bluuebunny/crossref_metadata_2025 huggingface dataset:
CMS Wordpress Based Academic Information System Design at SMPN 17 Marusu
Authors: Nahlah Nahlah , Ruswandi Djalal Muhammad
Published: May 2022

School is one of the formal education facilities that must be able to provide the best services or facilities for students and also for parents. One of the right ways is to use information technology, namely accessing academic values through websites. The specific objective of this research is to produce an academic information system based on Wordpress Content Management System (CMS) that can be used by SMPN 17 Marusu in Maros Regency. The current website-based academic information system is made using a complex programming language and requires special skills to create the website. Wordpress is a platform for creating websites instantly. In this study, the information system was built using the prototype method so that it can be adapted to user needs. The resulting software is presented to the user, starting from the requirements analysis, design to implementation stage, and users are given the opportunity to provide input so that the resulting software really fits the user's needs. The prototype method consists of several stages, namely, requirements analysis, system analysis, system design, system testing, and system implementation. The design stage includes interface design, front end and back end using WordPress CMS. With this school website, it is hoped that it can help government programs in improving school performance and productivity.
Accelerating High-Dimensional Temporal Modelling Using Graphics Processing Units for Pharmacovigilance Signal Detection on Real-Life Data
Authors: Sabatier Pierre , Feydy Jean , Jannot Anne-Sophie
Published: May 2022

Adverse drug reaction is a major public health issue. The increasing availability of medico-administrative databases offers major opportunities to detect real-life pharmacovigilance signals. We have recently adapted a pharmacoepidemiological method to the large dimension, the WCE (Weigthed Cumulative Exposure) statistical model, which makes it possible to model the temporal relationship between the prescription of a drug and the appearance of a side effect without any a priori hypothesis. Unfortunately, this method faces a computational time problem. The objective of this paper is to describe the implementation of the WCE statistical model using Graphics Processing Unit (GPU) programming as a tool to obtain the spectrum of adverse drug reactions from medico-administrative databases. The process is divided into three steps: pre-processing of care pathways using the Python library Panda, calculation of temporal co-variables using the Python library “KeOps”, estimation of the model parameters using the Python library “PyTorch” – standard in deep learning. Programming the WCE method by distributing the heaviest portions (notably spline calculation) on the GPU makes it possible to accelerate the time required for this method by 1000 times using a computer graphics card and up to 10,000 times with a GPU server. This implementation makes it possible to use WCE on all the drugs on the market to study their spectrum of adverse effects, to highlight new vigilance signals and thus to have a global vigilance tool on medico-administrative database. This is a proof of concept for the use of this technology in epidemiology.
Phenotyping of Heart Failure with Preserved Ejection Faction Using Health Electronic Records and Echocardiography
Authors: Pierre-Jean Morgane , Donal Erwan , Cuggia Marc , Bouzillé Guillaume
Published: May 2022

Patients suffering from heart failure (HF) symptoms and a normal left ventricular ejection fraction (LVEF 50%) present very different clinical phenotypes that could influence their survival. This study aims to identify phenotypes of this type of HF by using the medical information database from Rennes University Hospital Center. We present a preliminary work, where we explore the use of clinical variables from health electronic records (HER) in addition to echocardiography to identify several phenotypes of patients suffering from heart failure with preserved ejection fraction. The proposed methodology identifies 4 clusters with various characteristics (both clinical and echocardiographic) that are linked to survival (death, surgery, hospitalization). In the future, this work could be deployed as a tool for the physician to assess risks and contribute to support better care for patients.
Meaningful Activity Replacement Recommendations in Dementia
Authors: Turner Jonathan , Wilson Michael , Nugent Ciarán , Berry Damon , Doyle Julie , O’Sullivan Dympna
Published: May 2022

Exercise of meaningful activities is important for people living with dementia, both for quality of life and to maintain the necessary basic activities of daily living. A method is proposed for recommendation of replacements for lost meaningful activities that accounts for the need to maintain activities of daily living.
Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database
Authors: Fujarski Michael , Porschen Christian , Plagwitz Lucas , Brenner Alexander , Ghoreishi Narges , Thoral Patrick , de Grooth Harm-Jan , Elbers Paul , Weiss Raphael , Meersch Melanie , Zarbock Alexander , von Groote Thilo Caspar , Varghese Julian
Published: May 2022

Acute kidney injury (AKI) is a common complication in critically ill patients and is associated with long-term complications and an increased mortality. This work presents preliminary findings from the first freely available European intensive care database released by Amsterdam UMC. A machine learning (ML) model was developed to predict AKI in the intensive care unit 12 hours before the actual event. Main features of the model included medications and hemodynamic parameters. Our models perform with an accuracy of 81.8% on moderate to severe AKI and 79.8% on all AKI patients. Those results can compete with models reported in the literature and introduce an ML model for AKI based on European patient data.
Usability Assessment of Conversational Agents in Healthcare: A Literature Review
Authors: Denecke Kerstin , May Richard
Published: May 2022

