Simulation Toolkit Research Articles

Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA

BackgroundThis study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve. MethodsA Python-based tool named “complexDNA” was developed to facilitate the design of both simple and complex DNA geometries. This tool was employed to construct a human cell geometry with individual pairs of chromosomes. Subsequently, the performance of this chromosomal model was compared to the standard human cell model provided in the “molecularDNA” Geant4-DNA example. ResultsSimulations using the new chromosomal model revealed minimal discrepancies in DNA damage yield and fragment size distribution compared to the default human cell model. Notably, the chromosomal model demonstrated significant computational efficiency, requiring approximately three times less simulation time to achieve equivalent results. ConclusionsThis work highlights the importance of incorporating chromosomal structure into human cell models for radiation biology research. The “complexDNA” tool offers a valuable resource for creating intricate DNA structures for future studies. Further refinements, such as implementing smaller voxels for euchromatin regions, are proposed to enhance the model’s accuracy.

PyICLab: An integrated Python-based toolkit for in-silico simulations of ion chromatography

PyICLab is an open-source Python-based package featuring an object-oriented programming (OOP) interface, providing tools for realistic and customized numerical simulations of ion chromatography (IC). In this paper, we showcase PyICLab's use in simulating diverse separation scenarios, including isocratic carbonate elution, gradient hydroxide elution, high-concentration and large-volume injections. The accuracy of the embedded models was validated by demonstrating strong correlations between predicted and experimental results. Additionally, PyICLab's capability to handle complex IC configurations was demonstrated through a simulation of a column-switching system for seawater analysis. PyICLab offers valuable resources for chromatographic optimization, method development, and educational purposes. It is available on PyPI at pypi.org/project/pyIClab. Interested readers can install PyICLab using the pip command in a Python 3.11 or higher environment.

A Monte Carlo study comparing dead-time losses of a gamma camera between tungsten functional paper and lead sheet for dosimetry in targeted radionuclide therapy with Lu-177.

Dead-time loss is reported to be non-negligible for some patients with a high tumor burden in Lu-177 radionuclide therapy, even if the administered activity is 7.4GBq. Hence, we proposed a simple method to shorten the apparent dead time and reduce dead-time loss using a thin lead sheet in previous work. The collimator surface of the gamma camera was covered with a lead sheet in our proposed method. While allowing the detection of 208-keV gamma photons of Lu-177 that penetrate the sheet, photons with energies lower than 208keV, which cause dead-time loss, were shielded. In this study, we evaluated the usefulness of tungsten functional paper (TFP) for the proposed method using Monte Carlo simulation. The count rates in imaging of Lu-177 administered to patients were simulated with the International Commission on Radiological Protection (ICRP) 110 phantom using the GATE Monte Carlo simulation toolkit. The simulated gamma cameras with a 0.5-mm lead sheet, 1.2-mm TFP, or no filter were positioned closely on the anterior and posterior sides of the phantom. The apparent dead times and dead-time losses at 24h after administration were calculated for an energy window of 208keV ± 10%. Moreover, the dead-time losses at 24-120h were analytically assessed using activity excretion data of Lu-177-DOTATATE. The dead-time loss without a filter was 5% even 120h after administration in patients with a high tumor burden and slow excretion, while those with a lead sheet and TFP were 0.22 and 0.58 times less than those with no filter, respectively. The count rates with the TFP were 1.3 times higher than those with the lead sheet, and the TFP could maintain primary count rates at 91-94% of those without a filter. Although the apparent dead time and dead-time loss with the lead sheet were shorter and less than those with TFP, those with TFP were superior to those without a filter. The advantage of TFP over the lead sheet is that the decrease in primary count rates was less.

Metric learning guided sinogram denoising for cone beam CT enhancement.

