Versatile Simulation Research Articles

Two-Step Chirality Transfer to Twisted Assemblies: Synergistic Interplay of Chiral and Aggregation Interactions.

Chirality plays a pivotal role in both the origin of life and the self-assembly of materials. However, the governing principles behind chirality transfer in hierarchical self-assembly across multiple length scales remain elusive. Here, we propose a concise and versatile simulation strategy using the patchy particle chain model to investigate the self-assembly of rods interacting through chiral and aggregation interactions. We reveal that chiral interaction possessing an entropic nature, amplifies the fluctuations and twists in the alignment of rods, while aggregation interaction serves as a foundational platform for aggregation and assembly. When both interactions exhibit moderate absolute and relative values, their synergistic interplay facilitates the chirality transfer from rods to assemblies, resulting in the formation of chiral mesoscale ordered structures. Furthermore, we observe a two-step chirality transfer process by monitoring the formation kinetics of the twisted assemblies. This work not only provides a comprehensive insight into chirality transfer mechanisms, but also introduces a versatile mesoscale simulation framework for exploring the role of chirality in hierarchical self-assembly.

Monte Carlo simulation of initial and volume ion recombination correction factor in plane parallel ionization chambers exposed to ion beams

Objective. Accurate reference dosimetry with ionization chambers (ICs) relies on correcting for various influencing factors, including ion recombination. Theoretical frameworks, such as the Boag and Jaffe theories, are conventionally used to describe the ion recombination correction factors. Experimental methods are time consuming, the applicability may be limited and, in some cases, impractical to be used in clinical routine. The development of simulation tools becomes necessary to enhance the understanding of recombination under circumstances that may differ from conventional use. Before progressing, it is crucial to benchmark novel approaches to calculate ion recombination losses under known conditions. In this study, we introduce and validate a versatile simulation tool based on a Monte Carlo scheme for calculating initial and volume ion recombination correction factors in air-filled ICs exposed to ion beams with clinical dose rates. Approach. The simulation includes gaussian distribution of ion positions to model the distribution of charge carriers along the chamber volume. It accounts for various physical transport effects, including drift, diffusion, space charge screening and free electron fraction. To compute ion recombination, a Monte Carlo scheme is used due to its versatility in multiple geometries, without exhibiting convergence problems associated with numerically solved procedures. Main results. The code is validated in conventional dose rates against Jaffe’s theory for initial recombination and Boag’s theory for volume recombination based on parameters derived from experimental data including proton, helium and carbon ion beams measured with a plane parallel IC. Significance. The simulation demonstrates excellent agreement, typically 0.05% or less relative difference with the theoretical and experimental data. The current code successfully predicts ion recombination correction factors, in a large variety of ion beams, including different temporal beam structures.

Large-scale photonic network with squeezed vacuum states for molecular vibronic spectroscopy

Although molecular vibronic spectra generation is pivotal for chemical analysis, tackling such exponentially complex tasks on classical computers remains inefficient. Quantum simulation, though theoretically promising, faces technological challenges in experimentally extracting vibronic spectra for molecules with multiple modes. Here, we propose a nontrivial algorithm to generate the vibronic spectra using states with zero displacements (squeezed vacuum states) coupled to a linear optical network, offering ease of experimental implementation. We also fabricate an integrated quantum photonic microprocessor chip as a versatile simulation platform containing 16 modes of single-mode squeezed vacuum states and a fully programmable interferometer network. Molecular vibronic spectra of formic acid and thymine under the Condon approximation are simulated using the quantum microprocessor chip with high reconstructed fidelity ( > 92%). Furthermore, vibronic spectra of naphthalene, phenanthrene, and benzene under the non-Condon approximation are also experimentally simulated. Such demonstrations could pave the way for solving complicated quantum chemistry problems involving vibronic spectra and computational tasks beyond the reach of classical computers.

