Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design

The COVID-19 pandemic has created a worldwide healthcare crisis. Convolutional Neural Networks (CNNs) have recently been used with encouraging results to help detect COVID-19 from chest X-ray images. However, to generalize well to unseen data, CNNs require large labeled datasets. Due to the lack of publicly available COVID-19 datasets, most CNNs apply various data augmentation techniques during training. However, there has not been a thorough statistical analysis of how data augmentation operations affect classification performance for COVID-19 detection. In this study, a fractional factorial experimental design is used to examine the impact of basic augmentation methods on COVID-19 detection. The latter enables identifying which particular data augmentation techniques and interactions have a statistically significant impact on the classification performance, whether positively or negatively. Using the CoroNet architecture and two publicly available COVID-19 datasets, the most common basic augmentation methods in the literature are evaluated. The results of the experiments demonstrate that the methods of zoom, range, and height shift positively impact the model's accuracy in dataset 1. The performance of dataset 2 is unaffected by any of the data augmentation operations. Additionally, a new state-of-the-art performance is achieved on both datasets by training CoroNet with the ideal data augmentation values found using the experimental design. Specifically, in dataset 1, 97% accuracy, 93% precision, and 97.7% recall were attained, while in dataset 2, 97% accuracy, 97% precision, and 97.6% recall were achieved. These results indicate that analyzing the effects of data augmentations on a particular task and dataset is essential for the best performance. Doi: 10.28991/ESJ-2023-SPER-01 Full Text: PD

Freight trip generation modeling and data collection processes in Latin American cities. Modeling framework for Quito and generalization issues

International audienceEver-growing urban areas and global population movement towards urbanization lead to major concerns regarding urban logistics and last mile operations. In Latin America, the problem becomes critical since volatile emerging economies and unstable political situations, which are common in the region, introduce additional limitations for strong logistics solutions. In the city of Quito (Ecuador), traffic regulations only consider time-schedule restrictions for vehicle mobility without any other policies that benefit urban goods movement. This is in part because there is a lack of knowledge on freight flows, mainly related to the difficulties to retrieve data to that purpose. This paper proposes a freight trip generation analysis in Quito, based on a methodology included in “MIT’s Better Cities for Logistics Toolkit”, which defines specific zones observation and data collection campaigns. More precisely, a procedure combining observation-based and declarative data collection processes is proposed. First, the opportunities of combining both observed and declared data to characterize freight trip generation are addressed on the basis of a literature review. Main issues of combining establishment based surveys and observations are addressed to generalize the proposed framework. Finally, application implications in a transferability perspective to other Latin American countries are addressed

Elucidating the burden of recurrent and chronic digital ulcers in systemic sclerosis: long-term results from the DUO Registry

Objectives Digital ulcers (DUs) occur in up to half of patients with systemic sclerosis (SSc) and may lead to infection, gangrene and amputation with functional disability and reduced quality of life. This study has elucidated the burden of SSc-associated DUs through identification of four patient categories based on the pattern of DU recurrence over a 2-year observation period.Methods Patients with SSc-associated DUs enrolled in the Digital Ulcers Outcome Registry between 1 April 2008 and 19 November 2013, and with 2years of observation and 3 follow-up visits during the observation period were analysed. Incident DU-associated complications were recorded during follow-up. Work and daily activity impairment were measured using a functional assessment questionnaire completed by patients after the observation period. Potential factors that could predict incident complications were identified in patients with chronic DUs.Results From 1459 patients, four DU occurrence categories were identified: 33.2% no-DU; 9.4% episodic; 46.2% recurrent; 11.2% chronic. During the observation period, patients from the chronic category had the highest rate of incident complications, highest work impairment and greatest need for help compared with the other categories. Independent factors associated with incident complications included gastrointestinal manifestations (OR 3.73, p=0.03) and previous soft tissue infection (OR 5.86, p=0.01).Conclusions This proposed novel categorisation of patients with SSc-associated DUs based on the occurrence of DUs over time may help to identify patients in the clinic with a heavier DU burden who could benefit from more complex management to improve their functioning and quality of life