高效液相色谱法快速测定鱼类鲜度指标K值及方法学优化

对行业标准SC/T3048-2014测定鱼类鲜度K值(三磷酸腺苷,二磷酸腺苷,腺苷酸,肌苷酸,肌苷和次黄嘌呤)的分析方法进行优化。优化后的条件:以高氯酸溶液为提取剂,样品经高速均质结合超声波辅助提取后,用氢氧化钠溶液调节pH至6.0~6.4,一级水定容。采用C_(4)柱(4.6mm×250mm,5μm)分离,以磷酸二氢钾溶液(0.048mol/L,pH=5.7)~甲醇/磷酸二氢钾溶液(9/1,v/v)为流动相进行梯度洗脱,检测波长254nm,流速1.0mL/min,柱温30℃,外标法定量。优化结果表明:6种化合物的分析时间由行业标准方法的38min减少至11min。该方法的加标回收率(77.2%~112%),相对标准偏差(4.69%~7.81%),检出限(0.648~2.15mg/kg)和定量限(2.16~7.18mg/kg)均优于行业标准方法。结果表明该方法操作简便,分析快速,干扰较小,灵敏度高和重现性好,具有一定的实用价值。

乙肝病毒基因组序列扩增的条件优化和结果分析

目的优化乙型肝炎病毒(HBV)不同长度基因片段PCR扩增条件,为后续机制研究提供实验基础。方法运用PCR或巢式PCR(Nested PCR)方法扩增6例临床乙肝患者HBV DNA,针对特异性目的片段,通过优化Mg2+浓度、引物浓度、Taq酶用量、DMSO浓度等,获得特异性扩增条带,PCR产物直接测序并使用CHROMAS、MEGA等生物学软件进行结果分析。结果通过综合分析各种实验条件对PCR扩增结果的影响,最终确认在50μL扩增体系中,2.5 mmol/L Mg2+、200 nmol/L特异性引物、1.5 U Taq酶、56℃退火在本实验室可获得良好的扩增效果,适量二甲基亚砜(DMSO)可减少非特异性干扰,同时建议对短片段可酌量减少Taq酶使用量。生物信息学分析示6例HBV感染标本中1例为B基因型,5例为C基因型,共发现S基因21个核酸变异及9个氨基酸可改变。结论建立了适合本实验室的HBV DNA扩增体系以及后续生物信息学分析方法,明确了不同的扩增体系条件对PCR扩增效果具有重要影响。

EFFICIENT METHOD FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION BY CONSIDERING UNCERTAINTY

A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.

A decision tree based decomposition method for oil refinery scheduling

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years.However, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem,though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category(i.e. adjusting scale). Then,a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.

Nd:YAG laser micro-welding of ultra-thin FeCo-V magnetic alloy:optimization of weld strength

The main aim of this research is to optimize the tensile strength of laser welded FeCo-V alloy.A mathematicalrelationship was developed to predict tensile strength of the laser beam welded FeCo-V foils by incorporating process parameterssuch as lamping current,welding speed,pulse duration and focused position.The procedure was established to improve the weldstrength and increase the productivity.The results indicate that the pulse duration and welding speed have the greatest influence ontensile strength.The obtained results showed that the tensile strength of the weld joints increase as a function of increasing pulseduration reaching to a maximum at a pulse duration value of2.25ms.Moreover,the tensile strength of joints increases with decreasein welding speed reaching to a maximum at a welding speed of125mm/min.It has been shown that increase in pulse duration anddecrease in welding speed result in increased effective peak power density and hence formation of more resistant welds.At higherpulse durations and lower welding speeds,the tensile strength of weld joints decreases because of formation of solidificationmicrocracks in the fusion zone.

Full waveform inversion based on deep learning and optimal nearly analytic discrete method

In this study,we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete(ONAD)method.The forward-modeling method combines the ONAD method with recurrent neural network(RNN)for the fi rst time.RNN is a type of neural network that is suitable for sequential data,which uses information from both previous and current times to obtain output information.We express the ONAD method using an RNN framework to advance the time iteration of an acoustic equation.This process can simplify programming using RNN and convolution kernels.Next,we use deep learning based on the proposed forward-modeling method to study full waveform-inversion problems.Because the main purpose of inversion is to minimize the error between real and synthetic data,inversion is essentially an optimization problem.Many new optimizers are available in the framework of deep learning,such as the Adam and Nadam optimizers,which are used for optimizing velocity model in the inversion process.We perform six numerical experiments.The first two experiments demonstrate the forward-modeling results,which indicate that the forward-modeling method can effectively suppress numerical dispersion and improve computational effi ciency.The other four experiments demonstrate the inversion results,which show that the method proposed in this paper can eff ectively realize inversion imaging.We compare several optimizers used in deep learning and find that the Nadam optimizer has faster convergence and better effectiveness based on the ONAD method combined with RNN.

