Open-source Module Research Articles

ProSPyX: software for post-processing images of X-ray ptychography with spectral capabilities.

X-ray ptychography is a coherent diffraction imaging technique based on acquiring multiple diffraction patterns obtained through the illumination of the sample at different partially overlapping probe positions. The diffraction patterns collected are used to retrieve the complex transmittivity function of the sample and the probe using a phase retrieval algorithm. Absorption or phase contrast images of the sample as well as the real and imaginary parts of the probe function can be obtained. Furthermore, X-ray ptychography can also provide spectral information of the sample from absorption or phase shift images by capturing multiple ptychographic projections at varying energies around the resonant energy of the element of interest. However, post-processing of the images is required to extract the spectra. To facilitate this, ProSPyX, a Python package that offers the analysis tools and a graphical user interface required to process spectral ptychography datasets, is presented. Using the PyQt5 Python open-source module for development and design, the software facilitates extraction of absorption and phase spectral information from spectral ptychographic datasets. It also saves the spectra in file formats compatible with other X-ray absorption spectroscopy data analysis software tools, streamlining integration into existing spectroscopic data analysis pipelines. To illustrate its capabilities, ProSPyX was applied to process the spectral ptychography dataset recently acquired on a nickel wire at the SWING beamline of the SOLEIL synchrotron.

Ocean wave tracing v.1: a numerical solver of the wave ray equations for ocean waves on variable currents at arbitrary depths

Abstract. Lateral changes in the group velocity of waves propagating in oceanic or coastal waters cause a deflection in their propagation path. Such refractive effects can be computed given knowledge of the ambient current field and/or the bathymetry. We present an open-source module for solving the wave ray equations by means of numerical integration in Python v3. The solver is implemented for waves on variable currents and arbitrary depths following the Wentzel–Kramers–Brillouin (WKB) approximation. The ray tracing module is implemented in a class structure, and the output is verified against analytical solutions and tested for numerical convergence. The solver is accompanied by a set of ancillary functions such as retrieval of ambient conditions using OPeNDAP, transformation of geographical coordinates, and structuring of data using community standards. A number of use examples are also provided.

Post-mortem digital forensic analysis of the Garmin Connect application for Android

The Garmin Vivosmart 4 smartband can monitor various health metrics, including heart rate, oxygen saturation, body composition, and stress levels. It is a quite popular fitness tracking device, as its Android companion application – Garmin Connect – has been downloaded more than 10 million times and can provide critical forensic artifacts such as timestamped GPS-based locations. In this work, we analyze the Garmin Connect application to identify i) relevant digital forensic artifacts, and ii) assess methods to retrieve cloud-based data relevant to a digital forensic examination. For this purpose, we first establish a test scenario where the paired device/application collects data in regular real-world situations using a rooted smartphone running Android 11. The smartphone is then examined to gain insights into the data stored by the application and identify meaningful digital artifacts.To ease and automate the task of digital forensic practitioners, we have developed the Garmin Connect for Android Analyzer (GC4AA) set of Python 3 modules tailored for the digital forensic framework Android Logs Events And Protobuf Parser (ALEAPP). These open-source modules parse dumps of a Vivosmart 4 data directory and create reports displaying several digital artifacts, such as health metrics, GPS data and routes, and phone notifications. They automate the information-gathering process and produce a report specially tailored for Garmin Connect data, highlighting the most relevant artifacts. Our results show that the analysis of paired Garmin Collect/Vivosmart 4 with GC4AA can yield more digital forensic artifacts than existing open-source tools, including the following new artifacts: i) Daily Summary data; ii) GPS data; iii) Response Cache data; iv) Network Logs; v) Facebook API tokens; vi) Device Synchronization cache; vii) SpO2 reading charts. Our contributions include a graphical presentation of the collected data, greatly improving its readability and analysis.

