Monte Carlo Simulations Research Articles

Shaping membrane vesicles by adsorption of hinge-like nanoparticles.

The adsorption of particles onto fluid membranes can lead to membrane-mediated interactions between particles that promote their self-assembly and lead to changes in membrane morphology. However, in contrast with rigid particles, relatively little is known about deformable particles, which introduce additional complexities due to the mutual deformability of the particles and the membrane. Here, we use Monte Carlo simulations and umbrella sampling to investigate the equilibrium properties of hinge-like particles adsorbed on membrane vesicles by means of anisotropic, attractive interactions. We vary the hinge stiffness, adhesive area fraction, patterning of adhesive regions, and number of adsorbed particles. Depending on their properties, isolated particles can conform to the vesicle, induce invaginations of the membrane, or exhibit multistable behavior in which they sample distinct classes of configurations due to the interplay of particle and membrane deformations. With two adsorbed particles, the properties of the particles can be used to promote aggregation, bias the particles to different parts of the vesicle, or stabilize the coexistence of both cases. With multiple adsorbed particles, the number and type control their organization and collective impact on the vesicle, which can adopt shapes ranging from roughly spherical to dumbbell-like and multi-lobed. Our results highlight how modifying the mechanical properties and patterned adhesion of deformable particles, which is possible with DNA nanotechnology, influences their self-assembly and the resulting shapes of both the particles and vesicles.

First-principles thermodynamic investigation on the α phases in TiO and TiNb binary system.

O and Nb are two representative alloying elements of Ti to form high-temperature and corrosion resistance α Ti alloys. The investigation on the thermodynamic characteristics of α Ti-O and Ti-Nb has attracted much attention in recent years. However, in this regard, a satisfied experimental technique or modeling scheme is still yet to be developed due to the appearance of a variety of oxides in Ti-O and the mechanical instability present in Ti-Nb. Herein, we combined first-principles calculations with the cluster expansion method to investigate the ground-state characteristics for α Ti-O and α Ti-Nb systems. The atomic bonding interactions in these two systems were first revealed based on the calculated electronic structures. Afterward, the Debye-Grüneisen model and Monte Carlo simulations were employed together to investigate the thermodynamic properties of α phases in these two systems, and the effect of vibrational entropy on the order-disorder transition temperatures of the phases in α Ti-O system was first examined. A good agreement with experimentally reported phase boundaries is obtained in the Ti-Nb system by handling the mechanical instabilities introduced by the highly distorted structures. In addition, the cluster expansion coefficients for the Ti-O and Ti-Nb system offer a good starting point to investigate the phase equilibrium in Ti-Nb-O ternary alloy. We also believe the insights provided here would be helpful for those who would like to seek an efficient scheme they are confident with to investigate the phase thermodynamic properties of other hcp Ti-based alloys.

Markov models for clinical decision-making in radiation oncology: A systematic review.

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Giant Carrier Mobility in a Room-Temperature Ferromagnetic VSi2N4 Monolayer.

Using density functional theory (DFT), we investigate that two possible phases of VSi2N4 (VSN) may be realized, one called the "H phase" corresponding to what is known from calculation and herein the other new "T phase" being stabilized by a biaxial tensile strain of 3%. Significantly, the H phase is predicted to display a giant carrier mobility of 1 × 106 cm2 V-1 s-1, which exceeds that for most 2D magnetic materials, with a Curie temperature (TC) exceeding room temperature and a band gap of 2.01 eV at the K point. Following the H-T phase transition, the direct band gap shifts to the Γ point and increases to 2.59 eV. The Monte Carlo (MC) simulations also indicate that TC of the T phase VSN can be effectively modulated by strain, reaching room temperature under a biaxial strain of -4%. These results show that VSN should be a promising functional material for future nanoelectronics.

DNA-damage RBE assessment for combined boron and gadolinium neutron capture therapy.