Cone beam computed tomography (CBCT) is a widely available modality, but its clinical utility has been limited by low detail conspicuity and quantitative accuracy. Convenient post-reconstruction denoising is subject to back projected patterned residual, but joint denoise-reconstruction is typically computationally expensive and complex. In this study, we develop and evaluate a novel Metric-learning guided wavelet transform reconstruction (MEGATRON) approach to enhance image domain quality with projection-domain processing. Projection domain based processing has the benefit of being simple, efficient, and compatible with various reconstruction toolkit and vendor platforms. However, they also typically show inferior performance in the final reconstructed image, because the denoising goals in projection and image domains do not necessarily align. Motivated by these observations, this work aims to translate the demand for quality enhancement from the quantitative image domain to the more easily operable projection domain. Specifically, the proposed paradigm consists of a metric learning module and a denoising network module. Via metric learning, enhancement objectives on the wavelet encoded sinogram domain data are defined to reflect post-reconstruction image discrepancy. The denoising network maps measured cone-beam projection to its enhanced version, driven by the learnt objective. In doing so, the denoiser operates in the convenient sinogram to sinogram fashion but reflects improvement in reconstructed image as the final goal. Implementation-wise, metric learning was formalized as optimizing the weighted fitting of wavelet subbands, and a res-Unet, which is a Unet structure with residual blocks, was used for denoising. To access quantitative reference, cone-beam projections were simulated using the X-ray based Cancer Imaging Simulation Toolkit (XCIST). In both learning modules, a data set of 123 human thoraxes, which was from Open-Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression challenge, was used. Reconstructed CBCT thoracic images were compared against ground truth FB and performance was assessed in root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). MEGATRON achieved RMSE in HU value, PSNR, and SSIM were 30.97±4.25, 37.45±1.78, and 93.23±1.62, respectively. These values are on par with reported results from sophisticated physics-driven CBCT enhancement, demonstrating promise and utility of the proposed MEGATRON method. We have demonstrated that incorporating the proposed metric learning into sinogram denoising introduces awareness of reconstruction goal and improves final quantitative performance. The proposed approach is compatible with a wide range of denoiser network structures and reconstruction modules, to suit customized need or further improve performance.

Contributions of Cosmic-Ray Components to the HPGe Gamma Spectrometer Background Spectrum within the 0°-45° Zenith Angle Range

This study investigates the contributions of various cosmic-ray-induced components to the energy response of a High-Purity Germanium (HPGe) gamma spectrometer within the 0°-45° zenith angle range. The analysis focuses on muons, neutrons, protons, electrons, positrons, and photons. It also examines the impact of particle showers induced by muons interacting with the lead chamber and reaching the HPGe detector. Utilizing the GEANT4 simulation toolkit, we provide a detailed examination of these components' influence on the spectrum. The results confirm that within this zenith angle range, muons account for approximately 91% of the recorded cosmic-ray induced energy spectrum and contribute 54.6% to the overall cosmic-ray-induced background spectrum. The study also highlights the significant role of showers, especially those resulting from muon interactions with the lead shielding, in shaping the low-energy spectrum below 3 MeV.

Designing an E-Bike City: An automated process for network-wide multimodal road space reallocation

Effective and timely decarbonization of urban mobility requires systemic changes to transportation systems. High-quality cycling networks are seen as one of such measures and multiple scholars have developed automated approaches for a quick generation of such interventions. However, a common shortcoming is that they mostly ignore the tradeoffs and dependencies in allocating scarce road space to different modes. In this paper, we introduce an automated process for generating alternative multimodal transport networks within the boundaries of existing road space. Based on the user’s configuration, the resulting networks can prioritize separated cycling infrastructure, dedicated lanes for public transport, or a dense provision of on-street parking spaces. Also, the prioritization and the resulting designs can follow a variety of design principles, such as one-way streets, or impermeable superblocks. The outputs can be visualized on a map and used in common transport simulation toolkits. A case study in Zurich is used to demonstrate the process on a real-world network and discuss the results. The underlying software package SNMan (Street Network Manipulator) is available as open-source software and can be utilized by researchers and planners to envision alternative urban mobility futures in any place in the world.

Growth and characterization of highly efficient Cs[formula omitted]Cu[formula omitted]I[formula omitted] single crystal for [formula omitted]-ray detection

Metal halide perovskites, particularly zero-dimensional (0D) variants, have garnered significant attention as scintillator materials for various applications such as γ-ray spectroscopy, X-ray imaging, and security. Among these, Cs3Cu2I5 has emerged as a promising candidate owing to its exceptional characteristics, such as good energy resolution, high light output, and high stopping power. Although there is sufficient data available on the scintillation properties of Cs3Cu2I5 Single Crystal (SC) grown using various methods, there still remains a scarcity of data on the scintillation properties of SCs grown using solvent evaporation method at room temperature. In the present work, Cs3Cu2I5 SCs have been grown using the solvent evaporation method at room temperature and characterised for their structural, optical, and scintillation properties. The SC shows an orthorhombic structure with a bandgap of 3.58 eV. The optical properties of the SC reveal the existence of Self-Trapped Exciton (STE). The SC exhibits an energy resolution of 5.80 ±0.05% at 662 keV and a light output of 41,000 photons/MeV, making it suitable for γ-rays detection. The intrinsic photopeak efficiencies of Cs3Cu2I5 SC for γ-rays are reported for the first time. GEANT4 simulation toolkit has been used to perform realistic simulations, and the simulated and experimental efficiencies are compared. Simulations show that Cs3Cu2I5 scintillator has twice the efficiency of the NaI:Tl scintillator.

