氢氟酸处理InP/GaP/ZnS量子点的光学性能及其发光二极管应用

采用氢氟酸(HF)原位注入法制备了InP/GaP/ZnS量子点。通过紫外/可见/近红外光谱、光致发光光谱、透射电镜、球差校正透射电镜、X射线衍射、X射线光电子能谱等测试手段分析了HF对InP量子点的发光性能影响。实验结果表明,HF刻蚀减少了量子点表面氧化缺陷状态,有效控制了InP核表面的氧化,并且原子配体形式的F-钝化了量子点表面的悬挂键,显著提升了量子点的光学性能。HF处理的InP/GaP/ZnS量子点具有最佳的发光性能,PLQY高达96%。此外,用HF处理InP/GaP/ZnS量子点制备的发光二极管,其发光的电流效率为6.63 cd/A,最佳外量子效率(EQE)为3.83%。

青年职场“Gap day”现象探究

青年职场“Gap day”现象引起了社会的广泛关注和热烈讨论。青年职场“Gap day”现象的典型特征是:通过语言上的拼贴与再造,表达部分职业青年的困顿疲倦情绪;通过行为上的放空与逃离,排解青年在工作中积攒的情绪和压力;通过价值上的戏谑与自嘲,赋予青年的短暂休息以正向意义。青年职场“Gap day”现象的深层原因是:青年在高压生存环境中的“弹性姿态”、在加速社会运行中的“精神逃离”和在社会时钟齿轮下的“自我审视”。对青年职场“Gap day”现象的有效治理,既要着眼于青年发展,运用公共政策实现青年发展的普遍性诉求,又要立足于青年就业,采取切实措施促进青年实现高质量充分就业。

基于5GAP模型的高校食堂服务质量差距分析与对策探讨

食堂服务是高校后勤服务体系中最重要的环节之一,高校食堂管理必须要有的放矢地改进工作。5GAP模型是一种直接有效的服务环节分析工具,更注重找出内部的服务缺陷。在以往的高校食堂研究中,多以顾客满意度的外部视角分析服务质量问题,本文通过5GAP模型以内部视角分析发现引发高校食堂服务质量问题的根源,以期能对高校食堂服务质量的改进和提升起到一定作用。

共混GAP基含能热塑性弹性体的氢键行为与力学性能

为了改善GAP热塑性弹性体的力学性能,采用了DSC、低场核磁测试、静态力学测试和动态力学测试的方法对两种异氰酸酯固化的弹性体共混体系的氢键行为和力学性能进行分析,建立微观结构与宏观性能的关系。结果表明,由于分子结构原因,HMDI固化的GAP热塑性弹性体和IPDI固化的弹性体显示出不同的氢键行为和力学性能,通过物理共混得到了兼具抗拉强度及断裂延伸率的弹性体,50℃下抗拉强度高于1.5MPa,-40℃下延伸率不低于300%,相较于纯HMDI固化的弹性体,低温延伸率提升了约150%,IPDI固化的弹性体高温抗拉强度提升了约1.4MPa,说明通过共混可得到性能更为均衡的含能热塑性弹性体。

