Abstract
This study aims to investigate the elastic buckling behavior of tapered sandwich beams with functionally graded cellular cores and carbon nanotube–reinforced facesheets. The main objective is to clarify how tapering, porosity distribution, and nanocomposite reinforcement influence the critical buckling load, thereby providing insights useful for the design of lightweight and mechanically efficient structural components. To achieve this, stress transformations at specific angles are performed to accurately determine the effective material properties corresponding to different reinforcement patterns along the beam thickness. The governing equilibrium equations are derived using the virtual displacement principle and the variational method, and are solved numerically by means of the differential quadrature method. A comprehensive parametric study is conducted to evaluate the effects of geometric characteristics, porosity coefficient, porosity distribution patterns, carbon nanotube reinforcement, and transformation angle on the critical buckling loads. The results demonstrate that tapered beams generally exhibit reduced buckling resistance compared to beams of uniform thickness, and that improper distribution of reinforcements along the thickness can lead to significant deviations in buckling performance.
Address
Zahra Khoddami Maraghi and Ehsan Arshid: Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
Abdelouahed Tounsi: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia/ Material and Hydrology Laboratory, University of Sidi Bel Abbes, Faculty of Technology, Civil Engineering Department, Algeria
Abstract
This paper presents an in-depth investigation concerning the stability, temporary position, and visual design of spherical shells having functional graded carbon nanotube (FG-CNT) reinforced composites (FG-CNTRC) is investigated in this study. The analysis is based on Higher-order theories, in particular, the 12-variable displacement field (HOST12), which adequately describes the kinematic behavior of the shell. The material properties of FG-CNTRC were based on the Rule of Mixtures, thus the geometrically and materially nonlinear distribution and interaction of carbon nanotubes with matrix was described and incorporated. The stability of the FG-CNTRC spherical shells are investigated by developing the governing equations of motion via Hamilton's principle, thus accounting for material and geometrical parameters. To discretize the governing equations a hyperbolic differential quadrature method (HDQM) is employed based on Chebyshev–Gauss–Lobatto grid points, ensuring high accuracy and convergence in the results. Complete stability analyses are carried out by varying the effects of the volume fraction of the carbon nanotube and the geometry of the shell for visual design. The results point out the critical buckling loads and two deformation characteristics, along with the improvements in performance for FG-CNTRC shells versus typical materials. The findings are also compared to results reported by other authors in the literature showing the improved accuracy and reliability of the proposed approach. This study both reinforces the move toward sophisticated higher-order theories for the design of nanoreinforced structures and addresses an emerging field of knowledge related to the topic of nanocomposites for use in structural engineering.
Abstract
Endometriosis is a gynecological condition, which is chronic in nature and is associated with ectopic expansion of endometrial tissue resulting in the onset of pelvic pain, infertility, and diminished quality of life. There has been a drawback of conventional pharmacological treatment that is characterized by bad bioavailability, systemic effects, and a lack of targeting. Nano-drug delivery systems offer the potential solution to get past those obstacles since it allows site-specific delivery, prolonged release profile, and enhanced therapeutic efficacy. Animal models of endometriosis are used in the present study to assess the pharmacokinetics, biodistribution and efficacy of nanoformulated drugs in relation to the conventional agents. Liposomes, polymeric nanoparticles and dendrimer-based systems are examined as nanocarriers to achieve drug targeting to endometriotic lesions. In addition, machine learning is incorporated to get the best treatment protocols that predict the drug release profile, lesion regression, and systemic safety depending on the multi-parameter datasets. A translational platform of personalized therapeutic approaches has been achieved by investigating the discovery of the in-silico predictions and translation of the experimental results poised in animal models. Such integrative solution points to the promise of nanotechnology and artificial intelligence to transform the way endometriosis is treated, promising more effective, safer, and patient-specific options.
