Simulation Approach Research Articles

Performance Evaluation of Refractive Index Biosensor in THz Regime for Clinical Applications: A Simulation Approach

Abstract A novel innovative HC-PCF sensor model in THz regime is introduced integrated with an optimization approach. The suggested sensor architecture provides crucial advantages precise identification of healthy and ill tissues in the healthcare industry. The HC-PCF, meticulously constructed with specific dimensions, significantly increases the sensor sensitivity and specificity to 99.37% and 99.75% respectively. Healthcare industries are at the core of investigations and are undoubtedly crucial to modernize the prognosis procedures. It is a discipline that is continually expanding and searching for new approaches to raise the standard for efficacy, sensitivity, and accuracy. Recently, THz PCF has emerged with incredible potential in all the areas of biomedical applications. The importance in using THz sensors in this research is to detect the ill tissues, an important component in the categorizing diabetes. The integrated sensor architecture provides higher level of sensitivity, with a confinement loss of 0.05 in 0.23s processing time for a RI range of 1.28-1.39. This research highlights the capability of combining PCF with optimization to improvise the healthcare industry, offering an economical and efficient diagnostic solution across the fields.

Effect of Oxidized Tin Interfacial Atoms on Methylcyclohexane Dehydrogenation Catalyzed by γ‐Alumina Supported Platinum Clusters

AbstractTin (Sn) doped platinum (Pt) nanoparticles supported on γ‐alumina are important materials catalyzing various reactions, such as the dehydrogenation of methylcyclohexane (MCH) into toluene, involved in liquid organic hydrogen carriers process, and naphtha catalytic reforming. The role of oxidized forms of Sn, remains misunderstood. Relying on catalysts' models made of a Pt13cluster on the (100) surface of γ‐alumina, the location and effects of Sn4+and Sn2+ interfacial atoms are investigated by density functional theory (DFT) calculations. A simulated annealing approach based on ab initio molecular dynamics enables to explore the configurations of the catalytic systems. The Pt clusters' stabilities and that of the adsorbed molecular intermediates are linked to the electronic properties of the cluster and to its ductility modulated by the adsorbed species, the alumina support and the Sn atoms. The Gibbs free energy profiles related to the reaction mechanism of the MCH dehydrogenation into toluene are quantified on the basis of transition state search. The energy span concept reveals that the dual (either detrimental or beneficial) impact of Sn oxidized forms on the intrinsic reactivity of the Pt13 cluster depends on temperature. Based on this DFT analysis, we attempt to clarify the debated role of Sn oxidized species.

Advancing early detection of biological events by digital holographic microscopy and simulation of microorganisms

There is a global need to advance bio-aerosol sensing for CBRN (Chemical, Biological, Radiological, and Nuclear) applications by compact and cost-effective devices. Employing digital holographic microscopy (DHM) and deep learning, we developed a system called HoloZcan to automate the analysis of airborne microbial pathogens and particles. DHM provides valuable information, but obtaining data from biological specimens for robust investigations is challenging. This paper introduces a custom simulation approach using the open-source software Meep and the finite-difference time-domain (FDTD) method to overcome limitations of existing Mie-based simulators, especially when dealing with complex microbial shapes. The simulation tool enables the modelling of specific microorganisms, offering a safer and more flexible alternative for CBRN research by bypassing ethical and logistical constraints associated with live pathogens. The study details the simulation workflow, built upon the construction of a database of optical properties of biological materials, for realistic simulations of light-microbe interactions. Evaluations on homogeneous and non-homogeneous objects demonstrate the tool’s limited intrinsic errors and superior sensitivity to refractive index changes compared to traditional Mie-based simulations. This work significantly advances our capability to accurately simulate and analyse CBRN-related scenarios, enhancing comprehensive research in bio-aerosol sensing.Graphical abstract

Quantum Probabilistic Model Checking for Time-Bounded Properties

Probabilistic model checking (PMC) is a verification technique for analyzing the properties of probabilistic systems. However, existing techniques face challenges in verifying large systems with high accuracy. PMC struggles with state explosion, where the number of states grows exponentially with the size of the system, making large system verification infeasible. While statistical model checking (SMC) avoids PMC’s state explosion problem by using a simulation approach, it suffers from runtime explosion, requiring numerous samples for high accuracy. To address these limitations in verifying large systems with high accuracy, we present quantum probabilistic model checking (QPMC), the first method leveraging quantum computing for PMC with respect to time-bounded properties. QPMC addresses state explosion by encoding PMC problems into quantum circuits that superpose states within qubits. Additionally, QPMC resolves runtime explosion through Quantum Amplitude Estimation, efficiently estimating the probabilities of specified properties. We prove that QPMC correctly solves PMC problems and achieves a quadratic speedup in time complexity compared to SMC.

