, positioning it as a valuable resource for enhancing workflow integration and clinical collaboration. [ABSTRACT FROM AUTHOR]

In plasticulture production systems, the conventional practice involves broadcasting pre-emergent herbicides over the entire surface of raised beds before laying plastic mulch. However, weed emergence predominantly occurs through the transplant punch-holes in the mulch, leaving most of the applied herbicide beneath the plastic, where weeds cannot grow. To address this issue, we developed and evaluated a precision spraying system designed to target herbicide application to the transplant punch-holes. A dataset of 3378 images was manually collected and annotated during a tomato experimental trial at the University of Florida. A YOLOv8x model with a p2 output layer was trained, converted to TensorRT® to improve the inference time, and deployed on a custom-built computer. A Python-based graphical user interface (GUI) was developed to facilitate user interaction and the control of the smart sprayer system. The sprayer utilized a global shutter camera to capture real-time video input for the YOLOv8x model, which activates or disactivates a TeeJet solenoid for precise herbicide application upon detecting a punch-hole. The model demonstrated excellent performance, achieving precision, recall, mean average precision (mAP), and F1score exceeding 0.90. Field tests showed that the smart sprayer reduced herbicide use by up to 69% compared to conventional broadcast methods. The system achieved an 86% punch-hole recognition rate, with a 14% miss rate due to challenges such as plant occlusion and variable lighting conditions, indicating that the dataset needs to be improved. Despite these limitations, the smart sprayer effectively minimized off-target herbicide application without causing crop damage. This precision approach reduces chemical inputs and minimizes the potential environmental impact, representing a significant advancement in sustainable plasticulture weed management. [ABSTRACT FROM AUTHOR]

By 2027 more than 530 M homes will likely adopt at least one type of automated system. This means that a growing number of residents will be living with automated technology in the home, everyday. But living with smart homes is full of conflicts between what residents find appropriate and what technology does instead. Previous research, centering end-user needs, has often focused on smooth living experiences through graphical user interfaces and improved predictions. In this research, we take the more-than-human lens of co-performance to put crises in everyday practices in view, and to conceptualize a new notion of interface. Based on ethnographic data from 11 households, our findings illustrate how crises reveal conflicting ideas of appropriateness, how residents reconfigure their co-performances with technology in response to everyday crises, and how new interfaces are enacted as a result. We conclude by illuminating how researchers and designers should not look at the conflicts and crises emerging in the more-than-human home as something of which to get rid. Instead, they are opportunities for residents and buildings to respond to one another in the context of everyday life and to enact interfaces that were not pre-designed into the building. [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]

Graphical user interface (GUI) browsing and retrieval tools are becoming essential to interaction design research and practice. These tools allow GUI designers to browse large amounts of data and recover inspiring or relevant designs for their task. Unfortunately, data-driven market analysis or business intelligence (BI) applied to GUIs have mostly been left aside. To address this research gap, we elicit designers' needs and responses regarding the development of Enricommender, a high-fidelity prototype of a market analysis recommender and reporting system. We identify and discuss key design challenges, as reported by more than 200 real-world designers, as well as their workflows and overall expectations towards such a BI system. Ultimately, this article sets the foundation for developing future GUI-oriented BI applications. RESEARCH HIGHLIGHTS Insights about ideation and design of graphical user interfaces. Elicited preferences and current practices from 200 real-world designers. A business intelligence prototype for comparative analysis and design recommendations. [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]

Purpose: This paper aims to present research carried out on the influence of GUI graphical elements design for an integrated mission management system (IMMS) display flight planning process. Design/methodology/approach: Surveys and research were conducted among students/pilots to explore graphic presentation methods for flight planning displays. Guidelines for graphical layout of the IMMS flight planning interface are proposed. Findings: A research concept was obtained, enabling GUI tests for IMMS using prepared templates and questionnaires. Practical implications: This study improves cockpit information readability, understanding and presentation, particularly for flight planning elements such as terrain, weather, traffic and zones influencing route organisation. Social implications: This study targets possible improvements to the flight path planning process in aviation, inducing a reduction in errors related to human factors while processing the visual data on-board. Originality/value: The study verified the impact of drawing and rendering methods on IMMS flight planning, suggesting that current display methods may be error-prone when showing hazard information from multiple sources on a single screen. [ABSTRACT FROM AUTHOR]

