Development of a new irradiation-embrittlement prediction model for reactor pressure-vessel steels

Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction models may fail to properly describe the embrittlement trend curves of Chinese domestic RPV steels with relatively low Cu content.Based on the screened surveillance data of Chinese domestic and similar international RPV steels,we have developed a new fluencedependent model for predicting the irradiation-embrittlement trend.The fast neutron fluence(E>1 MeV)exhibited the highest correlation coefficient with the measured TTS data;thus,it is a crucial parameter in the prediction model.The chemical composition has little relevance to the TTS residual calculated by the fluence-dependent model.The results show that the newly developed model with a simple power-law functional form of the neutron fluence is suitable for predicting the irradiation-embrittlement trend of Chinese domestic RPVs,regardless of the effect of the chemical composition.

Dynamic evolution and trend prediction of multi-scale green innovation in China

Numerous studies deal with spatial analysis of green innovation(GI).However,researchers have paid limited attention to analyzing the multi-scale evolution patterns and predicting trends of GI in China.This paper seeks to address this research gap by examining the multi-scale distribution and evolutionary characteristics of GI activities based on the data from 337 cities in China during 2000-2019.We used scale variance and the two-stage nested Theil decomposition method to examine the spatial distribution and inequalities of GI in China at multiple scales,including regional,provincial,and prefectural.Additionally,we utilized the Markov chain and spatial Markov chain to explore the dynamic evolution of GI in China and predict its long-term development.The findings indicate that GI in China has a multi-scale effect and is highly sensitive to changes in spatial scale,with significant spatial differences of GI decreasing in each scale.Furthermore,the spatiotemporal evolution of GI is influenced by both geospatial patterns and spatial scales,exhibiting the“club convergence”effect and a tendency to transfer to higher levels of proximity.This effect is more pronounced on a larger scale,but it is increasingly challenging to transfer to higher levels.The study also indicates a steady and sustained growth of GI in China,which concentrates on higher levels over time.These results contribute to a more precise understanding of the scale at which GI develops and provide a scientific basis and policy suggestions for optimizing the spatial structure of GI and promoting its development in China.

GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction

Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.

Changing trends of disease burden of gastric cancer in China from 1990 to 2019 and its predictions:Findings from Global Burden of Disease Study

Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective prevention strategies.Methods:The data on incidence,mortality,and disability-adjusted life-years(DALYs)of GC in China from1990 to 2019 were obtained from the Global Burden of Disease Study(2019).The estimated annual percentage change(EAPC)was calculated to evaluate the temporal trends of disease burden of GC,and the package Nordpred in the R program was used to perform an age-period-cohort analysis to predict the numbers and rates of incidence and mortality in the next 25 years.Results:The number of incident cases of GC increased from 317.34 thousand in 1990 to 612.82 thousand in2019,while the age-standardized incidence rate(ASIR)of GC decreased from 37.56 per 100,000 in 1990 to 30.64 per 100,000 in 2019,with an EAPC of-0.41[95%confidence interval(95%CI):-0.77,-0.06].Pronounced temporal trends in mortality and DALYs of GC were observed.In the next 25 years,the numbers of new GC cases and deaths are expected to increase to 738.79 thousand and 454.80 thousand,respectively,while the rates of incidence and deaths should steadily decrease.The deaths and DALYs attributable to smoking were different for males and females.Conclusions:In China,despite the fact that the rates of GC have decreased during the past three decades,the numbers of new GC cases and deaths increased,and will continue to increase in the next 25 years.Additional strategies are needed to reduce the burden of GC,such as screening and early detection,novel treatments,and the prevention of risk factors.

