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Computer tools allow to ease the daily life of everyone, especially for people with disabilities. The optimal choice of pointing interfaces or types of settings can be difficult to achieve. Although there exist a few tests to compare and evaluate the performance of computer access technology (CAT), most of them only provide results as qualitative terms. Besides, the current choice is based mainly on clinical observations or non-standardized tests. To objectify the recommendations in CAT, support funding for people with disabilities and measure the functional repercussions of a therapeutic action, it is necessary to carry out comparative tests with measurable criteria. This paper presents the development of a personalized, free, and dedicated evaluation platform of pointing interfaces and aids. The quantitative evaluation process is described in detail, including the configuration of different exercises with settable parameters that permits to build a customized evaluation process with increased difficulties, the definition of performance indicators and the statistical analysis methods for quantified comparative tests. Finally, we present the assessment results of four people with different disabilities using multiple pointing interfaces, which verify the effectivity of this evaluation platform with the help of occupational therapists. [ABSTRACT FROM AUTHOR]

One of the alternative ways to obtain low-cost energy is to use biogas generated by the digestion process from sewage sludge. This paper presents an analysis of the processes in four anaerobic digesters (AD) - A, B, C and D. The study analyzed the amount of biogas produced in each digester tank and compared them with each other. Using data sets consisting of parameters relating to the pre-sludge and surplus sludge diverted to each tank, the effect of the proportion of these parameters on biogas production efficiency was studied. Based on this data, several models using different machine learning techniques were built and compared, which can be used to support the biogas production optimization process. A free convenient web tool written in Python language - AD2Biogas Predictor Tool - was also given away for sewage treatment plants to conveniently estimate the predicted amount of biogas produced on a given day using the implemented models. The main objective of the study is to understand how the studied parameters affect the efficiency of the process and identify potential optimization strategies, as well as to propose a model for biogas yield prediction based on sludge characteristics. The result of the study is to contribute to increasing the efficiency of sludge management in wastewater treatment plants and increasing biogas production, both in the form of developed models and a software tool

The interest in peptides and especially in peptidomimetic structures has risen enormously in the past few years. Novel modification strategies including nonnatural amino acids, sophisticated cyclization strategies, and side chain modifications to improve the pharmacokinetic properties of peptides are continuously arising. However, a calculator tool accompanying the current development in peptide sciences towards modified peptides is missing. Herein, we present the application PICKAPEP, enabling the virtual construction and visualization of peptidomimetics ranging from well‐known cyclized and modified peptides such as ciclosporin A up to fully self‐designed peptide‐based structures with custom amino acids. Calculated parameters include the molecular weight, the water–octanol partition coefficient, the topological polar surface area, the number of rotatable bonds, and the peptide SMILES code. To our knowledge, PICKAPEP is the first tool allowing users to add custom amino acids as building blocks and also the only tool giving the possibility to process large peptide libraries and calculate parameters for multiple peptides at once. We believe that PICKAPEP will support peptide researchers in their work and will find wide application in current as well as future peptide drug development processes. PICKAPEP is available open source for Windows and Mac operating systems (https://www.research-collection.ethz.ch/handle/20.500.11850/681174). [ABSTRACT FROM AUTHOR]

Wearable sensors, among other informatics solutions, are readily accessible to enable noninvasive remote monitoring in healthcare. While providing a wealth of data, the wide variety of such sensing systems and the differing implementations of the same or similar sensors by different developers complicate comparisons of collected data. An online application as a platform technology that provides uniform methods for analysing balance data is presented as a case study. The development of balance problems is common in neurodegenerative conditions, leading to falls and a reduced quality of life. While balance can be assessed using, for example, perturbation tests, sensors offer a more quantitative and scalable way. Researchers can adjust the platform to integrate the sensors of their choice or upload data and then preprocess, featurise, analyse, and visualise them. This eases performing comparative analyses across the sensors and datasets through a reduction of heterogeneity and facilitates easy integration of machine learning and other advanced data analytics, thereby targeting personalising medical insights. [ABSTRACT FROM AUTHOR]

A bibliometric analysis based on records from a single citation database may be limited in its comprehensiveness and, therefore, in the reliability of its results. The process of combining and deduplicating records from multiple citation index databases for the purpose of a bibliometric analysis is often manual and requires significant effort, especially for larger amounts of data. This paper presents an open-source tool for automatically preprocessing and deduplicating records based on similarity and user-configurable strategies. To validate the capabilities of the tool, the authors of this paper first manually deduplicated records from Scopus and Web of Science on a use-case analysis for 11,307 records. The performance of the tool was then evaluated against the manually deduplicated results. From the results of the best performing similarity configuration on a deduplication use case, the tool minimizes the time researchers would spend on data wrangling for combining Scopus and WoS up to 99% precision and 98% F-measure. The tool developed has practical implications for bibliometric studies. For instance, we conducted a bibliometric analysis of the most productive researchers at a university using a single citation database, as well as merged data from multiple citation databases. The study used the VOSviewer tool and showed that utilizing merged data may produce different outcomes compared to those obtained from a study based on a single citation database. [ABSTRACT FROM AUTHOR]

