Open Source Solutions Research Articles

An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

Open-Source Solutions in Archival Institutions

Open-source solutions offer a cost-effective alternative for archival institutions facing budget constraints. The absence of licensing fees and the ability to modify and customize the software without vendor lock-in make open source particularly appealing. This financial flexibility allows institutions to allocate resources to other critical aspects of preservation and access. In the article the author presents various advantages and disadvantages of using open-source software.

Rapid Development of A Spatial Digital Twins Using Open-source Solutions

Abstract. City-model spatial Digital Twins are a three-dimensional virtual self-updating twin of a city which is updated by real-time data. This paper explores the effectiveness of open-source data and tools that can generate a Digital Twin to be hosted on a distributed system. A spatial Digital Twin was successfully generated from a city-model made from open-source spatial data with access to real-time data. The Digital Twin application was hosted on a three-tier systems architecture. The exception to the open-source data was a Digital Surface Model (DSM) obtained from a private source. However, the DSM was an optional component. The output only met the minimum requirements of a Digital Twin while the visualization was basic where the live data was limited to global weather data rather than city-specific data. The investigation indicated that even with little data, generating a 3D city scene with an automated live data flow meets the spatial Digital Twin’s minimum requirements according to Piroumian (2023). The main contribution of the study is highlighting methods for generating and hosting a spatial Digital Twin and its associated data onto the web through using a three-tier architecture to store, host and display the Twin onto the web. The areas identified for further research include investigating variations in the distributed systems for hosting and publishing a spatial Digital Twin onto the web. A secondary subject of investigation is exploring methodologies for improving the detail of the spatial Digital Twin’s data for areas that do not receive much live data.

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.

Manufacturing floor mapping and presence tracking with a physics-based game engine

The manufacturing shop floor exists in a dynamic environment with constantly changing components regarding position and features. Presence tracking on the manufacturing shop floor is essential to enhance effective operations and safety. Internet of Things technology solutions have been developed to track and monitor machine and operators’ movements and materials on the shop floor to enhance machine availability and worker safety and improve overall operational effectiveness. However, for SMMs with limited financial resources and infrastructure, there is a need for a financially friendly approach to shop floor presence tracking and monitoring. This work presents the use of Internet-of-things and physics-based engines to develop a scalable and customizable solution to track presence on a manufacturing floor. The paper aims to improve and promote the adoption of I-4.0 technologies and digital manufacturing by SMMs with the development of an affordable, repeatable, customizable, open-source solution. The article describes a methodology for mapping, modeling, monitoring, and reporting presence on a manufacturing floor using physics-based engines. The methodology describes mapping the manufacturing floor into zones, the design of the IoT solution, selecting and placing sensors, and presenting the data using dashboards and physics-based engines. The paper investigates the methodology by implementing a case study on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) manufacturing floor using the Unity3D game engine, open-source software, and off-the-shelf motion sensors.

Design an RF Up-Down Convertor using Software Defined Radio and GNU Radio

There is a need to design of RF Up/Down- converter for (from C/Ku to/from L band) signals required for several applications. The proposed work presents a design of an RF Up/Down Converter that makes use of GNU Radio and Software-Defined Radios (SDRs). The converter helps in frequency translation between the C/Ku and L bands, leading to a cost-effective and versatile solution for RF signal processing. By using open-source GNU Radio software, the proposed system enhances accessibility, enabling its deployment in diverse applications, from satellite communication to radar systems. The converter’s unique features include real-time processing capabilities, customization through an intuitive graphical interface, and Python scripting. This idea presents the design considerations, signal processing techniques, and performance evaluation of the RF Up/Down Converter. The advantages of an open-source solution over other available alternatives are in terms of cost, flexibility, and rapid prototyping. Simulation and hardware results demonstrate the efficacy of the proposed work.

