Open Source Research Articles

Accuracy of thick and thin intraocular lens power formulas using paraxial vergence calculation

Purpose: To compare the prediction errors of several thick IOL formulas to a thin lens approach using variations of the same paraxial vergence calculation formula. Setting: Department of Ophthalmology, Randers Regional Hospital, Denmark Design: Prospective, non-interventional study. Methods: We prospectively and consecutively performed optical low coherence reflectometry biometry in 132 eyes with subsequent phacoemulcification and insertion of the same aspherical IOL model. Clinical refraction was performed two months postoperatively. We retrospectively used the same paraxial vergence formula and varied only the methods for calculating the postoperative IOL position, thickness, and curvatures to construct four formulas: Næser I formula based on thick lens calculation using the manufacturer´s cutting card information; Næser II formula based on thick lens calculation using calculated IOL data from open sources; Næser III formula based on thick lens calculation and a fixed IOL thickness of 0.62 mm; Næser IV formula based on thin lens calculation with fixed IOL position 0.31 mm anterior to the postoperative capsule. Each formula was optimized separately, hereby assuring a mean error of zero. The prediction error (PE) was defined as the difference between the measured and predicted spherical equivalent refraction. Results: Mean absolute error amounted to 0.30 (±0.26) D for all four formulas with no statistically significant difference. PE averaged zero for short, normal, and long eyes. Conclusions: The additional optical information provided by thick IOL calculations appears insignificant compared to other sources of error, related to the accuracy of IOL manufacturing, natural lens refractive index, and postoperative refraction.

Safeguarding User-Centric Privacy in Smart Homes

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

Open Source Adoption for Digital Transformation and Data Management During the COVID-19 Crisis

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

Predicting the Impact of Parameter Uncertainty on Model-Guided Li-O2 Cathode Design

Abstract Despite their high theoretical capacity, actual performance in lithium-oxygen (Li-O2) batteries is limited by sluggish transport and kinetic processes. Cathodes must provide ample surface area to host solid reduction products, but must also support species transport to access these surfaces. Numerous modeling studies therefore attempt to optimize cathode design by identifying microstructures to balance these two needs. However, model validation has historically relied on literature-sourced transport properties, which vary greatly between studies. In this work, we develop an open source, 1-D, continuum-scale Li-O2 battery model to examine the impact of electrolyte properties on predicted Li-O2 battery performance and design. Results demonstrate that O2 diffusion and solubility have the greatest impact on optimal design. Varying O2 diffusivity within the range of literature values surveyed led to maximum energy density variations of nearly 400%. These variations have a meaningful impact on the associated design conclusions: the optimal cathode porosity varied between 55 and 75%, depending on the O2 diffusivity. Moreover, the impact of advanced micrsotructures, such as graded cathode porosity, varies greatly with changes in electrolyte transport parameter estimates. As such, fundamental studies are required to accurately measure key electrolyte properties to enable numerical simulation as a guide to Li-O2 cathode design.

Refraction-Aware Structure from Motion for Airborne Bathymetry

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.

EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI).

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding, or 3) online tools that are limiting for analysis of large data sets. Here, we present EasyFlow, an open-source flow cytometry analysis graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

BerryPortraits: Phenotyping Of Ripening Traits cranberry (Vaccinium macrocarpon Ait.) with YOLOv8.

BerryPortraits (Phenotyping of Ripening Traits) is open source Python-based image-analysis software that rapidly detects and segments berriesand extracts morphometric data on fruit quality traits such as berry color, size, shape, and uniformity. Utilizing the YOLOv8 framework and community-developed, actively-maintained Python libraries such as OpenCV, BerryPortraits software was trained on 512 postharvest images (taken under controlled lighting conditions) of phenotypically diverse cranberry populations (Vaccinium macrocarpon Ait.) from the two largest public cranberry breeding programs in the U.S. The implementation of CIELAB, an intuitive and perceptually uniform color space, enables differentiation between berry color and berry brightness, which are confounded in classic RGB color channel measurements. Furthermore, computer vision enables precise and quantifiable color phenotyping, thus facilitating inclusion of researchers and data analysts with color vision deficiency. BerryPortraits is a phenotyping tool for researchers in plant breeding, plant genetics, horticulture, food science, plant physiology, plant pathology, and related fields. BerryPortraits has strong potential applications for other specialty crops such as blueberry, lingonberry, caneberry, grape, and more. As an open source phenotyping tool based on widely-used python libraries, BerryPortraits allows anyone to use, fork, modify, optimize, and embed this software into other tools or pipelines.