Studying gene family evolution strongly benefits from insightful visualizations. However, the ever-growing number of sequenced genomes is leading to increasingly larger gene families, which challenges existing gene tree visualizations. Indeed, most of them present users with a dilemma: display complete but intractable gene trees, or collapse subtrees, thereby hiding their children's information. Here, we introduce Matreex, a new dynamic tool to scale up the visualization of gene families. Matreex's key idea is to use "phylogenetic" profiles, which are dense representations of gene repertoires, to minimize the information loss when collapsing subtrees. We illustrate Matreex's usefulness with three biological applications. First, we demonstrate on the MutS family the power of combining gene trees and phylogenetic profiles to delve into precise evolutionary analyses of large multicopy gene families. Second, by displaying 22 intraflagellar transport gene families across 622 species cumulating 5,500 representatives, we show how Matreex can be used to automate large-scale analyses of gene presence–absence. Notably, we report for the first time the complete loss of intraflagellar transport in the myxozoan Thelohanellus kitauei. Finally, using the textbook example of visual opsins, we show Matreex's potential to create easily interpretable figures for teaching and outreach. Matreex is available from the Python Package Index (pip install Matreex) with the source code and documentation available at https://github.com/DessimozLab/matreex. [ABSTRACT FROM AUTHOR]

Summary: Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data.

While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven. [ABSTRACT FROM AUTHOR]

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool's performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS. [ABSTRACT FROM AUTHOR]

Due to the great importance of student satisfaction with educational services, many HEIs conduct annual surveys. Analyzing the results of such surveys, tracking trends, and comparing the evaluation results to help governing bodies make data-driven decisions to take measures to improve the quality of courses is time-consuming and requires a lot of manual work. As a solution to the problem, this paper proposes a comprehensive approach to monitoring student satisfaction with the quality of blended learning courses. The developed software tool analyzes results and enables users with different roles to generate reports with aggregated results at different levels, allowing them to make informed decisions and take measures to ensure a higher quality of courses. The generated reports during the pilot experiments proved the tool's applicability. This tool can be implemented in any HEI, regardless of the software systems used. [ABSTRACT FROM AUTHOR]

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.

Abstract Background To increase uptake of implementation science (IS) methods by researchers and implementers, many have called for ways to make it more accessible and intuitive. The purpose of this paper is to describe the iPRISM webtool (Iterative, Practical, Robust Implementation and Sustainability Model) and how this interactive tool operationalizes PRISM to assess and guide a program’s (a) alignment with context, (b) progress on pragmatic outcomes, (c) potential adaptations, and (d) future sustainability across the stages of the implementation lifecycle. Methods We used an iterative human-centered design process to develop the iPRISM webtool. Results We conducted user-testing with 28 potential individual and team-based users who were English and Spanish speaking from diverse settings in various stages of implementing different types of programs. Users provided input on all aspects of the webtool including its purpose, content, assessment items, visual feedback displays, navigation, and potential application. Participants generally expressed interest in using the webtool and high likelihood of recommending it to others. The iPRISM webtool guides English and Spanish-speaking users through the process of iteratively applying PRISM across the lifecycle of a program to facilitate systematic assessment and alignment with context. The webtool summarizes assessment responses in graphical and tabular displays and then guides users to develop feasible and impactful adaptations and corresponding action plans. Equity considerations are integrated throughout. Conclusions The iPRISM webtool can intuitively guide individuals and teams from diverse settings through the process of using IS methods to iteratively assess and adapt different types of programs to align with the context across the implementation lifecycle. Future research and application will continue to develop and evaluate this IS resource.

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase. [ABSTRACT FROM AUTHOR]

Background: To increase uptake of implementation science (IS) methods by researchers and implementers, many have called for ways to make it more accessible and intuitive. The purpose of this paper is to describe the iPRISM webtool (Iterative, Practical, Robust Implementation and Sustainability Model) and how this interactive tool operationalizes PRISM to assess and guide a program's (a) alignment with context, (b) progress on pragmatic outcomes, (c) potential adaptations, and (d) future sustainability across the stages of the implementation lifecycle. Methods: We used an iterative human-centered design process to develop the iPRISM webtool. Results: We conducted user-testing with 28 potential individual and team-based users who were English and Spanish speaking from diverse settings in various stages of implementing different types of programs. Users provided input on all aspects of the webtool including its purpose, content, assessment items, visual feedback displays, navigation, and potential application. Participants generally expressed interest in using the webtool and high likelihood of recommending it to others. The iPRISM webtool guides English and Spanish-speaking users through the process of iteratively applying PRISM across the lifecycle of a program to facilitate systematic assessment and alignment with context. The webtool summarizes assessment responses in graphical and tabular displays and then guides users to develop feasible and impactful adaptations and corresponding action plans. Equity considerations are integrated throughout. Conclusions: The iPRISM webtool can intuitively guide individuals and teams from diverse settings through the process of using IS methods to iteratively assess and adapt different types of programs to align with the context across the implementation lifecycle. Future research and application will continue to develop and evaluate this IS resource. [ABSTRACT FROM AUTHOR]

