中控科技神指安全系统

北京中控科技公司推出神指安全系统软件包.包括服务器软件包和客户端软件包,由指纹系统登录、指纹网络登录、应用程序登录、文件加密/解密等功能组件组成。具有指纹验证、简化支持成本、加强安全性、易于实现等特点。 支持的操作系统:Windows XP、Windows 2000.Win-dows Me、Windows 98。

INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION

Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.

Research on Thermodynamic Properties of Polybrominated Diphenylamine by Neural Network

Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.

Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis

Prediction of melt index （MI）, the most important parameter in determining the product＇s grade and quality control of polypropylene produced in practical industrial processes, is studied. A novel soft-sensor model with principal component analysis （PCA）, radial basis function （RBF） networks, and multi-scale analysis （MSA） is proposed to infer the MI of manufactured products from real process variables, where PCA is carried out to select the most relevant process features and to eliminate the correlations of the input variables, MSA is introduced to a^quire much more information and to reduce the uncertainty of the system, and RBF networks are used to characterize the nonlinearity of the process. The research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in propylene polymerization processes.

A new simple screening method for the detection of paralytic shellfish poisoning toxins

The current testing for paralytic shellfish poisoning(PSP) in shellfish is based on the mouse bioassay(MBA).To alleviate animal welfare concerns,we evaluated the utility of using sublethal indicators of toxicity as an alternative to measuring time to death.Live mice were injected with a PSP congener and the changes in neurotransmitter levels were measured 60,90,and 120 min after injection.Acetylcholine(ACh) was the most sensitive marker for PSP toxicity.The changes in neurotransmitter levels were most pronounced in the blood.Thus,measurement of Ach levels in the blood may serve as a sensitive predictor for PSP that would not require sacrifice of the mice.This method was relatively simple,sensitive(less than 1 μg/kg weight,equivalent to 20 ng/mL),low maintenance,and rapid(less than 60 min).

Absolute Exponential Stability of Generalized Dynamical Neural Networks

This paper investigates the absolute exponential stability of generalized neural networks with a general class of partially Lipschitz continuous and monotone increasing activation functions. The main obtained result is that if the interconnection matrix T of the neural system satisfies that - T is an H matrix with nonnegative diagonal elements, then the neural system is absolutely exponentially stable(AEST). The Hopfield network, Cellular neural network and Bidirectional associative memory network are special cases of the network model considered in this paper. So this work gives some improvements to the previous ones.

Quantitative ultrastucture analysis of neuroendocrine cells of gastric mucosa on normal and pathological conditions

AIMS To study the quantitative ultrastucture of neu- roendocrine cells of gastric mucosa on normal anc pathological conditions including the duodenal ulcer (DU) and Zollinger-Ellison syndrome (ZES). METHODS The neuroendocrine cells of the gastric mucosa of eight normal subjects,six patients with DU and five patients with ZES were quantitatively investi- gated with electro microscope and ultrastructure image analyzer. RESULTS The volume density of neuroendocrine cells in DU was 1.3% and 0.8% (vs 1.6% and 0.9%,P>0.05) in gastric antrum and corpus respectively. In antrum,G cells was of 65% (P< 0.05),D cells decreased in cell density (3% vs 9.5%) and in number of cell per unit area (P<0.01). In corpus,the cell density of ECL cells increased (49% vs 30%,P<0.05);D cells and EC cells decreasec (2% P<0.01 and 4% P<0.05,respectively),and the number of D cell per unit area markedly decreased. In ZES,D cells in corpus decreased in cell density (4% vs 22%,P<0.01) and P cells also decreased (11% vs 24%,P<0.05). The density of ECL cells increased (65% vs 30%,P<0.01). CONCLUSIONS In DU and ZES,both the number and type of NE cells present some changes. Incresed gastrin in DU and ZES patients may be caused by the decrease of D cells and somatostatin secretion.

Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks

The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.

Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model,which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator,and covers several well-known neural networks,such as Hopfield neural networks,cellular neural networks(CNNs),bidirectional associative memory(BAM)networks,recurrent multilayer perceptrons(RMLPs).By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality(LMI)technique,some exponential synchronization criteria are derived.Using the drive-response concept,hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria.Finally,detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.

