The valley filter efficiency of monolayer graphene and bilayer graphene line defect model

In addition to electron charge and spin, novel materials host another degree of freedom, the valley. For a junction composed of valley filter sandwiched by two normal terminals, we focus on the valley efficiency under disorder with two valley filter models based on monolayer and bilayer graphene. Applying the transfer matrix method, valley resolved transmission coefficients are obtained. We find that: i) under weak disorder, when the line defect length is over about $15\rm nm$, it functions as a perfect channel (quantized conductance) and valley filter (totally polarized); ii) in the diffusive regime, combination effects of backscattering and bulk states assisted intervalley transmission enhance the conductance and suppress the valley polarization; iii) for very long line defect, though the conductance is small, polarization is indifferent to length. Under perpendicular magnetics field, the characters of charge and valley transport are only slightly affected. Finally we discuss the efficiency of transport valley polarized current in a hybrid system.Comment: 6 figure

Geometric effects of a quarter of corrugated torus

In the spirit of the thin-layer quantization scheme, we give the effective Shr\"{o}dinger equation for a particle confined to a corrugated torus, in which the geometric potential is substantially changed by corrugation. We find the attractive wells reconstructed by the corrugation not being at identical depths, which is strikingly different from that of a corrugated nanotube, especially in the inner side of the torus. By numerically calculating the transmission probability, we find that the resonant tunneling peaks and the transmission gaps are merged and broadened by the corrugation of the inner side of torus. These results show that the quarter corrugated torus can be used not only to connect two tubes with different radiuses in different directions, but also to filter the particles with particular incident~energies.Comment: 7 pages, 8 figure

LSF-IDM: Automotive Intrusion Detection Model with Lightweight Attribution and Semantic Fusion

Autonomous vehicles (AVs) are more vulnerable to network attacks due to the high connectivity and diverse communication modes between vehicles and external networks. Deep learning-based Intrusion detection, an effective method for detecting network attacks, can provide functional safety as well as a real-time communication guarantee for vehicles, thereby being widely used for AVs. Existing works well for cyber-attacks such as simple-mode but become a higher false alarm with a resource-limited environment required when the attack is concealed within a contextual feature. In this paper, we present a novel automotive intrusion detection model with lightweight attribution and semantic fusion, named LSF-IDM. Our motivation is based on the observation that, when injected the malicious packets to the in-vehicle networks (IVNs), the packet log presents a strict order of context feature because of the periodicity and broadcast nature of the CAN bus. Therefore, this model first captures the context as the semantic feature of messages by the BERT language framework. Thereafter, the lightweight model (e.g., BiLSTM) learns the fused feature from an input packet's classification and its output distribution in BERT based on knowledge distillation. Experiment results demonstrate the effectiveness of our methods in defending against several representative attacks from IVNs. We also perform the difference analysis of the proposed method with lightweight models and Bert to attain a deeper understanding of how the model balance detection performance and model complexity.Comment: 18 pages, 8 figure

Low-energy effective Hamiltonian involving spin-orbit coupling in Silicene and Two-Dimensional Germanium and Tin

Starting from the symmetry aspects and tight-binding method in combination with first-principles calculation, we systematically derive the low-energy effective Hamiltonian involving spin-orbit coupling (SOC) for silicene, which is very general because this Hamiltonian applies to not only the silicene itself but also the low-buckled counterparts of graphene for other group IVA elements Ge and Sn, as well as graphene when the structure returns to the planar geometry. The effective Hamitonian is the analogue to the first graphene quantum spin Hall effect (QSHE) Hamiltonian. Similar to graphene model, the effective SOC in low-buckled geometry opens a gap at Dirac points and establishes QSHE. The effective SOC actually contains first order in the atomic intrinsic SOC strength $\xi_{0}$, while such leading order contribution of SOC vanishes in planar structure. Therefore, silicene as well as low-buckled counterparts of graphene for other group IVA elements Ge and Sn has much larger gap opened by effective SOC at Dirac points than graphene due to low-buckled geometry and larger atomic intrinsic SOC strength. Further, the more buckled is the structure, the greater is the gap. Therefore, QSHE can be observed in low-buckled Si, Ge, and Sn systems in an experimentally accessible temperature regime. In addition, the Rashba SOC in silicene is intrinsic due to its own low-buckled geometry, which vanishes at Dirac point $K$, while has nonzero value with $\vec{k}$ deviation from the $K$ point. Therefore, the QSHE in silicene is robust against to the intrinsic Rashba SOC.Comment: 11 pages, 3 figure

How are typical urban sewage treatment technologies going in China: from the perspective of life cycle environmental and economic coupled assessment

Sewage treatment is an important public service, but it consumes a lot of energy and chemicals in the process of removing wastewater pollutants, which may cause the risk of pollution transfer. To find the corresponding hot issues, this paper took the lead in integrating life cycle assessment (LCA) with life cycle costing (LCC) to evaluate four most typical sewage treatment technologies with more than 85% share in China. It is found that anaerobic/anoxic/oxic (AAO) was the optimal treatment scheme with relatively small potential environmental impact and economic load. The normalized results show that the trends of the four technologies on eleven environmental impact categories were basically the same. Marine aquatic ecotoxicity potential accounted for more than 70% of the overall environmental impact. Contribution analysis indicates that electricity and flocculant consumption were the main processes responsible for the environmental and economic burden. Overall, electricity consumption was the biggest hot spot. Sensitivity analysis verifies that a 10% reduction in electricity could bring high benefits to both the economy and the environment. These findings are expected to provide effective feedback on the operation and improvement of sewage treatment