ABSTRACT
Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) has touched individuals from all walks of life in recent years, and the movement of people and commodities has become a crucial route for the disease's spread. Reasonable control of the transportation industry has become a vital to the epidemic's prevention and control. Simultaneously, a succession of tight blockade inspection measures in the transportation business have significantly restricted the movement of people and commodities, putting a strain on the industry. As a result, it is crucial to study the transportation industry's comprehensive measurement in this environment for future regulation and prevention. In this paper, the transportation industry's freight and passenger transport are used as indicators to assess the economy, and the data source is multi-source time series data from government statistics. To anticipate future economic trends in the transportation business, we chose freight and passenger transportation in the air, land, and marine industries, respectively. Due to the significant amount of missing data, this paper develops a time series data imputation approach for its specific missing situations in order to fill in the gaps and make future prediction tasks easier. In addition, this paper builds a long and short-term memory network to train the data in order to predict future transportation industry economic trends. © 2022 IEEE.
ABSTRACT
ZigBee Programming is an elective course for computer science and technology major, mainly teaching the use of CC2530 and Zstack protocol stack. ZigBee is a kind of short-range, low complexity, low-power, low-cost and two-way wireless communication technology, which is mainly used for data transmission between various electronic devices with short distance, low power consumption and low transmission rate. The main purpose of this course is to cultivate students' basic hardware concept understanding and hands-on development ability. In order to do a good job of online teaching during the COVID-19 pandemic, aiming at the problems existing in the online teaching of ZigBee programming course, we put forward some improvement measures for the teaching methods and evaluation methods, including new teaching content, teaching mode, usual performance evaluation and final examination in this paper, combined with the characteristics of the course and the actual online teaching situation. The questionnaire results show that the students are satisfied with this novel teaching method. Moreover, it also provides references for the online teaching of other computer hardware courses. © 2021 IEEE.
ABSTRACT
Purpose: As the world grapples with the pervasive effects of the coronavirus pandemic, a notable disconnect has emerged in the public's understanding of scientific and medical research. Particularly, the travel industry has become unquestionably vulnerable amid the COVID-19 outbreak;this pandemic has interrupted the industry's operations with devastating economic consequences. This paper aims to highlight the importance of deconstructing barriers between medical science and public awareness related to COVID-19, taking tourism as a case in point. It also discusses the role of interdisciplinary research in facilitating the tourism and hospitality industry's recovery and alleviating tourists' uncertainties in the wake of COVID-19. Design/methodology/approach: This paper offers a synthesis of news coverage from several media outlets, framed within the literature on knowledge transformation across disciplines. This framing focuses on the medical sciences (e.g. public health) and social sciences (e.g. tourism management) to identify gaps between medical scientific knowledge and public awareness in the context of COVID-19. The authors' experience in public health and tourism management further demonstrates a missing link between academic research and the information made available in public health and everyday settings. A potential research agenda is proposed accordingly. Findings: This paper summarizes how salient issues related to knowledge transfer can become intensified during a global pandemic, such as medical research not being communicated in plain language, which leads some citizens to feel apathetic about findings. Reporting on the prevalence and anticipated consequences of disease outbreaks can hence be difficult, especially early in the development of diseases such as COVID-19. Research limitations/implications: By assuming a cross-disciplinary perspective on medical/health and social science research, this paper encourages academic and practical collaboration to bring medical research to the masses. This paper also outlines several research directions to promote public health, safety and sustainability through tourism. Practical implications: This paper highlights that it is essential for medical knowledge to be disseminated in a manner that promotes public understanding. The tourism and hospitality industry can benefit from an essential understanding of medical findings, particularly during this pandemic. Without a firm grasp on COVID-19's origins and treatment, the tourism and hospitality industry will likely struggle to recover from this catastrophe. Social implications: Taking COVID-19 as a case in point, this study advocates leveraging the strengths of disparate domains to bring medical findings to a wider audience and showcase cutting-edge developments for the greater good. This study also emphasizes the importance of engaging the general public in reputable scientific research findings to increase public awareness in a professional and accurate manner. Originality/value: This paper presents a unique and critical discussion of the gap between medical science knowledge and public awareness, as well as its implications for tourism and hospitality recovery after COVID-19, with a focus on applying medical scientific knowledge to post-pandemic industry recovery. © 2021, Jun Wen, Haifeng Hou, Metin Kozak, Fang Meng, Chung-En Yu and Wei Wang.
ABSTRACT
With the influence of novel coronavirus, wearing masks is becoming more and more important. If computer vision system is used in public places to detect whether a pedestrian is wearing a mask, it will improve the efficiency of social operation. Therefore, a new mask recognition algorithm based on improved yolov3 is proposed. Firstly, the dataset is acquired through network video;secondly, the dataset is preprocessed;finally, a new network model is proposed and the activation function of YOLOv3 is changed. The average accuracy of the improved YOLOv3 algorithm is 83.79%. This method is 1.18% higher than the original YOLOv3. © 2021, Springer Nature Switzerland AG.