Data-based instruction (DBI) is a process in which teachers use progress data to make ongoing instructional decisions for students with learning disabilities. Curriculum-based measurement (CBM) is a common form of progress monitoring, and CBM data are placed on a graph to guide decision-making. Despite the central role that graph interpretation plays in the successful implementation of DBI, relatively little attention has been devoted to investigating this skill among special education teachers. In the present study, we examined the data decisions of 32 U.S. pre-service special education teachers (29 females and 3 males). Participants viewed data presented sequentially on CBM progress graphs and used a think-aloud procedure to explain their reasoning each time they indicated they would make instructional changes. We also asked participants to make the same type of decisions in response to static CBM progress graphs depicting 10 weeks of data. Overall, there was inconsistency in pre-service teachers' responses related to when or why they would make an instructional change. Decisions were often influenced by graph-related features, such as variability in the data. Furthermore, responses suggested misunderstandings that led to premature instructional change decisions and reliance on individual data points. [ABSTRACT FROM AUTHOR]

Purpose: The Language ENvironment Analysis (LENA) technology uses automated speech processing (ASP) algorithms to estimate counts such as total adult words and child vocalizations, which helps understand children's early language environment. This ASP has been validated in North American English and other languages in predominantly monolingual contexts but not in a multilingual context like India. Thus, the current study aims to validate the classification accuracy of the LENA algorithm specifically focusing on speaker recognition of adult segments (AdS) and child segments (ChS) in a sample of bi/ multilingual families from India. Method: Thirty neurotypical children between 6 and 24 months (M = 12.89, SD = 4.95) were recruited. Participants were growing up in bi/multilingual environment hearing a combination of Kannada, Tamil, Malayalam, Telugu, Hindi, and/or English. Daylong audio recordings were collected using LENA and processed using the ASP to automatically detect segments across speaker categories. Two human annotators manually annotated -900 min (37,431 segments across speaker categories). Performance accuracy (recall and precision) was calculated for AdS and ChS. Results: The recall and precision for AdS were 0.62 (95% confidence interval [CI] [0.61, 0.63]) and 0.83 (95% CI [0.8, 0.83]), respectively. This indicated that 62% of the segments identified as AdS by the human annotator were also identified as AdS by the LENA ASP algorithm and 83% of the segments labeled by the LENA ASP as AdS were also labeled by the human annotator as AdS. Similarly, the recall and precision for ChS were 0.65 (95% CI [0.64, 0.66]) and 0.55 (95% CI [0.54, 0.56]), respectively. Conclusions: This study documents the performance of the ASP in correctly classifying speakers as adult or child in a sample of families from India, indicating recall and precision that is relatively low. This study lays the groundwork for future investigations aiming to refine the algorithm models, potentially facilitating more accurate performance in bi/multilingual societies like India. [ABSTRACT FROM AUTHOR]

The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (IRI) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has revolutionized the design and production of complex geometries by offering unprecedented creative freedom over traditional manufacturing. Despite its growing prominence, AM lacks automated and standardized design rules tailored to specific AM processes, resulting in time-consuming and expert-dependent manual verification. To address these limitations, this research introduces a novel design for additive manufacturing (DfAM) framework consisting of two complementary models designed to automate the design process. The first model, based on a decision tree algorithm, evaluates part compliance with established AM design rules. A modified J48 classifier was implemented to enhance data mining accuracy by achieving a 91.25% classification performance accuracy. This model systematically assesses whether input part characteristics meet AM processing standards, thereby providing a robust tool for verifying design rules. The second model features an AM design rule engine developed with a Python-based graphical user interface (GUI). This engine generates specific recommendations for design adjustments based on part characteristics and machine compatibility, offering a user-friendly approach for identifying potential design issues and ensuring DfAM compliance. By linking part specifications to various AM techniques, this model supports both researchers and engineers in anticipating and mitigating design flaws. Overall, this research establishes a foundation for a comprehensive DfAM expert system. [ABSTRACT FROM AUTHOR]

