Nonlinear Model Research Articles

Population pharmacokinetic and exposure-toxicity analyses of nab-paclitaxel after pegylated recombinant human granulocyte colony-stimulating factor administration in patients with metastatic breast cancer.

This study aimed to establish a population pharmacokinetic (PK) model to evaluate the dynamic relationship between the concentrations of total and unbound paclitaxel, and the exposure-response analysis of albumin-bound paclitaxel (nab-paclitaxel) after pegylated recombinant human granulocyte colony-stimulating factor (PEG-G-CSF) administration in patients with metastatic breast cancer. A total of 653 concentrations corresponding to total paclitaxel and 334 concentrations corresponding to unbound paclitaxel were analyzed in 24 subjects who randomized received a single 260 mg/m2 dose of two nab-paclitaxel formulations with a 21-35-day washout period. PEG-G-CSF was administered to all the patients in each cycle to prevent neutropenia. The exposure-response relationships were evaluated using the exposure to total, albumin-coated, and unbound paclitaxel, as well as the reduction in neutrophil count. The exposure data were analyzed using nonlinear mixed-effect modeling. A linear regression model was used to test the statistical significance of the correlation between percentage of reduction in neutrophil count and exposure. The PK characteristics of total paclitaxel were described using a three-compartment model with first-order elimination, and a mechanism-based model incorporating linear release of nab-paclitaxel and the saturated binding of unbound paclitaxel to plasma components was established. The release ratio of paclitaxel from nab-paclitaxel was estimated to be 4.60% and the maximum unbound fraction (2.76%) was reached at the end of the infusion. The study found that a longer duration of total paclitaxel concentration > 0.19 µmol/L was significantly correlated with a reduction in neutrophil count (r2 = 0.23, P = 0.00062). Specifically, a duration of > 8.6h was a predictor of a decreased neutrophil count. The decrease in neutrophils induced by nab-paclitaxel was significantly correlated with the duration above a total paclitaxel concentration of 0.19 µmol/L despite the use of PEG-G-CSF.

Game theoretic approach for the synthesis and optimization of economically optimal coalitions in integrated renewable energy and polygeneration networks

This study presents the generation of an integrated resource network that encompasses a bioenergy supply chain and a polygeneration hub. The resource network is designed using a superstructure that comprises 3 layers. The first layer is a supply chain network of seasonally available renewable and non-renewable energy sources and is connected to the second layer by a transport system comprised of rail, road, and pipeline. The second layer, which is the polygeneration hub, comprises a boiler to produce high-pressure steam, steam turbines for generating power, a multi-effect evaporation system for desalinating salt-water and an absorption refrigeration system to produce chilled water. The option of connecting the boiler to a solar-thermal and heat storage subnetwork for feedwater pre-heating is included in the second layer. Piping and electrical cables connect the polygeneration layer to the third layer, which comprises industrial heat demand, represented by heat exchanger network, and electrical power demand. The model is applied to a case study in which the subnetworks in the overall integrated superstructure layers are considered as individual players in an industrial symbioses network with the possibility to be in a cooperative game. The objective function of the model, which is represented as a mixed integer nonlinear programming model, involves the minimization of the summation of the players' marginal contribution, with equal weighting applied for each player, to the coalition's total annual cost. The result obtained involves preference for a coalition among participating subnetworks that include combined heat and power, multi-effect evaporation, and heat exchanger network.

Filtering of 3D point clouds using maximum likelihood algorithm

Recently, the 3D point cloud (PC) has become more popular as an innovative object representation. However, there is usually noise and outliers in the raw point cloud. It is essential to eliminate the noise from the point cloud and outlier data while maintaining the features and finer details intact. This paper presents a comprehensive method for filtering and classification point clouds using a maximum likelihood algorithm (ML). TOPCON GLS-2000 3D terrestrial laser scanners (TLS) have been used to collect the 3D PC data set; the scan range is up to 350 m. About 30 m apart from the study area. ScanMaster software has been used to import, view, and filter point cloud information. The position information of the points is linked with the training point cloud and the filtered point cloud to derive the nonlinear model using MATLAB software. To evaluate the quality of the denoising results, two error metrics have been used: the average angle (δ) and distance (Dmean) between the ground truth point and the resulting point. The experimental findings demonstrate that the suggested approach can effectively filter out background noise while improving feature preservation. The filtering and classifying technique is more effective and efficient compared to the selected filtering methods when applied to 3D point clouds containing a large number of points and a variety of natural characteristics.

