Monte Carlo Simulations Research Articles

Population Pharmacokinetic Modeling of Cefepime, Meropenem, and Piperacillin-Tazobactam in Patients with Cystic Fibrosis.

Patients with cystic fibrosis (CF) experience recurrent bacterial pulmonary exacerbations. Management of these infections is increasingly challenging due to decreased antimicrobial susceptibility to beta-lactam antibiotics. The pharmacokinetics of these agents are inadequately characterized in patients with CF. One hundred fifty-five pediatric and adult participants with CF receiving cefepime (n=82), meropenem (n=42), or piperacillin-tazobactam (n=31) were enrolled. Opportunistic blood samples were obtained during hospitalization. Population PK analysis was conducted using nonlinear mixed-effects modeling. Clinical and demographic characteristics were evaluated as potential covariates. Monte Carlo simulations were performed to evaluate probability of target attainment (PTA) for different dosing regimens. Estimated creatinine clearance, and total or lean body weight, affected the pharmacokinetics of cefepime and meropenem. No covariates were identified for piperacillin and tazobactam. In the cefepime group, a 3-h infusion achieved higher PTA than a 0.5-h infusion for all participants. Estimated breakpoints (the respective minimum inhibitory concentration (MIC) up to which ≥90% of patients are predicted to reach a PK/PD target) were two- to four-fold higher in pediatric participants receiving a 3-h vs. 0.5-h infusion. In the meropenem group, increased creatinine clearance led to reduced PTA. In the piperacillin-tazobactam group, total daily dose and mode of administration were principal drivers of PTA. Standard dosing regimens fail to achieve specific MIC targets in patients with CF. Therefore, clinicians should incorporate local antibiograms and PK models to determine optimal dosing. Further PK optimization to account for interindividual differences could be achieved by real-time beta-lactam therapeutic drug monitoring.

Fuel-Efficient and Fault-Tolerant CubeSat Orbit Correction via Machine Learning-Based Adaptive Control

The increasing deployment of CubeSats in space missions necessitates the development of efficient and reliable orbital maneuvering techniques, particularly given the constraints on fuel capacity and computational resources. This paper presents a novel two-level control architecture designed to enhance the accuracy and robustness of CubeSat orbital maneuvers. The proposed method integrates a -optimized sequence at the high level to leverage natural perturbative effects for fuel-efficient orbit corrections, with a gated recurrent unit (GRU)-based low-level controller that dynamically adjusts the maneuver sequence in real-time to account for unmodeled dynamics and external disturbances. A Kalman filter is employed to estimate the pointing accuracy, which represents the uncertainties in the thrust direction, enabling the GRU to compensate for these uncertainties and ensure precise maneuver execution. This integrated approach significantly enhances both the positional accuracy and fuel efficiency of CubeSat maneuvers. Unlike traditional methods, which either rely on extensive pre-mission planning or computationally expensive control algorithms, our architecture efficiently balances fuel consumption with real-time adaptability, making it well-suited for the resource constraints of CubeSat platforms. The effectiveness of the proposed approach is evaluated through a series of simulations, including an orbit correction scenario and a Monte Carlo analysis. The results demonstrate that the integrated -GRU system significantly improves positional accuracy and reduces fuel consumption compared to traditional methods. Even under conditions of high uncertainty, the GRU-based control layer effectively compensates for errors in thrust direction, maintaining a low miss distance throughout the maneuvering period. Additionally, the GRU’s simpler architecture provides computational advantages over more complex models such as long short-term memory (LSTM) networks, making it more suitable for onboard CubeSat implementations.

Probabilistic Analysis of Orbital Characteristics of Rotary Systems with Centrally and Off-Center Mounted Unbalanced Disks

Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery.

A polyhedron-based metal-organic framework with electronegative donor sites for efficient C2H6/C2H4 separation