Physics-based discrete models for magneto-mechanical metamaterials

Magneto-mechanical metamaterials are emerging smart materials whose mechanical responses can be tailored through structure architecture and magnetic interactions. The latter provides additional freedom in the material design space and leads to novel behaviors due to its nonlocal nature. The enriched functionalities open new possibilities in various applications, such as actuators, energy absorbers, and soft robots. However, the nonlinear and nonlocal coupling between elastic and magnetic forces poses a great challenge in the modeling and simulation of these systems, further hindering theory-based rational design strategies. Here, we focus on a class of magneto-mechanical metamaterials comprising elastic solids embedded with rigid permanent magnets. The clear separation between elastic and magnetic forces simplifies the design and fabrication process, yet their nonlocal interplay still allows for complex behaviors. We present a simulation framework for such magneto-mechanical metamaterials by combining a lattice spring model for the elastic solid with the dipole model for the magnetic interactions and implementing it in the LAMMPS molecular dynamics software. We demonstrate the capabilities of our framework by simulating a few representative structures, including shape-locking lattice metamaterials, a soft cellular solid with controllable buckling, and a metamaterial chain with phase-transforming behavior. For the shape-locking lattice metamaterials, we successfully capture the magnetic-actuation-driven reconfiguration and the nonlinear mechanical response of the curved lattices. For the soft cellular solid, we identify its buckling patterns under external non-uniform magnetic fields and simulate a buckling evolution process consistent with experiments. For the metamaterial chain, we include the strong long-range interactions among the embedded magnets and reproduce the controllable phase transitions in the experiments. Our work provides a simple yet versatile simulation methodology to investigate the nonlinear mechanical behaviors in the presence of strong external and internal magnetic forces, which will facilitate the design and analysis of magneto-mechanical materials. It can also be applied to other magnetically-driven smart structures, such as soft robots.

GPT-4 as a biomedical simulator

BackgroundComputational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems. MethodsWe introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients. ResultsIn qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival. ConclusionThis proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.

Advancements and Challenges in IoT Simulators: A Comprehensive Review.

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term "Internet of Things", which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators' capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.

Hydrodynamic lubrication behavior under miscellaneous operational conditions via CGB numerical model

The current study is basically focused on investigating the possible ways to enhance the lubricating oil film performance and hence ship energy efficiency of marine journal bearings in general terms. For the intended purpose, a novel numerical Circumferential Grooved Bearing (CGB) model was developed to assess journal bearing operating conditions based on versatile simulation analyses. Prior to all conducted test trials, the model validation has been confirmed based on the conformity between numerical outcomes and those derived from experimental procedures depending on Universal Journal Bearing Test Rig. CGB performance was evaluated at slow speed variations from 25 rpm up to 125 rpm and different applied loads utilizing the two lubricant oil grades of 5W40 and 0W30. Reducing shaft rotational speed by 80% was found to incur a reduction in the maximum oil film pressure value by 1.17 bar and 0.97 bar for the two aforementioned lubricants respectively. Further, the increase of applied loads from 1079 N up to 1471 N at a constant speed of 75 rpm reduces the maximum oil film pressure ratio by 0.14 and 0.18 for the two oil grades subsequently.

Hierarchical Control for Cyber-Physical Systems via General Approximate Alternating Simulation Relations

Traditional hierarchical control methodologies refine controllers from an abstract to a concrete system using a simulation relation established over the state sets of both systems. However, such a relation may yield a large error between the abstract and concrete systems, thus constraining the efficacy of hierarchical control architectures. To address this limitation, we introduce a more versatile simulation relation, namely general approximate alternating simulation relation, which also makes use of the input set of the abstract system. We demonstrate that our novel simulation relation effectively captures the similarity between the abstract and concrete system outperforming conventional simulation relations. Furthermore, it strengthens the hierarchical control architecture by reducing the error between the outputs of a concrete and its abstract system. We provide methods for establishing this new relation in linear control systems alongside their continuous abstractions. The practicality and effectiveness of our approach are underscored through its application in an illustrative example.

