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.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

Precision measurement and suppression of low-frequency noise in a current source with double-resonance alignment magnetometers

Low-noise high-stability current sources have essential applications such as neutron electric dipole moment measurement and high-stability magnetometers. Previous studies mainly focused on frequency noise above 0.1 Hz while less on the low-frequency noise/drift. We use double resonance alignment magnetometers(DRAMs) to measure and suppress the low-frequency noise of a homemade current source(CS) board. The CS board noise level is suppressed by about 10 times in the range of 0.001-0.1 Hz and is reduced to 100 n A/√Hz at 0.001 Hz. The relative stability of CS board can reach2.2 × 10^(-8). In addition, the DRAM shows a better resolution and accuracy than a commercial 7.5-digit multimeter when measuring our homemade CS board. Further, by combining the DRAM with a double resonance orientation magnetometer,we may realize a low-noise CS in the 0.001-1000 Hz range.

Functionalized alginate-based bioinks for microscale electrohydrodynamic bioprinting of living tissue constructs with improved cellular spreading and alignment

Bioprinting has been widely investigated for tissue engineering and regenerative medicine applications.However,it is still difficult to reconstruct the complex native cell arrangement due to the limited printing resolution of conventional bioprinting techniques such as extrusion-and inkjet-based printing.Recently,an electrohydrodynamic(EHD)bioprinting strategy was reported for the precise deposition of well-organized cell-laden constructs with microscale filament size,whereas few studies have been devoted to developing bioinks that can be applied for EHD bioprinting and simultaneously support cell spreading.This study describes functionalized alginate-based bioinks for microscale EHD bioprinting using peptide grafting and fibrin incorporation,which leads to high cell viability(>90%)and cell spreading.The printed filaments can be further refined to as small as 30μm by incorporating polyoxyethylene and remained stable over one week when exposed to an aqueous environment.By utilizing the presented alginate-based bioinks,layer-specific cell alignment along the printing struts could be observed inside the EHD-printed microscale filaments,which allows fabricating living constructs with cell-scale filament resolution for guided cellular orientation.

On The Cauchy Problem for A 1D Euler-Alignment System in Besov Spaces

In this paper, we investigate a 1D pressureless Euler-alignment system with a non-local alignment term, describing a kind of self-organizing problem for flocking. As a result, by the transport equation theory and Lagrange coordinate transformation, the local well-posedness of the solutions for the 1D pressureless Euler-alignment in Besov spaces with 1≤p∞ is established. Next, the ill-posedness of the solutions for this model in Besov spaces with 1≤p and is also deduced. Finally, the precise blow-up criteria of the solutions for this system is presented in Besov spaces with 1≤p .

Influence of highway space alignment continuous degradation in 3- dimensional space on autonomous vehicle trajectory deviation based on PreScan simulation

The advent of autonomous vehicles(AVs)is expected to transform the current transportation system into a safe and reliable one.The existing infrastructures,operational criteria,and design method were developed to meet the requirements of human drivers.However,previous studies have shown that in the traditional horizontal and vertical combined design methods,where the two-dimensional alignment elements change,there are varying changes in curvature and torsion,which cause the continuous degradation of the spatial curve and torsion.This continuous degradation will inevitably cause changes in the trajectory of Autonomous Vehicles(AVs),thereby affecting driving safety.Therefore,studying the characteristics of autonomous vehicles trajectory deviation has theoretical significance for optimizing highway alignment safety design.Driving simulation tests were performed by using PreScan and Simulink to calibrate the lateral deviation.A machine learning approach called the Gradient Boosting Decision Tree(GBDT)algorithm was implemented to build a model and express the relationship between space alignment parameters and lane deviation.The results showed that the AV’s driving trajectory is significantly affected by the space alignment factors when the vehicle is driving in the inner lane,the downhill section,and the left-turn section.These findings will provide a novel perspective for road safety research based on autonomous vehicle driving trajectories.

Vector meson spin alignment by the strong force field

Observation of unexpectedly large global spin alignment of φ vector mesons in non-central heavy-ion collisions by STAR experiment may reveal the non-perturbative nature of quark interaction in hot matter through fluctuating strong force field with short correlation length.