Impact of temperature and relative humidity variability on children’s allergic diseases and critical time window identification

BackgroundThe effects of temperature and relative humidity on different types of children’s allergic diseases have not been comprehensively evaluated so far. This study aims to assess the impact of temperature and relative humidity variability on children’s allergic diseases and to identify the critical time window.MethodsWe collected outpatient data on allergen testing in children between July 2020 and January 2022 from the Affiliated Children’s Hospital of Nanjing Medical University. We defined the 1st, 10th, 90th, and 99th percentiles as extreme cold, moderate cold, moderate hot, and extreme hot for temperature, and as low, moderate high, and extreme high for relative humidity, respectively. A distributed lag nonlinear model (DLNM) combined with a binomial regression model was used to assess the possible nonlinear relationship at different periods. Subgroup analysis by gender and age was conducted.ResultsWe found that extreme and moderate cold temperatures were positively associated with skin allergies and total allergies (28 days: OR = 4.69, 95% CI: 2.88, 7.63; OR = 3.36, 95% CI: 2.39, 4.73) and (28 days: OR = 3.76, CI: 2.43, 5.81; OR = 2.71, 95% CI: 2.00, 3.68), respectively. Moderate and extreme hot temperatures were negatively associated with food allergies (28 days: OR = 0.13, 95% CI: 0.04, 0.41 and OR = 0.04; 95% CI: 0.01, 0.27). Low relative humidity was negatively associated with respiratory allergies, skin allergies, and total allergic diseases (28 days: OR = 0.26, 95% CI: 0.10, 0.71; OR = 0.29, 95% CI: 0.15, 0.55; and OR = 0.42, 95% CI: 0.26, 0.68). Meanwhile, extreme high relative humidity was negatively associated with respiratory allergies, and positively associated with skin allergies, food allergies, and total allergies (28 days: OR = 0.16, 95%CI: 0.07, 0.37; OR = 3.60, 95% CI: 2.52, 5.14; OR = 15.61, 95% CI: 3.23, 75.56; and OR = 2.33, 95% CI: 1.73, 3.15). A stronger relationship between temperature, relative humidity, and allergic diseases was observed in children under 5 years, specifically girls.ConclusionsOur study provides evidence that temperature and relative humidity variability may be associated with allergic diseases, however, the directionality of the relationship differs by allergic type.

Study on the stability and ultra-low frequency oscillation suppression method of pumped storage power plant with dual units sharing one pipeline

This paper aims to investigate the stability of the pumped storage power plant (PSPP) with dual units sharing one pipeline (DUSOP) and the method of suppressing the ultra-low frequency oscillation (ULFO) characteristics. Firstly, four nonlinear models of pumped storage regulation system (PSRS) with DUSOP are established under the design scenarios of no surge tank, upstream surge tank (UST), downstream surge tank, and upstream and downstream dual surge tanks. Then the stability of the PSRS under different surge tank arrangements (STAs) in the long diversion system design scenarios (LDSDS) is comparatively analyzed. Finally, based on the concept of stability margin, the influence mechanism of the operating points on the ULFO characteristics is investigated in the LDSDS with UST, and the influence of the governor parameters, hydrodynamic parameters, and mechanical parameters on the damping ratio and frequency of the system is also analyzed. The results show that among STAs, the design scheme of arranging the UST is more reasonable for PSPP with LDSDS from the comprehensive consideration of the economy of the project and the stability of the system. For PSPP with UST, on the line of equal stability margin, increasing the proportional gain, reducing the unit inertia time constant, reducing the water flow inertia time constant, and appropriately increasing the area of the surge tank can increase the frequency and reduce the amplitude, which is conducive to the suppression of ULFO. In the stability domain range, increasing the proportional gain increases the frequency and the system stability increases and then decreases. Increasing the integral gain decreases the frequency and then increases the system stability. At the same time, the frequency of the unit can be changed by adjusting the parameters of the system to avoid resonance between the frequency of the unit and the frequency of the power grid and to ensure the stability and safety of the unit operation.

