Atherosclerosis, often termed the “silent killer,” progresses unnoticed until significant arterial buildup has occurred, underscoring the importance of early detection, prediction, and treatment. Especially around the carotid artery bifurcation, atherosclerotic plaque deposits frequently lead to disruptions in blood flow. Computational fluid dynamics (CFD) and fluid–structure interaction (FSI) models have been used to study the fluid dynamics of these disruptions in great detail. Yet, these numerical techniques have not been used to fully analyze the elastic response of plaques to hemodynamic stresses of the carotid artery. In order to examine the biomechanics of blood flow through calcified plaque deposits with elastic properties in an idealized 3D carotid sinus model, this work uses a two-way FSI approach utilizing the Arbitrary Lagrangian–Eulerian technique. Total deformation, von-Mises stress on plaques, flow velocity, and WSS-based parameters were among the metrics that were examined and contrasted with a model of a healthy artery. Atherosclerotic obstruction drove notable changes in vessel deformation, an FSI-specific metric, close to the bifurcation apex. Around the carotid sinus, elevated concentrations of velocity, oscillatory shear index (OSI), and wall shear stress (WSS) were particularly prominent. These findings suggest a potential atherosclerotic expansion, with rising OSI reflecting an increased risk of further stenosis or thrombus formation. In the event of plaque rupture, the elevated velocity and WSS levels near the sinus could further exacerbate these risks.
A novel concept of bipolar fuzzy shadowed numbers (BFSNs) is presented in this paper to address the uncertain behaviour in the parameters of the classical transportation problem. In this problem, the negative component captures aspects of dissatisfaction, such as penalties that arise from delays, spoiling, or unsatisfied demand, while the positive component represents aspects of satisfaction, such as feasible and timely deliveries. For analysing BFSNs, arithmetic operations including addition, subtraction and scalar operations are formally described. Then, two deshadowing approaches are presented to deal with the transportation problem based on BFSNs. Approach I is component-based approach that solves the positive and negative components separately after deshadowing the BFSNs. Approach II is an aggregation-based method that utilizes a weighted deshadowing function to combine both components into a single crisp value. An application of BFSNs is exhibited through the transportation of medical goods. Analysis of the obtained results from both the approaches is performed, and their effect on transportation decisions is discussed as well. It has been observed through the obtained results that the bipolar structure is preserved in Approach I, while Approach II yields a single optimized crisp value by aggregation.
White matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI) plays an important role in neurosurgical planning. In spite of many improvements in U-Net architecture, the existing methods fail to perform the delineation of white matter tracts efficiently due to dependence on intermediate computations resulting in errors. The multimodal data is used to segment major white matter tracts and study the effect of glioma on white matter using diffusion coefficients. The attention gating mechanism is employed along with the 3D-Vnet emphasizing on the focussed white matter regions. Each Attention gate(AG) module associated with the decoder part of the Vnet extracts complementary information from the gating signal. The method is applied to pairs of T1-weighted (T1w) and principal direction of diffusion (PDD) maps. The validation is provided on two different dataset viz. the Sheba75 and Human Connectome Project (HCP). The proposed method encompasses the focal loss to handle class imbalance issues and for the efficient prediction of true positives (TP). The proposed AGVnet achieved a mean Dice score of 0.854 on the HCP dataset, demonstrating superior performance over existing segmentation methods such as 3D U-Net and TractSeg. Also, the tract analysis is carried out to study the impact of Glioma tumor through diffusion coefficients such as FA, MD, RD, and AD values in both normal and tumorous patients. The decrease in FA and RD values is observed mainly due to the effect of the tumor on white matter tracts. A novel deep learning-based approach, AGVnet, for accurately delineating white matter tracts on diffusion tensor imaging. The selection of essential features at both the channel and spatial levels leads to the achievement of better segmentation accuracy, especially in complex cases where small and intricate white matter tracts are involved.
