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Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO. [ABSTRACT FROM AUTHOR]

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

Measuring systemic risk plays an important role in financial risk management to control systemic risk. By means of a vine copula grouped-CoES method, this paper aims to measure the systemic risk of Chinese financial markets. The empirical study indicates that the banking industry has a low risk and a strong ability to resist risks, but also contributes the most of the systemic risk. On the other hand, insurance companies and securities have high ES but low Δ CoES, indicating their low risk tolerance and small contribution to the systemic risk. Furthermore, this study employs a sliding window in Monte Carlo simulation to forecast systemic risk. The findings of this paper suggest that different types of financial industries should adopt different systemic risk measures. [ABSTRACT FROM AUTHOR]

This paper proposes a novel longitudinal reference trajectory tracking guidance framework for Mars atmospheric entry. Unlike traditional methods that require strict adherence to pre-defined safe trajectories, this guidance framework incorporates path constraints into the closed-loop tracking process to ensure safety. The framework comprises a safety module and a guidance law module. The safety module employs an online path constraints management algorithm that converts real-time path constraints into equivalent control constraints and applies them to the guidance module. The guidance law module uses the anti-saturation generalized super-twisting based sliding mode (ASGSTSM) guidance law, which provides sufficient rapidity and robustness to achieve trajectory tracking while accounting for uncertainties and control saturation. Simulation results demonstrate that the proposed guidance framework effectively ensures compliance of the entry vehicle with path constraints. The ASGSTSM guidance law exhibits notable speed and robustness, making it adaptable within the guidance framework. Particularly, in Monte Carlo simulations introducing significant uncertainty, incorporating the ASGSTSM guidance law within the guidance framework resulted in convergence in all trajectory tracking processes, whereas guidance laws in the control group led to numerous failures. Additionally, the paper offers practical principles for designing guidance laws within this universal framework. This facilitates future research to easily replace the guidance law module based on specific mission requirements. [ABSTRACT FROM AUTHOR]

The stochastic P-bifurcation behavior of bi-stability in a generalized van der Pol oscillator with the fractional damping under colored noise and thermal excitation is investigated. Firstly, using the principle of minimal mean square error and linearization method, the non-linear stiffness terms can be equivalent to a linear stiffness which is a function of the system amplitude, and the original system is simplified to an equivalent integer order van der Pol system. Secondly, the system amplitude stationary probability density function is obtained by the stochastic averaging, and then based on the singularity theory, the critical parametric conditions for the system amplitude stochastic P-bifurcation are found. Finally, the types of the stationary probability density function of the system amplitude are qualitatively analyzed in each area divided by the transition set curves. The consistency between the analytical results and the numerical results acquired from Monte-Carlo simulation also testifies the theoretical analysis in this paper and the method used in this paper can directly guide the design of the fractional order controller to adjust the response of the system. [ABSTRACT FROM AUTHOR]

In this paper, the transformed sine Dagum distribution is proposed using the Dagum distribution as a baseline distribution in the transformed sin-G family of distributions. The purpose of this paper is to propose and explore a modified version of the Dagum distribution, based on the transformed sin-G family of distributions. The density of the transformed sine Dagum distribution shows that it can adequately model datasets that are positively skewed, approximately symmetric and decreasing. The hazard rate plot shows that the proposed distribution can adequately model both monotonic and non-monotonic hazard rate datasets. Statistical properties such quantile function, moments, inverse moments, moments generating functions, mean and median deviation and order statistics are derived. The plots of the skewness and kurtosis show that the skewness is always positive and the kurtosis is increasing. Maximum likelihood estimation is used to estimate the parameters of the transformed sine Dagum distribution. Monte Carlo simulations are performed to ascertain the behavior of the estimators. The results show that the estimators are consistent. The proposed distribution is applied to two real life datasets. The results show that the proposed distribution provide a reasonable parametric fit to the datasets compared with the other competing distribution. [ABSTRACT FROM AUTHOR]