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

Intelligent HEV Fuzzy Logic Control Strategy Based on Identification and Prediction of Drive Cycle and Driving Trend

Real-time drive cycles and driving trends have a vital impact on fuel consumption and emissions in a vehicle. To address this issue, an original and alternative approach which incorporates the knowledge about real-time drive cycles and driving trends into fuzzy logic control strategy was proposed. A machine learning framework called MC_FRAME was established, which includes two neural networks for self-learning and making predictions. An intelligent fuzzy logic control strategy based on the MC_FRAME was then developed in a hybrid electric vehicle system, which is called FLCS_MODEL. Simulations were conducted to evaluate the FLCS_MODEL using ADVISOR. The simulation results indicated that comparing with the default controller on the drive cycle NEDC, the FLCS_MODEL saves 12.25% fuel per hundred kilometers, with the HC emissions increasing by 22.7%, the CO emissions reducing by 16.5%, the NOx emissions reducing by 37.5% and with the PM emissions reducing by 12.9%. A conclusion can be drawn that the proposed approach realizes fewer fuel consumption and less emissions.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

STUDY ON TREND PREDICTION AND VARIATION ONTHE FLOW INTO THE LONGYANGXIA RESERVOIR

The Longyangxia Gorge Key Water Control System is the first of the stairstep power sations along the Longyangxi-a-Qingtongxia river section. It has been playing an very important role in providing power, protecting flood and ice run supplying and irrigation etc. in the northwestern China. Therefore, the study on trend prediction, variation on the flow into the Longyangxia Reservoir are of the great social and economic benefits. In the medium-and-long-range runoff forecast, all kinds of regression equation are often used for predicting future hydrologic regime. However, these regression models aren’t appropriate to super long -range runoff forecast because of the restricting on weather data and so on. So a new super long-range runoff forecast model don’t depend on Reai-time weather data and called “Period correcting for residual error series GM (1, 1) model” is presented based on analyzing for the relational hydrologic data and the variation on the flow into the Longyangxia Reservoir, and the forecast model was applied successfully to predict the recent and super long -term trends of the flow into the Longyangxia Reservoir. The results indicate that the annual flow into the Longyangxia Reservoir is in the ending minimum period of the runoff history. The runoff increasing is expected in for the coming years.

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

Adaptive Wavelets Based on Second Generation Wavelet Transform and Their Applications to Trend Analysis and Prediction

In order to make trend analysis and prediction to acquisition data in amechanical equipment condition monitoring system, a new method of trend feature extraction andprediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisitiondata by means of second generation wavelet transform ( SGWT), Firstly, taking the vanishing momentnumber of the predictor as a constraint, the linear predictor and updater are designed according tothe acquisition data by using symmetrical interpolating scheme. Then the trend of the data isobtained through doing SGWT decomposition , threshold processing and SGWT reconstruction. Secondly,under the constraint of the vanishing moment number of the predictor, another predictor based on theacquisition data is devised to predict the future trend of the data using a non-symmetricalinterpolating scheme, A one-step prediction algorithm is presented to predict the future evolutiontrend with historical data. The proposed method obtained a desirable effect in peak-to-peak valuetrend analysis for a machine set in an oil refinery.

Trends and multi-model prediction of hepatitis B incidence in Xiamen

Background:This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022,and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027.Methods:Data were obtained from the China Information System for Disease Control and Prevention(CISDCP).The Joinpoint Regression Model analyzed temporal trends,while the Age-Period-Cohort(APC)model assessed the effects of age,period,and cohort on hepatitis B incidence rates.We also compared the predictive performance of the Neural Network Autoregressive(NNAR)Model,Bayesian Structural Time Series(BSTS)Model,Prophet,Exponential Smoothing(ETS)Model,Seasonal Autoregressive Integrated Moving Average(SARIMA)Model,and Hybrid Model,selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years.Results:Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend,with rates higher in men than in women.Higher incidence rates were observed in adults,particularly in the 30-39 age group.Moreover,the period and cohort effects on incidence showed a declining trend.Furthermore,in the best-performing NNAR(10,1,6)[12]model,the number of new cases is predicted to be 4271 in 2023,increasing to 5314 by 2027.

Study on Ann-Based Multi-Step Prediction Model of Short-Term Climatic Variation

In the context of 1905–1995 series from Nanjing and Hangzhou, study is undertaken of estab-lishing a predictive model of annual mean temperature in 1996–2005 to come over the Changjiang (Yangtze River) delta region through mean generating function and artificial neural network in combination. Results show that the established model yields mean error of 0.45°C for their abso-lute values of annual mean temperature from 10 yearly independent samples (1986–1995) and the difference between the mean predictions and related measurements is 0.156°C. The developed model is found superior to a mean generating function regression model both in historical data fit-ting and independent sample prediction. Key words Climate trend prediction. Mean generating function (MGF) - Artificial neural network (ANN) - Annual mean temperature (AMT)

The application of modeling and prediction with MRA wavelet network

As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.

