distance methods
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Influenza A virus is a segmented RNA virus whose genome consists of 8 single-stranded negative-sense RNA segments. This unique genetic structure allows viruses to exchange their segments through reassortment when they infect the same host cell. Studying the determination and nature of influenza A virus reassortment is critical to understanding the generation of pandemic strains and the spread of viruses across species. Reassortment detection is the first step in influenza A virus reassortment research. Several methods for automatic detection of reassortment have been proposed, which can be roughly divided into two categories: phylogenetic methods and distance methods. In this article, we proposed a reassortment detection method that does not require multiple sequence alignment and phylogenetic analysis. We extracted the codon features from the segment sequence and expressed the sequence as a feature vector, and then used the clustering method of self-organizing map to cluster the sequence for each segment. Based on the clustering results and the epidemiological information of the virus, the reassortment detection was implemented. We used this method to perform reassortment detection on the collected 7,075 strains from Asia and identified 516 reassortment events. We also conducted a statistical analysis of the identified reassortment events and found conclusions consistent with previous studies. Our method will provide new insights for automating reassortment detection tasks and understanding the reassortment patterns of influenza A viruses.

This paper presents an innovative technique of traffic management on a route divided into fixed block distances for trains equipped with a continuous area data transmission system that ensures information exchange between a train and a control center. The originality of the technique is focused on the optimization of the following train’s speed profile in terms of energy consumption under the need to maintain a minimum distance to the preceding train and to assume the smooth running of the following train, using elements of fixed and mobile block distance methods. The obtained results are an answer to the question of whether it is possible to obtain a smoother movement of the train and savings in mechanical energy consumption if the speed profile of this train is adjusted to the conditions before the train, such as information about the speed of the preceding train.

Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x, y, and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose.1

Objectives: Commentaries have suggested that initial emphasis on the higher susceptibility and mortality rates of older adults to COVID-19 has instigated ageism within the public discourse and policy decisions. Using the health belief model, the current study examined ageism in interaction with other factors influencing intention to social distance. Methods: Threat of contracting COVID-19, benefits and barriers to social distancing, benevolent and hostile ageism, and intention to social distance were examined in 960 adults ( M = 37.81 years, SD = 11.65). Results: Benevolent and hostile ageism were significant moderators for both perceived threat and barriers on intent to social distance; hostile ageism also moderated benefits on intent to social distance. Discussion: The current study demonstrates how ageism influences behavior during a pandemic. With initial reports of COVID-19 presenting older adults as a homogenous group, ageism negatively interacted with intention to social distance and may place older adults at greater risk.

Distance internship rises as global pandemic Covid-19 starts in the beginning of 2020. It forces the higher education institution to re-manage the rules of internship for second year students. As part of the regulation, the internship for Polban students was suggested to be done through distance methods. One of the NGOs that provide distance internships for English Department students is IQR Foundation. This study then investigates the enactment of students' copywriting skills and the challenges during distance internship. The data were collected by combining observation and study literature. The results show this distance internship enables students to apply copywriting skills through three enactments: composing script, creating English subtitles, and collaborating in news writing. Compared to the conventional one, distance internship leads to three main challenges: deciding concepts, using English as the language of communication, and handling the timeline. Distance internship, in many ways, has created many chances for students to grow up and to implement their knowledge in a new paradigm despite the pandemic as the boundary.

This article analyzes the issues of implementation of innovations and respective methodology of teaching humanities in universities. The aim of this article is to reveal challenges related with implementation and use of advanced innovative methods of teaching humanities in universities. This work substantiates the concept that modern processes occurring in education are direct consequence of complicated and deep transformations in culture, economy, policy of the World. Adaptation of life to wide-scale transformations is impossible without holistic comprehension of motives, trends and prospects of further social development, including education. The novelty of the work is in development of certain theoretic models of education development, which is rapidly moving away from the differentiation and fragmentation of scientific disciplines, modules and is concentrated on their integration. Another novelty in the existing paradigm of education can be presented by targeting of potential not just on distance methods but on creation of innovative education systems based, for instance, on technologies of virtual reality (VR). This work proves that in VR the educational components or prototypes can be presented by such units as virtual laboratory (workshop), lecture spaces, displaying of considered entities, network educational establishments.