Separating Phages From Other Virus Families and Classifying the Different Phage Families By GI-Clusters

Background: Phages are the most abundant biological entities, but the commonly used clustering techniques are difficult to separate them from other virus families and classify the different phage families together.Results: This work uses GI-clusters to separate phages from other virus families and classify the different phage families, where GI-clusters are constructed by GI-features, GI-features are constructed by the togetherness with F-features, training data, MG-Euclidean and Icc-cluster algorithms, F-features are the frequencies of multiple-nucleotides that are generated from genomes of viruses, MG-Euclidean algorithm is able to put the nearest neighbors in the same mini-groups, and Icc-cluster algorithm put the distant samples to the different mini-clusters. For these viruses that the maximum element of their GI-features are in the same locations, they are put to the same GI-clusters, where the families of viruses in test data are identified by GI-clusters, and the families of GI-clusters are defined by viruses of training data.Conclusions: From analysis of 4 data sets that are constructed by the different family viruses, we demonstrate that GI-clusters are able to separate phages from other virus families, correctly classify the different phage families, and correctly predict the families of these unknown phages also.

Computer Simulation of Exchange Bias Field at the Ferromagnetic-Antiferromagnetic Boundary

This paper presents computer simulation results for a bilayer system with ferromagnetic and antiferromagnetic films. The dependence of the exchange bias field on the external magnetic field for this system is calculated. The Heisenberg model and the Wolf cluster algorithm are used for calculations. The reason for the appearance the bias field is the interaction between spins at the films interface. An increase the external magnetic field leads to a nonlinear increase the bias field. There are two reasons for nonlinearity. First, the external magnetic field suppresses antiferromagnetic ordering. Second, an external magnetic field-ordered ferromagnetic has an inverse effect on the antiferromagnetic film.

Phase Transition and Magnetoelectric Effect in 2D Ferromagnetic Films on a Ferroelectric Substrate

In this paper, we investigate the behavior of 2D ferromagnetic (FM) films on a ferroelectric (FE) substrate with a periodic structure. The two-dimensional Frenkel–Kontorova (FK) potential simulates the substrate effect on the film. The substrate potential corresponds to a cubic crystal lattice. The Ising model and the Wolf cluster algorithm are used to describe the magnetic behavior of a FM film. The effect of an electric field on a FE substrate leads to its deformation, which is uniform and manifests itself in a period change of the substrate potential. Computer simulation shows that substrate deformations lead to a decrease in the FM film Curie temperature. If the substrate deformations exceed 5%, the film deformations become inhomogeneous. In addition, we derive the dependence of film magnetization on the external electric field.

Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN

As the passenger flow distribution center cooperating with various modes of transportation, the comprehensive passenger transport hub brings convenience to passengers. With the diversification of passenger travel modes, the passenger flow scale gradually increases, which brings significant challenges to the integrated passenger hub. Therefore, it is urgent to solve the problem of early warning and response to the future passenger flow to avoid congestion accidents. In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. In addition, the passenger flow data are studied and trained by combining with the GRNN neural network model based on parameter optimization (GA); the passenger flow prediction model is obtained. Finally, the passenger flow of Chengdu East Railway Station has been taken as an example, which is divided into 16 models, and each type of passenger flow is predicted, respectively. Compared with the traditional method, the results show that the model can predict the passenger flow with high accuracy.

K-means Cluster Algorithm Applied for Geometric Shaping Based on Iterative Polar Modulation in Inter-Data Centers Optical Interconnection

The demand of delivering various services is driving inter-data centers optical interconnection towards 400 G/800 G, which calls for increasing capacity and spectrum efficiency. The aim of this study is to effectively increase capacity while also improving nonlinear noise anti-interference. Hence, this paper presents a state-of-the-art scheme that applies the K-means cluster algorithm in geometric shaping based on iterative polar modulation (IPM). A coherent optical communication simulation system was established to demonstrate the performance of our proposal. The investigation reveals that the gap between IPM and Shannon limit has significantly narrowed in terms of mutual information. Moreover, when compared with IPM and QAM using the blind phase searching under the same order at HD-FEC threshold, the IPM-16 using the K-means algorithm achieves 0.9 dB and 1.7 dB gain; the IPM-64 achieves 0.3 dB and 1.1 dB gain, and the IPM-256 achieves 0.4 dB and 0.8 dB gain. The robustness of nonlinear noise and high capacity enable this state-of-the-art scheme to be used as an optional modulation format not only for inter-data centers optical interconnection but also for any high speed, long distance optical fiber communication system.

An Algorithm for Arranging Operators to Balance Assembly Lines and Reduce Operator Training Time

Industry 4.0 is transforming how costs, including labor costs, are managed in manufacturing and remanufacturing systems. Managers must balance assembly lines and reduce the training time of workstation operators to achieve sustainable operations. This study’s originality lies in its use of an algorithm to balance an assembly line by matching operators to workstations so that the line’s workstations achieve the same targeted output rates. First, the maximum output rate of the assembly line is found, and then the number of operators needed at each workstation is determined. Training time is reduced by matching operators’ training and skills to workstations’ skill requirements. The study obtains a robust, cluster algorithm based on the concept of group technology, then forms operator skill cells and determines operator families. Four numerical examples are presented to demonstrate the algorithm’s implementation. The proposed algorithm can solve the problem of arranging operators to balance assembly lines. Managers can also solve the problem of worker absences by assigning more than one operator with the required skillset to each workstation and rearranging them as needed.

International Gynaecological Cancer Society (IGCS) 2020 Annual Global Meeting: Twitter activity analysis

ObjectivesTwitter is the most frequently used social media platform by healthcare practitioners, at medical conferences. This study aimed to analyze Twitter conversations during the virtual International Gynecological Cancer Society 2020 conference to understand the interactions between Twitter users related to the conference.MethodsTweets using the hashtag ‘#IGCS2020’ were searched using the Twitter Search Application Programming Interface (API) during the period 10–13 September 2020. NodeXL Pro was used to retrieve data. The Clauset-Newman-Moore cluster algorithm clustered users into different groups or ‘clusters’ based on how users interacted.ResultsThere were 2009 registrants for the virtual IGCS 2020 conference. The total number of users within the network was 168, and there were 880 edges connecting users. Five types of edges were identified as follows: ‘replies to’ (n=18), ‘mentions’ (n=221), ‘mentions in retweets’ (n=375), retweets (n=198), and tweets (n=68). The most influential account was that of the IGCS account itself (@IGCSociety). The overall network shape resembled a community where distinct groups formed within the network. Our current analyses demonstrated that less than 10% of the total members interacted on Twitter.ConclusionThis study identified the most influential Twitter users within the ‘#IGCS2020’ community. he results also confirmed the community network shape of the #IGCS2020 hashtag and found that the most frequent co-related words were ‘ovarian’ and ‘cancer’ (n=39).