DESCRIPTION AND PREDICTION OF CATASTROPHE OF VIBRATION STATE FOR FAULTY ROTORS

A sudden increase of vibration amplitude with no foreboding often results in an abrupt breakdown of a mechanical system.The catastrophe of vibration state of a faulty rotor is a typical nonlinear phenomenon,and very difficult to be described and predicted with linear vibration theory.On the basis of nonlinear vibration and catastrophe theory,fhe eatastrophe of the vibration amplitude of the faulty rotor is described;a way to predict its emergence is developed.

Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

Preface to the Special Issue:Towards Improving Understanding and Prediction of Arctic Change and its Linkage with Eurasian Mid-latitude Weather and Climate

The dramatic changes in the Arctic climate system during recent decades are one of the most prominent features of global climate change.Two most striking and fundamental characteristics are the amplified near-surface warming at a rate twice the global average since the mid 20th century(e.g.,Blunden and Arndt,2012;Huang et al.,2017),and the rapid

A j,υ model for the analysis and prediction of tides

In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable.

Mitigation and prediction of disruption on the HL-2A Tokamak

Injection of high-Z impurities into plasma has been proved to be able to reduce the localized thermal load and mechanical forces on the in-vessel components and the vacuum vessel, caused by disruptions in Tokamaks. An advanced prediction and mitigation system of disruption is implemented in HL-2A to safely shut down plasmas by using the laser ablation of high-Z impurities with a perturbation real-time measuring and processing system. The injection is usually triggered by the amplitude and frequency of the MHD perturbation field which is detected with a Mirnov coil and leads to the onset of a mitigated disruption within a few milliseconds. It could be a simple and potential approach to significantly reducing the plasma thermal energy and magnetic energy before a disruption, thereby achieving safe plasma termination. The plasma response to impurity injection, a mechanism for improving plasma thermal and current quench in major disruptions, the design of the disruption prediction warner, and an evaluation of the mitigation success rate are discussed in the present paper.

Grey forewarning and prediction for mine water inflowing catastrophe periods

Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

Additive manufacturing(AM)has emerged as an advanced technique for the fabrication of complex near-net shaped and lightweight metallic parts with acceptable mechanical performance.The strength of AM metals has been confirmed comparable or even superior to that of metals manufactured by conventional processes,but the fatigue performance is still a knotty issue that may hinder the substitution of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates.As essential complements to high-cost and time-consuming experimental fatigue tests of AM metals,models for fatigue performance prediction are highly desirable.In this review,different models for predicting the fatigue properties of AM metals are summarized in terms of fatigue life,fatigue limit and fatigue crack growth,with a focus on the incorporation of AM characteristics such as AM defect and processing parameters into the models.For predicting the fatigue life of AM metals,empirical models and theoretical models(including local characteristic model,continuum damage mechanics model and probabilistic method)are presented.In terms of fatigue limit,the introduced models involve the Kitagawa–Takahashi model,the Murakami model,the El-Haddad model,etc.For modeling the fatigue crack growth of AM metals,the summarized methodologies include the Paris equation,the Hartman-Schijve equation,the NASGRO equation,the small-crack growth model,and numerical methods.Most of these models for AM metals are similar to those for conventionally processed materials,but are modified and pay more attention to the AM characteristics.Finally,an outlook for possible directions of the modeling and prediction of fatigue properties of AM metals is provided.

Simulation of Gas Oil Hydrotreater Heat Exchange Tube and Crystallization Prediction of NH_(4)Cl by Thermodynamic Equilibrium

