Search Results (270)

Optical clearing agents (OCAs) offer a promising approach to enhance skin transparency by reducing scattering and improving photon transmission, which is critical for non-invasive optical diagnostics such as glucose sensing and vascular imaging. However, the complex multilayered structure of skin and anatomical variability across different regions pose challenges for accurately evaluating OCA performance. In this study, we developed a multilayer Monte Carlo (MC) simulation model integrated with a depth- and time-resolved diffusion model based on Fick’s law to quantitatively assess the combined effects of OCA penetration depth and refractive index change on optical clearing. The model incorporates realistic skin parameters, including variable stratum corneum thicknesses, and was validated through in vivo experiments using glycerol and glucose at different concentrations. Both the simulation and experimental results demonstrate that increased stratum corneum thickness significantly reduces blood absorption of light and lowers the clearing efficiency of OCAs. The primary influence of stratum corneum thickness lies in requiring a greater degree of refractive index matching rather than necessitating a deeper OCA penetration depth to achieve effective optical clearing. These findings underscore the importance of considering regional skin differences when selecting OCAs and designing treatment protocols. This work provides quantitative insights into the interaction between tissue structure and optical response, supporting improved application strategies in clinical diagnostics. Full article

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns. Full article

In this work, the symmetries of a H₂⁺ molecule confined within potential energy wells of various shapes are highlighted. This system has been long regarded as a model for small molecules trapped in crystalline cavities and molecular cages; in this context, the role of symmetry assumes significant importance. Symmetries are determined by the well shape, molecular position, and orientation. They allow the classification of H₂⁺ states, the identification of fixed nodal surfaces for the identification of excited states in Monte Carlo simulations, and the estimation of potential energy surfaces. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered media from TD reflectance generated via Monte Carlo (MC) simulations. Optical properties for 384 bilayered tissue models representing human head or limb tissues were obtained from the literature at three near-infrared wavelengths. MC data were fit using the layered DT model to simultaneously recover transport coefficients in both layers. Bottom-layer absorption was recovered with errors under 0.02 cm⁻¹, and top-layer scattering was retrieved within 3 cm⁻¹ of input values. In contrast, recovered bottom-layer scattering had mean errors exceeding 50%. Total hemoglobin concentration and oxygen saturation were reconstructed for the bottom layer to within 10 μM and 5%, respectively. Extracted transport coefficients were significantly more accurate when obtained using layered DT compared to the conventional, semi-infinite DT model. Our results suggest using improved theoretical modeling to analyze TD reflectance analysis significantly improves recovery of deep-layer absorption. Full article

Galactic cosmic rays (GCRs), composed of protons and atomic nuclei, are accelerated in sources such as supernova remnants and pulsar wind nebulae, reaching energies up to the PeV range. As they propagate through the interstellar medium, their interactions with dense regions like molecular clouds produce secondary particles, including gamma-rays and neutrinos. In this study, we use the Geant4 Monte Carlo toolkit to simulate secondary particle production from GCR interactions within the Perseus molecular cloud, a nearby star-forming region. Our model incorporates realistic cloud composition, a wide range of incidence angles, and both hadronic and electromagnetic processes across a broad energy spectrum. The results highlight molecular clouds as significant sites of multi-messenger emissions and contribute to understanding the propagation of GCRs and the origin of diffuse gamma-ray and neutrino backgrounds in the Galaxy. Full article