Conversational agents (CA) are chatbot-based systems supporting the interaction with users through text, speech, or other modalities. They are used in an increasing number of medical use cases. Even though usability is considered a prerequisite for the success of mHealth apps using CA, there is still no standard procedure to study usability of health CA. In this paper, we report the results from a systematic literature review aiming at identifying study designs, tools, and metrics used to assess usability in health CA. We searched three bibliographic databases (PubMed, Scopus, IEEE Xplore) for papers reporting on CA in healthcare to extract information on the usability assessment of those CA. From 273 retrieved results, we included 66 papers for full text review. 34 of them reported on usability assessments. A broad range of tools is used (e.g. SUS, UEQ), but also individual questionnaires are exploited. The examined studies use scenario-based setups but assess also real-world usage. Exploratory setups are rarely reported. Due to the differences in the study designs and assessment tools, it is impossible to compare usability among CA. Thus, we recommend to develop a standardised procedure that can be always applied and which can be enriched by assessments needed for evaluating usability of CA-specific features.
Introducing Lightweight IT – A Way to Build Flexibility for Healthcare Organizations?
Authors: Severinsen Gro-Hilde , Silsand Line , Malm Nicolaisen Kristian , Pedersen Rune , Ellingsen Gunnar
Published: May 2022

The paper addresses socio technical relations of implementing a lightweight IT app in Norway. The focus is on how such technology will influence the possibilities for an organization connected to a large-scale heavyweight IT infrastructure to provide more rapid changes in line with healthcare worker’s needs. The research question is therefore: How can lightweight IT support rapid organizational changes? The empirical site is one of the first health trusts in Norway implementing lightweight technology integrated to their Electronic Health Record (EHR). The lightweight technology is a nursing app for registering early- warning score (NEWS) bedside the patients. The paper is based on a qualitative interpretive approach, and the results are discussed in line with information infrastructure theory.
Building an i2b2-Based Population Repository for COVID-19 Research
Authors: Pedrera-Jimenez Miguel , Garcia-Barrio Noelia , Hernandez-Ibarburu Gema , Baselga Blanca , Blanco Alvar , Calvo-Boyero Fernando , Gutierrez-Sacristan Alba , Quiros Víctor , Cruz-Bermudez Juan Luis , Bernal José Luis , Meloni Laura , Perez-Rey David , Palchuk Matvey , Kohane Isaac , Serrano Pablo
Published: May 2022

Reuse of Electronic Health Records (EHRs) for specific diseases such as COVID-19 requires data to be recorded and persisted according to international standards. Since the beginning of the COVID-19 pandemic, Hospital Universitario 12 de Octubre (H12O) evolved its EHRs: it identified, modeled and standardized the concepts related to this new disease in an agile, flexible and staged way. Thus, data from more than 200,000 COVID-19 cases were extracted, transformed, and loaded into an i2b2 repository. This effort allowed H12O to share data with worldwide networks such as the TriNetX platform and the 4CE Consortium.
Deep SNOMED CT Enabled Large Clinical Database About COVID-19
Authors: Gaudet-Blavignac Christophe , Ehrsam Julien , Turbe Hugues , Keszthelyi Daniel , Zaghir Jamil , Lovis Christian
Published: May 2022

In spring 2020, as the COVID-19 pandemic is in its first wave in Europe, the University hospitals of Geneva (HUG) is tasked to take care of all Covid inpatients of the Geneva canton. It is a crisis with very little tools to support decision-taking authorities, and very little is known about the Covid disease. The need to know more, and fast, highlighted numerous challenges in the whole data pipeline processes. This paper describes the decisions taken and processes developed to build a unified database to support several secondary usages of clinical data, including governance and research. HUG had to answer to 5 major waves of COVID-19 patients since the beginning of 2020. In this context, a database for COVID-19 related data has been created to support the governance of the hospital in their answer to this crisis. The principles about this database were a) a clearly defined cohort; b) a clearly defined dataset and c) a clearly defined semantics. This approach resulted in more than 28 000 variables encoded in SNOMED CT and 1 540 human readable labels. It covers more than 216 000 patients and 590 000 inpatient stays. This database is used daily since the beginning of the pandemic to feed the “Predict” dashboards of HUG and prediction reports as well as several research projects.
Proposal of Semantic Annotation for German Metadata Using Bidirectional Recurrent Neural Networks
Authors: Ulrich Hannes , Uzunova Hristina , Handels Heinz , Ingenerf Josef
Published: May 2022

The distributed nature of our digital healthcare and the rapid emergence of new data sources prevents a compelling overview and the joint use of new data. Data integration, e.g., with metadata and semantic annotations, is expected to overcome this challenge. In this paper, we present an approach to predict UMLS codes to given German metadata using recurrent neural networks. The augmentation of the training dataset using the Medical Subject Headings (MeSH), particularly the German translations, also improved the model accuracy. The model demonstrates robust performance with 75% accuracy and aims to show that increasingly sophisticated machine learning tools can already play a significant role in data integration.
Aggregations of Substance in Virtual Drug Models Based on ISO/CEN Standards for Identification of Medicinal Products (IDMP)
Authors: Vander Stichele Robert , Kalra Dipak
Published: May 2022

