Abstract
Researchers achieved a new method which improves the mechanical strength of sports equipment through their research on nanoengineered composite materials together with modern metamaterial technology. The study examines sports equipment frame performance during structural tests which evaluate their endurance to impacts and their dynamic performance through arc-type auxetic metamaterial tests which researchers used with nanocomposite materials. The proposed design uses auxetic structures which have negative Poisson's ratio properties to achieve superior energy absorption results which create better stiffness distribution and enhanced structural stability during mechanical loading. The team established a complete theoretical framework together with a numerical system to investigate how reinforced frames respond under nonlinear dynamic conditions which occur during low-velocity impact tests. The advanced Hertzian contact system enables the model to create accurate simulations of contact points together with destruction processes which occur during impact situations. The research team applied parametric analysis methods to study architectural design elements which they combined with material properties and nanocomposite reinforcement characteristics to evaluate their effects on stress distribution patterns and vibration response behavior and impact reduction capabilities. The auxetic metamaterial designs show better load transfer capabilities and lower stress concentration levels when compared to traditional composite design methods. The nanoengineered reinforcement provides two advantages because it enhances damping capacity while improving structural strength which results in better impact protection and improved overall system performance. The research shows how researchers combine nanotechnology and metamaterial engineering to develop sports equipment designs which enhance both durability and safety and operational performance. The research results establish important information for designing high-performance sports equipment while creating a basis to develop multifunctional nanocomposite materials which have specific mechanical characteristics for future engineering needs.
Address
Xiaofei Ma, 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, Rize 53100, Türkiye/ Turgut Kiran Maritime Faculty, Recep Tayyip Erdogan University, Rize 53900, Türkiye
Abstract
This study investigates the influence of graphene origami (GOri) reinforcement and multi-field piezoelectric/piezomagnetic loading on the natural frequency characteristics of a sandwich curved beam. The structure consists of a GOri-reinforced composite core integrated with piezoelectric and piezomagnetic layers. A higher-order shear deformable model incorporating thickness stretch effects is developed to establish the kinematic relationships. The constitutive equations are derived by combining: Micromechanical models for the effective material properties of the GOri-reinforced core, Multi-field coupling equations for the piezoelectric/piezomagnetic layers. The governing equations of motion are obtained using Hamilton's principle and solved analytically. The natural frequency responses are systematically analyzed with respect to: GOri foldability parameters and reinforcement content, Multi-field (electro-magneto-elastic) loading conditions, Geometric parameters of the curved beam, Elastic foundation characteristics. The model is validated through comparison with existing literature before presenting new results. This work provides fundamental insights into the dynamic behavior of smart sandwich structures with tunable graphene-based reinforcements.
Key Words
auxetic metamaterial; composite smart curved beam; foldability parameter; graphene origami; multi-field loading; out-of-plane normal strain
Address
X. Feng: School of Mechanical Engineering, Production Engineering Group, Kuala Lumpur, Malaysia
Abstract
Over the past years, nanotechnology has become a promising approach to delivering drugs more effectively, whereas machine learning methods offer great solutions of optimizing the design of the treatment, depending on the unique aspects of a patient. Uterine fibroids belong to the category of the most frequent benign tumor in women of childbearing age and may cause serious clinical issues as pain in the area of the pelvis, irregular bleeding, and infertility. Traditional methods of treatment are not very personalized and can cause either poor therapeutic outcomes or unwanted side effects. This paper has shown a machine-learning-based design and assessment of nanotherapeutics to achieve the customization of the treatment of uterine fibroids. A sample population comprising of 420 cases was created that contained clinical and therapeutic important variables such as the age of patients, body mass index (BMI), fibroid size, severity of symptoms, type of nanoparticles, dose of drug, and ligand of targeting. These were inputs to be used in predicting treatment efficacy which is the expected level of effectiveness of the nanotherapeutic intervention. To assess the associations between patient properties, nanocarrier properties, and the therapeutic outcomes expected, both statistical analysis and graphic visualisation were used to investigate the relationships. The findings show that patient related variables as well as the parameters used in the design of nanotherapeutics play a major role in determining the performance of the treatment. Specifically, predicted treatment efficacy is enhanced by the optimization of the dosage of drugs as well as by the choice of targeting ligands. The results indicate the possibility of combining machine learning and nanomedicine to allow individualized treatment plans to increase precision of treatment and better clinical results in patients with uterine fibroids.