In this work, HNTs@ZIF-67 composites were synthesized using the in situ growth method and incorporated into 6FDA-TFMB to prepare mixed-matrix membranes (MMMs). Scanning electron microscope (SEM) and transmission electron microscope (TEM) proved that the HNTs@ZIF-67 composite not only retained the hollow structure of HNTs, but also formed a continuous ZIF-67 transport layer on the surface of HNTs. The results of gas permeability experiments showed that with the increase in HNTs@ZIF-67 incorporation, the He permeability and He/CH₄ selectivity of MMMs showed a trend of increasing first and then decreasing. When the loading is 5 wt%, the He permeability and He/CH₄ selectivity of MMMs reach 116 Barrer and 305, which are 22.11% and 79.41% higher than the pure 6FDA-TFMB membrane. The results of density functional theory (DFT) and Monte Carlo (MC) calculations reveal that He diffuses more easily inside ZIF-67, HNTs and 6FDA-TFMB than CH₄, and ZIF-67 shows larger adsorption energy with He than HNTs and 6FDA-TFMB, indicating that He is easily adsorbed by ZIF-67 in MMMs. Based on experimental and molecular simulation results, the mechanism of HNTs@ZIF-67 improving the He/CH₄ separation performance of MMMs was summarized. With the advantage of a smaller molecular kinetic diameter, He can diffuse through ZIF-67 on the tube orifice of HNTs@ZIF-67 and enter the HNTs’ hollow tube for rapid transmission. At the same time, He can also be rapidly transferred in the continuous ZIF-67 transport channel layer, which improves the He permeability and the He/CH₄ selectivity of MMMs. Full article

The squared Bessel process plays a central role in stochastic analysis, with broad applications in mathematical finance, physics, and probability theory. While explicit expressions for its transition probability density function (TPDF) under constant parameters are well known, analytical results in the case of time-dependent dimensions remain scarce. In this paper, we address a significantly challenging problem by deriving an analytical formula for the TPDF of a conic combination of independent squared Bessel processes with time-dependent dimensions. The result is expressed in terms of a Laguerre series expansion. Furthermore, we obtain closed-form expressions for the conditional moments of such conic combinations, represented via generalized hypergeometric functions. These results also yield new analytical formulas for the TPDF and conditional moments of both squared Bessel processes and Bessel processes with time-dependent dimensions. The proposed formulas provide a unified analytical framework for modeling and computation involving a broad class of time-inhomogeneous diffusion processes. The accuracy and computational efficiency of our formulas are verified through Monte Carlo simulations. As a practical application, we provide an analytical valuation of an interest rate swap, where the underlying short rate follows a conic combination of independent squared Bessel processes with time-dependent dimensions, thereby illustrating the theoretical and practical significance of our results in mathematical finance. Full article

The Fricke gel dosimeter, a hydrogel-based chemical dosimeter containing dissolved ferrous sulfate, measures 3D radiation dose distributions by oxidizing Fe²⁺ to Fe³⁺ upon irradiation. This study investigates the variation in Fricke yield, G(Fe³⁺), from a radiation–chemical perspective in both standard and gel-like Fricke systems of varying viscosities, under low- and high-linear energy transfer (LET) conditions. We employed our Monte Carlo track chemistry code IONLYS-IRT, using protons of 300 MeV (LET~0.3 keV/µm) and 1 MeV (LET~25 keV/µm) as radiation sources. To assess the impact of viscosity on G(Fe³⁺), we systematically varied the diffusion coefficients of all radiolytic species in the Fricke gel, including Fe²⁺ and Fe³⁺ ions. Increasing gel viscosity reduces Fe³⁺ diffusion and stabilizes spatial dose distributions but also lowers G(Fe³⁺), compromising measurement accuracy and sensitivity—especially under high-LET irradiation. Our results show that an optimal Fricke gel dosimeter must balance these competing factors. Simulations with lower sulfuric acid concentrations (e.g., 0.05 M vs. 0.4 M) further revealed that G(Fe³⁺) values at ~100 s are nearly identical for both low- and high-LET conditions. This study underscores the utility of Monte Carlo simulations in modeling viscosity effects on Fricke gel radiolysis, guiding dosimeter optimization to maximize sensitivity and accuracy while preserving spatial dose distribution integrity. Full article