Address
Jing Gong, Li Liao, Liujing Zheng, Xiuyue Liao, Fei Xiang, Fengxia Yang and Xin Zheng: Department of Pharmacy, Chongqing Changshou District Traditional Chinese Medicine Hospital
Jie Lou: College of Pharmacy and Bioengineering, chongqing university of technology
Xiaojuan Tang: Department of Pharmacy, Chongqing Tongliang Hospital of Traditional Chinese Medicine
Abstract
Nanotechnology has created new opportunities in precision medicine, providing a chance to monitor the biological signals, molecular markers, and micro-environmental changes at the ultrasensitive level, which occurs before complications. The issue of postoperative complications that arise after performing spinal fusion surgeries is a burning clinical issue, and most of the time, it brings about long-term recovery, increased healthcare expenses, and deteriorated patient outcomes. We suggest the incorporation of nano-enabled AI prediction models in this work that utilizes data collected by nano-sensors, nanomaterials-based diagnostics, and traditional clinical data to improve risk stratification in spinal fusion. Working on a nanoscale, these systems bead on minor physiological changes, including inflammatory biomarkers, metabolic changes, and tissue healing, obscured by conventional techniques. Combined with artificial intelligence, nano-derived datasets offer unmatched granularity, increasing the forecasting performance and allowing timely detection of patients who are at a significant risk of unfavorable events. This model of Nano-AI fills the gap between nano-medicine and computational modeling to provide an innovative solution to customized care postoperative. Finally, the nano-integrated predictive analytics can transform the paradigm of surgical risk assessment, which will inform proactive solutions and support patient safety in sophisticated spinal surgeries.
Key Words
AI-powered risk prediction; machine learning in healthcare; postoperative complications; spinal fusion surgery; surgical outcome optimization
Address
Li Chunhui and Zhang Wenjia: Department of Neurosurgery, The First Hospital of Hunan University of Chinese Medicine, No. 95 Shaoshan Middle Road, Yuhua District, Changsha, Hunan, Postal Code 410021
Wu Yue: Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020
Abstract
In the musical instrument industry, a significant challenge is the durability of wood, especially when subjected to changes in humidity. Wood tends to absorb moisture, leading to expansion or contraction, which negatively impacts the instrument's sound and overall performance. To address this, various methods have been developed to enhance wood and minimize its moisture absorption. One such approach involves using specialized chemicals that improve wood's resistance to water and boost its durability. Nonetheless, challenges remain, such as the variability in wood's properties, not only among different trees but also within various sections of a single tree. These inconsistencies complicate the production of musical instruments and render the process time-consuming and difficult. Moreover, wood has limited resistance to impact. This research presents a new generation of polymer composites that have the potential to replace wood in musical instrument manufacturing. These composites replicate the qualities of wood while offering improved resistance to both moisture and impact. Furthermore, integrating silver nanoparticles into these materials can further enhance their performance. Nanoparticles develop a robust protective layer on wood, significantly reducing moisture absorption and enhancing both its longevity and stability. This advancement represents a noteworthy innovation in material preservation. A key advantage of these moisture-resistant materials is their ability to diminish unwanted noises in musical instruments. These materials provide greater stability in fluctuating humidity and environmental conditions, enhancing sound quality and minimizing extraneous noises during performance. Consequently, employing these advanced composites could improve the overall sound and playability of musical instruments, offering musicians a more enjoyable experience.
Key Words
musical instruments; polymer composites; silver nanoparticles; unpleasant and destructive sounds; wood
Address
Linfu Ta, Mingge Li: National Academy of Music "Prof. Pancho Vladigerov", 94, Evlogi i Hristo Georgievi Blvd., Sofia 1142, Bulgaria
Kaun-yu Cheg: Institute of Sciences and Design of AL-Kharj, Dubai, United Arab Emirates
Abstract
Immersive sound is part and parcel of the cinema experience, but most standard headphones cannot accommodate to the nuances of moving pictures. The paper proposes machine learning empowered smart nanomaterial headphones to aid movie based real-time and acoustic adaptation. The system uses nanomaterial transducers that are highly sensitive and adaptable and which are coupled with machine learning models with an adaptive approach so that it personalizes the sound delivery during film playback. This is done by constantly examining the changes in the soundtrack, determining exactly what sounds are being set, dialogue, music, special effects, and putting optimal acoustic output to sustain emotional appeal and discourse clarity. Finally, real time adaptations also take into consideration ambient noise and listener preferences allowing an extremely personalized and cinematic movie experience out of the theater. Experiments show improved speech recognition in conversations, a higher sense of spatial audio technology, better depth coverage and adaptive noise cancellation compared to standard technology. This study focuses on the paradigm shift of nanomaterials and AI that will render a new dimension to movie experience via the latest generation of wearable audio devices.
