The linear and nonlinear inverse Compton scattering between microwaves and electrons in a resonant cavity

In a free space, the Sunyaev-Zel'dovich (SZ) effect is a small spectral distortion of the cosmic microwave background (CMB) spectrum caused by inverse Compton scattering of microwave background photons from energetic electrons in the plasma. However, the microwave does not propagate with a plane waveform in a resonant cavity, the inverse Compton scattering process is a little different from that in a free space. By taking the Fourier expansion of the microwave field in the cavity, the coefficients of the first-order and the higher-order terms describe the local-space effect on the linear and nonlinear inverse Compton scattering respectively. With our theoretical results, the linear or nonlinear inverse Compton scattering cross section between microwave photons and electrons has important applications on the energy calibration of the extremely energetic electron beam, the sources of the terahertz waves, the extreme ultra-violet (EUV) waves or the mid-infrared beams.Comment: 8 pages, 5 figure

Oral vinorelbine and continuous low doses of cyclophosphamide in pediatric rhabdomyosarcoma: a real-world study

Introduction: Metronomic maintenance therapy (MMT) has significantly improved the survival of patients with high-risk rhabdomyosarcoma in clinical trials. However, there remains a lack of relevant data on its effectiveness in real-world situations.Methods: We retrospectively retrieved data of 459 patients < 18 years of age diagnosed with rhabdomyosarcoma at Sun Yat-sen University Cancer Center from January 2011 to July 2020 from our database. The MMT regimen was oral vinorelbine 25–40 mg/m2 for twelve 4-week cycles on days 1, 8, and 15, and oral cyclophosphamide 25–50 mg/m2 daily for 48 consecutive weeks.Results: A total of 57 patients who underwent MMT were included in the analysis. The median follow-up time was 27.8 (range: 2.9–117.5) months. From MMT to the end of follow-up, the 3-year PFS and OS rates were 40.6% ± 6.8% and 58.3% ± 7.2%, respectively. The 3-year PFS was 43.6% ± 11.3% in patients who were initially diagnosed as low- and intermediate-risk but relapsed after comprehensive treatment (20/57), compared with 27.8% ± 10.4% in high-risk patients (20/57) and 52.8% ± 13.3% in intermediate-risk patients who did not relapse (17/57). The corresponding 3-year OS for these three groups was 65.8% ± 11.4%, 50.1% ± 12.9%, and 55.6% ± 13.6%, respectively.Conclusion: We present a novel study of MMT with oral vinorelbine and continuous low doses of cyclophosphamide in real-world pediatric patients with RMS. Our findings showed that the MMT strategy significantly improved patient outcomes and may be an effective treatment for high-risk and relapsed patients

Fermi-LAT Detection of a New Starburst Galaxy Candidate: IRAS 13052-5711

A likely starburst galaxy (SBG), IRAS 13052–5711, which is the most distant SBG candidates discovered to date, was found by analyzing 14.4 yr of data from the Fermi large-area telescope. This SBG’s significance level is approximately 6.55 σ in the 0.1–500 GeV band. Its spatial position is close to that of 4FGL J1308.9–5730, determined from the Fermi large telescope fourth-source Catalog (4FGL). Its power-law spectral index is approximately 2.1, and its light curve for 14.4 yr has no significant variability. These characteristics are highly similar to those of SBGs found in the past. We calculate the SBG’s star formation rate (SFR) to be 29.38 M _⊙ yr ^−1 , which is within the SFR range of SBGs found to date. Therefore, IRAS 13052-5711 is considered to be a likely SBG. In addition, its 0.1–500 GeV luminosity is (3.28 ± 0.67) × 10 ^42 erg s ^−1 , which deviates from the empirical relationship of the γ -ray luminosity and the total infrared luminosity. We considered a hadronic model to explain the GeV spectrum of IRAS 13052-5711

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm

Instantaneous Frequency Estimation Based on Modified Kalman Filter for Cone-Shaped Target

The instantaneous frequency (IF) is a vital parameter for the analysis of non-stationary multicomponent signals, and plays an important role in space cone-shaped target recognition. For a cone-shaped target, IF estimation is not a trivial issue due to the proximity of the energy of the IF components, the intersections among different IF components, and the existence of noise. Compared with the general parameterized time-frequency (GPTF), the traditional Kalman filter can perform better when the energy of different signal components is close. Nevertheless, the traditional Kalman filter usually makes association mistakes at the intersections of IF components and is sensitive to the noise. In this paper, a novel IF estimation method based on modified Kalman filter (MKF) is proposed, in which the MKF is used to associate the intersecting IF trajectories obtained by the synchroextracting transform (SET). The core of MKF is the introduction of trajectory correction strategy in which a trajectory survival rate is defined to judge the occurrence of association mistakes. When the trajectory survival rate is below the predetermined threshold, it means that an association mistakes occurs, and then the new trajectories generated by the random sample consensus algorithm are used to correct the wrong associations timely. The trajectory correction strategy can effectively obviate the association mistakes caused by the intersections of IF components and the noise. The windowing technique is also used in the trajectory correction strategy to improve computational speed. The experimental results based on the electromagnetic computation data show that the proposed method is more robust and precise than the traditional Kalman filter. Moreover, the proposed method has great performance advantages compared with other methods (i.e., the multiridge detection, the ant colony optimization, and the GPTF methods) especially in the case of low signal noise ratio (SNR)