This study investigates the valuation of Euro-convertible bonds (ECBs) using a novel Markov-modulated cojump-diffusion (MMCJD) model, which effectively captures the dynamics of stochastic volatility and simultaneous jumps (cojumps) in both the underlying stock prices and foreign exchange (FX) rates. Furthermore, we introduce a Markov-modulated Cox–Ingersoll–Ross (MMCIR) framework to accurately model domestic and foreign instantaneous interest rates within a regime-switching environment. To manage computational complexity, the least-squares Monte Carlo (LSMC) approach is employed for estimating ECB values. Numerical analyses demonstrate that explicitly incorporating stochastic volatilities and cojumps significantly enhances the realism of ECB pricing, underscoring the novelty and contribution of our integrated modeling approach. Full article

Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance. Full article

Aimed at dealing with the problems of high reliability solution cost and low solution accuracy under random excitation, especially Gaussian white noise excitation, this paper proposes a probability density evolution and reliability analysis method for nonlinear gear transmission systems under Gaussian white noise excitation based on the path integration method. This method constructs an efficient probability density evolution framework by combining the path integration method, the Chapman–Kolmogorov equation, and the Laplace asymptotic expansion method. Based on Rice’s theory and combined with the adaptive Gauss–Legendre integration method, the transient and cumulative reliability of the system are path integration method calculated. The research results show that in the periodic response state, Gaussian white noise leads to the diffusion of probability density and peak attenuation, and the system reliability presents a two-stage attenuation characteristic. In the chaotic response state, the intrinsic dynamic instability of the system dominates the evolution of the probability density, and the reliability decreases more sharply. Verified by Monte Carlo simulation, the method proposed in this paper significantly outperforms the traditional methods in both computational efficiency and accuracy. The research reveals the coupling effect of Gaussian white noise random excitation and nonlinear dynamics, clarifies the differences in failure mechanisms of gear systems in periodic and chaotic states, and provides a theoretical basis for the dynamic reliability design and life prediction of nonlinear gear transmission systems. Full article

We derive an infinite-series representation for the transition probability density function (PDF) of the time-inhomogeneous 3/2 model, expressing all coefficients in terms of Bell-polynomial and generalized Laguerre-polynomial formulas. From this series, we obtain explicit expressions for all conditional moments of the variance process, recovering the familiar time-homogeneous formulas when parameters are constant. Numerical experiments illustrate that both the density and moment series converge rapidly, and the resulting distributions agree with high-precision Monte Carlo simulations. Finally, we demonstrate that the same approach extends to a broad family of non-affine, time-varying diffusions, providing a general framework for obtaining transition PDFs and moments in advanced models. Full article

The spontaneous deposition of platinum ($Pt$) atoms on $Au\left(111\right)$ surfaces is systematically investigated through kinetic Monte Carlo simulations within the Embedded Atom Model framework. The kinetic model aims to capture both stoichiometric, atomic-scale interactions and the more relevant processes that describe the kinetics of a physical problem. Various deposition rates are examined, encompassing a thorough exploration of $Pt$ adsorption up to a coverage degree of $\theta =0.25$. The resulting 2D islands exhibit a ramified structure, mirroring the experimental methodologies. For the first time, this study extensively analyzes the dependence of both the mean island size and island density on spontaneous deposition, thereby offering valuable insights into the intricate dynamics of the system. Full article

First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring the fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize the neutron flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5 × 10⁵ cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive data set with the potential for machine learning applications. The neutron flux throughout the pile is measured using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistically accurate Monte Carlo model using OpenMC 0.15.0 and MCNP 6.3. The experimental measurements reveal that the testbed has a neutron environment with a total neutron flux approaching 9.5 × 10³ n/cm² × s and a thermal flux of 6.5 × 10³ n/cm² × s. This work establishes that the modified Purdue subcritical pile can provide fair neutron and gamma fluxes within a large volume to enable the radiation testing of integral electronic components and can be a versatile research instrument with the potential to support material testing and limited isotope activation, while generating valuable training data sets for machine learning algorithms in nuclear applications. Full article