Simulation of ionization charge carrier cascade time and density for a new radiation detection method based on modulation of optical properties.

In time-of-flight PET, image quality and accuracy can be enhanced by improving the annihilation photon pair coincidence time resolution, which is the variation in the arrival time difference between the two annihilation photons emitted from each positron decay in the patient. Recent studies suggest direct detection of ionization tracks and their resulting modulation of optical properties, instead of scintillation, can improve the CTR significantly, potentially down to less than 10 ps CTR. However, the arrival times of the 511keV photons are not predictable, leading to challenges in the spatiotemporal localization characterization of the induced charge carriers in the detector crystal. To establish an optimized experimental setup for measuring ionization induced modulation of optical properties, it is critical to develop a versatile simulation algorithm that can handle multiple detector material properties and time-resolved charge carrier dynamics. We expanded our previous algorithm and simulated ionization tracks, cascade time and induced charge carrier density over time in different materials. For designing a proof-of-concept experiment, we simulated ultrafast electrons and free-electron x-ray photons for timing characterization along with alpha and beta particles for higher spatial localization. With 3 MeV ultrafast electrons, by reducing detector crystal thickness, we can effectively reduce the ionization cascade time to 0.79ps and deposited energy to 198.5keV, which is on the order of the desired 511keV energy. Alpha source simulations produced a cascade time of 2.45ps and charge carrier density of 6.39×1020cm-3 . Compared to the previous results obtained from 511keV photon-induced ionization track simulations, the cascade time displayed similar characteristics, while the charge density was found to be higher. These findings suggest that alpha sources have the potential to generate a stronger ionization-induced signal using the modulation of optical properties as the detection mechanism. This work provides a guideline to understand, design and optimize an experimental platform that is highly sensitive and temporally precise enough to detect single 511keV photon interactions with a goal to advance CTR for ToF-PET.

Design and Evaluation of Control Algorithms for MEMS Devices in MATLAB/Simulink

The field of Micro-Electro-Mechanical Systems (MEMS) Has witnessed significant advancements in recent years, enabling the development of miniaturized devices with diverse applications. Efficient control algorithms play a crucial role in optimizing the performance of MEMS devices, ensuring accurate sensing and actuation capabilities. This paper presents the design and evaluation of control algorithms for MEMS devices using MATLAB/Simulink, a versatile simulation environment. The proposed research focuses on the development of control algorithms tailored specifically for MEMS devices. The design process involves modeling the MEMS system in MATLAB/Simulink and incorporating real-world constraints and dynamics. To evaluate the performance of the control algorithms, extensive simulations are conducted. The outcomes of this research provide valuable insights into the design and evaluation of control algorithms for MEMS devices in MATLAB/Simulink. The results demonstrate the effectiveness and efficiency of the developed algorithms in enhancing the performance of MEMS systems. Furthermore, the research contributes to expanding the knowledge base of MEMS control design, paving the way for advanced applications and innovations in this rapidly evolving field.

Effects of datalink target data on air-to-air missile performance

Modern air-to-air missiles rely on data updated via a datalink about the position and velocity of a target until their own seeker can lock on to the target. The quality of the datalink target data depends on the errors of position and velocity updates, delay of these updates and lost updates. This paper introduces a simulation framework for analyzing the utilization of this data. The framework consists of models describing the target, the missile, and the generation of the datalink target updates. The versatile simulation experiments presented in the paper analyze the effects of the quality of the datalink data on the performance of different air-to-air missiles. The measure of performance is the probability of kill. The results of the simulations imply that the quality of the final updates before attempting the transition to using the missile’s seeker have the largest effect on the performance. Unless a large percentage of the target updates are lost or the seeker’s lock on to the target is delayed, the missile can typically get within a lethal miss distance of the target. The framework presented in this paper is suitable for evaluating the performance of all types of guided weapons.

Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host-Guest Interactions in Metal- and Covalent Organic Frameworks.

In recent years, research focused on synthesis, characterization, and application of metal-organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB) in conjunction with a suitable constrained molecular dynamics (MD) simulation protocol provides a versatile and flexible platform for the study of pristine MOFs as well as guest@MOF systems. Although being a semi-empirical quantum mechanical method, SCC DFTB inherently accounts for polarization and many-body contributions, which may become a limiting factor in purely force field-based simulation studies. A number of examples such as CO2, indigo, and drug molecules embedded in various MOF hosts are discussed to highlight the capabilities of the presented simulation approach. Furthermore, a promising extension of the outlined simulation strategy toward the treatment of covalent organic frameworks utilizing state-of-the-art neural network potentials providing a description at DFT accuracy and force field cost is outlined.

System design and analysis methods for optimal electric vehicle thermal management

The thermal management system (TMS) in an electric vehicle (EV) encounters many challenges due to the stringent thermal requirements of EV components and concurrent range reduction in cold conditions. Efficient systems require thermal architectures with highly interconnected components to satisfy a wide range of operating conditions. This work addresses the lack of a design methodology for clean sheet EV TMS designs by introducing a decision tree to enable analysis-driven design space exploration using simulation and modeling tools. A versatile simulation framework is developed inside MATLAB-Simulink using Simscape to capture dynamic physical interactions of coolant and refrigerant thermal systems and is validated at both the component and system-levels. Direct and indirect configurations for cabin conditioning are analyzed to compare relative performance. The indirect configuration is found to have a 1.6–1.8x longer conditioning time and a coefficient of performance decrease of 18–31% and 31–41% for heating and cooling, respectively. A previously unexplored general integrated loop architecture is created for concept-level analysis of various EV TMS configurations. Operating modes are formulated for all possible driving conditions and are switched with a control strategy. A detailed analysis is done for an idealized system to study the system-level performance, and critical modes are identified for different driving conditions. The range increase of the heat pump relative to positive temperature coefficient heating varies from 4 to 33%, with an extra 1–4.4% possible by idealized waste heat recovery. Waste heat recovery also increases the heating capacity by 28%, making heat pump operation feasible at low temperatures. The applicability of the decision tree in the context of various EV TMS designs of leading manufacturers and existing literature is also discussed.

A review of the Geant4 simulation platform for applications involving optical-based sensing and dosimetry

Geant4 is a versatile simulation platform for numerous applications involving transport of ionizing radiation, including ionizing photons. Moreover, the physics needed for tracking optical photons is also embedded in the Geant4 simulation toolkit. From its introduction two decades ago there have been a variety of reports concerning the use of the Geant4 code in optical photons transport applications. Further to this, in particular for the interests herein, characterizations of radioluminescence radiation detectors have also been conducted. Noting the rapid development of such detectors, reliant on light collection and detection, there has been an implicit need for optimization of the different components forming the respective optical devices. Compared to ionizing radiation transport modeling less attention has been paid to this capability among the radiation physics community and Geant4 users. This review first describes in brief terms the physics built into Geant4 to provide for the transport of optical photons. This is followed by a retrospective on reported applications, discussion of the strengths and deficiencies of optical transport analyses by Geant4 and contemplation of possible future applications.

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis.

The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS)LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.

A Clonal Evolution Simulator for Planning Somatic Evolution Studies.

Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regard to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions. Versatile simulation tools are needed to explore and optimize potential study designs if researchers are to deploy multiomic technologies most effectively. In this study, we present a simulator allowing for the generation of synthetic data from a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. Users can input various properties of the somatic evolutionary system, mutation classes, and biotechnology options, and then generate samples of synthetic sequence reads and their corresponding ground truth parameters for a given study design. We demonstrate the utility of the simulator for testing and optimizing study designs for various experimental queries.

