ABSTRACT
The research was conducted to know the impact of changes in distribution strategies and prices on buying decisions made by retail stores or minimarkets due to government policies on social restrictions of the COVID-19. This study uses quantitative research with population data distribution in one of Indonesia's major cities. It uses a structural model in data analysis with all latent variables linked. They were measuring instruments used using Smart PLS. This study found that with changes in distribution strategies by combining all types of intensive, exclusive, and selective, significant results were obtained at prices and purchasing decisions by customers. The main finding in the study was that the implementation of distribution strategies for government policy adjustments was very effective in reaching customers in all corners of the city so that price adjustments and customer purchase decisions could occur on an ongoing basis. Copyright © The Authors(s) 2023. Published by Inderscience Publishers Ltd.
ABSTRACT
Coronavirus Disease-2019 (COVID-19) is a new type of coronavirus that attacks the respiratory tract and can be contagious. More severe cases of this infection can cause pneumonia, acute respiratory syndrome, kidney failure and even death. Detection of Covid-19 is a crucial step to identifying suspected Covid-19 patients early. CT Scan is a significant modality to detect Covid-19 disease. Automatic detection of Covid-19 on CT-Scan implemented various convolutional neural network models such as Mobile-Net, RestNet50, and DenseNet. These three architectures were implemented for Covid-19 detection but were not compared on the same database. In this study, the authors propose a performance comparison of these three models to detect Covid-19 using an open-source dataset of 2038 CT-Scan images. The methods involved data preparation, preprocessing, training, testing, and performance evaluation. The result showed that the best accuracy is obtained by the DenseNet121 model with 95.98% and MobileNet with 95.09%, while ResNet50 has 92.00% accuracy. Although the accuracy of ResNet50 is lower than the other two models, the travel time of the ResNet50 model is faster than the DenseNet121 model, with an average travel time of 16 seconds per epoch. © 2022 IEEE.
ABSTRACT
COVID-19 has significantly influenced living in recent years. Almost all countries have carried out all limitations to reduce its spread. Detection is highly required for further handling of COVID-19. In this study, the detection was performed using classification on 1,184 X-ray images, specifically 404 X-ray images of COVID-19 positive people, 390 X-ray images of normal people and 390 X-ray images of pneumonia positive people. The image data were extracted with the Haar wavelet algorithm and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN);each had three classification models. The Quadratic SVM model obtained the best result with an accuracy of 79.8%. © 2022 IEEE.
ABSTRACT
The SARS-Cov-2 strain caused COVID-19, inflicting mild to moderate respiratory problems. The spread of COVID-19 is extremely fast which has resulted in the number of victims who have been declared dead to date, up to 2,587,225. There are several ways to reduce the spread of COVID-19, one of which is early detection. Currently, there are alternative methods used for early detection, one of which is the neural network method. Deep learning is one type of artificial neural network that is often used for the detection of several kinds of diseases. In this study, we classify CT-Scan images of the lungs based on two classes, namely CT-COVID and CT-NonCOVID, using two models, Inception-v3 and Inception-v4. The total CT-Scan image data used is 2038 and comes from the Kaggle.com website. Results obtained were then compared with standard performance metrics and then analyzed between the best models among the models used in the COVID-19 classification. From the results of the study, the Inception-v3 model obtained an average accuracy value of 93.96%, a precision value of 90.57%, a recall value of 95.65%, a specificity value of 92.81% and an f-score value of 92.51% and The Inception-v4 model obtained an average accuracy value of 86.41%, a precision value of 77.01%, a recall value of 91.18%, a specificity value of 83.77% and an f-score value of 83.38%. Based on the research results, the method with the best performance in COVID-19 classification is the Inception-v3 model because the Inception-v3 model has more layers, with a total of 48 layers and utilizes the idea of factorization that is more suitable for CT-Scan image classification which has low contrast visualization. © 2022 IEEE.