Total harmonic distortion estimation in piezoelectric micro-electro-mechanical-system loudspeakers via a FEM-assisted reduced-order-model

Piezoelectric micro-electro-mechanical-system (MEMS) loudspeakers are attracting growing research interest in the last years due to the increasing interest towards miniaturization required by new wireless audio devices. Finite Element Models (FEM) and Lumped Element Models (LEM) able to accurately simulate their linear response have been recently proposed in the literature. However, a nonlinear model suitable to predict the Total Harmonic Distortion (THD) of these devices is to date still missing. In this work, we present a FEM-assisted lumped-parameters equivalent circuit for THD estimation which accounts for geometric nonlinearities and piezoelectric hysteresis. The loudspeaker nonlinear electro-mechanical domain is simulated through a Reduced Order Model (ROM) which considers as basis function the pre-stressed undamped actuated mode of the device computed via FEM, to account for loudspeaker diaphragms of arbitrarily complex geometry. Parameters of the acoustical circuit are computed through analytical formulas. The good matching between numerical predictions and experimental results, carried out on a piezoelectric MEMS loudspeaker prototype for in-ear condition, demonstrates the accuracy of the proposed tool.

Impact of surface ultraviolet radiation intensity on hospital admissions for ischemic and hemorrhagic stroke: A large-scale database study using distributed lag nonlinear analysis, 2015-2022, in Harbin, China

ObjectivesOur aim is to evaluate the impact of surface ultraviolet radiation intensity on hospital admissions for stroke and to compare the correlation and differences among different subtypes of strokes. Materials and methodsWe collected daily data on surface ultraviolet radiation intensity, temperature, air pollution, and hospital admissions for stroke in Harbin from 2015 to 2022. Using a distributed lag non-linear model, we determined the correlation between daily surface ultraviolet radiation intensity and the stroke admission rate. Relative risks (RR) with 95% confidence intervals (CI) and attributable fractions (AF) with 95% CI were calculated based on stroke subtypes, gender, and age groups. ResultsA total of 132,952 hospitalized stroke cases (including hemorrhagic and ischemic strokes) were included in the study. We assessed the non-linear effects of ultraviolet intensity on hospitalized patients with ischemic and hemorrhagic strokes. Compared to the maximum morbidity benchmark ultraviolet intensity (19.2 × 10^5 for ischemic stroke and 20.25 for hemorrhagic stroke), over the 0-10 day lag period, the RR for extreme low radiation (1st percentile) was 0.86 (95% CI: 0.77, 0.96), and the RR for extreme high radiation (99th percentile) was 0.86 (95% CI: 0.77, 0.96). In summary, -4.842% (95% CI: -7.721%, -2.167%) and -1.668% (95% CI: -3.061%, -0.33%) of ischemic strokes were attributed to extreme low radiation intensity with a lag of 0 to 10 days and extreme high radiation intensity with a lag of 0 to 5 days, respectively. The reduction in stroke hospitalization rates due to low or high ultraviolet intensity was more pronounced in females and younger individuals compared to males and older individuals. None of the mentioned ultraviolet intensity intensities and lag days had a statistically significant impact on hemorrhagic stroke. ConclusionsOur study fundamentally suggests that both lower and higher levels of surface ultraviolet radiation intensity in Harbin, China, contribute to a reduced incidence of ischemic stroke, with this effect lasting approximately 10 days. This finding holds significant potential for public health and clinical relevance.

Seismic behavior of CFDST frame with metallic dampers: Damage-controlled design and performance assessment

Current investigations on performance-based seismic design of structures generally employed story drift as performance objectives. However, the story drift could only partially reveal the global damage of a structure and not control the damage of important components. In order to control the damage of various components in a structure, this paper proposed a new damage-controlled seismic design method using the Park & Ang indexes of components. Firstly, the Park & Ang damage index was simplified as an equivalent ductility damage index for various components. The energy balance concept was used to observe the hysteretic energy demand which was subsequently distributed to all stories. Thus, the components of each story could be designed based on their damage indexes under design-based and maximum-considered earthquakes. Four concrete filled double skin tubular (CFDST) moment resisting frames equipped with metallic dampers with various layouts were designed based on the proposed design method. Time history analyses on these four structures were performed after validating the accuracy of nonlinear model by Opensees. Analysis results showed that the components in four structures exhibited expected damage states as illustrated by the design method. It also verified that proposed seismic design method was effective to control the damage of various components, which was beneficial for protecting the important components in a structure by setting low damage indexes for them. The seismic design method and analysis results also provided a reference for the design of moment resisting frame with energy devices.

