A sense of purpose in life is inextricably linked with a firm conviction of exercising control over it, and having one’s priorities set straight. Health in turn, is affected by stress, by salutogenetic factors, as presented by A. Antonovsky. Disturbances of sense of purpose in life result in psychosomatic disorders. Hypothesis: there is a relevant statistical correlation between the level of sense of purpose in life and noopsychosomatic disorders. Methods: 1) Crumbaugh-Maholick Purpose-in-Life Test, 2) K. Mausch Questionnaire of Psychosomatic Ailments. Test group: 683 pedagogy students from the University of Szczecin. There is a relevant statistical correlation between the level of sense of purpose in life and psychosomatic disorders. The research results require comparison with other post-soviet countries, as well as democratic ones.
Recent results of the authors have demonstrated that the elevation of extracellular adenosine induced by the combined administration of dipyridamole, a drug inhibiting the cellular uptake of adenosine, and adenosine monophosphate (AMP), a soluble adenosine prodrug, mediates radioprotective effects in mice. Furthermore, it has been shown that this action is induced by at least two mechanisms: (1) protection by hypoxia as a result of the effects of treatment on the cardiovascular system (bradycardia, vasodilation), and (2) an enhanced regeneration of the radiation-perturbed hematopoiesis. Here, it was ascertained that the joint use of an optimal dose of noradrenaline given with dipyridamole and AMP combination eliminates the hypothermic and hypoxic effects of the treatment, but preserves the radioprotective action of dipyridamole and AMP combination in terms of hematopoietic recovery and partially also survival enhancing effects of the drugs in gamma-irradiated mice. These findings might be of importance for attempts to obtain available and tolerable radioprotective pharmacological prescriptions for clinical use.
A boundary vector generator is a data barrier amplifier that improves the distribution model of the samples to increase the classification accuracy of the feed-forward neural network. It generates new forms of samples, one for amplifying the barrier of their class (fundamental multi-class outpost vectors) and the other for increasing the barrier of the nearest class (additional multi-class outpost vectors). However, these sets of boundary vectors are enormous. The reduced boundary vector generators proposed three boundary vector reduction techniques that scale down fundamental multi-class outpost vectors and additional multi-class outpost vectors. Nevertheless, these techniques do not consider the interval of the attributes, causing some attributes to suppress over the other attributes on the Euclidean distance calculation. The motivation of this study is to explore whether six normalization techniques; min-max, Z-score, mean and mean absolute deviation, median and median absolute deviation, modified hyperbolic tangent, and hyperbolic tangent estimator, can improve the classification performance of the boundary vector generator and the reduced boundary vector generators for maximizing class boundary. Each normalization technique pre-processes the original training set before the boundary vector generator or each of the three reduced boundary vector generators will begin. The experimental results on the real-world datasets generally confirmed that (1) the final training set having only FF-AA reduced boundary vectors can be integrated with one of the normalization techniques effectively when the accuracy and precision are prioritized, (2) the final training set having only the boundary vectors can be integrated with one of the normalization techniques effectively when the recall and F1-score are prioritized, (3) the Z-score normalization can generally improve the accuracy and precision of all types of training sets, (4) the modified hyperbolic tangent normalization can generally improve the recall of all types of training sets, (5) the min-max normalization can generally improve the accuracy and F1-score of all types of training sets, and (6) the selection of the normalization techniques and the training set types depends on the key performance measure for the dataset.
Normative naturalism is primarily a metaphysical doctrine: there are normative facts and properties, and these fall into the class of natural facts and properties. Many objections to naturalism rely on additional assumptions about language or thought, but often without adequate consideration of just how normative properties would have to figure in our thought and talk if naturalism were true. In the first part of the paper, I explain why naturalists needn’t think that normative properties can be represented or ascribed in wholly non-normative terms. If so, certain prominent objections to normative naturalism fail. In the second part, I consider the objection that normative properties are “just too different” from (other) natural properties to themselves be natural properties. I argue that naturalists have no distinctive trouble making sense of thought and talk involving forms of “genuine” or “authoritative” normativity which can drive a non-question-begging form of the objection.