After some initial hesitancy at the beginning of the COVID-19 pandemic, the academic community agreed that the infection process is mostly airborne and generally associated with closed environments. Therefore, assessing the indoor infection probability is mandatory to contain the spread of the disease, especially in those environments, like school classrooms, hospital wards or public transportation, with higher risk of overcrowding. For this reason, we developed a software tool in Python to compute infection probability and determine those mechanisms that contribute to reduce its diffusion in closed settings. In this paper, we will briefly illustrate the model we used and focus our attention on the description of the main features of the software and give some examples of how it can be used in clinical practice to predict the spread of the disease in the rooms of a generic ward, optimize room occupancy or drive healthcare workers activity schedule. Finally, some limitations and further implementations of our work will be reported. [ABSTRACT FROM AUTHOR]

Background: Pharmacokinetic nomograms, equations, and software are considered the main tools available for Therapeutic Drug Monitoring (TDM). Model-informed precision dosing (MIPD) is an advanced discipline of TDM that allows dose individualization, and requires a software for knowledge integration and statistical calculations. Due to its precision and extensive applicability, the use of these software is widespread in clinical practice. However, the currently available evidence on these tools remains scarce. Objectives: To review and summarize the available evidence on MIPD software tools to facilitate its identification, evaluation, and selection by users. Methods: An electronic literature search was conducted in MEDLINE, EMBASE, OpenAIRE, and BASE before July 2022. The PRISMA-ScR was applied. The main inclusion criteria were studies focused on developing software for use in clinical practice, research, or modelling. Results: Twenty-eight software were classified as MIPD software. Ten are currently unavailable. The remaining 18 software were described in depth. It is noteworthy that all MIPD software used Bayesian statistical methods to estimate drug exposure and all provided a population model by default, except NONMEN. Conclusions: Pharmacokinetic software have become relevant tools for TDM. MIPD software have been compared, facilitating its selection for use in clinical practice. However, it would be interesting to standardize the quality and validate the software tools. [ABSTRACT FROM AUTHOR]

With the rise of cloud infrastructures, micro-services, frameworks, and reference architectures for every conceivable domain and quality attribute, someone might think that architectural knowledge is hardly needed anymore. But all the architect of today needs to select from the rich array of tools and infrastructure alternatives out there, instantiate, configure them, and create an architecture. Software architecture tools mean any software that helps automation and create architecture, according to requirements. The purpose of these tools is to reduce human effort, speed up software development, and increase reliability. This paper aims to perform a literature review of software architecture tools and to propose architectures for the requirements of the Organization of Small Industries and Industrial Towns of Iran (OSIITI). We surveyed more than 50 software architecture tools for use in practical situations and large-scale projects such as OSIITI's needs. The results of this survey identified five classes, namely (a) Modeling Tools to model architectures; (b) Code-Based Tools (Diagrams-As-Code) to perform syntactic and semantic consistency checking of the models; (c) Automated Tools to generate executable source code automatically that implements the models; (d) Diagramming Tools and (e) Icons-Based Tools to support for trace links between models and requirements or models and tests interfaces. For each class, several software tools are provided with their major features. These classes and tools are very helpful for organizations such as OSIITI that want to develop software, in both small and large-scale projects. A couple of architectures, based on layered and service-oriented patterns are proposed for OSIITI. [ABSTRACT FROM AUTHOR]

The present paper continues the authors' research in machining process optimization, including the direction of machining parameters optimization. The paper develops an innovative method, via surface integrity, for determining the technological route and the related depths of cut, with respect to machining front faces of cast iron parts. For correctly establishing the depths of the cut, the errors that appear within surface and subsurface layers during the casting process, as well as during machining, must be gradually eliminated. These errors make it necessary to consider the concept of surface integrity. This paper presents the modality of integrating the components of surface integrity into the depth of cut. For the practical use of this method, a new software tool based on a series of mathematical models and a small database was conceived. A case study illustrates how the method is applied and the software tool used to solve a specific application in the case of a belt pulley. [ABSTRACT FROM AUTHOR]

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions.