This paper illustrates an interferometric inverse synthetic aperture radar based on three‐dimensional imaging and rotational velocity estimation from a multistatic setup with three ground‐based radars with non‐orthogonal baselines. The radar backscattering along the orbit is simulated based on physical optics approximation. The paper illustrates a novel shape‐dependent and rotation‐invariant feature called normalised eigenvalues for classification. A database of interferometric point clouds is generated using radar back scattering at different aspect angles and classified using various classification algorithms. The classification accuracy at varying SNR levels was evaluated using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper discusses the challenging issues that reliability practitioners face in conducting destructive tests that lead to completely censored lifetimes, especially in estimating the defect rate of products. Manufacturers need to measure the defect rate for quality control purposes, but obtaining enough defective devices for accurate estimation is not easy when the defect rate is relatively low. To address the issues, a Bayesian approach for estimating the defect rate is proposed in this paper. The proposed method is devised to make up for the heavy computational burdens of the Metropolis-Hastings algorithm. To quantify the uncertainty in the Bayesian estimation, a nonparametric bootstrap technique is employed to construct a credible interval for the defect rate. The performance of the proposed method is evaluated through a variety of Monte Carlo simulation studies. The efficiency of the proposed Bayesian estimation procedure is validated using a real-world dataset of return-springs in DC motor systems under an accelerated destructive degradation test. • Estimate the defect rate of products in conducting destructive tests with completely censored lifetime data. • Propose a computationally efficient Bayesian estimator of an analytical form for the defect rate with masking. • Propose a nonparametric bootstrap technique to construct a credible interval for the defect rate. • The proposed method is validated using accelerated destructive degradation testing data of return-springs. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) have been found in many packaged food products such as salt, tea bags, milk, and fish. In a previous study by this group, MPs were found to leach into hot water from the plastic lining of disposable paper cups. No studies were found in the literature quantifying health risks or lifetime intake of MPs. At present, it is not possible to quantify health risks due to MPs because dose-response and toxicity assessments are not available. Therefore, the objective of the current study was to assess the intake of MPs and associated contaminants like fluoride that are released into these hot beverages. MPs in the previous study were quantified in terms of particle counts only and a simple method was adopted in the present study to convert the microplastics count into its respective mass. Chronic daily intake (CDI) and lifetime intake (LTI) of MPs through the ingestion pathway were calculated. CDI and Hazard Quotient (HQ) due to fluoride ingestion were also estimated following USEPA guidelines. Monte Carlo (MC) simulations were used to account for the variability in input variables such as concentration of MPs, body weight, averaging time, exposure duration, exposure frequency and ingestion rate to evaluate the impact on CDI and LTI values. The CDI was used to estimate the LTI of MPs and HQ for fluoride ingestion. MC simulations with 100,000 iterations resulted in an average CDI of 0.03 ± 0.025 mg of microplastic per kg of body weight per day and 7.04 ± 8.8 μg fluoride per kg body weight per day. This study takes us one step closer to estimating the human health risk due to the ingestion of microplastics and other contaminants through food items. [Display omitted] • Mass of microplastics from paper cups into hot beverages was estimated. • Chronic daily intake analysed using Monte Carlo simulations. • Average chronic daily intake was 0.03 ± 0.025 mg microplastic/kg body weight day−1. • Chronic daily intake of fluoride was 7.04 ± 8.8 μg/kg body weight per day. • Fluoride levels were higher or close to drinking water standard of 1.5 mg/L. [ABSTRACT FROM AUTHOR]

Tri-rhythmical nature has attracted extensive attention from scholars in describing the dynamical behaviors of self-sustained systems. In this paper, we consider a tri-rhythmic van der Pol system and give a bifurcation analysis of a stochastic tri-rhythmic self-sustained system under joint noise perturbation. Based on an approximate approach, we give the stationary probability density function of amplitudes, and we find that the noise and time-delay feedback, regulating the velocity time-delay feedback strength parameter, may not cause transitions among unimodal, bimodal and trimodal in the tri-rhythmic system. More stochastic bifurcations appear by regulating the time delay in this system. The system, surprisingly, undergoes five times of stochastic bifurcations when the time delay is monotonically increased. It is shown that the time delay is more sensitive to the tri-rhythmic system and a much stronger dependence on the system. From a biological point of view, the reaction rate of biological molecules can be enhanced or diminished by the change of the noise intensity or correlation time of Gaussian colored noise. More surprisingly, an increase in displacement feedback will delay the reaction rate; however, the effect of an increase in velocity feedback on the reaction rate depends on the time delay. A detailed research on the parameter space indicates that time delay and colored noise parameters can effectively control the multirhythmicity of the system. Finally, this paper verifies the effectiveness of the theoretical solution through the numerical results of Monte Carlo simulation of the original system. These results may help to further explore forking bifurcations in real-world applications. This research provides new insight into the understanding of nontrivial effects of joint noise on the tri-rhythmic system. [ABSTRACT FROM AUTHOR]

