Monte Carlo Simulations Research Articles

Generalized ghost pilgrim dark energy fractal cosmology with observational constraint

Abstract In this work, we explore dark energy models, mainly ghost, generalized ghost, and generalized ghost pilgrim dark energy models within the framework of fractal cosmology. To obtain solutions for the field equations, we employed a parameterization of the deceleration parameter, as proposed by R. K. Tiwari. By utilizing Markov Chain Monte Carlo (MCMC) analysis, we impose constraints on the free parameters of the derived solutions. The analysis is based on observational datasets, including 57 data points from the Observational Hubble Data (OHD) and, 1048 points from the $Pantheon$ Supernovae sample. This approach allows us to assess the viability of the dark energy models in describing the current cosmic expansion. According to the effective equation-of-state parameter, the model maintains itself in the quintessence era and ultimately switches into the Einstein-de Sitter model. Furthermore, we investigate the statefinder, jerk, snap, and lerk parameters. The energy conditions of each model satisfy the weak and null energy conditions but violate the strong energy condition. We find that the Om(z) curves for the data samples exhibit a consistently negative slope throughout the entire range.

Digital instrument simulator to optimize the development of hyperspectral systems: application for intraoperative functional brain mapping.

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. These areas can be assessed by monitoring cerebral hemodynamics and metabolism. Robust quantification of these biomarkers is complicated to perform during neurosurgery due to the critical context of the operating room. In actual devices, the inhomogeneities of the optical properties of the exposed brain cortex are poorly taken into consideration, which introduce quantification errors of biomarkers of brain functionality. Moreover, the best choice of spectral configuration is still based on an empirical approach. We propose a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This simulator can provide realistic modeling of the cerebral cortex and the identification of the optimal wavelengths to monitor cerebral hemodynamics (oxygenated and deoxygenated hemoglobin Hb) and metabolism (oxidized state of cytochromes and and cytochrome-c-oxidase oxCytb, oxCytc, and oxCCO). The digital instrument simulator is computed with white Monte Carlo simulations of a volume created from a real image of exposed cortex. We developed an optimization procedure based on a genetic algorithm to identify the best wavelength combinations in the visible and near-infrared range to quantify concentration changes in , Hb, oxCCO, and the oxidized state of cytochrome and (oxCytb and oxCytc). The digital instrument allows the modeling of intensity maps collected by a camera sensor as well as images of path length to take into account the inhomogeneities of the optical properties. The optimization procedure helps to identify the best wavelength combination of 18 wavelengths that reduces the quantification errors in , Hb, and oxCCO by 47%, 57%, and 57%, respectively, compared with the gold standard of 121 wavelengths between 780 and 900nm. The optimization procedure does not help to resolve changes in cytochrome and in a significant way but helps to better resolve oxCCO changes. We proposed a digital instrument simulator to optimize the development of hyperspectral systems for intraoperative brain mapping studies. This digital instrument simulator and this optimization framework could be used to optimize the design of hyperspectral imaging devices.

Scaling Violation in Power Corrections to Energy Correlators from the Light-Ray Operator Product Expansion

In recent years, energy correlators have emerged as a powerful tool to explore the field theoretic structure of strong interactions at particle colliders. In this Letter we initiate a novel study of the nonperturbative power corrections to the projected N-point energy correlators in the limit where the angle between the detectors is small. Using the light-ray operator product expansion as a guiding principle, we derive the power corrections in terms of two nonperturbative quantities describing the fragmentation of quarks and gluons. In analogy with their perturbative leading-power counterpart, we show that power corrections obey a classical scaling behavior that is violated at the quantum level. This crucially results in a dependence on the hard scale Q of the problem that is calculable in perturbation theory. Our analytic predictions are successfully tested against Monte Carlo simulations for both lepton and hadron colliders, marking a significant step forward in the understanding of these observables. Published by the American Physical Society 2024

