Tuning optical properties of rhombic hybrid Au-Ag nanoparticles: A discrete dipole approximation calculation

Optimization of metallic nanoparticles was presented in this paper by aid of computational numerical calculation. The optical extinction spectra of rhombic hybrid Au-Ag nanoparticles have been calculated by the discrete dipole approximation (DDA) aided design method. Both material and the thickness of the particles can be used to effectively tune localized surface plasmon resonance. On the basis of the calculated extinction spectra, the crucial parameters of the nanostructure arrays such as thickness can be determined. Using this DDA aided approach, a hybrid Au-Ag nanoparticles array is put forth and designed with the optimized parameter of thickness of metal thin films (h = 5 nm, and h = 25 nm). This study shows that the material of the particles have significant effect on the optical properties. The DDA aided design method can provide the optimized structure parameters for the hybrid nanostructures

The burden of cardiovascular diseases attributable to metabolic risk factors and its change from 1990 to 2019: a systematic analysis and prediction

BackgroundMetabolic disorders are the most important risk factors for cardiovascular diseases (CVDs). The purpose of this study was to systematically analyze and summarize the most recent data by age, sex, region, and time, and to forecast the future burden of diseases.MethodsData on the burden of CVDs associated with metabolic risk factors were obtained from the Global Burden of Disease (GBD) Study 2019; and then the burden of disease was assessed using the numbers and age-standardized rates (ASR) of deaths, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) and analyzed for temporal changes, differences in age, region, sex, and socioeconomic aspects; finally, the burden of disease was predicted using an autoregressive integrated moving average (ARIMA) model.ResultsFrom 1990 to 2019, the numbers of deaths, DALYs, YLDs, and YLLs attributed to metabolic risk factors increased by 59.3%, 51.0%, 104.6%, and 47.8%, respectively. The ASR decreased significantly. The burden of metabolic risk factor-associated CVDs was closely related to socioeconomic position and there were major geographical variations; additionally, men had a significantly greater disease burden than women, and the peak shifted later based on the age group. We predicted that the numbers of deaths and DALYs would reach 16.5 million and 324.8 million, respectively, by 2029.ConclusionsThe global burden of CVDs associated with metabolic risk factors is considerable and still rising, and more effort is needed to intervene in metabolic disorders

Improving the wind‐induced human comfort of the Beijing Olympic Tower by a double‐stage pendulum tuned mass damper

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/1/tal1704_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154522/2/tal1704.pd

Fast and Secure Key Generation with Channel Obfuscation in Slowly Varying Environments

Physical-layer secret key generation has emerged as a promising solution for establishing cryptographic keys by leveraging reciprocal and time-varying wireless channels. However, existing approaches suffer from low key generation rates and vulnerabilities under various attacks in slowly varying environments. We propose a new physical-layer secret key generation approach with channel obfuscation, which improves the dynamic property of channel parameters based on random filtering and random antenna scheduling. Our approach makes one party obfuscate the channel to allow the legitimate party to obtain similar dynamic channel parameters, yet prevents a third party from inferring the obfuscation information. Our approach allows more random bits to be extracted from the obfuscated channel parameters by a joint design of the K-L transform and adaptive quantization. Results from a testbed implementation show that our approach, compared to the existing ones that we evaluate, performs the best in generating high entropy bits at a fast rate and is able to resist various attacks in slowly varying environments. Specifically, our approach can achieve a significantly faster secret bit generation rate at roughly 67 bit/pkt, and the key sequences can pass the randomness tests of the NIST test suite

Fast and Secure Key Generation with Channel Obfuscation in Slowly Varying Environments

Physical-layer secret key generation has emerged as a promising solution for establishing cryptographic keys by leveraging reciprocal and time-varying wireless channels. However, existing approaches suffer from low key generation rates and vulnerabilities under various attacks in slowly varying environments. We propose a new physical-layer secret key generation approach with channel obfuscation, which improves the dynamic property of channel parameters based on random filtering and random antenna scheduling. Our approach makes one party obfuscate the channel to allow the legitimate party to obtain similar dynamic channel parameters, yet prevents a third party from inferring the obfuscation information. Our approach allows more random bits to be extracted from the obfuscated channel parameters by a joint design of the K-L transform and adaptive quantization. Results from a testbed implementation show that our approach, compared to the existing ones that we evaluate, performs the best in generating high entropy bits at a fast rate and is able to resist various attacks in slowly varying environments. Specifically, our approach can achieve a significantly faster secret bit generation rate at roughly 67 bit/pkt, and the key sequences can pass the randomness tests of the NIST test suite

