Exploring the effects of pandemics on transportation through correlations and deep learning techniques

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing. As a result, urban traffic congestion patterns have changed dramatically, and understanding these changes is crucial for effective emergency traffic management and control efforts. While previous studies have focused on natural disasters or major accidents, only a few have examined pandemic-related traffic congestion patterns. This paper uses correlations and machine learning techniques to analyze the relationship between COVID-19 and transportation. The authors simulated traffic models for five different networks and proposed a Traffic Prediction Technique (TPT), which includes an Impact Calculation Methodology that uses Pearson's Correlation Coefficient and Linear Regression, as well as a Traffic Prediction Module (TPM). The paper's main contribution is the introduction of the TPM, which uses Convolutional Neural Network to predict the impact of COVID-19 on transportation. The results indicate a strong correlation between the spread of COVID-19 and transportation patterns, and the CNN has a high accuracy rate in predicting these impacts.

The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study.

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.

Erythroferrone Expression in Anemic Rheumatoid Arthritis Patients: Is It Disordered Iron Trafficking or Disease Activity?

PURPOSE: Erythroferrone (ERFE) is well acknowledged for its inhibitory function on hepcidin synthesis in the liver during stress erythropoiesis, thereby ensuring sufficient iron supply to bone marrow erythroblasts. Hepcidin plays an indispensable role in the pathogenesis of anemia of chronic disease (ACD). Thus, ERFE was suggested to protect against ACD in various diseases. Rheumatoid arthritis (RA) is commonly involved with ACD and high hepcidin levels, with a further increase of the latter in active states. The present study is a case-control study that aimed to determine the pattern of ERFE expression in RA patients with concomitant ACD and study its relationship with hepcidin, erythropoietin (EPO) and disease activity. PATIENTS AND METHODS: Fifty-five RA patients with ACD were categorized into active and inactive RA using the disease activity score (DAS28); 15 healthy subjects were included as control subjects. ERFE was measured for patients and control subjects using quantitative real-time polymerase chain reaction, in addition to testing for CBC, ESR, CRP, iron profile parameters and hepcidin. EPO was assessed for patients of both active and inactive RA groups. RESULTS: ERFE and hepcidin showed the highest levels in active RA; ERFE values were similar in control subjects and inactive RA patients, while hepcidin was significantly higher in inactive RA than control subjects. Patients with high ERFE levels had higher RBC, Hct, MCV, hepcidin and EPO levels. Stepwise regression analysis has identified DAS28 and disease duration as the best predictors of ERFE values, whereas ERFE and hepcidin were independent predictors of disease activity. CONCLUSION: We introduce ERFE as a novel marker of RA activity. Although the inhibitory effect of ERFE on hepcidin is not evident, our results still indicate that ERFE may have a beneficial erythropoietic effect in the context of ACD in RA disease activity.