Monte Carlo Approach Research Articles

Investing in security-as-a-service for e-commerce infrastructure by small and medium enterprises: a Monte Carlo approach

Purpose This paper aims to examine the conditions under which small and medium enterprises (SMEs) invest in security services when they migrate their e-commerce applications to the cloud environment. Using a risk management perspective, the paper assesses the impact of security service pricing, security incident prevalence and virulence to estimate SME security spending at the market level and draw out implications for SMEs and security service providers. Design/methodology/approach Security risks are inherently characterized by uncertainty. This study uses a Monte Carlo approach to understand the role of uncertainty in the decision to adopt security services. A model relating key security constructs is assembled based on key constructs from the domain. By manipulating security service costs and security incident types, the model estimates the market-level adoption of services, security incidents and damages incurred, along with measures of their relative dispersion. Findings Three key findings emerge from this study. First, adoption of services and protection is higher when tiered security services are provided, indicating that SMEs prefer to choose their security services rather than accept uniformly priced products. Second, SMEs are considered price-sensitive, resulting in a maximum level of spending in the market. Third, results indicate that security incidents and damages can be much higher than the mean in some cases, and this should serve as a cautionary note to SMEs. Originality/value Security spending has been modeled at the firm level. Adopting a market-level perspective represents a novel contribution. Additionally, the Monte Carlo approach provides managers with tangible measures of uncertainty, affording additional information and insight when making security service adoption decisions.

Tuning structure, stability and magnetism in vanadium doped zinc sulfide for spintronic applications: Insights from first-principles and Monte Carlo approaches

This work utilizes a multi-scale computational approach combining first-principles density functional theory (DFT) and Monte Carlo statistical simulations to provide fundamental insights into the impacts of vanadium (V) doping on the structural, electronic, magnetic, and thermodynamic properties of wurtzite zinc sulfide (ZnS). 2 × 2 × 2 ZnS supercells with controlled V doping concentrations from 12.5%–25% substituting zinc sites were constructed. The introduction of V dopants induces systematic expansions in lattice parameters and cell volume, attributed to the larger ionic radius of V2+ than Zn2+, while the average compressibility shows minimal variation. However, V incorporation is found to soften ZnS by over 15% at higher dopant contents, increasing compliance and anisotropy. Stable ferromagnetic spin couplings emerge, mediated by host carriers without precipitating V secondary phases. The band structure develops half-metallic electronic properties with the lifting of spin degeneracy and preferential spin-up band metallicity induced by V-3d and S-3p hybridization effects. This results in 100% spin polarization at the Fermi level. Monte Carlo simulations based on a tight-binding Hamiltonian constructed from maximally localized Wannier functions to accurately calculate the exchange interactions between V spins successfully reproduce the magnetic phase transition behavior. The results confirm high Curie temperatures exceeding 300 K at 25% V, attributable to the significant strengthening of short-range ferromagnetic couplings over antiferromagnetic interactions. The integrated computational approach provides fundamental insights into the microscopic origin of carrier-mediated ferromagnetism in V-doped wurtzite ZnS dilute magnetic semiconductors. The systematic first-principles calculations and statistical mechanics modeling establish clear structure–property relationships linking V doping configurations and concentrations to tuned mechanical response, enhanced stability, electronic structure modifications, spin polarization, and magnetic ordering temperatures in ZnS. The results present design pathways and predictive capabilities to guide experimental efforts toward emerging high-performance magnetic materials for spintronic technologies.