Science Letters:Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade Ⅰ-Ⅳ) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma Ⅰ-Ⅳ), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade Ⅰ-Ⅱ with grade Ⅲ-Ⅳ ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade Ⅰ-Ⅱ Astrocytoma was 86.7%, accuracy ofⅢ-Ⅳ Astrocytoma was 84.6% were obtained when grade Ⅰ-Ⅱ Astrocytoma was compared with grade Ⅲ-Ⅳ ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and glioma from brain benign tumors.

Global exponential periodicity of a class of impulsive neural networks

By the Lyapunov function method, combined with the inequality techniques, some criteria are established to ensure the existence, uniqueness and global exponential stability of the periodic solution for a class of impulsive neural networks. The results obtained only require the activation functions to be globally Lipschitz continuous without assuming their boundedness, monotonicity or differentiability. The conditions are easy to check in practice and they can be applied to design globally exponentially periodic impulsive neural networks.

Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network

Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification.

Application of a Neural Network Technique for Prediction of the Water Quality Index in the Dong Nai River, Vietnam

Recent trends in environmental management of water resource have enlarged the demand for predicting techniques that can provide reliable, efficient and accurate water quality. In this case study, the authors applied the Artificial Neural Networks （ANN） to estimate the water quality index on the Dong Nai River flowing through Dong Nai and Binh Duong provinces. The information and data including 10 water quality parameters of the Dong Nai River at 23 monitoring stations were collected during the recorded time period from 2010 to 2014 to build water quality prediction models. The results of the study demonstrated that the Water Quality Index （WQI） forecasted with GRNN was very significant and had high correlation coefficient （R2 = 0.974 and p = 0.0） compared to the real values of the WQI. Moreover, the ANN models provided better predicted values than the multiple regression models did.

Two synchronous somatostatinomas of the duodenum and pancreatic head in one patient

Somatostatinomas are extremely rare neuroendocrine tumors of the gastrointestinal tract,f irst described in the pancreas in 1977 and in the duodenum in 1979.They may be functional and cause somatostatinoma or inhibi-tory syndrome,but more frequently are non-functioning pancreatic endocrine tumors that produce somatostatin alone.They are usually single,malignant,large lesions,frequently associated with metastases,and generally with poor prognosis.We present the unique case of a 57-year-old woman with two synchronous non-function-ing somatostatinomas,one solid duodenal lesion and one cystic lesion within the head of the pancreas,that were successfully resected with a pylorus-preserving Whipple's procedure.No secondaries were found in the liver,or in any of the removed regional lymph nodes.The patient had an uneventful recovery,and remains well and symptom-free at 18 mo postoperatively.This is an extremely rare case of a patient with two synchro-nous somatostatinomas of the duodenum and the pancreas.The condition is discussed with reference to the literature.

Surgical anatomy of the innervation of pylorus in human and Suncus murinus, in relation to surgical technique for pylorus-preserving pancreaticoduodenectomy

AIM： To clarify the innervation of the antro-pyloric region in humans from a clinico-anatomical perspective. METHODS： The stomach, duodenum and surrounding structures were dissected in 10 cadavers, and immersed in a 10mg/L solution of alizarin red S in ethanol to stain the peripheral nerves. The distribution details were studied to confirm innervations in the above areas using a binocular microscope. Similarly, innervations in 10 Suncus murinus were examined using the method of whole-mount imm unohistochemistry. RESULTS： The innervation of the pyloric region in humans involved three routes： One arose from the anterior hepatic plexus via the route of the suprapyloric/supraduodenal branch of the right gastric artery; the second arose from the anterior and posterior gastric divisions, and the third originated from the posterior-lower region of the pyloric region, which passed via the infrapyloric artery or retroduodenal branches and was related to the gastroduodenal artery and right gastroepiploic artery. For Suncus murinus, results similar to those in humans were observed. CONCLUSION： There are three routes of innervation of the pyloric region in humans, wherein the route of the right gastric artery is most important for preserving pyloric region innervation. Function will be preserved by more than 80% by preserving the artery in pylorus-preserving pancreaticoduodenectomy （PPPD）. However, the route of the infrapyloric artery should not be disregarded. This route is related to several arteries （the right gastroepiploic and gastroduodenal arteries）, and the preserving of these arteries is advantageous for preserving pyloric innervation in PPPD. Concurrently, the nerves of Latarjet also play an important role in maintaining innervation of the antro-pyloric region in PPPD. This is why pyloric function is not damaged in some patients when the right gastric artery is dissected or damaged in PPPD.

