Search Results (545)

Polymer hybrid materials have attracted considerable interest because combining different reinforcing constituents can provide improved mechanical performance, durability, lightweight characteristics, and structural efficiency. However, designing their behaviour remains challenging due to heterogeneous microstructures, constituent interactions, interfacial behaviour, reinforcement architecture, and manufacturing conditions. This review critically examines analytical, micromechanical, numerical, and reliability-based modelling approaches for polymer hybrid materials, covering their classification, mechanical characteristics, and predictive frameworks. Their applications in predicting tensile, flexural, and impact behaviour, damage, failure, and material degradation are discussed. The role of experimental characterization in determining material properties, model calibration, and numerical validation is also considered. Machine learning is emerging as a complementary tool for surrogate modelling within multiscale frameworks. The reviewed studies demonstrate variations between theoretical, numerical, and experimental results due to differences in material systems, fibre architecture, manufacturing and testing conditions, and modelling assumptions. This review highlights the need for reliable material datasets and unified modelling frameworks to improve predictive accuracy and support the design and optimization of polymer hybrid materials. Full article

Complex distribution-network topologies limit the accuracy of traveling-wave fault location, because the zero-mode velocity decays nonlinearly while conventional double-ended methods still require synchronized clocks. A clock-offset-invariant multi-terminal scheme combining mathematical morphology (MM), support vector regression (SVR), and particle swarm optimization (PSO) is proposed to remove dependence on double-ended synchronization and constant zero-mode velocity. SVR first fits the nonlinear mapping between fault distance and zero-mode velocity to establish a velocity-decay predictor. Backward fault-current traveling waves measured at multiple nodes are then processed through cycle subtraction and dynamic-window denoising, constrained by the underlying zero-mode physics. A morphological-gradient operator identifies the line- and zero-mode arrival times, from which a cross-dispersion sum-of-products equation that eliminates zero-mode velocity is constructed. Preliminary auxiliary-node solutions are obtained, and PSO performs a global search by minimizing the residual between the measured modal time difference and the theoretical value obtained from the SVR-predicted velocity. Weighted fusion yields the final fault distance. Simulations of a 35 kV distribution network in the power systems computer-aided design/electromagnetic transients including DC (PSCAD/EMTDC) environment show that the method maintains high accuracy at different fault locations and under high transition resistance within the tested parameter range, demonstrating adaptability and reliability under these conditions. Full article

The interaction of chemicals, nanoparticles, and circulating cells with the endothelium depends on the magnitude of the mechanical shear produced by the flow of blood. When simulating those interactions with microphysiological systems (MPSs), it is critical to reproduce those shear conditions faithfully. For example, unidirectional flow of a specific magnitude keeps the endothelium healthy with normal barrier tissue function, while bidirectional flow mimics disease conditions with compromised barrier function. Additionally, in MPS, recirculating fluid may be necessary to retain tissue-derived factors and metabolites. However, existing MPS designs struggle to achieve medium recirculation of small volumes of liquid with precise flow control. Here, we present an MPS design that is highly versatile and overcomes this limitation. We demonstrate how the device can produce a wide range of fluidic flow rates that can accommodate both low shear conditions suitable for tissues that typically are only exposed to interstitial flow and high shear conditions suitable for barrier tissues that experience blood flow. We demonstrate the device’s functionality by culturing human umbilical vein endothelial cells (HUVEC) and confirming their flow-aligned morphology through immunostaining of the adherens junction protein (VE-cadherin) and actin filaments. Furthermore, we present a mathematical model that can be used to calculate operating parameters for culturing any tissue under optimum conditions. We also discuss how the device can be adjusted to recirculate liquid volumes ranging from 100 µL to 5 mL. This versatile system holds promise for commercial applications, including the investigation of expensive compounds that are limited to very small volume samples such as rare cells (e.g., circulating tumor cells) or engineered therapeutic cells with barrier tissues. By offering precise control over a wide range of flow conditions with medium recirculation of small liquid volumes, our device addresses a critical gap in current MPS technology. Full article

