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By means of a novel time‐dependent cumulated variation penalty function, a new class of real‐time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns: in particular, the breathing motion data of the patients during robotic radiation therapy. It is illustrated that for both simulated and empirical data involving changes in mean, trend, and amplitude, the proposed methods outperform existing forecasting methods based on support vector machines and artificial neural network in terms of prediction accuracy. Moreover, the proposed methods are designed so that real‐time updates can be done efficiently with O(1) computational complexity upon the arrival of a new signal without scanning the old data repeatedly.

It is common to apply dimension reduction techniques like principal component analysis before performing cluster analysis of multivariate data. However, it is not guaranteed that the most useful in...

Through free-radical copolymerization of the Yb3+-centered near-infrared (NIR) luminescent complex monomer [Zn(L1)(4-vinyl-Py)Yb(L2)3] (2) (H2L1 = N,N’-bis(salicylidene)−1,2-diamine; 4-vinyl-Py = 4-vinyl-pyridine and HL2 = (Z)−3-methyl-1-phenyl-4-(2,2,2-trifluoro-1-hydroxyethylidene)−1H-pyrazol-5(4H)-one) with NVK (N-vinylcarbazole), the obtained [Zn(L1)(4-vinyl-Py)Yb(L2)3]-grafted polymer Poly(NVK-co-2) with additional PVK (Poly(N-vinylcarbazole)) to Yb3+ ion Foster energy transfer is used as the emitting material of polymer light-emitting diode (PLED), exhibiting the Yb3+-centered NIR outputs at 980 nm with a maximum efficiency of 0.049% (at 5.62 mA/cm2) and a maximum irradiance of 78.68 μW/cm2 (at 16 V), respectively. This superior performance to those of previously reported organo-Yb3+-doped polymer systems, together with the characters of low turn-on voltage of 7 V and weak efficiency-roll-off (0.049–0.031% upon 5.62–233.6 mA/cm2), indicates that organo-Yb3+-grafted polymer is an effective strategy to develop PLEDs with the wavelength extended to 980 nm.

Through self-assembly of the typical β-diketone ligand HBTFA (HBTFA = 4,4,4-trifluoro-1-phenyl-1,3-butanedione) with LnCl3·6H2O in the presence of Et3N, the six-tenth in the η2-Mode I and the four-tenth in the μ-(O)-η2-bridging Mode II of the ten BTFA− ligands, together with the involvement of two μ3-(OH)− anions, two μ1-coordinating H2O and two μ2-bridging H2O molecules, endow the formation of homoleptic cyclic tetranuclear complexes [Ln4(BTFA)10(μ3-OH)2(H2O)4] (Ln = La, 1; Eu, 2; Tb, 3 or Gd, 4), respectively. Moreover, attributing to effective ligand-to-Ln3+ transfer energy, color-purity red-light (ΦLEu = 20.1%) for complex 2 while few near-white-light (Φem = 6.4%) for complex 3 are exhibited, respectively, despite the (μ3-OH)−-and-H2O-induced OH-oscillators surrounded from the four Ln3+ ions.

Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them. With the popularity of IoT devices, malware on Android-based IoT devices is also increasing. People&rsquo, s lives and privacy security are threatened. To reduce such threat, many researchers have proposed new methods to detect Android malware. Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families. However, they cannot detect unknown malware and are easily evaded by malware that is confused or packaged. Many new solutions use syntactic features and machine learning techniques to classify Android malware. It has been known that analysis of the Function Call Graph (FCG) can capture behavioral features of malware well. This paper presents a new approach to classifying Android malware based on deep learning and OpCode-level FCG. The FCG is obtained through static analysis of Operation Code (OpCode), and the deep learning model we used is the Long Short-Term Memory (LSTM). We conducted experiments on a dataset with 1796 Android malware samples classified into two categories (obtained from Virusshare and AndroZoo) and 1000 benign Android apps. Our experimental results showed that our proposed approach with an accuracy of 97 % outperforms the state-of-the-art methods such as those proposed by Nikola et al. and Hou et al. (IJCAI-18) with the accuracy of 97 % and 91 % , respectively. The time consumption of our proposed approach is less than the other two methods.

This paper highlights the computational explosion issues in the autoregressive moving average approach of frequency estimation of sinusoidal data with a large sample size. A new algorithm is proposed to circumvent the computational explosion difficulty in the conditional least-square estimation method. Notice that sinusoidal pattern can be generated by a non-invertible non-stationary autoregressive moving average (ARMA) model. The computational explosion is shown to be closely related to the non-invertibility of the equivalent ARMA model. Simulation studies illustrate the computational explosion phenomenon and show that the proposed algorithm can efficiently overcome computational explosion difficulty. Real data example of sunspot number is provided to illustrate the application of the proposed algorithm to the time series data exhibiting sinusoidal pattern.

Two series of OAc–-dependent unique homoleptic nonanuclear [Ln9(L1)3(HL1)2(μ3-OH)4(OAc)10] (1–7) and hexanuclear {[Ln6(L2)4(HL2)(μ3-OH)(OAc)2(H2O)2]·(OAc)} (8–14) are obtained from the self-assembly of the partially deprotonated OH-H2Salen ligand H3L1 (N,N′-bis(salicylidene)(propylene-2-ol)-1,3-diamine) or OH-MeO-H2Salen ligand H3L2 (N,N′-bis(3-methoxysalicylidene)(propylene-2-ol)-1,3-diamine) with Ln(OAc)3 (Ln = La, Eu, Tb, Gd, Nd, Yb, or Er), respectively. The result of their photophysical properties shows that the sensitization for single-component near-white-light of complex 2, efficient Eu3+-centered red-light (ΦLEu = 13.4%) of complex 9, or or Tb3+-centered yellowish-green-light (ΦLTb = 8.1% and 21.3%) of complexes 3 and 10, and Nd3+- or Yb3+-centered NIR luminescence (η = ΦLLn/ΦLnLn = 67–75% and 76–85%) for complexes 5–6 and 12–13, arose from the ligands’ 3π–π* excited state, due to effective intramolecular energy transfer and multiple Ln3+-to-Ln3+ electron-communications.

To improve the ability of water to wet coal in the process of coal seam water injection, different ionic liquids and surfactant compound solutions were added to water. Analyses of surface tension, reverse spontaneous imbibition, and contact angle were used to optimize the different compound solutions. The results showed that when the mixture ratio of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIm][BF4]) and sodium dodecyl sulfate (SDS) was 3:1, and the concentration of the composite solution reached 0.1 wt%, the liquid surface tension reached a minimum of 28.946 mN/m, and the contact angle between the solution and the coal sample was 41.3°, which was lower than that of any concentrations of the other composite solutions. This solution could effectively wet the coal. In addition, infrared spectroscopy showed that different concentrations of the [BMIm][BF4] and SDS composite solution had different effects on the content of functional groups in coal. Further, based on the relationship between the content of the functional groups and contact angle, the relationship between the content of ring hydroxyl and OH⋯O in the hydroxyl structure and the size of the contact angle was found to be the most significant. Based on the above findings, a new type of composite wetting agent was prepared, and the wetting mechanism between coal and the wetting agent was clarified from the perspective of functional groups. The research results have important guidance and practical significance for improving the effect of increasing moisture and reducing dust in the process of coal seam water injection.