Authorized and Rogue LTE Terminal Identification Using Wavelet Coefficient Graph with Auto-encoder

The wide popularity of 4G/5G mobile terminals increase the requirements of wireless security. Radio frequency fingerprint (RFF) technology can strengthen 4G/5G air interface accessing security at the physical layer. In this paper, a wavelet transform (WT) coefficient graphs RFF extraction with auto-encoder (AE) based rogue terminal detection scheme is proposed. At first, WT coefficients at 48 scales are extracted from the transient-power-off part of LTE physical random access channel (PRACH) preamble. Then, an AE network structure aimed for 2D WT coefficient graph is designed for rogue terminal detection. We successfully distinguish 7 mobile phones and 1 USRP under the proposed mechanism, where the authorized terminals from the same manufacturer can be identified with an accuracy of 90.08%. In addition, extensive experiments are carried out at LOS and NOLS scenarios, respectively, the proposed LTE identification scheme has demonstrated robustness in dynamic environments

Global Burden of Aortic Aneurysm and Attributable Risk Factors from 1990 to 2017

Background: To date, our understanding of the global aortic aneurysm (AA) burden distribution is very limited. Objective: To assess a full view of global AA burden distribution and attributable risk factors from 1990 to 2017. Methods: We extracted data of AA deaths, disability-adjusted life years (DALYs), and their corresponding age-standardized rates (ASRs), in general and by age/sex from the 2017 Global Burden of Disease (GBD) study. The current AA burden distribution in 2017 and its changing trend from 1990 to 2017 were separately showed. The spatial divergence was discussed from four levels: global, five social-demographic index regions, 21 GBD regions, and 195 countries and territories. We also estimated the risk factors attributable to AA related deaths. Results: Globally, the AA deaths were 167,249 with an age-standardized death rate (ASDR) of 2.19/100,000 persons in 2017, among which the elderly and the males accounted for the majority. Although reductions in ASRs were observed in developed areas, AA remained an important health issue in those relatively underdeveloped areas and might be much more important in the near future. AA may increasingly affect the elderly and the female population. Similar patterns of AA DALYs burden were noted during the study period. AA burden attributable to high blood pressure and smoking decreased globally and there were many heterogeneities in their distribution. Discussion: AA maintained an incremental public health issue worldwide. The change pattern of AA burden was heterogeneous across locations, ages, and sexes and it is paramount to improve resource allocation for more effective and targeted prevention strategies. Also, prevention of tobacco consumption and blood pressure control should be emphasized

Hybrid RFF Identification for LTE Using Wavelet Coefficient Graph and Differential Spectrum

The growing popularity of 4 G/5 G mobile devices has led to an increase in demand for wireless security. Radio frequency fingerprint (RFF) technique is an emerging approach for device authentication using intrinsic and unique hardware impairments. In this paper, we propose an RFF-based method to identify rogue/unknown long term evolution (LTE) terminals. This is achieved by combining wavelet transform (WT) coefficient graphs and differential spectrum. The proposed method involves extracting 48 levels of wavelet coefficients from the transient power-off of the physical random access channel (PRACH) signal and representing them in a WT graph. The steady-state part of the PRACH signal after a frequency domain differential processing between the adjacent spectrum is extracted. To detect unknown attack devices, an identification scheme based on an autoencoder (AE) is designed. Two different AE network structures are designed based on the proposed features, and a hybrid identification structure is proposed. An experimental evaluation system is set up with seven mobile phones from three categories and one universal software radio peripheral (USRP) software-defined radio (SDR) platform. Training and testing datasets are collected under different conditions such as location, working times, and dates. Experimental results show that rogue devices can be identified with an accuracy up to 98.84% for different categories and 90.27% for different individuals