激活企业的“神经”系统——杭州家友超市信息系统管理有感

目前,国内的超市采用POS-MIS系统进行管理已经比较普遍了,各连锁超市公司都纷纷花巨资建立或改造自己的信息系统,希望通过信息系统的优势在竞争中立于不败之地.然而,我们发现有一个现象,同样使用信息系统软件,有的用户运用起来得心应手,而有的用户使用起来束手束脚,大叹物非所值,这究竟是什么原因呢?杭州家友超市从1998年至今,一直使用上海一家软件公司开发的商业自动化软件,在用的过程中,深深体会到信息系统水平的提高,除了要依靠一套科学合理的商业软件外,关键还在于企业如何结合自身的特点,运用信息系统加强企业的内部管理.

Analgesic effects of receptin, a chemically modified cobratoxin from Thai-land cobra venom

Objective To investigate the analgesia induced by receptin （REC）, a chemically modified cobratoxin （CTX, a long-chain postsynaptic α-neurotoxin from Thailand cobra venom）, and the effects of atropine and naloxone on antinociceptive activity of REC in rodent pain models. Methods REC was administered intraperitoneally （5 mg/kg, 7.07 mg/kg, or 10 mg/kg, i.p.） or intra-cerebral venticularly （62.5 μg/kg, i.c.v.）. The antinociceptive action was determined using the hotlate test, the acetic acid writhing test and tail flick assay in mice and rats. The involvement of cholinergic and the opioid peptidergic systems in REC-induced analgesia were examined by pretreatment of animals with atropine （Atr; 0.5 mg/kg, i.m. or 10 mg/kg, i.p.） or naloxone （Nal; 3 mg/kg, i.p.）. The effect of REC on motor activity was tested using the Animex test in mice. Results REC （5 mg/kg, 7.07 mg/kg or 10 mg/kg, i.p.） exhibited a dose-dependent analgesic action in mice as determined with hot-plate test and acetic acid writhing test. The significant analgesia of REC was seen 2 h to 3 h after its administration. In the rat-tail flick assay, the administration of REC at 62.5 μg/kg （1/160 of systemic dose; i.c.v.）produced marked analgesic effects. Atropine at 0.5 mg/kg （i.m.）, 10 mg/kg （i.p.） or naloxone at 3 mg/kg （i.p.） failed to block the analgesic effects of REC. REC at the highest effective dose of 10 mg/kg did not change the spontaneous mobility of mice. Conclusion These results demonstrate that REC has analgesic effect. This activity appears to be mediated through the peripheral nervous system though central nervous system may contribute to REC＇s analgesic effects. The central cholinergic system and opioid peptidergic system appear not to be involved in the antinociceptive action of REC.

Novel technique for craniospinal radiotherapy with patient supine

A simple procedure to plan, verify and implement craniospinal irradiation（CSI） with patients supine is presented. Treatment is conducted with a single posterior spinal field abutting two lateral cranial fields. The opposed lateral fields are half-blocked and the inferior line is perpendicular. The posterior field uses some fixed field parameters so that the cephalad edge of the posterior field is coplanar with the caudal edges of the lateral fields and it is independent of the height of the couch. A steel-shot ball is used to measure the size of overlap or gap at the junction using portal images of an electron portal image device or portal films. The results of analyzing the portal images show that the errors of the junction are within ± 1 mm. The dose-volume histograms （DVHs）show that there are not unbearable hot or cold spots in the clinic target volumes （CTVs）. Supine craniospinal treatment is a reliable and convenient alternative to treatment in the prone position and avoids the technical difficulties of the latter. The use of fixed field geometry greatly facilitates treatment planning and effectively reduces the amount of time of setup, verification and treatment.

Regulation of axonal regeneration following the central nervous system injury in adult mammalian

It has been well established that the recovery ability of central nervous system （CNS） is very poor in adult mammals. As a result, CNS trauma generally leads to severe and persistent functional deficits. Thus, the investigation in this field becomes a ＂hot spot＂. Up to date, accumulating evidence supports the hypothesis that the failure of CNS neurons to regenerate is not due to their intrinsic inability to grow new axons, but due to their growth state and due to lack of a permissive growth environment. Therefore, any successful approaches to facilitate the regeneration of injured CNS axons will likely include multiple steps： keeping neurons alive in a certain growth-state, preventing the formation of a glial scar, overcoming inhibitory molecules present in the myelin debris, and giving direction to the growing axons. This brief review focused on the recent progress in the neuron regeneration of CNS in adult mammals.