Dynamic reliability evaluation of large-scale reinforced concrete (RC) structures is one of the most challenging problems in engineering practices. Although extensive endeavors have been devoted to mechanical analysis of concrete structures in the past decades, it was recognized that the randomness from both structural parameters and excitations have significant effects on the dynamic behaviors of structures with complex nonlinearity, damage, energy dissipation, and plasticity. Thus, great difficulty exists in evaluating the nonlinear stochastic responses and dynamic reliability of the real-world complex structures of large degrees of freedom. In the present paper, a physically driven method for refined probabilistic response and seismic reliability evaluation of real-world RC structures is proposed via synthesis of the refined mechanical analysis and the physically-based uncertainty propagation. In this method, the material parameters can be treated as probabilistically dependent random variables characterized by vine copulas, and the ground motion is modeled by non-stationary Clough–Penzien spectrum. The uncertainty propagation of arbitrary response quantity(-ies) of interest is governed by the dimension-reduced probability density evolution equation (DR-PDEE). The intrinsic drift coefficients in the DR-PDEE are the physically driven force for the uncertainty propagation and can be identified via data from representative dynamic analyses of the structure. The time-variant reliability of the structures can be captured by solving the physically driven DR-PDEE, which cannot be achieved by the general Monte Carlo simulation (MCS) due to the prohabitively large computational cost. Finally, a practical engineering application is shown in this paper for the probabilistic response and seismic reliability evaluation of a 24-story RC shear wall structure with nearly 280,000 degrees of freedom (DOFs). [ABSTRACT FROM AUTHOR]

This paper compares three approaches for predicting the bridge response under moving vehicles. The first approach represents the vehicle as a concentrated moving load, while the second and third approaches simulate the vehicle using single (1-dof) and two (2-dof) degrees of freedom models, respectively. The vehicle–bridge models have been implemented following a finite-difference formulation. These models have been validated against the experimental displacement response of a mid-span prestressed reinforced concrete bridge. Parametric analyses have been conducted to investigate the impact of road roughness on the discrepancies between the models' predictions. An increasing level of road roughness, modeled according to ISO-8608, tends to conceal the mutual differences between the models' outcomes. Furthermore, a Monte Carlo simulation has been employed to fit the probability distributions of two indicators, comparing the bridge displacement responses in three roughness scenarios and considering variable velocity and vehicle characteristics. The primary objective is to assess whether there are scenarios where vehicle–bridge interaction modeling becomes indispensable for accurately estimating the vehicle's effect on the bridge response. The paper presents expressions for four probability density functions of the chosen metric, facilitating the comparison between the 1-dof and 2-dof models with the concentrated load approach. [ABSTRACT FROM AUTHOR]

The presented paper considers the problem of covering a maximum number of n given points in the plane by m equal disks of radius r. A point is covered if it is inside one or more than one disk. The disks need to be placed in the plane in such a way that a maximum number of points are covered. To solve the problem, an objective function, called energy, is introduced in such a way that the greater the covering is, the lower the energy is. Thus, a configuration of disks with minimum energy is a configuration with maximum covering. To find a configuration of disks that minimizes the energy, a stochastic algorithm based on the Monte Carlo simulated annealing technique is proposed. The algorithm overcomes potential local minima, which, as shown in the paper, are quite likely to occur. The computational complexity of the algorithm is O(mn). The algorithm is tested on several cases demonstrating its efficiency in finding global minima of the energy, i.e. configurations with maximum covering. [ABSTRACT FROM AUTHOR]

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France. [ABSTRACT FROM AUTHOR]

• Shared camera measurements in a dual-agent system for pose estimation using a MEKF. • Stereo-vision measurements remove range ambiguity for relative pose estimation. • Relative navigation estimation accuracy improves for proximity operations using stereo-vision measurements. This paper presents a novel autonomous relative navigation architecture for inspector spacecraft in proximity operations to a known structure in a lunar orbit. Autonomous on-orbit servicing and assembly of large space structures are at the forefront of research for advancing space exploration beyond low-earth orbit. Communication delays with ground stations and limited computational power located on the inspector spacecraft make current relative navigation methods unfeasible and in turn, make it difficult to maintain an accurate estimate of the relative position and orientation of the inspector spacecraft with respect to the structure. The proposed method utilizes the camera measurements from two identical inspector spacecraft, each equipped with a single camera, to estimate the relative position and orientation of the structure. This shared measurement strategy overcomes the deficiencies in monocular vision-based relative navigation techniques, such as the range ambiguities, as is proven in the simulated results of this paper. The proposed stereo-vision system in this work allows for a deterministic solution of the relative position and orientation from the measurements of each inspector spacecraft to the structure. The stereo-vision system was tested with a Multiplicative Extended Kalman Filter (MEKF) for each spacecraft to estimate the relative states of the structure while maintaining a fixed relative position to the structure. The performance of the proposed stereo-vision system was evaluated in terms of accuracy and reliability of the estimated relative position and orientation of two inspector spacecraft to the structure over 100 Monte Carlo simulations for three case studies. The results showed that the stereo-vision system significantly outperforms the monocular vision system even when fewer points on the structure are visible, as is the case for proximity operations. Additionally, the stereo-vision system is consistently more accurate for the relative velocity, attitude, and angular velocity states regardless of how many points are visible. The proposed stereo-vision relative navigation system has the potential to be applied autonomously in proximity operations of spacecraft for various space missions, such as rendezvous and docking, inspection, and on-orbit servicing. [ABSTRACT FROM AUTHOR]

