Multi-relaxation time lattice Boltzmann simulation of inertial secondary flow in a curved microchannel

The inertial secondary flow is particularly important tbr hydrodynamic lbcusing and particle manipulation m biomed- ical research. In this paper, the development of the inertial secondary flow structure in a curved microchannel was investi- gated by the multi relaxation time lattice Boltzmann equation model with a force term. The numerical results indicate that the viscous and inertial competition dominates the development of secondary flow structure development. The Reynolds number, Dean number, and the cross section aspect ratio influence significantly on the development of the secondary vor- texes. Both the intensity of secondary flow and the distance between the normalized vortex centers are functions of Dean numbers but independent of channel curvature radius. In addition, the competition mechanism between the viscous and inertial effects were discussed by performing the particle focusing experiments. The present investigation provides an improved understanding of the development of inertial secondary flows in curved microchannels.

Impact of time between meniscal injury and isolated meniscus repair on post-operative outcomes:A systematic review

BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases

Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.

Simulating high Reynolds number flow in two-dimensional lid-driven cavity by multi-relaxation-time lattice Boltzmann method

By coupling the non-equilibrium extrapolation scheme for boundary condition with the multi-relaxation-time lattice Boltzmann method, this paper finds that the stability of the multi-relaxation-time model can be improved greatly, especially on simulating high Reynolds number （Re） flow. As a discovery, the super-stability analysed by Lallemand and Luo is verified and the complex structure of the cavity flow is also exhibited in our numerical simulation when Re is high enough. To the best knowledge of the authors, the maximum of Re which has been investigated by direct numerical simulation is only around 50 000 in the literature; however, this paper can readily extend the maximum to 1000 000 with the above combination.

Three-dimensional multi-relaxation-time lattice Boltzmann front-tracking method for two-phase flow

We developed a three-dimensional multi-relaxation-time lattice Boltzmann method for incompressible and immiscible two-phase flow by coupling with a front-tracking technique. The flow field was simulated by using an Eulerian grid, an adaptive unstructured triangular Lagrangian grid was applied to track explicitly the motion of the two-fluid interface, and an indicator function was introduced to update accurately the fluid properties. The surface tension was computed directly on a triangular Lagrangian grid, and then the surface tension was distributed to the background Eulerian grid. Three benchmarks of two-phase flow, including the Laplace law for a stationary drop, the oscillation of a three-dimensional ellipsoidal drop, and the drop deformation in a shear flow, were simulated to validate the present model.

Multi-relaxation-time lattice Boltzmann front tracking method for two-phase flow with surface tension

In this paper, an improved incompressible multi-relaxation-time lattice Boltzmann-front tracking approach is proposed to simulate two-phase flow with a sharp interface, where the surface tension is implemented. The lattice Boltzmann method is used to simulate the incompressible flow with a stationary Eulerian grid, an additional moving Lagrangian grid is adopted to track explicitly the motion of the interface, and an indicator function is introduced to update the fluid properties accurately. The interface is represented by using a four-order Lagrange polynomial through fitting a set of discrete marker points, and then the surface tension is directly computed by using the normal vector and curvature of the interface. Two benchmark problems, including Laplace＇s law for a stationary bubble and the dispersion relation of the capillary wave between two fluids are conducted for validation. Excellent agreement is obtained between the numerical simulations and the theoretical results in the two cases.

Multi-relaxation-time lattice Boltzmann simulations of lid driven flows using graphics processing unit

Large eddy simulation （LES） using the Smagorinsky eddy viscosity model is added to the two-dimensional nine velocity components （D2Q9） lattice Boltzmann equation （LBE） with multi-relaxation-time （MRT） to simulate incompressible turbulent cavity flows with the Reynolds numbers up to 1 × 10^7. To improve the computation efficiency of LBM on the numerical simulations of turbulent flows, the massively parallel computing power from a graphic processing unit （GPU） with a computing unified device architecture （CUDA） is introduced into the MRT-LBE-LES model. The model performs well, compared with the results from others, with an increase of 76 times in computation efficiency. It appears that the higher the Reynolds numbers is, the smaller the Smagorinsky constant should be, if the lattice number is fixed. Also, for a selected high Reynolds number and a selected proper Smagorinsky constant, there is a minimum requirement for the lattice number so that the Smagorinsky eddy viscosity will not be excessively large.

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

Partition-Time Masking:一种唇语识别数据增强方法

提出了一种唇语识别数据增强方法Partition-Time Masking。该方法直接作用于输入数据,通过将输入划分为多个子序列再分别进行Mask操作最后再将各子序列按序拼接,使得模型能对部分帧缺失的输入具有更强的鲁棒性,从而增强泛化能力。实验前根据划分的子序列数目与掩码值来源不同而设计了5种增强策略,并与唇语识别研究中最重要的数据增强方法Time Masking进行了对比实验。实验在LRW数据集和LRW1000数据集上进行,实验结果表明Partition-Time Masking方法对模型性能提升的效果要优于Time Masking方法,其中子序列数目为3、掩码值选择各子序列平均帧时为最优策略,该策略使得目前最佳的唇语识别模型DC-TCN的性能从89.6%提高到90.0%。

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

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.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.