Regional Data Platform of West and Central African Herbaria

In April 2021, a Biodiversity Information for Development (BID) project was launched to deliver a regional data platform of West and Central African herbaria, which just concluded in April 2023. A dataset containing 168,545 herbarium specimens from 6 different countries: Togo, Gabon, Ivory Coast, Benin, Guinea Conakry and Cameroon, is now visible on the Global Biodiversity Information Facility (GBIF) website and will be regularly updated. A checklist datatset (Radji 2023a) and an occurrence dataset (Radji 2023b) obtained from herbarium parts are also available on GBIF. In addition, a Living Atlases portal for herbaria in West and Central Africa has been created to allow users to search, display, filter, and download these data. This application reuses open source modules developed by the Atlas of Living Australia (ALA) community (Morin et al. 2021). In addition to that, the RIHA platform (Réseau Informatique des Herbiers d'Afrique / Digital Network of African Herbaria) enables herbarium administrators to manage their own data. Thanks to all these tools, the workflow (Fig. 1) for data publication on GBIF is carried out regularly and easily and the addition of new member herbaria from West and Central Africa can be easily incorporated.

WEB PLATFORMS FOR CULTURAL HERITAGE MANAGEMENT: THE PARCO ARCHEOLOGICO DEL COLOSSEO CASE STUDY

Abstract. This paper describes the digitization test of Fonte Giuturna (Giuturna spring) in the Roman Forum area, from survey to data management through the in-use monitoring system, the WebApp SyPEAH of the Parco Archeologico del Colosseo. The location of Giuturna Spring, characterized by the presence of heterogeneous archaeological remains from different ages, was surveyed in May 2022 as part of a research project that aimed to superintend the entire Cultural Heritage digitization pipeline to provide the Archaeological Park Administration the digitization guidelines as a tool to standardize future surveys and data deliveries.Inspired by the desire to build a system of protection and conservation at the service of sustainable exploitation, SyPEAH is a web platform based on open-source modules designed to manage archaeological records with a WebGIS approach. It supports the use of several 3D data formats, including point clouds. The paper focuses on the web platform, describing the web app’s main features, especially in terms of point cloud data management. Moreover, possible future development of the platform intended to implement usability for single archaeological objects is described.

SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).

Application of Image Analysis to Length Measurement of Carbon Nanotubes— Evaluation of the applicability of a software utilizing open-source modules —

Application of Image Analysis to Length Measurement of Carbon Nanotubes— Evaluation of the applicability of a software utilizing open-source modules —

Development and evaluation of an open-source virtual reality C-Arm simulator

ABSTRACT C-Arm positioning for interventional spine procedures is often associated with a steep learning curve. This task requires mentally reconstructing 3D surgical tools and patient anatomy from a 2D X-ray image, which is non-trivial and acquired through years of experience. Standard training via apprenticeship-based programs must be limited due to the unnecessary exposure to ionizing radiation. To this end, we propose a Virtual Reality C-Arm simulator for interventional spine procedure training. We implemented the simulator as an open-source module in Slicer, and evaluated its efficacy through a user study, recruiting medical residents and expert clinicians. Users showed an overall significant improvement in C-Arm placement with regards to angular accuracy (mean ~2 degree improvement), and total procedure time (mean 11 minutes less time). The face and content validity was evaluated positively through a Likert scale questionnaire, with a mean score of 4 (out of 5) or higher for each of the questions. The results show the simulator provides effective training for C-Arm positioning, while eliminating the exposure to ionizing radiation associated with the current training standard. Although this work is catered towards spinal procedures, the system is extendable to other fields, such as cardiac and orthopaedic, and will be explored in future works.

FCMpy: a python module for constructing and analyzing fuzzy cognitive maps

FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.

PICNIK: A python package with isoconversional computations for non-isothermal kinetics

Isoconversional computations are widely-used methods to determine activation energies for thermally stimulated processes. pICNIK (python Isoconversional Computations for Non-Isothermal Kinetics) is an open-source module designed with the purpose of being a seed to a complete Python package to facilitate kinetic computations. It is object oriented with two classes: DataExtraction and ActivationEnergy. Five isoconversional methods were implemented: Friedmann's method, the Kissinger-Akahira-Sunose method, Ozawa-Flynn-Wall method, Vyazovkin and advanced Vyazovkin. This module allows to compute the activation energy from thermogravimetric data in minutes and the results can be exported as spreadsheet format or comma separated values files instead of traditional tedious and time consuming data processing. The module was validated with simulated data and two study cases: vaporization of n-decane and the thermal degradation of polypropylene. Program summaryProgram Title: pICNIKCPC Library link to program files:https://doi.org/10.17632/dpwtnmjpj5.1Developer's repository link:https://github.com/ErickErock/pICNIKCode Ocean capsule:https://codeocean.com/capsule/0740039Licensing provisions: MITProgramming language: PythonNature of problem: Numerical determination of the parameters of the Arrhenius function and the reaction model under the isoconversion hypothesis.Solution method: A Python module with implemented isoconversional methods. The algorithms implemented include several computations such as linear regressions, numerical integration and differentiation, and minimizing functions, all over several data sets.Additional comments including restrictions and unusual features: The initial data processing is focused on thermogravimetry and may not work for calculating conversion to other properties. However, the object-oriented implementation allows to overcome this limitation easily. Methods for calculating the Arrhenius pre-exponential factor and the reaction model remain to be implemented. It is a small module conceived to be further developed by the community. Another limitation of the code is that it does not include a graphical interface.

