Open Source Website Research Articles

Power Flow Analysis in Urban Distribution Networks with Implementation of Grid-Connected Photovoltaic Systems

In the last few years, renewable energies became more socially and economically relevant, and among them, photovoltaic systems stand out. Residential self-consumption of electricity is a field with great potential, and implementation of grid-connected photovoltaic systems (GCPS) is in full rise. The installation of distributed generation systems in residential environments could alter the performance of low-voltage distribution networks, since these are designed for unidirectional power flow and adding these generators means fluctuations in power-flows. For these reasons, a study of the fundamental magnitudes of three low-voltage distribution networks located in Madrid was made for various photovoltaic penetration rates, making use of simulations via the software OpenDSS and subsequent analysis of results. The research concludes that, among other aspects, GCPS produce load flow variations that are dependent on: the penetration rates; the distance from the point of interest and the distribution transformer, increasing the voltage variation between the most productive hours and the night hours with that distance; and on the rate between consumption and generation, so that when it diminishes, the self-sufficiency of the system increases, and with it the voltage of all the buses that tend to the rated voltage. Moreover, there are wide seasonal fluctuations: specifically, in summer months, generation profiles override consumption fluctuations, while in winter months consumption guides voltage and power profiles. Both the code implemented and the results of the analysis were published in an open source website using a free software license.

Accessible from the open web: a qualitative analysis of the available open-source information involving cyber security and critical infrastructure

Abstract In order to efficiently manage and operate industrial-level production, an increasing number of industrial devices and critical infrastructure (CI) are now connected to the internet, exposed to malicious hackers and cyberterrorists who aim to cause significant damage to institutions and countries. Throughout the various stages of a cyber-attack, Open-source Intelligence (OSINT) tools could gather data from various publicly available platforms, and thus help hackers identify vulnerabilities and develop malware and attack strategies against targeted CI sectors. The purpose of the current study is to explore and identify the types of OSINT data that are useful for malicious individuals intending to conduct cyber-attacks against the CI industry. Applying and searching keyword queries in four open-source surface web platforms (Google, YouTube, Reddit, and Shodan), search results published between 2015 and 2020 were reviewed and qualitatively analyzed to categorize CI information that could be useful to hackers. Over 4000 results were analyzed from the open-source websites, 250 of which were found to provide information related to hacking and/or cybersecurity of CI facilities to malicious actors. Using thematic content analysis, we identified three major types of data malicious attackers could retrieve using OSINT tools: indirect reconnaissance data, proof-of-concept codes, and educational materials. The thematic results from this study reveal an increasing amount of open-source information useful for malicious attackers against industrial devices, as well as the need for programs, training, and policies required to protect and secure industrial systems and CI.

Feature Selection for financial data – comparison

Data analysis is currently one the key for the success of good condition of the companies. Feature selection as a preprocessing of data method the estimator accuracy scores can be improved, as well the performance on very high-dimensional data set can be boosted.The paper presents an application of the filter Feature Selection method to check how the data set after the application of the selected method will look like. The goal of this article is to compare the feature selection method with the application without usage of Feature Selection about the set of prepared financial data. For this purpose, the experiments have been executed with usage of Jupyter Notebook with Python as a programming language. The data source for the data set was Stooq, which is an open source website for tracking of changes on the financial markets. For this purpose, several joint-stock companies of various sectors from Polish Stock Exchange have been selected. The Feature Selection has been applied before the data has been entered into the Artificial Neural Networks model. The results have been compared using the MSE value. The lowest MSE value has been obtained where no Feature Selection method has been applied, however MSE value for Filter Method is one order of magnitude smaller. For the experiments without Feature Selection 112 input variables have been used, for the Filter Method only 4 variables have been incorporated.

