MC Method Research Articles

Computational models of direct and indirect X-ray breast imaging detectors for in silico trials.

To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers areneeded. The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of theirsystems. We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open-source GPU-accelerated Monte Carlo x-ray imaging simulation code (MC-GPU). We build on the previous version of the MC-GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)-approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x-ray photon till its first interaction within the x-ray detector. On the other hand, the signal spread in the x-ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron-hole pairs or optical photons) using analytical equationsin the detector. The analytical optical spread model for the indirect detector includes the depth-dependent spread and collection of optical photons and relies on a pre-computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x-ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measureddata. We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with directdetectors. We have presented computational models of three commercially available FDA-approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC-GPU codes that can be used to replicate three systems are available in open source format throughGitHub.

An iDTMC-based Monte Carlo depletion of a 3D SMR with intra-pin flux renormalization

Improved deterministic truncation of the Monte Carlo (iDTMC) solution method has been applied to neutronic depletion analyses of a nuclear reactor in this work. The theoretical background on the iDTMC method and material depletion calculation is briefly discussed, and the concept of an intra-pin renormalization scheme is presented to determine the detailed power and flux profiles with the pin-wise homogenized iDTMC solutions. The iDTMC method is applied and evaluated in a 100 MWth 3-dimensional small modular reactor (SMR) problem. Burnup-dependent multiplication factors, pin power distributions, and material densities are estimated by the iDTMC method and compared with the reference and standard Monte Carlo solutions. Numerical performance is investigated in terms of real standard deviation, root-mean-square and relative errors, computing time, and figure-of-merit. The numerical results demonstrate that the iDTMC method is superior to the standard MC method in estimating the burnup-dependent reactor eigenvalue and 3D pin-wise power distribution.

Seismic reliability evaluation of spatially correlated pipeline networks by quasi-Monte Carlo simulation

The reliability assessment of pipeline networks under nature disasters helps to improve the disaster prevention capability and overall resilience of pipeline networks. For the earthquake hazard, the seismic damage of pipelines in the network is usually correlated due to the spatial correlation of critical parameters, including characteristics of pipeline, soil properties and ground motions. This paper proposes a framework to evaluate the seismic reliability of pipeline network under spatially correlated parameters based on the quasi-Monte Carlo (QMC) simulation. The influence of spatial correlated random variables on the seismic reliability of pipeline network is investigated with explicit consideration of the correlated random variables including the peak ground velocity, the wall thickness and the yield stress of pipe segment. The framework has been implemented in seismic reliability evaluation of two pipeline networks. The result shows that the QMC method has better simulation accuracy than the standard MC method under same sampling numbers. The spatial correlations have different influences on the seismic connectivity reliability of pipeline networks with different topological redundancy and parallelism of connective paths from sources to user nodes. For the pipeline network with greater redundancy and parallelism, the model without considering the spatial correlations overestimates the network connectivity reliability.

Application of unified constraint-dependent Master Curve in fracture assessment of cracked pressure vessels

In this work, the application of the unified constraint-dependent Master Curve (UCMC) in fracture assessment based on the newly developed T0-Ad* correlation equation (T0 is the MC reference temperature, and Ad* is a new unified constraint parameter) has been investigated for cracked pressure vessels. A large number of three-dimensional (3D) finite element analyses have been conducted to calculate the parameter Ad* and the crack driving force parameter KJ for various axial semi-elliptical surface cracks. Based on the KJ-Ad* two-parameter, the locations of crack initiation along the crack fronts were predicted. The fracture prediction results of the UCMC method have been comparatively analyzed with those of the conventional standard MC (SMC) method. It has been shown that for most shallow cracks with lower constraint, the application of the UCMC method can reduce the conservatism of the SMC method. For some deeper and longer cracks with higher constraint, the UCMC method may eliminate slight non-conservative fracture assessment of the SMC method. The UCMC method can incorporate both in-plane and out-of-plane constraints in cracked structures, and it may be possibly applicable to wide ranges of structural geometries, crack sizes, materials, temperatures and loading levels.