Reduction of Vision-Based Models for Fall Detection

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models.

Forensic and legal medicine in Chile: Cinderella without a prince.

Forensic medicine is of enormous importance in the functioning of contemporary justice systems worldwide. Therefore, in order to characterize the current state of medicolegal and forensic activity in Chile, a non-systematic review of the biomedical and legal literature on the subject was carried out. An analysis of open sources of information was also incorporated, including the National Registry of Individual Health Care Providers, the latest public accounts of the Forensic Medical Service, relevant background information available on the active transparency portal of that institution, exempt resolutions included in the "Chile Law" database (of the Department of Legislative and Documentary Services of the Library of the National Congress) and the lists of judicial experts appointed by the Courts of Appeals of the country for the biennium 2024 to 2025. We note that Chile maintains an unacceptable historical debt in terms of academic development and training of qualified professionals in forensic matters. Likewise, national scientific productivity in this field is practically null. Currently, forensic medicine is the medical specialty with the deepest shortage of professionals nationwide. Consequently, as in the early part of the last century, medical expert opinions are frequently performed by professionals untrained in forensic medicine. This scenario, taking into account the attitudes of certain lawyers and judges (detailed in the article), increases the risk of a miscarriage of justice. National public policies must take urgent action to address the deficits and provide solutions.

Geographically corrected clustering applied to establish medical service areas

Abstract. In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.

Essential steps for establishing a large‐scale passive acoustic monitoring for an elusive forest bird species: the Eurasian Woodcock (Scolopax rusticola)

In light of global biodiversity loss, comprehensive monitoring of species and biodiversity is fundamental for effective conservation management. Technology‐driven approaches, particularly in the field of bioacoustics, have gained importance, with autonomous recording units (ARUs) such as the AudioMoth being a notable example. However, a systematic application of this technology in large‐scale passive acoustic monitoring (PAM) schemes is challenging because of a lack of species‐specific calibration techniques and information on survey design. Using Eurasian Woodcock Scolopax rusticola as our focal species, we present application‐oriented guidelines for testing and implementing a terrestrial, single‐species PAM scheme. In this process, we assessed the species‐specific detection space of AudioMoths, considering the influence of recording setup and location. ARUs had similar detection rates as a human observer when set up in locations with low vegetation. A further comparative analysis of bioacoustic and traditional roding surveys confirmed the comparability of the two methods. The last step comprised an evaluation of BirdNET as a means of automated species detection. Although BirdNET reliably detected Woodcock occurrence, our results underline the need for rigorous testing of computer‐aided data analysis as well as recording hardware before establishing PAM. We demonstrate that low‐cost open source ARUs and open access analysis tools can yield results on a par with traditional survey methods, and we provide specific recommendations for PAM for Eurasian Woodcock.

Common open source WebGIS platform for spatial and non-spatial data to enhance health services in Sri Lanka

This study explores the transformative potential and current limitations of Geographic Information Systems (GIS) in the context of Sri Lanka's health sector and broader development initiatives. Despite the global GIS market's expected growth to billions by 2025, driven by its adoption in infrastructure, urban planning, and the integration with emerging technologies, its application in critical areas such as public health, disaster management, and citizen services remains underutilized. This underutilization stems from a lack of ground-level information, limited knowledge of GIS capabilities, restricted access to essential geospatial data, and the high cost of GIS software. This paper discusses the significant hurdles in obtaining updated and comprehensive citizen, household, and health-related information, which impedes effective planning and decision-making within the health sector of Sri Lanka. It further examines the initiatives by the Ministry of Health, supported by the Asian Development Bank, to implement a GIS-based Health Mapping System focused on infrastructure and inventory management of health facilities, yet lacking in integrating critical citizen data for demand analysis of health services. The study underscores the necessity for a comprehensive "Geographical Citizen Information System for the Health Sector in Sri Lanka" akin to those in developed countries, to host, process, and analyze spatial and non-spatial information for enhanced policymaking and development planning. Through an analysis of current challenges and the exploration of potential solutions, this paper advocates for an integrated approach to harness the full capabilities of GIS technologies, thereby enabling more informed and effective governance in Sri Lanka’s Health sector.

