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CONTENTS
Volume 20, Number 2, February 2026
 

Abstract
The combination of nano-enabled communication technology with machine-learning-enabled adaptive intelligence is a revolutionary model of intelligent and personalized learning system. The present study suggests a nano-inspired educational communication system that is powered by real time data collection, predictive engagement modeling, and content personalization to maximize the learning process. With simulation of 300 interactions of learners we test important performance measurements such as nano-interface signal quality, communication latency, system response efficiency, engagement prediction, learning personalization, assessment accuracy, and learning gain. The findings reveal that fidelity of signals and lower latency can enhance greatly system responsiveness, whereas machine-learning-based personalization and adaptive feedback can serve as an improvement to engagement and learning. In multidimensional analysis, it can be seen that the three factors of high engagement, high personalization, and high system responses produce maximum benefits on learning. These results confirm the effectiveness of the suggested framework and emphasize its possibilities to make a step forward to intelligent, real-time, and adaptive education platforms.

Key Words
adaptive learning systems; intelligent tutoring systems; machine learning; nano-enabled communication; personalized education

Address
Zhang Qianqian: School of Chinese Language and Literature, Changji University, Changji,China, 831100

Wang Peng: School of Aviation Academy, Changji University, Changji, China, 831100

Abstract
Patient-specifics and the nature of the implant surfaces are essential to the long-term performance of joint replacement implants, but a traditional method of designing them continues to be based on generalized assumptions that cannot be tailored to one person. This paper is intended to introduce an artificial intelligence (AI)-based framework to optimalization of nano-coated orthopedic implants to match individual patient characteristics. To train prediction machine learning models, a complete dataset that includes characteristics of the patient, properties of the implant materials, nano-coating factors, and AI-based design indicators is created. An integrated output in terms of mechanical stability, wear resistance, and potential to allow integration of the implant is the index of implant performance. The findings show that AI-optimized designs are always superior relative to traditional designs in all forms of nano-coatings and carbon-based nano-coatings are especially more effective. The given strategy points out the potential of AI to learn the intricate nonlinear relationships between biological and nano-engineered variables, which creates a solid avenue toward personalized joint replacement and better implant survival.

Key Words
artificial intelligence; machine learning optimization; nano-coatings; orthopedic implants; personalized joint replacement

Address
Huang Shengxiang: Department of Pediatric Orthopedics, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, 410007, China

Zhou Weili, Zhang Zhuo: Department of Orthopedics, Changsha Third Hospital, 176 Laodong West Road, Changsha City

Abstract
Vertebral compression fractures (VCFs) cause a significant clinical problem especially in osteoporotic groups of patients, whose delayed or incomplete recovery may result in chronic pain and diminished quality of life. After vertebral augmentation, precise estimation of fracture healing is not an easy task as bone morphology and biomaterial properties interact in a complex way. This paper suggests a predictive modeling system to combine radiomics aspects of vertebral imaging with physicomechanical attributes of PMMA-nanocomposite bone cement to predict the outcome of fracture healing. A carefully constructed dataset of 450 samples containing both quantitative radiomics descriptors such as texture, shape, and intensity, and PMMA characteristics such as elastic modulus, porosity, and nanoparticle concentration were created. The main outcome variable was a continuous fracture healing score. Exploratory data analysis showed that the variability was under control, feature distributions were normalized and correlation between predictors and the healing outcome was moderate, thus, the data is appropriate to multivariate and machine-learn methods. Correlation analysis indicated that both radiomics and PMMA related variables have a significant contribution to prediction of healing with the cement elastic modulus being the highest associated. The format of the dataset facilitates the exploration of the synergistic interaction between imaging biomarkers and biomaterial properties, which makes it possible to develop and verify the models. The offered data framework indicates the opportunity to integrate radiomics and nanocomposite material features to enhance the prognostic accuracy in the treatment of VCF. This will offer a basis of individual treatment planning and optimization of biomaterial design in vertebral augmentation procedures.

