The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and loading parameters and simulated using finite element analysis with a Neo-Hookean constitutive model. Two complementary neural architectures were explored: graph neural networks (GNNs), which operate directly on resampled and feature-enriched boundary data, and convolutional neural networks (CNNs), which process image-based representations of undeformed and deformed cross-sections. The GNN models consistently achieved low root-mean-square errors (≈0.021) and stable correlations across training, validation, and test sets, particularly when augmented with displacement and directional features. In contrast, CNN models exhibited limited predictive accuracy: quarter-section inputs regressed toward mean values, while full-ring and filled-section inputs improved after Bayesian optimization but remained inferior to GNNs, with higher RMSEs (0.023–0.030) and modest correlations (R2). To the best of our knowledge, this is the first work to combine boundary deformation observations with graph-based learning for inverse load identification in hyperelastic vessels. The results highlight the advantages of boundary-informed GNNs over CNNs and establish a reproducible dataset and methodology for future investigations. This framework represents an initial step toward a new direction in mechanics-informed machine learning, with the expectation that future research will refine and extend the approach to improve accuracy, robustness, and applicability in broader engineering and biomedical contexts.
Since there is no market for bond issuance by companies in the Iraqi market and the difficulty of borrowing, companies must resort to proprietary financing to finance their investments. However, in the framework of the literature of financial management, the type of financing used by the company sends signals to investors and therefore reflected on the market value. Therefore, the problem of the study revolves around the variables of the study (Equity financing within the framework the signal theory, price of common stock in the Iraqi market).
The study aims to verify the impact of the capital increase through the issuance of new stock on the price of
... Show MoreThe concept of deficit in public budget becomes a chronic economic phenomenon in most of the world, whether the advanced countries or developing countries. Despite the difference in the visions of the economic schools to accept or reject the deficit in public budget but the opinion that prevailed is the necessity of the state to reduce the public spending which led to a continuous deficits in the public budget which consequently increased the government borrowing ,increase income taxes and wealth, consequently this weakened the in motivation in private investment which contributed to the increase of in factionary stagnation , so that governments have to cover the lack of local funding sources which become difficult to be eq
... Show MoreFrictional heat is generated when the clutch starts to engag. As a result of this operation the surface temperature is increased rapidly due to the difference in speed between the driving and driven parts. The influence of the thickness of frictional facing on the distribution of the contact pressure of the multi-disc clutches has been investigated using a numerical approach (the finite element method). The analysis of contact problem has been carried out for a multiple disc dry clutch (piston, clutch discs, separators and pressure plate). The results present the distribution of the contact pressure on all tShe surfaces of friction discs that existed in the friction clutch system. Axisymmetric finite element models have been developed to ac
... Show MoreIn this work, the effect of aluminum (Al) dust particles on the DC discharge plasma properties in argon was investigated. A magnetron is placed behind the cathode at different pressures and with varying amounts of Al. The plasma temperature (Te) and density (ne) were calculated using the Boltzmann equation and Stark broadening phenomena, which are considered the most important plasma variables through which the other plasma parameters were calculated. The measurements showed that the emission intensity decreases with increasing pressure from 0.06 to 0.4 Torr, and it slightly decreases with the addition of the NPs. The calculations showed that the ne increased and Te decreased with pressure. Both Te and ne were reduced by increasing
... Show MoreNon-thermal plasmas have become popular as plasma technology has advanced in various fields, including waste management, aerospace technology, and medicinal applications. They can be used to replace combustion fuels in stationary hall motors and need little effort to keep running for longer periods of time. To improve overall system performance, non-reactive gases such as )Xe, Ar, and Kr) are utilized in pure or mixed form to generate plasma. Since DC glow discharge is a fundamental topic of importance, these gases have been researched. The paper concentrates on 2-D modeling and simulation. DC glow-discharge tubes are utilized with argon gas to create plasma and learn about its properties. The magnitude of the electron density, increases wi
... Show MoreAnalyzing plantar pressure trajectories is crucial for assessing foot behavior in dynamic gait stability. We propose the identification of foot symmetry and the detection of deformities by analyzing the trajectories of the center of pressure (CoP) and peak pressure (PP). First, using a foot pressure mapping system, plantar pressure data are acquired during a normal gait cycle. After the data have been acquired, post processing extracts both the CoP and PP trajectories over the spatiotemporal domain of foot motion for each foot independently. For this purpose, we used the optical flow technique which accurately estimates the direction of foot motion. The extracted trajectories of each foot are then segmented into, the medial and lateral regi
... Show MoreIn order to understand the effect of the number of piles (N), the history response of dynamic pile load in piled raft system and deflection time history of piled raft under repeated impact load applied on the center of piled raft resting on loose sand, laboratory model tests were conducted on small-scale models. The results of experimental work are found to be dynamic load increase with increase height of drop, the measured repeated dynamic load time history on the center of piled raft was close approximately to three a half sine wave shape with small duration in about (0.015 Sec). The maximum peak of impact loads occurs in pile and deflection time history occur after at the time of the peak repeated impact loads, dynamic pile load
... Show MoreThis paper presents the study and analysis, analytically and numerical of circular cylindrical shell pipe model, under variable loads, transmit fluid at the high velocity state (fresh water). The analytical analysis depended on the energy observation principle (Hamilton Principle), where divided all energy in the model to three parts , strain energy, kinetic energy and transmitted energy between flow and solid (kinetic to potential energy). Also derive all important equations for this state and approach to final equation of motion, free and force vibration also derived. the relations between the displacement of model function of velocity of flow, length of model, pipe thickness, density of flowed with location coordinate x-axis and angle
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