Background Preoperative esophagogastroduodenoscopy (EGD) may affect the management of bariatric patients although this is not consistent universally. The present prospective study evaluated the effect of preoperative EGD findings in obese Saudi patients, including upper digestive symptoms (UDS) and comorbidities, on their planned surgery. Methods From January 2018 to May 2019, we conducted a 4-center retrospective observational study to evaluate the endoscopic findings among Saudi patients aged 18-65 years with a body mass index (BMI) >40 kg/m 2 . Preoperative data included UDS, comorbidities, Helicobacter pylori ( H. pylori ) infection assessed during a histopathological examination, and EGD findings. Results 717 patients underwent EGDs, and 432 underwent bariatric surgery. The mean BMI was 44.3±6.3 kg/m 2 , and the mean age was 27.8±11.8 years. The overall UDS prevalence was 49%, with the most frequent being gastroesophageal reflux disease 54% (387/717), followed by dyspepsia 44% (315/717). H. pylori infection was detected in 287/672 (42.4%) patients. The total percentage of patients with normal EGD was 36% (258/717). A delayed bariatric procedure was performed in 15% of the patients for the following reasons: 2.3% had large polyps of >1 cm (either hyperplastic or cystic polyps); 1.62% had esophagitis grade C and D based on the Los Angeles classification; 0.7% had Barrett’s esophagus; and 5.7% had peptic ulcer disease. Conclusions Our findings confirmed that obesity carries a profound health burden with a significant impact on health expenditures. Routine preoperative EGD in the obese Saudi population appears to be mandatory to identify factors that may change, delay, or postpone the bariatric procedure.

The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system’s efficacy for reliable UAS detection at distances exceeding 70 m.

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UASs (Unmanned Air Systems) are universally used in many activities, spanning from leisure-commercial to military applications. Accordingly, as the number of UASs operating in the sky increases, so does the need to detect and identify them, in order to ensure their legitimate use. This paper introduces a continuous wave (CW) Doppler radar implementation that can be used to provide early warning for flying-by small UASs. By applying Fast Fourier Transform (FFT) to the returned signal’s Doppler frequency, estimations can be made regarding the presence of aerial bodies inside an Area of Interest (AoI). Achieving reliable detection with a low false alarm rate (FAR) while keeping the size and power demands of the system to minimum was a challenge that was successfully met. The proposed system was extensively tested in outdoor environments; measurement results are presented and parameters such as radar power, antenna gain, and noise are discussed.