The functional performance and in-service quality of products are strongly influenced by surface roughness, which is a direct outcome of material removal processes. In general, surface roughness is function by the input parameters of the machining process and the extent of tool wear, the increase of which leads to an increase cutting forces, torque, acoustic emission level, vibrations, and temperature. Finding the dependence between machining parameters, tool wear indicators, and surface roughness parameters enables real-time prediction of surface quality and contributes to appropriate processing quality. In this study, based on data obtained through experiment conducted using the Taguchi design of experiment, predictive models were developed using multiple regression analysis and artificial neural networks (ANN). These models establish a relationship between input drilling parameters, axial drilling force, and the maximum height of the surface roughness profile.

Background: Observational and experimental studies consistently show that physical activity supports improvements in both physical and mental health. Various exercise modalities have been proposed as intervention protocols, ranging from aerobic training to traditional Eastern relaxation practices that incorporate elements of physical movement. Objective: The aim of this article was to analyze and synthesize evidence on the impact of different forms of physical activity on psychological stress and related symptoms of anxiety and depression among healthcare workers. Methods: A focused PubMed search was conducted for studies published in the last five years addressing physical activity interventions targeting stress, anxiety, or depression in healthcare workers. Both randomized and non-randomized intervention studies, pilot trials, and relevant systematic reviews were included to provide a descriptive synthesis because of heterogeneity in interventions and outcomes. Results: A review of PubMed-indexed studies published in the past five years identified 7,475 articles exploring the relationship between physical activity and stress levels in diverse populations. Of these, 284 studies specifically examined healthcare workers, with 72 addressing these outcomes in the context of the COVID-19 pandemic. Interventions included aerobic exercises, yoga, tai chi, qigong, and other movement-based relaxation techniques, with considerable variation in session duration, frequency, and follow-up periods. Reported outcomes varied, but most studies demonstrated reductions in perceived stress and improvements in anxiety and depression symptoms. Several studies suggested that more intensive exercise protocols may be less effective, while shorter and lower-intensity interventions often produced stronger psychological benefits. Conclusion: Various forms of physical activity can effectively reduce stress and symptoms of anxiety and depression among healthcare workers. Interventions do not necessarily need to involve high-intensity or long-duration activity to achieve meaningful improvements. Short, low-intensity protocols may represent a feasible and beneficial resource for supporting the psychological well-being of healthcare professionals.

Background: SARS-CoV-2, though primarily a respiratory pathogen, exhibits multi-organ tropism, with the liver among the commonly affected organs. Elevations in liver enzymes are frequent in hospitalized COVID-19 patients, yet acute and pronounced hepatocellular injury in young, clinically stable individuals is uncommon. The underlying mechanisms may include direct viral cytopathic effects mediated by ACE2 receptors, immune-mediated injury, systemic inflammation, and metabolic stress. Notably, hepatic involvement can develop independently of respiratory compromise. Objective: The aim of this case report was to describe three adult patients with serologically confirmed SARS-CoV-2 infection and mild respiratory symptoms who presented with acute liver injury in the absence of other identifiable causes. Case report: During a confirmed COVID-19 wave in Bosnia and Herzegovina, a targeted outpatient study was performed in a family medicine setting. Routine liver testing was conducted for all suspected COVID-19 cases. Laboratory evaluation included liver enzymes, hepatitis serology, autoimmune markers, and inflammatory parameters, with imaging (ultrasound or MRI) to exclude structural pathology. Three previously healthy male patients (aged 25–45) developed acute liver injury during mild febrile illness, all with serologically confirmed SARS-CoV-2 infection (positive IgM and IgG). Retrospective assessment revealed unrecognized metabolic dysfunction-associated steatotic liver disease (MASLD) in all cases. Initial symptoms were fatigue and myalgia without respiratory distress. Laboratory findings demonstrated marked elevations of AST and ALT (>1000 U/L), GGT (>900 U/L), and raised ferritin and D-dimer, while bilirubin remained normal. This biochemical profile - disproportionately elevated transaminases and GGT with preserved bilirubin - was consistent across cases, suggesting SARS-CoV-2–related hepatocellular injury. All patients recovered rapidly with supportive outpatient care, without progression to liver failure. Conclusion: Clinically stable COVID-19 patients may experience acute hepatocellular injury, particularly those with underlying metabolic dysfunction such as MASLD. The recurring biochemical pattern of significantly elevated AST, ALT, GGT, and ferritin with normal bilirubin suggests a distinctive SARS-CoV-2–associated liver injury phenotype. Recognition of this presentation is essential for appropriate evaluation and management. Routine liver function monitoring should be considered in COVID-19 patients, regardless of respiratory symptom severity, especially in those with metabolic risk factors.

