Automated fluorimetry in quality control of pasteurized and ultra-high temperature-treated starch soup

Automated monitoring of the microbiological quality of heat-processed foods by the resazurin reduction test was applied to microtitration plate incubator-fluorimeter technology. The appearance and disappearance of the fluorescing peak of resorufin was monitored on microtitration trays. Pasteurized or ultra-high temperature-treated starch-based soup was used as the model food system. Bacillus subtilis spores (ultra-high temperature treatment) and vegetative cells of Enterococcus faecalis (pasteurization) were inoculated into the soup before the heat treatment at levels which resulted in some survival. The timing of appearance of maximum fluorescence correlated with the number of bacteria in pre-incubated samples. Automated resazurin-reduction fluorimetry was compared with conventional plating, turbidometry and microcolony count by the direct epifluorescent filter technique. The results of the resazurin test correlated well with those of all the other methods tested. Fluorimetry had the advantage that the results could be read within 1–5h and the reproducibility was superior to the other methods.     

非线性编辑中特效图文素材的应用

Cool 3D是一款特效图文制作软件，适用于动画片段、3D文字、图形等制作场合。Cool 3D中制作的特效素材应用于非线性编辑中，可以丰富素材的表现形式，提高节目编辑制作的整体质量。     

Theory and characteristics of novel variable reluctance 1X resolver

Although brushless resolvers have been used widely as angular position transducers, they are expensive due to their intricate construction, involving a rotary transformer to supply the exciting coils wound on the rotor poles with the current. It is shown theoretically in this paper that the resolver without rotary transformer or brushes can be realized by simple construction, which consists of the stator core with both 4-poles exciting windings and 2-poles output ones and the rotor core carrying no windings. In this resolver the rotor core has minimum gap at one side and a maximum gap at another side of the diameter. It is characterized by an outer surface form that makes the fluctuation part of gap permeance very in proportion to cos θ, where θ represents the angular position of a point in the air gap with respect to the origin on the rotor, the point of minimum air gap. The method determining the rotor form to embody the aforementioned gap permeance variation is also shown. It has been confirmed not only by simulation but also experiment that the 2-phase output voltages of a model designed based on the theory have sinusoidal waveforms with very small harmonic contents. Moreover, the rotor position detected by processing the output voltages through the conventional resolver/digital converter was within acceptable engineering accuracy.     

入侵检测在存储区域网中的应用

从SAN(存储区域网)的安全隐患出发,分析了SAN在应用环境中受到的三种典型威胁。结合现行的入侵检测技术给出一个可行的解决方案。     

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.     

Discourse cues to deception in the case of multiple receivers

Behavioral cues to deception are instrumental in detecting deception. As one of the primary sources of deception behavior, text has been analyzed at the level of sub-sentence or message but not the discourse of interaction. Additionally, empirical studies on cues to deception in the case of multiple receivers remain nonexistent. To fill these voids, we propose a discourse framework and six hypotheses about deception behaviors in a multi-receiver environment. The deception behaviors are operationalized by discourse features based on an analysis of real-world data. The results of statistical analysis validate the efficacy of discourse features in discriminating deceivers from truth-tellers.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

Face detection based on skin color likelihood

We propose a face detection method based on skin color likelihood via a boosting algorithm which emphasizes skin color information while deemphasizing non-skin color information. A stochastic model is adapted to compute the similarity between a color region and the skin color. Both Haar-like features and Local Binary Pattern (LBP) features are utilized to build a cascaded classifier. The boosted classifier is implemented based on skin color emphasis to localize the face region from a color image. Based on our experiments, the proposed method shows good tolerance to face pose variation and complex background with significant improvements over classical boosting-based classifiers in terms of total error rate performance.