Use of multivariate analysis for the improvement in prediction accuracy of bacterial aerobic plate count by flow cytometry

Flow cytometry (FCM) and aerobic plate count (APC) by the culture method were performed on green tea samples spiked with Escherichia coli type strain NCTC9001 (ATCC11775) solutions of different concentrations. In FCM, fluorescence signals from multiple stained bacteria and other fluorophores are detected using detector channels, and recorded as events with a voltage at each channel. FCM data were analyzed in two ways: conventional and multivariate analysis. In the former, the number of events with voltages larger than the defined threshold values was regarded as the predicted APC. In the latter, voltage histograms of all channels were obtained and merged horizontally to serve as explanatory variables. Then a partial least squares regression (PLSR) model was built to predict APC from the histogram data. The coefficient of determination (R²) and the root mean square error (RMSE) between APC by the culture method and that predicted by conventional FCM were 0.916 and 1.08 cfu/ml². The APC values predicted by the PLSR model and those measured were in good agreement with R² of 0.982 and RMSE of 0.417 cfu/ml, which verified the potential of the proposed method for improving APC prediction accuracy by FCM.     

EEM fluorescence spectroscopy as a fast method to assess the brine composition of salted herring

Ripening of barrel-salted herring (Clupea harengus) is evaluated by the use of fluorescence spectroscopy and protein determinations. During ripening, protein degradation takes place in the herring and protein is extracted into the brine. The present study aims at identifying parameters which are correlated to the ripening characteristics of barrel-salted herring and which can provide a better understanding of the ripening process. Front face fluorescence landscapes were obtained by measuring directly on the brine from barrel-salted herring. These data were analyzed by parallel factor analysis (PARAFAC), which revealed four fluorophores, tryptophan (two states), vitamin B6 and riboflavin. All four parameters showed an increase in concentration during the storage period corresponding to an increase in protein content that varied from 3 g/100 g at day 60 to 5 g/100 g after 277 days of storage. It was not possible to see a difference in the development of the four fluorophores during the ripening period. The protein content was predicted from the fluorescence landscapes by partial least squares (PLS). The use of unfolded fluorescence spectra gave an RMSECV of 0.26 g/100 g and a correlation between the measured protein content and the predicted values of 0.86.     

Community structure detection in social networks based on dictionary learning

Discovering community structures is a fundamental problem concerning how to understand the topology and the functions of complex network. In this paper, we propose how to apply dictionary learning algorithm to community structure detection. We present a new dictionary learning algorithm and systematically compare it with other state-of-the-art models/algorithms. The results show that the proposed algorithm is highly effectively at finding the community structures in both synthetic datasets, including three types of data structures, and real world networks coming from different areas.     

A sparse version of the ridge logistic regression for large-scale text categorization

The ridge logistic regression has successfully been used in text categorization problems and it has been shown to reach the same performance as the Support Vector Machine but with the main advantage of computing a probability value rather than a score. However, the dense solution of the ridge makes its use unpractical for large scale categorization. On the other side, LASSO regularization is able to produce sparse solutions but its performance is dominated by the ridge when the number of features is larger than the number of observations and/or when the features are highly correlated. In this paper, we propose a new model selection method which tries to approach the ridge solution by a sparse solution. The method first computes the ridge solution and then performs feature selection. The experimental evaluations show that our method gives a solution which is a good trade-off between the ridge and LASSO solutions.     

中国煤炭资源生产重心变迁趋势分析：基于煤炭资源综合评价体系

中国煤炭资源生产重心趋势判断不能仅仅依据资源潜力，还需考虑资源开发利用经济成本、社会成本、环境效益等多方面的约束，判断依据实质上与煤炭资源综合评价一致,因此，采用综合评价体系帮助判断中国煤炭资源生产重心发展趋势。在建立中国煤炭资源综合评价体系后，对中国煤炭资源进行综合评价，并对评价结果建立回归模型。回归模型分析表明，中国煤炭资源生产重心西移趋势将会进一步加强，甘肃、宁夏将在中短期内成为西移重点，在中长期内，新疆地位将会凸显，生产重心西移格局将更加明显。     

SAR测雨反演算法MREA的研究

介绍了利用X-SAR的归一化雷达散射截面(NRCS)来反演地表降雨量的算法:改进型回归经验算法(MREA),依据测雨反演算法的基本原理,重点分析了MREA算法的理论依据,对于矩形降雨分布在地表降雨量R=50 mm/h和R=100 mm/h的条件下用MREA算法进行反演,指出了NRCS随x轴坐标不断变化这一导致MREA算...     

2-巯基乙醇Antoine常数计算

2-巯基乙醇属于热敏性物质。实测了2-巯基乙醇蒸汽压数据,在此基础上计算出了2-巯基乙醇临Antoine常数,估算出了2-巯基乙醇的常压沸点。此数据为2-巯基乙醇生产工程设计提供物性基础数据。     

用回归方程计算的磁性铁（MFe）分析老钻孔在利用中的可靠性

通过对用回归方程计算的磁性铁(MFe)的验证情况。说明对于矿山地质工作．在老钻孔的利用上．用回归方程计算的结果可靠性低。不能用于直接指导生产，至于今后如何利用矿山现有的老钻孔资料，应重新考虑新的方法。     

Total and organic mercury concentrations in the white muscles of swordfish (Xiphias gladius) from the Indian and Atlantic oceans

A total of 226 swordfish samples collected from Taiwanese fishing vessels in the Indian and Atlantic oceans were examined for total mercury (THg) and organic Hg (OHg). Analysis of 56 pooled white muscle samples showed that THg and OHg concentrations ranged from 0.056 to 3.97 (1.3 ± 0.97) and from 0.043 to 3.92 (1.01 ± 0.82) µg g^-1 flesh mass, respectively. These values were similar to those from various previous studies during the past three decades. THg and OHg were significantly linearly correlated with fork length (FL, cm) of the fish from Indian and Atlantic oceans; however, there was no significant OHg%-FL relationship. OHg and THg also were significantly correlated. Fishes with FL ≤ 140 cm met the methyl Hg (meHg) regulatory standard set by the European Commission Decision (meHg ≤ 1.0); and fish with FL ≤ 211 cm met the Taiwanese Food and Hygiene Standard (meHg ≤ 2.0). Weekly swordfish consumption rates and amounts are recommended accordingly.     

Additive Composition of Supervised Self-Organizing Maps

The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined by prior knowledge of the problem that permits to split the processing into tasks of small dimensionality. The sub-tasks can be implemented with neural networks, although the learning examples cannot be used anymore to supervise directly each of the networks. This article addresses the problem of learning in a modular context, developing in particular additive compositions. A simple rule allows defining efficient training, and combining, for example, several Supervised-SOM networks. This technique is important because it introduces interesting generalizations in many modular compositions, permitting data fusion or sequential combinations of neural networks. This revised version was published online in August 2006 with corrections to the Cover Date.