Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (C_d), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, C_d, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R² (0.99) and with R² (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.

The current work discusses the production and characterization of new biodegradable nanoparticles for biomedical applications based on insect chitosan. Chitosan has numerous features due to the presence of primary amine groups in repeating units, such as antibacterial and anticancer activities. When polyanion tripolyphosphate is added to chitosan, it creates nanoparticles with higher antibacterial activity than the original chitosan. In this study, the ionic gelation technique was used to make wasp chitosan nanoparticles (WCSNPs) in which TEM and FTIR were used to investigate the physicochemical properties of the nanoparticles. In addition, the antibacterial activities of chitosan nanoparticles against extended-spectrum beta-lactamase (ESBL)- and carbapenemase-producing Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeruginosa were evaluated. The extracted wasp chitosan exhibited high solubility in acetic acid and met all standard criteria of all characterization testes for nanoparticles; the zeta potential indicated stable WCSNPs capable of binding to cellular membrane and increasing the cellular uptake. The produced WCSNPs showed growth inhibition activity against all tested strains, and the bacterial count was lower than the initial count. The inhibition percent of WCSNPs showed that the lowest concentration of WCSNPs was found to be effective against tested strains. WCSNPs’ antibacterial activity implies that they could be used as novel, highly effective antibacterial agents in a variety of biological applications requiring antibacterial characteristics.

Recirculating aquaculture systems (RAS) represent a promising solution for sustainable fish production, but their commercial viability is hampered by a critical barrier: adapting intelligent control systems to new fish species requires extensive, species-specific data collection and lengthy retraining periods (45–60 days). This challenge creates significant economic and operational hurdles for multi-species facilities, limiting their flexibility to adapt to market demands. This study addresses this fundamental limitation by introducing a novel framework that integrates transfer learning and federated intelligence to enable rapid, cost-effective, cross-species adaptation of deep reinforcement learning controllers. Building on our previous work with deep deterministic policy gradient (DDPG), we developed a modular neural architecture with species-agnostic and species-specific components. The system was validated across five distinct RAS configurations using three commercially important species: tilapia, rainbow trout, and European sea bass. The framework achieved 87.3% of optimal performance for a new species with just 14 days of adaptation data, a dramatic improvement over traditional approaches. Furthermore, the federated learning implementation enabled continuous, privacy-preserving model improvement across multiple facilities, demonstrating a 23.5% collective performance improvement over individually trained systems. Economic analysis confirmed the framework’s commercial viability, with adaptation costs 76% lower than developing new species-specific systems and a projected return on investment of 4–14 months. This research advances adaptive intelligent systems for aquaculture, offering a scalable and economically viable approach to precision RAS management. By significantly reducing implementation barriers, this work paves the way for wider commercial adoption, supporting the sustainable intensification required to meet global protein demands.

The present study focuses on the risk assessment of heavy metal contamination in aquatic ecosystems by evaluating the current situation of heavy metals in seven locations (North Amer El Bahry, Amer, Bakr, Ras Gharib, July Water Floud, Ras Shokeir, and El Marageen) along the Suez Gulf coast that are well-known representative sites for petroleum activities in Egypt. One hundred and forty-six samples of surface sediments were carefully collected from twenty-seven profiles in the intertidal and surf zone. The hydrochemical parameters, such as pH and salinity (S‰), were measured during sample collection. The mineralogy study was carried out by an X-ray diffractometer (XRD), and the concentrations of Al, Mn, Fe, Cr, Cu, Co, Zn, Cd, and Pb were determined using inductively coupled plasma mass spectra (ICP-MS). The ecological risks of heavy metals were assessed by applying the contamination factor (CF), enrichment factor (EF), geoaccumulation index (I_geo), pollution load index (PLI), and potential ecological risk index (RI). The mineralogical composition mainly comprised quartz, dolomites, calcite, and feldspars. The average concentrations of the detected heavy metals, in descending order, were Al > Fe > Mn > Cr > Pb > Cu > Zn > Ni > Co > Cd. A non-significant or negative relationship between the heavy metal concentration in the samples and their textural grain size characteristics was observed. The coastal surface sediment samples of the Suez Gulf contained lower concentrations of heavy metals than those published for other regions in the world with petroleum activities, except for Al, Mn, and Cr. The results for the CF, EF, and I_geo showed that Cd and Pb have severe enrichment in surface sediment and are derived from anthropogenic sources, while Al, Mn, Fe, Cr, Co, Ni, Cu, and Zn originate from natural sources. By comparison, the PLI and RI results indicate that the North Amer El Bahry and July Water Floud are considered polluted areas due to their petroleum activities. The continuous monitoring and assessment of pollutants in the Suez Gulf will aid in the protection of the environment and the sustainability of resources.

Recirculating aquaculture systems (RAS) represent a sustainable approach to intensive fish production, but their operational complexity poses significant challenges for fish farmers. Addressing these challenges requires managing the complexity of multi-objective optimization with shifting priorities, ensuring interpretability of sophisticated AI models for practical users, and adapting control strategies to the dynamic requirements of fish development stages. This study introduces an adaptive multi-objective reinforcement learning framework that dynamically adjusts priorities across different growth stages, addressing a critical gap in current RAS management approaches. The proposed system implements a hierarchical Deep Deterministic Policy Gradient architecture with growth stage-specific policies, complemented by an interpretable visualization framework that makes complex AI decisions accessible to aquaculture practitioners. Experimental validation in a commercial-scale RAS facility with tilapia demonstrated substantial improvements in both production efficiency and system usability. Key findings include an 18.7% improvement in feed conversion ratio compared to standard implementations, while maintaining exceptional water quality stability (98.3% of time within optimal ranges). Particularly noteworthy was the system’s performance during growth phase transitions—historically challenging periods in RAS management—where adjustment time was reduced by 42.5% while maintaining stable environmental conditions. User studies revealed that the interpretable visualization components significantly enhanced farmers’ trust and understanding, with 89% of participants expressing confidence in the system compared to 65% for previous approaches. These results demonstrate how adaptive reinforcement learning with appropriate interpretability tools can bridge the gap between advanced AI techniques and practical aquaculture management, potentially accelerating adoption of intelligent control systems in commercial aquaculture.

Aquaculture soil-water systems in Saudi Arabia face unique challenges due to the country's arid climate and extreme weather events. This study investigates the influence of abiotic factors on soil-water dynamics, dissolved oxygen levels, and fish mortality in Saudi Arabian aquaculture ponds. A novel environmental severity scale incorporating soil temperature, water quality parameters, and atmospheric conditions was developed to quantify their impact on aquaculture systems. Using data from 10 aquaculture facilities and controlled experiments, the relationships between environmental parameters, soil-water characteristics, dissolved oxygen levels, and fish mortality rates was analyzed. Multiple regression analysis revealed soil temperature as the dominant factor (93.01%) in predicting dissolved oxygen concentrations in pond water. Soil analysis showed significant correlations between soil organic matter content and dissolved oxygen levels (r = -0.87, p < 0.001), with critical thresholds identified at 50% and 75% severity levels. Fish mortality rates demonstrated a strong positive correlation with declining soil-water quality parameters (r = 0.91, p < 0.001), particularly when environmental severity exceeded 60%. A logistic regression model showed excellent performance in predicting system stress based on soil-water parameters. These findings provide a foundation for developing integrated soil-water management strategies for sustainable aquaculture operations in arid regions.