Novi regulatorni okvir za industrijski dizajn u EU, utemeljen na Uredbi (EU) 2024/2822 i Direktivi (EU) 2024/2823, donosi značajnu modernizaciju sustava zaštite koji nije mijenjan više od 20 godina. Glavne novosti uključuju proširenje definicije proizvoda, klauzula o popravku, suzbijanje 3D ispisa, promjenu terminologije, kao i moderne prikaze dizajna. Veći dio pravila počeo se primjenjivati od svibnja 2025., dok države članice imaju rok do kraja 2027. za potpuno usklađivanje nacionalnih zakona s novom Direktivom (EU) 2024/2823, kako bi se modernizirao sustav i prilagodio izazovima digitalnog doba. U radu će se analizirati razlozi zbog koji su usvojena nova pravila, te sudska praksa koja je oblikovala nova rješenja odnosno kako Sud EU-a kroz svoju praksu stvara pravni okvir koji paketu reformi dizajna daje snagu i koherentnost. Ključne riječi: industrijski dizajn, Uredba (EU) 2024/2822, Direktiva (EU) 2024/2823, klauzula o popravku, suzbijanje 3D ispisa.

International migrants face well-documented barriers to healthcare access, yet the extent to which these barriers shape patterns of disease co-occurrence remains poorly understood. Drawing on a nationwide dataset of approximately 13 million hospital admissions from around 4 million individuals in Austria (2015-2019), we constructed and compared comorbidity networks between Austrian nationals and non-Austrian migrants, matched 1:1 by age, sex, and time of first hospital admission (272,779 per group). Following matching, metabolic and cardiovascular diagnoses, including type 2 diabetes and myocardial infarction, were more common among non-Austrians, while depression was more common among Austrians. Comorbidity network analysis showed that among all disease pairs that differed significantly between groups, 70% showed stronger co-occurrence in Austrian patients and 30% in non-Austrian patients. Distinct sex-specific patterns appeared: Austrian males showed stronger associations between alcohol use disorder and mental health diagnoses, whereas non-Austrian males more frequently presented with acute somatic conditions. Among non-Austrian women, a pronounced cluster of recurrent depression, somatoform disorders, and dorsalgia was observed. We interpret the disproportionately fewer comorbidity links observed in non-Austrians not as evidence of lower disease burden, but as a likely reflection of structural access barriers, including language differences, cultural factors, and crisis-oriented admission patterns, that prevent comprehensive diagnostic assessment, though a contribution from the healthy migrant effect cannot be excluded. These findings stress the need for culturally aware care strategies and earlier identification of high-risk multimorbidity profiles in migrant populations.

Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown success in single-physics problems, yet their effectiveness in capturing nonlinear interactions in coupled systems (such as thermo-mechanical or electro-thermal coupling) remains underexplored. Here we pose a practical question: should the architecture of a neural operator reflect the strength of physical coupling it aims to model? We compare single-branch and multi-branch designs, in both feedforward and sequential recurrent forms, across three representative systems: a reaction-diffusion problem with heterogeneous sources, a nonlinear thermo-electrical problem with temperature-dependent conductivity and Joule heating, and a viscoplastic thermo-mechanical model of steel solidification. Single-branch networks consistently outperform multi-branch variants in tightly coupled regimes by encouraging shared latent representations, whereas multi-branch designs remain favorable for decoupled or single-physics tasks. Once trained, these surrogates deliver full-field predictions up to 1.8 × 104 times faster than physics-based solvers.

