BUE Scholar (The British University in Egypt)
Not a member yet
4435 research outputs found
Sort by
New Black Hole Solution in f(R) Theory and Its Related Physics
No full textRecent observations suggest that General Relativity (GR) faces challenges in fully explaining phenomena in regimes of strong gravitational fields. A promising alternative is the (Formula presented.) theory of gravity, where R denotes the Ricci scalar. This modified theory aims to address the limitations observed in standard GR. In this study, we derive a black hole (BH) solution without introducing nonlinear electromagnetic fields or imposing specific constraints on R or the functional form of (Formula presented.) gravity. The BH solution obtained here is different from the classical Schwarzschild solution in GR and, under certain conditions, reduces to the Schwarzschild (A)dS solution. This BH is characterized by the gravitational mass of the system and an additional parameter, which distinguishes it from GR BHs, particularly in the asymptotic regime. We show that the curvature invariants of this solution remain well defined at both small and large values of r. Furthermore, we analyze their thermodynamic properties, demonstrating consistency with established principles such as Hawking radiation, entropy, and quasi-local energy. This analysis supports their viability as alternative models to classical GR BHs
Application of Electrospun Polyvinyl Alcohol/Sodium Alginate Nanofibers as Biosensor
No full textThe determination of glycine is considered crucial for various biological functions and systems. So, this study presents nanofibers as a biosensor for glycine, utilizing the electrospinning method to prepare nanofibers with specific diameters. The sodium alginate (Na Alg) extracellular matrix was added to a Polyvinyl Alcohol (PVA) solution, resulting in a homo- geneous, bead-free electrospun PVA/Na Alg nanofiber membrane. The combination of PVA and Na Alg solutions was optimized to achieve the optimal composition and electrospinning settings, forming homogeneous and bead-free PVA/ Na Alg nanofiber architectures. The Fourier Transform Infrared Spectroscopy (FTIR) is utilized to analyze the chemical composition of PVA or PVA/NaAlg. The influence of ferric chloride (FeCl2) and glycerin on the morphological, struc- tural, and optical attributes of PVA and PVA/NaAlg nanofibers was investigated. Scanning electron microscopy (SEM) images demonstrated the successful synthesis of nanofibers with diameters ranging from 120 to 400 nm. The XRD results validated the semicrystalline characteristics of the obtained nanofibers. Optical examination indicated that the synthe- sized nanofibers transitioned from ultraviolet-transparent to blocking materials due to their electrospinnability. The optical band gap demonstrated an increase in the material\u27s conductivity, suggesting that PVA/Na Alg/Gly nanofibers could be employed as biosensors for glycine amino acids
Optimal Roof Strategy for Mitigating Urban Heat Island in Hot Arid Climates : Simulation and Python-Based Multi-Criteria Decision Analysis
No full textImpact of the Adjunctive Use of Omega-3 in PeriodontitisPatients With Diabetes on Local and Systemic Chemerin Levels:A Randomized Clinical Trial
No full textAims: The present study aspired to evaluate the impact of the adjunctive use of omega-3 with nonsurgical periodontal therapy onclinical parameters as well as local and systemic chemerin levels as a marker of cardiovascular disease risk in periodontitis patientswith diabetes.Methods: This randomized clinical trial was performed on thirty periodontitis patients with type II diabetes divided into two equalgroups, both treated by nonsurgical periodontal treatment with the adjunctive use of daily 1000 mg Omega-3FAs in group I onlyfor 6 months. Patients were reexamined after 2 weeks (baseline), 3, and 6 months for recording the clinical parameters as follows:plaque index (PI), gingival index (GI), probing depth (PD), and clinical attachment loss (CAL). Chemerin levels were assessed inboth serum and GCF samples, HbA1c levels were also assessed.Results: Omega-3FAs-treated group recorded a more statistically significant improvement in clinical parameters compared to thecontrol group, particularly concerning PD and CAL. A statistically significant reduction of HbA1c levels between baseline, 3 and6 m values was encountered in Omega-3FAs treated group, while no significant difference was evident in the control group.Additionally, Omega-3FAs treated group recorded a more statistically significant reduction of GCF and serum chemerin levels incomparison to the control group after 6 months of therapy.Conclusion: The adjunctive use of omega-3FAs with nonsurgical periodontal therapy has resulted in significant improvement ofclinical periodontal parameters and glycemic control in periodontitis patients with type II diabetes, alongside the additional benefitof reducing both local and systemic chemerin levels, a biomarker for cardiovascular ris
