“大时代”中的神学思考——吴耀宗处境化的神学方法

本文探讨了吴耀宗神学思考的多种思想渊源。认为他的神学思考的旨趣是要在当时的处境下对基督教加以合乎处境的阐释。虽然在实践上，吴耀宗反对自由主义，但在神学上，他所持守的仍然是自由主义的神学立场。他的努力，是致力于在中国当时的处境下。秉承自由主义的神学立场，对基督教信仰加以新的阐释。也就是说。他所做的是一个处境化的工作。

浅谈神学方法论

本文剖析了葛朗兹和弗兰克(Stanley J.Grenz and John R.Franke)所著《基要主义之后》一书提出的神学方法论,并结合本文作者自身的教会信仰经历作了反思。

试论施莱尔马赫的哲学诠释方法——以《论宗教》中的“修辞”为中心

《论宗教》是德国浪漫主义神学家施莱尔马赫的代表作。学界对这部著作的传统解读,常常先入为主地以哲学理性的方式,如黑格尔或柏拉图意义上“辩证法”和康德意义上的“先验论证”展开。本文以1799年版《论宗教》第二讲对“宗教本质”的论述为中心,阐明了施莱尔马赫所使用的诠释方法(作为“表达”的修辞),以及这种方法在“情感”“直观”和“宗教”等概念中的内在性,以此表明《论宗教》所使用的特殊描述方法恰恰不能与传统的哲学认识方法等同起来。

麦奎利与《探索人性》

约翰·麦奎利是当代著名的哲学神学家，多年担任牛津大学钦定玛格丽特夫人讲座教授，并于1983～1984年度登上举世闻名的“吉福德讲坛”。其“吉福德讲稿”《探索神性》的姊妹篇《探索人性》一书，从哲学角度和神学角度出发，系统梳理了人性和人生中基本的二十个难题，是对宗教哲学人论或神哲学人类学极有价值的贡献。麦氏著作已有一些中文译本，但对其系统的介绍尚缺。本文以其《探索人性》为中心，对麦氏思想和学术历程作简略的评介。

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

Neurotrophic and metabotrophic potential of nerve growth factor and brain-derived neurotrophic factor: Linking cardiometabolic and neuropsychiatric diseases

One of biggest recent achievements of neurobiology is the study on neurotrophic factors. The neurotrophins are exciting examples of these factors. They belong to a family of proteins consisting of nerve growth fac-tor （NGF）, brain-derived neurotrophic factor （BDNF）, neurotrophin-3 （NT-3）, NT-4/5, NT-6, and NT-7. Today, NGF and BDNF are well recognized to mediate a diz-zying number of trophobiological effects, ranging from neurotrophic through immunotrophic and epitheliotro-phic to metabotrophic effects. These are implicated in the pathogenesis of various diseases. In the same vein, recent studies in adipobiology reveal that this tissue is the body’s largest endocrine and paracrine organ producing multiple signaling proteins collectively termed adipokines, with NGF and BDNF being also produced from adipose tissue. Altogether, neurobio-logy and adipobiology contribute to the improvement of our knowledge on diseases beyond obesity such as cardiometabolic （atherosclerosis, type 2 diabetes, and metabolic syndrome） and neuropsychiatric （e.g. , Alzheimer’s disease and depression） diseases. The present review updates evidence for （1） neurotrophic and metabotrophic potentials of NGF and BDNF linking the pathogenesis of these diseases, and （2） NGF- and BDNF-mediated effects in ampakines, NMDA receptor antagonists, antidepressants, selective deacetylase inhibitors, statins, peroxisome proliferator-activated receptor gamma agonists, and purinergic P2X3 recep-tor up-regulation. This may help to construct a novel paradigm in the feld of translational pharmacology of neuro-metabotrophins, particularly NGF and BDNF.

