Abstract
With the rapid maturation process of nanotechnology, it is now possible to develop extremely sensitive, low-weight, and nondestructive nano-sensors capable of delivering physiological and biomechanical measurements with the highest level of accuracy ever. This paper discusses how data on nano-sensors can be incorporated alongside machine learning to forecast sports injury at risk and communicate an emergency during a professional and mass sporting event. Nano-sensors in wearable devices are employed to provide rich data sets in dynamically changing settings to ensure the ongoing detection of significant indicators such as joint loading, muscle fatigue, hydration, and micro-level effects. Machine intelligence models developed on these datasets will identify signs of a potential injury early and take proactive measures to intervene on behalf of the athletes and organizers of the event. In addition, the framework can be applied to sports crisis communication by using predictive understanding to provide information to the stakeholders to minimize the danger of adverse publicity to the event following a crisis associated with injuries at a major event. The results indicate that machine learning tools operating on nano- sensors lead to significantly increased accurate and timely injury prediction and data-driven evidence on transparent and effective crisis communication. The article attracts attention to the two-fold usefulness of nano-enabled technologies to increase the safety of athletes and the confidence of people in organization of sport events.
Key Words
crisis communication in sports events; machine learning; nano-sensors; sports injury prediction; wearable technology
Address
Yuelin Si: Institute for Sport Business, Loughborough University, London, E20 3BS, UK
Zicheng Zhao: Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA
Pengfei Wei: College of Physical Education and Health, Changsha Medical University, 410219, China
Abstract
The automotive industry extensively uses advanced high-strength steels for structural applications, raising challenges related to the mechanical performance of resistance spot-welded joints. This study numerically investigates the tensile–shear behavior of spot-welded steel plates made of TRIP800, DP450, and DP980 using a three-dimensional finite element approach. The objective is to evaluate the influence of material type and applied load on stress distribution, mechanical response, and safety factors. The results show significant stress concentration near the weld nugget, with a symmetrical distribution in the transverse direction and an asymmetrical pattern along the loading direction. DP450 and TRIP800 exhibit earlier loss of elastic behavior compared to DP980, although all materials maintain adequate safety margins at maximum load levels. These findings underscore the significance of material selection in ensuring the structural integrity of spot-welded automotive components.
Key Words
dual phase; finite element method; spot resistance; spot welding; transformation induced plasticity (TRIP)
Address
Cherfi Mohamed, Abderahmane Sahli, I.M.A Ghermaoui: LMPM, Djillali Liabes University of Sidi Bel-Abbes, Algeria
Abdelmadjid Moulgada, Mohammed E.S. Zagane: LMPM, Djillali Liabes University of Sidi Bel-Abbes, Algeria/ Ibn Khladoun, University of Tiaret, Algeria
Murat Yaylaci: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Türkiye/ Turgut Kiran Maritime Faculty, Recep Tayyip Erdogan University,53900, Rize, Türkiye/ Dijitalpark Teknokent, Murat Yaylaci-Luzeri R&D Engineering Company, 53100, Rize, Türkiye
Djafar Ait Kaci: Ibn Khladoun, University of Tiaret, Algeria
Merve Terzi,Şevval Öztürk: Department of Civil Engineering, Recep Tayyip Erdogan University, 53100, Rize, Türkiye
Ecren Uzun Yaylaci: Faculty of Fisheries, Recep Tayyip Erdogan University, 53100, Rize, Türkiye
Abstract
Nano-biosensors have upended the approach of measuring breast cancer recurrence in a non-invasive way because it can detect biomolecular changes at very tiny levels in the patient samples. The study reviewed the use of nano-biosensor technology in conjunction with machine learning algorithms to predict recurrence risk based on multi-parametric biomarker data. The features of population of patients, i.e., circulating tumor cells, exosome population, miRNA expression, protein biomarkers, and biosensor-detected electrochemical and optical signals were modeled by creating a synthetic dataset. To perform better prediction, demographic and clinical factors, i.e., age of the patient, body mass index, and treatment history, were also included. Thus, the machine learning model is based on such correlations of these biosensor-derived features and the recurrence risk, which allows for estimating the likelihood of diseases returning. Correlations showed that the major outputs of the nano-biosensors such as electrochemical and optical signals are significantly correlated (0.5-0.8) with the recurrence probability, which confirms their value as predictive signals. Scatter plot and distribution graphs also identified trends and variations of the patient sub-groups, indicating the nano-biosensor data ability to be used to stratify patients and monitor them individually. This method offers a scalable, non-invasive method of early cancer recurrence detection in the breast through convergence between nano-biosensor measurements and sophisticated predictive modeling strategies. This promises to change patient outcomes by improving nanoscale diagnostics and permitting constant patient monitoring without invasive approaches. This paper has proven that features based on nano-biosensors combined with machine learning can attain moderate to high predictive power with respect to breast cancer recurrence risks, thus showing non-invasive monitoring on a nanoscale is promising.