PKULAST-An Extendable Model for Land Surface Temperature Retrieval From Thermal Infrared Remote Sensing Data

Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from thermal infrared (TIR) remote sensing data. In this study, we developed a python package, dubbed Peking University LAnd Surface Temperature (PKULAST) model to address the retrieval of LST from TIR remote sensing data with different LST retrieval algorithms, such as Single Channel algorithm, Split Window algorithm, and the Temperature and Emissivity Separation algorithm. Extendable data structures in the model covers common conceptual models used in LST algorithms implementation, such as spectral response functions, surface spectral libraries (ASTER, ECOSTRESS etc.), atmospheric profiles (NCEP, ERA5, MERRA-2, etc.), making it a convenience to construct the processing chain of developing new LST retrieval algorithms and generating LST products. The ASTER imageries are tested to assess the performance of the PKULAST to derive accurate LST. The LST result is then evaluated using long-term ground-based longwave radiation observations collected at seven sites from 2000 to 2021 under cloud-free scenes, and the result shows that the PKULAST model can obtain LST with an uncertainty of 2.7 K in bias, and 4.4 K in RMSE. With the help of predefined extendable data structures and workflows for different sensors, atmospheric profile libraries and surface spectral libraries, new algorithms for retrieving LST can be developed easily. The PKULAST promises to be a tool that seamlessly interoperates with developing LST retrieval algorithms and generating LST scientific products.

An Improved Kidney Tumor Prediction Using Deep Convolutional Neural Network-Restricted Boltzmann Machine Technique in Medical Image Segmentation

Automatic medical image segmentation has become increasingly important as contemporary medical imaging has become more widely available and used. Existing image segmentation solutions however lack the necessary functionality for simple medical image segmentation pipeline design. Pipelines that have already been deployed are frequently standalone software that has been optimised for a certain public data collection. As a result, the open-source python module deep-Convolutional neural network-Restricted Boltzmann Machine (deep CNNRBM) was introduced in this research work. The goal of Deep CNN-purpose RBMs is to have an easy-touse API that allows for the rapid creation of medical image segmentation transmission lines that include data augmentation, metrics, data I/O pre-processing, patch wise analysis, a library of pre-built deep neural networks, and fully automated assessment. Similarly, comprehensive pipeline customisation is possible because of strong configurability and many open interfaces. The dataset of Kidney tumor Segmentation challenge 2019 (KiTS19) acquired a strong predictor with respect to the standard 3D U-net model after cross-validation using deep CNNRBM. To that purpose, deep CNN-RBM, an expressive deep learning medical image segmentation architecture is introduced. The CNN sub-model captures frame-level spatial features automatically while the RBM submodel fuses spatial data over time to learn higher-level semantics in kidney tumor prediction. A neural network recognises medical picture segmentation, which is initiated using RBM to second-order collected data and then fine-tuned using back propagation to be more differential. According to the simulation outcome, the proposed deep CNN-RBM produced good classification results on the kidney tumour segmentation dataset.

