<p><strong>Introduction. </strong>Lyme neuroborreliosis (LNB) can manifest during the early and late stages of Lyme disease (LD). The aim of this study is to determine epidemiology and clinical findings in patients with LNB.&nbsp;<br /><strong>Methods.</strong> The research was conducted in Banja Luka at the University Clinical Center of Republic of Srpska (UCC RS) during a four-year period from 10/2017 to 10/2021. The research included 51 patients admitted to the hospital with some neurological symptoms that could lead to the LNB diagnosis. Patients had lumbar puncture with cytochemical analysis of cerebrospinal fluid (CSF), ELISA anti-Borrelia IgM/IgG in serum and CSF, Immunoblot anti-Borrelia IgM/IgG in serum, and other documentation about epidemiology and clinical findings in LNB. &nbsp;<br /><strong>Results.</strong> A statistically significant (p=0.017) higher proportion of female patients was observed. Most patients were between 50 and 60 years old. Most of them were retirees (31.37%), farmers, medical technicians followed with forestry workers, students etc. Most of examinees did not have information on previous tick bite (64.71%). The largest number (72.55%) did not have data on previous skin changes. The largest number of neurological symptoms at admission related to the feeling of tingling and weakness in the extremities, headache, dizziness and some ophthalmological symptoms. Patients also suffered from consciousness and speech disorder, anxiety, paresis n.VII. The largest number of examinees had some neurological symptoms up to three months prior the admission to the hospital. &nbsp;<br /><strong>Conclusion. </strong>Symptoms of LNB can be quite non-specific or possibly even mimic other neurological diseases. Involvement&nbsp;<br />of peripheral nervous system is dominant in adult patients. It is necessary to work on early diagnosis of LD and it is mandatory to report it to the national epidemiological service.&nbsp;</p>

<p><strong>Introduction. </strong>Antibiotic resistance is a major threat to public health globally. The aim was to examine the impact of the COVID-19 pandemic on the distribution and antimicrobial resistance of pathogenic microorganisms isolated from samples&nbsp;<br />obtained during standard hospital care in one hospital center. &nbsp;<br /><strong>Methods.</strong> Data were obtained retrospectively from a database of the hospital microbiology laboratory. Study sample consisted of 3012 samples tested before the pandemic in 2019 and 3130 samples from the pandemic period in 2021.&nbsp;<br /><strong>Results.</strong> There was no statistically significant difference in the occurrence of agents that were resistant to all antibiotics between the observed years, but there was the difference in the occurrence of those agents between departments, with the highest frequency in the intensive care unit and the COVID-19 department (p&lt;0.001). Isolation of Acinetobacter bacteria increased 2.7 times, and Clostridioides difficile 6.4 times during 2021 compared to 2019. Statistically significant differences were registered in resistance to: imipenem, piperacillin-tazobactam, ceftayidime, cefepime, glycopeptides, aminoglycosides, levofloxacin and ciprofloxacin in 2021 compared to 2019.&nbsp;<br /><strong>Conclusion. </strong>Our results suggest possible influence of COVID-19 on antimicrobial resistance and input a need for a new larger study addressing this issue.</p>

<p><strong>Introduction. </strong>Test anxiety implies an intense pathological fear in situations of preparing and taking exams which is a significant problem for 15&ndash;70% of medical students. The frequent use of psychoactive substances is recognized as a problem for young people. The aim was to determine the frequency and degree of test anxiety and the association with misuse of psychoactive substances in medical students of the Faculty of Medicine in Foča.<br /><strong>Methods.</strong> The research was conducted according to the principle of a cross-sectional study at the Faculty of Medicine in Foča. Students were offered a custom-made questionnaire containing general and specific questions for test anxiety and use and dependence of psychoactive compounds and social networks. Test anxiety was evaluated by Westside anxiety scale.&nbsp;<br /><strong>Results.</strong> The sample included 145 respondents, 69.7% females and 30.3% males. The results indicate that the degree of test anxiety does not differ in male and female students, but test anxiety is less pronounced in younger students and students with medium academic success. The level of test anxiety was correlated with the rate of psychoactive compound use. Psychoactive substances were used by 50.3% of students, primary for anti-anxiety, than for psychostimulation and for hypnotic effects. The most commonly used classes of psychoactive substances were plant anxiolytics, followed by benzodiazepines, marijuana, alcohol and narcotics/cocaine (18.6%, 12.4%, 12.4%, 8.3%, and 4.8%, respectively).<br /><strong>Conclusion.</strong> A significant number of students reach for anxiolytics and other drugs that can create addiction. Elevated levels of test anxiety are positively correlated with increased consumption of psychoactive compounds. However, the frequency of use of psychoactive compounds surpasses the frequency of altogether moderate, high and extreme test anxiety.</p>