Roles of NF-κB in central nervous system damage and repair

NF-κB family is a kind of nuclear factors in B lymphocyte that can bind to the immunoglobulin κ-chain enhancer and enhance transcriptional activity. NF-κB/Rel proteins, as a dimeric transcription factor, control the expression of genes that regulate a broad range of biological processes through canonical and non-canonical pathways. In the central nervous system, NF-κB controls inflammatory reactions and the apoptotic cell death following nerve injury. It also contributes to the infarction and cell death in stroke models and patients. However, NF-κB is essential for neurosurvival as well. NF-κB activation is a part of recovery process that may protect neurons against oxidative-stresses or brain ischemia-induced apoptosis and neurodegeneration. Inhibition of NF-κB may reduce its neuroprotection activity. Hence the dual opposite effects of NF-κB on cells. The ultimate survival or death of neurons depends on which, where and when the NF-κB factors are activated.

Molecular actions guiding neural regeneration in planarian

Planarian is among the simplest animals that possess a centralized nervous system （CNS）, and its neural regenera- tion involves the replacement of cells lost to normal ＇wear and tear＇ （cell turnover）, and/or injury. In this review, we state and discuss the recent studies on molecular control of neural regeneration in planarians. The spatial and temporal expression patterns of genes in intact and regenerating planarian CNS have already been described relatively clearly. The bone morphogenetic protein （BMP） and Wnt signaling pathways are identified to regulate neural regeneration. During neural regeneration, conserved axon guidance mechanisms are necessary for proper wiring of the nervous system. In addition, apoptosis may play an important role in controlling cell numbers, eliminating unnecessary tissues or cells and remodeling the old tissues for regenerating CNS. The bilateral symmetry is established by determination of anterior-posterior （A-P） and dorsal-ventral （D-V） patterns. Moreover, neurons positive to dopamine, serotonin （5-HT）, and gamma-aminobutyric acid （GABA） have been detected in planarians. Therefore, planarians present us with new, experimentally accessible contexts to study the molecular actions guiding neural regeneration.

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

Prediction Model of Dissolved Oxygen Fuzzy System in Aquaculture Pond Based on Neural Network

A dissolved oxygen fuzzy system predicting model based on neural network was put forward in this study. 106 groups of data were used to confirm the fitness of the predicting model. The first 80 groups of data were acted as training input and the other 26 groups of data were acted as the confirmed data in the system. The result showed that the testing data was approximately the same as the predicted data. So it gave a new way to solve the problem that the status of the water quality couldn＇t be predicted in time and it＇s hard to watching and measuring the factors dynamic.

NEURAL NETWORK SMITH PREDICTIVE CONTROL FOR TELEROBOTS WITH TIME DELAY

A neural network Smith predictive control strategy is proposed to deal with inpu t and feedback time delays in telerobot systems. The delay time is assumed to b e invariant and unknown. The proposed control structure consists of a slave syst em and a master controller. In the slave system, a recurrent neural network (RNN ) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant, which is used to linearize the slave s ystem. The master controller is a Smith predictor for the linearized slave syste m, which provides prediction and maintains the desirable tracking performance. S tability propriety is guaranteed based on the Lyapunov method. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of th e proposed control strategy.

In- and Anti-transition of Firing Patterns Induced by Random Long-range Connections in Coupled Hindmarsh-Rose Neurons System

The effects of random long-range connections （shortcuts） on the transitions of neural firing patterns in coupled Hindmarsh-Rose neurons are investigated, where each neuron is subjected to an external current. It is found that, on one hand, the system can achieve the transition of neural firing patterns from the fewer-period state to the multi-period one, when the number of the added shortcuts in the neural network is greater than a threshold value, indicating the occurrence of in-transition of neural firing patterns. On the other hand, for a stronger coupling strength, we can also find the similar but reverse results by adding some proper random connections. In addition, the influences of system size and coupling strength on such transition behavior, as well as the internality between the transition degree of firing patterns and its critical characteristics for different external stimulation current, are also discussed.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

FAULT-TOLERANT INTEGRATED NAVIGATION SYSTEM BASED ON NEURONS

In this paper, the multisensor data fusion technique of a fault tolerant integrated navigation system is discussed. A neural approach for data fusion is proposed for multisensor integrated systems. The simulation results show that this neural approach for data fusion is feasible.

