Search Results (31)

Optimizing pavement maintenance and rehabilitation (M&R) strategies is essential, especially in developing countries with limited budgets. This study presents an integrated framework combining a deterioration prediction model and a genetic algorithm (GA)-based optimization model to plan cost-effective M&R strategies for flexible pavements, including asphalt concrete (AC) and double bituminous surface treatment (DBST). The GA schedules multi-year interventions by accounting for varied deterioration rates and budget constraints to maximize pavement performance. The optimization process involves generating a population of candidate solutions representing a set of selected road sections for maintenance, followed by fitness evaluation and solution evolution. A mixed Markov hazard (MMH) model is used to model uncertainty in pavement deterioration, simulating condition transitions influenced by pavement bearing capacity, traffic load, and environmental factors. The MMH model employs an exponential hazard function and Bayesian inference via Markov Chain Monte Carlo (MCMC) to estimate deterioration rates and life expectancies. A case study on Cambodia’s road network evaluates six budget scenarios (USD 12–27 million) over a 10-year period, identifying the USD 18 million budget as the most effective. The framework enables road agencies to access maintenance strategies under various financial and performance conditions, supporting data-driven, sustainable infrastructure management and optimal fund allocation. Full article

Transformer health assessment enables predictive maintenance strategies that extend equipment lifespan, minimize resource consumption, and support sustainable power system operations. However, traditional methods often rely on simple health indicators, which fail to effectively capture the complex relationships within transformer health data. To address this issue, this article proposes a joint training method based on a wide and deep model, enhanced with Bayesian inference and Markov chain Monte Carlo (MCMC) techniques. The model combines a wide component, which uses linear regression to identify global patterns in transformer health parameters, and a deep neural network that learns complex nonlinear relationships, such as those in thermal aging data. Bayesian inference is integrated to quantify uncertainties in the predictions, while MCMC is employed for robust parameter estimation during training. This combination enables a more accurate, interpretable, and comprehensive assessment of transformer conditions. Experimental results on realistic datasets show that the proposed method significantly improves prediction accuracy and reliability compared to existing approaches. Specifically, the joint wide and deep model outperforms traditional methods by 6.6% in classification accuracy, demonstrating its potential for application in smart grid systems. This research contributes to sustainable power system management by enabling more efficient resource utilization and supporting the transition to sustainable energy systems. Full article

To increase the calculation speed of the computational fluid dynamics (CFD)-based simulation for the gas–solid flow in fluidized beds, a Markov chain model (MCM) was developed to simulate the particle movement in a two-dimensional (2D) circulating fluidized bed (CFB) riser. As a statistic model, the MCM takes the results obtained from a CFD–discrete element method (DEM) as samples for calculating transition probability matrixes of particle movement. The transition probability matrixes can be directly used to describe the macroscopic regularities of particle movement and further used to simulate the particle motion combined with the Monte Carlo method. Particle distribution snapshots, residence time distribution (RTD), and mixing obtained from both MCM and CFD-DEM are compared. The results indicate that the MCM offers a computational speed that is approximately 100 times faster than that of the CFD-DEM. The discrepancy in the mean particle residence time, as computed by the two models, is under 2%. Furthermore, the MCM provides an accurate depiction of time-averaged particle motion. In sum, the MCM can well describe the time-averaged particle mixing compared to the CFD-DEM. Full article

We perform observational confrontation and cosmographic analysis of $f(T,T_G)$ gravity and cosmology. This higher-order torsional gravity is based on both the torsion scalar, as well as on the teleparallel equivalent of the Gauss–Bonnet combination, and gives rise to an effective dark-energy sector which depends on the extra torsion contributions. We employ observational data from the Hubble function and supernova Type Ia Pantheon datasets, applying a Markov chain Monte Carlo sampling technique, and we provide the iso-likelihood contours, as well as the best-fit values for the parameters of the power-law model, an ansatz which is expected to be a good approximation of most realistic deviations from general relativity. Additionally, we reconstruct the effective dark-energy equation-of-state parameter, which exhibits a quintessence-like behavior, while in the future the Universe enters into the phantom regime, before it tends asymptotically to the cosmological constant value. Furthermore, we perform a detailed cosmographic analysis, examining the deceleration, jerk, snap, and lerk parameters, showing that the transition to acceleration occurs in the redshift range $0.52\le z_{tr}\le 0.89$, as well as the preference of the scenario for quintessence-like behavior. Finally, we apply the Om diagnostic analysis to cross-verify the behavior of the obtained model. Full article