Agent-based neural models often encode transmission within neuron state updates, which can make it difficult to separately log and quantify spatial recruitment patterns, delay structure, and environment-mediated feedback effects. We present LANA (Local Adaptive Neural Agents), a dual-agent neural agent-based model in which neurons and propagating signals are represented as distinct interacting entities embedded in a dynamic environmental field. The model combines discrete leaky integrate-and-fire neuron dynamics, mobile signal agents, synaptic links with distance-dependent delays, and a bounded environment-to-neuron feedback mechanism. LANA is intended as a normalized phenomenological mesoscopic framework for mechanism-level comparison rather than as a circuit-specific biophysical reconstruction. To support interpretability and reproducibility, we report a compact internal verification block for the implemented operators, including delay propagation, environmental decay and diffusion, threshold activation, and refractory enforcement. We then compare the full LANA model against a matched neuron-only baseline and summarize spatial recruitment using first-spike maps, cumulative recruitment times, and wavefront speed as a secondary descriptive metric. Finally, we evaluate two controlled operating regimes, a resting regime (S1) and a hyperexcitable regime (S2), under fixed network size, stimulation schedule, and matched random seeds. Relative to the baseline, the full model sustains and spreads activity more effectively and provides spatially resolved recruitment summaries, including first-spike timing and cumulative recruitment measures, that are not available in the same form when transmission is represented only through neuron-level updates. Relative to S1, S2 exhibits earlier activation, higher firing activity, stronger environmental accumulation, and faster cumulative recruitment. Local and factorial sensitivity analyses further identify the parameters that most strongly govern these regime differences. Together, these results position LANA as a normalized mesoscopic and computationally tractable framework for studying how excitability, transport state dynamics, delayed coupling, and environment-mediated feedback jointly shape emergent activity in controlled simulation settings.

Simple Summary The Carpathian water buffalo is a regional livestock resource in Central and Eastern Europe, but little is known about its genetic background. This study compared buffalo from Hungary and Romania using genome-wide marker data generated with a cattle genotyping array. The results show that both populations retain moderate genetic diversity. The Romanian animals had slightly higher diversity, whereas the Hungarian animals showed more long homozygous genome segments, which may indicate stronger recent relatedness or a more restricted breeding structure. The candidate regions for positive selection included genomic regions that may be associated with immune response, reproduction, growth, milk production and heat or cold tolerance. Because the marker array was designed for cattle and sample sizes differed between countries, the findings should be considered a first genomic baseline rather than final evidence of causal variants. The results can support future conservation planning and more detailed studies using buffalo-specific genomic tools.

Background/Objectives: Hypertrophic cardiomyopathy (HCM) is a complex myocardial disease in which structural remodeling extends beyond conventional parameters such as maximal wall thickness (MWT). This study aimed to evaluate isoperimetric analysis as a geometry-based approach for characterizing left ventricular (LV) morphology and to compare its performance with fractal analysis in distinguishing HCM from non-HCM subjects. Methods: This retrospective study included 120 subjects (60 HCM and 60 controls) who underwent clinically indicated CMR. Endocardial contours were delineated on short-axis cine images at end-diastole. The isoperimetric index (IPI), cavity-corrected IPI (CC-IPI), and fractal dimension (FD) were calculated. Group comparisons, correlations, and receiver operating characteristic analyses were performed. Results: Patients with HCM showed significantly higher IPI and CC-IPI values than controls (p < 0.001), including in sex-stratified analyses. IPI and CC-IPI correlated strongly with MWT (r = 0.867) and LV mass index (r = 0.807 and 0.803, respectively), moderately with native T1 (r = 0.567), and weakly with end-diastolic volume index (r = 0.256 and 0.253, respectively) and stroke volume (r = 0.237 and 0.229, respectively). No association was observed with late gadolinium enhancement. IPI and CC-IPI showed a strong linear correlation (R2 = 0.997), with both indices demonstrating high discriminatory performance (AUC = 1.00). Conclusions: HCM is associated with notable alterations in LV endocardial geometry beyond myocardial hypertrophy alone. IPI and CC-IPI provide simple, size-normalized, and physiologically interpretable morphologic descriptors that complement conventional CMR parameters and FD-based contour complexity analysis.

