The Study of Medium- and Short-term Prediction for Artux Earthquake (M_S=6.9) and Usunan Earthquake (M_S=5.8)

In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.

“Small earthquake modulation window” and its applications in medium－term prediction

Based on the observations of many years, it has been found that “small earthquake modulation windows” exist inthe situation of some special geological structures, which respond sensitively to the variations of regional stressfields and the activities of earthquake swarms greater than moderate strong magnitude, and can supply some precursory information. More than two “small earthquake modulation windows” can also provide a general orientation of the first main earthquake of a earthquake cluster. Compared with “seismic window” based on frequency itis no doubt that the “modulation-window” has an unique characteristic of applicational significance to mediumterm earthquake prediction with a time scale of two or three years.

Seismicity Quantification and Its Application to Medium-term Earthquake Prediction

Bed on the analysis of each parameter describing seismicity,we think A(b)-value can betterquantitatively describe the feature of the enhancement and quietness of seismicity in this paper. Thedata of moderate or small earthquakes during 1972～1996 in North China are used in space scanningof A(b)-value. The result shows that 2～3 years before most strong earthquakes there wereObviously anomaly zones of A(b)-value with very good prediction effect. Some problems about themedium-term prediction by using A(b)-value are also discussed.

Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application

The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.

The middle-long term prediction of the February 3,1996 Lijiang earthquake(M_S=7) by the "criterion of activity in quiescence

Earthquake activities in history are characterized by active and quiet periods. In the quiet period, the place where earthquake M_≥6 occurred means more elastic energy store and speedy energy accumulation there. When an active period of big earthquake activity appeared in wide region, in the place where earthquake (M_≥6) occurred in the past quiet period, the big earthquake with magnitude of 7 or more often occur there. We call the above-mentioned judgement for predicting big earthquake the 'criterion of activity in quiescence'. The criterion is relatively effective for predicting location of big earthquake. In general, error of predicting epicenter is no more than 100 km. According to the criterion, we made successfully a middle-term prediction on the 1996 Lijiang earthquake in Yunnan Province, the error of predicted location is about 50 km. Besides, the 1994 Taiwan strait earthquake (M_s=7.3), the 1995 Yunnan-Myanmar boundary earthquake (M_s=7.2) and the Mani earthquake (M_s=7.9) in north Tibet are accordant with the retrospective predictions by the 'criterion of activity in quiescence'. The windows of 'activity in quiescence' identified statistically by us are 1940-1945, 1958-1961 and 1979-1986. Using the 'criterion of activity in quiescence' to predict big earthquake in the mainland of China,the earthquake defined by 'activity in quiescence' has magnitude of 6 or more; For the Himalayas seismic belt, the Pacific seismic belt and the north-west boundary seismic belt of Xinjiang, the earthquake defined by 'activity in quiescence' has magnitude of 7, which is corresponding to earthquake with magnitude of much more than 7 in future. For the regions where there are not tectonically and historically a possibility of occurring big earthquake (M_s=7), the criterion of activity in quiescence is not effective.

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.

Stroke: Medium and long-term mortality and associated factors in French-speaking West Africa, case of Benin

Introduction: Stroke is the leading cause of mortality and physical disability in sub-Saharan Africa. Objective: Determining medium-term and long-term mortality for stroke and identifying associated factors. Method: It consists in a cross-sectional, prospective, descriptive and analytical study that was conducted from April 1 to August 31, 2013 in the Neurology Department of CNHU-HKM in Cotonou. It involved patients who have known stroke for at least 6 months, and were all admitted and discharged later on. The disease survivors were re-contacted and examined again at home or at hospital. Then, the number of deceased was systematically recorded with precision of death time-limit. Results: The overall mortality rate was 29%. Mortality was higher with patients over 70 years with a frequency of 57.1%. The medium-term mortality rate was 25% against 4% for long-term. The average time-limit for death occurrence after the vascular incident was 7 months ± 6.4 months. Prognostic factors of mortality were: the age of the patient (IC95% = 7.73 [1.49 - 39.99], p = 0.015 ), marital status (IC95% = 0.27 [0.08 to 0.94], p = 0.039 ) and the presence of aphasia (IC95% = 5.52 [1.45 to 20.94 ], p = 0.012). Conclusion: Stroke mortality still remains significant, even after the patients have been discharged from hospital. A good psychological family support and efficient aphasia coverage are essential for its reduction.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

