Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Stable anode-free zinc-ion batteries enabled by alloy network-modulated zinc deposition interface

Newly-proposed anode-free zinc-ion batteries(ZIBs)are promising to remarkably enhance the energy density of ZIBs,but are restricted by the unfavorable zinc deposition interface that causes poor cycling stability.Herein,we report a Cu-Zn alloy network-modulated zinc deposition interface to achieve stable anode-free ZIBs.The alloy network can not only stabilize the zinc deposition interface by suppressing 2D diffusion and corrosion reactions but also enhance zinc plating/stripping kinetics by accelerating zinc desolvation and nucleation processes.Consequently,the alloy network-modulated zinc deposition interface realizes high coulombic efficiency of 99.2%and high stability.As proof,Zn//Zn symmetric cells with the alloy network-modulated zinc deposition interface present long operation lifetimes of 1900 h at 1 m A/cm^(2)and 1200 h at 5 m A/cm^(2),significantly superior to Zn//Zn symmetric cells with unmodified zinc deposition interface(whose operation lifetime is shorter than 50 h),and meanwhile,Zn3V3O8cathodebased ZIBs with the alloy network-modified zinc anodes show notably enhanced rate capability and cycling performance than ZIBs with bare zinc anodes.As expected,the alloy network-modulated zinc deposition interface enables anode-free ZIBs with Zn3V3O8cathodes to deliver superior cycling stability,better than most currently-reported anode-free ZIBs.This work provides new thinking in constructing high-performance anode-free ZIBs and promotes the development of ZIBs.

Regulatable Orthotropic 3D Hybrid Continuous Carbon Networks for Efficient Bi-Directional Thermal Conduction

Vertically oriented carbon structures constructed from low-dimen-sional carbon materials are ideal frameworks for high-performance thermal inter-face materials(TIMs).However,improving the interfacial heat-transfer efficiency of vertically oriented carbon structures is a challenging task.Herein,an orthotropic three-dimensional(3D)hybrid carbon network(VSCG)is fabricated by depositing vertically aligned carbon nanotubes(VACNTs)on the surface of a horizontally oriented graphene film(HOGF).The interfacial interaction between the VACNTs and HOGF is then optimized through an annealing strategy.After regulating the orientation structure of the VACNTs and filling the VSCG with polydimethylsi-loxane(PDMS),VSCG/PDMS composites with excellent 3D thermal conductive properties are obtained.The highest in-plane and through-plane thermal conduc-tivities of the composites are 113.61 and 24.37 W m^(-1)K^(-1),respectively.The high contact area of HOGF and good compressibility of VACNTs imbue the VSCG/PDMS composite with low thermal resistance.In addition,the interfacial heat-transfer efficiency of VSCG/PDMS composite in the TIM performance was improved by 71.3%compared to that of a state-of-the-art thermal pad.This new structural design can potentially realize high-performance TIMs that meet the need for high thermal conductivity and low contact thermal resistance in interfacial heat-transfer processes.

An Intelligent Identification Approach of Assembly Interface for CAD Models

Kinematic semantics is often an important content of a CAD model(it refers to a single part/solid model in this work)in many applications,but it is usually not the belonging of the model,especially for the one retrieved from a common database.Especially,the effective and automatic method to reconstruct the above information for a CAD model is still rare.To address this issue,this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics.First,as the geometry of an assembly interface is formed by one or more adjacent faces on each model,a face-attributed adjacency graph integrated with face structure fingerprint is proposed.This can describe each CAD model as well as its assembly interfaces uniformly.After that,aided by the above descriptor,an improved graph attention network is developed based on a new dual-level anti-interference filtering mechanism,which makes it have the great potential to identify all representative kinds of assembly interface faces with high accuracy that have various geometric shapes but consistent kinematic semantics.Moreover,based on the abovementioned graph and face-adjacent relationships,each assembly interface on a model can be identified.Finally,experiments on representative CAD models are implemented to verify the effectiveness and characteristics of the proposed approach.The results show that the average assembly-interface-face-identification accuracy of the proposed approach can reach 91.75%,which is about 2%–5%higher than those of the recent-representative graph neural networks.Besides,compared with the state-of-the-art methods,our approach is more suitable to identify the assembly interfaces(with various shapes)for each individual CAD model that has typical kinematic pairs.