Rhegmatogenous retinal detachment (RRD) is a vision-threatening retinal disorder in which successful anatomical reattachment does not always result in satisfactory functional visual recovery. Optical coherence tomography (OCT) has become an essential non-invasive imaging modality for evaluating retinal microstructure and identifying biomarkers associated with postoperative visual outcomes. This narrative review summarizes and critically evaluates current evidence regarding the prognostic significance of OCT biomarkers following RRD repair. Available studies indicate that primary outer retinal biomarkers, particularly the integrity of the ellipsoid zone (EZ) and external limiting membrane (ELM), are the most reliable predictors of postoperative best-corrected visual acuity. Preservation of these structures reflects photoreceptor viability and is strongly associated with improved visual recovery. Secondary biomarkers, including hyperreflective foci, intraretinal cystic changes, retinal folds, persistent subretinal fluid, and preretinal hyperreflective dots, provide complementary information regarding inflammatory activity, retinal remodeling, and postoperative complications such as epiretinal membrane formation. Although these biomarkers have considerable clinical value for prognostication, patient counseling, and postoperative monitoring, their interpretation is limited by heterogeneity among studies, differences in imaging protocols, and the lack of standardized assessment criteria. Future large-scale prospective studies are needed to validate OCT biomarkers and establish standardized approaches for their integration into routine clinical practice.

Indoor air quality may be compromised by elevated radon levels, a naturally occurring radioactive gas. Prolonged exposure to increased radon levels can adversely affect human health. This paper presents the results of indoor radon activity concentration measurements conducted at 20 locations using a passive method. CR-39-based radon detectors were deployed over three different exposure periods, from January to April. The aim of the study was to compare and analyse average indoor radon activity concentrations obtained over different detector exposure periods, and to determine whether reliable information on indoor air quality can be obtained using shorter exposure durations. The measured indoor radon activity concentrations ranged from 6.0 to 727 Bq/m 3 , 10.1 to 561 Bq/m 3 , and 15.1 to 480 Bq/m 3 for the three-month, two-month, and one-month exposure periods, respectively. Comparative analysis of the results showed that the mean ratio of radon activity concentrations for one-month to two-month, two-month to three-month, and one-month to two-month exposure periods were 1.24, 1.57, and 1.85, respectively. To establish the relationship between radon activity concentrations obtained for different exposure periods, linear regression analysis was performed. The analysis of the estimated relative percentage differences showed that the results for different exposure periods agreed within ±20% at approximately 53% of the locations.

Background/Objectives: Pharmacists play a key role in managing urinary tract infection (UTI) symptoms by providing medications, self-care advice, and over-the-counter treatments, while referring patients to a doctor when necessary. This study aimed to examine the practices of community pharmacists in managing UTI symptoms and to gain insight into their attitudes toward antibiotic use for this condition. Methods: A cross-sectional study was conducted among community pharmacists in Serbia using a previously validated online questionnaire, assessed for content and face validity and pilot-tested among pharmacists. Results: A total of 430 community pharmacists participated in the study. Patients more often consulted pharmacists before visiting a doctor than after (median 5 vs. 3 per week; p < 0.001). For uncomplicated UTIs, pharmacists primarily recommended increased fluid intake (92.8%), herbal teas (94.7%), and food supplements (85.6%), whereas for complicated UTIs, most referred patients to a doctor (95.4%). Attitudes, perceived competence, and support for over-the-counter antibiotic availability were significantly associated with gender, years of experience, and specialization. Pharmacists who agreed that antibiotics are the most effective treatment for uncomplicated urinary tract infections were more likely to refer patients to a doctor (p < 0.01). Conclusions: Pharmacists are frequently consulted for UTI management and emphasize non-antibiotic approaches for uncomplicated cases. Their attitudes influence counseling practices, highlighting the need for standardized UTI counseling services, antimicrobial stewardship education, and structured communication training to support appropriate antibiotic use.

