BIBLIOMETRIC ANALYSIS OF PUBLICATIONS OF SCIENTISTS IN OPEN ACCESS JOURNALS AS A TOOL TO INCREASE THE PUBLISHING ACTIVITY OF THE UNIVERSITY

Objective. The study is aimed at increasing the publishing activity and better presentation of scientific results of Admiral Makarov National University of Shipbuilding by expanding scientific publications in open access logs. Chronological frameworks of the studied scientific articles – 2017–2021. Methods. In order to obtain relevant empirical data, the authors of the research analyzed publications of the Admiral Makarov National University of Shipbuilding scientists in open access logs using products and services of scientometric database Scopus. The algorithm of "step-by-step" actions in the Scopus database was considered in relation to: the determination of the most relevant topics; formation of the circles for the most cited publications; determining the representation level of publications made by the University scientists in the Scopus database on the topic of the study. Results. During the study, the authors showed that complete and objective evaluation in the effectiveness of scientific activity of the institution is possible only with the analysis of open resources of scientific information; the percentage of publications in the open access of Admiral Makarov National University of Shipbuilding authors-scientists is growing: in five years, the number of scientific articles of open access has increased twice; the most popular publication model is GoldAPC; there are the most cited publications in foreign editions that relate to the first and second quartile. Conclusions. The authors firstly conducted a bibliometric study of scientists’ publications in open access journals as a tool for raising the publishing activity of the university on the basis of products and services of the Scopus scientometric database. The research materials can be used to further increase of publishing activity both for individual scientists and institutions as a whole.

An IoT Crossdomain Access Decision-Making Method Based on Federated Learning

Crossdomain collaboration allows smart devices work together in different Internet of Things (IoT) domains. Trusted third party-based solutions require to fully understand the access information of the collaboration participants to implement crossdomain access control, which brings privacy risk. In this paper, we propose a federated learning-based crossdomain access decision-making method (FCAD), which builds a crossdomain access decision-making model without sharing privacy information of collaboration participants. Crossdomain access logs are extracted to construct a training dataset. Data enhancement method is used to address the uneven distribution of the dataset. Federated learning and gradient aggregation methods are used to prevent privacy leaks. The experiments on the public dataset show that FCAD obtains a prediction accuracy of 83.6% in the existing crossdomain access system.

Analisis Kesiapan Penerapan Process Mining pada Sistem Manajemen Pembelajaran Universitas Telkom

<p>Sistem manajemen pembelajaran (<em>Learning Management System/ LMS</em>) berbasis komputer telah banyak digunakan untuk mengelola pembelajaran dalam institusi pendidikan, termasuk universitas. LMS merekam dan mengelola akses pengguna secara otomatis dalam bentuk <em>event log</em>. Data dalam <em>event log</em> tersebut dapat dianalisis untuk mengenali pola penggunaan LMS sebagai pertimbangan pengembangan LMS. Salah satu metode yang dapat diadopsi adalah <em>process mining</em>, yaitu menganalisis data <em>event log</em> berbasis proses. Analisis data berbasis proses ini bertujuan untuk memodelkan proses yang terjadi dan terekam dalam LMS, mengecek kesesuaian pelaksanaan proses dengan prosedur, dan mengusulkan pengembangan proses di masa mendatang. Makalah ini mengeksplorasi kesiapan data penggunaan LMS di Universitas Telkom sebagai subjek penelitian untuk dianalisis dengan pendekatan <em>process mining</em>. Sepanjang pengetahuan kami, belum ada penelitian sebelumnya yang melakukan analisis data berbasis proses pada LMS ini. Kontribusi penelitian ini adalah eksplorasi peluang untuk menganalisis proses pembelajaran dan pengembangan metode pembelajaran berbasis LMS. Analisis kesiapan LMS dilakukan berdasarkan daftar pengecekan komponen yang dibutuhkan dalam <em>process mining</em>. Makalah ini mengikuti tahap-tahap utama dalam <em>Process Mining Process Methodology</em> (PM²). Studi kasus yang dieksplorasi adalah proses pembelajaran pada satu mata kuliah dalam satu semester berdasarkan <em>event log </em>yang diekstrak dari LMS. Hasil penelitian ini menunjukkan bahwa analisis data dalam LMS ini dapat digunakan untuk menganalisis performansi pembelajaran di Universitas Telkom dari kelompok pengguna yang berbeda-beda dan dapat dikembangkan untuk menganalisis data pada studi kasus yang lebih besar. Studi kelayakan ini diakhiri dengan diskusi tentang kelayakan LMS untuk dianalisis dengan <em>process mining</em>, evaluasi oleh tim ahli LMS, dan usulan pengembangan LMS di masa mendatang. <em></em></p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><em>Computer-based Learning Management Systems (LMS) are commonly used in educational institutions, including universities. An LMS records and manages user access logs in an event log. Data in an event log can be analysed to understand patterns in the LMS usage to support recommendations for improvements. One promising method is process mining, which is a process-based data analytics working on event logs. Process mining aims to discover process models as recorded in the LMS, conformance checking of process execution to the defined procedure, and suggest improvements. This paper explores the feasibility of Telkom University LMS usage data to be analysed using process mining. To the best of our knowledge, there was no previous research doing process-based data analytics on this LMS. This paper contributes to explore opportunities to analyse learning processes and enhance LMS-based learning methods. The feasibility study is based on a data component checklist for process mining. This paper is written following the main stages on the Process Mining Project Methodology (PM2). We explore a case study of the learning process of a course in a semester, based on an event log extracted from the LMS. The results show that data analytics on this LMS can be used to analyse learning process performance in Telkom University, based on different user roles. This feasibility study is concluded with a discussion on the feasibility of the LMS to be analysed using process mining, an evaluation by the representative of the LMS expert team, and a recommendation for improvements.</em></em></p>

