Detection of liner surface defects in solid rocket motors using multilayer perceptron neural networks

Debonding problems along the propellant/liner/insulation interface are a critical point to the integrity and one of the major causes of structural failures of solid rocket motors. Current solutions are typically restricted to methods for assessing the integrity of the rocket motors structure and visually inspecting their components. In this context, this paper presents an improved algorithm to detect liner surface defects that may compromise the bonding between the solid propellant and the insulation. The use of Local Binary Patterns (LBP) provides a structural and statistical approach to texture analysis of liner sample images. Along with color information extraction, these two methods allow the representation of image pixels by feature vectors that are further processed by a Multilayer Perceptron (MLP) neural network classifier. The MLP neural network analyzes liner sample images and classifies each pixel into one of three classes: non-defect, foreign object, and defect. Several tests were executed varying different parameters to find the optimal MLP configuration, and as a result, the best classification accuracy of 99.08%, 90.66%, and 99.48% was achieved for the corresponding classes. Moreover, the defect size estimate showed that the MLP classifier correctly identified defects less than 1 mm long, with a relatively small number of training examples. Positive results indicate that the algorithm can identify liner surface defects with a performance similar to human inspectors and has the potential to assist or even automate the liner inspection process of solid rocket motors.     

Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation part one: the applicability of a fuzzy rule based approach

This work presents the applicability of applying a fuzzy logic approach to the calculation of noontime erythemal UV irradiance for the plain areas of Egypt. When different combinations of data sets were examined from the test performance point of view, it was found that 91% of the whole series was estimated within a deviation of less than +/-10 mW/m(2), and 9% of these deviations lay within the range of +/-15 mW/m(2) to +/-25 mW/m(2).     

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10 + 2 °C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R² = 0.99) between actual and predicted data.     

Evaluation of laser induced breakdown spectroscopy for multielemental determination in soils under sewage sludge application

Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique for simple, direct and clean analysis, with great application potential in environmental sustainability studies. In a single LIBS spectrum it is possible to obtain qualitative information on the sample composition. However, quantitative analysis requires a reliable model for analytical calibration. Multilayer perceptron (MLP), an artificial neural network, is a multivariate technique that is capable of learning to recognize features from examples. Therefore MLP can be used as a calibration model for analytical determinations. Accordingly, the present study proposes to evaluate the traditional linear fit and MLP models for LIBS calibration, in order to attain a quantitative multielemental method for contaminant determination in soil under sewage sludge application. Two sets of samples, both composed of two kinds of soils were used for calibration and validation, respectively. The analyte concentrations in these samples, used as reference, were determined by a reference analytical method using inductively coupled plasma optical emission spectrometry (ICP OES). The LIBS-MLP was compared to a LIBS-linear fit method. The values determined by LIBS-MLP showed lower prediction errors, correlation above 98% with values determined by ICP OES, higher accuracy and precision, lower limits of detection and great application potential in the analysis of different kinds of soils.     

Determination of the enantiomers of 3-tert.-butylamino-1,2-propanediol by high-performance liquid chromatography coupled to evaporative light scattering detection

A method for the separation and quantitation of the enantiomers of 3-tert.-butylamino-1,2-propanediol by high-performance liquid chromatography and evaporative light scattering detection has been developed. Separation of the enantiomers was performed in normal-phase liquid chromatography on a Chiralpak AS chiral stationary phase. The influence of the gas nature, gas pressure and temperature of the drift tube of the evaporative light scattering detector on the detection sensitivity was investigated. The method was validated in terms of linearity, limit of quantitation, accuracy and precision. The enantiomeric excess of (S)-3-tert.-butylamino-1,2-propanediol, used for the industrial synthesis of (S)-timolol, was measured from 0 to 94%.     