Abstract The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for tools and solutions to simplify and automate the processing of the large and complex datasets that result from these analyses. Constellation is one such of these tools, developed by our group over the last two years to perform unsupervised trend detection for repeating, polymeric units in HRMS data of complex mixtures such as natural organic matter, oil, or lignin. In this work, we develop two new unsupervised algorithms for finding chemically-meaningful changing units in HRMS data, and incorporate a molecular-formula-finding algorithm from the open-source CoreMS software package, both demonstrated here in the Constellation software environment. These algorithms are evaluated on a collection of open-source HRMS datasets containing polymeric analytes (PEG 400 and NIST standard reference material 1950, both metabolites in human plasma, as well as a swab extract containing polymers), and are able to successfully identify all known changing units in the data, including assigning the correct formulas. Through these new developments, we are excited to add to a growing body of open-source software specialized in extracting useful information from complex datasets without the high costs, technical knowledge, and processor-demand typically associated with such tools.

Objectives. Classification of information systems (IS) security indicators and the creation of the method of improved software tool based on its results (in comparison with similar software tool developed earlier by the authors) for auditing information security management systems of organizations in the Republic of Belarus.Methods. During the development and improvement of the software tool using the method of systeminformation analysis and the approaches to its implementation were identified based on following capabilities: organization of questionnaires of heads of departments and services whose specialists work with IS designed to information processing not classified as state secrets or IS with limited dissemination; assessment of the level of compliance of the organization's IS information protection system with the requirements established by the legislation of the Republic of Belarus and other national regulatory legal acts; systematization of recommendations for improving the level of compliance of the organization's IS protection system with the established requirements.Results. Based on the results of the developed improved software tool approbation, it was found that the use of this tool makes it possible to reduce by 20–30 % the financing of costs for the implementation of the auditing the information security management systems of an organization.Conclusion. The developed improved software tool, compared to analogues, is characterized by reduced cost due to the following properties: ease of launch and configuration; independence from the type of operating system; the possibility of organizing both local and remote access. The developed improved software tool was tested in the branch "Long-Distance Communication Branch" of RUE "Beletelecom".

This paper considers the problem of quality measurement of business process models using the n-dimensional space distances. Business process models are graphical schemes similar to flowcharts or activity diagrams utilized in software engineering, usually represented using the BPMN (Business Process Model and Notation) or EPC (Event-driven Process Chain) notations. Business process modeling aims to capture current enterprise workflows for their analysis and then improvement using automation through IT (Information Technology) systems deployment. Therefore, designed business process models should be of high quality, so all “weak spots” of described organizational workflows could be properly identified and replaced with more efficient solutions. Whereas process models, which are not understandable, un-structured, and/or uncertain, may cause even more issues when used to improve organizational activities. Therefore, this study proposes quality measures and a software tool that can be used to detect errors in BPMN and EPC business process models using distances in n-dimensional space. The formal problem statement based on the graph-based description of business process models was given. Business process model quality characteristics and measures that consider features of both BPMN and EPC notations were proposed. Distances in n-dimensional space were suggested for usage to measure the quality of business process models. The algorithm for business process model quality measurement was proposed. The software tool to measure the quality of BPMN and EPC business process models was designed and developed. The quality of sample business process models was measured using the software tool. Obtained results were analyzed and discussed. This paper considers the Euclidean distance only, while other existing n-dimensional space distance measures or, on the opposite, similarity measures, can be used to evaluate business process model quality in further research.

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples. [ABSTRACT FROM AUTHOR]

The study and early diagnosis of cardiac arrhythmias are of great interest today. For several years, new studies have highlighted the potential of the photoplethysmographic wave to detect these events. However, for the design and evaluation of these algorithms, it is necessary to have electrocardiographic and photoplethysmographic signals acquired simultaneously and annotated by specialists. There is low availability of annotated photoplethysmography signals for the study of arrhythmias, as well as tools that make it possible to perform analysis and annotations on these signals. This work shows the software "PPG & ECG Annotation Tool" for annotating electrocardiography (ECG) and photoplethysmography (PPG) records. The proposed software facilitates three main types of annotations: characteristic points, for example, R-peaks of the electrocardiogram, arrhythmic events with adjustable duration, and signal quality. The tool contains algorithms for detecting fiducial points ECG and PPG signals and enables their correction by the user. It also allows computing time intervals and heart rates in selected segments. The software is designed for medical specialists, does not require programming skills, and the user interface is simple and intuitive. The software is available for Windows and Linux platforms. [ABSTRACT FROM AUTHOR]

Analysis of sparse signals has been attracting the attention of the research community in recent years. Several approaches for sparse signal recovery have been developed to provide accurate recovery from a small portion of available data. This paper proposes an improved combined approach for both accurate and computationally efficient signal recovery. Particularly, the proposed approach uses the benefits of the gradient-based steepest descent method (that belongs to the convex optimization group of algorithms) in combination with a specially designed thresholding method. This approach includes solutions for several commonly used sparse bases – the discrete Fourier, discrete cosine transform, and discrete Hermite transform, but can be adapted for other transformations as well. The presented theory is experimentally evaluated and supported by empirical data. Various analytic and real-world signals are used to assess the performance of the proposed algorithm. The analyses are performed for different percentages of available samples. The complexity of the presented algorithm can be seen through the analog hardware implementation presented in this paper. Additionally, the user-friendly graphical interface is developed with a belonging signal database to ease usage and testing. The interface allows users to choose various parameters and to examine the performance of the proposed tool in different scenarios and transformation bases. [ABSTRACT FROM AUTHOR]

