Mechanical properties and deformation mechanisms of nanocrystalline u-10mo alloys by molecular dynamics simulation

U-Mo alloys were considered to be the most promising candidates for high-density nuclear fuel. The uniaxial tensile behavior of nanocrystalline U-10Mo alloys with average grain sizes of 8–23 nm was systematically studied by molecular dynamics (MD) simulation, mainly focusing on the influence of average grain size on the mechanical properties and deformation mechanisms. The results show that Young’s modulus, yield strength and ultimate tensile strength follow as average grain size increases. During the deformation process, localized phase transitions were observed in samples. Grain boundary sliding and grain rotation, as well as twinning, dominated the deformation in the smaller and larger grain sizes samples, respectively. Increased grain size led to greater localized shear deformation, resulting in greater stress drop. Additionally, we elucidated the effects of temperature and strain rate on tensile behavior and found that lower temperatures and higher strain rates not only facilitated the twinning tendency but also favored the occurrence of phase transitions in samples. Results from this research could provide guidance for the design and optimization of U-10Mo alloys materials

Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned faster than normal knowledge, and ii) trigger patterns have a greater effect on image classification than normal class patterns. Our approach generates the images with newly learned knowledge by identifying the differences between the old and new global models, and filters trigger images by evaluating the effect of these generated images. By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign. Comprehensive experiments demonstrate that our approach can defend against almost all the existing types of backdoor attacks and outperform all the seven state-of-the-art defense methods with both IID and non-IID scenarios. Especially, our approach can successfully defend against the backdoor attack even when 80\% of the clients are malicious

Joint state of charge and state of health estimation of lithium-ion battery using improved adaptive dual extended Kalman filter based on piecewise forgetting factor recursive least squares.

This work aims to improve the accuracy of state of charge estimation for lithium-ion battery, as well as to accurately estimate state of health. This study presents a piecewise forgetting factor recursive least squares method based on integral separation with a second-order resistor-capacitor model and uses a novel adaptive filter based on error covariance correction on the conventional dual extended Kalman filter. The experiments show that the error of SOC estimation is less than 0.61% and the error of SOH is less than 0.09% under different complex conditions, the proposed method can effectively improve the estimation accuracy and robustness

Land use/land cover change and driving effects of water environment system in Dunhuang Basin, northwestern China

The Dunhuang Basin, located in northwestern China, is famous for its oases and geological remains. However, some problems of the eco-environment have raised public concern in recent decades. Land use/land cover change (LUCC) has been considered essential reference for studying eco-environment across the world. In the present study, the land use/land cover was divided into natural water, salt marshes, Aeluropus littoralis, natural vegetation, barren land, and desertified land. The LUCC was analyzed using four temporal Landsat images (from around 1975, 1990, 2000, 2010, respectively) and RapidEye images in 2010. Firstly, vegetation degeneration is the most serious problem, and 926.74 km2 turned into bare land in the past 35 years. The total area of bare land increased mainly occurred during 1975–1990. The area of desertified land increased rapidly from 2000 to 2010. Secondly, wetlands have experienced extreme shrinking; some areas degenerated into salt marshes, subsequently vanished. Salt marsh areas have been continually decreasing and gradually degenerating into saline and alkaline lands and bare land. In relation to the driving forces of LUCC, according to collected data and interpretation results by remote sensing images, the surface water environment is destructive due to three reservoirs impede surface water supplementation to the soil and natural vegetation. In addition, excessive pumping of groundwater occurred in the study area. Based on the local soil profiles of vadose zones and dynamic change of groundwater level, the groundwater flow system is another key factor, which developed along with the spatial distribution of groundwater recharge, runoff, and discharge conditions. Furthermore, large-scale activities connected to the reclamation of commercial farmlands have also promoted the LUCC

A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

Realization of strong coupling between deterministic single-atom arrays and a high-finesse miniature optical cavity

We experimentally demonstrate strong coupling between a one-dimensional (1D) single-atom array and a high-finesse miniature cavity. The atom array is obtained by loading single atoms into a 1D optical tweezer array with dimensions of 1$\times$11. Therefore, a deterministic number of atoms is obtained, and the atom number is determined by imaging the atom array on a CCD camera in real time. By precisely controlling the position and spacing of the atom array in the high finesse Fabry--Perot cavity, all the atoms in the array are strongly coupled to the cavity simultaneously. The vacuum Rabi splitting spectra are discriminated for deterministic atom numbers from 1 to 8, and the $\sqrt{N}$ dependence of the collective enhancement of the coupling strength on atom number $N$ is validated at the single-atom level.Comment: Main text: 7 pages, 5 figures; Supplementary material: 5 pages, 4 figure