A Steady-State Energy-Based Monte Carlo Method for Phonon Transport With Arbitrary Temperature Difference

Abstract A steady-state Monte Carlo scheme is developed for phonon transport based on the energy-based deviational phonon Boltzmann transport equation (PBTE). Other than tracking trajectories and time evolution of each packet in the transient methods, this steady-state method determines the paths of energy packets from being emitted to the steady-state through statistics of scattering probability. By reconsidering and developing the periodic heat flux boundary condition, we extend the capability of this method to systems with arbitrary temperature differences. This steady-state energy-based Monte Carlo (SEMC) method has been verified by comparing predictions with results from the previous discrete-ordinates method, the analytical solution, and transient MC methods for phonon transport in or across thin films. The present SEMC algorithm significantly improves the computational efficiency for a steady phonon transport process instead of time evolution by a transient algorithm.

Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms

The detection of meat texture is of great value because it is the key factor that drives consumer purchasing decisions. In this study, a hyperspectral imaging (HSI) system was utilized to determine the texture parameters of Tan mutton. In order to observe the influence of mutton spectra during different refrigeration periods for modeling, hyperspectral images of the Tan mutton samples were collected in the 900–1700 nm spectral range, and the correction models of Tan mutton texture parameters were established. The four machine learning algorithms, such as partial least squares regression (PLSR), least squares support vector machine (LSSVM), random forest (RF), and decision trees (DT), were developed to establish the spectral models based on the characteristic bands selected by different extraction strategies including interval variable iterative space shrinkage approach (iVISSA), competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA) and variable combination population analysis (VCPA). The results showed that the LSSVM-iVISSA-CARS models exhibited excellent performance in predicting hardness and gumminess with root-mean-square errors (RMSEP) of 5.259 and 3.051 as well as the coefficient of determination for the prediction data set (R p 2 ) of 0.986 and 0.984 respectively. Good performances were achieved with R p 2 of 0.987 and RMSEP of 4.970 with the LSSVM-iVISSA-SPA model for chewiness, respectively. Therefore, HSI has potential for the evaluation and prediction of texture parameters in Tan mutton. • Spectra at 900–1700 nm was applied for detecting texture parameters of Tan mutton. • The OSC and MC methods were both utilized to preprocess X-block and Y-block. • Informative wavelengths were selected using iVISSV-CARS and iVISSV-SPA approaches. • The LSSVM model yielded the best result in predicting hardness, gumminess and chewiness of Tan mutton.

A robust biostatistical method leverages informative but uncertainly determined qPCR data for biomarker detection, early diagnosis, and treatment.

As a common medium-throughput technique, qPCR (quantitative real-time polymerase chain reaction) is widely used to measure levels of nucleic acids. In addition to accurate and complete data, experimenters have unavoidably observed some incomplete and uncertainly determined qPCR data because of intrinsically low overall amounts of biological materials, such as nucleic acids present in biofluids. When there are samples with uncertainly determined qPCR data, some investigators apply the statistical complete-case method by excluding the subset of samples with uncertainly determined data from analysis (CO), while others simply choose not to analyze (CNA) these datasets altogether. To include as many observations as possible in analysis for interesting differential changes between groups, some investigators set incomplete observations equal to the maximum quality qPCR cycle (MC), such as 32 and 40. Although straightforward, these methods may decrease the sample size, skew the data distribution, and compromise statistical power and research reproducibility across replicate qPCR studies. To overcome the shortcomings of the existing, commonly-used qPCR data analysis methods and to join the efforts in advancing statistical analysis in rigorous preclinical research, we propose a robust nonparametric statistical cycle-to-threshold method (CTOT) to analyze incomplete qPCR data for two-group comparisons. CTOT incorporates important characteristics of qPCR data and time-to-event statistical methodology, resulting in a novel analytical method for qPCR data that is built around good quality data from all subjects, certainly determined or not. Considering the benchmark full data (BFD), we compared the abilities of CTOT, CO, MC, and CNA statistical methods to detect interesting differential changes between groups with informative but uncertainly determined qPCR data. Our simulations and applications show that CTOT improves the power of detecting and confirming differential changes in many situations over the three commonly used methods without excess type I errors. The robust nonparametric statistical method of CTOT helps leverage qPCR technology and increase the power to detect differential changes that may assist decision making with respect to biomarker detection and early diagnosis, with the goal of improving the management of patient healthcare.