妊娠合并慢性乙型肝炎血清HBV pgRNA、PreS1抗原表达与肝内胆汁淤积症的相关性分析

目的 探究妊娠合并慢性乙型肝炎病人血清乙型肝炎病毒(HBV)前基因组RNA(HBV pgRNA)、前S1抗原(PreS1Ag)水平变化及与妊娠期肝内胆汁淤积症(ICP)发生的相关性。方法 选取2017年6月至2020年6月在雅安市人民医院进行孕期检查的慢性乙型肝炎孕妇279例作为研究对象,根据入组病人是否患有ICP分为合并ICP组43例、未合并ICP组236例。比较两组病人血清中HBV pgRNA、PreS1 Ag水平,并分析两组血清HBV pgRNA、PreS1 Ag表达与HBV DNA表达水平相关性;比较两组病人妊娠结局,并分析合并ICP组病人血清HBV pgRNA表达水平及PreS1 Ag阳性表达率与妊娠结局的关系。受试者操作特征(ROC)曲线分析血清HBV pgRNA、PreS1 Ag光密度[D(λ)]值诊断慢性乙型肝炎孕妇合并ICP的效能。结果 合并ICP组慢性乙型肝炎病人血清HBV表面抗原(HbsAg)水平[(3.71±0.92)log IU/mL]、HBV e抗原(HbeAg)阳性率[65.12%(28/43)]、HBV DNA含量[(8.03±1.69)log copies/mL]、天冬氨酸转氨酶(AST)[(79.68±15.73)U/L]、丙氨酸转氨酶(ALT)[(72.08±16.95)U/L]、PreS1 Ag阳性表达率[88.37%(38/43)]、PreS1 Ag D(λ)值水平(1.24±0.25)及HBV pgRNA表达水平[(5.17±1.25)log copies/mL]明显高于未合并ICP组[(2.26±0.74)log IU/mL、24.15%(57/236)、(5.19±1.07)logcopies/mL、(23.01±12.47)U/L、(21.76±10.51)U/L、67.80%(160/236)、(0.92±0.23)、(3.02±0.98)logcopies/mL](P<0.05)。血清HBV pgRNA诊断慢性乙型肝炎孕妇合并ICP的曲线下面积(AUC)为0.89,灵敏度为81.40%,特异度为80.50%。PreS1 Ag D(λ)值诊断慢性乙型肝炎孕妇合并ICP的AUC为0.83,灵敏度为76.70%,特异度为79.20%。二者联合诊断的AUC为0.91,灵敏度为93.00%,特异度为78.40%。合并ICP组、未合并ICP组血清PreS1 Ag阳性的慢性乙型肝炎病人血清HBV DNA、HBV pgRNA表达水平均明显高于PreS1 Ag阴性表达病人(P<0.05)。合并ICP组、未合并ICP组血清HBV pgRNA、PreS1 Ag D(λ)值均与HBV DNA表达水平呈正相关(P<0.05)。合并ICP组产后出血、早产的发生率明显高于未合并ICP组(P<0.05)。发生不良妊娠结局的慢性乙型肝炎合并ICP病人血清PreS1 Ag阳性表达率、PreS1 Ag D(λ)值、HBV pgRNA表达水平均明显高于未发生不良妊娠结局病人(P<0.05)。结论 合并ICP的慢性乙型肝炎病人血清HBV pgRNA表达水平、PreS1 Ag阳性表达率及D(λ)值水平均明显升高,对ICP有一定诊断价值,且两者水平变化均与HBV DNA含量有关,并可能预示病人不良妊娠结局的发生。