Key Words
correlation analysis; machine learning; PMMA-nanocomposite; radiomics; vertebral compression fractures

Address
Feng Xu: Department of Trauma, Reconstruction and Repair Surgery, Guizhou Hospital of Beijing Jishuitan Hospital, No.25 South Shachong Road, Nanming District, Guiyang City, Guizhou Province

Qian Zhang, Chun Hua Pan, Yun Xu: Department of Pain Medicine, The Second Affiliated Hospital of Guizhou Medical University, No.3 Kangfu Road, Kaili City, Guizhou Province


Abstract
Recent research into nano technology has created new methods for assessing and developing functionally graded nanostructures which will serve future engineering applications. The study examines the propagation of nonlinear wave patterns through tri-directional functionally graded (TD-FG) nanoplates which combine ceramic aluminum oxide and metal stainless steel SUS304 through a performance and economic assessment. The material properties are assumed to vary continuously along the length, width, and thickness directions to achieve enhanced mechanical performance while enabling cost-efficient material distribution. The governing framework uses nonlocal strain gradient theory to describe size-dependent effects which become evident at nanoscale dimensions. The researchers used a quasi-three-dimensional refined theory to achieve precise measurements of transverse shear deformation and thickness stretching without needing shear correction factors. The von-Karman assumptions enable researchers to study wave propagation through intermediate deformations because they include geometric nonlinearity. The process of deriving the nonlinear coupled equations of motion and their boundary conditions starts from Hamilton's principle which is implemented through a consistent variational framework. The displacement field gets described through a harmonic representation while an iterative solution method handles the nonlinear relationship between wave characteristics and structural response. The assessment of combined performance together with economic factors shows how tri-directional material gradation and nanoscale parameters and nonlinear effects impact wave dispersion and structural performance. The results demonstrate that optimized gradation patterns can achieve two objectives which are to improve wave propagation performance and to decrease material usage which creates design standards for advanced nano-engineered components that aerospace and micro-electromechanical and high-performance mechanical systems will use.

Key Words
economic analysis; functionally graded nanoplates; nonlocal strain gradient theory; nonlinear wave propagation; size-dependent effects

Address
Muye Guo, Yufei Wu: Harbin University of Commerce, Harbin, 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
The process of managing diabetes requires continuous blood glucose level assessment through precise testing methods to achieve effective monitoring. The research presents a combined system which implements nano-biosensors and data-driven intelligence to deliver ongoing glucose level assessment and personalized therapy enhancement. The portable nano-biosensor system employs nanoscale enzymatic and electrochemical detection methods to track glucose level fluctuations while transmitting secured information to a cloud-based analytics system. The advanced machine learning models study temporal glucose data to predict future hyperglycemic and hypoglycemic events which deliver personalized insulin dosage recommendations and lifestyle improvement strategies to users. The system demonstrated high glucose detection performance through preliminary validation tests which maintained detection accuracy during regular daily activities. The combination of nanotechnology and predictive analytics creates a personalized diabetes management system which enhances patient compliance and safety while delivering better long-term health results. The appraisal on the basis of structured physiological and lifestyle data proved that the suggested framework produced credible glucose forecasting performance with high levels of feature association that preserve correlation coefficients between 0.5 and 0.8 in the important variables. The findings show better prediction stability and better personalization than the traditional glucose monitoring methodologies.

Key Words
continuous glucose monitoring; machine learning; nano-biosensor; predictive analytics; personalized diabetes management

Address
Haixia Shi: Department of Endocrinology, Tongliao People's Hospital, Tongliao, Inner Mongolia 028005, China

Yaru Zhou: Department of Endocrinology, The First People's Hospital of Keerqin District, Tongliao, Inner Mongolia 028000, China

Lijun Zhang: Department of Orthopedics, Tongliao Traditional Chinese Medicine Hospital, Tongliao, Inner Mongolia 028005, China

Abstract
Athletes need improved sports equipment which drives companies to develop products with better strength and enhanced durability through advanced nanocomposite technology. The research establishes a thorough examination which tests the dynamic performance and structural strength of tennis equipment that uses nanocomposite materials under short term load testing. Engineers create structural models through a three-dimensional elasticity framework which enables them to simulate reinforced structure behavior when elastic foundations produce actual impact conditions. The system dynamics equations originate from energy principles while Rayleigh-Ritz approximation method provides a quick method to calculate displacement fields natural frequencies and temporary vibration patterns. The proposed model incorporates material heterogeneity, nanoscale reinforcement effects, and time-dependent damping characteristics to provide a realistic representation of structural performance. The research investigates how various nanocomposite properties together with reinforcement structures and viscoelastic support parameters affect dynamic stability and vibration reduction. The results show that using nanocomposite reinforcement increases stiffness and decreases vibration amplitudes and enhances energy dissipation abilities which result in better structural reliability and performance efficiency of sports equipment. The research demonstrates that nanoscale reinforcement materials interact with viscoelastic support systems to determine how materials respond to transient changes. The research results help designers create advanced sports structures while developing high-tech athletic equipment that combines exceptional mechanical strength with long-lasting durability and effective vibration control.

