Ecological viticulture represent an upward trend in many countries. Unlike conventional viticulture, it avoids the use of chemical fertilizers and other additives, minimizing the impact of chemicals on the environment and human health. The aim of this study was to investigate the influence of nanofiltration (NF) process on volatiles and chemical composition of conventional and ecological Cabernet Sauvignon red wine. The NF process was conducted on laboratory Alfa Laval LabUnit M20 (De Danske Sukkerfabrikker, Nakskov, Denmark) equipped with six NF M20 membranes in a plate module, at two temperature regimes, with and without cooling and four pressures (2.5, 3.5, 4.5 and 5.5 MPa). Different processing parameters significantly influenced the permeate flux which increased when higher pressure was applied. In initial wines and obtained retentates, volatile compounds, chemical composition and elements concentration were determined. The results showed that the higher pressure and retentate cooling was more favourable for total volatiles retention than lower pressure and higher temperature. Individual compound retention depended on its chemical properties, applied processing parameters and wine composition. Nanofiltration process resulted in lower concentrations of ethanol, acetic acid (>50%), 4-ethylphenol and 4-ethylguaiacol (>90%). Different composition of initial feed (conventional and ecological wine) had an important impact on retention of elements.

Wine aroma represents one of the most important quality parameters and it is influenced by various factors, such as climate conditions, viticulture and vinification techniques, storage conditions, etc. Wines produced from conventionally and ecologically grown grapes of the same variety have different chemical compositions and aroma profiles. The composition of wine can also be influenced by the additional treatment of wine, such as the concentration of wine by reverse osmosis (RO). The aim of this study was to investigate the influence of four different pressures (2.5, 3.5, 4.5 and 5.5 MPa) and two temperature regimes (with and without cooling) on the aroma profile and chemical composition of conventional and ecological Cabernet Sauvignon red wine during concentration by reverse osmosis. The results showed that different processing parameters influenced the permeate flux, the retentate temperature and the compounds retention. Higher working pressures (4.5 and 5.5 MPa) and the regime, with cooling, resulted in a higher retention of the total aroma compounds than the opposite processing parameters. The retention of individual compounds depended also on their chemical properties and their interactions with the membrane surface. The reverse osmosis membranes proved to be permeable for ethanol, acetic acid or undesirable 4-ethylphenol and 4-ethylguaiacol that made them applicable for their correction or removal.

Preventing large-scale illegal migration is one of the EU’s highest priorities. In this study, we analyze the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifically targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia’s external EU borders. The model’s methodological framework is based on the integration of Geographic Information Systems (GISs), Multi-Criteria Analysis (MCA), and the Analytic Hierarchy Process (AHP). The model utilizes various environmental and infrastructure variables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorized risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. The model was quantitatively verified using a weighted detection-versus-background design against 7481 geocoded border crossing incidents, demonstrating high predictive skill and robust calibration (Continuous Boyce Index up to 0.97) when controlling for patrol effort bias and spatial autocorrelation. High-resolution historical satellite imagery showing activities related to illegal migration was used for the generation of labeled datasets for AI training. Features such as suspicious vans, river boats, tire tracks, tents, illegal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applications, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimized operations.

Preventing large-scale illegal migration is one of the EU's highest priorities. In this study, we analyse the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifical-ly targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia's external EU borders. The model’s methodological framework is based on the integration of Geo-graphic Information Systems (GIS), Multi-Criteria Analysis (MCA), and the Analytic Hi-erarchy Process (AHP). The model utilizes various environmental and infrastructure var-iables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorised risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. High-resolution historical satellite imagery showing activities re-lated to illegal migration is used for model verification and generation of labelled da-tasets for AI training. Features such as suspicious vans, river boats, tyre tracks, tents, il-legal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applica-tions, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimised op-erations.