In this study representation of chemical substances in IDMP is reviewed, with an exploration of aggregation levels for substance used in the virtual drug data models of RxNorm, SNOMED-CT, ATC/INN, and the Belgian SAM database, for products with a single substance and combinations of substances. Active moiety and available solid states forms are explored for diclofenac, amoxicillin, carbamazepine, amlodipine, with regard to their representation in coding systems such as WHODrug, SMS, UNII, CAS, and SNOMED-CT. By counting the number of medicinal products in Belgium for amlodipine in each level of aggregation, concepts for grouper of substances and two levels of grouper of medicinal products are illustrated. Recommendations are made for the further development of IDMP and its link to international drug classifications.
Evaluation and Challenges of Medical Procedure Data Harmonization to SNOMED-CT for Observational Research
Authors: Reinecke Ines , Kallfelz Michael , Sedlmayr Martin , Siebel Joscha , Bathelt Franziska
Published: May 2022

The relevance of health data research on real world data (RWD) is increasing. To prepare national RWD for international research, harmonization with standard terminologies is required. In this paper, we evaluate to what extent the German OPS vocabulary in OHDSI covers codes present in RWD and mappings to SNOMED-CT. The evaluation identified a mapping gap of 21.1% in the RWD set.
The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes
Authors: Frexia Francesca , Mascia Cecilia , Wittner Rudolf , Plass Markus , Müller Heimo , Geiger Jörg , Holub Petr
Published: May 2022

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.
Support-Vector Machine-Based Classifier of Cross-Correlated Phoneme Segments for Speech Sound Disorder Screening
Authors: Mahmut Emilian-Erman , Nicola Stelian , Stoicu-Tivadar Vasile
Published: May 2022

This paper presents a Support-Vector Machine (SVM) based method of classification of cross-correlated phoneme segments as part of the development of an automated Speech Sound Disorder (SSD) Screening tool. The pre-processing stage of the algorithm uses cross-correlation to segment the target phoneme and extracts data from the new homogeneously trimmed audio samples. Such data is then fed into the SVM-based classification script which currently achieves an accuracy of 97.5% on a dataset of 132 rows. Given the global context of an increasing trend in the incidence of Speech Sound Disorders (SSDs) amongst early-school aged children (5–6 years old), the constraints imposed by the new Corona virus pandemic, and the (consequent) shortage of professionally trained specialists, an automated screening tool would be of much assistance to Speech-Language Pathologists (SLPs).
Patient Data Sharing and Reduction of Overtime Work of Nurses by Innovation of Nursing Records Using Structured Clinical Knowledge
Authors: Tsuru Satoko , Tamamoto Tetsuro , Nakao Akihiro , Tanizaki Kouichi , Yahagi Naohisa
Published: May 2022

Half of nurses’ overtime hours are due to records. Nursing records, which are mainly narrative records, cost a large amount of money. However, it has been pointed out that there are problems with their quality and post-use. In this study, we analyzed the value of nursing records for physicians. As a result, we found that the use of standard observation terms in nursing records can create an environment in which patients’ conditions can be shared. To create this environment, the physicians of the clinical path committee classified hospitalized patients in terms of disease, treatment, and examination, and created a list of 778 process paths. Physicians, nurses, and researchers collaborated to develop digital contents with high-priority observation items and care actions adapted to patient conditions for each path. We developed a clinical support system equipped with these digital contents. In May 2019, we installed the system in a 900-bed university hospital. Then, in October 2020, we installed the system in a 400-bed general hospital. We used “nurses’ overtime hours for recording” and “reduction rate” as indicators of the usefulness of this system. In the 900-bed university hospital, we compared the previous year’s results for March, the end of the fiscal year. This overtime hours were 2,944 hours 00 minutes in March 2019 and 2,141 hours 55 minutes in March 2020. 27% reduction was indicated. The respective bed occupancy rates were 90.80 percent and 90.60 percent, with no difference. In the 400-bed general hospital, This overtime hours were compared to the previous year, covering November and December after one month of implementation. 386 hours in November 2019 and 204.5 hours in November 2020. 47% reduction indicated. 366 hours in December 2019 and 214.5 hours in December 2020. A reduction of 41% was shown. These results suggest that the implementation of this system can both improve the quality of team care and reduce overtime.
The Use of Machine Learning Techniques to Predict Diabetes in Patients with Cystic Fibrosis
Authors: Bogovič Tajda , Kokol Peter , Završnik Jernej , Blažun Vošner Helena
Published: May 2022

The accuracy of the prognosis of diabetes in patients with cystic fibrosis is crucial, as it highly connected with mortality and other complications. The prognosis of diabetes is a time-consuming process. Usually, it is performed by medical staff and can often lead to misdiagnosis. The aim of the study was to analyze and evaluate risk factors of developing diabetes in patients diagnosed with Cystic Fibrosis by using classification machine learning techniques. The ECFS data register was used to train and test the models. Visualization of our results using SHAP values highlights that most important features are age, antibiotic treatment, FEV1 value and lung transplant as risk predictors for diabetes.
DeepUnity Capture: An Application for Digital Photo Documentation
Authors: Weishaupt Johanna
Published: May 2022