Learning 3D human–object interaction graphs from transferable context knowledge for construction monitoring

We propose a novel framework for detecting 3D human–object interactions (HOI) in construction sites and a toolkit for generating construction-related human–object interaction graphs. Computer vision methods have been adopted for construction site safety surveillance in recent years. The current computer vision methods rely on videos and images, with which safety verification is performed on common-sense knowledge, without considering 3D spatial relationships among the detected instances. We propose a new method to incorporate spatial understanding by directly inferring the interactions from 3D point cloud data. The proposed model is trained on a 3D construction site dataset generated from our crafted simulation toolkit. The model achieves 54.11% mean interaction over union (mIOU) and 72.98% average mean precision(mAP) for the worker–object interaction relationship recognition. The model is also validated on PiGraphs, a benchmarking dataset with 3D human–object interaction types, and compared against other existing 3D interaction detection frameworks. It was observed that it achieves superior performance from the state-of-the-art model, increasing the interaction detection mAP by 17.01%. Besides the 3D interaction model, we also simulate interactions from industrial surveillance footage using MoCap and physical constraints, which will be released to foster future studies in the domain.

Fast and Thermal Neutron Removal Cross-Section for Ceramic Glass Aluminum Oxynitride

This study investigates the effectiveness of transparent aluminum oxynitride (AlON) in neutron shielding, focusing on both fast and thermal neutrons. Using conventional radiation attenuation parameters, the macroscopic neutron removal cross-sections of AlON were calculated for varying neutron energies and material thicknesses. The Geant4 simulation toolkit was employed to model and analyze the neutron interactions with AlON. The results indicate that AlON exhibits a high neutron shielding capacity for fast neutrons, performing better than traditional shielding materials such as concrete and lead. However, for thermal neutrons, AlON's performance was less effective, only surpassing lead but not concrete or water. The findings suggest that while AlON is highly effective for fast neutron shielding, it may require complementary materials to adequately shield thermal neutrons. This could involve using AlON in combination with other materials to create a more comprehensive neutron shielding solution. AlON shows significant potential as a neutron shielding material, particularly for fast neutrons. Its integration with additional shielding materials could enhance its overall effectiveness, making it suitable for various nuclear and radiation protection applications.

Skin Absorbed Dose Coefficients for Human Legs from Beta Radiation as a Function of Height

External exposure to skin from beta-emitter radionuclides following severe reactor accidents or nuclear testing can result in beta burning and other health complications. The skin absorbed dose coefficient (SADC) measures the energy deposition into the skin during such accidents. The U.S. Environmental Protection Agency has published several reports to measure the possible energy deposition into the skin in such accidents. However, the most recent SADC published by Federal Guidance Report (FGR) 12 was computed only at one meter above the contaminated surface. Therefore, it was necessary to develop a model to estimate the absorbed dose coefficients for skin at different heights. In this manuscript, Geant4, a Monte Carlo simulator toolkit, was used to estimate the absorbed dose coefficients from electron sources located on the soil surface with energies ranging from 0.1 to 4 MeV. The energy deposited from primary electrons, secondary electrons, and photons in a 50 µm thick layer of epidermis tissue (Basal Cells Layer) located at a depth of 50 µm from the skin surface was estimated at several discrete heights of human leg phantom. More than 40% of the total energy deposited comes from secondary electrons and photons in energy sources of 0.1 and 0.2 MeV on average, but for higher energies, this percentage is less than 1%, which indicates primary electrons are the main source of the deposited energy in the skin. Furthermore, the results showed the energy deposited into skin closer to the ground was 50–100% higher than the previously estimated doses for 1 m above the ground. The results from Geant4 showed a great correlation (R2 = 0.972) with the FGR 12 data at one meter height, and they were aligned with the published values from FGR 12, which validated the simulation results. Therefore, the calculated dose coefficients for different energy sources and different heights could be used in radiation protection measurements.

ACE: A general-purpose non-Markovian open quantum systems simulation toolkit based on process tensors.