Abstract Background: Alcohol use and abuse remain prevalent in China, though mounting evidence shows that even drinking in moderation is detrimental to health. While many countries’ intake is on the decline, China’s alcohol consumption is rising fast and is on the path to overtaking countries like the United States, even on a per capita level. Objectives: This paper aims to analyze the danger of lax enforcement of laws and regulations against alcohol use and abuse and underscores the imperative for effective health interventions to curb problematic alcohol consumption in China. Results: Different from their Western counterparts, Chinese drinkers often consume more spirit with a high percentage of alcohol in social settings that encourage the “ganbei culture”—making toasts with alcohol filled to the brim and downed in one go to show respect—which perpetuates excessive drinking at a fast pace. Though the country has various laws and regulations in place to curb problematic alcohol consumption, like workplace drinking, their impacts are dismal. Considering that China has 1.4 billion people, the country’s uncurbed alcohol consumption trend could have a detrimental effect on national strategic objectives like “Healthy China 2030” and international ones like the Sustainable Development Goals. To further compound the situation, prevalent campaigns promoted by liquor companies—like alcohol-infused coffee, chocolate, and ice cream—may groom young people to develop alcohol consumption habits, if not addictions, for generations to come. Conclusions: We developed the Framework of 5Vs of China’s “Ganbei Culture to shed light on the issue, with the hope that it, along with the overarching insights of this paper, can assist health professionals and policymakers in better guarding and improving public health against the harms of alcohol use and abuse in China and beyond.

This workshop is designed to explore the potential synergies between established research on Social and Assistive Robots within HRI and the emerging field of Autonomous Vehicle and Other Road Users (AV-ORU) interactions. It examines the bidirectional applicability of principles, methodologies, and insights and seeks to initiate a detailed discussion on the distinctions and parallels between HRI and AV-ORU interactions to promote more meaningful, context-aware exchange and prevent the oversimplification of transferability. The goal is to encourage the integration of methodologies, broaden the ethical considerations in play, improve the focus on user-centric research in interactions and ultimately address shared challenges, innovative solutions towards sophisticated, ethically sound, and socially integrated autonomous systems.

Although Bosnia and Herzegovina has had a rich history in medicines and traditional medicines, it historically had poor activity regarding the field of medicinal chemistry in the country. However, this has changed recently as Bosnia and Herzegovina has shown immense potential in this field. A significant milestone occurred in 2019, with the establishment of the Organization Pharmaceutical Research Institute. This non‐governmental organization aims to improve medicinal chemistry in Bosnia and Herzegovina. Through research, partnerships, and educational initiatives, the organization has made substantial strides in promoting pharmaceutical research, education, and innovation. Moreover, the country‘s membership in the European Federation for Medicinal Chemistry and Chemical Biology (EFMC) has further facilitated collaboration with European experts, access to cutting‐edge knowledge and technologies, and harmonization with European standards. Looking to the future, this organization endeavors to improve healthcare, encourage innovation in medicinal chemistry, and promote the development of new therapies. With the efforts to establish an Association of Chemists in Bosnia and Herzegovina, the nation‘s scientific community is poised to flourish, contributing to the advancement of medicinal chemistry and healthcare in the region.

