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Over the years, several architecture of smart home has been proposed to enable the use of ambient intelligence. However, the major issue with most of them lies in their lack of high reliability and scalability. Therefore, the first contribution of this paper introduces a novel distributed architecture for smart homes, inspired by private cloud architectures, which is reliable and scalable. This implementation aims at simplifying and encouraging both the deployment of new software components as well as their reutilization to achieve the activity recognition process inside smart homes. The second contribution of this paper is the introduction of the LIARA Environment for Modular Machine Learning (LE2ML), a new machine learning workbench. Its design relies on a microservices architecture to provide a better scalability as well as smaller and faster deployments. Experiments demonstrate that our architecture is resilient to both a node failure and a total power outage. Moreover, the workbench obtained similar results, as regards the performance of the recognition, when compared to previously proposed methods.

With the increasing availability of high-performance computing (HPC) platforms, uncertainty quantification (UQ) and sensitivity analyses (SA) can be efficiently leveraged to optimize design parameters of complex engineering problems using modeling and simulation tools. The workflow involved in such studies heavily relies on HPC resources and hence requires pre-processing and post-processing capabilities of large amounts of data along with remote submission capabilities. The NEAMS Workbench addresses all aspects of the workflows involved in these studies by relying on a user-friendly graphical user interface and a python application program interface. This paper highlights the NEAMS Workbench capabilities by presenting a semiautomated coupling scheme between Dakota and any given package integrated with the NEAMS Workbench, yielding a simplified workflow for users. This new capability is demonstrated by running a SA of a turbulent flow in a pipe using the open-source Nek5000 CFD code. A total of 54 jobs were run on a HPC platform using the remote capabilities of the NEAMS Workbench. The results demonstrate that the semiautomated coupling scheme involving Dakota can be efficiently used for UQ and SA while keeping scripting tasks to a minimum for users. All input and output files used in this work are available in https://code.ornl.gov/neams-workbench/dakota-nek5000-study .

We systematically compared two coding approaches to generate training datasets for machine learning (ML): (i) a holistic approach based on learning progression levels and (ii) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for undergraduate physiology, each targeting five levels of a developing flux learning progression in an ion context. Human-coded datasets were used to train two ML models: (i) an 8-classification algorithm ensemble implemented in the Constructed Response Classifier (CRC), and (ii) a single classification algorithm implemented in LightSide Researcher’s Workbench. Human coding agreement on approximately 700 student responses per item was high for both approaches with Cohen’s kappas ranging from 0.75 to 0.87 on holistic scoring and from 0.78 to 0.89 on analytic composite scoring. ML model performance varied across items and rubric type. For two items, training sets from both coding approaches produced similarly accurate ML models, with differences in Cohen’s kappa between machine and human scores of 0.002 and 0.041. For the other items, ML models trained with analytic coded responses and used for a composite score, achieved better performance as compared to using holistic scores for training, with increases in Cohen’s kappa of 0.043 and 0.117. These items used a more complex scenario involving movement of two ions. It may be that analytic coding is beneficial to unpacking this additional complexity.

The rapid evolution of mass spectrometry (MS)-based lipidomics has enabled the simultaneous measurement of numerous lipid classes. With lipidomics datasets becoming increasingly available, lipidomic-focused software tools are required to facilitate data analysis as well as mining of public datasets, integrating lipidomics-unique molecular information such as lipid class, chain length, and unsaturation. To address this need, we developed lipidr, an open-source R/Bioconductor package for data mining and analysis of lipidomics datasets. lipidr implements a comprehensive lipidomic-focused analysis workflow for targeted and untargeted lipidomics. lipidr imports numerical matrices, Skyline exports, and Metabolomics Workbench files directly into R, automatically inferring lipid class and chain information from lipid names. Through integration with the Metabolomics Workbench API, users can search, download, and reanalyze public lipidomics datasets seamlessly. lipidr allows thorough data inspection, normalization, and uni- and multivariate analyses, displaying results as interactive visualizations. To enable interpretation of lipid class, chain length, and total unsaturation data, we also developed and implemented a novel lipid set enrichment analysis. A companion online guide with two live example datasets is presented at https://www.lipidr.org/. We expect that the ease of use and innovative features of lipidr will allow the lipidomics research community to gain novel detailed insights from lipidomics data.

Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

Translation memory systems aim to reuse previously translated texts. Be-cause the operational unit of the first-generation translation memory sys-tems is the sentence, such systems are only useful for text types in which full-sentence repetition frequently occurs. Second-generation sub-sentential translation memory systems try to remedy this problem by providing addi-tional translation suggestions for sub-sentential chunks. In this paper, we compare the performance of a sentence-based translation memory system (SDL Trados Translator’s Workbench) with a sub-sentential translation memory system (Similis) on different text types. We demonstrate that some text types (in this case, journalistic texts) are not suited to be translated by means of a translation memory system. We show that Similis offers useful additional translation suggestions for terminology and frequent multiword expressions.