We describe a general-purpose computational toolkit for simulating open quantum systems, which provides numerically exact solutions for composites of zero-dimensional quantum systems that may be strongly coupled to multiple, quite general non-Markovian environments. It is based on process tensor matrix product operators (PT-MPOs), which efficiently encapsulate environment influences. The code features implementations of several PT-MPO algorithms, in particular Automated Compression of Environments for general environments comprised of independent modes as well as schemes for generalized spin boson models. The latter includes a divide-and-conquer scheme for periodic PT-MPOs, which enable million time step simulations for realistic models. PT-MPOs can be precalculated and reused for efficiently probing different time-dependent system Hamiltonians. They can also be stacked together and combined to provide numerically complete solutions of small networks of open quantum systems. The code is written in C++ and is fully controllable by configuration files, for which we have developed a versatile and compact human-readable format.

Quantitative analysis of dose dependent DNA fragmentation in dry pBR322 plasmid using long read sequencing and Monte Carlo simulations

Exposure to ionizing radiation can induce genetic aberrations via unrepaired DNA strand breaks. To investigate quantitatively the dose–effect relationship at the molecular level, we irradiated dry pBR322 plasmid DNA with 3 MeV protons and assessed fragmentation yields at different radiation doses using long-read sequencing from Oxford Nanopore Technologies. This technology applied to a reference DNA model revealed dose-dependent fragmentation, as evidenced by read length distributions, showing no discernible radiation sensitivity in specific genetic sequences. In addition, we propose a method for directly measuring the single-strand break (SSB) yield. Furthermore, through a comparative study with a collection of previous works on dry DNA irradiation, we show that the irradiation protocol leads to biases in the definition of ionizing sources. We support this scenario by discussing the size distributions of nanopore sequencing reads in the light of Geant4 and Geant4-DNA simulation toolkit predictions. We show that integrating long-read sequencing technologies with advanced Monte Carlo simulations paves a promising path toward advancing our comprehension and prediction of radiation-induced DNA fragmentation.

A comparative study of crystal geometry, material, and reconstruction algorithms on MicroPET scanner performance

Small-animal PET, also known as microPET, has been developed to enhance image quality by improving spatial resolution and sensitivity due to its smaller ring and crystal size. However, the reduction of crystal size and the presence of gaps between cuboidal crystals could reduce sensitivity. MicroPET with tapered arrays has been developed to simultaneously improve sensitivity and spatial resolution. This study compared the performance of 14 × 14 and 28 × 28 array microPETs using LSO and CZT crystal materials. The impact of crystal design and reconstruction algorithms on image quality and performance parameters was investigated, using GATE Monte Carlo simulation toolkit. The findings showed that sensitivity increased with crystal thickness, particularly in LSO. The 14 × 14 arrays consistently exhibited higher sensitivity, while the 28 × 28 arrays had superior spatial resolution. NECR values increased with crystal thickness, with LSO outperforming CZT. Scatter and random fractions were low. Different reconstruction algorithms had negligible effects on FWHM and CNR but influenced SNR, with the COSEM algorithm achieving significantly reduced noise. The small size of crystals and tapered arrays contributed to improved sensitivity and spatial resolution. However, there was a trade-off between sensitivity and spatial resolution. LSO crystals showed better performance, but the choice between array sizes depended on specific imaging requirements. This study provides insights into optimizing microPET design for enhanced imaging capabilities.

Optimization of the resampling method in the weighted ensemble simulation toolkit with parallelization and analysis (WESTPA).

Optimization of the resampling method in the weighted ensemble simulation toolkit with parallelization and analysis (WESTPA).

Simulations on the performances of neutron shielding glass materials and secondary radiation risks

Glasses with neutron shielding properties are extremely important for nuclear industries. Neutron interactions with ten different neutron shielding glass types were simulated by using GEANT4 Monte Carlo simulation toolkit. Simulation results were evaluated in terms of shielding performances, deposited energy properties and produced secondary radiation. Important evaluations were made in view of radiation safety, especially on secondary particles and radioactivity resulting from interactions between neutrons and glass samples. All simulated glass samples were found to emit risky secondary particles and radioactive nuclei after interacting with fast neutrons.

