Empirical modelling of submersed macrophytes in Yangtze lakes

Submersed macrophytes in Yangtze lakes have experienced large-scale declines due to the increasing human activities during past decades. To seek the key factor that affects their growth, monthly investigations of submersed macrophytes were conducted in 20 regions of four Yangtze lakes during December, 2001-March, 2003. Analyses based on annual values show that the ratio of Secchi depth to mean depth is the key factor (50% of macrophyte biomass variability among these lakes is statistically explained). Further analyses also demonstrate that the months from March to June are not only the actively growing season for most macrophytes, but the key time the factor acts. Five key-time models yielding higher predictive power (r(2) reaches 0.75,0.76,0.77,0.69 and 0.81) are generated. A comparison between key-time models and traditional synchronic ones indicates that key-time models have higher predictive power. Analyses of transparency thresholds during macrophyte growing season and the limitations of the models are presented. The models and other results may benefit the work concerning submersed macrophyte recovery in Yangtze lakes. (c) 2005 Elsevier B.V. All rights reserved.

alpha-decay half-lives and Q(alpha) values of superheavy nuclei

The alpha-decay half-lives of recently synthesized superheavy nuclei (SHN) are investigated by employing a unified fission model (UFM) where a new method to calculate the assault frequency of alpha emission is used. The excellent agreement with the experimental data indicates the UFM is a useful tool to investigate these alpha decays. It is found that the alpha-decay half-lives become more and more insensitive to the Q(alpha) values as the atomic number increases on the whole, which is favorable for us to predict the half-lives of SHN. In addition, a formula is proposed to compute the Q(alpha) values for the nuclei with Z >= 92 and N >= 140 with a good accuracy, according to which the long-lived SHN should be neutron rich. Several weeks ago, two isotopes of a new element with atomic number Z = 117 were synthesized and their alpha-decay chains have been observed. The Q(alpha) formula is found to work well for these nuclei, confirming its predictive power. The experimental half-lives are well reproduced by employing the UFM with the experimental Q(alpha) values. This fact that the experimental half-lives are compatible with experimental Q(alpha) values supports the synthesis of a new element 117 and the experimental measurements to a certain extent.

中学生的性别—理科刻板印象及其影响因素

Gender stereotype has a great effect on an individual’s cognition and behavior. Notably, stereotyped cognition about gender and science exerts an influence on an individual’s academic or career choice. In order to weaken the negative effect of gender-science stereotype and facilitate girls’ participation in science, this study examined the development of implicit gender-science stereotype and influence factors with implicit association test and questionnaires in a sample of secondary school students. The present work showed that: Firstly, there were no gender differences and gender predominance in performance of math and physics during secondary school years. However, girls tended to attribute success in math and physics to unstable factors, or the failure to stable factors. The reverse was true for boys’ attribution. This gender difference in attribution was especially evident in their study of physics. Secondly, 7th to 11th grade students implicitly regarded science as male domain, with the exception of 7th grade boys, who thought both boys and girls can study science well. On the whole, this gender-science stereotype was more and more evident as the specialization of science subjects’ progresses through secondary school, and this inclination decreased with increasing grade. Thirdly, the negative correlation between explicit and implicit stereotype which appeared in girls from 8th grade grew stronger with increasing grade and became significant in 10th grade. On the contrary, the significantly positive correlation existed in 7th -11th boys. Fourthly, the experience including attitude toward science, science interests and self –efficacy in math and physics had significantly negative effect on girls’ implicit gender-science stereotype, and significantly positive effect on boys’. It was showed that gender moderated the effect of experience in the study of science and implicit gender-science stereotype, and the attitude toward science mediated the relationship between science interests, self-efficacy and implicit gender-science stereotype. Fifthly, the perceived teacher’s class behaviours by students and the perceived parents’ gender stereotype by children had strong predictive power on students’ implicit gender-science stereotype. And the perceived teachers’ and parents’ performance expectancies can influence gender-science stereotype indirectly through self-efficacy in related subjects and attitude toward science. In conclusion, the present study showed that cognitive bias about gender and science existed in Chinese secondary school students. The information conveyed from teachers and parents interacting with students’ experience in the study of science affect the formation of stereotyped cognition.

不同智力水平儿童的自我调控特点

Self-regulation has recently become an important topic in cognitive and developmental domain. According to previous theories and experimental studies, it is shown that self-regulation consist of both a personality (or social) aspect and a behavioral cognitive aspect of psychology. Self-regulation can be divided into self-regulation personality and self-regulation ability. In the present study researches have been carried out from two perspectives: child development and individual differences. We are eager to explore the characteristics of self-regulation in terms of human cognitive development. In the present study, we chose two groups of early adolescences one with high intelligence and the other with normal intelligence. In Study One Questionnaires were used to compare whether the highly intelligent group had had better self-regulation personality than the normal group. In Study Two experimental psychology tasks were used to compare whether highly intelligent children had had better self-regulation cognitive abilities than their normal peers. Finally, in Study Three we combined the results of Study One and Study Two to further explore the neural mechanisms for highly intelligent children with respect to their good self-regulation abilities. Some main results and conclusions are as follows: (1) Questionnaire results showed that highly intelligent children had better self-regulation personalities, and they got higher scores on the personalities related to self-regulation such as, self-reliance, stability, rule-consciousness. They also got higher scores on self-consciousness which meant that they could know their own self better than the normal children. (2) Among the three levels of cognitive difficulties in self-regulation abilities, the highly intelligent children had faster reaction speed than normal children in the primary self-regulation tasks. In the intermediate self-regulation tasks, highly intelligent children’s inhibition processing and executive processing were both better than their normal peers. In the advanced self-regulation tasks, highly intelligent children again had faster reaction speed and more reaction accuracy than their normal peers when facing with conflict and inconsistency experimental conditions,. Regression model’s results showed that primary and advanced self-regulation abilites had larger predictive power than intermediate self-regualation ability. (3) Our neural experiments showed that highly intelligent children had more efficient neural automatic processing ability than normal children. They also had better, faster and larger neural reaction to novel stimuli under pre-attentional condition which made good and firm neural basis for self-regualation. Highly intelligent children had more mature frontal lobe and pariental functions for inhibition processing and executive processing. P3 component in ERP was closely related to executive processing which mainly activated pariental function. There were two time-periods for inhibition processing—first it was the pariental function and later it was the coordination function of frontal and pariental lobes. While conflict control task had pariental N2 and frontal-pariental P3 neural sources, highly intelligent children had much smaller N2 and shorter P3 latency than normal children. Inconsistency conditions induced larger N2 than conditions without inconsistency, and conditions without inconsistency (or Conflict) induced higher P3 amplitudes than with Inconsistency (or Conflict) conditions. In conclusion, the healthy development of self-regulation was very important for children’s personality and cognition maturity, and self-regulation had its own specific characteristics in ways of presentation and ways of development. Better understanding of self-regulation can further help the exploration of the nature of human intelligence and consciousness.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

Dynamic power management approaches based on neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system. by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level policies. We proposed two PAY policies-Back propagation Power Management (BPPM) and Radial Basis Function Power management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79,145,1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.