Key Words
high-tech tennis sports equipment; nanocomposite materials; Rayleigh-Ritz method; three-dimensional elasticity; transient vibrations; viscoelastic foundations

Address
Fengjun Shen: College of Physical Education, Kashi University, Kashi, Xinjiang, 844000, 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, Rize 53100, Türkiye/ Turgut Kiran Maritime Faculty, Recep Tayyip Erdogan University, Rize 53900, Türkiye


Abstract
Healthcare-associated infections and, in particular, the surgical site infections (SSIs) are acute problems regardless of the enhancements in the antiseptic agents, sterilization procedures, and the observation of the environment. The primary techniques of the conventional approach to disinfection are the application of chemical agents, the effectiveness of which can be negatively determined by improper use, microorganism resistance, and the rapid recontamination of the surface. One of the newer methods that have been implemented in recent years with promising results is nanostructured surfaces to supplement environmental hygiene as well as reduce the number of microbes in clinical settings. The antimicrobial properties of the nanoscale-engineered surfaces are inherent to the physical action principles that comprise the membrane disruption, reduced microbial adhesion, and biofilm prevention in most of the cases that do not require chemical biocides. Nanostructured materials may also be implemented synergistically with legacy disinfectants to offer superior disinfectants, longer antimicrobial and greater reliable hygienic monitoring. The paper shall discuss the relevance of nanostructured surfaces in environmental hygiene monitoring and how this could be relevant in SSI prevention. We take into consideration the modern trends in nanofabrication processes, processes of antimicrobial protection as well as the compatibility with existing disinfectants, and new methods of determining the surface and microbial survival condition. The other problem of interest is the problem of durability, cost, safety and regulatory issues. The concept of nanostructured surfaces can be regarded as a novel development in the healthcare hygiene practices, based on an integration of materials science and infection prevention and environmental monitoring. They are able to contribute to the existence of stronger infection control measures, reduced reliance on chemical disinfectants, and improved patient outcomes in the surgical and high-risk clinical environments.

Key Words
cleaning complianceons; disinfectants; log reduction; nanostructured surfaces; surgical site infections (SSIs)

Address
Jia Fang: Chun'an First People's Hospital Infection Control Department, No.1869, North Huanhu Road, Qiandaohu Town, Chun'an Country, Hangzhou City, Zhejiang Province, China

Meiyun Wu: Department of Rheumatology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, No 201, Hubin South Road, Siming District, Xiamen City, Fujian Province, China

Abstract
Nanomedicine was found as a hopeful solution to the targeted therapy of urological cancer which provides greater drug delivery and targeting. Nevertheless, it is difficult to design nanomedicine preparations to deliver the most effective therapies because of the complicated interplay between the biology of tumors and the properties of nanoparticles. This paper presents a machine learning (ML)-inspired methodology to optimization of nanomedicines in urological treatment of cancer. The model can be used to determine the efficacy of different formulations of nanomedicine in the treatment of various stages of urological cancers by combining the predictions of the treatment responses with clinical data including the tumor size, the drug loading and the properties of the nanoparticles. We are showing how ML algorithms can optimize the size of nanoparticles, drug loading and ligand targeting to individual treatment strategies. A cascading of 3D visuals and statistical studies were used to explain the determinants of the important variables that affected treatment reaction as part of tumor size, dose of drug, and the existence of targeting ligands. We have demonstrated that the optimal nanoparticle formulations based on the unique characteristics of tumours enhance therapeutic efficacy and minimise toxicity which can lead to a means of effective and individualised cancer therapy. This paper demonstrates how ML can inform the design of personalized nanomedicines, which will be a major breakthrough in the context of precision oncology of urological cancers.

Key Words
drug delivery; machine learning; nanomedicine; precision medicinel; urological cancers

Address
Xinghui Wu, Han Yan, Zhanyu Sheng, Maolin Xiang, Pengcheng Wang: The First Hospital of Changsha, (The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University), Changsha 410005, China

Chungang Yan: The Fourth Hospital of Changsha, Changsha 410005, China


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