In wireless communication, small cells are low-powered cellular base stations that can be used to enhance the coverage and capacity of wireless networks in areas where traditional cell towers may not be practical or cost-effective. Unmanned aerial vehicles (UAVs) can be used to quickly deploy and position small cells in areas that are difficult to access or where traditional infrastructure is not feasible. UAVs are deployed by telecommunication service providers to provide aerial network access in remote rural areas, disaster-affected areas, or massive-attendance events. In this paper, we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network to a Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy and data rate efficiency of the strategic beaconing policy (at Nash equilibrium).

The ability to differentiate between various fish species plays an essential role in aquaculture. It helps to protect their populations and monitor their health situations and their nutrient systems. However, old machine learning methods are unable to detect objects in images with complex backgrounds and especially in low-light conditions. This paper aims to improve the performance of a YOLO v5 model for fish recognition and classification. In the context of transfer learning, our improved model FishDETECT uses the pre-trained FishMask model. Then it is tested in various complex scenes. The experimental results show that FishDETECT is more effective than a simple YOLO v5 model. Using the evaluation metrics Precision, Recall, and mAP50, our new model achieved accuracy rates of 0.962, 0.978, and 0.995, respectively.

(1) Background: This study aimed to compare the immunogenicity of the mix-and-match CoronaVac/BNT162b2 vaccination to the homologous CoronaVac/CoronaVac regimen. (2) Methods: We conducted a simple-blinded randomized superiority trial to measure SARS-CoV-2 neutralization antibodies and anti-spike receptor binding domain (RBD) IgG concentrations in blood samples of participants who had received the first dose of CoronaVac vaccine followed by a dose of BNT162b2 or CoronaVac vaccine. The primary endpoint for immunogenicity was the serum-neutralizing antibody level with a percentage of inhibition at 90% at 21-35 days after the boost. A difference of 25% between groups was considered clinically relevant. (3) Results: Among the 240 eligible participants, the primary endpoint data were available for 100 participants randomly allocated to the mix-and-match group versus 99 participants randomly allocated to the homologous dose group. The mix-and-match regimen elicited significantly higher levels of neutralizing antibodies (median level of 96%, interquartile range (IQR) (95-97) versus median level of 94%, IQR (81-96) and anti-spike IgG antibodies (median level of 13,460, IQR (2557-29,930) versus median level of 1190, IQR (347-4964) compared to the homologous group. Accordingly, the percentage of subjects with a percentage of neutralizing antibodies > 90% was significantly higher in the mix-and-match group (90.0%) versus the homologous (60.6%). Interestingly, no severe events were reported within 30 days after the second dose of vaccination in both groups. (4) Conclusions: Our data showed the superiority of the mix-and-match CoronaVac/BNT162b2 vaccination compared to the CoronaVac/CoronaVac regimen in terms of immunogenicity, thus constituting a proof-of-concept study supporting the use of inactivated vaccines in a mix-and-match strategy while ensuring good immunogenicity and safety.

Multipair (MP) massive multiple-input–multiple-output (MIMO) two-way relaying system is a promising solution that is able to provide excellent spectral efficiency performance. It consists in multiple pairs ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2K$</tex-math></inline-formula> ) of single-antenna user terminals that exchange information through of a relay using a large number of antennas <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{t}\gg 2K$</tex-math></inline-formula> . The relay is equipped with a large number of energy-efficient power amplifiers (PAs) and ensure the multipair two-way forwarding. In this article, we first present an analytical study focused on the effects of PAs on the considered system over block fading Rayleigh channel. We derive closed-form expressions for the symbol error rate. Then, we develop a new efficient distributed learning approach for MP two-way massive MIMO. Specifically, we design the MP two-way massive MIMO system by leveraging the concept of the autoencoder, whereas the end-to-end communication system is designed using one deep neural network (NN). Interestingly, the proposed scheme, referred to as AE-MP-mMIMO, is suited for varying block fading Rayleigh channel scenarios. Indeed, we propose to adopt one stage precoder/decoder for each UE and two-stage precoding scheme for the relay: 1) an NN-Tx is used to address the PA nonlinearity and 2) a linear zero-forcing precoder is adopted to remove the multiuser interference. The NN-Tx and the UE's precoder/decoder are trained off-line and when the channel varies, only the zero-forcing precoder changes on-line. Numerical simulations show the capability of the proposed approach to achieve competitive performance with a significantly lower complexity compared to existing literature.