Gulf University for Science & Technology

In this work, an eight-port MIMOn-s antenna with dual-band operation is presented. The proposed work involves several wireless applications with FR1 and FR2 millimeter-wave bands. Also, a need for conformal antenna with capability of reduced SAR can cater the need for applications in flexible electronics. The antenna includes an oval-shaped radiating patch with a circular slot printed on Rogers RT Duroid TM5880 substrate on one plane and rectangular defected-ground-structure on the other side with a dimension of 70.71 × 70.71 mm². The radiating patch and ground are asymmetric-fed producing measured bandwidth of Band 1 = 3.59–3.85GHz useful for WiMAX application and Band 2 = 8.40–70.0GHz for application in Industry-Scientific-Medical (ISM)-24.0 GHz, Ultra-wideband (UWB)-24.0GHz and millimeter-wave 5G-FR2 bands (n257-n263). The property of the substrate thickness = 0.254mm is utilized for bending angles of 15°, 30°, and 45° ensuring no change in -10.0dB impedance bandwidth making it suitable for flexible-electronics applications. The SARn-s is evaluated with antenna placed on tissue (skin, fat, muscle) model at SAR/frequency and found to be ≤ 1.60 W/Kg at key frequency values in the operating bandwidth of interest corresponding to 0.0331 W/Kg at 3.50GHz, 0.0435 W/Kg at 10.0GHz, 0.0131 W/Kg at 15.0GHz, 0.0209 W/Kg at 24.0GHZ, 0.0171 W/Kg at 26.0GHz, 0.0116 W/Kg at 28.0GHz, 0.00334 W/Kg at 38.0GHz, 0.00336 W/Kg at 39.0GHz, 0.00296 W/Kg at 41.0GHz, 0.00149 W/Kg at 47.0GHz and 0.0851 W/Kg at 60.0GHz. The diversity performance is also evaluated with measured ECCn-s < 0.02, DGn-s > 9.9998dB, TARCn-s < -8.0dB, and CCLn-s < 0.10b/s/Hz. The measured peak-gain varied between 2.965dBi-16.258dBi with maximum peak-gain of 16.13dBi at 60.0GHz including 2-D radiation patterns at WiMAX and other bands. The multi-band flexible-ability and acceptable SARn-s suggest the proposed well suited for WiMAX and future 5G applications in FR2 bands.

This paper explores an optimal control problem of weakly coupled abstract hyperbolic systems with missing initial data. Hyperbolic systems, known for their wave‐like phenomena and complexity, become even more challenging with weak coupling between subsystems. The study introduces no‐regret and low‐regret control strategies to handle missing information and achieve optimal performance. By deriving the Euler–Lagrange optimality system, it characterizes these control approaches in the context of weak coupling. Additionally, the paper establishes the existence and uniqueness of a no‐regret and low‐regret control, emphasizing the influence of uncertain coupling parameters. These findings are optimal control strategies for abstract weakly coupled hyperbolic systems under uncertainty. Finally, as highlighted in our conclusion, future research could explore integrating memory effects through fractional derivatives to improve the modeling of viscoelasticity, diffusion with memory, and wave damping.

Background Early prediction of pregnancy complications is important for adequate and timely prevention, management, and reducing maternal/fetal pathogenesis. Objective To study the prognostic value of cytokines as predictors of pregnancy complications using unbiased artificial intelligence/machine learning (AI/ML) methods. Methods For this study, we used our previously published data on 127 women with pregnancy complications and 97 women with a history of normal delivery and undergoing a normal delivery. A panel of seven cytokines were analyzed from activated peripheral blood mononuclear cells (PBMC). AI/ML methods such as kNN, SVM, decision tree, and ensemble classification were applied to explore the possible use of AI/ML to compare and predict normal gestation and normal delivery as opposed to different pregnancy complications such as recurrent spontaneous miscarriage (RSM), preterm delivery (PTD), pregnancy-induced hypertension (PIH), and premature rupture of fetal membranes (PROM). Results The study examined cytokine levels in various pregnancy conditions, revealing significant differences, particularly in the levels of IL-2 and IFN-γ, across age-matched comparisons. Additionally, binary classification tasks demonstrated notable accuracies and f-measures for methodologies such as Ensemble (Bagged), QDA, and SVM (Cubic), showcasing their effectiveness in distinguishing between normal delivery and different pregnancy complications. Conclusion The study provides a machine learning-based methodology for the prediction of pregnancy complications based on levels of cytokines produced by peripheral blood cells.