The research on which this poster is based deals with the requirements engineering of an application for medical photo documentation. Against this backdrop, the poster sets out the underlying concepts of medical informatics, the process of medical photo documentation and the standardized procedure of requirements engineering. Using these standards and methodologies, requirements for a mobile photo documentation solution have been elicited, prepared, documented and modeled. As a result of this work, a standardized specification according to ISO/IEC/IEEE 29148, a demonstration model as well as a functional prototype of the application to be designed have been established. From this prototype, an application was developed that is now in routine clinical use and is constantly being refined.
Influenza Screening Using Patient-Generated Health Data in Post COVID-19 Pandemic
Authors: Choo Hyunwoo , Kim Myeongchan , Lee Dohyung , Shin Soo-Yong
Published: May 2022

It is very important to ensure reliable performance of deep learning model for future dataset for healthcare. This is more pronounced in the case of patient generated health data such as patient reported symptoms, which are not collected in a controlled environment. Since there has been a big difference in influenza incidence since the COVID-19 pandemic, we evaluated whether the deep learning model can maintain sufficiently robust performance against these changes. We have collected 226,655 episodes from 110,893 users since June 2020 and tested the influenza screening model, our model showed 87.02% sensitivity and 0.8670 of AUROC. The results of COVID-19 pandemic are comparable to that of before COVID-19 pandemic.
Discovery of COVID-19 Symptomatic Experience Reported by Twitter Users
Authors: Jiang Keyuan , Zhu Minghao , Bernard Gordon R.
Published: May 2022

Since the beginning of the COVID-19 pandemic, patients shared their personal experiences of the viral infection on social media. Gathering their symptomatic experiences reported on Twitter may help better understand the infectious disease and supplement our knowledge of the disease gathered by healthcare workers. In this study, we identified personal experience tweets related to COVID-19 infection using a pre-trained and fine-tuned language model, and annotated the machine-identified tweets in order to extract the information of infection status, symptom concepts, and the days the symptomatic experience occurred. Our result shows that the top 10 most common symptoms mentioned in the collected Twitter data are in line with those published by WHO and CDC. The symptoms along with the day information appear to provide additional insight on how the infection progresses in infected individuals.
The Tiel Shift Towards Person Centered Care
Authors: Hofdijk Jacob , Cillessen Felix , Asin Javier
Published: May 2022

The health system in the Netherlands is one of the best in the world and it is a challenge to keep this affordable and accessible for everyone. A shift from care to lifelong maintenance of health is highly needed, but the drivers of change are missing. Obviously, the involvement of citizens is essential, but to introduce the change the indispensable incentives must be identified. The believe in integrated network care is growing, but it requires a fresh view on the integration of prevention and care, the involvement of the “old players” to create a new governance model, the supporting IT and adequate funding arrangements. In the Tiel region the implementation of the INCA model for integrated care in the primary care setting might make the difference for this journey.
Towards a Computational Approach for the Assessment of Compliance of ALCOA+ Principles in Pharma Industry
Authors: Durá Marta , Sánchez-García Ángel , Sáez Carlos , Leal Fátima , Chis Adriana E. , González-Vélez Horacio , García-Gómez Juan M.
Published: May 2022

The pharmaceutical industry is a data-intensive environment and a heavily-regulated sector, where exhaustive audits and inspections are performed to ensure the safety of drugs. In this context, processing and evaluating the data generated in the manufacturing lines is a relevant challenge since it requires compliance with pharma regulations. This work combines data integrity metrics and blockchain technology to evaluate the compliance-degree of ALCOA+ principles among different levels of drug manufacturing data. We propose the DIALCOA tool, a software to assess the compliance-degree for each ALCOA+ principle, based on the assessment of data from manufacturing batch reports and its different levels of information.
Using Robots in Medical Informatics Education
Authors: Bürkle Thomas , Bender Matthias , Glauser Robin
Published: May 2022

Although robots have been used for quite some time in education on school and university level, we found no reports of robots being used in the teaching of medical informatics. Thus we present the timetable and structure of a one week, 2 ECTS blocked course for robots in medical informatics initiated in autumn 2022. 19 participants completed the first iteration. We report about the requirements in terms of an appropriate programming environment, the combination among robots and our medical informatics lab and the results of the student’s evaluation of the first instance as well as the experiences with the two types of robots used.
Software Architecture for Automated Assessment of Prescription Writing
Authors: Khatami Alireza , Holbrook Anne , Levinson Anthony J. , Keshavjee Karim
Published: May 2022

Prescribing skills are a crucial competency in medical practice considering the increasing numbers of medications available and the increasingly complex patients with multiple diseases faced in clinical practice. Medical students need to become proficient in these skills during training, as required by medical licensing colleges. Not only is teaching the fundamentals of safe and cost-effective prescribing to medical students challenging but evaluating their prescribing skills by faculty members is difficult and time consuming. The COVID-19 pandemic has accelerated the interest in clinically relevant online exams, including automated assessment of short answer style questions. The goal of this project was to design a software to automate the assessment of learners’ prescriptions written during low stakes formative assessments. After establishing the components of a legal prescription with multiple medications, and identifying the sources of errors in prescribing and prescribing assessment, we designed and validated an architecture and developed a prototype for automated parsing of learner prescriptions.
“ai4health” – Development and Conception of a Learning Programme in Higher and Continuing Education on the Fundamentals, Applications and Perspectives of AI in Healthcare
Authors: Kalthoff Daniel , Prien Marieke , Götz Nina-Alexandra
Published: May 2022