A comparison of the environmental impact of turboprop and turbofan-powered aircraft

PurposeThis study aims to examine turboprop- and turbofan-powered aircraft, with the same seating capacity flying on the same route and trajectory, and investigate their environmental effects.Design/methodology/approachThe integrated aircraft noise and emissions modeling platform developed by EUROCONTROL is used for the calculation of fuel burn, CO2, H2O and other gas emissions (NOx, SOx, CO, HC, soot and other trace compounds) for the per phase of flight.FindingsThe striking findings are that turboprop-powered aircraft offer lower required thrust, fuel consumption and total emissions for a short-haul flight, but turbofan-powered aircraft have lower particulate matter, CO and HC emissions than turboprop-powered aircraft. This study suggests that turboprop-powered aircraft are superior to turbofan-powered aircraft in terms of environmental impact for a short-haul flight.Practical implicationsThe current research conducts comprehensively fuel consumption and amounts of emissions aspects of turboprop- and turbofan-powered aircraft for sustainable development of airlines by a versatile simulation approach and sheds light on airlines intending to create fleets.Originality/valueThe research offers a systematic aircraft selection for investigators, scientists, airline operators, policy analysts and legislators, by a comprehensive computer simulation method that acknowledges consistently the fuel consumption and detailed emissions analysis of turboprop- and turbofan-powered aircraft.

HexSE: Simulating evolution in overlapping reading frames.

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another and vice versa. To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with any distribution of open reading frames in linear or circular genomes. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias and the distribution of selection biases (dN/dS) in the respective reading frames. Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License version 3 and are available at https://github.com/PoonLab/HexSE.

Smart Eco Greenhouse VESNA

Recent challenges, including increasing global warming, reduction of carbon footprint, and limited resources of freshwater, enforce the implementation of smart solutions. This paper introduces the educational-oriented project VESNA (Versatile Simulator for Near-zero Emissions Agriculture, https://vesna.uiam.sk) focused on the development of a smart ecological greenhouse for micro-organic farming. This project provides an opportunity for students in the field of automation and control engineering to apply obtained theoretical knowledge and practical skills to monitor and control a real process. The prototype of the smart greenhouse is developed by the university students within the courses on both, bachelor's and master's levels. Moreover, the project emphasizes the soft-skills including teamwork, problem-solving, and presentation skills. Due to the COVID-19 pandemic, the students experienced a challenge to solve part of the project in a fully remote framework. The paper introduces detailed insight into the equipped hardware solution enabling both, autonomous and remote control of many controlled variables affecting the micro-climate inside the smart greenhouse. The educational goals, structure, and organization of the courses are also introduced. The detailed evaluation of the student's feedback on the proposed courses is analyzed to investigate the educational value and the benefits of the courses for the students.

IonMonger 2.0: software for free, fast and versatile simulation of current, voltage and impedance response of planar perovskite solar cells

The second generation of the open-source MATLAB-based software tool IonMonger, for solving drift–diffusion models of charge transport in planar perovskite solar cells, is presented here. This version is based upon a generalisation of the original drift–diffusion model of charge carrier and ion motion in the perosvkite cell, as described in Courtier (J Comput Electron 18:1435–1449, 2019). The generalised model has the flexibility to capture (1) non-Boltzmann statistics of charge carriers in the transport layers, (2) steric effects for the ions in the perovskite layer, (3) generation of charge carriers from light made up of a spectrum of different wavelengths and, (4) Auger recombination. The updated software is significantly more stable than the original version and also adds the ability to simulate impedance spectroscopy measurements as well as transient voltage and/or illumination protocols. In addition, it is fully backwards compatible with the original version and displays improved performance through refinement of the underlying numerical methods. Furthermore, the software has been made accessible to a wider user base by the addition of IonMonger Lite, a version that leverages MATLAB’s live scripts and eliminates the need for a detailed knowledge of MATLAB’s syntax.