Sampling Calculation Research Articles

Computational screening of single-cluster catalysts anchored on g-C3N4 for nitroreduction of 3-nitro-1,2,4-trizole-5-one (NTO)

3-nitro-1,2,4-triazole-5-one (NTO) is widely used as an insensitive explosive in high-safety weapon systems; however, it poses significant environmental risks due to its high solubility and anionic nature. Despite 3-amino-1,2,4-triazole-5-one (ATO) has been identified as the key intermediate during NTO degradation, developing highly active and selective catalysts for the NTO to ATO process remains challenging. Using computational screening with density functional theory (DFT), ab initio molecular dynamics (AIMD) simulations, and enhanced sampling approach, this study presents a rational design among metal trimer (M3, where M = Ag, Au, Cu, Ir, Os, Pt, Pd, Rh, and Ru) cluster anchored on N-vacancy and perfect graphitic carbon nitride (g-C3N4) as robust single-cluster catalysts (SCCs) with promising efficiency for NTO nitroreduction. Through a three-step process involving structural optimization, AIMD simulation, and enhanced sampling calculation, we found that Ru shows remarkable activity and selectivity for NTO nitroreduction, particularly in a solvent environment with a higher concentration of H. Notably, the Ru3@1CN SCC demonstrates the ability to directly reduce NTO to ATO in the first two steps. This work opens new avenues for NTO nitroreduction in contamination control and production of functionalized chemicals, providing valuable guidance for selective hydrogenation of other nitro compounds as well.

Quantifying particulate matter emission rates from naturally ventilated dairy buildings by considering roof opening contributions

Roof openings are typically fitted to naturally ventilated dairy building (NVDB) for better ventilation but their impact on air pollutant emission calculations has not been fully considered. Particulate matter (PM) emission rate (ER) for NVDB rely on the total ventilation rate (VR), outdoor PM concentration and average indoor PM concentration sampled either under the roof (Roof Sampling) or in the cubicle area (Cubicle Sampling), which may show large deviations due to its spatiotemporal variation of PM concentrations and complex airflow patterns. This study utilised a novel ER calculation method (Joint Sampling) that computes the respective emission from the roof and sidewall openings by matching each outlet's VR and PM concentration. By year-round field measurements of PM2.5 and the total suspended particulates (TSP), results showed that annual average ERs of PM2.5 and TSP were 10.8 mg h−1 cow−1 and 45.7 mg h−1 cow−1 for Roof Sampling, 12.7 mg h−1 cow−1 and 40.7 mg h−1 cow−1 for Cubicle Sampling, and 11.7 mg h−1 cow−1 and 45.9 mg h−1 cow−1 for Joint Sampling. Considering the Joint Sampling results were relatively true, Roof Sampling exhibited a maximum underestimate of PM2.5 emissions of 20.8% when sidewall curtains were fully opened, whilst Cubicle Sampling demonstrated a maximum overestimate of TSP of 10.2% when the aperture was closed. Using Joint Sampling, the roof opening contributed 39.3% and 24.4% of the annual PM2.5 and TSP emissions. When sidewall openings are partially or fully closed, the Joint Sampling calculation is preferable to estimate the ER of PM.

KNOWLEDGE AND BEHAVIORS OF FAMILY PHYSICIANS AND INTERNAL MEDICINE PHYSICIANS REGARDING THE USE OF MAGNESIUM FOR SUPPLEMENTATION AND TREATMENT