The value of simplified models of radionuclide transport for the safety assessment of nuclear waste repositories: A benchmark study

In order to assess sites for a deep geological repository for storing high-level nuclear waste safely in Germany, various numerical models and tools will be in use. For their interaction within one workflow, their reproducibility, and reliability version-controlled open-source solutions and careful documentation of model setups, results and verifications are of special value. However, spatially fully resolved models including all relevant physical and chemical processes are neither computationally feasible for large domains nor is the data typically available to parameterize such models. Thus, simplified models are crucial for the pre-assessment of possible sites to narrow down the list of suitable candidates for which detailed site investigations and fully resolved models will be done at a later stage. Still, the accuracy of these simplified models is of importance as the pre-assessment of suitable sites will be based on them. In this study, we compare the modelling capabilities of TransPyREnd, a one-dimensional transport code based on finite differences, specifically developed for the fast estimation of radionuclide transport by the German federal company for radioactive waste disposal (BGE), with OpenGeoSys, which is a modelling platform based on finite elements in up to three spatial dimensions. Both codes are used in the site selection procedure for the German nuclear waste repository. The comparison of the model results of TransPyREnd and OpenGeoSys is augmented by comparisons with an analytical solution for a homogeneous material. For the purpose of numerical benchmarking, we consider a geological profile located in southern Germany as an example where the hypothetical repository is located in a clay-stone formation. TransPyREnd and OpenGeoSys yield overall similar results. However, both codes use different discretizations which impact is the highest for strongly sorbing compounds, while the difference gets negligible for less sorbing and more diffusive compounds as higher diffusion tends to blur the initial conditions. Overall, the OpenGeoSys model is more exact whereas the TransPyREnd model has considerable faster run times. We found in our example, that significant substance amounts are only leaving the host rock formation, if apparent diffusion is high, for which case both codes give similar results, while relative differences are considerable for strongly sorbing compounds. However, in the latter case no significant substance amount of radionuclides leaves the host-rock formation, thus deeming the differences in the model results minor for the overall safety assessments of sites.

A framework for virtual learning in industrial engineering education: development of a reconfigurable virtual learning factory application

Advances in digital factory technologies are offering great potential to innovate higher education, by enabling innovative learning approaches based on virtual laboratories that increase the involvement of students while delivering realistic experiences. This article introduces a framework for the development of virtual learning applications by addressing multidisciplinary requirements. The implementation of the framework can be eased by the use of the proposed virtual learning factory application (VLFA), an open-source solution that takes advantage of virtual reality to support innovative higher-education learning activities in industrial engineering. A complete design and development workflow is described, starting from the identification of the requirements, to the design of software modules and underlying technologies, up to the final implementation. The framework and the VLFA have been tested to implement a serious game related to the design and analysis of manufacturing systems, also collecting the feedback of students and teachers.

An Open Approach to Autonomous Vehicles

The evolution of autonomous vehicle (AV) technology is rapidly transforming the automotive industry, driving innovations in safety, efficiency, and user experience. This research paper explores the impact of open approaches on the development and deployment of autonomous vehicles. By leveraging open-source software and hardware platforms, such as OpenPilot and Apollo, the study highlights how collaborative, transparent methodologies foster innovation and accelerate technological advancements. The paper examines the technical, regulatory, and ethical challenges associated with open-source AV development, including data management, sensor integration, and compliance with safety standards. Through a comprehensive analysis of successful open-source projects and emerging trends, the research identifies the benefits of open approaches in promoting cross-disciplinary collaboration and addressing complex challenges. The findings underscore the potential of open-source solutions to drive the future of autonomous vehicle technology, offering insights into their role in shaping industry standards and regulatory frameworks

PyFMLab: Open-source software for atomic force microscopy microrheology data analysis

Background Atomic force microscopy (AFM) is one of the main techniques used to characterize the mechanical properties of soft biological samples and biomaterials at the nanoscale. Despite efforts made by the AFM community to promote open-source data analysis tools, standardization continues to be a significant concern in a field that requires common analysis procedures. AFM-based mechanical measurements involve applying a controlled force to the sample and measure the resulting deformation in the so-called force-distance curves. These may include simple approach and retract or oscillatory cycles at various frequencies (microrheology). To extract quantitative parameters, such as the elastic modulus, from these measurements, AFM measurements are processed using data analysis software. Although open tools exist and allow obtaining the mechanical properties of the sample, most of them only include standard elastic models and do not allow the processing of microrheology data. In this work, we have developed an open-source software package (called PyFMLab, as of python force microscopy laboratory) capable of determining the viscoelastic properties of samples from both conventional force-distance curves and microrheology measurements. Methods PyFMLab has been written in Python, which provides an accessible syntax and sufficient computational efficiency. The software features were divided into separate, self-contained libraries to enhance code organization and modularity and to improve readability, maintainability, testability, and reusability. To validate PyFMLab, two AFM datasets, one composed of simple force curves and another including oscillatory measurements, were collected on HeLa cells. Results The viscoelastic parameters obtained on the two datasets analysed using PyFMLab were validated against data processing proprietary software and against validated MATLAB routines developed before obtaining equivalent results. Conclusions Its open-source nature and versatility makes PyFMLab an open-source solution that paves the way for standardized viscoelastic characterization of biological samples from both force-distance curves and microrheology measurements.

