Temperature-regulation liquid gating membrane with controllable gas/liquid separation

Membrane separation technology with the ability to regulate gas/liquid transport and separation is critical for environmental fields, such as sewerage treatment, multiphase separation, and desalination. Although numerous membranes can dynamically control liquid-phase fluids transport via external stimuli, the transport and separation of gas-phase fluids remains a challenge. Here, we show a temperature-regulation liquid gating membrane that allows in-situ dynamically controllable gas/liquid transfer and multiphase separation by integrating a thermo-wettability responsive porous membrane with functional gating liquid. Experiments and theoretical analysis have demonstrated the temperature-regulation mechanism of this liquid gating system, which is based on thermo-responsive changes of porous membrane surface polarity, leading to changes in affinity between the porous membrane and the gating liquid. In addition, the sandwich configuration with dense Au-coated surfaces and heterogeneous internal components by a bistable interface design enables the liquid gating system to enhance response sensitivity and maintain working stability. This temperature-regulation gas/liquid transfer strategy expands the application range of liquid gating membranes,which are promising in environmental governance, water treatment and multiphase separation.

Development of miniaturized SAF-LIBS with high repetition rate acousto-optic gating for quantitative analysis

The self-absorption effect in laser-induced breakdown spectroscopy(LIBS)reduces the accuracy of quantitative measurement results.The self-absorption-free LIBS(SAF-LIBS)has been proved to directly capture the optically thin plasma spectra by setting an appropriate exposure time.In this work,a novel SAF-LIBS technique with high repetition rate acousto-optic gating is developed,in which an acousto-optic modulator is used as the shutter to diffract the optically thin fluorescence,and a high repetition rate laser is used to produce quasi-continuous plasmas to enhance the integral spectral intensity,so that the CCD spectrometer can replace an intensified CCD(ICCD)and echelle spectrometer in SAF-LIBS.Experimental results show that the average absolute prediction error of aluminum is reduced to 0.18%,which is equivalent to that of traditional SAF-LIBS.This technique not only effectively shields continuous background radiation and broadened spectral lines in optically thick plasma,but also has advantages of miniaturization,low cost,convenience and reliability.

引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

基于GRU-DRSN的双通道人体活动识别

人体活动识别(human activity recognizition, HAR)在医疗、军工、智能家居等领域有很大的应用空间。传统机器学习方法特征提取难度较大且精度不高。针对上述问题并结合传感器时序特性,提出了一种融合CBAM(convolutional block attention module)注意力机制的GRU-DRSN双通道并行模型,有效避免了传统串行模型因网络深度加深引起梯度爆炸和消失问题。同时并行结构使得两条支路具有相同的优先级,使用深度残差收缩网络(deep residual shrinkage network, DRSN)提取数据的深层空间特征,同时使用门控循环结构(gated recurrent unit, GRU)学习活动样本在时间序列上的特征,同时进行提取样本不同维度的特征,并通过CBAM模块进行特征的权重分配,最后通过Softmax层进行识别,实现了端对端的人体活动识别。使用公开数据集(wireless sensor data mining, WISDM)进行验证,模型平均精度达到了97.6%,与传统机器学习模型和前人所提神经网络模型相比,有更好的识别效果。

Gating技术与汉语听觉词汇加工

尝试对Gating技术的实施和运用 ,对数据进行探索性分析。被试为 47名北京师范大学本科学生。实验中被试听到依次加长的单字词语音片段 ,每听完一个语音片段要尽快判断该语音是什么字音 ,并迅速写出想到的第一个符合该读音的汉字 ,同时在七点量表上评定作出判断的确定性等级。结果显示 ,Gating技术作为探讨汉语听觉词汇加工的一条途径可以提供丰富的信息 ,它不仅能得到词汇的语音识别点 (IsolationPoint ,IP) ,也揭示出它与声调存在一定关系。对错误反应的分析还表明 :声调信息可以独立于音段信息而被识别 ;即时影响声调识别的因素不一定局限于区别性特征 ;听觉词汇识别受到听觉语言特有的方向性特点的影响 ,这种影响不仅体现在音段信息。

11.2 W/mm power density AlGaN/GaN high electron-mobility transistors on a GaN substrate

In this letter,high power density AlGaN/GaN high electron-mobility transistors(HEMTs)on a freestanding GaN substrate are reported.An asymmetricΓ-shaped 500-nm gate with a field plate of 650 nm is introduced to improve microwave power performance.The breakdown voltage(BV)is increased to more than 200 V for the fabricated device with gate-to-source and gate-to-drain distances of 1.08 and 2.92μm.A record continuous-wave power density of 11.2 W/mm@10 GHz is realized with a drain bias of 70 V.The maximum oscillation frequency(f_(max))and unity current gain cut-off frequency(f_(t))of the AlGaN/GaN HEMTs exceed 30 and 20 GHz,respectively.The results demonstrate the potential of AlGaN/GaN HEMTs on freestanding GaN substrates for microwave power applications.