BACKGROUND Tuberculosis (TB) is the leading global cause of death from a single infectious agent. Recent reductions in global health funding have threatened TB control, making comprehensive assessment of TB, HIV-related TB, and drug-resistant TB burdens before these disruptions essential for shaping effective responses. The WHO End TB Strategy sets targets of a 95% reduction in TB deaths and a 90% reduction in TB incidence between 2015 and 2035. Using results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, this study aims to assess the burden of TB and multidrug-resistant TB (MDR-TB) across 204 countries and territories, and to evaluate progress towards the WHO End TB incidence and mortality targets. METHODS We quantified TB mortality using the Cause of Death Ensemble modelling platform with global vital registration, surveillance, verbal autopsy, and minimally invasive tissue sampling data. For TB morbidity estimation, we simultaneously modelled incidence, prevalence, and mortality by age and sex using DisMod-MR 2.1. A population attributable fraction (PAF) approach was applied to stratify morbidity and mortality estimates by HIV and drug-resistance status. We also calculated disability-adjusted life-years (DALYs) as the sum of years of life lost and years lived with disability. For the risk factor analysis, a comparative risk assessment framework was used and PAFs were derived for alcohol use, smoking, and high fasting plasma glucose to determine the proportion of TB burden associated with these risk factors. FINDINGS In 2023, there were an estimated 9·11 million (95% uncertainty interval 8·04-10·3) incident cases of all-form TB, 1·22 million (0·98-1·49) deaths, and 54·6 million (43·8-65·5) DALYs globally. HIV-related TB comprised 781 000 (690 000-879 000) incident cases and 210 000 (142 000-279 000) deaths, contributing 11·0 million (7·56-14·3) DALYs. MDR-TB accounted for 466 000 (198 000-1 080 000) incident cases, 102 000 (31 700-238 000) deaths, and 3·96 million (1·31-9·01) DALYs. From 2015 to 2023, global all-form TB incidence rates declined by 19·2% (17·8-20·5) and deaths declined by 22·6% (4·7-35·7); declines were larger for drug-susceptible TB than for MDR-TB. Sub-Saharan Africa and south Asia had the highest mortality burdens in 2023; reductions in all-form TB incidence and mortality were uneven between 2000 and 2023, with limited progress in both measures in Latin America and the Caribbean. Removing smoking, alcohol use, and high fasting plasma glucose would reduce global TB deaths to 768 000 (592 000-970 000) and DALYs to 34·9 million (27·8-43·8) in 2023; MDR-TB deaths would decrease to 77 200 (23 400-183 000) and DALYs to 3·12 million (1·03-7·29). INTERPRETATION Global progress towards WHO End TB targets is disparate and fragile. Although many regions achieved meaningful gains, others have stagnated in recent years. The complexity of TB prevention is amplified by divergent MDR-TB trends, the persistent burden of HIV, and growing exposure to modifiable risk factors. Recent volatility in global health financing threatens to further destabilise this vulnerable epidemiological landscape; concerted action is urgently needed to temper disruptions and preserve progress. FUNDING Gates Foundation.

Residential rooftop photovoltaic (PV) systems are widely deployed in low-voltage networks, where maximizing self-consumption is essential. However, most studies assume balanced three-phase loads, neglecting the inherently single-phase nature of residential appliances. This paper evaluates the impact of phase-aware load modeling on self-consumption in a residential PV system by comparing aggregated and phase-aware representations under normal operation and load-shifting. The load-shifting problem is formulated as a mixed-integer linear programming (MILP) model. Results show that aggregated models systematically overestimate both baseline self-consumption and the benefits of load-shifting. Phase-aware modeling provides more realistic and physically feasible results, although with smaller achievable improvements. These findings highlight the importance of incorporating phase-level modeling for accurate assessment and optimization of residential PV systems.

AI-native decision systems — in which generative artificial intelligence generates, executes, and adapts decisions in real time — are shifting the centre of gravity of analytical effort. Whereas the principal challenge used to be the extraction of insight, it is now becoming the validation of AI outputs while action is still being taken upon them. Existing approaches address this problem only partially: explainable artificial intelligence and governance frameworks treat validation as an ex post layer; reinforcement learning reduces it to a single scalar reward; control theory and the broader cybernetic tradition formalise the closed loop but neglect its sociotechnical constraints; while the PRIME–INSPECT framework establishes a governance foundation, yet leaves real-time validation implicit. This paper proposes a validation-centric architecture that links the GAVA decision loop (Generate–Act–Validate– Adapt), introduced here, with the PRIME–INSPECT framework and elevates validation to a central analytical function comprising four subdimensions: statistical, operational, cognitive, and governance-related. The proposed architecture is empirically examined on a dual sample of IT professionals and top-management representatives, using descriptive statistics, multiple regression, mediation, moderation, and structural equation modelling. The results confirm the central role of trust, the negative effect of perceived risk, and the importance of top management support, while robustness checks separated by the IT and TMT subsamples further strengthen the structural model. Taken as a whole, the findings support the view that validation should be treated as a first-order organisational stage in AI-native decision systems.