THREE GEOPOLITICAL SCENARIOS WITH GLOBAL IMPACT

Although it seems chaotic, the international environment is shaped by predictable forces, nurtured by interest and lust for power and domination. However, sometimes, as Nassim Nicholas Taleb pointed out, "black swans" can happen, meaning events that come as a total surprise and have a major effect. This article refers to three predictable geopolitical developments with significant impact on the global power competition, namely: the re-election of Donald J. Trump as US President; Chinese global financial initiative, including a new Asian bloc currency and a new gold/currency benchmark; and the extension of the asymmetric war between Israel and HAMAS. Three foreseeable geopolitical dynamics, which can however be influenced by chance (“black swans”). The study is linear, starting with the hypotheses, continuing with data collection, data analysis and synthesis and ending with conclusions. The research methods belong to geopolitics and combine data analysis and synthesis with inductive-deductive and ambispective methods. The data used are exclusively from open sources and include official documents, literature and journalistic sources.

Predicting the probability of failure in medicinesʼ public procurement

The quality and timeliness of medicinesʼ supply to the healthcare system through public procurement is an urgent task of public policy in all countries of the world, including Russia. Failure to close (failure of tenders, termination of already concluded contracts) procurement procedures in such a socially significant area carries risks for the population, provokes the emergence of hidden transaction costs for the budget system to eliminate the consequences of procurement failures. In their previous works, the authors identified the factors that lead to the failure to close tenders for the purchase of remedies, and initially assessed the consequences of their influence on procurement procedures. The purpose of this article is to present a mathematical model of the probability of non-closure tenders based on the obtained results. To achieve this goal, through processing more than 1 million notifications of public procurement of remedies for 2022–2023, collected from the open sources, the tasks with no methodological solutions were completed. Thus, a set of features accompanying the failure to close procedures for the purchase of remedies was compiled. The composition of identified features was analyzed; their influence on non-closure of the procedure was assessed. A model of the probability of non-closure of the procedure was constructed, and the results were interpreted. Unlike previously published studies, the forecast model was implemented on the ensembles of decision trees using gradient boosting. This made possible to significantly improve the quality of the forecast for each factor affecting the probability of non-closure of the bidding. The results obtained in the article are not only scientifically novel, but can also be used by regulatory and control bodies in public procurement to develop methodological recommendations for customers on establishing optimal conditions for concluding and fulfilling contracts, which will reduce procurement risks and damage to the state budget.

Using Deep Learning, Optuna, and Digital Images to Identify Necrotizing Fasciitis

Necrotizing fasciitis, which is categorized as a medical and surgical emergency, is a life-threatening soft tissue infection. Necrotizing fasciitis diagnosis primarily relies on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound scans, surgical biopsy, blood tests, and expert knowledge from doctors or nurses. Necrotizing fasciitis develops rapidly, making early diagnosis crucial. With the rapid progress of information technology and systems, in terms of both hardware and software, deep learning techniques have been employed to address problems in various fields. This study develops an information system using convolutional neural networks (CNNs), Optuna, and digital images (CNNOPTDI) to detect necrotizing fasciitis. The determination of the hyperparameters in convolutional neural networks plays a critical role in influencing classification performance. Therefore, Optuna, an optimization framework for hyperparameter selection, is utilized to optimize the hyperparameters of the CNN models. We collect the images for this study from open data sources such as Open-i and Wikipedia. The numerical results reveal that the developed CNNOPTDI system is feasible and effective in identifying necrotizing fasciitis with very satisfactory classification accuracy. Therefore, a potential future application of the CNNOPTDI system could be in remote medical stations or telemedicine settings to assist with the early detection of necrotizing fasciitis.

OpenOPAF: An Open Source Multimodal System for Automated Feedback for Oral Presentations

Providing automated feedback that facilitates the practice and acquisition of oral presentation skills has been one of the notable applications of multimodal learning analytics (MmLA). However, the closedness and general unavailability of existing systems have reduced their potential impact and benefits. This work introduces OpenOPAF, an open-source system designed to provide automated multimodal feedback for oral presentations. By leveraging analytics to assess body language, gaze direction, voice volume, articulation speed, filled pauses, and the use of text in visual aids, it provides real-time, actionable information to presenters. Evaluations conducted on OpenOPAF show that it performs similarly, both technically and pedagogically, to existing closed solutions. This system targets practitioners who wish to use it as-is to provide feedback to novice presenters, developers seeking to adapt it for other learning contexts, and researchers interested in experimenting with new feature extraction algorithms and report mechanisms and studying the acquisition of oral presentation skills. This initiative aims to foster a community-driven approach to democratize access to sophisticated analytics tools for oral presentation skill development.