Key Words
acoustic adaptation; AI; machine learning; movie; smart nanomaterials
Address
Zixuan Li: Institute for Advanced Studies, University of Malaya, Malaysia, 50603
Amirrudin Bin Kamsin: Department of Computer System & Technology, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia,50603
Hanafi Bin Hussin: Department of South East Asian Studies, Faculty of Arts and Social Sciences, University of Malaya, Malaysia, 50603
A. Horri: Department of Civil Engineering, University of Zabol, Zabol, Iran
Abstract
In this paper, we propose a new framework that merges physics-informed deep neural networks (PINNs) with novel material modeling to assess the performance of tennis handles reinforced with graphene oxide powder nanocomposites. The tennis handle is modeled as a thin shell structure within the cylindrical coordinate framework to accurately capture the complex curved geometry of the handle as well as its vibration response under dynamic loading conditions. The effective mechanical properties of the nanocomposite reinforced structure use the Halpin–Tsai micromechanical model to represent the ability of the graphene oxide powders to reinforce the polymeric matrix. The structural response is captured in terms of a higher-order shear deformation theory (HSDT) based on Taylor's series expansion, which is an improvement compared to classical and conventional first-order shear models typically used due to its ability to account for variation in shear strain through thickness. The governing motion equations are developed through Hamilton's principle, accounting for both inertial and elastic energy contributors. To tackle the resulting high-dimensional system, a PINN architecture with Legendre polynomial expansions provides a physics-constrained and computationally efficient surrogate representation for detailed vibration and stability analyses. Legendre polynomials allow the neural network to have a larger representation capacity while grasping smoothness and orthogonality within the solution space. Results show that the stiffness, damping, and energy absorption capacity of tennis handles improved significantly with graphene oxide nanocomposites. Also, the proposed PINN framework achieved better accuracy than traditional numerical methods, such as finite differences, computer finite element analysis, calculations made using Matlab simulation toolboxes, and higher-order polynomial interpolation. This hybrid physics–AI methodology improves sports by assisting with the optimization of tennis handle designs and also provides a generalized method for the use of physics-informed machine learning in sports equipment design.
Key Words
Hamilton's principle; higher-order shear deformation theory; graphene oxide powder nanocomposites; physics-informed deep neural networks; sports engineering
Address
Ying Ying Tao: Department of Physical Education, Communication University of Zhejiang, Hangzhou, Zhejiang, 310000, China
Liquan Chen: School of Culture and Tourism, Quzhou College of Technology, Quzhou, Zhejiang, 324000, China
Murat Yaylaci: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Turkey/ Turgut Kiran Maritime Faculty, Recep Tayyip Erdogan University, 53900, Rize, Turkey
Abstract
The current work provides a mathematical model to evaluate the stability and instability of carbon nanotube (CNT)-reinforced fibers as they are employed in clothing design. We model the textile thread as a double-clamped beam, which indicates the mechanical behavior of the fiber. The fibers are multi-scale hybrid nanocomposites (MHC) or combinations of multi-scale composite, integrated with CNTs for added strength and elasticity. We apply Halpin–Tsai model characterizations for composite materials so that we can evaluate the effective properties of the composite substance and incorporate interaction between CNTs and the matrix. The deformation of the thread is modeled by developing a quasi-3D hybrid type (q-3DHT) formula using higher order shear deformation theory (HSDT). The higher order shear deformation theory represents shear deformation and rotational inertia and provides a more accurate representation of the behavior of the material system. A variational method to derive the governing equations of motion for the system exposed to a harmonically induced transverse force is used in the approach. Following this, the stability of the system is assessed using the differential quadrature approach (DQA), which is an adaptation of the Dubner and Abate development for effectively inverting Laplace transforms. This mathematical framework provides insight for dynamic stability of CNT enhanced fibers in the textile industry and provide strategies for optimizing garment design. The model ultimately has potential implications of incorporating CNT reinforcement for developing high-performance fibers that perform well against external force and still have comfort and durability with clothing design.