Nonlinear modeling and analysis of a metatronic amplifier using harmonic balance-finite difference frequency domain method

Nonlinear modeling and analysis of a metatronic amplifier using harmonic balance-finite difference frequency domain method

Handling Volatility and Nonlinearity in Wind Speed Data: A Comparative Analysis between ARIMA-GARCH and ARIMA-MLP

One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess the internal structure of the wind speed daily data in two stations in Johor; Senai and Mersing. Due to the existence of some nonlinearity features in the residuals part of ARIMA modelling, two nonlinear models were introduced to capture the internal structure of the residual data. Both conventional time series models; GARCH, and machine learning model; MLP was applied to model the residuals of ARIMA model. The out-sample performance in forecasting accuracy was compared between the ARIMA-GARCH model and the ARIMA-MLP model. The findings proves that MLP model has outperformed GARCH model in capturing the dynamics in the residual data by providing the lowest error measurements. Thus, the machine learning approaches has proven its superiority against the conventional time series nonlinear model in handling the nonlinearity in the daily wind speed series for wind speed forecasting.

Fast correntropy-based multi-view clustering with prototype graph factorization

As a consequence of the ability to incorporate information from different perspectives, multi-view clustering has gained significant attention. Nevertheless, 1) its high computational cost, particularly when processing large-scale and high-dimensional multi-view data, restricts its applications in practice; and 2) complex noise in real-world data also challenges the robustness of existing algorithms. To tackle the above challenges, we develop a fast correntropy-based multi-view clustering algorithm with prototype graph factorization (FCMCPF). FCMCPF first adopts prototype graphs to effectively mitigate the complexity associated with graph construction, thereby reducing it from a quadratic complexity to a linear one. Then, it decomposes these prototype graphs under the correntropy criterion to robustly find the cluster indicator matrix without any post-processing. To solve the non-convex and non-linear model, we devise a fast half-quadratic-based strategy to first convert it into a convex formulation and then swiftly complete the optimization via the matrix properties of orthogonality and trace. The extensive experiments conducted on noisy and real-world datasets illustrate that FCMCPF is highly efficient and robust compared to other advanced algorithms, with comparable or even superior clustering effectiveness.

VCPNET: A new dataset to benchmark vegetation carbon phenology metrics

Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change.

Method for determining the Lyapunov exponent of a continuous model using the monodrome matrix

The Lyapunov exponent is a measure of the sensitivity of a dynamic system to any changes and disturbances. It is therefore applicable in many fields of science, including physics, chemistry, economics, psychology, biology, medicine, and technology. Therefore, there is a need to have effective methods for determining the value of this exponent. In particular, it concerns the definition of its sign. The positive value of the Lyapunov exponent confirms the sensitivity of the system, especially when the system is chaotic. A negative value indicates the stability of the dynamic system being tested. The numerical value of the Lyapunov exponent indicates the degree of sensitivity of the dynamic system under study. Although the mathematical definition of the Lyapunov exponent is clear and simple, in practice determining its value is not a trivial matter, especially for continuous models. This paper presents a method for determining the Lyapunov exponent for continuous models. This method is based on the monodromy matrix. The work, for example, presents research on the following models of physical systems: the Van der Pol model with external forcing, the nonlinear mathematical pendulum model with external forcing and the Lorenz weather model.

The traveling purchaser problem for perishable foods

This paper addresses the well-known traveling purchaser problem (TPP) considering restrictions related to perishable food, which is called the traveling purchaser problem for perishable foods (TPP-PF). In addition to the main assumptions of the capacitated TPP, the TPP-PF takes into account the release times of perishable foods at markets. In this context, a product can only be purchased at a market after it becomes available for sale. The TPP-PF also considers that the quality of perishable products decreases due to waiting on the market shelves and during transportation, and it additionally takes the deterioration cost of the foods into account. The aim of the problem is to find the best procurement and route plan for the purchaser to minimize the total transportation cost of the temperature-controlled vehicle, the damage cost of perishable foods, and the purchasing cost. Since the deterioration cost of products based on the waiting time is determined through exponential functions, the problem is formulated as a mixed-integer non-linear programming model. To find efficient results for the problem, an adaptive large neighborhood search (ALNS) algorithm is introduced with new problem-specific destroy/repair operators. Furthermore, an additional mechanism is included in the ALNS to change the direction of the search when the algorithm cannot improve the best solution after a number of iterations. In order to analyze the performance of the proposed ALNS, a new benchmark problem set is generated using a well-known TPP benchmark set. The proposed ALNS is first performed for the TPP-PF instances and compared to GUROBI and a simulated annealing algorithm. Following the TPP-PF experiments, the ALNS is carried out for the TPP instances and compared to six state-of-the-art solution approaches. The computational results show that the proposed ALNS outperforms those approaches by finding better results for almost all instances.

