Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.     

A Bayesian Surprise Approach in Designing Cognitive Radar for Autonomous Driving

This article proposes the Bayesian surprise as the main methodology that drives the cognitive radar to estimate a target’s future state (i.e., velocity, distance) from noisy measurements and execute a decision to minimize the estimation error over time. The research aims to demonstrate whether the cognitive radar as an autonomous system can modify its internal model (i.e., waveform parameters) to gain consecutive informative measurements based on the Bayesian surprise. By assuming that the radar measurements are constructed from linear Gaussian state-space models, the paper applies Kalman filtering to perform state estimation for a simple vehicle-following scenario. According to the filter’s estimate, the sensor measures the contribution of prospective waveforms—which are available from the sensor profile library—to state estimation and selects the one that maximizes the expectation of Bayesian surprise. Numerous experiments examine the estimation performance of the proposed cognitive radar for single-target tracking in practical highway and urban driving environments. The robustness of the proposed method is compared to the state-of-the-art for various error measures. Results indicate that the Bayesian surprise outperforms its competitors with respect to the mean square relative error when one-step and multiple-step planning is considered.     

基于模糊认知图的我国自贸区洗钱风险影响因素研究

自贸区洗钱风险的影响因素相互关联,通过专家认知判断自贸区洗钱风险各影响因素间相互关系。运用模糊认知图的迭代推理机制,借助灰色关联度,计算各影响因素与自贸区洗钱风险的关联度,识别影响自贸区洗钱风险的主要因素。研究表明,自贸区内反洗钱法律法规健全度、上游犯罪规模、反洗钱监管当局和义务主体履职效力、司法合作水平、上游犯罪结案率是影响自贸区洗钱风险的主要因素,这为我国自贸区反洗钱实务工作提供了决策参考。     

Effect of Yakae-Prajamduen-Jamod Traditional Thai Remedy on Cognitive Impairment in an Ovariectomized Mouse Model and Its Mechanism of Action

Cognitive impairment is a neurological symptom caused by reduced estrogen levels in menopausal women. The Thai traditional medicine, Yakae-Prajamduen-Jamod (YPJ), is a formula consisting of 23 medicinal herbs and has long been used to treat menopausal symptoms in Thailand. In the present study, we investigated the effects of YPJ on cognitive deficits and its underlying mechanisms of action in ovariectomized (OVX) mice, an animal model of menopause. OVX mice showed cognitive deficits in the Y-maze, the novel object recognition test, and the Morris water maze. The serum corticosterone (CORT) level was significantly increased in OVX mice. Superoxide dismutase and catalase activities were reduced, while the mRNA expression of IL-1β, IL-6, and TNF-α inflammatory cytokines were up-regulated in the frontal cortex and hippocampus of OVX mice. These alterations were attenuated by daily treatment with either YPJ or 17β-estradiol. HPLC analysis revealed that YPJ contained antioxidant and phytoestrogen constituents including gallic acid, myricetin, quercetin, luteolin, genistein, and coumestrol. These results suggest that YPJ exerts its ameliorative effects on OVX-induced cognitive deficits in part by mitigating HPA axis overactivation, neuroinflammation, and oxidative brain damage. Therefore, YPJ may be a novel alternative therapeutic medicine suitable for the treatment of cognitive deficits during the menopausal transition.     

The Role of Entropy in Construct Specification Equations (CSE) to Improve the Validity of Memory Tests: Extension to Word Lists

Metrological methods for word learning list tests can be developed with an information theoretical approach extending earlier simple syntax studies. A classic Brillouin entropy expression is applied to the analysis of the Rey’s Auditory Verbal Learning Test RAVLT (immediate recall), where more ordered tasks—with less entropy—are easier to perform. The findings from three case studies are described, including 225 assessments of the NeuroMET2 cohort of persons spanning a cognitive spectrum from healthy older adults to patients with dementia. In the first study, ordinality in the raw scores is compensated for, and item and person attributes are separated with the Rasch model. In the second, the RAVLT IR task difficulty, including serial position effects (SPE), particularly Primacy and Recency, is adequately explained (Pearson’s correlation R=0.80) with construct specification equations (CSE). The third study suggests multidimensionality is introduced by SPE, as revealed through goodness-of-fit statistics of the Rasch analyses. Loading factors common to two kinds of principal component analyses (PCA) for CSE formulation and goodness-of-fit logistic regressions are identified. More consistent ways of defining and analysing memory task difficulties, including SPE, can maintain the unique metrological properties of the Rasch model and improve the estimates and understanding of a person’s memory abilities on the path towards better-targeted and more fit-for-purpose diagnostics.     

化学学历案促进深度学习的发生——化学平衡移动复习课

学历案是关于学习经历或过程的方案，利用学历案构建一个“以学为主、以教促学”的教学模式，改变了教师的主导地位，凸显了学生的主体地位。笔者尝试借助学历案，以花青素为载体，通过生活化的化学实验，将化学平衡常数K作为分析工具，进行证据推理，以外界条件使花青素溶液颜色变化，到花青素的工业合成为明线；以化学平衡移动的本质到化学平衡移动原理的应用为暗线，双线结合，学生自主建构“化学平衡移动”的认知模型，从而促进深度学习的发生。     

Epidemic dynamics and spatial segregation driven by cognitive diffusion and nonlinear incidence

We propose susceptible-infected-susceptible epidemic reaction–diffusion models with cognitive movement and nonlinear incidence $S^q{I}^p$ $(p,q>0)$ in a spatially heterogeneous environment. The cognitive dispersal term takes either random diffusion or symmetric diffusion. Building upon the $L^{\infty }$-estimates of positive solutions under $p,q>0$, we state the asymptotic dynamics for , $q>0$. The numerical results reveal spatial segregation of susceptible and infected populations: (a) the heterogeneous random diffusion can segregate the population and reduce the infection fraction significantly; (b) the segregation phenomenon disappears as the ratio $p/q$ approaches one from below; (c) the disease-free region strengthens the segregation induced by heterogeneous random diffusion; (d) the segregation governed by random diffusion is more sensitive to the incidence mechanism; (e) the distribution of steady states driven by symmetric diffusion is always similar to that by homogeneous diffusion.