Objective: To identify indications for magnesium use and assess physicians' knowledge of different formulations of magnesium in medication and supplements Method: A descriptive study was conducted online via Google Forms between January 18 and April 20, 2023. The study population consisted of family physicians (general practitioners, residents, and specialists) and internal medicine physicians (residents and specialists) in Ankara. All physicians were attempted to be reached, and no sampling calculation was made. The survey consisted of 15 questions. Results: A total of 119 physcians completed the survey. The mean age of the physicians who participated in our study was 30.47±4.94 years (min=25, max=48) and 62.2% were female. Of them, 69.7% were family physicians, 30.3% were internal medicine physicians, 77.3% were research assistants and 22.7% were specialists. The most common indications for magnesium treatment were muscle cramps (89.1%) and hypomagnesemia (87.4%); the most commonly used preparations were magnesium oxide (79.8%) and magnesium sulfate (32.8%). The most common indications of using magnesium were cramps (32.4%) and insomnia (13.5%); the most frequently used preparations were magnesium oxide (26.3%), magnesium citrate+malate+glycinate (13.1%), and magnesium biglycinate (13.1%). Physicians who had been practicing for more than 5 years prescribed magnesium more (p=0.010), recommended magnesium supplements more (p=0.020), and used magnesium supplements themselves more (p=0.025) than physicians who had been practicing for 5 years or less. It was found that specialists questioned patients' use of vitamin-mineral supplements more (p=0.044) and recommended magnesium supplements more (p=0.001) than residents. It was found that family physicians prescribed the magnesium used by patients more (p<0.001) and recommended more magnesium-rich nutrition to patients (<0.001) than internal medicine physicians. Conclusion: The physicians who participated in our study preferred magnesium forms covered by social security institution reimbursement for their patients, whereas they preferred forms with higher bioavailability for their own use. It was observed that family physicians provided more magnesium-rich dietary recommendations and prescribed the magnesium used by the patients than internal medicine physicians.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems.

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Prevention of Dengue Fever Based on Dominant Risk Factors within Elementary School Environment in Bandung City

Background: Dengue fever is an environment-based disease that is endemic in Bandung City. Bandung City with an average temperature of 25.86°C has a humid and cool climate which is a favorable environment for Aedes aegypti. The average amount of rain per day is 21.3 days per month with an average rainfall of 200.4 mm. These environmental conditions favor the breeding of Aedes aegypti. Objective: The study aimed to obtain the dominant variables in the 3M Plus PSN.Penelitian ini merupakan penelitian kuantitatif dengan desain cross sectional. Method: The population in this study were all elementary school students in Bandung City. Through the Cluster Sampling calculation, 973 minimum samples from 13 elementary schools were obtained. With a minimum sample for each elementary school totaling 76 6th-grade students. Results: The results of the study showed that a Knowledge P-value of 0.023, Attitude P-value 0.036, Perception P-value of 0.000, and motivation P-value of 0.018 were associated with DHF Prevention through 3M Plus PSN. Exposure to information media on dengue prevention through the 3M Plus PSN is not associated with dengue prevention through the 3M Plus PSN. The variable that most influenced the prevention of Dengue Fever through the 3M Plus PSN was perception with a P-value of 0.000 CI of 2.118 (1.639-2.737). Conclusion: Knowledge, perception, attitude, and motivation are related to dengue fever prevention through 3M Plus PSN. Perception is the most dominant factor associated with the prevention of dengue fever through the 3M Plus PSN.

Motherhood Role from a Postpartum Perspective: Effects Reflected by High-Risk and Normal Pregnancies.

High-risk pregnancies not only heighten concerns about the baby's health but also have the potential to impact the mother-infant relationship by shifting the mother's focus towards her own health needs. This study aims to delve into the intricacies of motherhood roles among women experiencing high-risk pregnancies compared to those with uncomplicated pregnancies, aiming to shed light on the disparities between the two groups. The participants of this descriptive, comparative, and correlational study consisted of literate mothers admitted to a hospital in Eastern Turkey, diagnosed with high-risk pregnancies, and with no prior history of psychological support. Due to an inability to reach the entire population, an unknown sampling method was employed for sampling calculation. The sample comprised 133 mothers with high-risk pregnancies and an equal number of healthy mothers, totaling 266 participants. Data were collected using the "Maternal Introduction Form" and the "Maternal Role Gaining Scale", and analyses were conducted using the SPSS Statistical Programme. Given the non-normal distribution of the variables, nonparametric tests were applied post reliability analysis. There is a statistically significant difference (p < 0.05) in the scores of maternal attitude and anxiety, as well as maternal role and the Maternal Role Gaining Scale, based on various demographic factors such as marriage duration, spouse's education, family economic status, pre-pregnancy health issues, medication use, hospitalization status and reason, assigned sex of the baby and desired gender, total pregnancies, mode of delivery, postpartum difficulties, support for baby care, feeding method, high-risk pregnancy diagnosis, and week of diagnosis. A Bonferroni corrected analysis also revealed significant differences between mothers with and without high-risk pregnancies.