AI applications play an increasingly important role in all areas of healthcare. Therefore, a basic understanding of AI technology for health professionals seems necessary. However, to date there is no learning programme in Germany that includes technological basics, applications, and perspectives of AI in healthcare for interdisciplinary health professions. The ai4health project investigates which basic knowledge and competences health professionals need to acquire for an informed handling of AI applications in healthcare, and what the appropriate didactic approach is. Through the qualitative research by interviews and a workshop, six relevant areas of competences were identified. The two most important areas are ELSA and relevant AI applications. Explainability was also highlighted as an important point. The implementation of the topics in a blended learning course for interdisciplinary health professionals and educators in the healthcare sector is now planned.
Discovering Key Topics in Emergency Medical Dispatch from Free Text Dispatcher Observations
Authors: Ferri Pablo , Sáez Carlos , Félix-De Castro Antonio , Sánchez-Cuesta Purificación , García-Gómez Juan M.
Published: May 2022

The objective of this work was to discover key topics latent in free text dispatcher observations registered during emergency medical calls. We used a total of 1374931 independent retrospective cases from the Valencian emergency medical dispatch service in Spain, from 2014 to 2019. Text fields were preprocessed to reduce vocabulary size and filter noise, removing accent and punctuation marks, along with uninformative and infrequent words. Key topics were inferred from the multinomial probabilities over words conditioned on each topic from a Latent Dirichlet Allocation model, trained following an online mini-batch variational approach. The optimal number of topics was set analyzing the values of a topic coherence measure, based on the normalized pointwise mutual information, across multiple validation K-folds. Our results support the presence of 15 key topics latent in free text dispatcher observations, related with: ambulance request; chest pain and heart attack; respiratory distress; head falls and blows; fever, chills, vomiting and diarrhea; heart failure; syncope; limb injuries; public service body request; thoracic and abdominal pain; stroke and blood pressure abnormalities; pill intake; diabetes; bleeding; consciousness. The discovery of these topics implies the automatic characterization of a huge volume of complex unstructured data containing relevant information linked to emergency medical call incidents. Hence, results from this work could lead to the update of structured emergency triage algorithms to directly include this latent information in the triage process, resulting in a positive impact in patient wellbeing and health services sustainability.
Deep Learning-Based Brain Hemorrhage Detection in CT Reports
Authors: Bayrak Gıyaseddin , Toprak Muhammed Şakir , Ganiz Murat Can , Kodaz Halife , Koç Ural
Published: May 2022

Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance.
Performance of Machine Learning Methods to Classify French Medical Publications
Authors: Zaghir Jamil , Goldman Jean-Philippe , Bjelogrlic Mina , Keszthelyi Daniel , Gaudet-Blavignac Christophe , Turbé Hugues , Lokaj Belinda , Lovis Christian
Published: May 2022

Many medical narratives are read by care professionals in their preferred language. These documents can be produced by organizations, authorities or national publishers. However, they are often hardly findable using the usual query engines based on English such as PubMed. This work explores the possibility to automatically categorize medical documents in French following an automatic Natural Language Processing pipeline. The pipeline is used to compare the performance of 6 different machine learning and deep neural network approaches on a large dataset of peer-reviewed weekly published Swiss medical journal in French covering major topics in medicine over the last 15 years. An accuracy of 96% was achieved for 5-topic classification and 81% for 20-topic classification.
Changes in Patient Characteristics in Telehealth Usage During Times of Crisis
Authors: Khairat Saif , Zebrowski Ashlyn , Kwong Elizabeth , Edson Barbara , Gianforcaro Robert
Published: May 2022

There is limited knowledge on whether increased telehealth usage may enhance health access to communities during natural disasters, particularly for emergency medical services. This study aimed to elucidate telehealth usage during three hurricanes in NC between 2018 and 2020 and assessed demographics of users including gender and age, insurance status, and daily rate of visits in relation to respective hurricanes. From 10,056 telehealth visits, we found that age and insurance coverage were significantly different between crisis and non-crisis times. Patients found comparative satisfaction during both times. This study suggests the use of phone and video visits to enable better access to parents with children under the age of 18 years and uninsured patients.
Характеристика окремих видів контролю за діяльністю закладів вищої освіти України
Authors: Avdieiev О. О.
Published: May 2022

Досліджено та узагальнено види контролю за діяльністю закладів вищої освіти України. Розмежовано поняття «вид контролю» та «форма контролю». Проаналізовано наявну практику поділу контролю на певні види. Запропоновано власну класифікацію видів контролю за діяльністю закладів вищої освіти за різноманітними критеріями та наголошено на необхідності доповнити Закон України «Про вищу освіту» окремим розділом із назвою «Контроль за діяльністю закладів вищої освіти України», в якому слід визначити його поняття, принципи, загальні вимоги до його здійснення, види і форми, систему суб’єктів, які його здійснюють, та їх повноваження.
Щодо проблемних питань притягнення особи до адміністративної відповідальності: організаційно-правові засади
Authors: Rezanov S. A.
Published: May 2022