Address
Li Guoping: Shaoxing Maternity and Child Health Care Hospital, Department of Maternal and Child Health, Shaoxing City, Zhejiang Province, 312000, China
Yao Na: Chongqing Materials Research Institute Co., Ltd, China
Guo Xiaping: Zhuji Hospital of Traditional Chinese Medicine, Department of Gynecology, Zhuji City, Zhejiang Province, 311800, China
Abstract
Lumbar fusion surgery and its public health are intervention with significant variability in the outcomes and overall risks and benefits for the patients, therefore requiring state-of-the-art risk assessment models. In this paper, an Explainable Artificial Intelligence (XAI) approach is developed for the application in spinal fusion surgeries and public health with the Machin learning (ML) methods combined with nanotechnology. Consequently, the proposed framework captures patient's demographic, public health index, clinical, and biomechanical characteristics for prediction of surgical outcomes, pos-surgical complications, and recovery curves. Feature importance analysis and SHapley Additive exPlanations (SHAP) are used for decision justification to let clinicians understand which factors contribute to the high-risk score. The proposed XAI model is tested on clinical datasets from previous years and year ended to be more accurate compared to traditional statistical models yet enough compact for interpretation. The results reveal the application of explainable AI combined with nanotechnology in the enhancement of treatment plans and public health for various patients, the minimization of possible surgery complications, as well as, the introduction of improved chances of success. Results show that older patients with osteoporosis face higher physiological strain because it results in delayed postoperative healing along with greater surgical complications because of their decreased hemoglobin levels and insufficient bone repair. The duration of surgeries increases when patients have low hematocrit levels and lower vitamin D concentrations which calls for unique preoperative optimization methods to enhance surgical results.
Key Words
nanotechnology; public health; risk stratification; machin learning; spinal fusion surgeries; XAI approach
Address
Xinglong Zhong, Anqi Hu, Hao Chen, Xianghua Ma: Strategy & Development Department, Hengrui Pharmaceutical, Shanghai 201210, China
Abstract
The effects of Al and Cr on the microhardness and phase separation of the α' phase in Fe-Cr-Al alloys aged at 750 K are studied by using the hardness measurement and phase-field (PF) simulation, and the correlations between the mechanical properties and morphology kinetics are clarified. The separation of α' phase shows the substantial effect on the hardness increment, by addition of Al, the increment of hardness is suppressed in the low Cr content Fe-25Cr at.% alloy, while the increment of hardness is intensified in the high Cr content alloys. The Cr depletion and Al enrichment at the interface of α/α' phases is showed in Fe-25Cr-10Al at.% alloy rather than in the high Cr content alloys. With the Cr content changing from 25 to 35 at.%, the early stage variation rate of volume fraction and later stage coarsening rate are quickened, correspondingly, the average particle distance goes through a descent of early stage and ascent of growth and coarsening stage. Large volume fraction and small particle distance of α' phase explains the high hardness of the high Cr content alloys. In addition, the time exponents of average particle radius are close to 0.3 at the growth and coarsening stage, while less than 0.3 at the later coarsening stage, a dependence of the time exponent on the phase separation stage is revealed.
Key Words
Fe-Cr-Al alloys; kinetics evolution; microhardness; phase-field
Address
Shi Chen, Yongsheng Li, Shujing Shi, Zhengwei Yan, Jing Chen, Dong Wang: School of Materials Science and Engineering, Nanjing University of Science and Technology,
Nanjing, 210094, China/ MIIT Key Laboratory of Advanced Metallic and Intermetallic Materials Technology, Nanjing, 210094, China
Abstract
This study presents a theoretical framework for analyzing multi-field coupled behavior in shear deformable sandwich beams, targeting advanced sports equipment such as a functionally graded baseball bat. The structure comprises a graphene origami-reinforced copper matrix core sandwiched between piezoelectric (top) and piezomagnetic (bottom) face-sheets, enabling interaction with external electric and magnetic fields. A higher-order thickness-stretched theory is employed to overcome limitations of classical models, capturing transverse shear and normal strain effects essential for thick sandwich configurations. The formulation integrates two-dimensional constitutive relations for elastic, piezoelectric, piezomagnetic, and thermo-elastic responses, with appropriate field equations describing electric potential and magnetic induction distributions. Equilibrium equations are derived systematically via the principle of virtual work, incorporating mechanical, thermal, electrical, and magnetic contributions to yield the full set of governing equations and boundary conditions. The proposed model provides a foundational basis for designing smart baseball bats capable of vibration damping through applied electric potentials or impact energy harvesting via the piezomagnetic effect under thermal environments, supporting the development of sensor-integrated intelligent sports equipment.