The methodology for calculating the phase-lag and amplification-factor for both explicit and implicit multistep methods for first-order differential equations was recently developed by one of the authors. The objective of this study is to develop low-order Adams–Bashforth–Moulton predictor–corrector algorithms that eradicate phase-lag, amplification-factor. The stability regions of the newly established methodologies will also be highlighted. Furthermore, we will examine our results from numerical experiments employing the newly developed approaches.

The research focuses on optical solitons and employs the generalized auxiliary equation technique to obtain soliton resolutions for the nonlinear Kairat-X equation. This equation considers wave number groups influenced by time and velocity dispersion in non-linear mediums. Because of their stability and numerous uses in signal processing, telecommunications, and quantum physics, optical solitons are appreciated. Novel periodic, exponential, and other soliton solutions are shown in the work, and the dynamics of the model are thoroughly examined using phase portraits, quasi-periodic patterns, Lyapunov exponents, 3D attractors, 2D power spectra, and sensitivity analysis. Various simulations show how noise intensity variations affect system sensitivity and instability through the assessment of stochastic sensitivity along with Poincaré, and Lyapunov analysis. These results provide a significant addition to the discipline.

This study examines the validity and reliability of the Kyrgyz version of the Online Gaming Motivations Scale, initially developed in English and comprising 12 items across 3 factors: achievement, social and immersion. The scale was translated into Kyrgyz, and subject matter experts verified language accuracy. Following content validation, the construct validity and reliability of the Kyrgyz version were assessed through factor analysis using data from 329 adolescents. Confirmatory factor analysis was conducted to evaluate construct validity, while reliability was assessed using Cronbach’s alpha internal consistency coefficients. The results of these analyses confirmed that the Kyrgyz version of the scale demonstrates both validity and reliability.

We report on facile hydrothermal synthesis of 2H and 1T/2H mixed-phase MoS2 nanosheets using different organic sulfur precursors with varying sulfur release tendencies. 1T/2H MoS2 demonstrated direct solar light-driven photocatalysis...

This study investigates the propagation of electromagnetic waves in a perfectly electric conducting cylindrical waveguide with a central chamber filled with cold plasma embedded in vacuum which is covered by dielectric layer in conducting cylinder. The mathematical modeling formulates a boundary value problem which is solved by using the mode-matching technique to analyze the scattering characteristics. This technique relies on the projection of solution on orthogonal bases. The development and application of orthogonality relations are useful to convert the differential system into linear algebraic systems, truncated and inverted for the solution. The validity of the truncated solution is confirmed by verifying the matching conditions, which demonstrate the perfect alignment of electric and magnetic fields at the two interfaces. The investigation focuses on the power flux in different regions of the waveguide, considering both transparency and non-transparency regimes. Computational results demonstrate energy propagation versus the properties of the medium and geometrical parameters of configuration. It is found that the plasma radius alterations do not significantly affect transmission, but influence the number of cut-on modes. In the transparency regime, one cut-on mode, consistently, exists across all chamber regions. In the non-transparency regime, the plasma region has no cut-on mode while the vacuum and dielectric regions exhibit one cut-on mode.

This study introduces a method for analyzing the propagation of electromagnetic waves in cylindrical structures with central chambers facilitating beam-plasma interactions, particularly relevant for slow-wave structures in backward wave oscillators. The boundary value problem, governed by the Helmholtz equation, is resolved using the mode-matching technique, yielding an exact solution. The analysis elucidates key phenomena, including reflection, transmission, orthogonality relations, and power flux variations with frequency and material properties. By examining the effects of plasma frequency and beam radius on phase velocity, group velocity, and interaction efficiency, the study provides insights into optimizing wave propagation and energy transfer. The results demonstrate that higher plasma frequencies and reduced beam radii enhance scattering characteristics, offering practical guidance for designing efficient electromagnetic devices.