I argue that the emerging sciences of synthetic morphology and diverse intelligence suggest non-physicalist models of mind and show how they can be empirically investigated. Whence the anatomical, physiological, molecular-biological, and behavioral properties of engineered new beings that have never before existed, and do not have a history of selection? Understanding, predicting, and guiding new forms of life and mind requires characterizing a structured latent space of patterns. Developmental, synthetic, and behavioral biology should take seriously, and exploit, the kinds of non-physicalist ideas that are already a staple of Platonist mathematics. I propose the following hypotheses. (1) Patterns in this space span a highly variable degree of agency, comprising a spectrum ranging from static truths studied by mathematicians to active ones studied by behavioral scientists (i.e., some patterns on the same spectrum as mathematical truths are kinds of minds). (2) The relationship between mind and body is the same as the relationship between causally instructive mathematical facts and physics. (3) Living beings have no monopoly on the “free lunches” provided by the ingression of these patterns into the physical world. While traditional computationalist views of living and cognitive systems are insufficient, my framework erases artificial distinctions between organisms and machines, framing all physical constructs (natural or engineered) as being, to various degrees, in-formed by patterns from the latent space. I sketch a research program, already begun, inspired by these ideas. Such frameworks, while contradicting long-held assumptions of both mechanists and organicists, could have many implications for evolutionary biology, regenerative medicine, AI, and the ethics of synthbiosis with the forthcoming immense diversity of morally important beings. Full article

Background/Objectives: Cardiac autonomic modulation, as assessed by heart rate variability (HRV), is a key indicator of physiological status. Although aging has traditionally been associated with reduced variability, morphological characteristics may be its main correlates. This study aimed to identify phenotypes of suppressed vagal modulation in women using a machine learning approach, specifically by correcting for HRV’s intrinsic mathematical dependence on heart rate (HR). Methods: Seventy-five women (30–69 years old) were recruited during fitness center enrollment. To control for hormonal fluctuations, the younger cohort (30–49 years old) was assessed during the follicular phase, whereas the older cohort (50–69 years old) was postmenopausal. Electrocardiogram data for HRV, bioimpedance, and anthropometric measurements were collected. Intrinsic vagal modulation was isolated by adjusting root mean square of successive differences (RMSSD) for the mean R–R interval (RMSSD_adj). The machine learning pipeline used LASSO for feature selection and multivariate logistic regression to identify suppressed vagal phenotypes (RMSSD_adj ≤ 0.0232). Model stability was verified using 1000 bootstrap iterations, and a cumulative Z-score index (ISCA) was used to characterize the morphologic–hemodynamic burden. Results: LASSO identified central adiposity, measured by the waist-to-hip ratio, as the primary independent correlate of suppressed vagal phenotypes (RMSSD_adj ≤ 0.0232). The model achieved an area under the curve (AUC)–ROC of 0.633 (95% CI: 0.490–0.771) with high specificity (0.900) and sensitivity of 0.360. No significant differences in intrinsic vagal modulation were observed between age cohorts (p > 0.05). However, women classified as having a high morphologic–hemodynamic burden (high-overload ISCA) exhibited a significantly higher resting heart rate, averaging 8.3 bpm more than the low-overload group (p < 0.05), reflecting a higher physiological demand in this phenotype. Conclusions: The autonomic status of women is characterized more accurately by morphological phenotypes than by chronological age. Integrated kinanthropometric monitoring is essential for identifying reduced autonomic resilience regardless of birth year. Full article