Connecting West and Central African Herbaria Data: A new Living Atlases regional data platform

The label transcription and imaging of specimens in key African herbaria has been ongoing since the early 2000s. Many collections in Benin, Cameroon, Côte d’Ivoire, Gabon, Guinea Conakry, and Togo are now fully transcribed and partially digitized. More than 200 000 transcribed specimens are available with the following distribution: Benin: 45 000 Cameroon: 70 000 Côte d’Ivoire: 18 000 Gabon: 70 000 Guinea Conakry: 5 000 Togo: 15 000 Benin: 45 000 Cameroon: 70 000 Côte d’Ivoire: 18 000 Gabon: 70 000 Guinea Conakry: 5 000 Togo: 15 000 In April 2021, a BID project was started to deliver a regional data platform of West and Central African herbaria. Biodiversity Information for Development (BID) is a multi-year programme funded by the European Union and led by GBIF with the aim of enhancing capacity for effective mobilization and use of biodiversity data in research and policy in the 'ACP' nations of sub-Saharan Africa, the Caribbean and the Pacific. Our project's funding runs from April 2021 to April 2023. At this stage of the project, we are working on defining the information technology (IT) architecture (Fig. 1) and selecting the tools that we will be using to achieve our goals. In the talk, we will present our conclusions through architecture schemas and tools demonstrations. Each of the 6 countries will have its own PostgreSQL database, storing its data. They will also have access to the RIHA data management platform (Réseau Informatique des Herbiers d'Afrique / Digital Network of African Herbaria). This is a web application, developed in PHP, allowing full management of the data by herbarium administrators (Fig. 2). An Integrated Publishing Toolkit (IPT) will fetch these herbaria data from the databases, create the Darwin Core archives, and connect these data automatically to gbif.org on a periodic basis (Fig. 3). On the databases, we will use a PostgreSQL view to ease conversion from the RIHA data model to the Darwin Core model. On the IPT, we will create one dataset per country, linked to each PostgreSQL view. The SQL query will be configured to only fetch validated data, depending on the herbarium administrator's validation in the RIHA platform. The automatic and periodic data transmission to gbif.org is a feature available in the IPT, and recently improved by the GBIF France team, which contributes to the IPT development. Another part of the automatic data workflow will be to feed a Living Atlases portal for the West and Central African herbaria. This web application will allow public users to search, display and download herbaria data from West and Central Africa (Fig. 4). Internally, this Living Atlases application will reuse open source modules developed by the Atlas of Living Australia (ALA). The application is mainly written in Java, uses JQuery/Bootstrap for the interface and relies on SolR and Spark in the backend. It has been developed to be easily reusable, by only modifying configuration and doing web customization (HTML / CSS), hiding most of the backend technological complexity. The automatic data workflow will transfer datasets generated by the IPT, in Darwin Core Archive format, to the Living Atlases portal backend. A technical task orchestrator, yet to be selected, will implement this feature. Living Atlases subportals, limited to data of one participating country, could be easily set up, leveraging the existing backend resources (Fig. 5). One of the benefits of the Living Atlases portal is that we can easily deploy additional front end applications with limited data, configured by a filter (here, a filter on the data owner country). Only configuration and web customization (HTML / CSS) are required. All the backend modules, especially the ones storing data, are shared by the multiple front-ends, limiting the hardware consumption and data administration. The full automation of the workflow will allow this platform to run at a very low maintenance cost for IT administrators. Moreover, adding a new herbarium member from West and Central Africa will be quite easy thanks to the architecture of the Integrated Publishing Toolkit and Living Atlases tools (Fig. 6).

Generative locally linear embedding: A module for manifold unfolding and visualization

<h2>Abstract</h2> Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral method for dimensionality reduction and manifold unfolding. It embeds data using the same linear reconstruction weights as in the input space. In this paper, we propose an open source module which not only implements LLE, but also includes implementations of two generative LLE algorithms whose linear reconstruction phases are stochastic. Using this module, one can generate as many manifold unfoldings as desired for data visualization or feature extraction.

Attendance Management System using Google Facenet for Facial Recognition

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

Graphical User Interface for Shuffling of Sections based on Student Profile

This project GUI for shuffling of sections is done to automate the hectic work of shuffling students into sections has been programed in python using open source module using pandas and tkinter the overall result achieved to this program is that students got shuffled into sections with same ratio of male and female in all section, and average of ranks of students of all sections are similar as to maintain equality and integrity. This program gives a GUI for the administrator to access the file with data of students stored to manipulate that data. In this project we will implement using python programming language .in python, we will use module pandas, TKinter. Pandas to manipulate data of students from an excel file through python program, TKinter is used to add GUI to the program to select the file to be manipulated pandas is a software library written for the python language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series In particular, it offers data structures and operations for manipulating numerical tables and time series. TKINTER is a software library for creating library for creating GUI using python language.

