This paper presents a detailed investigation into the forced vibration behavior of a nano-rectangular plate under the influence of moving nanoparticles, incorporating Coulomb friction effects. By combining Eringen's nonlocal continuum theory with the first-order shear deformation theory, the study examines the impact of the nonlocal parameter on the nanoplate's forced vibration response. The nanoplate, supported by a viscoelastic foundation modeled with a damper and Winkler modulus, is subjected to moving nanoparticles, considering their weight, inertia, and friction. The governing equations of motion and boundary conditions are derived using Mindlin's displacement field relations and Hamilton's principle. The analytical Galerkin method and Eigenfunction expansion are employed to transform the dimensionless partial differential equations into dimensionless ordinary differential equations under simply supported boundary conditions. The model's validity is confirmed through comparison with existing research. Additionally, this research explores the effects of key parameters, including the nonlocal parameter, foundation stiffness and damping coefficients, nanoparticle inertia, and velocity, on the dynamic amplitude factors of in-plane and out-of-plane displacements in detail.

The friction stir process (FSP), which is widely used on plate-type materials, was applied to AA5754 alloy, commonly used in engineering applications, under three different parameter settings. The effects of this process on the strength and elongation values were then evaluated. In the next stage, the material properties of the contact problem were designed using finite element-based (FEM) modeling techniques, and the effects of the changes in the strength and elongation values of the material on the contact stress and distance were determined. As a result of the examinations, it was determined that in all the changing parameters, the strength values increased compared to the initial state of the material after FSP. The elongation values may decrease or increase depending on the specific FSP parameters used. Regarding the contact stress and contact distance values, it was determined that the contact stress values increased in direct proportion to the strength of the material. It was determined that the changes in the elongation values of the material were more effective in the change of contact distances.

Thermo-mechanical aging plays an important role in varnish on wooden string instruments for acoustic damping and the stability of the structure, which affects both the quality of sound and the life span of the instrument. In this paper, machine learning methods are proposed to predict a change in acoustic and structural properties with time due to the varnish aging under environmental conditions. We gather data on temperature, humidity, varnish composition, and acoustic performance parameters in order to build predictive models using techniques like neural networks, regression analysis, and reinforcement learning. These models predict changes in acoustic damping and structural integrity by indicating the patterns in the thermo-mechanical responses of varnished wood. Results obtained in this work have provided insight into the most favorable conditions of environment and varnish formula to minimize aging effects, besides furnishing instrument makers and restorers with guidelines based on data which help prolong tonal quality and structural durability. The study opens paths to further uses of machine learning as far as the prediction of aging effects is concerned, with big impacts on both musical and preservative fields.

Smart hybrid meta-nanomaterials are realized through the integration of functionally graded graphene origami auxetic (FG-GOA), hybrid nanocomposite, with smart layers. This study applies the Higher-Order Shear Deformation Theory (HSDT) to investigate the nonlinear vibration of a novel piezo-electrically composite plate that is constructed with FG-GOA center and smart porous multi-scale hybrid nanocomposite (SPHNC), like Graphene Platelets (GPLs), Carbon Fibers (CF) and Polyvinylidene Fluoride (PVDF), for layers. To find out the equations that control the smart FG-GOA plate, the Generalized Differential Quadrature Method (GDQM) is implemented in order to solve the mathematical model of smart FG-GOA plates. In addition, the nonlinear frequency ratio and dynamic properties of the SPHNC are measured. Furthermore, the impact of the porosity parameter, the height of the FG-GOA core, and the height of the SPHNC layer have been investigated and shown in each figure.

This research models the novel smart NPR cellular plate's large amplitude oscillation utilizing the Mindlin plate theory. The plate consists of an Auxetic metamaterial core and smart composite layers. By using the micromechanical concept, one may ascertain the characteristics of the NPR cellular core and smart composite layers. Then, using von Karman nonlinearity terms and Hamilton's principle, the equations controlling the smart NPR cellular sandwich plates' behavior may be developed. The GDQM is then used to answer these mathematical problems. It is also possible to determine the smart NPR cellular plate's frequency ratio by taking the outside voltage. Each Figure also shows the calculated impacts of the NPR cellular geometrical properties and the NPR cellular layer thickness. All of these variables had an effect on the frequency ratio.