Early detection of breast cancer (BC) is essential for effective treatment and improved prognosis. This study compares the performance of various machine learning (ML) algorithms, including convolutional neural networks (CNNs), logistic regression (LR), support vector machines (SVMs), and Gaussian naive Bayes (GNB), on two key datasets, Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Histopathological Image Classification (BreaKHis). For the BreaKHis dataset, the CNN achieved an impressive accuracy of 92%, with precision, recall, and F1 score values of 91%, 93%, and 91%, respectively. In contrast, LR achieved 88% accuracy, with corresponding precision, recall, and F1 score values of 86%, 87%, and 89%, respectively. SVM and GNB demonstrated 90% and 84% accuracy, respectively, with similar precision, recall, and F1-score metric performances. In the WDBC dataset, LR achieved the highest accuracy of 97.5%, with nearly 97% values for precision, recall, and F1 score. In contrast, CNN attained 96% accuracy with equal recall, precision, and F1 score values of 96%. SVM and GNB followed closely with 95% and 94% accuracy, respectively. Minimising the false negative rate (FNR) and false omission rate (FOR) is vital for improving model reliability, with the LR excelling in the WDBC dataset (FNR: 5.9%, FOR: 4.8%) and the CNN performing best in the How to cite this article Chetry M, Feng R, Babar S, Sun H, Zafar I, Mohany M, Afridi HI, Khan NU, Ali I, Shafiq M, Khan S. 2025. Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes. PeerJ BreaKHis dataset (FNR: 8.3%, FOR: 7.0%). The results demonstrate that CNN outperforms traditional models across both datasets, highlighting its potential for early and accurate BC detection.

Green governance plays a crucial role in sustainable performance to improve the green environment; thus, how to contribute to environmental, social, and carbon emissions reporting has attracted the attention of regulators and policymakers. Green governance is often linked to sustainability performance. However, more evidence is needed on the contribution of green governance to sustainability performance. Under this lens, our present study seeks to discover the contribution of green external audits, green committees, environmental monitoring teams, and green governance practices on sustainability performance. Moreover, this study also estimated the threshold effect of green governance on the relationship between green external audits, green committees, environmental monitoring teams, and sustainability performance. A total of 905 non‐financial firms listed in G5 countries were incorporated for the period 2013–2023, and data were collected from the professional Data‐Stream portal. The quantile fixed effect, GMM‐based quantile, and static and dynamic threshold models were employed to investigate the proposed model. Our empirical results revealed that the external assurance of green audits, committees, and governance practices improves sustainability performance. Furthermore, sustainable governance practices are a crucial determinant of ESG reporting and carbon emission performance. However, environmental monitoring teams are unable to contribute to sustainability performance. Moreover, carbon emissions and ESG performance highly responded to green external audits under green governance threshold scores of 59.23% and 59.53%, respectively. In addition, green committee decisions boost environmental and ESG performance when under green governance threshold scores surpassed 59.23% and 59.53%, respectively. Based on our research findings, this study supports the stakeholder theory and legitimacy theory.

In a digitized world where artificial intelligence (AI) is rapidly infiltrating every aspect of our lives, it is crucial to utilize generative AI tools effectively. While the demand for AI technologies is increasing rapidly, challenges arise in their practical use. Accordingly, this study aims to develop a novel scale to measure users’ level of competence in prompt engineering. The psychometric properties of the Prompt Engineering Competence Scale (PECS) were evaluated using data obtained from 437 users. An exploratory factor analysis was performed to investigate the factor structure of the PECS. The results revealed a one-factor structure with a Cronbach's alpha value of 0.92. Confirmatory factor analysis results indicated that the one-factor model provided a good fit to the data. In the final stage, the item discrimination index was calculated to improve further the reliability and validity of the newly developed scale. The results suggest that the scale items were able to reliably discriminate users. These findings collectively indicate that the PECS is a valid and reliable instrument for measuring users’ level of competence in prompt engineering.

This paper examined the characteristics of heat transfer of ternary hybrid nanofluid (THNF) flow toward an electromagnetic hydrodynamic (EMHD) plate under the influence of various factors (temperature jump, different nanoparticles, stretching/shrinking, radiation, porous plate, and nanoparticle shape). The novelty lies in the comprehensive analysis of THNFs composed of Fe3O4–SWCNT–MWCNT nanoparticles within water–ethylene glycol mixtures (Water–EG [70%:30%] and Water–EG [50%:50%]). Mathematically, the study reduced the partial differential equations (PDEs) to a set of ordinary differential equations (ODEs) for a solution. Thereafter, using the similarity transformation, the PDEs models are diminished to a set of ODEs. This models were resolved analytically with the Adomian decomposition method (ADM) using Mathematica software code and numerically via the Runge–Kutta–Fehlberg method (RK45) using the Mathematica package. The main outcome of the research reveals the significant impact of nanoparticle shape, concentration, and base fluid composition on the temperature profile of the nanofluid, with spherical nanoparticles exhibiting cooler profiles due to reduced drag forces. The findings contribute insightful observations into the heat transfer demeanor of THNFs, offering a systematic framework for studying complex fluid dynamics phenomena. This work benefits researchers in the thermal management systems field and engineering applications, providing a deeper knowledge of heat transfer mechanisms in nanofluid flows and offering implications for optimizing thermal systems for enhanced efficiency and performance.

