<p><strong>Introduction.</strong> Cardiovascular complications are one of the leading causes of mortality releted to diabetes mellitus typ 2 (T2DM). Dyslipidemia is one of the associated risk factors for coronary artery disease (CAD) in patients with T2DM. The aims of our study were: to determine the characteristics of lipid disorders in persons with T2DM; to determine the cumulative impact of investigated risk factors (gender, age, genetic predisposition, smoking habits, diabetes mellitus, hypertension, obesity) for the occurence of the coronary artery disease; to determine the influence of lipid profile on coronary artery disease development. <strong>Methods.</strong> A cross-sectional study was conducted in the Educative Center of the Primary Health Center Banja Luka in the period 01.11.2021&ndash;30.04.2022. Adult patients (&ge;18 years) with T2DM were recruited into the study. The data about socio-demographic characteristics, lifestyle and clinical factors were collected using structural questionnaire as a tool. For all subjects, anthropometric measurements, blood pressure readings, and laboratory findings (fasting blood glucose, HbA1c, lipid profile) were taken.<strong> Results.</strong> A total of 221 patients with T2DM participated in the study, 52.03% were males. Hypertriglycerdidemia was found in 63.81% subjects, hypercholesterolemia in 56.60%, low HDL-cholesterol in 49.77% subjects and increased level of LDL-cholesterol in 39.37% subjects. Metabolic dyslipidemia (increased triglyceride levels and low HDL levels), representing the major predictor of CAD, was found in 35.29% subjects. Older age, physical inactivity, obesity, hypertension and high levels of fasting glucose in blood were significantly related to dyslipidaemia in patients with T2DM. <strong>Conclusion.</strong> The representation of dyslipidemia in our subjects with T2DM is high, what increases the risk for coronary artery disease. Therefore, it is necessary not only to implement the therapy for glucoregulation, but also the secondary preventive measures for dyslipidemia, and that is the cardiovascular prevention.</p>

O trabalho tem como objetivo analisar o nível de conhecimento dos gestores do ramo de Farmácia que utilizam o ERP “InovaFarma” e sua percepção de como os dados retornados podem ser utilizados para obter maiores informações administrativas para apoio na tomada de decisões. Tem também como objetivo verificar se o sistema é “alimentado” de forma correta para que possam obter informações precisas e certeiras.  Por meio de questionário será observado até que ponto os gestores buscam informações nas ferramentas que possuem a disposição e como podem utilizá-las para auxiliar no dia a dia. O método de pesquisa utilizado foi a pesquisa descritiva, onde utilizou-se a técnicas de pesquisa bibliográfica, coleta de dados, análise e interpretação dos resultados. Os gestores que utilizam de forma frequente e muito frequente as ferramentas disponíveis encontram-se mais aptos a realizarem investimentos mais assertivos, e conseguem tomar decisões de forma mais facilitada, além de que ao realizarem uma “alimentação” mais constante das informações financeiras da empresa, conseguem disponibilizar aos demais participantes das tomadas de decisão informações mais transparentes e que podem ser comprovadas de acordo com os dados inseridos no sistema. Palavras-chave: ERP. Gestores. Informações. Tomada de decisão.

Digitization of public administration is a choice that has no alternative. The analysis of public administration in Bosnia and Herzegovina was research through the trends of changes in public administration and refers to: working conditions, access to work execution, developments with IT personnel, business conditions, investments, needs for new technologies, equipment and security. The key factor in every public administration is personnel. In the paper, we have presented an analysis of the employment of IT personnel at the level of BiH and the facts that emerged from the conducted analyses. Management of IT personnel, their need, recruitment and stimulation in public administration is a big challenge. An analysis of the current situation, trends and solutions is given through the available data. In order to digitize public administration, investments are also needed, of course these investments should be well thought out and guided by examples of good practice. Based on available data, analyses of investments in ICT in public administration at the level of institutions of BiH and Brčko District were carried out. An example of good practice was presented and solutions were given.

Precision agriculture is becoming increasingly important in modern agriculture as it allows farmers to optimize production and increase yields. This includes the use of sensors and technologies to collect and analyze data on soil, crops, weather, and other relevant factors. However, existing technology still has limitations such as accuracy and coverage over large areas. In order to solve this, new sensors and technologies are being developed, especially those based on artificial intelligence and machine learning, which allow for greater accuracy in data collection. In addition, new technologies such as drones and satellite imagery are being used to map crops and optimize agricultural production. This paper analyzes some of the latest developments in precision agriculture, providing insight into the future development and application of this technology. This work is particularly relevant to farmers, researchers, and companies involved in the development of sensors and technologies for precision agriculture.

As one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection.

In this study we demonstrate the appropriate use of statistically based filtering methods for feature selection and describe the application to Heart Rate Variability (HRV) features used to distinguish between arrhythmia and normal sinus rhythm electrocardiogram (ECG) signals. The initial set of HRV features is evaluated using both correlation and statistical significance tests. Normality assumption is assessed for each feature in order to select appropriate correlation methods and significance tests. In addition, the impact of outliers on the statistical test results is illustrated by an explorative analysis of correlation before and after outlier removal. Finally, a reduced set of features is selected, and the decision process guided by correlation and statistical significance test results is described and discussed.

