IRS辅助的SWIPT系统中基于能效优先的波束成形设计与优化

以智能反射面(intelligent reflecting surface,IRS)辅助的无线携能通信(simultaneous wireless information and power transfer,SWIPT)系统为背景,研究了该系统中基于能效优先的多天线发送端有源波束成形与IRS无源波束成形联合设计与优化方法。以最大化接收端的最小能效为优化目标,构造在发送端功率、接收端能量阈值、IRS相移等多约束下的非线性优化问题,用交替方向乘子法(alternating direction method of multipliers,ADMM)求解。采用Dinkelbach算法转化目标函数,通过奇异值分解(singular value decomposition,SVD)和半定松弛(semi-definite relaxation,SDR)得到发送端有源波束成形向量。采用SDR得到IRS相移矩阵与反射波束成形向量。结果表明,该系统显著降低了系统能量收集(energy harvesting,EH)接收端的能量阈值。当系统总电路功耗为?15 dBm时,所提方案的用户能效为300 KB/J。当IRS反射阵源数与发送天线数均为最大值时,系统可达最大能效。     

Noncentrosymmetric Tetrel Pnictides RuSi4P4 and IrSi3P3: Nonlinear Optical Materials with Outstanding Laser Damage Threshold

Noncentrosymmetric (NCS) tetrel pnictides have recently generated interest as nonlinear optical (NLO) materials due to their second harmonic generation (SHG) activity and large laser damage threshold (LDT). Herein nonmetal-rich silicon phosphides RuSi₄P₄ and IrSi₃P₃ are synthesized and characterized. Their crystal structures are reinvestigated using single crystal X-ray diffraction and ²⁹Si and ³¹P magic angle spinning NMR. In agreement with previous report RuSi₄P₄ crystallizes in NCS space group P1, while IrSi₃P₃ is found to crystallize in NCS space group Cm, in contrast with the previously reported space group C2. A combination of DFT calculations and diffuse reflectance measurements reveals RuSi₄P₄ and IrSi₃P₃ to be wide bandgap (E_g) semiconductors, E_g = 1.9 and 1.8 eV, respectively. RuSi₄P₄ and IrSi₃P₃ outperform the current state-of-the-art infrared SHG material, AgGaS₂, both in SHG activity and laser inducer damage threshold. Due to the combination of high thermal stabilities (up to 1373 K), wide bandgaps (≈2 eV), NCS crystal structures, strong SHG responses, and large LDT values, RuSi₄P₄ and IrSi₃P₃ are promising candidates for longer wavelength NLO materials.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

What is the optimal cutoff surface for ore bodies with more than one mineral?

In mine planning problems, cutoff grade optimization defines a threshold at every time period such that material above this value is processed, and the rest is considered waste. In orebodies with multiple minerals, which occur in practice, the natural extension is to consider a cutoff surface. We show that in two dimensions the optimal solution is a line, and in n dimensions it is a hyperplane.     

The Picone identity: A device to get optimal uniqueness results and global dynamics in Population Dynamics

This paper infers from a generalized Picone identity the uniqueness of the stable positive solution for a class of semilinear equations of superlinear indefinite type, as well as the uniqueness and global attractivity of the coexistence state in two generalized diffusive prototypes of the symbiotic and competing species models of Lotka–Volterra. The optimality of these uniqueness theorems reveals the tremendous strength of the Picone identity.     

Cyberbullying: Effect of emergency perception on the helping tendencies of bystanders

The effect of the emergency perception of bystanders of cyberbullying victims on helping behaviors is often neglected in research on cyberbullying. In this study, we explored the influence of this cognitive factor on cyber-bystanders’ helping tendencies as well as elucidated possible underlying processes. The results of two studies were reported. In Study 1, 150 undergraduates read a true case of a girl experiencing cyberbullying. The results indicated that when the participants perceived the victim’s situation to be more critical (i.e., higher emergency perception), their helping tendencies were stronger, partly through increased state empathy followed by feelings of responsibility to help. In Study 2, we randomly assigned 300 undergraduates to two groups. The low emergency group read the same cyberbullying case as Study 1, whereas the cyberbullying case read by the high emergency group contained additional emergency information of the victim. The results indicated that the high emergency group expressed stronger helping tendencies than did the low emergency group. This effect was caused by a stronger perception that the victim was in an emergency situation, which not only strengthened the participants’ helping tendencies directly but also indirectly through increasing their state empathy and feelings of responsibility to help.     

Regularity of optimal sets for some functional involving eigenvalues of an operator in divergence form

In this paper we consider minimizers of the functional$\min \{{\lambda }_1(\mathrm{\Omega })+\cdots +{\lambda }_k(\mathrm{\Omega })+\mathrm{\Lambda }|\mathrm{\Omega }|,:\mathrm{\Omega }\subset D\text{ open}\}$ where $D\subset {\mathbb{R}}^d$ is a bounded open set and where $0<{\lambda }_1(\mathrm{\Omega })\le \cdots \le {\lambda }_k(\mathrm{\Omega })$ are the first k eigenvalues on Ω of an operator in divergence form with Dirichlet boundary condition and with Hölder continuous coefficients. We prove that the optimal sets ${\mathrm{\Omega }}^{⁎}$ have finite perimeter and that their free boundary $\partial {\mathrm{\Omega }}^{⁎}\cap D$ is composed of a regular part, which is locally the graph of a $C^{1,\alpha }$-regular function, and a singular part, which is empty if $d<d^{⁎}$, discrete if $d=d^{⁎}$ and of Hausdorff dimension at most $d-d^{⁎}$ if $d>d^{⁎}$, for some $d^{⁎}\in \{5,6,7\}$.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.