Delta University for Science and Technology

Mahmoud A.Q., Soliman T.A., Elkhooly T.A., Harhash A., Eid E.G.

Abstract Objectives Zirconia (ZrO2) has been used in dental restorations due to its increased mechanical properties, biocompatibility, low degree of bacterial adhesion, and acceptable optical properties. One of the major drawbacks of ZrO2 is its short-term durable bond with resin cement. The objective of this study was to evaluate the effect of different primers embedded with silanized nanographene oxide (SGO) sheets on the wettability of ZrO2 surface and bond strength durability between resin cement and ZrO2. Materials and Methods Four hundred ZrO2 specimens were divided into four main groups as each group had 100 specimens according to the type of the primer: rely X ceramic primer (Group I), monobond N primer (Group II), monobond plus primer (Group III), and Z prime plus primer (ZP, Group IV). Each main group was subdivided into five subgroups according to SGO concentrations by weight blended into primers: (1) 0% (control), (2) 0.1%, (3) 0.3%, (4) 0.6%, and (5) 0.9% as each subgroup had 20 specimens. Immediate shear bond strength (SBS) test was done for half of the specimens per each subgroup (10 specimens) by universal testing machine, the other half of the specimens per each subgroup (10 specimens) were exposed to thermocycling for 10,000 cycles that is equivalent to 1 year of clinical use at controlled temperatures (5–55°C) by thermocycler then SBS test by universal testing machine was done. Water contact angle test was done for all specimens per each subgroup (20 specimens) by computer software and an optical tensiometer. Results The SBS was nonsignificantly decreased after thermocycling for all primers embedded with SGO except for ZP primer. The best wettability of ZrO2 surface was found in (ZP) primer group embedded with (0.9% SGO) with a mean value of 20.60. Conclusion Primers embedded with SGO could increase the wettability of the ZrO2 surface and bond strength durability between resin cement and ZrO2 even after thermocycling aging. The clinical significance of this study was the possible increase of the wettability of ZrO2 surface and SBS of resin cement to ZrO2 with promising long-term stability when commercial primers embedded with SGO were used. This could reduce the risk of debonding between resin cement and ZrO2 crowns or veneers.

Al‐Muntaser A.A., Alzahrani E., Al‐Rasheedi A., Al‐Harthy E.A., Alwafi R., Asnag G.M., Tarabiah A.E., Saeed A.

AbstractThis study aims to develop novel PVDF/PMMA‐based polymer nanocomposites (PNCs) filled with copper nanoparticles (Cu NPs) for capacitive energy storage applications. The unique conductive properties of Cu NPs were utilized to enhance the dielectric and energy storage properties of the polymer blend significantly. Cu NPs were incorporated at low concentrations (1.5 and 3 wt.%), providing a cost‐effective approach to improving material performance. Structural analyses using XRD and FTIR revealed that Cu NPs disrupt the crystalline structure of the polymer blend, increasing the amorphous phase and facilitating charge carrier mobility. UV/visible spectroscopy demonstrated a reduction in the optical bandgap energy, indicating strong electronic interactions between Cu NPs and the polymer matrix. Impedance spectroscopy and dielectric measurements confirmed that Cu NPs enhance interfacial polarization, resulting in higher dielectric constants and improved conductivity at low frequencies while maintaining low dielectric loss. Notably, the 3 wt.% Cu NP nanocomposite achieved an energy storage density of ~3.8 × 10−3 J/m3 at low frequencies, more than double that of the pure PVDF/PMMA blend. These findings indicate that PVDF/PMMA‐Cu nanocomposites could be promising materials for capacitive energy storage applications.Highlights PVDF/PMMA/Cu nanocomposites were prepared using the solution‐casting method. CuNPs in PVDF/PMMA blends enhance optical, structural, and electrical properties. Improved dielectric properties and conductivity in PNCs were demonstrated. Fabricated capacitors exhibited improved performance and higher energy storage.