Background: The COVID-19 pandemic is likely to have unprecedented and unforeseeable consequences, from those on a worldwide/global level to those at the local level–at the level of local communities and families, and individuals (and not just humans, but all other living beings), of which the future will testify in various ways. Objective: This work presents principles of treatment health care protection of people who has consequences of the colaps of helatcare sytem in Bosnia and Herzegovina (BiH) during Corona pandemic time and proposals how to make it better, with a focus on indications and restrictions for use different methods of education medical staff and how to improve therapeutic modalities by professional staff by education with new ICTs. Methods: System analysis of Public health sector in BiH which has felt down as consequences of great influence of rough political system generaly and at Cantonal and Community levels need to be meta analized by publich health care experts and by most influential health managers who can help to improve distroid current Health Care System at all levels, from Primary to Terciray level of ih heakth care protection. Results: The health system in BiH was unprepared for the COVID-19 pandemic. The unpreparedness of the health system varied in individual areas. The reasons for the unpreparedness of the health system in responding to the pandemic are the organization of the state of BiH by the Dayton Peace Agreement, according to which the health system is fragmented and regulated by law, without the possibility of its harmonization, coordination and interconnection. This pandemic period has clearly shown that “public health” as a medical discipline has been and remains a peripheral concern of society, that is, it has always been put on the back burner. The reason for this is the constant favoring of clinical disciplines, which is not disputed as a need, but this situation has shownhow much public health is needed and called upon when solving such pandemics, which have become a planetary problem. Proof of the previous statement is the very small number of epidemiologists and social medicine specialists in BiH. Conclusion: The response of the health system of BiH as a whole to the outbreak of the Covid-19 pandemic was inadequate, untimely, unprepared, unprofessional, chaotic, misinformed and with a lot of politicking, quasi-leadership and, in the end, it will turn out to be influenced by crime. This led to a high rate of illness and death, led to a great burden on the unprepared health system and setbacks in economic and social terms.

The primary objective of this study was to develop a predictive model for the supply of live livestock in the Republic of Croatia for the period 2024-2029, based on the analysis of historical data. Livestock production has long been a strategically important sector in Croatia, supported by a strong tradition and a notable presence of indigenous breeds. Nevertheless, despite these advantages, the supply of live livestock per production unit has demonstrated persistent negative trends. The study applies the Autoregressive Integrated Moving Average (ARIMA) model to analyze time series data from 2019 to 2023 in order to identify structural patterns and forecast future supply dynamics. Supplementary statistical and econometric methods are employed to examine variation, autocorrelation, and the significance of fluctuations within the series. The analysis also highlights that the cost of production, as a key non-price determinant, plays a decisive role in shaping livestock supply trends. Findings indicate a regressive trend across most livestock sectors, emphasizing the need for targeted policy measures to stabilize and enhance future production.

