Latin Hypercube Sampling Technique Research Articles

Sustainable chemometric methods boosted by Latin hypercube technique for quantifying the recently FDA-approved combination of bupivacaine and meloxicam in the presence of bupivacaine carcinogenic impurity: Comprehensive greenness, blueness, and whiteness assessments

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

Improved Deflection Prediction Model for PSC Box Girder with Stay Cable System during Tensioning Phase

To improve the accuracy of deflection prediction for prestressed concrete (PSC) box-girder bridges with a stay cable system (SCS) during tensioning, this study employs the Latin hypercube sampling (LHS) technique to sample random variables affecting long-term deflection of the main girder. A long-term deflection randomness analysis model is established, and the shear stiffness degradation factor is included to quantify the contribution of diagonal web cracking to the main girder deflection. Uncertainty and sensitivity analysis are conducted for the Dongming Huanghe River Highway Bridge. An objective function is applied to select the optimal combination of random variables, and a modified model is established and verified for accuracy. Results show that diagonal web-cracking accounts for 8.8% of total deflection and cannot be ignored, and the prestressed tension control stress, creep uncertainty coefficient, and concrete density significantly impact long-term deflection. The modified model predicts deflection with greater accuracy than the mean value model.

On the efficacy of conditioned and progressive Latin hypercube sampling in supervised machine learning

In this paper, Latin Hypercube Sampling (LHS) method is compared as per its effectiveness in supervised machine learning procedures. Employing LHS saves computer's processing time and in conjunction with Latin hypercube design properties and space filling ability, is considered as one of the most advanced mechanisms in terms of sampling. Although more data usually deliver better results, when using LHS techniques, same quality outputs can be produced with less data and, as a result, storage cost and training time are reduced. Conditioned Latin Hypercube Sampling (cLHS) is one of those techniques, successfully performing in supervised machine learning tasks. Unfortunately, the minimum sufficient training dataset size cannot be known in advance. In this case, progressive sampling is recommended since it begins with a small sample and progressively increases its size until model accuracy no longer improves. Combining Latin hypercube sampling and the idea of sequentially incrementing sampling, we test Progressive Latin Hypercube Sampling (PLHS) while monitoring the performance of the sampling-based training as the sample size grows. PLHS and cLHS algorithms are applied in datasets with discrete variables securing that each sample is provided with the Latin hypercube design properties and preserves the principal ability of LHS for space filling, as illustrated in respective sample projecting diagrams. The performance of the above LHS methods in supervised machine learning is evaluated by the degree of training of the model, which is certified through the accuracy of the produced confusion matrices in test files. The results from the use of the above Latin Hypercube Sampling techniques compared against benchmark sampling method empirically prove that machine learning training process becomes less costfull, while remaining reliable.

Optimization of parameters of the OWC wave energy converter device using MLP and XGBoost models

In the present work, we have studied the performance of a breakwater-integrated quarter-circle-shaped front wall OWC device under the influence of irregular incident waves. Firstly, the boundary value problem associated with the hydrodynamics of OWC device is handled for solution using the dual boundary element method (BEM). To examine the complex relationships between all input features and the target variable in a time-efficient manner, supervised machine learning models are developed. Here, two different models: (i) multilayer perceptron (MLP) model based on an artificial neural network, and (ii) a tree ensemble model, namely the XGBoost model are developed. The submergence depth of the front wall of the OWC device, chamber length, rotational speed, and diameter of the turbine blade are considered as input attributes, and the average annual power generated by the OWC device is considered as the output attribute. The MLP model is employed to optimize these input parameters, leveraging the insights provided by the XGBoost model to maximize the annual average power generation. From the dual BEM based numerical results, and using the Latin hypercube sampling technique, 3750 samples were generated to train, validate, and test the machine learning models. Using the XGBoost model with the support of accumulated local effect plots, we find four specific regions of the input space in which the annual average power extraction will be maximum. Hereafter, an extended input database is generated with twenty equally spaced levels for each parameter and the dataset is passed through the developed MLP model to find the optimized values of the parameters of the OWC device which maximizes the power generation. It is obtained that the maximum power generation is attained for y0/h=−0.65, r/h=3, 2.8≤D≤3 and 70≤N≤80∪105≤N≤116.