血清preS1水平与慢性乙型肝炎患者肝纤维化的关系及对癌变的诊断价值

目的分析血清乙型肝炎病毒(hepatitis B virus,HBV)前S1蛋白(precursor S1 protein,preS1)与慢性乙型肝炎(chronic hepatitis B,CHB)肝纤维化及癌变进展的相关性。方法对2019年10月—2021年10月期间在青海红十字医院接受检查的228例乙肝表面抗原(hepatitis B surface antigen,HBsAg)阳性慢性HBV感染者进行回顾性分析,其中CHB患者75例、肝硬化(liver cirrhosis,LC)患者93例(LC组)、肝细胞癌(hepatocellular carcinoma,HCC)患者60例(HCC组)。根据LC和HCC组肝组织活检分析肝脏炎症活动及肝纤维化程度。结果HCC组血清preS1水平[496.32(457.63,988.0)ng/mL]和LC组[338.72(247.93,554.61)ng/mL]血清preS1水平均显著高于CHB组[113.69(87.09,177.40)ng/mL],且差异具有统计学意义(P均<0.05)。HCC组血清preS1水平亦高于LC组(P=0.002)。经受试者工作特征曲线分析,血清preS1水平鉴别诊断CHB与LC的曲线下面积(area under the curve,AUC)是0.881(95%CI:0.830~0.932),鉴别诊断CHB/LC与HCC的AUC是0.861(95%CI:0.815~0.908)。3组患者的血清preS1水平与HBsAg(rs=0.799,P<0.001)呈强正相关和Log HBV DNA(rs=0.262,P<0.001)呈弱正相关。此外LC组和HCC组血清preS1水平与肝脏炎症活动分级(rs=0.201,P=0.009)及肝纤维化分期也呈弱正相关性(rs=0.295,P<0.001)。结论血清preS1水平与血清HBsAg、HBV DNA水平和肝脏炎症和纤维化进展呈正相关,有可能成为鉴别诊断HBV相关慢性肝病肝硬化或癌变的候选标志物。

Influence of Alignment Errors on Contact Pressure during Straight Bevel Gear Meshing Process

Straight bevel gears are widely applied in automotive, aerospace, chemical and many other fields as one of the most common type of gears. Currently, the researches on straight bevel gears have focused on the fields of fatigue, wear, noise and vibration, while little attention is paid to the effect of multiple alignment errors on the gear tooth wear. To study the influence of alignment errors on the gear tooth wear, a simulated model of a straight bevel gear pair is established. Then, the contact pressure on the tooth surface is analyzed under the various alignment errors according to the Archard wear relationship. The main combinations of alignment errors played vital roles on the tooth wear are investigated. The result shows that under the single alignment error, the contact pressure moves to the tooth heel and increases greatly at here when ？P=0.1 or ？G=0.1; when ？E=–0.03, the contact pressure greatly increases at the tooth heel, but it obviously increases at the tooth toe when ？E=0.03; the alignment error ？γ=1 has little effect on the contact pressure on the tooth surface. Moreover, the combination of ？P, ？G, ？E〈0 and ？γ is the most dangerous type among the multiple alignment errors. This research provides valuable guidelines for predicting the tooth wear under various alignment errors.

Photo-induced flexible semiconductor CdSe/CdS quantum rods alignment

The anisotropic absorption and emission from semiconductor CdSe/CdS quantum rods(QRs)provide extra benefits among other photoluminescence nanocrystals.Using photo-induced alignment technique,the QRs can be oriented in liquid crystal polymer matrix at a large scale.In this article,a 2D Dammann grating pattern,within“SKL”characters domains aligned QRs in composite film,was fabricated by multi-step photo exposure using several photo masks,and a continuous geometric lens profile pattern aligned QRs was realized by the single step polarization converting holographic irradiation method.Both polarized optical microscope and fluorescence microscope are employed to determine the liquid crystal director profiles and QRs anisotropic excitation properties.We have been able to orient the QRs in fine binary and continuous patterns that confirms the strong quantum rod aligning ability of the proposed method.Thus,the proposed approach paves a way for photoinduced flexible QRs alignments to provide a highly specific and difficult-to-replicate security application at a large scale.

Spectral modulation of third-harmonic generation by molecular alignment and preformed plasma

We demonstrate spectral modulation of third-harmonic generation from molecular alignment effects. The third harmonic spectrum is broadened or narrowed under different influences of cross-phase modulations originating from various molecular alignment revivals. Furthermore, the spectrum and spatial distribution of the generated third harmonic pulse change dramatically in the presence of a preformed plasma. Under the influence of a preformed plasma, a narrower third harmonic spectrum is observed, and the conical third-harmonic pulse increases while the axial part decreases. The investigation provides an effective method to modulate the spectral characteristic and spatial distribution of third-harmonic generation from intense femtosecond filament.

