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In this research work, a novel non-linear mathematical model has been proposed considering susceptible, quarantined, infected, recovered, and removed compartments before and after the 1st dose and 2nd dose of vaccination. For this dynamics model, the novel coronavirus COVID-19, a contagious disease, is taken as a case study in which its transmission, impact of vaccination, and mitigation have been discussed. This model may be helpful in numerous fields of epidemiology and dynamical systems; moreover, Mason Graph has been used to describe the mathematical model. The stability analysis and disease-free equilibrium points have been deliberated for the model. In this work, the semi-analytical technique Variational Iteration Method has been employed, which will assist researchers in the future by showing that if the rate of immunized personnel rises, then the infection rate decreases. It has been observed that the non-vaccinated personnel decrease with the passage of time due to the awareness campaign programs of the governments. Furthermore, it was observed that the removed rate also decreases with the passage of time as the immunized personnel rises. Mathematical software MAPLE has been used to calculate the analytical solutions of the aforementioned mathematical model.

In this study, we introduce a machine learning optimized graphene-based biosensor tailored for the early and accurate detection of breast cancer, aiming to elevate diagnostic reliability and clinical efficacy. The device employs a multilayer Ag–SiO₂–Ag architecture to amplify optical response, achieving a peak sensitivity of 1785 nm/RIU. Machine learning models are used to optimize structural parameters, enabling systematic refinement of detection accuracy and reproducibility. The optimized design demonstrates superior sensitivity compared with conventional biosensor configurations, underscoring its effectiveness in bioanalytical applications. The proposed platform offers a precise and robust solution for breast cancer screening and monitoring, with strong potential for clinical translation. To further refine sensor efficacy, a comprehensive parametric optimization approach is employed, strategically enhancing its sensitivity metrics. The sensor’s heightened precision and responsiveness position it as a promising tool in biomedical diagnostics, particularly for early-stage breast cancer screening and monitoring.

Accurate assessment of transformer insulating paper is vital for reliable operation and optimal transformer management, with the Degree of Polymerization (DP) serving as a primary indicator of insulation health. Direct DP measurement is often impractical, prompting this study to explore machine learning models for predicting DP using 2-Furfuraldehyde (2-FAL), a cellulose degradation byproduct measurable in transformer oil. This approach classifies insulation into four categories—Fresh (DP: 700–1200), Lightly Aged (DP: 450–700), Moderately Aged (DP: 250–450), and Worstly Aged (DP < 250)—based on DP values, offering a streamlined alternative to conventional multi-gas diagnostic methods. Supervised machine learning algorithms were developed using IEEE C57.104-2019 standard data, employing regression (Linear Regression, Polynomial Regression, Random Forest Regressor) to predict continuous DP and classification (Logistic Regression, Support Vector Machine with RBF kernel, Random Forest Classifier) to categorize insulation condition. Model performance was evaluated using regression metrics (Mean Squared Error, Mean Absolute Error, R² Score) and classification metrics (accuracy, precision, recall, F1-score). The Random Forest Regressor (R²: 0.894) and Classifier (accuracy: 0.925) demonstrated superior performance, enabling precise, non-invasive DP estimation and condition assessment. These findings highlight the efficacy of 2-FAL-based machine learning models for transformer health monitoring, facilitating predictive maintenance and enhancing operational reliability.

Frictional moving load-induced dynamic response of a porous piezoelectric micro/nano plate with superficial parabolic discontinuity. The present paper aims to analyze the complex dynamic response of micro/nano-scale components through investigating the stress distribution within a Nonlocal Porous Piezoelectric Layer (NPPEL) of finite thickness. The study specifically focuses on quantifying the combined effects of material porosity, size-dependent elasticity, and geometrical surface imperfections when the layer is subjected to a load moving across its upper boundary. This comprehensive model provides a more realistic assessment of reliability for small-scale smart devices. The layer’s constitutive behavior is modeled using Eringen’s nonlocal elasticity theory to account for the essential size effects present at the micro/nano scale. The governing equations for the coupled porous and piezoelectric medium are derived, incorporating appropriate boundary conditions for a moving load. Crucially, the superficial parabolic discontinuity on the upper surface is handled analytically through a robust perturbation technique, allowing for the derivation of closed analytical forms for the resulting shear and normal stresses. The final solutions are then computed using Mathematica to illustrate the transient stress fields. Numerical results demonstrate that the nonlocal parameter is highly effective at amplifying the magnitude of the stresses, which is characteristic of the stiffening effect in nonlocal models. The depth and factor of the parabolic irregularity significantly amplify the stress concentrations at the interface, indicating a critical pathway for potential failure. Furthermore, the frictional coefficient of the moving load plays a non-linear role in dictating the shear stress distribution, providing crucial insight into contact mechanics at the nanoscale. The core novelty lies in the simultaneous analytical incorporation of nonlocal effects, porosity, and an arbitrary surface irregularity under dynamic moving load conditions–a combination highly relevant to microfabrication. The model is directly applicable to enhancing the design and performance assessment of MEMS/NEMS pressure sensors, ultra-thin piezoelectric energy harvesters, and other micro-electromechanical devices where surface quality and size effects dictate device lifespan and reliability.

