Monte Carlo Simulations Research Articles

Inverse Probability of Treatment Weighting Using the Propensity Score With Competing Risks in Survival Analysis.

Inverse probability of treatment weighting (IPTW) using the propensity score allows estimation of the effect of treatment in observational studies. We had three objectives: first, to describe methods for using IPTW to estimate the effects of treatments in settings with competing risks; second, to illustrate the application of these methods using empirical analyses; and third, to conduct Monte Carlo simulations to evaluate the relative performance of three methods for estimating time-specific risk differences and time-specific relative risks in settings with competing risks. In doing so, we provide guidance to applied biostatisticians and clinical investigators on the use of IPTW in settings with competing risks. We examined three estimators of time-specific risk differences and relative risks: the weighted Aalen-Johansen estimator, an estimator that combines IPTW with inverse probability of censoring weights (IPTW-IPCWs), and a double-robust augmented IPTW estimator combined with IPCW (AIPTW-IPCW). The design of our simulations reflected clinically realistic scenarios. Our simulations found that all three estimators tended to result in unbiased estimations of time-specific risk differences and time-specific relative risks. However, the weighted Aalen-Johansen estimator and the AIPTW-IPCW estimator tended to result in estimates with greater precision compared to the IPTW-IPCW estimator. In our empirical analyses, we illustrated the application of these methods by estimating the effect of statin prescribing on the risk of subsequent cardiovascular death in patients discharged from the hospital with a diagnosis of acute myocardial infarction.

Retrieval of water cloud microphysical properties with polarized LiDAR based on optimization method: A polarimetric Monte Carlo simulation

Retrieval of water cloud microphysical properties with polarized LiDAR based on optimization method: A polarimetric Monte Carlo simulation

Probabilistic Small-Signal Modeling and Stability Analysis of the DC Distribution System

With the advent of large-scale electronic transportation, the construction of electric vehicle charging stations (EVCSs) has increased. The stochastic characteristic of the charging power of EVCSs leads to a risk of destabilization of the DC distribution network when there is a high degree of power electronification. Current deterministic stability analysis methods are too complicated to allow for brief descriptions of the effect of probabilistic characteristics of EVCSs on stability. This paper develops a probabilistic small-signal stability analysis method. Firstly, the probabilistic information of the system is obtained by combining the s-domain nodal impedance matrix based on the point estimation method. Then, the probability function of stability is fitted using the Cornish–Fisher expansion method. Finally, a comparison experiment using Monte Carlo simulation demonstrates that this method performs well in balancing accuracy and computational efficiency. The effects of line parameters and system control parameters on stability are investigated in the framework of probabilistic stability. This will provide a probabilistic perspective on the design of more complex power systems in the future.

Energy-Oriented Economic Evaluation of Phase Shifters Utilizing Risk-Adjusted Lifecycle Cost Analysis

In response to the growing need for efficient and sustainable energy systems, phase shifters are pivotal in enhancing the flexibility and reliability of power grids while facilitating the integration of renewable energy sources. This paper conducts a comprehensive economic evaluation of various phase shifter technologies, including symmetrical dual-core mechanical phase shifters, electronic phase shifters, and controllable phase shifters, using a lifecycle cost (LCC) analysis framework tailored for the energy sector. The assessment employs probability distributions and Monte Carlo simulation methods to analyze their economic performance, considering key energy-related cost factors such as initial investment, operational efficiency, maintenance, and decommissioning. An uncertainty analysis is introduced to refine the accuracy of LCC calculations, while sensitivity analysis highlights the influence of cost fluctuations—especially in initial investment and operating costs—on overall performance. The results reveal that while large-capacity phase shifters involve higher upfront costs, their superior operational efficiency and lower long-term unit costs can provide substantial economic advantages for sustainable energy applications. These findings offer valuable insights for optimizing energy equipment selection and guiding investment decisions in the transition toward greener and more resilient energy systems.

Economic feasibility of low-trophic aquaculture within U.S. Offshore wind energy areas

Offshore wind energy is developing along the Atlantic coast of the United States and is expected to create use conflicts. A solution proposed to reduce conflicts among seafood production and ocean energy production is to co-locate low-trophic aquaculture within offshore wind energy areas. This study applied a techno-economic analysis to assess the economic feasibility of locating low-trophic aquaculture within offshore wind energy areas off the U.S. Atlantic coast. Costs and revenues were compiled for ten candidate species in four regions of the U.S. Atlantic to determine which species, locations, grow-out methods, and production scale combinations may be profitable or worth further consideration. Costs and revenue distributions were estimated via a Monte Carlo simulation analysis where profit margins and breakeven analyses were assessed to determine which combinations could be economically feasible. At 100-mt and 500-mt scales of production, 87% of profit margins were negative, indicating most scenarios of offshore co-located aquaculture would not be profitable. However, high value species such as eastern oyster and soft-shell clam and high yield species such as sugar and winged kelp have the greatest potential for offshore aquaculture, with profit margins ranging from 4% to 31%. Generally, costs of production will need to decrease for co-located offshore aquaculture to be economically viable in the U.S. Atlantic.