On the Flood Disasters in the Lower Jingjiang Reaches: Grey Prediction Model and Application

In the light of the historical substantial data (covering a 70-year period) collected in the Lower Jingjiang segment and aided by topological grey method, here we attempt to characterize the occurrence and future trend of flood calamities in the study area. Our findings indicate that overall the high-frequent flood disasters with middle to lower damage prevail at present. A series of dramatic flood waves will appear in the years of 2016, 2022, 2030 and 2042, particularly a destructive flood will occur between 2041 and 2045 in the Lower Jingjiang reaches. Typical of sensitive response to flood hazards in close association with its special geographical location, the lower Jingjiang segment hereby can reflect the development trend of floods in the middle Yangtze reaches. According to the results, a good fitness was revealed between the prediction and practical values. This actually hints that the topological grey method is an effective mathematical means of resolving problems containing uncertainty and indetermination, thus providing valuable information for the flood prediction in the middle Yangtze catchment.

ON THE PREDICTION OF TROPICAL CYCLONE LANDFALL AND INFLUENCE ON SOUTHERN CHINA USING MONTHLY OLR ANOMALIES FOR PRIME SUMMER

With the OLR data, the landfall and activity of tropical cyclones (TC) in southern China over a 20-year period (1975～1994) are studied. The result shows that the variation of the monthly anomalous OLR is somewhat teleconnected with the TC activity in southern China. The former is used to predict short-term climate for the latter over months with frequent or no TC influence. To some extent, the relationship between the TC activity in southern China and the monthly mean OLR anomalies is dependent on the climatological location of the subtropical high in northwestern Pacific region.

An intelligent prediction method of fractures in tight carbonate reservoirs

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

New indexes and methods in earthquake prediction research

This paper gives a brief introduction to a few new indexes and methods published in recent issues of seismological literature which have been explored especially by the authors and many of their collaborators for applying in earthquake prediction research. The new indexes include the statistical indexes of seismicity (Morishita index Iδ, the parameters C and b-value spectrum derived from the magnitude-frequency relation, etc. )and indexes describing the dynamical characteristics of seismic waves obtained from digitized seismologicrecords (wave form linearities, spectral characteristics, etc. ). The new methods fall into two categories:namely the methods of non-linear sciences (fractal analysis, self-similarity and self-organization structure,neural network) and graphical analysis methods of multi-dimensional data (face analysis, projection pursuit,chronogeometric analysis ).

ANALYSIS OF CRITERIA PREDICTING THE TENDENCY OF PILOT INDUCED OSCILLATION FOR LINEAR NON-LINEAR SYSTEM

Some problems encountered in applying Smith's technique to predict the PIO tendency for non-linear pilot-vehicle loop, are thoroughly analyzed. Subsequently, modified PIO predictable criteria are developed, in addition, to make also a certain improvement on Smith's PIO definition and PIO types. These modified criteria are applied to predict PIO tendency of various different configurations on the variable stability aircraft NT-33 in case of supposed non-linearity, and predicted results are compared with the flight tests and analytical results in the case of linear hypothesis given in Ref. (4)

A Multipath Routing Algorithm Based on Traffic Prediction in Wireless Mesh Networks

The technology of QoS routing has become a great challenge in Wireless Mesh Networks (WMNs). There exist a lot of literatures on QoS routing in WMNs, but the current algorithms have some deficiencies, such as high complexity, poor scalability and flexibility. To solve the problems above, a multipath routing algorithm based on traffic prediction (MRATP) is proposed in WMNs. MRATP consists of three modules including an algo-rithm on multipath routing built, a congestion discovery mechanism based on wavelet-neural network and a load balancing algorithm via multipath. Simulation results show that MRATP has some characteristics, such as better scalability, flexibility and robustness. Compared with the current algorithms, MRATP has higher success ratio, lower end to end delay and overhead. So MRATP can guarantee the end to end QoS of WMNs.