Prediction of solar activities:Sunspot numbers and solar magnetic synoptic maps

The evolution of solar magnetic fields is significant for understanding and predicting solar activities.And our knowledge of solar magnetic fields largely depends on the photospheric magnetic field.In this paper,based on the spherical harmonic expansion of the photospheric magnetic field observed by Wilcox Solar Observatory,we analyze the time series of spherical harmonic coefficients and predict Sunspot Number as well as synoptic maps for Solar Cycle 25.We find that solar maximum years have complex short-period disturbances,and the time series of coefficient g_(7)~0 is nearly in-phase with Sunspot Number,which may be related to solar meridional circulation.Utilizing Long Short-Term Memory networks(LSTM),our prediction suggests that the maximum of Solar Cycle 25 is likely to occur in June 2024 with an error of 8 months,the peak sunspot number may be 166.9±22.6,and the next solar minimum may occur around January 2031.By incorporating Empirical Mode Decomposition,we enhance our forecast of synoptic maps truncated to Order 5,validating their relative reliability.This prediction not only addresses a gap in forecasting the global distribution of the solar magnetic field but also holds potential reference value for forthcoming solar observation plans.

Proposal and evaluation of a new norm index-based QSAR model to predict pEC50 and pCC50 activities of HEPT derivatives

The search and development of anti-HIV drugs is currently one of the most urgent tasks of pharmacological studies. In this work, a quantitative structure-activity relationship （QSAR） model based on some new norm indexes, was obtained to a series of more than 150 HEPT derivatives （1-[（2-hydroxyethoxy）methyl]-6-（phenylthio）thymine） to find their pEC50 （the required effective concentration to achieve 50% protection of MT-4 cells against the cytopathic effect of virus） and pCC50 （the required cytotoxic concentration to reduce visibility of 50% mock infected cell） activities. The model efficiencies were then validated using the leave-one-out cross validation （LOO-CV） and y- randomization test. Results indicated that this new model was efficient and could provide satisfactory results for prediction of pECso and pCC50 with the higher R2 train and the higher Rt2est. By using the leverage approach, the applicability domain of this model was further investigated and no response outlier was detected for HEFT derivatives involved in this work. Comparison results with reference methods demonstrated that this new method could result in significant improvements for predicting pEC50 and pCC50 of anti-HIV HEPT derivatives. Moreover, results shown in this present study suggested that these two absolutely different activities pECso and pCC50 of anti-HIV HEPT derivatives could be predicted well with a totally similar QSAR model, which indicated that this model mizht have the potential to be further utilized for other biological activities of HEFT derivatives.

Neural network prediction of solar cycle 24

The ability to predict the future behavior of solar activity has become extremely import due to its effect on the environment near the Earth. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of space weather. The level of solar activity is usually expressed by in- ternational sunspot number （Rz）. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. We predict a solar index （Rz） in solar cycle 24 by using a neural network method. The neural network technique is used to analyze the time series of solar activity. According to our predictions of yearly sunspot number, the maximum of cycle 24 will occur in the year 2013 and will have an annual mean sunspot number of 65. Finally, we discuss our results in order to compare them with other suggested predictions.

Method of forecasting seismic energy induced by longwall exploitation based on changes in ground subsidence

A method of forecasting total seismic energy induced by longwall exploitation, based on changes in ground subsidence, is presented in the form of a linear regression model with one with one independent variable. In the method, ground subsidence is described with a cross-section area of a subsidence trough Pw along a line of observations in the direction of an advancing longwall front, approximately along the axis of the longwall area. Total seismic energy is determined on the basis of seismic energy data of tremors induced by exploitation. The presentation consists of a detailed method and evaluation of its predictive ability for the area of longwall exploitation within the region of one of the coal mines in the Upper Silesian Coal Basin. This method can be used for forecasting the total seismic energy released by tremors within the area directly connected with the exploitation, in which the seismic activity induced by this exploitation occurs. The estimation of the parameters of the determined model should each time be carried out with investigations of the correctness of the model. The method cannot be applied when the number of recorded phenomena is small and when there is insufficient data to make it possible to calculate the index Pw.

Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.