Against the background of increasing styling convergence in the new energy vehicle (NEV) market, rear-end styling has gradually become a key visual interface for communicating brand identity, shaping product differentiation, and eliciting users’ Kansei cognition. However, existing Kansei design studies on automotive styling have mainly focused on whole-vehicle forms or front-face morphology, while systematic modeling methods for local rear-end styling remain limited. Under small-sample conditions, the nonlinear mapping between the Kansei semantic space and styling parameters also faces the risk of overfitting. To address these issues, this study proposes a data-driven Kansei Engineering (KE) framework integrating non-negative matrix factorization (NMF), grey relational analysis (GRA), and deep feedforward neural network (DFNN), aiming to achieve a continuous translation from Kansei need identification to parametric scheme generation for rear-end styling. First, the original seven-dimensional Kansei evaluations were aggregated into three latent Kansei dimensions through the non-negative low-rank decomposition of NMF. Second, GRA was used to screen key morphological features and reduce modeling complexity at the feature level. Third, DFNN and random forest (RF) were constructed as prediction models, and DFNN showed better average test RMSE and R² than RF. Finally, the optimal codes predicted by the DFNN were transformed into design schemes constrained by morphological coding, and their consistency in expressing the target Kansei images was verified, thereby establishing an engineering constraint-oriented and interpretable decoding pathway distinct from free-association-based Kansei design. The ablation experiment indicates that the performance advantage of the proposed framework does not arise solely from DFNN, but from the mathematical coupling among NMF-based semantic aggregation, GRA-based feature screening, and DFNN-based nonlinear mapping. This framework reformulates Kansei design as a hierarchical decomposition and modeling process, establishing a data-driven decision-support tool jointly driven by mathematical algorithms and artificial intelligence for NEV rear-end styling design. Full article

Background/Objective: The morphology of the flow–volume curve has emerged as a potential source of additional functional information by enabling the analysis of concavity, slope-based metrics, area-derived measures, and mathematical models extracted from the expiratory tracing. Therefore, the aim of this study was to map and describe the available evidence on morphological analysis methods of the expiratory flow–volume curve for the identification and characterization of fixed airflow obstruction. Methods: A scoping review was conducted following the methodological frameworks proposed by Arksey and O’Malley, Levac et al., the Joanna Briggs Institute, and the PRISMA Extension for Scoping Reviews (PRISMA-ScR). Studies published between 1 January 1990, and 31 December 2025, that evaluated morphological flow–volume curve metrics for the diagnosis or characterization of Chronic Obstructive Pulmonary Disease (COPD) were included, without language restrictions. Searches were performed in PubMed/MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, OpenGrey, and Google Scholar. Study selection was conducted by independent reviewers, with disagreements resolved by consensus, and data extraction was performed using a standardized form. Results were synthesized narratively and organized according to metric families. Results: The search identified 13,577 records; after duplicate removal and screening, 24 studies met the inclusion criteria. The evidence included observational studies, diagnostic validation studies, longitudinal cohorts, and methodological modeling studies. Identified metrics were grouped into four main categories: concavity and geometric indices of the flow–volume curve, expiratory slope and flow-decay metrics, area-based or volumetric-derived measures, and mathematical or computational models applied to the tracing. Concavity indices, the β-angle, slope-ratio, Peak Index, and the D parameter showed consistent associations with airflow obstruction, emphysema, small airway disease, or functional impairment. Expiratory slope metrics and Flow Decay demonstrated high diagnostic performance in several studies, whereas area-based measures such as AEX, AEX-FV, AreaFE%, and AUC3/AT3 integrated the overall loss of expiratory flow during forced expiration. Mathematical and computational models suggested that the complete shape of the curve contains additional diagnostic and prognostic information, although methodological variability and the need for external validation remain important limitations. Conclusions: Morphological analysis of the flow–volume curve represents a promising approach to complement conventional spirometry in the identification and characterization of fixed airflow obstruction. These metrics may provide additional information regarding airflow limitation, non-uniform lung emptying, emphysema, small airway disease, hyperinflation, and clinically relevant outcomes. Full article