Herein, we report the first mechanochemical strategy for the Ru-catalyzed deoxygenative borylation of free phenols via C–O bond cleavage. This Ru-catalyzed phenolic borylation approach has been successfully extended to the Suzuki–Miyaura-type cross-coupling of phenols with aryl bromides. The protocol accepts a wide scope of phenolic substrates, allowing the synthesis of aryl pinacolboranes and biphenyl structures in excellent yields and serving as a better alternative to classical cross-coupling reactions in the context of pot, atom, and step economy synthesis.

This research explores mixed convection resulting from simultaneous natural and forced heat exchange in a lid‐driven staggered cavity occupied by a Newtonian fluid. All boundaries maintain no‐slip conditions, except the top wall, which moves with the velocity. Assumptions are made, including two cases of uniform and nonuniform heated left wall and the maintenance of cold walls throughout the cavity, which disrupts thermal equilibrium. CFD simulations are carried out by employing finite element (FEM) routine available in COMSOL Multiphysics software to elucidate the dimensionless problem and obtain the optimal results with desired accuracy. In this study we incorporated detailed examination of flow parameters, such as Reynolds number Grashof number , Prandtl number and Richardson number on dominant flow motions in the designed staggered cavity. Local heat flux and kinetic energy for uniform and non‐uniform cases provide insights into heat distribution. Increased Reynolds numbers decrease fluid kinetic energy, while higher Grashof values enhance thermal buoyancy forces, raising the Nusselt number. For uniform heat source the average Nusselt number rises significantly by the Grashof number especially for higher Prandtl numbers indicating strong convective heat transfer. Lower Prandtl numbers exhibit minimal increases, showing weaker convection. While for non‐uniform heat source While also rises with , the overall values and slopes are lower compared to the uniform heat source case. High Prandtl numbers still enhance heat transfer, but the effect is less pronounced, and low fluids show minimal sensitivity to . Higher Reynolds numbers also accelerate the clockwise rotational structure, as indicated by the streamlines.

With refinements in electromagnetics, diverse medical applications have evolved to detect diseases efficaciously. Breast cancer, a dominant cause of mortality among women worldwide, necessitates early diagnosis and screening for timely medical intervention. This research establishes the design, simulation, and analysis of an advanced triangular slotted circular flexible Ultra-wideband (UWB) antenna optimized for breast cancer detection and healthcare monitoring. The proposed antenna employs an extensive frequency range of 2.95 GHz to 24.2 GHz, accomplishing an impressive impedance bandwidth of 156%. It authenticates directional and omnidirectional radiation patterns with compact dimensions of 46.3 × 52.6 × 1.076 mm³. Key aspects divulge a resonance frequency at 14.35 GHz with a significant input reflection coefficient of −37.8 dB. The antenna achieves a peak gain of 3.16 dB at 5.8 GHz, with efficiencies of 59.56% and 66.88% at 5.8 GHz and 4.48 GHz, respectively. A meticulous case study involving SAR evaluation confirms the antenna’s safe exposure levels. For a flat human phantom, SAR values are 0.774 W/kg at 13.5 GHz and 0.712 W/kg at 14.35 GHz for 10 gm of tissue. For the breast phantom model, SAR values are 0.201 W/kg at 11.4 GHz and 0.152 W/kg at 14.35 GHz for 10 gm of tissue. Besides that, the antenna’s flexible design promises an excellent execution under several bending conditions, making it ideal for wearable applications. These findings establish the antenna as an efficient solution for breast cancer detection and healthcare monitoring, combining safety, flexibility, and the aptness to ameliorate early diagnosis while lowering mortality rates. Wearable antennas are pivotal for advanced healthcare applications. This section presents the literature and discusses the work related to flexible UWB antenna designed for breast cancer detection and healthcare monitoring, tackling challenges in early diagnosis and patient care.