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time–frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time–frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods. [ABSTRACT FROM AUTHOR]

Most engineering structures serve in complex and harsh dynamic environments and suffer from nonlinear vibrations generated by random excitations such as strong winds and waves, which need to be mitigated. To this end, a novel viscoelastic nonlinear energy sink (VNES) device is proposed for the vibration control of structures, whereby the random vibration of a nonlinear oscillator with a VNES device is analyzed in this paper. Specifically, the mathematical model of the structure-VNES is formulated. An approximate technique for the treatment of viscoelastic damping element modeled by the fractional derivative is developed, whereby an equivalent nonlinear system without fractional derivative is derived. The averaged Itô equations associated with the amplitude envelopes are then obtained by resorting to the stochastic averaging method. The closed-form solution of the stationary response is solved from the Fokker–Planck–Kolmogorov (FPK) equation and verified by the Monte Carlo simulation (MCS) data simultaneously. The effects of system parameters and noise intensity on the stationary response are sequentially examined. By comparing the VNES system to the case without attachment and one equipped with the commonly used tuned mass damper (TMD) device, the performance and robustness of the VNES are highlighted. The conclusions of this work may contribute to the efficacy of the application of the VNES in practice. [ABSTRACT FROM AUTHOR]

To reduce total test time and increase the efficiency of statistical analysis of a life-testing experiment adaptive progressive Type-II censoring scheme has been proposed. This paper addresses the statistical inference of the unknown parameters, reliability, and hazard rate functions of logistic exponential distribution under adaptive progressive Type-II censored samples. Maximum likelihood estimates (MLEs) and maximum product spacing estimates (MPSEs) for the model parameters, reliability, and hazard rate functions can not be obtained explicitly, hence these are derived numerically using the Newton–Raphson method. Bayes estimates for the unknown parameters and reliability and hazard rate functions are computed under squared error loss function (SELF) and linear exponential loss function (LLF). It has been observed that the Bayes estimates are not in explicit forms, hence an approximation method such as Markov Chain Monte Carlo (MCMC) method is employed. Further, asymptotic confidence intervals (ACIs) and highest posterior density (HPD) credible intervals for the unknown parameters, reliability, and hazard rate functions are constructed. Besides, point and interval Bayesian predictions have been derived for future samples. A Monte Carlo simulation study has been carried out to compare the performance of the proposed estimates. Furthermore, three different optimality criteria have been considered to obtain the optimal censoring plan. Two real-life data sets, one from electronic industry and other one from COVID-19 data set containing the daily death rate from France are re-analyzed to demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

With the dramatic increase in the number of resident space objects, the risk of collision between space objects and debris is growing. To protect spacecraft from such collisions, it is crucial to determine the generation patterns of space debris and analyze the evolution of debris clouds. In this paper, a continuous distribution formula is derived for each parameter of the breakup debris based on the China Aerodynamics Research and Development Center spacecraft breakup model (CARDC-SBM). Using CARDC-SBM, we theoretically establish a normal distribution for the separation velocity of the breakup debris in relation to the breakup mass center. Based on this foundation, the mean and standard deviation of the debris separation velocity distribution are empirically modeled using polynomial and exponential functions. Consequently, an empirical formula with a broad range of applicability is derived, which significantly reduces the computation time of debris cloud simulations. The initial velocity distribution given by this empirical formula is employed as the initial condition for the evolution of debris clouds from the perspective of a boundary value problem. Simulations using CARDC-SBM and subsequent analysis of the debris cloud evolution resulting from a breakup event reveal that the distribution of each parameter is determined solely by the relative collision velocity and the spacecraft's nominal density in the impact breakup scenario. Our results show that the empirical formula for the debris cloud distribution provides good agreement with Monte Carlo simulations. • A continuous distribution formula is derived for each parameter of the impact breakup debris based on CARDC-SBM. • We point out and validate that normal distribution can characterize the velocity of debris produced by CARDC-SBM. • Empirical formulas are developed to compute the debris separation velocity distribution with low computational burden. • The proposed method can provide initial condition for space debris clouds evolution. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios. • The uncertainty-resilient rendezvous trajectory optimization algorithm provides not only a nominal trajectory but an explicit optimal state-feedback control policy. • Rendezvous trajectory uncertainty caused by initial state errors, dynamics errors, and navigation errors is propagated using an unscented transformation method. • The proposed method considers common input constraints and path constraints in space rendezvous missions. [ABSTRACT FROM AUTHOR]

