Emirates International University Digital Repository
Augmented Framework for Enhanced Phishing Detection and Prevention
Network security has become a hot topic as the Internet has grown in popularity. Phishing attacks are a type of cybercrime when a hacker poses as a reliable source to gain confidential data from a user of the internet. Therefore, more efficient phishing detection is required for better cyber defense. A framework for improving phishing detection based on machine learning is presented in this study. The framework attempts to enhance the speed-to-accuracy ratio in identifying fraud risks by examining the body of currently available literature on phishing attacks and associated remedies. The proposed model improves the effectiveness of phishing detection systems by highlighting the importance of data collection and filtering procedures. To create new security measures and respond to evolving threats, regular updates and ongoing research are necessary. Therefore, this study provides information for upcoming studies on cybersecurity and the preservation of personal data.
Copyright (c) 2026 "This Open Access article is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), permitting unrestricted use, distribution, and adaptation provided the original author and source are properly credited."
Extract Classification Rules from COVID-19 Datasets using Multi-Objective Advanced Genetic Algorithm
The rapid proliferation of Coronavirus Disease 2019 (COVID-19) has resulted in a substantial escalation of suspected and confirmed cases globally, posing a significant threat to public health, economic stability, and international relations This underscores the urgent need for alternative diagnostic strategies that leverage artificial intelligence to enhance pandemic response efficacy. This research endeavors to augment predictive accuracy through the Multi-Objective Advanced Genetic Algorithm (MoAGA), which automatically generates classification rules from datasets. To this end, the Multi-Objective Single Genetic Algorithm (MoSGA) was enhanced by incorporating a distributed population strategy alongside an optimized migration procedure. Furthermore, the proposed method was refined through the integration of two fuzzy control systems, which dynamically adjust crossover and mutation rates, culminating in the development of the Multi-Objective Fuzzy Genetic Algorithm (MoFGA). This innovative approach optimizes both population diversity and fitness. The methodology's efficacy was validated against COVID-19-related datasets, with performance assessed through metrics such as prediction accuracy, maximum fitness, and average fitness. Results indicated that the MoAGA achieved a prediction accuracy of 99.30%, with a maximum fitness of 0.88 and an average fitness of 0.86. This novel methodology demonstrates a superior ability to uncover rule sets with markedly higher predictive precision than existing techniques, enhancing the potential for optimal global solutions.
A hybrid approach for accurate geothermal temperature prediction in the western region of Yemen
The growing need for clean and reliable energy sources has propelled geothermal energy to the forefront of research. Its inherent advantages of baseload power generation and minimal environmental impact make it a highly attractive option. Nonetheless, effective utilization of geothermal resources relies heavily on the ability to accurately predict subsurface temperatures within geothermal reservoirs. In Yemen, a critical barrier to geothermal development is the lack of robust empirical correlations for predicting these crucial temperatures. This study presents a novel approach by developing a hybrid Particle Swarm Optimization Neural Network) PSONN (model to overcome this challenge. A dataset encompassing 1402 data points was collected from 108 geothermal wells located in Yemen's western region. The model incorporates key parameters influencing geothermal temperatures as inputs, including: Latitude; Longitude, Temperature gradient, Surface temperature, Depth, Elevation. Subsurface temperature serves as the model's output variable. The data were then strategically divided into two sets: 70 % designated for training the PSONN model and 30 % reserved for subsequent testing and validation of its predictive capabilities. The research successfully establishes the PSONN model as a highly effective tool for subsurface temperature prediction. This is supported by the exceptionally low Average Absolute Percent Relative Error (APRE) of 0.541, indicating a minimal deviation between predicted and actual values. Additionally, the low Standard Deviation (SD) of 0.11 signifies a high degree of consistency in the model's predictions. The findings suggest that the PSONN model achieves high predictive accuracy, with performance metrics (e.g., APRE = 0.541, SD = 0.11, R = 0.999) comparable to or exceeding those reported in prior studies (e.g., Haklidir, 2019; Altay et al., 2022). Further comparative analysis with existing methods is warranted to fully establish its relative advantages.
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Development of Sustainable PEO/HPMC-CuO Nanocomposite Polymer Electrolytes for Advanced Dielectric Capacitors and Energy Storage Applications
This study focuses on the development and characterization of novel PEO/HPMC-based nanocomposites incorporating copper oxide (CuO) to enhance their physiochemical properties for optoelectronic applications. X-ray diffraction analysis verified the effective incorporation of nanoparticles and indicated a reduction in the degree of crystallinity, declining from 28.24% in the pristine PEO/HPMC to 17.29% in the PEO/HPMC containing 9%CuO. This increase in amorphous character is beneficial for charge mobility. Fourier-transform infrared spectroscopy showed characteristic functional groups of the components with no major chemical structural changes, indicating good compatibility. Optical band gap analysis using Tauc’s method revealed a significant reduction in both direct and indirect band gaps. The direct band gap decreased from 5.19 eV (pure PEO/HPMC) to 4.48 eV (PEO/HPMC containing 9%CuO), reflecting the formation of localized states and stronger interfacial interactions. Electrical conductivity (σac) improved with both filler content and frequency, indicating enhanced ionic mobility and conductive network formation. Dielectric studies showed increased dielectric constant (ε′) and loss (ε″), especially at low frequencies, due to space charge and interfacial polarization. These results confirm the promising multifunctional behavior of the nanocomposites, making them strong candidates for flexible electronic and optoelectronic device applications.
