Explore the vast range of titles available.

Purpose This research aims to explore the transmission of seismic surface waves through a two magneto-strictive materials i.e. cobalt ferrite and Terfenol-D when embedded in a plate-substrate configuration with non-ideal interface. The study focuses on understanding the impact of width of the plates, imperfect parameter, heterogeneity parameter on both the materials cobalt ferrite and Terfenol-D under magnetically open and short conditions. Methodology To achieve this, the study employs a variable-separable technique following Direct Sturm-Liouville method and appropriate boundary conditions to derive frequency relations for both magnetically open and short circuit scenarios. Numerical simulations are conducted to investigate the effects of width of the plates, imperfect parameter, heterogeneity parameter on both the materials cobalt ferrite and Terfenol-D under magnetically open and short conditions. Findings The research findings indicate that the phase velocity is increasing more in Terfenol-D as compared to Cobalt ferrite, either the case magnetically open or closed. Graphical comparisons highlight the impact of width plates, imperfect parameter, heterogeneity parameter on the characteristics on wave propagation clearly. Research limitations The study is confined to linear wave propagation and does not consider nonlinear effects. Additionally, the analysis is based on idealized material properties and interface conditions. Practical implications The results of this research can contribute to the design and optimization of sensors, energy harvesters, and wave manipulation devices utilizing piezomagnetic materials. Understanding the behaviour of surface waves in these structures is crucial for their effective application. Originality This study offers a comprehensive analysis of surface wave propagation in two different types of piezomagnetic composite structure by considering heterogeneity and interface conditions. The comparative study of different piezomagnetic models and the incorporation of heterogeneity and interface conditions contribute to the originality of the research.

This article explores that the study on bending of magneto-electric-elastic nanobeams relies on nonlocal elasticity theory. The Vlasov’s model foundation utilizes the silica aerogel foundation. The guiding expressions of nonlocal nanobeams in the considered framework are used extensively and where parabolic third-order beam theory is achieved after using Hamilton’s principle. Parametric work is introduced to scrutinize the influence of the magneto-electro-mechanical loadings, nonlocal parameter, and aspect ratio on the deflection characteristics of nanobeams. It is noticed that the boundary conditions, nonlocal parameter, and beam geometrical parameters have significant effects on dimensionless deflection of nanoscale beams.

Efficient prediction of citrus fruit diseases is essential for maintaining orchard health and productivity. Traditional diagnostic methods, often relying on manual inspection, are labor-intensive and prone to inaccuracies. Deep learning techniques, especially Convolutional Neural Networks (CNNs), offer an automated and accurate alternative. This study introduces a novel model integrating CNN with Gradient Boosting (GB) and optimized using the Nesterov-Accelerated Adaptive Moment Estimation (Nadam) optimizer to enhance prediction accuracy. The model employs a custom CNN architecture combined with GB, leveraging Nadam for faster convergence and improved performance. Trained on a dataset of 3,000 citrus fruit images sourced from Kaggle, the model follows a structured process of preprocessing, feature extraction, integration of GB with CNN, and optimal prediction. Comparative analysis using metrics such as accuracy, precision, F1 score, and recall demonstrates the model's effectiveness, achieving an accuracy of 98.03% and precision of 98.04%. This robust approach addresses limitations of traditional methods by enabling automated feature extraction and reliable disease prediction. The proposed CNN-GB-Nadam model significantly enhances efficiency and reliability, providing a valuable tool for protecting citrus fruit health and improving orchard management practices.Article HighlightsThe hybrid CNN-GB model, optimized with Nadam, achieves 98% accuracy, enhancing citrus fruit disease detection.Automating disease detection aids timely interventions, improving orchard health and agricultural productivity.Combining deep learning with explainability ensures reliable, practical tools for farmers and stakeholders.

Assuming different types of imperfect interfaces composed of a magnetoelectroelastic (MEE) structure, the current work investigates the transmission of a Love-type wave in a MEE solid cylindrical structure. The spatially variable quasi-classical technique is applied to derive the analytical solution of the layers. The substantial impact of factors related to the imperfect interface on the wave phase velocities is illustrated numerically. The Love-type wave's dispersion relation has been established as the determinant for electrically and magnetically open and short cases. Moreover, the article investigates the consequences of six different imperfect parameters namely mechanical imperfection, electrical imperfection, magnetic imperfection, magneto-mechanical imperfection, electro-mechanical imperfection, and magneto-electrical imperfection parameters in magnetically and electrically open and short scenarios are covered. The findings demonstrate that, in comparison to the short case, the electric and magnetic open case has a higher phase velocity. Here are some key findings: imperfection parameters strongly affect the phase velocity and attenuation coefficient curves and the bonding parameter's prominent influence is inversely proportional to the attenuation coefficient and well-proportional to the phase velocity. Identifying the piezoelectric and piezomagnetic connection and its possible use in the construction of sensors, actuators, energy harvesters, and nano-electronics is the result of this theoretical investigation. This is the first time that a polar coordinate system was used in the quasi-classical method of solving differential equations. The results argue that the outcomes of this specific model have an immense ability to deal with various commercial and industrial applications in acoustical engineering, geotechnical design, ultrasonic technology, and SAW devices.