Optimization of High-Temperature CO2 Capture by Lithium Orthosilicate-Based Sorbents Using Response Surface Methodology

The major challenge in the current context of the rising world energy demand is to limit the global temperature increase for mitigating climate change. This goal requires a large reduction of CO2 emissions, mainly produced by power generation and industrial processes using fossil fuels. In this study, a novel methodology for K2CO3-doped Li4SiO4 sorbents production for CO2 capture at high temperatures was adopted based on the Design of Experiments (DoE). This innovative approach systematically tested different synthesis (temperature and K2CO3 content) and adsorption conditions (sorption temperature and CO2 concentration), allowing for the assessment of individual and interactive effects of process parameters. The Response Surface Methodology (RSM) was employed to obtain non-linear predictive models of CO2 uptake and Li4SiO4 conversion. The results of RSM analysis evidenced a maximum adsorption capacity of 196.4 mg/g for a sorbent produced at 600 °C and with 36.9 wt% of K2CO3, tested at 500 °C and 4 vol% of CO2. Whereas at 50 vol% of CO2, the best uptake of 295.6 mg/g was obtained with a sorbent synthesized at 600 °C, containing less K2CO3 (17.1 wt%) and tested at a higher temperature (662 °C). These findings demonstrate that K2CO3-doped Li4SiO4 sorbents can be tailored to maximize CO2 capture under various operating conditions, making them suitable for use in industrial processes.

Non-linear critical speed in high-speed ballasted railways with ground reinforcement

When constructing or upgrading high-speed railway lines on soft soils, reinforcing the soil foundation becomes crucial. This not only involves small track displacements but also increases the critical speed, thereby ensuring safe and efficient regular operations. Nevertheless, existing studies on the critical speed phenomenon in high-speed railway tracks with ground reinforcement are limited, and they all assume a linear elastic behavior for both the soils and reinforcement materials. The main novelty of this research is that it presents the first study on the effect of non-linear soil behavior on the critical speed of high-speed railway ballasted tracks with soil reinforcement and introduces a novel simplified approach based on an equivalent homogeneous soil layer by replacing the soil layer containing inclusions/columns. To accomplish this, a full 3D non-linear numerical model was developed and experimentally validated. The findings from a detailed and comprehensive parametric analysis demonstrate a significant influence of non-linear behavior on the critical speed, resulting in reductions of up to 30% compared to the linear elastic scenario. Furthermore, it is found that the soil-reinforcement stiffness contrast, the area replacement ratio, and the plasticity index of the soil foundation play a crucial role in the critical speed. Other aspects such as the installation pattern and the thickness of soft soil have a relatively lower impact on the critical speed.

Machine Learning Investigation For Tri-Magnetized Sutterby Nanofluidic Model with Joule Heating In Agrivoltaics Technology

This paper aims to establish a panoramic foundation for investigating the impact of sunlight on a recently formulated water-based tri-nano hybrid Sutterby liquid (TNHF) under provided magnetic field, directed through photovoltaic solar panels by exploiting the knacks of machine intelligent computing paradigm. The comparative performance of hybrid and tri-nano fluidic system with water as a base fluid is exhaustively analyzed and discussed. This research is unique, as it has a distinguished figurative comparison analysis between two different types of nanofluidic materials, which helps to choose the best one for use in agrivoltaics systems, and replace the materials already in use to enhance the efficiency and performance coefficients. Furthermore, the description of the composition scheme makes this research more feasible and applicable. A numerical dataset of nonlinear mathematical model is generated by employing finite difference scheme in the recently introduced Python bvp-solver algorithm, then it is embedded into artificial intelligence (AI)-based Levenberg Marquardt neural network algorithm (LMNNA). A significant outcome of the research indicates that the integration TNHF results in a notably faster enhancement of heat transfer rate and temperature framework as compared to traditional hybrid fluid. It is observed that introducing three distinct nanomaterials of specific thermophysical characteristics enhances the thermal exchanging profile and faces an obvious flow rate dissipation in solar plate channels. The standard numerical and AI-generated results are documented to portray the stability, accuracy and efficacy of scheme in terms of iterative learning curves on MSE, error analysis, histograms and regression statistics. Additional perquisites of the methodology include cost effectiveness, time-saving ability, robustness, stability and its extendibility.