New scheme of anticipating synchronization for arbitrary anticipation time and its application to long-term prediction of chaotic states

How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory. Different from ＇traditional techniques （or regimes）＇ proposed in the previous literature, the present scheme guarantees that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.

Research on TSIP method for medium－term earthquake prediction

This paper introduces the space increased probability of strong earthquakes (SIP)-a new design based on the algorithm CN of time increased probability of strong earthquake (TIP). The authors have done a prediction research passing in review of eight strong earthquakes with M>6 in the last 20 years in East China. The result shows that six of the eight strong earthquakes were in the space-time domain of the time and space probability of strong earthquake (TSIP) prediction. The prediction accuracy is 75%, the space-time domain rate of the TSIP precaution is 5%, the diagnosed value of R is 0. 70. So the TSIP as a method of medium-term earthquake prediction has good practicality, efficiency and prospects of applying.

Time distribution characteristics of regional macroseismic activity in the Sichuan-Yunnan region and its significance to mid-long term prediction

The earthquakes with Ms≥6.0 are often gathered into belts or clusters and are roughly consistent with tectonic structure trends in the Sichuan-Yunnan (Chuan-Dian) region. The middle south part(98°-106°E, 21°-34°N) of South-North Seismic Zone can be zoned into seven small areas. There all were strong quakes with M_s≥7.0 historically in each small area. Ten earthquakes with M_s≥7.0 have occurred in this region since 1970 and they appeared in five small areas respectively. The relationships between occurrence-time and cumulative frequencies of strong quakes in these five areas are shown to be an exponential distribution or power function. By examining the inner coincidence it is indicated that these relationships are of definite significance to mid-long term macroseismic prediction of each area.

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.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

Long-Term Outcomes after Coronary Artery Bypass Grafting with Risk Stratification

Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-term outcomes following coronary artery bypass graft (CABG) surgery. Methods: We retrospectively revised the electronic medical records of 2330 patients who underwent adult Cardiac surgery between August 2016 and December 2022 at Madinah Cardiac Center, Saudi Arabia. Three hundred patients fulfilled the eligibility criteria of CABG operations with a complete follow-up period of at least 24 months, and data reporting. The collected data included patient demographics, comorbidities, laboratory data, pharmacotherapy, echocardiographic parameters, procedural details, postoperative data, in-hospital outcomes, and follow-up data. Our follow-up was depending on the clinical status (NYHA class), chest pain recurrence, medication dependence and echo follow-up. A univariate analysis was performed between each patient risk factor and the long-term outcome to determine the preoperative, operative, and postoperative factors significantly associated with each long-term outcome. Then a multivariable logistic regression analysis was performed with a stepwise, forward selection procedure. Significant (p < 0.05) risk factors were identified and were used as candidate variables in the development of a multivariable risk prediction model. Results: The incidence of all-cause mortality during hospital admission or follow-up period was 2.3%. Other long-term outcomes included all-cause recurrent hospitalization (9.8%), recurrent chest pain (2.4%), and the need for revascularization by using a stent in 5 (3.0%) patients. Thirteen (4.4%) patients suffered heart failure and they were on the maximum anti-failure medications. The model for predicting all-cause mortality included the preoperative EF ≤ 35% (AOR: 30.757, p = 0.061), the bypass time (AOR: 1.029, p = 0.003), and the duration of ventilation following the operation (AOR: 1.237, p = 0.021). The model for risk stratification of recurrent hospitalization comprised the preoperative EF ≤ 35% (AOR: 6.198, p p = 0.023), low postoperative cardiac output (AOR: 3.622, p = 0.007), and the development of postoperative atrial fibrillation (AOR: 2.787, p = 0.038). Low postoperative cardiac output was the only predictor that significantly contributed to recurrent chest pain (AOR: 11.66, p = 0.004). Finally, the model consisted of low postoperative cardiac output (AOR: 5.976, p < 0.001) and postoperative ventricular fibrillation (AOR: 4.216, p = 0.019) was significantly associated with an increased likelihood of the future need for revascularization using a stent. Conclusions: A risk prediction model was developed in a Saudi cohort for predicting all-cause mortality risk during both hospital admission and the follow-up period of at least 24 months after isolated CABG surgery. A set of models were also developed for predicting long-term risks of all-cause recurrent hospitalization, recurrent chest pain, heart failure, and the need for revascularization by using stents.

