First-Order Symmetry Energy Induced by Neutron-Proton Mass Difference

The 1st-order symmetry energy coefficient of nuclear matter induced merely by the neutron-proton （n p） mass difference is derived analytically, which turns out to be completely model-independent. Based on this result, （npDM） the 1st-order symmetry energy Esym,1 （A） of heavy nuclei such as 2~spb induced by the np mass difference is investigated with the help of a local density approximation combined with the Skyrme energy density functionals. Although /U（npDM） Esym,1 （A） is small compared with the second-order symmetry energy, it cannot be dropped simply for an accurate estimation of nuclear masses as it is still larger than the rms deviation given by some accurate mass formulas. It is therefore suggested that one perhaps needs to distinguish the neutron mass from the proton one in the construction of nuclear density funetionals.

A new polar motion prediction method combined with the difference between polar motion series

After the first Earth Orientation Parameters Prediction Comparison Campaign(1 st EOP PCC),the traditional method using least-squares extrapolation and autoregressive(LS+AR)models was considered as one of the polar motion prediction methods with higher accuracy.The traditional method predicts individual polar motion series separately,which has a single input data and limited improvement in prediction accuracy.To address this problem,this paper proposes a new method for predicting polar motion by combining the difference between polar motion series.The X,Y,and Y-X series were predicted separately using LS+AR models.Then,the new forecast value of X series is obtained by combining the forecast value of Y series with that of Y-X series;the new forecast value of Y series is obtained by combining the forecast value of X series with that of Y-X series.The hindcast experimental comparison results from January 1,2011 to April 4,2021 show that the new method achieves a maximum improvement of 12.95%and 14.96%over the traditional method in the X and Y directions,respectively.The new method has obvious advantages compared with the differential method.This study tests the stability and superiority of the new method and provides a new idea for the research of polar motion prediction.

IMPROVEMENT OF REGIONAL PREDICTION OF SEA FOG ON GUANGDONG COASTLAND USING THE FACTOR OF TEMPERATURE DIFFERENCE IN THE NEAR-SURFACE LAYER

The relationship between the factor of temperature difference of the near-surface layer(T_(1000 hPa)-T_(2m))and sea fog is analyzed using the NCEP reanalysis with a horizontal resolution of l°xl°(2000 to 2011) and the station observations(2010 to 2011).The element is treated as the prediction variable factor in the GRAPES model and used to improve the regional prediction of sea fog on Guangdong coastland.(1) The relationship between this factor and the occurrence of sea fog is explicit:When the sea fog happens,the value of this factor is always large in some specific periods,and the negative value of this factor decreases significantly or turns positive,suggesting the enhancement of warm and moist advection of air flow near the surface,which favors the development of sea fog.(2) The transportation of warm and moist advection over Guangdong coastland is featured by some stages and the jumping among these states.It also gets stronger over time.Meanwhile,the northward propagation of warm and moist advection is quite consistent with the northward advancing of sea fog from south to north along the coastland of China.(3) The GRAPES model can well simulate and realize the factor of near-surface temperature difference.Besides,the accuracy of regional prediction of marine fog,the relevant threat score and Heidke skill score are all improved when the factor is involved.

Numerical Analysis of Upwind Difference Schemes for Two-Dimensional First-Order Hyperbolic Equations with Variable Coefficients

In this paper, we consider the initial-boundary value problem of two-dimensional first-order linear hyperbolic equation with variable coefficients. By using the upwind difference method to discretize the spatial derivative term and the forward and backward Euler method to discretize the time derivative term, the explicit and implicit upwind difference schemes are obtained respectively. It is proved that the explicit upwind scheme is conditionally stable and the implicit upwind scheme is unconditionally stable. Then the convergence of the schemes is derived. Numerical examples verify the results of theoretical analysis.

Quantitative prediction of oil saturation of unconsolidated sandstone reservoir based on time-lapse seismic “relative difference method”: Taking Zeta oil field in West Africa as an example

In view of the disadvantage that the absolute difference of time-lapse seismic(the difference between monitoring data and base data) is not only related to the change of oil saturation, but also closely related to the thickness of reservoir, a time-lapse seismic "relative difference method"(the ratio of monitoring data to base data) not affected by the thickness of reservoir but only related to the change of fluid saturation, is proposed through seismic forward modeling after fluid displacement simulation. Given the same change of fluid saturation, the absolute difference of time-lapse seismic conforms to the law of "tuning effect" and seismic reflection of "thin bed", and the remaining oil prediction method based on absolute difference of time-lapse seismic is only applicable to the reservoirs with uniform thickness smaller than the tuning thickness or with thickness greater than the tuning thickness. The relative difference of time-lapse seismic is not affected by reservoir thickness, but only related to the change of fluid saturation. It is applicable to all the deep-sea unconsolidated sandstone reservoirs which can exclude the effect of pressure, temperature, pore type and porosity on seismic. Therefore, the relation between the relative difference of time-lapse seismic and the change of fluid saturation, which is obtained from seismic forward modeling after Gassmann fluid displacement simulation, can be used to quantitatively predict the change of reservoir water saturation and then the distribution of the remaining oil. The application of this method in deep sea Zeta oil field in west Africa shows that it is reasonable and effective.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

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.