This study examines the phytochemical composition, antioxidant, antifungal, and insecticidal properties of Origanum syriacum (Syrian oregano plant) and Cymbopogon wimterianus (Java citronella plant) extracts. Their potential applications in food preservation and pest control are explored based on their bioactive properties. The phytochemical screening indicated a rich presence of secondary metabolites in the extract. The hydrodistillation of plant leaves resulted in an extraction yield of 4.3% Syrian oregano essential oil. The major component of the essential oil was carvacrol (79.30%). The Syrian oregano ethanolic extract contained 110.674 ± 1.842 mg GAE/g total phenols and 52.57 ± 0.086 mg RE/g total flavonoids, and exhibited a high antioxidant activity with a half-maximal inhibitory concentration (IC50) equal to 168.28 μg/mL. Flatbread was prepared with additions of Syrian oregano and Java citronella powders, followed by analysis of moisture content, visual appearance, and sensory characteristics. The results showed that the powders of Syrian oregano and Java citronella have promising food preservative effects. These findings were supported by a significant decrease in fungal growth in several samples and a shelf life extension of one day. The inclusion of a 2% mixture of Syrian oregano and Java citronella powder in the flatbread resulted in the sample receiving the highest overall acceptability mark from consumers, while also extending its shelf life. To assess the insecticidal activity, weevils (Sitophilus granarius L.) were exposed to Syrian oregano and Java citronella essential oils. The insecticidal activity was at its peak when Syrian oregano and Java citronella essential oils were combined resulting in 7% lethal dose (LD50) towards grain weevils. Future research should focus on optimizing extraction methods, evaluating long-term storage effects, and assessing the broader applicability of these extracts in various food products and agricultural settings.

This study investigates a widely recognized ninth-order numerical technique within the explicit two-step family of methods (a.k.a. hybrid Numerov-type methods). To boost its performance, we incorporate an economical step-size control algorithm that, after each iteration, either preserves the current step length, reduces it by half, or doubles it. Any additional off-grid points needed by this strategy are computed using a local interpolation routine. Indicative numerical experiments confirm the substantial efficiency gains realized by this method. It is particularly adept at resolving differential equations with oscillatory dynamics, delivering high precision with fewer function evaluations. Furthermore, a detailed Mathematica implementation is supplied, enhancing usability and fostering further research in the field. By simultaneously improving computational efficiency and accuracy, this work offers a significant contribution to the numerical analysis community.

Three-dimensional iron(ii) clathrochelate-bridged secondary arylamine copolymers (TAC1–3) were synthesized via a facile microwave-assisted Buchwald–Hartwig cross-coupling reaction, utilizing a custom-designed diamine clathrochelate precursor along with a range of tribrominated aryl surrogates. The target copolymers exhibited impressive iodine vapor adsorption capabilities, with TAC3 achieving an outstanding uptake of 1500 wt% (15 g g⁻¹). Kinetic analysis identified a predominant pseudo-2nd-order adsorption model, whilst recyclability tests confirmed the materials’ durability, sustaining high efficiency albeit over multiple adsorption–desorption cycles. The TAC1–3 copolymers demonstrated notable iodine uptake from cyclohexane solutions with TAC3 achieving a maximum capacity of 1.11 g g⁻¹ at an initial iodine concentration of 1000 mg L⁻¹. Moreover, TAC1–3 demonstrated exceptional performance in aqueous systems, achieving adsorption capacities up to 5.95 g g⁻¹ in I2 solutions and 5.34 g g⁻¹ in I3⁻ (KI/I2) solutions.

The banking industry faces significant challenges, from high customer churn rates to threatening long-term revenue generation. Traditionally, churn models assess service quality using customer satisfaction metrics; however, these subjective variables often yield low predictive accuracy. This study examines the relationship between customer attrition and account balance using decision trees (DT), random forests (RF), and gradient-boosting machines (GBM). This research utilises a customer churn dataset and applies synthetic oversampling to balance class distribution during the preprocessing of financial variables. Account balance service is the primary factor in predicting customer churn, as it yields more accurate predictions compared to traditional subjective assessment methods. The tested model set achieved its highest predictive performance by applying boosting methods. The evaluation of research data highlights the critical role of financial indicators in shaping effective customer retention strategies. By leveraging machine learning intelligence, banks can make more informed decisions, attract new clients, and mitigate churn risk, ultimately enhancing long-term financial results.