Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Seismic behavior of steel tubular bridge columns equipped with low-yield-point steel plates in the root replaceable pier

To enable rapid recovery of a steel bridge column after an earthquake,a novel tubular-section steel bridge column equipped with low-yield-point(LYP)steel tubular plates in the root replaceable pier is proposed.For the purpose of discussing the seismic behavior of the novel steel bridge column,quasi-static tests and finite element simulation analyses of the specimens were carried out.The effects of parameters such as the axial compression ratio,eccentricity,and thickness and material strength of the tubular plate in the energy-dissipating zone are discussed.Experimental results from seven specimens that were subjected to four failure modes are presented.The damage to the quasi-static specimens is localized to the replaceable energy-dissipating pier.The seismic behavior of the novel steel bridge columns is significantly influenced by the axial compression ratio and eccentricity of specimens.Numerical results show that the high stress area of the specimens is mainly concentrated in the connection zone between the LYP steel tubular plate and the bottom steel plate,which is consistent with the position of the quasi-static specimen when it was prone to fracture.Finally,a calculation formula is proposed to facilitate the capacity prediction of this new steel tubular bridge column under repeated loading.

Improved performance of Ge n^+/p diode by combining laser annealing and epitaxial Si passivation

A method to improve Ge n＋/p junction diode performance by excimer laser annealing （ELA） and epitaxial Si passi- vation under a low ion implantation dose is demonstrated. The epitaxial Si passivation layer can unpin the Fermi level of the contact of Al/n-Ge to some extent and reduce the contact resistance. In addition, the fabricated Ge n ：/p junction diode by ELA plus epitaxial Si passivation exhibits a decreased reverse current density and an increased forward current density, resulting in a rectification ratio of about 6.5 x 10^6 beyond two orders magnitude larger than that by ELA alone. The reduced specific contact resistivity of metal on n-doped germanium and well-behaved germanium n＋/p diode arc beneficial for the performance improvement of Ge n-MOSFETs and other opto-electronic devices.

Improved performance of Au nanocrystal nonvolatile memory by N2-plasma treatment on HfO2blocking layer

The N2-plasma treatment on a HfO2 blocking layer of Au nanocrystal nonvolatile memory without any post annealing is investigated. The electrical characteristics of the MOS capacitor with structure of Al–Ta N/HfO2/Si O2/p-Si are also characterized. After N2-plasma treatment, the nitrogen atoms are incorporated into HfO2 film and may passivate the oxygen vacancy states. The surface roughness of HfO2 film can also be reduced. Those improvements of HfO2 film lead to a smaller hysteresis and lower leakage current density of the MOS capacitor. The N2-plasma is introduced into Au nanocrystal（NC） nonvolatile memory to treat the HfO2 blocking layer. For the N2-plasma treated device, it shows a better retention characteristic and is twice as large in the memory window than that for the no N2-plasma treated device. It can be concluded that the N2-plasma treatment method can be applied to future nonvolatile memory applications.

Time-resolved laser flash photolysis study on transient reaction between excited triplet state of anthraquinone derivatives (AQS) and 2-deoxythymidine

The transient absorption spectra and kinetics of excited triplet state of anthraquinone derivatives 2-anthraquinonesulfonatesodium (AQS) and 2-deoxythymidine (dT) have been investigated in CH3CN-H2O (97:3) using the time-resolved laser flash photolysis technique (KrF, 248 nm). The absorption spectra of dT radical cation and the radical anion of AQS have been observed. From dynamic and thermodyrnamic analysis, the mechanism of this transient reaction has been initially analysed.

Research on the Prediction of Load Regulation Capacity for Supercritical Thermal Power Unit

Both the modeling and the load regulation capacity prediction of a supercritical power plant are investigated in this paper. Firstly, an indirect identification method based on subspace identification method is proposed. The obtained identification model is verified by the actual operation data and the dynamic characteristics of the system are well reproduced. Secondly, the model is used to predict the load regulation capacity of thermal power unit. The power, main steam pressure, main steam temperature and other parameters are simulated respectively when the unit load is going up and down. Under the actual constraints, the load regulation capacity of thermal power unit can be predicted quickly.

Multiparameter collaborative optimization of the vibrating screen based on the behavior of oil sunflower seed penetrating screen holes

The behavior of oil sunflower seeds penetrating screen holes is an important factor that affects the screening performance of oil sunflower seeds.In this study,a double-deck reverse-motion vibrating screening device for oil sunflower seed screening was designed.The force condition and motion law of the oil sunflower seeds on the screen surface were analyzed.This study compared the effect of particle filling amount of discrete element model of oil sunflower seeds on the simulation effects.The screening process was numerically simulated using the coupled Discrete Element Method and Multibody Dynamics(DEM-MBD)technique with the screening percentage of oil sunflower seeds as the index.The influence of the operating parameters of the vibrating screen on the screening effect was analyzed using a multiparameter collaborative optimization scheme.The results of this study can provide a reference for the numerical simulation of crop screening behavior and the development of screening devices.

Tilting sheaves for weighted projective lines of weight type （p）

We give a complete classification of tilting sheaves over weighted projective lines of weight type （p）.

Domain wall state diagram for SrTiO_(3)/BaTiO_(3) superlattice structures

The domain wall structure of ferroelectric/paraelectric superlattices can be much more complex due to the influence of the superlattice stacking structure,the in-plane strain induced by the substrate and environmental temperature.In this study,we employed a phase field model to investigate the domain wall state of the SrTiO_(3)/BaTiO_(3) superlattice structure.The domain wall thickness for the SrTiO_(3)/BaTiO_(3) layer was measured using a hyperbolic function.Based on the simulation results,here,we show a domain wall state diagram to distinguish the hard and soft domain states.The polarization profiles across hard/soft domain walls were illustrated and analyzed.Our simulation results offer a useful concept for the control of the domain wall state in the ferroelectric superlattice.

Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing

The noncontact blade tip timing(BTT)measurement has been an attractive technology for blade health monitoring(BHM).However,the severe undersampled BTT signal causes a significant challenge for blade vibration parameter identification and fault feature extraction.This study proposes a novel method based on the minimum variance distortionless response(MVDR)of the direction of arrival(DoA)estimation for blade natural frequency estimation from the non-uniformly undersampled BTT signals.First,based on the similarity between the general data acquisition model for BTT and the antenna array model in DoA estimation,the circumferentially arranged probes on the casing can be regarded as a non-uniform linear array.Thus,BTT signal reconstruction is converted into the DoA estimation problem of the non-uniform linear array signal.Second,MVDR is employed to address the severe undersampling issue and recover the BTT undersampled signal.In particular,spatial smoothing is innovatively utilized to enhance the estimation of covariance matrix of the BTT signal to avoid ill-condition or singularity,while improving efficiency and robustness.Lastly,numerical simulation and experimental testing are employed to verify the validity of the proposed method.Monte Carlo simulation results suggest that the proposed method behaves better than conventional methods,especially under a lower signal-to-noise ratio condition.Experimental results indicate that the proposed method can effectively overcome the severe undersampling problem of BTT signal induced by physical limitations,and has a strong potential in the field of BHM.

Physiognomy： Personality Traits Prediction by Learning

Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.