This paper presents present an example of the method of selecting an optimal model of fixed access network and compare various well-known access network technology choices in heterogeneous environment, in order to selecting an optimal solution. Architecture scenarios and technologies used for experimenting are P2P (Point to Point)) based on Ethernet, P2MP (Point to MultiPoint) based on GPON (Gigabit Passive Optical Network) technology and FTTB/C (Fibre to The Building/Cabinet) based on VDSL technology. After short reminder of FOAN (Fibre Optics Access Network) architectures, topologies and technologies and brief comparisons of P2P vs. GPON and FTTH (Fibre to The Home) vs. FTTB, the sample areas, which are subject of analysis are described in detail. A comparison of proposed solutions in four chosen areas is shown.

Background/Aim: The primary objective of this study was to conduct comprehensive research and analysis of patient-generated content related to Achilles tendon surgery on the social media platform Instagram. The aim was to gain deeper insights into patients' experiences during the perioperative period, which may contain valuable information pertinent to their condition, information of which the physicians may be unaware. Methods: A search was conducted on the Instagram database, covering a period of 78 months from February 2015 to August 2021. Posts utilizing the hashtags "#achillessurgery" and "#achillesrepair" were included in the analysis. Posts were assessed using a binary scoring system, considering variables such as tone, media format, return to work, rehabilitation or physical therapy, return to sports, images related to the ankle (e.g., scars, stitches, casts, dressings, walking boots), activities of daily living, and pain. Results: A total of 500 posts were reviewed, and a positive tone was observed in 77.8% of these. The average Instagram like ratio for these posts was computed as 9%. Statistical analysis revealed a significant relationship between post content and tone status (P=0.001). Specifically, within the positive tone group, informative content about the disease was comparatively lower and statistically significant when compared to exercise training and patient experience (P=0.001). Additionally, exercise training posts were found to be significantly lower than patient experience posts (P=0.001) within the positive tone group. In contrast, within the negative tone group, patient experience posts were significantly higher compared to informative content and exercise training posts (P=0.001). Moreover, the quantity of informative posts markedly surpassed the number of posts related to exercise training (P=0.001). Conclusion: In the positive tone category, people tended to emphasize personal experiences and participate in posts related to exercise training rather than actively seeking or exchanging information about the disorder. On the other hand, within the negative tone group, individuals were more likely to share adverse experiences, pursue support, and seek a deeper understanding from others. Their priority may also lie in sharing and gaining information specifically about the disorder. Our exploration of the use of social media platforms to delve into patient perspectives on Achilles tendon surgery provides an alternative insight into patients' experiences with the surgical process. By comprehending the content shared by patients on social media, surgeons may gain an improved opportunity to assess and address the surgical experience of their patients more effectively, ultimately contributing to enhanced patient care.

The paper presents an automated method for solving traditional single side 2D jigsaw puzzles, focusing solely on shape features. Termed as semi-apictorial puzzles, our approach utilizes pictorial content solely for image segmentation, not for puzzle matching. Through enhancements in background separation, corner extraction, and feature matching, our method simplifies and accelerates puzzle reconstruction. A key contribution is the introduction of an edge matching technique that employs approximate triangles to evaluate a possible match, which notably improves computational efficiency and reduces algorithm complexity. Experimental results demonstrate that the proposed method outperforms existing solutions, enabling the handling of a larger puzzles within a reasonable timeframe.

This paper presents the development and implementation of a flexible industrial machine model for automated visual inspection, called ETFCam, designed to improve the learning outcomes of electrical engineering students in the field of machine vision and robotics. Unlike prefabricated didactic models, which are typically “closed” systems with a predefined set of experiments, custom didactic systems for teaching and training built from scratch tend to be more flexible and provide a deeper insight in engineering, machine design and planning, while being more cost-effective. The proposed system is based on a 3DOF stepper motor-based manipulator, a DC motor driven conveyor, a pneumatic actuated gripper and a machine vision system. The paper discusses several applications of such a system in an educational environment, with a special focus on machine vision applications. Due to the fact that the system is versatile, open, modular, and easy to upgrade, it has unlimited potential and possibilities for further development. In addition, it provides students with a perfect testbed for learning new engineering skills in many areas such as schematic drawing and understanding, PLC based control, sensing, and machine vision.