The integration of real, physical objects into virtual scenarios enables direct haptic feedback for the user. In addition to the interaction with small, hand-held, hybrid objects, i.e., objects that exist both in the virtual and real environments, it is also possible to interact with more massive objects, such as tables and chairs. A virtual assembly scenario for both a seated and a standing assembly workstation to be used in conjunction with a standing-height drill station was developed and matching physical and virtual representations of the furniture, a movable work chair, was added. The virtual world was dynamically aligned to the real object by means of tracking hardware, so that virtuality and reality shared the same spatial position in relation to the user. Within a study, the participants had to carry out a manual assembly task on both workstations, thus physically and dynamically interacting with and using (sitting down on, getting up from) the real chair integrated in the virtual scenario of the seated workbench. The chair was absent in the scenario of the standing assembly workbench. The collected data on subjective cognitive workload, user experience and presence are supposed to provide an initial indication of the influence of such physical objects in the virtual world. Furthermore, the question of how trust in physical objects can be increased in the virtual world is investigated.

The Input Devices and Music Interaction Laboratory (IDMIL) Digital Audio Workbench (DAWb) is a web application designed for experimentation with key concepts in digital audio theory with interactive visualizations of each stage of the Analog-to-Digital Conversion (ADC) and Digital-to-Analog Conversion (DAC) processes. By experimenting with the simulation settings, numerous key concepts in digital signal theory can be illustrated, such as aliasing, quantization, critical sampling, anti-aliasing filtering and dithering. The interactive interface allows the simulation to be explored freely; users can modify parameters and examine the resulting signals visually through numerous graphs or listen to the resulting signals. The workbench has been extensively used during the 200-level Introduction to Digital Audio course at McGill University in Fall 2020.

Model-Driven Engineering (MDE) has emerged as a methodology, grounded in theory and tooling, to design and develop software systems with models at their core. MDE makes use of modelling languages, ranging from general-purpose like the standard UML, to dedicated modelling languages like Domain-Specific Modelling Languages. Those are supported by modelling editors, with languages that typically offer graphical and textual notations as concrete syntax. However, being concentrated on vision, these tools ignore other senses and communication channels like audition (voice and sound) that could be used in industrial settings, for accessibility purposes, or simply as complementary to the visual approaches.We are building a modelling workbench platform that, similarly to modelling workbenches dealing with diagrammatic languages, allows a software language engineer to model a domain-specific language and generate a voice/audio editor where the domain end-users can operate (Create, Read, Update and Delete), and navigate diagrams through speech recognition and voice synthesis tools.One of the problems of editors that use voice is the fixed interaction paradigms that contribute to poor user experience. In this paper, we propose an interaction mechanism that can recognise vocal and non-vocal sounds as well as gestures adding two senses in the definition of Domain-Specific Languages’ concrete syntax definition, not usually explored in the model-driven tools. we have built a prototype and we carried out a pilot empirical study, with preliminary positive results, to access the presented prototype in terms of usability, productivity and learning curve.

This work is focused on the topical problem of developing efficient software tools to design intelligent systems, in particular, integrated expert systems (IESs), which have powerful functionality and scalable architecture. Automated planning methods are employed to facilitate IES development at the design and requirement analysis stages. General problems of developing a modern software tool base for IES design are discussed. In this context, the intelligent software environment of the AT-TECHNOLOGY workbench for IES development based on the problem-oriented methodology is described. The problem of planning the IES prototyping process is investigated, which forms the basis for implementing the basic components of the intelligent software environment (intelligent planner and technological knowledge base). An automation method based on heuristic search is proposed. The use of the intelligent software environment is illustrated by the development of a dynamic IES prototype for controlling and planning medical resources in serious road traffic accidents.

Sasis merupakan bagian penting dari sebuah kendaraan, yang membentuk kerangka dari sebuah kendaraan. Sasis harus cukup kaku untuk menahan kejutan, twist, getaran dan tekanan lainnya. Pertimbangan penting dalam desain sasis adalah memiliki kekakuan lentur yang memadai disertai kekuatan untuk karakteristik handling yang lebih baik. Oleh karena itu tegangan maksimum, dan defleksi merupakan kriteria penting untuk desain sasis. Tujuan kajian ini untuk menentukan desain paling optimal dari ketiga desain sasis tipe C, I dan Rectangular Box (Hollow). Mendapatkan ketebalan sasis paling minimum pada sasis bus tipe Rectangular Box (Hollow) dari metode optimasi ASO dan MOGA. Serta membandingkan hasil optimasi yang didapatkan dari kedua metode optimasi tersebut. Pada kajian ini digunakan 2 jenis software yaitu software CAD (SOLIDWORKS 2015) untuk pembuatan model sasis bus dan software Finite Element Analysis (ANSYS Workbench v.16) untuk melakukan simulasi pembebanan static structural dan optimasi. Hasil pada kajian ini menunjukan bahwa tegangan maksimum terjadi pada cross section C besar tegangan 73,235 Mpa, dan defleksi sebesar 1,5455 mm. Hasil analisis pembebanan statik menunjukan bahwa desain tipe Rectangular Box (Hollow) jauh lebih optimal daripada tipe C ataupun I. Berdasarkan optimasi pada sasis bus tipe Rectangular Box (Hollow) dengan dua metode tersebut, hasil terbaik menggunakan metode ASO dengan nilai massa optimum sebesar 682,57 kg (persentase penurunan massa sebesar 1,224 %) dengan equivalent (von-misses) stress sebesar 53,557 MPa dan total deformasi sebesar 1,1847 mm. Sedangkan hasil optimasi metode MOGA, massa hasil optimasi sebesar 686,83 kg dengan equivalent (von-misses) stress 53,517 MPa dan total deformasi sebesar 1,1809 mm. Terjadi penurunan massa sebesar 0,608%.