Sequential optimal experimental design for vapor-liquid equilibrium modeling

We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in particular, in vapor-liquid equilibrium modeling. As a sequential design method, our method iteratively alternates between performing experiments, updating parameter estimates, and computing new experiments. Specifically, our sequential design method computes a whole batch of new experiments in each iteration and this batch of new experiments is designed in a two-stage locally optimal manner. In essence, this means that in every iteration the combined information content of the newly proposed experiments and of the already performed experiments is maximized. In order to solve these two-stage locally optimal design problems, a recent and efficient adaptive discretization algorithm is used. We demonstrate the benefits of the proposed methodology on the example of the parameter estimation for the non-random two-liquid model for narrow azeotropic vapor-liquid equilibria. As it turns out, our sequential optimal design method requires substantially fewer experiments than traditional factorial design to achieve the same model precision and prediction quality. Consequently, our method can contribute to a substantially reduced experimental effort in vapor-liquid equilibrium modeling and beyond.

Modeling and Control of Multi‐Stack Fuel Cell Air System based on Nonlinear Model Predictive Control Method

Hydrogen is crucial for achieving SDGs by driving energy transition and combating climate change. Proton exchange membrane fuel cell technology, leveraging hydrogen, faces challenges in meeting high‐power demands. The multistack fuel cell system (MFCS) tackles this by integrating multiple substacks, yet its air supply needs meticulous control. Proportional integral derivative (PID) decoupling from single‐stack falls short of MFCS. This article proposes nonlinear model predictive control (NMPC) for optimized air flow and pressure decoupling. Modeling MFCS's air system and designing a predictive model, it is aimed to ensuring precise control of air flow and pressure in each substack. The decoupling experiments show that NMPC outperforms PID, accurately managing air flow and pressure and reducing load fluctuations. For air mass flow, NMPC cuts mean‐absolute error (MAE) by 64.56% and root‐mean‐square error (RMSE) by 81.36%. For pressure, MAE drops 81.23% and RMSE 83.59%. Comprehensive step load tests confirm NMPC's precise, dynamic regulation too, compared to PID, NMPC lowers average MAE for air mass by 20.67%, pressure by 32.22%. RMSE improvements of 31.08% and 33.23% highlight NMPC's strength. NMPC's quick response mitigates coupling issues, enhancing vehicle load adaptability.

Association between prenatal air pollutant exposure and autism spectrum disorders in young children: A matched case-control study in Canada

The direction and magnitude of association between maternal exposure to ambient air pollutants across gestational windows and offspring risk of autism spectrum disorders (ASD) remains unclear. We sought to evaluate the time-varying effects of prenatal air pollutant exposure on ASD. We conducted a matched case-control study of singleton term children born in Ontario, Canada from 1-Apr-2012 to 31-Dec-2016. Provincial birth registry data were linked with applied behavioural analysis services and ambient air pollutant datasets to ascertain prenatal exposure to nitrogen dioxide (NO2), ground-level ozone (O3), fine particulate matter (PM2.5), and ASD diagnoses. Covariate balance between cases and controls was established using coarsened exact matching. Conditional logistic regression was used to assess the association between prenatal air pollutant exposure and ASD. Distributed lag non-linear models (DLNM) were used to examine the effects of single-pollutant exposure by prenatal week. Sensitivity analyses were conducted to assess the impact of exposure period on the observed findings. The final sample included 1589 ASD cases and 7563 controls. Compared to controls, cases were more likely to be born to mothers living in urban areas, delivered by Caesarean section, and assigned male sex at birth. NO2 was a consistent and significant contributor to ASD risk after accounting for co-exposure to O3, PM2.5 and covariates. The odds ratio per interquartile range increase was 2.1 (95%CI 1.8–2.3) pre-conception, 2.2 (2.0–2.5) for the 1st trimester, 2.2 (1.9–2.5) for the 2nd trimester, and 2.1 (1.9–2.4) for the 3rd trimester. In contrast, findings for O3 and PM2.5 with ASD were inconsistent. Findings from DLNM and sensitivity analyses were similar. Exposure to NO2 before and during pregnancy was significantly associated with ASD in offspring. The relationship between prenatal O3 and PM2.5 exposure and ASD remains unclear. Further investigation into the combined effects of multi-pollutant exposure on child neurodevelopment is warranted.