– Today, when looking at the quality of an online item, the feedback itself plays a very important role. Based on the feedback we can decide whether the desired item is good or not, get a picture of the seller and so on. Many companies that have online shops display the most positive feedback while hiding bad ones or display only a few of them. In this research, we will help people by automating the process of deciding whether a feedback is positive or negative, which will give them time for other jobs and save money for hiring people who will work on the feedback. Since feedback on online articles is very important today, the process of determining positive and negative feedback should be made as quick and easy as possible. In this research, we will show a very simple and fast way to classify feedback as positive or negative, which means that the main question of this research is how to facilitate and speed up the process of determining the polarity of the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called Naïve Bayes, it gave the accuracy of around 80%.

The incidence of thyroid dysfunction in the area of central Bosnia with special reference to hyperthyroidism was being monitored. Considering that there were disagreements among earlier authors and even their contradictory results, the goal was to determine the percentage ratio between the thyroid hyperfunction and other dysfunctions and to identify the degree of correlation between thyroid hormones and the age structure of women. The study included 76 women aged 20 to 82 years with increased values of thyroid hormones and 49 women with reduced values of thyroid hormones. Thirty female patients with baseline thyroid hormone levels comprised a control group. Hyperthyroidism was observed in 60.8 % of women, with the highest percentage of getting ill in their fourth and fifth decade of life (64 %). The monitoring of TSH, FT4 and FT3 hormone values in thyroid dysfunction compared to women with thyroid gland hormone reference values by age groups showed that there was a statistically significant difference in the age group between 40 and 59 years (p < 0.05). For the age group of 60 years and older, there was no significant difference (p > 0.05). The TSH hormone value and the age of women are positively correlated and mutually dependent (p < 0.05). The observed reduction in the thyroid hormone values with an increase in women’s age can be considered an adaptation of the organism to the reduced need for energy and represents an important metabolic parameter of the biological process of aging.

Lafora progressive myoclonus epilepsy (Lafora disease, LD) is a fatal autosomal recessive neurodegenerative disorder (with an onset in teenage years in previously normal adolescents). This paper represents a view of a patient diagnosed with Lafora progressive myoclonus epilepsy, over a course of seven years. A description of the initial manifestation of symptoms, doctors' attempts to combat the symptoms with drug treatment, further attempts towards reaching the correct diagnosis, the final confirmation of the Lafora diagnosis (mutation in the NHLRC1 gene), and the current state of the patient is presented. The absence of a positive family history, the lack of staff specialized in dealing with this or similar pathology, and the diagnostic inability to characterize this type of disorder in Bosnia and Herzegovina have led to a fair delay in diagnosing and beginning of an adequate pharmacological treatment. Overall, recent identification of LD cases in Bosnia and Herzegovina warrants an establishment of a Centre for Genetic Testing in order to ensure more humane counseling of an entire family whose family member(s) might be diagnosed with this devastating and currently an incurable disorder.

In the world of global competition, customers have increasing demands that companies must meet in order to remain active in the global market. For this reason, it is necessary to use new technologies in the production processes, i.e. to implement Industry 4.0. In other words, we need to create a connected company through the digital transformation that enables production processes to discover new ways to increase productivity and improve overall business performance. Companies need to get involved and start a digital system, and from supplier to customer. It is a key to the hidden value that can contribute to the company’s productivity, compliance, profitability, as well as the quality of the finished product, and eventually the introduction of flexible industrial automation of production processes. The aforementioned technologies and Internet of Things connect the physical and virtual world with a purpose to better collect and analyze data, transforming them into information that reaches decision-makers. To do this, it is necessary to implement smart sensors that provide information at all times. The implementation of Industry 4.0 in production processes is unthinkable without smart sensors and provides the following: faster product development time, lower overall costs, improved use of production processes and their optimization, as well as company risk management. The paper will outline the motives for the implementation of smart sensors and applications of smart sensors in production processes. Keywords: production process, smart sensor, Industry 4.0, production automation, digital transformation, flexible automation.

The aims of the present study were to validate the discriminatory potential of Cameriere's third molar maturity index (I3M) cut-off value of I3M < 1.1 and whether closed apices of permanent second mandibular molar (I2M = 0.0) and I3M < 1.1 indicate the legal age of 14 years and older. A digital orthopantomograms of 804 healthy South Indian children, aged between 10 and 18 years, were collected retrospectively, and mineralization of the third and second left mandibular molars was analyzed by Cameriere's method. The proportion of accurately classified (Ac) individuals using the cut-off value of I3M < 1.1 was 79.4% for males, with Bayes post-test probability (Bayes PTP) of 68.8%. For females, Ac was 79.9% and Bayes PTP was 70.9%. The sensitivity was 94.8% and 98.2% for males and females, and the specificity was 58.7% and 62.8%, respectively. The combination of both variables, I3M < 1.1 and I2M  = 0.0, increased the Ac to 87.2% and 94.4% in males and females, the specificity to 98.6% and 99.2%, and Bayes PTP to 98.2% and 94.4%, respectively. In conclusion, the suggested cut-off value of I3M < 1.1 is of moderate accuracy in discriminating individuals between 14 years or older from those under 14 years in the studied population. A combination of both variables, I2M = 0.0 and I3M < 1.1, the discriminating test achieves an excellent specificity and Bayes PTP, which is mandatory for the forensic and medicolegal purposes.