Video Surveillance Systems (VSSs) are integral parts of road tunnels. Currently, they perform a number of functions, such as ensuring the protection of tunnel technologies, monitoring tunnel operation, or recognising license plates. If VSSs are to be designed to protect the tunnel and its technologies from unauthorised intentional human activity, it is necessary to ensure that they are designed in such a way as to meet the essential functionality requirement of the physical protection system (PPS). In this case, the VSS, as one of the alarm systems, should perform the function of early intrusion detection. The verification of the functionality of the PPS is possible using a software tool to model and simulate various intrusion scenarios. This article provides an example of the use of the SATANO software evaluation tool. The VSS enables multiple applications, such as monitoring, detection, knowledge, and identification. In this paper, the defined current standardised requirements for the design of VSSs in tunnels are considered from the point of view of their possible use for intelligent video analysis, enabling the recognition of various risk situations (e.g., faults or accidents of vehicles). Using the software tool, IP Video Design Tool, the requirements for the design of cameras in tunnels are assessed and adapted from the perspective of the use of intelligent video analysis. In the event that there is a requirement to use the VSS during emergency situations (e.g., fire), it is necessary to assess the operating conditions and period of time through which the VSS would operate in a given tunnel. This article presents the results of the simulation of the spread of a fire in a tunnel and its impact on the operation of the VSS. [ABSTRACT FROM AUTHOR]

Manual assembly operations are often the bottlenecks of production processes. Usually, in order to find out, what the throughput of these manual assembly operations will be, it is first necessary analysing them in the context of the workstation and the whole processes as well. For these analyses, one of the most useful tools is the use of simulation. Despite existing several complete software for these purposes, they often demand significant time and effort to create good simulations. Sometimes, however, it is just enough to simulate a few key aspects of the assembly process to get key results, which are often the base for immediate and/or further process improvement. In these cases, creating a special analytical tool that allows efficiently obtaining a sufficiently good solution to the given problem becomes a viable and convenient option. This paper presents an assembly simulation and analysis tool designed and created in Microsoft Excel by the authors. The tool is useful for quick analyses of assembly tasks without the need of having to engage in creating complex 3D models and extra analyses. The proposal is implemented into a practical case study, which allows verifying its validity. [ABSTRACT FROM AUTHOR]

Video content on the Internet continues to grow. As a result, streaming platforms must ensure a certain level of quality when preparing their content. To this end, several metrics have been developed by the research community to evaluate video quality. This work integrates 14 video metrics and the SI-TI indicators into a container image to create a cross-platform tool, VQMTK. The tool offers a web interface and a Bash script that combines all metrics into a single tool. Performance tests have demonstrated that the tool is capable of handling all the integrated metrics using 4K video samples. The tool can be used in scientific and educational environments.

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool’s performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS.

Business process models are essential business process management artifacts that help describe visually ongoing business activities to facilitate communication between information technology and business stakeholders. Business process models are used to find inefficient spots within described workflows and resolve detected shortcomings by automation via configurable software solutions or unified workflow engines. However, this is impossible when using syntactically or semantically poor business process models. It is the same as building a house using the blueprint with windows on the floor and typos in text labels. Therefore, it is extremely important to keep created business process models clear and relevant to the actual workflows they describe. Hence, in this paper, we propose the approach and the software tool to calculate semantic quality measures of business process models. The proposed approach uses a special procedure to extract the modeling domain statements using natural language processing techniques. According to the proposed approach, the initial textual descriptions of business process models should be tokenized. Then obtained tokens should be turned to the lower case style and cleansed to remove non-alphabetic tokens and stop words. Finally, the remaining tokens should be stemmed and the existing duplicates should be removed. The same procedure is then repeated for text labels attached to the business process model activities. Then, tokens present in the result of textual description’s processing but missing in the result of labels’ processing are considered incomplete (i.e. incorrect in the modeling domain). Similarly, tokens present in the result of labels’ processing but missing in the result of textual description’s processing are considered invalid (i.e. irrelevant to the modeling domain). Therefore, respective semantic quality measures can be calculated. The software tool is created using the Python programming language because of its powerful natural language processing packages.

