This paper presents spline-based coupling methods for partitioned multiphysics simulations, specifically designed for isogeometric analysis (IGA) based solvers. Traditional vertex-based coupling approaches face significant challenges when applied to IGA solvers, including geometric accuracy issues, interpolation errors, and substantial communication overhead. The methodology draws on the IGA mathematical framework to deliver coupling solutions that preserve the high-order continuity and exact geometric representation of splines. We develop two complementary strategies: (1) a spline-vertex coupling method that enables efficient interaction between IGA and conventional solvers, and (2) a fully isogeometric coupling approach that maximizes accuracy for IGA-to-IGA communication. Both theoretical analysis and extensive numerical experiments demonstrate that our spline-based methods significantly reduce communication overhead compared to traditional approaches while simultaneously enhancing geometric accuracy through exact boundary representation and maintaining higher-order solution continuity across the coupled interfaces. We quantitatively confirm the communication efficiency benefits through systematic measurements of both transfer times and data volumes across various mesh refinement levels, with experimental results closely aligning with our theoretical predictions. Our benchmark studies further demonstrate the geometric fidelity advantages through exact boundary representation, while also highlighting how the inherent mathematical structure of splines naturally preserves solution derivatives across interfaces without requiring additional computation or specialized transfer algorithms. This work not only provides efficient coupling strategies tailored to IGA-based solvers but also establishes a practical bridge between IGA and traditional discretization methods in partitioned multiphysics simulations. By offering viable options for coupling conventional solvers with IGA-based components, our approach enables broader adoption of IGA in established simulation workflows while ensuring accurate and high-performance interface communications.
Differential equation-driven evolution strategies are often associated with boundary-driven topology optimization methods, such as the level set method. However, differential equations can also be utilized effectively in density-based approaches. This paper presents a design update scheme formulated using differential equations to evolve elemental densities in topology optimization. The proposed scheme transforms the differential equation into an absolute increment format, closely related to the optimality criteria (OC) method, which is traditionally implemented in a relative increment format in density-based methods. The relative increment format of the OC method typically ensures an efficient and stable optimization process, whereas the absolute increment format tends to enable a more active and responsive optimization process, potentially leading to optimized results with improved performance. Furthermore, the absolute increment format can be converted into a relative one if needed. This study explores compliance minimization problems for both isotropic composite and single-material cases. Detailed MATLAB implementations for these cases are presented and thoroughly explained. Numerical examples demonstrate that the differential equation-driven update scheme effectively addresses density distribution optimization problems, offering an alternative to classical density methods.
Hearing loss is a sensory damage, a hidden disability that affects numerous adults globally. However, gesture recognition plays a significant role in overcoming several problems and challenges in human life, particularly for persons with hearing impairments and speech disabilities. The ability of machines to comprehend human actions and interpret sensory data is exploited across a wide range of applications. Automatic gesture recognition is used to present a response in a computerised learning environment to practice sign language for the deaf community. Deaf people worldwide face challenges conveying their feelings to others. To address this, a smart system powered by artificial intelligence and the Internet of Things was developed to improve communication for the deaf community. The large number of SLs hinders the application’s performance. Current progress in artificial intelligence and deep learning helps automate and enhance gesture recognition systems. This paper proposes an Assistive Communication System Using Deep Sparse Autoencoder with Feature Learning (ACS-DSAEFL) approach to aid the hearing disabled. The ACS-DSAEFL approach aims to develop an effective framework that enhances real-time communication and assistive interactions for people with hearing disabilities. Initially, the image pre-processing stage applies noise removal using a Gaussian filter (GF) and contrast enhancement via histogram equalisation to improve image quality. To achieve effective feature representation, the Inception-enhanced vision transformer (IEViT) model is employed to extract both local and global features. Finally, the stacked sparse denoising autoencoder (SSDAE) is implemented for classification. The comparison assessment of the ACS-DSAEFL technique showed an accuracy of 98.63% compared with other methods on the HaGRID dataset.