Incentivising Agroforestry Through Carbon Revenue: Augmenting Farmers’ Income in India

Background: The present study explores and assesses the potential of carbon revenue to augment the financial returns of farmers from agroforestry plantations. Government of India aims at doubling the farmers’ income and this study can go a long way in achieving this aim. The income from carbon revenue will be over and above the market value of farm-grown wood and hence will be an additional incentive. Methods: Under the present study, the assessment of carbon stocks for Eucalyptus and Poplar plantations was done in Yamunanagar district of Haryana using stratified random sampling and standard calculation tools explained under methodology. The results of this study and data from other existing studies, covering different parts of the country, were used for estimating the carbon credits and carbon revenue for total area under agroforestry in India i.e. 28.4 MHA. Result: The findings suggest that agroforestry, if incentivized with carbon revenue, has a potential of increasing the income of farmers by 40-50% (upto Rs. 25,000/ha/year). It could be achieved by developing agroforestry based carbon finance projects for Voluntary Carbon Markets (VCM) and by promoting agroforestry plantations for net carbon emission neutrality of corporate using their CSR funds. The additional returns could also act as a buffer and as an alternative like minimum support price (MSP) for farmers in case of “price-crash” of agroforestry produce due to various reasons. The study indicates that there is urgent need to have a policy in India for registering agroforestry plantations under carbon finance projects thereby contributing to net neutrality of corporates, ultimately benefitting the farmers through financial incentives.

Optimizing Sampling Strategies for Estimating Riverine Nutrient Loads in the Yiluo River Watershed, China

Accurately estimating nutrient loads is crucial for effective management and monitoring of aquatic ecosystems. This study evaluated the uncertainty in different sampling frequencies and calculation methods for estimating total nitrogen (TN) and total phosphorus (TP) loads in the Yiluo River watershed, a tributary of the Yellow River in China. Using daily TN and TP concentration data from 2019 to 2020, we conducted a bootstrapping analysis to evaluate the accuracy of nine different load estimation methods at different sampling frequencies. Our results showed that Method 3 (M_3, constant concentration interpolation) and Method 7 (M_7, flow-weighted concentration method), when used with a biweekly sampling frequency, had the lowest Standard Deviation of the Percentage errors (STD) (7.70% and 8.60% for TN, 12.0% and 18.8% for TP, respectively) and Mean Relative Error (MRE) values (0.078% and −1.60% for TN, 0.305% and 2.33% for TP, respectively) on an annual scale. For monthly TN and TP load estimates, M_7 can control the MRE within ±20% at a biweekly sampling frequency. Furthermore, the uncertainty in TN and TP load estimates was generally larger during the summer months (June–September), emphasizing the important role of storm events in nutrient export. Extreme events (&lt;10% of the time) contributed approximately 50% of the annual nutrient loads. The findings of this study provide a scientific basis for optimizing water quality monitoring schemes and management strategies in agricultural watersheds.

Vision Transformer With Quadrangle Attention.

Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the flexibility of transformers to adapt to objects of varying sizes, shapes, and orientations. To address this issue, we propose a novel quadrangle attention (QA) method that extends the window-based attention to a general quadrangle formulation. Our method employs an end-to-end learnable quadrangle regression module that predicts a transformation matrix to transform default windows into target quadrangles for token sampling and attention calculation, enabling the network to model various targets with different shapes and orientations and capture rich context information. We integrate QA into plain and hierarchical vision transformers to create a new architecture named QFormer, which offers minor code modifications and negligible extra computational cost. Extensive experiments on public benchmarks demonstrate that QFormer outperforms existing representative vision transformers on various vision tasks, including classification, object detection, semantic segmentation, and pose estimation. The code will be made publicly available at QFormer.

