Minia University (also spelled Minia) was established in Minia, in 1976 by virtue of Republican Decree No. (93), separating it from Assiut University. The campus is north of Minia. Its emblem is Nefertiti Bust.
Abstract Background Diabetes mellitus is a global epidemic leads to multiple serious health complications, including nephropathy. Diabetic nephropathy is a serious kidney-related complication of type 1 or 2 diabetes that is prevalent in almost 40% of the people with diabetes. We examined whether folic acid and melatonin can reduce progression of nephropathy in rats of type 1 diabetes mellitus by controlling the level of oxidative stress, glucose, lipids, and cytokines. Methods Forty-two male albino rats were distributed into six groups, (n = 7 per group). Five of the groups were induced with diabetes by a single intraperitoneal injection of freshly prepared streptozotocin at a dose of 50 mg/kg body weight. After the induction of diabetes, the rats were treated with folic acid (100 mg/kg) and melatonin (10 mg/kg) separately and in combination daily for 6 weeks, whereas, the other diabetic group was treated with glibenclamide (5 mg/kg). One of the diabetic groups served as a positive control. One-way ANOVA was used to compare those five subfields ability followed by LSD multiple comparisons. Results The data indicated that diabetes significantly altered the body weight, lipids and kidney function. Diabetic rats exhibited a significant increase in plasma levels of urea, uric acid, creatinine, sodium, tumor necrosis factor alpha (TNF-α), interleukin-6(IL-6), cholesterol, triglycerides, and low-density lipoprotein (LDL). In contrast, plasma total protein, potassium, high-density lipoprotein (HDL) and interleukin-10 (IL-10) decreased significantly in diabetic rats compared to the control rats. Moreover, levels of renal malondialdehyde (MDA) and nitric oxide (NO) were significantly increased while the levels of renal glutathione(GSH), superoxide dismutase(SOD), and catalase (CAT) were significantly decreased in diabetic rats comparison to those in the control rats. Hence, diabetic rats treated with folic acid and melatonin alone as well as in combination showed improvements with respect to the indices in addition to a significant recovery observed via histopathology when compared to the diabetic group. Conclusions These results revealed that treatment with folic acid in combination with melatonin in diabetic rats was more effective than treatment with either of folic acid or melatonin alone to alleviate the symptoms of diabetic nephropathy.
Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5% and 97.1% , respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.
To mitigate the drinking water crisis, it is imperative to enhance the water productivity of solar stills, particularly in arid regions. This study presents the first application of eggshell halves filled with either white sand grains or processed eggshell powder as cost-effective natural energy storage and corrugation materials to improve the production of hemispherical solar stills. To investigate the proposed techniques, three identical hemispherical solar still were developed and evaluated under identical weather conditions. The configurations included: a conventional still with egg-shaped metal basin coated with nanoparticles (HSD-SET), a modified still with eggshell halves filled with white sand grains (MHSD-SET&ES_SG), and a modified still with eggshell halves filled with eggshell powder (MHSD-SET&ES_ESP). Additionally, the microstructure and morphological properties of eggshells were examined using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) to ascertain their chemical composition. The findings showed a significant enhancement in water productivity, with increases of up to 51.94 % (9.64 kgm- 2day- 1) and 85.64 % (11.87 kgm- 2day- 1), for the MHSDSET&ES_SG and MHSD-SET&ES_ESP configurations, respectively, compared to the conventional HSD-SET (6.35 kgm- 2day- 1). It is noted that the productivity improvement for the MHSD-SET&ES_ESP was 22.18 % higher than that of MHSD-SET&ES_SG. Furthermore, the adoption of these modified configurations led to reductions in productivity cost by 48.81 % and 79.77 % and shortened the payback time by 32.85 % and 40.92 % respectively. Finally, the annual CO2 emissions were reduced by 2.83 and 3.33 tons for the MHSD-SET&ES_SG and MHSDSET&ES_ESP configurations, respectively.
Electrolytic water hydrogen production is an effective method for achieving the absorption of excess renewable energy and peak shaving and valley filling in the power system. However, when facing large-scale and longdistance hydrogen transportation needs, existing hydrogen transportation strategies struggle to transport hydrogen economically and flexibly from hydrogen production plants (HPPs) to various hydrogen users. To this end, this paper proposes a distributionally robust optimization (DRO) scheduling model for the electric-hydrogen integrated energy system (EHIES) based pipeline-road collaborative hydrogen transportation (PRCHT). Firstly, by analyzing the transportation mechanism of hydrogen-blended pipelines and combining the relationship between the pipeline's storage and the gas pressure at both ends, this work constructs a quasi-dynamic transportation model for natural gas hydrogen blending with a variable hydrogen blending ratio. Next, by employing the improved McCormick technique and piecewise linearization method, the quasi-dynamic model is transformed into a mixed-integer linear programming (MILP) model. Furthermore, by integrating the trailer-based hydrogen transportation model, a PRCHT model is developed. Finally, considering the high uncertainty in wind power output, a DRO scheduling model for the integrated electricity-hydrogen energy system based on Wasserstein distance is proposed. The DRO model is then transformed into a MILP problem using the conditional value-at-risk (CVaR) approximation method. The simulation results demonstrate that the proposed scheduling model reduces the total system cost by 19.43 % compared to the constant hydrogen blending ratio benchmark, while preventing 22.68 % of potential hydrogen load shedding relative to the natural-gas-pipeline-exclusive transport model. Meanwhile, the employed algorithm improves computational efficiency and achieves a robust optimization of the scheduling decisions by balancing system robustness and economic performance.
Accurate liver, COVID-19, and brain disease diagnosis is crucial for effective medical treatment and improved patient outcomes. In Computer-Aided Diagnosis (CAD) systems, segmentation is the foundational step, which plays a pivotal role in accurately delineating regions of interest for subsequent analysis. Among various techniques, multilevel thresholding segmentation is a specialized approach for processing medical images. However, its computational complexity and challenges in achieving satisfactory segmentation results limit its widespread application. To address these issues, this paper proposes an Enhanced Crested Porcupine Optimizer (ECPO) tailored for multilevel thresholding in medical image segmentation. The ECPO integrates two novel strategies: Enhanced Solution Quality (ESQ) and Gaussian Distribution, improving the exploration and exploitation capabilities of the original Crested Porcupine Optimizer (CPO). The optimization performance of ECPO is rigorously evaluated on 12 classical benchmark functions using CEC’2022 test functions, demonstrating superior results compared to CPO and other state-of-the-art algorithms. Subsequently, the ECPO is applied to segmenting medical images from three datasets focusing on liver cancer, COVID-19, and brain diseases. Utilizing Otsu and Kapur methods. Experimental results indicate that ECPO achieves the best segmentation outcomes in terms of fitness values, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The experimental results reveal that ECPO achieves the most accurate and effective segmentation outcomes across all datasets, outperforming other competitive algorithms. These findings underscore the potential of ECPO as a robust and efficient solution to the multilevel thresholding segmentation challenges in medical imaging.