Remaining Useful Life Prediction of Rolling Element Bearings Based on Different Degradation Stages and Particle Filter

A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is improved by a method based on the Paris formula and the Foreman formula allowing the establishment of different degradation stages.The remaining useful life of rolling element bearings can be predicted by the adjusted model with inputs of physical data and operating status information.The late operating trend is predicted by the use of the particle filter algorithm.The rolling bearing full life experimental data validate the proposed method.Further,the prediction result is compared with the single SSM and the Gamma model,and the results indicate that the predicted accuracy of the proposed method is higher with better practicability.

An Analysis of the Difference between the Multiple Linear Regression Approach and the Multimodel Ensemble Mean

An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.

Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

PROGRESS IN THE STUDY OF RETROSPECTIVE NUMERICAL SCHEME AND THE CLIMATE PREDICTION

The retrospective numerical scheme(RNS)is a numerical computation scheme de- signed for multiple past value problems of the initial value in mathematics and considering the self- memory property of the system in physics.This paper briefly presents the historical background of RNS,elaborates the relation of the scheme with other difference schemes and other meteorological prediction methods,and introduces the application of RNS to the regional climatic self-memory model, simplified climate model,barotropic model,spectral model,and mesoscale model.At last,the paper sums up and points out the application perspective of the scheme and the direction for the future study.

GEKF,GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering （EKF）, the unscented Kalman filtering （UKF） and the Gaussian particle filtering （GPF） to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal （These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper）. Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey-Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF.

Research on software development of air temperature prediction in coal face

With ever-increasing depth of coal mine and the continuous improvement of mechanization, heat damage has become one of the major disasters in coal mine exploitation. Established the temperature prediction models suitable for different kinds of tunnels through analysis of the heat of shafts, roadways and working faces. The average annual air temperature prediction equation from the inlets of shafts to the working faces was derived. The formula was deduced using combine method of iteration and direct calculation. The method can improve the precision of air temperature prediction, so we could establish the whole pathway air temperature prediction model with high precision. Emphasizing on the effects of leakage air to air temperature of working face and using the ideology of the finite difference method and considering the differential equation of inlet and outlet at different stages, this method can significantly improve the accuracy of temperature prediction. Program development uses Visual Basic 6.0 Language, and the Origin software was used to fit the relevant data. The predicted results shows that the air temperature generally tends to rapidly increase in the air inlet, then changes slowly on working face, and finally increases sharply in air outlet in the condition of goaf air leakage. The condition is in general consistent with the air temperature change tendency of working face with U-type ventilation system. The software can provide reliable scientific basis for reasonable ventilation, cooling measures and management of coal mine thermal hazards.

HTI介质下五维地震脆性稳定预测方法研究

岩石的脆性性质是页岩油气藏勘探开发过程中工程甜点预测的重要指标之一。以横向各向同性介质(HTI)为例,建立了各向异性假设下的脆性指示因子与背景杨氏模量、背景泊松比及各向异性参数的关系,形成了五维地震脆性稳定预测方法。首先,基于各向同性假设预测储层的杨氏模量和泊松比;其次,基于复频域反演获得各向异性参数的低频信息作为初始模型,结合方位振幅差异反演技术稳定预测储层的3个各向异性参数;最后,通过背景杨氏模量和泊松比以及各向异性参数计算各向异性储层的脆性指示因子,实现HTI介质脆性的稳定预测。该方法可以充分利用宽频地震数据的低频信息,并将六参数直接反演转化为二次三参数反演,理论上提升了反演过程的稳定性。实际资料应用结果表明,该方法针对页岩油的脆性预测具有良好的效果。

高等级混凝土路面切缝时机的预测预警方法

基于有限差分法对混凝土路面的温度场、临界拉应力、早期强度性能等进行预测计算,以此提出一种实用的路面切缝时机的预测预警方法,并在我国当前唯一的一条采用机制砂配制的滑模摊铺混凝土路面高速公路上进行了验证。结果表明:通过数值模型预测的最早切缝时间与采用回弹强度法确定的切缝时间一致,表明该模型预测的强度结果与真实(回弹)强度接近。通过数值模型预测路面板内的最大拉应力,从而对最晚切缝时间做出预警,可以保障实际的断板率非常低(<2‰)。基于大量模型计算结果和实际工程数据,表明上午铺筑的路面应当天切缝,下午铺筑的路面应次日清晨以后切缝。当混凝土摊铺时的温度低于气温,且保温条件良好时,模型结果表明切缝时间窗口总体上延长2~4 h;当混凝土摊铺时的温度高于气温,或者寒潮来临、保温条件差时,切缝时间窗口应提前2 h。