A Review of Ensemble Learning-Based Solutions for Phishing Website Detection

Phishing is the deception of a trustworthy person in an electronic connection in order to obtain confidential information from individuals or organisations usernames, passwords, and credit card numbers are just a few examples. Phishers imitate legitimate websites by creating websites that are visually and semantically identical. As technology advances, phishing techniques have become more sophisticated, necessitating the use of antiphishing measures to detect phishing attacks. To solve the phishing attacks problems. We got the data for the Phishing website from the Kaggle open source website, which is a Google Limited Liability Company-owned online community of data scientists and machine learning experts ( LLC). We are using Ensemble learning to detecting website. We are also analize accurary. We compared the results of multiple machine learning methods for predicting phishing websites

PENINGKATAN KAPABILITAS GURU DAN KARYAWAN SMAN 7 SURAKARTA DALAM MANAJEMEN KONTEN WEBSITE OPEN SOURCE

Over time, Information Technology has become more advanced and has made the public to be literate towards technology, especially educational staff who must continue to provide knowledge to their students. Blog or weblog is a term used to describe a development of a personal website that makes it easy for users to display personal information on the Internet in an easy way. However, not all employees and teachers at the school are able to manage content from the website, one of which is SMAN 7 Surakarta. This is due to the lack of counseling in managing the content of this website. From the description above, we are interested in doing community service entitled “Improving the Capabilities of Teachers and Employees of SMAN 7 Surakarta in Open Source Website Content Management.”

Covid19Risk.ai: An Open Source Repository and Online Calculator of Prediction Models for Early Diagnosis and Prognosis of Covid-19

Background: The current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice. Methods: In this pilot study, we performed a literature search in the PubMed and WHO databases to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and were published online together with supporting documentation and links to the associated articles. Results: The open source website currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. Conclusion: We have successfully demonstrated in this pilot study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

MoleculARweb: A Web Site for Chemistry and Structural Biology Education through Interactive Augmented Reality out of the Box in Commodity Devices

Augmented/virtual realities (ARs/VRs) promise to revolutionize STEM education. However, most easy-to-use tools are limited to static visualizations, which limits the approachable content, whereas more interactive and dynamic alternatives require costly hardware, preventing large-scale use and evaluation of pedagogical effects. Here, we introduce https://MoleculARweb.epfl.ch, a free, open-source web site with interactive AR webpage-based apps that work out-of-the-box in laptops, tablets, and smartphones, where students and teachers can naturally handle virtual objects to explore molecular structure, reactivity, dynamics, and interactions, covering topics from inorganic, organic, and biological chemistry. With these web apps, teachers and science communicators can develop interactive material for their lessons and hands-on activities for their students and target public, in person or online, as we exemplify. Thousands of accesses to moleculARweb attest to the ease of use; teacher feedback attests to the utility in online teaching and homework during a pandemic; and in-class plus online surveys show that users find AR engaging and useful for teaching and learning chemistry. These observations support the potential of AR in future education and show the large impact that modern web technologies have in democratizing access to digital learning tools, providing the possibility to mass-test the pedagogical effect of these technologies in STEM education.

Designing Inclusion: Using 3D Printing to Maximize Adapted Physical Education Participation

While assistive technology is often suggested as a way to increase, maintain, or improve functional ability for individuals with disabilities within physical activity (PA) settings, cost and availability of such items are often noted as barriers. In recent years, 3D printing has become available to the general public through the adoption of 3D printers in schools, libraries, and universities. Through individual design and rapid prototyping, 3D printing can support physical educators in accommodating student need for assistive technology through a multitude of modification possibilities. This article will highlight the capacity for 3D printed assistive technology within educational settings, and will illustrate how teachers, APE specialists, and other related service personnel can utilize this technology to support student success in PE and PA settings. This article will also assist practitioners with locating, uploading, and utilizing existing collections of 3D assistive technology models from open-source websites, such as Thingiverse.