Doped-δ-doped transferred electron device for sustained terahertz signal generation

Abstract The performance of a novel transferred electron device structure aimed at sustaining high-frequency signals in the terahertz (THz) range is investigated. The device uses a highly doped δ-layer to split the n-doped device into two distinct regions, forming a doped-δ-doped configuration. The first region generates high-speed electrons toward the δ-layer, while the second region utilizes negative differential resistance to modulate electron speeds and sustain oscillations. An ensemble self-consistent Monte Carlo model is employed to analyze electron dynamics and THz signal generation in this structure under a constant bias. The design demonstrates superior performance, achieving a fundamental operating frequency of 427 GHz in a 600 nm length InP device, nearly a 50% increase over conventional notch-doped design, while maintaining the current harmonic amplitude. This design achieves higher frequencies without reducing device length and increasing doping density, effectively addressing the trade-off of the Kroemer criterion. The study of the effects of varying doping densities and region lengths on device performance, highlighting the importance of optimizing these parameters to sustain current oscillations and efficiently generate THz signals. This design offers a promising solution for a compact and efficient THz source.

Cross-Sections for Projectile Ionization, Electron Capture, and System Breakdown of C5+ and Li2+ Ions with Atomic Hydrogen

For many disciplines of science, all conceivable collisional cross-sections and reactions must be precisely known. Although recent decades have seen a trial of large-scale research to obtain such data, many essential atomic and molecular cross-section data are still missing, and the reliability of the existing cross-sections has to be validated. In this paper, we present projectile ionization, electron capture, and system breakdown cross-sections in carbon (C5+) ions and lithium (Li2+) ion collisions with atomic hydrogen based on the Monte Carlo models of classical and quasi-classical trajectories. According to our expectation, the QCTMC results show higher results in comparison to standard CTMC data, emphasizing the role of the Heisenberg correction constraint, especially in the low-energy regime. On the other hand, at high energy, the Heisenberg correction term has less influence as the projectile momentum increases. We present the total cross-sections of projectile ionization, electron capture, and system breakdown in C5+ ions and Li2+ ion collisions with atomic hydrogen in the impact energy range from 10 keV to 160 keV, which is of interest in astrophysical plasmas, atmospheric sciences, plasma laboratories, and fusion research.

Monte Carlo simulation for dosage optimization of the best available therapy for bloodstream infections secondary to carbapenemase-producing Klebsiella pneumoniae in critically ill patients

ObjectivesWe aimed to use Monte Carlo simulation, based on pharmacokinetic/ pharmacodynamic (PK/PD) targets, to investigate and determine the optimal dosage of the available combination therapies for carbapenem-resistant Klebsiella pneumoniae (CRKP) in critically ill patients. MethodsWe collected CRKP clinical isolates from Phramongkutklao Hospital between October 2020 and June 2022. A molecular study of resistant genes was performed using polymerase chain reaction (PCR). Broth microdilution checkerboards were used to evaluate the mono- and synergistic antibiotic activities. Monte Carlo simulation was used to determine the optimal antibiotic regimens, based on the probability of target achievement (PTA) and cumulative fraction of response (CFR). ResultsThe 54 CRKP isolates were resistant to tigecycline (100%), colistin (75.9%), amikacin (70.4%), and gentamicin (63.0%). The most common carbapenemase genotype was blaoxacillinases (OXA)-48-like (42.6%), followed by blaNew Delhi metallo beta lactamase (NDM) (29.6%) and coexistence of blaOXA-48-like and blaNDM (22.2%). Based on the PTA, synergistic and additive activities against CRKP isolates were observed with appropriate dosages of tigecycline–colistin (67.9%), tigecycline–gentamicin (62.2%), and tigecycline–amikacin (51.4%). ConclusionsTigecycline–colistin was the best available combination therapy for critically ill patients with CRKP, especially NDM. When used in combination with tigecycline, a colistin creatinine clearance (CrCl) of <90 mL/min can raise the CFR target and less nephrotoxicity.

