Evaluating the somatic cell count (SCC) at the level of the herd or individual cows allows for efficient monitoring of mammary gland health. By analysing SCC, it is possible to identify subclinical cases of mastitis that do not manifest through visible clinical signs on the udder or changes in milk. This study was conducted on a modern dairy farm of the Holstein-Friesian breed in the municipality of Čapljina, Bosnia and Herzegovina. The total number of cows included in the study during 2022 and 2023 ranged between 325 and 335. Milk samples were preserved with azidiol and transported to the laboratory. Milk quality was assessed by determining the SCC in milk using the Fluoro-opto-electronic method, and by analysing the fat, protein, and lactose contents. The devices used in the study were CombiFoss 6200 – MilkoScan FT and Fossomatic FC 6000. A strong positive correlation was found between SCC and milk proteins, but not with milk fat. A significant negative correlation was found between SCC and lactose. There was no significant difference in the number of somatic cells by year, although there was a significant difference by season within the studied years. Winter stands out as the season with the lowest SCC, followed by spring and summer, while autumn had the highest count. Autumn also showed the largest oscillations in SCC, while spring had the smallest. Somatic cell counts over 200,000/mL were recorded from July to December 2022 and from May to November 2023. Zoohygiene conditions and milking hygiene measures should be additionally adjusted in summer and autumn to maintain the desired standards achieved in winter and spring.

Extant literature captures the benefits and risks concerning young adults’ use of digital technologies and platforms, but it does not unilaterally recognize the drivers of problematic digital behavior. Those drivers might differ across dimensions of young adults’ digital lives, their socioeconomic backgrounds, and other demographic determinants. In this study, we analyze the determinants of addictive digital behavior of economics and business students from a Southeast European (SEE) sample of 372 participants. We measure digital addictive behavior regarding Internet use, with a focus on mobile phones, using established psychological scales. Our results show that age is generally associated with lower problematic digital behavior (significant in the full sample), while female students report higher PRIUSS-3 scores than male students. Higher ICT proficiency is associated with lower PRIUSS-3 and MPPUS-10 scores. Daily screen time is associated with higher MPPUS-10 scores, but it does not significantly predict PRIUSS-3 in the multivariable model. The empirical results can be used to frame the higher education policies and targeted interventions in the SEE region.

Teaching quality is crucial for positive student outcomes, yet it can be limited by challenges in the classroom. In this study, we examine how teacher-reported limitations to teaching (e.g., student absenteeism, disruptive behavior, lack of engagement, language barriers) relate to student-reported teaching quality. We also explore whether teacher job satisfaction mediates this relationship. Using data from the Norwegian extension of the TIMSS 2019 Grade 9 student questionnaire, we assess three dimensions of teaching quality based on the three basic dimensions (TBD) framework. Results show that more frequent limitations to teaching are linked to lower student-reported quality. We find that more satisfied teachers deliver clearer instruction and achieve better classroom management. However, we find no evidence that limitations to teaching are associated with lower job satisfaction, nor that job satisfaction mediates the relationship between limitations and teaching quality. We discuss implications for teacher education and educational policymaking.

This article extends the analysis of Atkinson, Foley, and Ganz in"Beyond the Spoiler Effect: Can Ranked-Choice Voting Solve the Problem of Political Polarization?". Their work uses a one-dimensional spatial model based on survey data from the Cooperative Election Survey (CES) to examine how instant-runoff voting (IRV) and Condorcet methods promote candidate moderation. Their model assumes an idealized electoral environment in which all voters possess complete information regarding candidates'ideological positions, all voters provide complete preference rankings, etc. Under these assumptions, their results indicate that Condorcet methods tend to yield winners who are substantially more moderate than those produced by IRV. We construct new models based on CES data which take into account more realistic voter behavior, such as the presence of partial ballots. Our general finding is that under more realistic models the differences between Condorcet methods and IRV largely disappear, implying that in real-world settings the moderating effect of Condorcet methods may not be nearly as strong as what is suggested by more theoretical models.

This research assessed management strategies for overtourism in Zadar County. Overtourism has become apparent in both city and seaside destinations, affecting residents’ quality of life. This study defines overtourism as a challenge for urban management, emphasizing that exploring strategies to address overtourism also influences the management of sustainability and quality of life in urban areas. Here, a methodological framework was created with five strategies, each evaluated against seven criteria. The evaluation was carried out by the directors of the county’s tourist boards. Since these strategies have not yet been implemented, the directors had to rate them with some uncertainty, as they lacked complete information about the criteria and potential effects. To handle this uncertainty, the intuitionistic fuzzy set (IFS) approach was used. Additionally, the SiWeC method determined the importance of the criteria, and the TOPSIS method ranked the strategies. Results, based on ratings from 12 directors, indicated that Digital Support and Environmental Sustainability are the most important criteria. Strategy C, which aims to redirect tourists to lesser-known locations within the county, performed best, maintaining visitor numbers while helping preserve the region’s natural resources. This research has shown that strategies for managing overtourism help reduce the pressure tourists place on urban environments, thereby improving the quality of life and sustainable development of these environments.