Neutron capture therapy (NCT) by 10B and 157Gd agents is a unique irradiation-based method which can be used to treat brain tumors. Current study aims to quantitatively evaluate the relative biological effectiveness (RBE) and dose distributions during the combined BNCT and GdNCT modalities through a hybrid Monte Carlo (MC) simulation approach. Snyder head phantom as well as a cubic hypothetical tumor was at first modeled by Geant4 MC Code. Then, the energy spectra and dose distribution relevant to the released secondary particles during the combined Gd/BNCT were scored for different concentrations of 157Gd and 10B inside tumor volume. Finally, the scored energy spectra were imported to the MCDS code to estimate both RBESSB and RBEDSB values for different 157Gd concentrations. The results showed that combined Gd/BNCT increases the fluence-averaged RBESSB values by about 1.7 times when 157Gd concentration increments from 0 to 2000µg/g for both considered cell oxygen levels (pO2=10% and 100%). Besides, a reduction of about 26% was found for fluence-averaged RBEDSB values with an increment of 157Gd concentration in tumor volume. From the results, it can be concluded that combined Gd/BNCT technique can improve tumor coverage with higher dose levels but in the expense of RBEDSB reduction which can affect the clinical efficacy of the NCT technique.

Simulating the head of a TrueBeam linear particle accelerator and calculating the photoneutron spectrum on the central axis of a 10-MV photon using particle and heavy-ion transport system code.

Photon energy is higher than the (γ,n) threshold, allowing it to interact with the nuclei of materials with high z properties and liberate fast neutrons. This represents a potentially harmful source of radiation for humans and the environment. This study validated the Monte Carlo simulation, using the particle and heavy-ion transport code system (PHITS) on a TrueBeam 10-MV linear particle accelerator's head shielding model and then used this PHITS code to simulate a photo-neutron spectrum for the transport of the beam. The results showed that, when comparing the simulated to measured PDD and crosslines, 100% of the γ-indexes were<1 (γ3%/3mm) for both simulations, for both phase-space data source and a mono energy source. Neutron spectra were recorded in all parts of the TrueBeam's head, as well as photon neutron spectra at three points on the beamline.

Experimental and Monte Carlo based dosimetric investigation of a novel 3mm radiosurgery 3MV beam using the microSilicon detector.

The ZAP-X system is a novel gyroscopic radiosurgical system based on a 3MV linear accelerator and collimator cones with a diameter between 4 and 25mm. Advances in imaging modalities to detect small and early-stage pathologies allow for an early and less invasive treatment, where a smaller collimator matching the anatomical target could provide better sparing of surrounding healthy tissue. A novel 3mm collimator cone for the ZAP-X was developed. This study aims to investigate the usability of a commercial diode detector (microSilicon) for the dosimetric characterization of this small collimator cone; and to investigate the underlying small field perturbation effects. Profile measurements in five depths as well as PDD and output ratio measurements were performed with a microSilicon detector and radiochromic EBT3 films. In addition, comprehensive Monte Carlo simulations were performed to validate the measurement observations and to quantify the perturbation effects of the microSilicon detector in these extremely small field conditions. It is shown that the microSilicon detector enables an accurate dosimetric characterization of the 3mm beam. The profile parameters, such as the FWHM and 20%-80% penumbra width, agree within 0.1 to 0.2mm between film and detector measurements. The output ratios agree within the measurement uncertainty between microSilicon detector and films, whereas the comparisons of the PDD results show good agreement with the Monte Carlo simulations. The analysis of the perturbation factors of the microSilicon detector reveals a small field correction factor of approximately 3% for the 3mm circular beam and a correction factor smaller than 1.5% for field diameters above 3mm. It could be shown that the microSilicon detector is well-suitable for the characterization of the new 3mm circular beam of the ZAP-X system.

Electron beam response corrections for an ultra-high-dose-rate capable diode dosimeter.

Ultra-high-dose-rate (UHDR) electron beams have been commonly utilized in FLASH studies and the translation of FLASH Radiotherapy (RT) to the clinic. The EDGE diode detector has potential use for UHDR dosimetry albeit with a beam energy dependency observed. The purpose is to present the electron beam response for an EDGE detector in dependence on beam energy, to characterize the EDGE detector's response under UHDR conditions, and to validate correction factors derived from the first detailed Monte Carlo model of the EDGE diode against measurements, particularly under UHDR conditions. Percentage depth doses (PDDs) for the UHDR Mobetron were measured with both EDGE detectors and films. A detailed Monte Carlo (MC) model of the EDGE detector has been configured according to the blueprint provided by the manufacturer under an NDA agreement. Water/silicon dose ratios of EDGE detector for a series of mono-energetic electron beams have been calculated. The dependence of the water/silicon dose ratio on depth for a FLASH relevant electron beam was also studied. An analytical approach for the correction of PDD measured with EDGE detectors was established. Water/silicon dose ratio decreased with decreasing electron beam energy. For the Mobetron 9 MeV UHDR electron beam, the ratio decreased from 1.09 to 1.03 in the build-up region, maintained in range of 0.98-1.02 at the fall-off region and raised to a plateau in value of 1.08 at the tail. By applying the corrections, good agreement between the PDDs measured by the EDGE detector and those measured with film was achieved. Electron beam response of an UHDR capable EDGE detector was derived from first principles utilizing a sophisticated MC model. An analytical approach was validated for the PDDs of UHDR electron beams. The results demonstrated the capability of EDGE detector in measuring PDDs of UHDR electron beams.

