The Computational Thematic Analysis Toolkit

As online communities have grown, Computational Social Science has rapidly developed new techniques to study them. However, these techniques require researchers to become experts in a wide variety of tools in addition to qualitative and computational research methods. Studying online communities also requires researchers to constantly navigate highly contextual ethical and transparency considerations when engaging with data, such as respecting their members' privacy when discussing sensitive or stigmatized topics. To overcome these challenges, we developed the Computational Thematic Analysis Toolkit, a modular software package that supports analysis of online communities by combining aspects of reflexive thematic analysis with computational techniques. Our toolkit demonstrates how common analysis tasks like data collection, cleaning and filtering, modelling and sampling, and coding can be implemented within a single visual interface, and how that interface can encourage researchers to manage ethical and transparency considerations throughout their research process.

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.

The Neuroscience Experiments System (NES)–A Software Tool to Manage Experimental Data and Its Provenance

Computational tools can transform the manner by which neuroscientists perform their experiments. More than helping researchers to manage the complexity of experimental data, these tools can increase the value of experiments by enabling reproducibility and supporting the sharing and reuse of data. Despite the remarkable advances made in the Neuroinformatics field in recent years, there is still a lack of open-source computational tools to cope with the heterogeneity and volume of neuroscientific data and the related metadata that needs to be collected during an experiment and stored for posterior analysis. In this work, we present the Neuroscience Experiments System (NES), a free software to assist researchers in data collecting routines of clinical, electrophysiological, and behavioral experiments. NES enables researchers to efficiently perform the management of their experimental data in a secure and user-friendly environment, providing a unified repository for the experimental data of an entire research group. Furthermore, its modular software architecture is aligned with several initiatives of the neuroscience community and promotes standardized data formats for experiments and analysis reporting.

Rxn Rover: Automation of Chemical Reactions with User-Friendly, Modular Software

The automation of chemical reactions in research and development can be an enabling technology to reduce cost and waste generation in light of technology transformation towards renewable feedstocks and energy...

Organizing Form, Experimentation, and Performance: Innovation in the Nascent Civilian Drone Industry

Our aim is to explore whether the benefits to firms of using community-based innovation extend to nascent markets: uncertain, high-velocity settings with novel, often complex products. Grounded in a rare empirical comparison, we closely track the two ventures (one using community-based innovation and the other firm-based) that pioneered the na2scent civilian drone market. We unpack how each addressed the three major innovations that shaped this setting. Our primary insight is that the firm organizing form for innovation performs best relative to communities in nascent markets. Firms have a coordination advantage that enables quickly and accurately targeting experimentation and problem-solving processes to reduce the many specific uncertainties that characterize these markets. Although communities can help, their task self-selection advantage works best in stable settings such as established markets with simple products (e.g., modular software) and in ambiguous settings in which low-cost randomness pays off. Broadly, we contribute a theoretical framework that identifies how organizing form and problem type jointly shape innovation performance. Most important, uncertainty forms a boundary condition for when firms should rely on firm-based (versus community-based) organizing for innovation.

MeTiS: a modular pipeline for extracting 3D-printable brain-surface models from conventional and ultra-high field MRI

Aim: To develop a modular software pipeline for robustly extracting 3D brain-surface models from MRIs for visualization or printing. No other end-to-end pipeline specialized for neuroimaging does this directly with an interchangeable combination of methods. Materials & methods: A software application was developed to dynamically generate Nipype workflows using interfaces from the Analysis of Functional NeuroImages, Advanced Normalization Tools, FreeSurfer, BrainSuite, Nighres and the FMRIB Software Library suites. The application was deployed for public use via the LONI pipeline environment. Results: In a small, head-to-head comparison test, a pipeline using FreeSurfer for both the skull stripping and cortical-mesh extraction stages earned the highest subjective quality scores. Conclusion: We have deployed a publicly available and modular software tool for extracting 3D models from brain MRIs to use in medical education.

Generalised Image Processing Method for Quantitative Analysis of Nucleus, Cell and Focal Adhesion Clusters

Focal adhesion clusters (FAC) are dynamic and complex structures that help cells to sense physicochemical properties of their environment. Research in biomaterials, cell adhesion or cell migration often involves the visualization of FAC by fluorescence staining and microscopy, which necessitates quantitative analysis of FAC and other cell features in microscopy images using image processing. Fluorescence microscopy images of human umbilical vein endothelial cells (HUVEC) obtained at 63x magnification were quantitatively analysed using ImageJ software. A generalised algorithm for selective segmentation and morphological analysis of FAC, nucleus and cell morphology is implemented. Further, a method for discrimination of FAC near the nucleus and around the periphery is implemented using masks. Our algorithm is able to effectively quantify different morphological characteristics of cell components and shows a high sensitivity and specificity while providing a modular software implementation.

A remote and immersive setup for pandemic-safe surgical education

Existing challenges in surgical education (See one, do one, teach one) as well as the Covid-19 pandemic make it necessary to develop new ways for surgical training. This is also crucial for the dissemination of new technological developments. As today’s live transmissions of surgeries to remote locations always come with high information loss, e.g. stereoscopic depth perception, and limited communication channels. This work describes the implementation of a scalable remote solution for surgical training, called TeleSTAR (Telepresence for Surgical Assistance and Training using Augmented Reality), using immersive, interactive and augmented reality elements with a bi-lateral audio pipeline to foster direct communication. The system uses a full digital surgical microscope with a modular software-based AR interface, which consists of an interactive annotation mode to mark anatomical landmarks using an integrated touch panel as well as an intraoperative image-based stereo-spectral algorithm unit to measure anatomical details and highlight tissue characteristics.We broadcasted three cochlea implant surgeries in the context of otorhinolaryngology. The intervention scaled to five different remote locations in Germany and the Netherlands with lowlatency. In total, more than 150 persons could be reached and included an evaluation of a participant’s questionnaire indicating that annotated AR-based 3D live transmissions add an extra level of surgical transparency and improve the learning outcome.

A fuzzy approach for evaluation and selection of performance testing tools for modular software development

Performance of a software is an important feature to determine the quality of the software developed. Performance testing of modular software is a time consuming and costly task. Several performance testing tools (PTTs) are available in the market which help software developers to test their software performance. In this paper, we propose an integrated multiobjective optimization model for evaluation and selection of best-fit PTT for modular software system. The total performance tool cost is minimized and the fitness evaluation score of the PTTs is maximized. The fitness evaluation of PTT is done based on various attributes by making use of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model allows the software developers to select the number of PTTs as per their requirement. The individual performance of the modules is considered based on some performance properties. The reusability constraints are considered, as a PTT can be used in the same module to test different properties and/or it can be used in different modules to test same or different performance properties. A real-world case study from the domain of enterprise resource planning (ERP) is used to show the working of the suggested optimization model.