This paper presents a rigorous performance evaluation of an all-photonic format conversion framework designed to transition a 40-Gbps return-to-zero on–off keying (RZ-OOK) data stream directly into a binary phase-shift keying (BPSK) format using a nonlinear optical loop mirror (NOLM). Operating entirely within the photonic transport layer, the proposed subsystem exploits cross-phase modulation (XPM) inside a 500 m highly nonlinear fiber (HNLF) loop to achieve a precise nonlinear π -phase shift at a calibrated control pump peak power of 21.5 dBm. To validate its operational viability under realistic wavelength division multiplexing (WDM) deployment constraints, the continuous-waveform propagation is simulated under severe localized inter-symbol interference (ISI), chromatic dispersion, and stochastically distributed amplified spontaneous emission (ASE) noise floors. The performance of the restored constellation space is benchmarked via decision-directed tracking, demonstrating a highly compliant corrected signal Error Vector Magnitude (EVM) of 12.44
This study investigated whether different OpenSim musculoskeletal models preserve the intrinsic multivariate structure of sagittal-plane gait kinematics in clinical populations. Four publicly available clinical gait datasets were analyzed, including post-stroke, hip osteoarthritis, fall-risk, and vestibulopathy cohorts. Marker trajectories were standardized and processed using four musculoskeletal models (Gait2354, Gait2392, Rajagopal, and Lai-Uhlrich). Following quasi-static scaling and inverse kinematics, hip flexion, knee flexion, and ankle dorsiflexion trajectories were time-normalized to one gait cycle. Representational consistency was evaluated using Dynamic Time Warping (DTW), principal component analysis (PCA), and nonlinear manifold embedding. Median DTW distances indicated negligible waveform distortion between the closely related Gait2354 and Gait2392 models, moderate differences between the Gait models and Rajagopal or Lai-Uhlrich models which varies from 0.19 to 0.42, and generally lower distortion between Rajagopal and Lai-Uhlrich which varies from 0.05 to 0.29. Distal joints exhibited larger divergence, with ankle median DTW distances reaching 0.66 in the fall-risk cohort. PCA showed that the first principal component frequently explained more than 80
Reliable prediction of fatigue lifetime in concrete infrastructure subjected to variable-amplitude loading remains a fundamental challenge in computational mechanics, particularly in arid environments where harsh climatic conditions, limited experimental data, and evolving infrastructure demands coexist. In Saudi Arabia, large-scale investments in transportation, energy, and sustainable urban development further amplify the need for predictive tools that are both computationally scalable and mechanically admissible. This work develops a variational, operator-based physics-based machine learning ( ϕ ML) framework for fatigue lifetime prediction that rigorously bridges fatigue damage mechanics and data-driven surrogate modeling. Fatigue lifetime is formulated as the output of a nonlinear operator acting on loading histories and environmental descriptors and is approximated within an explicitly constrained admissible operator space. Thermodynamic consistency, irreversibility of damage evolution, boundedness of internal variables, and nonnegative mechanical dissipation are enforced through an explicit variational constraint formulation at the operator level. The resulting framework provides a unified admissibility-preserving approach for fatigue lifetime prediction while complementing existing physics-informed and thermodynamics-informed learning methodologies. A hybrid operator decomposition is introduced, combining a low-fidelity mechanistic fatigue predictor with a data-adaptive correction operator. This structure preserves physical interpretability in data-scarce regimes while providing a flexible representation of sequence-dependent and environment-coupled fatigue effects under variable loading. To demonstrate practical applicability, the framework is evaluated under environmental scenarios representative of Saudi Arabian infrastructure systems. These scenarios are defined using SBC 304 exposure classifications together with publicly available climatic datasets and are employed solely to characterize representative benchmark conditions rather than to impose code-specific constraints within the governing mathematical formulation. Numerical benchmark studies conducted using computational reference datasets generated from the governing fatigue damage model indicate that the proposed operator-based ϕ ML framework can achieve lower prediction errors, reduced worst-case deviations, and improved satisfaction of admissibility constraints relative to the benchmark methods considered in this work. The reported results should therefore be interpreted as methodological benchmarking outcomes against a common computational reference model rather than as experimental validation against physical fatigue test data. Within the numerical scenarios investigated, the proposed framework exhibits comparatively stable predictive performance under reduced data regimes, suggesting potential suitability for fatigue assessment problems where available training data are limited. By unifying variational principles, fatigue mechanics, and operator-based learning, this study provides a physically informed computational framework for fatigue lifetime assessment and may serve as a basis for future integration with digital twin and infrastructure-monitoring technologies.