Time-dependent fragility analysis of a nuclear containment structure under internal pressure and chloride-induced corrosion

The prestressed concrete containment structure, serving as the ultimate barrier against nuclear fuel leakage, holds paramount importance in ensuring the uninterrupted operation of nuclear power plants. Particularly in the aftermath of the Fukushima nuclear disaster, the influence of material aging and degradation of containment structures has been emphasized. Consequently, a probabilistic performance assessment of containment structures under chloride-induced corrosion condition is of significant relevance. This study presents a method for time-dependent fragility analysis of nuclear containment structures subjected to internal pressure, considering the effect of chloride-induced corrosion of steel reinforcement. A highly efficient and accurate finite element model with multi-layered shell element is formulated for damage and failure analysis. Furthermore, a corrosion degradation model of reinforced concrete, aligned with the attributes of multi-layered shell elements, is derived to reduce the reduce parameter samples generated by Latin Hypercube sampling technique. Material uncertainties and time-dependent effects are jointly integrated into the finite element analysis process, culminating in the establishment of fragility curves for the containment structure under various service lifetimes. The results indicate that within first 40 years of service, the load-bearing capacity of the containment structure has slightly decreased, while the probabilities of functional and structural failures in the containment structure will significantly increase after 60 years of service.

A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents

RBI, referring to a risk-based approach to inspection planning, is an established pipeline integrity management method. Both corrosion and dents are the primary threats to pipeline integrity. However, they are often treated separately in RBI without considering their interactions. This coupling may lead to a synergic effect on integrity degradation. The present study proposes an RBI planning framework for pipelines considering external corrosion and dents. Time-dependent pipeline deterioration by dents and corrosion is modeled probabilistically using a Dynamic Bayesian Network (DBN), in-line inspection (ILI) data, and corrosion propagation knowledge. Two failure scenarios (leakage and burst) are considered. The hybrid method, integrating Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) technique, estimates the pipeline's Probability of Failure (PoF) over time. The pipeline failure risk is quantified by monetizing the Consequence of Failure (CoF). An optimization model of loss-maintenance total expected cost is introduced to determine the optimum inspection period using maximum acceptable risk (MAR) and the lowest total expected cost. A cost-benefit analysis (CBA) is finally implemented to choose appropriate risk reduction measures. The proposed framework is robust and well-validated by a case study on an in-service pipeline.

A multiobjective optimization of 3D - slot jet configuration for enhancement of film cooling in an annular combustor liner

This study reports the results of a combined experimental and numerical investigation of the performance of a three-dimensional film cooling slot configuration and its optimization using an evolutionary-based genetic algorithm. The primary objective of the optimization is to maximize the area-averaged film cooling effectiveness (ηad.avg) and minimize its standard deviation (ση) at a fixed coolant mass flux for a single liner. The experimental study is conducted on flow and heat transfer characteristics to validate the numerical model under laboratory conditions (low temperature and pressure). A design space is created using the Latin hypercube sampling technique, and it is solved using steady-state RANS simulations with the validated numerical model to estimate the ηad.avg and ση under actual engine conditions (high temperature and pressure). A surrogate model is developed using the kriging technique to predict the objective functions with the geometrical parameters of the slot. Following this, the optimum configuration is identified using the genetic algorithm. The geometrical parameters are slot jet diameter (d), slot jet pitch (p), lip taper angle (α), and lip length (L). The numerical results show that the optimum configuration outperforms the two reference configurations of the baseline combustor. At a blowing ratio (BR) of 1, the optimum configuration enhances the ηad.avg by 8% and reduces the ση by 6.6% compared to reference -1 configuration. Similarly, compared to reference -2, the optimum configuration enhances the ηad.avg by 19.8% and reduces the ση by 5.2.%. Furthermore, under actual engine conditions, a numerical study is conducted on a subsequent row of liners to determine the optimum length of the cooling ring. The numerical results show that the optimum slot configuration cools an additional liner length (G/S) of 5.5 compared to the reference-1 slot and results in a reduction of one pair of cooling rings in the combustor, which contributes to a 16.6% less coolant mass flux.

