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Power system operations are unavoidably influenced by forecast uncertainties. The forecast uncertainty's effect on operations was quantified in individual systems, however, was seldom investigated in a hybrid system. This study aims to quantify the effect of forecast uncertainties on the hybrid hydro–photovoltaic system operation. First, the operation model is built considering streamflow and photovoltaic forecast uncertainties with long- and short-time scales. Second, total release uncertainty is determined from the sum of squares for releases under different forecast scenarios. Finally, a variance partitioning method is used to divide the total release uncertainty into the uncertainties induced by individual forecast errors. Longyangxia hybrid hydro–photovoltaic power plant from China was selected as a case study. Results provide following insights: (1) long-term inflow forecast uncertainty accounts for 98.89% of the contribution to the long-term release uncertainty, while photovoltaic uncertainty accounts for 1.11%; (2) long-term inflow forecast uncertainty still dominates the short-term release uncertainty, with the contribution of 62.06%; and (3) the total release uncertainty decreases in long-term hybrid operation compared with the reservoir operation, indicating the complementary relationship between long-term hydropower and photovoltaic power. These implications highlight the importance of improving forecast accuracies for varied energy sources at different time scales.

In the extensive age, dear designate perplexity and relatively supercilious show charge in the traditive parcel extend project composition, the double discriminator GAN is ply to the bale work indicate composition. On the basis of BicycleGAN, a topic discriminator is added, and the analogous privation sine and external province are reformed. In the proof, the input aim is a likeness suit of “margin idol + source cast,” and the product goal is the copy with the top 10 chance of genuineness. The trial arise are appraised from the three aspects of variegation, PSNR importance, and SSIM appraise. The arise shows that the effigy produced by the double discriminator GAN not only has improved variegation but also better peculiarity of show lowdown. Therefore, it is practicable to devote the double discriminator GAN to the look sketch of parcel products. On the one side, it can stipulate designers with sketch breath, and on the other side, it can also rescue manpower and significant cause and disapprove duty ability.

Dynamically dimensioned search algorithm (DDS) has been proved to be an effective algorithm for solving optimization problems in actual applications, such as distributed watershed model calibration, reservoir operation, pump-and-treat problem, and Radiation Therapy (IMRT) beam angle optimization. However, it always traps in local optimal, especially for multimodal problems. To further improve the performance of DDS, this article proposes a novel chaotic dynamically dimensioned search algorithm (CDDS) by incorporating chaos theory into DDS. The improvement is performed by using three strategies: chaotic initialization, a new Gaussian mutation operator, and a chaotic search. The chaotic map generates an initial population to improve the quality of the initial solution. Chaotic initialization can enhance the exploration ability of DDS because of its intrinsic ergodic and stochastic property. A new Gaussian mutation operator is used to increase convergence speed and the exploitation ability. Meanwhile the chaotic search is utilized to jump out of the local optimal. The optimal CDDS among 13 CDDS variants is compared with other optimization algorithms on 20 classical benchmark test functions, 14 shifted rotated benchmark functions from CEC2005, and 30 benchmark functions from CEC2014 in terms of exploitation and exploration. We testify the applicability and effectiveness on a cascade reservoirs operation optimization problem in real world tasks. The experimental results and analyses demonstrate the superiority of the proposed algorithm in increasing the solution quality and accelerating the convergence.

Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space. We consider drug features, i.e., substructures, targets, enzymes, transporters, pathways, indications, side effects, and off side effects, to calculate drug-drug similarities, and assume them as manifolds in feature spaces. In this paper, we present a novel computational method named "Manifold Regularized Matrix Factorization" (MRMF) to predict potential drug-drug interactions, by introducing the drug feature-based manifold regularization into the matrix factorization. In the computational experiments, the MRMF models, which utilize known drug-drug interactions and the drug feature-based manifold, produce the area under precision-recall curves (AUPR) up to 0.7963. We test manifold regularizations based on different drug features, and the MRMF models can produce robust performances. Compared with other state-of-the-art methods, the MRMF models can produce better performances in the cross validation and case study. The manifold regularization is the critical factor for the high-accuracy performances of our method. MRMF is promising and effective for the prediction of drug-drug interactions.

We investigate the spectral approximation of optimal control governed by Stokes equations with integral state constraint. A good choice for basis functions leads the discrete system with sparse mat...

