The influence of climate changes on heavy metals accumulation in Alosa immaculata from the Danube River Basin.

This research report provides a comprehensive overview of the historical trends in heavy metal concentrations in the Pontic shad (Alosa immaculata) populations from both the Danube River and the Black Sea, while also exploring the potential influence of global warming on metal accumulation. Through bibliometric modeling analysis, it reveals significant limitations in existing international research, particularly the lack of comprehensive data on the impact of hydroclimatic changes on heavy metal accumulation in Alosa immaculata. Recognizing the critical importance of studies on heavy metal bioaccumulation in Danube shad, this research underscores their significance in defining tolerance thresholds, quantifying the impact of toxic elements along the aquatic food chain, and enhancing the economic sustainability of ichthyofauna monitoring efforts. Furthermore, these studies contribute invaluable insights into the complex dynamics of aquatic ecosystems, offering essential decision-making support for optimizing commercial fishing management practices on the Danube and ensuring robust support systems for industrial fishing endeavors.

Effects of cooking methods and co-ingested foods on mercury bioaccessibility in pontic shad (Alosa immaculata).

Human mercury (Hg) exposure is mostly caused by eating fish. However, there are major differences between the measured and predicted mercury concentration on Hg bioavailability. This study investigated the effects of cooking (steaming, baking, frying, marinating, and smoking) and selected components' co-ingestion on Hg bioaccessibility. Baking and frying reduced Hg bioaccessibility compared to the raw sample. The bioaccessible Hg fraction in fish was assessed through in vitro digestion method. Hg bioaccessibility varied from 4.31 to nearly 24.95% and the Hg recovery rate varied from 63.44 to 78.74%. Co-ingested garlic and broccoli with pontic shad had a positive effect on decreasing fish Hg bioaccessibility. The antioxidant activity of co-ingested food items was also calculated and correlated with mercury bioaccessibility. These results highlighted a possible positive role of plant-based foods and other food processing techniques in the bioaccessibility reduction of other chemical contaminants found in food sources.

An Analytical Framework on Utilizing Various Integrated Multi-Trophic Scenarios for Basil Production.

Here, we aim to improve the overall sustainability of aquaponic basil (Ocimum basilicum L.)-sturgeon (Acipenser baerii) integrated recirculating systems. We implement new AI methods for operational management together with innovative solutions for plant growth bed, consisting of Rapana venosa shells (R), considered wastes in the food processing industry. To this end, the ARIMA-supervised learning method was used to develop solutions for forecasting the growth of both fish and plant biomass, while multi-linear regression (MLR), generalized additive models (GAM), and XGBoost were used for developing black-box virtual sensors for water quality. The efficiency of the new R substrate was evaluated and compared to the consecrated light expended clay aggregate-LECA aquaponics substrate (H). Considering two different technological scenarios (A-high feed input, B-low feed input, respectively), nutrient reduction rates, plant biomass growth performance and additionally plant quality are analysed. The resulting prediction models reveal a good accuracy, with the best metrics for predicting N-NO3 concentration in technological water. Furthermore, PCA analysis reveals a high correlation between water dissolved oxygen and pH. The use of innovative R growth substrate assured better basil growth performance. Indeed, this was in terms of both average fresh weight per basil plant, with 22.59% more at AR compared to AH, 16.45% more at BR compared to BH, respectively, as well as for average leaf area (LA) with 8.36% more at AR compared to AH, 9.49% more at BR compared to BH. However, the use of R substrate revealed a lower N-NH4 and N-NO3 reduction rate in technological water, compared to H-based variants (19.58% at AR and 18.95% at BR, compared to 20.75% at AH and 26.53% at BH for N-NH4; 2.02% at AR and 4.1% at BR, compared to 3.16% at AH and 5.24% at BH for N-NO3). The concentration of Ca, K, Mg and NO3 in the basil leaf area registered the following relationship between the experimental variants: AR > AH > BR > BH. In the root area however, the NO3 were higher in H variants with low feed input. The total phenolic and flavonoid contents in basil roots and aerial parts and the antioxidant activity of the methanolic extracts of experimental variants revealed that the highest total phenolic and flavonoid contents were found in the BH variant (0.348% and 0.169%, respectively in the roots, 0.512% and 0.019%, respectively in the aerial parts), while the methanolic extract obtained from the roots of the same variant showed the most potent antioxidant activity (89.15%). The results revealed that an analytical framework based on supervised learning can be successfully employed in various technological scenarios to optimize operational management in an aquaponic basil (Ocimum basilicum L.)-sturgeon (Acipenser baerii) integrated recirculating systems. Also, the R substrate represents a suitable alternative for replacing conventional aquaponic grow beds. This is because it offers better plant growth performance and plant quality, together with a comparable nitrogen compound reduction rate. Future studies should investigate the long-term efficiency of innovative R aquaponic growth bed. Thus, focusing on the application of the developed prediction and forecasting models developed here, on a wider range of technological scenarios.

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models.

In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.