Key Words
auxetic metamaterial; electro-magneto-elastic results; folding; graphene origami; initial electric/ magnetic potentials; shear and normal deformation theory
Address
Rong Qi: 1Institute of physical Education, Shanghai normal University, Shanghai 200234, China
Mintao Li: 2School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou 325000, Zhejiang, China
Mostafa Habibi: Technical Sciences, Chennai, India/ Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey/ Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, 600077, India
Abstract
This paper combines deep learning (DL) with traditional algorithms to solve the problems of noise interference, sparse data and incomplete local structure in architectural 3D PC (Point Cloud) data registration and fusion. Combining the RPMNet (Robust Point Matching Network)++ model with the ICP (Iterative Closest Point) algorithm improves the accuracy of PC registration and fusion effect, providing accurate 3D data support for building information modeling, bridge construction and smart city construction. RPMNet++ is used to extract high-dimensional features from PC, a bidirectional attention mechanism enhances the global representation of features, and the Copula correlation model analyzes and removes noise points. The ICP algorithm is introduced to fine-tune the initial registration results, achieving an efficient combination of global and local optimization. The experimental design covers noise-free PC, noisy PC and unnormalized PC scenes. The data source is four subsets of Paris-Lille-3D. Comparative experiments are conducted with mainstream methods such as PointNet (Point Network), DCP (Deep Closest Point), DeepICP (Deep Iterative Closest Point), FGR (Fast Global Registration), PRNet (Point Recognition Network) and LCCP-Net (Locally Convex Connected Patches Network). The anisotropic error (rotation error 0.00911, translation error 0.00009) and isotropic error (rotation error 0.01901, translation error 0.00021) of the proposed method in noise-free PC registration are both the lowest. The average inter-cloud chamfer distance of the noise-free points of the proposed method is 0.000002. Ablation experiments show that the average F-norm of the PC data registration method in this paper is 2.58. And it also shows significant advantages in processing time, with an average processing time of 0.47 seconds. This method achieves high robustness and high-efficiency registration for complex PC scenes, providing reliable technical support for the application of three-dimensional PC.
Key Words
3D architecture; deep learning; point cloud data; registration and fusion; traditional algorithms
Address
Chenglong Huang, Chi-Ho Lin, Suan Lee, Jie Zhang: School of Computer Science, Semyung University, 65 Semyung-ro, Jecheon-si 27136, Chungcheongbuk-do, Republic of Korea
Abstract
The research examines how extreme weather conditions affect the stability and aerodynamic capabilities of stadium roofs that use nano materials. The modern architectural design for large stadium roofs uses a doubly curved panel system which achieves better structural performance and flexible design options. The roof structure will achieve better mechanical performance through the use of graphene nanoplatelet (GPL) reinforced composites which create panel materials that will provide higher stiffness and lower weight and better environmental performance. The analysis uses rain-induced loading as an extreme weather condition because it considers the extra weight and damping effects and fluid-structure interaction which occurs during precipitation. The governing equations of motion for the doubly curved panels are formulated based on classical shell theory which is modified through the addition of nanoparticle reinforcement effects. An effective medium method enables material property evaluation by demonstrating how graphene nanoplatelets affect material performance. The first-order piston theory provides an effective method for measuring unsteady aerodynamic pressures which affect curved surfaces during high wind conditions. Through the evaluation of structural/aerodynamic systems one obtains important information about system data that indicates natural frequencies and damping ratios, as well as the dynamic performance of a structure. The results of the research indicate that the addition of graphene nanoplatelets results in an increase in the rigidity/stability of the roof system and decreases the amount of rain-induced loading that the roof system will experience due to vibration. Additionally, the aerodynamic performance of the roof system is enhanced/helped by its ability to accommodate this high-velocity airflow. Furthermore, the findings demonstrated that nano-reinforced materials permit architects to develop new roof structures for future use by stadiums.
Key Words
aeroelastic stability; doubly curved panels; graphene nanoplatelets; nano-reinforced sport stadium roofs; rain-induced loading
Address
Zhuoxuan Li, Zilun Wang: 1School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
Wen Li: School of Business, Suzhou University of Science and Technology, Suzhou 215009, China