Наголошено, що ефективна протидія та запобігання різним видам адміністративних правопорушень є вагомим напрямом правової політики будь-якої сучасної держави. Визначено, що держава гарантує непорушність прав і свобод людини та громадянина. Для їх забезпечення на уповноважені органи, зокрема Національну поліцію, покладається обов’язок підтримання правопорядку і захисту прав, свобод та законних інтересів. Аргументовано, що Національна поліція посідає головне місце в системі органів, які здійснюють протидію та запобігання адміністративним правопорушенням. Акцентовано, що окремих змін потребує нормативне закріплення провадження у справах про адміністративні правопорушення, яке здійснюється органами поліції, зокрема це стосується порядку встановлення особи правопорушника, доказування його вини, фіксації та надання доказів і здійснення адміністративного затримання.
Процесуальні аспекти проведення огляду як слідчої (розшукової) дії: окремі дискусійні питання
Authors: Koniushenko Y. Yu.
Published: May 2022

Статтю присвячено окремим дискусійним питанням проведення огляду як слідчої (розшукової) дії. Звернено увагу на відсутність законодавчого тлумачення поняття «огляд», наведено доктринальні його визначення та надано авторське бачення щодо праворозуміння цього поняття. Під час визначення видів огляду підтримано позицію науковців, що освідування є окремою слідчою (розшуковою) дією, хоча має багато спільного з оглядом. Окрему увагу приділено огляду місця події як невідкладній слідчій (розшуковій) дії, в ході чого визначено, що оперативні працівники не мають права його проводити. З урахуванням зарубіжного досвіду висловлено думку про необхідність відмови від участі понятих під час проведення слідчих (розшукових) дій.
Cumpliendo sueños
Authors: Ruiz Enríquez Hugo Milton
Published: May 2022

Este libro reseña la historia de la fundación de la UPEC, desde que el proyecto de creación fue presentado al Congreso Nacional en mayo del 2003. El libro, además, hace un recorrido detallado por toda la estructura organizacional de la universidad, indicando con detalle su proceso evolutivo y estado funcional.
Interpretation of Palaeozoic geoforms with the use of seismic attributes in a region of the Eastern Plains, Colombia
Authors: Esquivel Laura Carolina , Villamizar Fanny , Molina Indira
Published: May 2022

This study focused on producing maps of Palaeozoic geoforms in the subsoil of a region of the Eastern  Planes basin, Colombia. The results provide novel Palaeozoic  information for the study area suggesting a  possible shallow marine depositional environment in the  Ordovician and a slightly deeper shallow marine environment in the Cambrian. This information was  obtained from the analysis of both seism stratigraphic  sequences and gamma ray well logs and from the seismic  interpretation of possible geological structures at depth,  using a seismic cube, four 2D seismic lines and four wells.  The results were complemented with the application of the  seismic attributes of coherence, variance, RMS amplitude  and spectral decomposition to highlight geological characteristics such as structures and lithology. The  information was integrated and analyzed to generate maps  of geoforms corresponding to the Middle Ordovician,  Lower Ordovician and Cambrian sequences.
ДЖЕРЕЛОЗНАВЧИЙ АНАЛІЗ ПРОБЛЕМАТИКИ ГІБРИДНИХ ВІЙН В СУЧАСНОМУ ДИСКУРСНОМУ ПРОСТОРІ УКРАЇНИ
Authors: Радченко Олександр Віталійович , Радченко Оксана Олександрівна
Published: May 2022

Стаття пропонує авторську систематизацію наукових та експертних поглядів і підходів до проблематики гібридних війн та їх впливу на забезпечення інформаційного суверенітету держави в сучасному дискурсному просторі. Обґрунтовано нагальність дослідження інформаційних загроз, негативних зовнішніх впливів на стан національної безпеки України, що особливо актуалізувалося після 2014 року, коли не в останню чергу завдяки успішному застосуванню інформаційних спецоперацій Росії вдалося анексувати Крим та розгорнути військове протистояння Сході України через забезпечення позитивного сприйняття частиною місцевого населення цих регіонів агресивних дій РФ. Визначено, що кінцевою метою розв’язаної Росією гібридної геополітичної інформаційної війни є цивілізаційний вибір українського народу: рухатися в напрямку європейської інтеграції чи повертатися в лещата православно-російської цивілізації. На основі сгенерованої в інтернет-сервісі Word’s Хмарки тегів виокремлено 5 основних кластерів наукового розуміння проблематики феномену інформаційної війни. Розкрито основні підходи до наукового розуміння зазначеного соцієтального феномену в контексті: філософсько-онтологічної сутності інформаційної війни; понятійно-категоріального апарату; виокремлення ключових загроз національній безпеці держави; механізмів і технологій ведення гібридних війн; суб’єкт-об’єктної структуризації та практичних кейсів. Зроблено висновок про необхідність розгортання в Україні системної діяльності органів публічного врядування, насамперед, інституту Президента України, Кабінету Міністрів України, Збройних сил, СБУ та МВС з формування сучасної ефективної системи захисту національного інформаційного суверенітету, яка б включала підготовку фахівців сфери розпізнавання, запобігання й протидії проявам інформаційної війни, ведення контрпропагандистських інформаційних операцій тощо.
ЗАБЕЗПЕЧЕННЯ ПРАВ І СВОБОД ЛЮДИНИ В СИСТЕМІ ПУБЛІЧНОГО УПРАВЛІННЯ В УМОВАХ ПАНДЕМІЇ COVID-19
Authors: Коломоєць Юлія Олександрівна
Published: May 2022