A Deep Web Query Interfaces Classification Method Based on RBF Neural Network

This paper proposes a new approach for classification for query interfaces of Deep Web, which extracts features from the form＇s text data on the query interfaces, assisted with the synonym library, and uses radial basic function neural network （RBFNN） algorithm to classify the query interfaces. The applied RBFNN is a kind of effective feed-forward artificial neural network, which has a simple networking structure but features with strength of excellent nonlinear approximation, fast convergence and global convergence. A TEL_8 query interfaces＇ data set from UIUC on-line database is used in our experiments, which consists of 477 query interfaces in 8 typical domains. Experimental results proved that the proposed approach can efficiently classify the query interfaces with an accuracy of 95.67%.

High-precision grating sensor demodulation device's network interface based on DM9000A

High-precision fiber Bragg grating se nsor demodulation instrument with wide-range dynamic scanning can effectivel y improve the measuring range of the optical fiber grating sensor.Ethernet com munication module is an extremely important part of the high-precision grating sensor demodulation device.Network interface based on Ethernet control chip D M9000A is used to send and receive the Bragg grating sensing pulse.The network transformer YL18-2050S is used to convert and filter the pulse from network.The transmitting and receiving program of grating demodulation,hardware circuit of Ethernet transmission interface are designed.The experimental results show that the network interface can achieve accurate and real-time transmissi on of the grating sensing information at high speed.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

AS-Interface接口技术及应用

阐述了AS Interface(ActuratorSensorInterface,简称AS I)接口技术的特点及其在自动控制系统中的应用。介绍了AS I接口技术的主要特性,产品的主要构成。分析了接口基本内容和AS I模块,给出了AS I的两种典型应用方案。可以看出,AS I是一个传感器 执行器 接口的技术,用两芯电缆传递数据和电源,用于自动化控制层的最底层,用简单经济的方式将二进制的数字化执行器和传感器连接起来,既可以直接连接到控制箱中,单独或与PLC,IPC一起独立就地运行;或与上层现场总线(Profibus,Interbus,Devi ceNet,CAN,Modbus,Modbus+,CC Link,Ethernet等)相连,作为现场总线的一个分支,应用于网络系统,从而满足工业控制的各种要求。

Multi-agent mobile networking observation experiment at the air-sea interface of ocean eddy

Current climate forecasting has advanced to the stage of investigating mesoscale air-sea interactions. Recent studies have identified significant structural differences between the cores and edges of mesoscale eddies;however, the effects of these structural variations on air-sea fluxes and the Marine Atmospheric Boundary Layer(MABL) remain underexplored. Traditional observations often fail to capture the detailed structures of eddies, necessitating enhanced observations at high spatiotemporal resolution for mesoscale eddies. To address this, efforts have been made to develop multi-agent platforms and expendable air-sea interface observation technologies. A task-oriented observation scheme was developed to monitor the spatial characteristics of mesoscale eddies. The South China Sea(SCS) is rich in mesoscale eddies with rapid motion changes, requiring enhanced observations of the air-sea interface using multi-agent mobile networking. An anticyclonic eddy was observed in the eastern region of the Xisha Islands in the SCS, and we examined variations in air-sea fluxes across different regions within the eddy.

Ratio of Fe-Al compound at interface of steel-backed Al-graphite semi-solid bonding plate

The ratio of Fe-Al compound at the bonding interface of solid steel plate to Al-7graphite slurry was used to characterize the interracial structure of steel-Al-7graphite semi-solid bonding plate quantitatively. The relationship between the ratio of Fe-Al compound at interface and bonding parameters （such as preheat temperature of steel plate, solid fraction of Al-7graphite slurry and rolling speed） was established by artificial neural networks perfectly. The results show that when the bonding parameters are 516 ℃ for preheat temperature of steel plate, 32.5% for solid fraction of Al-7graphite slurry and 12 mm/s for rolling speed, the reasonable ratio of Fe-Al compound corresponding to the largest interfacial shear strength of bonding plate is obtained to be 70.1%. This reasonable ratio of Fe-Al compound is a quantitative criterion of interracial embrittlement, namely, when the ratio of Fe-Al compound at interface is larger than 70.1%, interfacial embrittlement will occur.

Inversion of 3D density interface with PSO-BP method

BP( Back Propagation) neural network and PSO( Particle Swarm Optimization) are two main heuristic optimization methods,and are usually used as nonlinear inversion methods in geophysics. The authors applied BP neural network and BP neural network optimized with PSO into the inversion of 3D density interface respectively,and a comparison was drawn to demonstrate the inversion results. To start with,a synthetic density interface model was created and we used the proceeding inversion methods to test their effectiveness. And then two methods were applied into the inversion of the depth of Moho interface. According to the results,it is clear to find that the application effect of PSO-BP is better than that of BP network. The BP network structures used in both synthetic and field data are consistent in order to obtain preferable inversion results. The applications in synthetic and field tests demonstrate that PSO-BP is a fast and effective method in the inversion of 3D density interface and the optimization effect is evident compared with BP neural network merely,and thus,this method has practical value.