The present investigation deals with the study of thermo-fluidic behavior of copper–alumina/water (Cu–Al2O3/H2O) HNF over a permeable stretching sheet with the influence of Darcy–Forchheimer drag. The synergistic dispersion of different NFs in the base liquid greatly enhances the overall thermal conductivity of the base liquid. So, hybrid nanofluids are attractive candidates for applications such as precision coating, polymer processing and microscale heat management. The research model assumes thermal conductivity, Prandtl number and viscosity to describe realistic thermophysical behavior. The flow controlling nonlinear PDEs are transformed into the corresponding ODEs by using the similarity transformations and solved by two approaches, i.e., SCMLW (Spectral Collocation Method with Legendre Wavelets) and bvp4c solver that allow the direct validation of the results. The present study has important applications in polymer extrusion, porous heat exchangers, geothermal systems, microelectronic cooling, aerospace thermal management, biomedical devices, and packed-bed reactors where efficient heat transfer and porous medium transport are essential. The numerical results reveal that increasing the Darcy–Forchheimer number from Fr = 0.1 to 1.5 enhances the fluid velocity and reduces the thermal boundary layer thickness, while the skin friction coefficient decreases from approximately − 0.116 to − 0.153 due to nonlinear inertial drag effects. It is also observed that the hybrid nanofluid retains thermal energy for a longer duration compared to mono nanofluids, indicating superior heat transfer capability. Unlike previous studies that often assume constant properties or neglect nonlinear inertial drag, the present work includes viscous dissipation and validates results using both SCMLW and MATLAB’s bvp4c solver, providing reliable benchmark solutions and new insights into coupled momentum and heat transfer phenomena.
The phenomenon of two-phase flow, where a liquid jet is issued from a central nozzle and atomised by the high-speed annular gas stream in a coaxial setup, is considered in this numerical study. The purpose of this paper is to investigate primary jet breakup and jet unsteadiness during the coaxial airblast atomization process. The present simulation aims to establish a better understanding of the flapping phenomenon of the liquid jet. In this work, jet instabilities were characterised at two different axial locations downstream of the atomiser exit. In addition to that, the turbulence statistics were analysed in a two-phase flow. Furthermore, the state of turbulence in the various regimes of the jet flow was studied using anisotropic invariant maps. The DDES (delayed detached eddy simulation) technique is adopted to compute the gas–liquid flow, while the volume of fluid technique is used to capture the gas–liquid interface. The simulation results have shown good qualitative and quantitative agreement with the in-house experimental observations. The influence of fluid flow through the central nozzle on the turbulence statistics is examined by comparing the current two-phase case with a hypothetical case, where air flows through the annular passage, and liquid flow through the central nozzle is absent.
This paper develops Thinai coherence geometry for finite systems of local observations which are required to lift to a single global interpretation. For a finite case, we define its coherence complex to be the family of all report-index subfamilies whose constraints admit a common lift. This complex converts coherence, defect, and repair into one object: defect circuits are minimal nonfaces, maximal coherent subfamilies are facets, and inclusion-minimal deletion repairs are complements of facets. We prove a Helly bound for tame lift-region geometries and a realization theorem showing that finite Sperner hypergraphs with no singleton edges occur as defect-circuit hypergraphs for Boolean endpoint systems with non-box constraints. Weighted repairs form polyhedral chambers, and matroidal coherence complexes give a greedy repair theory. Temporal coherence is reduced to ordinary coherence over discrete finite-horizon admissible path space. Positive observation channels, pullback consensus, central gates, typed adjudication, and coherence excess are then organized as structural enrichments of the same liftability invariant.
Variable-order fractional calculus has emerged as a powerful modeling paradigm for systems whose memory and hereditary properties evolve dynamically over time. This paper presents a comprehensive study of variable-order fractional differential equations with variable coefficients and time-delay terms, where the fractional derivative is defined in the Caputo sense. The presence of both variable order and delay introduces non-trivial coupling effects that require careful analytical treatment. We rigorously establish Ulam–Hyers stability for the proposed system, confirming that solutions exhibit robust behavior under small perturbations in initial data and forcing terms. To obtain approximate solutions, we implement the Adams–Bashforth–Moulton predictor-corrector algorithm, a multi-step method well suited to fractional-order systems due to its favorable convergence and efficiency properties. Extensive computational simulations are conducted across a range of variable-order functions, illustrating the influence of order variation on solution trajectories and validating the theoretical stability findings. The results affirm the practical effectiveness of the proposed framework for analyzing complex variable-order fractional delay systems arising in physics, engineering, and biological modeling.
This study investigates the impact of radiation and chemical reactions to the magneto-hydrodynamic (MHD) flow of a Williamson nanofluid over a stretched wedge under slip conditions, within a Darcy–Forchheimer porous medium. The model incorporates effects such as thermophoresis, mixed convection, Brownian motion, viscous dissipation, and suction/blowing, along with heat generation and radiation absorption, to provide a comprehensive representation of thermal and mass transport. Using similarity transformations, the governing equations are reduced to a system of nonlinear ordinary differential equations, which are solved using MATLAB’s bvp4c solver. Results are validated and presented through detailed tables and plots, highlighting the influence of key parameters on fluid behavior. Notably, increasing the Forchheimer number (Fr) enhances the flow velocity at χ = 0.2 but reduces it at χ = 2.2. Additionally, higher thermal slip and suction parameters lead to a decline in temperature. These insights offer potential applications in engineering and industrial systems involving MHD flows.
