Open-source Online Research Articles

Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty.

By predicting short-term postoperative outcomes before surgery, patients undergoing cervical laminoplasty (CLP) surgery could benefit from more accurate patient care strategies that could reduce the likelihood of adverse outcomes. With this study, we developed a series of machine learning (ML) models for predicting short-term postoperative outcomes and integrated them into an open-source online application. National surgical quality improvement program database was utilized to identify individuals who have undergone CLP surgery. The investigated outcomes were prolonged length of stay (LOS), non-home discharges, 30-day readmissions, unplanned reoperations, and major complications. ML models were developed and implemented on a website to predict these three outcomes. A total of 1740 patients that underwent CLP were included in the analysis. Performance evaluation indicated that the top-performing models for each outcome were the models built with TabPFN and LightGBM algorithms. The TabPFN models yielded AUROCs of 0.830, 0.847, and 0.858 in predicting non-home discharges, unplanned reoperations, and major complications, respectively. The LightGBM models yielded AUROCs of 0.812 and 0.817 in predicting prolonged LOS, and 30-day readmissions, respectively. The potential of ML approaches to predict postoperative outcomes following spine surgery is significant. As the volume of data in spine surgery continues to increase, the development of predictive models as clinically relevant decision-making tools could significantly improve risk assessment and prognosis. Here, we present an accessible predictive model for predicting short-term postoperative outcomes following CLP intended to achieve the stated objectives.

A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries

All-solid-state batteries (ASSBs) are a class of safer and higher-energy-density materials compared to conventional devices, from which solid-state electrolytes (SSEs) are their essential components. To date, investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern. However, obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship. To provide a solution to these challenges, developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structure-performance relationships and find out new design guidelines for reasonable SSEs. Herein, a dynamic experimental database containing >600 materials was developed in a wide range of temperatures (132.40–1261.60 ​K), including mono- and divalent cations (e.g., Li+, Na+, K+, Ag+, Ca2+, Mg2+, and Zn2+) and various types of anions (e.g., halide, hydride, sulfide, and oxide). Data-mining was conducted to explore the relationships among different variates (e.g., transport ion, composition, activation energy, and conductivity). Overall, we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications. This database is dynamically updated, which can be accessed via our open-source online system.

MA-QC: Free online software for prostate MR quality control and PI-QUAL assessment

PurposeTo validate MRI Analyzer Quality Control (MA-QC), a free and open-source online software designed to facilitate MR data acquisition quality control and PI-QUAL score calculation. Material and methodsMA-QC is a web-based software, designed for analysing DICOM data related to MR acquisition parameters. The software allows automatic extraction of 18 technical criteria, and manual input of 12 visual criteria, to calculate the PI-QUAL score. We collected 100 prostate MRI datasets from four MR device manufacturers to test data compatibility, automatic sequence recognition, and robustness of technical criteria extraction from DICOM data. The main issue was to determine the spatial resolution in the phase and frequency directions, due to variable encoding of the DICOM datasets. ResultsAcquisition data could be extracted from all sample examinations (100%), with a median analysis speed of 15.2 ± 4.4 images per second and mean processing time of 96 [11–326] seconds per examination. MA-CQ automatically detected the optimal T2-w, DWI and DCE sequences in 71 out of 100 (71%) cases, and required manual selection of at least one sequence in 29 out of 100 (29%) cases to get the best parameters. Display of technical criteria for the 3 sequences was instantaneous. PI-QUAL score could be calculated in all cases. ConclusionThis software brings substantial help in the quality assessment of prostate MRI examinations, by providing fast extraction of series data and the 18 technical parameters of PI-QUAL. PI-QUAL scoring can be performed in less than two minutes, helping to focus on the visual criteria, allowing use of this software in the clinical workflow in the aim of improving overall image quality in prostate MR imaging.

