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Hypersensitivity Assessment of Non-Carious Cervical Lesions After Treatment with Different Desensitizing Protocols: A Randomized Clinical Trial
No full textTechnologies used in vehicular networks for effective eco-friendly intelligent transportation system in smart cities
No full textToday, Vehicular Networks (VNs) can significantly improve the safety and sustainable growth of Intelligent Transportation Systems (ITSs) for smart cities. VNs offer efficient solutions to persistent urban problems such as congested roads and roadway safety. VNs adopt a wide range of technologies such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X) communications. These technologies allow cars to communicate with each other and with road infrastructure such as Roadside Units (RSUs) in real time, as well as enable the sharing of data. This can effectively mitigate accidents, enhance traffic flow, and minimize air pollution. Our paper extensively explores the state-of-the-art VN technologies used to establish safe and sustainable transportation systems. Additionally, our study highlights the importance of connectivity among automobiles for smarter urban environments to enhance transportation and support environmentally friendly growth. This is done by utilizing the Veins simulator in our work for conducting different mobility and traffic simulations. We formulate various scenarios, where we use the vehicles’ speed for our performance evaluation. Furthermore, we use OMNeT++ and SUMO for the accurate simulation of traffic motion in the city of Cairo, where results demonstrate that the speed of vehicles employing Vehicular Ad-Hoc Networks (VANETs) communications is not affected by traffic congestion
Using Machine Learning to Predict Women at Risk Having a Child With Congenital Heart Defects
No full textCongenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and 311 with healthy children. After preprocessing the data, ten machine learning models were tested, including Random Forest (RF), Decision Tree (DT), and MLP Classifier. RF achieved the highest accuracy at 97.37%, followed by DT at 96.49%, and MLP at 92.96%. The results show AI’s potential in predicting CHD, supporting early diagnosis and improving infant outcomes
Intracanal microbial reduction after root canal preparation with different tapered instruments. An ex-vivo study
No full textAim: The objective of this ex vivo study was to assess the dual impact of instruments taper and irrigation activation methods on bacterial reduction in infected root canals. Materials and methods:42 extracted human molar teeth were inoculated with Enterococcus faecalis for 2 weeks to develop a mature biofilm. The samples were divided into four experimental groups and two control groups. The experimental groups underwent root canal preparation using two different instruments: XP Shaper (taper 0.04) and Reciproc Blue R25 (taper 0.08). Additionally, two different activation methods were tested: mechanical (XP Finisher) and sonic (EDDY). Bacterial reduction was assessed using Colony Forming Unit (CFU) analysis and Confocal Laser Scanning Microscopy (CLSM) to evaluate the efficacy of the treatments. Results: Results showed significant microbial reduction in all groups after root canal preparation. Root canal preparation with the Reciproc Blue R25 (0.08 taper) achieved a significantly higher bacterial reduction compared to the XP-Endo Shaper (0.04 taper). Additionally, EDDY-activated irrigation resulted in a greater bacterial reduction than the XP Finisher. The interaction between instrument taper and activation method was statistically significant (p\u3c 0.001), indicating that both factors contribute to the efficacy of microbial reduction in root canals.
Conclusion: Both the instrument taper and the method of irrigant activation significantly affected the reduction of intracanal bacterial load. The use of larger tapers and sonic activation provides microbial reductions that may improve clinical outcomes in endodontic treatments
Upcycling Waste to Wealth: CuO-SiO₂/reduced graphene nanocomposite from pomegranate peels for one-pot low-temperature conversion of waste oils into valuable fatty acid monomers
No full textThe utilisation of heterogeneous catalysts in producing fatty acid monomers can minimise the separation cost and hence reduce the price of the fatty acid monomers. This study reports for the first time a novel, environ mentally benign, highly active copper oxide-silica oxide/reduced graphene oxide (CuO-SiO 2 /RGO), heteroge neous nano-catalyst derived from waste pomegranate peels, for the one-pot, low-temperature synthesis of fatty acid monomers from high-acid-value waste vegetable oil (WVO). The synthesised nano-catalyst was extensively characterised using XRD, FT-IR, TEM, SEM, EDX and TGA-DTA. Further, it was utilised to synthesise fatty acid- rich oleic phenoxypropyl acrylate (OPA) monomer from high acid value WVO via a single-step reaction. The process parameters for the synthesis of OPA monomer using CuO-SiO 2 ◦ /RGO catalyst have been optimised using response surface methodology (RSM) and found to be 8.5:1 reactant molar ratio, 3.5 % (w/w) catalyst loading, 54 C temperature, and 9.5 h reaction time, where the highest OPA monomer yield was 95.73 % under optimum conditions. The CuO-SiO 2 /RGO exhibited stable catalytic performance after regeneration with an OPA yield of 93.1 ± 0.37 % after five consecutive runs. The plausible reaction mechanism unveiled that the direct synthesis of OPA monomer from high acid value WVO occurred through both transesterification and esterification reactions simultaneously on the surface of CuO and SiO2 catalyst supported on RGO sheets
Utilization Of Colors, Lighting, and Materials as a Narrative AI-Tool to Convey Human Emotions in Museums’ Interior Spaces. A Case Study of The Museum of Islamic Art Cairo, Egypt.