Copyright (c) 2012 ECS journal of solid state science and technology Publication type: e-Journal ISSN: 2162-8777 Publication Year: 2012 - Present Publisher: IOP Publishing Language: English Country: United States of America URL: https://iopscience.iop.org/journal/2162-8777 Authors: Electrochemical Society. https://marketplace.copyright.com/rs-ui-web/mp/search/journal/21628777
Optimizing PEO/HPMC polymer blends with Al2O3/MoO3 as hybrid nanofiller for enhanced dielectric performance and energy storage systems
This study investigates the synergistic effects of aluminum oxide nanoparticles (Al₂O₃ NPs) and molybdenum trioxide nanobelts (MoO₃ NBs) on the structural, optical, and electrical properties of hydroxypropyl methylcellulose (HPMC) and polyethylene oxide (PEO) polymer blends prepared using solution casting method. Structural characterization revealed reduced crystallinity by XRD analysis and strong interfacial interactions by FTIR spectroscopy, confirming effective nanofillers complexation with PEO/HPMC matrix. UV–Vis analysis revealed significant reductions in both direct and indirect optical band gaps upon nanofillers incorporation. The nanofillers also improved dielectric performance, with AC conductivity increasing from 2.63 × 10−10 S/cm (pure blend) to 8.85 × 10−9 S/cm (1.2 wt% nanofiller) at 1 kHz. Dielectric results showed optimal performance at 1.2 wt% Al₂O₃-MoO₃ loading, exhibiting the highest dielectric constant among all formulations. Nyquist plots analysis demonstrated improved bulk conductivity and emerging double-layer capacitance, suggesting improved charge storage capabilities at electrode-electrolyte interfaces. SEM analysis revealed homogeneous dispersion of Al₂O₃-MoO₃ nanofillers in PEO/HPMC matrix at loadings ≤1.2 wt%, while significant aggregation was observed at higher concentrations. With their superior dielectric response and enhanced ionic conductivity, these Al₂O₃/MoO₃-PEO/HPMC nanocomposite films show outstanding promise for both nanodielectric devices and advanced energy storage systems.
α-MoO3 Nanobelts Boosted the Structural, Optical, Thermal, and Dielectric Properties of PEO/PVP Blends for Emerging Optoelectronic/Energy-Storage Applications
This study presents a comprehensive investigation of the optical, thermal, structural, and electrical properties of poly(ethylene oxide) (PEO)/polyvinylpyrrolidone (PVP) blends embedded with molybdenum trioxide nanobelts (MoO3 NBs). The nanocomposites were fabricated via solution casting with varying concentrations of MoO3 NBs to assess their influence on the polymer matrix. X-ray diffraction (XRD) analysis demonstrated the semicrystalline nature of PEO/PVP blends and revealed structural modifications induced by the nanofiller. Fourier transform infrared (FTIR) spectroscopy showed electrostatic interactions between the polymer matrix and MoO3 NBs, facilitated by intermolecular and intramolecular hydrogen bonding. The indirect optical band gap decreased from 4.57 eV (pure blend) to 3.54 eV (4.0 wt % MoO3), indicating enhanced optoelectronic properties. Thermogravimetric analysis (TGA) confirmed improved thermal stability upon nanobelt addition, while scanning electron microscopy (SEM) revealed a uniform dispersion of MoO3 NBs within the polymer matrix. Furthermore, the nanocomposites exhibited significantly enhanced AC conductivity compared to the pure matrix, along with tunable dielectric properties consistent with non-Debye relaxation behavior. These findings highlight the potential of PEO/PVP-MoO3 nanocomposite samples as tailorable dielectric materials for advanced applications in flexible solid-polymer electrolytes and high-performance energy-storage systems.
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Development of PVDF/PMMA-Cu nanocomposites with enhanced dielectric properties and energy storage density for capacitor applications
This 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.
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Enhanced the structural, optical, electrical, and dielectric properties of PEO/CMC blend via TiO2 and ZnO nanoceramics: nanocomposites for capacitor applications
Flexible and high-performance dielectric materials are critical for modern energy storage systems. Herein, polymer nanocomposite (PNC) films, based on polyethylene oxide (PEO) and carboxymethyl cellulose (CMC) blend, were reinforced with TiO2/ZnO nanoceramics. The nanoceramics were synthesized via a sol-gel route and incorporated into the PEO/CMC matrix at concentrations ranging from 0.2 to 1.6 wt.% through the solution casting method. Structural analysis confirmed successful nanofiller incorporation, reducing crystallinity from 86.9% to 65.7% and increasing amorphous content. FTIR and UV–Vis spectroscopy revealed interfacial interactions, decreasing optical band gaps and increasing Urbach energy, indicative of enhanced defect states. Dielectric performance of the PEO/CMC-TiO2/ZnO PNC film with nanofillers’ content of 1.6 wt.% was markedly improved, with ε′ exceeding 104 and tanδ reduced below 1.5 at low frequencies at an applied electric field of 50 kV.cm−1. Where the capacitance–frequency analysis showed a notable increase in capacitance (0.1 μF to 8 μF), while electric modulus and Jonscher exponent analysis revealed a transition from dipolar to interfacial polarization and a conduction shift toward localized hopping. These enhancements position the TiO2/ZnO-reinforced PEO/CMC system as a viable material for high-performance dielectric capacitors.
