Abstract
The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.

Abstract
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to improve the local learning model while avoiding excessive air-to-ground and ground-to-air communications. Moreover, the proposed de-ployment framework considers the stringent energy constraints of UAVs and the problem of class imbalance, where we show that considering these design parameters significantly improves the performances of the UAV swarm in terms of classification accuracy, energy consumption and availability of UAVs when compared with several baseline algorithms.

Abstract
In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks' elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions.

Abstract
Background: The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard. Objective: This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors. Methods: A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B). Results: A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Abstract
Hexapods are very common in astronomy as a mechanism to provide a stiff mount or a precision alignment tool. Here, we present a lumped model for a general symmetric hexapod that allows us to compute the load distribution under external forces, the hexapod's resolution, and the identification of singularity loci within the workspace. We also developed a script to analyze this parametric model, which is publicly available. We use this model to develop and design a hexapod for mid-infrared ELT imager and spectrograph, one of the extremely large telescope's first light instruments. The designed hexapod solution can survive strict earthquake conditions that can go up to 5g, and position and align the 11 ton instrument with submillimetric and arcsecond precisions. Although the model presented is not as precise or as realistic as a finite element WO analysis, it provides, in a fraction of a second, a very good first approximation. Therefore, unlike Eh methods, the model is able to study many geometries in a short time. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
Context. Close young binary stars are unique laboratories for the direct measurement of pre-main-sequence (PMS) stellar masses and their comparison to evolutionary theoretical models. At the same time, a precise knowledge of their orbital parameters when still in the PMS phase offers an excellent opportunity for understanding the influence of dynamical effects on the morphology and lifetime of the circumstellar as well as circumbinary material. Aims. The young T Tauri star WW Cha was recently proposed to be a close binary object with strong infrared and submillimeter excess associated with circum-system emission, which makes it dynamically a very interesting source in the above context. The goal of this work is to determine the astrometric orbit and the stellar properties of WW Cha using multi-epoch interferometric observations. Methods. We derive the relative astrometric positions and flux ratios of the stellar companion in WW Cha from the interferometric model fitting of observations made with the VLTI instruments AMBER, PIONIER, and GRAVITY in the near-infrared from 2011 to 2020. For two epochs, the resulting uv-coverage in spatial frequencies permits us to perform the first image reconstruction of the system in the K band. The positions of nine epochs are used to determine the orbital elements and the total mass of the system. Combining the orbital solution with distance measurements from Gaia DR2 and the analysis of evolutionary tracks, we constrain the mass ratio. Results. We find the secondary star orbiting the primary with a period of T = 206.55 days, a semimajor axis of a = 1.01 au, and a relatively high eccentricity of e = 0.45. The dynamical mass of M-tot = 3.20 M-circle dot can be explained by a mass ratio between similar to 0.5 and 1, indicating an intermediate-mass T Tauri classification for both components. The orbital angular momentum vector is in close alignment with the angular momentum vector of the outer disk as measured by ALMA and SPHERE, resulting in a small mutual disk inclination. The analysis of the relative photometry suggests the presence of infrared excess surviving in the system and likely originating from truncated circumstellar disks. The flux ratio between the two components appears variable, in particular in the K band, and may hint at periods of triggered higher and lower accretion or changes in the disks' structures. Conclusions. The knowledge of the orbital parameters, combined with a relatively short period, makes WW Cha an ideal target for studying the interaction of a close young T Tauri binary with its surrounding material, such as time-dependent accretion phenomena. Finding WW Cha to be composed of two (probably similar) stars led us to reevaluate the mass of WW Cha, which had been previously derived under the assumption of a single star. This work illustrates the potential of long baseline interferometry to precisely characterize close young binary stars separated by a few astronomical units. Finally, when combined with radial velocity measurements, individual stellar masses can be derived and used to calibrate theoretical PMS models.