No full textThis study explores the relationship between museum interior design and human emotional responses, focusing on how key design elements—color, lighting, and materials—can be optimized to enhance visitor engagement and emotional well- being. Conducted at the Museum of Islamic Art in Cairo, Egypt, the research examines two critical spaces along the visitor journey: the Medicine Hall, introducing the experience, and the Writings Hall, situated toward the end of the route, where fatigue and emotional disengagement often occur. Over a nine-week period, a mixed-methods approach was employed, integrating structured observation, behavioral mapping, and AI-assisted image modifications to generate controlled design variations, alongside a questionnaire assessing basic emotional responses (pleasant, happy, sad, angry, joyful) from 120 participants with diverse backgrounds. Findings reveal that combinations such as natural materials paired with warm colors and natural light significantly enhance positive emotions, while artificial lighting, cool colors, and synthetic materials tend to reduce emotional engagement. By accounting for contextual factors like cultural influences and social norms, this research provides nuanced, evidence-based insights into how interior design impacts visitor emotions, offering actionable guidelines for creating emotionally resonant, human-centered museum environments
Analyzing the Impact of Urban Expansion on City Functions Using Space Syntax Techniques
No full textt This study aims to evaluate the impact of spatial configurations within the street network—shaped by recent urban transformations—on indicators of urban vitality in Greater Cairo. It investigates the spatial relationships between the physical components of the network at both the local (neighborhood) and global (citywide) scales. Recent urban shifts have led to noticeable changes in street vitality patterns, resulting in significant disparities across different neighborhoods.
The research employs Space Syntax techniques to measure both local integration (Integration R800) and global integration (Integration Rn) of the street network as projected for the year 2025. It further analyzes the “foreground” structure—represented by major roads connecting city sectors—and the “background” structure—represented by internal local streets.
Findings reveal that the interplay between local and global integration levels plays a critical role in shaping urban vitality. Areas with high integration values on both scales tend to exhibit elevated levels of urban activity, reflected in dense pedestrian movement and a diverse mix of commercial and service functions. Conversely, areas with low integration values show diminished vitality.
Regression analysis also indicates a strong correlation between angular integration values—both local and global—and key indicators of urban vitality. These results underscore the importance for planners and decision-makers to prioritize the integration of foreground and background street structures in future planning for Greater Cairo, in order to enhance urban vitality and improve overall quality of life within the city
Ferric metal-organic frameworks (MOFs)-based electrospinning fibers for supercapacitors
No full textFerric-based metal-organic frameworks (Fe-MOFs) were synthesized and incorporated into poly(methyl methacrylate) (PMMA) using electrospinning to produce PMMA_Fe-MOF nanofibers. The materials were analyzed using X-ray diffraction (XRD), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR), and scanning electron microscopy (SEM). The electrospun materials were directly integrated into nickel foam (NF) electrodes for energy storage applications, e.g., supercapacitors. The electrochemical performance was assessed using cyclic voltammetry (CV), galvanostatic charge-discharge curves (GCDC), linear sweep voltammetry (LSV), and electrochemical potentiokinetic reactivation (EPR). The PMMA_Fe-MOF electrodes exhibited outstanding capacitive performance, achieving specific capacitances of up to 1017.5 F/g at 1 A/g for the 2.5 % Fe-MOF loading. Our findings highlights the promise of electrospun Fe-MOF-based composites as highperformance electrode materials for supercapacitors, featuring a straightforward, binder-free manufacturing procedure. The materials’ structural stability and electrochemical characteristics indicate potential use in energy storage applications
Innovative resistive plate chambers for the CMS phase-2 upgrade: Project summary, construction, and quality assurance
No full textIn view of the Phase-2 of the LHC physics program, called High Luminosity LHC (HL-LHC), the CMS muon system will be upgraded to maintain a robust muon triggering and reconstruction performance. Therefore RE3/1 and RE4/1 stations of the forward muon system will be equipped with new improved Resistive Plate Chambers (iRPC) as dedicated detectors for muon triggering in CMS. The new iRPC detectors have a different design and geometry with respect to the present RPC system to improve the rate capability and survive the harsh background condition during HL-LHC. This work summarizes the iRPC project including the iRPC design and production process, details of the ongoing detector production, quality control procedures at the production sites and results of performance studies with high energy muon beam under intense gamma radiation at the CERN Gamma Irradiation Facility (GIF++) facility
AI – Driven Climate Modeling For Local Adaptation
No full textLiterature review below explores how artificial intelligence (AI) is revolutionizing climate adaptation at regional scales worldwide. As the impact of climate worsens and becomes variable, conventional region-scale models will be limited to being unsuitable in providing credible, place-based projections required by affected groups (IPCC, 2023). Emergent AI technology innovation such as physics-based machine learning to artificial data generation systems for low-resourced areas is the answer to potential remedies (Google AI, 2024). It cites two of the best leaders in today\u27s time: Rotterdam\u27s flood alert system from sensor networks (Deltares, 2023), and Los Angeles\u27 city and satellite heat map project (Nature Cities, 2025). Study reminds that whereas the AI technology in fact possesses the capacity to increase the resilience of the climate within the region, its non-use for the common benefit is characterized by its potential to be limited to highly technologically developed regions unless policy purposeful action is undertaken. Climate AI can be inclusive and fair as well, the report concludes by explaining how and by referring to the use of hybrid systems, ethics, and open platforms with indigenous knowledge (UNESCO, 2025)