Accelerating the penetration of clean electricity to promote the low carbonization of high-speed railways: A probabilistic framework

Transitioning from carbon-intensive travel modes to high-speed railways (HSR) is widely recognized as a crucial pathway to achieving the ‘carbon peak and carbon neutrality’ goals in the transport sector. However, it remains unclear whether HSR can meet carbon peak goals on time, under the high uncertainty of future electricity development. This study integrates scenario analysis and Monte Carlo simulation into life cycle assessment, proposing a probabilistic framework for dynamically simulating the carbon dioxide (CO2) emissions over the entire life cycle of HSR. Monte Carlo simulation-Latin hypercube sampling method is introduced to model the uncertainty in the development of electricity. The results show that with the gradual increase in the proportion of clean electricity, the time when the CO2 emission intensity of HSR is lower than that of electric cars and electric coaches is most probable to occur in the 2nd–3rd years and the 5th–14th years of operation, respectively. The emission trend of HSR is primarily influenced by the passenger growth rate and the depth of transition to clean electricity, with minimal impact from the loading factor and initial passenger volume. When the passenger growth rate reaches 5 % by 2030, transitioning solely to clean electricity can peak CO2 emissions from HSR by 2030. However, with passenger growth rates of 6 %, 7 %, 8 %, and 9 %, the peak time is most probable to be delayed by 6–7 years, 13–14 years, 16 years, and 19 years, respectively. These findings suggest that achieving CO2 emission peak goals in the transport system requires collaborative efforts across multiple sectors, including transport, energy, and industry sectors. The results contribute to a deeper understanding of the pathways for achieving carbon peak in the transport sector.

Application of Mont Carlo Simulation and Artificial Neural Network Model to Probabilistic Health Risk Assessment in Fluoride-Endemic Areas

Groundwater contamination with fluoride is a considerable public health concern that affects millions of people worldwide. The rapid growth of urbanization has led to increase in groundwater contamination. The health risk assessment focuses on both acute and chronic health consequences as it investigates the extent and effects of fluoride exposure through contaminated groundwater. Fluoride exposure, especially in endemic locations, has serious health consequences, including dental and skeletal fluorosis. An accurate assessment of these hazards is essential for public health planning and mitigation actions. The present study uses Monte Carlo Simulation (MCS) and an Artificial Neural Network (ANN) model to perform a Probabilistic Health Risk Assessment on populations in fluoride-endemic areas. Analysis of the results of the study reveals that the concentration of fluoride ranged from 0.58 to 3.80 mg/L with an average of 2.30 mg/L across the Kasganj district, which was higher than permissible limits given by BIS and WHO. The highest value of hazard quotient of 3.29 for Children is found to be in the Durga Colony area, while the lowest value of the hazard quotient of 0.31 for adults is found to be in the Nadrai Gate area. The assessment of health risks revealed a high probability of non-carcinogenic disease from the consumption of groundwater containing fluoride. The ANN model has the R2 value of 0.9989 in training and 0.9870 in testing while RMSE value in training and testing was 0.02230 and 0.0267. The findings suggest that before being used, the groundwater in Kasganj, Uttar Pradesh, India, needs to be treated and made drinkable. The results emphasize the critical need for ongoing monitoring, public education initiatives, and implementing feasible mitigating techniques to lower fluoride exposure. The findings show that this hybrid model is excellent at addressing the numerous uncertainties associated with fluoride use, hence improving the reliability of health risk estimates in fluoride-endemic locations. The results offer vital information to help policymakers and local health officials create focused measures to safeguard public health in Kasganj.

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.