Personalized multipeptide vaccines are currently being discussed intensively for tumor immunotherapy. In order to identify epitopes-short, immunogenic peptides-suitable for eliciting a tumor-specific immune response, human leukocyte antigen-presented peptides are isolated by immunoaffinity purification from cancer tissue samples and analyzed by liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). Here, we present MHCquant, a fully automated, portable computational pipeline able to process LC-MS/MS data automatically and generate annotated, false discovery rate-controlled lists of (neo-)epitopes with associated relative quantification information. We could show that MHCquant achieves higher sensitivity than established methods. While obtaining the highest number of unique peptides, the rate of predicted MHC binders remains still comparable to other tools. Reprocessing of the data from a previously published study resulted in the identification of several neoepitopes not detected by previously applied methods. MHCquant integrates tailor-made pipeline components with existing open-source software into a coherent processing workflow. Container-based virtualization permits execution of this workflow without complex software installation, execution on cluster/cloud infrastructures, and full reproducibility of the results. Integration with the data analysis workbench KNIME enables easy mining of large-scale immunopeptidomics data sets. MHCquant is available as open-source software along with accompanying documentation on our website at https://www.openms.de/mhcquant/ .

This paper presents an unsupervised method for recognizing assembly work done by factory workers by using wearable sensor data. Such assembly work is a common part of line production systems and typically involves the factory workers performing a repetitive work process made up of a sequence of manual operations, such as setting a board on a workbench and screwing parts onto the board. This study aims to recognize the starting and ending times for individual operations in such work processes through analysis of sensor data collected from the workers along with analysis of the process instructions that detail and describe the flow of operations for each work process. We propose a particle-filter-based factory activity recognition method that leverages (i) trend changes in the sensor data detected by a nonparametric Bayesian hidden Markov model, (ii) semantic similarities between operations discovered in the process instructions, (iii) sensor-data similarities between consecutive repetitions of individual operations, and (iv) frequent sensor-data patterns (motifs) discovered in the overall assembly work processes. We evaluated the proposed method using sensor data from six workers collected in actual factories, achieving a recognition accuracy of 80% (macro-averaged F-measure).

Background Due to the restriction of display mode, in most of the virtual reality systems with multiple people in the same physical space, the program renders the scene based on the position and perspective of the one user, so that other users just see the same scene, resulting in vision disorder. Methods To improve experience of multi-user co-located collaboration, in this study, we propose a fire drill system supporting co-located collaboration, in which three co-located users can collaborate to complete the virtual firefighting mission. Firstly, with multi-view stereoscopic projective display technology and ultra wideband (UWB) technology, co-located users can roam independently and watch virtual scenes through the correct perspective view based on their own position by wearing dedicated shutter glasses, thus carrying out different virtual tasks, which improves the flexibility of co-located collaboration. Secondly, we design simulated firefighting water-gun using the micro-electromechanical system sensor, through which users can interact with virtual environment, and thus provide a better interactive experience. Finally, we develop a workbench including a holographic display module and multi-touch operation module for virtual scene assembly and virtual environment control. Results The controller can use the workbench to adjust the virtual layout in real time, and control the virtual task process to increase the flexibility and playability of system. Conclusions Our work can be employed in a wide range of related virtual reality applications. Keywords: Virtual reality, Co-located collaboration, Multi-view display, Ultra wideband, Simulated firefighting water-gun

The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 years. The main focus of our recent website upgrade work has been the acceleration of analyses in the face of increasing protein sequence database size. We additionally discuss any new software, the new hardware infrastructure, our webservices and web site. Lastly we survey updates to some of the key predictive algorithms available through our website.

This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms.

In the present work, mass of an industrial manipulator-link is minimized using topology optimization method. Topology optimization is established substantial method for mass reduction of structural and machine components. A single link of manipulator is considered for optimization. For optimizing the design region, minimum compliance is chosen as objective function. While robotic arm rotates continuously about a fixed end, applied force acts dynamically on the other end. In such case topology obtained at one angular position is not optimum for other angular positions. Hence, a single topology is not sufficient for dynamic loading condition. Here, a weighted density parameter approach is proposed to consider the dynamic effect and produce a single weighted-topology. To compare the performance of weighted-topology, performance values such as maximum Von-Mises stress and deflection are computed at all discrete angular positions. A MATLAB based routine is developed to generate the weighted-topology and to simulate the performance values. Further, the obtained results are validated using simulation software ANSYS Workbench 18.1. The optimal design of weighted-topology is proven as better compare to any other individual topologies at different angular positions.