Abnormal maternity and self-destruction： A psychoanalytical survey of the governess in Rebecca

This paper attempts to survey a minor character--the governess in Rebecca with the psychoanalytical theory, especially emphasizing her abnormal maternity towards Rebecca and trying to dig out its profound causes and its several abnormal characteristics. At last, the following conclusion is drawn： It is just this abnormal maternity that leads to the governess＇ self-destruction.

Apply the visual management in the nursing education to carry forward the spirit of Nightingale

Apply the visual management in the teaching of the nursing education, and strengthen the cultivation of the spirit of Nightingale of the nursing students. According to the characteristics of the method of the visual management, using the visual and iconic teaching methods, construct the scene and the atmosphere of the rich cultural atmosphere, to stimulate the students＇ human emotional experience. Through several approaches, carry forward the spirit of Nightingale education of the nursing students, and make the students establish the lofty values of the nursing occupation, to provide the nursing care of high quality for the patients.

A Direct Feedback Control Based on Fuzzy Recurrent Neural Network

A direct feedback control system based on fuzzy recurrent neural network is proposed, and a method of training weights of fuzzy recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simulation results indicate that fuzzy recurrent neural network controller has perfect dynamic and static performances .

Research on High Resolution Satellite Image Classification Algorithm based on Convolution Neural Network

Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.

EFFICIENT GRADIENT DESCENT METHOD OFRBF NEURAL ENTWORKS WITHADAPTIVE LEARNING RATE

A new algorithm to exploit the learning rates of gradient descent method is presented, based on the second-order Taylor expansion of the error energy function with respect to learning rate, at some values decided by "award-punish" strategy. Detailed deduction of the algorithm applied to RBF networks is given. Simulation studies show that this algorithm can increase the rate of convergence and improve the performance of the gradient descent method.

Classification and Identification of Nuclear, Biological or Chemical Agents Taken from Remote Sensing Image by Using Neural Network

In the context of new risks and threats associated to nuclear, biological and chemical （NBC） attacks, and given the shortcomings of certain analytical methods such as principal component analysis （PCA）, a neural network approach seems to be more accurate. PCA consists in projecting the spectrum of a gas collected from a remote sensing system in, firstly, a three-dimensional space, then in a two-dimensional one using a model of Multi-Layer Perceptron based neural network. It adopts during the learning process, the back propagation algorithm of the gradient, in which the mean square error output is continuously calculated and compared to the input until it reaches a minimal threshold value. This aims to correct the synaptic weights of the network. So, the Artificial Neural Network （ANN） tends to be more efficient in the classification process. This paper emphasizes the contribution of the ANN method in the spectral data processing, classification and identification and in addition, its fast convergence during the back propagation of the gradient.

Neuronal apoptosis after hypothermia circulatory arrest and intermittent antegrade cerebral perfusion

Objective To observe the influence of hypotherrnic circulatory arrest （HCA） on the apoptotic processes of neurons in the hippocampus and the expression of the related genes Bcl-2 and Bax, and compare to the intermittent antegrade cerebral perfusion. Methods Eighteen dogs were randomly divided into three groups： control group （6 animals, underwent normal temperature cardiopulmonary bypass, NCPB）, HCA group （6 animals, underwent HCA for 1 h）, and HCA ＋ IACP group （6 animals, underwent HCA for 1 h, combined with intermittent antegrade cerebral perfusion （IACP） every 15 min）. The hippocampus tissue was retrieved 2h after the CPB discontinued The expression of Bcl-2 and Bax were examined with immunohistochemistry method. The cytomorphologic changes of the hippocampus tissue were investigated with transmission electron microscopy （TEM）. Results The immunohistochemical staining showed that Bax protein levels were significantly higher in HCA group than in the other two groups （P〈0.01）, while Bcl-2 protein levels were significantly higher in HCA ＋ IACP group than that of the other two groups （P〈0.01）. Meanwhile, the TEM results showed that there was no apoptosis of neurons in control group, but neuronal apoptotic changes could be clearly observed in HCA group, and only a small amount of apoptotic neurons were seen in HCA ＋ IACP group. Conclusions HCA alone can induce neuronal apoptosis in the hippocampus. IACP during the HCA period has a protective effect on the cerebral tissue through suppressing apoptosis by decreasing Bax expression and increasing Bcl-2 expression.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.