Key Words
breast cancer recurrence; machine learning prediction; nano-biosensors; nanoscale diagnostics; non-invasive cancer monitoring
Address
Deng Dan, Nie Shaozhong: Department of Breast and Thyroid Surgery, Yongzhou Central Hospital (Affiliated Hospital of Nanhua University), No. 396 Yiyun Road, Lengshuitan District, Yongzhou City, Hunan Province
Wu Yue: Department of Orthopedics, Beijing Chaoyang Hospital, affiliated with Capital Medical University
Abstract
In this research, w discusses the use of nano-scale Virtual Reality (VR) modeling as a mechanism to understand the nature of corporate visual identity (CVI) on consumer purchase intention. Although other studies have analyzed VR in marketing and CVI as a brand perception determinant, there is a lack of research studies that have utilized these dimensions in a simulation-based research. A nano-scale VR environment was created in this work to depict minor differences in the logo design, color schemes, typography, and spatial brand components. One hundred and twenty participants were shown controlled VR brand scenarios and their answers were then a subject of structural equation modelling. The findings indicate that the effect of immersive nano-scale VR modeling is significant in increasing the level of consumer perception of CVI coherence (β = 0.67, p < 0.01), which consequently has a positive impact on purchase intention (β = 0.58, p < 0.01). Moreover, the brand familiarity was also observed to mediate this relationship whereby more pronounced effects were observed in less familiar brands. The results indicate that nano-scale VR modelling is an effective technique, which can be used to test and optimise corporate visual identity strategy before being put into practice in the market. The study can add value to the practice of marketing and design because it gives empirical evidence of the potential role that advanced VR simulations can play in the decision-making of consumers and the performance of a brand.
Address
Yuan Wang, Muhamad Abdul Aziz Ab. Gani, Shahrunizam Sulaiman: College of Creative Arts, Universiti Teknologi MARA (UiTM), Perak Branch, Seri Iskandar Campus, 32610 Seri Iskandar, Perak, Malaysia
Lu Yixiang: College of Business, City University of Hong Kong, Hong Kong SAR
Abstract
Rheumatoid arthritis generally manifests evidence of chronic inflammation of the synovium; one major cell type responsible for this condition is heterogeneously classified macrophage populations. Nanotherapeutics would selectively undertake this task, but adapting the agents will require considerable carefulness, rendering the design optimization rather tricky. The development of a machine-learning (ML)-guided framework for a rational design of nanoparticles with discrete synovial macrophage subtype targeting will hence be the goal of this study. A dataset of 400 nanoparticle formulations was used to investigate the effects of size, surface charge, drug loading, and ligand density on cellular uptake, cytokine suppression (TNF-α and IL-6), cytotoxicity, and synovial targeting efficiency. Ensemble ML approaches, such as random forest and neural nets, produce solid predictions of therapeutic outcome and the feature-related design parameters governing that outcome. Cellular uptake, ligand density, and drug loading efficiency are identified as independent determinants of anti-inflammatory response, while size and charge play secondary yet significant roles. Multi-dimensional analyses illustrate a trade-off between efficacy and safety and reveal subtype-specific responses, with M2 macrophages showing high cytokine suppression at low cytotoxicity, whereas M1 macrophages demonstrated increased uptake with moderate levels of inflammatory modulation. This integrated ML approach has allowed us to gain mechanistic insight into nanoparticle-macrophage interactions and to rapidly in silico optimize RA nanotherapeutics, thus paving the way for personalized and efficient design strategies against inflammatory diseases.
Address
Guo Hongling: Pharmacy Department, Changsha Hospital of Traditional Chinese Medicine (Changsha No. 8 Hospital),
Changsha City, Hunan Province, PR China
Zhou Weili: Department of Orthopedics, Changsha Third Hospital, 176 Laodong West Road, Changsha City
Wu Yue: Department of Orthopedics, Beijing Chaoyang Hospital, affiliated with Capital Medical University, No. 8, Gongti South Road, Chaoyang District, Beijing
Abstract
In this study, the wave propagation behavior of anisotropic slosh pipe structures, which serve as fundamental components in various dynamic training devices, is analytically investigated. A first-order shear deformation theory is employed to derive the governing equations for flexural wave propagation in anisotropic cylindrical shells partially or fully filled with a viscous fluid. Four different anisotropic materials—Balsa, Sweetgum, Yellow Poplar, and Mahogony Honduras—are examined to highlight the influence of material symmetry on dynamic performance. The internal fluid is modeled as Newtonian, laminar, fully developed, and axially symmetric, and its interaction with the shell is formulated using the Navier–Stokes equations. Hamilton's principle is applied to obtain coupled fluid–structure equations, which are solved analytically to determine wave frequency and phase velocity characteristics. Parametric analyses are conducted to assess the effects of flow velocity, radius-to-thickness ratio, and both circumferential and longitudinal wave numbers. The results demonstrate that increasing flow velocity induces a damping effect that reduces both wave frequency and phase velocity, while stiffer anisotropic materials such as hexagonal systems exhibit higher dynamic response levels. These findings provide valuable insights into the design and performance optimization of anisotropic slosh pipes used in advanced instability-based and fluid-dynamic training equipment.