A Smoothed Particle Hydrodynamics Approach for One-Dimensional Dam Break Flow Simulation with Boussinesq Equations

A Smoothed Particle Hydrodynamics Approach for One-Dimensional Dam Break Flow Simulation with Boussinesq Equations

Spatial distribution of damage potential of the 2023 Pazarcik Turkey earthquake using inelastic-response spectra of recorded and simulated ground motions

On 6 February 2023, a devastating earthquake of magnitude 7.7 struck the southeastern part of Turkey, followed by multiple aftershocks. The sequence of earthquakes caused extensive damage in Southern Turkey and Northern Syria. This study evaluates the destructive potential of ground shaking resulting from the Pazarcik mainshock by developing spatial distribution of inelastic demand spectra across the region by using the recorded ground motions. These spectra provide an additional step toward more realistic estimates of the damage potential of ground shaking than the traditional elastic response spectra. Given the recorded ground motions, we also developed simulated ground-motion time series at numerous un-instrumented sites. We used these recorded and simulated motions to estimate ductility demands across the affected area. Constant-ductility spectra were derived using recording stations within 100 km of the fault rupture. The results indicated that in the near-fault area, and for structural periods of 0.5 and 1.0 s, the yield strength demand at a ductility level of 3 exceeded the levels specified in the local seismic design code for a 475-year return period. Our findings demonstrate that with more refinement and efficiency of the ground-motion simulation approach, the development of near-real-time spatial distribution of ductility demand is a promising approach for rapid seismic damage assessment of earthquakes.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

Tumoral Pharmacokinetic-Pharmacodynamic Simulation of Doxorubicin-Encapsulated Polymeric Micelles Using a Tissue-Isolated Tumor Perfusion System.

Pharmacokinetic (PK) elucidation of polymeric micelles delivering anticancer drugs is crucial for accurate antitumor PK-pharmacodynamic (PK-PD) simulations. Particularly, establishing a methodology to quantify the tumor inflow and outflow of anticancer drugs encapsulated in polymeric micelles is an essential challenge. General tumor biodistribution experiments are disadvantageous in that inflow quantification is easy, but outflow quantification is challenging. We addressed this issue by proposing a quantification method that combines a tissue-isolated tumor perfusion system with microdialysis. This method aims to determine tumoral drug inflow and outflow by quantifying the drugs released from the polymeric micelles via a tumor-embedded microdialysis probe and perfusate, respectively. Furthermore, we evaluated the feasibility of this method by perfusing pH-sensitive polyethylene glycol-poly(aspartate-hydrazone-doxorubicin/phenylalanine)n (PPDF-Hyd-DOX) in a tissue-isolated tumor perfusion system, and we quantified tumor inflow and outflow released DOX. Based on the quantitative results, we performed compartmental analyses by incorporating the gamma-distributed delay function and calculated the PK rate constants. These parameters were input into a tumor-bearing rat compartment model for ex vivo-in vivo extrapolation (EVIVE) of the rat plasma PPDF-Hyd-DOX concentrations and simulated intratumorally released DOX concentrations. The simulation profiles demonstrated a good fit with the Walker 256 intratumoral released DOX concentration profiles previously reported. This EVIVE-PK model was coupled with the threshold natural-growth tumor PD model, and PK-PD analysis was performed. This model exhibited a better fit to the tumor weight profile of Walker 256-bearing rats treated with PPDF-Hyd-DOX than that of our previously reported PK-PD model. Thus, EVIVE, based on a tissue-isolated tumor perfusion system with microdialysis, is a promising approach for the PK-PD simulation of polymeric micelle anticancer therapy.

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: a novel dynamic ARDL simulation approach

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: a novel dynamic ARDL simulation approach

Prediction of Energy Resolution in the JUNO Experiment

Abstract This paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Analysis the dynamic rollover threshold and roll safety thresholds of 4-axle truck vehicle: a simulation approach

Analysis the dynamic rollover threshold and roll safety thresholds of 4-axle truck vehicle: a simulation approach

LCV Vehicle in-cab noise prediction and reduction using SEA based simulation approach

