《中庸系统神学》序言

陈慰中博士，福建省漳平市人。1930年1月出生于厦门鼓浪屿。1945年随家出国，旅居南洋、美、英等国，后定居加拿大维多利亚。在美国获得生物化学博士后，继往英国学习神学，得神学硕士学位，被按立为英国国家教会圣公会牧师。1973年应邀至加拿大维多利亚参加创办国际学院，任生物化学教授多年，翌年被选为英国牛津皇家显微学终生院士、1984年创办加拿大中华学院，任院长。1990年创办李约瑟中国古代科技博物馆，任馆长。有多种著作介绍中国文化及古代科技发明成果。曾多次应邀来华，在多处大学讲学，并被聘为名誉教授。

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.

析丁主教有关上帝是爱的论述

自从《丁光训文集》出版以来,引起中国教会教牧同工重视,不少同工及神学院师生亦积极撰写较有质量的神学研讨性文章。 《丁光训文集》的神学导向是什么?中国教牧同工是否值得探讨?我想应该有一个共识,丁主教是一位信仰虔诚、熟悉圣经,且富有神学功底,对中国教会具有很深厚感情的主教;同时他的政治态度非常明朗,热爱新中国,热爱中国人民,拥护中国共产党的领导。

拓宽视野,更深刻地认识教会——《荣耀的教会》序

中国教会恢复后的二十年来,出版了不少书籍,但论教会的书却不多见,教会是系统神学中的一个大题目,也是众信徒不可须臾离开的客观实际。如何正确认识教会,对神学思想建设,对按“三自”原则办好教会,对带领信徒灵命成长,都有十分重要的意义。季剑虹同工的《荣耀的教会》(以下简称《荣耀》)是本好书,很值得同工同道认真阅读。此书很好地解决了五对矛盾: 一是救赎与创造。《荣耀》一开头从神永远的计划说起。

持守信望爱 活出真善美——杨济余同道访谈录

基督徒如何在当代社会与周围人群开展对话,寻求理解,不仅需要基督徒对信仰本身的体认和践行,更需要有对周围文化的深入理解和反思,本期邀请杨济余同道谈谈他对信仰、世界、文化等问题的看法,以期启发读者再思信仰真谛,活出美善人生。

New technologies to investigate the brain-gut axis

Functional gastrointestinal disorders are commonly encountered in clinical practice, and pain is their commonest presenting symptom. In addition, patients with these disorders often demonstrate a heightened sensitivity to experimental visceral stimulation, termed visceral pain hypersensitivity that is likely to be important in their pathophysiology. Knowledge of how the brain processes sensory information from visceral structures is still in its infancy. However, our understanding has been propelled by technological imaging advances such as functional Magnetic Resonance Imaging, Positron Emission Tomography, Magnetoencephalography, and Electroencephalography （EEG）. Numerous human studies have non-invasively demonstrated the complexity involved in functional pain processing, and highlighted a number of subcortical and cortical regions involved. This review will focus on the neurophysiological pathways （primary afferents, spinal and supraspinal transmission）, brainimaging techniques and the influence of endogenous and psychological processes in healthy controls and patients suffering from functional gastrointestinal disorders. Special attention will be paid to the newer EEG source analysis techniques. Understanding the phenotypic differences that determine an individual＇s response to injurious stimuli could be the key to understanding why some patients develop pain and hyperalgesia in response to inflammation/injury while others do not. For future studies, an integrated approach is required incorporating an individual＇s psychological, autonomic, neuroendocrine, neurophysiological, and genetic profile to define phenotypic traits that may be at greater risk of developing sensitised states in response to gut inflammation or injury.

Prevalence of depression and anxiety in patients with chronic digestive system diseases: A multicenter epidemiological study

AIM To investigate the prevalence of depression and anxiety in patients with chronic digestive system diseases.METHODS A total of 1736 patients with chronic digestive systemdiseases were included in this cross-sectional study, including 871 outpatients and 865 in-patients. A selfdesigned General Information for Patients of the Department of Gastroenterology of General Hospitals questionnaire was used to collect each patient's general information, which included demographic data(including age, sex, marital status, and education) and disease characteristics(including major diseases, disease duration, principal symptoms, chronic pain, sleep disorder, and limited daily activities).RESULTS The overall detection rate was 31.11%(540/1736) for depression symptoms alone, 27.02%(469/1736) for anxiety symptoms alone, 20.68%(359/1736) for both depression and anxiety symptoms, and 37.44%(650/1736) for either depression or anxiety symptoms. Subjects aged 70 years or above had the highest detection rate of depression(44.06%) and anxiety symptoms(33.33%). χ2 trend test showed: the higher the body mass index(BMI), the lower the detection rate of depression and anxiety symptoms(χ2trend = 13.697, P < 0.001; χ2trend = 9.082, P = 0.003); the more severe the limited daily activities, the higher the detection rate of depression and anxiety symptoms(χ2trend = 130.455, P < 0.001, χ2trend = 108.528, P < 0.001); and the poorer the sleep quality, the higher the detection rate of depression and anxiety symptoms(χ2trend = 85.759, P < 0.001; χ2trend = 51.969, P < 0.001). Patients with digestive system tumors had the highest detection rate of depression(57.55%) and anxiety(55.19%), followed by patients with liver cirrhosis(41.35% and 48.08%). Depression and anxiety symptoms were also high in subjects with comorbid hypertension and coronary heart disease. CONCLUSION Depression and anxiety occur in patients with tumors, liver cirrhosis, functional dyspepsia, and chronic viral hepatitis. Elderly, divorced/widowed, poor sleep quality, and lower BMI are associated with higher risk of depression and anxiety.

Comparison of social interaction and neural activation in the main olfactory bulb and the accessory olfactory bulb between Microtus mandarinus and Microtus fortis

To gain insight into the function of AOB and MOB during different social interaction and in different vole species,the behaviors and neural activation of the olfactory bulbs in social interactions of mandarin voles Microtus mandarinus and reed voles Microtus fortis were compared in the present research.Mandarin voles spent significantly more time attacking and sniffing their opponents and sniffing sawdust than reed voles.During same sex encounters,mandarin voles attacked their opponents for a significantly longer time and sniffed its opponent for shorter time compared with male-female interactions.However,no significant behavioral differences were found during encounters of two individual reed voles,regardless of gender composition of the pair.Using c-Fos as an indicator of neural activation,we observed that neural activation was significantly higher in almost all sub-regions of the main olfactory bulb(MOB)and the accessory olfactory bulb(AOB)of mandarin voles compared with reed voles.Numbers of c-Fos-ir neurons in almost all sub-regions of the AOB and the MOB during male-female interactions were also higher than those in interactions of the same sex.Anterior-posterior ratios of Fos-ir neurons in the AOBM(AOBMR)and the AOBG(AOBGR)in male-female interaction were significantly higher than those in interaction of the same sex.The AOBMR of male mandarin voles and reed voles were larger than those of females in male-female interactions.Behavioral patterns are consistent with cellular activity patterns.Consistent level of neural activation in MOB and AOB suggests important roles of both the main olfactory bulb and the accessory olfactory bulb in social interaction in two species.

Effects of combined pre- and post-natal protein deprivation on the myenteric plexus of the esophagus of weanling rats:A histochemical,quantitative and ultrastructural study

AIM: To evaluate the effects of protein deprivation on the myenteric plexus of the esophagus of weanling rats. METHODS: Pregnant female Wistar rats were divided into 2 groups: nourished (N),receiving normal diet,and undernourished (D),receiving a protein-deprived diet,which continued after birth. At twenty-one days of age,13 esophagi from each group were submitted to light microscopy and morphometrical analysis employing the NADH diaphorase,NADPH diaphorase and acetylcholinesterase techniques. Three other esophagi from each group were evaluated with transmission electron microscopy (TEM). RESULTS: In both the NADH- and the NADPH-reactive mounts,the neurons of the N mounts were more intensely stained,while in the D esophagi only the larger neurons were reactive. Many myenteric neurons of N were intensely reactive for AChE activity but only a few neurons of D exhibited these aspects. Ultrastructural analysis revealed that the granular reticulum of N showed large numbers of ribosomes aligned on the outer surface of its regularly arranged membrane while the ribosomes of D were disposed in clusters. The chromatin was more homogeneously scattered inside the neuron nucleus of N as well as the granular component of the nucleolus was evidently more developed in this group. Statistically significant differences between N and D groups were detected in the total estimated number of neurons stained by the NADPH technique. CONCLUSION: The morphological and quantitative data shows that feeding with protein-deprived diet in 21-d old rats induces a delay in the development of the myenteric neurons of the esophagus.