Ethylene is an important chemical raw material which is widely used in the production of chemical fibers, plastics, rubber, coatings, as well as medicines, and its separation from the gas mixtures of C2H6/C2H4 is of great importance but a challenging task due to their similar physical properties and molecular dimensions. Herein, we report the C2H6 and C2H4 sorption studies on a robust anionic metal-organic framework (MOF) {(NH2Me2)[Co3(μ3OH)(TPT)(TZB)3](H2O)6(DMA)6}n (Co-MOF) formed by connection of Co3(OH) clusters by two N-rich organic ligands 2,4,6-tri(4-pyridyl)-1,3,5-triazine (TPT) and 4-(1H-Tetrazol-5-yl)benzoic acid (H2TZB). The prepared Co-MOF features a polyhedron stacking network structure composed of trigonal bipyramidal cages and octahedral cage which both contribute to a high C2H6 uptake capacity of 6.48 mmol/g and a decent C2H6/C2H4 selectivity of 1.54 at 298 K and 1 bar. Grand Canonical Monte Carlo (GCMC) simulation results agree well with the experimental tendency, both in the adsorption isotherms and sorption heats, which demonstrated that the suitable pore surfaces with electronegative donor sites generated multiple CH ⋅⋅⋅O/N and CH ⋅⋅⋅π interactions between the framework and C2H6, resulting in a stronger interaction between the MOF skeleton and C2H6 molecule than C2H4 as reflected by the dispersion-corrected density functional theory (DFT) calculation.

Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (SS316L) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M23C6, γ’, and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate for periods of up to 3000 h. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25 % volume fraction and mechanical properties within 6 % relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ∼1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that microstructural evolution under variable conditions can be predicted using a statistics-based model informed by a practicably obtainable dataset. The potential applications of this type of modeling framework are discussed.

A Comprehensive Review of Diffusing Alpha-Emitters Radiation Therapy (DaRT): From Dosimetry to Its Biological Effectiveness

Diffusing alpha-emitters radiation therapy (DaRT) represents a groundbreaking development in cancer therapy, offering a solution to the limitations of conventional radiation therapy. By deploying <sup>224</sup>Ra embedded seeds, DaRT achieves targeted delivery of high-dose alpha particles directly to tumor sites, showing considerable efficacy in tumor control and minimal damage to adjacent healthy tissues. This comprehensive review analyzes the published literature regarding mechanisms, seed production, dose calculation, measurement, and biological experiments related to DaRT. It includes in-depth discussions on mathematical models, Monte Carlo simulations for dose distribution, real-time <i>in vivo</i> dosimetry developments, and biological experiments both in vitro and <i>in vivo</i>. Clinical trial outcomes are also examined to evaluate the therapy’s effectiveness in various cancer types. DaRT utilizes <sup>224</sup>Ra-labeled seeds, using the decay chain of <sup>224</sup>Ra to deliver alpha particles effectively within a tumor. Several asymptotic diffusion-leakage models were developed to calculate the alpha dose distribution of DaRT. <i>In vivo</i> dosimetry techniques have been developed for real-time monitoring. Biological experiments demonstrated the cytotoxic effects of DaRT across various cancer cells, with varying radiosensitivity. Additionally, the enhanced effects of combined therapy with chemotherapy and immunotherapy were suggested by many <i>in vivo</i> studies. Clinical trials have shown high complete response rate in squamous cell carcinoma, with minimal side effects, suggesting DaRT’s feasibility and safety. DaRT emerges as a highly localized cancer treatment method with minimal side effects compared to traditional radiation therapy. It directly ablates tumors and potentially enhances immune responses, indicating a significant advance in cancer therapy. Future research and ongoing clinical trials will further elucidate its efficacy across different cancer types and in combination with other treatments.

Cost-effectiveness of sodium-glucose cotransporter-2 inhibitors in the treatment of diabetic nephropathy in Japan.

To assess the cost-effectiveness of diabetic nephropathy treatment with sodium-glucose cotransporter-2 (SGLT2) inhibitors in Japanese clinical practice, considering diabetes-related complications. A population-based Monte Carlo simulation was used to estimate the cost-effectiveness for people with diabetic nephropathy who initiated pharmacotherapy with an SGLT2 inhibitor plus conventional treatment or conventional treatment alone, based on quality-adjusted life-years (QALYs) and healthcare costs. The Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation study (CREDENCE) and the Japanese Society for Dialysis Research statistical survey were the primary sources of probability and mortality, while Japanese Health Insurance Claims Data were the cost source. The state transition model included diabetic nephropathy, hospitalization due to cardiovascular disease, dialysis, and death. One-way and probabilistic sensitivity analyses were used to explore model uncertainty. Using the threshold of JPY 5 000 000 per QALY, SGLT2 inhibitor plus conventional treatment was more cost-effective than conventional treatment alone, with an incremental cost-effectiveness ratio of JPY 654 309 per QALY. Treating 100 000 people, SGLT2 inhibitor plus conventional treatment prevented 2234 deaths and reduced 5793 fewer heart failure cases, 3967 fewer myocardial infarctions and stroke events. Sensitivity analysis affirmed the robustness of these results for patients aged under 70 years. The SGLT2 inhibitor treatment appeared to be cost-effective for the overall population of our study and particularly for younger patients (<70 years old). For older patients (≥70 years old), the cost-effectiveness was less clear and may require further evaluation. Decision-makers should consider this age-based heterogeneity when making recommendations about SGLT2 inhibitor treatment.

Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain).

Andalusia is the second largest region in Spain, and it has developed a comprehensive mental health (MH) plan that encourages the consolidation of the balanced care model. However, its geographical and socioeconomic disparity is a great challenge for a community-based MH system. Both the assessment of the implementation of the MH plan and the development of new tools to support decision-making can be considered critical. The present study aims (i) to assess how the integration of different types of MH care may influence system performance and (ii) to check the performance evolution of the integration process geographically regarding the small MH areas of Andalusia. The performance of the Andalusian MH system was assessed by combining Monte Carlo simulation, fuzzy inference and data envelopment analysis. The relative technical efficiency was the main performance indicator. A correct integration of appropriate types of MH care, according to population needs, increases the performance of the Andalusian MH system both from global and regional perspectives. The spatial representation (based on small MH areas) of the results highlights how the performance depends on specific geographical characteristics. By analyzing the identified spatial clusters, defined by different management patterns depending on user and socioeconomic characteristics, benchmark areas and areas for improvement can be studied to design evidence-informed policies and interventions. A global analysis of MH system performance was carried out, including both the successive integration of different types of care and its spatial evolution. Although an appropriate integration of different types of MH care has a positive effect on the Andalusian MH system, this process has different profiles depending on specific geographically based user and socioeconomic characteristics. The balanced care model can be considered the paradigm for assessing the performance of a large and populated territory such as Andalusia, which has a community-based MH system. This methodological approach (performance assessment and spatial analysis) may be used as a guide for developing future evidence-informed policies and managerial interventions.

Robust topology optimization of multi-material structures with overhang angle constraints using the material field series-expansion method

This paper presents a novel topology optimization method to enhance the manufacturability and practicality of multi-material structures. Traditional topology optimization techniques excel at generating high-performance designs, but the performance of these designs is often compromised due to the load perturbations encountered in real-world applications or post-processing for additive manufacturing (AM). This work addresses this challenge by incorporating three key features directly into the optimization process. First, the method integrates overhang angle constraints, ensuring designs are inherently manufacturable with AM. Second, it utilizes the material field series-expansion method for a significant reduction in design variables while simultaneously providing a clear and efficient description of material distribution within the optimized designs. Finally, to enhance design robustness under real-world scenarios with uncertain loads, the method employs Monte Carlo simulation, which is decoupled from the optimization process to reduce the computational cost. Numerical examples demonstrate the efficiency and effectiveness of the proposed method in generating robust designs of multi-material structures well-suited for AM. The proposed framework offers a promising pathway for the realization of complex, high-performance composite structures.

Implementing Sobol's Global Sensitivity Analysis to SFRC's Flexural Strength Predictive Equation

Steel fibers are essential for SFRC since they strengthen the material’s resistance to bending and cracking stresses and guarantee its endurance. However, the uncertainty of input parameters such as concrete compositions and steel fibers causes the stochasticity of flexural strength. The study uses big data to analyze the influence of fibers and other concrete compositions on the flexural strength properties of SFRC. For this purpose, the study focuses on developing predictive models for SFRC flexural properties based on a comprehensive database comprising two hundred and seven experimental results recorded by seventeen researchers. Bayesian Model Averaging is employed to identify significant components that influence the overall flexural strength and to develop a predictive flexural strength model. Monte Carlo simulation generates big data by utilizing the probability distribution of input variables and the predictive flexural strength model. The study used Sobol’s global sensitivity analysis method to assess various input parameters’ sensitivity to SFRC flexural strength based on the generated database. The impact order of input variables on the flexural strength is identified, as determined by the Sobol’ Indice.