The thesis deals with the creation of a 3D model of the slewing bearing and its functional parts on which the axial load acts. In order to investigate the load applied to the bearing as a whole and to examine the effect of forces on the rolling body and the rolling tracks, it was necessary to create a computational 3D model with computational finite element mesh. Model of slewing bearing was designed in the Creo Parametric 3D modeling program. The individual bearing elements have been designed to create a functional mesh on each of them in the work environment of the Ansys Workbench. In order to carry out bearings stress tests and examine their behavior at various parameters such as bearing size, then the parameterization of bearing geometry and the interconnection of the cad program with the calculation program were performed. This has accelerated the process of introducing the model from the Creo model into the Ansys Workbench computing program. Based on many analyzes and results, graphs were constructed and the analytically obtained results were verified by calculations based on Hertz´s contact theory.

The number of Web APIs for accessing information and services is continuously increasing, and yet, no tools exist to automate the time-consuming and error-prone process of invoking those APIs and composing their responses. The recent emergence of widely-adopted, standardized, Web-API description formats and the development of Linked Data technologies for data integration have motivated our work on the LRA (Linked REST APIs) methodology [1, 2]. LRA relies on RDF service specifications to automate the development process around the usage of Web APIs. This automation represents a great opportunity to systematize and improve the quality of service-oriented application development. However, LRA’s reliance on SPARQL as the user-interaction model may hinder its adoption, because it requires developers to learn the intricacies of the unconventional graph data model and its associated datasets. In this paper we have developed the LRA Workbench ($$LRA_{Wbench}$$ L R A Wbench ), which takes advantage of the emergent schema of Web-API specifications, in order to simplify the formulation of LRA-compliant SPARQL queries. Our empirical evaluation of the $$LRA_{Wbench}$$ L R A Wbench usability demonstrates that our tool significantly improves the performance of developers formulating SPARQL queries for LRA. A subsequent study on the effectiveness of the $$LRA_{Wbench}$$ L R A Wbench demonstrated that developers using LRA tend to produce code with considerable better structural complexity, in less time, than developers manually composing APIs.

Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

The protocol details one of many methods available to process and assemble sequence data using CLC workbench, SortMeRNA, and MegaHit.

International audience; The functional aspect of the parts and assemblies is the engine that generates constructive solutions that satisfy customer requirements. When specifying design solutions, the designer always thinks in terms of function to be satisfied. However, this information is immediately lost once the design solution is validated. In this context this paper will first contribute to the enrichment of the "computer aided design, (CAD)" modeler database by integrating data related to product functions. Then, it will enrich the data related to assembly constraints by the fit aspect. It would be useful if the designer can specify, when creating an assembly constraint between a shaft and a hub (either through bearings, bushings or by direct contact), the type of constraint fit (tight, loose or uncertain). This information will be necessary, for example, when automatically generating assembly or disassembly drawings. The main objective of the proposed paper is the development of two applications. The first application named "computer aided design part function, (CAD PF)", used for the enrichment of the parts database by the part function. The second one named "computer aided design adjustment constraint type, (CAD ACT)", used for the enrichment of the database of assemblies by the type of constraint adjustment during their creation. These tools will be modeled by the graphical "system modeling language, (SysML)" and will be implemented using FreeCAD "Applications Programming Interfaces, (APIs)" which is an open source application.

This work focuses on the experimental studies of a new technology for the development of integrated expert systems (IES) based on the further development of the problem-oriented methodology and intelligent software environment of the AT-TECHNOLOGY workbench through the integration of an ontological approach to software design for applied IES and methods of intelligent planning and management of IES development processes with different architectural typology.

The Rail Track Rocket Sled (RTRS) is a captive flight test facility utilised for the simulation of actual flight conditions of aerospace and other aeronautical systems. Several features that result from high-velocity physics are considered during a test. This paper deals with the modelling of the pivoted slipper and its components at 40 ton normal load and its analysis. The model is prepared on SolidWorks and analysis is done on ANSYS. The Static Structural Workbench of ANSYS is utilised to carry out the load test on a pivoted slipper. Aerodynamic drag, sliding, lateral and vertical movement of the slipper are not considered in the paper. The paper shows equivalent Von-Mises stresses and total deformations experienced by the slipper and its components due to reactions developed during load application. The load is applied normal to the upper face of the box attached to the slipper set. The material used in the analysis is structural steel for full slipper set. Slipper set consists of slipper, pin, bush and box. The bush is welded to the box of the sled. It is found that side pins and bushes developed maximum deflection and stress. Thus, side pins and bush should be made with higher yield strength material.