This paper presents KF-RRT algorithm: a novel approach to path planning for robotic manipulators in dynamic environments. It is based on a modified RRT algorithm combined with Kalman filtering technique. RRT modification implies two aspects. The first one is related to continuous update of struc-ture/ordering within the tree to accommodate for online execution of the algorithm. The second one relies on forest-based replanning by combining connected components. On the other hand, Kalman filter is used to track/predict the motion of obstacles. Virtually augmented obstacles influence the growth of trees, which yields the improved safety margin of the resulting motion. KF-RRT is validated within a simulation study, where it is compared to comneting algorithms,

The main focus of this study is early-stage flame detection, where the number of flame pixels in the image is very scarce. To address this challenge, a custom-made dataset was created specifically for early-stage flame detection, encom-passing challenging environmental conditions. The DeepLabv3+ architecture with ResNet-50 backbone was employed for training and weighted cross-entropy was used to effectively handle the imbalanced nature of the dataset. As a result, the model achieved a mean Intersection over Union (mIoU) value of 0.7519, demonstrating robust performance in challenging conditions. The model exhibited accurate flame pixel detection and flame shape identification in images with low flame content but high smoke levels. Additionally, the model performed well in night-time conditions, accurately identifying flame regions and shapes. An important aspect of the model's performance was its ability to correctly identify images with no flames, thereby reducing false alarms and making it suitable for UAV-based flame detection tasks.

With the advancements of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it is now possible to greatly speed up the processes of predicting certain anomalies and prevent unforeseen situations and disasters. One example of such an environmental disaster is the problem of early-stage flame segmentation. It is not only important to create a model capable of pattern recognition with high accuracy but also to optimize it for real-time execution. In this paper, we demonstrate the capabilities of Deeplabv3+ for early-stage flame segmentation on a custom-made dataset with challenging conditions, and near real-time execution with the adoption of the Open VINO toolkit. Acceleration of the inference process in the range of 70.46% to 93.46% is achieved, while the speed of the inference process when using the GPU with FP16 precision is increased by almost 2 times when compared to FP32 precision. The impact of our findings is significant, as early-stage flame segmentation is a critical component of disaster prevention in environmental settings. Our results demonstrate the potential of using the OpenVINO toolkit for the acceleration of the inference process.

The prediction of the dynamics of High-Speed Craft (HSC) with prismatic hulls is commonly performed by designers using semi-empirical formulations based on Savitsky’s classic method. However, the accuracy of this prediction decreases with the presence of warp, when the deadrise of the hull change along its length, which is typical for small passenger ferries, even when considering the effective deadrise and trim angle concept proposed by Savitsky in 2012. The present work assessed the dynamics of three planing warped hulls and one prismatic monohull developed by the University of Naples, using a morphing grid approach implemented in OpenFOAM to capture the motion of the vessel. Numerical results on resistance, wetted area, dynamic trim angle, wall shear stress, and pressure distribution were compared with the method proposed by Savitsky, and previously published results where possible. Results suggested that it is possible to improve Savitsky prediction by changing the location where the equivalent deadrise angle is evaluated. This single modification will allow to extend the application of Savitsky method for a wider range of warp rates.

In this paper, the control of the electric vehicle with in-wheel motor drives is presented. Electric vehicle control is implemented through a drive motor control strategy based on the theory of discrete-time sliding mode. The speed controller is obtained as a combination of discrete-time first order sliding mode control and discrete-time realization of super twisting control algorithm that is commonly used in second-order sliding mode. The design of the proposed speed controller is performed using a discrete-time model of electrical drive. Various tests were performed in Matlab/Simulink software to validate the electronic differential system, vehicle model and engine control algorithm for different types of vehicle movement.

Due to increasingly widespread electoral corruption, citizens are slowly starting to lose trust in the fairness of democratic elections. The main objective of VoteChain is the elimination of the aspect of trust from the electoral process, in order to make voting more secure, transparent, and easily accessible. This paper proposes and implements a robust system that enhances voting efficiency by creating an electronic platform on top of a distributed Bitcoin Cash blockchain ledger. Blockchain represents a time-stamped series of immutable data records shared across a distributed network. When utilized in the context of voting, it guarantees full anonymity, vote integrity, and a fair, incontrovertible ledger with verifiable election results to all voters. Moreover, the system offers the ability to vote via any Internet-enabled computer or smartphone, dramatically decreasing the overall election organization costs. The system is envisioned as an application that connects to the Bitcoin Cash blockchain network via a custom feature-rich library. After discussing the system's characteristics, design, and underlying technology, this paper presents an example election scenario explaining how VoteChain works in-depth. In the end, the system's possible shortcomings are outlined, along with its prospective evolution and potential improvements that can be implemented.