With increase in consumer demand vehicle OEMs are developing quieter vehicles at a design stage using acoustic simulation tools to reduce development cycle times avoiding costly proto builds and test iterations. Present work describes application of statistical energy analysis (SEA) simulation approach for in cab noise prediction and noise reduction through sound package optimization for LCV vehicle. In first step complete vehicle in-cab SEA model is prepared with baseline sound package concept. It consists of comprehensive definition of structural panels, interior and exterior cavities. Noise control treatment is modelled as multi-layer poro-elastic materials. Airborne noise excitations for sources engine, transmission, intake and exhaust muffler are applied. Source strengths for above sources were derived from dyno-coupled powertrain noise tests in the anechoic chamber. Load case of rated speed was considered neglecting tire and wind noise during simulation. Contribution analysis was carried out to find out sensitivity of different panels on interior noise at various frequencies. It was observed that bottom middle floor panel and rear glass panel were major noise contributors. Noise mitigation was carried out by optimizing floor panel sound package and intake air filter within given space constraints. These modifications resulted in overall 2 dB in-cab noise reduction

Innovative Strategies for designing acoustic encapsulations for e-motors to tackle NVH performance challenges

The automotive industry's rapid transition towards E-mobility is presenting novel challenges for NVH engineers having broadband frequency composition and tonal characteristics. The purpose of the paper is to present innovative strategies for electric motor NVH development by directly applying acoustic encapsulation made of poro-elastic materials onto the casing of the electric motor. The first strategy is a pure simulation approach with combination of finite element analysis (FEA) and statistical energy analysis (SEA). Finite Element Analysis is used to calculate radiated power of electric motor casing for Maxwell electromagnetic excitations on stator teeth. This radiated power is then applied as excitation on the SEA model of the electric motor casing for prediction of exterior noise using Statistical Energy Analysis. The second strategy is a hybrid approach of simulations and experimental measurements. Electric motor is characterized as a source in terms sound power from experimental measurements of sound pressure as per ISO Standard: ISO 3745:2003. This power is applied as excitation in the interior of SEA model of the electric motor for prediction of exterior noise. Both the strategies allow design of optimized sound package on the SEA model of electric motor using statistical energy analysis to achieve the NVH performance.

Advantages of Accounting for Stochasticity in the Premium Process

In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium amounts are modelled by independent and identically distributed random variables. While this model has been studied in the existing literature and certain explicit results are known under more restrictive assumptions, these results are relatively difficult to apply in practice. In this paper, we investigate the factors that differentiate this model and the classical risk model. After reviewing various known results of this model, we derive a simulation approach for obtaining the probability of ultimate ruin based on importance sampling, which does not require specific distributions for the premium and the claim. We demonstrate this approach first with examples where the distribution of the sampling random variable can be identified. We then provide additional examples where we use the fast Fourier transform to obtain an approximation of the sampling random variable. The simulated results are compared with the known results for the probability of ruin. Using the simulation approach, we apply this model to a real-life auto-insurance data set. Differences with the classical model are then discussed. Finally, we comment on the suitability and impact of using this model in the context of solvency risk management.

Continuous and funnel-gate configurations of a permeable reactive barrier for reclamation of groundwater laden with tetracycline: experimental and simulation approaches

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%.

PenGym: Realistic training environment for reinforcement learning pentesting agents

Penetration testing, or pentesting, refers to assessing network system security by trying to identify and exploit any existing vulnerabilities. Reinforcement Learning (RL) has recently become an effective method for creating autonomous pentesting agents. However, RL agents are typically trained in a simulated network environment. This can be challenging when deploying them in a real network infrastructure due to the lack of realism of the simulation-trained agents.In this paper, we present PenGym, a framework for training pentesting RL agents in realistic network environments. The most significant features of PenGym are its support for real pentesting actions, full automation of the network environment creation, and good execution performance. The results of our experiments demonstrated the advantages and effectiveness of using PenGym as a realistic training environment in comparison with a simulation approach (NASim). For the largest scenario, agents trained in the original NASim environment behaved poorly when tested in a real environment, having a high failure rate. In contrast, agents trained in PenGym successfully reached the pentesting goal in all our trials. Even after fixing logical modeling issues in simulation to create the revised version NASim(rev.), experiment results with the largest scenario indicated that agents trained in PenGym slightly outperformed, and were more stable, than those trained in NASim(rev.). Thus, the average number of steps required to reach the pentesting goal was 1.4 to 8 steps better for PenGym. Consequently, PenGym provides a reliable and realistic training environment for pentesting RL agents, eliminating the need to model agent actions via simulation.