The study aims to design and analyze a portable power hacksaw capable of cutting small workpieces mainly wood, plastic, and commonly used metal. This topic was chosen as today's hacksaws lack portability and lubrication facilities. Theoretical calculations were done to select the hacksaw material and dimensions based on the loads experienced by it. The CAD design was prepared using Solidworks. Three-dimensional Finite Element Analysis was used to conduct a structural analysis of the model. Parameters like total deformation and von-Mises stress of various components of the hacksaw were evaluated. Different cutting blade materials were analyzed on Ansys workbench as well. CFD analysis was performed using Ansys Fluent on the nozzle to study the pressure points and the respective velocity streamlines of the coolant. Design optimization was carried out for various components of the hacksaw using a generative design approach. The finite element analysis results indicated that the proposed design was structurally safe and optimized. CFD analysis of the nozzle provided valuable insights regarding lubricant flow characteristics. Blade material analysis allowed the selection of an appropriate blade material for cutting purposes. The introduction of a lubrication regime provided a novel approach to tackling issues like high blade temperatures and workpiece surface finish.

Part of the process to ensure the safety of radioactive waste disposal is the predictive modeling of the solubility of all relevant toxic components in a complex aqueous solution. To ensure the reliability of thermodynamic equilibrium modeling as well as to facilitate the comparison of such calculations done by different institutions it is necessary to create a mutually accepted thermodynamic reference database. To meet this demand several institutions in Germany joined efforts and created THEREDA 15 (Moog et al., 2015). THEREDA is a suite of programs at the base of which resides a relational databank. Special emphasis is put on thermodynamic data along with suitable Pitzer coefficients which allow for the calculation of solubilities in high-saline solutions. Registered users may either download single thermodynamic data or ready-to-use parameter files for the geochemical speciation codes 20 PHREEQC, Geochemist’s Workbench, CHEMAPP, or TOUGHREACT. Data can also be downloaded in a generic JSON-format to allow for the import into other codes. The database can be accessed via the world wide web: www.thereda.de Prior to release, the released part of the database is subjected to many tests. Results are compared to results from earlier releases and among the different codes. This is to ensure that by additions of new and modification of existing data no adverse side 25 effects on calculations are caused. Furthermore, our website offers an increasing number of examples for applications, including graphical representation, which can be filtered by components of the calculated system. References Moog, H. C., Bok, F., Marquardt, C. M., and Brendler, V.: Disposal of Nuclear Waste in Host Rock formations featuring high-saline solutions - Implementation of a Thermodynamic Reference Database (THEREDA). Appl. Geochem., 55, 72-84, doi: 30 10.1016/j.apgeochem.2014.12.016, 2015.

A progressive tool carries out a sequence of basic sheet metal operations like blanking, piercing, notching, coining, bending etc. at multiple stations during each stroke of press in order to produce a final component. The challenging part of the project was to design a progressive tool for mold tag because of the complexity involved in it. The objective of the project is to design a progressive tool for mold tag to meet the customer specification, analyse the punches to determine the stress induced using ANSYS workbench and validate the results with theoretical calculations, fabricate and trial run the designed progressive tool for the component. The methodology of the project includes detailed study of component drawing and generation of blank development & strip layout using AutoCAD software. Based on the strip layout design various forces are calculated. Total force is used for the design of elements of progressive tool. The design of progressive tool includes 2D drawing and 3D modelling. The 2D drawing is carried out by AutoCAD software and 3D modelling by Autodesk Fusion 360 software. After the modelling of different punches and dies, the FEM analysis is performed on punches and dies using ANSYS workbench to determine whether the stress and strain are within the allowable limits. It is followed by the manufacturing and assembly of elements of progressive tool. Finally, the tool is fabricated to produce mold tag component.

The spread of hydrogen energy usage has been majorly checked by issues concerning its storage. May it be stationary storage, locomotive storage, or vehicular storage. Most prominently used hydrogen storage methods such as compressed form, cryogenic form, and chemical methods hydrogen storage still lack behind in meeting the gravimetric density and volumetric density targets set by US Department of Energy. Prominently preferred multilayer composite design which utilizes carbon fiber as its stress bearing component is still under development and has its own merits and demerits such as high cost and thick cylinder walls. This study focuses to take an alternative approach toward the design aspect of these high pressure storage tanks along with the selection of new materials. It compares various properties such as material density and tensile strength of extensively used carbon fiber with proposed tank materials used for this study, i.e., S-glass fiber as well as silicon carbide. The combination of these two new materials provides desired properties to the tank. To study and analyze, various identical models of the tanks were crafted using CAD software and numerical analysis was performed in Ansys Workbench version 16.0. Obtained results in the form of safety factor, highest directional deformation, and equivalent stress were studied and presented under the application of 700 bar pressure.

Chapter 3 introduced Amazon RDS, and you learned how to deploy it on the Amazon cloud. You created an instance of an Amazon RDS MySQL database in AWS and configured the database. You also created tables in this database and inserted data into it using MySQL Workbench.