In this paper, the compensation characteristics and hysteresis loops of a ferrimagnetic mixed-spin diluted fullerene-like (XY)540 are studied based on Monte Carlo method. We show the ground-state phase diagrams in order to explore the stable configuration states. The temperature dependence of magnetization, magnetic susceptibility, internal energy, specific heat, and entropy are presented. The phase diagram including compensation temperature Tcomp and phase change temperature Tc is obtained. The multi-loop hysteresis phenomena under the influence of diverse physical parameters are observed. Finally, the critical exponents of the fullerene-like (XY)540 are obtained. [ABSTRACT FROM AUTHOR]

In this paper, we study asymptotic model change detection in multivariate linear regression by using the Kolmogorov–Smirnov function of the partial sum process of recursive residuals. We approximate the rejection region and also the power function of the test by establishing a functional central limit theorem for the sequence of the partial sum processes of the recursive residuals of the observations. When the assumed model is true, the limit process is given by the standard multivariate Brownian motion which does not depend on the regression functions. However, when the assumed model is not true (some models change), the limit process is represented by a vector of deterministic trend plus the standard multivariate Brownian motion. The finite sample size rejection region and the power of the test are investigated by means of Monte Carlo simulation. The simulation study shows evidence that the proposed test is consistent in the sense that it attains the power larger than the size of the test when the hypothesis is not true. We also demonstrate the application of the proposed test method to Indonesian economic growth data in which we test the adequacy of three-variate low-order polynomial model. The test result shows that the growth of the Indonesian economy is neither simultaneously constant nor linear. The test has successfully detect the appearance of a change in the model which is mainly caused by the COVID-19 pandemic in 2020. [ABSTRACT FROM AUTHOR]

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes. [ABSTRACT FROM AUTHOR]

In recent years, composite structures have been used in a large number of applications in aerospace, machinery, marine, and civil engineering. However, there are inevitably many uncertainties in the whole life cycle of composite structures, which can easily lead to structural damage and failure. Therefore, it is important to analyze the reliability and sensitivity of composite structures. At present, most of the contributions use the first-order reliability method (FORM) and the second-order reliability method (SORM) to study the reliability of composite structures and compare them with the results of the Monte Carlo simulation. However, both methods have their limitations. FORM cannot guarantee the calculation accuracy for the highly nonlinear limit state equation, and the calculation efficiency of SORM is too low. Therefore, this paper proposes to use importance sampling (IS) and backpropagation neural network-based Monte Carlo (MC-BPNN) to study the reliability, sensitivity, and dispersion of delamination growth of composite laminates. The results show that compared with FORM and SORM, IS and MC-BPNN have higher calculation accuracy and efficiency and can more accurately evaluate the failure degree of composite structures and ensure their safe operation in the field of aerospace equipment. The universality of this method can make it being widely used in the reliability and sensitivity analysis of different composite materials as well as dispersion analysis. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem. [ABSTRACT FROM AUTHOR]

This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry–clear, humid–hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh). [ABSTRACT FROM AUTHOR]

This paper extends the threshold regression to threshold effect in varying coefficient model. We allow for either cross-section or time series observations. Estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. The distribution of threshold estimates is found to be non-standard. Under some sufficient conditions, we show that the proposed estimator for regression slopes is root-n consistent and asymptotically normally distributed, and that the proposed estimator for the varying coefficient is consistent and also asymptotically normal distributed but at a rate slower than root-n. Consistent estimators for the asymptotic variances of the proposed estimators are provided. Monte Carlo simulations are presented to assess the performance of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the relationship between environmental regulation and regional technological innovation study. [ABSTRACT FROM AUTHOR]

Controlled de-orbiting is crucial for a low earth orbiting (LEO) satellite or capsule intending to land in a desired location and to prevent damage to people and property on the ground caused by debris. Drag modulation is one possible mechanism to control de-orbiting, exploiting atmospheric drag variation to reduce the necessary orbital energy from the initial conditions to the re-entry interface. In this context, this paper proposes a novel targeted de-orbiting Artificial Neural Network (ANN) and drag-based guidance algorithm for a LEO artificial satellite. It relies on the combination of empirical and analytical relations with Deep Neural Networks (DNNs) to estimate the main parameters of the optimal control law in real time. The training set is composed of several optimal control solutions generated with a previously developed optimal control algorithm, exploiting the formulation in equinoctial modified orbital parameters to manage the large time scale of the problem and the computational cost. An innovative procedure for the training set generation led to the achievement of general results with a limited number of samples. The successful outcome of the guidance algorithm on over 1000 cases demonstrates its robustness and generalization of results. To conclude, a Linear Quadratic Regulator (LQR) feedback control is applied to one case to deal with a more realistic and uncertain density model. • Analytical optimal guidance for a satellite de-orbiting from Low Earth Orbit. • A novel approach based on Deep Neural Networks. • Prediction of the key parameters of the optimal control law in real time. • A Monte Carlo analysis showed the algorithm's robustness and generalization. • Successful feedback control thanks to a Linear Quadratic Regulator (LQR). [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. [ABSTRACT FROM AUTHOR]