Study on Quantum Finance Algorithm: Quantum Monte Carlo Algorithm based on European Option Pricing

As one of the major methods for the simulation of option pricing, Monte Carlo method assumes random fluctuations in the distribution of asset prices. Under certain uncertainties process, different evolution paths could be simulated so as to finally yield the expectation value of the asset price, which requires a lot of simulations to ensure the accuracy based on huge and expensive calculations. In order to solve the above computational problem, quantum Monte Carlo (QMC) has been established and applied in the relevant systems such as European call options. In this work, both MC and QM methods are adopted to simulate European call options. Based on the preparation of quantum states in QMC algorithm and the construction of quantum circuits by simulating a quantum hardware environment on a traditional computer, the amplitude estimation (AE) algorithm is found to play a secondary role in accelerating the pricing of European options. More importantly, the payoff function and the time required for the simulation in QMC method show some improvements than those in MC method.

Study on the Umbilical Cord-Mesenchymal Stem Cell Manufacturing Using Clinical-Grade Culture Medium.

Mesenchymal stem/stromal cell (MSC)-based therapies have been gaining increasing attention owing to their application in various diseases and conditions. In this study, we aimed to identify the optimal condition for industrial-scale MSC manufacturing. MSCs were isolated from umbilical cord (UC) tissues by implementing the explant method (Exp) or a collagenase based-enzymatic digestion method (Col), using a good manufacturing practice-compatible serum-free medium developed in-house. Microarray analysis demonstrated that the gene expression profiles of Exp-MSCs and Col-MSCs did not significantly differ according to the method of isolation or the culture conditions used. The isolated UC-MSCs were then subjected to expansion using conventional static culture (ST) or microcarrier-based culture in stirred-tank bioreactors (MC). Metabolomic and cytokine array analyses were conducted to evaluate the biochemical status of the MSCs. However, no remarkable differences in the metabolic profile and cytokine secretome between ST-MSCs and MC-MSCs were observed. On the contrary, we observed for the first time that the hydrophobic components of ST-MSCs and MC-MSCs were different, which suggested that the cell membrane distribution of fatty acids and lipids was altered in the process of adaptation to shear stress in MC-MSCs. These results establish the flexibility of the isolation and expansion method for UC-MSCs during the manufacturing processes and provide new insights into the minor differences between expansion methods that may exert remarkable effects on MSCs. In conclusion, we demonstrated the feasibility of both Exp-MSCs and Col-MSCs and MC and ST culture methods for scale-up and scale-out of MSC production, as well as the equivalence of these cells. As for the industrialized mass production of MSCs, enzyme-based methods for isolation and cell expansion in a bioreactor were considered to be more suitable. The methods developed, which underwent comprehensive evaluation in this study, may contribute toward the provision of sufficient MSC sources and the establishment of cost-effective MSC therapies. Impact statement Our in-house-developed good manufacturing practice-grade serum-free medium could be used for both isolation (Exp and Col) and expansion (ST and MC) of umbilical cord (UC)-mesenchymal stem/stromal cells (MSCs). Characteristics of the obtained UC-MSCs were widely assessed with regard to gene expression, metabolome, and secretome. Cellular characteristics and efficacy were observed to be equivalently maintained among whichever technique was applied. In addition, our research presents the first evidence that bioreactor and microcarrier-based MSC cultures alter the fatty acid and phospholipid composition of MSCs. These results provide new insights into the differences between expansion methods that may exert remarkable effects on MSCs.

A Comparison of Conventional Empirical Formula and MCNPX Code in the Estimations of Photon and Neutron Skyshine Rates for an 18MV Radiotherapy Bunker

Purpose: A physical phenomenon, scattering the radiation by the atmosphere above the room to the points at ground level around the linac treatment room is known as skyshine radiation. This study aimed to estimate photon and neutron skyshine from a linac in a high-energy radiation therapy facility. Materials and Methods: The empirical method of NCRP report 151 and MC simulations were employed to estimate skyshine radiation dose from the 18MV linac photon beam. A linac and its bunker were modeled and skyshine dose equivalent from photons and secondary neutrons were derived and compared in the control room, corridor, sidewalk and, parking. Results: The photon skyshine dose rates calculations by the MC method varied from 0.43 µSv/h at the sidewalk to 6.2 µSv/h at the control room. The ratios of NCRP to MCNP calculations varied from 3.58 for the corridor to 16.14 for the control room. For the neutron skyshine dose rate at distances shorter than 20m, it was found to be 10.4 nSv/h and the ratios of the NCRP to MCNP were 1.26 at the control room and 3.34 at the sidewalk. Conclusion: It was concluded that the empirical method overestimates photon and neutron skyshine dose rates in comparison to the MCNPX code. The refinement of the proposed empirical method of NCRP 151 and application of MC methods are strongly suggested for more reliable calculations of skyshine radiations.