I present rst-workbench, a software package that simplifies the installation and usage of numerous end-to-end Rhetorical Structure Theory (RST) parsers. The tool offers a web-based interface that allows users to enter text and let multiple RST parsers generate analyses concurrently. The resulting RST trees can be compared visually, manually post-edited (in the browser) and stored for later usage.

The Software Analysis Workbench (SAW) is a verification tool that has shown promise in verifying cryptographic implementations in C and Java as evidenced by the verification of Amazon’s s2n HMAC. That result uses an idealized abstraction for SHA256 to simplify the proof obligations. The OpenSSL SHA256 implementation supported by s2n presents verification problems for SAW because some functions do not complete SAW’s symbolic execution or verification in the backend Satisfiability Modulo Theories (SMT) solvers. Fortunately, SAW provides a compositional framework to reduce the proof complexity, replacing function calls with overrides which are nothing more than contracts stating the input to output relation of the functions. This paper illustrates the SAW compositional framework applied to the OpenSSL SHA256 implementation. It shows a refactoring of the implementation that manages symbolic variables to work with the symbolic execution engine and lends itself to overrides while preserving its connection to the original source. Early results suggest the approach effective in applying SAW to legacy cryptographic implementations that are critical to the security of almost all existing applications.

This paper presents the design, implementation, and usage details of ARCADE, an extensible workbench for supporting the recovery of software systems' architectures, and for evaluating architectural change and decay. ARCADE has been developed and maintained over the past decade, and has been deployed in a number of research labs as well as within three large companies. ARCADE's implementation is available at https://bitbucket.org/joshuaga/arcade and the video depicting its use at https://tinyurl.com/arcade-tool-demo.

During the crushing of a stone, sharpness of the blade decreases due to which deformations are observed on the blade, which reduce the efficiency of the blade. Hence it becomes necessary to check the blade structure for its crushing ability so that safety is achieved while crushing the stone. The FE model was developed to observe the stresses and deflections which are caused while crushing. Various iterations are performed and graphs are plotted and analyzed to see the deflections caused on the blade. This paper is presented to evaluate the frequencies and deflections caused on the blade and also to evaluate the stress caused on the rock, which gives better efficiency while crushing the rock. The model was created using CAD software, and Modal analysis was performed by using ANSYS Workbench 19.2.

While language workbenches---tools to define software languages together with their IDEs---are yet to become ubiquitous in industry, a noticeable amount of domain modeling is still done using word processors and spreadsheet calculators. We suggest an approach to use a word processor to define (modeling) languages in example-driven way: a language is defined by giving examples of code written in it, which are then annotated to specify abstract syntax, formatting rules, dynamic semantics, and so on. Such a definition can be used to validate similar documents and to generate an API for processing models, or it can serve as a front-end and later be transformed to an (equivalent) definition in a language workbench. We discuss how a similar approach for language definition can be implemented in a form of a language workbench.

This paper presents MIST, an all-in-one tool capable of generating a complete environment to verify C/C++ firmwares starting from informal specifications. Given a set of specifications written in natural language, the tool guides the user in translating each specification into an XML formal description, capturing a temporal behavior that must hold in the design. Our XML format guarantees the same expressiveness of linear temporal logic, but it is designed to be used by designers that are not familiar with formal methods. Once each behavior is formalized, MIST automatically generates the corresponding test-bench and checker to stimulate and verify the design. In order to guide the verification process, MIST employs a clustering procedure that classifies the internal states of the firmware. Such classification aims at finding an effective ordering to check the expected behaviors and to advise for possible specification holes. MIST has been fully integrated into the IAR System Embedded Workbench. Its effectiveness and efficiency have been evaluated to formalize and check a complex test-plan for an industrial firmware.

The current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the "local search" category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even in the presence of non-differentiable functions, as is the case in the discrete nature of including/excluding model components in search of the best performing mixed-effects PK/PD model. A genetic algorithm implemented in an R-based NONMEM workbench for identification of near optimal models is presented. In addition to the GA capabilities, the workbench supports modeling efforts by: (1) Organizing and displaying models in tabular format, allowing the user to sort, filter, edit, create, and delete models seamlessly, (2) displaying run results, parameter estimates and precisions, (3) integrating xpose4 and PsN to facilitate generation of model diagnostic plots and run PsN scripts, (4) running regression models between post-hoc parameter estimates and covariates. This approach will further facilitate the scientist to shift efforts to focus on model evaluation, hypotheses generation, and interpretation and applications of resulting models.

Fog Computing, as a distributed middleware layer, is expected to bring Cloud capabilities closer to the IoT edge devices. Industrial IoT systems may benefit from the geographically distributed features of the fog nodes, to enhance several QoS requirements such as delay. However, heterogeneous IIoT data traffics require a specific process for each type of them. Thus we refer to a priority classification scheme to slice a fog-enabled IIoT network. For this purpose, we perform a multi-objective optimization algorithm enabled by a machine learning workbench to set a slicing strategy, taking into account the specific and relevant QoS metrics of each priority class. Our slicing model performs better results, in terms of data-rate, e2e delay and network usage, compared to a non-sliced reference model.