Hassan H.M., Abou-Hany H.O., Shata A., Hellal D., El-Baz A.M., ElSaid Z.H., Haleem A.A., Morsy N.E., Abozied R.M., Elbrolosy B.M., Negm S., El-kott A.F., AlShehri M.A., Khasawneh M.A., Saifeldeen E.R., et. al.

Parkinson’s disease (PD) is the main neurodegenerative disorder affecting motor activity, there are different pathophysiological pathways contributing to its development including oxidative stress, neuroinflammation, Lewy’s bodies accumulation, and impaired autophagy. Vinpocetine is an herbal extract with antioxidant and anti-inflammatory activities that may counteract pathophysiologic neurodegeneration pathways. Moreover, Lactobacillus is a probiotic that can modulate the gut-brain axis and provide the body with the needed precursors of antioxidants and anti-inflammatory mediators. In the current study PD was induced experimentally in Sprague Dawley rats with rotenone (2.5 mg/kg, i.p, daily) for 60 days, vinpocetine; Vinpo (20 mg/kg, orally, daily) and Lactobacillus; Lacto (2.7 × 108 CFU/ml, orally, daily) were applied as protective treatment. Vinpocetine and Lactobacillus treatment significantly ameliorated motor function by increasing distance traveled and rearing frequency in the open field test with a concomitant increase in falling time from both the accelerating rotarod and the wire screen test. Moreover, vinpocetine and Lactobacillus treatment upregulates tyrosine hydroxylase expression (the rate-limiting enzyme in dopamine synthesis), leading to enhanced dopamine synthesis and improved dopaminergic function with regression of histopathological hallmarks. Antioxidant GSH levels were significantly increased after vinpocetine and Lactobacillus treatment with a significant decrease in MDA content in brain homogenates. Furthermore, vinpocetine and Lactobacillus treatment significantly decreased striatal inflammatory markers; nitrite, IL-1β and TNF-α. Proteinopathies were regressed with a substantial decrease in striatal α-synuclein and tau content. In conclusion, vinpocetine and Lactobacillus treatment reduced rotenone neurotoxicity with improved dopamine release and motor activity with correction of oxidative burden, neuro-inflammation, and proteinopathy.

Khelifa A., Kabeel A.E., Attia M.E., Muthu Manokar A., Abdel-Aziz M.M.

Bentegri H., Rabehi M., Kherfane S., Nahool T.A., Rabehi A., Guermoui M., Alhussan A.A., Khafaga D.S., Eid M.M., El-Kenawy E.M.

Predicting the compressive strength of Compressed Earth Blocks (CEB) is a challenging task due to the nonlinear relationships among their diverse components, including cement, clay, sand, silt, and fibers. This study employed PyCaret, an automated machine learning platform, to address this complexity by developing and evaluating predictive models. The analysis demonstrated that fiber content exhibited a strong positive correlation with cement content, with a correlation coefficient of 0.9444, indicating a significant influence on compressive strength. Multiple machine learning algorithms were tested using metrics such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess model performance. Among these, the Extra Trees Regressor showed the best predictive capability with R2 = 0.9444 (highly accurate predictions), RMSE = 0.4909 (low variability in prediction errors) and MAE = 0.1899 (minimal average prediction error). The results confirm that PyCaret effectively automates the machine learning workflow, enabling accurate modeling of complex material behavior. The Extra Trees Regressor outperformed other algorithms due to its ability to handle highly nonlinear and multivariate datasets, making it particularly well-suited for predicting the compressive strength of CEB. This approach offers a significant advantage over traditional laboratory testing, which is time-consuming and resource-intensive. By incorporating machine learning techniques, especially using PyCaret’s streamlined processes, the prediction of CEB strength becomes more efficient and reliable, providing a practical tool for engineers and researchers in material science.

Doghish A.S., Elazazy O., Mohamed H.H., Mansour R.M., Ghanem A., Faraag A.H., Elballal M.S., Elrebehy M.A., Elesawy A.E., Abdel Mageed S.S., Saber S., Nassar Y.A., Abulsoud A.I., Abdel‐Reheim M.A., Elawady A.S., et. al.