This study addresses the inverse problem of parameter identification in mathematical models of tumor growth under limited and noisy experimental data. Three classical growth models—logistic, Richards, and Gompertz—are investigated in the context of structural and practical identifiability. It is demonstrated that, despite structural identifiability, parameter estimation remains highly unstable due to the ill-posed nature of the inverse problem. A comparative analysis of the Levenberg–Marquardt method and a genetic algorithm shows that improvements in optimization strategies alone do not resolve this instability and may lead to overfitting. To overcome this limitation, a Tikhonov regularization framework is introduced for the Gompertz model, ensuring stable and physically interpretable parameter estimates. The regularized formulation provides a balance between data fidelity and parameter stability, resulting in improved agreement with experimental observations. To further validate the identified parameters, a reaction–diffusion partial differential equation model is employed. Numerical simulations demonstrate that regularized parameters lead to significantly different spatial tumor morphologies, including more compact structures with sharper interfaces, highlighting the impact of inverse problem regularization on forward model predictions. The results confirm that the primary limitation in tumor growth modeling lies in the ill-posedness of the inverse problem rather than in the choice of optimization algorithm. The proposed framework provides a robust approach for parameter identification and improves the reliability of predictive tumor growth models. Full article

Changes in river channel morphology under the influence of natural and anthropogenic factors pose a serious threat to the stability of aquatic ecosystems and water resource use, especially in regions with limited hydrological information. This study presents, for the first time, a three-dimensional numerical investigation of channel processes in the Talas River (Kazakhstan), employing the Volume of Fluid (VOF) method for free-surface flow simulation and a non-Newtonian model for sediment transport and riverbed morphodynamics. To verify the developed mathematical model, experimental data on the flow in the L-shaped channel and Earthfill dam break were used, which provided high reliability of the calculated results. The calculations showed a significant increase in the channel area in the studied section of the Talas River (from 41,334.92 m² to 56,890.17 m²) for the period from 2019 to 2024, mainly due to the intensification of the dynamics of currents and the formation of additional vortex zones with a diameter of 50 to 200 m. It was found that in places of local flow acceleration, water velocity increased up to 4.5 m/s, leading to bank erosion and channel widening, whereas after redistribution of channel flows, the maximum velocity decreased to 2.8 m/s, ensuring stabilisation of morphological changes. The results of the study underline the need for an integrated approach to river morphodynamics management using numerical modelling to predict channel changes, minimise flood risks and optimise the use of water resources. The presented computational approach can be adapted to analyse hydrodynamic processes in other poorly studied river systems, which significantly expands its scientific and practical value. Full article

The surface roughness (Ra) and coefficient of friction (COF) of acrylonitrile butadiene styrene (ABS) parts fabricated through fused deposition modelling (FDM) are key determinants of their functional service performance. However, these two objectives often present a trade-off relationship in single-objective optimisation. To achieve synergistic optimisation of surface quality and surface performance of FDM ABS parts, this paper proposes a multi-objective optimisation framework integrating response surface methodology (RSM) with a chaotic whale optimisation algorithm (CWOA). A Box–Behnken design (BBD) was employed to establish quadratic regression models for Ra and COF as functions of layer thickness (0.16–0.24 mm), extrusion ratio (0.9–1.0), infill density (20–100%) and extrusion temperature (240–270 °C). Based on analysis of variance (ANOVA) and response surface analysis, a non-linear mathematical model with dual responses was constructed. Pareto-dominated CWOA was introduced for multi-objective optimisation, and the optimal process parameter combination was selected using the TOPSIS method. The results showed that the optimal parameters were: layer thickness 0.16 mm, extrusion ratio 0.9, infill density 66%, and extrusion temperature 270 °C. Under these conditions, the experimentally measured Ra was 7.7921 μm (2.04% error from the predicted value) and COF was 0.1504 (2.80% error from the predicted value). Compared with the benchmark reference (the average value of the BBD central experimental runs), a synchronous decrease in Ra and COF was achieved (Ra reduced by 27.4% and COF reduced by 14.9%). Metallographic morphology analysis revealed that the optimal specimen surface exhibited wide filament ridges, narrow and well-defined inter-filament valleys, and no obvious forming defects, achieving synergistic low roughness and low friction. The proposed RSM-CWOA framework provides an effective method for multi-objective optimisation of FDM processes and can be extended to other polymer additive manufacturing systems. Full article