This paper studied the genetic algorithm (GA) deeply for the problem of hydrological frequency parameter estimation of P-III distribution. For simple GA, the convergence speed and global searching ability are contradictory. An improved algorithm named GA using rejection and interpolation mechanism (GA-RIM) are proposed. Firstly, the rejection mechanism is adopted to ensure the diversity of population and avoid useless operations. Secondly, the strategy of preserving excellent individual is used to guarantee the population to converge to the optimal solution. Thirdly, interpolation mechanism is used to ensure the population to explore in the total domain of definition and mutate adaptively according the diversity of population. The GA-RIM, GA and other four usual methods are used to estimate hydrological parameter of two examples. Through simulation experiments, it was found that the GA-RIM is superior than GA and other methods in terms of convergence speed, precision and global searching ability.

Pair-copula has become a hot spot in the research of both theory and application of statistics. Therefore, proper construction of pair-copula is an area worthy of study. Considering the asymmetry o...

Recently some generalized autoregressive conditional heteroskedasticity (GARCH) models are proposed and applied to various hydrologic variables to capture and remove the ARCH effect, which has been observed frequently in the residuals from linear autoregressive moving average (ARMA) models fitted to hydrologic time series. As a nonlinear phenomenon of variance behavior, the ARCH effect reveals partially nonstationarity and nonlinearity of hydrological processes. This paper deals with the variation of a river basin using the ARMA-GARCH error model, which combines an ARMA model for modelling the mean behavior and a GARCH model for modelling the variance behavior of the residuals from the ARMA model. Based on the heteroscedasticity of hydrological variable series, the time-varying regional variance is proposed to check the variation of a river basin for the first time. As a study case, the method is applied to four deseasonalized daily discharge series from the middle reach of Yangtze River, China. Through the analyses of the conditional variance in different streamflow series, it is concluded that: (1) The ARCH effect exists in all the studied series which means the stream processes is nonstationary in terms of the variance; (2) The variations of time-varying variances are similar for the series from adjacent hydrological stations, and the similarity degree increases from upstream to downstream; (3) The regional variance is time-varying and can be used for further regional research.

In this paper, a novel multi-objective grey wolf optimizer (MOGWO) based on multiple search strategies (i.e., adaptive chaotic mutation strategy, boundary mutation strategy, and elitism strategy) which we shall call MMOGWO is proposed to solve multi-objective optimization problems (MOPs). The algorithm uses a fixed-sized external archive that is adaptively maintained according to a grid-based approach to preserve the non-dominated solutions found during the search process. Then, the archive is used to define the social hierarchy and simulate the hunting behaviors of grey wolves. In the proposed algorithm, an adaptive chaotic mutation strategy based on a chaotic cubic map and modified generational distance (GD) is applied to the archive to dynamically adjust the convergence speed and balance the exploration and exploitation. To prevent the population diversity loss, a boundary mutation strategy based on the concept of multi-level parallel is employed to manage boundary constraint violations. Moreover, a non-dominated sorting and crowding distance-based elitism strategy is also incorporated into the algorithm for exploiting more potential Pareto optimal solutions and preserve the diversity of solutions in the approximated set. The proposed algorithm is evaluated on a wide range of multi-objective optimization problems (MOPs), and compared with other state-of-the-art multi-objective optimization algorithms in terms of often-used performance metrics with the help of statistical analysis, average ranks test and Wilcoxon Signed-Rank Test (WSRT). It is revealed by the experimental results that the algorithm is highly competitive and significantly outperforms other well-known algorithms on most of the test problems. On obtaining satisfactory performance for test problems, to investigate the performance of the MMOGWO for solving real-world optimization problems with various constraints, MMOGWO is further applied to handle the multi-objective optimal scheduling problem (MOOSP) of cascade hydropower stations (CHSs) based on a novel constraints handling method designed in this paper. Simulation results indicate that, compared with other algorithms, MMOGWO can produce better quality solutions and it can be considered as a promising alternative tool to deal with multi-objective real-life engineering problems with complex constraints by equipping with constraints handling methods.

Lion swarm optimization (LSO) algorithm that based on the natural division of labor among lion king, lionesses and lion cubs in a pack of lions is recently introduced. To evaluate the exploration and the exploitation of the LSO algorithm comprehensively, an intensive study based on optimization problems is necessary. In this work, we firstly present the revised version of the LSO algorithm in detail. Secondly, the efficiency of LSO is evaluating using quantitative analysis, convergence analysis, statistical analysis, and robustness analysis on 60 classical numerical test problems, encompassing the Uni-modal, the Multi-modal, the Separable, the Non-separable, and the Multi-dimension problems. For comparison purposes, the results obtained by the LSO algorithm are compared against a large set of state-of-the-art optimization methods. The comparative results show that the LSO can provide significantly superior results for the US, the UN, and the MS problems regarding convergence speed, robustness, success rate, time complexity, and optimization accuracy compared with the other optimizers, and present very competitive results in terms of those indicators compared with the other optimizers. Finally, to check the applicability and robustness of the LSO algorithm, a case study on optimal dispatch problem of China’s Wujiang cascade hydropower stations shows that the LSO can obtain well and reliable optimal results with average generation of 122.421180 108 kW ⋅ h, 103.463636 108 kW ⋅ h, and 99.3826340 108 kW ⋅ h for three different scenarios (i.e. the wet year, the normal year and the dry year), which are satisfying compared with that of the GA, the improved CS, and the PSO in terms of optimization accuracy. Besides, regarding the convergence speed, the results are also competitive. Therefore, we can conclude that the LSO is an efficient method for solving complex problems with correlative decision variables with simple structure and excellent convergence speed.

Bacterial small non-coding RNAs (sRNAs) play important roles in various physiological processes, and predicting sRNAs is an important task. In this paper, we develop a computational method for the sRNA prediction by using sRNA sequence-derived features. We investigate a variety of sRNA sequence-derived features, and evaluate the usefulness of features for the sRNA prediction. Then, we develop the sequence learning ensemble method, which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs, and the genetic algorithm is adopted to optimize the parameters in the ensemble system. In the computational experiments, we compile a balanced dataset and four imbalanced datasets, and evaluate our method on these datasets by using 5-fold cross validation. The sequence learning ensemble method can achieve AUC scores greater than 0.9, and outperforms existing state-of-the-art sRNA prediction methods. In conclusion, the proposed method has a great potential for sRNA prediction. The source codes, datasets and supplementary are available in http://www.bioinfotech.cn/BIBM2017/SLEM.

In this paper, an improved meshless method for numerical solution of Helmholtz equation is presented. To overcome the unacceptable huge matrix condition number, we combine some Tikhonov regularization tools with radial basis function to solve the equations. Compared to the finite element method, a better result in numerical experiments will be approached using the proposed method. Furthermore, higher accuracy can be obtained by adopting suitable radial basis functions which is impossible for traditional RBF meshless method.

In this paper, a family of modified Chebyshev–Halley’s methods free from second derivative is presented. Per iteration the new methods require three evaluations of the function and one of its first derivatives. A detailed convergence analysis of the new methods shows that the new methods are at least fifth-order convergent and especially, the modified super-Halley’s method is sixth-order convergent. Numerical examples are given to illustrate the efficiency and performance of the new methods.

Based on data characteristics and nonparametric test, a new statistical temporal change analysis approach is proposed. The new approach consists of data characteristics analysis, temporal change analysis (including both change point and trend analysis), and result interpretation. Data characteristics are firstly investigated, especially with respect to the assumptions of independence and normality. Then proper nonparametric methods are chosen based on the detected characteristics of the observed data to analyze change points and monotonous linear trend for each of the segments divided by the change points. To avoid shortcoming of the traditional approach of carrying out the trend analysis before change point analysis, it is proposed in this paper that change point detection be performed before trend analysis. At last, statistical analysis results are interpreted according to the physical mechanism of observations. As a study case, the proposed approach has been carried out on three annual discharge series of the Yangtze River at the Yichang hydrological station. The investigations of data characteristics show that the observed data do not meet the assumptions of being independent and identically Gaussian-distributed. So the nonparametric Pettitt’s test was adopted to detect abrupt changes in the mean levels, followed by trend analysis using the nonparametric Mann-Kendall (MK) test. Results indicate the proposed approach is both reliable and reasonable for the temporal change analysis.