Due to limited motor capabilities, people with upper limb disabilities have trouble utilizing a typical mouse while operating a computer. Different wearable Assistive Mouse Controllers (AMCs) have been developed to overcome their challenges. However, these people may not be able to realize the importance, ease of use, and social approval of these AMCs due to their fear of new technology, lack of confidence, and lack of ingenuity. These may negatively affect their attitude and intention toward accepting AMCs for equitable human-computer interaction. This study presents the development of a sensor-based head-mounted AMC, followed by an empirical analysis of its acceptance using the Technology Acceptance Model (TAM) from the socioeconomic perspective of Bangladesh. In a similar vein, we examined the effects of three additional psychological constructs-technology anxiety, confidence, and innovation, on its acceptance along with the original components of the TAM. A total of 150 individuals with stroke-induced upper limb disability participated in an online survey, and their responses were analyzed using confirmatory factor analysis and structural equation modeling, following the general least square method. Analysis revealed, about 96.44% of the participants had positive attitude towards the AMC, and almost 88.56% of them had positive intentions to accept it. Furthermore, about 68.61% of them expressed signs of anxiety, 96.35% were confident, and 94.16% of them had an innovative mindset in terms of device usage. The findings imply that individuals with an innovative mentality are more capable of comprehending the practical implications of a new technology than those without one. It is also feasible to reduce technological anxiety and boost a user's confidence while using an AMC by combining an innovative mentality with straightforward device interaction techniques. Additionally, peer encouragement and motivation can significantly enhance their positive attitude towards accepting the AMC for facilitating their interaction with a computer.
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these complex materials with high accuracy while satisfying physics-based constraints. However, most of these approaches disregard the uncertainty in the estimates and the spatial heterogeneity of these materials. In this work, we leverage recent advances in generative models to address these issues. We use as building block neural ordinary equations (NODE) that - by construction - create polyconvex strain energy functions, a key property of realistic hyperelastic material models. We combine this approach with probabilistic diffusion models to generate new samples of strain energy functions. This technique allows us to sample a vector of Gaussian white noise and translate it to NODE parameters thereby representing plausible strain energy functions. We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries. We extensively test our method with synthetic and experimental data on biological tissues and run finite element simulations with various degrees of spatial heterogeneity. We believe this approach is a major step forward including uncertainty in predictive, data-driven models of hyperelasticity.
Fast numerical predictions have become an indispensable component of modern engineering design workflows, whether in interactive design within computer-aided design (CAD) environments or in multi-query numerical tasks such as design optimization and uncertainty quantification. Depending on the context, "fast" may refer to near real-time predictions within a few seconds, or simply to methods that are significantly faster than high-fidelity simulations, for example those based on the finite element method (FEM). With the aim of providing a tool that not only enables such accelerated predictions but also integrates seamlessly into established workflows, we introduce the concept of IGANets. IGANets are spline-based, physics-informed machine learning models that can be integrated naturally between CAD representations and numerical analysis tools, particularly those based on isogeometric analysis (IGA). Unlike purely data-driven approaches, IGANets do not inherently rely on precomputed training data; instead, they are formulated in a collocation setting directly from physical models. In this paper, we present the IGANets concept and demonstrate its feasibility through numerical experiments for the Poisson equation and linear elasticity. In addition, we investigate a multi-instance linear-elasticity setting with varying I-beam-like geometries and boundary conditions in order to assess the generalization capability of the framework. The results show that IGANets can predict solutions for previously unseen problem instances within the training range with improved accuracy as the number of training samples increases.
In this paper, an accessible pipeline of automated teaching of the Quran to deaf and hard-of-hearing students is proposed based on the identification of Arabic Sign Language (ArSL) postures that match the words of S The piping includes an instructional method that is accessible to both deaf and hard-of-hearing students, necessitating the use of Arabic Sign Language postures to correspond to the words. A designed list of 2,054 labeled images was obtained with local institutions working with deaf users as guidance. In order to be linguistic and unambiguously semantically designated with Qur’anic terms, DIN 31,635 transliterations is used as a canonical internal representation of all class annotations, and Arabic forms are used to present them. Two complementary approaches are evaluated: (i) a pose-keypoint classification approach using MediaPipe features, trained with multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF) classifiers; and (ii) an image-based model utilizing a ResNet50 backbone. Performance evaluation is conducted using an internal train/validation/test split and assessed based on accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC metrics. Within this evaluation framework, the ResNet50-based model achieves near-perfect performance, while the keypoint-based models also demonstrate high performance. Such results are limited to the dataset and the evaluation protocol that were adopted and cannot be generalized as performance guarantees. To this end, we explicitly note the following constraints: a single sutra, a small dataset size, the use of fixed frames instead of continuous signing (and co-articulation not modeled), the possibility of bias of the signers and regional variants, and low input of the lower-body landmarks due to framing. Future directions will include more generalization by signer-independent evaluation, extension to a wider range of sūrahs and ArSL variants, continuous sign-language recognition, and on-device real-time implementation of an inclusive Qur’anic education.