Effective mitigation and response to disasters require fast and accurate classification and localisation of natural disasters. We herein propose EcoDisasterLocNet, a hybrid architecture of Grad-CAM + + and DenseNet-201 that integrates fine-grained and interpretable disaster localisation with accurate spatial classification. The architecture is learned on a balanced and augmented 10,537-image dataset spread over four classes: earthquake, wildfire, flood, and cyclone. 75% of the images were utilised for training while 15% and 10% of the images respectively tested and validated the architecture. Optimised DenseNet-201 produced the best results where it obtained 99.90% training accuracy, 94.24% validation accuracy, and 95.16% testing accuracy along with 95.26%/95.16%/95.19% precision/recall/F1-score. This hybrid ensemble (ERI-2025) was initially evaluated alongside standalone CNN models. Grad-CAM and Grad-CAM + + visualisations were used to emphasise disaster-related Grad-CAM and Grad-CAM + + visualisations were used to emphasise disaster-related regions (e.g., fissures, wildfires, flood regions, cyclone eyes) for localisation. Grad-CAM + + enhanced the IoU from 0.27 to 0.51 to 0.39–0.51 while maintaining a Dice coefficient of 2.00. The superiority of DenseNet-201 in both classification and localisation tasks was confirmed by comparative evaluation against other CNN architectures The hybrid design of the proposed framework is adaptable to multimodal and spatiotemporal datasets in future research, and it provides a scalable, interpretable, and high-precision solution for real-time disaster monitoring.

This manuscript presents a detailed analytical study on the propagation behavior of circumferential shear horizontal (SH) waves in a tri-layered cylindrical composite structure, comprising an inner functionally graded (FG) layer, a viscoelastic (VE) middle layer, and an initially stressed piezomagnetic (PM) outer layer. Unlike prior studies that primarily focus on phase velocity analysis, this work offers a novel and in-depth exploration of stress distribution characteristics across the multilayered system. The model incorporates both mechanical and magneto-mechanical interface imperfections and examines the effects of geometric dimensions, material gradation, angular wave modes, and initial stresses on wave dynamics. A robust mathematical framework is developed using the equations of motion and constitutive relations specific to each material, leading to a dispersion relation formulated under suitable boundary conditions. Numerical simulations demonstrate that the phase velocity and surface wave responses are notably influenced by gradation parameters, layer radii, and imperfection factors. Most significantly, detailed stress profiles are constructed to reveal how material damping, gradation, and interface conditions interact to shape the internal stress landscape. This dual-phase velocity and stress analysis offers enhanced insight into wave-material interactions, providing a valuable design reference for next-generation piezomagnetic sensors, actuators, and composite structural components.

Chest X-rays of COVID-19 patients helped detect and diagnose the virus early and assess the severity of the infection. Therefore, assessing the severity of Covid-19 infection plays an important role in determining the patient's condition and distinguishing cases that need intensive clinical care. But there are challenges facing doctors and radiologists because of vital signs, different areas of infection, and the wide differences between many cases. Therefore, deep learning techniques play an important role in solving these challenges for early detection of Covid-19 disease in X-ray images and distinguishing it from other pneumonias. In this study, three CNN models, AlexNet, ResNet-18 and GoogleNet, are proposed to diagnose a data set collected from multiple sources. Each model diagnosed a multi-class data set (four classes) and a two-class data set. All dataset images were processed and removed from the data before they were entered into CNN networks. Because the data set is unbalanced, a data augmentation technique was applied to balance the data set between collecting classes. Characteristics were extracted in a hybrid way between CNN models and Gray-level Cooccurrence Matrix (GLCM), Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT) algorithms and combined all the algorithms into a single vector for each image. All networks achieved superior performance in diagnosing COVID-19 and distinguishing it from other pneumonias. GoogleNet reached an accuracy, sensitivity, specificity, and AUC of 94.10%,95%, 97.75% and 96.13%, respectively with the dataset of multiple classes. while ResNet-18 achieved an accuracy, sensitivity, specificity, and AUC of 98.60%, 98%, 98%, and 97.10%, respectively with two-class (COVID-19 and normal).