Inertisation options for BG method and optimisation using CFD modelling

Spontaneous combustion(sponcom) is one of the issues of concern with the blasting gallery(BG) method of coal mining and has the potential to cause fires, and impact on production and safety, greenhouse gas(GHG) emissions and huge costs involved in controlling the aftermath situations. Some of the research attempts made to prevent and control coal mine fires and spontaneous combustion in thick seams worked with bord and pillar mining methods are presented in this paper. In the study, computational fluid dynamics(CFD) modelling techniques were used to simulate and assess the effects of various mining methods, layouts, designs, and different operational and ventilation parameters on the flow of goaf gases in BG panels. A wide range of parametric studies were conducted to develop proactive strategies to control and prevent ingress of oxygen into the goaf area preventing spontaneous combustion and mine fires.

Optimal multilevel thresholding based on molecular kinetic theory optimization algorithm and line intercept histogram

Among all segmentation techniques, Otsu thresholding method is widely used. Line intercept histogram based Otsu thresholding method(LIH Otsu method) can be more resistant to Gaussian noise, highly efficient in computing time, and can be easily extended to multilevel thresholding. But when images contain salt-and-pepper noise, LIH Otsu method performs poorly. An improved LIH Otsu method(ILIH Otsu method) is presented, which can be more resistant to Gaussian noise and salt-and-pepper noise. Moreover, it can be easily extended to multilevel thresholding. In order to improve the efficiency, the optimization algorithm based on the kinetic-molecular theory(KMTOA) is used to determine the optimal thresholds. The experimental results show that ILIH Otsu method has stronger anti-noise ability than two-dimensional Otsu thresholding method(2-D Otsu method), LIH Otsu method, K-means clustering algorithm and fuzzy clustering algorithm.

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

Statistical approach for the culture conditions optimization of magnetotactic bacteria for magnetic cells production

The culture of Magnetospirillum magneticum WM-1 depends on several control factors that have great effect on the magnetic cells concentration. Investigation into the optimal culture conditions needs a large number of experiments So it is desirable to minimize the number of experiments and maximize the information gained from them. The orthogonal design of experiments and mathematical statistical method are considered as effective methods to optimize the culture condition of magnetotactic bacteria WMol for high magnetic cells concentration. The effects of the four factors, such as pH value of medium, oxygen concentration of gas phase in the serum bottle, C：C （mtartaric acid： m=succinic acid） ratio and NaNO3 concentration, are simultaneously investigated by only sixteen experiments through the orthogonal design L16（44） method. The optimal culture condition is obtained. At the optimal culture condition （ pH 7.0, an oxygen concentration 4.0%, C：C （mtartaric acid： m=succinic acid） ratio 1：2 and NaNO3 100 mg 1^-1）, the magnetic cells concentration is promoted tO 6.5×10^7 cells ml^-1, approximately 8.3% higher than that under the initial conditions. The pH value of medium is a very important factor for magnetic cells concentration. It can be Proved that the orthogonal design of experiment is of 90% confidence. Ferric iron uptake follows MichaelisoMenten kinetics with a Km of 2.5 pM and a Vmax of 0.83 min^-1.

Research on Optimizing Computer Network Structure based on Genetic Algorithm and Modified Convex Optimization Theory

In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in the background and one of its core problems is to solve the optimization problem. Unlike traditional batch algorithm, stochastic gradient descent algorithm in each iteration calculation, the optimization of a single sample point only losses could greatly reduce the memory overhead. The experiment illustrates the feasibility of our proposed approach.

Research on the optimal dynamical systems of three-dimensional Navier-Stokes equations based on weighted residual

In this paper, the theory of constructing optimal dynamical systems based on weighted residual presented by Wu & Sha is applied to three-dimensional Navier-Stokes equations, and the optimal dynamical system modeling equations are derived. Then the multiscale global optimization method based on coarse graining analysis is presented, by which a set of approximate global optimal bases is directly obtained from Navier-Stokes equations and the construction of optimal dynamical systems is realized. The optimal bases show good properties, such as showing the physical properties of complex flows and the turbulent vortex structures, being intrinsic to real physical problem and dynamical systems, and having scaling symmetry in mathematics, etc.. In conclusion, using fewer terms of optimal bases will approach the exact solutions of Navier-Stokes equations, and the dynamical systems based on them show the most optimal behavior.

A learning method for energy optimization of the plug-in hybrid electric bus

The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.