The long-term prediction of the oil-contaminated water from the Sanchi collision in the East China Sea

The condensate and bunker oil leaked from the Sanchi collision would cause a persistent impact on marine ecosystems in the surrounding areas. The long-term prediction for the distribution of the oil-polluted water and the information for the most affected regions would provide valuable information for the oceanic environment protection and pollution assessment. Based on the operational forecast system developed by the First Institute of Oceanography, State Oceanic Administration, we precisely predicted the drifting path of the oil tanker Sanchi after its collision. Trajectories of virtual oil particles show that the oil leaked from the Sanchi after it sank is mainly transported to the northeastern part of the sink location, and quickly goes to the open ocean along with the Kuroshio. Risk probability analysis based on the outcomes from the operational forecast system for years 2009 to2017 shows that the most affected area is at the northeast of the sink location.

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

Determination of Regions With Medium-Term Risk of Strong Earthquakes: Pre-warning Active Faults

To predict the area with frequent seismicity and the future risky region of strong earthquakes on the time scale of one or several years is a very important and urgent problem that needs to be solved.On the basis of active fault research,pre-warning active faults that have been active recently will be discussed; then the medium-term risky region of strong earthquakes will be delimited around the pre-warning active faults.This method proves to be effective.

Gravity observations along the eastern margin of the Tibetan Plateau and an application to the Lushan MS 7.0 earthquake

This paper introduces relative and absolute gravity change observations in the eastern portion of the Tibetan Plateau. We analyze and discuss a change that occurred in 2010 in the gravity along the eastern margin of the plateau and the relationship between this change and the 2013 Lushan Ms7.0 earthquake. Our results show that： （1） before the Lushan MsT.0 earthquake, gravity anomalies along the eastern margin of the Tibetan Plateau changed drastically. The Lushan earthquake occurred at the bend of the high gradient zone of gravity var- iation along the southern edge of the Longmenshan fault zone. （2） The 2013 Lushan earthquake occurred less than 100 km away from the epicenter of the 2008 Wenchuan earthquake. Lushan and Wenchuan are located at the center of a four- quadrant section with different gravity anomalies, which may suggest that restoration after the Wenchuan earthquake may have played a role in causing the Lushan earthquake. （3） A medium-term prediction based on changes in gravity anoma- lies was made before the Lushan Ms7.0 earthquake, in par- ticular, a prediction of epicenter location.

Comparison of Long-Term Forecasting of June-August Rainfall over Changjiang-Huaihe Valley

In terms of an Artificial Neural Network (ANN) established is a long-term prediction model for June-August flood/drought in the Changjiang-Huaihe Basins and a regression forecasting expression is formulated with the aid of the same factors and sample size for comparison. Results show that the ANN is superior in predictions and fittings due to its higher self-adaptive learning recognition and nonlinear mapping especially in the years of severe flood and drought. This shows great promise in using ANN in the research of flood/drought prediction on a long-range basis.