A new simulation approach of air source heat pump adopting the holistic process distributed parameter method and hybrid PID-bisection control algorithm

Air source heat pump (ASHP) faces problems of performance deterioration when operating at low ambient temperature due to the low compression ratio, high discharge temperature, frost accumulation, etc., and may even become nonfunctional at sub-zero-centigrade ambient temperature, requiring attention and tools for studying. This paper proposed a kind of holistic process distributed parameter simulation approach of ASHP system adopting the hybrid PID-bisection (PID: proportional-integral-derivative) control algorithm. Main components of the ASHP are modeled with the distributed parameter method. An adiabatic compression model of two-phase fluid based on thermodynamics is proposed. The PID-bisection control algorithm and variable speed integral PID & bisection control algorithm are proposed and applied to iterative computation of the model. A single/two-stage compression ASHP with an intercooler is simulated by this approach. The average deviation of simulation results of the model from experimental data is not more than 7 %, and the maximum deviation is not more than 18 %. For simulation of single-stage compression mode, the maximum error is not more than 4%. For simulation of two-stage compression mode under low-evaporating-temperature operating conditions, the maximum error is not more than 4 %. Computational speed of the ASHP model is significantly improved by using the PID-bisection control algorithm, and could be further accelerated by using the variable speed integral PID & bisection control algorithm. The proposed simulation approach is not only effective in sophisticated simulation of performance of single-stage and two-stage compression ASHP, but also potential for research on the optimization of the ASHP in cold regions.

Investigations on the Leak Resistance Performance and the Difference Mechanism of Composite Materials under Several Typical Curing Processes

This study aims to investigate the leak resistance of carbon fiber composite products formed by various typical curing processes. Firstly, the leak rates of specimens produced through different curing methods were measured, and the defect were statistically analyzed. After that, the simulation approach was applied to numerically study the impact of these defect characteristics on leak rates was examined from the three factors of porosity, void distribution and void morphology, specimen with prefabricated defects was prepared, and its leak performance were tested to validate the simulation results. Finally, the differences in leak resistance among specimens under different curing processes were analyzed from the perspective of curing defects, and specific defect characteristics contributing to enhanced leak resistance were identified.

In-depth exploration of the interface mechanism of aqueous ammonium-ion batteries with ionic liquids

Aqueous ammonium-ion batteries have emerged as a research focus in recent years because of their distinctive advantages. Nevertheless, issues such as the side reaction of water, electrode element dissolution and others have an impact on their stability. The experimental findings demonstrate that the system incorporating ionic liquids (ILs) exhibits gratifying electrochemical performance. The Molecular Dynamics (MD) simulation approach was employed to investigate the alterations of NH4Cl aqueous batteries prior to and subsequent to the addition of imidazolium ionic liquids (ILs), with a focus on the impact on the anode's interface. It is found that the addition of ILs can reduce the interfacial water’s content at low charge. Interestingly, in the case of high charge, the water content at the interface didn’t decrease; instead, it increased. This phenomenon is associated with the initial increase followed by a decrease in the number of ionic liquid (IL) cations. Three conformations for IL cations were found near the interface, which are “parallel”, “slant” and “vertical”, and the proportion of the three had a certain influence on the content of interfacial water. The "parallel" configuration is more effective in reducing the interfacial water content, establishing it as the dominant conformation. The findings reported above will offer new theoretical support for the design and modification of aqueous ammonium ion electrolytes.

A novel meshless method for numerical simulation of fractured-vuggy reservoirs

This paper proposes a novel meshless numerical simulation method for fractured-vuggy reservoirs. Initially, model discretization involves extracting fracture and vug feature nodes along the contours of the reservoir bodies and in alignment with fracture orientations, tailored to the characteristics of the fractured-vuggy oil deposit. Subsequently, a connection element model is established within the influence domain. Based on the seepage equations of the connection system, a computational method for the parameters of the connection elements (connection transmissibility and connection volume) is defined. In addition, a sequential method is employed to solve for the pressure and saturation, achieving rapid prediction of the reservoir's production dynamics. On this basis, an automatic history matching algorithm is utilized for real-time fitting of oil–water dynamic indicators, inverting the characteristic parameters of the connection element model, and quantitatively characterizing the injection–production connection elements. The research findings indicate that the method is capable of rapidly fitting and predicting the dynamics of oil reservoir production. Furthermore, conceptual model examples have substantiated that it achieves comparable computational efficiency and superior computational accuracy when compared to the traditional finite difference (volume) method under the same nodal conditions. Additionally, the node arrangement for fractures and vugs is comparatively flexible, and it can ensure the integrity of the flow paths with a limited number of nodes to enhance predictive accuracy. Therefore, this method can effectively meet the rapid prediction requirements for fractured-vuggy reservoirs and also provides a novel meshless computational approach for the numerical simulation of such reservoirs.