Calculation and optimization of inter-regional available transfer capability considering wind power uncertainty and system improvement measures

The intermittency and uncertainty of wind energy make transmission congestion management increasingly complex. In this paper, we investigate the impact of wind energy on available transmission capacity (ATC) and then explore ways to mitigate transmission congestion from the grid and load side, respectively. The impact of a stochastic variable is considered by applying Latin Hypercube Sampling (LHS) and backward curtailment techniques to generate typical scenarios. ATC is improved by the optimal allocation of Thyristor Controlled Series Compensation (TCSC) on the grid side and the use of Demand Response (DR) on the load side. The source-grid-load cooperative optimisation model for ATC is solved by an improved particle swarm optimisation (PSO) algorithm. Based on the IEEE-30 bus system, an experimental scheme is designed and analytical calculations are performed. The results show that the joint application of TCSC and DR can help to improve ATC in a comprehensive way. The work done in this paper can achieve the purpose of promoting renewable energy consumption by improving the utilisation efficiency of the transmission network without changing the network structure.

SIRC epidemic model with cross-immunity and multiple time delays.

Multi-strain diseases lead to the development of some degree of cross-immunity among people. In the present paper, we propose a multi-delayed SIRC epidemic model with incubation and immunity time delays. Here we aim to examine and investigate the effects of incubation delay [Formula: see text] and the impact of vaccine which provides partial/cross-immunity with immunity delay parameter ([Formula: see text]) on the disease dynamics. Also, we study the impact of the strength of cross-immunity [Formula: see text] on the disease prevalence. The positivity and boundedness of the solutions of the epidemic model have been established. Two different types of equilibrium points (disease-free and endemic) have been deduced. Expression for basic reproduction number has been derived. The stability conditions and Hopf-bifurcation about both the equilibrium points in the absence and presence of both delays have been discussed. The Lyapunov stability conditions about the endemic equilibrium point have been established. Numerical simulations have been performed to support our analytical results. We quantitatively demonstrate how oscillations and Hopf-bifurcation allow time delays to alter the dynamics of the system. The combined impacts of both the delays on disease prevalence has been studied. Through parameter sensitivity analysis, we observe that the infected population decreases with an increase in vaccination rate and the system starts to stabilize early with the increase in cross-immunity rate. Global sensitivity analysis for the basic reproduction number has been performed using Latin hypercube sampling and partial rank correlation coefficients techniques. The combined effect of vaccination rate with transmission rate and vaccination rate with re-infection probability (i.e. strength of cross-immunity) on [Formula: see text] have been discussed. Our research underlines the need to take cross-immunity and time delays into account in the epidemic model in order to better understand disease dynamics.

Fragility Analysis of Transmission Towers Subjected to Downburst Winds

A downburst is a typical local highly intensive wind all over the world, which is attributed to be the main cause of wind damage to transmission lines in inland areas worldwide. The collapse accidents of transmission towers under the downburst still occur every year. Therefore, it is of great significance to assess the safety of the transmission towers under downbursts. The motivation of the present study is to propose a fragility assessment method for transmission towers under the action of a downburst considering the uncertainty of wind-resistance capacity and the stochastic wind load effect. First, the downburst wind field of the transmission tower with different wind attack angles and different radial distances is simulated according to the mixed stochastic model. Then, random material characteristic samples are generated by the Latin hypercube sampling technique and applied to establish uncertain finite element models for transmission towers. Next, the static nonlinear buckling analysis is carried out by numerical methods to determine the ultimate capacity under the downburst wind load. The parameter analysis of different wind attack angles and radial distances between the downburst and the tower is conducted to determine the most unfavorable location of the maximum response. The failure mode of the transmission tower and the probabilities of the initial failure main members are summarized. Finally, the fragility curves of the transmission tower under the downburst and the atmospheric boundary layer (ABL) wind are compared. The results show that the maximum response is located at R = 1.6D. Most of the initial buckling members are located close to the first section of the tower. The fragility curves of the tower under the downburst are more dangerous than the ABL wind with the attack angle increasing from 0° to 90°. Furthermore, considering the probability model of intensity and direction of the downburst and based on the previous fragility analysis, the collapse probability of the transmission tower caused by the downburst is obtained. By probability analysis of the parameters, including layout conditions, different directions, and different wind speeds, it is found that the most favorable arrangement is 157.5°, and the most unfavorable arrangement is 112.5°.