The hydrotreater system heat exchanger is one of the main pieces of heat exchange equipment in petrochemical enterprises.In recent years,oil resources have shown a deterioration trend of high sulfur and high acid content,with corrosion risk being prominent in oil processing.Taking the multi-medium flow corrosion risk of the hydrotreater heat exchanger pipeline in a petrochemical enterprise as the research object,based on the parameter characteristics of corrosive NH_(3) and HCl media under a high-temperature and high-pressure environment,the ammonium salt crystallization and deposition mechanism under multi-phase flow is revealed.The thermodynamic equilibrium curve is modified based on the thermodynamic principle and fugacity coefficient variation,and the prediction model of ammonium chloride crystallization in hydrotreater heat exchanger under high temperature and high pressure is constructed according to the modification.This study uses the mixture model,the flow-thermal coupling method,and the discrete phase model method to carry out the numerical simulation of multiphase flow and the numerical prediction of particle distribution characteristics in the heat exchanger pipeline of the hydrotreater heat exchange equipment,so as to realize the quantitative prediction of the particle crystallization deposition distribution in the pipeline.The results show that with the decrease of temperature,the crystallization occurs first on both sides of the center of the tube bundle,and more crystallization occurs in the lower half of the U-shaped tube,which may seriously lead to problems such as pipe blockage and under-deposit corrosion.

Risk estimation and prediction of the transmission of coronavirus disease-2019(COVID-19)in the mainland of China excluding Hubei province

Background In December 2019,an outbreak of coronavirus disease(later named as COVID-19)was identified in Wuhan,China and,later on,detected in other parts of China.Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures,estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed.COVID-19 daily data of the mainland of China excluding Hubei province,including the cumulative confirmed cases,the cumulative deaths,newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020,were archived from the National Health Commission of China(NHCC).We parameterize the model by using the Markov Chain Monte Carlo(MCMC)method and estimate the control reproduction number(Rc),as well as the effective daily reproduction ratio-Re(t),of the disease transmission in the mainland of China excluding Hubei province.Results The estimation outcomes indicate that Rc is 3.36(95%CI:3.20–3.64)and Re(t)has dropped below 1 since 31 January 2020,which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission.Moreover,our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected,and may even cause a second wave of epidemic or outbreaks.By calculating the effective reproduction ratio,we prove that the contact rate should be kept at least less than 30%of the normal level by April,2020.Conclusions To ensure the pandemic ending rapidly,it is necessary to maintain the current integrated restrict interventions and self-protection measures,including travel restriction,quarantine of entry,contact tracing followed by quarantine and isolation and reduction of contact,like wearing masks,keeping social distance,etc.People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April.If all the above conditions are met,the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.

Helmholtz Solving and Performance Optimization in Global/Regional Assimilation and Prediction System

Despite efficient parallelism in the solution of physical parameterization in the Global/Regional Assimilation and Prediction System(GRAPES),the Helmholtz equation in the dynamic core,with the increase of resolution,can hardly achieve sufficient parallelism in the solving process due to a large amount of communication and irregular access.In this paper,optimizing the Helmholtz equation solution for better performance and higher efficiency has been an urgent task.An optimization scheme for the parallel solution of the Helmholtz equation is proposed in this paper.Specifically,the geometrical multigrid optimization strategy is designed by taking advantage of the data anisotropy of grid points near the pole and the isotropy of those near memory equator in the Helmholtz equation,and the Incomplete LU(ILU)decomposition preconditioner is adopted to speed up the convergence of the improved Generalized Conjugate Residual(GCR),which effectively reduces the number of iterations and the computation time.The overall solving performance of the Helmholtz equation is improved by thread-level parallelism,vectorization,and reuse of data in the cache.The experimental results show that the proposed optimization scheme can effectively eliminate the bottleneck of the Helmholtz equation as regards the solving speed.Considering the test results on a 10-node two-way server,the solution of the Helmholtz equation,compared with the original serial version,is accelerated by 100,with one-third of iterations reduced.

Estimation of boundary parameters and prediction of transmission loss based upon ray acoustics

Estimation of boundary parameters and prediction of transmission loss using a coherent channel model based upon ray acoustics and sound propagation data collected in field experiments are presented. Comparison between the prediction results and the experiment data indicates that the adopted sound propagation model is valuable, both selection and estimation methods on boundary parameters are reasonable, and the prediction performance of transmission loss is favorable.