Rapid urban-industrial development has intensified surface warming in global cities, including India, posing critical challenges for sustainable urban environments. While advanced AI and remote sensing methods have mapped urban heat patterns, a fundamental thermodynamic understanding of how cities generate, absorb, store, and dissipate heat with the urban land transformation remains underexplored. This study conceptualizes the urban geo-thermodynamics mechanism as a comprehensive framework to quantify urban surface energy exchanges, heat flux dynamics, and thermal responses in the Haldia urban-industrial region (103.84 km²) of eastern India. The analysis employs Landsat-derived impervious surface expansion, land surface temperature (LST), and normalized difference vegetation index (NDVI), NASA POWER radiation fluxes, world settlement footprint 3D structural (2023) and material stock (2024) data, and census-based population records (1991–2021). The integrated mathematical formulations were developed after the remote sensing-GIS-based statistical analysis for the urban energy balance through the Urban Thermodynamic Index (UTI), Urban Heat Retention Efficiency (UHRE), and Urban Cooling Potential (UCP) indices, which were developed from energy balance equations linking net radiation (Q*), anthropogenic flux (${\mathrm{Q}}_{\mathrm{F}}$), sensible and ground heat (${\mathrm{Q}}_{\mathrm{H}}$, ${\mathrm{Q}}_{\mathrm{G}}$), and latent heat flux (${\mathrm{Q}}_{\mathrm{E}}$). The results reveal a 36% increase in UTI and a 28% rise in UHRE between 1991 and 2021, indicating enhanced surface heat accumulation and anthropogenic energy input associated with built-up area and population growth (22.87–53.37 km²) and (1452–2375 person/km²). In contrast, UCP declined by 22%, reflecting reduced evaporative cooling due to vegetation loss, with the regression-based calibration (R² = 0.89; RMSE = 0.74 °C) validating strong correspondence with observed LST. These findings demonstrate a quantifiable link between thermodynamic processes and the transformation of the urban morphological landscape. The proposed mathematical-thermodynamic structure provides a scientific, GIS-based statistical method for urban heat risk assessment, energy-efficient planning, and geo-thermal environmental management, supporting global initiatives toward climate-resilient and sustainable urban development. Full article

The transition from continuous swarm intelligence algorithms to discrete combinatorial domains remains a critical challenge in bio-inspired computing. Traditional binarization techniques frequently induce premature convergence in highly constrained landscapes. This paper presents a chaotic discretization framework that replaces the classical behavior of the two-step binarization technique to regulate the balance between exploration and exploitation. The proposal systematically integrates three leading continuous metaheuristics in the literature, with twenty-four binarization configurations, across three distinct NP-hard problem archetypes: capacity-constrained (0–1 Knapsack), sparse (Set Covering), and mathematically degenerate flat landscapes (Unicost Set Covering). Nonparametric statistical tests confirm that chaotic discretization acts as a powerful regulator in the landscape (p < 0.05). Empirical evidence shows that the highest-performing chaotic mapping is heavily influenced by the specific landscape morphology evaluated: the 0–1 Knapsack Problem is statistically optimized by the Circle map under standard rules; the Set Covering Problem achieves optimal median performance with the Tent map under elitist formulations, although severe matrix constraints ultimately force statistical ties; and the Unicost Set Covering Problem utilizes the nonlinear sequences of the sinusoidal map under complementary operators to break convergence stagnation. Full article