Full-waveform inversion by model extension: Practical applications

Producing reliable acoustic subsurface velocity models still remains the main bottleneck of the oil and gas industry’s traditional imaging sequence. In complex geologic settings, the output of conventional ray-based or wave-equation-based tomographic methods may not be sufficiently accurate for full-waveform inversion (FWI) to converge to a geologically satisfactory earth model. We create a new method referred to as full-waveform inversion by model extension (FWIME) in which a wave-equation migration velocity analysis (WEMVA) technique is efficiently paired with a modified version of FWI. We find that our method is more powerful than applying WEMVA and FWI sequentially, and that it can converge to accurate solutions without the use of a good initial guess or low-frequency energy. We determine FWIME’s potential on five realistic and challenging numerical examples that simulate complex geologic scenarios often encountered in hydrocarbon exploration. We guide the reader step by step throughout the optimization process. We find that our method can simultaneously invert all wave types with the same simple mechanism and without the need for a user-intensive hyperparameter tuning process. In an open-source online repository, we provide our C++/compute unified device architecture (CUDA) numerical implementation accelerated with graphics processing units, encapsulated in a Python interface. All the numerical examples developed are accessible through Python notebooks and fully reproducible.

Security Threat Mitigation for Smart Contracts: A Comprehensive Survey

The blockchain technology, initially created for cryptocurrency, has been re-purposed for recording state transitions of smart contracts—decentralized applications that can be invoked through external transactions. Smart contracts gained popularity and accrued hundreds of billions of dollars in market capitalization in recent years. Unfortunately, like all other computer programs, smart contracts are prone to security vulnerabilities that have incurred multibillion-dollar damages over the past decade. As a result, many automated threat mitigation solutions have been proposed to counter the security issues of smart contracts. These threat mitigation solutions include various tools and methods that are challenging to compare. This survey develops a comprehensive classification taxonomy of smart contract threat mitigation solutions within five orthogonal dimensions: defense modality, core method, targeted contracts, input-output data mapping, and threat model. We classify 133 existing threat mitigation solutions using our taxonomy and confirm that the proposed five dimensions allow us to concisely and accurately describe any smart contract threat mitigation solution. In addition to learning what the threat mitigation solutions do, we also show how these solutions work by synthesizing their actual designs into a set of uniform workflows corresponding to the eight existing defense core methods. We further create an integrated coverage map for the known smart contract vulnerabilities by the existing threat mitigation solutions. Finally, we perform the evidence-based evolutionary analysis, in which we identify trends and future perspectives of threat mitigation in smart contracts and pinpoint major weaknesses of the existing methodologies. For the convenience of smart contract security developers, auditors, users, and researchers, we deploy and maintain a regularly updated comprehensive open-source online registry of threat mitigation solutions, called Security Threat Mitigation (STM) Registry at https://seit.egr.msu.edu/research/stmregistry/ .

The (Mis)Information Game: A social media simulator

Given the potential negative impact reliance on misinformation can have, substantial effort has gone into understanding the factors that influence misinformation belief and propagation. However, despite the rise of social media often being cited as a fundamental driver of misinformation exposure and false beliefs, how people process misinformation on social media platforms has been under-investigated. This is partially due to a lack of adaptable and ecologically valid social media testing paradigms, resulting in an over-reliance on survey software and questionnaire-based measures. To provide researchers with a flexible tool to investigate the processing and sharing of misinformation on social media, this paper presents The Misinformation Game—an easily adaptable, open-source online testing platform that simulates key characteristics of social media. Researchers can customize posts (e.g., headlines, images), source information (e.g., handles, avatars, credibility), and engagement information (e.g., a post’s number of likes and dislikes). The platform allows a range of response options for participants (like, share, dislike, flag) and supports comments. The simulator can also present posts on individual pages or in a scrollable feed, and can provide customized dynamic feedback to participants via changes to their follower count and credibility score, based on how they interact with each post. Notably, no specific programming skills are required to create studies using the simulator. Here, we outline the key features of the simulator and provide a non-technical guide for use by researchers. We also present results from two validation studies. All the source code and instructions are freely available online at https://misinfogame.com.

Wikipedia page views for health research: a review.