AI Based Hazardous Asteroid Detection

As the need for early detection and mitigation of potential threats from near-Earth objects grows, this study presents a comprehensive approach to predicting hazardous asteroids using machine learning techniques. Given the increasing importance of protecting our planet from potential impact events, accurately classifying and predicting hazardous asteroids is cru- cial. The study proposes a comprehensive system for predicting and mitigating asteroid hazards using advanced data processing, machine learning, and simulation techniques. The proposed study collect real-time and historical asteroid data, process and clean this data, extract relevant features, and train machine learning models to predict potentially hazardous asteroids. Additionally, Monte Carlo simulations are employed to assess the collision risks of these asteroids with Earth. A forecasting module predicts future asteroid behavior based on historical and simulated data. The system also includes an alert mechanism that notifies relevant stakeholders when a potentially hazardous asteroid is detected

Evaluation of phthalate acid esters (PAEs) in kefir samples by using magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI)/GC–MS method: A health risk assessment study

In this study, by magnetic chitosan coated with polyaniline (Fe3O4@CHI@PANI) −gas chromatography/mass spectrometry (GC/MS) method was applied to analyze six PAE compounds in kefir samples. The results obtained from kefir samples showed that the lowest and highest mean of PAEs were associated to dimethyl phthalate (DMP was not detected or nd) and di-n-octyl phthalate (DNOP, 2.12 µg/kg), respectively. Also according to results the average of DEHP (Bis(2-ethylhexyl)phthalate) and total PAEs in all samples were 1.31 ± 0.72 and 4.58 ± 1.87 µg/kg, respectively. Furthermore, maximum and minimum mean of total PAEs were detected in the packages with a volume of 1000 mL (5.47 ± 2.03 μg/kg) and packages with a volume of 1500 mL (3.69 ± 1.23 μg/kg), respectively, and the maximum and minimum mean total PAE (μg/kg) were related to samples with a date less than the expiration date (5.42 ± 1.46) and samples with a date greater than the production date (3.73 ± 1.91), respectively. The level of these contaminants was lower than the existing standard levels. The assessment of human health risks, comprising risks of non-carcinogenic and carcinogenic were calculated with Monte Carlo simulation (MCS) and sensitivity analysis under certain removal efficiencies from 5 to 95 %. The result of the carcinogenic risk for DEHP showed that adults are not threatened by the consumption of kefir (1.06E-05), while children are exposed to negligible risk the consumption of kefir (1/48E-04). In all samples, the total non-carcinogenic risk of PAE compounds was less than 1 (TTHQ was 2.03E-04 and 4.33E-05 for children and adults, respectively), this values presenting there would be unlikely risks of non-carcinogenic of PAEs in kefir for consumers. In conclusion, it can be stated it doesn’t pose any threat to Iranian consumers (adults and children).

Payoff-driven migration promotes the evolution of trust in networked populations

Establishing and maintaining trustworthy behaviors in large populations of self-interested individuals is a significant problem which received extensive attentions from research fields such as evolutionary biology, economics, sociology and engineering. As a well-known model that captures social trust dilemmas, the N-player trust game is adopted to investigate the evolution of trust in groups consisting of investors and trustees. In addition, to model real scenarios in dynamic environments, we introduce a novel migration mechanism, called adaptive environment-benefit-driven migration, which considers the influence of individuals’ payoffs on migration. Through theoretical analysis and Monte Carlo simulations, we demonstrate the effectiveness of the migration mechanism on facilitating trust behaviors in the framework of evolutionary game theory. We find that the population density plays a crucial role in establishing trust. Simulation results reveal that trust can be fostered in a dynamic environment influenced by individual migration with the medium vacancy rate. This work significantly contributes to the understanding of mutual trust behaviors in networked social systems from a more realistic perspective.

