In Egypt, the climate has a direct impact on the dried date fruit production. The traditional drying method or open sun drying (OSD) leads to pollution of the final product caused by sand-laden winds, rain, or animals and harmful insects, etc. This study is aimed at designing, implementing, and experimentally validating a solar dryer based on IoT technology and integrated with a PV system in Aswan, Egypt. The purpose of the dryer is to monitor and control the quality of the three most popular date fruit varieties and determine the most effective drying method, with an algorithm that operates the system automatically for optimal performance and high-quality products. The automatic solar dryer (ASD) significantly affects the final moisture content and color characteristics and reaches the equilibrium moisture content (EMC) at a faster rate for dried date samples compared to the OSD. The drying rate of ASD was 29.03% (Sakkoti), 31.04% (Malkabi), and 25.49% (Gondaila) higher than OSD. Also, the dried date fruit samples reached EMC on the ASD after 8 days for both Malkabi and Gondaila and 9 days for Sakkoti, while it took 14 to 15 days on OSD. The maximum open circuit voltage , short circuit current , and output power were 41.70 V, 8.84 A, and 365.09 W, respectively. All values of total color change () after open-air drying of dry date varieties were higher than solar drying for both drying systems. This study can be then more helpful for producers of dried foods.

Optimizing feeding rates in recirculating aquaculture systems (RAS) is crucial for ensuring fish growth, health, and system efficiency. This research introduces a novel approach to RAS feeding control using a Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm. The developed system integrates feeding rate optimization with comprehensive water quality management to improve overall performance and stability. The DDPG controller demonstrated superior tracking accuracy, reduced feed consumption, and improved operational stability compared to traditional control methods such as Model Predictive Control (MPC), PID, and Bang-Bang control. The learned policy-maintained feeding rates within optimal ranges while adapting to dynamic system requirements and environmental conditions. The integration of water quality monitoring and control further enhanced system stability, ensuring critical parameters remained within target ranges. Comparative analysis revealed the DDPG controller’s advantages in terms of faster recovery times after environmental perturbations, improved long-term stability, and significant economic benefits through reduced operational costs and increased efficiency. The robustness and adaptability of the system were validated through comprehensive testing under various fault conditions, growth phases, and system scales. The successful development and evaluation of the DDPG-based RAS feeding control system represents a significant advancement in aquaculture management. The demonstrated improvements in efficiency, stability, and economic performance establish the potential for this approach to revolutionize feeding practices in commercial RAS operations. Further research and development efforts can build upon these findings to advance the state-of-the-art in intelligent aquaculture management systems and promote the sustainability and profitability of the industry.

Rapid urbanization and industrialization around the world have created massive amounts of organic residues, which have been prioritized for conversion into valuable resources through the composting process to keep their harmful effect at a minimum. This research aimed to assess the influence of active and passive aeration on composting mass of sugar beet residues in the case of using additives (e.g., charcoal only or manure only or combination). Some physicochemical properties of composting mass were analyzed on certain days of composting. Some parameters including temperature–time profile, carbon to nitrogen ratio (C/N ratio), moisture content, electrical conductivity, pH, germination and microbial population enumeration of compost were measured. Cress germination test was conducted for each medium of germination which contains a mixture of soil and compost (at a ratio of 3:1) taken from each treatment. The results showed that temperature–time profile data of composting mass showed an irregularity. Forcedly aerated composting mass did not demonstrate a thermophilic phase while passively aerated ones did not show a mesophilic phase. Carbon to nitrogen (C/N) ratio reduction was greater in most forcedly aerated composting mass than passively aerated on days from 1 to 33 of composting period. The results further showed that electrical conductivity decreased at the end of the composting period where it ranged from 2.55 to 3.1 dS/m. Germination medium containing forcedly aerated compost treated with a combination of charcoal and manure achieved the highest germination index which was higher than the control sample by 58.63% followed by forcedly aerated composting mass treated by charcoal only which exceeded the control sample by 5.35%. Strong correlation coefficient (r > 0.80) for the relationship between germination index and number of bacteria was obtained on day 17th of composting period.

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.

This study investigates green-synthesized copper oxide nanoparticles (CuONPs) via lemon balm extract and their incorporation into polyacrylonitrile (PAN) nanofibers for food packaging applications. UV-Vis spectroscopy indicates the successful formation of CuONPs, characterized by a characteristic absorption peak at 250–350 nm and minimal aggregation. Photoluminescence (PL) analysis revealed an intense emission peak at 350–450 nm, indicating the presence of oxygen vacancies that enhance photocatalytic activity and reactive oxygen species generation. FT-IR spectroscopy confirmed Cu–O stretching vibrations at 542–588 cm⁻¹ and hydroxyl groups at 3398–3451 cm⁻¹, demonstrating the role of phenolic substances as reducing and capping agents. XRD analysis confirmed phase-pure monoclinic CuONPs with the most intense peak at 2θ = 35.5° corresponding to the (200) plane. TEM revealed rod-shaped nanoparticles with diameters of 10.6–17.7 nm, while SEM showed particles of 30–50 nm. EDX spectroscopy validated elemental composition with no impurities detected. Incorporation of CuONPs into PAN nanofibers at 0.3, 0.7, and 1.0 wt.% produced dose-dependent improvements. PAN/1.0CuONPs nanofibers achieved an ultimate tensile strength of 18.7 MPa and 690% elongation, representing 38% enhancement over pure PAN. The TGA demonstrated improved thermal stability, with the onset of degradation delayed to 330–340 °C. Antibacterial testing against Staphylococcus aureus , Escherichia coli , and Listeria monocytogenes showed that PAN/1.0CuO nanofibers exhibited zones of inhibition of 29.4 ± 0.3 mm, 31.4 ± 0.7 mm, and 25.2 ± 0.3 mm, representing a 5-fold enhancement over pure PAN. DPPH assay revealed 88% radical-scavenging capacity. These findings establish CuONPs-incorporated PAN nanofibers as promising candidates for active food packaging with superior mechanical, thermal, antibacterial, and antioxidant properties.