基于FUZZY-BN-FTA的厂区架空燃气管道泄漏可能性研究

为研究厂区架空燃气管道泄漏的故的可能性,提出模糊数学方法、贝叶斯网络模型以及故障树模型相结合的集成模型。通过GeNIe软件建立架空燃气管道泄漏事故的模糊贝叶斯网络模型;引入Leaky Noisy-or Gate扩展模型对模糊贝叶斯网络中节点的条件概率表进行修正,结合基本事件先验概率预测架空燃气管道泄漏的概率值,并引入3个基本事件重要度确定基本事件的重要性排序,同时进行敏感性分析确定主要影响因素,提出控制措施。研究结果表明:厂区车辆撞击、管道疲劳损耗、管材质量不佳、安装设计不合理、焊接缺陷为导致架空燃气管道泄漏的主要因素。研究结果可为工业企业安全管理人员有针对性地制定控制措施,减少事故发生率提供参考。

中文重叠关系抽取的动态分层级联标记模型

构建了动态分层级联标记中文重叠关系抽取(RWG-LSA)模型:首先基于预训练语言模型和gated机制构建了动态字词融合特征学习模型(RWG),有效避免了主体标记模块的特征缺失和无法并行计算等问题;其次引入动态权局部自注意力(LSA),自主学习到主体层面的语义特征;最后在有效融合了输入序列的全局和主体局部特征的基础上,实现RWG-LSA模型对文本中实体对和关系的抽取。在SKE中文数据集上的实验表明,本模型对重叠关系抽取有显著效果,F1值达到了82.44%。

白车身侧围总拼精度提升方案的创新与应用

本文系统阐述了奇瑞公司焊装车间主线侧围总拼柔性GATE系统精度提升的创新方案。该方案通过对GATE系统的定位机构进行优化调整,满足公司生产需求。通过对新的OPENGATE系统刚性CAE分析以及现场实际验证确定了该方案有效。

Gating范式:探讨听觉词汇加工的途径

传统的反应时任务只能为听觉词汇加工提供定量数据,而不能提供定性信息,Gating范式的优势在于可以提供以上两种信息,使研究者能够全面、细致地了解听觉词汇加工的即时过程。而且它的适用群体广泛,可被用于研究不同年龄段的发展。文章较详细地介绍了Gating范式的基本特点及其在汉语研究中的应用,期待它被更多的研究者所认识和使用。

Flexible planar micro supercapacitor diode

Supercapacitor diode is a novel ion device that performs both supercapacitor energy storage and ion diode rectification functions.However,previously reported devices are limited by their large size and complex processes.In this work,we demonstrate a screen-printed micro supercapacitor diode(MCAPode)that based on the insertion of a finger mode with spinel ZnCo_(2)O_(4) as cathode and activated carbon as anode for the first time,and featuring an excellent area specific capacitance(1.21 mF cm^(-2)at 10 mV s^(-1))and high rectification characteristics(rectification ratioⅠof 11.99 at 40 mV s^(-1)).Taking advantage of the ionic gel electrolyte,which provides excellent stability during repeated flexing and at high temperatures.In addition,MCAPode exhibits excellent electrochemical performance and rectification capability in"AND"and"OR"logic gates.These findings provide practical solutions for future expansion of micro supercapacitor diode applications.

A quantum blind signature scheme based on dense coding for non-entangled states

In some schemes, quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. By considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non-entangled quantum blind signature scheme based on dense encoding. The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources. After receiving particles, the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature. Then the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification.Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection,and higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature. By simultaneously utilizing the secondary distribution and rearrangement of non-entangled quantum states, a non-entangled quantum state representation of three bits of classical information is achieved, reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency while, at the same time, this scheme meets the requirements of unforgeability, non-repudiation, and prevention of information leakage.

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography

Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Investigation of helicity-dependent photocurrent of surface states in(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate

Helicity-dependent photocurrent(HDPC)of the surface states in a high-quality topological insulator(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate grown by chemical vapor deposition(CVD)is investigated.By investigating the angle-dependent HDPC,it is found that the HDPC is mainly contributed by the circular photogalvanic effect(CPGE)current when the incident plane is perpendicular to the connection of the two contacts,whereas the circular photon drag effect(CPDE)dominates the HDPC when the incident plane is parallel to the connection of the two contacts.In addition,the CPGE of the(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate is regulated by temperature,light power,excitation wavelength,the source–drain and ionic liquid top-gate voltages,and the regulation mechanisms are discussed.It is demonstrated that(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplates may provide a good platform for novel opto-spintronics devices.

Te掺杂的GaSb材料载流子特性研究

非故意掺杂的GaSb材料呈现p型导电,限制了GaSb材料在InAs/GaSb超晶格红外探测器等领域的应用。探究N型GaSb薄膜电学特性对估算超晶格载流子浓度以及制备超晶格衬底、缓冲层、电极接触层等提供了一定的理论依据。Te掺杂能够以抑制GaSb本征缺陷的方式实现N型GaSb薄膜的制备,利用分子束外延(Molecular Beam Epitaxy,MBE)技术,设置GaTe源温分别为420℃、450℃、480℃,分别在GaSb衬底与GaAs衬底上生长不同GaTe源温度下掺杂的GaSb薄膜,通过霍尔测试探究GaSb薄膜的电学特性。在77 K的霍尔测试中,发现在GaAs衬底上生长的GaSb薄膜均显示为N型半导体,载流子浓度随源温升高而增加。与非故意掺杂的GaSb相比,源温为420℃、450℃时由于载流子浓度增加而导致的杂质散射,迁移率大幅提高,且随温度升高而增加,但在480℃时,由于缺陷密度减小,迁移率大大减小。在GaSb衬底上生长7000ABe掺杂的GaSb缓冲层,再生长5000ATe掺杂的GaSb薄膜。结果发现,由于P型缓冲层的存在,当源温为420℃时,薄膜显示为P型半导体,空穴载流子的存在导致薄膜整体载流子浓度增加,且空穴和电子的补偿作用使迁移率大幅降低。源温为450℃、480℃时,薄膜仍为N型半导体,载流子浓度随温度增加,且为GaAs衬底上生长的GaSb薄膜载流子浓度的2~3倍;迁移率在450℃时最高,480℃时减小。设置GaTe源温为450℃时GaSb薄膜的载流子浓度较高且迁移率较高,参与超晶格材料的制备能够使整个材料的效果最佳。

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.