The impact of vaccines for diarrhoea on antibiotic use among children in five low-resource settings: a comparative simulation study

Vaccines for diarrhoea could have the ancillary benefit of preventing antibiotic use. We aimed to quantify and compare the expected impact of enteric vaccines on antibiotic use via Monte Carlo simulations. We analysed data from a longitudinal birth cohort, which enrolled children from 2009 to 2012 from Bangladesh, India, Nepal, Pakistan, and Tanzania. We used Monte Carlo simulations to estimate hypothetical vaccine impact in nine vaccination scenarios (including six single vaccines and three combination vaccines) on antibiotic- treated diarrhoea, overall antibiotic courses, and antibiotic exposures to bystander pathogens. For each vaccine scenario, we randomly selected target pathogen-specific diarrhoea episodes to be prevented according to the specified vaccine efficacy and estimated the absolute and relative differences in incidence of antibiotic use outcomes between vaccine and no vaccine scenarios. Among 1119 children, there were 3029 (135·3 courses per 100 child-years) antibiotic-treated diarrhoea episodes. Based on simulated results, a Shigella vaccine would cause the greatest reductions compared with the other single pathogen vaccines in antibiotic courses for all-cause diarrhoea (6·1% relative reduction; -8·2 courses per 100 child-years [95% CI -9·4 to -7·2]), antibiotic courses overall (1·0% relative reduction; -8·2 courses per 100 child-years [-9·4 to -7·2]), and antibiotic exposures to bystander pathogens (1·2% relative reduction; -15·9 courses per 100 child-years [-18·5 to -13·8]). An adenovirus-norovirus-rotavirus vaccine would cause the greatest reductions in antibiotic use (12·2 courses per 100 child-years [-13·7 to -11·0]) compared with the other combination vaccines. However, projected vaccine effects on antibiotic use in 2021 were 45-74% smaller than those estimated in 2009-12 accounting for reductions in diarrhoea incidence in the past decade. Vaccines for enteric pathogens could result in up to 8-12 prevented courses of antibiotics per 100 vaccinated children per year. Combination vaccines will probably be necessary to achieve greater than 1% reductions in total antibiotic use among children in similar low-resource settings. Wellcome Trust and Bill & Melinda Gates Foundation.

Generating Near-Optimal Road Condition–Capacity Improvement Decisions Using Monte Carlo Simulations

Generating Near-Optimal Road Condition–Capacity Improvement Decisions Using Monte Carlo Simulations

Study of Merger Highlights Use of Probabilistic Financial Analysis

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 221033, “Enhancing Management Insight Into Mergers and Acquisitions Using Probabilistic Financial Analysis,” by Lachlan Hughson, SPE, 4-D Resources Advisory; Brooks J. Klimley, SPE, Brooks J. Klimley and Associates; and Michael J. Orlando, University of Colorado Denver. The paper has not been peer reviewed. _ The complete paper analyzes the 2021 merger of Cabot Oil and Gas with Cimarex Energy to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers and acquisitions (M&amp;A) and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared with a probabilistic approach based on Monte Carlo simulation using a readily available Excel add‑in.​​​​​ Introduction On 24 May 2021, Cabot Oil and Gas Corporation and Cimarex Energy Company announced a $17 billion, all‑stock merger of equals whereby Cabot would issue 4.0146 of its own shares for each Cimarex share. Per the joint proxy statement/prospectus filed with the Securities and Exchange Commission on 23 August 2021, the exchange ratio was deemed “fair, from a financial point of view” by Cabot’s financial adviser, J.P. Morgan Securities (JPM), and Cimarex’s financial adviser, Tudor, Pickering, Holt and Co. (TPH), based on their respective deterministic analyses. This paper reviews and compares these deterministic analyses to a probabilistic analysis based on publicly available data. The financial advisers are industry‑leading, with a wealth of experience and insight into undertaking a fairness opinion for a merger transaction of this type. This paper does not question their analysis or conclusions. Instead, it seeks to analyze the deterministic data used by the advisers to highlight how a probabilistic approach can significantly upgrade the valuation exercise and enhance the M&amp;A analysis process. Probabilistic Approach Overview A probabilistic approach to financial modeling simply means that, instead of including one input value per cell in an Excel model and then running multiple iterations based on selectively changing the single input cells, as done with a deterministic approach, a specific range of values is incorporated into a particular cell for an input that experiences variability. A probabilistic model, therefore, generates a range of output values for a specific calculation in a particular cell, defined by their associated frequency of occurrence instead of merely one value with no sense for its particular probability. Importantly, a probabilistic approach allows users to integrate significantly more commercial data into their financial models using both probability distribution and time‑series functions to best incorporate the variability of the actual inputs. These probabilistic input distributions can be estimated from historical data or through choosing the likely input range based on commercial experience. The user does not have to decide which individual data points to include to best represent the scenario being considered or run multiple scenarios to mimic the functionality of the Monte Carlo simulation underpinning a probabilistic approach.