Wikipedia is an open-source online encyclopedia and one of the most-read sources of online health information. Likewise, Wikipedia page views have also been analyzed to inform public health services and policies. The present review analyzed 29 studies utilizing Wikipedia page views for health research. Most reviewed studies were published in recent years and emanated from high-income countries. Together with Wikipedia page views, most studies also used data from other internet sources, such as Google, Twitter, YouTube, and Reddit. The reviewed studies also explored various non-communicable diseases, infectious diseases, and health interventions to describe changes in the utilization of online health information from Wikipedia, to examine the effect of public events on public interest and information usage about health-related Wikipedia pages, to estimate and predict the incidence and prevalence of diseases, to predict data from other internet data sources, to evaluate the effectiveness of health education activities, and to explore the evolution of a health topic. Given some of the limitations in replicating some of the reviewed studies, future research can specify the specific Wikipedia page or pages analyzed, the language of the Wikipedia pages examined, dates of data collection, dates explored, type of data, and whether page views were limited to Internet users and whether web crawlers and redirects to the Wikipedia page were included. Future research can also explore public interest in other commonly read health topics available in Wikipedia, develop Wikipedia-based models that can be used to predict disease incidence and improve Wikipedia-based health education activities.

An Innovative Approach for Fake News Detection using Machine Learning

This research aims to increase people's awareness of fake news on online social networks and help them determine the reliability of information they consume. It investigates methods for detecting fake news sources, authors, and subjects on online social networks. The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. Random Forest (RF) and Naive Bayes (NB) had accuracy scores of 98.25% and 94.74%, respectively.

Statistics for ecologists: A frequentist and bayesian treatment of modern regression models By JohnFieberg (Ed.). 2022. An open‐source online textbook. pp. 526. $0.00 online: (https://fw8051statistics4ecologists.netlify.app/)

The Journal of Wildlife ManagementEarly View e22446 BOOK REVIEW Statistics for Ecologists: A Frequentist and Bayesian Treatment of Modern Regression ModelsBy John Fieberg (Ed.). 2022. An open-source online textbook. pp. 526. $0.00 online: (https://fw8051statistics4ecologists.netlify.app/) Conner A. Ties, Corresponding Author Conner A. Ties [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorBenjamin W. Hoose, Corresponding Author Benjamin W. Hoose [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJustinn J. Jones, Corresponding Author Justinn J. Jones [email protected] Department of Ecology and Conservation Biology, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorLindsay A. Martinez, Corresponding Author Lindsay A. Martinez [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJared Schlottman, Corresponding Author Jared Schlottman [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this author Conner A. Ties, Corresponding Author Conner A. Ties [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorBenjamin W. Hoose, Corresponding Author Benjamin W. Hoose [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: benjamin.hoose[email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJustinn J. Jones, Corresponding Author Justinn J. Jones [email protected] Department of Ecology and Conservation Biology, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorLindsay A. Martinez, Corresponding Author Lindsay A. Martinez [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJared Schlottman, Corresponding Author Jared Schlottman [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this author First published: 20 June 2023 https://doi.org/10.1002/jwmg.22446Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issuee22446 RelatedInformation