Assessment of the Fish Stock Status of the Spangled Emperor Lethrinus nebulosus Along the Coast of Balochistan, Pakistan

The sustainable exploitation of fishery resources in Pakistan was assessed using the catch-based Monte Carlo method (CMSY) and the length-based Bayesian biomass (LBB) method to evaluate the data-limited fishery of the Spangled Emperor, Lethrinus nebulosus. CMSY relies on catch data, resilience parameters, and quantitative stock status metrics, while LBB exclusively uses length–frequency (LF) data for stock assessments. This study utilized twenty-two years of catch–effort and LF data from 7230 fish along the Balochistan coastline in Pakistan. The study revealed that the relative biomass of the exploited stock, with a B/BMSY ratio of 0.557, indicates significant depletion. The relative exploitation rate (F/FMSY = 2.47) confirms that the stock is being severely overfished. The discrepancy between the optimal length at first capture (Lc_opt = 43.1 cm) and the length at first capture (Lc = 38.8 cm) further proves the overexploitation of L. nebulosus. The convergence of findings from both methodologies strengthens the reliability of stock status estimates. By integrating diverse data types and analytical frameworks, this study provides valuable insights into the sustainability of L. nebulosus populations. This dual approach not only underscores the importance of varied data sources but also informs management strategies for effective fisheries conservation, contributing to a deeper understanding of resource dynamics along the Balochistan coast of Pakistan.

Finding high posterior density phylogenies by systematically extending a directed acyclic graph

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees.

Spectral estimation for mixed causal-noncausal autoregressive models

. Mixed causal-noncausal autoregressive (MAR) processes driven by non Gaussian noise can replicate the non linear dynamics induced by local explosive episodes observed in financial bubbles. MAR models cannot be identified using second-order moments because they share spectral density with a set of different representations. In this study, we propose an identification and estimation method based on the third-order spectral density cumulant that can recover the complete probability structure of the errors without assuming any prior knowledge of the probability distribution function. Monte Carlo experiments demonstrated the estimation and identification performances. Furthermore, we illustrated the adequacy of our method through an empirical application to eight monthly commodity prices. The results show that MAR models can effectively capture the explosiveness and bubble phenomena generated in the commodities market.

OpenSSS: an open-source implementation of scatter estimation for 3D TOF-PET

BackgroundScatter correction is essential for quantitative and accurate time-of-flight (TOF) PET imaging. It is implemented by an accurate scatter estimation algorithm, to calculate the statistical distribution of scattered photons among the measured coincidences. However, to our knowledge, scatter estimation algorithms that account for TOF and that are compatible with custom geometries are not available in open-source reconstruction libraries, such as CASToR and STIR. To this end, we have developed an open-source implementation of the TOF-aware single-scatter-simulation (SSS) algorithm: openSSS.ResultsopenSSS is validated on NEMA phantoms and patient data, for three PET geometries, compared to Monte-Carlo simulations and two proprietary vendor-specific reconstruction platforms. The reconstructed images have similar contrast recovery and background variability, deviating by up to 3.7%-point on contrast recovery and 1.8 on background variability and looking visually similar.ConclusionWe have developed and validated an open-source scatter estimation library to complement reconstruction frameworks. By enabling vendor-independent clinical-grade reconstructions on custom scanner geometries, openSSS represents a crucial step in transparent research on quantitative PET and novel PET scanner designs.

Accuracy of a whole-body single-photon emission computed tomography with a thallium-bromide detector: Verification via Monte Carlo simulations.