Magnetic hysteresis mechanism in Lu0.9Sr0.1Cr0.5Fe0.5O3 studied by Monte Carlo simulations

In this work we report magnetic properties of the orthorhombic perovskite Lu0.9Sr0.1Cr0.5Fe0.5O3 synthesized by a wet chemical method. As in LuCr0.5Fe0.5O3 the compound with Sr shows the magnetization reversal phenomenon, but the magnetic order and the compensation temperature occur at higher temperatures. Interestingly, in M vs H curves a hysteresis loop is observed when Cr4+ and Cr3+ ions coexist as a consequence of the aliovalent substitution of Lu3+ by Sr2+ in the B sites of the perovskite. To explain this behavior, we performed numerical simulations with a magnetic model for Lu1−x Sr x Cr0.5Fe0.5O3 perovskites with x= 0 and x = 0.1. We found that the ferromagnetic coupling of Fe3+ and Cr4+ through superexchange interactions (according the empiric Goodenough–Kanamori–Anderson rules) increases the magnetization at high fields and that the presence of ferromagnetic clusters explains the hysteretic behavior found in simulations.

Design and characterisation of a minibeam collimator utilising Monte Carlo simulation and a clinical linear accelerator.

Spatially fractionated radiotherapy is showing promise as a treatment modality. Research has primarily focused on applications in proton therapy, however, there is some evidence that minibeam effects may also be present in photon therapy.&#xD;&#xD;This study presents a 3D printed tungsten minibeam collimator intended to produce peak-to-valley dose ratios (PVDR) of between seven and ten with a 1 MeV, bremsstrahlung generated, photon beam. The design of the collimator is motivated by a Monte Carlo study estimating the peak to valley dose ratio for different collimator designs at different energies. This collimator was characterised on a clinical linear accelerator (Elekta VersaHD).&#xD;&#xD;It was found that 1 MeV photon sources may generate PVDRs in the region of seven. The performance of the fabricated collimator was measured on a 250 keV superficial treatment unit and an Elekta VersaHD running in unflattened mode with a 6 MV beam. On the superficial unit the measured PVDR was greater than 10. On the Elekta VersaHD units the PVDR was measured to be between approximately 1.5 and 2.0 at 3 cm deep.

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

A Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC

A Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC

Novel estimation of African swine fever transmission parameters within smallholder villages in Lao P.D.R.

African Swine Fever (ASF) disease transmission parameters are crucial for making response and control decisions when faced with an outbreak, yet they are poorly quantified for smallholder and village contexts within Southeast Asia. Whilst disease-specific factors - such as latent and infectious periods - should remain reasonably consistent, host, environmental and management factors are likely to affect the rate of disease spread. These differences are investigated using Approximate Bayesian Computation with Sequential Monte-Carlo methods to provide disease parameter estimates in four naïve pig populations in villages of Lao People's Democratic Republic. The villages represent smallholder pig farmers of the Northern province of Oudomxay and the Southern province of Savannakhet, and the model utilised field mortality data to validate the transmission parameter estimates over the course of multiple model generations. The basic reproductive number between-pigs was estimated to range from 3.08 to 7.80, whilst the latent and infectious periods were consistent with those published in the literature for similar genotypes in the region (4.72 to 6.19days and 2.63 to 5.50days, respectively). These findings demonstrate that smallholder village pigs interact similarly to commercial pigs, however the spread of disease may occur slightly slower than in commercial study groups. Furthermore, the findings demonstrated that despite diversity across the study groups, the disease behaved in a consistent manner. This data can be used in disease control programs or for future modelling of ASF in smallholder contexts.