Subcutaneous methotrexate (SC-MTX) remains the first-line treatment for rheumatoid arthritis (RA) patients who have failed oral therapy, offering superior bioavailability and clinical efficacy. However, existing fixed-dosage protocols (7.5–25 mg weekly) fail to account for individual patient variability, resulting in 30–40
Localized muscle fatigue is an exercise-induced decline in the force-generating capacity of muscles. The patients with severe motor disabilities, such as tetraplegics, can use their facial muscles to control assistive devices. However, repeated use of these muscles often leads to fatigue which in turn alters the characteristics of facial EMG. The facial EMG signals are stochastic, nonstationary, and multicomponent, and their characteristic changes under fatiguing contractions are not yet well established. In this work, facial EMG signals are recorded from the left and right frontalis muscles of fifty healthy subjects under a standard experimental protocol. The first and last six-second segments of the signals correspond to nonfatigue and fatigue conditions, respectively. These signals are preprocessed and decomposed using a three-level maximum overlap discrete wavelet packet transform (MODWPT). The first two different wavelets are employed for the decomposition, namely Daubechies-4 (db4) and discrete Meyer (dmey). In order to quantify the time-scale representations, wavelet energy is extracted from all scales. Finally, these features are used to develop a multilayer perceptron (MLP) network for detecting the fatigue state. MODWPT is capable of representing the nonstationary and multicomponent variations of facial EMG under both fatigue and nonfatigue conditions. Wavelet energy is higher in the nonfatigue state across all scales and both the wavelets (p < 0.05). The MLP based on db4 achieves a maximum accuracy of 94.35
This article discusses a constrained Travelling Salesman Problem (TSP), in which the traveler determines the shortest route to take in order to place a limit on the total journey time and costs. In actual life, the length of a tour and its total cost might be scheduled. The goal of the proposed TSP is the expense of travel. It is a cost optimization based TSP. The overall cost of travel cannot be more than the proposed TSP’s total travel allowance and time ceiling. The expenses and duration of travel are regarded as type-2 fuzzy (T2F) variables. Using a defuzzification technique, we came across the crisp equivalency of fuzzy objective or fuzzy cost. An approach motivated by ant colony optimization (ACO) has been employed to solve the hypothesized TSP. To solve the suggested TSP, two features–"probabilistic selection" and "neighborhood path search"-have been added to the fundamental ACO. Furthermore, we have adopted a 2-optimal strategy for the ACO technique to obtain a better path quickly. A few common benchmark problem examples or datasets have been explored in order to illustrate the utility of the depicted approach. In addition, this paper computes a few benchmark cases that have been redefined in a random T2F circumstance.
Emerging anthropogenic micropollutants (EAMs) pose a significant threat to humans and the environment due to their persistence and resistance to the conventional wastewater treatment process. EAMs primarily originate from municipal, industrial, agricultural, and pharmaceutical sources and are increasingly detected in water resources worldwide. Advanced wastewater treatment technologies (AWTTs) offer a potential solution for effective removal of EAMs. This review overviews the recent advancements in AWTTs, highlighting innovative treatment approaches and materials like bioaugmentation, membrane bioreactor (MBR), adsorption and advanced oxidation processes (AOP). MBRs and adsorption achieve high removal efficiencies (90–99
Psychosocial stress while driving leads to driver inattention, a major contributing factor in many traffic accidents. Fast and accurate detection of a driver’s mental state is critical to mitigating health risks and preventing road crashes. Identifying driver inattention using wearables remains a significant challenge for advanced driver monitoring systems. In this study, we propose the use of Third-Order Cumulant (TOC) features for automated classification of driver inattention states using Textile Electrocardiogram (tECG) signals. ECG data was collected from 15 subjects using textile electrodes at 256 Hz during two scenarios: normal driving and driving while engaging in a phone call (inattention state). A 2D third-order cumulant matrix was computed from each ECG segment, and discriminative features were extracted using a 1D slice integration method. The extracted features were used to train classifiers including Support Vector Machine, Random Forest (RF), Decision Tree, and 1D Convolutional Neural Network (1D-CNN), evaluated using Leave-One-Subject-Out cross-validation. Results show that the proposed approach effectively identifies driver inattention states, with RF and TOC features yielding a weighted F1-score of 73.44
This study examines the radiative mixed convective flow of a hybrid nanofluid over an inclined, permeable, moving plate under the combined influence of thermophoresis, viscous dissipation, an externally applied magnetic field, and porous medium resistance modeled via the Darcy–Forchheimer relation. The governing nonlinear partial differential equations are reduced to a system of ordinary differential equations and solved numerically using the MATLAB bvp4c solver, with particular attention to entropy generation analysis. Results indicate that increasing the Forchheimer parameter (Fr) enhances inertial resistance, leading to significant suppression of velocity, temperature, and concentration profiles, while increasing the local Nusselt ( Nu_x ) and Sherwood ( Sh_x ) numbers. Higher Fr values reduce entropy generation ( N_s ) due to weakened velocity and thermal gradients, while slightly elevating the Bejan number (Be) in the near-wall region. A generative chemical reaction parameter ( K_c ) enriches species concentration and marginally raises N_s close to the surface, with minimal far-field impact. The findings demonstrate that porous medium inertia and chemical reactions can be effectively tuned to control heat and mass transfer rates in hybrid nanofluid systems, offering valuable design insights for advanced thermal management, energy conversion, and process engineering applications.
