Examining the impact of the COVID-19 pandemic on container carrier performance

The COVID-19 pandemic has affected various industries in different ways depending on their type of business. We attempt to understand the impact of the pandemic on the performance of 26 major container carriers. To capture performance differences before and during the pandemic, we collect data the years 2019 and 2020. Further, we divide carriers' business processes into two stages, i.e., asset acquisitions and transport operations. We employ relevant data envelopment analysis (DEA) models to this effect. The mean efficiency score in 2020 was higher than in 2019. Overall, container carriers increased their performance during the COVID-19 pandemic. However, seven companies showed lower efficiency in 2020. Similarly, eight companies were efficient in their operations but not in asset acquisition decisions. Our results show that firm strategy is important in coping with external shocks. The contribution of the paper is in measuring the impact of external shocks, such as the COVID-19 pandemic, on the performance of container carriers, both in regard of their acquisitions and their operations. © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

Assessing the effects of emerging plastics on the environment and public health

Plastic pollution has been a growing concern globally, especially under the COVID-19 pandemic due to the use of personal protective equipment. While many engineers and environmental scientists have been addressing global plastic pollution, special attention must also be paid to smaller plastics such as micro- and nano-plastics. These smaller plastics pollute and affect the health of the public, environment, and marine life. Risk assessment of plastics is required to evaluate currently available treatment technologies and identify the significance of plastic pollution. Assessing the Effects of Emerging Plastics on the Environment and Public Health assesses the harmful effects of plastics on the environment and public health. It evaluates the potential risks of micro- and nano-plastics and reviews applications of modeling tools and prevention approaches using technologies to minimize plastic pollution through the development of biodegradable plastics or recycling and reusing plastics in environmentally friendly ways. Covering topics such as agroecosystems, preventive measures, and bioplastics, this book is an indispensable resource for environmentalists, environmental engineers, government officials, libraries, business leaders, students and educators of higher education, academicians, and researchers. © 2022 by IGI Global. All rights reserved.

Edge Deployment Framework of GuardBot for Optimized Face Mask Recognition With Real-Time Inference Using Deep Learning

Deep learning based models on the edge devices have received considerable attention as a promising means to handle a variety of AI applications. However, deploying the deep learning models in the production environment with efficient inference on the edge devices is still a challenging task due to computation and memory constraints. This paper proposes a framework for the service robot named GuardBot powered by Jetson Xavier NX and presents a real-world case study of deploying the optimized face mask recognition application with real-time inference on the edge device. It assists the robot to detect whether people are wearing a mask to guard against COVID-19 and gives a polite voice reminder to wear the mask. Our framework contains dual-stage architecture based on convolutional neural networks with three main modules that employ (1) MTCNN for face detection, (2) our proposed CNN model and seven transfer learning based custom models which are Inception-v3, VGG16, denseNet121, resNet50, NASNetMobile, XceptionNet, MobileNet-v2 for face mask classification, (3) TensorRT for optimization of all the models to speedup inference on the Jetson Xavier NX. Our study carries out several analysis based on the models' performance in terms of their frames per second, execution time and images per second. It also evaluates the accuracy, precision, recall & F1-score and makes the comparison of all models before and after optimization with a main focus on high throughput and low latency. Finally, the framework is deployed on a mobile robot to perform experiments in both outdoor and multi-floor indoor environments with patrolling and non-patrolling modes. Compared to other state-of-the-art models, our proposed CNN model for face mask recognition based on the classification obtains 94.5%, 95.9% and 94.28% accuracy on training, validation and testing datasets respectively which is better than MobileNet-v2, Xception and InceptionNet-v3 while it achieves highest throughput and lowest latency than all other models after optimization at different precision levels.

Assessing the efficiency of the Covid-19 control measures and public health policy in OECD countries from cultural perspectives

Purpose: To identify sources of the success and failure of COVID-19 control measures and develop best-practice public health policy in mitigating the spread of COVID-19, this paper aims to evaluate the efficiency of various combinations of government COVID-19 control measures among OECD countries. This paper also identifies which factors critically influence the efficiency of COVID-19 control measures. Design/methodology/approach: This paper employed two-stage network SBM (slacks-based measure of efficiency) models with variable returns-to-scale and constant returns-to-scale, respectively, among various forms of data envelopment analysis (DEA) models. As a post hoc analysis, the authors used Tobit regression for examining the causal relationship between a nation's cultural dimensions and its COVID-19 control measure's efficiency scores. Findings: The authors found that the pervasive less individualistic and higher uncertainty avoiding culture positively influenced the efficient control of COVID-19 outbreaks since such a culture helped the government impose its mandatory COVID-19 control measures without people's strong resistance to those measures. Originality/value: Many public health policymakers are wondering why COVID-19 control measures are not effective in coping with the COVID-19 outbreaks. This paper helps the government find the most efficient combination of COVID-19 controls measures for curbing the spread of the stubborn coronavirus. This paper is one of the first attempts to identify pandemic risk mitigation factors from a cultural perspective. © 2021, Emerald Publishing Limited.

Impact on mental health of the COVID-19 outbreak among community pharmacists during the sanitary lockdown period.

OBJECTIVES: COVID-19 outbreak can impact mental health including health care workers. The aim of this study was to assess the psychological impact of COVID-19 in French community pharmacists. MATERIAL AND METHODS: We carried out a postal-based survey to assess the psychological impact of COVID-19 in French owner community pharmacists based on three validated self-report questionnaires: Perceived Stress scale, Impact of Event Scale-revised and Maslach Burnout Inventory. RESULTS: The sample consists of 135 community pharmacists. Twenty-three pharmacists reported significant post-traumatic stress symptoms (17%). High burnout symptoms were found in 33 (25%), 46 (34.9%) and 4 (3%) participants. Females scored higher than males for all questionnaires (P=0.01). CONCLUSIONS: This study is the first study which showed the psychological impact of COVID-19 in community pharmacists. Based on validated self-report questionnaires, up to 35% of pharmacists reported psychological disturbances. Interventions to promote psychological well-being of healthcare workers need to be developing.