This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University's Anvil supercomputer, the parallel adaptive anisotropic meshing method utilizes a hierarchical load balancing model (designed for large, cc-NUMA shared memory architectures) and contains an optimized local reconnection operation that performs three times faster than its original implementation from previous studies. While utilizing a new user-defined sizing function, we also show an adaptive isotropic method that generates meshes with good quality and fidelity of up to approximately 50 million elements in less than a minute while the adaptive anisotropic method is shown to generate approximately the same number of elements in about 5 min.
There is a new nature-inspired algorithm called salp swarm algorithm (SSA), due to its simple framework, it has been widely used in many fields. But when handling some complicated optimization problems, especially the multimodal and high-dimensional optimization problems, SSA will probably have difficulties in convergence performance or dropping into the local optimum. To mitigate these problems, this paper presents a chaotic SSA with differential evolution (CDESSA). In the proposed framework, chaotic initialization and differential evolution are introduced to enrich the convergence speed and accuracy of SSA. Chaotic initialization is utilized to produce a better initial population aim at locating a better global optimal. At the same time, differential evolution is used to build up the search capability of each agent and improve the sense of balance of global search and intensification of SSA. These mechanisms collaborate to boost SSA in accelerating convergence activity. Finally, a series of experiments are carried out to test the performance of CDESSA. Firstly, IEEE CEC2014 competition fuctions are adopted to evaluate the ability of CDESSA in working out the real-parameter optimization problems. The proposed CDESSA is adopted to deal with feature selection (FS) problems, then five constrained engineering optimization problems are also adopted to evaluate the property of CDESSA in dealing with real engineering scenarios. Experimental results reveal that the proposed CDESSA method performs significantly better than the original SSA and other compared methods.
This study presents a novel real-time Egyptian currency recognition system designed to assist visually impaired individuals in performing financial transactions independently and securely. The system leverages advanced deep learning models-YOLOv8, YOLOv9, and YOLOv10 to achieve high accuracy and low latency in identifying Egyptian banknotes. Evaluated on a comprehensive dataset of 2,000 annotated images, the models incorporate innovations such as context aggregation, GELAN, and NMS-free training to enhance performance. A review of prior systems highlights their limitations, especially concerning regional currencies. YOLOv10 achieved the best performance, with a precision of 0.9678, F1 score of 0.9715, and mAP@0.5 of 0.9934, surpassing both YOLOv8 and YOLOv9. Compared to traditional techniques, this approach offers significant improvements in accuracy and processing speed, providing a scalable and practical solution for accessible AI applications. These contributions promote financial independence and inclusion for visually impaired users, supporting ongoing advances in assistive technology.
Fluid-structure interaction (FSI) simulation of biological systems presents significant computational challenges, particularly for applications involving large structural deformations and contact mechanics, such as heart valve dynamics. Traditional arbitrary Lagrangian-Eulerian methods encounter fundamental difficulties with such problems due to mesh distortion, motivating immersed techniques. This work presents a novel open-source immersed FSI framework that strategically couples two mature finite element libraries: MFEM, a GPU-ready and scalable library with state-of-the-art parallel performance developed at Lawrence Livermore National Laboratory, and FEBio, a nonlinear finite element solver with sophisticated solid mechanics capabilities designed for biomechanics applications developed at the University of Utah and Columbia University. This coupling creates a unique synergy wherein the fluid solver leverages MFEM's state-of-the-art parallel performance, while the immersed solid exploits FEBio's comprehensive suite of hyperelastic and viscoelastic constitutive models and advanced solid mechanics modeling targeted for biomechanics applications. FSI coupling is achieved using a fictitious domain/distributed Lagrange multiplier methodology with variational multiscale stabilization for enhanced accuracy on under-resolved grids expected with unfitted meshes used in immersed FSI. A fully implicit, monolithic scheme provides robust coupling for strongly coupled fluid-solid interactions characteristic of cardiovascular applications. The framework's modular architecture facilitates straightforward extension to additional physics and element technologies. Several test problems are considered to demonstrate the capabilities of the proposed framework, including a three-dimensional semilunar heart valve simulation. This platform addresses a critical need for open-source immersed FSI software combining advanced biomechanics modeling with high-performance computing infrastructure. The online version contains supplementary material available at 10.1007/s00366-026-02371-9.
