Analysis of Impact of Competency, Professionalism, and Audit Deadline on Remote Audit Effectiveness

Auditors must begin to develop their skills to face industry 4.0. The spread of Covid-19 has further encouraged auditors to conduct remote audits and provides an opportunity to rethink the way audits are conducted. In this study, the researcher wanted to find out how the influence of competence, professionalism, and audit deadlines on the effectiveness of remote audits. This research is quantitative, with data processing using primary data obtained from distributing questionnaires to auditors who work at Public Accounting Firms in Jakarta. Statistical analysis using multiple linear regression, before conducting a feasibility test through validity, reliability and classical assumption tests. The results showed that the variables of competence, professionalism, and audit time limit had a significant effect on the effectiveness of remote auditing. © 2022 ACM.

Leadership in fostering vocational school partnerships in the time of COVID-19

This paper sought to explore school leadership practices, the management of school partnerships with the industry, and the challenges faced in implementing partnerships during the COVID-19 pandemic. Embracing an exploratory research design including a sequential model, the present research involved 89 principals, vice principals, and productive teachers from government-funded secondary schools who directly engage in the management of school partnerships with the industry. Findings reveal that school leadership practices implemented a participatory approach. School management was employed to optimize the role of vice principals, program heads, and productive teachers. The challenge of establishing partnerships during the COVID-19 pandemic lies in implementing an independent learning policy assisted by information and communication technology. © 2022 International Journal of Health Sciences.

High-Flow and High-Pressure Oxygen Mixing for Ventilator System

The treatment of patients with acute respiratory failure due to COVID-19 needs ventilator machine support to provide oxygenation and ventilation processes. The most critical control in the machine is how to provide precision-inspired oxygen fraction (FiO2) to the patients during the treatment. Generally, the low-flow nasal cannula can provide 4-6 liters per minute of supplemental oxygen with approximately 37 - 45% FiO2. In this paper, we present high-flow and high-pressure oxygen mixing to provide up to 100% FiO2 and 60 liters per minute. The proposed system also employs the close-loop feedback control to achieve a desirable FiO2 set point. It can be used for a Continuous Positive Airway Pressure (CPAP) and Bilevel Positive Airway Pressure (BiPAP) ventilator systems with oxygen as well as a High Flow Nasal Cannula (HFNC) system for acute respiratory patient's syndrome. © 2021 IEEE.

On the Comparison of Social Distancing Violation Detectors with Graphical Processing Unit Support

Social distancing or sometimes referred as physical distancing is claimed as the best spread stopper in the present COVID-19 pandemic. Social distancing monitoring by using computer vision becomes an important technological aspect in the current pandemic. This type of technology ensures automatic human object detection followed by physical distance measurement. The actual distances are measured as the number of pixels separating two centroids. The social distancing violations are known based on the measured distances. In this works, we compare three deep learning methods used for social distancing monitoring i.e YOLOv3, YOLOv3-Tiny, and MobileNetSSD. Those methods are executed with and without GPU support, and we assess the their performances in terms of speed and detection accuracies. The results show that the use of GPU significantly increases the speed of both YOLOv3 and YOLOv3-Tiny, but not for MobilenetSSD. GPU support increases about 300 % the Frame-per-Second (FPS) rate of YOLOv3 and the highest FPS rate is achieved for YOLOv3-Tiny. The results also indicate that YOLOv3 offers the best detection accuracies compared to YOLOv3-Tiny and MobilenetSSD, but in the exchange of heavy computational process.

Physical Distancing Monitoring with Background Subtraction Methods

World Health Organization (WHO) has confirmed that the spreading of coronavirus disease 2019 (COVID-19) could be avoided by keeping the physical distance at least 3 feet (1 meter). Then, we have a motivation to employ computer vision techniques to monitor social distancing violations. The principle of the works are to detect persons, then to assess the physical distancing violation from their distance. Most of the researchers have tried to utilize object detection methods such as faster RCNN, Yolo, and SSD to detect persons from the frame. Those methods rely on, the support of Graphics Processing Unit (GPU) to execute their heavy computation. In this works, we propose social distancing monitoring by applying background subtraction methods based on Gaussian Mixture Models (GMM) i.e. Geometric Multigrid (GMG), k-Nearest Neighbor (KNN), Mixture of Gaussian (MOG), and Mixture of Gaussian 2 (MOG2). These methods have been used to filter persons from the frame with computational process. Some parameters evaluation measures have been determined to check the best method suitable for this works. In terms of performance, better methods are ranked as KNN, MOG, MOG2, and GMG.

Adjustment to COVID-19 Era: Ongoing process at biobank at faculty of medicine, public health and nursing UGM (FKKMK UGM), Yogyakarta- Indonesia

Biobank at FKKMK UGM has just established for 2 years, when COVID-19 pandemic hit. Initially FKKMK UGM Biobank focused its service to non infectious related biosamples, due to limited infrastructure and expertise. On the first three months of COVID-19 pandemic, biobank was shut down. As the pandemic is expected to last longer, biobank must resume its operation. Adjustment is needed for this facility to enable it to cover service for ''potential infectious'' biosamples. To achieve the objective, based on current capacity, several activities are needed to be done, included : 1. Identification of potential needs and risks ;2. Preparation for capacity building ;3. System adjustment. These activities are planned to be finalized by mid 2021, while the faculty experiencing financial constriction. With the guidance frominstitutional biosafety team, by the end of 2020, Biobank FKKMK UGM determined that samples in virus-transfer medium (VTM) are prohibited, while alreadyprepared frozen COVID-19 related blood samples can be stored in biobank. We determine 3-layers packing system for biosamples entering biobank, and train our user to adhere. A home-made disinfecting chamber is in place to automatically clean the surface of outer biosample packing. Processing laboratory is disinfected regularly with ultraviolet light, and all technician wear adequate personal protection equipment (PPE) while working. Biosamples process is done in biosafety class II chamber, and biosamples are processed carefully to avoid vapour and spill-out. Despite the adjustment, adequate and formal training in handling this highly infectious biosamples is still needed. While SARS-CoV2 infection soaring, vaccination program among laboratory personnel becomes paramount. Collaboration between biobank and facility equipped with negative pressure processing laboratory needs to be initialized;however, at present, this facility is being used to support COVID-19 diagnostic laboratory in the campus.

Disaster Mitigation Education in the COVID-19 Pandemic: A Case Study in Indonesia

Disaster mitigation education - even during the current coronavirus (COVID-19) pandemic - must be carried out to face the problem of flooding. It can be argued that the worst impacts of floods can be prevented by way of a young generation that is armed with Higher Order Thinking Skills (HOTS) and disaster mitigation behaviors acquired through environmental education. This study measured HOTS related to disaster mitigation behaviors of the young generation during the COVID-19 pandemic in Indonesia. The research utilized a descriptive methodology, with a data collection technique conducted through online surveys. The collected data was referred to a survey that designed questions to be scaled against a HOTS skill hierarchy, and then assigned a score based on the skill weight in that hierarchy. The results highlight that most of the young generation's HOTS scores were averaged in the very low category (26.88), even though the score for flood disaster mitigation behavior was in the high category (77.40). The low score is mostly attributable to the lack of disaster mitigation education at schools, universities, and for the public in general. The study concluded that the young generation's Higher Order Thinking Skills (HOTS) must be improved. Developing innovative learning tools and disseminating information are a necessary prerequisite for increasing these skills. © Copyright 2020, Mary Ann Liebert, Inc.