Unmanned Aerial Vehicle (UAV) object detection is a core technology for cross-modal, wide-area surveillance. However, operating at high altitudes under complex flight conditions imposes extreme physical constraints, generating massive sub-pixel targets, elongated morphologies, and dense object clustering. These challenges are critically important because conventional lightweight detectors deployed on edge devices often suffer from severe high-frequency edge loss during spatial downsampling, morphological distortion of anisotropic targets, and the mathematical suppression of weak feature gradients during cross-scale fusion, ultimately leading to severe missed detections. To overcome these inherent bottlenecks, this paper proposes WaveUAV-YOLO, a lightweight architecture prioritizing frequency-domain edge preservation and stable gradient propagation. First, a Wavelet High-Frequency Downsampling (WHFD) module augments standard convolutions by utilizing Haar wavelet decomposition to explicitly capture and compensate for the lost boundary cues of sub-pixel targets. Second, an Asymmetric Multi-scale Bottleneck without Dimensionality Compression (C2f_AMSB) cancels forced channel compression and introduces asymmetric convolutions to effectively adapt to elongated targets. Third, a Mean-Normalized Feature Aggregation (FFM_Concat) prevents deep background features from suppressing weak shallow signals during fusion. Extensive experiments on VisDrone2019, DIOR, NWPU, and HIT-UAV demonstrate that WaveUAV-YOLO (11.70 M parameters) achieves competitive or superior detection precision against recent lightweight UAV detectors. Furthermore, it achieves an end-to-end inference speed of ≈50 FPS on a standard desktop GPU, validating a favorable balance between sub-pixel detection reliability and computational efficiency for adverse UAV environments. Full article

Acute myocardial ischemia, caused by a sudden reduction in coronary blood flow, initiates metabolic disturbances that lead to severe pathophysiological consequences. These include electrophysiological alterations, such as changes in action potential morphology and impaired electrotonic coupling between cardiomyocytes, and mechanical dysfunction, characterized by reduced contractile force and subsequent mechanical discoordination across the ventricular wall. This study employs multi-scale mathematical modeling to investigate the effects of acute ischemia on the electromechanical activity of a single human cardiomyocyte and a one-dimensional myocardial tissue. We identify the conditions for conduction block initiation and the parameters governing conduction restoration in ischemic tissue, and analyze the underlying mechanisms. Our simulations demonstrate that conduction slowing in the one-dimensional strand under ischemia directly results from the hyperkalemia-induced reduction in the fast sodium current (i_Na). This i_Na reduction is enhanced by direct electromechanical coupling and mechano-electric/mechano-calcium feedback in the mechanically and electrically interacting cardiomyocytes of the one-dimensional tissue. Under 15 min ischemia conditions, i_Na decreases to a level insufficient to sustain excitation propagation, causing conduction block. Under the conditions of this simulation, where gap junction conductance was held unchanged, the block occurred via the i_Na reduction which is itself amplified by mechano-calcium feedback. Furthermore, our model suggests a potential compensatory mechanism against conduction block in ischemic myocardium. Experimental evidence indicates that ischemia can disrupt gap junctions. A moderate reduction in the electrodiffusion coefficient along the strand, simulating reduced gap junction conductance, can convert persistent conduction block into a transient form and even eliminate it completely, facilitating the maintenance of excitation wave propagation. Full article

Axillary Lymph Nodes (ALNs) can be affected by breast cancer, and the number of affected ALNs is a determinant factor in breast cancer staging. Microwave imaging (MWI) has emerged as a promising technique for ALN assessment, addressing limitations in conventional imaging modalities. This study investigates, for the first time, the classification of ALNs and axillary regions from microwave signals, without image reconstruction. Classification is performed considering realistic morphological characteristics of ALNs reported in the literature and is based solely on geometric differences, which differ from targets previously explored in microwave-based classification studies. Eighty ALN numerical models were mathematically generated based on state-of-the-art anatomical descriptions. Microwave signals were simulated for three scenarios of different complexity, involving one and two ALNs, representing healthy and metastasised conditions. The methodology evaluated multiple combinations of signal types, feature extraction methods, and classifiers, including scenarios with multiple targets, reflecting clinically relevant axillary conditions and limited angular views inherent to axillary imaging. Classification accuracy reached 95% for single-ALN scenarios using kNN, while more complex two-ALN cases achieved accuracies up to 83.3% using SVM. These results demonstrate the potential of microwave signal-based classification to differentiate healthy and metastasised ALNs and axillary regions, supporting future integration with MWI image interpretation. Full article