Microfabrication,surface modification,and laser guidance techniques to create a neuron biochip

In this report we illustrate our application of soft lithography-based microfabrication,surface modification,and our unique laser cell-patterning system toward the creation of neuron biochips. We deposited individual forebrain neurons from Day 7 embryonic chicks into two rows of eight in a silicon microstructure aligned over a microelectrode array (MEA). The polydimethylsiloxane (PDMS) membrane with microstructures to confine cells and guide network connectivity was aligned to the electrodes of a MEA. Both the MEA and the PDMS membrane were treated with O2 plasma,Poly-L-Lysine,and Laminin to aid in cell attachment and survival. The primary advantage of our process is that it is quicker and simpler than previous cell-placement methods and may make highly defined neuronal network biochips more practical.

Constitution Identification System for Traditional Chinese Medicine (TCM) Based on Correlation Between TCM Constitution and Physical Examination Indices

Objective Identification of one’s constitution based on a combination of features and back propagation neural network theory is needed in modern medicine and traditional Chinese medicine(TCM).We describe a method to identify one’s constitution based on TCM constitution classification and a physical index model.Methods We created a constitution identification system based on neural network using Visio Studio development tool.We report the initial implementation of the system,the accuracy of which was verified using actual data.Results We found a relatively strong correlation between TCM constitution and physical indicators.Conclusion Finally,our report describes a possible application of the proposed system.

Theoretical Perspective of Social Entrepreneurship： A Study of Determinants of Social Entrepreneurship in the Context of Saudi Arabia

The present paper discusses the different theories on entrepreneurship in general and social entrepreneurship in particular to investigate the different determinants of social entrepreneurship in the context of Saudi Arabia. The present research is a conceptual study. The data were collected to analyze the entrepreneurship theories in Saudi Arabia＇s context. The data related to entrepreneurship spirit, perception of society towards entrepreneurship, and other macroeconomic indicators are collected from different research journals, websites of different corporate houses, and government sources to analyze the social entrepreneurship theories, such as failure thesis, interdependence theories, welfare state theory, supply-side theory, entrepreneurship education, etc.. The theoretical hypothesis of interdependence theory, entrepreneurship spirit, and general education and the theoretical analysis of this paper in light of Saudi Arabia are in line with the theory. But the hypothesis generated out of failure thesis theory, welfare state theory, entrepreneurship education, and individualistic and collective society culture is not in line with these theories.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

Effects of functional training on geometric indices of heart rate variability

Background： Geometric methods provide an analysis of autonomic modulation using the geometric properties of the resulting pattern, and represent an interesting tool in the analysis of heart rate variability （HRV）. The aim of this study was to evaluate the impact of functional training on cardiac autonomic modulation in healthy young women using the geometric indices of HRV. Methods： Data were analyzed from 29 women, and were stratified into a functional training group （FTG, n = 13; 23.00 ± 2.51 years; 21.90± 2.82 kg/m2） and a control group （CG, n = 16; 20.56 ± 1.03 years; 22.12±3.86 kg/m2）. The FTG received periodized functional training for 12 weeks. The cardiac autonomic modulation of both groups was evaluated before and after this training, and a qualitative analysis was performed using the Poincar6 plot. Results： There was a significant increase in the difference of the triangular index （RRTri）, SDI, SD2, and RR intervals in the FTG as compared to the CG, and the qualitative analysis from the Poincar6 plot showed an increase in the dispersion of beat-to-beat and long-term RR intervals in the functional group after training. No changes were observed in the triangular interpolation of RR interval histogram （TINN） or SD1/SD2. Conclusion： Functional training had a beneficial impact on autonomic modulation, as characterized by increased parasympathetic activity and overall variability, thus highlighting the clinical usefulness of this type of training.