A rapid seismic fragility and risk analysis of electrical substation equipment considering modeling uncertainties

This study aims to evaluate the influences of modeling-related uncertainties on the seismic response and fragility function of the four vulnerable components of electrical substation equipment, taking into account the dynamic interaction between apparatuses via bus slider-rigid bus conductors. The 3D finite element model for interconnected disconnect switch- circuit breaker-current transformer-surge arrester system is developed in OpenSees software. A sensitivity analysis is conducted to evaluate and rank the importance of individual random variables on the seismic responses. To incorporate modeling uncertainties into the failure fragility function, probabilistic structural interconnected simulations are generated using the Latin hypercube sampling technique. The fragility curves for each apparatus in the connected system are derived using incremental dynamic analysis (IDA). An efficient procedure based on the endurance time (ET) method is presented for estimating response distributions and fragility parameters of the interconnected apparatuses while considering modeling uncertainty. The proposed approach provides acceptable results compared to those from the IDA while tremendously reducing computer run-time. This investigation also puts forth a rapid detailed seismic risk analysis framework using scenario sampling analysis. To this end, the ET response distributions are exploited as equipment response models within reliability-based risk analysis. Results indicate that the damping ratio and porcelain elastic modulus have a substantial effect on the seismic responses of equipment. In addition, modeling uncertainties affect the median and dispersion values of the failure fragilities by up to −5.9% and +22% on average for all equipment cases, respectively. According to the loss curves, it is observed that the circuit breaker contributes the greatest to the system's overall loss, whereas the surge arrester contributes the least.

Analyzing the Dynamics of a Periodic Typhoid Fever Transmission Model with Imperfect Vaccination

We present a nonautonomous compartmental model that incorporates vaccination and accounts for the seasonal transmission of typhoid fever. The dynamics of the system are governed by the basic reproductive number R0. This demonstrates the global stability of the disease-free solution if R0<1. On the contrary, if R0>1, the disease persists and positive periodic solutions exist. Numerical simulations validate our theoretical findings. To accurately fit typhoid fever data in Taiwan from 2008 to 2023, we use the model and estimate its parameters using Latin hypercube sampling and least squares techniques. A sensitivity analysis reveals the significant influence of parameters such as infection rates on the reproduction number. Increasing vaccination coverage, despite challenges in developing countries, reduces typhoid cases. Accessible and highly effective vaccines play a critical role in suppressing the epidemic, outweighing concerns about the efficacy of the vaccine. Investigating possible parameter changes in Taiwan highlights the importance of monitoring and managing transmission rates to prevent recurring annual epidemics.

Time-series forecasting of a typical PWR system response under Control Element Assembly withdrawal at full power

To expedite the decision-making process under Nuclear Power Plant (NPP) accident conditions, at a reduced computational cost, a Machine Learning (ML) time-series meta-model is proposed to map the relationships between the NPP's real-time parameters to its transient response and hence forecast its future response. To this end, three variations of Recurrent Neural Networks (RNNs), namely: the Long-Short-Term-Memory (LSTM), Gated Recurrent Unit (GRU) and a series combination of a convolutional neural network (CNN) with LSTM were developed. The NPP response was simulated using the thermal hydraulics best estimate code, MARS-KS, and cross-validated against the Design Control Document (DCD) of APR1400. The time-series database required to train, test, and validate the time-series Machine Learning (ML) expert algorithm was acquired by coupling DAKOTA software with the best estimate thermal hydraulics system code, MARS-KS to conduct a Best Estimate Plus Uncertainty (BEPU) analysis. The Monte-Carlo random propagation approach and the Latin Hypercube Sampling technique were used to sample and propagate key uncertain parameters until a statistically significant sample of the NPP transient responses was achieved. All three RNN models were developed and optimized with the help of the Talos tool, and were found to exhibit reasonable performance in forecasting the key most probable NPP transient responses, Furthermore, the uncertainty of the ML predictions was assessed by integrating a Bayesian Neural Network (BNN) with the LSTM model. The prediction accuracy ranged from 86% to 96%, when evaluated on an unseen test dataset. Based on a voting system with five performance metrics (MAE, MSE, RMSE, R2 and Accuracy) the LSTM model and its derivative CNN+LSTM outperformed the GRU model; while the CNN +LSTM is found to be the most computationally efficient.