Мета роботи є систематизація наукових підходів до проблеми забезпечення прав та свобод людини в системі публічного управління в умовах пандемії COVID-19. У статті проаналізовані основні нормативно-правові документи, спрямовані на встановлення обмежувальних заходів, пов’язаних з розповсюдженням пандемії COVID-19, які обмежують права та свободи людини. Виконавча влада, на наш погляд, має право планувати та організовувати санітарно-протиепідеміологічні, профілактичні і навіть обмежувальні заходи, але обмежувати конституційні права та свободи громадян, минаючи волю органів народного представництва, у суспільстві не має право. У виключних випадках, коли обставини вимагають негайного вирішення, це можливо лише на дуже нетривалий час, який необхідний представницькому органу для повноцінного обговорення та прийняття рішення. Методологія: системний аналіз, узагальнення, синтез, аналіз, моделювання. Наукова новизна. Вжиті державою заходи, як було зазначено, впливають на реалізацію низки прав та свобод людини. Зокрема, у житловій, трудовій сферах, у сфері соціального забезпечення та соціального захисту громадян. Крім того, зростає екзистенціальна незахищеність у поєднанні з відсутністю ефективної освіти у галузі прав людини. Потрібно зберегти і покращити якість освіти у зв’язки із переходом на дистанційне навчання. Так, під час онлайн навчання виявилися найбільш значущими ризиками, які пов’язані з дегуманізацією, розривом соціальних зв’язків. Процес цифровізації всіх сфер життя суспільства та пов’язана з ним зміна форматів освіти у закладах вищої освіти – це прогресивний, неминучий, а й суперечливий феномен. Деякі претензії щодо зниження оплати за навчання у зв’язку з цим не враховують, що навантаження на викладачів та навчальні заклади також збільшились. Трудове законодавство України передбачає можливість дистанційної роботи. Окремо стоїть питання навантаження на працівника, іноді працівник виконує більше завдань, ніж передбачено його трудовим договором. Висновки. Держава та суспільство усвідомлюють небезпеку пандемії для здоров’я нації та свідомо вживають адекватних заходів щодо протидії їй. За 2019–2021 роки світова спільнота отримала певний досвід протидії COVID-19, однак не завжди цей досвід був вдалий. Так, виникли протести населення проти обов’язкової вакцинації, або обмеження права осіб пересування в межах певних територій тощо. Часто держави вдавалися до локдаунів, вимог самоізоляції, закриття виробництв, обов’язкової вакцинації, обов’язкове застосування засобів індивідуального захисту тощо. Тому до впровадження обмежувальних заходів, пов’язаних з пандемією COVID-19, потрібно підходити виважено з урахуванням громадської думки та можливостей соціально-економічного становища країни та добробуту громадян.
Comparison of BIM and Conventional Method to Analyze Dimension and Volume of Abutment
Authors: Anugrah Yuliano Rayhan , Martina Nunung , # Safri
Published: May 2022

The development of the industrial era 4.0 is rapid, one of which is the BIM (Building Information Modeling) method for surveying activities, namely 3D Scanning. The 3D Scanning method, which can obtain millions of points in point clouds in one scan, is expected to provide higher accuracy and more efficient and effective operating time than conventional methods. Conventional methods, which still apply human plotting, require more time and human resources because they have to move from one point to another, and their accuracy is highly dependent on the quality of human resources. This research was conducted at the Interchange Project of the Batang Industrial Estate, Central Java, and aimed to compare the dimensions, volume, and shop drawings of the 3D Scanning method with the conventional method and the advantages of the 3D Scanning and Modeling method. The results obtained in this study are that there is an average dimension difference of 0.007 m from the shop drawing image, then there is a difference in the average coordinate point of 0.028 m which causes the Abutment position to shift from the shop drawing image. BIM is one method for get the volume of structures on Interchange Project with careful results and faster process. The calculated volume is based on shop drawings. The results showed that the difference between the conventional BIM method in the volume of concrete was 1.156%, and in the granular heap, there was a difference of 1.292%. The advantages of these 2 BIM methods are how they operate, which has started to implement an automation system and requires less time than conventional methods.
Analisis Pengiriman Data dari LoRa Gateway ke Network Server
Authors: Rosyida Zain Ayu , Hudi Siti Azka Rahmawati , Neforawati Indri
Published: May 2022

