ABSTRACT
Purpose: This study sought to analyze the correlation between artificial intelligence (AI) and libraries and examine whether there were any shifts in research trends related to these two topics during the coronavirus pandemic. Design/methodology/approach: The study gathered secondary data from the Scopus website using the keywords "AI,” "library” and "repository,” from 1993 to 2022. Data were re-analyzed using the bibliometric software VOSviewer to examine the trending country's keyword relations and appearance and Biblioshiny to study the publication metadata. Findings: Index keywords, such as "human,” "deep learning,” "machine learning,” "surveys” and "open-source software,” became popular during 2020, being closely related to digital libraries. Additionally, the annual scientific production of papers increased significantly in 2021. Words related to data mining also had the most significant growth from 2019 to 2022 because of the importance of data mining for library services during the pandemic. Practical implications: This study provides insight for librarians for the implementation of AI to support repositories during the pandemic. Librarians can learn how to maximize the AI-based repository services in academic libraries during the pandemic. Furthermore, academic libraries can create policies for repository services using AI. Social implications: This study can lead researchers, academicians and practitioners in conducting research on AI in library repositories. Originality/value: As research on AI and digital repositories remains limited, the study identifies themes and highlights the knowledge gap existing in the field. © 2023, Emerald Publishing Limited.
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Objective: We aimed to assess the association between RAAS inhibitors (ACEi or ARB) and the clinical outcomes of COVID-19 patients with hypertension in the First Wave, Second Wave and Third Wave at Secondary Referral Hospital, Indonesia Methods: This cross sectional study was based on an electronic medical record in the Dr Adhyatma MPH Tugurejo General Hospital, Semarang, Indonesia. All patients more than 18 years old who confirmed COVID-19 case in the first wave (November 2020 to January 2021), second wave (May 2021 to July 2021) and third wave (January 2022 to February 2022) were included in this study. Data about demographic, comorbidities, features of antihypertensive drugs and clinical outcomes were collected. The clinical outcomes included mortality and prolonged hospitalization (length of stay (LOS) > 10 days). The statistical measures calculated were Chi square test and Fischer's exact test. Results: From three periods of peak wave, data from 1560 COVID-19 patients was collected in the first wave (304;19.5%), the second wave (1038;66.5%), and the third wave (218;14.0%). Subjects were dominated by males (50.4%) and a median of age 50 (19 -91) years old. The common comorbidities in studies were diabetes mellitus with 318 (20.4%) and hypertension with 254 (16.3%). In the main analysis, 99 (39%) patients with hypertension receiving RAAS inhibitors were compared with 155 (61%) patients receiving other antihypertensive drugs. Our results showed that RAAS inhibitors was not associated with a higher risk of COVID-19 mortality (RR:1.01;95% CI: 0.61-1.67;p:0.969) or prolonged LOS (RR:0.86;95%CI: 0.60-1.26;p:0.408). Conclusions: In COVID-19 patients with hypertension, the use of RAAS inhibitors showed no increased mortality or prolonged LOS compared to patients without RAAS inhibitors. COVID-19 patients and patients exposed to the COVID-19 pandemic should not discontinue ACEi or ARBs.
ABSTRACT
COVID-19 pandemic has deteriorated nature-based tourism sectors around the world. Indeed, tourism site managers' perceptions of and mitigation strategies to COVID-19 play a significant role in ensuring tourism operationalization and its management amidst pandemics. Using the case of Curug Cipeteuy, a community-based tourism destination in Gunung Ciremai National Park (GCNP), we investigate managers perceptions on COVID-19 and understand their mitigation strategies amidst the COVID-19 pandemic. A questionnaire survey, depth interviews, and field observation were conducted. We found that while managers' opinion of COVID-19 was relatively low, they hold high agreement in supporting tourism development amidst the COVID-19 pandemic. These could be related to those managers who are proud of tourism and its promising benefits to their area. Furthermore, both structural and non-structural mitigation strategies were done to overcoming pandemic impacts and inviting visitors.
ABSTRACT
National parks (NPs) have become a rapidly growing segment of nature-based tourist destinations. However, the COVID-19 pandemic has impacted tourism activities across Indonesian NPs. While existing studies have scrutinized the impact of COVID-19 on tourism, studies that focused on clustering residents adjacent of NPs to support tourism amidst the COVID-19 pandemic remain underexplored. To unveil how residents are clustered, we assess residents in Ngadisari and Wonokitri villages based on their distinctive opinions of perceived benefits (PB), perceived costs (PC), perceived health risks (PHR), and support for tourism (ST). Totally, 354 respondents were obtained. We found that residents are clustered into two clusters named tourism supporters and cautious supporters. Tourism supporters are identified by their high agreement on PB and ST, and low agreement with PC. In contrast, Cautious supporters hold lower agreement in PB and ST, and higher agreement on PC and PHR. Our findings revealed that residents are heterogeneous and constitute a distinctive group of opinions and interests. Therefore, the design of policies and interventions should be adjusted based on those clusters.
ABSTRACT
Existing elevators use a wide variety of methods in their systems to provide a direct response to the input received by first checking the inputs to operate. This response is related to the movement of the elevator itself. The research was conducted to minimize the movement of the elevator. In addition, as a form of effort to reduce the spread of Covid 19, the touchless button is introduced to the elevator operating system. The provision of the fuzzy logic method will make the elevator system manage the input given to determine priorities in the elevator movement. There are several input variables in the fuzzy method: position, distance, direction, and elevator capacity. Fuzzy logic will manage these variables, which are then obtained in the form of elevator movement priority. PLC used in this study is PLC Outseal Mega V1.1 as a controller on the elevator. The input uses a proximity infrared sensor so that it can provide input without touch. The use of functions on the PLC such as comparators, timers, and counters can realize an elevator system based on fuzzy logic calculations. The output used is a relay that will operate the DC motor. The proposed method realizes an innovative elevator operating system to stop the spread of covid 19. © 2021 IEEE.