ABSTRACT
To investigate the operational improvements of vessels under the impact of COVID-19, this work has developed a Computational Fluid Dynamics model combined with Lagrangian particles to study the airborne transmission of COVID-19 viruses inside a ship. Initially a generic model was established to enable validation against experimental results for the diffusion of flu virus in an idealised room. Following this, the room geometry was replaced by the superstructure of a full-scale crew boat. Considering the boat advancing in open water, simulations were conducted to study the particulate flow due to a person coughing and speaking, with the boat’s forward door open and closed. The results have shown that, when the forward door is open, a significant airflow can carry the viruses to make extensive contacts with the passengers. This led to the suggestion of keeping the door closed. However, when the forward door is shut, face-to-face speaking can generate viruses that can float in the air for a long time, and it was found that the viruses mainly stay within a half-meter distance in front of the speaking person, before sinking to attach to the deck. Thus, a social-distancing suggestion on seat arrangement has been highlighted to minimise the risk of contagion. Overall, this work is expected to inform guidelines on hygienic and reconfiguring means for operators to counter COVID-19 and potentially the spread of similar viruses in the future. Copyright © 2022 by ASME.
ABSTRACT
The case of COVID-19, which is still a pandemic in our environment, has claimed many victims to health workers who work on the frontline. In this era of the COVID-19 pandemic, personal protective equipment (PPE) is essential to reduce the risk of transmitting infectious diseases to health workers. This study aimed to determine the correlation between knowledge about Covid-19 and PPE with compliance with using PPE for nurses at Purbalingga Hospital. This type of research is an observational analytic study with a cross-sectional design. The research subjects were nurses who worked in Purbalingga Hospital, with a sample of 60 people. Sampling using random sampling method, data collection through self-assessment using a validated questionnaire. Data analysis used the frequency distribution test (univariate) and the Spearman rank correlation test to analyze the direction and significance of the correlation between knowledge about Covid-19 and knowledge about PPE and nurses’ compliance in using PPE. The study results prove: 1) There is a positive and significant correlation between knowledge about Covid-19 and compliance with using PPE for nurses at Purbalingga Hospital. This is evidenced by the correlation value of 0.858 and p-value of 0.000<(0.05). Based on these results, the research hypothesis, "There is a positive and significant correlation between knowledge about Covid-19 and adherence to using PPE for nurses at Purbalingga Hospital," was accepted. 2) There is a positive and significant correlation between knowledge about PPE and compliance with using PPE for nurses at Purbalingga Hospital. This is evidenced by the correlation value of 0.975 and p-value of 0.000<(0.05). Based on these results, the research hypothesis, "There is a positive and significant correlation between knowledge about PPE and adherence to using PPE for nurses in Purbalingga Hospital," is accepted. © 2022
ABSTRACT
The increasing intensity of the use of technology through distance learning in the Covid-19 era occurred at the training site for the best achievements of participants. Combination research from The Unified Theory of Acceptance and Use of Technology (UTAUT) and Felder-Silverman Learning Style Model (FSLSM) as a goal to determine the effect of the performance of supervisory leadership training participants at the National Institute of Public Administration (NIPA) in Samarinda. This quantitative study collected 197 data from the survey and then processed the data using warp PLS 6, with the aim of researching participant behavior in e-coaching and learning styles on performance. Results show variables of performance expectancy, effort expectancy has a significant positive effect on behavioral intentions in coaching, and the relationship between behavioral intentions in coaching is significantly positive on performance, while the social influence on behavioral intentions in coaching has an insignificant negative effect, learning style on behavioral intentions in coaching has a positive and insignificant effect and learning style on performance has a significant negative effect so that indications of increased behavioral intentions in coaching will reduce the social influence and there is not always a change in learning style, but when there is an increase in performance there will be a change in the learning style of the participants. © 2021
ABSTRACT
This study is aimed to analyze the variables of external environment, organizational resources, organizational capabilities, and business competitiveness. The study priorities strategy and programs as basic for developing the competitiveness of creative industry in Indonesia. The number of respondents who participated in this survey was 200, while the key informants were 10 people. Method of analysis involved descriptive statistics, and analytical hierarchy process (AHP). Then, data were processed by using both IBM SPSS 24, and Expert Choice 11. The results show that creative industry competitiveness has relatively declined during covid-19 pandemic. Although external environment support, organizational resources, and organizational capabilities were at good shape. The priority strategy for competitiveness development should be focused on strengthen the organizational capabilities by considering the dynamics of external environmental factors and internal resource capacity. Then, the priority programs developed sequentially are increasing partnerships with suppliers, distributors and customers, analyzing social and economic aspects, developing human resource capacity, and using information and communication technology in products and services. In addition, another important program is strengthening the supply chain system. © 2021 by the authors;licensee Growing Science, Canada.