Simulation of Traffic Flow Model with Traffic Controller Boundary

This paper considers a fluid dynamic traffic flow model appended with a closure linear velocity-density relationship which provides a first order hyperbolic partial differential equation (PDE) and is treated as an initial boundary value problem (IBVP). We consider the boundary value in such a way that one side of highway treat like there is a traffic controller at that point. We present the analytic solution of the traffic flow model as a Cauchy problem. A numerical simulation of the traffic flow model (IBVP) is performed based on a finite difference scheme for the model with two sided boundary conditions and a suitable numerical scheme for this is the Lax-Friedrichs scheme. Solution figure from our scheme indicates a desired result that amplitude and frequency of cars density and velocity reduces as time grows. Also at traffic controller point, velocity and density values change as desired manner. In further, we also want to introduce anisotropic behavior of cars(to get more realistic picture) which has not been considered here. Doi: 10.12777/ijse.5.1.25-30 [How to cite this article: Sultana, N., Parvin, M. , Sarker, R., Andallah, L.S. (2013). Simulation of Traffic Flow Model with Traffic Controller Boundary. International Journal of Science and Engineering, 5(1),25-30. Doi: 10.12777/ijse.5.1.25-30

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown

Power transformer health condition evaluation: A deep generative model aided intelligent framework

This paper presents a deep generative model-aided intelligent framework for effective health condition evaluation of power grid transformers. The health assessment of a power transformer is required to guarantee the stable and sustainable operation of the grid and to precisely convert the electrical energy. A power transformer must undergo a series of tests to determine its state of health and identify its health index. In this paper, we develop a novel approach to identify and classify the health condition of power transformers using a machine learning approach. The proposed framework is structured by using a multi-layer perception generative model with a logistic regression classifier. The developed model uses the twelve input layers which enables the model to effectively compressed the dataset and eight categories in the output classification layers. The effectiveness of the proposed model is examined on the real-world testing data set of 31 categories of six hundred and eight transformers. The obtained performance using the proposed framework confirms its efficacy in precisely evaluating the transformer's health condition. The obtained results have also been compared with the existing machine-learning models. The comparisons show that the proposed model outperforms the state-of-the-art models by achieving 99% of accuracy. 2023 Elsevier B.V.Scopu

Insights into novel inhibitors intending HCMV protease a computational molecular modelling investigation for antiviral drug repurposing

Human cytomegalovirus (HCMV) is an important opportunistic pathogen that is the most significant viral cause of congenital birth abnormalities and is responsible for a high morbidity and death rate in immunocompromised patients. HCMV's rising severity and the limitations of current vaccines in preventing infection highlight the urgent need for effective antiviral drugs. This study aimed to identify small compounds using extensive & appropriate in-silico drug design techniques, including molecular docking, Post-docking MM-GBSA, ADMET, MD simulation, PCA, and DCCM were employed in this study capable of binding to the viral protease and inhibiting its activity, thereby preventing proteolytic processing during capsid maturation. 3516 compounds from life chemical were used in molecular docking, and the top four compounds having high binding affinity and promising ADMET properties with life chemicals ID: F3407-0101 (CID: 49667672), F6559-3323 (CID: 121022124), F6456-1266 (CID: 71810903), and F3411-7969 (CID: 50785034). MD simulation was also used to assess the stability of the protein-ligand complex structure. Finally, after MD simulation, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analysis were performed using trajectories, and we can suggest the best drug candidate which is CID: 50785034 (N-(3-fluoro-4-methylphenyl)-2-({7-oxo-8-[(oxolan-2-yl) methyl]-5-thia-1,8,10,11-tetraazatricyclo [7.3.0.0^ {2,6}] dodeca-2(6),3,9,11-tetraen-12-yl}sulfanyl)acetamide), another two compounds CID: 121022124, and CID: 71810903 which comes after CID: 50785034. All three compounds may have the potential to be developed as a therapy option for HCMV infection

Cost of oral cholera vaccine delivery in a mass immunization program for children in urban Bangladesh.

Cholera poses a substantial health burden in the developing world due to both epidemic and endemic diseases. The World Health Organization recommends oral cholera vaccines for mass vaccination campaigns in addition to traditional prevention practices and treatments in resource-poor settings. In many developing countries like Bangladesh, the major challenge behind implementing mass vaccination campaigns concerns the affordability of the oral cholera vaccine (OCV). Vaccination of children with OCV is not only an impactful approach for controlling cholera at the population level and reducing childhood morbidity but is also considered more cost-effective than vaccinating all ages. The aim of the study was to estimate the cost of an OCV campaign for children from a societal perspective using empirical study. A total of 66,311 children aged 1 to 14 years old were fully vaccinated with two doses of the OCV Shanchol while 9,035 individuals received one dose of this vaccine. The estimated societal cost per individual for full vaccination was US$6.11, which includes the cost of vaccine delivery estimated at US$ 1.95. The cost per single dose was estimated at US$2.86. The total provider cost for full vaccination was estimated at US$ 6.01 and the recipient cost at US$ 0.10. Our estimation of OCV delivery costs for children was relatively higher than what was found in a similar mass OCV campaign for all age groups, indicating that there may be additional cost factors to consider in targeted vaccine campaigns. This analysis provides useful benchmarks for the possible costs related to delivery of OCV to children and future OCV cost-effectiveness models should factor in these possible cost disparities. Attempts to reduce the cost per dose are likely to have a greater impact on the cost of similar vaccination campaigns in many resource-poor settings