基于SSA-LSTM-GF的混凝土坝变形预测方法

针对混凝土坝变形分析预测的复杂性,应用相空间重构思想和融合建模理念,提出了一种基于SSA-LSTM-GF的混凝土坝变形分析预测方法。SSA-LSTM-GF方法利用奇异谱分析法(SSA)将变形实测数据序列分解为趋势分量、周期分量和剩余分量,并将剩余分量视为噪声分量予以剔除;采用长短期记忆神经网络(LSTM)模型和高斯拟合(GF)算法分别进行周期分量和趋势分量的分析预测,并将二者结果进行叠加重构,得到最终预测结果。实例验证结果表明,SSA可以达到较好的数据分解和消噪效果,LSTM模型针对周期分量的预测性能优越,GF算法能够很好地实现趋势分量的拟合预测和部分信息的挖掘提取,LSTM模型和GF算法的成果重构效果良好,SSA-LSTM-GF方法具有一定的可行性和应用价值。     

Starch?Sulfuric Acid (SSA) as Catalyst for a One‐Pot Synthesis of 1,5‐Diaryl‐1H‐pyrazoles

Protocols with starch? sulfuric acid (SSA) as reusable catalyst for the synthesis of aryl‐1H‐pyrazoles are described. SSA acted as an efficient and environmentally friendly catalyst for the regioselective condensation of Baylis? Hillman adducts 1 with phenylhydrazine hydrochloride leading to the new 1,5‐diaryl‐1H‐pyrazole 2a – 2e in excellent yields (Scheme and Table 1).     

Microstructure characterization of short-chain branching polyethylene with differential scanning calorimetry and successive selfnucleation/annealing thermal fractionation

A series of the copolymers of ethylene with 1-hexene(M1–M9) synthesized by metallocene catalyst Et[Ind]2ZrCl2/MAO was studied by differential scanning calorimetry and successive self-nucleation and annealing(SSA) thermal fractionation. The distribution of methylene sequence length(MSL) in the different copolymers was determined using the SSA method. The comonomer contents of samples M4 and M5 are 2.04 mol% and 2.78 mol%, respectively. Both M4 and M5 have low comonomer content and their MSL distribution profiles exhibit a monotonous increase trend with their MSL. The longest MSL of M5 is 167, and its corresponding molar percent is 43.95%, which is higher than that of M4. Moreover, the melting temperature(Tm) of M5 is also higher than that of M4. The comonomer contents of samples M7, M8, and M9 are 8.73 mol%, 14.18 mol% and 15.05 mol%, respectively. M7, M8, and M9 have high comonomer contents, and their MSL distribution profiles display unimodality. M7 has a lower peak value of 33 and a narrow MSL distribution, resulting in a Tm lower than that of M8 and M9. The MSL and its distribution are also key points that influence the melting behavior of copolymers. Sometimes, MSL and its distribution of copolymers have a greater impact on it than the total comonomer contents, which is different from traditional views.     

Effect of sequence distribution on the isothermal crystallization kinetics and successive self-nucleation and annealing (SSA) behavior of poly(ε-caprolactone-co-ε-caprolactam) copolymers

Two types of miscible poly(ε-caprolactone-co-ε-caprolactam) copolymers were studied. In both cases catalyzed hydrolytic ring-opening polymerization was employed. For the first type, the comonomers were added simultaneously to obtain random copolymers. For the second type, the comonomers were added sequentially to obtain block copolymers. Successive self-nucleation and annealing (SSA) and isothermal crystallization studies were performed to both types of copolymers. The SSA results reflect the differences in molecular microstructure: block versus random copolymers. In a wide composition range only the polycaprolactam sequences were capable of crystallization in the random copolymers. Avrami indexes of approximately 3-4 were obtained corresponding to the spherulitic crystallization of these units within the copolymers. The block copolymer samples experienced a relatively small reduction of crystallization kinetics with composition, and this was attributed to the dilution effect caused by the miscible non-crystalline polycaprolactone units. On the other hand, for the random copolymers, the rate of crystallization strongly increased with polycaprolactam content while the energy barrier for secondary nucleation decreased exponentially. The comparison between miscible block and random copolymers provides a unique opportunity to distinguish the dilution effect of the polycaprolactone units (a moderate effect) on the isothermal crystallization and melting of the polyamide phase from the molecular microstructural effect in the random copolymers case (a dramatically strong effect), where the polycaprolactam sequences are interrupted statistically by polycaprolactone sequences.     

