Milk and dairy products are nutritionally rich foods, providing energy and high-quality proteins along with a number of essential micronutrients. In this way, the dairy sector supports the existence of people in food chains around the world. Human activities affect the Earth's climate because they cause the release of huge amounts of greenhouse gases, which are retained in the Earth's atmosphere along with the gases that are naturally present in it. Food and drink are considered to be responsible for 20 to 30% of the impact on the environment, in which meat and dairy products are the most important. Research and methodologies are mostly focused on the emission of greenhouse gases (GHG), but numerous other impacts on the environment must also be taken into account, such as impacts on land and water through various types of pollution. In addition, the consumption of scarce water reserves and energy resources is also taken into account. Therefore, a sustainable approach to food and beverage production has no alternative. The dairy sector faces challenges to which it responds with numerous organized and synchronized actions and projects in order to successfully restructure and fit into the overall human activities to reduce pollution. Sustainability itself has 3 dimensions, ecological, economic and social. The aim of this paper is to present the current situation of sustainability in the dairy sector in the world and numerous activities undertaken by various actors in the dairy sector, from farm entities and dairy companies to various associations and umbrella organizations such as the International Dairy Federation, the European Dairy Association as well as non-specific dairy-strictly unrelated bodies. All activities are carried out in coordination and under the auspices of the United Nations and its organizations, primarily the Food and Agriculture Organization.

U radu se upoređuju tri metode za estimaciju i kompenzaciju poremećaja u diskretnim sistemima sa kliznim režimom (KR). Prva metoda koristi Luenbergerov opserver poremećaja, druga - nominalni model upravljanog objekta, a treća - integral signala klizne funkcije. Cilj rada je da pokaže ekvivalentnost navedenih metoda u primeni na diskretne sisteme upravljanja sa KR u nominalnim uslovima na jednostavnom sistemu upravljanja objektom prvog reda tipa čistog integratora. Poseban doprinos rada je modifikovani estimator poremećaja koji kombinuje Luenbergerov estimator sa estimatorom na osnovu integrala klizne funkcije. Teorijski rezultati su ilustrovani simulacionim eksperimentima.

Contemporary technologies, including digitalization and artificial intelligence, increasingly influence everyday life and social relations, often giving rise to new forms of criminal offenses. Consequently, the fight against cybercrime demands more sophisticated mechanisms for detecting and proving such offenses. On the other hand, technological advancements present additional challenges for the efficient administration of criminal procedures, particularly with the application of artificial intelligence. This paper examines the current and applicable forms of modern technologies in criminal law, with a particular focus on criminal procedural law, highlighting the use of digital tools and artificial intelligence. The first part of the paper presents concrete examples of their application in combating cybercrime, evaluating both their advantages and potential risks. The second part analyzes key international documents of the Council of Europe and the European Union, which outline principles for the application of modern technologies, with a particular emphasis on the protection of human rights, the rule of law, and the preservation of moral values.

The aim of the research is to determine the effectiveness of ultrasonic extraction of nettle seeds (Urtica dioica) whose products can be used as potential metal corrosion inhibitors. Different solvents were used (water, methanol, ethanol, acetone) in terms of total phenol content (TPC), flavonoids (TFC) and antioxidant activity (DPPH and FRAP). The results show that the aqueous extracts have the highest TPC and antioxidant capacity in both tests. The high correlation between TPC and antioxidant activity (R ≈ 0.98) confirms that phenolic compounds are key contributors to antioxidant capacity. These findings suggest that nettle seeds can be examined as a potential metal corrosion inhibitor.

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.

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.

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.

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.

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.