The application of additive manufacturing technologies for producing parts from polymer composite materials has gained significant attention due to the ability to create fully functional components that leverage the advantages of both polymer matrices and fiber reinforcements while maintaining the benefits of additive technology. Polymer composites are among the most advanced and widely used composite materials, offering high strength and stiffness with low mass and variable resistance to different media. This study aims to experimentally investigate the impact of selected process parameters, namely, wall thickness, raster angle, printing temperature, and build plate temperature, on the flexural properties of carbon fiber reinforced polyamide (CFrPA) fused deposition modeling (FDM) printed samples, as per ISO 178 standards. Additionally, regression and artificial neural network (ANN) models have been developed to predict these flexural properties. ANN models are developed for both normal and augmented inputs, with the architecture and hyperparameters optimized using random search technique. Response surface methodology (RSM), which is based on face centered composite design, is employed to analyze the effects of process parameters. The RSM results indicate that the raster angle and build plate temperature have the greatest impact on the flexural properties, resulting in an increase of 51% in the flexural modulus. The performance metrics of the optimized RSM and ANN models, characterized by low MSE, RMSE, MAE, and MAPE values and high R2 values, suggest that these models provide highly accurate and reliable predictions of flexural strength and modulus for the CFrPA material. The study revealed that ANN models with augmented inputs outperform both RSM models and ANN models with normal inputs in predicting these properties.

Accurate determination of chromosome centromere location is of high importance in cytogenetics, particularly in karyotyping, chromosome classification and determination of exposure to genotoxic environmental effects. This study investigates the ability of CNN to accurately predict the human chromosome centromere location and the effect centering chromosomes in images, by predicted centromere location, has on classification accuracy. Dataset, used to train and test CNN models, contained 8283 annotated individual chromosome images. Prior to performing centromere detection, followed by chromosome classification, the individual chromosome images are preprocessed using sequence of filtering algorithms. The CNN model achieved an average error of 0.5586 and 0.4543 in predicting x and y coordinates of centromere location, respectively. The achieved classification accuracy of randomly oriented and centered chromosomes in images, is 71.10 and 96.73%, respectively. Achieved increase in chromosome classification accuracy of 25.63% highlights importance of chromosome centromere detection, importance of positional variation removal, and high performance of CNN in prediction of centromere location and chromosome classification.

In this paper, the relationship between PTA (purified terephthalic acid) price and oil price was tested and a linear regression model was established. The following prediction models were tested on a historical oil price time series: the Same Slope, the Weighted Moving Averages, the Simple Exponential Smoothing, and the Exponential Smoothing With Additive Trend or the Holt's model. In order to determine the unknown parameters of the used models, nonlinear mathematical programming was used. Performance analysis on the given time series showed that the Same Slope model yielded better results than other tested models. The oil forecast given by the Same Slope model was then combined with the initial regression equation to establish the final PTA predicting model.

University students experience numerous health-related behavioral changes, including the adoption of unhealthy dietary habits. This study aimed to assess the nourish status in a sample of students from Faculty of Medicine of Sarajevo University and correlate it with students eating habits. A cross-sectional survey of 68 students was performed during February and March 2016, at the Sarajevo University. Verbal informed consent was obtained from all participants before completing the self-administered questionnaire that included questions on their eating habits and anthropometrics measures, weight and height. Body mass index was used to assess students nourish status. Statistical analyses were performed using the Statistical Package for Social Sciences software (version 13.0). This study showed that the majority of the students (69.12%) were of normal weight. Intakes of fruit and raw vegetables were more common among students with BMI≤24.9 kg/m 2 than students with BMI≥25 kg/m 2 (P=0.0004 and P=0.046 respectively). Consumption frequency of coca cola and beverages was less common (P=0.005) among students with BMI≤24.9 kg/m 2 . This study gives baseline information about weight status and eating habits among a sample of university students. Regulating the energy density of food could be used as an approach for successful body weight control.