Seeking to meet the significant learning needs in the electrical machine laboratory, a workbench with two small synchronous machines (low cost BLDC – Brushless Direct Current motors) is developed. They are mechanically and electrically coupled. Such a configuration is known as synchronous feedback or back-to-back load testing. A metalworking rotary table varies the mechanical power angle (torque angle) between machines. A real time SCADA – Supervisory Control and Data Acquisition, monitors the speed of rotation, voltages and currents in the time domain, the voltage and current phasors and acts on the machines drive. The power angle is calculated from the phasor diagram. The student interacts with the equipment by setting it in motion, accelerating the machines, and varying the mechanical angle through the rotary table. It can then differentiate the operation of machines such as generator and motor, the variation in load caused by the power angle between them, the synchronism, the power factor and the power angle. Progressively, and starting from the student’s action on the object of study, all concepts related to synchronous machines are learned. The workbench is low cost and uses safe voltages for risk free operation.

Word embeddings have been widely used in lexical semantics and neural networks in Natural Language Processing. This article investigates the semantic representations using word embedding technologies by verifying them on a human constructed domain ontology. The domain of Traditional Chinese Medicine (TCM) is used as a workbench in this study, because this domain is knowledge-rich and has a large-scale domain ontology with well-defined entity types and relation types. This article releases a dataset, named “TCMSem”, to capture TCM domain experts’ intuitions of semantic relatedness. This data set is designed to cover the medical entities and relations with as many semantic types as possible so as to initiate a diverse and comprehensive evaluation on word embeddings. Experimental results show that word embeddings have demonstrated higher proficiencies in the detection of synonyms and collocations than other types of semantic relations. Furthermore, the semantic relatedness of thousands of terms of major categories in TCM is visualized using the taxonomy defined in the ontology.

This paper shows the comparative study of Al alloy wheel rim and composite wheel rim to analyse the strength and weight of 14 inches wheel under same load conditions by using computational modelling and analysis. For this paper model, the wheel rim is created by using a 3D CAD software Creo 3.0, and analysis is done by using ANSYS. For this paper, we select few selected orientations of the composite materials, such as [±35°]FW, [±45°]FW, [±55°]FW, [±75°]FW and Al alloy of grade Al6061 which is commonly used by wheel manufacturing companies. As we know Al alloy is used nowadays commercially for its good strength and aesthetic look, and composite materials are also used by super car manufacturers wheel manufacturers also making wheel for super cars to make them lighter in weight and good in aesthetic looks. In this present paper, design and analysis of the composite wheel with various fibre orientation angles are done with the help of ANSYS composite pre- & post (ACP)-module in ANSYS Workbench 15.0.

Open image in new window is a workbench for modeling statement-level transformation rules on Open image in new window programs with the aim to formally verify their correctness. It is based on Abstract Execution, a verification framework for abstract programs with a high degree of proof automation, and interfaces with the Open image in new window program prover. We describe the user interface and functionality of Open image in new window , and illustrate its capabilities along the application to proving conditional correctness of a code refactoring rule.

The significance intention of this research work is to design a structure for predicting ECG signal abnormalities. This proposed model incorporates three-section pre-processing, feature extraction, and structural designing. The novelty of this work lies in structural designing done with Biomedical Toolkit to improve its prediction accuracy. The structural design happens with the aid of optimization techniques to precise its predicting accuracy. The preliminary research carried out with a benchmark dataset to design and validate the designed optimal structure. The developed system does the preliminary investigation and predicts the heart abnormalities of a person. Hence anyone skeptic of heart problem can analyze his/her ECG using this efficient technique. The entire implementation executes in the working platform LabVIEW (Laboratory Virtual Instrument Engineering Workbench) to process the system to ECG signal abnormalities prediction.

World Wide Web has become a huge collection of documents and the amount of documents available is increasing on a daily basis. How to correctly classify the vast documents into a particular category and locate any document of interest easily has become a challenge researchers have been trying to solve for decades and different researchers have attempted different algorithms using different platform to achieve this aim. In this paper, a University web site was used as a case study and a machine learning workbench called WEKA (Waikato Environment for Knowledge Analysis) which provides a general-purpose environment for automatic classification, regression, clustering and feature selection was used as a machine learning platform. Running Naive Bayes with 10-fold cross validation on the selected web data gives a 77% correctly classified instances in zero second with relative absolute error of 68.9937%. This shows the ability of Naive Bayes algorithm to accurately classify vast amount of web document in a short time.