基于Roussouly分型的脊柱形态和GAP评分对成人脊柱畸形术后临床疗效的影响

目的评估成人脊柱畸形(ASD)手术前后的Roussouly矢状面脊柱形态及全脊柱序列比例(GAP)评分对术后临床疗效的影响。方法回顾性分析2016年1月至2021年12月在宁夏医科大学总医院行手术治疗的72例ASD患者临床资料,其中男性20例,女性52例,年龄(46.9±19.0)岁。将纳入患者按照“当前”[依据骶骨倾斜角(SS)]和“理论”[依据骨盆入射角(PI)]Roussouly分型脊柱形态进行划分。根据一个恒定参数(PI)判断出患者“理论”形态,然后将此种类型的理想形态参数与实际参数作对比,若术后3个月患者的“当前”Roussouly脊柱形态与其“理论”Roussouly脊柱形态各项参数均符合则归入匹配组,否则归入非匹配组。根据相关参数计算两组患者手术前后GAP评分,并收集术前、术后3个月和末次随访时的疼痛视觉模拟评分(VAS)、Oswestry功能障碍指数(ODI)及术前和末次随访时SRS-22评分(包括总分、疼痛、功能、自我形象、心理健康和治疗满意度),同时记录患者随访时机械并发症发生情况。比较两组患者的影像学参数、GAP评分、临床疗效评分及机械并发症中的差异。结果72例ASD患者术前以RoussoulyⅠ型和Ⅱ型脊柱形态为主,术后以RoussoulyⅡ型和Ⅲ型为主。所有患者术后影像学参数和临床疗效评分较术前均得到改善(P均<0.05)。组间比较,非匹配组末次随访骨盆倾斜角和躯干整体倾斜角大于匹配组(P均<0.05),术后3个月及末次随访,匹配组GAP评分均低于非匹配组(P均<0.05),匹配组较非匹配组在术后3个月和末次随访有更好的VAS评分、ODI评分及SRS-22评分(包括总分、疼痛、功能和治疗满意度)(P均<0.05)。匹配组术后机械并发症发生率更低(P<0.05),腰椎前凸顶点位于理想位置可降低机械并发症发生率。结论ASD术后脊柱矢状面匹配理想Roussouly分型的患者临床疗效和GAP评分更好、机械并发症发生率更低,PI术中应尽可能矫正至理想Roussouly分型脊柱形态。

基于GIS的漳州市松岭村乡村旅游适宜性评价及GAP分析

以“全国乡村旅游扶贫重点村”漳州市松岭村为研究对象,借助地理信息系统(GIS),从资源禀赋潜力出发,构建乡村旅游适宜性评价体系,利用地理探测器计算各旅游适宜性影响因子的权重,基于乡村旅游适宜性评价结果对旅游空间进行开发空缺(GAP)分析,并提出旅游规划优化策略。旨在优化景观资源在空间整体布局中的合理利用,为乡村旅游规划提供新思路。研究结果表明:(1)松岭村旅游适宜性受基础设施因子影响最大,受坡向因子影响最小;(2)松岭村旅游适宜等级可分为高适宜、较高适宜、中适宜、较低适宜、低适宜区5类,其中高适宜区面积最小,仅占松岭村总面积的4.64%,低适宜区面积最大,占总面积的58.00%;(3)借鉴GAP分析思路,识别松岭村景观资源点开发潜力分级区块,各适宜等级区均存在较多景观资源点未开发。建议结合现有特色景观资源点,优先开发较高及高潜力区块、积极开发中潜力区、适度开发较低及低潜力区,以期在保留旅游资源特色的同时最大化利用其景观资源。

LiF/GAP协同作用改善硼粉的燃烧性能

以氟化锂/缩水甘油叠氮化物聚合物(LiF/GAP)为界面层对微米硼进行改性,研究LiF/GAP协同作用对硼粉热行为、燃烧性能和凝聚相燃烧产物的影响。结果表明,通过硅烷偶联剂处理后,硼粉表面黏结性得以提高,LiF能够较好地包覆在硼粉表面;GAP在400℃之前抑制了LiF与硼表面氧化膜的反应,使LiF的除膜效应延后至硼粉氧化增重阶段,从而将更多的活性硼暴露在空气中发生氧化,促进了硼粉的燃烧;LiF和GAP协同作用显著改善了硼粉的燃烧性能,尤其当LiF和GAP质量分数均为10%时,作用效果最为明显。此外,LiF和GAP协同作用有效抑制了燃烧过程中硼粉团聚,使其凝聚相燃烧产物粒径降低。