Computing equilibrium free energies through a nonequilibrium quench.

Many methods to accelerate sampling of molecular configurations are based on the idea that temperature can be used to accelerate rare transitions. These methods typically compute equilibrium properties at a target temperature using reweighting or through Monte Carlo exchanges between replicas at higher temperatures. A recent paper [G. M. Rotskoff and E. Vanden-Eijnden, Phys. Rev. Lett. 122, 150602 (2019)] demonstrated that accurate equilibrium densities of states can also be computed through a nonequilibrium "quench" process, where sampling is performed at a higher temperature to encourage rapid mixing and then quenched to lower energy states with dissipative dynamics. Here, we provide an implementation of the quench dynamics in LAMMPS and evaluate a new formulation of nonequilibrium estimators for the computation of partition functions or free energy surfaces (FESs) of molecular systems. We show that the method is exact for a minimal model of N-independent harmonic springs and use these analytical results to develop heuristics for the amount of quenching required to obtain accurate sampling. We then test the quench approach on alanine dipeptide, where we show that it gives an FES that is accurate near the most stable configurations using the quench approach but disagrees with a reference umbrella sampling calculation in high FE regions. We then show that combining quenching with umbrella sampling allows the efficient calculation of the free energy in all regions. Moreover, by using this combined scheme, we obtain the FES across a range of temperatures at no additional cost, making it much more efficient than standard umbrella sampling if this information is required. Finally, we discuss how this approach can be extended to solute tempering and demonstrate that it is highly accurate for the case of solvated alanine dipeptide without any additional modifications.

Relationship of sleep with diurnal cortisol rhythm considering sleep measurement and cortisol sampling schemes

Information on the relationships between the previous night's sleep and the next-day diurnal cortisol rhythm is inconsistent due to confounding factors such as sleep measurements (trait/state sleep and objective/subjective sleep) and cortisol sampling schemes. Therefore, this study aimed to investigate these relationships, considering the confounding factors. College students (n = 79) wore actigraphy for 3 days to undergo an evaluation of previous night-time sleep objectively and reported their subjective sleep parameters in a sleep diary. In addition, participants provided six salivary cortisol samples daily. Furthermore, six cortisol sampling schemes were created to reflect diurnal cortisol rhythms, and two different methods were used to calculate the index of diurnal cortisol slope (DCS). A multilevel model was created to examine the impact of both trait and state sleep on next-day diurnal cortisol rhythm. The results revealed that higher objective state sleep efficiency and longer objective state total sleep time were associated with a higher cortisol awakening response (CAR). Moreover, higher objective trait sleep efficiency and longer objective trait total sleep time were associated with higher waking cortisol levels and steeper DCS. In addition, a minimum of four saliva samples were required at different time points, including upon waking, 30 min after waking, 1 h after waking, and at bedtime, to explore the relationship of sleep efficiency/total sleep time with waking cortisol, CAR, and DCS. Furthermore, the index of the peak-to-bed slope was appropriately employed to examine the relationship between sleep efficiency and DCS, whereas the wake-to-bed slope was effective for examining the relationship between total sleep time and DCS. In summary, this study clarified the relationship between sleep and next-day diurnal cortisol rhythm and suggested a cost-effective cortisol sampling schedule and calculation methods.