Key Words
anisotropic slosh pipe; dynamic training devices; first-order shear deformation theory; viscous fluid flow; wave propagation behavior
Address
Wenfeng Tang: Guangzhou College of Technology and Business, Guangzhou 510850, Guangdong, China
Min Hao: Physical Education Teaching and Research Section, Basic Course Department, Wuhan Donghu College, Wuhan 430212, Hubei, China
Mostafa Habibi: 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, India
Abstract
The rising demand for strong lightweight materials which can withstand test of time has resulted in using nano-reinforced composite materials for roof panel systems which protect current sports stadiums against intense dynamic forces. The researchers created an analytical-computational framework which enhances stadium roof panel strength through graphene platelet-reinforced composite materials and deep neural network verification which functions as an advanced machine learning method. The roof system is modeled as a doubly curved graphene platelet–reinforced composite panel exposed to dynamic loading conditions that simulate wind gusts and seismic excitations. The effective material properties of the nano-reinforced composite are evaluated by incorporating the contribution of graphene platelets within the polymeric matrix. The panel's structural behavior operates under first-order shear deformation theory which defines transverse shear deformation through a specific shear correction factor. The researchers use energy principles to derive governing equations of motion which they solve analytically using Navier's solution technique that employs double trigonometric series expansions. The Laplace transform handles analytical work for dynamic system behavior through its ability to evaluate transient response which needs its inverse transformation to be solved using a modified Dubner and Abate numerical method. The research confirms roof panel dynamic response through deep neural network training which uses analytical method datasets to produce fast computational results. The research findings demonstrate that analytical methods and machine learning approaches generate identical results which confirm the system's accuracy. The results deliver important information which assists in designing nano-reinforced stadium roof panels that provide superior stability and strength, and vibration control performance.
Key Words
deep neural network verification; dynamic loading analysis; first order shear deformation theory; graphene platelet–reinforced composites; sport stadium roof panels
Address
Zixuan Wang: School of Physical Education and Health Sciences, Mudanjiang Normal University, Mudanjiang, Heilongjiang,157011, 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
The regeneration of cartilage is a significant clinical problem because self-healing of this tissue is less pronounced and because of its complicated mechanobiological activity. Recent breakthroughs in nanotechnology have shown that nanoparticle-modified scaffolds can be greatly useful in cartilage repair through the mechanical strength, bioactivity, and cellular reactions. Weakly linear interactions between nanoparticle properties, scaffold behavior, biological environment and mechanical stimulation however, render conventional trial-and-error optimization time consuming and costly. This paper is an attempt to build a machine learning-inspired predictive model to model and optimize the results of nano-enhanced cartilage regeneration. A file with numerous samples was developed, including the most important input variables, including the type of nanoparticles, size, and concentration, scaffold pore structure and elasticity, cell seeding density, culture time and level of mechanical stimulation. The main output variable created as a cartilage regeneration index is a composite index of tissue quality, extracellular matrix formation and functional recovery. Trained supervised machine learning models were used to embrace the multifaceted nonlinear associations between inputs and regeneration performance. The findings show that the specified predictive models are capable of providing accurate estimates of the cartilage regeneration results in a broad design space. Important findings derived during feature importance analysis are that culture time, nanoparticle concentration, mechanical stimulation, and scaffold porosity are supreme factors determining regeneration efficiency. The constructed framework presents a potent surrogate modeling platform that is able to inform the rational design and optimization of nano-engineered cartilage scaffolds as well as drastically save on the experimental work. This paper presents the possibility of machine learning being a useful decision-support tool in the field of advanced cartilage tissue engineering and regenerative medicine.
Address
Zou Shu: Department of orthopedics, The Fourth Hospital of Changsha,No. 200, Section 4, Jinxing North Road, Wangcheng District, Changsha City, Hunan Province, Changsha 412002, Hunan, China
Li Jiameng: Department of Spine Surgery, The Fourth Hospital of Changsha, No. 200, Section 4, Jinxing North Road, Wangcheng District, Changsha City, Hunan Province, Changsha 412002, Hunan, China
Zhou Weili: Department of Joint Surgery, Changsha Third Hospital, No. 176 Laodong West Road, Changsha City
Wu Yue: Department of Orthopedics, Beijing Chaoyang Hospital, affiliated with Capital Medical University, No. 8, Gongti South Road, Chaoyang District, Beijing