Enhancement of heat dissipation efficiency in the CSNS target through the innovative design of a serial cooling water channel

This paper presents a coupling of the Monte Carlo method with computational fluid dynamics (CFD) to analyze the flow channel design of an irradiated target through numerical simulations. A novel series flow channel configuration is proposed, which effectively facilitates the removal of heat generated by high-power irradiation from the target without necessitating an increase in the cooling water flow rate. The research assesses the performance of both parallel and serial cooling channels within the target, revealing that, when subjected to equivalent cooling water flow rates, the maximum temperature observed in the target employing the serial channel configuration is lower. This reduction in temperature is ascribed to the accelerated flow of cooling water within the serial channel, which subsequently elevates both the Reynolds number and the Nusselt number, leading to enhanced heat transfer efficiency. Furthermore, the maximum temperature is observed to occur further downstream, thereby circumventing areas of peak heat generation. This phenomenon arises because the cooling water traverses the target plates with the highest internal heat generation at a lower temperature when the flow channels are arranged in series, optimizing the cooling effect on these targets. However, it is crucial to note that the pressure loss associated with the serial structure is two orders of magnitude greater than that of the parallel structure, necessitating increased pump power and imposing stricter requirements on the target container and cooling water pipeline. These findings can serve as a reference for the design of the cooling channels in the target station system, particularly in light of the anticipated increase in beam power during the second phase of the China Spallation Neutron Source (CSNS Ⅱ).

A Comprehensive Monte Carlo Simulation Study on Multiple Comparison Methods after ANOVA

Multiple comparison methods are applied to control the type I error rate at the nominal level. In this study, we investigate the performance of multiple comparison methods after analysis of variance (ANOVA) is implemented under different conditions. We include Bonferroni, Holm, Hochberg, Hommel, Benjamini-Hochberg (BH), and Benjamini-Yekutieli (BY) correction methods. Monte-Carlo simulation study is applied to assess their performances under different patterns, including sample size and group number combinations. Wide inferences are drawn on considered methods, and suggestions are provided for selecting appropriate methods. Moreover, the methods are implemented on three different types of real-life data sets to emphasize the importance of these correction methods in the research.

Methodology of Shipboard Spare Parts Requirements Based on Whole Part Repair Strategy

This paper introduces an assessment method for shipboard spare parts requirements based on a whole-part repair strategy, aimed at enhancing the availability and combat effectiveness of naval equipment. Addressing the shortcomings of traditional repair strategies, this study innovatively adopts a whole-part rotation repair approach to reduce repair times and improve the rapid response capability of equipment. An evaluation model for support probability and fill rate is established, and Monte Carlo simulation techniques are applied to simulate the impact of different maintenance strategies on spare parts demand and equipment availability. This study also conducts a sensitivity analysis of key parameters, including Mean Time Between Failures (MTBF), repair demand probability, and faulty part repair cycle, to assess their influence on spare parts requirements and equipment availability. The results indicate that the whole-part repair strategy can effectively reduce spare parts demand and enhance equipment availability. In conclusion, the whole-part repair strategy demonstrates a distinct advantage in shipboard spare parts management, optimizing inventory management while ensuring combat readiness. This research provides a novel analytical approach for naval logistics and maintenance planning.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Target Attainment and Population Pharmacokinetics of Cefazolin in Patients with Invasive Staphylococcus aureus Infections: A Prospective Cohort Study

This study aimed to determine cefazolin target attainment in patients with invasive Staphylococcus aureus (S. aureus) infections and to develop a population pharmacokinetic (PK) model. Adult patients with invasive S. aureus infections treated with cefazolin bolus infusions were included. Unbound and total trough and mid-dose cefazolin concentrations were measured, and strain-specific MICs were determined. The primary outcome was the proportion of patients attaining 100% fT&gt;MIC at all time points evaluated. A population PK model was developed, using non-linear mixed-effects modelling. Overall, 51 patients were included, with a total of 226 unbound and total cefazolin concentrations measured (mean: 4.4 per patient). The median daily dosage in patients with an estimated glomerular filtration rate of &gt;60 mL/min/m2 was 8 g. The median age was 74 years (interquartile range (IQR) 57–82) and 26% were female. A history of chronic kidney disease and acute kidney injury were present in 10/51 (19.6%) and 6/51 (11.7%), respectively. Achievement of 100% fT&gt;MIC occurred in 86% of the patients and decreased to 45% when a target of 100% fT&gt;4xMIC was evaluated. The mean unbound cefazolin fraction was 27.0% (standard deviation (SD) 13.4). Measured and estimated mean cefazolin trough concentrations differed significantly [13.1 mg/L (SD 23.5) vs. 7.4 mg/L (SD 7.9), p &lt; 0.001]. In the population PK model, elevated estimated creatinine clearance and bolus instead of continuous application were covariates for target non-attainment. In conclusion, cefazolin target achievement was high, and the measurement of the unbound cefazolin concentration may be favored. The Monte Carlo simulations indicated that target attainment was significantly improved with continuous infusion.