Three composition-dependent features of the mixed-modifier effect in ternary ZnO–Na2O–B2O3 glasses
Three ternary glass systems with chemical compositions xZnO.(30-x)Na2O.70B2O3, xZnO.(40-x)Na2O.60B2O3, and xZnO.(50-x)Na2O.50 B2O3 were successfully prepared using the melt-quenching method. The structure and electrical properties of the prepared glasses were characterized using Fourier-transform infrared (FTIR) spectroscopy and direct-current (DC) conductivity measurements, respectively. A new form of the “mixed modifier effect” was observed in the ternary ZnO–Na2O–B2O3 glasses. This effect was examined through analyzing the relationship between each of activation energy, conductivity, and mobility with the ratio of modifier ion concentrations (NZn(m)/NNa), interionic separation distance of Na+ ions (NNa−1/3), and NNa−3/2. The activation energy varies non-monotonically with ZnO, ranging from 0.81–1.13 eV (70B; maximum at 20 mol% ZnO), 0.76–1.13 eV (60B; maximum at 30 mol% ZnO), and 0.83–1.17 eV (50B; maximum at 40 mol% ZnO). Molecular dynamics simulations further support the experimental trends: the Na⁺ diffusion activation energy increases with ZnO content (e.g., 0.276–0.374 eV in 70B, 0.278–0.362 eV in 60B, and 0.293–-0.343 eV in 50B), confirming Na⁺ as the dominant mobile species and providing atomistic insight into the composition-dependent mixed-modifier features in ZnO–Na2O– B2O3 glasses.
Heterogeneous stacking strategy for modeling flowing bottom-hole pressure of oil wells
Accurately predicting Flowing Bottom-Hole Pressure (FBHP) is critical for optimizing oil and gas production. Existing predictive methods often rely on oversimplified or complex, yet computationally expensive, models that fail to capture the intrinsic nonlinearities of well dynamics, leading to inaccurate predictions and potential economic losses. This paper introduces a three-layer heterogeneous stacking ensemble model to address the latter challenge. In particular, the key novelty of the developed work is a hierarchical architecture that integrates five distinct Machine Learning (ML) base learners, two meta-learners, and a final super-learner, i.e., an additional meta-model that combines the outputs of the meta-learners to capture complex, non-linear relationships in the data. When evaluated on a field dataset (total dataset samples N=795; test set samples N=199), the proposed Super Learner Stacking model (ST-S) demonstrated superior predictive performance on the independent test set, achieving R-squared (R2) = 0.857±0.006 and Root Mean Squared Error (RMSE) = 146.382±2.806. In addition, the ST-S model outperformed all individual models and simpler stacking ensembles reported in the article. As a result, the developed ST-S model provides a robust, data-driven tool for FBHP prediction, achieving high predictive accuracy without resorting to computationally expensive methods, thereby supporting improved well management and production optimization.
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Silver nanoparticle-enhanced PVA–PEO polymer electrolytes for energy storage and dielectric applications
Poly(vinyl alcohol) (PVA)/polyethylene oxide (PEO) blend electrolytes incorporating silver nanoparticles (Ag NPs) were fabricated via a conventional solution-casting technique and systematically investigated for their structural, optical, electrical, and dielectric properties. Transmission electron microscopy confirmed that the synthesized Ag nanoparticles exhibit a nearly spherical morphology with a mean diameter of 29.87 nm. X-ray diffraction analysis revealed a progressive reduction in the crystallinity of PVA/PEO blend matrix with increasing Ag nanoparticles content, indicating enhanced amorphous character and strong polymer–nanoparticle interactions. Fourier-transform infrared spectroscopy confirmed interactions between Ag nanoparticles and the hydroxyl and ether functional groups of the blend. UV–visible optical studies demonstrated a concentration-dependent red shift in the absorption edge and a significant reduction in both indirect and direct optical band gap energies, decreasing from 4.99 and 5.30 eV for the pristine PVA/PEO blend to 4.15 and 4.56 eV, respectively, at higher Ag loadings. Electrical and impedance spectroscopy revealed a pronounced enhancement in ionic conductivity upon Ag incorporation, attributed to reduced bulk resistance, increased segmental mobility, and the formation of conductive pathways within the nanocomposite matrix. Furthermore, dielectric analysis showed high dielectric constants at low frequencies governed by Maxwell–Wagner–Sillars interfacial polarization. The combined improvements in structural, optical, electrical and dielectric properties highlight the potential of PVA–PEO/Ag nanocomposite electrolytes for energy storage applications, particularly high-performance dielectric capacitors and multifunctional polymer systems.
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PVA/HPMC/Fe₂O₃–MoO₃ nanocomposites with tunable optical, dielectric, and electrical properties for energy-storage and optoelectronic applications
This work investigates the synergistic impact of iron oxide (Fe₂O₃) nanorods and molybdenum trioxide (MoO₃) nanobelts incorporated into a polyvinyl alcohol/hydroxypropyl methylcellulose (PVA/HPMC) blend via solution casting. The structure–property relationships of PVA/HPMC/Fe₂O₃–MoO₃ nanocomposites were analyzed using XRD, FTIR, UV–Vis spectroscopy, and electrical/impedance measurements. Structural analyses (XRD, FTIR) revealed reduced PVA/HPMC blend crystallinity and enhanced interfacial interactions, confirming effective integration of Fe₂O₃–MoO₃ nanofillers. Optical studies showed increased absorption, a bathochromic shift, and the appearance of a ligand-to-metal charge-transfer band, accompanied by reduced direct and indirect bandgaps due to defect-induced localized states and intensified interfacial charge transfer. Electrical and dielectric measurements indicated significant improvements in AC conductivity, charge transport, and dielectric constant, with the highest value (~908) achieved at 2.00 wt% nanofillers loading. Overall, the synergistic incorporation of Fe₂O₃ and MoO₃ nanofillers significantly enhances the optical, dielectric, and electrical performance of the PVA/HPMC matrix, making these nanocomposites promising for optoelectronic devices, flexible capacitors, and solid-state energy-storage applications. In addition, their improved dielectric response and charge transport characteristics suggest potential use in flexible sensors, electromagnetic interference shielding materials, and smart wearable electronic systems.