Probing a novel neutral heavy gauge boson within the mono-Z′ portal at the HL-LHC
No full textThis study examines the production of dark matter events in association with a Z′ boson decaying via leptonic channels in simulated proton–proton collisions at the Large Hadron Collider. The analysis focuses on collisions at a center-of-mass energy of s=14TeV under high-luminosity conditions, corresponding to an integrated luminosity of 1000fb−1. Using Monte Carlo simulations interpreted within the Effective Field Theory (EFT) framework, this work investigates potential signatures of new physics. In the absence of such signals, upper limits are set on key EFT parameters, including the theory\u27s cutoff scale and the mass of the Z′ boson
Real-time path planning in dynamic environments using LSTM-augmented A∗ search
No full textThis paper presents a novel predictive heuristic framework for simulated real-time path planning in dynamic environments, integrating Long Short-Term Memory (LSTM) neural networks, Kalman filtering, and the A∗ search algorithm. The proposed LSTM-Augmented A∗ method uses historical obstacle trajectories to predict future positions, significantly reducing the computational overhead associated with frequent re-planning and enhancing collision avoidance. To robustly handle prediction uncertainties and measurement noise, an adaptive Kalman filter is integrated alongside the LSTM predictions, forming a hybrid prediction model. The entire study—including obstacle modeling, hybrid prediction methods, path-planning algorithm implementation, and performance validation—is conducted using MATLAB® and its Deep Learning Toolbox simulations. Initially, extensive simulations in synthetic environments are used to evaluate the framework’s responsiveness to complex spatiotemporal obstacle dynamics. Subsequently, rigorous validation is performed using established benchmark simulation maps, notably Berlin_0_256.map, which represent realistic complexities and noisy conditions. Simulation results demonstrate substantial improvements in path efficiency, computational speed, prediction accuracy, path smoothness, and safety metrics. The proposed integration of LSTM predictions and Kalman filtering within the A∗ heuristic enables proactive, near-optimal path generation while preserving theoretical guarantees of admissibility and completeness. These findings underline the robustness and practical applicability of combining deep-learning predictions with classical heuristic methods in simulated dynamic path-planning scenarios
Precise modelling of commercial photovoltaic cells/modules of different technologies using hippopotamus optimizer
No full textAccurate parameters’ identifications of photovoltaic models is essential for precise simulation and analysis of integrated and standalone photovoltaic systems which is directly influencing performance assessments. Accordingly, this study investigates the procedures of the hippopotamus optimizer for optimal parameters’ identifications of photovoltaic single and double-diode models, as well as the Sandia photovoltaic array performance model. The single and double-diode models simulate the steady-state I-V and P-V principal curves, while the Sandia model predicts maximum power points under various environmental conditions. Reducing root mean quadratic error is adapted as the optimization objective, subjected to operational and design viable constraints. The hippopotamus optimizer\u27s performance is tested on eight commercial photovoltaic units with diverse technologies, including silicon, poly-crystalline, mono-crystalline, cadmium telluride, copper indium gallium selenide, and amorphous silicon/microcrystalline silicon cells. Thru extensive simulations and comparisons with other optimizers in the literature, the hippopotamus optimizer shows its effectiveness in achieving lowest values of the root mean quadratic errors, indicating a high correlation among modeled and actual dataset points. For instance, using the single-diode model, the optimizer achieves best root mean quadratic error values of 28.210671 mA, 2.039979 mA, 13.79826 mA, 1.721864 mA, and 0.7728666 mA for Kyocera KC200GT, PhotoWatt PWP201, STP6-120/36, and STM6-40/36 modules and RTC France photovoltaic silicon cell, respectively. These results highlight the optimizer\u27s potential as a powerful tool for enhancing photovoltaic model accuracy. Consequently, the hippopotamus optimizer contributes to improved performance predictions and design precision in photovoltaic applications