Forecasting step-like landslide displacement through diverse monitoring frequencies

The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected.This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area(TGRA)as a case study.We collected surface automatic monitoring data at different intervals,ranging from daily to monthly.The Ensemble Empirical Mode Decomposition(EEMD)algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency.Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models:Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU).The predictions from these models were integrated to derive cumulative displacement forecasts,enabling a comparative analysis of prediction accuracy across different monitoring frequencies.The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting,with optimal performance observed at moderate monitoring intervals.Intriguingly,the daily mean average error(MAE)decreases sharply with increasing monitoring frequency,reaching a plateau.These findings were corroborated by a parallel analysis of the Bazimen landslide,suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.

基于机器学习的人体热舒适度建模与预测

热舒适度是衡量室内环境质量和影响人类健康的重要指标之一,是建筑、空调控制等系统智能化的重要参考依据,同时能够有效降低建筑热环境控制的能源需求.目前可穿戴设备如智能手环、柔性传感器等已广泛应用,可构建人体的健康大数据.但由于存在个体差异因素,不同个体对相同热环境所表现的生理热反应不同,基于单一个人的热舒适模型难以对群体热状态实现有效地预测.考虑到以往研究样本量相对较小、模型复杂难以部署等局限性,本文建立人工气候室,利用环境传感器和可穿戴设备收集了60名受试者的热舒适数据,采用机器学习实现人体热舒适度建模与预测.研究考虑身高、体重、性别等个体差异因素,采用XGBoost、随机森林和SVC共3种机器学习算法,得到了基于人体生理参数的增强型预测热态模型并对热舒适度进行分类.结果表明:对皮肤温度及其梯度进行归一化处理发现,归一化过程能够将冷不舒适、舒适、热不舒适3种状态拉开,有利于SVC算法在高维空间寻找最优超平面,对特征进行分类.对比归一化前后随机森林模型的特征重要性发现,归一化过程降低了体重、身高、性别等个体差异对模型预测效果的影响程度;在XGBoost、随机森林和SVC这3种机器学习算法中,SVC在测试集上的准确率和3种热状态的AUC值都高于XGBoost和随机森林,其分类效果和泛化能力最好.

Linear Quadratic Optimal Control for Systems Governed by First-Order Hyperbolic Partial Differential Equations

This paper focuses on linear-quadratic(LQ)optimal control for a class of systems governed by first-order hyperbolic partial differential equations(PDEs).Different from most of the previous works,an approach of discretization-then-continuousization is proposed in this paper to cope with the infinite-dimensional nature of PDE systems.The contributions of this paper consist of the following aspects:(1)The differential Riccati equations and the solvability condition of the LQ optimal control problems are obtained via the discretization-then-continuousization method.(2)A numerical calculation way of the differential Riccati equations and a practical design way of the optimal controller are proposed.Meanwhile,the relationship between the optimal costate and the optimal state is established by solving a set of forward and backward partial difference equations(FBPDEs).(3)The correctness of the method used in this paper is verified by a complementary continuous method and the comparative analysis with the existing operator results is presented.It is shown that the proposed results not only contain the classic results of the standard LQ control problem of systems governed by ordinary differential equations as a special case,but also support the existing operator results and give a more convenient form of computation.

基于机器学习建立高炉压差预测模型的研究

压差变化作为高炉监控指标的一个关键参数,了解其变化趋势对操作者而言是至关重要的。本文通过对现场测量数据的分析,解决了原数据中存在的异常值、缺失值等问题。用Pearson特征的选择方法对标准化后的数据选取特征变量,通过将随机森林模型,GBDT模型和Adaboost模型的预测结果进行对比。结果表明GBDT模型相对于随机森林和Adaboost模型而言具有较大优势,随机森林预测模型精度R2为0.78,Adaboost预测模型精度为0.76,GBDT预测模型精度R2达到了0.91。GBDT预测结果最精确,在模型训练所需时间上GBDT用时远远少于其他两个模型。综上所述GBDT模型总体表现出最高的预测准确性和最少的训练时间,具有更好的适用性。

Pharmacometabolomic prediction of individual differences of gastrointestinal toxicity complicating myelosuppression in rats induced by irinotecan

Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan(CPT-11),and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually,two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine.