No abstract

Precise and reliable monthly runoff prediction plays a vital role in optimal management of water resources but non-stationarity and skewness of monthly runoff time series can pose major challenges for developing appropriate prediction models. To address these issues, this paper proposes a novel hybrid prediction model based on Elman neural network (Elman), variational mode decomposition (VMD) and Box-Cox transformation (BC), named VMD-BC-Elman model. Firstly, the observed runoff is decomposed into sub-time series using VMD for the better frequency resolution.Secondly, the input datasets were transformed into normal distribution using Box-Cox, and as a result, skewedness in the data was removed and the correlation between the input and output variables enhanced. Finally, Elman is used to simulate the respective sub-time series. The proposed model is evaluated using monthly runoff time series at Zhangjiashan, Zhuangtou and Huaxian hydrological stations in Wei River Basin in China. The model performances are compared with those of single models (SVM, Elman), decomposition-based (VMD-SVM, VMD-Elman et.al) and BC-based models (BC-SVM and BC-Elman) by employing four metrics. The results show that the hybrid models outperform single models, and VMD-BC-Elman model performs best in all considered hybrid models with NSE greater than 0.95, R greater than 0.98, NMSE less than 4.73%, and PBIAS less than 0.39% in both training and testing periods. The study indicates that VMD-BC-Elman model is a satisfactory data-driven approach to predict the non-stationary and skewed monthly runoff time series, representing 2 an effective tool for predicting monthly runoff series.

Wireless sensor network technologies have been widely used in modern life especially in Internet of Things. Since battery energy of sensor nodes is limited, balancing the energy consumption of each node on a transmitting path is an important issue. In this article, we propose a multi-hop clustering routing method with fuzzy inference and multi-path tree. The algorithm identifies the best path from the source node to the destination node by following two steps: (1) dividing the wireless sensor nodes by an efficient clustering routing method and (2) determining the optimal path by a combination of the fuzzy inference approach and multi-path method, taking into account the remaining energy, the minimum hops, and the traffic load of node. Simulation results show that the proposed protocol can efficiently reduce the energy consumption of the network, balance network load, and prolong the survival time of the network.

Precise and reliable monthly runoff prediction plays a vital role in optimal management of water resources but non-stationarity and skewness of monthly runoff time series can pose major challenges for developing appropriate prediction models. To address these issues, this paper proposes a novel hybrid prediction model based on Elman neural network (Elman), variational mode decomposition (VMD) and Box-Cox transformation (BC), named VMD-BC-Elman model. Firstly, the observed runoff is decomposed into sub-time series using VMD for the better frequency resolution. Secondly, the input datasets were transformed into normal distribution using Box-Cox, and as a result, skewedness in the data was removed and the correlation between the input and output variables enhanced. Finally, Elman is used to simulate the respective sub-time series. The proposed model is evaluated using monthly runoff time series at Zhangjiashan, Zhuangtou and Huaxian hydrological stations in Wei River Basin in China. The model performances are compared with those of single models (SVM, Elman), decomposition-based (VMD-SVM, VMD-Elman et.al) and BC-based models (BC-SVM and BC-Elman) by employing four metrics. The results show that the hybrid models outperform single models, and VMD-BC-Elman model performs best in all considered hybrid models with NSE greater than 0.95, R greater than 0.98, NMSE less than 4.73%, and PBIAS less than 0.39% in both training and testing periods. The study indicates that VMD-BC-Elman model is a satisfactory data-driven approach to predict the non-stationary and skewed monthly runoff time series, representing an effective tool for predicting monthly runoff series.

There are two molecules in the asymmetric unit of the title compound, C 23 H 33 NO, in which the dihedral angles between the aromatic rings are 72.1 (3) and 89.0 (2) . One of the molecules features a tert-butyl group disordered over two sets of sites in a 0.545 (13):0.455 (13) ratio. Both molecules feature an intramolecular O-HÁ Á ÁN hydrogen bond, which closes an S(6) ring. Neither of the N-H groups participates in hydrogen bonds, perhaps due to steric crowding. Structure descriptionInterest in the title compound arises from the highly catalytic activity of phenoxy-imine ligated group IV metal complexes (now known FI catalysts) for olefin polymerization (Fujita et al., 2014; Makio et al., 2011). The structure of the title compound (phenoxyamine) is similar to phenoxy-imine ligands of FI catalysts, but phenoxy-amines are considered to be conformationally more flexible to chelate metal ions as they are not constrained to be planar (Sreenivasulu & Vittal, 2003; Yang et al., 2003). Moreover, group IV metal complexes bearing phenoxy-amine ligands have exhibited highly catalytic activities for the polymerization of olefins (Alesso et al., 2011; Wan et al., 2013). As a part of our studies in this area, we have prepared the title compound ( Fig. 1) and determined its crystal structure.There are two molecules in the asymmetric unit, in which the dihedral angles between the aromatic rings are 72.1 (3) for the C1 molecule and 89.0 (2) for the C24 molecule. Both molecules feature an intramolecular O-HÁ Á ÁN hydrogen bond (Fig. 2, Table 1), which closes an S(6) ring. Neither of the N-H groups participate in hydrogen bonds, perhaps due to steric crowding. Synthesis and crystallizationA solution of (R)--phenylethylamine (1.21 g, 10 mmol) and 3,5-di-tert-butylsalicylaldehyde (2.34 g, 10 mmol) in 15 ml of ethanol was stirred for 12 h at 25 C, and then the resulting precipitate was collected to give the corresponding iminophenol. A suspension of above obtained iminophenol in 20 ml of ethanol was cooled to 0 C, and NaBH 4 (0.57 g, 15 mmol) was added portionwise with stirring at 0 C over a period of 30 min. After the reaction mixture was stirred 12 h at 25 C, the solvents were removed under reduced pressure and the residue was dissolved in 20 ml H 2 O and then extracted with CH 2 Cl 2 . The organic layer was washed with brine and dried over Na 2 SO 4 , and the solvents were removed under reduced pressure and the residue was recrystallized from ethanol solution to give the title compound (2.40 g, 70.6% yield) as white needle-like crystals. 1 136.6, 129.4, 128.2, 127.2, 124.1, 123.6, 122.9, 58.1, 51.9, 35.6, 34.8, 32.4, 30.3, 24.0. Elemental analysis (calcd %) for C 23 H 33 NO: C, 81.37; H, 9.80; N, 4.13. Found: C, 81.25; H, 9.77; N, 4.12.The title compound (20 mg) was dissolved in dichloromethane (2 ml). The solution was allowed to evaporate slowly over several days to yield colorless blocks. RefinementCrystal data, data collection and structure refinement details are summarized in Table 2. One of the tert-butyl groups in the...