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Optimized neural networks for efficient modeling of crude oil production
The accurate prediction of crude oil production is crucial for effective management of oil reservoir operations. This paper leverages recent advancements in machine learning techniques and metaheuristic optimization algorithms, specifically deep learning (DL) and metaheuristic (MH) approaches, to construct a robust and efficient oil production prediction model. Real-world datasets from two diverse countries, Yemen and China, are employed in model development. The study focuses on optimizing a multilayer perceptron (MLP) using the Runge–Kutta optimizer (RUN). The primary goal is to enhance the MLP parameters through the application of the RUN algorithm. Rigorous evaluation experiments gauge the efficacy of the resulting prediction model (RUN-MLP), demonstrating impressive performance across three widely recognized evaluation metrics: root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (). Comparative analyses involve multiple MLP-modified models employing various MH algorithms, with the RUN-MLP consistently exhibiting competitive performance. The findings underscore the computational efficiency of the RUN optimization algorithm. Additionally, the study employs the Friedman test as a statistical tool to elucidate differences between RUN and its competitors.
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Mandibular condyle detection using deep learning and double attractor-based energy valley optimizer algorithm
The temporomandibular joint (TMJ) constitutes a bilateral ginglymoarthrodial joint, wherein each condyle interacts with its corresponding glenoid fossa of the temporal bone. There is a critical need to understand better and accurately characterize the temporomandibular joint’s diverse and variable morphological features, which can reveal significant variability across individuals, genders, and age groups. Within this study, we present an innovative condyle detection technique harnessing the potential of deep learning and feature selection (FS) models. Our approach encompasses a multi-stage process, commencing with using YOLOv8 to identify the region of interest (ROI). Subsequently, leveraging a sophisticated deep learning model, we extract salient features from the identified ROI. We modified the Energy Valley Optimizer (EVO) as an FS technique. To substantiate the efficacy of our developed method, a comprehensive dataset of 3000 panoramic images is employed, meticulously classified by two experienced maxillofacial Radiologists into four distinctive types: flat, pointed, angled, and round. The evaluation and comparison results confirm the efficiency of the proposed method in detecting condyle based on various evaluation performance indicators.
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Tunable structural, optical, and electrical performance of PEMA/PMMA–CoCl₂ composites for advanced optoelectronics and energy storage applications
This work investigates the effects of CoCl₂ doping on the structural, optical, and impedance characteristics of PEMA/PMMA blends for the advancement of sophisticated polymeric multifunctional materials. Composite films containing CoCl₂ were prepared using the solution-casting method and characterized by various analytical techniques. XRD and FTIR analysis revealed reduced crystallinity with significant interactions in the doped samples, SEM revealed a homogeneous morphology with slight porosity as a result of filler incorporation. UV–Vis spectra have demonstrated a systematic decrease of both direct and indirect band gaps, which evidences an effective tuning of the electronic structure via controlled doping. Electrical studies have demonstrated a significant increase of ionic conductivity at higher CoCl₂ content, which was further supported by impedance spectra that have revealed a lower bulk resistance and better charge transport. Conducing equivalent-circuit models further confirmed and quantified these improvements in conductivity. This is a dual-polymeric (PEMA/PMMA) matrix doped with CoCl₂, hence attaining the simultaneous control of structural order, optical properties, and ionic transport each rarely observable in convention polymer films. The optimized 5.0 wt.% composite exhibits an excellent balance between the conductivity and structural stability and, hence, is considered a promising candidate for tunable optoelectronic and energy storage applications.
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Effect of encapsulated multi-walled carbon nanotubes on the thermal, electrical, and dielectric properties of PEO/SA nanocomposites
Polymer nanocomposites combining dielectric properties with controlled conductivity are critical for advanced energy storage applications. This study investigates polyethylene oxide/sodium alginate (PEO/SA) blends reinforced with multi-walled carbon nanotubes (MWCNTs). PEO/SA/MWCNTs nanocomposites were fabricated using solution casting, with TEM confirming uniform dispersion of MWCNTs (diameters: 13.30–22.16 nm) without aggregation. DSC analysis revealed enhanced thermal stability, demonstrated by increased glass transition (Tg ↑ 10%), melting (Tm ↑ 12%), and decomposition temperatures, attributed to strong interfacial interactions. Electrical properties showed a significant improvement in conductivity: threefold (to 2.81 × 10⁻⁹ S.cm− 1 at 100 Hz) at a concentration of 0.40 wt% MWCNTs, with further improvement at elevated temperatures. Dielectric studies demonstrated exceptional permittivity (ε′ = 1.66 × 103at a concentration of 0.40 wt% MWCNTs, a 447% increase compared to the pure blend) and frequency-dependent behavior, with both ε′ and ε″ values decreasing steadily before stabilizing above 1 MHz. The loss tangent (Tanδ) demonstrated dual dependency: increasing with MWCNTs concentration/temperature but decreasing with frequency due to reduced interfacial polarization. These results establish PEO/SA/MWCNTs nanocomposites as promising next-generation dielectric materials for flexible energy storage and optoelectronic systems.
