Physical-Layer Key Generation Based on Multipath Channel Diversity Using Dynamic Metasurface Antennas

Physical layer key generation(PKG)technology leverages the reciprocal channel randomness to generate the shared secret keys.The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels.To improve the wireless key generation rate,we propose a multipath channel diversity-based PKG scheme.Assisted by dynamic metasurface antennas(DMA),a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation.The proposed algorithm first estimates the angle of arrival(AOA)based on the reconfigurable radiation pattern of DMA,and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain.After multipath separation and parameter estimation,multi-dimensional independent path gains are utilized for generating secret keys.Finally,we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio(SNR)region.The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.

APOE and APOC1 gene polymorphisms are associated with cognitive impairment progression in Chinese patients with late-onset Alzheimer's disease

Current evidence shows that apolipoprotein E （APOE）, apolipoprotein CI （APOC1） and low density lipoprotein receptor-related protein （LRP） variations are related to late-onset Alzheimer＇s disease. However, it remains unclear if genetic polymorphisms in these genes are associated with cognitive decline in late-onset Alzheimer＇s disease patients. We performed a 30-month longitudi- nal cohort study to investigate the relationship between Alzheimer＇s disease and APOE, APOC1, and LRP. In this study, 78 Chinese Han patients with late-onset Alzheimer＇s disease were recruit- ed form Guangxi Zhuang Autonomous Region in China. APOE, APOC1, and LRP genotyping was performed using polymerase chain reaction-restriction fragment length polymorphisms. The Mini-Mental State Examination and Clinical Dementia Rating Scale were used to assess pa- tients＇ cognitive function. After a 30-month follow-up period, we found a significant reduction in Mini-Mental State Examination total score, a higher proportion of patients fulfilling cognitive impairment progression criteria, and a higher proportion of APOC1 H2 carriers in APOE 4 carriers compared with non-carriers. In addition, the APOE 4 allele frequency was significantly higher in the cognitive impairment progression group compared with the non-cognitive im- pairment progression group. In conclusion, APOE e4 plays an important role in augmenting cognitive decline, and APOC1 H2 may act synergistically with APOE ~4 in increasing the risk of cognitive decline in Chinese patients with late-onset Alzheimer＇s disease.

Tunnelling of electrons via the neighboring atom

As compared to the intuitive process that the electron emits straight to the continuum from its parent ion,there is an alternative route that the electron may transfer to and be trapped by a neighboring ionic core before the eventual release.Here,we demonstrate that electron tunnelling via the neighboring atomic core is a pronounced process in light-induced tunnelling ionization of molecules by absorbing multiple near-infrared photons.We devised a siteresolved tunnelling experiment using an Ar-Kr+ion as a prototype system to track the electron tunnelling dynamics from the Ar atom towards the neighboring Kr+by monitoring its transverse momentum distribution,which is temporally captured into the resonant excited states of the Ar-Kr+before its eventual releasing.The influence of the Coulomb potential of neighboring ionic cores promises new insights into the understanding and controlling of tunnelling dynamics in complex molecules or environment.

An Urban Road Risk Assessment Framework Based on Convolutional Neural Networks

In contemporary cities,road collapse is one of the most common disasters.This study proposed a framework for assessing the risk of urban road collapse.The framework first established a risk indicator system that combined environmental and anthropogenic factors,such as soil type,pipeline,and construction,as well as other indicators.Second,an oversampling technique was used to create the dataset.The framework then constructed and trained a convolutional neural network(CNN)-based model for risk assessment.The experimental results show that the CNN model(accuracy:0.97,average recall:0.91)outperformed other models.The indicator contribution analysis revealed that the distance between the road and the construction site(contribution:0.132)and the size of the construction(contribution:0.144)are the most significant factors contributing to road collapse.According to the natural breaks,a road collapse risk map of Foshan City,Guangdong Province,was created,and the risk level was divided into five categories.Nearly 3%of the roads in the study area are at very high risk,and 6%are at high risk levels,with the high risk roads concentrated in the east and southeast.The risk map produced by this study can be utilized by local authorities and policymakers to help maintain road safety.