Five neutral heteroleptic mononuclear vanadium(IV) hydrazone complexes ([VOL(bpy)]), derived from 2-hydroxy-5-methylacetophenone and various acid hydrazides (furoic, thiophene, benzoic, nicotinic, and isoniazid), were synthesized and shown to exhibit improved antidiabetic efficacy in streptozotocin-induced diabetic rats, with reduced toxicity and minimal bioaccumulation compared to maltolato- and picolinato-based vanadium species. Structural identity was established by spectroscopic methods. Crystal structures were obtained for four complexes, providing insight into their solid-state chemistry. Stability studies in simulated intestinal and gastric fluids showed that the complexes largely retained their integrity under intestinal conditions, whereas decomposition occurred in the highly acidic gastric environment within several minutes. In vivo experiments revealed a structure-antihyperglycemic activity relationship. The nicotinic-containing complex showed the highest activity, reducing blood glucose levels by 67% within 7 days of treatment, while the remaining complexes improved glycemic control by more than 50%. Bioaccumulation studies demonstrated <1.1% uptake in the liver and kidneys and negligible accumulation in the brain. The presented vanadium compounds enhance antidiabetic potential by addressing key limitations, particularly bioaccumulation and toxicity, associated with vanadium agents previously evaluated in clinical trials.

Digital twins (DTs) are increasingly used to monitor and secure Industrial Control Systems (ICS), yet detecting stealthy False Data Injection Attacks (FDIAs) that manipulate system states within normal physical bounds remains challenging. Deep learning anomaly detectors often over-generalize such subtle manipulations, while classical fault detection methods do not scale well in highly correlated multivariate systems. We propose a closed-loop Information-Theoretic Digital Twin (IT-DT) framework for real-time anomaly detection. N4SID identification is combined with steady-state Kalman filtering to quantify residual distribution shifts via closed-form KL divergence, capturing both mean deviations and malicious cross-covariance shifts. Evaluations on the SWaT and WADI datasets show that IT-DT achieves F1-scores of 0.832 and 0.615, respectively, with better precision than deep learning baselines such as TranAD. Computational profiling indicates that the analytical approach requires minimal memory and provides approximately a 600x inference speedup over transformer-based methods on CPU hardware. This makes the framework suitable for resource-constrained industrial edge controllers without GPU acceleration.

This research aimed to examine which of the selected strategies can most effectively influence households to reduce their total municipal waste and thus protect the environment. To achieve this goal, a sample of 202 households from the Brčko District of BiH was used. Respondents evaluated six strategies against ten criteria, expressing their assessments through linguistic values. These linguistic inputs were modeled using symmetric fuzzy numbers, ensuring a consistent and mathematically robust representation of uncertainty and subjective judgment. The research used the fuzzy SiWeC (Simple Weight Calculation) method to determine the importance of the criteria, and the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), ARAS (Additive Ratio Assessment), and SAW (Simple Additive Weighted) methods to rank the strategies. The application of several methods in decision-making helps validate results and verify the robustness of strategy selection. These methods identified “waste reduction efficiency” as the most important criterion and “Strategy 3—Packaging return machines” as the most effective overall. Furthermore, analysis of demographic subgroups revealed significant variations in the perceived value of alternative strategies. Consequently, this study concludes that to optimize municipal waste management, strategies should be tailored to specific demographic profiles. This targeted approach would enhance waste reduction at the source, divert more waste from landfills, and promote the broader implementation of circular economy principles. The use of symmetric fuzzy numbers provided a reliable and stable foundation for this multi-criteria decision-making analysis.

<p><strong>Aim </strong>To identify predictors of all-cause mortality and 6-month rehospitalisation in patients with hypertensive crisis, focusing on inflammatory indices, metabolic markers measured at admission, and antihypertensive treatment profiles.</p> <p><strong>Methods </strong>This prospective observational study included 210 adult patients with hypertensive crisis. Demographic, clinical, and therapeutic data were collected, including data on comorbidities, antihypertensive drug use, and treatment adherence. Laboratory parameters obtained at admission included neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), homocysteine, and uric acid. Patients were followed for 12 months. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to identify independent predictors.</p> <p><strong>Results </strong>Mortality occurred in 10.9% of patients, and 27.1% were rehospitalised within 6 months. Deceased patients exhibited significantly higher levels of PLR (p=0.0329), SII (p=0.0355), homocysteine (p=0.0488), and uric acid (p=0.021). In multivariate analysis, homocysteine (OR=3.55; p&lt;0.001), uric acid (OR=1.03; p=0.007), PLR (OR=1.04; p=0.047), and SII (OR=1.01; p=0.030) remained independently associated with mortality. Chronic kidney disease (OR=2.15, p=0.012) and poor treatment adherence (OR=1.92; p=0.017) were also significant predictors. ROC analysis demonstrated moderate discriminative power, with AUC values of 0.68 for PLR, 0.66 for SII, 0.65 for homocysteine, and 0.63 for uric acid.</p> <p><strong>Conclusion</strong> Elevated inflammatory indices and metabolic markers, particularly homocysteine and uric acid, were independently associated with increased mortality risk. Additionally, chronic kidney disease and suboptimal adherence to antihypertensive therapy significantly contributed to adverse outcomes. These findings underscore the importance of comprehensive risk assessment and personalised management in this high-risk population.</p>