Integration of Machine Learning Models into Three-Dimensional Numerical Simulation of Polymer Electrolyte Membrane Fuel Cells

Numerical simulations are powerful tools for understanding phenomena in Polymer Electrolyte Membrane Fuel Cells (PEMFCs) and developing PEMFCs with high performance and durability. We have been developing a three-dimensional (3D) PEMFC simulator P-Stack [1]. P-Stack can perform 3D cell/stack simulations for long-time operation about 10 ~ 100 hours at most. However, fuel cell degradation occurs on longer time scales. To simulate such very long time scales within acceptable computational time, the computational speed need to be improved innovatively. As one of the approaches, we have integrated machine learning models into the simulation.In the power generation calculation of P-Stack, an in-plane current density distribution is determined by calculating a one-dimensional (1D) electrochemical reaction model at each point. In the present study, the 1D electrochemical reaction model is replaced with a machine learning model. The learning data is the result of “pseudo-1D” power generation calculation of 500 cases generated by P-Stack. Here, “pseudo-1D” means the power generation calculation for sampling is performed for small cell geometry, guaranteeing that in-plane distribution is almost homogeneous. It also suitable for sampling calculation because computational cost is very low. In the data generation by P-Stack, the input parameters of P-Stack were determined by Latin hypercube sampling. The input parameters for the machine learning model are the potential, the concentration of each gas species in the anode and cathode catalyst layers, and the temperature. The prediction parameters are current density, cell resistance, overvoltages, molar flux of each gas species and heat flux.As shown in Figure 1, the machine learning model well predicts the target parameters, and the coefficient of determination was about 0.9 for various prediction targets of the verification data. As a result, machine learning could be used to predict power generation. We have implemented this machine learning model into P-Stack. We will also discuss the stability and accuracy of computation coupled with machine learning model and P-Stack, and how much the calculation can be accelerated by our approach.Reference[1] T. Tsukamoto, T. Aoki, H. Kanesaka, T. Taniguchi, T. Takayama, H. Motegi, R. Takayama, S. Tanaka, K. Komiyama, M. Yoneda, J. Power Sources 488 (2021) 229412 Figure 1

THE INFLUENCE OF WORK MOTIVATION AND WORK DISCIPLINE ON EMPLOYEE PERFORMANCE PT BURANGKENG MAJU TEHNIK (BMT) IN EAST BEKASI

The research conducted aims to determine simultaneously and partially the independent variables, namely Work Motivation and Work Discipline, the dependent variable Employee Performance. The method used in this research is quantitative method, where in this study using Purposive Sampling calculation with the slovin formula. The population in this study were all employees totaling 100 employees with a sample of 80 respondents. Based on the results of the study, in the partial test (T) the Work Motivation variable does not have a partial positive effect on Employee Performance with a value of 1.968 (t-count)&gt; 1.66412 (t-table) with a significant value of 0.053 &lt;0.05. And the Work Discipline variable has a positive influence on Employee Performance with a value (t-count) of 5.216&gt; 1.66412 (t-table) with a significant value of 0.000 &lt;0.05. Furthermore, in the simultaneous test (f) the Work Motivation and Work Discipline variables simultaneously have a positive effect on the Employee Performance Variable with a value (f-count) of 29.536&gt; 2.72 (f-table) with a significant value of 0.000 &lt;0.05. Then the determination test (R2) resulted that the correlation between the two variables of Work Motivation (X1) and Work Discipline (X2) on Employee Performance (Y).Keywords: Work Motivation, Work Discipline, Employee Performance

A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

AbstractSoil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core‐scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core‐scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.

Understanding the sources of error in MBAR through asymptotic analysis.

Many sampling strategies commonly used in molecular dynamics, such as umbrella sampling and alchemical free energy methods, involve sampling from multiple states. The Multistate Bennett Acceptance Ratio (MBAR) formalism is a widely used way of recombining the resulting data. However, the error of the MBAR estimator is not well-understood: previous error analyses of MBAR assumed independent samples. In this work, we derive a central limit theorem for MBAR estimates in the presence of correlated data, further justifying the use of MBAR in practical applications. Moreover, our central limit theorem yields an estimate of the error that can be decomposed into contributions from the individual Markov chains used to sample the states. This gives additional insight into how sampling in each state affects the overall error. We demonstrate our error estimator on an umbrella sampling calculation of the free energy of isomerization of the alanine dipeptide and an alchemical calculation of the hydration free energy of methane. Our numerical results demonstrate that the time required for the Markov chain to decorrelate in individual states can contribute considerably to the total MBAR error, highlighting the importance of accurately addressing the effect of sample correlation.