ABSTRACTRecently, many studies focused on the billions of native bacteria found inside and all over the human body, commonly known as the microbiota, and its interactions with the eukaryotic host. One of the niches for such microbiota is the gastrointestinal tract (GIT), which harbors hundreds to thousands of bacterial species commonly known as enteric bacteria. Changes in the enteric bacterial populations were linked to various pathologies such as irritable bowel syndrome and obesity. The gut microbiome could affect the health status of individuals. MicroRNAs (miRNAs) are one of the extensively studied small‐sized noncoding RNAs (ncRNAs) over the past decade to explore their multiple roles in health and disease. It was proven that miRNAs circulate in almost all body fluids and tissues, showing signature patterns of dysregulation associated with pathologies. Both cellular and circulating miRNAs participate in the posttranscriptional regulation of genes and are considered the potential key regulators of genes and participate in cellular communication. This manuscript explores the unique interplay between miRNAs and enteric bacteria in the gastrointestinal tract, emphasizing their dual role in shaping host‐microbiota dynamics. It delves into the molecular mechanisms by which miRNAs influence bacterial colonization and host immune responses, linking these findings to gut‐related diseases. The review highlights innovative therapeutic and diagnostic opportunities, offering insights for targeted treatments of dysbiosis‐associated pathologies.

Anwar M.A., Sayed G.A., Hal D.M., Hafeez M.S., Shatat A.S., Salman A., Eisa N.M., Ramadan A., El-Shiekh R.A., Hatem S., Aly S.H.

Abstract Across diverse cultures, herbal remedies have been used to alleviate oral discomfort and maintain dental hygiene. This review presents studies on herbal remedies with remarkable antimicrobial, anti-inflammatory, antioxidant, anticancer, anticaries, analgesic, and healing properties. The manuscripts demonstrate the depth of scientific inquiry into herbal remedies used for the management of various oral and dental health conditions. These include gingivitis, oral ulcers, mucositis, periodontitis, oral pathogens, carcinoma, xerostomia, and dental caries. Researchers have investigated the phytochemical and pharmacological properties of plant-derived compounds and their extracts evaluated their interactions with oral pathogens and inflammatory processes. The convergence of traditional knowledge and rigorous scientific investigation offers a compelling narrative, fostering a deeper understanding of herbal remedies as viable alternatives to conventional dental interventions. This work has the potential to provide patients with access to gentle, yet effective solutions, and simultaneously offer dental health professionals the opportunity to enrich their knowledge, and ability to provide personalized, holistic care. This review highlights the symbiotic relationship between herbal medicine and scientific understanding, emphasizing the importance of disseminating this knowledge to benefit both practitioners and patients, enabling evidence-based decision-making in dental care. The exploration of herbal remedies offers a promising alternative, potentially mitigating some of these side effects while promoting oral health in a more natural and holistic manner.

Alsheheri S.Z., Salama R.S.

Recently, the valorization of agricultural and industrial wastes has gained significant attention for the synthesis of high-value nanomaterials. In this study, we investigate the synthesis and characterization of composite materials comprising activated carbon (AC) derived from pistachio shells, alumina nanoparticles (Al2O3) sourced from recycled aluminum cans, and silver ferrite nanoparticles (AgFeO2) for potential energy storage applications. The nanocomposites were characterized using XPS, FTIR, BET, SEM, TEM, and EDX techniques to analyze their structural, chemical, and morphological properties. XPS analysis revealed the oxidation states and chemical interactions between the components, confirming the successful integration of AgFeO2 into the AC and alumina matrix. FTIR spectra indicated the presence of hydroxyl, carbonyl, and ferrite functional groups. Textural analysis demonstrated that the composites possessed a hybrid microporous-mesoporous structure, with significant surface area retention and optimized pore sizes. TEM and SEM imaging showed uniform nanoparticle dispersion, highlighting the composites’ high structural integrity. Electrochemical evaluation indicated superior capacitive performance, with the 10 wt% AgFeO2-Alum-AC composite achieving the highest specific capacitance (480 F/g at 0.7 A/g) and excellent cycling stability. These findings establish the AgFeO2-modified Alum-AC composite as a viable material for high-performance supercapacitors.