A Multi-Interface Remote Monitoring and Control System Architecture Based on Embedded Server

This paper presents a multi-interface embedded server architecture for remote real-time monitoring system and distributed monitoring applications. In the scheme,an embedded microprocessor( LPC3250 from NXP) is chosen as the CPU of the embedded server with a linux operation system( OS) environment. The embedded server provides multiple interfaces for supporting various application scenarios. The whole network is based on local area network and adopts the Browser / Server( B / S) model. The monitoring and control node is as a browser endpoint and the remote node with an embedded server is as a server endpoint. Users can easily acquire various sensors information through writing Internet protocol address of remote node on the computer browser. Compared with client / server( C / S) mode,B / S model needs less maintain and can be applicable to large user group. In addition,a simple network management protocol( SNMP) is used for management of devices in Internet protocol( IP) networks. The results of the demonstration experiment show that the proposed system gives good support to manage the network from different user terminals and allows the users to better interact with the ambient environment.

On-Line Privacy Behavior: Using User Interfaces for Salient Factors

The problem of privacy in social networks is well documented within literature;users have pri- vacy concerns however, they consistently disclose their sensitive information and leave it open to unintended third parties. While numerous causes of poor behaviour have been suggested by re- search the role of the User Interface (UI) and the system itself is underexplored. The field of Per- suasive Technology would suggest that Social Network Systems persuade users to deviate from their normal or habitual behaviour. This paper makes the case that the UI can be used as the basis for user empowerment by informing them of their privacy at the point of interaction and remind- ing them of their privacy needs. The Theory of Planned Behaviour is introduced as a potential theoretical foundation for exploring the psychology behind privacy behaviour as it describes the salient factors that influence intention and action. Based on these factors of personal attitude, subjective norms and perceived control, a series of UIs are presented and implemented in con- trolled experiments examining their effect on personal information disclosure. This is combined with observations and interviews with the participants. Results from this initial, pilot experiment suggest groups with privacy salient information embedded exhibit less disclosure than the control group. This work reviews this approach as a method for exploring privacy behaviour and propos- es further work required.

基于控保协同的有源配网主动注入式保护方法

由于逆变型分布式电源(inverter-interfaced distributed generation,IIDG)的故障特性主要由其控制策略及电网故障情况决定,在不同故障场景和工况下,传统的被动式保护方法存在保护整定和协调困难的问题。为此,提出一种基于控保协同的有源配网主动注入式保护方法,故障期间控制IIDG主动向电网注入特征信号,保护处利用检测到的特征信号来获取IIDG的基波信息,从而消除IIDG对各支路电流及现有电流保护的影响。由于保护利用的故障特征是由IIDG主动注入的,因此该方法在不同场景和工况下都有较好的适用性,且不需要增加额外的通信和检测设备,具有经济性好和实用性高的优点。仿真结果验证了所提方法的有效性。

融合图注意力网络和注意力因子分解机的服务推荐方法

为了解决服务推荐过程中的特征稀疏问题、提高服务的语义表示能力,进而提升推荐的准确性和有效性,提出基于图注意力网络(graph attention networks,GAT)研究服务推荐方法,引入服务的组合权重和组合的结构信息,综合利用多种服务特征,提高服务推荐质量。将GAT和注意力因子分解机(attention factorization machine,AFM)结合在一起,利用多头自注意力机制,学习每个节点在图邻域中的重要性;进行信息聚合,处理网络中的不同图结构,以适应服务动态变化的场景。实验结果显示,在数据平衡的情况下,提出的方法性能表现均好于对比方法;在数据不平衡的情况下,提出的方法大部分性能指标也表现良好,达到了提升服务推荐准确性和有效性的目标。

适用于逆变型分布式电源T 接的配电网线路纵联保护方案

针对逆变型分布式电源(IIDG)T接于配电网导致双端电流纵联差动保护难以适用的问题,提出一种基于线路端点正序电流的新型纵联保护方案。建立以并网点正序电压主导的IIDG压控电流源模型。根据线路本端保护测量所得正序电气量分别求解对端正序电流计算值。在此基础上,将同一端点测量值与计算值的相量差幅值作为保护动作量,并利用发生区、内外故障时保护动作量的差异形成故障识别判据。考虑计算误差及互感器传变误差对保护整定值的影响构建完整的保护方案。该方案在保护动作量计算过程中无须实时获取IIDG运行状态,仅在现有双端纵联通道内对线路两端的电气量进行通信即可准确识别区内、外故障,通信压力小。仿真结果表明所提保护方案可靠有效,受IIDG出力影响小且具备一定抗过渡电阻能力。