The properties of the progenitors of gamma-ray bursts and of their environment are encoded in their luminosity function and cosmic formation rate. They are usually recovered from a flux-limited sample based on Lynden-Bell's |$c^{-}$| method. However, this method is based on the assumption that the luminosity is independent of the redshift. Observationally, if correlated, people use non-parametric |$\tau$| statistical method to remove this correlation through the transformation, |$L^{\prime }=L/g(z)$|⁠ , where z is the burst redshift, and |$g(z)=(1+z)^{k}$| parametrizes the underlying luminosity evolution. However, the application of this method to different observations could result in very different luminosity functions. By the means of Monte Carlo simulation, in this paper, we demonstrate that the origin of an observed correlation, measured by the |$\tau$| statistical method, is a complex combination of multiple factors when the underlying data are correlated. Thus, in this case, it is difficult to unbiasedly reconstruct the underlying population distribution from a truncated sample, unless the detailed information of the intrinsic correlation is accurately known in advance. In addition, we argue that an intrinsic correlation between luminosity function and formation rate is unlikely eliminated by a misconfigured transformation, and the |$g(z)$|⁠ , derived from a truncated sample with the |$\tau$| statistical method, does not necessarily represent its underlying luminosity evolution. [ABSTRACT FROM AUTHOR]

Bootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the analysis of data where predictors are collinear. This paper develops a nonparametric bootstrap-quantile approach for the estimation of ridge parameter in the linear regression model. The proposed method is illustrated using some popular and widely used ridge estimators, but this idea can be extended to any ridge estimator. Monte Carlo simulations are carried out to compare the performance of the proposed estimators with their baseline counterparts. It is demonstrated empirically that MSE obtained from our suggested bootstrap-quantile approach are substantially smaller than their baseline estimators especially when collinearity is high. Application to real data sets reveals the suitability of the idea. [ABSTRACT FROM AUTHOR]

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the spectral properties of mimetic finite-difference operators and their application in the robust fluid–structure interaction (FSI) analysis of aircraft wings under uncertain operating conditions. By delving into the eigenvalue behavior of mimetic Laplacian operators and extending the analysis to stochastic settings, we develop a novel stochastic mimetic framework tailored for addressing uncertainties inherent in the fluid dynamics and structural mechanics of aircraft wings. The framework integrates random matrix theory with mimetic discretization methods, enabling the incorporation of uncertainties in fluid properties, structural parameters, and coupling conditions at the fluid–structure interface. Through spectral and localization analysis of the coupled stochastic mimetic operator, we assess the system's stability, sensitivity to perturbations, and computational efficiency. Our results highlight the potential of the stochastic mimetic approach for enhancing reliability and robustness in the design of aircraft wings, paving the way for optimization algorithms that integrate uncertainties directly into the design process. Our findings reveal a significant impact of stochastic perturbations on the spectral radius and eigenfunction localization, indicating heightened system sensitivity. The introduction of randomized singular value decomposition (RSVD) within our framework not only enhances computational efficiency but also preserves accuracy in low-rank approximations, which is critical for handling large-scale systems. Moreover, Monte Carlo simulations validate the robustness of our stochastic mimetic framework, showcasing its efficacy in capturing the nuanced dynamics of FSI under uncertainty. This study contributes to the fields of numerical methods and aerospace engineering by offering a rigorous and scalable approach for conducting uncertainty-aware FSI analysis, which is crucial for the development of safer and more efficient aircraft. [ABSTRACT FROM AUTHOR]

Autocollimators are widely used optical axis-measuring tools, but their measurement errors increase significantly when measuring under non-leveled conditions and they have a limited measurement range due to the limitations of the measurement principle. To realize axis measurement under non-leveled conditions, this paper proposes an autocollimator axis measurement method based on the strapdown inertial navigation system (SINS). First, the measurement model of the system was established. This model applies the SINS to measure the change in attitude of the autocollimator. The autocollimator was then applied to measure the angular relationship between the measured axis and its own axis, based on which the angular relationship of the axis was measured via computation through signal processing and data fusion in a multi-sensor system. After analyzing the measurement errors of the system model, the Monte Carlo method was applied to carry out a simulation analysis. This showed that the majority of the measurement errors were within ±0.002° and the overall measurement accuracy was within ±0.006°. Tests using equipment with the same parameters as those used in the simulation analysis showed that the majority of the measurement errors were within ±0.004° and the overall error was within ±0.006°, which is consistent with the simulation results. This analysis proves that this method solves the problem of the autocollimator being unable to measure the axis under non-leveled conditions and meets the needs of axis measurement with the application of autocollimators under a moving base. [ABSTRACT FROM AUTHOR]

