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.

Modeling and analysis of the ocean dynamic with Gaussian complex network

The techniques for oceanographic observation have made great progress in both space-time coverage and quality, which make the observation data present some characteristics of big data. We explore the essence of global ocean dynamic via constructing a complex network with regard to sea surface temperature. The global ocean is divided into discrete regions to represent the nodes of the network. To understand the ocean dynamic behavior, we introduce the Gaussian mixture models to describe the nodes as limit-cycle oscillators. The interacting dynamical oscillators form the complex network that simulates the ocean as a stochastic system. Gaussian probability matching is suggested to measure the behavior similarity of regions. Complex network statistical characteristics of the network are analyzed in terms of degree distribution, clustering coefficient and betweenness. Experimental results show a pronounced sensitivity of network characteristics to the climatic anomaly in the oceanic circulation. Particularly, the betweenness reveals the main pathways to transfer thermal energy of El Niño–Southern oscillation. Our works provide new insights into the physical processes of ocean dynamic, as well as climate changes and ocean anomalies.

Correlation between TERT C228T and clinic-pathological features in pediatric papillary thyroid carcinoma

The aims of the present study were to reveal the prevalence of the TERT C228 T mutation in pediatric papillary thyroid carcinoma(PPTC) and to further investigate the role of the TERT C228 T mutation in PPTC. We also tested another TERT mutation, TERT C250 T, although this was not detected in PPTC patients. In this study, 48 patients with PPTC(41 with classic PPTC) were enrolled. DNA was extracted from PPTC tissues and TERT C228 T mutation analysis was performed. Chi-squared analysis,Fisher’s exact test, and a t-test were applied to test the significance of differences. The TERT C228 T mutation presented in 13(27.1%) of the 48 PPTC patients and 10(24.4%) of the 41 classical PPTC patients. There were significant differences between PPTC patients with the TERT C228 T mutation and those without in terms of modified radical neck dissection, multifocality,capsular invasion, extrathyroidal invasion, and American Joint Committee on Cancer(AJCC) tumor stage(P<0.05). In classical PPTC, there were additional significant differences in other clinic-pathological features, such as AJCC nodal stage(P=0.009)and American Thyroid Association(ATA) PPTC stage(P=0.021) between patients with and without the TERT C228 T mutation.These findings indicate that the TERT C228 T mutation is significantly correlated with certain aggressive clinic-pathological features of PPTC.

MYC-associated protein X binding with the variant rs72780850 in RNA helicase DEAD box 1 for susceptibility to neuroblastoma

Neuroblastoma(NB)is one of the most common malignant tumors in children,with variable clinical behaviors and a 15%death rate of all malignancies in childhood.However,genetic susceptibility to sporadic NB in Han Chinese patients is largely unknown.To identify genetic risk factors for NB,we performed an association study on 357 NB patients and 738 control subjects among Han Chinese children.We focused on DEAD box 1(DDX1),a putative RNA helicase,which is involved in NB carcinogenesis.The potential association of DDX1 polymorphisms with NB has not been discovered.Our results demonstrate that rs72780850(NM_004939.2:c.-1555 T>C)located in the DDX1 promoter region is significantly associated with higher expression of DDX1 transcript and increased NB risk(odds ratio=1.64,95%confidence interval=1.03%–2.60%,P=0.004),especially in aggressive NB compared with ganglioneuroma and ganglioneuroblastoma in a dominant model(TC+CC vs.TT).Furthermore,the MYC-associated protein X(MAX)transcription factor showed stronger binding affinity to the DDX1 rs72780850 CC allele compared with the TT allele,explaining the molecular mechanism of the increased NB risk caused by the rs72780850 polymorphism.Our results highlight the involvement of regulatory genetic variants of DDX1 in NB.

Identification of potential pathogenic mutations in Chinese children with first branchial cleft anomalies detected by whole-exome sequencing

Importance:First branchial cleft anomalies(FBCAs)are rare congenital malformations,accounting for<8%of all branchial cleft anomalies.However,little is currently known about the cause of FBCAs at the molecular level.Objective:To identify genomic alterations related to the genetic etiology of FBCAs in Chinese children.Methods:We performed whole-exome sequencing of samples from 10 pediatric patients with FBCAs.Data analysis was carried out using the Burrow-Wheeler Alignment software package,and the dbSNP database for comparisons.Rare variants were further validated by Sanger sequencing.Insertion/deletions(indels)were examined using the Genome Analysis Toolkit.Results:We identified 14 non-synonymous mutations in seven potential FBCA-susceptibility genes(TRAPPC12,NRP2,NPNT,SH3RF2,RHPN1,TENM4,and ARMCX4).We also detected 133 shared small indels in 125 genes.Gene Ontology analysis indicated that most of the identified genes played critical roles in development and differentiation pathways involved in regulating organ development.Interpretation:We characterized the mutational landscape in pathways involved in development and differentiation in Chinese children with FBCA.The results identified potential pathogenic genes and mutations related to FBCA,and provide molecular-level support for the branchial theory of FBCA pathogenesis.

Impact of acoustic wave propagation characteristics on aerodynamic damping of rotor blades in a transonic axial compressor

The aeroelastic stability associated with the acoustic wave propagation characteristics of transonic rotor blades was numerically investigated in this study.The influence of the vibration frequency on the aerodynamic damping of the first bending mode was primarily considered at different speeds,including both work and stall points.We found that the state of acoustic wave propagation associated with aeroelastic instability is closely related to rotational speed.At low speeds near stall points,the risk of aeroelastic instability is confined to the upstream cut-off state.However,at high speeds near stall points,aeroelastic instability may occur in both the downstream cut-off state and the acoustic wave propagation state of the upstream cut-off frequency,further expanding the range of the acoustic wave propagation state in which aeroelastic instability can arise.The research findings show that for suction surfaces,aerodynamic work is affected not only by acoustic wave propagation characteristics,but also by shock waves,radial flow,and reflux in the flow field.However,for pressure surfaces,the acoustic wave propagation characteristics play a significant role.When aeroelastic instability occurs,negative damping predominantly arises from the pressure surface.To investigate why lower-order modes are more prone to aeroelastic instability,specific simulations were conducted for the first bending and twisting modes under different operating conditions in the downstream cut-on state.When the vibration frequency significantly exceeds the downstream cutoff frequency,the blade phase is minimally influenced by acoustic wave propagation characteristics,and the rotor is aeroelastically stable in these phases.Simultaneously,there is an approximately linear increase in the unsteady pressure amplitude with increasing vibration frequency,and the aerodynamic work is predominantly influenced by the unsteady pressure amplitude.