Significant restrictions have been found in the selection of piezoelectric materials and the direction of wave propagation in earlier studies on surface acoustic wave sensors. The primary goal of the current work is to investigate how wave propagation direction influences the performance of SAW macro- and nano-sensors in an effort to remove such barriers in the technological revolution of SAW sensors. A proposed model is established to study Shear Horizontal (SH) and anti-plane SH wave propagation in piezoelectric materials with surface effects. The theoretical forms are constructed and used to present the wavenumber of surface waves in any direction of the piezoelectric medium, based on the Extended Stroh formalism. In addition, we take into account surface elasticity theory in order to obtain the phase velocity equation based on the wavenumber expression. The model incorporates surface elasticity, piezoelectricity, and permittivity to account for nanoscale surface phenomena. Two configurations are examined: an orthotropic piezoelectric material layer over an elastic framework and a piezoelectric material half-space with a nano substrate. Analytical expressions for frequency equations are derived for both symmetric and anti-symmetric waves. Numerical results highlight the critical thickness of the piezoelectric layer, where surface energy significantly influences dispersion properties. The effects of surface elasticity and density on wave velocity are analyzed, revealing a spring force-like influence on boundaries. The research investigates SH wave transmission in anisotropic, transversely isotropic piezoelectric nanostructures. The findings could aid in designing SAW devices and piezoelectric sensors, as well as producing more effective surface acoustic wave sensors, based on recent theoretical work summaries.

Small cell carcinoma (SCC) of the kidney is included in extrapulmonary SCC which is a group of extremely rare but highly aggressive cancers. There have been only a few case reports and small retrospective series in the literature describing the malignancy in kidneys. Most of the published reports describe the entity as a variant mixed with other tumor subtypes such as urothelial carcinoma, adenocarcinoma, and squamous cell carcinoma. Pure-form SCC in kidneys is exceedingly rare. Fluorodeoxyglucose positron emission tomography-computed tomography plays an essential role in the accurate staging evaluation of this cancer.

Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.

Askin's tumors are rare malignant neoplasms located in the thoracopulmonary region and mainly occur in children and adolescents. In this report, we describe a case of histologically proven Askin's tumor in a 24-year-old male. The patient was admitted with a history of 3-month lower back pain and with a rare presentation of paraparesis.

Background: Emergency laparoscopic cholecystectomy (ELC) has emerged as a viable alternative to delayed elective surgery for acute gallstone disease, although its widespread adoption is hindered by cultural barriers. This study compares outcomes between elective and emergency laparoscopic cholecystectomy and evaluates the impact of implementing an ELC pathway on elective waiting times, patient outcomes, and overall service delivery. Methods: A prospective cohort study was conducted between December 2021 and December 2023, including all patients undergoing emergency or elective laparoscopic cholecystectomy. One-to-one propensity score matching, correlation statistics, and multivariate logistic regression were used to analyse outcomes. Results: Of 585 patients, 314 (53.4%) underwent emergency and 271 (46.3%) elective cholecystectomies. After matching, 474 patients were analysed (237 per group). The ELC pathway achieved an 81.4% first-presentation procedure rate, with 69.2% managed as day cases and 84.4% discharged the following day. Emergency cases had longer operative times (+9 min), higher rates of subtotal cholecystectomy (8.9% vs. 3.0%, p < 0.001), and more frequent postoperative ERCP (16.9% vs. 4.6%, p < 0.001). Other outcomes were comparable. Introduction of the ELC pathway significantly reduced elective waiting times from a median of nine to three months (R = −0.219, R2 = 0.059, p < 0.001) and preoperative admissions (IQR 0–1, R = −0.223, R2 = 0.050, p = 0.002). Conclusions: An ELC pathway is a safe and effective alternative to elective gallstone surgery, offering substantial benefits to patients and healthcare systems, while serving as a strategic, cost-conscious approach to reducing surgical waiting times and preoperative admissions. Its success hinges upon surgical expertise in acute decision making, skill in performing subtotal cholecystectomy, and access to institutional resources such as advanced imaging and ERCP services.

This study investigates the propagation of surface seismic waves at the loosely bonded interface of a visco-piezoelectric composite structure, incorporating the flexoelectric effect. The structure consists of a viscoelastic layer placed over a piezoelectric substrate, with the upper layer's shear stiffness modelled using the Kelvin–Voigt approach. An analytical method based on the separation of variables is employed to derive the complex dispersion relations for both electrically open- and short-circuit boundary conditions. Numerical simulations reveal the significant influence of various parameters on the wave's phase velocity and attenuation coefficient. Furthermore, a graphical comparison of three rheological models—Maxwell, Newton, and Kelvin–Voigt—is presented. The results show that the attenuation is lower in the Maxwell and Newton models compared to the Kelvin–Voigt model. Key findings include the bonding parameter's direct proportionality with phase velocity and inverse relationship with attenuation, and the pronounced impact of flexoelectricity on both phase velocity and attenuation. This theoretical framework offers insights into the piezo-flexoelectric coupling, with potential applications in designing sensors, actuators, energy harvesters, and nano-electronic devices.