Background: Regular physical training in young athletes leads to physiological cardiovascular adaptations, often manifested as electrocardiographic (ECG) changes. Identifying predictors of such changes is essential for distinguishing normal adaptations from potential pathological findings. Objective: The aim of this study was to investigate body mass index (BMI), systolic and diastolic blood pressure (SBP/DBP), and heart rate (HR) as potential predictive factors for sinus arrhythmia, incomplete right bundle branch block (IRBBB), and ST-segment elevation in young endurance and strength athletes. Methods: This retrospective-prospective study included 60 male athletes aged 12–17 years (30 endurance, 30 strength athletes) who underwent a five-year follow-up with regular ECG monitoring. Anthropometric and cardiovascular parameters (BMI, SBP, DBP, HR) were recorded, and associations with ECG findings were analyzed using descriptive statistics, Student’s t-test, Mann-Whitney U test, Chi-square test, and logistic regression. Results: Endurance athletes showed a significant increase in BMI during follow-up (p = 0.035), while in strength athletes BMI was significantly associated with sinus arrhythmia (p = 0.045). Systolic blood pressure at the end of the study significantly differed in endurance athletes with and without ST-segment elevation (p = 0.029). However, logistic regression analysis demonstrated that BMI, SBP, DBP, and HR were not independent predictors of ECG abnormalities in either group. Conclusion: Basic cardiovascular parameters such as BMI, blood pressure, and heart rate do not appear to independently predict ECG changes in young athletes. Other factors, including training intensity and genetic predisposition, likely play a greater role. Preventive cardiovascular screening remains crucial for the early detection of clinically relevant abnormalities in this population.

Background: Assessment of the fetal nervous system - both in its anatomical structure and functional behaviour - has long been a challenge in perinatal medicine. Recent advances in ultrasound technology, especially 3D and 4D ultrasound, now allow detailed real-time observation of fetal anatomy and behavior. The development and maturation of the fetal brain in utero (and its continuity into extrauterine life) is a complex dynamic process: fetal neurobehavior is thought to follow a reproducible, gestational-age–dependent pattern that reflects neurological integrity. If normative fetal neurodevelopmental stages could be recognized and standardized, then deviations - abnormal neurobehaviors - could be identified, enabling prompt prenatal diagnosis of nervous-system pathology. Objective: The aim of this study was to emphasize the potential of 4D ultrasound–based fetal neurobehavioral evaluation (specifically with the Kurjak Antenatal Neurodevelopmental Test, KANET) in detecting abnormal neurobehavior prenatally, and to underline how this method may allow early identification of fetuses at risk for neurodevelopmental impairment. Methods: Review of the concept of fetal neurobehavioral assessment using 4D ultrasound. The KANET test applies 4D ultrasound to observe fetal behavior (movements, facial expressions, general/isolated movements) across gestation, akin to how neonates are neurologically assessed postnatally. By standardizing a scoring system for fetal behaviors relative to gestational age, KANET distinguishes between normal, borderline, and abnormal fetal neurobehavior. Evidence from multicenter studies and clinical/practice settings is considered to assess the feasibility and predictive value of KANET. Results: a) 4D ultrasound makes it possible to observe a wide repertoire of fetal behaviors (limb movements, facial expressions, mouth movements, hand-to-face, general movements), with increasing complexity and organization through gestation - reflecting central nervous system (CNS) maturation. PubMed+2De Gruyter Brill+2; b) Application of KANET in both low-risk and high-risk pregnancies (including growth-restricted and diabetic pregnancies) has shown significant differences in fetal behavior patterns. PubMed+2journaljammr.com+2; c) Postnatal follow-up in some studies found that fetuses with abnormal prenatal KANET scores indeed displayed adverse neurological outcomes - suggesting KANET’s potential as a predictive tool. PubMed+2PubMed+2; d) A recent systematic review (2025) found consistent evidence that behaviors observed via 4D ultrasound (e.g., yawning, hand-to-face, startle, general movements) increase in complexity between approx. 24–34 weeks gestation, coinciding with known neurodevelopmental milestones (e.g., thalamocortical connectivity). PubMed+1; e) However, despite growing evidence for structured fetal behavior as a marker of neural integration, the review cautions that such behaviors cannot yet be equated with consciousness or subjective awareness. PubMed+1.- Conclusion: The advent of 3D/4D ultrasound - and standardized tools like KANET - enables non-invasive prenatal assessment not only of fetal anatomy but also of functional neurodevelopment. Observing and scoring fetal behavior provides a promising avenue for early detection of neurodevelopmental abnormalities. While current evidence supports the use of KANET in clinical practice to identify fetuses at risk for neurodevelopmental impairment, interpretation should remain cautious: observed behaviors likely reflect maturation and neural integration but do not equate to consciousness. Further large-scale, long-term follow-up studies are needed to solidify the predictive validity and clinical utility of prenatal neurobehavioral assessment.