Interaction of particles with unsteady non-linear viscous flows has widespread implications in physiological and biomedical systems. One key application where this plays a fundamental role is in the mechanism and etiology of embolic strokes. Specifically, there is a need to better understand how large occlusive emboli traverse complex vascular geometries, and block a vessel disrupting blood supply. Existing modeling approaches resort to key simplifications in terms of embolic particle shape, size, and their coupling to fluid flow. Here, we devise a novel computational model for resolving embolus-hemodynamics interactions for large non-spherical emboli approaching near occlusive regimes in anatomically real vascular segments. The formulation relies on extending an immersed finite element approach, coupled with a six degree-of-freedom particle dynamics model. The geometric complexities and their manifestation in embolus-flow and embolus-wall interactions are handled using a parametric shape representation, and projection of vessel signed distance fields on the particle boundaries. We illustrate our methodology and algorithmic details, as well as present examples of benchmark cases and convergence of our technique. Thereafter, we demonstrate a parametric study of large emboli for Large Vessel Occlusion (LVO) strokes, showing that our methodology can capture the non-linear tumbling dynamics of emboli originating from their interactions with the flow and vessel walls; and resolve near-occlusive scenarios involving lubrication effects around the embolus and flow re-routing to non-occluded branches. This is a key methodological advancement in stroke modeling, as to the best of our knowledge this is the first modeling framework for LVO stroke and occlusion biofluid mechanics. Finally, even though we present our framework from the perspective of LVO strokes, the methodology as developed is broadly generalizable to two-way coupled fluid-particle interaction in unsteady viscous flows for a wide range of applications. The online version contains supplementary material available at 10.1007/s00366-026-02364-8.
Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp's amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods.
Numerical modelling of structural self-contact and crack propagation presents significant challenges due to the inherently discontinuous and non-differentiable nature of the underlying physical phenomena. Traditional contact models demand explicit definition and tracking of contact points, while fracture models often rely on predefined crack initiation sites, sharp interfaces, and re-meshing. This study introduces a novel framework that overcomes these limitations within a unified and numerically stable variational formulation. The contact phenomenon is described through the hyperelastic third medium contact model and fracture is represented by a phase field. Structures are embedded in a third medium that stiffens under compression, enabling the transfer of forces between structural members. Crack propagation occurs in regions in which it is energetically favourable for the system to evolve toward a fully damaged state, specifically where the critical energy release rate is exceeded. Careful treatment is required when coupling the two phenomena, particularly concerning the void material behaviour. This work presents an efficient and differentiable numerical model that captures both nonlinear phenomena within a unified framework. This framework will allow designers and engineers to efficiently analyse complex nonlinear structural behaviours, previously requiring separate models that involved pre-defined crack initiation sites and contact points. Lastly, the differentiable nature of the model facilitates straightforward future integration into topology optimisation pipelines, providing designers the ability to intentionally design for and leverage self-contact interactions and material failure as functional, performance-enhancing features.
Modern wheelchairs, with advanced and robotic technologies, could not reach the life of millions of disabled people due to their high costs, technical limitations, and safety issues. This paper proposes a gesture-controlled smart wheelchair system with an IoT-enabled fall detection mechanism to overcome these problems. It can recognize gestures using Convolutional Neural Network (CNN) model along with computer vision algorithms and can control the wheelchair automatically by utilizing these gestures. It maintains the safety of the users by performing fall detection with IoT-based emergency messaging systems. The development cost of the overall system is cheap and is lesser than USD 300. Hence, it is expected that the proposed smart wheelchair should be affordable, safe, and helpful to physically disordered people in their independent mobility.
Alzheimer's disease is characterised by the spreading of misfolded proteins and progressive structural changes in the brain. Despite significant clinical research, understanding how microscopic protein dynamics translate into macroscopic tissue degeneration remains a major challenge. In this work, we present a three-dimensional, finite element-based computational framework to model disease progression by combining multi-protein transport and brain tissue deformation within anatomically realistic geometries. The propagation of toxic tau and amyloid-[Formula: see text] proteins is described using reaction-diffusion equations of the Fisher-Kolmogorov type, incorporating prion-like growth mechanisms and anisotropic transport along white matter fibre tracts. Brain atrophy is represented through a hyperelastic constitutive model driven by protein-dependent volume loss. Subject-specific simulations are achieved through an automated preprocessing pipeline that generates finite element meshes and reconstructs axonal orientation fields from medical imaging data. The model reproduces key morphological patterns observed in Alzheimer's disease and shows good quantitative agreement with longitudinal imaging measurements. Overall, the proposed framework offers an extensible computational platform for studying Alzheimer's disease progression across subject-specific brain geometries. The models developed, including the image processing framework (BrainImage2Mesh) and the coupled bio-chemo-mechanical COMSOL finite element implementation, are made freely available to download at https://mechmat.web.ox.ac.uk/codes. The online version contains supplementary material available at 10.1007/s00366-026-02313-5.
Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. Today's finite element analysis packages come with a set of pre-programmed material models, which may exhibit restricted validity in capturing the intricate mechanical behavior of these materials. Regrettably, incorporating a modified or novel material model in a finite element analysis package requires non-trivial in-depth knowledge of tensor algebra, continuum mechanics, and computer programming, making it a complex task that is prone to human error. Here we design a universal material subroutine, which automates the integration of novel constitutive models of varying complexity in non-linear finite element packages, with no additional analytical derivations and algorithmic implementations. We demonstrate the versatility of our approach to seamlessly integrate innovative constitutive models from the material point to the structural level through a variety of soft matter case studies: a frontal impact to the brain; reconstructive surgery of the scalp; diastolic loading of arteries and the human heart; and the dynamic closing of the tricuspid valve. Our universal material subroutine empowers all users, not solely experts, to conduct reliable engineering analysis of soft matter systems. We envision that this framework will become an indispensable instrument for continued innovation and discovery within the soft matter community at large.
Understanding the mechanical behaviour of defective materials is key to predicting failure and enhancing performance. Traditional fracture mechanics often requires assumptions about geometry and loading that are unavailable in experimental systems. We present a MATLAB-based computational toolbox that extracts configurational forces and mixed-mode SIFs directly from experimentally measured displacement or deformation gradient fields, like digital image/volume correlation and high (angular) resolution electron backscatter diffraction. The toolbox implements path-independent energy integrals, including the J- and M-integrals, and introduces a novel mode decomposition formulation that isolates mode I-III SIFs contributions without predefined specimen geometries, applied loads, or boundary conditions. Applications to microcracks, dislocations, and fatigue cracks demonstrate its robust, geometry-independent characterisation, which can enable data-driven analysis of defect behaviour in anisotropic and complex materials. The framework is material-agnostic in principle and operates directly on experimental fields; however, its current implementation assumes small-strain kinematics, making it most applicable to linear and anisotropic elastic and elastoplastic materials such as metals and ceramics. The online version contains supplementary material available at 10.1007/s00366-025-02262-5.
Weak imposition of essential boundary conditions (i.e., weak BCs) for the Navier-Stokes equations of incompressible flows allows a certain amount of controlled numerical flow slip on the solid surface. Numerical flow slip mimics the presence of a thin boundary layer that would otherwise need to be captured using a fine mesh resolution. As a result, weak BCs enable the use of coarser meshes near solid walls without sacrificing numerical solution accuracy, which significantly reduces the computational costs, especially for 3D, wall-bounded turbulent flows. However, weak BCs for compressible flows are not as well understood as those for the incompressible-flow case. In particular, numerical instabilities were observed in some cases where the weak BCs were simultaneously imposed for the velocity and temperature fields. In the present effort, to address these stability issues, we develop a methodology for the design of compressible-flow weak BC operators and demonstrate the improved performance of the resulting weak BC formulations using challenging 2D and 3D test cases.
Visually impaired individuals actively utilize devices like computers, tablets, and smartphones, due to advancements in screen reader technologies. Integrating freely available deep learning models, image captioning can further enhance these readers, providing an affordable assistive tech solution. This research outlines the critical software requirements necessary for image captioning tools to effectively serve this demographic. Two qualitative investigations were conducted to determine these requirements. An online survey was first conducted to identify the main preferences of visually impaired users in relation to audio descriptive software, with findings visualized using word clouds. A subsequent study evaluated the proficiency of existing deep learning captioning models in addressing these stipulated requirements. Emphasizing the need for comprehensive image data, the results highlighted three primary areas: 1) characteristics of individuals, 2) color specifics of objects, and 3) the overall context of images. The research indicates that current captioning tools are not entirely effective for the visually impaired. Based on the delineated requirements and suggested future research paths, there is potential for the development of improved image captioning systems, advancing digital accessibility for the visually impaired.