Kafrelsheikh University

Conventional multiple-jet impingement cooling can provide high local heat transfer, yet under confined conditions its effectiveness is often limited by localized cooling footprints, non-uniform temperature fields, and complex jet-to-jet interference. To address this limitation, the present study numerically investigates the effects of inward-focused jet orientation and jet-to-jet spacing on the flow and thermal characteristics of a confined three-jet impingement system. Three configurations are considered: a baseline vertical arrangement, symmetric inward-focused arrangements, and mixed vertical-inclined arrangements. Simulations are performed for jet spacings of 8, 10, and 12 mm using the SST k-omega turbulence model. The results show that inward focusing improves cooling through momentum exchange and redirection, whereby part of the incoming axial jet momentum is converted into wall-parallel motion before and immediately after impingement. This mechanism broadens the cooled footprint, enhances lateral surface sweeping, and strengthens thermal boundary-layer disruption relative to the vertical baseline. The symmetric inward-focused cases show the clearest and most consistent improvement over the baseline, with the 45 degrees, 225 degrees configuration providing the most robust symmetric performance at 8 and 10 mm, while the 67.5 degrees, 247.5 degrees arrangement becomes particularly attractive at 12 mm. These balanced inwardfocused cases improve heat transfer while maintaining better thermal uniformity than strongly asymmetric arrangements. Although the mixed 90 degrees, 202.5 degrees configuration yields the best overall average thermal performance, the mixed 90 degrees, 247.5 degrees case demonstrates the penalty associated with excessive asymmetry. Overall, the study identifies moderate and balanced inward focusing as a promising strategy for compact, thermally uniform, and high-performance impingement cooling systems.

Water pollution and shortages are critical global challenges, and the green synthesis of nanomaterials provides a sustainable solution for water purification, targeting the removal of dyes, heavy metals, and bacteria. Utilizing agricultural waste (e.g., cladosiphon, bamboo, cane stalks), this approach offers eco-friendly, cost-effective alternatives to conventional methods. Recent advancements in green synthesis techniques have led to the development of carbon-based nanomaterials like carbon dots, quantum dots, nanofibers, nanotubes, and metal oxides such as ZnO, TiO2, and IONPs. A bibliometric analysis of 270 relevant articles (2015-2024) reveals a significant rise in publications, peaking at 57 articles in 2023, with India leading the field with 89 publications. CVarious methods have shown impressive pollution reduction: CNTs at 0.05 g/L removed 99.8% of Methylene Blue (MB), while TiO2 eliminated 100% of RR250 dye in 60 min. Green-synthesized nanomaterials, like iron and silver, also demonstrate potential, with iron nanoparticles achieving 100% Cr (VI) removal and silver from Acacia ehrenbergiana reducing RhB dye by 96%. This review emphasizes the novelty of hybrid synthesis methods that combine green chemistry with traditional techniques, offering a useful solution for addressing challenges in scalability. By integrating these approaches, the potential of green-synthesized nanomaterials for large-scale water treatment is significantly enhanced. Future studies will focus on optimising these synthesis processes through AI-driven techniques and incorporating life cycle assessments (LCA) to evaluate the long-term environmental impact and commercial feasibility of these materials, ensuring their broader applicability and sustainability.

The paper introduces a powerful, data-driven framework for predicting the electrochemical behavior of Polyindole (PIn)-based supercapacitor electrodes through the integration of traditional machine learning (ML) techniques with modern deep learning (DL) architectures. Motivation behind this study comes from the necessity for reliable and accurate models that would enable quick and efficient prediction of the behavior of the conducting polymer electrodes and thus minimize expenses in terms of time, effort, and resources spent on experimental tests needed for characterization of their energy storage capabilities. PIn was prepared through potentiostatic electrochemical polymerization of indole on a graphite sheet. The deposition time was carefully varied to control film growth and active mass loading, and the fabricated electrodes were characterized using cyclic voltammetry (CV) within a potential window from -0.2 to 1.2 V at scan rates ranging from 5 to 75 mV/s. These measurements were then used to assemble a large dataset of over 30,000 experimental points, and provide this dataset to all five ML models Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ridge Regression (RR) and four DL networks Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a hybrid network using RNN-LSTM. The performance of the model was determined by the coefficient of determination (R-2), mean absolute error (MAE), and root mean square error (RMSE). The findings indicate that KNN was best in a high-rate situation (R-2 = 0.9387, MAE = 0.000289, RMSE = 0.000644 when 5 mV/s), whereas ANN was more robust because it sustained a high predictive accuracy (R-2 > 0.9) at all scan rates. Comprehensively, the proposed framework reflects the complementary efficiency of both classical and DL algorithms and enables the application of hybrid deep architectures for reliable predictive modeling of conducting-polymer Supercapacitor (SC) electrodes.

This study aimed to evaluate the effects of dietary supplementation with the hexane fraction of Ulva fasciata extract (UH) on growth performance, immune responses, and antioxidant status in Nile tilapia (Oreochromis niloticus). Nile tilapia fingerlings (initial weight 3.13 ± 0.03 g) were fed diets supplemented with 0, 50, 100, or 150 mg kg⁻1 UH for 60 days. The findings demonstrated that dietary supplementation with the hexane fraction of Ulva fasciata extract (UH150, UH100, and UH50) significantly increased final weight gain, specific growth rate, and improved feed conversion ratio compared to UH0 (control diet). According to the intestinal villi histomorphometric evaluation, fish that received UH exhibited improvements in villi length, goblet cell numbers, and intervillous spaces, particularly in UH100 (p < 0.05). However, UH150 demonstrated the most significant improvements in villi width. The highest WBC counts, phagocytic activity, lysozyme activity, superoxide dismutase and catalase activity—alongside normal ranges for hematological and biochemical parameters—were detected in fish that received 100 mgkg−1 UH. The Ulva hexane fraction extract meals at 50, 100, and 150 mg kg−1 triggered upregulation of growth hormone receptor (GHr), insulin-like growth factor (IGF-1), myostatin (MSTN), tumor necrosis factor alpha (TNF-α), heat shock protein 70 (HSP 70), fatty acid synthase (FAS), and lipoprotein lipase (LPL) with the best results being stated in UH150. In conclusion, UH dietary inclusion improved growth performance, feed utilization efficiency, and immune-physiological response of Nile tilapia.