The powered aeroassisted orbital maneuver (AOM) is an emerging type of AOM manner, which can fulfill various complicated orbital transfer missions more flexibly and efficiently by virtue of aerodynamic forces and synergetic thrust. In this paper, an adaptive control strategy is presented for the reference trajectory tracking guidance of powered AOM under system uncertainties and input constraints. The presented controller is designed with an attack angle control law and a bank angle control law. The constrained control inputs are smoothly described through a hyperbolic tangent-type function. Then, the attack angle and bank angle control laws are separately developed by utilizing the first-order and second-order backstepping control methods. Besides, the parametric adaptive mechanism is incorporated to identify the squares of the upper bounds of the total uncertain items. Benefiting from this design, the presented controller can not only resist system uncertainties, but also accommodate input constraints. The stability evaluation shows that the radial distance and velocity tracking errors under the presented controller can eventually stabilize to the bounded minor fields around zero. Lastly, the derived results are verified through comparative studies with an existing controller and Monte Carlo simulations under different conditions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for assessing the uncertainty in vibration and static responses of laminated composite beams resulting from uncertainty in material properties and distributed loads. The proposed method utilizes surrogate models developed using polynomial chaos expansion (PCE) based on a relatively small sample size. These training samples are computed using a high-order shear beam model in which the governing equations are derived using Hamilton's principle, and solved by Ritz's approach using a trigonometric series approximation. The proposed PCE model's coefficients are estimated using the spectral projection and linear regression techniques. The first four statistical moments and probability distributions of the mid-span displacement and the fundamental frequency of laminated composite beams are predicted. Global sensitivity analysis is also conducted to assess how material property variation and stochastic loads affect the beam's deflection and the fundamental frequency. The accuracy and efficiency of the proposed PCE models are compared with those from Monte Carlo simulation (MCS). A remarkable reduction in the computational cost of PCE models compared to MCS is observed without compromising the predictions' accuracy. As most real-world systems are subjected to multiple sources of uncertainty, this study provides a state-of-the-art method to quantify such uncertain parameters more efficiently and allow for a better reliability assessment in composite beam design. [ABSTRACT FROM AUTHOR]

Sharing of risks and rewards is considered to be one of the key benefits and principles of integrated project delivery (IPD). Despite its importance, risk–reward strategies are not implemented widely in IPD construction projects due to the lack of a well-defined basis for establishing adequate allocation plans. This paper fills this knowledge gap. This research followed a multistep methodology. First, the authors calculated the risk control valuations of all potential combinations of coalitions for IPD stakeholders. This was performed using interrelated steps of risk identification and quantification, risk assignment based on associated contractual analysis, establishment of a coordination network, and Monte Carlo simulation. Second, the authors adopted cooperative game theoretic solutions—including Shapely, Owen, and Myerson partition-graph restricted game values—to allocate risk–reward shares for three IPD relational structures. Third, for each IPD relational structure, the authors evaluated the stability and associated negotiation power of coalitions for the IPD stakeholders using the propensity-to-disrupt ratio. Ultimately, the outcomes of this study show that having a multiparty agreement, in which all stakeholders—the owner, designer, contractor, and subcontractor—have open communication channels, creates the most balanced coalition. In this case, all stakeholders have equal willingness to cooperate in the project. Furthermore, as the engagement of the subcontractor in the IPD coalition decreases, the liability and contribution of the contractor increase in terms of project risk control. The latter emphasizes the importance of the technical capabilities of the contractor in the case of restricted subcontractor's engagement in the project. This research contributes to the body of knowledge by offering a basis for negotiation among various IPD stakeholders in terms of the degree of the subcontractor's engagement on the one hand and the proportionate share of each stakeholder on the other hand. [ABSTRACT FROM AUTHOR]

This paper is the first attempt, to the best of the authors' knowledge, to explore the effect of the stochastic material parameters on the dynamic response of functionally graded graphene platelets reinforced composites plate subject to a moving load. A novel stochastic calculation scheme is presented in this study, which considers the spatial variability of structural material parameters for a more precise and effective analysis of plate structures. Using the radial point interpolation method (RPIM), the governing equations of the plate are derived based on the first-order shear deformation theory and Hamilton's principle. The elastic moduli of the graphene platelets (GPLs) and the matrix are modeled as separate random fields, which are discretized using the Karhunen–Loève expansion (KLE) method. The random variables obtained by KLE were utilized in conjunction with the improved point estimation method (PEM) and Newmark- β method to determine the stochastic dynamic response. By comparing the results with those obtained using Monte Carlo method, the correctness and effectiveness of the proposed stochastic calculation scheme of PEM-RPIM are confirmed. Subsequently, the scheme was used to compute the coefficient of variation of the maximum dynamic deflection at the center of the plate, and also the sensitivity analysis was conducted. The results indicate that the distribution pattern and the weight fraction of GPLs have an impact on deflection sensitivity. Moreover, the deflection sensitivity is found to be significantly higher in response to variations of the random field E GPL . [ABSTRACT FROM AUTHOR]