Research on the Causes and Coping Strategies of the Land Subsidence Caused by the Tunnel Construction Projects

In this paper, we conduct research on the causes and coping strategies of the land subsidence caused by the tunnel construction projects. We analyze the issues from the following of the perspectives. （1） Analysis method. To solve large scale system of the development of computer hardware and the numerical calculation method, we use the basic analysis to deal with it. （2） The empirical of methods. Ground motion is usually leads to the basic development of the inclined tunnel surface vertical displacement, the result of the movement process can turn to a settling tank. （3） Machine learning based approaches. In one of biggest difficulties when using neural network method is to obtain all possible parameters related to ground subsidence, we use the machine learning model to handle the challenge. In the final part, we show prospect for the future research, we will combine more numerical analysis tools to optimize the current methodology.

A MATHEMATICAL APPROACH TO DESYNCHRONIZATION OF COUPLED OSCILLATORS： APPLICATION TO A NEURONAL ENSEMBLE

Synchronization of neurons plays an important role in vision, movement and memory. However, many neurological disorders such as epilepsies, Parkinson disease and essen- tial tremor are related to excessive synchronization of neurons. In the line of therapy, stimulations to these pathologically synchronized neurons should be capable of breaking synchrony. As the first step of designing an effective stimulation, we consider desynchro- nization problem of coupled limit-cycle oscillators ensemble. First, the desynchronization problem is redefined in a nonlinear output regulation framework. Then, we design an output regulator （stimulation） which forces limit-cycle oscillators to track exogenous sinusoidal references with different phases. The proposed stimulation is robust against variations of oscillators＇ frequencies. Mathematical analysis and simulation results reveal the efficiency of the proposed technique.

A novel linear microprobe array for the fabrication of neural microelectrodes

A novel linear microprobe array(LMPA)has been developed by a conventional microfabrication method from silicon.The LMPA leverages the properties of conventional microwire with additional features of naturally formed regular spacing.With the help of periodic microprobe arrays and double-side V-grooves fabricated in advance between each pair of the two microprobes’rear ends,the number of microprobe units for assembly in one array can be flexibly chosen by cleavage fracture from the LMPA.The fabrication method was demonstrated and the prototype device was assessed by electrochemical impedance spectroscopy(EIS)and in vivo test.The SNR of the spikes recorded was 6.

Autism genetics: Methodological issues and experimental design

Autism is a complex neuropsychiatric disorder of developmental origin, where multiple genetic and environmental factors likely interact resulting in a clinical continuum between "affected" and "unaffected" individuals in the general population. During the last two decades, relevant progress has been made in identifying chromosomal regions and genes in linkage or association with autism, but no single gene has emerged as a major cause of disease in a large number of patients. The purpose of this paper is to discuss specific methodological issues and experimental strategies in autism genetic research, based on fourteen years of experience in patient recruitment and association studies of autism spectrum disorder in Italy.

Preface

Clinical medicine and experiments have shown that electrophysiological activities on neuronal disease systems such as the epilepsy and Parkinson can exhibit the evolutions of complex dynamical behaviors and their transitions, which are closely related to the generation mechanism of neuronal diseases. Traditionally, electrophysiological activities have been analyzed from the statistical methods. Although some ideal results have been obtained, mechanisms of complex electrophysiological activities in neuronal systems cannot yet be disclosed. Dynamics modelling can help researchers to explore the mechanisms of electro- physiological activities of neuronal disease systems. By constructing reasonable physiological dynamical model, inner relation between the dynamics model and representation behaviors of the neuronal disease systems can be further studied. In addition, based on the constructed network model, we can also explore mechanisms of the evolutions of dynamical behaviors and their transitions of the initiation, propagation and termination of different kinds of the seizures. Finally, we can design the feasible control method to regulate dynamics behaviors of the seizures so as to realize the healthy neuronal firings.