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge. Full article

This paper studies the optimal mission abort decisions for safety-critical mission-based systems with multiple components. The considered system operates in a random shock environment and is required to accomplish a mission during a fixed mission period. If the failure risk of the system is very high, the main mission can be aborted to avoid higher failure cost. The main contribution of this study lies in the design and optimization of mission abort policies for multi-component systems with multiple abort criteria. Moreover, multi-level transitions are considered in this study to characterize the different shock-resistance abilities for components in different states. Mission abort decisions are determined based on the number of components in either defective or failed state. The problem is formulated in the framework of the finite Markov chain imbedding method. We use the Monte-Carlo simulation method to derive the mission reliability and system survivability. Numerical studies and sensitivity analysis are presented to validate the obtained result. Full article

Tidal orbital decay plays a vital role in the evolution of hot Jupiter systems. As of now, this has only been observationally confirmed for the WASP-12 system. There are a few other candidates, including WASP-4 b, but no conclusive result could be obtained for these systems as of yet. In this study, we present an analysis of new TESS data of WASP-4 b together with archival data, taking the light–time effect (LTE) induced by the second planetary companion into account as well. We make use of three different Markov chain Monte Carlo models: a circular orbit with a constant orbital period, a circular orbit with a decaying orbit, and an elliptical orbit with apsidal precession. This analysis is repeated for four cases. The first case features no LTE correction, with the remaining three cases featuring three different timing correction approaches because of the large uncertainties of the ephemeris of planet c. Comparison of these models yields no conclusive answer to the cause of WASP-4 b’s apparent transit timing variations. A broad range of values of the orbital decay and apsidal precession parameters are possible, depending on the LTE correction. However, the LTE caused by planet c can explain on its own—in full—the observed transit timing variations of planet b, with no orbital decay or apsidal precession being required at all. This work highlights the importance of continued photometric and spectroscopic monitoring of hot Jupiters. Full article

In this paper, we creatively price the discretely sampled variance swaps under the mean-reverting Gaussian model (MRG model in short) with regime-switching asymmetric double exponential jump diffusion. We extend the traditional MRG model by further considering the trend of the financial market as well as a sudden and unexpected event of the market. This new model is meaningful because it uses observable Markov chains that represent market states to adjust its parameters, which helps capture the movement of the market and fluctuations in asset prices. By utilizing the characteristic function and the conditional transition characteristic function, we obtain analytical solutions for pricing formulae. Note that this is our first effort to provide the analytical solution for the ordinary differential equations satisfied by the Feynman–Kac theorem. To achieve this, we have developed a new methodology in Proposition 2 that involves dividing the sampling interval into more detailed switching and non-switching intervals. One significant advantage of our closed-form solution is its high computational accuracy and efficiency. Subsequent semi-Monte Carlo simulations will provide specific validation results. Full article

Throughout this study, locally rotationally symmetric (LRS) Bianchi type-V space-time is pondered with Tsallis holographic dark energy (THDE) with the Granda–Oliveros (GO) cut-off in the Sáez–Ballester (SB) theory of gravity. A parameterization of the deceleration parameter $\left(q\right)$ has been suggested: $q=\alpha -\frac{\beta }{H^2}$. The proposed deceleration parameterization demonstrates the Universe’s phase transition from early deceleration to current acceleration. Markov chain Monte Carlo (MCMC) was utilized to have the best-fit value for our model parameter and confirm that the model satisfies the recent observational data. Additional parameters such as deceleration parameter q with cosmographic parameters jerk, snap, and lerk have also been observed physically and graphically. The constructed model is differentiated from other dark energy models using statefinder pair analysis. Some important features of the model are discussed physically and geometrically. Full article

A particular form of the time-dependent deceleration parameter is used to examine the accelerated expansion of the universe and the phase transition in this expansion in the context of $f(R,T)$ gravity theory for the flat FRW model. The modified field equations are solved under the choice of $f(R,T)=R+2f\left(T\right)$. The best fit values of the model parameters that would be consistent with the recent observational datasets that are estimated. For this estimation, 57 points from Cosmic Chronometers (CC) datasets and 1048 points from Pantheon supernovae datasets are used. Bayesian analysis and likelihood function are applied together with Markov Chain Monte Carlo (MCMC) method at $1\sigma$ and $2\sigma$ confidence levels. Then, the physical behavior of parameters such as density, pressure and cosmographic parameters corresponding to these constrained values of the model parameters are analyzed. Looking at the deceleration parameter, it is seen that the universe has passed from a decelerating expansion phase to an accelerating phase. As a result, it has been shown that the cosmological model $f(R,T)$ that we discussed can explain the accelerating expansion of the late universe well without resorting to any dark energy component in the energy-momentum tensor. Full article