#4942 GENOTYPE-PHENOTYPE ANALYSIS IN FAMILIAL RENAL GLUCOSURIA PATIENTS: THE CRUCIAL ROLE OF CONSERVED RESIDUES

Abstract Background and Aims Familial renal glucosuria (FRG) is a hereditary disorder caused by mutations in SLC5A2, the gene encoding sodium-glucose cotransporter 2 (SGLT2) in proximal tubule cells. In this study, we aimed to characterize the genotype-phenotype relationship in FRG patients using data from our cohort and literature review. Method We sequenced SLC5A2 in a cohort of 21 FRG patients and measured the renal threshold of glucose (RTG) in 15 patients using urinary glucose excretion in a 4-hour oral glucose tolerance test (OGTT). We built an open-source online calculator to facilitate the calculation of RTG. Besides, we reviewed published literature and obtained information on SLC5A2 variants and 24-hour urinary glucose (24hUG) in FRG patients. Results We identified 27 rare SLC5A2 variants, including 13 novel variants (G484D, R564W, A212S, c.574+1G>C, W649*, S592Cfs*6, Q579*, A89T, Y339*, V39F, G491E, A464E, and G360D), in our cohort and yielded 107 SLC5A2 variants from literature review. RTG in our cohort ranged from 1.0 to 9.2mmol/L. Patients carrying two SLC5A2 variants had lower RTG (3.9 vs. 6.2 mmol/L, p = 0.057) and larger amounts of 24-hour urinary glucose excretion (24hUG) (52.39 vs. 7.20 g/1.73m2, p = 2.7*10−8) than those carrying a single variant. Patients with homozygous missense or in-frame indels had mean 24hUG of 82.30g/1.73m2, comparable to those with homozygous truncating variants (81.66g/1.73m2) and significantly more than those with homozygous splicing variants (35.38g/1.73m2, p = 0.026). Patients with homozygous missense variants involving conservative residues had more 24hUG (104.76g/1.73m2) than those with variants at non-conservative residues (46.37g/1.73m2, p = 0.0031). Conclusion We built a comprehensive map showing the impact of SLC5A2 variant type, variant location, and zygosity on glucosuria severity. Our results highlighted that conserved residues play an essential role in maintaining the transport function of SGLT2.

Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study

One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti, due to its endemic status in Brazil.

Climate change by the numbers: Leveraging mathematical skills for science learning online

The purpose of this preregistered study was to test an online intervention that presents participants with novel numbers about climate change after they estimate those numbers. An experimental study design was used to investigate the impact of the intervention on undergraduate students’ climate change understanding and perceptions that human caused climate change is plausible. Findings revealed that posttest climate change knowledge and plausibility perceptions were higher among those randomly assigned to use the intervention compared with those assigned to a control condition, and that supplementing this experience with numeracy instruction was linked with the use of more explicit estimation strategies and greater learning gains for people with adaptive epistemic dispositions. Findings from this study replicate and extend prior research, support the idea that novel data can support knowledge revision, identify estimation strategies used in this context, and offer an open-source online intervention for sharing surprising data with students and teachers.

Ovitrap Monitor - Online application for counting mosquito eggs and visualisation toolbox in support of health services

Ovitraps are a widely used method for mosquito detection and monitoring, especially Aedes mosquitoes. Eggs present in ovitraps must be routinely counted to generate up-to-date information on potential spread of mosquito-borne diseases. This task is tedious, time consuming and prone to errors if done manually by eye counting. In this contribution, we introduce the Ovitrap Monitor, an online open source and user-friendly integrated application that semi-automatically counts mosquito eggs from low-medium resolution mobile phone pictures. A high correlation was found between counts performed manually by a technician and those obtained with the app using an extensive dataset of more than 750 ovitrap pictures. The application features an intuitive user interface and time-series plots and maps to facilitate data flow and speed up evidence-based decision-making within health organisations battling mosquito-borne diseases. Besides being open source, the Ovitrap Monitor is also backed by test data to guarantee its implementation through benchmarking and enforce research in the public health field.

Meta-analysis of lean and obese RNA-seq datasets to identify genes targeting obesity.

Obesity is a global crisis leading to several metabolic disorders. Modernization and technology innovation has been easier for next generation sequencing using open-source online software galaxy, which allows the users to share their data and workflow mapping in an effortless manner. This study is to identify candidate genes for obesity by performing differential expression of genes. RNA-Seq analysis was performed for six different datasets retrieved from GEO database. 258 datasets from obese patients and 55 datasets from lean patients were analysed for differentially expressed genes (DEGs). DEGs analysis showed 1971 upregulated genes and 615 downregulated genes with log2FC count ≥ 2.5 and p-value < 0.05. The Gene enrichment analysis performed using Gene Ontology resource highlighted pathways associated to obesity such as cholesterol metabolism, Fat digestion and absorption and glycerolipid metabolism. Using string database protein-protein interactions network was built and the network clusters were visualized using Cytoscape software. The protein-protein interactions of the upregulated and downregulated genes were mapped to form a network, wherein PNLIP (Pancreatic lipase) and FTO (Fat mass and obesity associated protein) gene clusters were visualized as densely connected clusters in MCODE. PNLIP and FTO with its associated genes were identified as candidate genes for targeting obesity.