Assessing safety functionalities in the design and validation of driving automation

This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach.

Fuzzy logic approach for seismic fragility analysis of RC frames with applications to earthquake-induced damage and construction quality

This study presents a fuzzy logic model which is able to consider various uncertainties for predicting the seismic fragility curves of the structure. The fuzzy models are generated based on a database (generated by the Latin Hypercube Sampling technique) which includes structure’s modeling parameters as input variables. Also, the output section of the database comprises the structure’s fragility parameters at three different limit states. Several classification decision tree-based Mamdani fuzzy logic models are constructed by converting the above-mentioned numerical database to qualitative labels. The appropriate number of membership functions and also pruning levels of the related decision tree are detected by automatically introducing the classification tree-based rules to the fuzzy model and performing about two million fuzzy analyses while evaluating the associated error versus the number of rules. On the basis of 200 structural models, a set of interpretable Mamdani fuzzy models are generated to predict the fragility curve parameters at different seismic performance levels. These fuzzy models can effortlessly perform further assessments such as sensitivity analysis and seismic performance assessment while considering the impact of earthquake damage and construction quality.

A multi-objective approach for location and layout optimization of wave energy converters

Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN’s sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.

APPLICATION OF SEMI-VARIOGRAM ANALYSIS IN MEASURING SPATIAL VARIABILITY AND DISTRIBUTION OF SELECTED SOIL PROPERTIES IN NORTHEAST AKWA IBOM STATE, NIGERIA

Application of semi-variogram analysis in measuring spatial variability and distribution of selected soil properties in Northeast Akwa Ibom State, was carried out.The aim was to assess spatial variability and distribution of selected soil properties in the study area for effective site-specific soil management and precision agriculture using semi-variogram analysis. A digital elevation model (DEM) of the study area was acquired from United State Geological Survey (USGS) at 30m resolution. Slope gradient map that is capable of capturing the short-scale spatial variability of soil properties in the study area was generated from the DEM to guide field sampling. Modified conditioned Latin hypercube sampling technique was used in selecting observation points. Soil samples were collected from each observation point at 0-30 cm and 30-60 cm depths using soil auger. A total of 152 soil samples were collected for laboratory analysis. Analysed data of depth interval of 0-30cm and 30-60 cm were integrated to form depth interval of 0-60 cm. The data were subjected to normality test to ascertain the normal distribution of the data. Selected soil properties were subjected to semi-variogram analysis. The study revealed that slope gradient was able to captured short scale spatial variation in some soil properties understudy. Soil texture of the flat/nearly flat was sand in both surface and subsurface soil and sand in the surface soil and loamy sand in the subsurface soil in gently sloping and sloping. Soil pH was slightly acid in in flat/nearly flat and gently sloping and strongly acid in the sloping area in both surface and subsurface soil. Organic carbon was very high in the flat/nearly flat and gently sloping and high in sloping topography in both surface and subsurface soil. Total N was low in the sloping area and moderately low in gently sloping and nearly flat /flat. Base saturation was very high in the sloping topography and high in the gently sloping and nearly flat /flat. The results of semi-variogram analysis showed that all the selected soil properties exhibited spatial dependence within some distances. The range was 136.2 m for sand, 76.4m for silt, 1.6 m for clay, 1.7m for soil pH, 9.4m for organic carbon, 7.1m for total N, 9.2m for available P and 7.8 m for exchangeable K in the study area. Beyond this range, there is no longer relationship between sample points and sample values are not related to one another. The strength of the spatial dependence of sand, silt, soil pH, organic carbon, total N and available P was moderate; exchangeable K was strong while clay was weak. The semi- variance (sill) was 57.4 for sand, 23.8 for silt, 7.15 for clay, 0.21 for soil pH, 1.18 for organic carbon, 0.002 for total N, 85.1 for available P, and 0.03 for exchangeable K. The nugget variance or nugget effect was 25.9 for sand, 10.2 for silt, 5.9 for clay, 0.06 for soil pH, 0.60 for organic carbon, 0.001 for total N, 33.4 for available P and 0.003 for exchangeable K. The best fitted models were Exponential for sand and silt; Gaussian for available P and Spherical for clay, pH, organic carbon, total N and exchangeable K.

Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load

An optimal dispatching strategy for a multi-source complementary power generation system taking source–load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and power load instability on power grid dispatching. The uncertainty problem is first converted into common situations for study, such as load power forecasting and solar and wind power. The backward scenario reduction and Latin hypercube sampling techniques are used to create these common situations. Based on this, a multi-timescale coordinated optimum scheduling control method for a multi-source complementary power generation system taking the demand response into account is presented, and the optimal operation of a wind–PV–thermal-pumped storage hybrid system is examined. The time-of-use power price optimizes the electrical load in the day-ahead pricing mode, and the two types of demand response loads are selected in the day-ahead scheduling. Second, the lowest system operating cost and the minimal day-ahead and intra-day adjustment of each source are established as the optimization targets in the day-ahead and intra-day phases of the multi-timescale coordinated scheduling model of the multi-source complementary system. The example study demonstrates that the scheduling strategy may increase the amount of renewable energy consumed, minimize load fluctuations, increase system stability, and further reduce operating expenses, proving the viability and efficiency of the suggested strategy.

A global sensitivity analysis method for safety influencing factors of RCC dams based on ISSA-ELM-Sobol

Structural material parameters will directly affect the stability and deformation of roller compacted concrete (RCC) dams. To analyze the influence degree of structural material parameters of RCC dams on the safety evaluation indexes, this study proposes a global sensitivity analysis method based on extreme learningmachines optimized by an improved sparrow search algorithm with Sobol method (ISSA-ELM-Sobol). First, the safety evaluation indexes of the RCC dam and their influencing factors are determined; second, a reasonable finite element model is established; third, the sample set is generated using the Latin hypercube sampling technique; fourth, the ISSA-ELM model is established to replace the finite element calculation; finally, based on the established ISSA-ELM model, the Sobol method is used for global sensitivity analysis. A case study for a typical RCC dam in Sichuan province of China showed that the material properties of the foundation and the cohesion of potential sliding surfaces have the most obvious influence on the safety of RCC dams; the ISSA proposed in this study can overcome the problem that sparrow search algorithm is easy to mature prematurely and fall into a local optimum. The ISSA-ELM model established in this study has significant advantages in solving nonlinear regression problems.

Fractional-order modeling and optimal control of a new online game addiction model based on real data

The spread of online games has become more and more serious in recent years, which not only endangers the healthy growth of young people, but also has a certain potential harm to society. Therefore, how to effectively control the spread of games has become an urgent problem to be solved. To solve this problem, a new fractional model of online game addiction is developed in this paper. In the aspect of qualitative analysis, the non-negativity and boundedness of the model solutions are demonstrated. All equilibria and basic reproduction number are solved. The global asymptotic stability of all equilibria are proved by constructing ingenious Lyapunov functions. In terms of quantitative analysis, different from previous fractional order literature, we use a new method to fit the real data of game users in China from 2010 to 2021, and obtain the optimal fractional order α=0.98942 and model parameters. The estimated results show that the value of the basic reproduction number during this period is R0=7.1045. By setting different values of the parameters, the effects of all parameters on the model are shown. With the Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) techniques, the influence of all parameters in the basic reproduction number R0 are quantified and the means to control the spread of the game were generalized: isolation and treatment. Finally, the Fractional Euler Method (FEM) and the fractional Forward Backward Sweep Method (FBSM) are used together to solve the fractional optimal control problem and the optimal control strategy is determined. The results of this paper provide a new research idea for parameter estimation of fractional equation, and a more reliable result for controlling the spread of online games.