A Combined CNN-LSTM Network for Ship Classification on SAR Images.

Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.

Digital Biomarker-Based Studies: Scoping Review of Systematic Reviews.

Background: Sensors and digital devices have revolutionized the measurement, collection, and storage of behavioral and physiological data, leading to the new term digital biomarkers.

Objective: This study aimed to investigate the scope of clinical evidence covered by systematic reviews (SRs) of randomized controlled trials involving digital biomarkers.

Methods: This scoping review was organized using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

Disease-Related Costs Published in The Middle East and North Africa Region: Systematic Review and Analysis of Transferability.

Background: In the Middle East and North Africa (MENA) the scarcity of local cost data is a key barrier to conducting health economic evaluations. We systematically reviewed reports of disease-related costs from MENA and analysed their transferability within the region.

Methods: We searched PubMed and included full text English papers that reported disease-related costs from the local populations of Algeria, Bahrain, Egypt, Iraq, Jordan, Saudi Arabia, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Syria, Tunisia, United Arab Emirates and Yemen between 1995 and 2019.

Walking Gait Step Length Asymmetry Induced by Handheld Device.

The modeling and feature extraction of human gait motion are crucial in biomechanics studies, human localization, and robotics applications. Recent studies in pedestrian navigation aim at extracting gait features based on the data of low-cost sensors embedded in handheld devices, such as smartphones. The general assumption in pedestrian dead reckoning (PDR) strategy for navigation application is that the presence of a device in hand does not impact the gait symmetry and that all steps are identical.