Investigating the Magnetic Characteristics of Carbon Bi-nanotube-like Composite with the Monte Carlo Approach: Focus on Vacancies

Investigating the Magnetic Characteristics of Carbon Bi-nanotube-like Composite with the Monte Carlo Approach: Focus on Vacancies

Analytical Modelling and Performance Characterization of Single Electron Transistor

One important component of the present nanotechnology research field is single electron transistor (SET) which has unrealized quality to operate at breakneck speeds with ultralow power consumption. The single electron transistor is a novel nanoscale switching device that can regulate the motion of a single electron in addition to maintaining its scalability down to the atomic level. In this context, scalability refers to the ability of electronic device to function better as their dimensions get smaller. The basic device properties that this single electron transistor (SET) operates on "quantized current-voltage (I-V) characteristic," "single electron tunneling effect," and "Coulomb blockade," are also covered. This study presents some of the key SET-related topics, such as modelling and simulation methods. Simulation of Nanostructures Method (SIMON) is circuit simulator for tunneling device with just a single electron device based on Monte Carlo approach. It enables simulation of any kind of circuits with voltage source, junctions in tunnels and capacitors in quasi stationary mode and transient mode. The most significant feature of SET that is applied in the research work is Coulomb blockade oscillation, so at the conclusion of this paper, a number of simulations utilizing the SIMON simulator are shown. The simulation results show that for gate capacitance CG = 2af, C∑ = 3af, IBIAS = 2nA, tunnel barrier resistance RT > 4.1KΩ and operating temperature T=21K the model reflects real SET characteristic with maximum voltage gain.

GATE Monte Carlo approach to heterogeneity dose distribution in small fields used in radiation therapy

The Accurate dosage prediction in Radiation Therapy is challenging, prompting a need for precision beyond conventional clinical Treatment Planning Systems (TPS). Monte Carlo-based methods are sought for their superior accuracy. The aim of this study is to compare dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, focusing on smaller fields. This study was initiated with a homogeneous validation of the TrueBeam STX system, using measurements obtained from the Centre Hospitalier Interregional Edith Cavell (CHIREC) in Brussels. The validation compared dosimetric functions (Percentage Depth Dose (PDD), Dose profile (DP) and Collimator scatter fraction (CSF)) employing the GAMMA index with a 2% / 2 mm criterion tolerance. Following this, heterogeneous studies examined dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, with a specific focus on smaller fields. Simulations were conducted using both platforms on chest phantoms with heterogeneous slabs representing bone, lung, and heart, each housing a central tumor. The impact of electronic equilibrium on tumors for different small field sizes was evaluated. Results showed a remarkable 99% agreement between measurements and GATE calculations in the homogeneous validation of the TrueBeam STX system. However, in heterogeneous studies, ACUROS consistently overestimated lung doses by up to 8% compared to GATE simulation, especially evident with a flattening filter and smaller beam sizes at density interfaces. This highlights significant dose estimation discrepancies between ACUROS and GATE, emphasizing the need for precise calculations. The findings support exploring Monte Carlo-based methods for enhanced accuracy in Radiation Therapy treatment planning.

Multi-objective electricity generation expansion planning towards renewable energy policy objectives under uncertainties

Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches. The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts, and incorporate changing policy objectives into the constraints to meet policy makers’ expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system. The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers. Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made.

Expression of Concern: Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach

Expression of Concern: Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach

Exploring Universe acceleration through observational constraints via Hubble parameter reconstruction

In this article, we introduce an innovative parametric representation of the Hubble parameter, providing a model-independent means to explore the dynamics of an accelerating cosmos. The model's parameters are rigorously constrained through a Markov Chain Monte Carlo (MCMC) approach, leveraging a comprehensive dataset consisting of 31 data points from cosmic chronometers (CC), 1701 updated observations of Pantheon supernovae type Ia (SNeIa), and 6 data points from baryonic acoustic oscillations (BAO). Our analysis delves into the behavior of various cosmological parameters within the model, including the transition from a decelerating phase to an accelerating one, as well as the density parameters and the equation of state (EoS) parameter. The outcomes of our investigation reveal that the equation of state parameter aligns with characteristics reminiscent of the phantom model, supporting the prevailing understanding of our universe's current state of acceleration. This research contributes valuable insights into the ongoing cosmic expansion and underscores the utility of our novel parametric approach.