基于径向基神经网络和遗传算法的槽内式选择性超声电合成甲基苯甲醛

以混合二甲苯为原料, Mn(Ⅲ)为氧化剂, 硫酸溶液为电解质, 采用槽内式超声电合成甲基苯甲醛. 探讨了选择性电合成甲基苯甲醛的可能性, 通过径向基(RBF)神经网络和遗传算法(GA)对选择性电合成甲基苯甲醛3种异构体的比例、 电流效率与混合二甲苯的用量、 硫酸浓度和电流强度的关系建立预测模型, 并运用GA确定模型中RBF神经网络的目标均方误差(Goal)和径向基函数的分布(Spread). 然后根据预测模型, 使用GA对电合成条件进行优化, 分别获得了电合成产物中对位甲基苯甲醛占优、 邻位和对位甲基苯甲醛占优以及电流效率最高时的电合成条件. 当采用上述条件进行实验时, 模型给出的预测结果分别为: 对位甲基苯甲醛占优的质量分数可达90.01%, 邻位和对位甲基苯甲醛占优的质量分数为80.38%, 电流效率达到最高时的邻位、 间位和对位甲基苯甲醛的质量分数分别为16.80%, 8.43%和74.77%; 而与之相对应的实际实验结果分别为90.10%和79.91%, 以及17.20%, 8.49%和74.31%, 二者之间的最大相对误差小于±2.24%, 表明所建立模型的预测值与实测值基本吻合.     

A machine learning approach to select features important to stroke prognosis

Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-term disability in the world. Selection of features related to stroke prognosis is highly valuable for effective intervention and treatment. In this study, an integrated machine learning approach was used to select the features as prognosis factors of stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importance of features was ranked by the Shapiro-Wilk algorithm and the Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features. Furthermore, the importance of selected features was determined by Random-Forest-Classifier and Shapiro-Wilk algorithm. Finally, twenty-three selected features were used by SVC, MLP, Random-Forest, and AdaBoost-Classifier to predict the RVISINF (Infarct visible on CT) of acute stroke on IST dataset. It was suggested that the selected features could be used to infer the long-term prognosis of acute stroke at a high accuracy, and it also could be used to extract factors related to RVISINF, which is associated with large artery occlusion (LAO) in ischemic stroke patient.     

穿山甲色谱指纹谱的建立及其在基源鉴别和质量等级区分方面的应用

中药穿山甲为鳞鲤科动物穿山甲的鳞甲,常用于治疗乳汁不通、痈肿疮毒、风湿痹痛等症,饮片来源上经常存在基源不清、等级不明的问题。针对以上问题,目前已发展出了多种分析方法,包括薄层色谱、质谱和DNA检测,但由于特异性低、设备成本高、步骤繁琐等不足,以上方法均不适合作为穿山甲质量监测的常规手段,而色谱指纹谱技术恰好可以弥补以上不足。该文旨在建立穿山甲的色谱指纹谱,并探讨其在等级及基源鉴别方面的可行性。穿山甲粉末经1 mol/L HCl加热水解得供试液,色谱柱采用Waters Symmetry 300 C18,以0.1%(v/v)三氟乙酸/水、0.1%(v/v)三氟乙酸/乙腈为流动相,280 nm为检测波长,采用梯度洗脱获得穿山甲指纹谱。经考察,该方法的精密度、日内日间重复性及样品稳定性均表现良好(RSD<5%)。以12批中华穿山甲一等品的平均指纹谱为对照指纹谱,同时确认17个共有峰,继而以共有峰绝对峰面积为原始数据,计算样品指纹谱与对照指纹谱的相似度。结果表明,中华穿山甲与其他动物来源穿山甲的相似度不高于0.776,体现了相似度评价在基源鉴别上的有效性;但不同等级穿山甲之间交叉严重,表明相似度评价在等级鉴别上的局限性。以24批不同等级中华穿山甲为分析对象建立判别分析模型,经十折交叉验证,模型无偏差正确率为95.83%,说明该模型在等级区分上可行性很高。该文通过建立穿山甲的色谱指纹谱,并结合不同数据处理方法验证了其在基源鉴别和等级区分方面的可行性,为保证穿山甲临床用药的科学性、准确性提供新的思路。     

Estimating UV erythemal irradiance by means of neural networks

In recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000-2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of -0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.     