Reaction rebalancing: a novel approach to curating reaction databases

PurposeReaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.MethodsThe SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.ResultsThe rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.ConclusionThe SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.Scientific ContributionSynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

On the Automated Generation of Life Contingencies Problems and Solutions

Purpose: The purpose of this project is to provide an open source solution to this problem that is customizable, efficient, and easy to use. Methodology: We introduce an open-source software package named AutoSULT to address this problem. This package utilizes the computing abilities of R and the typesetting abilities of R Markdown, LaTeX, and other existing R packages. Findings: Our software efficiently generates and typesets customized questions with step-by-step solutions for several types of problems that are commonly used in introductory life contingencies courses. We highlight some of the advantages of this tool and demonstrate its uses. Unique contribution to theory, practice and policy: We believe that this software can serve as a valuable resource for both instructors and students of introductory life contingencies courses.

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Cleaning ECG with Deep Learning: A Denoiser Tested in Industrial Settings

As the popularity of wearables continues to scale, a substantial portion of the population has now access to (self-)monitorization of cardiovascular activity. In particular, the use of ECG wearables is growing in the realm of occupational health assessment, but one common issue that is encountered is the presence of noise which hinders the reliability of the acquired data. In this work, we propose an ECG denoiser based on bidirectional Gated Recurrent Units (biGRU). This model was trained on noisy ECG samples that were created by adding noise from the MIT-BIH Noise Stress Test database to ECG samples from the PTB-XL database. The model was initially trained and tested on data corrupted with the three most common sources of noise: electrode motion artifacts, muscle activation and baseline wander. After training, the model was able to fully reconstruct previously unseen signals, achieving Root-Mean-Square Error values between 0.041 and 0.023. For further testing the model’s robustness, we performed a data collection in an industrial work setting and employed our model to clean the noisy data, acquired from 43 workers using wearable sensors. The trained network proved to be very effective in removing real ECG noise, outperforming the available open-source solutions, while having a much smaller complexity compared to state-of-the-art Deep Learning approaches.

Clusterslice: Slicing resources for zero-touch Kubernetes-based experimentation

ClusterSlice is an open-source solution for automated Kubernetes-centered experimentation. It introduces well-designed abstractions that reduce experimentation complexity with improved reliability and reproducibility. Its main capabilities are: (i) automated declarative operation, e.g., through declarative specifications of experimentation slices; (ii) infrastructure-as-a-service (i.e., the utilization of heterogeneous physical and virtual resources), platform-as-a-service (e.g., multiple composable Kubernetes flavors and network plugins), and application-as-a-service (e.g., plug-and-play application features) capabilities; (iii) multi-cluster and multi-domain support, i.e., inter-cluster operation over multiple open testbeds, virtualization systems and domains; and (iv) experimentation automation, e.g., support of automated experiments of various research topics, including network plugin performance assessments and anomaly detection workflows. Here, we provide the basic architectural attributes of ClusterSlice and proof-of-concept results highlighting its above capabilities.

Influence of open-source software on Bangladesh academic library service sustainability: a conceptual framework

Purpose This research aims to develop a conceptual framework that explores the influence of open-source software (OSS) on the sustainability of library services within the context of academic libraries in Bangladesh. Design/methodology/approach The study uses a comprehensive research methodology that includes literature review and analysis to construct a robust conceptual framework. This study investigates the various dimensions of OSS adoption and its impact on library service sustainability. Findings The research findings reveal the critical factors and mechanisms through which OSS can positively affect the sustainability of library services. This study identifies key drivers and challenges associated with the adoption of open-source solutions in the context of Bangladesh academic libraries. Practical implications The framework developed in this research offers practical insights for academic libraries in Bangladesh seeking to adopt OSS solutions. This study guides how to leverage these technologies to enhance the sustainability of library services in a cost-effective and efficient manner. Originality/value This study contributes to the academic literature by presenting a novel conceptual framework tailored to the unique context of Bangladesh academic libraries. This study adds value by addressing the specific challenges and opportunities related to OSS adoption and its implications for library service sustainability in this region.

Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education.

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.