Towards an Ultra-Affordable Three-Dimensional Bioprinter: A Heated Inductive-Enabled Syringe Pump Extrusion Multifunction Module for Open-Source Fused Deposition Modeling Three-Dimensional Printers

Abstract The extrusion systems of the widespread Fused Deposition Modeling (FDM) three-dimensional (3D) printers enable printing only with materials in the filament form. This property hinders the usage of these FDM 3D printers in many fields where the printing materials are in forms other than filaments. Thus, this paper proposes a Heated Inductive-enabled Syringe Pump Extrusion (HISPE) multifunction open-source module with a potential application in bioprinting (i.e., extrusion-based bioprinting). The proposed HISPE module is designed to be cost-effective, simple, and easy to replicate. It is capable of replacing the conventional extrusion system of any open-source cartesian FDM 3D printer. This module widens both the range of the FDM 3D printing materials (e.g., bioinks, biopolymers, blends of materials, or composites) and their forms (e.g., hydrogels, powder, pellets, or flakes). The capabilities of the proposed module were investigated through 3D printing bone scaffolds with a filament diameter of 400 µm and pore size of 350 µm by a polycaprolactone (PCL) biodegradable polymer in the pellets form. The morphological accuracy of the printed scaffolds was investigated by scanning electron microscope (SEM). The investigation results confirm the accurateness of the proposed HISPE module in printing high-precision models.

WheatGene: A genomics database for common wheat and its related species

Common wheat (Triticum aestivum) is a hexaploid plant (AABBDD) derived from genetically related tetraploid wheat T. turgidum (AABB) and a diploid goatgrass Aegilops tauschii (DD). Recent advances in sequencing technology and genome assembly strategies allow the acquisition of multiple wheat genomes, calling for a centralized database to store, manage and query the genomics information in a manner to reflect their evolutionary relationship and to perform effective comparative genome analysis. Here, we built WheatGene, a database that contains five wheat genomes of 318,102 genes and 945,900 transcripts and their expression information in 998 RNA-seq samples that can be searched and compared in an interactive manner. WheatGene was developed with Drupal, a popular content management system and the toolkit Tripal managed the biological information. The database was accessible through a web browser with species, search, gene expression, tools, and literature entries. Tools available were BLAST, synteny viewer, map viewer, JBrowse, data downloads, gene expression heatmap and bar chart, and homologs viewer. Moreover, the map viewer connected genomics data with genetic maps and QTL that can be searched for markers for molecular breeding. WheatGene was developed with open-source modules and libraries. WheatGene is available at http://wheatgene.agrinome.org.

A Seamless Pseudonymization and Randomization Workflow for REDCap.

The interaction of multiple computer systems during multi-center randomized controlled trials (RCTs) is a hurdle for IT-specialists as well as medical staff. A common workflow for the initial registration of a patient requires the generation of a pseudonym by a pseudonymization service, a manual transmission of the pseudonym to a randomization service, and a manual transfer of the pseudonym and assigned study arm into an electronic data capture (EDC) system. This interaction is often time consuming and error prone due to multiple system changes. Objective of this work is to enhance a commonly used EDC system, Research Electronic Data Capture (REDCap), as a single source of interaction for multi-center RCTs. This is achieved by providing two modules for a seamless integration of a pseudonymization service, i.e., Mainzelliste, and a randomization service, i.e., RandIMI. Thus, no site-specific system changes are required, which increases time efficiency and reduces errors. From a technical perspective, only authentication credentials and firewall exposure for a single system must be managed. To evaluate the usability of our implementation, the system usability scale was employed. The increase of time efficiency was measured in laboratory conditions by a comparison of the time for patient registrations with and without our modules. An "excellent" usability was shown and an average time reduction by nearly 64 %. Both open-source modules are available from the REDCap Repository of External Modules.

Renewable energy analysis in indigenous communities using bottom-up demand prediction

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system’s analysis by developing scripts in the LK scripting language and integrating System Advisor Model software’s open-source modules for modeling of renewable energy systems. The results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.