The problem of reproducibility of scientific research is a serious issue in biomedical sciences. In addition to experimental repeatability, limiting the (pre-) analytical variance is also essential. To address this problem in the field of metabolomics, we have designed KIMBLE, the KNIME-based Integrated MetaBoLomics Environment, a novel platform for the processing and analysis of NMR metabolomics data. It consists of an elaborate NMR metabolomics workflow in the KNIME workflow management system that handles both targeted and untargeted metabolomics. The workflow provides a self-documenting way of transforming raw time-domain NMR data into metabolic insights. Parameters for the quantification of a number of interesting metabolites in urine are included in the workflow, and several useful statistical analysis and visualization tools are incorporated as well. The workflow comes with an interesting sports-induced ketosis dataset so that new users can easily get acquainted with the platform. The user is free to adapt and extend the workflow to his or her personal needs. The KIMBLE workflow, the KNIME software and all the required libraries are installed in a VirtualBox virtual machine that allows for facile installation and use by non-experts.

Purpose The purpose of this paper is to study a method to optimize the arrangement of the devices on a smart assembly workbench, which help to reduce fatigue and improve efficiency for the worker. Design/methodology/approach The optimization priority is studied based on the users’ decisions, a mathematical model of the layout optimization is established from ergonomic perspective and an improved algorithm is adopted to solve the built the mathematical model. Findings Ergonomic software Jack is chosen to simulate the four layout schemes obtained. Through comparative analysis of the simulation results, it is proven that the optimal solution can be obtained using the improved algorithm. Originality/value The mathematical model built on observation comfort, operation comfort and device accessibility, as well as the improved algorithm in this paper, has some reference values for the layout design of smart assembly workbench.

We devise and develop a new workbench integrating, among other elements, two touch screens and a holographic display with touch film for a novel form of collaborative design of virtual scenes. Two users sitting at opposite ends of a table are enabled to collaboratively build 3D virtual scenes. They design a 2D planar graph via the regular touch screens, while the corresponding 3D scene is displayed on the holographic display screen simultaneously. One user’s interactions with the touch screen or touch film can be observed by the other instantaneously. A smart phone or tablet can also connect into this system remotely so that more users can join the cooperative work. This workbench has many advantages in co-location work, such as the convenience of user communication and the flexibility of the provided views. It improves the efficiency of the design and the quality of the virtual scene construction.

The main aim of this paper is to acquaint readers of the journal “Open Education” with the accumulated experience of construction and practical use in the educational process of Cybernetics Department of the National Research Nuclear University MEPhI of a special class of intelligent tutoring systems, based on the architectures of tutoring integrated expert systems. The development is carried out on the problem-oriented methodology basis and intelligent software environment of AT-TECHNOLOGY workbench. They provide automation of support of all the stages of construction and maintenance of the life cycle of such systems. In the context of basic models, methods, algorithms and tools that implement the conceptual foundations of a problem-oriented methodology, and which are evolutionarily developed and experimentally investigated in the process of constructing various architectures of training integrated expert systems, including webbased ones, some features of the generalized model of intellectual learning and its components are considered (in particular, the competence-based model of the trainee, the adaptive tutoring model, the ontology model of the course /discipline et al.) as well as methods and means of their realization in the current versions of tutoring integrated expert systems. In current versions of tutoring integrated expert systems examples of implementation of typical intelligent tutoring problems are described for the generalized ontology “Intelligent systems and technologies” (individual planning of the method of studying the training course, intelligent analysis of training tasks, intelligent support for decision making). A brief description of the conceptual foundations of the model of the intelligent software environment of the AT-TECHNOLOGY workbench is given and a description of some components of the model is presented with a focus on the basic components – intelligent planner, standard design procedures and reusable components, which provide automated support for the prototyping processes of tutoring integrated expert systems. A brief description of the models and approaches, used in the formalization of the planning and management processes of prototyping is presented. Schemes for the implementation of the standard design procedure “Construction of training IES and web-IES” in different modes of operation are described, as well as the technology of using reusable components for the development of tutoring integrated expert systems. Quantitative estimations of various components/ parameters of tutoring integrated expert systems are given and further prospects for their application are analyzed.

The existence of Massive datasets that are generated in many applications provides various opportunities and challenges. Especially, scalable mining of such large-scale datasets is a challenging issue that attracted some recent research. In the present study, the main focus is to analyse the classification techniques using WEKA machine learning workbench. Moreover, a large-scale dataset was used. This dataset comes from the protein structure prediction field. It has already been partitioned into training and test sets using the ten-fold cross-validation methodology. In this experiment, nine different methods have been tested. As a result, it became obvious that it is not applicable to test more than one classifier from the (tree) family in the same experiment. On the other hand, using (NaiveBayes) Classifier with the default properties of the attribute selection filter has a great time consuming. Finally, varying the parameters of the attribute selections should be prioritized for more accurate results.

This research study proposes a novel method of inter-related problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition. The well-known FG-NET and MORPH Album 2 image gallery were used and anthropometric features were extracted from landmark points on the face. The landmarks were pre-processed with the procrustes algorithm before feature extraction was performed. The Weka machine learning workbench was used to compare the performance of traditional techniques such as the K nearest neighbor (Knn) and Multi-LayerPerceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated.