Moisture content in Dall's porpoise (Phocoenoides dalli) tissues: a reference base for conversion factors between dry and wet weight trace element concentrations in cetaceans

Concentration of trace elements measured by dry weight basis has become more commonly used in recent studies on cetaceans than wet weight basis, which was used more in earlier studies. Because few authors present moisture content data in their papers, it is difficult to compare the concentrations of trace elements between various studies. Therefore, we felt that it would be useful if a reference conversion factor (CF) for tissue types could be found to convert between wet weight and dry weight data on trace element concentrations. We determined the moisture contents of 14 tissues of Dall's porpoise (Phocoenoides dalli), and then, calculated the CF values for those tissues. Because the moisture content of each tissue differs from other tissues, it is necessary to use a specific CIF for each tissue rather than a general CF for several tissues. We have also found that CIF values for Dall's porpoise tissues are similar to the same tissues in other cetaceans. Therefore CF values from Dall's porpoise can be reliably used to convert between wet and dry weight concentrations for other cetacean tissues as reference data. (C) 2002 Elsevier Science Ltd. All rights reserved.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Discussion on the basic mathematical models of neurons - Double synaptic weight neuron in general-purpose neurocomputer, theory and application

Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

低分子有机酸对辣椒生长发育及叶片活性氧代谢的影响

采用盆栽试验与室内分析相结合的方法,研究了6种低分子有机酸和一种有机酸盐对辣椒生长发育和叶片活性氧代谢的影响。结果表明:柠檬酸、乙酰丙酸和有机酸钾处理不仅可显著提高辣椒根系干质量,增加辣椒vC含量,而且提高了辣椒的产量。甲酸、柠檬酸、乙酰丙酸和有机酸钾处理使根系活力比对照分别提高83%、93.8%、96.75%和99.5%。柠檬酸、乙酰丙酸和有机酸钾处理提高了辣椒叶片的SOD和POD活性,降低了膜脂过氧化产物MDA含量,延缓了叶片衰老。但是低分子有机酸处理对CAT活性的影响较小。

施氮和灌水对冬小麦叶片光合特性和籽粒产量的影响

大田条件下设置不同的施氮和灌水水平,研究施氮量和灌水量对冬小麦(Triticum aestivum L.)叶片光合作用生理及籽粒形成的影响。结果表明,施氮和灌水提高叶片Fv/Fm和Pn,并显著提高冬小麦籽粒产量、单位面积穗数和每穗粒数。施氮明显降低千粒重,而适量灌水明显提高千粒重。

新型土壤改良剂对夏玉米生理生长特性的影响

通过大田玉米试验,验证新型土壤改良剂对夏玉米生育期土壤水分、紧实度及玉米生理生长特性的影响。结果表明,施用改良剂PJG和PFL夏玉米全生育期平均株高、叶面积分别高于对照20.7%、19.75%和51.88%、72.37%;2种改良剂对干物质积累的影响存在差异,影响效果依次为茎干重>叶干重>根干重。夏玉米光合速率和叶绿素含量受土壤改良剂影响较大,PJG和PFL分别高于对照29.96%、24.48%和73.36%、68.53%。在0~10 cm土层内,施用PJG和PFL后土壤紧实度分别低于对照44.44%和42.91%。施用改良剂PJG后,0~20 cm土层土壤含水量维持在田间持水量的77.9%左右,未施用改良剂土壤,夏玉米生育期表层土壤含水量起伏变化较大。土壤改良剂PJG在夏玉米的施用效果略好于PFL。

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.