Solving stochastic chemical kinetics by Metropolis-Hastings sampling

This study considers using Metropolis-Hastings algorithm for stochastic simulation of chemical reactions. The proposed method uses SSA (Stochastic Simulation Algorithm) distribution which is a standard method for solving well-stirred chemically reacting systems as a desired distribution. A new numerical solvers based on exponential form of exact and approximate solutions of CME (Chemical Master Equation) is employed for obtaining target and proposal distributions in Metropolis-Hastings algorithm to accelerate the accuracy of the tau-leap method. Samples generated by this technique have the same distribution as SSA and the histogram of samples show it''s convergence to SSA     

Estimating possible impact of human activity on the warm season temperature in Beijing since the industrial era

Global surface temperature shows an increasing trend over the past 100 years.Anthropogenic and natural factors have been invoked to explain this warming, but their relative contribution and the spatial variability remain controversial.Based on a 2650-year temperature record from a stalagmite near Beijing, we explore the possible impacts of human activity on the warm season temperature in Beijing since the industrial era using the method of singular spectrum analysis (SSA).The results suggest that the warm s...     

教学效能感的结构:最小空间分析的运用

好的结构无疑是研究一个心理变量的基础.对于教学效能感的结构,不管是理论的解释还是实际的测量都受到不停的争议和质疑.该研究用474个样本检测了TES和中文教学效能感量表（俞国良和辛涛等,1995）的结构,指出了一般教学效能感（GTE）和个人教学效能感（PTE）两因子模型的局限性.按严格的程序翻译了俄亥俄州教师效能感量表（OSTES）,并施测在相同的被试上,因素分析的结果呈单维结构.介绍并应用最小空间分析（SSA）重新探索OSTES的结构.最小空间分析的结果支持OSTES教学策略、课堂管理和学生参与三因子的存在,尤其是教学策略和课堂管理因子呈现出一条清晰的界线.     

时间序列分维数提取算法的研究

混沌时间序列的分维数是描述其混沌特性的一个重要参数．这里介绍的改进的分维数提取算法是在传统的Ｇ－Ｐ关联维数分析算法的基础上，结合奇异谱分析技术，构造了基于奇异谱分析的关联维数提取算法，克服了原始Ｇ－Ｐ算法抗噪声干扰能力较差的缺点，同时具有可靠性较高、结果精度较理想、运算速度较快等优点     

Nanometer SiO2 modified with 5-sulfosalicylic acid as selective solid-phase extractant for Fe(III) determination by ICP-AES from biological and natural water samples

A new modified nanometer SiO₂ using 5-sulfosalicylic acid (SSA) as a solid-phase extractant was used for separation, preconcentration and determination of Fe(III) in aqueous solutions by inductively coupled plasma atomic emission spectrometry (ICP-AES). Its adsorption and preconcentration behaviour for Fe(III) in aqueous solutions was investigated using static procedures in detail. The optimum pH value for the separation of Fe(III) on the newly designed sorbent was 3.5. Complete elution of the adsorbed Fe(III) from the nanometer SiO₂-SSA was carried out using 2.0 mL of 0.01 mol L^− 1 of HCl. The time of 90% sorption was less than 2 min for Fe(III) at pH 3.5. Common coexisting ions did not interfere with the separation and determination of Fe(III) at pH 3.5. The maximum static adsorption capacity of the sorbent at optimum conditions was found to be 44.01 mg of Fe(III) per gram of sorbent. The relative standard deviation (RSD) of the method under optimum conditions was 3% (n = 5). The procedure was validated by analyzing three certified reference materials (GBW 08301, GBW 08504, GBW 08511), the results obtained were in good agreement with standard values. The nanometer SiO₂-SSA was successfully employed in the separation and preconcentration of the investigated Fe(III) from the biological and natural water samples yielding 100-folds concentration factor.