Modeling, simulation, and prediction of global energy indices: a differential approach

Modeling, simulation, and prediction of global energy indices remain veritable tools for econometric, engineering, analysis, and prediction of energy indices. Thus, this paper differentially modeled, simulated, and non-differentially predicated the global energy indices. The state-of-the-art of the research includes normalization of energy indices, generation of differential rate terms, and regression of rate terms against energy indices to generate coefficients and unexplained terms. On imposition of initial conditions, the solution to the system of linear differential equations was realized in a Matlab environment. There was a strong agreement between the simulated and the field data. The exact solutions are ideal for interpolative prediction of historic data. Furthermore, the simulated data were upgraded for extrapolative prediction of energy indices by introducing an innovative model, which is the synergy of deflated and inflated prediction factors. The innovative model yielded a trendy prediction data for energy consumption, gross domestic product, carbon dioxide emission and human development index. However, the oil price was untrendy, which could be attributed to odd circumstances. Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming effect of independent indices on the dependent index. Clearly, this paper has accomplished interpolative and extrapolative prediction of energy indices and equally recommends for further investigation of the untrendy nature of oil price.

Computational analysis and prediction for exons of PAC579 genomic sequence

To isolate the novel genes related to human hepatocellular carcinoma (HCC), we sequenced P1-derived artificial chromosome PAC579 (D17S926 locus) mapped in the minimum LOH (loss of heterozygosity) deletion region of chromosome 17p13.3 in HCC, Four novel genes mapped in this genomic sequence area were isolated and cloned by wet-lab experiments, and the exons of these genes were located. 0-60 kb of this genomic sequence including the genes of interest was scanned with five different computational exon prediction programs as well as four splice site recognition programs. After analyzing and comparing the computationally predicted results with the wet-lab experiment results, some potential exons were predicted in the genomic sequence by using these programs.

MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL

When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.

Analysis and Prediction of '94-'95 Auto Market in China

After a sluggish market in 1994, both the sale and the production of automobiles will be raised constantly in China, especially in the second half of 1995. In order to understand this, we shall begin with the analysis of the auto market in 1994. Retrospect of China’s Auto Market in

Research on Carbon Peak and Carbon Neutrality Technological Pathways in the Chinese Papermaking Industry

Against the backdrop of the dual carbon goals,the papermaking industry in China faces significant pressure to reduce emissions and lower carbon intensity.Based on historical data of energy consumption in the pulp and paper industry in China from 2000 to 2020,this study analyzed the current status of paper production and energy consumption in China.Two methods were employed to predict the growth trend of paper production in China,and three carbon dioxide emission accounting methods were compared.The study used an accounting method based on the industry’s overall energy consumption and predicted the carbon dioxide(CO_(2))emissions of the Chinese papermaking industry from 2021 to 2060 under three scenarios.The study identified the timing for achieving carbon peak and proposed the measures for carbon neutrality.The results indicated that:(1)the CO_(2)emissions of the Chinese papermaking industry in 2020 were 111.98 million tons.(2)Under low-demand,high-demand,and baseline scenarios,the papermaking industry is expected to achieve carbon peak during the“14th Five-Year Plan”period.(3)In 2060,under the three scenarios,CO_(2)emissions from the papermaking industry will decrease by 11%-31%compared to the baseline year.However,there will still be emissions of 72-93 million tons,requiring reductions in fossil energy consumption at the source,increasing forestry carbon sequestration and utilization of Carbon Capture,Utilization and Storage(CCUS)technology,and taking measures such as carbon trading to achieve carbon neutrality.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Prediction of Fracture-Cavity System in Carbonate Reservoir： A Case Study in the Tahe Oilfield

The carbonate rocks in Tahe oilfield, which suffered from multi-period polycycle karstification and structure deformation, are heterogeneous reservoirs that are rich in pores, cavities,and fractures. The reservoirs are diversified in scale, space configuration, and complex in filling. For this kind of reservoir, a suite of seismic prestack or poststack prediction techniques has been developed based on the separation of seismic wave fields. Through cross-verification of the estimated results,a detailed description of palaeogeomorphology and structural features such as pores, cavities, and fractures in unaka has been achieved, the understanding of the spatial distribution of reservoir improved.