Uplink Secure Receive Spatial Modulation Empowered by Intelligent Reflecting Surface

With the emergence of the fifth generation (5G) era, the development of the Internet of Things (IoT) network has been accelerated with a new impetus, making it imperative to strive for a more reliable and efficient network environment. To accomplish this, we introduce and investigate a novel proposal for the intelligent reflecting surface (IRS) enabled uplink secure receive spatial modulation (SM), named IRS-USRSM, to resolve the security issues arising from the open wireless transmission environment in the 5G IoT network. In the IRS-USRSM scheme, we assume that the passive eavesdropper is directly connected to the uplink user and occasionally connected to the IRS. To achieve enhanced secrecy with finite alphabet inputs, a joint transmitter perturbation and IRS reflection design for physical layer security is proposed to guarantee secure and reliable transmission of IRS-USRSM. Specifically, two categories of IRSbased random phase compensation strategies, namely, random perturbation compensation and random path synthesize, along with maximum likelihood detection and suboptimal detection are proposed to meet the variant design requirements between achieved performance and system cost. Furthermore, in order to evaluate the performance limits of the IRS-USRSM, the closedform results of average bit error probabilities and discrete-input continuous-output memoryless channel capacities are derived using the method of moment generating function. Simulation results are presented to verify the correctness of our theoretical analyses, as well as to demonstrate the efficiency and superiority of the proposed IRS-USRSM scheme

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Three Essays on Cross-Sectionally Dependent Panel Data Models

This thesis develops the panel data models that are designed to capture and explain observed comovements among macroeconomic/finance variables. In Chapter 1, we develop a unifying econometric framework for analysing the heterogeneous spatial panel data models with common factors. In particular, a CCEX-IV estimation procedure is developed to tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors. Asymptotic properties of the proposed estimators are established and Monte Carlo simulations confirm their satisfactory finite sample performances. The proposed method is then applied to analyse the growth of UK house prices over 1997Q1-2016Q4. Chapter 2 extends the previous analysis to a dynamic framework and proposes a spatial-temporal autoregressive model with unobserved factors. An iterative procedure is developed for the consistent estimation of parameters. The properties of the proposed estimators are investigated both theoretically and via extensive Monte Carlo simulations. Moreover, we develop network connectedness measures that can track the evolving influence of any node on others at both individual and regional levels through using the diffusion FEVDs and multipliers. We finally employ the method to analyse the synchronisation of international business cycles using the data for 79 countries over 1970-2019. While the first two chapters study the conditional mean effects, we investigate the conditional distributional effects in Chapter 3. Specifically, we develop a two-step procedure for estimating the dynamic quantile panel data model with unobserved common factors. The proposed estimator is shown to be consistent and follow an asymptotic normal distribution, but it is subject to asymptotic bias due to the incidental parameters. We then apply the split-panel jackknife approach to correct the bias and confirm its satisfactory performance by Monte Carlo simulations. Finally, the proposed method is applied to an analysis of bilateral trade flows for 380 country pairs over 1960-2018

Unit root quantile autoregression testing with smooth structural changes

By incorporating the flexible Fourier form into quantile autoregression model, this paper proposes three new unit root test statistics, which are robust to both non-Gaussian condition and structural changes. Since their limiting distributions are non-standard, a bootstrap procedure is developed to calculate their critical values. Monte Carlo simulation results show that, while Koenker and Xiao (2004) tests are quite conservative under various kinds of error distributions and structural changes, the newly proposed tests have good size performance except for a little size distortion occasionally. Moreover, our tests have much higher performance especially when the sample size is small

Estimation and Inference in Heterogeneous Spatial Panels with a Multifactor Error Structure

We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal instrumental variables. We develop the individual and Mean Group estimators, and establish their consistency and asymptotic normality. By contrast, the Pooled estimator is shown to be inconsistent in the presence of parameter heterogeneity. Monte Carlo simulations confirm that the finite sample performance of the proposed estimators is quite satisfactory. We demonstrate the usefulness of our approach with an application to the house price growth for Local Authority Districts in the UK over 1997Q1-2016Q4

Estimation and test for quantile nonlinear cointegrating regression

In order to investigate the nonlinear relationship among economic variables at each quantile level, this paper proposes a quantile nonlinear cointegration model in which the nonlinear relationship at each quantile level is approximated by a polynomial. The parameter estimator in the proposed model is shown to follow a nonstandard distribution asymptotically due to serial correlation and endogeneity. Therefore, this paper develops a fully modified estimator which follows a mixture normal distribution asymptotically. Moreover, a test statistic for the linearity and its asymptotic distribution are also derived. Monte Carlo results show that the proposed test has good finite sample performance

Dynamic network quantile regression model

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016

Dynamic network quantile regression model

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016

Carbide precipitation during tempering of hybrid steel 60

The effects of carbide precipitation on mechanical performance of Hybrid Steel 60, known as a novel bearing steel, have not been investigated. In this study, the austenite transformation temperatures of Hybrid Steel 60 during heating were revealed by the thermal expansion curve. The temperature and effective activation energy of the second phase precipitation were determined by the differential scanning calorimetry (DSC) curve. Different solid solution structures after austenitization were detected using various cooling rates. The solubility temperature was determined based on hardness and residual austenite content. The carbides precipitated at the peak temperature were qualitatively identified using XRD. It was discovered that the temperature points Ac1 and Ac3 of the steel were 786 °C and 864 °C, respectively. In addition, the effect of solid solution temperature on quenching hardness is minimal, while the cooling rate has a greater impact on hardness, reaching a peak at 5 °C s ^−1 . The primary carbide phase in Hybrid Steel 60 is the M _7 C _3 and VC. When the temperature ranges from 500 °C to 550 °C, M _23 C _6 begins to precipitate. As a result, after tempering at 525 °C, the hardness peak value reached 566 HV