This paper presents a multiple-input fully differential operational transconductance amplifier (MI-FD OTA) with very low power consumption. To obtain a differential MOS pair with minimum supply voltage and minimum power consumption, the multiple-input bulk-driven MOS transistor operating in the subthreshold region is used. To show the advantage of the MI-FD OTA, a fifth-order Chebyshev filter was used to realize a low-pass filter capable of operating with a supply voltage of 0.5 V and consuming 60 nW at a nominal setup current of 3 nA. The proposed filter uses five MI-FD OTAs and five capacitors. The total harmonic distortion (THD) was 0.97% for a rail-to-rail sinusoidal input signal. The MI-FD OTA and the filter application were designed and simulated in the Cadence environment using a 0.18 µm CMOS process from TSMC. The robustness of the design was confirmed by Monte Carlo analysis and process, voltage, and temperature corner analysis. [ABSTRACT FROM AUTHOR]

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations' load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations' operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs. [ABSTRACT FROM AUTHOR]

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner. [ABSTRACT FROM AUTHOR]

An implicit limit state function is predicted with a support vector machine (SVM) for reliability analysis of engineering structures to reduce the number of finite element analyses. The accuracy and predictability of the SVM method are reduced considerably by noise in data. In this paper, density-based spatial clustering of applications with noise (DBSCAN) is applied to reduce the noise in training samples for SVM regression. Then, the SVM model is linked with Monte Carlo simulation (MCS) to find out the reliability of the engineering structures. Four different examples of static and dynamic problems are solved to show acceptability and efficiency of the proposed method. It is observed that the proposed method is suitable for a smaller number of performance function calls. Direct MCS, artificial neural network-based MCS and response surface methods have been used to examine the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Solving optimization problems in a fuzzy environment is an area widely addressed in the recent literature. De-fuzzification of data, construction of crisp more or less equivalent problems, unification of multiple objectives, and solving a single crisp optimization problem are the general descriptions of many procedures that approach fuzzy optimization problems. Such procedures are misleading (since relevant information is lost through de-fuzzyfication and aggregation of more objectives into a single one), but they are still dominant in the literature due to their simplicity. In this paper, we address the full fuzzy linear programming problem, and provide solutions in full accordance with the extension principle. The main contribution of this paper is in modeling the conjunction of the fuzzy sets using the "product" operator instead of "min" within the definition of the solution concept. Our theoretical findings show that using a generalized "min" operator within the extension principle assures thinner shapes to the derived fuzzy solutions compared to those available in the literature. Thinner shapes are always desirable, since such solutions provide the decision maker with more significant information. [ABSTRACT FROM AUTHOR]

In order to help control the spread of space debris and junk, deorbit sail devices have been used for deorbiting techniques; however, this approach is still considered risky due to potential failure or malfunction as it need to be exposed to the harsh space environment for a prolonged period of time, particularly in low-Earth orbit. China has made new strides forward in managing space junk, as it has successfully unfolded a 25-square-meter deorbit sail developed by the Shanghai Academy of Spacecraft Technology in-orbit for the payload capsule of a recently launched rocket. Accordingly in this paper, a full life-cycle modelling based on Monte Carlo method is developed to reveal the damage behavior of the 25-square-meter deorbit sail surface under the combined effects of atomic oxygen erosion and deorbit sail defects. The simulation results show that as the atomic oxygen fluence increases and the material defect width widens, the interaction effect of the two factors becomes more and more pronounced, which decisively influences the erosion rate of the sail surface. Morphological characterization of aluminized film PET material, activated silanisation modified material and plasma-polymerization coating material before and after atomic oxygen erosion combined defects is carried out and the atomic oxygen erosion of the deorbit sail surfaces is quantitatively assessed. The assessment results show that compared to the PET material and activation silanisation surface modification material, the plasma polymeric coated material showed much better flexibility, folding resistance and irradiation resistance to prevent geological fold and to shield from the atomic oxygen erosion in space. The atomic oxygen erosion combined defects assessment method studied in this paper provides valuable reference data for the subsequent serialisation of deorbit sail products and other typical lightweight film materials for space applications. • Introduction of Monte Carlo method for material surface damage simulation analysis. • Compare the performance of off-orbit sail surface materials under different protection treatments. • Standardize the process of space flexible film material performance evaluation system. [ABSTRACT FROM AUTHOR]

Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]