Single-photon emission computed tomography (SPECT) devices equipped with cadmium-zinc-telluride (CZT) detectors achieve high contrast resolution because of their enhanced energy resolution. Recently, thallium bromide (TlBr) has gained attention as a detector material because of its high atomic number and density. This study evaluated the clinical applicability of a SPECT system equipped with TlBr detectors using Monte Carlo simulations, focusing on 99mTc and 177Lu imaging. This study used the Simulation of Imaging Nuclear Detectors Monte Carlo program to compare the imaging characteristics between a whole-body SPECT system equipped with TlBr (T-SPECT) and a system equipped with CZT detectors (C-SPECT). The simulations were performed using a three-dimensional brain phantom and a National Electrical Manufacturers Association body phantom to evaluate 99mTc and 177Lu imaging. The simulation parameters were accurately set by comparing them with the actual measurements. The T-SPECT system demonstrated improved energy resolution and higher detection efficiency than the C-SPECT system. In 99mTc imaging, T-SPECT demonstrated 1.71 times higher photopeak counts and improved contrast resolution. T-SPECT exhibited a significantly lower impact of hole tailing and higher-energy resolution (4.50% for T-SPECT vs. 7.34% for C-SPECT). Furthermore, T-SPECT showed higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values, indicating better image quality. In 177Lu imaging, T-SPECT showed 2.76 times higher photopeak counts and improved energy resolution (3.94% for T-SPECT vs. 5.20% for C-SPECT). T-SPECT demonstrated a higher contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) across all acquisition times, maintaining sufficient counts even with shorter acquisition times. Moreover, T-SPECT acquired higher low-frequency values in power spectrum density (PSD), indicating more accurate internal image reproduction. T-SPECT offers superior energy resolution and detection efficiency than C-SPECT. Moreover, T-SPECT can provide higher contrast resolution and sensitivity in clinical imaging with 99mTc and 177Lu. Furthermore, the Monte Carlo simulations are confirmed to be a valuable guide for the development of T-SPECT.

Monte Carlo calculated beam quality correction factors for high energy photon and electron fields.

Monte Carlo calculated beam quality correction factors for high energy photon and electron fields.

Appraisal of potential toxic elements pollution, sources apportionment, and health risks in groundwater from a coastal area of SE China.

Appraisal of potential toxic elements pollution, sources apportionment, and health risks in groundwater from a coastal area of SE China.

Reliability inference for dual stress factors accelerated degradation test based on the nonlinear Wiener process with three-source variability

. Accelerated degradation test plays a prominent role in reliability assessment and lifetime prediction for highly reliable products. The literature on accelerated degradation modeling primarily focuses on single stress factor situations. Therefore, this article proposes a nonlinear Wiener process-based dual stress factors accelerated degradation model with interaction, which simultaneously accounts for temporal variability, individual variability, and measurement variability. The maximum likelihood estimates (MLEs) of the model parameters are obtained using the profile likelihood approach and the Nelder-Mead algorithm, along with the MLEs for the reliability metrics of interest under normal operating conditions. We then provide bootstrap confidence intervals of the model parameters using the parametric percentile bootstrap method. The performance of the proposed method is assessed through the Monte Carlo simulation. Finally, a real-world example is presented to illustrate the application of our method.

Integrated analysis of circular economy policy innovations in developing countries through experts' perspectives.

Integrated analysis of circular economy policy innovations in developing countries through experts' perspectives.

Evaluation of dose calculation method with a combination of Monte Carlo method and removal-diffusion equation in heterogeneous geometry for boron neutron capture therapy

Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.

Computational Design of 3D Porous Aluminum Nitride Assembled From AlN‐Biphenylene Nanoribbons for Reversible Hydrogen Storage

AbstractHydrogen fuel with zero CO2 emission is of current interest for global carbon neutralization. In this study, a 3D porous aluminum nitride (p‐AlN) framework assemble from AlN‐biphenylene nanoribbons and investigate its performance in reversible hydrogen storage is presented. Using density functional theory (DFT), it is showed that the p‐AlN is dynamically and thermally stable, and exhibiting a semiconductor nature with a bandgap of 3.57 eV. The adsorption energy of H2 is in the range of −0.104 to −0.087 eV/H₂. According to ab initio molecular dynamics (AIMD) simulations, the H2 molecules remain stable above liquid nitrogen temperature (77 K). The studied system offers gravimetric (volumetric) capacities of 4.95 wt.% (67.86 g L−1) at 77 K/35 bar, and 1.41 wt.% (18.71 g L−1) at 298 K/100 bar, as revealed by grand canonical Monte Carlo (GCMC) simulations based on force field parameters fitted from DFT results.

Efficient Quantum Gibbs Samplers with Kubo–Martin–Schwinger Detailed Balance Condition

Lindblad dynamics and other open-system dynamics provide a promising path towards efficient Gibbs sampling on quantum computers. In these proposals, the Lindbladian is obtained via an algorithmic construction akin to designing an artificial thermostat in classical Monte Carlo or molecular dynamics methods, rather than being treated as an approximation to weakly coupled system-bath unitary dynamics. Recently, Chen, Kastoryano, and Gilyén (arXiv:2311.09207) introduced the first efficiently implementable Lindbladian satisfying the Kubo–Martin–Schwinger (KMS) detailed balance condition, which ensures that the Gibbs state is a fixed point of the dynamics and is applicable to non-commuting Hamiltonians. This Gibbs sampler uses a continuously parameterized set of jump operators, and the energy resolution required for implementing each jump operator depends only logarithmically on the precision and the mixing time. In this work, we build upon the structural characterization of KMS detailed balanced Lindbladians by Fagnola and Umanità, and develop a family of efficient quantum Gibbs samplers using a finite set of jump operators (the number can be as few as one), akin to the classical Markov chain-based sampling algorithm. Compared to the existing works, our quantum Gibbs samplers have a comparable quantum simulation cost but with greater design flexibility and a much simpler implementation and error analysis. Moreover, it encompasses the construction of Chen, Kastoryano, and Gilyén as a special instance.

