To understand the effects of arecoline administration on the muscarinic cholinergic signaling pathway, rats were injected with arecoline, 10 mg/kg i.p., and the carbachol‐stimulated phosphoinositide breakdown in rat brain cortical slices was examined. In vivo administration of arecoline resulted in inhibition of carbachol‐stimulated phosphoinositide turnover in rat brain cortical slices. Arecoline was a partial agonist with peak effects of 30% of the maximum as obtained with carbachol. Coaddition of arecoline inhibited the carbachol‐stimulated phosphoinositide breakdown. Pretreatment of rat brain cortical slices with arecoline in vitro resulted in a dose‐dependent inhibition of carbachol‐stimulated [3H]inositol monophosphate accumulation. The inhibition occurred rapidly, with half‐maximal inhibition occurring at 15 min and maximal inhibition achieved within 60 min. The inhibition of phosphoinositide breakdown was recovered 1 h after arecoline was removed. When synaptoneurosomes were used for the ligand binding studies, arecoline pretreatment was found to have decreased the maximal ligand binding (Bmax) without inducing any marked change in binding affinity (KD). The influence could be recovered by incubating the synaptoneurosomes in the absence of arecoline for 2 h. Taken together, these data suggest that the underlying mechanism by which phosphoinositide turnover is inhibited is arecoline‐induced receptor sequestration.
In the paper, design and Implementation of cloud-dust based intelligent system is proposed. For achieving applications of intelligent system, such as records, surveillance, assessments, predictions, diagnosis, prescription, scheduling and fool-proofing checks, an architecture named Cloud-Dust is developed. The intelligent system is separated into the cloud system and the dust system. The dust system contains (1) Wireless sensors network (2) Features extraction circuits (3) Intelligent computing circuits (4) Embedded system. It can play a role as real-time preprocessor very well, just like an intelligent agent. However, the cloud system contains (1) Cloud database (2) Intelligent computing engine (3) Ubiquitous human-machine-interface. It can flexibly use computing resources and integrate information from many different dust systems. By the experiments, we can find the advantages of the cloud-dust based intelligent system. It meets the both needs of real-time and integration for intelligent systems. So it is necessary to develop the cloud-dust based system for design and implementation of the intelligent system.
In the paper, a cloud-dust based intelligent maximum power analysis system for photovoltaic is proposed. In order to resolve NP problem for photovoltaic, factors of photovoltaic are integrated to cloud-dust based intelligent maximum power analysis system for computing. This study is the development of the maximum power analysis system for photovoltaic, to improve the solar panels effects of the different region and enable them to get maximum efficiency of the power generation. The design methodology of this study includes: (1) The monitoring and control Module (2) The prediction and evaluation module (3) The performance diagnosis module (4) The maintenance prescription module. At last, we can find the advantages of the cloud-dust based intelligent maximum power analysis system for photovoltaic. It increases overall competitive performance of products, reduces cost of products and consummation rates of human resources.
This paper developes a new method for the pixel classification of an intensity image. A neural networks of non -linear analog neurons have been shown extremely effective. This problem is considered as an optimally classification of an image based on their original activation. Optimization is defined in terms of energy which is a function of neurons the output values which vary continuously. The neurons are modelled as amplifiers which have sigmoid monotonic input -output relations. A synapse between two neurons is defined by a conductance which connects the output of neuron to the input of another neuron. The net input current to any neuron is the sum of the currents flowing through the set of resistors connecting its input to the outputs of the other neurons. We have formulate the problems in terms of desired optima, subject to certain constraints. ABSTRACTThis paper developes a new method for the pixel classification of an intensity image. A neural networks of non linear analog neurons have been shown extremely effective. This problem is considered as an optimally classification of an image based on their original activation. Optimization is defined in terms of energy which is a function of neurons the output values which vary continuously. The neurons are modelled as amplifiers which have sigmoid monotonic input output relations. A synapse between two neurons is defined by a conductance which connects the output of neuron to the input of another neuron. The net input current to any neuron is the sum of the currents flowing through the set of resistors connecting its input to the outputs of the other neurons. We have formulate the problems in terms of desired optima, subject to certain constraints. 1J J where R is a constant, I. is the external input current and V. = g(u.) is the sigmoid monotonic transfer function.
The main purpose of this research is applying an intelligent strategy to improve the customer satisfaction prediction system of airlines service. Historically, the measurement of customer satisfaction collects by questionnaire. We collect and computing the customer satisfaction of each flight by the cloud computing based prediction system. It compares the customer questionnaire, then continuous modifying the accuracy of prediction system by neural network methodology. It is more efficiency and precisely to improve the customer satisfaction for the long-term perspective. We are proposing a parameter of evaluation module that selected 12 influence factors from MEPH and 12 satisfaction evaluation factors of airlines service (by customer perception of service quality). In this study, we are using back-propagation neural network method to build the module. The module could be used by calculating the airlines service satisfaction from the quality factors such as material, machine, product, and staff. However, we could get the prediction of service satisfaction through the way of data fusion by the 12 satisfaction indicators. The results could be used to make the service quality strategy, in order to lead a higher customer satisfaction. The findings help airlines managers to predict their customer satisfaction more efficiently, making changes to service quality strategy easily to meet the customers’ satisfaction level. Even if the managers pre-set a customers’ satisfaction level, a real-time cloud computing could help managers deploy the resources to achieve the goal. After all, in order to prove the feasibility of the parameters and the intelligent evaluation methodology, this study will collect data and test the evaluation of quality from an experimentation of airlines service system in Kaohsiung city in Taiwan.
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