A weighted dual cumulative sum chart for monitoring the process mean

AbstractThe cumulative sum (CUSUM) charts are well‐structured and widely used for detecting small‐to‐moderate changes in the process parameters. However, their performance can be limited when the shift size is unknown or varies over time. Various methods have been developed to address this issue, including dual CUSUM (DCUSUM), adaptive CUSUM, adaptive EWMA, and weighted CUSUM charts, all of which offer better sensitivity compared to traditional CUSUM charts. In this study, we propose a new weighted DCUSUM chart designed to effectively detect the mean shifts in a normally distributed process. Monte Carlo simulations are used to estimate the zero‐state and steady‐state average run‐length (ARL) profiles of the control charts. The ARL performances of the control charts are evaluated in terms of expected relative ARL (ERARL) and expected weighted run‐length (EWRL). Our results show that the proposed chart may outperform existing counterparts in terms of the ARL, ERARL, and EWRL measures, indicating superior performance in detecting a range of the mean shift sizes. Finally, we apply the existing and proposed charts to a real dataset, showcasing their practical utility in process monitoring.

Health Risk Assessment of Nitrate, Nitrite, and Fluoride Ions in Water Reservoirs of Mehriz, Iran in 2023

Introduction: High concentrations of nitrate, nitrite, and fluoride in drinking water can cause adverse health effects. This study aims to investigate concentration and health risks assessment of nitrate, nitrite, and fluoride ions in water reservoirs of Mehriz city and Bahadoran district. Materials and Methods: Monthly sampling was done from water reservoirs of Mehriz city and Bahadoran district for 6 months. Nitrate, nitrite, and fluoride concentrations were measured in the samples using a spectrophotometer. Then, health risk assessment and sensitivity analysis were performed on the obtained data using Crystal Ball software and Monte Carlo simulation method. Results: Nitrate, nitrite, and fluoride concentrations were lower than the standard limitation in all of the studied areas. Risk assessment findings indicated that hazard quotient (HQ) values of nitrite in Miankoh-Movahedin, Bidok and Bahadoran water reservoirs were less than 1 for all age groups. HQ values of nitrate were also below 1 for all age groups except children. HQ values of fluoride in Bahadoran water reservoir were below 1 for all age groups except children. Conclusion: Health risk of consuming water containing nitrates and fluorides is high for children. Based on sensitivity analysis, the concentration of nitrate and fluoride in drinking water is the key factor in raising health risks. Reducing nitrate and fluoride concentrations in drinking water can reduce health risks in the population.

Weighted Expectile Regression Neural Networks for Right Censored Data.

As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression (WER) method assumes that the censoring variable and covariates are independent, and that the covariates effects has a global linear structure. However, these two assumptions are too restrictive to capture the complex and nonlinear pattern of the underlying covariates effects. In this article, we developed a novel weighted expectile regression neural networks (WERNN) method by incorporating the deep neural network structure into the censored expectile regression framework. To handle the random censoring, we employ the inverse probability of censoring weighting (IPCW) technique in the expectile loss function. The proposed WERNN method is flexible enough to fit nonlinear patterns and therefore achieves more accurate prediction performance than the existing WER method for right censored data. Our findings are supported by extensive Monte Carlo simulation studies and a real data application.

Numerical Simulation of Spectral Radiation for Hypersonic Vehicles

The spectral radiation of hypersonic vehicles and surrounding flow fields is crucial for optical target detection. A novel approach combines Low-Discrepancy Sequences with the Reverse Monte Carlo Method to simulate the spectral radiation of hypersonic missiles. The effects of chemical non-equilibrium reactions and high-emissivity coating failures on the spectral radiation of a typical biconical hypersonic missile were investigated. Significant differences were found among chemical equilibrium, non-equilibrium, non-reactive, and catalytic wall models. High-emissivity coating failures occur mainly in the high-temperature regions of the nose cone and fins. The non-equilibrium model shows a transition peak distribution of NO in the head shock layer within the ultraviolet gamma band, with integrated ultraviolet radiation (200–300 nm) three times higher than the equilibrium model. In the 1–3 μm band, the non-equilibrium model’s radiation intensity at a 120° horizontal detection angle is about 1.46 times that of the equilibrium model. Using real coating emissivity, the 3–5 μm band radiation intensity is about 5% higher, and the 8–14 μm band is about 8.51% lower than the uniform emissivity model. When high-emissivity coating emissivity fails by 40%, the 3–5 μm band intensity decreases by about 15.38%, and the 8–14 μm band intensity decreases by about 12.67%.