Deep learning for estimation of Kirkpatrick-Baez mirror alignment errors

A deep learning-based automated Kirkpatrick-Baez mirror alignment method is proposed for synchrotron radiation.We trained a convolutional neural network(CNN)on simulated and experimental imaging data of a focusing system.Instead of learning directly from bypass images,we use a scatterer for X-ray modulation and speckle generation for image feature enhancement.The smallest normalized root-mean-square error on the validation set was 4%.Compared with conventional alignment methods based on motor scanning and analyzer setups,the present method simplified the optical layout and estimated alignment errors using a single-exposure experiment.Single-shot misalignment error estimation only took 0.13 s,significantly outperforming conventional methods.We also demonstrated the effects of the beam quality and pretraining using experimental data.The proposed method exhibited strong robustness,can handle high-precision focusing systems with complex or dynamic wavefront errors,and provides an important basis for intelligent control of future synchrotron radiation beamlines.

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond(B5G),Unmanned Aerial Vehicles(UAVs)are increasingly important to improve the performance of dense cellular networks.As a conventional metric,coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment.In recent years,stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems.In this paper,an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed.An assumption was considered that all users are distributed according to Poisson Cluster Process(PCP)around base stations,in particular,Thomas Cluster Process(TCP).Using thismodel,the impact of beam alignment errors on the coverage probabilitywas investigated.Initially,the ProbabilityDensity Function(PDF)of directional antenna gain between the user and its serving base station was obtained.Then,association probability with each tier was achieved.A tractable expression was derived for coverage probability in both Line-of-Sight(LoS)andNon-Line-of-Sight(NLoS)condition links.Numerical results demonstrated that at low UAVs altitude,beam alignment errors significantly degrade coverage performance.Moreover,for a small cluster size,alignment errors do not necessarily affect the coverage performance.

A New Pre-alignment Approach Based on Four-Quadrant-Photo-Detector for IC Mask

In this paper, a new pre-alignment approach based on Four-Quadrant-Photo-Detector （FQPD） for IC mask is presented. The voltage outputs from FQPDs are the functions of alignment mark＇s position offsets with respect to FQPDs. The functions are obtained with least squares error （LSE）-based polynomial fitting after the normalization of experimental data. As the acquired functions are not monotonic about their variables, the alignment mark＇s position offset cannot be given by direct inverse operation on the obtained functions. However, the piecewise polynomial fitting gives the inverse function, with which the alignment mark＇s position offset can be predicted according to the voltage outputs of FQPDs. On the basis of prediction, a pre-alignment control strategy is proposed. The feasibility and robustness of the pre-alignment approach is shown by experiments. Furthermore, the results demonstrate that the maximum error of mask＇s position offset in the X- and Y- directions is less than 15μm after coarse pre-alignment. Keywords： Four-Quadrant-Photo-Detector （FQPD）, pre-alignment, IC mask, polynomial fitting

New measuring method of fiber alignment in precision torsion pendulum experiments

Testing the extreme weak gravitational forces between torsion pendulum and surrounding objects will indicate new physics which attracts many interests.In these measurements,the fiber alignment plays a crucial role in fulfilling high precision placement measurement,especially in measuring the deviation between the fiber and source mass or other objects.The traditional way of the fiber alignment requires to measure the component of the pendulum body and then transfer to the torsion fiber by some complicated calculations.A new method is reported here by using a CCD camera to get the projection image of the torsion fiber,which is a direct and no-contact measurement.Furthermore,the relative position change of the torsion fiber can also be monitored during the experiment.In our experiment,the alignment between the fiber and the center of the turntable has been operated as an example.Our result reaches the accuracy of several micrometers which is higher than the previous method.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

植物PRE类转录因子基因的研究进展

多效唑是一种赤霉素合成抑制剂,抗多效唑(paclobutrazol-resistance,PRE)基因家族编码的蛋白质可与多效唑发生拮抗作用,这些蛋白质具有螺旋-环-螺旋(basic Helix-Loop-Helix,bHLH)结构,在高等植物中参与转录调控从而在植物发育的许多方面发挥关键作用.PRE基因通过参与各种激素(如赤霉素,油菜素内酯,生长素等)、温度和光响应信号通路来调节植物的生长和发育,对植物生长发育具有重要意义.综述PRE基因在植物中的研究进展,主要包括PRE基因的基本性质、分类、功能以及所参与的植物生理过程等方面,为进一步研究植物PRE类转录因子基因提供理论基础.

Anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base

The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.