CSSP2: an improved method for predicting contact-dependent secondary structure propensity

The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i ± 4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i ± 4) and >(i, i ± 4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information.     

Knowledge acquisition and development of accurate rules for predicting protein stability changes

Knowing the mechanisms by which protein stability change is one of the most important and valuable tasks in molecular biology. The conventional methods of predicting protein stability changes mainly focus on improving prediction accuracy. However, it is desirable to extract domain knowledge from large databases that is beneficial to accurate prediction of the protein stability change. This paper presents an interpretable prediction tree method (named iPTREE) that produces explanatory rules to explore hidden knowledge accompanied with high prediction accuracy and consequently analyzes the factors influencing the protein stability changes. To evaluate iPTREE and the knowledge upon protein stability changes, a thermodynamic dataset consisting of 1615 mutants led by single point mutation from ProTherm is adopted. Being as a predictor for protein stability changes, the rule-based approach can achieve a prediction accuracy of 87%, which is better than other methods based on artificial neural networks (ANN) and support vector machines (SVM). Besides, these methods lack the ability in biological knowledge discovery. The human-interpretable rules produced by iPTREE reveal that temperature is a factor of concern in predicting protein stability changes. For example, one of interpretable rules with high support is as follows: if the introduced residue type is Alanine and temperature is between 4 °C and 40 °C, then the stability change will be negative (destabilizing). The present study demonstrates that iPTREE can easily be used in the application of protein stability changes where one requires more understandable knowledge.     

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

香水百合香气成分的气相色谱保留指数三维定量构效关系研究

采用比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）方法,研究了香水百合中38种香气成分分子结构与气相色谱保留指数值之间的定量构效关系。用外部测试集验证法和留一交叉验证法对模型的稳健性和预测能力进行了检验,并通过CoMSIA模型和CoMFA模型的分子场三维等势图研究了这些化合物分子中不同化学结构对保留指数值的影响。检验结果表明,所建立的CoMSIA模型和CoMFA模型都具有较好的稳健性和预测能力,且能够合理解释结构对保留指数值的影响,可应用于对香水百合香气成分的色谱保留指数值的预测。与CoMFA模型相比,CoMSIA模型的预测准确度更高,在香水百合香气成分的色谱定量构效关系研究中,显然有更好的应用前景。     

基于支持向量机的药物诱导磷脂质病预测模型

本文应用一种组合遗传算法和共轭梯度法的支持向量机(GA-CG-SVM)方法建立了药物诱导磷脂质病分类预测模型.首先对描述符进行了优化,选出了19个描述符用于模型的构建,所建模型对训练集的预测准确率为81.6%,对测试集的预测精度为87.5%,说明所建SVM分类模型不仅能正确预测训练集药物诱导的磷脂质病,也对其他化合物具...     

A new modification of the UNIQUAC equation including temperature dependent parameters

The UNIQUAC equation was modified by introduction of a linear temperature dependence of the volume and surface area parameters, r_i and q_i. The slope of r_i and q_i functions were found to be the same for hydrocarbons and pyridine. The modified equation was used for prediction of vapor–liquid equilibria (VLE) in binary mixtures of hydrocarbons and pyridine with hydrocarbons as well as for the prediction of the excess enthalpy (H^E) in binary mixtures formed by pyridine with aliphatic alkanes. The results obtained were compared with predictions by UNIFAC and further with UNIQUAC equation and its modification involving temperature dependant coordination number z. The proposed temperature dependence of the r_i and q_i parameters enables prediction of the VLE at various temperatures and leads to reasonable values of H^E. The necessary input reduces to one set of isothermal VLE data. One set of UNIQUAC interaction parameters u_ij is sufficient for representation of VLE in a wide range of temperature and to obtain a reasonable prediction of H^E.