Generation of lossy mode resonances(LMR)using perovskite nanofilms

The results presented here show for the first time the experimental demonstration of the fabrication of lossy mode resonance(LMR) devices based on perovskite coatings deposited on planar waveguides. Perovskite thin films have been obtained by means of the spin coating technique and their presence was confirmed by ellipsometry, scanning electron microscopy, and X-ray diffraction testing. The LMRs can be generated in a wide wavelength range and the experimental results agree with the theoretical simulations. Overall, this study highlights the potential of perovskite thin films for the development of novel LMR-based devices that can be used for environmental monitoring, industrial sensing, and gas detection, among other applications.

Train post‑derailment behaviours and containment methods:a review

Railway accidents,particularly serious derailments,can lead to catastrophic consequences.Therefore,it is essential to prevent derailment escalation to reduce the likelihood of severe derailments.Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment.However,despite the increasing attention on this field from academia and industry in recent years,there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research.For this reason,this paper presents a comprehensive review of existing studies on train post-derailment behaviours,encompassing various topics such as post-derailment contact-impact models,dynamic modelling and simulation techniques,and the primary factors influencing post-derailment behaviours.Significantly,this review introduces and elucidates substitute guidance mechanisms(SGMs),which serve as railway-specific passive safety protection and accident prevention measures.The various types of SGMs are depicted,and their ongoing developments and applications are explored in depth.The review additionally points out several unresolved challenges including the adverse effects of SGMs,and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods.This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods.

Research and Implementation of Time Synchronous Dynamic Password Based on SM3 Hash Algorithm

With the rapid development of information technology, demand of network & information security has increased. People enjoy many benefits by virtue of information technology. At the same time network security has become the important challenge, but network information security has become a top priority. In the field of authentication, dynamic password technology has gained users’ trust and favor because of its safety and ease of operation. Dynamic password, SHA (Secure Hash Algorithm) is widely used globally and acts as information security mechanism against potential threat. The cryptographic algorithm is an open research area, and development of these state-owned technology products helps secure encryption product and provides safeguard against threats. Dynamic password authentication technology is based on time synchronization, using the state-owned password algorithm. SM3 hash algorithm can meet the security needs of a variety of cryptographic applications for commercial cryptographic applications and verification of digital signatures, generation and verification of message authentication code. Dynamic password basically generates an unpredictable random numbers based on a combination of specialized algorithms. Each password can only be used once, and help provide high safety. Therefore, the dynamic password technology for network information security issues is of great significance. In our proposed algorithm, dynamic password is generated by SM3 Hash Algorithm using current time and the identity ID and it varies with time and changes randomly. Coupled with the SM3 hash algorithm security, dynamic password security properties can be further improved, thus it effectively improves network authentication security.

各向异性黏土中的深基坑开挖单撑失效数值模拟研究

近年来,随着基坑建设深度需求的不断增大,多道支撑式支护体系的使用大幅增长,而结构局部连接失效导致内支撑体系渐进失效破坏的案例时有发生。针对该工程问题,以往的研究通常将土体理想化,忽略了土体力学特性存在各向异性的特征,导致计算结果存在一定的偏差。本文基于天津某地铁车站基坑项目,采用NGI-ADP土体本构模型考虑土体的各向异性,建立三维有限元模型模拟不同初始位置单撑失效(OSF),通过荷载转移率和荷载增加率这两个指标,量化单撑失效后基坑支撑系统的受力变化。分析结果表明:土体的各向异性对支撑轴力的影响不容忽视;同一道支撑的不同初始位置单支撑失效后,基坑支撑的轴力响应是相似的;单撑失效后,在失效支撑附近荷载传递存在就近现象,主要在竖直方向上传递大幅荷载。

All-fiber ellipsometer for nanoscale dielectric coatings

Multiple mode resonance shifts in tilted fiber Bragg gratings(TFBGs)are used to simultaneously measure the thickness and the refractive index of TiO_(2) thin films formed by Atomic Layer Deposition(ALD)on optical fibers.This is achieved by comparing the experimental wavelength shifts of 8 TFBG resonances during the deposition process with simulated shifts from a range of thicknesses(T)and values of the real part of the refractive index(n).The minimization of an error function computed for each(n,T)pair then provides a solution for the thickness and refractive index of the deposited film and,a posteriori,to verify the deposition rate throughout the process from the time evolution of the wavelength shift data.Validations of the results were carried out with a conventional ellipsometer on flat witness samples deposited simultaneously with the fiber and with scanning electron measurements on cut pieces of the fiber itself.The final values obtained by the TFBG(n=2.25,final thickness of 185 nm)were both within 4%of the validation measurements.This approach provides a method to measure the formation of nanoscale dielectric coatings on fibers in situ for applications that require precise thicknesses and refractive indices,such as the optical fiber sensor field.Furthermore,the TFBG can also be used as a process monitor for deposition on other substrates for deposition methods that produce uniform coatings on dissimilar shaped substrates,such as ALD.

System reliability-based robust design of deep foundation pit considering multiple failure modes

Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.

CurveNet:Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition

In computer vision fields,3D object recognition is one of the most important tasks for many real-world applications.Three-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3D object recognition.In this paper,we propose to use the principal curvature directions of 3D objects(using a CAD model)to represent the geometric features as inputs for the 3D CNN.Our framework,namely CurveNet,learns perceptually relevant salient features and predicts object class labels.Curvature directions incorporate complex surface information of a 3D object,which helps our framework to produce more precise and discriminative features for object recognition.Multitask learning is inspired by sharing features between two related tasks,where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label classification.Experimental results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object classification.We further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3D object as the inputs.A Cross-Stitch module was adopted to learn effective shared features across multiple representations.We evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.

A perceptual quality metric for 3D triangle meshes based on spatial pooling

In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the dis- tortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the dis- tortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score. We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores.

Revealing the underlying mechanisms behind TE extraordinary THz transmission

Transmission through seemingly opaque surfaces,so-called extraordinary transmission,provides an exciting platform for strong light–matter interaction,spectroscopy,optical trapping,and color filtering.Much of the effort has been devoted to understanding and exploiting TM extraordinary transmission,while TE anomalous extraordinary transmission has been largely omitted in the literature.This is regrettable from a practical point of view since the stronger dependence of the TE anomalous extraordinary transmission on the array’s substrate provides additional design parameters for exploitation.To provide high-performance and cost-effective applications based on TE anomalous extraordinary transmission,a complete physical insight about the underlying mechanisms of the phenomenon must be first laid down.To this end,resorting to a combined methodology including quasi-optical terahertz(THz)time-domain measurements,full-wave simulations,and method of moments analysis,subwavelength slit arrays under s-polarized illumination are studied here,filling the void in the current literature.We believe this work unequivocally reveals the leaky-wave role of the grounded-dielectric slab mode mediating in TE anomalous extraordinary transmission and provides the necessary framework to design practical high-performance THz components and systems.