Stochastic seismic acoustic impedance inversion via a Markov-chain Monte Carlo method using a single GPU card

Seismic acoustic impedance inversion plays an important role in understanding subsurface structures and obtaining subsurface properties. The stochastic approach is one of the methods used for impedance inversion, and it aims to produce more reliable results by accounting for modeling uncertainty. Stochastic inversion represents the uncertainty of a subsurface model as a probability distribution and uses this distribution to estimate model parameters. In this study, seismic acoustic impedance inversion was performed using a Markov-chain Monte Carlo approach, which is a sampling method used in the stochastic process. Utilizing Bayesian inference based on prior information and observed data, we implemented acceptance probabilities and performed acoustic impedance inversion for field data through iterative calculations. We employed a single GPU card to execute the inversion algorithm and conducted a comparative analysis with the results obtained using a cluster composed of multiple CPU cores. Through a computational speed analysis, the efficiency of the algorithm using a GPU was verified, while uncertainty analysis was employed for algorithm validation. Through these analyses, we confirmed the feasibility of applying our developed GPU algorithm to tasks that require the inversion of extensive data, such as high-resolution seismic exploration and CO2 storage monitoring.

Exact perturbation approximations for the conditional moments of a multifactor CIR term structure model with a weak mean-reversion influence

In this paper, we derive exact series expressions for the conditional moments of a multivariate Cox-Ingersoll-Ross term structure model with a weak mean-reversion effect assumption. First, we construct the perturbation solution for the system of Riccati equations, which illustrates the conditional characteristic function of this term structure model. Second, we derive the power series expression for the perturbation solution. Via the power series solutions, we establish the exact series expressions for conditional moments. Furthermore, we demonstrate the error bounds for these perturbation approximations of the conditional moments. Numerical analysis indicates that our method is more efficient, accurate and superior to the Monte Carlo approach with the Euler scheme for computing the conditional moments.

A Monte Carlo approach for capacity and delay analyses of multiple interacting airports in Istanbul metroplex

Abstract The Istanbul metroplex airspace, home to Atatürk (LTBA), Sabiha Gökçen (LTFJ), and Istanbul (LTFM) international airports, is a critical hub for international travel, trade and commerce between Europe and Asia. The high air traffic volume and the proximity of multiple airports make air traffic management (ATM) a significant challenge. To better manage this complex air traffic, it is necessary to conduct detailed analyses of the capacities of these airports and surrounding airspace. In this study, Monte Carlo simulation is used to determine the ultimate and practical capacities of the airport and surrounding airspace and compare them to identify any differences or limitations. The traffic mix, runway occupancy time and traffic distribution at airspace entry points are randomised variables that directly impact airport and airspace capacities and delays. The study aims to determine the current capacities of the runways and routes in the metroplex airspace and project the future capacities with the addition of new facilities. The results demonstrated that the actual bottleneck could be experienced in airspace, rather than runways, which was the focus of the previous literature. Thus, this study will provide valuable insights for stakeholders in the aviation industry to effectively manage air traffic in the metroplex airspace and meet the growing demand.

Fast and accurate evaluation of deep-space galactic cosmic ray fluxes with HelMod-4/CUDA

The accurate knowledge of cosmic ion fluxes is essential for fundamental physics, deep space missions, and exploration activities in the solar system. In the HelMod-4 model the Parker transport equation is solved using a Monte Carlo approach to evaluate the solar modulation effect on local interstellar spectra of Galactic Cosmic Rays (GCRs). This work presents the latest updates to the HelMod-4 model parameters, focusing on the descending phase of solar cycle 24. The updates are motivated by the latest high-precision measurements from the AMS-02 detector which revealed for the first time with high accuracy the features of GCR fluxes’ evolution during a period of positive interplanetary magnetic field polarity. Furthermore, we present HelMod-4/CUDA, a GPU-accelerated approach for solving the Parker equation in the heliosphere using a stochastic differential equation method. The code is an evolution of the HelMod-4 code, porting the algorithm to GPU architecture using the CUDA programming language. This approach achieves significant speedup compared to a CPU implementation. The HelMod-4/CUDA code has been validated by comparing its results with the most precise and updated experimental GCR spectra observed during high and low solar activity periods, both in the inner and outer heliosphere, at the Earth location, and outside the ecliptic plane. The comparison shows that HelMod-4 and HelMod-4/CUDA can be equivalently used to provide solar-modulated spectra with a similar degree of accuracy in reproducing observed data.