Perkembangan IoT di Indonesia mendorong perkembangan jaringan yang dibutuhkan perangkat IoT untuk dapat berkomunikasi antara sensor dan aktuator. Salah satu jaringan yang membantu proses komunikasi tersebut ialah LoRa. LoRa (Long Range)  sendiri merupakan protokol jaringan luas berdaya rendah dan tahan terhadap gangguan dari sinyal-sinyal yang tidak diinginkan. LoRa memiliki keterbatasan yaitu tidak bisa melakukan pengiriman data langsung ke network server, maka dibutuhkan perangkat yang dapat menjembatani pengiriman data agar sampai ke network server yaitu LoRa Gateway. Karena penggunaan LoRa Gateway yang sangat penting dalam hal tersebut, dilakukanlah analisis untuk melihat seberapa besar data yang dipakai dan protokol apa saja yang digunakan saat terjadinya komunikasi dari Gateway LoRa ke Network Server. Analisis yang dilakukan dengan menggunakan aplikasi Wireshark. Metode yang digunakan adalah pengumpulan data, pengolahan data, dan analisis data. Kesimpulan analisis ini adalah bahwa Spreading Factors (SF) dan interval waktu pengiriman data berpengaruh pada besar kilobytes dan kecepatan pengiriman data ke network server.
MODELING THE OPTIMAL PORTFOLIO CHOICE IN THE TOURISM INDUSTRY IN TERMS OF THE COVID-19 PANDEMIC
Authors:   Ivan Franko National University of Lviv, Hurnyak Ihor , Hrytsyshyn Anna ,   Ivan Franko National University of Lviv
Published: May 2022

The present pandemic COVID-19 has resulted in global challenges, economic and healthcare crises, and posed spillover impacts on the global industries, including tourism and travel that the major contributor to the service industry worldwide. The tourism and leisure industry has faced the COVID-19 tourism impacts hardest-hit and lies among the most damaged global industries. Changes in the market of travel services in the world in terms of the pandemic were reasonably expected to move in the negative direction. On the other hand, the diversification of capital markets allows leading companies successfully to maneuver in the midst of this range of negative factors. The article deals with a number of cases to explain this mechanism. The optimal diversified portfolio in the field of tourism is identified as a result of such analysis. On the base of created portfolio, the tendencies of price stabilization in the market of this type of services were revealed. The article uses a wide range of Python packages, machine learning tools and Fama-French modeling.
SUSTAINABLE DEVELOPMENT OF UKRAINIAN REGIONS: SPATIAL PANEL METHOD
Authors:   Ivan Franko National University of Lviv, Zomchak Larysa , Koval Lilia ,   Ivan Franko National University of Lviv
Published: May 2022

Today, the concept of sustainable development is probably familiar to everyone and no one undertakes to deny the critical need for such an approach to further world development. A holistic and scientific approach to modeling regional processes is important. This investigation is aimed at identifying the main causes of pollution in the regions, and making recommendations for further policy and reforms in Ukraine. The purpose of the investigation is to study the sustainable development of the regions of Ukraine. This paper used the method of spatial-panel modeling, namely modeling the dependence of environmental pollution on capital investment, GRP, population and environmental costs, based on regional data for 2004–2020. The main task is to build a spatial-panel model of sustainable development of the regions of Ukraine, analysis of the results, providing recommendations on certain areas of sustainable development policy in Ukraine. The following research methods are used in the investigation: spatial modeling method, spatial-panel modeling method, spatial-panel modeling with fixed effects, hypothesis testing method, econometric methods. The paper analyzes the current state and probability of achieving different directions of sustainable development of the regions of Ukraine, based on the goals and objectives of sustainable development until 2030. The works of the scientists and the methods of research and modeling of regional processes used by them are considered. The mathematical apparatus of research, namely the econometric aspects of spatial modeling is analyzed. SAR and SEM models of pollution of the regions of Ukraine by the method of spatial-panel modeling are constructed taking into account GRP, population, investment, as well as the cost of preserving the environment, using the programming language R in the RStudio environment. SAR and SEM models were built, and a rather strong influence of spatial effects on the level of pollution in the regions has been revealed. Models are analyzed, the main problems are identified. Policy recommendations are provided to achieve sustainable development of the Ukrainian regions on the basis of spatial-panel modeling.
СУЧАСНІ РЕАЛІЇ ТА ПЕРСПЕКТИВИ ПІДГОТОВКИ ФАХІВЦІВ У СФЕРІ ТОРГІВЛІ ТА ПІДПРИЄМНИЦТВА
Authors: Черевична Н.І. , Гапонцева О.В. , Фролова Т.В.
Published: May 2022

У статті розглядаються актуальні проблеми підготовки фахівців галузі підприємництва, торгівлі, сфери гостинності та шляхи їх вирішення. На сьогодні торгівля, дрібне та середнє підприємництво є основними і одними з найзатребуваніших та привабливіших складових, які впливають на розвиток і збалансованість ринку праці і тягнуть за собою позитивні структурні зміни в національній економіці. У сфері підприємництва та торгівлі є багато престижних професій, які потребують професійної та вищої освіти. У зв'язку з цим якісна освіта розглядається в даний час як один з основних індикаторів якості життя, інструмент соціальної та культурної згоди та економічного зростання. Створення сприятливих умов для стимуляції молоді до підприємницької діяльності та формування професійних компетентностей та практичних навичок сьогодні – вже через 10 років визначатиме імідж країни у світі. Адже саме від розвитку сучасного молодіжного підприємництва залежатимуть темпи економічного зростання та розвитку нашої країни у майбутньому.