The ability to predict the success or failure of smoking cessation efforts will be useful for clinical practice. Stress response is regulated by two primary neuroendocrine systems. Salivary cortisol has been used as a marker for the hypothalamuspituitary- adrenocortical axis and salivary α-amylase as a marker for the sympathetic adrenomedullary system. We studied 62 chronic smokers (34 women and 28 men with an average age of 45.2±12.9 years). The levels of salivary cortisol and salivary α-amylase were measured during the period of active smoking, and 6 weeks and 24 weeks after quitting. We analyzed the men separately from the women. The men who were unsuccessful in cessation showed significantly higher levels of salivary α-amylase over the entire course of the cessation attempt. Before stopping smoking, salivary cortisol levels were higher among the men who were unsuccessful in smoking cessation. After quitting, there were no differences between this group and the men who were successful in cessation. In women we found no differences between groups of successful and unsuccessful ex-smokers during cessation. In conclusions, increased levels of salivary α-amylase before and during smoking cessation may predict failure to quit in men. On the other hand, no advantage was found in predicting the failure to quit in women. The results of our study support previously described gender differences in smoking cessation., M. Dušková ... [et al.]., and Obsahuje bibliografii a bibliografické odkazy
Transportation of people and goods represents a still more significant
component of the human culture. Its influence is extremely high today and will increase greatly in the future.
Almost all the contemporary transportation systems are based on the necessity of interaction between the transportation tool (vehicle, plane, ship), the transport control system and the human subject. Though a large effort is put into the development of automatic transport systems, none of the present attempts is fully automatic, in all of them the human subject plays a non-neglectable role with considerably high impact on the reliability and safety of the transportation function. Among such functions the driving and control activity dominates.
The drivers, pilots, captains and transportation systems dispatchers and controllers are usually exposed to considerably long and exhausting Services, which could last up to 8 and even more hours.
It is generally known that the human subject is not able to retain in the state of vigilance without brakes and relaxation. Usually, its ability to concentrate his/her attention to some activity (like driving of systém control) decreases considerably soon, mainly after 45 or 60 niinutes only.
The decrease of human subject attention in the course of his/her activity is not monotone of course, it can involve several periods of temporary increases and decreases. However, without exception, if the exposition is long enough, the subject attention finally falls under the limit of acceptability for safe and reliable activity of the particular type. The subject activity becomes dangerous for him/her, his/her environment and for the driven vehicle, plane, ship or the controlled transportation system too. Finally, the subject falls in the stage of the so-called micro-sleep, in which he/she is not able to produce the particular driving or control activity at all.
A considerably large effort was given to the analysis of negative impacts of this factor. Unfortunately, the used methodology for such analyses differs up to now in many countries, so that the results are not quite comparable. However, one can estimate that between 15 and 40% of all the accidents on the roads are caused by the non-satisfactory level of the human subject attention. If we take into account that the average economic loss of one mortal road accident is estimated to more than 1 million Euro and if the density of such accidents is taken into account as well, we come to a tremendous figure, which has to be enlarged more by the estimation of losses of non-mortal accidents.
Intrahepatic cholestasis of pregnancy (ICP) is a frequent liver disorder, mostly occurring in the third trimester. ICP is not harmful to the mothers but threatens the fetus. The authors evaluated steroid alterations in maternal and mixed umbilical blood to elucidate their role in the ICP development. Ten women with ICP were included in the study. Steroids in the maternal blood were measured by Gas Chromatography-Mass Spectrometry (GC-MS) (n=58) and RIA (n=5) at the diagnosis of ICP, labor, day 5 postpartum, week 3 postpartum and week 6 postpartum. The results were evaluated by ANOVA consisting of the subject factor, between subject factors ICP, gestational age at the diagnosis of ICP and gestational age at labor, withinsubject factor Stage and ICP × Stage interaction. The 17 controls were firstly examined in the week 36 of gestation. ICP patients showed reduced CYP17A1 activity in the C17,20 lyase step thus shifting the balance between the toxic conjugated pregnanediols and harmless sulfated 5α/β-reduced-17-oxo C19 steroids. Hence, more toxic metabolites originating in maternal liver from the placental pregnanes may penetrate backward to the fetal circulation. As these alterations persist in puerperium, the circulating steroids could be potentially used for predicting the predisposition to ICP even before next pregnancy., P. Šimják, M. Hill, A. Pařízek, L. Vítek, M. Velíková, M. Dušková, R. Kancheva, J. Bulant, M. Koucký, Z. Kokrdová, K. Adamcová, A. Černý, Z. Hájek, L. Stárka., and Obsahuje bibliografii
In this paper, we suggest Evolution Algorithms (EA) for development of neural network topologies to find the optimal solution of some problems. Topologies are modified in feed-forward neural networks and in special cases of recurrent neural networks.
We applied two approaches to the tuning of neural networks. One is classical, using evolution principles only. In the other approach, the adaptation phase (training phase) of the neural network is raade in two steps. In the hrst step we use the genetic algorithm to find better than random starting weights (nearly optimal values), in the second step we use the backpropagation algorithm to finish the adaptation phase. This means that the starting weights for the backpropagation algorithm are not random values, but approximately optimum values. In this context, the fitness of a chromosome (neural network) is a function of its estimated test error (its estimated generalization ability).
Some results obtained by these methods are demonstrated in a prediction of Geo-Magnetic Storms (GMS) and Handwriting Recognition (HWR).
