Renewable energy development in Libya faces numerous obstacles that hinder its progress. This paper aims to identify these obstacles and propose effective strategies to overcome them. Based on the literature review and expert opinions, eight obstacles were identified: lack of infrastructure, dependence on fossil fuels, lack of a stable investment climate, political instability, weak regulatory framework, varying environmental conditions, lack of public awareness, and technological barriers. The analytic hierarchy process (AHP) method was used to calculate the weights of these obstacles. The results showed that lack of infrastructure was the most critical obstacle, followed by dependence on fossil fuels. Seven strategies were suggested to overcome these obstacles: encouraging private sector investment, providing financial incentives, strengthening the regulatory framework, capacity building, promoting public awareness, technology transfer, and international cooperation. The combined compromise solution (CoCoSo) method was used to rank these strategies based on their effectiveness. The results showed that encouraging private sector investment was the most important strategy to overcome the obstacles. The findings of this paper can support decision-makers in Libya to take the right decisions and allocate resources effectively to overcome the identified obstacles and promote renewable energy development. Additionally, the paper provides insights into other countries facing similar challenges in the development of renewable energy.

<p><strong>Introduction. </strong>The aim of this article was to present a case of a foreign body in the external auditory canal described as a pseudotumor of the middle ear, as well as to point out diagnostic and therapeutic aspects of this problem.</p> <p><strong>Case report. </strong>An 8-year-old girl was hospitalized several times in our department due to the surgery of left-sided chronic otitis media. Mastoidectomy and posterior atticotomy were performed during initial hospitalization. Six months later, she was admitted for the second act of the left-sided tympanoplasty. However, two months prior hospitalization, symptoms regarding the right ear appeared: sense of fullness, gradual hearing loss and occasional pain. An otoscopic finding on the right indicated the presence of &ldquo;tumefaction&rdquo; in the external auditory canal with a surface that was markedly hyperemic. This pseudotumor was finally interpreted as the foreign body in the external auditory canal.</p> <p><strong>Conclusion. </strong>Foreign bodies in the external auditory canal (EAC) are relatively common in pediatric population. The medical history sometimes is not reliable, and the clinical and radiological interpretation of pseudotumor in the external auditory canal or middle ear must include this possibility in the differential diagnosis as well. Surgical exploration and pathohistological diagnosis are necessary to make a definitive diagnosis and to avoid potential complications.</p>

<p><strong>Introduction.</strong> Vitamin D is a liposoluble vitamin that has many important roles in the human body. Daily requirements for vitamin D are met through intake of food and exposure to sunlight. The high frequency of vitamin D deficiency is a public health problem that can be corrected using food supplements (FS), which is why its consumption is increasing. The quality of FS, including the content of active components, depends on the good manufacturing practice which is not strictly regulated for the production of FS, as well as the formulation, packaging and storage. Because of that, the quality of FS can be variable. The aim of our pilot study was to examine the conformity of the actual determined content of vitamin D and the declared content in 49 FS, in the form of tablets and capsules, present on the market in Republic of Srpska. <strong>Method.</strong> Determination of vitamin D content was performed by high-performance liquid chromatography (HPLC). <strong>Results.</strong> The range of the determined content of vitamin D in relation to the declared content, expressed as a percentage, was from 64,4% to 188,8%, whereby the deviation is not statistically significant (mean: 100.50%; Cl 95% -0.54 to 0.17; p=0.313 tablets; mean: 98.02%; Cl 95% -3.00 to 0.42; p=0.127 capsules). When measurement uncertainty is taken into account, only one sample (2.0%) was outside the legally allowed range (88.8% more than labeled). <strong>Conclusion.</strong> Therefore, it has been shown that most of the examined products are of satisfactory quality in terms of vitamin D content, but it is necessary to continuously monitor the food supplements that are on the market.</p>

<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>

The issue of water contamination by heavy metal ions as highly persistent pollutants with harmful influence primarily on biological systems, even in trace levels, has become a great environmental concern globally. Therefore, there is a need for the use of highly sensitive techniques or preconcentration methods for the removal of heavy metal ions at trace levels. Thus, this research investigates a novel approach by examining the possibility of using pomegranate (Punica granatum) peel layered material for the simultaneous preconcentration of seven heavy metal ions; Cd(II), Co(II), Cr(III), Cu(II), Mn(II), Ni(II) and Pb(II) from aqueous solution and three river water samples. The quantification of the heavy metals was performed by the means of FAAS technique. The characterization of biomaterial was performed by SEM/EDS, FTIR analysis and pHpzc determination before and after the remediation process. The reusability study, as well as the influence of interfering ions (Ca, K, Mg, Na and Zn) were evaluated. The conditions of preconcentration by the column method included the optimization of solution pH (5); flow rate (1.5 mL/min), a dose of biosorbent (200 mg), type of the eluent (1 mol/L HNO3), sample volume (100 mL) and sorbent fraction (<0.25 mm). The biosorbent capacity ranged from 4.45 to 57.70 μmol/g for the investigated heavy metals. The practical relevance of this study is further extended by novel data regarding adsorbent cost analysis (17.49 $/mol). The Punica granatum sorbent represents a highly effective and economical biosorbent for the preconcentration of heavy metal ions for possible application in industrial sectors.

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.

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.

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.

In this paper, error performance analysis for M-ary phase shift keying (PSK) system in the inverse gamma two-ray with diffuse power (IG/TWDP) composite fading channel is presented. Using Fourier series approach, the average symbol error probability (ASEP) expression is derived in terms of hypergeometric functions, which can be evaluated using standard software packages. Derived expression is used to investigate degradation of error performance cased by shadowing, in regard to those obtained by considering only the TWDP multipath fading. All obtained results are verified by Monte-Carlo simulation.

Implementation of credit scoring models is a demanding task and crucial for risk management. Wrong decisions can significantly affect revenue, increase costs, and can lead to bankruptcy. Together with the improvement of machine learning algorithms over time, credit models based on novel algorithms have also improved and evolved. In this work, novel deep neural architectures, Stacked LSTM, and Stacked BiLSTM combined with SMOTE oversampling technique for the imbalanced dataset were developed and analyzed. The reason for the lack of publications that utilize Stacked LSTM-based models in credit scoring lies exactly in the fact that the deep learning algorithm is tailored to predict the next value of the time series, and credit scoring is a classification problem. The challenge and novelty of this approach involved the necessary adaptation of the credit scoring dataset to suit the time sequence nature of LSTM-based models. This was particularly crucial as, in practical credit scoring datasets, instances are not correlated nor time dependent. Moreover, the application of SMOTE to the newly constructed three-dimensional array served as an additional refinement step. The results show that techniques and novel approaches used in this study improved the performance of credit score prediction.

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.

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.