Concentration of Potentially Toxic Elements (PTEs) in Rapid Coffee Products in Bandar Abbas, Iran: Probabilistic Non-Carcinogenic and Carcinogenic Risk Assessment.

Coffee is one of the most widely consumed beverages in the world. However, coffee plants are often exposed to potentially toxic elements (PTEs) pollution. The main aims of current study were to detect the PTEs in instant coffee and health risk assessment of consumers in Bandar Abbas city. To achieve this, 40 samples of instant coffee were randomly collected from various points in the city in 2023 and PTEs concentrations were measured using flame atomic absorption spectrometry (FAAS). The non-carcinogenic and carcinogenic risks were calculated using Monte Carlo simulation (MCS) method. The concentrations of Fe and Cu were higher than other PTEs, equaling 404.41 mg/kg and 0.0046 mg/kg, respectively. The non-carcinogenic risk assessment revealed that THQ (Fe > Pb > As > Cd > Ni > Cu) and TTHQ levels were less than 1 based on the 95% percentile in adults and children, indicating there is no possibility of a non-carcinogenic risk associated with instant coffee. The carcinogenic risk due to inorganic As in instant coffee was acceptable (2.63E-5 and 1.27E-5 based on the 95% percentile for adults and children, respectively), therefore PTEs in instant coffee do not endanger the health of consumers.

Radiation-induced defects in the InGaN/GaN superlattice structure

With the molecular dynamics method, this paper investigates radiation-induced defects in the In0.16Ga0.84N/GaN superlattice structure (SLS) and the In0.04Ga0.96N/GaN SLS. In the temporal evolution of cascades, most of vacancies recombine with interstitials. The Monte Carlo simulations about the proportions of PKAs induced by 3 MeV protons were also considered in this work for calculating the weighted averages of surviving defects. For the In0.16Ga0.84N/GaN SLS irradiated by protons, around 82.6 percent of surviving vacancies are Ga vacancies while around 88.9 percent of surviving interstitials are Ga interstitials. For the In0.04Ga0.96N/GaN SLS irradiated by protons, around 87.3 percent of surviving vacancies are Ga vacancies while around 88.6 percent of surviving interstitials are Ga interstitials. N vacancies, N interstitials, and In vacancies also exist in irradiated InGaN/GaN SLS. Details about different types of defects are presented in this paper, which helps explain the microscopic mechanism of irradiated InGaN/GaN SLS. Since different types of defects have different influences on electronic and optical properties, simulations about the proportions of various defects in irradiated InGaN/GaN SLS help experimentalists find the effective factors of radiation-related changes in electronic and optical properties.

An end-to-end deep convolutional neural network-based dose engine for parotid gland cancer seed implant brachytherapy.

Seed implant brachytherapy (SIBT) is a promising treatment modality for parotid gland cancers (PGCs). However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice. This paper aims to develop an end-to-end deep convolutional neural network-based dose engine (DCNN-DE) to achieve fast and accurate dose calculation for PGC SIBT. A DCNN model was trained using the patient's CT images and TG-43-based dose maps as inputs, with the corresponding MCS-based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I-125 SIBT patient cases, and its transferability was tested on an additional 16 non-PGC head and neck cancers (HNCs) I-125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance. On the PGC testing dataset, the DCNN-DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67%±0.47% for clinical target volume (CTV) D90 and 1.04%±1.33% for skin D0.1cc. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (p<0.001) and 3.1% (p<0.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non-PGC HNC dataset, the DCNN-DE exhibited good transferability, achieving a CTV D90 PAE of 1.88%±1.73%. The DCNN-DE can generate a dose map in less than 10 ms. We have developed and validated an end-to-end DCNN-DE for PGC SIBT. The proposed DCNN-DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.

Proximal survival analysis to handle dependent right censoring

Abstract Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assuming censoring and failure time are independent within covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with absolute certainty. In cases of dependent censoring, covariate measurements are often, at best, proxies of underlying prognoses. In this article, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators, and we provide generic assumptions under which they are consistent, asymptotically normal, and doubly robust. We examine the finite-sample performance of our proposed estimators via a Monte Carlo simulation and apply them to the SEER-Medicare dataset.

Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation

Global greenhouse gas emissions from the built environment remain high, driving innovative approaches to develop and adopt building materials that can mitigate some of those emissions. However, life-cycle assessment (LCA) practices still lack standardized quantitative uncertainty assessment frameworks, which are urgently needed to robustly assess mitigation efforts. Previous works emphasize the importance of accounting for the three types of uncertainties that may exist within any quantitative assessment: parameter, scenario, and model uncertainty. Herein, we develop a quantitative uncertainty assessment framework that distinguishes between different types of uncertainties and suggest how these uncertainties could be handled systematically through a scenario-aware Monte Carlo simulation (MCS). We demonstrate the framework’s decision-informing power through a case study of two multilevel ordinary Portland cement (OPC) manufacturing scenarios. The MCS utilizes a first-principles-based OPC life-cycle inventory, which mitigates some of the model uncertainty that may exist in other empirical-based cement models. Remaining uncertainties are handled by scenario specification or sampling from developed probability distribution functions. We also suggest a standardized method for fitting distributions to parameter data by enumerating through and implementing distributions based on the Kolmogorov–Smirnov test. The level of detail brought by the high-resolution parameter breakdown of the model allows for developing emission distributions for each process of OPC manufacturing. This approach highlights how specific parameters, along with scenario framing, can impact overall OPC emissions. Another key takeaway includes relating the uncertainty of each process to its contributions to total OPC emissions, which can guide LCA modelers in allocating data collection and refinement efforts to processes with the highest contribution to cumulative uncertainty. Ultimately, the aim of this work is to provide a standardized framework that can provide robust estimates of building material emissions and be readily integrated within any uncertainty assessment.

Quantum scaling of the spin lattice relaxation rate in the checkerboard J-Q model.

Motivated by recent progress on the experimental realization of proximate deconfined quantum critical point in a frustrated quantum magnet, we study the low-energy spin dynamics of a related checkerboard J-Q model by using quantum Monte Carlo simulations. The ground state of this model undergoes a weakly first-order quantum phase transition with an emergent O(4) symmetry between an antiferromagnetic state and a plaquette valence bond solid. The calculated spin lattice relaxation rate of nuclear magnetic resonance, 1/T1, exhibits distinct low-temperature behaviors depending on the ground states. With decreasing the temperature, 1/T1 rises up on the antiferromagnetic side, characterizing a crossover to the renormalized classical regime, whereas 1/T1 drops exponentially on the side of valence bond solid, reflecting the gap opening in the plaquette ordered phase. The extracted spin gap scales with the distance to the transition point as a power-law with an exponent φ ≈ 0.3, consistent with the scaling ansatz φ = νz with ν ≈ 0.3 and z = 1. Near the quantum phase transition, the temperature dependent 1/T1 shows a broad crossover regime where a universal scaling 1/T1 ∼ T η with η ≈ 0.6 is found. Our results suggest a quantum scaling regime associated with the emergent enhanced symmetry near this first-order quantum phase transition.&#xD.

Effects of Contact Surface Shape on Dynamic Lifetime and Strength of Molecular Bond Clusters under Displacement- and Force-Controlled Loading Conditions.

Many experimental and theoretical studies have shown that the mechanical properties of cells and the extracellular matrix can significantly affect the lifetime and strength of the adhesion clusters of molecular bonds. However, there are few studies on how the shape of the contact surface affects the lifetime and strength of the adhesion clusters of molecular bonds, especially theoretical studies in this area. An idealized model of focal adhesion is adopted, in which two rigid media are bonded together by an array of receptor-ligand bonds modeled as Hookean springs on a complex surface topography, which is described by three parameters: the surface shape factor β, the length of a single identical surface shape L, and the amplitude of surface shapes w. In this study, systematic Monte Carlo simulations of this model are conducted to study the lifetime of the molecular bond cluster under linear incremental force loading and the strength of the molecular bond cluster under linear incremental displacement loading. We find that both small surface shape amplitudes and large surface shape factors will increase the lifetime and strength of the adhesion cluster, whereas the length of a single surface shape causes oscillations in the lifetime and strength of the cluster, and this oscillation amplitude is affected by the surface shape amplitude and the factor. At the same time, we also find that the pretension in the cluster will play a dominant role in the adhesion strength under large amplitudes and small factors of surface shapes. The physical mechanisms behind these phenomena are that the changes of the length of a single surface shape, the amplitude of surface shapes, and the surface shape factor cause the changes of stress concentration in the adhesion region, bond affinity, and the number of similar affinity bonds.