Performance optimization of a tri-hybrid method for estimation of patient scatter into the EPID.

On‐treatment EPID images are contaminated with patient‐generated scattered photons. If this component can be accurately estimated, its effect can be removed, and therefore a corresponding in vivo patient dose estimate will be more accurate. Our group previously developed a "tri‐hybrid" (TH) algorithm to provide fast but accurate estimates of patient‐generated photon scatter. The algorithm uses an analytical method to solve for singly‐scattered photon fluence, a modified Monte Carlo hybrid method to solve for multiply‐scattered photon fluence, and a pencil beam scatter kernel method to solve for electron interaction generated scattered photon fluence. However, for efficient clinical implementation, spatial and energy sampling must be optimized for speed while maintaining overall accuracy.In this work, the most significant sampling issues were examined, including spatial sampling settings for the patient voxel size, the number of Monte Carlo histories used in the modified hybrid MC method, scatter order sampling for the hybrid method, and also a range of energy spectrum sampling (i.e., energy bin sizes).The total predicted patient‐scattered photon fluence entering the EPID was compared with full MC simulation (EGSnrc) for validation. Three phantoms were tested with 6 and 18 MV beam energies, field sizes of 4 × 4, 10 × 10, and 20 × 20 cm2, and source‐to‐imager distance of 140 cm to develop a set of optimal sampling settings.With the recommended sampling, accuracy and precision of the total‐scattered energy fluence of the TH patient scatter prediction method are within 0.9% and 1.2%, respectively, for all test cases compared with full MC simulation results. For the mean energy spectrum across the imaging plane, comparison of TH with full MC simulation showed 95% overlap.This study has optimized sampling settings so that they have minimal impact on patient scatter prediction accuracy while maintaining maximum execution speed, a critical step for future clinical implementation.

The calculations of pore structure parameters from gas adsorption experiments of shales: Which models are better?

Currently, low-pressure gas (carbon dioxide and nitrogen) adsorption experiments have been widely applied to describe pore structure features of shales. However, there are no standard models in calculations of pore structure parameters from low-pressure gas adsorption experiments, which limits comparison between various studies. To evaluate the applicability of calculating methods, 9 Chang 7 shale samples were performed gas adsorption experiments and pore structure parameters are obtained using Monte Carlo simulation (MC), Density Functional Theory (DFT) and Barrett-Joyner-Halenda (BJH) methods. The results show that MC and DFT can accurately characterize micropore structures, but MC model provides a wider pore size range than DFT. Therefore, we recommend that MC method should be used to get micropore parameters from CO2 adsorption data. Under the influence of tensile strength effect (TSE), a fake peak located at ~3.8 nm in pore size distribution (PSD) lines obtained from N2 desorption curves with BJH model, and an overestimation for pore structure parameters in meso-to macropore dimension based on this method was found. This phenomenon implied that the parameters from N2 desorption using BJH model are not applicable for the calculation of parameters for pore structure. What's more, compared with DFT method, BJH model based on adsorption curve ignores the effect of adsorption potential on location of pore condensation transition, which may lead to an underestimation of the shale pore structure parameters in narrow mesopore range (<10 nm). Therefore, DFT method was recommended to characterize the meso- and macroporous pore structure of shales based on N2 adsorption data.