In this paper, a new numerical method is proposed to solve one-dimensional Burgers’ equation using multiquadric (MQ) radial basis function (RBF) for spatial approximation and a second-order compact finite difference scheme for temporal approximation. The numerical results obtained by this way for different Reynolds number have been compared with the existing numerical schemes to show the accuracy and efficiency of the approach. To show the superiority of this meshless method, numerical experiments with non-uniform MQ interpolation node distribution are also performed.

The Pettitt method, which is a rank-based test method, has been widely used to detect change point in the mean value of observed series. Traditionally the rank-based test has been assumed to be distribution-free and not sensitive to outliers and skewed distributions. However, there has no evidence provided to prove this assumption. Based on the work of Yue and Wang (Stoch Environ Res Risk Assess 16:307–323, 2002), this study defines the success rate of detecting the given change point as the ability of the Pettitt method, and investigates the ability in various circumstances by means of Monte Carlo simulation. Experiment results demonstrate that, the ability of the Pettitt method depends on not only the pre-assigned significance level, but also various properties of the sample data, including the sample size, the magnitude of a shift and the change point position. Besides, the distribution type and the distribution parameters such as the coefficient of variation, the coefficient of skewness and the shape parameter also seriously influence the ability. As expected, it is easier for the method to detect the change point when the sample size is larger, or the magnitude of a change point is bigger, or the variation of the sample data is smaller. And the highest ability is obtained when the change point occurs at the middle position of the series. These simulation results would provide users an extensive and detailed understanding about the use of the Pettitt method for the detection of change point.

A fully higher-order compact (HOC) finite difference scheme on the 9-point two-dimensional (2D) stencil is formulated for solving the steady-state laminar mixed convection flow in a lid-driven inclined square enclosure filled with water-Al2O3 nanofluid. Two cases are considered depending on the direction of temperature gradient imposed (Case I, top and bottom; Case II, left and right). The developed equations are given in terms of the stream function-vorticity formulation and are non-dimensionalized and then solved numerically by a fourth-order accurate compact finite difference method. Unlike other compact solution procedure in literature for this physical configuration, the present method is fully compact and fully higher-order accurate. The fluid flow, heat transfer and heat transport characteristics were illustrated by streamlines, isotherms and averaged Nusselt number. Comparisons with previously published work are performed and found to be in excellent agreement. A parametric study is conducted and a set of graphical results is presented and discussed to elucidate that significant heat transfer enhancement can be obtained due to the presence of nanoparticles and that this is accentuated by inclination of the enclosure at moderate and large Richardson numbers.

Natural convection heat transfer from a heated thin plate located in the middle of a lid-driven inclined square enclosure has been analyzed numerically. Left and right of the cavity are adiabatic, the two horizontal walls have constant temperature lower than the plate’s temperature. The study is formulated in terms of the vorticity-stream function procedure and numerical solution was performed using a fully higher-order compact (FHOC) finite difference scheme on the 9-point 2D stencil. Air was chosen as a working fluid (Pr = 0.71). Two cases are considered depending on the position of heated thin plate (Case I, horizontal position; Case II, vertical position). Governing parameters, which are effective on flow field and temperature distribution, are Rayleigh number values (Ra) ranging from 103 to 105 and inclination angles γ (0° ⩽ γ

Background Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements. Results In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. We construct datasets for three species: Human, Mouse and Drosophila. For each species, we compile the balanced dataset and imbalanced dataset, and thus obtain six datasets to build and evaluate prediction models. In the computational experiments, the genetic algorithm-based weighted ensemble method achieves 10-fold cross validation AUC of 0.932, 0.937 and 0.995 on the balanced Human dataset, Mouse dataset and Drosophila dataset, respectively, and achieves AUC of 0.935, 0.939 and 0.996 on the imbalanced datasets of three species. Further, we use the prediction models trained on the Mouse dataset to identify piRNAs of other species, and the models demonstrate the good performances in the cross-species prediction. Conclusions Compared with other state-of-the-art methods, our method can lead to better performances. In conclusion, the proposed method is promising for the transposon-derived piRNA prediction. The source codes and datasets are available in https://github.com/zw9977129/piRNAPredictor. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1206-3) contains supplementary material, which is available to authorized users.

Background Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File.