An optimized evaluation method of the bearing capacity of reinforced concrete beam based on the Bayesian theory was proposed in this paper. This evaluation method optimized the traditional Markov Chain-Monte Carlo (MCMC) sampling method, and proposed an improved Metropolis–Hastings (MH) sampling method and a transitive MCMC (TMCMC) sampling method based on the MCMC theory. These two derived sampling methods solved the problem that the traditional MCMC algorithm makes it difficult to achieve convergence when the number of modified parameters is large. Therefore, on the basis of obtaining the measured sample information and the prior information of uncertain parameters, this paper first used multiple “model components” to form a model sample, then carried out a sensitivity analysis based on the relevant response indicators and selected the key parameters that had a great impact on the bearing capacity, carried out static load tests, and extracted and analyzed the experimental data. Then, based on a large amount of analysis data, the improved MH sampling method and TMCMC sampling method were used to establish a posterior probability distribution database. Finally, multiple posterior probability distributions were used to identify and predict the bearing capacity. The results showed that the method was feasible and effective for the evaluation of the bearing capacity of reinforced concrete beam. Full article

Using a hedging strategy to stabilize fuel price is very important for airline companies in order to reduce the cost of their main business. In this paper, we construct models for managing the risk of the hedging strategy. First, we use conditional value at risk (CVaR) to measure the risk of an airline company’s hedging strategy. Compared with the value at risk (VaR), CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transfer invariance. Therefore, CVaR is a consistent method of risk measurement. Second, time-varying state transition probability is introduced into our model in order to build a Markov Switching-GARCH (MS-GARCH). MS-GARCH takes dynamic changes of market state into account, a feature which has obvious advantages over the traditional constant state model. Additionally, we use a Markov chain Monte Carlo (MCMC) algorithm to estimate the parameters of MS-GARCH based on Gibbs sampling. We use fuel oil futures data from the Shanghai Futures Stock Exchange to implement and evaluate our model. In this paper, we empirically estimate the risk of airlines’ hedging strategy and draw the conclusion that our model is obviously effective in terms of the risk management of hedging, a use which has a certain guiding significance for reality. Full article

Monte Carlo simulations are a powerful technique and are widely used in different fields. When applied to complex molecular systems with long chains, such as those in synthetic polymers and proteins, they have the advantage of providing a fast and computationally efficient way to sample equilibrium ensembles and calculate thermodynamic and structural properties under desired conditions. Conformational Monte Carlo techniques employ a move set to perform the transitions in the simulation Markov chain. While accepted conformations must preserve the sequential bonding of the protein chain model and excluded volume among its units, the moves themselves may take the chain across itself. We call this a break in linear topology preservation. In this manuscript, we show, using simple protein models, that there is no difference in equilibrium properties calculated with a move set that preserves linear topology and one that does not. However, for complex structures, such as those of deeply knotted proteins, the preservation of linear topology provides correct equilibrium results but only after long relaxation. In any case, to analyze folding pathways, knotting mechanisms and folding kinetics, the preservation of linear topology may be an unavoidable requirement. Full article

A hierarchical random graph (HRG) model combined with a maximum likelihood approach and a Markov Chain Monte Carlo algorithm can not only be used to quantitatively describe the hierarchical organization of many real networks, but also can predict missing connections in partly known networks with high accuracy. However, the computational cost is very large when hierarchical random graphs are sampled by the Markov Chain Monte Carlo algorithm (MCMC), so that the hierarchical random graphs, which can describe the characteristics of network structure, cannot be found in a reasonable time range. This seriously limits the practicability of the model. In order to overcome this defect, an improved MCMC algorithm called two-state transitions MCMC (TST-MCMC) for efficiently sampling hierarchical random graphs is proposed in this paper. On the Markov chain composed of all possible hierarchical random graphs, TST-MCMC can generate two candidate state variables during state transition and introduce a competition mechanism to filter out the worse of the two candidate state variables. In addition, the detailed balance of Markov chain can be ensured by using Metropolis–Hastings rule. By using this method, not only can the convergence speed of Markov chain be improved, but the convergence interval of Markov chain can be narrowed as well. Three example networks are employed to verify the performance of the proposed algorithm. Experimental results show that our algorithm is more feasible and more effective than the compared schemes. Full article

Recently, flow models parameterized by neural networks have been used to design efficient Markov chain Monte Carlo (MCMC) transition kernels. However, inefficient utilization of gradient information of the target distribution or the use of volume-preserving flows limits their performance in sampling from multi-modal target distributions. In this paper, we treat the training procedure of the parameterized transition kernels in a different manner and exploit a novel scheme to train MCMC transition kernels. We divide the training process of transition kernels into the exploration stage and training stage, which can make full use of the gradient information of the target distribution and the expressive power of deep neural networks. The transition kernels are constructed with non-volume-preserving flows and trained in an adversarial form. The proposed method achieves significant improvement in effective sample size and mixes quickly to the target distribution. Empirical results validate that the proposed method is able to achieve low autocorrelation of samples and fast convergence rates, and outperforms other state-of-the-art parameterized transition kernels in varieties of challenging analytically described distributions and real world datasets. Full article