In this research work, a novel non-linear mathematical model has been proposed considering susceptible, quarantined, infected, recovered, and removed compartments before and after the 1st dose and 2nd dose of vaccination. For this dynamics model, the novel coronavirus COVID-19, a contagious disease, is taken as a case study in which its transmission, impact of vaccination, and mitigation have been discussed. This model may be helpful in numerous fields of epidemiology and dynamical systems; moreover, Mason Graph has been used to describe the mathematical model. The stability analysis and disease-free equilibrium points have been deliberated for the model. In this work, the semi-analytical technique Variational Iteration Method has been employed, which will assist researchers in the future by showing that if the rate of immunized personnel rises, then the infection rate decreases. It has been observed that the non-vaccinated personnel decrease with the passage of time due to the awareness campaign programs of the governments. Furthermore, it was observed that the removed rate also decreases with the passage of time as the immunized personnel rises. Mathematical software MAPLE has been used to calculate the analytical solutions of the aforementioned mathematical model.

Background/Objectives: Cancer patients using implanted venous access devices (ICVADs) for chemotherapy are at increased risk of venous thromboembolism (VTE), but the performance of risk assessment models (RAMs) in this setting is understudied. This study evaluated VTE incidence, risk factors, and the predictive performance of the Khorana, COMPASS-CAT, and ONKOTEV models. Methods: We retrospectively reviewed records of adult cancer patients treated with chemotherapy via ICVADs. The cumulative incidence (CI) of VTEs was estimated using the Fine–Gray method, and RAM performance was assessed by sensitivity, specificity, predictive values, accuracy, and AUC. Overall survival (OS) was analyzed using Kaplan–Meier and log-rank tests. Results: A total of 446 patients were included. The most common cancers were colorectal (29.6%), gastric (26%), pancreatic (18.4%), and breast (13.9%). During a median follow-up of 16.5 months, VTEs occurred in 82 patients (18.4%), including 43 (9.6%) that were ICVAD-related. Median time to VTE was 117 days and 68 days for ICVAD-related events. The CI of VTEs was 9% at 1 year and 18.4% at 2 years. ONKOTEV showed the best performance (accuracy of 74.4%, specificity of 85.7%, and AUC of 0.607), with 1-year incidence higher in the high-risk group (28.5% vs. 12.4%, p < 0.001). In contrast, all RAMs showed limited ability for ICVAD-related VTEs. VTE was independently associated with inferior OS (HR 1.39, p = 0.037). Conclusions: Cancer patients with ICVADs face a substantial risk of early VTEs. Among evaluated RAMs, ONKOTEV performed best for overall but not ICVAD-related events. Prospective studies are needed to guide prophylaxis strategies using validated RAMs.

Introduction Gram-negative bacteria (GNB) have become prominent across healthcare and community settings due to factors including lack of effective infection control and prevention (ICP) and antimicrobial stewardship programs (ASPs), GNB developing antimicrobial resistance (AMR), and difficulty treating infections. This review summarizes available literature on healthcare-associated infections (HAIs) in Middle Eastern pediatric patients. Methods Literature searches were performed with PubMed and Embase databases. Articles not reporting data on GNB, HAIs, pediatric patients, and countries of interest were excluded. Results The searches resulted in 220 publications, of which 49 met the inclusion criteria and 1 additional study was identified manually. Among 19 studies across Egypt reporting GNB prevalence among pediatric patients, Klebsiella species/ K. pneumoniae and Escherichia coli were typically the most common GNB infections; among studies reporting carbapenem resistance and multidrug resistance (MDR), rates reached 86% and 100%, respectively. Similarly, in Saudi Arabia, Klebsiella spp./ K. pneumoniae and E. coli were the GNB most consistently associated with infections, and carbapenem resistance (up to 100%) and MDR (up to 75%) were frequently observed. In other Gulf Cooperation Council countries, including Kuwait, Oman, and Qatar, carbapenem resistance and MDR were also commonly reported. In Jordan and Lebanon, E. coli and Klebsiella spp./ K. pneumoniae were the most common GNB isolates, and AMR rates reached 100%. Discussion This review indicated the prevalence of GNB-causing HAIs among pediatric patients in Middle Eastern countries, with studies varying in reporting GNB and AMR. Most publications reported antimicrobial susceptibility of isolated GNB strains, with high prevalence of extended-spectrum beta-lactamase-producing K. pneumoniae and E. coli isolates. A review of ASPs highlighted the lack of data available in the region. Conclusions Enhanced implementation of ICP, ASPs, and AMR surveillance is necessary to better understand the widespread burden of antimicrobial-resistant GNB and to better manage GNB-associated HAIs across Middle Eastern countries.