Efficient and reversible separation of NH_(3) by deep eutectic solvents with multiple active sites and low viscosities

The efficient separation and collection of ammonia(NH_(3))during NH_(3) synthesis process is essential to improve the economic efficiency and protect the environment.In this work,ethanolammonium hydrochloride(EtOHACl)and phenol(PhOH)were used to prepare a novel class of deep eutectic solvents(DESs)with multiple active sites and low viscosities.The NH_(3) separation performance of EtOHACl+PhOH DESs was analyzed completely.It is figured out that the NH_(3) absorption rates in EtOHACl+PhOH DESs are very fast.The NH_(3) absorption capacities are very high and reach up to 5.52 and 10.74 mol·kg1 at 11.2 and 100.4 kPa under 298.2 K,respectively.In addition,the EtOHACl+PhOH DESs present highly selective absorption of NH_(3) over N_(2) and H_(2) and good regenerative properties after seven cycles of absorption/desorption.The intrinsic separation mechanism of NH_(3) by EtOHACl+PhOH DESs was further revealed by spectroscopic analysis and quantum chemistry calculations.

Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.

SADDLE-POINT BASED SEPARATION OF TOUCHED OBJECTS IN 2-D IMAGE

In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.

Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.

Cyclomatic Complexity-Based Encapsulation, Data Hiding, and Separation of Concerns

Three design principles are prominent in software development-encapsulation, data hiding, and separation of concerns. These principles are used as subjective quality criteria for both procedural and object-oriented applications. The purpose of research is to quantify encapsulation, data hiding, and separation of concerns is quantified using cyclomatic-based metrics. As a result of this research, the derived design metrics, coefficient of encapsulation, coefficient of data hiding, and coefficient of separation of concerns, are defined and applied to production software indicating whether the software has low or high encapsulation, data hiding, and separation of concerns.

Numerical Simulation and Experiment on Thin-Shell Object Separation from Aircraft

The unsteady aerodynamic loads generated by the thin-shell object separating from aircraft affects flying safety.To investigate the loads,a method combining numerical simulation and experiment is proposed.Firstly,the motional tendency of the thin-shell object separating from aircraft is calculated,and then the high-speed air blowing test on ground is designed.Thereafter,the external store is employed to avoid colliding with the thin-shell object in air.Finally,the hanging and flight test is conducted by a high-speed unmanned aerial vehicle(UAV),and the feasibility of the thin-shell object separating from aircraft at high speed is proved.Consequently,the separating problem of a thin-shell object with an unconventional aerodynamic configuration is solved,and the collisions with aircraft is prevented.

Blind Source Separation based on Time-Frequency Morphological Characteristics for Rigid Acoustic Scattering by Underwater Objects

Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation （BSS） can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution （WVD） observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. A simulation experimental has been used to analyze the feasibility of the new method, with changing the pulse width of the transmitted signal, the relative amplitude and the time delay parameter. And simulation results show that the new method can not only separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.

Rational design of stratified material with spatially separated catalytic sites as an efficient overall water-splitting photocatalyst

The development of metal sulfide catalysts with remarkable activity toward efficient overall photocatalytic water splitting remains challenging owing to the dominant charge recombination and deficient catalytic active sites.Moreover,in the process of water oxidation catalysis,the inhibition of severe photocorrosion is an immense task,requiring effective photogenic hole-transfer kinetics.Herein,stratified Co-MnO_(2)@CdS/CoS hollow cubes with spatially separated catalytic sites were rationally designed and fabricated as highly efficient controllable catalysts for photocatalytic overall water splitting.The unique self-templated method,including a continuous anion/cation-exchange reaction,integrates a Co-doped oxidation co-catalyst(Co-MnO_(2))and a reduction co-catalyst(CoS)on the nanocubes with uniform interface contact and ultrathin two-dimensional(2D)nanometer sheets.We demonstrate that the stratified Co-MnO_(2)@CdS/CoS hollow cubes can provide an abundance of active sites for surface redox reactions and contribute to the separation and migration of the photoionization charge carriers.In particular,CoS nanoparticles dispersed on the walls of CdS hollow cubes were identified as reduction co-catalysts accelerating hydrogen generation,while Co-MnO_(2) nanosheets attached to the inner walls of the CdS hollow cube were oxidation co-catalysts,promoting oxygen evolution dynamics.Benefiting from the desirable structural and compositional advantages,optimized stratification of Co-MnO_(2)@CdS/CoS nanocubes provided a catalytic system devoid of precious metals,which exhibited a remarkable overall photocatalytic water-splitting rate(735.4(H_(2))and 361.1(O_(2))μmol h^(−1) g^(−1)),being among the highest values reported thus far for CdS-based catalysts.Moreover,an apparent quantum efficiency(AQE)of 1.32%was achieved for hydrogen evolution at 420 nm.This study emphasizes the importance of rational design on the structure and composition of photocatalysts for overall water splitting.