Predicting next-day discharge via electronic health record access logs

Objective Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users’ granular interactions with patients’ records by communicating various semantics and has been neglected in outcome predictions. Materials and Methods This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. Results The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919–0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860–0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. Conclusion EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

Exploring the COVID-19 Pandemic as a Catalyst for PHR Application User Behavior Change in Taiwan: A Development and Usability Study (Preprint)

BACKGROUND During the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan’s National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile applications with which insured individuals can retrieve their MHB data. However, the NHIA MHB service has its limitations, and the participation rate among insured individuals is low. OBJECTIVE We aimed to integrate the MHB SDK with our developed blockchain-enabled PHR mobile app, which enables patients to access, store, and manage their cross-hospital PHR data. We also collected and analyzed the application’s log data to examine patients’ MHB use during the COVID-19 pandemic. METHODS We integrated our existing blockchain-enabled mobile application with the MHB SDK to enable NHIA MHB data retrieval. The application utilizes blockchain technology to encrypt the downloaded NHIA MHB data. Existing and new indexes can be synchronized between the application and blockchain nodes, and high security can be achieved for PHR management. Finally, we analyzed the application’s access logs to compare patients’ activities during high and low COVID-19 infection periods. RESULTS We successfully integrated the MHB SDK into our application, thereby enabling patients to retrieve their cross-hospital medical data, particularly those related to COVID-19 rapid and polymerase chain reaction testing and vaccination information and progress. We retrospectively collected the application’s log data for the period July 2019 to June 2021. From January 2020, the preliminary results revealed a steady increase in the number of people applying to create a blockchain account for access to their medical data and the number of application subscribers among patients visiting the outpatient department (OPD) and emergency department (ED). Notably, for patients visiting the OPD and ED, the peak proportions with respect to the use of the application for OPD and ED notes and laboratory test results also increased year by year. The highest proportions were 52.40% for ED notes in June 2021, 88.10% for ED laboratory test results in May 2021, 34.61% for OPD notes in June 2021, and 41.87% for OPD laboratory test results in June 2021. These peaks coincided with Taiwan’s local COVID-19 outbreak lasting from May to June 2021. CONCLUSIONS This study developed a blockchain-enabled application, which can periodically retrieve and integrate PHRs from the NHIA MHB's cross-hospital data and the investigated hospital's self-pay medical data. Analysis of users’ access logs revealed that the COVID-19 pandemic substantially increased individuals’ use of PHRs and their health awareness with respect to COVID-19 prevention.

Italian Tracing System for Water Buffalo Milk and Processed Milk Products

This document describes the development of a tracing system for the buffalo supply chain, namely an online computer system in which farmers, dairies, and brokers must maintain records of the production of milk through to the production of derivatives. The system is jointly used throughout the Italian national territory by the Istituto Zooprofilattico Sperimentale del Mezzogiorno (IZSM) and the Sistema Informativo Agricolo Nazionale Italiano (SIAN), after being made mandatory and regulated with the publication of the Ministerial Decree of 9 September 2014. Farmers are obligated to communicate their daily production of bulk milk, the number of animals milked, the number of the delivery note of the sale, and the name of the purchaser; within the first week of the month, they must communicate the milk production of each animal milked. Dairies are required to communicate the milk and the processed product (mozzarella, yogurt, etc.) purchased on a daily basis. The intermediaries are required to communicate the daily milk purchased, both fresh and frozen, the semi-finished product, and the sale of the same. The tracing system linked to the project authorized by the Ministry of Health, called “Development, validation and verification of the applicability of an IT system to be used for the management of traceability in the buffalo industry”, provides operators with the monitoring of production and sales in real time through alerts and access logs. Currently, there are 1531 registered farmers, 601 non-PDO dairies, 102 PDO dairies, 68 non-PDO intermediaries, and 17 PDO intermediaries in Italy. The system provides support for the recovery of the buffalo sector; from the analysis of the data extrapolated from the tracing system of the buffalo supply chain for the years 2016 to 2019, this paper highlights that the application of the Ministerial Decree No. 9406 of 9 September 2014 and the tracing of the supply chain have increased the price of buffalo milk at barns from EUR 1.37/kg to EUR 1.55/kg from 2016 to 2019.