固化剂对GAP固化反应的影响研究

针对含能黏结剂GAP应用时固化剂的选择问题,采用红外、凝胶测试仪、非等温差示扫描量热法(Differential Scanning Calorimetry,DSC),研究了氰酸酯固化剂和丁二酸二丙炔醇酯固化剂对GAP固化反应的影响。实验表明,GAP分子结构中的羟基和叠氮基团都能参与反应,氰酸酯类固化剂与GAP的固化速率小于丁二酸二丙炔醇酯固化剂与GAP的固化速率,采用非等温DSC进行测试可以获得丁二酸二丙炔醇酯固化剂与GAP的固化放热峰,无法获得氰酸酯与GAP的固化放热峰。丁二酸二丙炔醇酯/GAP固化物的拉伸强度小于N-100/GAP固化物,延伸率大于N-100/GAP固化物。该研究为含能黏结剂GAP应用时固化剂的确定提供了试验基础数据。

Unsupervised Time Series Segmentation: A Survey on Recent Advances

Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.

GAP/N-100交联体系力学和热解机理的MD模拟

为探究聚叠氮缩水甘油醚(GAP)与多异氰酸酯(N-100)交联形成三维网状聚合物的力学性能、热分解机理和主要产物信息,采用perl语言结合分子动力学模拟软件编写了能实现GAP与N-100交联的脚本,建立了不同交联度的GAP/N-100分子模型,并预测了不同交联体系的力学性能,采用反应分子动力学对热解机理和产物进行了模拟。结果表明:通过自编脚本可以得到一系列不同交联度的交联模型,最终交联度为96.7%;随着交联度的增大,GAP/N-100体系的杨氏模量、剪切模量和体积模量均逐渐提高。GAP/N-100交联体系热解的初始分解机理为叠氮基团的脱落以及碳骨架的分解,热解反应的活化能Ea为13.411 kJ/mol,指前因子A为0.099 1/ps-1,热解的主要产物有N2、H_(2)、H_(2)O以及NH3,主要的中间产物为CH_(2)O。

Behavior of exciton in direct−indirect band gap Al_(x)Ga_(1−x)As crystal lattice quantum wells

Excitons have significant impacts on the properties of semiconductors.They exhibit significantly different properties when a direct semiconductor turns in to an indirect one by doping.Huybrecht variational method is also found to influence the study of exciton ground state energy and ground state binding energy in Al_(x)Ga_(1−x)As semiconductor spherical quantum dots.The Al_(x)Ga_(1−x)As is considered to be a direct semiconductor at AI concentration below 0.45,and an indirect one at the concentration above 0.45.With regards to the former,the ground state binding energy increases and decreases with AI concentration and eigenfrequency,respectively;however,while the ground state energy increases with AI concentration,it is marginally influenced by eigenfrequency.On the other hand,considering the latter,while the ground state binding energy increases with AI concentration,it decreases with eigenfrequency;nevertheless,the ground state energy increases both with AI concentration and eigenfrequency.Hence,for the better practical performance of the semiconductors,the properties of the excitons are suggested to vary by adjusting AI concentration and eigenfrequency.

Spin gap in quasi-one-dimensional S=3/2 antiferromagnet CoTi2O5

Quasi-one-dimensional(1D)antiferromagnets are known to display intriguing phenomena especially when there is a spin gap in their spin-excitation spectra.Here we demonstrate that a spin gap exists in the quasi-1D Heisenberg antiferromagnet CoTi2O5 with highly ordered Co2+/Ti4+occupation,in which the Co2+ions with S=3/2 form a 1D spin chain along the a-axis.CoTi2O5 undergoes an antiferromagnetic transition at TN~24 K and exhibits obvious anisotropic magnetic susceptibility even in the paramagnetic region.Although a gapless magnetic ground state is usually expected in a quasi-1D Heisenberg antiferromagnet with half-integer spins,by analyzing the specific heat,the thermal conductivity,and the spin-lattice relaxation rate(1/T1)as a function of temperature,we found that a spin gap is opened in the spin-excitation spectrum of CoTi2O5 around TN,manifested by the rapid decrease of magnetic specific heat to zero,the double-peak characteristic in thermal conductivity,and the exponential decay of 1/T1 below TN.Both the magnetic measurements and the first-principles calculations results indicate that there is spin-orbit coupling in CoTi2O5,which induces the magnetic anisotropy in CoTi2O5,and then opens the spin gap at low temperature.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.