CPA Methodology: educational technological design proposal to solve problems

Technology Enhanced Learning (TEL) is a teaching and learning approach that utilizes various forms of technology to enhance the learning experience (Cochrane, Redmond, and Corrin, 2018). One example of TEL is the use of digital fabrication, where we can find various open-source prototyping platforms, in educational settings. ADAPTA is an assistive technology educational event, in which elder people and students came together in meaningful and authentic domestic problem-solving educational activity, implementing the CPA methodology, based on a process of observation and prototyping, attempts to open a space for the design of assistive parts/devices for the elderly to improve your quality of life, through creation, testing, fabrication and usage of the pieces. A relevant aspect of the CPA methodology is that it allows the cultivation of 21st Century skills, through challenging projects that encourage the four Ps – Project, Passion, Peers and Play (Resnick, 2017).&#x0D; &#x0D; The CPA in Spanish, (APC in English for Autonomous Project Cell) methodology is a four-step process that can be used in conjunction with digital design processes and digital fabrication to support TEL. The four steps of the CPA methodology are: (a) Observation: This step involves students observing and documenting their surroundings or a specific problem they want to solve. This can involve collecting data, taking photos, or sketching out ideas in a real space (b) Documentation: In this step, students document their observations and ideas in a clear and organized manner. This can involve creating a written report, creating a visual representation of their ideas, using an open source online platform (c) Make/reflect: In this step, students used analog drawing and sketches, digital drawing, and digital fabrication. Later, the Arduino platform to create a prototype of their solution. This can involve programming the Arduino using a variety of programming languages, such as C++ or Python, and connecting various electronic components to the Arduino to create their desired outcome. As they work on their prototype, students should also be encouraged to reflect on their progress and make any necessary adjustments (d) Show and Tell: In the final step, students present their prototype to their peers and teachers/guides, demonstrating the functioning of their solution and explaining the process they followed to create it. This step allows students to share their learning with others and receive feedback on their work.&#x0D; &#x0D; Following the 2018 OECD objectives that aspire to an education that promotes individual, social and planetary well-being, we pay special attention to the domestic problems experienced by older adults. Considering that 16.2% of the country's inhabitants are over 60 years of age and, according to data from the Casen Survey, some 460,000 elderly people live in single-person households in Chile, it is important to pay attention to how aging creates difficulties in mobility and coexistence with the environment of the domestic space, such as the kitchen and bathroom, with the handling of forces and weights, difficult access, unattainable heights, etc. The workshop took place at a municipal space (Hub Providencia) in Santiago Chile, which met every week, showing them a variety of possible solutions to start observing in their homes, how they interacted with space and usability.&#x0D; Examples of possible solutions: 3d printed gadgets: open bottle, cary 3 bags, separate a book, open a bottle Nb 2.By following the CPA methodology, students were engaged in authentic problem-solving activities and learn valuable skills, such as critical thinking, communication, and collaboration. The use of digital drawing and fabrication, and Arduino in TEL could help the elder group to start designing from the drawing (pencil and paper). Afterwards this product was taken by the younger group of students, who were in charge of building the physical prototype. By providing hands-on opportunities for both group of students to engage in authentic problem-solving activities, they could converge on physical products that were finally given from the younger students to the elder ones.

The Use of Open-Source Online Course Content for Training in Public Health Emergencies: Mixed Methods Case Study of a COVID-19 Course Series for Health Professionals.