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Evaluating the Prediction Performance of Random Forest in Classification of Carbonate Lithology
Accurate lithology prediction in carbonate reservoirs is essential for hydrocarbon exploration but remains challenging due to their complex heterogeneity. Traditional methods (e.g., seismic and well-log analysis) often fail to capture subtle lithological variations, while machine learning approaches such as Random Forest (RF) remain underexplored for carbonates. Previous research has not sufficiently compared Random Forest with advanced models such as XGBoost and deep learning approaches, nor provided detailed feature importance analyses specific to carbonate lithology classification. This study employs a dataset comprising 4,624 samples characterized by ten petrophysical properties to evaluate the classification performance of RF. Our optimized RF framework demonstrates superior accuracy while reducing dependence on costly core sampling, thereby improving the precision of carbonate reservoir models.
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Simplified Deep Neural Network Models for Cardiovascular Disease Classification
Cardiovascular diseases encompass a range of conditions affecting the heart and blood vessels. Given their global impact, early detection of these diseases is crucial for saving lives and effectively managing morbidity and mortality. One such effective approach is to leverage deep learning to enhance classification performance in heart disease prediction (HDP). This paper presents two new simplified deep neural network (SDNN) models to assist cardiologists and vascular doctors in diagnosing heart disease that achieve 100% accuracy with feature selection, outperforming complex hybrid models of related works. The proposed models are tested on combined Kaggle datasets containing consistent reports of 918 people, including heart disease and non-heart disease reports. The models are investigated with/without applying feature selection methods and compared with different machine learning classifiers and recently proposed SDNN models. The experimental results show how the proposed SDNN models outperform other ML-based and DL-based classifiers in terms of accuracy and structure’s complexity. The proposed model, SDNN–HDP1, with dropout layers achieves 94.086% accuracy, while the second proposed model, SDNN–HDP2, without dropout layers achieves 95.69% accuracy without feature selection. The results of SDNN–HDP1 and SDNN–HDP2 reach 100% in terms of accuracy, precision, and recall with feature selection.
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TiO₂-reinforced PVA/HPMC/CMC ternary bio-blend electrolytes for advanced energy storage devices
This study develops and characterizes eco-friendly ternary polymer electrolytes composed of polyvinyl alcohol (PVA), hydroxypropyl methylcellulose (HPMC), and carboxymethyl cellulose (CMC), reinforced with titanium dioxide (TiO2) nanoparticles, for high-performance energy-storage applications. PVA/HPMC/CMC/TiO2 nanocomposites were fabricated via solution casting and characterized using XRD, FTIR, UV–Vis spectroscopy, SEM, TEM, and impedance spectroscopy to investigate their structural, optical, dielectric, and electrical properties. XRD results revealed that the incorporation of TiO2 suppressed crystallinity and promoted the formation of an amorphous phase, thereby favoring ion mobility. Optical analysis revealed band-gap narrowing with the addition of TiO2 (4.63 eV direct, 2.88 eV indirect), indicating enhanced electronic transitions and light absorption. Impedance measurements demonstrated optimized ionic conductivity (2.63 × 10−7 S cm−1 at 1.2 wt% TiO2) and a high dielectric constant (∼1264), resulting from interfacial polarization and additional conduction pathways. A pronounced decrease in bulk resistance (1.5 kΩ at 1.2 wt% TiO₂ vs. 8.2 kΩ for the pristine blend) further confirmed enhanced charge transport with reduced energy loss-key factors for high-performance capacitors and microelectronic devices. These findings highlight the crucial role of TiO2 nanoparticles in tuning and tailoring the functional properties of PVA/HPMC/CMC electrolytes, thereby providing a sustainable, scalable, and cost-effective platform for next-generation capacitive energy storage systems.
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Enhanced linear and nonlinear optical properties of poly(vinyl alcohol) films with manganese(III) tetraphenylporphyrin chloride additive for optoelectronic applications
Polymer-based composite materials with tunable optical properties have garnered significant attention due to their potential applications in optoelectronics and photonics. Among them, poly(vinyl alcohol) (PVA) has been extensively investigated owing to its excellent film-forming ability, optical transparency and compatibility with functional additives. In this study, composite films were fabricated by incorporating manganese(III) tetraphenylporphyrin chloride (MnTPPCl: a metalloporphyrin complex that absorbs visible light) into a PVA matrix at concentrations of 0.05, 0.1 and 0.25 wt% via the conventional solution casting method. The films were characterized using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, SEM and UV–visible–near infrared (NIR) spectrophotometry to examine their structural, morphological and optical properties, respectively. ATR-FTIR analysis confirmed the successful formation of molecular interactions between MnTPPCl and PVA, facilitated by hydrogen bonding and coordination. SEM micrographs revealed a smooth surface for pure PVA, whereas the incorporation of MnTPPCl introduced uniformly distributed spherical domains whose size and density increased with the dopant concentration. Optical studies revealed a redshift in the absorption edge, accompanied by the emergence of MnTPPCl-specific absorption bands. A decrease in the indirect optical bandgap (from 4.98 to 4.37 eV) and an increase in the absorption coefficient (up to 10−13 cm−1) were observed. Furthermore, enhancements in both optical conductivity and dielectric response were recorded. Nonlinear optical measurements demonstrated a pronounced improvement, with the third-order nonlinear susceptibility (χ(3)) increasing from 1 × 10−13 to 1.2 × 10−13 esu, and the nonlinear refractive index (n2) exhibiting a tenfold enhancement. These results demonstrate that MnTPPCl can effectively tune the structural and optical properties of PVA, making these composites promising materials for advanced optoelectronic, nonlinear optical and photonic device applications. © 2025 Society of Chemical Industry.