Quasielastic electromagnetic scattering cross sections and world data comparisons in the GENIE Monte Carlo event generator

The usage of Monte Carlo neutrino event generators (MC$\nu$EGs) is a norm within the high-energy $\nu$ scattering community. The relevance of quasielastic (QE) energy regimes to $\nu$ oscillation experiments implies that accurate calculations of $\nu A$ cross sections in this regime will be a key contributor to reducing the systematic uncertainties affecting the extraction of oscillation parameters. In spite of this, many MC$\nu$EGs utilize highly phenomenological, parameterized models of QE scattering cross sections. Moreover, a culture of validation of MC$\nu$EGs against prolific electron ($e$) scattering data has been historically lacking. In this work, we implement new $e A$ cross sections obtained from nuclear ab initio approaches in GENIE, the primary MC$\nu$EG utilized by the FNAL community. In particular, we utilize results from Quantum MC methods which solve the many-body nuclear problem in the Short-Time Approximation (STA), allowing consistent retention of two-nucleon dynamics which are crucial to explain available nuclear electromagnetic (electroweak) data over a wide range of energy and momentum transfers. This new implementation in GENIE is fully tested against the world QE electromagnetic data, finding agreement with available data below $\sim2\,$GeV of beam energy with the aid of a scaling function formalism. The STA is currently limited to study $A\leq12$ nuclei, however, its semi-inclusive multibody identity components are exportable to other many-body computational techniques such as Auxiliary Field Diffusion MC which can reach $A\leq40$ systems while continuing to realize the factorization contained within the STA's multinucleon dynamics. Together, these developments promise to make future experiments such as DUNE more accurate in their assessment of MC$\nu$EG systematics, $\nu$ properties, and potentially empower the discovery of physics beyond the Standard Model.

Evaluation of susceptibility and response in the surface of agents of surface mycoses (Trichophyton mentagrophytes; T. tonsurans) to antifungal drugs of interest in a medical clinic

Introduction: The resistance of fungal species to drugs usually used in clinics is of great interest in the medical field. Objective: To evaluate susceptibility and in vitro response of species of Trichophyton spp. to antifungal drugs of interest in clinical medicine. Methods: 12 samples of clinical isolates from humans were used, nine of T. mentagrophytes and three of T. tonsurans. Susceptibility tests were performed according to the agar diffusion (AD) and broth microdilution (BM) methods. Results: In the AD method, the species T. tonsurans presented a percentage of sensitivity of 33% in relation to amphotericin B and 66% to itraconazole, with 100% resistance to ketoconazole and fluconazole. T. mentagrophytes also showed 100% resistance to ketoconazole in this technique, with 11% sensitivity to ketoconazole, 22% to itraconazole and 22% of samples classified as sensitive dose dependent. In the MC method, the species T. tonsurans presented a sensitivity percentage of 66%, 55% and 33% in relation to ketoconazole, fluconazole and itraconazole, respectively. The T. mentagrophytes species presented sensitivity percentages of 11%, 11%, 33% and 55% for amphotericin B, itraconazole, ketoconazole and fluconazole, respectively. Conclusion: There was resistance in vitro of the species of T. mentagrophytes and T. tonsurans against the antifungal fluconazole and relative resistance against ketoconazole in the AD method. In BM, however, important percentages of sensitivity were observed for the two species analyzed in relation to the antifungals fluconazole and ketoconazole when compared to itraconazole and amphotericin B.

Effect of different target materials of LINAC head on photon spectrum

ABSTRACT The X-rays were examined, which consist of the effect of 6 MeV energized electrons on different thickness and material of targets, using the MC method. When the PDDs are examined, we can see that the curves obtained for the Target 4 and Target 8 containing only tungsten material are very close PDD values to each other. The highest TPR20,10 value was also obtained for Target 8. For Target 4 and Target 8, which are composed only of tungsten material, when we examine the mean photon energy distributions formed, we observe an increase in energy towards the edge of the field. We obtained the closest TPR20,10 value for Target 6 to the TPR20,10 value generated by the original LINAC target (Target 1). However, the mean photon energy distribution value obtained for Target 6 is higher than the mean photon energy distribution value obtained from the Target 1. The mean photon energy distribution generated by the selected material according to the desired X-ray penetration and the homogeneity of this energy should be considered. The results obtained in the study are of a guiding nature both in modeling studies and for companies that will make target production.