Ghorbal A.B., Grine A., Elbatal I., Almetwally E.M., Eid M.M., El-Kenawy E.M.

This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. This purified input dataset is used in the DPRNNs model, where both short and long-term temporal dependencies in the data are captured well. NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. From the Wilcoxon and ANOVA analyses, one can approve the specificity and consistency of the findings. Liebert and Ruple firmly rethink this rather simple output as a robust theoretic and empirical framework for evaluating and projecting CO2 emissions; they also view it as a helpful guide for policymakers fighting global warming. Further study can build up this theory to include other greenhouse gases and create methods enabling instantaneous tracking for sophisticated and responsive approaches.

Abou Ouf T., Makram A., Ghodya M.K.

Ali N.A., El-Gindy A.E., Wahba M.E., Mostafaa A.E.

AbstractThis work developed and validated a new UV-HPLC method for the separation and quantification of vonoprazan fumarate (VNP), amoxicillin (AMX), and clarithromycin (CLM), together in a ternary mixture and in laboratory-prepared tablets. A C18 column and a delightful 70:30 blend of 0.05 M KH2PO4 buffer pH 6 and acetonitrile were used as mobile phase. The flow rate was 1.5 mL min−1, while the wavelength of detection was 254 nm. Linear concentration ranges of (1.0–10.0 μg mL−1) vonoprazan fumarate, (1.0–125.0 μg mL−1) amoxicillin, and (25.0–1,200 μg mL−1) clarithromycin resulted in 0.061, 0.6213, and 7.302 μg mL−1 detection levels and 0.1849, 0.845, and 22.127 μg mL−1 quantification values for VNP, AMX, and CLM, respectively. The greenness of this method was evaluated using the National Environmental Methods Index assessment (NEMI), analytical Eco-scale Assessment (ESA) tool, Green Analytical Procedure Index assessment (GAPI), and Analytical Greenness metric assessment (AGREE), which authenticated the method to be eco-friendly. This method, which was systematically validated and used also to calculate the concentration of the three drugs in their dosage forms, showed high accuracy and precision. Additionally, it produced peaks that were clearly separated and resolved, demonstrating its robustness and specificity.

Habieeb R., Kabeel A.E., Abdelsalam M.M.

Desalination plays a role in tackling the global water scarcity issue. Enhancing the efficiency of desalination plants in terms of water recovery (WR) and salt rejection (SR) is crucial for cost-effectiveness and improvements. This study introduces AttnDesal, a neural network model crafted to forecast key performance indicators of desalination facilities. We can use Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks along with a way to pay attention to make the AttnDesal system find relationships between time series data, whether in the short and long term. The model underwent training and testing using a dataset, incorporating parameters such as feed temperature (Tf), feed flow rate (Qf), concentrate pressure (Cp), and energy consumption (kWh). The evaluation results reveal that AttnDesal exhibits performance, achieving a mean squared error (MSE) of 0.0024, a mean absolute error (MAE) of 0.0032 for WR, and an R squared value of 0.9818. Regarding SR prediction, the model obtained an MSE of 0.0019, an MAE of 0.0390, and an R squared value of 0.9795. These findings underscore the model’s precision and dependability in forecasting desalination plant efficiency. Comparisons between the actual. Predicted values prove that the model effectively captures the intricate relationships in the data. We suggest evaluating AttnDesal’s performance using datasets from water treatment and desalination facilities around the world to ensure its wide application and reliability. This article also covers the preprocessing steps, such as data cleaning, normalization, and reshaping, to prepare the data for training the model. By incorporating an attention mechanism, AttnDesal can focus on the parts of the input sequence, thereby improving prediction accuracy. The accurate forecasting of performance indicators by AttnDesal positions it as a tool for optimizing desalination plant operations, ultimately enhancing efficiency and cost-effectiveness. The model’s success highlights the potential of networks with attention mechanisms to advance desalination technology and tackle global water challenges.

