A study of real-time recognition of unmanned aerial vehicles in outdoor areas based on a random forest algorithm

With the widespread use of unmanned aerial vehicles (UAVs) in life, the real-time recognition of UAVs has become an important issue. The authors of this paper mainly studied the application of the random forest (RF) algorithm in the outdoor real-time recognition of UAVs. Mel-Frequency Cepstral Coefficient (MFCC) features were extracted from sound signals firstly, and then the RF method was combined with weighted voting to obtain the improved random forest (IRF) method to identify UAV sounds and environmental sounds. An experimental analysis was conducted. The modeling time of the IRF method increased by 9.52% compared with the RF method, and the recognition rate of the IRF method decreased with the increase of the distance from the microphone; however, the recognition rate of the IRF method was always higher than that of the RF method, and the recognition rate of the IRF method for the mixed samples was always higher than 90%. When the distance was 10 m, the IRF method still had a recognition rate of 91.29%. The experimental results verify the effectiveness of the IRF method for the outdoor real-time recognition of UAVs and its practical application feasibility

A Deep Learning Approach for Wireless Network Performance Classification Based on UAV Mobility Features

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios

Effect of Sarcopenia on Survival and Health-Related Quality of Life in Patients with Hepatocellular Carcinoma after Hepatectomy

Background: Although sarcopenia has been reported as a negative prognostic factor in patients with hepatocellular carcinoma (HCC), the lack of studies with a prospective design utilizing comprehensive sarcopenia assessment with composite endpoints is an important gap in understanding the impact of sarcopenia in patients with HCC. The aim of this study was to investigate the relationship between sarcopenia and postoperative 1-year mortality and health-related quality of life (HRQOL) based on sarcopenia assessment. Methods: The study cohort, who received resection surgery for HCC between May 2020 and August 2021, was assessed for sarcopenia based on grip strength, the chair stand test, skeletal muscle mass, and gait speed. The primary outcome measures were 1-year mortality and HRQOL determined using the QLQ-C30 questionnaire. In addition, we collected hospital costs, postoperative hospital stays, complications, 30-day and 90-day mortality, and 90- and 180-day readmission rates. Univariate and multivariate linear regression analyses were conducted to examine factors associated with global health status. Results: A total of 153 eligible patients were included in the cohort. One-year mortality was higher in patients with sarcopenia than in those without sarcopenia (p = 0.043). There was a correlation between sarcopenia and the surgical approach to global health status (p = 0.025) and diarrhea (p = 0.003). Conclusions: Preoperative sarcopenia reduces postoperative survival and health-related quality of life in patients with HCC

mtDNA D-loop mutations and mtDNA depletion among control and NRTI-treated children with AIDS.

<p>A: The ratio of mtDNA D-loop mutations in the control children (Group A, n = 50), children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84). B: mtDNA depletion in control children (Group A, n = 50), children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84); *P<0.01. C: mtDNA depletion in non-infected children (Group A) and 20 cases of age-matched, untreated, HIV-infected children (HIV-infection, n = 20), who came from our HIV blood samples bank and the total DNA in these samples were isolated for mtDNA loss specific assay.</p

Patient information.

<p>Patient information.</p

Long-term treatment with NRTIs can induce TK2 overexpression and reduce P53R2 expression in PBMCs.

<p>A: TK2 mRNA expression was quantified by qRT-PCR in control children (Group A, n = 50), children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84); ^#P<0.05. B: Western blot of TK protein expression in Group A, Group B and Group C. C: The graphics from 3B are shown in 3C. Data from triplicate independent experiments are presented as the mean ± SD. ^#P<0.05. D: P53R2 mRNA expression was quantified by qRT-PCR in control children (Group A, n = 50), children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84); *P<0.01. E: Western blot of P53R2 protein expression in group A, group B and group C. F: The graphics from 4B are shown in 4C. Data from triplicate independent experiments are presented as the mean ± SD. *P<0.01.</p

Primers and probes for real-time qPCR and D-loop PCR.

<p>Primers and probes for real-time qPCR and D-loop PCR.</p

P53R2 reduction parallels mtDNA depletion in PBMCs during longer-term treatment with NRTIs.

<p>A: The association between P53R2 mRNA expression and mtDNA copy number in Groups A, B and C. B: The association between P53R2 protein expression and mtDNA copy number in Groups A, B and C.</p

Plasma lactate and PBMC ATP levels among control and NRTI-treated children with AIDS.

<p>A: The concentration of plasma lactate in control children (Group A, n = 50),children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84); *P<0.01, ^#p<0.05. B: Linear relationship between the log of luciferase activity and ATP concentration. C: PBMC ATP levels in control children (Group A, n = 50), children with AIDS treated for less than 36 months (Group B, n = 68) and children with AIDS treated for 36 to 72 months (Group C, n = 84).</p