含逆变型分布式电源和分支负荷的配电网自适应电流差动保护

电流差动保护具有良好的选择性和灵敏性。但是,逆变型分布式电源(inverter-interfaced distributed generator,IIDG)和分支负荷接入改变了配电网的故障特性,现有配电网的电流差动保护面临拒动或误动的风险。为解决以上问题,该文在计及P/Q控制的IIDG和分支负荷的故障等值建模基础上,从故障点、IIDG和分支负荷的相对位置出发,挖掘配电网线路两端正序电流差的幅值特征以及正序电流故障分量的相位特征。在此基础上,提出一种正序电流幅值与故障分量相角阈值配合的双比例制动系数自适应差动保护方案,并且利用幅相平面分析所提保护判据的可靠性和灵敏性。最后通过PSCAD/EMTDC验证了所提保护方案的有效性,仿真结果表明该方案适应不同故障类型、位置和过渡电阻,且不受IIDG以及分支负荷接入的影响。

融入时空自适应图卷积的运动想象信号解析

基于运动想象(motor imagery,MI)脑电信号的脑机接口(brain-computer interface,BCI)技术在脑卒中患者运动功能康复医疗应用中得到广泛关注和研究。然而MI信号具有低信噪比和个体差异性大的特点,导致脑电信号噪声过大从而影响分类性能。因此,如何充分提取MI信号特征以得到更高的单被试分类精度,以及如何训练一个在跨被试上表现优秀的通用模型是MI-BCI系统用于实际应用时急需解决的问题。针对该问题,提出了一种面向不同被试的时空自适应图卷积网络模型,从时空两个维度提取MI特征信号进行分类。模型包括四个模块:空间自适应图卷积模块、时间自适应图卷积模块、特征融合模块和特征分类模块。空间自适应图卷积模块通过通道间的特征相似性动态构造空间图表示,摆脱人为构造的图表示限制。时间自适应图卷积模块将脑电信号的时间序列划为多个时间片段,计算时间段间的相似性,自适应构造脑电信号的时间图表示,消除了噪声影响。最后,进行特征融合并分类。结果表明,在BCIIV2a数据集上使用10-折交叉验证的方法和HGD数据集上分别达到了90.45%和91.64%的平均分类精度,与目前性能较好的方法相比达到了更高的准确率,证明了该模型的有效性;使用迁移学习对不同个体进行实验,平均准确率提高了1.66个百分点,证明了模型的鲁棒性。

基于通道组合-数据对齐-多尺度全局CNN的MI-EEG分类

由于运动想象脑机接口(MI-BCI)范式不需要视觉刺激,应用MI-BCI范式在提高人机交互系统舒适度方面具有重要意义。为实现辅助设备的异步控制,提高模型的鲁棒性,减少通道使用数量以降低BCI系统输入的复杂性,提出一种基于通道组合(channel combination,CC)-数据对齐(euclidean space data alignment,EA)-多尺度全局卷积神经网络(multiscale global convolutional neural network,MGCNN)的运动想象脑电分类方法。通过引入大脑静息状态下的脑电信号,扩展MI-BCI输出指令集;利用CC将22通道脑电数据重构为左右对称通道加中间通道的3通道形式,重构后的数据经过EA方法规范后作为网络输入;构建多尺度卷积模块与全局卷积模块,并行提取脑电信号的局部特征和ERS/ERD全局特征;利用迁移学习提升模型的解码能力。结果表明:该方法在BCI Competition IV 2a数据集上达到了99.28%的平均准确率和0.99的Kappa值,提高了运动想象脑电分类精度,为在线异步运动想象脑机接口的应用与发展作出了贡献。

对21世纪网络神经外科临床实践的思考

百年神经外科发展史可以划分为三个时期,即经典神经外科时期、显微神经外科时期和微创神经外科时期。进入21世纪,现代医学科学迅猛发展,神经外科跨入网络神经外科新时期。网络神经外科是以脑网络的拓扑特性为基础,以多模态智能神经功能影像和微创手术技术为手段,在诊治神经系统疾患过程中,使患者脑功能网络得到保护或重塑,藉此提高难治性神经系统疾患诊治水平,它将成为脑科学转化研究的纽带。