Method of Searching for Earthquake Disaster Evacuation Routes Using Multi-Objective GA and GIS

This study treats the determination of routes for evacuation on foot in earthquake disasters as a multi-objective optimization problem, and aims to propose a method for quantitatively searching for evacuation routes using a multi-objective genetic algorithm (multi-objective GA) and GIS. The conclusions can be summarized in the following three points. 1) A GA was used to design and create an evacuation route search algorithm which solves the problem of the optimization of earthquake disaster evacuation routes by treating it as an optimization problem with multiple objectives, such as evacuation distance and evacuation time. 2) In this method, goodness of fit is set by using a Pareto ranking method to determine the ranking of individuals based on their relative superiorities and inferiorities. 3) In this method, searching for evacuation routes based on the information on present conditions allows evacuation routes to be derived based on present building and road locations.?Further, this method is based on publicly available information;therefore, obtaining geographic information similar to that of this study enables this method to be effective regardless of what region it is applied to, or whether the data regards the past or the future. Therefore, this method has high degree of spatial and temporal reproducibility.

Accurate design of spatially separated double active site in Bi_(4)NbO_(8)Cl single crystal to promote Z-Scheme photocatalytic overall water splitting

The efficiency of photocatalytic overall water splitting was mainly limited by the slow reaction kinetics of water oxidation.How to design effective surface active site to overcome the slow water oxidation reaction was a major challenge.Here,we propose a strategy to accelerate surface water oxidation through the fabrication spatially separated double active sites.FeCoPi/Bi_(4)NbO_(8)Cl-OVs photocatalyst with spatially separated double active site was prepared by hydrogen reduction photoanode deposition method.Due to the high matching of the spatial loading positions of FeCoPi and OVs with the photogenerated charge distribution of Bi_(4)NbO_(8)Cl and corresponding reaction mechanisms of substrate,the FeCoPi and OVs on the(001)and(010)crystal planes of Bi_(4)NbO_(8)Cl photocatalyst provided surface active site for water oxidation reaction and electron shuttle reaction(Fe^(3+)/Fe^(2+)),respectively.Under visible light irradiation,the evolution O_(2)rate of FeCoPi/Bi_(4)NbO_(8)Cl OVs was 16.8μmol h^(-1),as 32.9 times as Bi_(4)NbO_(8)Cl.Furthermore,a hydrogen evolution co-catalyst PtRu@Cr_(2)O_(3)was prepared by sequential photodeposition method.Due to the introduction of Ru,the Schottky barrier between PbTiO_(3)and Pt was effectively reduced,which promoted the transfer of photogenerated electrons to PtRu@Cr_(2)O_(3)thermodynamically,the evolution H_(2)rate on PtRu@Cr_(2)O_(3)/PbTiO_(3)increased to 664.8 times.On based of the synchronous enhancement of the water oxidation performance on FeCoPi/Bi_(4)NbO_(8)Cl-OVs and water reduction performance on PtRu@Cr_(2)O_(3)/PbTiO_(3),a novel Z-Scheme photocatalytic overall water splitting system(FeCoPi/Bi_(4)NbO_(8)Cl-OVs)mediated by Fe^(3+)/Fe^(2+)had successfully constructed.Under visible light irradiation,the evolution rates of H_(2)and O_(2)were 2.5 and 1.3μmol h^(-1),respectively.This work can provide some reference for the design of active site and the controllable synthesis of OVs spatial position.On the other hand,the hydrogen evolution co catalyst(PtRu@Cr_(2)O_(3))and the co catalyst FeCoPi for oxygen evolution contributed to the construction of an overall water splitting system.

“三权分置”路径下数据产权客体的类型谱系

“数据二十条”在表述数据产权“三权分置”时分别使用了“数据资源”“数据”“数据产品”三项表达来称谓“三权”的客体,从政策上肯认并确立了数据产权的客体对象。从法律视角审视,前述三项政策表达仍需经由学理阐释来论证其在法律上的定位及其相互关系,使之成为具有规范性的法律表达。“三权分置”路径下,“数据”的核心特征应包括信息属性、物理属性和集合属性,并在数据生产和流通使用的“价值链”上延展形成“数据资源”—“数据集合”—“数据产品”的类型层级。在分级分类构建原则下,作为产权客体的“数据”还可在横向上形成以公共数据与企业数据为一级子类型、公用/私用公共数据与公开/不公开企业数据为二级子类型的类型谱系。由此,便可在“三权分置”路径下阐明作为产权客体的数据的规范属性与类型谱系,为数据产权“三权分置”的法律化奠定基础和前提。