Molecular modeling of CO2 affecting competitive adsorption within anthracite coal

This study aimed to investigate the adsorption properties of CO2, CH4, and N2 on anthracite. A molecular structural model of anthracite (C208H162O12N4) was established. Simulations were performed for the adsorption properties of single-component and multi-component gases at various temperatures, pressures, and gas ratios. The grand canonical ensemble Monte Carlo approach based on molecular mechanics and dynamics theories was used to perform the simulations. The results showed that the isotherms for the adsorption of single-component CO2, CH4, and N2 followed the Langmuir formula, and the CO2 adsorption isotherm growth gradient was negatively correlated with pressure but positively correlated with temperature. When the CO2 injection in the gas mixture was increased from 1 to 3% for the multi-component gas adsorption, the proportion of CO2 adsorption rose from 1/3 to 2/3, indicating that CO2 has a competing-adsorption advantage. The CO2 adsorption decreased faster with increasing temperature, indicating that the sensitivity of CO2 to temperature is stronger than that of CH4 and N2. The adsorbent potential energies of CO2, CH4, and N2 diminished with rising temperature in the following order: CO2 < CH4 < N2.

Efficient removal of toxic azo dyes from contaminated water by adsorption on the GO surface.

The purpose of this study is to examine the possibility of GO to be used as an adsorbent for five novel potentially hazardous azo-dyes for their removal from aqueous solution. Adsorption characteristics of GO for azo-dyes removal were investigated by means of experimental and computational DFT as well as Monte Carlo approaches. Experimental studies include the effect of adsorbent dose, contact time, and initial concentration, while computational investigation involves DFT and Monte Carlo (MC) simulations. Through DFT studies geometric, electronic, and thermodynamic parameters were explored and possible mechanism of interactions and adsorption energies by predicted through MC by searching lowest possible adsorption complexes. Experimental data were evaluated by Langmuir models in order to describe the equilibrium isotherms. Equilibrium data fitted well to the Langmuir model. Thermodynamic parameters i.e., free energy change, enthalpy change, and entropy change revealed that the removal of azo-dyes by adsorption on the surface of GO molecular sieves was spontaneous. Nature of the process was found to be physiosorption involving non-covalent interaction. The study unveiled that GO can be used as an efficient adsorbent material for the adsorption of azo-dyes from aqueous solution.

Optimal Operation and Market Integration of a Hybrid Farm with Green Hydrogen and Energy Storage: A Stochastic Approach Considering Wind and Electricity Price Uncertainties

In recent years, growing interest has emerged in investigating the integration of energy storage and green hydrogen production systems with renewable energy generators. These integrated systems address uncertainties related to renewable resource availability and electricity prices, mitigating profit loss caused by forecasting errors. This paper focuses on the operation of a hybrid farm (HF), combining an alkaline electrolyzer (AEL) and a battery energy storage system (BESS) with a wind turbine to form a comprehensive HF. The HF operates in both hydrogen and day-ahead electricity markets. A linear mathematical model is proposed to optimize energy management, considering electrolyzer operation at partial loads and accounting for degradation costs while maintaining a straightforward formulation for power system optimization. Day-ahead market scheduling and real-time operation are formulated as a progressive mixed-integer linear program (MILP), extended to address uncertainties in wind speed and electricity prices through a two-stage stochastic optimization model. A bootstrap sampling strategy is introduced to enhance the stochastic model’s performance using the same sampled data. Results demonstrate how the strategies outperform traditional Monte Carlo and deterministic approaches in handling uncertainties, increasing profits up to 4% per year. Additionally, a simulation framework has been developed for validating this approach and conducting different case studies.