Analysis of physical dose enhancement in nano-scale for nanoparticle-based radiation therapy: a Cluster and endothelial cell model

Objective(s): One major difficulty of conventional radiotherapy is the lack of selectivity between the tumor and the organs at risk. In nanoparticle aided radiotherapy, heavy elements are present at higher concentrations in the tumor than normal tissues. This study aimed to model the characteristics of secondary electrons generated from the interaction of clusters comprised of five different nanoparticles including Gold, Gadolinium, Iridium, Bismuth, and Hafnium atoms with low energy x-rays (similar to brachytherapy sources in terms of energy) as a function of nanoparticle size and beam energy. Materials and Methods: To better evaluate the contributions of secondary electrons in energy deposition, and also to develop a framework in analyzing further measurements in the future, we attempted to enhance and promote existing mathematical models for energy deposition in endothelial cells by nanoparticle-enhanced radiotherapy. Also, the MCNPX Monte Carlo code was used to model the identical geometry and the dose enhancement factor was calculated for all types of simulated nano-clusters.Results: Our results showed that for our model consist of a nano-cluster and an endothelial cell the DEF significantly depends on the energy of photons and L- and K-edge binding energy of the atoms inside the nano-cluster. However, for Gd at the energy 60 keV, a higher dose enhancement factor was seen.Conclusion: It can be concluded that the mathematical model considers the DEF variation with photon energy and the effect of NP type is considered in DEF calculations. However, the MC method has indicated very high sensitivity to photon energy, and NP type compared to the mathematical method.

A new analytical approach for photons conversion coefficients calculations of the human lens eye

In the previous paper [1], the air Kerma to Hp(3) conversion coefficients for monoenergetic photons from 0.02–10 MeV were calculated, using MCNP-6 and EGSnrc codes. The Hp(3)/Kair conversion coefficients have been calculated using a new analytical approach for which an equation on base non-linear model is proposed in this work. Our results in a good agreement were found, with the reference data El Bardouni, 2019 [1]. The analytical approach is successful and powerful such as in MC methods to calculate the conversion coefficients.

Physics-Based Computational Protein Design: An Update.

We describe methods for physics-based protein design and some recent applications from our work. We present the physical interpretation of a MC simulation in sequence space and show that sequences and conformations form a well-defined statistical ensemble, explored with Monte Carlo and Boltzmann sampling. The folded state energy combines molecular mechanics for solutes with continuum electrostatics for solvent. We usually assume one or a few fixed protein backbone structures and discrete side chain rotamers. Methods based on molecular dynamics, which introduce additional backbone and side chain flexibility, are under development. The redesign of a PDZ domain and an aminoacyl-tRNA synthetase enzyme were successful. We describe a versatile, adaptive, Wang-Landau MC method that can be used to design for substrate affinity, catalytic rate, catalytic efficiency, or the specificity of these properties. The methods are transferable to all biomolecules, can be systematically improved, and give physical insights.

Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data

Quasi-Monte Carlo (QMC) methods using quasi-random sequences, as opposed to pseudo-random samples, are proposed for use in the joint modelling of time-to-event and multivariate longitudinal data. The QMC integration framework extends the Monte Carlo Expectation Maximisation approaches that are commonly adopted, namely using ordinary and antithetic variates. The motivation of QMC integration is to increase the convergence speed by using nodes that are scattered more uniformly. Through simulation, estimates and computational times are compared and this is followed with an application to a clinical dataset. There is a distinct speed advantage in using QMC methods for small sample sizes and QMC is comparable to the antithetic MC method for moderate sample sizes. The new method is available in an updated version of the R package joineRML.

FAVE: A Fast and Efficient Network Flow AVailability Estimation Method With Bounded Relative Error

Capacity planning and sales projection are essential tasks for network operators. This work aims to help network providers to carry out network capacity planning and sales projection by answering: Given topology and capacity, whether the network can serve current flow demands with high probabilities? We name such probability as the “ flow availability ”, and present the flow availability estimation (FAVE) problem with generalizing the classical network connectivity based and maximum flow based reliability estimations. To quickly estimate flow availabilities, we utilize correlations among link and flow failures to figure out the importance of roles played by different links in flow failures (i.e., flow demands could not be satisfied). And we design three sequential importance sampling (SIS) estimation methods, which are: (1) Accurate and efficient : They achieve a bounded or even vanishing relative error with linear computational complexities. Hence they can provide more accurate estimations in less simulation time. (2) Robust and scalable: They maintain such estimation efficiencies even if only a partial SEED set information is available, or when the FAVE problem is extended to the multiple flows case. When applying to a realistic backbone network, our method can reduce the flow availability estimation cost by 900 and 130 times compared with MC and baseline IS methods; and also facilitate capacity planning and sales projection by providing better flow availability guarantees, compared with traditional methods.