ABSTRACT
The requirement for quantity and quality of human resources, especially occupations in the economics field, has played a significant role in recovering and improving the COVID-19 pandemic economic situation in Vietnam. Therefore, this encouraged economics majors to attract a large number of students to enrol in 2021. This study aims to determine the factors affecting the career choices of economic students in Vietnam. The research focuses on analysing six factors to determine the relationship between variables that help explain students' compatibility and their chosen majors. A survey questionnaire using simple random sampling collected 309 data points from economics students at a private university in Vietnam. Methodologies such as Cronbach's Alpha, exploratory factor analysis, confirmatory factor analysis, regression, and structural equation modelling were employed using SPSS and Amos software to check the correlation between factors and draw conclusions about factors affecting students' career choices. The results revealed that influencers, interests, financial resources and career opportunities were critical elements that influenced students' decisions in choosing a major. Interest (to pursue passion) was considered by students in choosing a major - which could also encourage them to develop their own capabilities. Additionally, the data proved that most job selections were based on future employability;therefore, career opportunities had the most positive impact on students' decisions. The findings of this study identify determinants of students' choice in economics majors and their relationships and can improve students' awareness and future orientation before deciding to choose a major. Moreover, this study provides valuable data for universities to formulate and develop strategies to attract students, such as career consulting. © 2023 The Author(s)
ABSTRACT
Objective: To compare in-person and virtual breast fellowship interview experiences from the perspective of fellowship program directors (PDs) and applicants. Methods: Three separate voluntary, anonymous, e-mail delivered surveys were developed for PDs, in-person interview applicants in 2019-2020, and virtual interview applicants in 2020-2021. PD and applicant survey responses regarding the two interview cycles were compared. Results: The response rate was 56% (53/95) for PDs, 19% (23/123) for in-person applicants, and 38% (49/129) for virtual applicants. PDs reported significantly lower cost for virtual compared to in-person interviews (P < 0.001). They reported no significant difference in number of applications received, number of applicants interviewed, applicant pool geographic regions, number of interview days offered, or format of interviews. Most PDs (31/53, 58%) felt the virtual format still allowed them to get to know the applicants well. Cost was significantly higher for in-person compared to virtual applicants (P < 0.001). More in-person applicants (11/23, 48%) listed cost as a barrier compared to virtual applicants (7/49, 14%) (P = 0.002). Virtual and in-person applicants reported a similar number of program applications, but virtual applicants completed more interviews (P = 0.012). Both groups preferred scheduled time to speak with the current fellows and a one-on-one interview format with two to four faculty members. Most applicants (36/49, 73%) felt the virtual format still allowed them to get to know each program well. Conclusion: Virtual interviews provide a reasonable alternative to in-person interviews for breast imaging fellowship applicants, with decreased cost being the main advantage.
ABSTRACT
This paper presents a numerical investigation of Ultraviolet (UV) disinfection performance against the recently explored SARS-CoV-2 Virus. The UV lighting source is of a vertical lamp supposed to be put in popular mobile UV-C devices. The Finite Volume Method (FVM) and Discrete Ordinates (DO) model are adopted to deal with UV irradiance. Various results for the formation of an effective disinfection zone, detailed disinfection rate, and UV exposure duration are discussed and analyzed in detail. Results show that the bactericidal influence against SARS-CoV-2 viruses, which is strongest in the horizontal central plane through the UV lamp, can be significantly increased with the upgrade in the lamp power utilized. Furthermore, the UV exposure duration is found to have a considerable effect on the disinfection performance. Specifically, the disinfection rate is greatly improved, resulting in a remarkable expansion in the effective disinfection zone with a longer exposure period. Furthermore, the exposure duration required for 90% total viruses being eliminated with different lamp wattages are reported. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Pedestrian detection and tracking in video surveillance systems is a complex task in computer vision research, which has widely used in many applications such as abnormal action detection, human pose, crowded scenes, fall detection in elderly humans, social distancing detection in the Covid-19 pandemic. This task is categorized into two sub-tasks: detection, and re-identification task. Previous methods independently treat two sub-tasks, only focusing on the re-identification task without employing re-detection. Since the performance of pedestrian detection directly affects the results of tracking, leveraging the detection task is crucial for improving the re-identification task. The total inference time is computed in both the detection and re-identification process, quite far from real-time speed. This paper joins both sub-tasks in a single end-to-end network based on Convolutional Neural Networks (CNNs). Moreover, the detection includes the classification and regression task. As both tasks have a positive correlation, separately learning classification and regression hurts the overall performance. Hence, this work introduces the Regression-Aware Classification Feature (RACF) module to improve feature representation. The convolutional layer is the core component of CNNs, which extracts local features without modeling global features. Therefore, the Cross-Global Context (CGC) is proposed to form long-range dependencies for learning appearance embedding of re-identification features. The proposed model is conducted on the challenging benchmark datasets, MOT17, which surpasses the state-of-the-art online trackers. © 2021, Springer Nature Switzerland AG.
ABSTRACT
The service robots are being ramped up rapidly in healthcare establishment, especially in the fight against the Covid-19 pandemic. This paper presents a new design and implementation of an Ultraviolet Disinfection Robot (UV Robot) which can be used in many medical institutions to disinfect patient rooms, the operating rooms, reduce cost of traditional disinfection and most importantly risks for patients and medical personnel. The robot is equipped with a high power UV-C lamps: five 100 W UV lamps covering 360° directions on the top and two 30 W lamps on two sides. The high intensity ultraviolet light emitted by the UV-C lamps makes it possible to kill 99% bacteria, germs molds, virus in around 30 s with a radius from 1 to 2.5 m depending on the type of microorganisms. A high disinfection efficiency in less time of the robot is confirmed by the experiments conducted in the hospital in Danang city, Vietnam. Compared to the available similar robots, this domestic product provides an economical and effective solution for Vietnamese medical institution in reducing the spread of harmful microorganisms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The COVID-19 pandemic requires everyone to wear a face mask in public areas. This situation expands the ability of a service robot to have a masked face recognition system. The challenge is detecting multi-view faces. Previous works encountered this problem and tended to be slow when implemented in practical applications. This paper proposes a real-time multi-view face mask detector with two main modules: face detection and face mask classification. The proposed architecture emphasizes light and robust feature extraction. The two-stage network makes it easy to focus on discriminating features on the facial area. The detector filters non-faces at the face detection stage and then classifies the facial regions into two categories. Both models were trained and tested on the benchmark datasets. As a result, the proposed detector obtains high performance with competitive accuracy from competitors. It can run 20.60 frames per second when working in real-time on Jetson Nano. © 2021, Springer Nature Switzerland AG.
ABSTRACT
The information about Coronavirus disease 2019 (COVID-19), especially about infected cases in every country is very urgent. In this paper, an algorithm to analyze the COVID19 infected case reports is introduced. Fifty-two (52) reported cases from LuatVietnam - a reputable Vietnamese online newspaper - were taken as input. The retrieved data were analyzed and classified. The analysis output was saved into a CSV file showing the essential extracted information about infected cases. Each output row contains Patient ID, Gender, Age, Address and Status. Based on the tested results, the algorithm achieved the accuracy of 86.67% with the average processing time per patient of 0.103 milliseconds. © 2020 IEEE.