This paper focuses on the design and implementation of a discrete digital PID (Proportional - Integral - Derivative) controller utilizing an FPGA (Field Programmable Gate Arrays) platform, which inherently supports parallel implementation of algorithms. Typically, cost-effective FPGA boards lacks peripherals, such as analog inputs and outputs, so they need to be added externally. The main hypothesis is that a DC motor system can be controlled with a low-cost variant of FPGA-based PID controllers. Therefore, an I2C (Inter-Integrated Circuit) based AD (Analog-to-digital) converter is added as input, while PWM (Pulse width modulation) based output signal is used as an output. The effectiveness of the designed regulator is demonstrated on an example of a DC (direct current) motor control. Additionally, for control and monitoring purposes, the FPGA is connected to the PC using the UART (Universal Asynchronous Receiver Transmitter) protocol. Experimental results indicate that the FPGA-based PID implementation offers solid performance.

This paper explores the application of FPGA programmable structures in the field of digital image signal processing (ISP). FPGAs offer high flexibility, speed and parallelism, making them ideal for general digital signal processing (DSP), as well as specific ISP tasks. The paper utilizes standard ISP algorithms such as morphological operations, filtering and edge detection to compare practical implementations of FPGA and CPU-based compute engines. Through illustrative examples and empirical results, we demonstrate the distinct advantages of employing FPGA for these use-cases, and contrast them with traditional CPU approaches, clearly showing FPGA capacity to significantly accelerate execution. The challenges that arise from resource-limited IOT-class hardware configurations are highlighted in the paper, namely resource optimization, memory management and maximal frequency.

This paper presents an end-to-end architecture for smart waste management, leveraging real-time data, IoT, AI, and machine learning to optimize operational efficiency and decision-making processes. The architecture is designed for both near real-time and batch data processing, ensuring continuous optimization and adaptation of waste collection routes and resource allocation. Machine learning models are employed to predict possible bad adverse scenarios and optimize operational plans. Additionally, business intelligence is utilized for data analysis and reporting, providing actionable insights based on real-time and historical data. The presented system is implemented on a scalable Kubernetes infrastructure, supporting the increasing data volumes and processing demands while maintaining system responsiveness and efficiency. This integrated approach demonstrates significant improvements in resource utilization, operational efficiency, and service delivery, highlighting the potential for smarter and more sustainable waste management practices. This research addresses the gap in combining IT architectures with AI models and IoT, paving the way for future advancements in smart waste management systems.

This paper introduces twisting sliding mode control method (TWSMC) to track 3D trajectories of a quadrotor unmanned aerial vehicle (UAV) exposed to bounded disturbances and perturbations. The key idea behind TWSMC is to introduce a nonlinear twisting term into the sliding surface design, which enables the system to switch between different sliding modes (SMs) smoothly, thereby reducing the chattering phenomenon and improving control performance. Moreover, a high-gain adaptation (HGA) algorithm is adopted in the TWSMC scheme to additionally attenuate the chattering effect, where the switching control gain increases during the convergent phase and decreases in the sliding phase. Through the comprehensive simulation study, it is shown that the proposed approach exhibits improved robustness and performance in tracking a reference under disturbances and perturbations.

The aim of the research is to assess the microbiological correctness and quality of sheep's milk, which is primarily used as a raw material for the production of Travnik - Vlasic cheese, and originates from the Vlasic - Travnik area, with an emphasis on assessing its compliance with the requirements of the current legislation for raw milk. In general, Travnik - Vlasic cheese is produced, according to the original technology, from unpasteurized milk, and therefore the purity of the raw material is extremely important. The samples were collected during the first months of lactation, March and April 2023, from 8 primary producers from the Vlasic - Travnik area, and were analyzed for the total number of bacteria and all samples meet the criteria of the regulations.