This paper examines the implementation of Industry 4.0 elements in enhancing the quality of cables and connectors in the automotive industry, with a focus on meeting ISO 16949 requirements. Modern quality control solutions are presented, including smart sensors, digital twins, and predictive analytics. Special emphasis is placed on multi-stage testing methods and process digitalization for quality monitoring. Through a case study from the company Leoni, the impact of QRQC, Q4.0, and Q-Loop systems on defect reduction in the production of BMW components is analyzed. The paper demonstrates how the integration of Industry 4.0 technologies enhances reliability, efficiency, and compliance with automotive industry standards.

The growing need for reducing ܥܱଶemissions in the context of sustainable development has intensified the search for efficient analytical approaches to understand and manage emission drivers. In this paper, three machine learning models were developed using multiple linear regression for the countries of Bosnia and Herzegovina, Croatia and Slovenia. Renewable energy consumption, ܲܯଶ,ହ air pollution, ܦܩܲ per capita, foreign direct investment, urban population, forest area, and total population were used as inputs in the models, while ܥܱଶ emissions for the period from 2000 to 2020 were used as outputs. The developed models for all three countries have good performance, with ܴଶvalues of 91,34%, 77,91%, and 77,20% respectively. For Bosnia and Herzegovina urban population increases ܥܱଶemission, while renewable energy consumption and forest area decrease ܥܱଶ emission. In Croatia ܲܯଶ,ହ was the most influential factor that increases ܥܱଶemission.In Slovenia population growth decreases ܥܱଶ emissions, whileGDP per capita increases ܥܱଶ emissions. Also, hypothesis testing for differences between means was performed for all variables between all three countries. The findings showed that for almost all variables there were statistically significant differences in mean differences between all countries. Regarding ܥܱଶ emission there are not enough statistical evidence that Bosnia and Herzegovina have higher ܥܱଶ emissions than Croatia, while both Bosnia and Herzegovina, and Croatia have significantly higher ܥܱଶ emissions than Slovenia. This research shows the potential of machine learning models as tools for data-driven policymaking in the transition towards Industry 5.0 and a sustainable industrial future.

In Bosnia and Herzegovina, black alder appears in scattered smaller forest stands, fragments and patches that are still not spatially separated and allocated in management plans, despite its high ecological importance. The objective of this study is to model a black alder ecological niche considering combined effects of climate, hydrological and air quality determinants to support decision-making of conservation and restoration activities on a local/regional level. Black alder occurrence was registered on 72 temporary sample plots representing about 1500 trees in the Bosna River basin corresponding to Level 6, EU-Hydro River Network Database. Six climatic variables (average annual temperature, minimum temperature, maximum temperature, sum of temperature above 5°, sum of precipitation, maximum precipitation), five hydrological variables (average annual flow, minimum flow, maximum flow, flow between 1961–1990 and water level) and five air quality variables (average annual concentration of air particulate matter of PM2.5 and PM10 mm, SO2, NO2, maximum CO2) were interpolated spatially on 10 m grain size based on hydro-meteorological data from 13 national stations. The MaxEnt method was used to predict spatial distribution model, where predicted occurrence probabilities are classified in habitat suitability classes. The MaxEnt model revealed high-quality spatial prediction (AUC=0.95). The most significant determinants were average annual sum of precipitation and average annual 24-hour maximum CO2 concentration (cumulative about a 72% contribution). The highest occurrence probabilities were related to areas with less than 1400 mm of annual sum precipitation and elevated CO2 linked to low NO2. The areas with high species occurrence are mainly located in continental Bosnian Internal Dinarides in the valley and partly on hilly and sub-mountainous positions overlapping pedunculated oak-hornbeam and Illyrian sub-mountainous beech forests. Modeled ranges of precipitations and air variables concentrations indicate that black alder prefers continental low hilly and plane positions covering forest edges, although some suitable ecological niches are predicted in sub-urban and peri-urban green areas. The obtained model of species distribution determined spatially ecological niches important for conservation and restoration to maintain ecological services and biodiversity as well as aesthetic and recreational roles of black alder, which are important for local communities.