Estimating the Size and Timing of the Maximum Amplitude of Solar Cycle 24

A simple statistical method is used to estimate the size and timing of maximum amplitude of the next solar cycle （cycle 24）. Presuming cycle 23 to be a short cycle （as is more likely）, the minimum of cycle 24 should occur about December 2006 （±2 months） and the maximum, around March 2011 （±9 months）, and the amplitude is 189.9 ± 15.5, if it is a fast riser, or about 136, if it is a slow riser. If we presume cycle 23 to be a long cycle （as is less likely）, the minimum of cycle 24 should occur about June 2008 （±2 months） and the maximum, about February 2013 （±8 months） and the maximum will be about 137 or 80, according as the cycle is a fast riser or a slow riser.

Analysis of foreshock sequence of the 1975 Haicheng earthquake of Ms 7.3

The 'earthquake nucleation' is discussed in this paper. The acceleration is a property of the nucleation phase and is a necessary condition of earthquake instability too. If the acceleration property of this nucleating process is described by the equation dΩ/dt=C/(tf-t)n, the process can be summarized briefly that the rate of cumulative seismic release is proportional to the inverse power of the remaining time to failure. Based on this principle, the foreshock sequence of the 1975 Haicheng earthquake with Ms7.3, was analysed backward. It is stated clearly that the time-to-failure and magnitude of the mainshock can be predicted successfully if the coefficient r2 attains to the maximum. In the estimation of mainshock time, the error can generally be less than, or far less than,one-half the remaining time between the time of the last used data point and the mainshock.

Variation in the emission rate of sounds in a captive group of false killer whales Pseudorca crassidens during feedings: possible food anticipatory vocal activity?

This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.

Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.

Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based(HECS)descriptors

Rational design of solid-state electrolytes(SSEs)with high ionic conductivity and low activation energy(Ea)is vital for all solid-state batteries.Machine learning(ML)techniques have recently been successful in predicting Li^(+) conduction property in SSEs with various descriptors and accelerating the development of SSEs.In this work,we extend the previous efforts and introduce a framework of ML prediction for E_(a) in SSEs with hierarchically encoding crystal structure-based(HECS)descriptors.Taking cubic Li-argyrodites as an example,an Ea prediction model is developed to the coefficient of determination(R^(2))and rootmean-square error(RMSE)values of 0.887 and 0.02 eV for training dataset,and 0.820 and 0.02 eV for test dataset,respectively by partial least squares(PLS)analysis,proving the prediction power of HECSdescriptors.The variable importance in projection(VIP)scores demonstrate the combined effects of the global and local Li^(+) conduction environments,especially the anion size and the resultant structural changes associated with anion site disorder.The developed E_(a) prediction model directs us to optimize and design new Li-argyrodites with lower Ea,such as Li_(6–x)PS_(5–x)Cl_(1+x)(<0.322 eV),Li_(6+x)PS_(5+x)Br_(1–x)(<0.273 eV),Li_(6+x)PS_(5+x)Br_(0.25)I_(0.75–x)(<0.352 eV),Li_(6+(5–n)y)P_(1–y)N_(y)S_(5)I(<0.420 eV),Li_(6+(5–n)y)As_(1–y)N_(y)S_(5)I(<0.371 eV),Li_(6+(5–n)y)As_(1–y)NySe_(5)I(<0.450 eV),by broadening bottleneck size,invoking site disorder and activating concerted Li+conduction.This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials.

ACTPred: Activity Prediction in Mobile Social Networks

A current trend for online social networks is to turn mobile.Mobile social networks directly reflect our real social life,and therefore are an important source to analyze and understand the underlying dynamics of human behaviors （activities）.In this paper,we study the problem of activity prediction in mobile social networks.We present a series of observations in two real mobile social networks and then propose a method,ACTPred,based on a dynamic factor-graph model for modeling and predicting users＇ activities.An approximate algorithm based on mean fields is presented to efficiently learn the proposed method.We deploy a real system to collect users＇ mobility behaviors and validate the proposed method on two collected mobile datasets.Experimental results show that the proposed ACTPred model can achieve better performance than baseline methods.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.