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The main goals of fifth generation (5G) systems are to significantly increase the network capacity and to support new 5G service requirements. Ultra network densification with small cells is among the key pillars for 5G evolution. The inter-small-cell 5G backhaul network involves massive data traffic. Hence, it is important to have a centralized, efficient multi-hop routing protocol for backhaul networks to manage and speed up the routing decisions among small cells, while considering the 5G service requirements. This paper proposes a parallel multi-hop routing protocol to speed up routing decisions in 5G backhaul networks. To this end, we study the efficiency of utilizing the parallel platforms of cloud computing and high-performance computing (HPC) to manage and speed up the parallel routing protocol for different communication network sizes and set recommendations for utilizing cloud resources to adopt the parallel protocol. Our numerical results indicate that the HPC parallel implementation outperforms the cloud computing implementation, in terms of routing decision speed-up and scalability to large network sizes. In particular, for a large network size with 2048 nodes, our HPC implementation achieves a routing speed-up of 37x. However, the best routing speed-up achieved using our cloud computing implementation is 15.5x, and is recorded using one virtual machine (VM) for a network size of 1024 nodes. In summary, there is a trade-off between a better performance for HPC vs. flexible resources of cloud computing. Thus, choosing best fit platform for 5G routing protocols depends on the deployment scenarios at 5G core or edge network.

ABSTRACT: Objectives: The association between toothbrushing and coronavirus disease 2019 (COVID-19) infections is unknown. The aim of this study was to test the hypothesis that the change in time and frequency of toothbrushing is associated with having COVID-19 symptoms. Methods: In this 8-month retrospective cohort study, we used the data from the Japan COVID-19 and Society Internet Survey (JACSIS; N = 22,366), which was conducted between August and September 2020. The logistic regression analyses were used to calculate the odds ratios (ORs) of having the 3 main COVID-19 symptoms (high fever, cough, and taste and smell disorder). Confounders were age, sex, educational attainment, equivalised income level, self-rated health, health literacy, and living area. Results: The mean age of the participants was 49 years (SD = ±17.3), and 49.2% were male. Overall 2704 (12.1%) participants changed (increased or decreased) the time and frequency of toothbrushing, whilst 19,662 (87.9%) did not change. Only 60 participants (0.3%) had the 3 main COVID-19 symptoms. All logistic regression models showed that those who had a change in time and frequency of toothbrushing had higher odds of having the 3 main COVID-19 symptoms compared to those who had unchanged time and frequency of toothbrushing. The ORs ranged from 6.00 (95% confidence interval [CI], 3.60–9.99) in the crude model to 4.08 (95% CI, 2.38–6.98) in the fully adjusted model. Conclusions: The change in time and frequency of toothbrushing from before to after the COVID-19 pandemic was associated with having the 3 main COVID-19 symptoms.

CBCT (cone beam computed tomography) is an imaging investigation that provides three-dimensional (3D) images of craniofacial structures. The purpose of this study is to determine the mechanical behavior of an orthodontic system where the lingual treatment technique was used in a 25-year-old female patient from whom a set of CBCT scans was used. CBCT images were processed through software programs such as Invesalius, Geomagic, and Solid Works, to create models containing virtual solids. These models were then imported into Ansys Workbench 2019 R3 (a finite element method software program) for successive simulations to generate displacement maps, deformations, stress distributions, and diagrams. We observed that in the lingual technique, the lowest force occurring on the maxillary teeth is at 1.1, while the highest force appears at 2.3. In the mandible, the lowest force occurs at 4.6, and the highest force at 3.1. The values of the forces and the results of the finite element method can represent a basis for the innovation of new orthodontic springs and also of bracket elements. Thus, by using new technologies, orthodontic practice can be significantly improved for the benefit of patients. Other virtual methods and techniques can be used in future studies, including the application of virtual reality for orthodontic diagnosis. [ABSTRACT FROM AUTHOR]

Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques.

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Copyright of Farmacia is the property of Societatea de Stiinte Farmaceutice Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)