基于改进YOLOv8和GMM图像点集匹配的双目测距方法

针对无人塔吊系统的研究需求,提出一种基于改进YOLOv8和GMM图像点集匹配的双目测距方法,对驾驶室外环境中的塔吊吊钩进行检测识别并测距。通过双目摄像头进行图像采集,引入FasterNet骨干网络和Slim-neck颈部连接层,对YOLOv8目标检测算法进行改进,有效检测画面中的塔吊吊钩并获取检测框的二维坐标信息;采用局部敏感哈希方法,并融合分阶段匹配策略,提升GMM图像点集匹配模型的匹配效率,针对检测框中的塔吊吊钩,进行特征点匹配;最后通过双目相机三角测量原理计算得出塔吊吊钩的深度信息。实验结果表明,改进后的YOLOv8算法与原算法相比,精确率P提高了2.9%,平均精度AP50提高了2.2%,模型复杂度降低了10.01 GFLops,参数量减少了3.37 M,在提升检测精度的同时实现了模型的轻量化。改进后的图像点集匹配算法与原算法相比,各个指标表现出更加良好的鲁棒性。最后在工程现场对塔吊吊钩进行识别与测距,误差可接受范围内有效完成了塔吊吊钩的检测识别与测距任务,验证了本方法的可行性。

一种基于YOLOX_s的雾天场景目标检测方法

文中提出了一个基于深度可分离卷积和注意力机制的雾天目标检测模型,旨在实现在雾天场景中对目标的快速、准确检测。该模型由去雾模块和检测模块组成,并在训练过程中共同训练。为确保模型在雾天场景中检测的准确性和实时性,在去雾模块方面,采用AODNet对输入图像进行去雾处理,以降低雾对图像中待检测目标的干扰,在检测模块中使用改进后的YOLOX_s模型,输出目标的分类置信度和位置坐标。为提升网络的检测性能,在YOLOX_s基础上采用深度可分离卷积和注意力机制来提高特征提取能力,扩大特征图感受野。所提模型能提高有雾场景中模型的检测精度,且不增加模型参数量和计算量。实验结果表明,所提模型在RTTS数据集和合成有雾目标检测数据集上均表现出色,有效提高了模型在雾天场景中的检测精度。与基准模型相比,平均精度(mAP@50_95)分别提升了1.9%和2.37%。

考虑节点重要度的城市轨道交通应急救援点选址研究

为实现对城市轨道交通中关键站点的识别和保护,提出1种考虑节点重要度的应急救援点选址方法,基于复杂网络理论构建城市轨道交通网络,从节点拓扑属性、车站属性、网络连通属性3方面出发建立节点重要度评价方法,构建节点重要度加权下的综合覆盖水平最大、最大救援响应时间最短和建设成本最低的多目标应急救援点选址模型,并以武汉地铁网络为例,基于熵权-TOPSIS法确定网络中共262个节点的重要度,设计变邻域遗传算法进行优化求解。研究结果表明:4种赋权方案下发现,相对不考虑节点重要度,本文方法所得目标在综合覆盖水平上分别提高1.44%,1.06%,2.51%和2.90%,同时可有效降低最大救援响应时间及建设成本,识别与保护关键站点。研究结果可为提升线网整体的应急能力提供指导。

基于轻量化CenterNet的智能车辆目标检测算法

针对当前目标检测算法参数较多,计算量较大,导致响应速度较慢,难以推广应用于智能车辆系统的问题,提出一种改进的CenterNet目标检测算法。即应用轻量化MobileNetV3网络替换原ResNet-50网络,降低计算量;应用深度可分离的PANet替换特征增强网络,获得多尺度特征信息融合后的特征,并引入SimAM注意力机制在特征融合前强化目标特征关注度,再用SiLU激活函数替换原目标检测网络中的ReLU激活函数,增强网络学习能力。最后提出CPAN-ASFF模块对深度可分离的PANet输出多尺度特征图进行融合,提高目标检测精度。应用优化后的KITTI数据集对改进后的CenterNet目标检测算法进行训练及检测验证,结果表明:其平均准确率为80.7%,比原始Center⁃Net目标检测算法提高了12个百分点,其检测速度为65 f/s,其参数量为8.91 M,较原算法减少72.73%,改进后的算法在遮挡目标、重叠目标以及与背景相似目标的检测效果上表现更优。且在SODA10M数据集中,文中提出的算法的检测精度与速度也都优于当前主流算法。该研究对算法的优化及实验为智能车辆在实际工程中的应用奠定了技术支撑。