A hybrid attention temporal sequential network for sleep stage classification

Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases, which has attracted extensive attention in recent years. Traditional methods for sleep stage classification, such as manual marking methods and machine learning algorithms, have the limitations of low efficiency and defective generalization. Recently, deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data. However, these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data. To solve these problems, a hybrid attention temporal sequential network model is proposed in this paper, choosing recurrent neural network to replace traditional convolutional neural network, and extracting temporal features of polysomnography from the perspective of time. Furthermore, intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation. And then, based on recurrent neural network and inter-temporal attention mechanism, this model further realized the fusion of inter-temporal contextual representation. Finally, the end-to-end automatic sleep stage classification is accomplished according to the above hybrid representation. This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website, which include a number of polysomnography. Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines. The overall accuracy of sleep stage classification could reach 0.801, 0.801 and 0.717, respectively. Meanwhile, the macro average F1-scores of the proposed model could reach 0.752, 0.728 and 0.700. All experimental results could demonstrate the effectiveness of the proposed model.

Automatic external defibrillator provided by unmanned aerial vehicle (drone) in Greater Paris: A real world-based simulation

AimTo reduce the delay in defibrillation of out-of-hospital cardiac arrest (OHCA) patients, recent publications have shown that drones equipped with an automatic external defibrillator (AED) appear to be effective in sparsely populated areas. To study the effectiveness of AED-drones in high-density urban areas, we developed an algorithm based on emergency dispatch parameters for the rate and detection speed of cardiac arrests and technical and meteorological parameters. MethodsWe ran a numerical simulation to compare the actual time required by the Basic Life Support team (BLSt) for OHCA patients in Greater Paris in 2017 to the time required by an AED-drone. Endpoints were the proportion of patients with “AED-drone first” and the defibrillation time gained. We built an open-source website (https://airborne-aed.org/) to allow modelling by modifying one or more parameters and to help other teams model their own OHCA data. ResultsOf 3014 OHCA patients, 72.2 ± 0.7% were in the “no drone flight” group, 25.8 ± 0.2% in the “AED-drone first” group, and 2.1 ± 0.2% in the “BLSt-drone first” group. When a drone flight was authorized, it arrived an average 190 s before BLSt in 93% of cases. The possibility of flying the drone during the aeronautical night improved the results of the “AED-drone first” group the most (+60%). ConclusionsIn our very high-density urban model, at most 26% of OHCA patients received an AED from an AED-drone before BLSt. The flexible parameters of our website model allows evaluation of the impact of each choice and concrete implementation of the AED-drone.

Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling

BackgroundCOVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs). MethodsThe dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics. ResultsThe accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity. ConclusionsThe proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease.

Effect of COVID-19 on the Financial Performance of Oil and Natural Gas Corporation Limited (ONGC)

This is a study which was conducted based on the secondary data of ONGC Ltd. collected from some open-source websites. Mostly researchers use different approaches to assess the financial statement of an organization/industry. Here basically the ratio analysis of ?Oil and Natural Gas Corporation Limited (ONGC)? signifies previous few years? financial performance. Companies strengths and weaknesses can be analyzed from here and more over it is going to be helpful to understand the company?s position in the market after COVID-19 pandemic where whole world was in a deep crisis. There are few ratios that are quite related to financial statements, used here in this study. ?Balance sheet? of 2015-2020 of ?ONGC Ltd.? have been used here & accordingly a comparative study is performed. Keywords: Ratio Analysis Balance Sheet Financial Analysis Companies Data COVID-19

Who Did What When? Acknowledging Collaborative Contributions in Digital History Projects