The construction of a practical platform for party building and ideological and political education based on information fusion

Abstract Party building and ideological and political education have received much attention in recent years, and the construction of its work practice platform is particularly important. In order to improve the level of party building and ideological and political education in colleges and universities, In this paper is based on the information fusion technique to fuse relevant factors after data, feature, and decision levels. Using sampling calculation, the percentage of relevant factors was calculated as 37%, 38%, and 25%, and the Kalman filter algorithm was used to test the information data. The two factors that accounted for more were selected as the platform construction indexes, and the information was fused with the maximum likelihood estimation and D-S algorithm to build the party construction and ideological and political education work practice platform. It is evident that the practice platform based on information fusion technology can promote the development of ideological and political education.

Parameter Sensitivity Analysis of SWAT Modeling in the Upper Heihe River Basin Using Four Typical Approaches

Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.

Analysis of residual stress distribution characteristics of laser surface hardening based on Voronoi model

The laser surface hardening’s parameters have a direct effect on the residual stress of the matrix. It is important to quantitatively reveal the influence degree of process parameters on the matrix residual stress for the formulation of optimal process. In this paper, a random polycrystalline model of the matrix is established based on the Voronoi method. The grain heterogeneity is introduced according to the nanoindentation results. The divided 7 different grain attributes are randomly assigned to each voronoi cell through a python script. A thermo-mechanical coupling model for the laser quenching process of SUS301L-HT stainless steel considering grain heterogeneity was established. The thermal stress field and temperature field distribution of the single laser surface hardening are calculated with different process parameters. On this basis, the BOX-Behnen Design method was used to establish a response surface model, and the response surface method was combined with the Monte Carlo sampling calculation method to calculate the sensitivity of the process parameters. The calculation results show that the analysis value D of the residual stress is a normally distributed. Laser power and laser scanning speed are the main factors affecting the average residual stress of the grain, and the effects of the two are opposite. The laser scanning speed has a greater influence on the residual stress of the matrix than the laser power.

Semi-analytic Fresnel diffraction calculation with polynomial decomposition.

The numerical method based on the fast Fourier transform (FFT) is generally applied to calculate the Fresnel diffraction field, which would suffer from sampling constraints. To break this limit, in this Letter, the semi-analytic Fresnel diffraction calculation method is proposed based on polynomial decomposition. The diffraction field is computed by using properly analytic Fresnel diffraction basis functions (FDBFs) according to the application requirements. Analytic FDBF is calculated based on Legendre or Chebyshev polynomials by using the object-domain frequency division multiplexing method. The proposed method offers arbitrary sampling, high-flexibility, and high-accuracy diffraction calculation in the full Fresnel region. The computational efficiency and accuracy of the proposed method are compared with FFT-based methods. It has potential application in light field analysis, wavefront sensing, and image processing.

Periodic event-triggered adaptive tracking control design for nonlinear discrete-time systems via reinforcement learning

In this paper, an event-triggered control scheme with periodic characteristic is developed for nonlinear discrete-time systems under an actor–critic architecture of reinforcement learning (RL). The periodic event-triggered mechanism (ETM) is constructed to decide whether the sampling data are delivered to controllers or not. Meanwhile, the controller is updated only when the event-triggered condition deviates from a prescribed threshold. Compared with traditional continuous ETMs, the proposed periodic ETM can guarantee a minimal lower bound of the inter-event intervals and avoid sampling calculation point-to-point, which means that the partial communication resources can be efficiently economized. The critic and actor neural networks (NNs), consisting of radial basis function neural networks (RBFNNs), aim to approximate the unknown long-term performance index function and the ideal event-triggered controller, respectively. A rigorous stability analysis based on the Lyapunov difference method is provided to substantiate that the closed-loop system can be stabilized. All error signals of the closed-loop system are uniformly ultimately bounded (UUB) under the guidance of the proposed control scheme. Finally, two simulation examples are given to validate the effectiveness of the control design.