Incident Duration Reliability Assessment Using Monte-Carlo Simulation and Kernel Density Estimation of Machine Learning-Based Models

Traffic incidents are a major cause of non-recurrent congestion and delays, making accurate incident duration (ID) prediction essential for effective traffic management. While machine learning (ML) and deep learning (DL) models have been developed to predict ID and its sub-periods (verification, response, and clearance times), their reliability has not been systematically assessed. This study introduces a novel framework to evaluate the reliability of these predictions using 4,000 traffic incident records. Non-parametric Kernel Density Estimation effectively captured variations in the data, outperforming traditional parametric methods. Monte Carlo simulation was then used to assess prediction reliability. Among the models tested, bagged ensemble trees provided the best balance between accuracy and complexity, showing strong reliability for predicting total ID and sub-periods. Adding 5% to 25% buffer adjustments further improved reliability by accounting for prediction uncertainties. This framework offers a robust tool for assessing prediction reliability, is adaptable to various ML and DL models, and represents a significant step forward in traffic incident management.

Design and optimization of a source (reflector/shielding) performance test system based on (241Am-Be)-paraffin thermal neutron irradiation device

Laboratory measurements and Monte Carlo simulations were conducted to investigate the feasibility of employing a (reflector/shield) to improve the efficiency of a thermal neutron irradiator utilizing a paraffin-moderated single 241Am-Be source with an activity of 1 Ci. A multi-parameter optimization was performed to evaluate the potential of natural lead (Pb) and 90 % enriched 208Pb lead (208Pb(90 %)) in terms of increasing the neutron production while minimizing the total dose equivalent rate. The laboratory-derived optimal configuration for the (reflector/shield) assembly demonstrated that Pb significantly increased the neutron flux by approximately 58 % and reduced the total dose equivalent rate to 0.00443 mSv/h, thereby allowing for a workload of 4500 h within the annual permissible dose limit of 20 mSv. The MCNP simulations validated the potentials of Pb and confirmed the superiority of 208Pb(90 %) in terms of increasing neutron flux to about 26 % compared to that measured for Pb, while also minimizing the total dose equivalent rate to 0.0037 mSv/h. Thus, enabling a workload of approximately 5400 h, which is higher by about 20 % compared to that permitted by Pb based on measurements. The geometrical configuration and results promoted the use of the proposed TNI device primarily for prompt gamma neutron activation analysis (PGNAA).

Coupling a process-driven model with geographic information system enables the spatial predication of cadmium in paddy soils

Policies on the management of paddy fields are usually made at a broad scale and from a long-term perspective, while predicting the spatial extent of cadmium (Cd) contamination in paddy soils remains challenging. In this study, we developed a process-driven spatial model to quantify the transport of Cd in paddy soils and validated it against observed data from a 10-year regional investigation in southern China. Using a geographic information system and Monte Carlo simulation, the model was then applied to evaluate the effectiveness of different remediation strategies for contaminated paddy fields at field-to-regional scales in a 100-year period. In the last decade, atmospheric emissions have accounted for 43.5% of the total Cd input in local paddy soils. However, the local clean air act failed to mitigate Cd contamination in 99.8% of study area over the period of 2020–2120 because straw return became the dominant contributor to Cd inputs. Improving aerosol emission reductions by 3% per year, stopping straw return to soil, and cleaning irrigation channels would take approximately 30 years (2020–2050) to protect 95% of local rice production from causing an excessive human Cd kidney burden, especially in the paddy fields located in mining-affected areas.