Using the example of Liberated Africans (www.liberatedafricans.org/), this paper examines the difficulties of citing, and assigning credit for, highly collaborative digital humanities research projects in the context of tenure, promotion and the job market. This digital history project traces the lives of tens of thousands of individuals the British recaptured during their efforts to suppress and abolish the slave trade leaving Africa after 1807. The creation, development, release and relaunch of new versions of open source websites and digital archives sometimes build and expand upon the historiography, archival digitization efforts, metadata development, previously published datasets, and often other digital projects. The creation, expansion and relaunch of Liberated Africans would not have been possible without the international and interdisciplinary collaboration and contributions of historians, archivists, computer programmers, digital humanities centers, graduate research assistants and granting agencies. This paper discusses the collaborative process involved in the relaunch of the second version of Liberated Africans in 2018 and serves as an example of how a timestamped project history can assess and recognize individual contributions within a major digital research project which depends on the involvement and input of an interdisciplinary network. This digital project history discusses the many contributors to the historiography, efforts to digitize primary source materials, initial website design, website redevelopment and dataset contributions. It is argued that the publishing of project histories is best practice for understanding the distinction between individual research contributions and service contributions within the burgeoning area of digital humanities research. Without opportunities to publish project histories in peer reviewed journals, digital projects might not be acknowledged as meaningful research contributions and might not receive the same recognition as traditional print publications.

Review of the London Stage Database

The London Stage Database is an open-access and open-source website that digitises the performance records contained in the print volumes of the London Stage, published in the 1960s. The database's flexible search function and intuitive interface open up new directions in research and will change the way we think about eighteenth-century theatre.

Impute.me: An Open-Source, Non-profit Tool for Using Data From Direct-to-Consumer Genetic Testing to Calculate and Interpret Polygenic Risk Scores.

To date, interpretation of genomic information has focused on single variants conferring disease risk, but most disorders of major public concern have a polygenic architecture. Polygenic risk scores (PRSs) give a single measure of disease liability by summarizing disease risk across hundreds of thousands of genetic variants. They can be calculated in any genome-wide genotype data-source, using a prediction model based on genome-wide summary statistics from external studies. As genome-wide association studies increase in power, the predictive ability for disease risk will also increase. Although PRSs are unlikely ever to be fully diagnostic, they may give valuable medical information for risk stratification, prognosis, or treatment response prediction. Public engagement is therefore becoming important on the potential use and acceptability of PRSs. However, the current public perception of genetics is that it provides “yes/no” answers about the presence/absence of a condition, or the potential for developing a condition, which in not the case for common, complex disorders with polygenic architecture. Meanwhile, unregulated third-party applications are being developed to satisfy consumer demand for information on the impact of lower-risk variants on common diseases that are highly polygenic. Often, applications report results from single-nucleotide polymorphisms (SNPs) and disregard effect size, which is highly inappropriate for common, complex disorders where everybody carries risk variants. Tools are therefore needed to communicate our understanding of genetic vulnerability as a continuous trait, where a genetic liability confers risk for disease. Impute.me is one such tool, whose focus is on education and information on common, complex disorders with polygenetic architecture. Its research-focused open-source website allows users to upload consumer genetics data to obtain PRSs, with results reported on a population-level normal distribution. Diseases can only be browsed by International Classification of Diseases, 10th Revision (ICD-10) chapter–location or alphabetically, thus prompting the user to consider genetic risk scores in a medical context of relevance to the individual. Here, we present an overview of the implementation of the impute.me site, along with analysis of typical usage patterns, which may advance public perception of genomic risk and precision medicine.

Absorbing What Is Useful: Technology, Classification Systems, and Intangible Knowledge

The following paper uses martial arts manuals to demonstrate the relationship between knowledge preservation and technology. It argues that technological constraints can influence the way information is organized, classified, and represented, thus affecting the way knowledge is translated into information. This paper explores how the technological limitations of a physical book cannot preserve the complexity of intangible knowledge in martial arts. The roles of hierarchy, transparency, standardization, and metadata are explored in the process of creating effective information-as-thing. Understanding the way technology affects the preservation of knowledge will better inform the preservation of intangible cultural knowledge in the cultural heritage field. Finally, a suggestion is made for an open-source website as an effective method for preserving intangible knowledge. 