基于元学习的小样本语义分割算法

针对现有的小样本语义分割模型对未知新类分割精度不高的问题,提出一种基于元学习的小样本语义分割算法.首先,利用深度可分离卷积改进传统主干网络,并在ImageNet数据集上进行了编码器的预训练.其次,利用预训练的主干网络将支持图片和查询图片映射到深度特征空间.最后,利用支持图片的真实掩码将支持特征分离为目标前景和背景,并借助vision transformer构造了一种自适应的元学习分类器.在PASCAL-5^(i)数据集上进行了大量的试验.结果表明:所提出模型在VGG-16、ResNet-50和ResNet-101主干网络上分别实现了47.1%、58.3%和60.4%的mIoU(即平均交并比)(1 shot),同时在5 shot设定下实现了49.6%、60.2%和62.1%的mIoU;在COCO-20^(i)数据集上实现了23.6%、30.3%和30.7%的mIoU(1 shot),同时在5 shot设定下实现了30.1%、34.7%和35.2%的mIoU.

An anionic In(Ⅲ)-based metal-organic framework with Lewis basic sites for the selective adsorption and separation of organic cationic dyes

In this paper, a new anionic metal-organic framework, [In(PBPTTBA)][(CH_3)_2 NH_2](BUT-29) has been synthesized through the reaction of tetratopic acid ligand with double Lewis pyridine sites, 4,4',4'',4'''-(4,4'-(1,4-phenylene)bis(pyridine-6,4,2-triyl)) tetrabenzoic acid(H_4 PBPTTBA) and In(NO_3)_2·5H_2O and fully characterized by single-crystal X-ray diffraction(SXRD), powder X-ray diffraction(PXRD),thermogravimetric analysis(TGA), infrared spectroscopy(IR), and elemental analysis(EA). BUT-29 can be used as an efficient adsorbent for the selective removal of organic cationic dyes in N,N0-dimethylformamide(DMF) solution. The adsorption capacities of BUT-29 toward methylene blue and crystal violet at 298 K can reach 1119 mg/g and 832 mg/g, respectively. Moreover, the adsorbed dyes can be released in the DMF solution of LiNO_3 gradually.

基于GA-SA组合算法的山区复杂环境无人机起降点选址

针对山区复杂环境下的物流链前端无人机货运起降点选址和任务分配进行研究。首先以建设成本最小和运输时间满意度最大为目标,综合考虑无人机自身性能和禁飞空域等因素,构建多约束条件下多目标函数的起降点选址和任务分配模型。采用遗传算法(genetic algorithm, GA)和模拟退火算法(simulated annealing algorithm, SA)的组合算法进行求解,首先通过遗传算法得出较优的可行解,再以此解作为退火算法的初始解进行模型求解。仿真结果表明,构建的多约束模型能够实现预期效果,并且采用的算法解决此类问题时具有良好的适用性。

基于伪点云特征增强的多模态三维目标检测方法

环境感知是自动驾驶汽车落地的关键技术之一,它对于提高自动驾驶汽车的安全性和可靠性至关重要.三维目标检测是其中的一项核心任务,旨在识别和定位三维空间中的物体,为后续决策提供重要的信息.点云和图像是该任务最常用的输入数据,点云由三维空间中不规则分布的点组成,而图像则是由二维空间上规则分布的像素组成.因此,点云和图像之间难以进行有效的融合.而伪点云作为一种点云表征的图像信息,近几年受到了该领域学者的广泛关注.现阶段基于伪点云的三维目标检测方法还存在伪点云特征提取粗糙和相应感兴趣区域(Region-of-Intersts,RoI)特征表征能力差的问题.本文针对上述问题开展研究,分别提出细粒度注意力卷积和多尺度分组稀疏卷积.细粒度注意力卷积将规则图像处理中常用的深度可分离卷积引入不规则点云的处理流程,并在此基础上嵌入通道和分组注意力机制,进行精细的特征提取,增强伪点云特征;多尺度分组稀疏卷积将格网池化后的Ro I特征分组,进行差异化特征学习,获取不同尺度的Ro I特征,增强伪点云Ro I格网特征的表征能力.基于此,本文在SFD(Sparse Fuse Dense)网络的伪点云特征提取流程中引入细粒度注意力卷积,同时在其伪点云Ro I特征学习流程中引入多尺度分组稀疏卷积,构建SFD++多模态三维目标检测网络.在权威KITTI自动驾驶数据集上的实验结果表明,SFD++每秒可以处理8.33帧数据,其精度在简单、中等和困难的三维汽车检测上达到95.74%、88.80%和86.04%,比次优SFD的精度高出0.15%、0.84%和0.58%.除此之外,一系列消融和补充实验结果验证了所提出卷积的有效性和相关参数设置的合理性.

突发自然灾害下的两阶段多目标应急物资中心选址问题研究

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