A novel hybrid rule network based on TS fuzzy rules is proposed to resolve the problems of fuzzy classification and prediction. The proposed model learns by using genetic algorithm and is able to cover the whole distribution regions of the samples. In the learning process: (1) fuzzy intervals of each dimension of the samples are partitioned evenly; (2) computing intervals (CIs) are established based on the even intervals; (3) linear weighted model of several normal probability distributions is used to describe the sample probability distribution on CIs; (4) membership degree of each CI is learnt to evaluate the importance of each CI, avoiding the problem that the optimal intervals are difficult to cover the original sample spaces; (5) dynamic rule selection mechanism is used to dynamically combine a small number of optimal rules linearly to achieve nonlinear approximation, reducing the computation load. Three experiments are performed: the experiments on Iris and Mackey-Glass chaotic time series show that HRN can achieve satisfactory results and is more effective in terms of generalization ability, whereas the experiment on exhaust gas temperature demonstrates that HRN can predict the EGT of aero engine effectively.
Cíle. Dosavadní výzkumy osobnostních korelátů a prediktorů generativity jsou převážně založeny na pětifaktorovém modelu osobnosti. Méně pozornosti je věnováno jiným osobnostním modelům. Jako další vhodný se jeví Cloningerův model osobnosti, který rozlišuje temperamentové a charakterové rysy osobnosti.
Soubor. Dva aspekty generativity – zájem (Loyola Generativity Scale) a jednání (Generative Behavior Checklist) – byly predikovány u souboru osob ve střední dospělosti (N = 83, 58 % žen, průměrný věk 53 let) na základě osobnostních rysů zjišťovaných o deset let dříve.
Hypotézy. Autoři předpokládali, že generativní zájem a generativní jednání budou předpovídány rysy vyhýbání se poškození, vyhledávání nového a sebepřesažení.
Statistická analýza. Data byla analyzována postupy korelační a regresní analýzy s využitím bootstrappingu.
Výsledky. Generativní zájem i generativní jednání mohou být predikovány na základě osobnostních rysů, nicméně v případě generativního zájmu ani jeden z prediktorů nevykazoval samostatný statisticky významný vliv. Signifikantním prediktorem generativního jednání byla dimenze sebepřesažení. Rys sebepřesažení definovaný jako představa o vlastní účasti ve světě jako celku nemá v rámci pětifaktorového modelu obdobu, výsledky tudíž vhodně doplňují dosavadní poznatky o osobnostních souvislostech generativity.
Limitace. Studie má dvě limitace: za prvé, výzkum byl proveden s relativně nízkým počtem osob, za druhé, jak generativita, tak osobnostní rysy byly v průběhu longitudinální studie zjišťovány pouze jednou, na různých věkových stupních. and Objectives. The research of personality correlates and predictors of generativity is largely based on five-factor model of personality. Less attention is paid to other personality models. One of another suitable models is Cloninger’s model of personality, which distinguishes temperament and character traits of personality.
Sample and setting. Two aspects of generativity, concern (Loyola Generativity Scale) and action (Generative Behavior Checklist), were predicted in a group of middle-aged people (N = 83, 58% women, mean age 53 years) based on personality traits ten years earlier.
Hypotheses. Authors assumed that generative concern and generative action would be predicted by the traits of harm avoidance, novelty seeking, and self-transcendence.
Statistical analysis. Data was analyzed using the correlation and regression analysis with the use of bootstrapping.
Results. Generative concern and generative action can be predicted on the basis of personality traits, but in the case of generative concern, none of the predictors showed a separate statistically significant effect. A significant predictor of generative action was the dimension of self-transcendence. The self-transcendence, defined as the concept of own participation in the world as a whole, is not included within the five-factor model, and the results thus suitably complement the existing knowledge of personality factors of generativity.
Study limitation. The study has two limitations: first, the research was performed with a relatively small sample size, and second, both generativity and personality traits were assessed only once during the longitudinal study, at different age levels.
Cash flow forecasting is indispensable for managers, investors and banks. However, which method is more robust has been argued under the condition of small size samples. With sliding window technique we create the Response Surface, Back Propagation Neural Network, Radial Basis Functions Neural Network and Support Vector Machine models respectively, which are examined by comparing performances of training and simulation. Performances of training models are measured by mean of squared errors while that of simulation is done by average relative errors of the results. By comparison, Support Vector Machine is most robust to forecast cash flow, followed by Radial Basis Function Neural Network, the third Back Propagation Neural Network and the last Response Surface Model. The optimal result of each model depends on the window size of the transmitter.
Studie ukazuje kontrast mezi ekonomickým přístupem jdoucím ve šlépějích Garyho Beckera a přístupem behaviorální ekonomie. Důraz je kladen na metodologické srovnání obou alternativ a vyhodnocení potenciálu, který má behaviorální ekonomie pro uskutečnění paradigmatické změny na půdě ekonomického myšlení. Ukazuji, že behaviorální ekonomii se dosud nepodařilo nabídnout teoretickou alternativu homo economicus. Také její potenciál pro dlouhodobé překonání teorie racionální volby v oblasti predikčního úspěchu, který je pro ekonomy standardním kritériem vyhodnocování teorií, může být omezený., The study shows a contrast between the Beckerian economic approach and behavioral economics. The methodological comparison of both alternatives is emphasized as well as the assessment of behavioral economics' potential to cause a paradigm shift in economic thinking. I argue that behavioral economics was unable to offer a theoretical alternative to homo economicus so far. Also, its potential to overcome the rational choice theory in the long run predictive success that is the standard benchmark for theory evaluation in economics may be limited., and Petr Špecián.
The paper presents the possibility of application of frontal neural rietworks, genetic and eiigenic algorithrns in predicting gross domestic product development by designing a prediction model whose accuracy is superior to the model ušed in practice [1]. The learning process is implemented by means of a newly designed algorithm based on the EuSANE algorithm [2].