With high-stakes industries such as healthcare, finance, and autonomous systems, an increasing number of cognitive artificial intelligence are being utilized, which presents new challenges for developing calibrated trust. The complexity of trust involves balancing reliance and skepticism. Mistrust and misalignment cause complacency towards automation, premature skepticism, and outright rejection of the automation systems. Previous literature describes trust via isolated theoretical perspectives (cognitive load theory, expectancy-disconfirmation theory, algorithmic fairness). But these analyses examine trust in fragments and overlook important multidimensional dynamics, as well as failing to sufficiently quantify the principles of human-centered design and advanced ensemble architecture to an underdeveloped extent for capturing nonlinear sociotechnical phenomena. This research presents the first synthesized and integrated model to examine trust using a psychological, organizational, and computationally grounded approach. The integrated model uses engineered human-centered metrics such as Trust Stability Index (TSI), Bias Penalty Factor (BPF), and Cognitive Stress Aggregate (CSA), combined with rigorous mathematical formulations and advanced Sobolev-space functional analysis. Of 16 models analyzed with varying architecture and ensemble methods, the Stacking Ensemble achieved the best performance ( R^2 = 0.9489, RMSE = 2.06, MAE = 1.54). Overall analysis of the model yielded excellent generalization, a high level of noise tolerance (96.98
Among the various ideas in graph theory, labelling of graphs has diverse applications in cryptography, coding theory, data security, telecommunication networks, etc. Labeling of a graph is any mapping that, under specific circumstances, converts a given collection of graph components to a given set of numbers. In this paper, we determine the exact value of reflexive edge irregularity strength of the barycentric subdivision of circulant graphs C_p[1,k_1] .
The purpose of current study is to analyse the flow and thermal behaviour of Sutterby hybrid nanofluids, with particular attention given to an inclined magnetic field, viscous dissipation, heat source, Cattaneo-Christov heat flux, and thermal radiation model. In this work, we consider the Ag -Au/Blood based hybrid nanoparticle in the non-Newtonian Sutterby fluid in order to investigate how much they create the enhancement in the thermal conductivity. As the inclined magnetic field interacts with the electrically conducting fluid, its impacts on flow properties are examined. A more precise description of heat flow is obtained by using the Cattaneo-Christov model, which considers the speed limitations of heat conduction. The thermal transport analysis are investigated by using the thermal radiation, viscous dissipation and heat source or sink. The physical phenomena stated above have an impact on the formulation of the governing equations of mass, momentum and energy transfer. The model is established in the form of dimensional PDEs and suitable transformations is applied to translate the PDEs into non-dimensional ODEs. A mathematical model is created, and suitable numerical technique (RungeKutta 4 th order) are used to find solutions. The findings demonstrate the combined impact of these variables on the fluid flow's temperature distribution, velocity profiles, and heat transfer rates. The results reveals that Lorentz force or retardation force causes the velocity profile to decrease as the magnetic field increases. When comparing the Ag/blood case to the Ag-Au/blood case based on the Sutterby Deborah number, the depth of the thermal boundary layer rapidly drops. The hybrid nanofluid's temperature rises while its velocity decreases due to the nanoparticle volume percentage parameter. The friction drag heightens along with the Sutterby Deborah number and Power-law index estimates.
The low thermal conductivity of air poses a significant challenge to the efficiency of solar air heaters, and this requires sophisticated heat transfer enhancement techniques. This study investigates the performance of a tubular solar air heater integrated with twisted tape inserts to address this limitation. A hybrid approach using 3D computational fluid dynamics combined with deep neural networks was adopted for systematic analysis of five dimensionless parameters-twisted tape length (lt /Dh), twisted tape diameter (dt /Dh), twisted tape thickness (tt /Dh), twisted tape pitch (pt /Dh), and Reynolds number. Quantitative results have shown that tape diameter increment coupled with pitch reduction are the major promoters for thermal enhancement, as an increase in (dt /Dh) from 0.322 to 0.483 increases the Nusselt number by 30%. On the other hand, tighter pitch ratio (pt /Dh) has resulted in a 4.7-fold increase in friction factor at Re = 10,000. The surrogate model with the DNN showed highly accurate prediction results with an average deviation of approximately 1-2% relative to the CFD results. For the global optimization experiment with the DNN, the maximum value of the thermo-hydraulic performance factor of 1.45 was found at Re = 10,000, (dt /Dh) = 0.48, and (pt /Dh) = 2.68, making it superior to the state-ofthe-art tubular swirl generators reported in the literature. The originality and creativity in the thesis lie in the conversion of a machine learning model from a prediction model to a design engine to expose the non-linear inter-relationship between the parameters. This hybrid approach involving CFD and ML not only captures the complex physical phenomenon associated with the twisted tape geometry in terms of affecting the flow and heat transfer mechanisms in an accurate manner, but it also generates a faster and more stable surrogate model for the performance predictions and optimizations with equal efficacy.
Deepfake technology is causing unprecedented threats to the authenticity of digital media, and demand is high for reliable digital media detection systems. This systematic review focuses on an analysis of deepfake detection methods using deep learning approaches, machine learning methods, and the classical methods of image processing from 2018 to 2025 with a specific focus on the trade-off between accuracy, computing efficiency, and cross-dataset generalization. Through lavish analysis of a robust peer-reviewed studies using three benchmark data sets (FaceForensics++, DFDC, Celeb-DF) we expose important truths to bring some of the field’s prevailing assumptions into question. Our analysis produces three important results that radically change the understanding of detection abilities and limitations. Transformer-based architectures have significantly better cross-dataset generalization (11.33% performance decline) than CNN-based (more than 15% decline), at the expense of computation (3–5× more). To the contrary, there is no strong reason to assume the superiority of deep learning, and the performance of traditional machine learning methods (in our case, Random Forest) is quite comparable (accuracy of 99.64% on the DFDC) with dramatically lower computing needs, which opens up the prospects for their application in resource-constrained deployment scenarios. Most critically, we demonstrate deterioration of performance (10–15% on average) systematically across all methodological classes and we provide empirical support for the fact that current detection systems are, to a high degree, learning dataset specific compression artifacts, rather than deepfake characteristics that are generalizable. These results highlight the importance of moving from an accuracy-focused evaluation approach toward more comprehensive evaluation approaches that balance either generalization capability, computational feasibility, or practical deployment constraints, and therefore further direct future research efforts towards designing systems for detection that could be deployed in practical applications.