Pressure nanosensors are widely used in industry today. Cheap price, simple measurement circuit, and low energy consumption are the reasons for the widespread use of these sensors. The structure of these systems includes membranes, Wheatstone bridge circuits for measurement, and piezoresistor elements for use as the resistance, respectively. The development of intelligent artificial hands relies heavily on nano-sensor technology to provide precise sensory feedback and enhance user control. However, existing nano-sensors often face limitations in sensitivity, durability, and seamless integration with neural control systems, creating a gap in achieving lifelike prosthetic functionality. This study aims to creatively adjust both the underlying attributes (material composition, sensor architecture, signal processing) and the actual attributes (durability, real-world performance, compatibility) of nano-sensors to improve their efficiency in intelligent prosthetics significantly. The novelty lies in combining advanced nano-materials, structural optimization, and Artificial Intelligence (AI)-driven signal processing for multi-sensor fusion, an approach not fully explored in previous research. The study identifies key sensor limitations and enhances performance through graphene-based materials, structural redesign, and AI-driven signal optimization. Simulations and performance modeling assess expected gains in response time, sensitivity, and integration efficiency for next-generation artificial hands. Experimental and simulation results demonstrate a gauge factor improvement to 11.94, representing a 73.8 % increase over Carbon Nano Tube (CNT)-only films, with linearity maintained at Coefficient of Determination (R²) = 0.996. Electrical noise was reduced by 34 %, conductivity improved from 2.31×10⁵ S/m to 2.78×10⁵ S/m, and response latency decreased from 14.2 ms to 8.6 ms. Durability testing confirmed <3 % drift after 10⁶ bending cycles and a 77.9 % lower degradation rate compared to Indium Tin Oxide (ITO) sensors, while control system integration expanded bandwidth by 74.5 % and improved error convergence by 31.6 %. Operational gains included a 54.4 % reduction in calibration time, a 127.2 % increase in data throughput, and a 43.3 % longer operational lifetime under continuous monitoring. These results confirm that the proposed Graphene-Carbon Nano Tube (G-CNT) sensor architecture, coupled with AI-based signal optimization, delivers a quantitatively superior solution for high-performance, long-life, and integration-ready tactile sensing in next-generation intelligent prosthetic systems.

This study investigates the free vibration characteristics of rectangular sandwich plates that are partially supported by two-parameter elastic foundations, while also accounting for environmental temperature variations. The sandwich plate is constructed with an auxetic core layer, which exhibits a negative Poisson's ratio (NPR), complemented by two functionally graded (FG) face layers. The analysis considers the foundation's capacity to support the plate in either a complete or partial manner. Utilizing Hamilton's principle, the governing equations are derived based on a higher-order shear deformation theory framework. The application of this analytical method to the governing partial differential equations (PDEs) yields a system of algebraic equations. Solving this system in accordance with the specified boundary conditions results in an eigenvalue problem, which facilitates the determination of the natural frequencies of the plate. The findings of this study are validated against existing literature. Furthermore, the investigation explores the impact of various parameters including temperature fluctuations, characteristics of the auxetic core, boundary conditions, elastic foundation distribution patterns, and spring coefficients on the free vibration behavior of the sandwich plate. The results indicate that the auxetic core, characterized by its negative Poisson's ratio, confers metamaterial properties, thereby suggesting that the proposed model possesses significant potential for diverse engineering applications. In this analysis, higher-order shear deformation theory (HSDT) is employed to examine the free vibration characteristics of the sandwich plate, which features an auxetic core and functionally graded faces, while being subjected to a thermal medium and resting on a two-parameter elastic foundation. Given that the material properties of the NPR auxetic core layer are influenced by three geometric parameters, a thorough examination of the effective material properties is conducted, leading to the computation of the natural frequencies of the sandwich plate.

Damage management is vital in the tunnels to achieve quality, safety, long life, and economical operation. The proposed model of educational management of tunnel damage evaluation and mitigation will use the methods of numerical solutions. It combines tunnel engineering, structural mechanics, and numerical analysis, and in turn provides a systematic framework of diagnosing damages, estimating damages, and responding to the damages. The model is computed using a computer technique like finite element analysis and is used to simulate a variety of tunnel conditions of damage and to predict how they will affect performance. Moreover, its educational aspect contributes to the optimizing of the decision-making process as it offers training to tunnel management staff so that they could have the knowledge on how to achieve proactive repairing and mitigation measures. The model proposed in this project stretches the tunnel management practices further by integrating the methods of data-based simulation with capacity-building so that maintenance of such infrastructure becomes more resilient and more knowledge-based.