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An Optimized Hybrid Intelligent System for High-Accuracy Dewpoint Pressure Estimation
Dewpoint pressure (DPP) is a critical property of gas condensate reservoir development. Accurately estimating this property remains a significant challenge. Existing empirical correlations and iterative methods lack sufficient accuracy due to complexity and computational intensity. However, despite their utilization involving complex computations, they have not achieved sufficient accuracy. Several individual intelligent systems have been utilized to predict this property with good accuracy, but the application of hybrid models is less common. Therefore, this study proposes two hybrid intelligent models—Particle Swarm Optimization combined with Neural Networks (PSONN) and Neuro-Fuzzy (NFuzzy)—to enhance prediction accuracy of dewpoint pressure. Approximately 860 collected data points were used to develop these hybrid models. Inputs such as temperature (T), hydrocarbon composition, specific gravity, and molecular weight of heptane plus were utilized to predict the dewpoint pressure. In this study, the performance of both intelligent hybrid systems is compared to the most widely published Artificial Intelligence (AI) models. Based on statistical error analysis results, the new hybrid models outperform the published models. The results confirm that the PSONN hybrid model achieved the best performance with an absolute percent relative error (APRE) of 2.47%.
Copyright (c) 2021 © 2021 UST Journals | All Rights Reserved ,Journal of Science and Technology is an Open Access Peer-Reviewed Journal Published Monthly by Faculty of Engineering and Computing – University of Science & Technology - Aden - Yemen.
Skin Prick Test and Patch Test: Environmental and Chemical Allergen Reactivity Pattern Among Yemeni Patients in Sana’a City, Yemen
Background: The prevalence of hypersensitivity reactions to different allergens varies according to the population studied and is subject to environmental and lifestyle factors. Early and delayed hypersensitivity are diagnosed with skin prick test and patch test. Objective: This study aimed to identify the common environmental and chemical allergens sensitizing Yemeni allergic patients. Subjects and Methods: A retrospective study was conducted using registries of patients who attended Elaj Medical Center in Sana’a city, Yemen, from January 1st to December 31st 2023. The collected data included sex, age, and the clinical laboratory test results of the skin prick test and the patch test. Results: The skin prick test was performed on sixty-five patients compared to twenty-two who were tested with a patch test. Most of the positive participants were male, aged between 18 and 40 years. The most frequent environmental allergens causing immediate hypersensitivity were Dermatophagoides farinae and Dermatophagoides pteronyssinus mites (64.6% and 69.2%, respectively), Aspergillus (47.7%), cat dander and dog dander (43.1% and 35.4%, respectively). Dermatophagoides farinae mites mostly sensitized females with an odds ratio of 3.43 (95% CI, 1.13– 10.42, p=0.05). The most common chemical allergens that caused delayed hypersensitization were paraphenylenediamine (45.5%), potassium bichromate (45.5%), cobalt sulfate (36.4%), thiuram mix (27.3%), Vaseline (27.3%), and nickel sulfate (27.3%). Polysensitization was more frequent. The age of patients was directly proportional to the number of allergens responsible for immediate hypersensitivity (r-0.298, p=0.016). Conclusion: Yemeni patients exhibited high sensitization to aeroallergens and chemical allergens. Females showed a higher propensity for polysensitization. The pattern of sensitization to environmental and chemical allergens in Yemen differs somewhat from neighboring Arab and Asian countries. Further multi-center studies are needed to support and establish a standard country-specific allergens panel for testing.
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An Optimized Framework Based on Data Exploration and Dynamic Ensemble-Based Models for Breast Cancer Prediction
Breast cancer (BC) is a major global health concern. Detecting BC at an early stage gives more treatment options and can help avoid more aggressive treatments. The use of machine learning (ML) in BC prediction offers significant potential for improving the accuracy and speed of diagnosis, personalizing treatment, and identifying high-risk patients. However, there are significant challenges associated with the use of ML, including the need for high-quality data and more flexible models with optimal parameters to achieve high efficiency. In this paper, we propose an optimized framework based on multi-stage data exploration. This framework is designed to provide a comprehensive approach to data exploration, ensuring that the data is well-prepared for ML. In addition, the framework includes dynamic ensemble-based classifiers, which combine multiple independent classifiers to improve accuracy and mitigate the risk of overfitting in conjunction with the cross-validation techniques. These classifiers are optimized using Bayesian hyperparameter tuning, which involves selecting the optimal values for the various hyperparameters of the model. This approach can significantly improve the prediction accuracy of the resulting model. The study evaluates the framework using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset and compares our results with other state-of-the-art models. The experimental results show that the best result is 100% for accuracy and recall with hyperparameters of (Ensemble Method = AdaBoost, Number of learners = 322, learning rate = 0.9350, and the Maximum number of splits = 1). The highest performance has been achieved with the proposed framework compared with the other models in terms of accuracy (mean = 99.35%, best = 100%, worst = 98.7%, and Standard Deviation = 0.325). The framework can potentially improve the accuracy and efficiency of BC prediction, ultimately leading to better outcomes for patients.
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Controlling the dielectric and optical properties of polyvinyl alcohol/polyethylene glycol blends by adding copper oxide nanoparticles for application in energy storage devices
Copper oxide nanoparticles (CuONPs) were prepared using the sol–gel technique. Polyvinyl alcohol/polyethylene glycol (PVA/PEG) blend filled with different concentration of CuONPs were synthesized using the solution casting way. TEM micrograph indicated the polydisperse and quasi-spherical shapes of CuONPs with an average size of 21.4 nm. FT-IR measurement showed that the intensity of the spectra reduced with increasing concentration of CuONPs. XRD study showed that PVA/PEG blend had a semi-crystalline structure; adding CuONPs decreased the crystallinity degree, revealing the proper intercalation of CuO nanoparticles within PVA/PEG polymeric system. UV/vis analysis indicated that the addition of CuONPs resulted in improved optical absorption and narrowed the allowed direct/indirect optical gap of the polymeric samples. Using frequency-dependent AC conductivity and electrical modulus, the dynamic ionic activity and the type of relaxation process of electrolytes samples were studied. The dielectric constant (ε′) and dielectric loss (ε′′) increase with concentrations of CuONPs in the low-frequency part, indicating the effects of space charge polarization. The impedance parameters were analyzed using a Nyquist diagram, which showed a single semicircle for all samples whose radius of curvature reduced with increasing the nanofiller loading, as well as the appearance of inclined spike for the two samples with the highest concentration of CuONPs. These parameters can be best fitted to two models of equivalent circuits. The obtained results confirm that these PVA/PEG/CuO nanocomposites are candidates for optoelectronic applications, thin film capacitors, energy storage, and flexible nanoelectrics for frequency-operated microelectronic devices.