Smart monitor of pacemaker patient by using IoT cloud in real time

People severing from heart diseases are increasing day after day, now it's more important to focus on this destructive problem and provide a good health care and remote patient monitoring .An effective monitoring system has been discovered to monitor patients who are implanting a pacemaker and need long-term follow-up. So extensive research has been done to monitor ECG and analyze it by using the free open source website and linking it with portable ECG system that are responsible for the presentation and storage of data to the doctor and patient. In this research we will develop a new method of ECG monitoring for patients who are implanting pacemakers and help them to transfer their ECG signal for long distances depending on the Internet things (IOT). Once the device is worn, it will start collecting ECG information and sent it directly via Wi-Fi to the IOT cloud. Reviewing ECG data through the web has led to the alleviation of the problem of the inability of the doctor to monitor the patient from the home as well as in the event that the doctor is being in another country ,the device also helps to discover the magnetic field that confuses the pacemaker by giving the patient a warning. The results of the test are able to detect that the system would be reliable for displaying real-time ECG data, which could help facilitate patient follow-up after implantation of the pacemaker at home with ease of use and cheaper price.

Open Sourcing the Pedagogy to Activate the Learning Process

Information systems graduates increasingly need to understand the collaborative, technology-driven practices inspired by open source software development that are fundamentally changing today's workplace. To meet this challenge, instructors must bring open source principles and technologies to active learning experiences. In this paper, the authors describe how nineteen undergraduates in a web development and design course at a Midwest university worked collaboratively with leading open-source software provider, Red Hat, to revamp the Teaching Open Source website. Accommodating this semester-length project required making significant revisions to course structure, instructional strategy, and assessments. The authors also describe the challenges of integrating these practices into the classroom and conclude with project reflections, including cautions and suggestions for instructors considering similar initiatives to move away from the “instructor as expert” paradigm to “meritocracy rule” thereby enabling students to make decisions with impacts beyond the classroom.

A do-it-yourself 3D-printed thoracic spine model for anesthesia resident simulation.

Central line placement, cricothyroidotomy, and lumbar epidural placement are common procedures for which there are simulators to help trainees learn the procedures. However, a model or a simulator for thoracic epidurals is not commonly used by anesthesia training programs to help teach the procedure. This brief technical report aims to share the design and fabrication process of a low-cost and do-it-yourself (DIY) 3D-printed thoracic spine model. Ten expert anesthesiology attendings and fifteen novice anesthesiology residents practiced with the model and were subsequently surveyed to assess their attitudes towards its fidelity and usefulness as a teaching tool. Responses were recorded with a Likert scale and found to be positive for both groups. Design files and an assembly manual were developed and made public through an open-source website.

Security Assessment Model for Database Relational Designs

The increasing number of data breaches has led many organizations to focus on securing their IT infrastructures and application architectures. However, the main causes of many of the latest attacks are not associated with these two architectures. The damage caused by most of the recent attacks could have been minimized if more attention was given to enhancing the security of all components of the database architecture. The existing enterprise database architecture frameworks do not consider this issue a priority; hence, it has received minimal attention. The enterprise database architecture is the most important architecture because it is responsible for defining how all types of data, whether security-critical or not, are stored and accessed. This paper focuses on addressing the lack of a complete solution to help enterprise system architects to address the security of their organizations from early stages. The novelty of this approach is that it specifies how to modify the required artifacts by the enterprise database architecture to address security-critical data. The approach also defines a number of security measurements that help enterprise architects in measuring the security of the organization database based on those artifacts. These metrics are developed based on the results of a cybersecurity experiment conducted on 100 randomly selected open-source websites. The paper's contributions also consist of the definition of a number of security refactoring rules that specify how to modify current enterprise databases to make them more secure. This paper uses an existing relational diagram for a health clinic database to illustrate the application of the model to an existing database. The validity and applicability of these metrics and refactoring rules are proved using an experiment conducted on a number of security-related databases.