The onset of the COVID-19 pandemic generated an urgent need for credible and actionable information to guide public health responses. The massive open-source online course (MOOC) format may be a valuable path for disseminating timely and widely accessible training for health professionals during public health crises; however, the reach and effectiveness of health worker-directed online courses during the pandemic remain largely unexplored. This study investigated the use of an open-source online course series designed to provide critical COVID-19 knowledge to frontline health workers and public health professionals globally. The study investigated how open-source online educational content can be optimized to support knowledge sharing among health professionals in public health emergencies, particularly in resource-limited contexts. The study examined global course enrollment patterns (N=2185) and performed in-depth interviews with a purposive subsample of health professionals enrolled in the course series (N=12) to investigate the sharing of online content in pandemic responses. Interviewed learners were from Ethiopia, India, Kenya, Liberia, Malawi, Rwanda, Thailand, Uganda, the United Arab Emirates, and the United States. Inductive analysis and constant comparative methods were used to systematically code data and identify key themes emerging from interview data. The analysis revealed that the online course content helped fill a critical gap in trustworthy COVID-19 information for pandemic responses and was shared through health worker professional and personal networks. Enrollment patterns and qualitative data illustrate how health professionals shared information within their professional networks. While learners shared the knowledge they gained from the course, they expressed a need for contextualized information to more effectively educate others in their networks and in their communities. Due to technological and logistical barriers, participants did not attempt to adapt the content to share with others. This study illustrates that health professional networks can facilitate the sharing of online open-source health education content; however, to fully leverage potential benefits, additional support is required to facilitate the adaptation of course content to more effectively reach communities globally.

Water-related conflicts: definitions, data, and trends from the water conflict chronology

Violence associated with freshwater resources has occurred throughout recorded history, with water triggering violence and armed conflict, water or water systems being used as weapons, and water or water systems becoming casualties during conflicts. Understanding the causes of water-related violence and regional and temporal trends is critical for identifying priority areas for conflict resolution and strategies to reduce the risk of future conflicts. The purpose of this paper is to introduce the structure, content, and design of The Water Conflict Chronology, an open-source online database, which tracks water-related conflicts from around the globe. Some analysis is provided with caveats about the limitations of the data, but further analysis will be forthcoming. The database is updated approximately annually using information from other conflict-related databases, news reports, eyewitness accounts, and a review of historical documents. As of October 2022, the Chronology had 1298 entries, from the earliest events around 2400 BCE through early 2022. Initial analysis of data shows trends in the nature, location, and social and political characteristics of water-related violence, including a sharp increase in water-related violence in recent years, especially where water has been a trigger or casualty of violence, a concentration of events in the Middle East, southern Asia, and the Sahel, and the increased targeting of civilian water systems during civil and regional armed conflicts.

Online Self-Assessment in Mathematics at the University of Bern

In the Swiss education system the cantons oversee educational matters, practically meaning there are 26 different school systems. This is also reflected in the graduation exam, which is organized locally in every high school. Based on this assessment and their preference, students are admitted to university without any additional entrance exam. This motivated us to create an online self-assessment. Its two mathematical parts serve a different purpose: one evaluates basic mathematical skills, and the other one deals with abstract reasoning that gives students a flavor of university mathematics. Additionally, the online self-assessment contains a set of metacognition questions regarding motivation, expectations and attitude towards mathematics. Our goal is that students to identify their strengths and help them make a more informed study choice.&#x0D; The technical tool used for the implementation is STACK, an open-source online assessment system in mathematics. Concerning this use case of STACK, we are currently collecting students’ feedback. We will present our first conclusions on this matter, which serve as a basis for further adjustments of the material.

A model-based infrastructure for the specification and runtime execution of self-adaptive IoT architectures

To meet increasingly restrictive requirements and improve quality of service (QoS), Internet of Things (IoT) systems have embraced multi-layered architectures leveraging edge and fog computing. However, the dynamic and changing IoT environment can impact QoS due to unexpected events. Therefore, proactive evolution and adaptation of the IoT system becomes a necessity and concern. In this paper, we present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems. Our proposal comprises the design of a domain-specific language (DSL) for the specification of such architectures, and a runtime framework to support the system behaviuor and its self-adaptation at runtime. The code for the deployment of the IoT system and the execution of the runtime framework is automatically produced by our prototype code generator. Moreover, we also show and validate the extensibility of such DSL by applying it to the domain of underground mining. The complete infrastructure (modeling tool, generator and runtime components) is available in a online open source repository.

Variational AutoEncoders-LSTM based fault detection of time-dependent high dimensional processes

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data; however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a Variational Autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study.