Smart Sensor Placement For Thermal Monitoring Of Concrete

Structural health monitoring (SHM) has become an inevitable part in a life span of a structure due to its potential to ensure the public safety and to increase the life span of the structure. Monitoring any kind of structures for various parameters, using wireless smart sensors has gained popularity in recent past. This paper discusses the development of low cost real time wireless smart sensor monitoring system to monitor early age concrete temperature in real time. Temperature of two early age concrete mixes (Mix1, Mix2) were measured in real time for 24 hours by using DS18B20 sensors connected with the Node Mc u, which is an open source IOT platform. Temperature measurements were saved and visualized in real time using Thing Speak TM which is an open IoT online platform with MATLAB analytics. The temperature sensor DS18B20 was selected such that it is suitable to measure temperature readings of the concrete without any interference of the chemical reactions in concrete. Calibration methods and temperature variation with different concrete mixes are also discussed. It could be seen that the wireless temperature monitoring system performed adequately and it can be considered as a better low cost alternative for traditional wired temperature monitoring system.

REGISTRY OF TYPES, FACTORS, AND CONSEQUENCES OF MEDICAL ERRORS IN THE DIAGNOSIS OF DIABETES MELLITUS TYPES

Aim. To identify and systematize the types, factors, and consequences of medical errors in diagnosing types of diabetes mellitus (DM) and to create a registry for them.Materials and Methods. A search, collection, evaluation, and synthesis of scientific data regarding the prevalence of diagnostic errors, their significance, types, causes (including general and diabetes-specific factors), and prevention methods were conducted using Google Scholar, Scopus, Web of Science, PubMed Medline, and Embase databases, as well as publications from open sources. The analysis included guidelines from leading medical societies and expert groups. Based on the systematization of literature data and the authors' clinical observations, a «Registry of Types, Factors, and Consequences of Medical Errors in the Diagnosis of Diabetes Mellitus Types» was created for the first time.Results. General factors contributing to errors in the diagnosis of diabetes mellitus (DM) include limited access to quality healthcare, a shortage of specialists in diabetology, low levels of teamwork, lack of availability of diagnostic tests for determining DM types, ineffective communication, unsatisfactory care coordination, and follow-up, as well as cognitive biases. Diabetes-specific factors include the heterogeneity of DM, atypical disease progression, comorbidities, environmental factors, and technical errors in DM diagnosis. The most typical diagnostic discrepancies involve the misidentification of type 1, type 2, and other specific types of DM. Implementing the created «Registry of Types, Factors, and Consequences of Medical Errors in the Diagnosis of Diabetes Mellitus Types» in endocrinology departments has significantly reduced the number of diagnostic discrepancies for DM types. Additionally, incorporating this registry into the educational process at higher education institutions has improved the competence of students and medical professionals on this issue.Conclusions. The «Registry of Types, Factors, and Consequences of Medical Errors in the Diagnosis of Diabetes Mellitus Types», created based on a synthesis of literature data and the results of clinical observations, contributes to improving the quality and accuracy of diagnosis, enhances the professional level of healthcare workers, and reduces the number of incorrect diagnoses and treatment prescriptions.

Data analytics for research on complex brain disorders

There exist many research tools for complex brain disorders both proprietary and open source. Those tools are mostly focused on specific use cases and don’t offer a holistic view of the research and diagnostic processes. The MES-CoBraD platform offers a holistic tool that participates in all the phases of the process from planning to execution. In it researchers can create workflows of the diagnostic and research procedures. The Data Analytics module provides the functionality described in these workflows, mainly the analytical functions. The tools are integrated with the rest of the platform in a seamless manner. They are capable of analyzing different categories of data including imaging data, electroencephalogram (EEG)/polysomnography (PSG), medical device data, and consumer technology data. In this paper we present a summary of how the MES-CoBraD platform operates, the role of the data analytics in it, the actual functions implemented and integrated in the Data Analytics module and a number of use cases which show how each specific function can be used during the research and clinical procedures.

Investigating Open Source LLMs to Retrofit Competency Questions in Ontology Engineering

Competency Questions (CQs) are essential in ontology engineering; they express an ontology's functional requirements as natural language questions, offer crucial insights into an ontology's scope and are pivotal for various tasks, e.g. ontology reuse, testing, requirement specification, and pattern definition. Despite their importance, the practice of publishing CQs alongside ontological artefacts is not commonly adopted. We propose an approach based on Generative AI, specifically Large Language Models (LLMs) for retrofitting CQs from existing ontologies and we investigate how open LLMs (i.e. Llama-2-70b, Mistral 7B and Flan-T5-xl) perform in generating CQs for existing ontologies. We compare these results with our previous efforts using closed-source LLMs and we reflect on the results.