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

The compressive strength (CS) of a concrete column confined with spiral stirrups is an important indicator for assessing the safety and stability of concrete structures. However, achieving accurate CS estimation for confined concrete remains challenging due to the complex confinement mechanism provided by spiral stirrups. In this study, three robust machine learning (ML) algorithms—support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost)—are employed to predict the CS value of the spiral stirrup-confined circular concrete columns. The hyperparameters of the ML models undergo fine-tuning via Bayesian optimization with 10-fold cross-validation, and the optimized ML models are evaluated for their predictive capabilities. Results show that compared to SVR and RF, XGBoost exhibits more stable generalization performance, achieving an average coefficient of determination (R2) of 0.944 for the 10-fold cross-validation, and demonstrates superior accuracy on the testing dataset with an R2 value of 0.967. To provide insights into the relationship between input features and the output CS value, Individual Conditional Exception (ICE) plots, one/two-dimensional Partial Dependence Plots (PDPs), and Shapley Additive Explanation (SHAP) techniques are utilized to interpret the optimized XGBoost model. Additionally, a friendly online graphical user interface (GUI) has been specially developed based on the optimized XGBoost model to facilitate convenient CS estimation for spiral stirrup-confined circular concrete column. [ABSTRACT FROM AUTHOR]

This research addresses housing customisation by using digital tools in co-design processes. This paper introduces the development of a Graphical User Interface (GUI) that assists the end-users' interaction with the design process. Although the participation of end users in the design process is considered essential, a communication gap persists between designers and end users, and there is a lack of tools to help inhabitants express their needs and desires. To bridge this gap, this research proposes using digital technologies to enhance end-users participation in the design process of their houses. In this paper, we show the results of the development and evaluation of an interface designed to help inhabitants to co-create their houses. We developed and tested a GUI for a housing co-design system. Interviews with professionals, housing cooperatives, and inhabitants informed the design process, allowing us to define user requirements and design tasks. The interface was tested with low and high-fidelity prototypes, receiving positive feedback from both experts and potential users. Architects were also involved in using the interface to comment on its usefulness for housing co-design. The tool demonstrated the potential to improve end-users' participation, contributing significantly to participatory processes in collective housing. This research ensures the tool's effectiveness by directly incorporating user input, aligning the interface with the user's needs and preferences. [ABSTRACT FROM AUTHOR]

There is already a consensus among central banks to accelerate the development of Central Bank Digital Currencies (CBDC). To facilitate a smooth money transfer process, the interactive experience of the CBDC user interface design is very important. However, much of the current research has focused on the technical feasibility of the CBDCs, and no scientific research has yet attempted to design an effective CBDC user interface. In this study, a series of CBDC user interfaces were designed to help study the interaction process of CBDCs in a simulated payment transaction context with a focus on the user interface and interactive experience of the CBDCs. The experiment in this study was a 2 (graphical metaphor) × 3 (gesture type) × 2 (visual feedback) mixed factorial design in which a total of 12 CBDC prototypes were created and evaluated based on users' task performance and a series of subjective evaluations. The results of our empirical study revealed that graphical metaphor user interface design can improve the interface usability and subjective evaluation of CBDCs, and may also trigger the user's sense of experience with traditional banknotes. Specifically, the use of animated cues for currency movement on the CBDC user interface may help evoke the user's experience of using cash. In addition, we found that the wave type gesture may cause users to spend more time completing the CBDC operational tasks. Moreover, the animated cue of money moving may have faster performance and a higher degree of rationality. The above-mentioned results may provide an important design reference for CBDC developers to create easy-to-use CBDC services. [ABSTRACT FROM AUTHOR]

The traditional analytical framework taken by neuroimaging studies in general, and lesion‐behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well‐suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion‐behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion‐behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on (a) whether they are addressing a classification versus regression problem and (b) whether their predictor data correspond to binary lesion images, continuous lesion‐network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass‐univariate, multivariate, and machine learning models, features built‐in and customizable routines for hyper‐parameter optimization, cross‐validation, model stacking, and significance testing, and automatically generates text‐based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit's features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive versus receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto‐temporal structural disconnection and with similar functional networks. Importantly, we find that lesion location and lesion‐derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30%–40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies. [ABSTRACT FROM AUTHOR]

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. [ABSTRACT FROM AUTHOR]

Precise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post‐surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time‐consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over‐segmentation or under‐segmentation of tumour regions. Introducing an interactive deep‐learning tool would empower radiologists to rectify these inaccuracies by adjusting the over‐segmented and under‐segmented voxels as needed. This paper proposes a network named Atten‐SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi‐scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten‐SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post‐operative glioblastoma dataset. The methodology outperformed state‐of‐the‐art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance‐95 got is 7.78 mm for the Uppsala University dataset. [ABSTRACT FROM AUTHOR]

