Paper Title

A MATHEMATICAL PERSPECTIVE ON DYNAMICAL CHARACTERISTICS OF BLOOD FLOW IN HUMAN ARTERIES

Abstract

The effects of an overlapping stenosis on blood flow characteristics in an artery have been studied. Blood has been represented by a couple stress fluid is observed that the resistance to the flow, pressure drop and wall shear stress increases with height and length of the stenosis. And it is noticed that the resistance to the flow and pressure drop decreases with couple stress fluid parameters. The effect of different physical characteristics on liquid impedance to flowing, velocity, and surface shearing stress are studied. Herschel–Bulkley equation has been taken to represent the non-Newtonian character of blood. The problem is investigated by a combined use of analytical and numerical techniques. The shape, length and height of the stenosis in an artery under various flow conditions play a pivotal role. Research has been carried out by many researchers on the nature of geometry of the stenosis in the artery, nature of the blood and its flow. Finite difference scheme has been used to solve the unsteady nonlinear Navier–Stokes equations in cylindrical coordinate’s system governing flow assuming axial symmetry under laminar flow condition so that the problem effectively becomes two-dimensional. The relations for surface shearing stress, pressure drop, flow resistance, and velocity are calculated analytically by applying a mild stenosis approximation.
KEYWORDS : Blood flow, flow resistance, Herschel–Bulkley, flowing, velocity, two-dimensional.

Paper Title

A HYBRID MACHINE LEARNING APPROACH FOR BONE FRACTURE DETECTION IN RADIOGRAPHIC IMAGES

Abstract

Accurate detection and classification of bone fractures from radiographic (X-ray) images is essential for clinical diagnosis, emergency treatment planning, orthopedic workflows, and automated medical decision support systems. Manual diagnosis is often challenging due to overlapping anatomical structures, subtle fracture lines, variable image quality, and inter observer differences among radiologists. Traditional machine-learning approaches rely on handcrafted features, which often fail to generalize across diverse fracture types and imaging conditions. This survey paper reviews existing fracture detection methodologies and evaluates deep-learning-based classification models. Special emphasis is given to the model, a hybrid transfer learning architecture that integrates Dense Net’s feature reuse with Inception’s multi-scale pattern extraction. Its architecture, workflow, accuracy, and clinical applicability are thoroughly discussed. Bone fractures are among the most common global health problems and require rapid and accurate diagnosis to prevent complications and reduce healthcare costs. Traditional diagnostic methods rely on the manual examination of X-ray images by experienced radiologists, a process that is time-consuming and prone to human error, especially in emergency departments with high patient volumes. This study proposes a lightweight hybrid approach that integrates deep learning–based feature extraction with classical machine learning algorithms for automatic bone fracture detection. These findings confirm that combining deep feature extraction with ensemble learning can effectively support automated diagnostic systems for bone fracture detection, enabling healthcare professionals to make faster and more reliable decisions.
KEYWORDS : Bone fracture detection, Medical imaging, Radiographs, Transfer learning, hybrid transfer learning

Paper Title

A MACHINE LEARNING FRAMEWORK FOR PROACTIVE NETWORK DIAGNOSIS AND OPTIMIZATION

Abstract

Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machine learning-based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. The exponential growth of data-intensive applications and heterogeneous network architectures has increased the demand for intelligent systems capable of predicting network performance and optimizing data flow in real time. Traditional static models are inadequate for addressing dynamic network conditions, latency variations, and bandwidth fluctuations. This paper presents a comprehensive review of existing methodologies and proposes a conceptual machine learning framework for predictive network performance and data flow optimization. The framework integrates supervised and reinforcement learning models with deep neural architectures to forecast traffic patterns, congestion probabilities, and throughput variations. defined networking (SDN), and edge intelligence for adaptive traffic routing and self-healing network behaviors.
KEYWORDS : Machine learning framework, Predictive Analytics, Network Performance Optimization, Software-Defined Networking

Paper Title

APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CLINICAL OUTCOMES FROM HEALTHCARE DATA

Abstract

Machine learning (ML) has emerged as a transformative force in healthcare, enabling more accurate and timely prediction of patient outcomes by analyzing complex and heterogeneous datasets. By leveraging diverse data sources, ML algorithms uncover subtle patterns and risk factors often missed by traditional statistical methods. This paper comprehensively explores the pivotal role of machine learning in healthcare outcome prediction, emphasizing a patient-centric approach that integrates individual patient characteristics and preferences to tailor care effectively. discussion spans the variety of ML techniques employed, the types of clinical and non-clinical data utilized, and specific applications across medical specialties that enhance clinical decision-making and enable proactive interventions. It also critically addresses challenges such as data privacy, model transparency, bias, and integration hurdles in clinical workflows. Finally, the paper outlines future directions where advancements in explainable AI, federated learning, and patient engagement can further refine predictive models to improve healthcare delivery and outcomes. The integration of machine learning (ML) into healthcare has revolutionized the prediction of patient outcomes, offering improved accuracy, speed, and personalization in clinical decision making. ML algorithms can analyze large volumes of patient data, including electronic health records (EHRs), genetic profiles, imaging data, and clinical notes, to detect patterns that might escape traditional statistical methods.
KEYWORDS : Machine Learning, Healthcare Outcomes, Predictive Models, Patient-Centric Care, Clinical Decision Support

