Stress is a part of human life, especially for urban citizens. Stress is inseparable characteristics of student life, especially exam days. Stress management is one of the first steps which can affect students success during the exams, especially in universities. Blood pressure is the first stress observation symptom to understand its level. Therefore, to understand the stress impact of university students during the exam weeks, a conditional experiment has been designed. 200 students were selected from Bosnian and Turkish female and male. The students` blood systolic, diastolic and heart rate were measured to detect the differences between non-exams days and exam days. The blood pressure measurement has been done 3 times in specific times, non-exam days, midterm and final days. Since non-exam days were taken as stress off days, they were supposed that these days were control data to compare with exam days to see the differences. As a result of the measurements, Bosnian females showed the highest increasing, systolic 13.2%, diastolic 9.3% and heart rate 8.5% during the midterm exam days. The group has been followed by Bosnian males, systolic 6.9%, diastolic 6.1% and heart rate 6.63 increased during the midterm days. Although Turkish students blood pressure and heart rate increased, the values were less than Bosnian students. Moreover, high correlation significance results belonged to Bosnian females and males, 0.722 and 0.698 respectively. Finally, it was concluded that if students have scholarship they have more blood pressure during the exams. While 95% of Bosnian females and 90% of Bosnian males have some scholarship, no Turkish students have scholarship demonstrated the differences between Bosnian and Turkish students blood measurements.

The relationship between single nucleotide polymorphisms (SNPs) and phenotypes is noisy and cryptic due to the abundance of genetic factors and the influence of environmental factors on complex traits, which makes the idea of applying artificial neural networks (ANNs) as universal approximates of complex functions promising. In this study, we compared different ANN architectures and input parameters to predict the adult length of Pacific lampreys, which is the primary indicator of their total migratory distance. Feedforward and simple recurrent network architectures with a different range of input parameters and different sizes of hidden layers were compared. Results indicate that the highest performing ANN had an accuracy of 67.5% in discriminating between long and short specimens. Sensitivity and specificity were 62.16% and 70.73%, respectively. Our results imply that feedforward ANN architecture with a single hidden neuron is enough to solve the problem of specimen classification. Nonetheless, while ANNs are useful at approximating functions with unknown relationships in the case of SNP data, additional work needs to be performed to ensure that the chosen SNP markers are related to functional regions related to the examined trait, as the use of non-specific markers will result in the introduction of noise into the dataset.