The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for tools and solutions to simplify and automate the processing of the large and complex datasets that result from these analyses. Constellation is one such of these tools, developed by our group over the last two years to perform unsupervised trend detection for repeating, polymeric units in HRMS data of complex mixtures such as natural organic matter, oil, or lignin. In this work, we develop two new unsupervised algorithms for finding chemically-meaningful changing units in HRMS data, and incorporate a molecular-formula-finding algorithm from the open-source CoreMS software package, both demonstrated here in the Constellation software environment. These algorithms are evaluated on a collection of open-source HRMS datasets containing polymeric analytes (PEG 400 and NIST standard reference material 1950, both metabolites in human plasma, as well as a swab extract containing polymers), and are able to successfully identify all known changing units in the data, including assigning the correct formulas. Through these new developments, we are excited to add to a growing body of open-source software specialized in extracting useful information from complex datasets without the high costs, technical knowledge, and processor-demand typically associated with such tools. [ABSTRACT FROM AUTHOR]

Background: Cheiloscopy is a reliable method of personal identification which may augment the established methods like dactylography, DNA (deoxyribonucleic acid) profiling, and dental records. Aim: This study aimed to determine the correlation of lip prints with ethnicity and gender of individuals in an attempt to bridge the gap between conventional manual methods and digital methods of cheiloscopy. Methods: Lip prints of 300 gender-matched subjects of Indian and Malaysian-Chinese descents were collected and analyzed using the Suzuki K and Tsuchihashi Y classification system. The lip sizes were measured and lip print patterns were analyzed. The analysis was carried out using manual and computer-aided methods. A customized software for lip print analysis and validate it with the manual lip print analysis was developed. Results: Independent sample t test showed a statistically significant difference between the width and length of the lips between males and females of the total population (p < 0.001). Pearson's chi-square test showed no statistically significant difference between the Indian males and females in the width of the lower lip (p = 0.074). In the Malaysian-Chinese population, there was a statistically significant difference between males and females in the length of the upper lip (p = 0.032) and lower lip (p = 0.035). The type V grooves were predominant in the total study population (51.3%) followed by type III pattern (38.7%). The new customized software could not provide reliable results. Conclusions: Lip sizes differed significantly among the Indian and Malaysian-Chinese subjects. There was no significant gender dimorphism in the distribution of lip print patterns. The results from manual and computer-aided methods were comparable. [ABSTRACT FROM AUTHOR]

Abstract Background Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. Results In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. Conclusions The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.

In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) conversions, syllabification, phonetic feature analysis and script grammar check. This open source software tool, released under MIT license, is developed as a one-stop solution to handle different speech related text processing tasks for automatic speech recognition, text to speech synthesis and non-speech natural language processing tasks including syllable subword based language modeling, phoneme diversity analysis and text sanity check. The tool is evaluated on a manually crafted gold standard lexicon. Mlphon performs orthographic syllabification with 99% accuracy with a syllable error rate of 0.62% on the gold standard lexicon. For grapheme to phoneme conversion task, overall phoneme recognition accuracy of 99% with a phoneme error rate of 0.55% is obtained on gold standard lexicon. Additionally an extrinsic evaluation of Mlphon is performed by employing the pronunciation lexicon created using Mlphon, in Malayalam automatic speech recognition (ASR) task. Performance analysis in terms of the computation time of lexicon creation process and the word error rate (WER) on ASR task are presented along with a comparison over other automated tools for lexicon creation. Pronunciation lexicons with more than 100k commonly used Malayalam words in phonemised and syllabified forms is created and they are published as open language resources along with this work. We also demonstrate the usage of Mlphon on different natural language processing applications - syllable subword ASR, assisted pronunciation learning, phoneme diversity analysis and text sanity check. Being a knowledge based solution with open source code, Mlphon can be adapted to other languages of similar script nature.

Dynamic programming (DP) is a widely used optimization method with several applications in various fields of science. The DP problem solving process can be divided in two phases: mathematical part and programming part. There are a number of researchers for whom the mathematical part is available, but they are not familiar with computer programming. In this paper we present a software tool that automates the programming part of DP and allows users to solve problems based only on their mathematical approach. The application builds up the “d-graph model” of the problem to be solved and applies the “d-variant” of the corresponding single source shortest path algorithm. In addition, we report experimental results regarding the e ciency of the tool relative to the Matlab implementation.

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.

Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of the published tools do not implement methods to consider complex objects, such as urban greenery in their models or they expect a rather complex 3D mesh to represent such objects. Their use in an environment that is difficult to represent geometrically, such as vegetation-covered areas, is rather limited. In this paper, we present a newly developed solar-radiation tool focused on solar-radiation modeling in areas with complex objects, such as vegetation. The tool uses voxel representations of space based on point-cloud data to calculate the illumination and ESRA solar-radiation model to estimate the direct, diffuse, and global irradiation in a specified time range. We demonstrate the capabilities of this tool on a forested mountain area of Suchá valley in the Hight Tatra mountains (Slovakia) and also in the urban environment of Castle Hill in Bratislava (Slovakia) with urban greenery. We compare the tool with the r.sun module of GRASS GIS and the Area Solar Radiation tool of ArcGIS using point-cloud data generated from the digital-terrain model of Kamenistá valley in High Tatra mountains in Slovakia. The results suggest a higher detail of the model in rugged terrain and comparable results on smooth surfaces when considering its purpose as a 3D modeling tool. The performance is tested using different hardware and input data. The processing times are less than 8 min, and 8 GB of memory is used with 4 to 16 core processors and point clouds larger than 100,000 points. The tool is, therefore, easily usable on common computers. [ABSTRACT FROM AUTHOR]