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

A new intelligent method for disease diagnosis based on rough set theory (RST) and the relevance vector machine (RVM) for classification is presented as the rough relevance vector machine (RRVM). The RRVM mixes rough set’s strong rule extraction ability with the excellent classification ability of the relevance vector machine through preprocessing initial information, reducing data, and training the relevance vector machine. Compared with traditional intelligence methods such as neural network (NN), support vector machine (SVM), and relevance vector machine (RVM), this method manages to identify disease samples objectively and effectively with less transcendental information.

Under influence of climate change and human activities, deterministic trend has been detected and reported in various hydrometeorological observation records. In order to correctly model the stochastic properties, the time series has to be stabilized by removing the trend. Both detrending and differencing have been proposed to fulfill such a task. But the influence of the two stabilizing approaches on the residual series is distinguishing. In this study, ARMA models are constructed based on the above two stabilization approaches for an annual minimum daily discharge series with a deterministic trend. Comparisons are made with respect to stabilization ability, model simulation, and forecasting. Results indicate that the model based on detrending is superior to the one based on differencing in almost all the selected comparison criteria. So detrending is suggested to remove the deterministic trend before using ARMA model to fit the observed data.

Climatic change has great implications for hydrological cycle and water resources planning. In order to assess this impact, a macro-scale and semi-distributed monthly water balance model was proposed and developed to simulate and predict the hydrological processes. GIS techniques were used as a tool to analyze topography, river networks, land-use, human activities, vegetation and soil characteristics. The model parameters were linked to these basin characteristics by regression and optimization methods. A parameterization scheme was developed and the model parameters were estimated for each grid element. Based on the different GCM and RCM outputs, the sensitivities of hydrology and water resources for China to global warming were studied. The proposed models are capable of producing both the magnitude and timing of runoff and water resources conditions. The semi-dry regions, such as Liaohe, Haihe, Ruanhe and Huaihe River basins in north China, The runoffs of these basins are small or even zero during dry season (from Oct. to May) and are very sensitive to temperature increase and rainfall decrease. While in the basins of the humid south China like Yangtze River basin, the runoffs are perennial and the base flow normally occupies a large portion of the total runoff volume. These humid basins are less vulnerable to climate change. Results of the study also indicated that runoff is more sensitive to variation in precipitation than to increase in temperature. Climate change challenges existing water resources management practices by additional uncertainty. Integrated water resources management will enhance the potential for adaptation to change.

Based on the finite difference scheme in time, the method of particular solutions using radial basis functions is proposed to solve one-dimensional time-dependent inhomogeneous Burgers’ equations. Two numerical examples with good accuracy are given to validate the proposed method.

As the rapid development of the Internet, mining users' useful information from Web log has become the hot research, which plays an important guiding role in developing and making use of the Web. Firstly, pre-dealing with Web log, analyzing the main factors of Web users' visiting interests. Afterwards rules mining model of Web users' visiting interest is formed that based on rough set theorem, from which we get the decision rule and minimum decision algorithm of Web users' visiting interest. Finally, the model and algorithm are proved to be practical and feasible through examples.

Aiming at disadvantages of fuzzy comprehensive evaluation (CE) in weight determination, according to rough set theorem and analytic hierarchy process, a weight determination method of complete data driving is presented, and improved fuzzy CE model is obtained. At first, evaluation index system is formed according to analytic hierarchy process. Then index system is reduced effectively by the use of attribute reduction in rough set theorem, and redundant attributes are deleted. Based on the concept of inclusion degree, we define attribute importance, and present determination method of weight. According to which, fuzzy CE model is formed, therefore a new method of CE is acquired. Finally, this method is proved to be practical and feasible through examples.

Aiming at Bayesian rough set model, some new concepts of knowledge reduction are denoted such as the upper approximation reduction and the lower approximation reduction, the upper approximation discernible matrix and the lower approximation discernible matrix, the judgment theorems with respect to those reductions are obtained, from which we provide new approaches to knowledge reductions in information systems.

Customer relationship management is hot in intelligent business study nowadays, in which the evaluation of customer value is one of the basic problems. Method of customer value mining based on rough logic is proposed in this paper, from which we got the decision rule of customer value evaluation and the minimal decision algorithm. This method is showed to be practical and feasible through examples.