Copyright (c) 2024 Springer Nature has partnered with Copyright Clearance Center's RightsLink service to offer a variety of options for reusing this content. https://s100.copyright.com/AppDispatchServlet?title=Controlling%20the%20dielectric%20and%20optical%20properties%20of%20polyvinyl%20alcohol%2Fpolyethylene%20glycol%20blends%20by%20adding%20copper%20oxide%20nanoparticles%20for%20application%20in%20energy%20storage%20devices&author=K.%20M.%20A.%20Saron%20et%20al&contentID=10.1007%2Fs10971-024-06320-0&copyright=The%20Author%28s%29%2C%20under%20exclusive%20licence%20to%20Springer%20Science%2BBusiness%20Media%2C%20LLC%2C%20part%20of%20Springer%20Nature&publication=0928-0707&publicationDate=2024-02-02&publisherName=SpringerNature&orderBeanReset=true
Enhancing optical, structural, thermal, electrical properties, and antibacterial activity in chitosan/polyvinyl alcohol blend with ZnO nanorods: polymer nanocomposites for optoelectronics and food/medical packaging applications
This work presents a comprehensive investigation into the development and characterization of novel nanocomposites composed of chitosan (Cs) and polyvinyl alcohol (PVA) with zinc oxide (ZnO) nanorods (NRs). The study begins with the successful synthesis of ZnO nanorods via the sol–gel technique with an average diameter of approximately 35 nm, as indicated by TEM image and histogram. These ZnO NRs were then seamlessly integrated into Cs/PVA polymer blend-based nanocomposite films through a casting process. The results reveal a series of noteworthy findings, where XRD patterns indicate an increase in intermolecular interactions, leading to softening of the Cs/PVA blend’s polymer chain backbone and disruption of crystalline regions. Complex interactions between ZnO NRs and functional groups within the Cs/PVA matrix are also evident from FTIR analysis, where the spectra show noticeable changes in the intensity and broadness of certain peaks when ZnO NRs concentrations increase. The optical feature are investigated by ultraviolet–visible tecnique (UV–Vis), where the surface plasmon resonance peak of ZnO NRs were observed and the optical energy gap were determined. Furthermore, thermogravimetric analysis (TGA) demonstrates improved thermal stability, owing to blend-NRs interactions that safeguard the structural integrity of Cs/PVA during exposure to elevated temperatures. DC electrical conductivity significantly increases with rising temperature and NRs content. The DC conductivity value of Cs/PVA blend filled with 12 wt% ZnO NRs at 373 K reached 3.39 × 10−9 S/cm, which increased by more than two orders of magnitude due to increased nanofiller bridging the gaps between localized states, and reducing potential barrier separation and facilitating charge carrier transfer. Furthermore, these nanocomposites exhibit enhanced antibacterial activity, with increased ZnO content correlating with elevated antimicrobial efficacy. These properties make the nanocomposites highly promising for potential in optoelectronic devices where their optical and electrical characteristics can be leveraged. Simultaneously, their demonstrated antibacterial properties make them appealing for applications in food and medical packaging, safeguarding product hygiene and safety.
Copyright (c) 2024 Springer Nature has partnered with Copyright Clearance Center's RightsLink service to offer a variety of options for reusing this content. https://s100.copyright.com/AppDispatchServlet?title=Enhancing%20optical%2C%20structural%2C%20thermal%2C%20electrical%20properties%2C%20and%20antibacterial%20activity%20in%20chitosan%2Fpolyvinyl%20alcohol%20blend%20with%20ZnO%20nanorods%3A%20polymer%20nanocomposites%20for%20optoelectronics%20and%20food%2Fmedical%20packaging%20applications&author=Abdu%20Saeed%20et%20al&contentID=10.1007%2Fs00289-024-05270-5&copyright=The%20Author%28s%29%2C%20under%20exclusive%20licence%20to%20Springer-Verlag%20GmbH%20Germany%2C%20part%20of%20Springer%20Nature&publication=0170-0839&publicationDate=2024-04-21&publisherName=SpringerNature&orderBeanReset=true
Study of Optical, Thermal, Electrical, and Impedance Properties of Li4Ti5O12-Based PEO/SA Biopolymer Blend Electrolytes for Lithium-Ion Batteries
Nanocomposites composed of polyethylene oxide (PEO) and sodium alginate (SA), containing varying contents of lithium titanium oxide nanoparticles (Li4Ti5O12NPs), were synthesized by solution casting technique. Li4Ti5O12 was incorporated into PEO/SA blend and is a valuable biopolymer for its biocompatibility, solubility and eco-friendliness. Structural analysis via X-ray diffraction spectroscopy revealed a decrease in the crystallinity of PEO/SA matrix with increasing nanoparticle content. Complementary Fourier transform infrared analysis verified the presence of strong molecular interactions between Li4Ti5O12 and the blend chains. Scanning electron microscopy verified a uniform dispersion of Li4Ti5O12 within PEO/SA blend, contributing to the improved properties of the electrolytes, while optical analysis showed a decrease in the bandgap energy, indicating enhanced light absorption and improved suitability for applications in nanodielectric devices. The thermal stability of PEO/SA/Li4Ti5O12 electrolyte samples was improved as shown by thermogravimetric analysis. Furthermore, a significant improvement in the ionic conductivity of the filled samples was observed, attributed to the reduced bulk resistance and improved charge transport pathways. Dielectric studies further showed improved dielectric permittivity and reduced dielectric losses for filled samples, enhancing the material’s charge storage capability. These findings highlight the potential of PEO/SA/Li4Ti5O12 biopolymer electrolytes for advanced applications in nanodielectric devices and lithium-ions batteries.