Seawater-oceanic crust interaction constrained by triple oxygen and hydrogen isotopes in rocks from the Saglek-Hebron complex, NE Canada: Implications for moderately low-δ18O Eoarchean Ocean

Estimations of Earth's earliest surface conditions assume a strong connection between the temperature and oxygen isotopic composition of oceans, balanced by surface weathering and submarine hydrothermal alteration. The oldest preserved supracrustal rocks provide rare opportunities to study and constrain the earliest surface conditions prevailing on the Earth. Here, we present a study of triple oxygen and hydrogen isotopes of hydrothermally altered Eoarchean metamorphosed basalts, ultramafic rocks, and detrital and chemical sediments, from the Saglek-Hebron Complex in northern Labrador, Canada. For the metavolcanic rocks, δ’18O values range from 4.83 ‰ to 8.56 ‰, while Δ’17O values vary from −0.076 ‰ to −0.023 ‰, both higher and lower than the mantle. Accounting for the effects of metamorphism on oxygen and hydrogen isotopic compositions, we demonstrate that triple oxygen isotopic values are preserved from the hydrothermal suboceanic stage, while none of the hydrogen isotope compositions (δD from −77.9 ‰ to −10.7 ‰) are interpreted as primary. Several metabasalt samples from the Saglek-Hebron Complex yielded Δ’17Ο values lower than modern mantle values, which cannot be explained by direct interaction with modern seawater and indicate complex upstream interactions. Our numerical models and Monte Carlo simulation considers one- and two-stage mechanisms of water-rock interaction, including the δ’18Ο and Δ’17Ο isotopic shift effects due to interaction between basalts and chemical sediment-derived fluids. The modelling favors Eoarchean seawater characterized by low δ’18Ο < −8 ‰ at Δ’17Ο up to 0.01 ‰. This model also works for higher Δ’17Ο at lower δ’18Ο. Our results also suggest that without proper modelling of multi-stage water-rock interaction, involving isotopic shifts and input of sediment-derived fluids, exposed sections of altered oceanic crust present only remote evidence of the original seawater. Due to the modeled isotopic shifts and fluid mixing, we favor “weak” coupling of seawater-oceanic crust interaction globally. This potentially reduces the relative importance of submarine hydrothermal alteration in explaining the oxygen isotopic record in submarine basalts across the geologic history.

Experimental and numerical characterization of E-glass/epoxy plain woven fabric composites containing void defects

The presence of voids in woven fabric composite (WFC) significantly affects the mechanical and thermomechanical properties. Void defects are introduced during the resin infiltration process due to various controlling parameters involved in the curing process, i.e., pressure, temperature, resin flow etc. In this study, the mechanical constants of 2D woven fabric composite were experimentally determined. A finite element (FE) based representative volume element (RVE) model has been validated against these experimental results. A multiscale-based FE model has been developed, and periodic boundary conditions are applied to the RVE model to evaluate the homogenized thermomechanical properties of WFC containing void defects. Present numerical model incorporates the geometrical microstructures of the post-cured woven composite and void contents obtained from X-ray microtomography. The influence of void defect and resin infiltration have been incorporated to evaluate the thermomechanical properties of plain WFC. A parametric study has been carried out with respect to the variation of void defects on thermoelastic properties of E-glass/epoxy plain WFC. The variation of void defects has been considered in micro and mesoscale models. Monte Carlo simulations further quantified the effects of void content on the thermomechanical constants of WFC. The presence of voids has been observed to have significant influences on the thermomechanical constants of yarn and woven fabric composites.