Paper Title

EFFECT OF MATERIAL EXTRUSION PARAMETERS ON THE MECHANICAL PERFORMANCE OF AUXETIC CELLULAR STRUCTURES

Abstract

The growing demand for lightweight, high-performance materials has led to increased interest in auxetic cellular structures, which exhibit a negative Poisson’s ratio and unique deformation behavior. This study investigates the mechanical performance of 3D printed auxetic cellular structures fabricated using material extrusion techniques, commonly known as fused deposition modeling (FDM). Auxetic geometries, such as re-entrant honeycomb and chiral structures, were designed and manufactured using thermoplastic materials to evaluate their structural response under various loading conditions. The research focuses on analyzing key mechanical properties, including compressive strength, stiffness, energy absorption, and deformation characteristics. Specimens were fabricated with varying printing parameters such as layer height, infill density, and printing orientation to assess their influence on mechanical performance. Experimental testing was conducted using universal testing machines, and the results were compared with numerical simulations to validate the behavior of the auxetic designs. The findings demonstrate that auxetic structures exhibit enhanced energy absorption capacity and improved resistance to deformation compared to conventional cellular structures. Additionally, the study highlights the critical role of material extrusion parameters in determining structural integrity and mechanical efficiency. The optimized auxetic designs show potential for applications in impact protection, biomedical implants, aerospace components, and flexible engineering systems. This research contributes to the understanding of the relationship between design geometry, manufacturing parameters, and mechanical performance of 3D printed auxetic structures, providing a foundation for future advancements in additive manufacturing and smart material design.
KEYWORDS : Auxetic cellular structures, mechanical performance, material extrusion techniques, auxetic designs

Paper Title

EFFECT OF MATERIAL EXTRUSION PARAMETERS ON THE MECHANICAL PERFORMANCE OF AUXETIC CELLULAR STRUCTURES

Abstract

The growing demand for lightweight, high-performance materials has led to increased interest in auxetic cellular structures, which exhibit a negative Poisson’s ratio and unique deformation behavior. This study investigates the mechanical performance of 3D printed auxetic cellular structures fabricated using material extrusion techniques, commonly known as fused deposition modeling (FDM). Auxetic geometries, such as re-entrant honeycomb and chiral structures, were designed and manufactured using thermoplastic materials to evaluate their structural response under various loading conditions. The research focuses on analyzing key mechanical properties, including compressive strength, stiffness, energy absorption, and deformation characteristics. Specimens were fabricated with varying printing parameters such as layer height, infill density, and printing orientation to assess their influence on mechanical performance. Experimental testing was conducted using universal testing machines, and the results were compared with numerical simulations to validate the behavior of the auxetic designs. The findings demonstrate that auxetic structures exhibit enhanced energy absorption capacity and improved resistance to deformation compared to conventional cellular structures. Additionally, the study highlights the critical role of material extrusion parameters in determining structural integrity and mechanical efficiency. The optimized auxetic designs show potential for applications in impact protection, biomedical implants, aerospace components, and flexible engineering systems. This research contributes to the understanding of the relationship between design geometry, manufacturing parameters, and mechanical performance of 3D printed auxetic structures, providing a foundation for future advancements in additive manufacturing and smart material design.
KEYWORDS : Auxetic cellular structures, mechanical performance, material extrusion techniques, auxetic designs

Paper Title

A MULTISCALE FEM FRAMEWORK FOR PREDICTING PROGRESSIVE FAILURE IN AEROSPACE COMPOSITE MATERIALS

Abstract

Failure in composite materials is known to initiate at the level of constituents. Further development of the failure process and ultimate failure of the composite structure depend on the fiber architecture and other geometrical details. A composite failure analysis must therefore be at multiple length scales. This paper will outline a multiscale modeling scheme and illustrate the approach with two examples: tensile fiber failure and transverse matrix cracking. For the case of fiber failure, a five-cylinder axisymmetric finite element model containing an initially broken fiber at the center will be used to conduct stress analysis and formation of a failure plane will be simulated by a crack growth procedure. The transverse crack formation will be analyzed as a linking up of fiber-matrix de bond cracks. Formation of these cracks will in turn be analyzed by an energy-based criterion. The local scale modeling will take account of the manufacturing induced irregularities and defects by appropriate representation of these in the failure analysis. Finally, an assessment of the multiscale approach as a rational alternative to the currently used failure theories, which are formulated on homogenized composites, will be presented and the challenges remaining to address in future will be outlined. This manuscript presents a multiscale stochastic failure modeling approach for fiber rein-forced composites. A homogenization based reduced-order multiscale computational model is employed to predict the progressive damage accumulation and failure in the composite.
KEYWORDS : Composite Failure, Multiscale Modeling, finite element model, Failure Theories

Paper Title

SYNTHESIS OF PHOTO-RESPONSIVE LIQUID CRYSTAL ELASTOMER ACTUATORS VIA UV-INDUCED CROSSLINKING TECHNIQUES

Abstract

Liquid crystal elastomers represent a versatile class of polymer materials with potential applications in soft robotics, adhesives, and smart materials. The integration of photo responsive molecules into LCEs enables spatiotemporal control, wavelength-selective actuation and remote operations, expanding their application space. However, the incorporation of sensitive photo responsive molecules is often hindered by the chemical methods and processing conditions required for the LCE fabrication. In this work, we introduce a general strategy for covalent incorporation of photo responsive moieties into LCEs through Diels–Alder chemistry utilizing late-stage functionalization. This approach facilitates the retention of material alignment and ther momechanical properties, while enabling the functionalization of thick, aligned polysiloxane elastomers. A wide range of photo responsive molecules, including azobenzenes, spirogyras, cyanine dyes, and donor–acceptor Ste house adducts, were successfully integrated, demonstrating this method\'s versatility. Furthermore, we leverage the reversible nature of Diels–Alder conjugation to achieve on-demand editing and exchange of photo responsive moieties within a single LCE, allowing for dynamic tuning of material properties. This platform offers a scalable and efficient route for developing multifunctional LCEs, providing new opportunities for advanced stimuli-responsive materials and broadening the scope of applications across various fields.
KEYWORDS : Liquid crystal elastomers, soft robotics applications across various fields photo responsive molecules,