We present TDS Simulator, a new software tool aimed at modelling thermal desorption spectroscopy (TDS) experiments. TDS is a widely used technique for quantifying key characteristics of hydrogen-material interactions, such as diffusivity and trapping. However, interpreting the output of TDS experiments is non-trivial and requires appropriate post-processing tools. This work introduces the first software tool capable of simulating TDS curves for arbitrary choices of material parameters and hydrogen trap characteristics, using the primary hydrogen diffusion and trapping models (Oriani, McNabb–Foster). Moreover, TDS Simulator contains a specific functionality for loading experimental TDS data and conducting the inverse calibration of a selected transport model, providing automatic estimates of the density and binding energy of each hydrogen trap type in the material. In its first version, TDS Simulator is provided as a MATLAB App, which is made freely available to the community and provides a simple graphical user interface (GUI) to make use of TDS Simulator straightforward. As reported in the present manuscript, the outputs of TDS Simulator have been extensively validated against literature data. Demonstrations of automatic determination of trap characteristics from experimental data through the optimization tool are also provided. The present work enables an efficient and straightforward characterization of hydrogen-material characteristics relevant to multiple applications, from nuclear fusion to the development of hydrogen-compatible materials for the hydrogen economy. TDS Simulator can be downloaded from https://mechmat.web.ox.ac.uk/codes. • TDS Simulator is a free, user–friendly App for simulating H desorption experiments. • Multiple H transport theories (Oriani, McNabb-Foster) and trap sites are considered. • The provided inference algorithm enables identifying trap quantities from test data. • Each of the elements implemented in TDS Simulator is extensively validated. • Lab data is analysed, proving strong inference abilities and bringing new insight. [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]

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (P B), injury (P I) and complication-free tumor control (P +) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses ( D ‾ ‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, P B , P I , and P + for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies. [ABSTRACT FROM AUTHOR]

Demonstrating software early and responding to feedback is crucial in agile development. However, it is difficult for stakeholders who are not on‐site customers but end users, marketing people, or designers, and so forth to give feedback in an agile development environment. Successful graphical user interface (GUI) test executions can be documented and then demonstrated for feedback. In our new concept, GUI tests from behavior‐driven development (BDD) are recorded, augmented, and demonstrated as videos. A GUI test is divided into several GUI unit tests, which are specified in Gherkin, a semi‐structured natural language. For each GUI unit test, a video is generated during test execution. Test steps specified in Gherkin are traced and highlighted in the video. Stakeholders review these generated videos and provide feedback, for example, on misunderstandings of requirements or on inconsistencies. To evaluate the impact of videos in identifying inconsistencies, we asked 22 participants to identify inconsistencies between (1) given requirements in regular sentences and (2) demonstrated behaviors from videos with Gherkin specifications or from Gherkin specifications alone. Our results show that participants tend to identify more inconsistencies from demonstrated behaviors, which are not in accordance with given requirements. They tend to recognize inconsistencies more easily through videos than through Gherkin specifications alone. The types of inconsistency are threefold: The mentioned feature can be incorrectly implemented, not implemented, or an unspecified new feature. We use a fictitious example showing how this feedback helps a product owner and her team manage requirements. We conclude that GUI test videos can help stakeholders give feedback more effectively. By obtaining early feedback, inconsistencies can be resolved, thus contributing to higher stakeholder satisfaction. [ABSTRACT FROM AUTHOR]

This study aimed to investigate and analyze the performance of twelve machine learning (ML) algorithms in estimating the construction time and cost of drill and blast tunnels. Thirteen tunnels located in different regions of Iran were selected, resulting in 900 data sets. Ten parameters were identified as influential factors affecting the construction time and cost of these tunnels. 80% of the data set was used for training, while the remaining 20% was reserved for testing. Additionally, 288 unseen data sets were utilized for evaluation purposes. All the algorithms demonstrated accurate performance on the test data sets, with R-squared values exceeding 0.93. However, only the Gaussian process regression algorithm achieved satisfactory results on the unseen data sets. Furthermore, a graphical user interface (GUI) was developed based on the trained ML models. This GUI allows real-time estimation of the time and cost of drill and blast tunnels and can be updated during construction. [ABSTRACT FROM AUTHOR]

PURPOSE: Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction. METHODS: The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user's choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment. RESULTS: Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P <.00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P =.0017), while firstorder_10Percentile (P =.0312) was statistically significant between good and partial/nonresponders. CONCLUSION: The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building. CARPI is a Python-based automated software application that enables radiomics and perfusion analysis of different types of tumors. [ABSTRACT FROM AUTHOR]