Molecular physiology of vesicular glutamate transporters in the digestive system

Glutamate is the major excitatory neurotransmitter in the mammalian central nervous system (CNS). Packaging and storage of glutamate into glutamatergic neuronal vesicles require ATP-dependent vesicular glutamate uptake systems, which utilize the electrochemical proton gradient as a driving force. Three vesicular glutamate transporters (VGLUT1-3) have been recently identified from neuronal tissue where they play a key role to maintain the vesicular glutamate level. Recently, it has been demonstrated that glutamate signaling is also functional in peripheral neuronal and non-neuronal tissues, and occurs in sites of pituitary, adrenal, pineal glands, bone, GI tract, pancreas,skin, and testis. The glutamate receptors and VGLUTs in digestivesystem have been found in both neuronal and endocrinal cells. The glutamate signaling in the digestive system may have significant relevance to diabetes and GI tract motility disorders. This review will focus on the most recent update of molecular physiology of digestive VGLUTs.

Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks

In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. Three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.

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 .

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

Multi-agent reinforcement learning using modular neural network Q-learning algorithms

Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope with extremely complex and dynamic environment due to the huge state space. To reduce the state space, modular neural network Q-learning algorithm is proposed, which combines Q-learning algorithm with neural network and module method. Forward feedback neural network, Elman neural network and radius-basis neural network are separately employed to construct such algorithm. It is revealed that Elman neural network Q-learning algorithm has the best performance under the condition that the same neural network training method, i.e. gradient descent error back-propagation algorithm is applied.

Neuron system shock superimposed response based on catastrophe dynamics

With the rapid development of computer science and artificial intelligence technology, the complexity and intelligence of the neural network models constructed by people have been greatly improved. When the complex neuron system is subjected to the impact of ＂catastrophic＂, its original characteristics may be changed, and the consequences are difficult to predict. Catastrophe dynamics mainly studies the source of the sudden violent change of nature and human society and its evolution. The impact of the system can be divided into endogenous and exogenous shocks. In this article, catastrophe theory is used to study the neuron system. Based on the mean field model of Hurst and Sornette, introducing the weight parameters, mathematical models are constructed to study the response characteristics of the neuron system in face of exogenous shocks, endogenous shocks, and integrated shocks. The time characteristics of the shock response of the neuron system are discussed too, such as the instantaneous and long-term response of the system in face of shocks, the different response forms according to the weight or linear superposition, and the influence of adjusting parameters on the neuron system. The research result shows that the authoritarian coefficient and weight coefficient have a very important influence on the response of neuron system; By adjusting the two coefficients, the purpose of disaster prevention, self-healing protection and response reducing can be well achieved.

Application of Neural Networks for Predictive Variables in Engineering

This paper proposes a method in order to detect the importance of the input variables in multivariate analysis problems. When there is correlation among predictor variables, the importance of each input variable, when adding variables in the model, can be detected from the knowledge stored in Artificial Neural Network （NN） and it must be taken into account. Neural networks models have been used with the analysis of sensibility, these models predict more accurately the relationship between variables, and it is the way to find a set of forecasting variables in order to be included in the new prediction model. The obtained results have been applied in a system to forecast the volume of wood for a tree, and to detect relationships between input and output variables.

The Obsessive Compulsive Disorder Symptoms after the Successful Treatment of the Pineal Germinoma： A Case Report

We report here, a young male patients referred with ＂Obsessive Compulsive Disorder＂ symptoms which emerged after the successful treatment of pineal germinoma. OCD （obsessive-compulsive disorder） is a frequent, chronic, and clinically disorder which may presents in several neurologic disorders, especially occurs, in early adult life. Essential features of OCD are obsessional thoughts, compulsive acts as the ritualistic behavior, anxiety, and specific cognitive impairments. The cause of obsessive-compulsive disorder isn＇t fully understood. One of the many theories of the pathophysioiogy about to OCD is includes with hyperactivity in certain subcortical and cortical regions of brain also, dysfunction of the cortico-striatal circuits, particularly implicated in orbitofrontal cortices and basal ganglions. Additionally, pineal gland functioning is remarkable for the mental health disorders, particularly in OCD. On the basis of the investigations to present case report, we discussed the probable reasons of OCD symptoms, emphasizing the role of pathophysiology including the cortico subcortical pathways in genesis of the symptoms.

A special hierarchical fuzzy neural-networks based reinforcement learning for multi-variables system

Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN)for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.

Correlation methods of base-level cycle based on wavelet neural network

The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrdation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.

Research on pattern recognition for marine steam turbine rotor axis orbit

The structure,function and recognition method of an axis orbit auto-recognizing system are presented in this paper.In order to make the best use of information of format and dynamic characteristics of marine steam turbine axis orbit,the structure and functions or neural network are applied to this system,which can be used to auto-recognize axis orbit of the system turbine rotor using BP neural network.