In this paper, the authors focused on recognizing tonsillectomy cases using statistical and acoustic measures together with different feature extraction techniques. The centroid, spread, skewness, kurtosis, jitter, shimmer, and F0 range analysis showed considerable differences between the pathological case and the normal case. Pathological cases in tonsillectomy showed noteworthy features that affect the speech signal, considered as an effect of the surgical process. Different feature extraction methods were examined in the study, including the linear prediction coding (LPC) and discrete wavelet transform (DWT), which was referred to as LPCFDWT, formants with DWT, which was termed as FADWT, and Mel-frequency cepstral coefficient (MFCC). The use of LPCFDWT with a fine Gaussian SVM classifier has a remarkable tonsillectomy recognition accuracy of 96.90
Agricultural price time series often exhibit complex, nonlinear, and nonstationary patterns, posing significant challenges for conventional forecasting methods. Although Empirical Mode Decomposition (EMD) is widely used for analyzing such data due to its adaptive and data-driven nature, it is inherently limited in handling bivariate data, as it cannot effectively capture the interdependencies between paired signals. To overcome this limitation, this study introduces a novel forecasting model that combines Bivariate Empirical Mode Decomposition (BEMD) with long short-term memory (LSTM) networks for interval-valued agricultural price forecasting. The model utilizes daily minimum and maximum potato prices from the Agra market, obtained from the ‘Agmarknet’ portal ( https://agmarknet.gov.in ), to construct an interval-valued bivariate time series. This series is transformed into a complex-valued signal, where the minimum (lower bound) and maximum (upper bound) prices represent the real and imaginary components, respectively. BEMD is then applied to decompose the signal into a set of Intrinsic Mode Functions (IMFs) and a residual component, each capturing distinct frequency characteristics. The real and imaginary parts of these decomposed components are extracted and modelled independently using LSTM networks. The individual forecasts of the IMFs and the residue are subsequently combined to produce the final interval forecasts. Comparative analysis reveals that the proposed BEMD–LSTM model significantly outperforms traditional EMD-based methods, as measured by Theil’s U statistic, Interval Mean Squared Error (IMSE), and Interval Mean Absolute Error (IMAE). These results underscore the enhanced capability of BEMD-based frameworks in capturing the dynamics of interval-valued agricultural price data, offering superior forecasting accuracy and robustness.
Wireless sensor networks (WSNs) are emerging as a vital technology for networking applications due to their wide range of applications and cost-effectiveness. Combinatorial design-based group key management provides a reliable and flexible security mechanism for secure group communication in WSNs by efficiently assigning the number of keys per node. In this work, we used a dataset of 400 designs from symmetric balanced incomplete block designs (SBIBD). The SBIBD is an arrangement of a finite set into subsets satisfying balance properties such as the set of elements being equal to the collection of blocks, the size of the block being equal to the number of repetitions of elements in a block, and every pair-wise element contains in exactly one block. The dataset was used to train four machine learning models: support vector machine, artificial neural networks, K-nearest neighbours, and extreme learning machine. We observed that among these four algorithms, the extreme learning machine demonstrated a higher accuracy in predicting re-keying values with the coefficient of determination, mean squared error, root mean squared error, and mean absolute error values, respectively.