Nowadays, the innovations of AI and other automated decision-making software are spreading to many different areas. The automotive field in particular is rapidly shifting towards the concepts of Advanced Driver Assistance Systems (ADAS), which could bring huge benefits in the future. However, before being able to use these tools, many assurances are required regarding their functioning and safety. To this end, several control techniques exist to evaluate the performance of this software, but a reliable and repeatable method for evaluating complex scenarios and corner cases is still lacking. In this paper, we propose a suite of tools for the generation and analysis of synthetic tests, aimed at evaluating and analyzing the functioning of autonomous driving systems in order to measure their effectiveness and drive their development. [ABSTRACT FROM AUTHOR]

An interactive tool has been developed to help design oral solid dosage form formulations. The tool enables quantitative explorations and comparisons of physical, bulk, and mechanical properties, and takes into account functional characteristics as well. In this manner, comparisons and clustering of both excipients and APIs can be carried out. These comparisons enable the generation of alternatives as well as surrogate identification, so as to spare resources and material. Multiple data sources were merged to create a "joint" data table with all relevant properties. Four main workflow activities are supported: Explore Materials, Search Similar APIs, Search Similar Excipients and Search Material Clusters. Multi-dimensional filtering can be superimposed to each functionality. Suggested visualizations are made particularly accessible by providing them as "standard plots". The underlying philosophy is to empower formulation scientists to explore options, rather than prescribe decisions on exclusively mathematical grounds. The tool described here is the first step towards a holistic optimization incorporating predictions of mixture properties. Methodology of use is illustrated through three material selection application examples., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Some or all co-authors may own F. Hoffmann-La Roche stock. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper]., (Copyright © 2024. Published by Elsevier B.V.)

Protein phosphorylation is a crucial post-translational modification implicated in cancer pathogenesis, offering potential diagnostic and therapeutic targets. Here, we developed PhosCancer, a user-friendly database for extracting biologically and clinically relevant insights from phosphoproteomics data. Leveraging data from the CNHPP and CPTAC, PhosCancer encompasses 174,587 phosphosites from 14 datasets spanning 12 cancer types. Through extensive statistical analyses and integration of annotations from external resources, PhosCancer serves as a convenient one-stop platform facilitating the exploration of phosphorylation profiles across different cancer types. Not only does PhosCancer encompass basic information, 3D structure, functional domains, and upstream kinases, but also provides quantitative associations with nine clinical features, and the relevance with hallmarks in both cancer-specific and pan-cancer views. PhosCancer is a valuable resource for cancer researchers and clinicians, promoting the identification of clinically actionable biomarkers and further facilitating the clinical applications of phosphoproteomic data., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s). Published by Elsevier Inc.)

Background: Pharmacokinetic nomograms, equations, and software are considered the main tools available for Therapeutic Drug Monitoring (TDM). Model-informed precision dosing (MIPD) is an advanced discipline of TDM that allows dose individualization, and requires a software for knowledge integration and statistical calculations. Due to its precision and extensive applicability, the use of these software is widespread in clinical practice. However, the currently available evidence on these tools remains scarce. Objectives: To review and summarize the available evidence on MIPD software tools to facilitate its identification, evaluation, and selection by users. Methods: An electronic literature search was conducted in MEDLINE, EMBASE, OpenAIRE, and BASE before July 2022. The PRISMA-ScR was applied. The main inclusion criteria were studies focused on developing software for use in clinical practice, research, or modelling. Results: Twenty-eight software were classified as MIPD software. Ten are currently unavailable. The remaining 18 software were described in depth. It is noteworthy that all MIPD software used Bayesian statistical methods to estimate drug exposure and all provided a population model by default, except NONMEN. Conclusions: Pharmacokinetic software have become relevant tools for TDM. MIPD software have been compared, facilitating its selection for use in clinical practice. However, it would be interesting to standardize the quality and validate the software tools.

The present paper continues the authors’ research in machining process optimization, including the direction of machining parameters optimization. The paper develops an innovative method, via surface integrity, for determining the technological route and the related depths of cut, with respect to machining front faces of cast iron parts. For correctly establishing the depths of the cut, the errors that appear within surface and subsurface layers during the casting process, as well as during machining, must be gradually eliminated. These errors make it necessary to consider the concept of surface integrity. This paper presents the modality of integrating the components of surface integrity into the depth of cut. For the practical use of this method, a new software tool based on a series of mathematical models and a small database was conceived. A case study illustrates how the method is applied and the software tool used to solve a specific application in the case of a belt pulley.