Reverse mapping of coarse grained polyglutamine conformations from PRIME20 sampling.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

Assessing the Nationwide Benefits of Vehicle–Grid Integration during Distribution Network Planning and Power System Dispatching

The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%).

Lifehistory Trade-Offs Influence Women’s Reproductive Strategies

Abstract Objective In a UK national census sample, women from the upper and lower socioeconomic (SES) classes achieve parity in completed family size, despite marked differences in both birth rates and offspring survival rates. We test the hypothesis that women adopt reproductive strategies that manipulate age at first reproduction to achieve this. Methods We use a Monte-Carlo modeling approach parameterized with current UK lifehistory data to simulate the reproductive lifehistories of 64,000 individuals from different SES classes, with parameter values at each successive time step drawn from a statistical distribution defined by the census data. Results We show that, if they are to achieve parity with women in the higher socioeconomic classes, women in lower socioeconomic classes must begin reproducing 5.65 years earlier on average than women in the higher SES classes in order to offset the higher class-specific mortality and infertility rates that they experience. The model predicts very closely the observed differences in age at first reproduction in the census data. Conclusions Opting to delay reproduction in order to purse an education-based professional career may be a high risk strategy that many lower SES women are unwilling and unable to pursue. As a result, reproducing as early as possible may be the best strategy available to them.

Safety Evaluation of Existing R.C. Buildings: Uncertainties Due to the Location of In Situ Tests

The paper aimed to investigate the influence, on the assessment of the structural safety level of an existing r.c building, of the different choices that the technician in charge of a structural evaluation (the “analyst”) can make regarding the structural elements to be tested to obtain a prescribed level of knowledge. To this end, the case study of a reinforced concrete framed structure built in the 1960s in Italy was investigated by means of numerical analyses. The probability distribution of the estimated safety levels was evaluated in the paper by means of a Monte Carlo approach, considering the alternative selections of elements done by a large number of analysts, and the probability of unsuccessful safety estimations is discussed for the knowledge levels considered in the Italian technical codes and the Eurocodes.

Self-consistent Atmosphere Representation and Interaction in Photon Monte Carlo Simulations

We present a self-consistent representation of the atmosphere and implement the interactions of light with the atmosphere using a photon Monte Carlo approach. We compile global climate distributions based on historical data, self-consistent vertical profiles of thermodynamic quantities, spatial models of cloud variation and cover, and global distributions of four kinds of aerosols. We then implement refraction, Rayleigh scattering, molecular interactions, and Tyndall–Mie scattering to all photons emitted from astronomical sources and various background components using physics first principles. This results in emergent image properties that include: differential astrometry and elliptical point spread functions (PSFs) predicted completely to the horizon, arcminute-scale spatial-dependent photometry variations at 20 mmag for short exposures, excess background spatial variations at 0.2% due to the atmosphere, and a PSF wing due to water droplets. We use a common atmosphere representation framework to self-consistently model all phenomena by simulating individual photons. We reproduce the well-known correlations in image characteristics: correlations in altitude with absolute photometry (overall transmission) and relative photometry (spectrally dependent transmission), anticorrelations of altitude with differential astrometry (nonideal astrometric patterns) and background levels, and an anticorrelation in absolute photometry with cloud depth. However, we also find further subtle correlations including an anticorrelation of temperature with background and differential astrometry, a correlation of temperature with absolute and relative photometry, an anticorrelation of absolute photometry with humidity, a correlation of humidity with lunar background, a significant correlation of PSF wing with cloud depth, an anticorrelation of background with cloud depth, and a correlation of lunar background with cloud depth.