Turing patterns on polymerized membranes: coarse-grained lattice modelling with an internal degree of freedom for polymer direction.

We numerically study Turing patterns (TPs) on two-dimensional surfaces with a square boundary in R3 using a surface model for polymerized membranes. The variables used to describe the membranes correspond to two distinct degrees of freedom: an internal degree of freedom for the polymer directions in addition to the positional degree of freedom. This generalised surface model enables us to identify non-trivial interference between the TP system and the membranes. To this end, we employ a hybrid numerical technique, utilising Monte Carlo updates for membrane configurations and discrete time iterations for the FitzHugh-Nagumo type Turing equation. The simulation results clearly show that anisotropies in the mechanical deformation properties, particularly the easy axes associated with the stretching and bending of the membranes, determine the direction of the TPs to be perpendicular or parallel to the easy axes. Additionally, by calculating the dependence of the maximum entropy on the internal degree of freedom, we can obtain information on the relaxation with respect to the polymer structure. This crucial information serves to remind us that non-equilibrium configurations can be captured within the canonical Monte Carlo simulations.

Robust cross-dock assignment problem with uncertain cost parameters

Amidst the unprecedented macroeconomic uncertainty following the pandemic, stakeholders in global supply chains face significant disruptions, necessitating robust decision-making frameworks. In this study, we present an enhanced solution approach to the Truck Dock Assignment with Temporary Storage (TDATS) problem, addressing critical uncertainties in supply chain and logistics operations. This deterministic model, TDATS, integrates operational, penalty, and storage costs, accounting for complex scenarios such as varying arrival times and temporary storage requirements. Additionally, we introduce a robust optimisation framework: Robust Truck Dock Assignment with Temporary Storage (R-TDATS), to effectively handle uncertainties in cost coefficients. Using polyhedral representations of uncertainty sets and the Bertsimas and Sim optimisation approach, the proposed solution provides decision-makers with conservative yet realistic solutions. Numerical experiments demonstrate the effectiveness of this method, showcasing its applicability to real-world instances of the problem. Moreover, the study also analyses the system's performance under higher levels of uncertainty by increasing the deviation of input variables to 25% and 50%. The results show a significantly increased Price of Robustness(PR) up to 39%, a Relative Increase (RI) in cost up to 88%, and Risk values up to 71%, particularly notable for the combined variation in all three cost coefficients (Co, Cp, Cs). Despite this, the magnitude of the cost increase remains relatively small, underscoring the importance of carefully adjusting and optimising input variables, such as demand forecasts and transportation schedules, to mitigate the impacts of uncertainty. An extensive sensitivity analysis using Monte Carlo simulations identifies a threshold point where increasing operational and storage costs reach a saturation level. Beyond this point, further increase in these costs have minimal impact, prompting a strategic shift towards penalty cost management. This includes nuanced trade-off analysis between operational/storage costs and penalty costs, guiding resource allocation and cost management strategies. Additionally, the analysis reveals a linear relationship between penalty costs and the unit penalty cost coefficient, facilitating the development of optimised penalty cost management strategies. The study offers valuable insights for decision-makers in the cross-docking sector, empowering them to make informed decisions that enhance the efficiency and resilience of supply chain networks amidst uncertainty.

Modified Kibria–Lukman Estimator for the Conway–Maxwell–Poisson Regression Model: Simulation and Application

This study presents a novel estimator that combines the Kibria–Lukman and ridge estimators to address the challenges of multicollinearity in Conway–Maxwell–Poisson (COMP) regression models. The Conventional COMP Maximum Likelihood Estimator (CMLE) is notably susceptible to the adverse effects of multicollinearity, underscoring the necessity for alternative estimation strategies. We comprehensively compare the proposed COMP Modified Kibria–Lukman estimator (CMKLE) against existing methodologies to mitigate multicollinearity effects. Through rigorous Monte Carlo simulations and real-world applications, our results demonstrate that the CMKLE exhibits superior resilience to multicollinearity while consistently achieving lower mean squared error (MSE) values. Additionally, our findings underscore the critical role of larger sample sizes in enhancing estimator performance, particularly in the presence of high multicollinearity and over-dispersion. Importantly, the CMKLE outperforms traditional estimators, including the CMLE, in predictive accuracy, reinforcing the imperative for judicious selection of estimation techniques in statistical modeling.