study performance
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2022 ◽  
Author(s):  
Jayanthi Shastri ◽  
Sachee Agrawal ◽  
Nirjhar Chatterjee ◽  
Harsha Gupta

Background: Accurate rapid antibody detection kits requiring minimum infrastructure are beneficial in detecting post-vaccination antibodies in large populations. ChAdOx1-nCOV (COVISHIELD) and BBV-152 (Covaxin) vaccines are primarily used in India. Methods: In this single-centre prospective study, performance of Meril ABFind was investigated by comparing with Abbott SARS-CoV-2 IgG II Quant (Abbott Quant), GenScript cPass SARS-CoV-2 neutralization antibody detection kit (GenScript cPass), and COVID Kawach MERILISA (MERILISA) in 62 vaccinated health care workers (HCW) and 40 pre-pandemic samples. Results: In the vaccinated subjects, Meril ABFind kit displayed high sensitivity of 93.3% (CI, 89.83%-96.77%), 94.92% (CI, 91.88%-97.96%), and 90.3% (CI, 86.20%-94.4%) in comparison to Abbott Quant, MERILISA, and GenScript cPass respectively. The results of the Meril ABFind in the COVISHIELD-vaccinated group were excellent with 100% sensitivity in comparison to the other three kits. In the Covaxin-vaccinated group, Meril ABFind displayed sensitivity ranging from 80% to 88.9%. In control samples, there were no false positives detected by Meril ABFind, while Abbott Quant, MERILISA, and GenScript cPass reported 2.5%, 10.0%, and 12.5% false positives, respectively. In the pre-pandemic controls, specificity of Meril ABFind was 100%, Abbott Quant 97.5%, MERILISA 90%, and GenScript cPass 87.5%. Conclusion: The Meril ABFind kit demonstrated satisfactory performance when compared with the three commercially available kits and was the only kit without false positives in the pre-pandemic samples. This makes it a viable option for rapid diagnosis of post vaccination antibodies.


Author(s):  
Mahdi Giozafat

Mobile money services give trade benefits such as bill payment, decreased transaction costs and time, expanded savings possibilities, sales, and convenience. Despite the benefits, traders in Uganda are still slow to adopt and use mobile money services. This article reflects on the findings of a study that looked at the barriers that merchants experience while utilizing and implementing mobile money services in Uganda. A self-administered questionnaire was utilized to obtain data from 394 survey respondents. A model for encouraging traders to use mobile money services is offered. The suggested model expands on the Unified Theory of Technology Acceptance and Use. According to regression study, performance expectancy, social factors, and sensitization components all have a substantial impact on the behavioral intention of mobile money service uptake for trade. On the other hand, security and effort expectation had no significant affect on traders' behavioral intention to use mobile money services. Furthermore, the data show that enabling conditions affected the utilization of mobile money services for commercial transactions. The suggested approach is adaptable and generic, and it may be used in other developing nations with comparable circumstances to Uganda.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juha Törmänen ◽  
Raimo P. Hämäläinen ◽  
Esa Saarinen

Purpose This study aims to introduce the perceived systems intelligence (SI) inventory, developed based on the earlier published self-report SI inventory (Törmänen et al., 2016). It can be used together with earlier managerial level tools for building a learning organization and included in general 360-style evaluations in personnel development. Design/methodology/approach The inventory is validated with confirmatory factor analysis with a model based on the self-report SI inventory, using data from full-time used employees and managers in the USA and UK. Perceived SI factor scores are correlated with the perceived study performance of the individual. Findings The perceived SI inventory is found to have good factorial validity, and it correlates strongly with evaluations of perceived study performance. Managers perceived high in performance are also found to score high in perceived SI. Perceived SI does not depend on gender, age, organization size or industry. Originality/value The perceived SI inventory is the first personnel level peer evaluation tool suggested for developing learning organizations. The new inventory makes peer evaluations possible and provides a new grassroots level tool for personnel development programs in learning organizations.


2021 ◽  
Author(s):  
Kai-Yuan Cheng ◽  
Lucas M. Harris ◽  
Yong Qiang Sun

Abstract. Container technology provides a pathway to facilitate easy access to unified modeling systems and opens opportunities for collaborative model development and interactive learning. In this paper, we present the implementation of software containers for the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), a unified atmospheric model for weather-to-seasonal prediction. The containerized SHiELD is cross-platform and easy to install. Flexibility of the containerized SHiELD is demonstrated as it can be configured as a global, a global-nest, and a regional model. Bitwise reproducibility is achieved on various x86 systems tested in this study. Performance and scalability of the containerized SHiELD are evaluated and discussed.


2021 ◽  
Vol 9 (08) ◽  
pp. 647-650
Author(s):  
Maryama Gul ◽  
◽  
P.K. Sanse ◽  

The objective of this study was to assess the performance of JK SFC. The establishment of State financial Corporations was, one of the steps taken, at the official level to promote the growth of small and mediumscale industries. The Jammu and Kashmir State Financial Corporation is a statutory Corporation established under SFCs Act 1951(Central Act 63 of 1951) which is facing heavy losses due to no source of funds and no recovery of loans. In the present study performance of JKSFC was analyzed with different tools like trend analysis and ratio analysis and average growth rate. The results of the study show that performance of JKSFC is declining during the research period due tofacing problems of liquidity & solvency.


2021 ◽  
Vol 38 (4) ◽  
pp. 947-953
Author(s):  
Onur Gedik ◽  
Ayşe Demirhan

The usage of mask is necessary for the prevention and control of COVID-19 which is a respiratory disease that passes from person to person by contact and droplets from the respiratory tract. It is an important task to identify people who do not wear face mask in the community. In this study, performance comparison of the automated deep learning based models including the ones that use transfer learning for face mask detection on images was performed. Before training deep models, faces were detected within images using multi-task cascaded convolutional network (MTCNN). Images obtained from face mask detection dataset, COVID face mask detection dataset, mask detection dataset, and with/without mask dataset were used for training and testing the models. Face areas that are detected with MTCNN were used as input for convolutional neural network (CNN), MobileNetV2, VGG16 and ResNet50. VGG16 showed best performance with 97.82% accuracy. MobileNetV2 showed the worst performance for detecting faces without mask with 72.44% accuracy. Comparison results show that VGG16 can be used effectively to detect faces without mask. This system can be used in crowded public areas to warn people without mask that may help the reduce the risk of pandemic.


2021 ◽  
Vol 5 (4) ◽  
pp. 688-696
Author(s):  
Aviv Yuniar Rahman

Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.  


2021 ◽  
pp. 106505
Author(s):  
Marcus R. Johnson ◽  
Merritt Raitt ◽  
Aliya Asghar ◽  
Debra L. Condon ◽  
Danielle Beck ◽  
...  

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