Integration of artificial intelligence with a customized Four-Probe station for I-V characteristic classification and prediction
No full textThe incorporation of Artificial Intelligence (AI) is pivotal in automating intricate technical tasks, significantly enhancing accuracy and efficiency while alleviating the burdens of repetitive monitoring traditionally borne by technicians. This study focuses on developing a customized four-probe station integrated with sophisticated AI models aimed at classifying current–voltage () characteristics and extracting essential parameters. Our methodology encompasses the fabrication of precision-engineered gold-plated probes, meticulously assembled with a three-dimensional (3D) moving head to ensure optimal contact and measurement fidelity across a variety of electronic and optoelectronic devices. Data acquisition is executed via a source meter unit, followed by rigorous post-processing utilizing advanced algorithms, including convolutional neural networks and random forest techniques. Notably, the gold-plated contacts enhance measurement accuracy by providing superior conductivity and minimizing contact resistance, while the movable head allows for dynamic adjustment, facilitating precise probe alignment for consistent data retrieval. The results demonstrate a remarkable capability in classifying characteristics with a root-mean-square (RMS) error of less than 1%, underscoring the system’s reliability and accuracy. Moreover, our predictive models effectively utilize previously recorded measurements to forecast the degradation profiles of devices, thus offering significant insights into device longevity and performance
IoT-Cloud Based System for Warehouse Management: A Conceptual Framework
No full textWarehouses consider as an important linking between production and final clients. Improving effectiveness, inside these facilities, will help increase of supply chain’s performance. Given the massive volume of data that need to be handled in real time, IoT and cloud computing platforms are essential in today’s warehouses. This paper proposes an IoT-Cloud based system that simplifies the streaming of information for all the processes within warehouses, from receiving to transportation. The system was realized using the interaction between Node RED, MongoDB cloud and Python environment. An illustration of the proposed system was provided by the display of the different implemented nodes in Node RED. A dynamic workspace in which different actors and objects interact with each other should be provided by the system. Future work will be dedicated to the implementation of the suggested model for the experimental data
Novel hybrid nanofibers for burn wounds: Fucoidan-coated cefdinir nanoparticles in PVA matrices
No full textNanofiber-based drug delivery systems hold significant potential for wound care due to their large surface area, porosity, and ability to deliver drugs in a controlled manner. This study aimed to develop and evaluate a novel hybrid nanofibers (NFs) mat combining Fucoidan-coated cefdinir-loaded Chitosan nanoparticles with polyvinyl alcohol (PVA) for enhanced burn wound healing. The hybrid NFs were fabricated using electrospinning and evaluated for physicochemical properties, in vitro drug release, ex vivo skin permeation, skin deposition, and burn wound healing efficacy. Swelling and weight loss studies were also evaluated, which highlighted the hydration and degradation profiles. Healing progression was assessed through the in vitro scratch wound assay and the in vivo murine burn wound healing model, supported by histopathological and immunohistochemical analyses. Fucoidan-coated cefdinir-loaded Chitosan nanoparticles incorporated into polyvinyl alcohol nanofibers (Fu-cCFD-CSNPs-PVA NFs) exhibited uniform, smooth, bead-free morphology with an average diameter of 295.12 nm ± 48.52 nm, optimal swelling capacity of 250.41 % ± 9.59, and controlled degradation of 48.55 % ± 1.44. The system demonstrated sustained cefdinir release of 84.00 % ± 2.23 within 48 h as well as enhanced ex vivo skin permeation, deposition, and antimicrobial efficacy. The in vitro studies showed improved human skin fibroblast proliferation and migration. In addition, the in vivo evaluations revealed accelerated wound closure with superior tissue regeneration in the murine burn wound healing model. Histopathological and immunohistochemical analyses confirmed enhanced TGF-β1 expression and organized collagen deposition. This multifunctional delivery system offers controlled drug release kinetics combined with the therapeutic benefits of Fucoidan, presenting an advanced approach for burn wound management. Thus, the developed hybrid NFs show potential as a novel antimicrobial burn wound dressing for local therapy