Joint radio frequency front-end and digital back-end antijamming scheme based on a metasurface antenna array

An array’s degree of freedom(DoF)determines the number of jamming incidents that can be managed and the antijamming performance.Conventional arrays can improve the DoF only by increasing the number of antennas.On the other hand,when the received signal is digitized,high-power jamming will reduce the number of bits used to represent the desired signal,further increasing the difficulty of back-end antijamming based on digital signal processing.In this paper,we propose a joint radio frequency(RF)front-end and digital back-end antijamming scheme based on a metasurface antenna array.The metasurface antennas can rapidly switch patterns when receiving signals,so that a single channel can be equivalent to multiple channels and increase the DoF.We use independent component analysis to estimate the channel and then optimize the array parameters under the minimum signal-to-jamming ratio constraint of each antenna.The proposed scheme works well under high-power jamming conditions by suppressing jamming at the RF front end and using a low-precision analog-to-digital converter.Simulation results show that the proposed scheme reduces the bit error rate of the received signals by one order of magnitude compared with the conventional array.

Integrated hetero-nanoelectrodes for plasmon-enhanced electrocatalysis of hydrogen evolution

Hetero-nanostructures of plasmonic metals and semiconductors have attracted increasing attention in the field of photocatalysis.However,most of the hetero-nanostructured catalysts are randomly arranged and therefore require comprehensive structural design for optimizing their properties.Herein,we report the robust construction of hierarchical hetero-nanostructures where gold(Au)nanorods and molybdenum disulfide(Mo S_(2))quantum sheets(QSs)are integrated in highly ordered arrays.Such construction is achieved through porous anodic alumina(PAA)template-assisted electrodeposition.The as-fabricated hetero-nanostructures demonstrate exciting electrocatalysis towards hydrogen evolution reaction(HER).Both plasmon-induced hot-electron injection and plasmonic scattering/reabsorption mechanisms are determinative to the enhanced electrocatalytic performances.Notably,broadband photoresponses of HER activity in the visible range are observed,indicating their superiority compared with random systems.Such integrated hetero-nanoelectrodes could provide a powerful platform for conversion and utilization of solar energy,meanwhile would greatly prompt the production and exploration of ordered nanoelectrodes.

Fishbone-like platinum-nickel nanowires as an efficient electrocatalyst for methanol oxidation

Platinum(Pt)-based electrocatalyst with low Pt content and high electrocatalytic performance is highly desired in fuel cell applications.Herein,we demonstrated that platinum-nickel(Pt-Ni)nanowires with an average composition of PtNi3 and a fishbone structure can be readily synthesized and used as an efficient electrocatalyst toward methanol oxidation reaction(MOR).The PtNi3 fishbone-like nanowires(PtNi3-FBNWs)present features such as richer Pt on the surface than in the bulk,high-index facets on the rough surface,and polyhedral facets at the ends of side chains.Such compositional and structural features could be determinative to the enhanced performance in the electrocatalysis of MOR.Compared with commercial 20%Pt/carbon black(Pt/C),the specific activity and mass activity of the PtNi3-FBNWs are enhanced by approximately 4.76 and 3.02 times,respectively.The stability of electrocatalysis is significantly improved as well.Such comprehensive enhancement indicates that the PtNi3-FBNWs would be a promising candidate toward MOR in fuel cells.

Toward Interoperable Multi‑hazard Modeling: A Disaster Management System for Disaster Model Service Chain

A natural hazard-related disaster event often causes a series of secondary disasters, forming a disaster chain. Modeling the evolution of disaster chains in multihazard scenarios is crucial for risk governance and urban resilience. However, existing multi-hazard models are limited by complex model design and fixed disaster types, making it impossible to ensure flexible reactions to complex and diverse scenarios. This study presents a disaster management system for disaster model service chain(DMSC) to implement interoperable multi-hazard modeling. To achieve efficient model interaction in the DMSC, a management module is designed to normalize heterogeneous single-hazard models based on disaster system theory and the Open Geospatial Consortium standards, enabling them to be accessible,reusable, and interoperable. The normalized models are then adaptively orchestrated through an orchestration module to establish optimal executable DMSCs for different multihazard scenarios. Taking an earthquake disaster chain as a case study, we demonstrate that the disaster management system shows stable and flexible performance for multihazard modeling.