Development Of Dedicated Software Research Articles

The Tecan SiLA2 SDK: A royalty-free, open-source framework to develop SiLA2 servers and clients.

Many devices in a laboratory only have proprietary interfaces. Integrating them into an automation platform such as a Robotic Liquid Handler (RLH) thus usually requires upfront technical configuration and often dedicated software development. In this paper, we present the Tecan SiLA2 SDK, a BSD-3 licensed, royalty-free open-source framework on the.NET platform that makes it very easy for developers to implement the open SiLA2 standard: A developer only has to specify a dedicated automation interface, annotate it with informational constraints such as units or MIME-constraints and then the code required to expose implementations of this interface through SiLA2 can be generated. To validate the applicability and the savings in terms of integration efforts, we conducted multiple case studies. First, we analyze the integration of the Tecan Spark microplate reader into a lab execution system (LES), allowing us admittedly rough comparisons of device integrations using SiLA2 vs. the previous proprietary interface. Next, we analyze the integration of a RLH into the voice control software LabVoice, demonstrating the flexibility of integrations made possible through SiLA2. Lastly, we review the integration potential of liquid handling hardware into custom control softwares using a semi-automated exposure of the Tecan MAPlinx software platform through SiLA2.

A gradient-based deep neural network model for simulating multiphase flow in porous media

Simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO2 storage sites, where CO2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and CO2 saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

Software Quality Assurance in INDIGO-DataCloud Project: a Converging Evolution of Software Engineering Practices to Support European Research e-Infrastructures

From the advent of Grid technology – as the new paradigm of distributed computing – to the current days of Cloud computing models, the continuous need of new tools and services to match the scientific community requirements has been addressed in Europe through dedicated software development projects for e–Infrastructure creation, operation and management. This work presents the most significant software quality breakthroughs obtained in one of such projects, INDIGO–DataCloud, the main challenges and barriers confronted throughout the lifespan of the project, and how they were partially or totally overcome. The knowledge base established throughout the last 15 years of diverse software development initiatives in Europe for sustaining distributed research e-Infrastructures, supported by the advances in the area of software engineering, definitely contributed to improve the quality and reliability of the software delivered, and consequently, the operational stability of the European e–Infrastructures. INDIGO–DataCloud project is a good evidence of such insights, where, unlike the preceding trend found in past projects, the enforcement of Software Quality Assurance practices has been present since the very early stages of the software lifecycle.

CMD-3 detector offline software development

CMD-3 is the general purpose cryogenic magnetic detector for VEPP-2000 electron-positron collider, which is being commissioned at Budker Institute of Nuclear Physics (BINP, Novosibirsk, Russia). The main aspects of physical program of the experiment are precision measurements of hadronic cross sections, study of known and search for new vector mesons, study of the nn̄and pp̄production cross sections in the vicinity of the threshold and search for exotic hadrons in the region of center of mass energy below 2 GeV. This contribution gives a general design overview and a status of implementation of CMD-3 offline software for reconstruction, simulation, visualization and storage management. Software design standards for this project are object oriented programming techniques, C++ as a main language, Geant4 as an only simulation tool, Geant4 based detector geometry description, CLHEP library based primary generators, ROOT toolbox as a persistency manager and Scientific Linux as a main platform. The dedicated software development framework (Cmd3Fwk) was implemented in order to be the basic software integration solution and a high level persistency manager. The key features of the framework are modularity, dynamic data processing chain handling according to the XML configuration of reconstruction modules and on-demand data provisioning mechanisms.

Experiences with a Development Tool for Fuzzy Modeling Applications

This paper gives a description of several fuzzy modeling applications implemented with a dedicated software development environment, TILShell 3.0. Two modeling applications are presented: the estimation of the polymer quality in a tubular polymerization reactor and the rule generation for a fuzzy position controller. Using TILGen, a built-in tool of TILShell, a set of fuzzy rules are extracted from input-output experimental data of the processes. The results are validated using process data and thus the conclusions about TILGen tool can be drawn.