The documentation of the tunneling process is a crucial task of every tunnel construction project. It provides evidence of the work performed and thus, serves as a basis for invoicing and for several further analyses. Therefore, continuous digitalisation of this documentation is essential. For this purpose, we provide a digital Tunneling Information Management System (TIMS), which is a prototypically implemented software tool for replacing the still common paper‐based documentation process of tunneling projects using the New Austrian Tunneling Method (NATM). The data model presented here defines the data structures managed by this tool. Based on this, the software architecture and the implementation of TIMS is shown. [ABSTRACT FROM AUTHOR]

The aim of the research presented in this work is to develop a new method for processing a vibration signal. Among the considered the main primary informative parameters of vibration signals used to assess the vibration state of rotary-type machines. An approach based on the use of decisive functions is proposed, characterized by their application in relation to both the primary informative parameters of vibration signals and the results of statistical processing of temporal trends of primary parameters. The work also provides examples of decision functions. Also presented is the combined format of the information table on the vibration state of a technical object which is used to create real decision support systems.

Dam break is an accident that may heavily threat downstream residents' life and property safety, especially in China. As revealed by accident investigation statistics, both flawed organizational behavior and inadequate downstream resident risk awareness have affected the safety risk of reservoir dams. Multiple information transferring mode and dynamic processes perform with the characteristics of social-technical systems. Based on the system dynamics approach, this study proposed a risk causation model aiming for factor interactions involving organizational, human, and technical system levels. The derived simulation model represented the historical risk evolution process of Gouhou reservoir in China and the rationality of the proposed model was verified. To further improve the efficiency of the organizational response and monitor real-time dam safety, a software tool called Dam Emergency Response Aids (DERA) was constructed to evaluate the potential safety benefits of risk control measures, and to overcome the defects of static emergency plans. By integrating relevant professional modules and data, the mobile application (APP) has been applied on the Jinniu Mountain reservoir dam in Nanjing of China and helped to maintain its excellent safety operation until now. It shows that the risk dynamics model proposed can improve the abilities of dam operating management organization for more effective responses under emergency circumstances. HIGHLIGHTS This research helps to remind the safety responsible organizations to focus their investment on publicizing and downstream resident training, which will increase their awareness of risks, enhance public understanding of the evacuation process and risk pre-judgment criteria, and maintain trusts in organization decision-making and community cohesion.;

The development of healthier and more sustainable food products, such as plant-based meat substitutes (PBMSs), have received significant interest in recent years. A thorough understanding of the aroma composition can support efforts to improve the sensory properties of PBMS products and promote their consumer acceptability. Here, we developed an integrated hardware and software approach for aroma analysis of roasted food based on simultaneous analysis with three complementary detectors. Following the standard procedure of aroma headspace sampling and separation using solid-phase microextraction-gas chromatography, the column flow was split into three channels for the following detectors for the selective detection of nitrogen and sulfur (N/S)-containing compounds: an electron ionization-mass spectrometry for identification through a library search, a nitrogen-phosphorous detector, and a flame-photometric detector (FPD)/pulsed-FPD. Integration of results from the different types of detectors was achieved using a software tool, called AromaMS, developed in-house for data processing. As stipulated by the user, AromaMS performed either non-targeted screening for all volatile organic compounds (VOCs) or selective screening for N/S-containing VOCs that play a major role in the aroma experience. User-defined parameters for library matching and the retention index were applied to further eliminate false identifications. This new approach was successfully applied for comparative analysis of roasted meat and PBMS samples.

Background: Boolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks. Results: In this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method's applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus. Conclusions: The proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system's stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings. [ABSTRACT FROM AUTHOR]

We focus on an estimation method based on deep learning in terms of fault correction time for the operation reliability assessment of open-source software (OSS) under the environment of an edge computing service. Then, we discuss fault severity levels in order to consider the difficulty of fault correction. We use a deep feedforward neural network in order to estimate fault correction times. In particular, we consider the characteristics of fault trends by using three-dimensional graphs. Therefore, we can increase the recognizability of the proposed method based on deep learning for large-scale fault data from the standpoint of fault severity levels under edge OSS operation. [ABSTRACT FROM AUTHOR]

This article presents a benchmarking framework, namely "ProvBench," with a specific focus on provenance of collected data, capable of identifying and measuring the impact of changes to hardware, operating system, software, middleware, and services that constitute a highly complex and heterogeneous research computing environment. The provenance is retained via detailed and automated recording of hardware details, runtime environment, software and libraries used, input data and results, as well as execution logs of the computation. This capability is particularly essential for constant monitoring and fast identification of abnormalities. The framework is compatible across different operating systems and varied software environments that support software modules. Its modular object‐oriented design allows for easy expansions, that is, adding new software and tests is straightforward. ProvBench is being actively used in our center for testing acquired equipment, evaluation of preproduction systems, assessing the impact of system and software changes, finding bad nodes, and other useful purposes with successful results. [ABSTRACT FROM AUTHOR]