Research on the temperature control of a cryogenic urine storage system based on improved adaptive LADRC

Abstract Routine urine tests play a vital role in the diagnosis of kidney and urinary system diseases, with test outcomes directly affecting disease diagnosis. However, if urine is exposed to room temperature for extended periods, it may lead to rapid degradation of the sample, thus affecting the accuracy of the test. Therefore, for urine that requires long-term storage for testing, it is necessary to control the temperature within an appropriate range during the collection process to effectively preserve the activity of urinary proteins and ensure the reliability of the test. To address this issue, a cryogenic urine storage system was developed, and a fuzzy LADRC temperature control algorithm combined with a Smith predictor was proposed, specifically designed for optimizing the temperature stability during urine collection and storage processes. Initially, the characteristics of the cryogenic storage system were analyzed. A simulation model combining the fuzzy LADRC with a Smith predictor was built using the MATLAB/Simulink simulation toolkit, and a comparative simulation was conducted. The simulation results showed that the proposed algorithm significantly improved the time-domain response performance compared to PID control, verifying the superiority of the algorithm over PID control. The final experimental results demonstrated that the fuzzy LADRC temperature control algorithm with the Smith predictor essentially eliminated overshoot, with improvements in speed and stability compared to PID control.

Exploring the Executable SysML Capabilities to Integrate and Operate Hardware in the Loop

AbstractThese days, systems engineers use advanced software tools and standardized modeling methods to break down complex systems into simpler, less complicated parts that are easier to understand. This includes CATIA Magic that allows for systems architecture, as well as more in‐depth Systems Modeling Language (SysML) modeling and simulation. The goal of this paper is to explore the capabilities of SysML to model and control hardware in the loop (HIL). The model of a weapon system was created using several different diagrams commonly used in SysML. When these diagrams are used together with the CATIA Magic simulation toolkit (Magic Model Analyzer), they can be made executable and used for simulation purposes. While CATIA Magic was selected to provide a digital model of the weapon system, a LEGO Mindstorms EV3 development kit was selected to create a mockup of a physical system. The CATIA Magic model reacts to the inputs received from the LEGO sensors and sends control signals to motors built into the LEGO model. In general, the integration between the LEGO Mindstorms and CATIA Magic can be done successfully, however, this study revealed certain limitations pertinent to the current capability of CATIA Magic to make connections between the SysML model and LEGO hardware. While these limitations did not allow for a full integration and in‐depth testing of the created dynamic model, as intended, the paper presents the current state of the project discussing what is involved in integrating CATIA Magic and LEGO Mindstorms kits and what functionality could be achieved by doing this. The paper pursues both the educational aspect that allows a deeper exploration of the modern system modeling tools, and the practical aspect, showing how a SysML model can be integrated with HIL to provide opportunities to better understand the design space at earlier stages of system development.

Generating Baikal-GVD high energy cascade-like neutrino events with a GEANT4-based simulation toolkit

Abstract Using GEANT4 and Cosmic Ray Monte Carlo (CRMC) software packages, we developed a new simulation toolkit for astrophysical neutrino telescopes. By configuring the Baikal-GVD detector and comparing the vertex position and direction of incident particles, as well as the channel-by-channel signals, to the events detected by Baikal-GVD, we successfully generated 13 high-energy cascades neutrino events with the toolkit. Our analysis revealed a systematic offset between the reconstructed shower position and the true interaction position, with a distance close to the scale of the shower maximum of -0.64±1.32 m. The results of the linear fit between real data and simulation demonstrate reliable outcomes, indicating that the simulation toolkit is capable of studying the performance of astrophysical neutrino telescopes.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the “cradle-to-grave” life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy

Objective. To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Approach. Phantoms of polyethylene, gelatin, or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent 11C, 10C and 15O radionuclides contributions in each voxel were determined from the extracted time activity curves. Next, the experimental configurations were simulated in Geant4 Monte Carlo versions 10.0 to 11.1, with three different fragmentation models—binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 model-version combinations. Total positron annihilation and parent isotope production yields predicted by each simulation were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Finally, we compared the depth of the maximum positron annihilation yield and the distal point at which the positron yield decreases to 50% of peak between each model and the experimental results. Main results. Performance varied considerably across versions and models, with no one version/model combination providing the best prediction of all positron-emitting fragments in all evaluated target materials and irradiation conditions. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50%-of-peak point (10.2), while BIC also performed well and INCL generally performed the worst across most Geant4 versions. Significance. The best predictions of the spatial distribution of positron annihilations and positron-emitting fragment production along the beam path during carbon and oxygen ion therapy was obtained using Geant4 10.2.p03 with BIC or QMD. These version/model combinations are recommended for future heavy ion therapy research.