Modeling of temperature-humidity for wood drying based on time-delay neural network

The temperature-humidity models of wood drying were developed based on Time-delay neural network and the identification structures of Time-delay neural network were given. The controlling model and the schedule model, which revealed the relation between controlling signal and temperature-humidity and the relation between wood moisture content and temperature-humidity of wood drying, were separately presented. The models were simulated by using the measured data of the experimental drying kiln. The numerical simulation results showed that the modeling method was feasible, and the models were effective.

Clinical Studies on Primary Central Nervous System Lymphoma:A Report of 31 Cases

OBJECTIVE To analyze the clinical manifestations, neuroimaging and pathological characteristics of primary central nervous system lymphoma （PCNSL） with a normal immunity, and to explore the methods of treatment and diagnosis. METHODS The clinical, laboratory, imaging data and pathological findings and therapeutic efficacy of 31 cases with pathologically proved PCNSL, during a period from July 1995 to June 2006, were analyzed retrospectively. The method of surgery, used in combination with chemotherapy and radiotherapy, was evaluated in 18 cases versus a simple surgical procedure used in 5. Among the total cases, a CHOP regimen was employed in 11 and Teniposide （VM26） plus Semustine （me-CCUN） was used in 7 cases. RESULTS PCNSL had a variety of clinical features, so that its misdiagnosis rate was high. The main clinical findings of PCNSL included intracranial hypertension and （focal） neurologic impairment. No positive result was found in the CSF cellular examination. All of the 31 cases were B-cell lymphoma. Twenty-four of the 31 cases were followed-up, with a follow-up period from 6 to 98 months. The median period of survival of the group who underwent surgery in combination with chemotherapy and radiotherapy was 20 months, while the group with simple surgical therapy was 10 months. CONCLUSION Specific clinical manifestations were usually absent in the patients with PCNSL, giving an uncertain preoperative diagnosis and a poor prognosis. Pathological examination is the only reliable method for a final diagnosis of the disease. The main objective of surgical therapy is to relieve the intracranial hypertension caused by the tumor. Recurrence may occur in a short period following the simple operation. Therefore combined therapy, i.e. surgery plus additional radiotherapy and chemotherapy, should be adopted. This is the key point for extending survival time and improving the quality of life.

Neural pathway for fever generation

Fever is an adaptive host response coordinated by the central nervous system （CNS） during systemic immune challenge. Recent research shed light on the mechanism of fever generation, particularly the underlying neural pathways. In this review, we first briefly summarize current views on the mechanism of sensing microbial infection by the nervous system, and the roles of prostaglandin E2 （PGE2） and its receptors in fever; then we focus on the neural circuits underlying fever generation, particularly their relationship with the distribution of PGE2 receptors within the CNS. At the end, an overall neurochemical model of fever generation is presented, pointing to the direction for future studies.

Design of hydraulic motor speed control system based on co-simulation of AMESim and Matlab_Simulink

In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.

A Single Neuron with Stochastic Resonance for Noisy Square Pulse Train Signal Transmission

A nonlinear single neuron is demonstrated to exhibit stochastic resonance by theoretical analysis and numerical simulations. This single neuron is used for noisy periodic signal transmission, and significant performance of raising input output SNR gain can be achieved. The research of this paper not only gives a very simple model of neuron with stochastic resonance, but also enlarges the application scope of neuron to the transmission of periodic signals.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

APPLICATION OF ASSOCIATIVE MEMORY NEURAL NETWORK IN HIGH VOLTAGE TRANSMISSIONLINE FAULT DIAGNOSIS

Effective methods of enhancing the fault-tolerance property are proposed for two kinds of associative memory (AM) neural network (NN) used in high voltage transmission line fault diagnosis. For feedforward NN (FNN),the conception of 'fake attaction region' is presented to expand the attraction region artificially,and for the feedback Hopfield bidirectional AM NN (BAM-NN),the measure to add redundant neurons is taken to enhance NN's memory capacity and fault-tolerance property. Study results show that the NNs built not only can complete fault diagnosis correctly but also have fairly high fault-tolerance ability for disturbed input information sequence. Moreover FNN is a more convenient and effective method of solving the problem of power system fault diagnosis.

A prediction comparison between univariate and multivariate chaotic time series

The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.