Saleh A.I., Rabie A.H., ElSayyad S.E., Takieldeen A.E., Khalifa F.

As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public data sets, at various of training samples percentages, showed that the proposed strategy achieves promising performance. Namely, the system yielded an overall accuracy of 98.91% with testing run time of 5.5 seconds, while using machine classifiers with small number of hyper-parameters. Additional experimental comparison reveals superior performance of the proposed system over literature approaches using various metrics. Statistical analysis also confirmed that the proposed AMDS outperformed other models after running 50 times. Finally, the generalizability of the proposed model is evaluated by testing its performance on external data sets for monkeypox and COVID-19. Our model achieved an overall diagnostic accuracy of 98.00% and 99.00% on external COVID and monkeypox data sets, respectively.

Zahra S., Samra M., El Gizawi L.

This study explored the integration of emotional intelligence (EI) with artificial intelligence (AI) to address emerging challenges in architectural education. An AI-supported teaching model was developed, utilizing AI tools to assess students’ emotional responses and enabling educators to adapt teaching strategies based on emotional data. This study employed a three-phase methodology: theoretical, analytical, and experimental phases. The theoretical phase involved a comprehensive literature review focusing on the role of EI in architectural education. In the analytical phase, a survey was conducted to evaluate students’ ability to overcome learning challenges using a case study from an Egyptian university. The experimental phase implemented an EI-driven teaching approach with a pilot group of students, incorporating instructor feedback and ChatGPT-4O for assessments in order to minimize potential bias. The results demonstrate that integrating EI into education significantly enhances students’ performance compared to traditional teaching methods. Furthermore, the findings contribute to the development of an AI-based model that provides personalized feedback and fosters a dynamic learning environment, aiming to achieve higher academic and behavioral standards among architecture students. This research offers theoretical and practical insights into advancing the integration of AI and EI in architectural education.

Mostafa S.K., Abdel-Rahman R.H., Mansour A.K., El-Sherbeny M.A.

It is pivotal to implement effective collaboration among professionals from different disciplines in the healthcare sector. As the complexity of patients’ health needs grows, so does the importance of developing innovative and efficient models of patient care that rely on interprofessional collaborative teamwork. Interprofessional education (IPE) is a valuable approach that improves communication, collaboration, and ultimately safe patient outcomes in healthcare settings. Students studying medicine and pharmacy are among the healthcare professionals who could potentially benefit from IPE. Despite the existence of newly added undergraduate curricula for interprofessional communication and professionalism, active implementation to achieve the required competencies and skills is not yet widespread, particularly in developing countries. This article presents a successful implementation of simulation-based team-oriented learning sessions among pharmacy and medical students at Delta University for Science and Technology in Egypt. The purpose of the IPE activity was to train students to work collaboratively as members of an interprofessional healthcare team by simulating a real-life situation. Student feedback was very positive with most pharmacy and medical students being satisfied with their IPE experience. Moreover, students’ feedback emphasized the significance of IPE and a simulated team-based environment in improving communication skills and collaborative practice. The high level of student satisfaction with the IPE sessions, particularly regarding the clarity of training objectives and the interactive nature of the experience, suggests that simulation-based learning is an effective tool for developing key interprofessional collaboration skills among healthcare students. This highlights the potential of IPE to cultivate future healthcare professionals who excel in interprofessional collaboration, ultimately leading to improved patient care and outcomes. Overcoming barriers to implementing IPE in Egypt needs a multifaceted strategy and a comprehensive long-term follow-up plan is critical for evaluating the lasting effect of IPE on students’ collaborative practices in real-life healthcare settings.