The University of Zadar (Croatian: Sveučilište u Zadru, Latin: Universitas Studiorum Iadertina) is a university located in Zadar, Croatia. The university in its present form was founded in 2002, but can trace its lineage to 1396, thus making it the oldest tertiary institution in Croatia and one of the oldest in Europe.
Rural settlements in Serbia are increasingly exposed to the combined pressures of demographic decline and climate-related hazards. This study integrates Geographic Information Systems (GIS), Remote Sensing (RS), census data, gridded population datasets, and climate projections to assess long-term rural vulnerability through 2100. Historical census information spanning 1948–2022 provides the demographic context, while the 1991 and 2022 rural populations are used for the baseline demographic projection. Landsat 8/9, Sentinel-2, hazard-event databases, and CMIP6-MIROC6 climate projections under SSP5-8.5, downscaled using CHELSA, are integrated in the spatial analysis. A dimensionless Climate-Hazard Exposure Index (CHEI), based on normalized drought, flood, extreme-rainfall, and temperature scores, and a Settlement Viability Index (SVI) are used to identify areas where demographic decline coincides with elevated climate-hazard exposure. The baseline demographic model estimates a reduction of the rural population from approximately 2.1 million inhabitants in 2022 to 1.44 million by 2050 and about 730,000 by 2100. Climate exposure is treated as an independent spatial layer rather than as a calibrated causal coefficient of population decline. Severe demographic viability risk is operationalized using a projected settlement population threshold of fewer than 100 inhabitants. The spatial classification achieved an overall accuracy of 80.0%, with precision of 83.0%, recall of 80.0%, and an F1-score of 81.5%. Demographic hindcasting is interpreted as an internal agreement check rather than independent predictive validation. Southern and eastern Serbia show the highest combined vulnerability, supporting the need for targeted adaptation, climate-resilient infrastructure, and sustainable rural-development policies.
In today's rapidly evolving transportation landscape, electric vehicles (EVs) play a key role in reducing dependence on fossil fuels. Developing accessible charging infrastructure is a critical priority for sustainable regional transport planning. This study introduces an integrated methodological framework for evaluating and optimizing the spatial distribution of electric vehicle charging stations (EVCSs) at the national scale, using the Republic of Croatia as a case study. The framework combines a composite EVCS Deficit Index (IDEV) derived using CRITIC-weighted multi-criteria analysis, a data-driven suitability model (EVLP) based on logistic regression (LR) with spatially stratified cross-validation, and network-based location-allocation optimization under explicit coverage constraints. IDEV integrates nine indicators capturing supply intensity, accessibility, impedance, load, and demand pressure. Local Indicators of Spatial Association (LISA) were used to identify statistically significant clusters of high deficits at the local government unit (LGU) level. The EVLP suitability surface was derived from 16 predictors. The LR model achieved high predictive performance, and Top-N evaluation confirmed strong prioritization capacity. Deficit-priority LGUs and high-suitability pixels were subsequently integrated into a constrained location-allocation model. Scenario analysis (k = 10, 25, 50, 100 new EVCS) revealed diminishing marginal coverage gains, while simultaneously demonstrating measurable improvements in EVCS accessibility within deficit-priority LGUs. The proposed framework moves beyond standalone suitability mapping or coverage optimization by linking spatial equity diagnostics, probabilistic modeling, and network allocation within a unified GIS workflow. The results provide a transferable methodological template for evidence-based EVCS planning at national scale.
This study investigated the influence of drying techniques such as convection hot-air drying, vacuum drying, and freeze drying with slow and flash pre-freezing on the total phenolic content and the profile of dominant phenolic compounds in cultivated blueberry (Vaccinium corymbosum L.). Although fresh blueberries exhibited higher total phenolic content (1350.85 mg GAE/100 g), total flavonol glycosides (66.20 mg/100 g), and total anthocyanins (218.23 mg/100 g) compared with dried samples, freeze-dried samples, particularly those subjected to flash pre-freezing, retained higher contents of these components in the dried material compared to other drying techniques. This could be attributed to the microstructural preservation of plant tissue during freeze drying. Furthermore, the study demonstrated that subsequent milling of freeze-dried samples, whether using a knife mill or a ball mill, also affects the availability of bioactive compounds in freeze-dried blueberry powders. The combination of flash pre-freezing followed by ball milling yielded the highest availability of bioactive components in the processed blueberry powder.
This research investigates the use of wood biomass ash (WBA) as a supplementary cementitious material (SCM) in blended cement formulations containing 6 and 12 wt
Fast development of digital and computer systems has profoundly shaped the evolution of artificial intelligence (AI) and expanded its use across almost every aspect of society. Medicine stands among the fields most deeply transformed by this revolution where AI can accelerate diagnostic processes, personalize treatments, support clinical decision-making and enhance education. Yet the same technological progress that enables these benefits also introduces new vulnerabilities and exposure to growing cyber threats. As in other areas of use of digital and computer technologies (especially advanced ones), the possibility of their misuse for various purposes and with different motives is increasing: from personal revenge, through organized crime (national and international) and influencing operations to espionage and terrorism. This paper explores the dual nature of AI in medicine: as both an enabler of progress and a potential vector of systemic risk, through the lenses of information and cybersecurity, intelligence, and resilience. It examines the technological and organizational dimensions of these challenges by jointly analyzing documented AI-enabled clinical infrastructures, data flows, and security controls alongside governance structures, institutional responsibilities, and human factors shaping system resilience. As researchers, clinicians, technologists, intelligence analysts, and security professionals, we believe that this human dimension must guide all our efforts to ensure that AI (today and in the future) serves its true purpose: strengthening medicine’s capacity to heal, protect, learn, prevent, and uphold the dignity of human life.
