Behavioral practices towards antibiotic use among health care workers - Sierra Leone, 2021: a facility-based cross-sectional study.

Introduction: globally, antimicrobial resistance (AMR) kills around 1.27 million 700,000 people each year. In Sierra Leone, there is limited information on antibiotic use among healthcare workers (HCWs). We assessed antibiotic prescribing practices and associated factors among HCWs in Sierra Leone. Methods: we conducted a cross-sectional survey among HCWs. We collected data using a questionnaire containing a Likert scale for antibiotic prescribing practices. We categorized prescribing practices into good and poor practices. We calculated adjusted odds ratios (aOR) to identify risk factors. Results: out of 337 (100%) HCWs, 45% scored good practice. Out of the total, 131 (39%) of HCWS considered fever as an indication of antibiotic resistance and 280 (83%) HCWs prescribed antibiotics without performing microbiological tests and 114 (34%) prescribed a shorter course of antibiotics. Factors associated with good practice were being a doctor (aOR=1.95; CI: 1.07, 3.56), the internet as a source of information (aOR=2.00; CI: 1.10, 3.66), having a high perception that AMR is a problem in the health-facility (aOR=1.80; CI: 1.01, 3.23) and there is a connection between one´s prescription and AMR (aOR=2.15; CI: 1.07, 4.32). Conclusion: this study identified a low level of good practice toward antibiotic prescription. We initiated health education campaigns and recommended continuous professional development programs on antibiotic use.

Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment.

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system's ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

A Novel Real-Time Reference Key Frame Scan Matching Method.

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions' environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.