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Optimizing Heart Disease Prediction Models through SMOTE: Addressing Data Imbalance
The problem of data imbalance poses a significant challenge in the field of medical diagnostics, particularly in heart disease prediction using machine learning models. This study investigates the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address this imbalance and improve the predictive accuracy of heart disease models. Through a rigorous methodology involving data collection, preprocessing, and the evaluation of 21 machine learning models, the study compares the performance of models trained on both original and SMOTE-balanced datasets. The findings indicate that models trained on SMOTE-balanced datasets showed statistically significant improvements in precision, F1-score, and often recall, with varied impacts on accuracy depending on the dataset. Key performance metrics such as accuracy, precision, recall, and F1-score are analyzed using t-tests to assess the statistical significance of improvements offered by SMOTE. Notably, the enhancements were particularly evident in precision and F1-score across the Cleveland and Statlog heart disease datasets, demonstrating SMOTE's ability to enhance model performance effectively. This study aims to demonstrate the potential of SMOTE in enhancing model performance, thus contributing to more effective and reliable heart disease diagnostics.
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Adhesive SiO2 Layer-Protected Premium Transparent AgNWs Deposited on Flexible Substrates
Flexible, highly conductive, and transparent silver nanowires (AgNWs) have emerged as vital materials for advanced applications in photovoltaics, touch screens, and optoelectronics. However, their practical deployment has been hindered by issues such as poor adhesion to diverse substrates (e.g., glass and plastic) and susceptibility to ambient oxidation. In this study, we present a novel approach to overcome these challenges through the synthesis and performance evaluation of transparent conductive electrodes (TCEs) composed of AgNWs with an adhesive SiO2 protective layer deposited on glass, paper, and plastic substrates. Using a combination of polyol synthesis, RF sputtering, and spray coating techniques, we achieved robust and stable TCEs. The SiO2 coating not only significantly improved resistance to oxidation but also enhanced adhesion, mechanical stability, and durability. SiO2/AgNWs/Glass and SiO2/AgNWs/Plastic electrodes demonstrated high transmittance values of 83.36% and 69.67% at 550 nm, along with low sheet resistance of 48.5 Ω sq–1 and 50.2 Ω sq–1, respectively. This work highlights the ability of the adhesive SiO2 layer to preserve optical and electrical properties while enhancing the substrate adherence and protection against degradation.
Copyright (c) 2025 Copyright © 2025 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/
Tailoring Structural, Optical, and Dielectric Properties of PVC/PMMA/PS/ZnO Nanocomposites for Capacitive Energy Storage Applications
Using a conventional casting method, flexible polymeric film nanocomposites composed of PMMA (polymethyl methacrylate), PS (polystyrene), PVC (polyvinyl chloride) and ZnO nanoparticles were synthesized. Fourier transform infrared (FTIR) spectroscopy identified distinct peaks corresponding to vibrational groups in the prepared samples. Upon doping the PVC/PMMA/PS blend with varying concentrations of ZnO NPs (2.5–10 wt%), most absorption intensities tend to diminish progressively as the ZnO contents have been increased to 5 wt%. Changes in FTIR vibrational bands indicated interactions between the PVC/PMMA/PS/ZnO nanocomposite constituents. The XRD patterns of the ZnO NPs-based composites have exhibited the same peaks of the pure blend; however, there is a notable increase in broadness and a significant reduction in intensity as the weight percentage of ZnO NPs rises from 2.5 to 10. This observation indicates the development of interactions between the polymer and nanoparticles. The redshift seen in the absorption edge of the samples filled with ZnO provided strong evidence that charge transfer complexes had formed inside the polymeric matrix. The indirect and direct energy gaps for allowable transitions decreased with increasing ZnO NP concentrations, ranging from 3.88 eV and 4.87 eV in the pure blend to 3.31 eV and 4.67 eV, respectively. The σAC value at 100 Hz was 8.41 × 10−13 S·cm−1 and increased with frequency, reaching 5.12 × 10−9 S·cm−1 at 106 Hz. Also, a modest improvement in σAC values is observed with the increase of ZnO NPs loading. The increase in conductivity can be ascribed to the improved amorphous nature of the synthesized nanocomposite facilitated by the incorporation of ZnO NPs. Dielectric studies showed that the best improvement was attained for the PVC/PMMA/PS/5 wt% of ZnO nanocomposite sample. Further, its imaginary part (ε″) exhibited a constructive decrease in its value with the increase in the ZnO loadings. These findings recommend these nanocomposites for potential applications in optoelectronics and energy storage devices.
Copyright (c) 2012 ECS journal of solid state science and technology https://iopscience.iop.org/journal/2162-8777
A Hybrid Transfer Learning Framework for Enhanced Oil Production Time Series Forecasting
Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
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