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Spinach (Spinacia oleracea L.), a valuable crop, suffers greatly from salt stress. This study investigates the potential of green silver nanoparticles (Ag-NPs) synthesized using Aloe vera extract to relieve the harmful effects of salt stress on spinach seed germination and growth. The experiment evaluated various seed germination and plant growth characteristics of spinach cultivar Viroflay RZ under five Ag-NP concentrations (0, 20, 40, 80, and 100 ppm) and four salinity levels (0, 50, 100, and 150 mM NaCl) in a controlled laboratory setting. This studymeasured seed germination percentage, rate and relative germination, vigor indices, plant height, root length, shoot and root dry weight, and chlorophyll content. Data analysis revealed that salinity stress significantly inhibited seed germination and all other studied parameters, especially at higher salt concentrations. The impact of green Ag-NPs on these traits varied considerably under salt stress. A complex statistical model showed a non-linear relationship between Ag-NP concentration and its effect, with an optimal concentration potentially alleviating the negative effects of salt stress. The study suggests that pre-treating spinach seeds with green Ag-NPs at an optimized concentration might enhance their tolerance to salt stress, potentially improving germination and growth under saline conditions. This research promotes using eco-friendly nanotechnology to mitigate the detrimental effects of salinity on agricultural productivity. [ABSTRACT FROM AUTHOR]

Purpose: To build, train, and assess the artificial neural network (ANN) system in estimating the residual valve rate after endoscopic valve ablation and compare the data obtained with conventional analysis. Methods: In a retrospective cross-sectional study between June 2010 and December 2020, 144 children with a history of posterior urethral valve (PUV) who underwent endoscopic valve ablation were enrolled in the study. MATLAB software was used to design and train the network in a feed-forward backpropagation error adjustment scheme. Preoperative and postoperative data from 101 patients (70%) (training set) were utilized to assess the impact and relative significance of the necessity for repeated ablation. The validated suitably trained ANN was used to predict repeated ablation in the next 33 patients (22.9%) (test set) whose preoperative data were serially input into the system. To assess system accuracy in forecasting the requirement for repeat ablation, projected values were compared to actual outcomes. The likelihood of predicting the residual valve was calculated using a three-layered backpropagating deep ANN using preoperative and postoperative information. Results: Of 144 operated cases, 33 (22.9%) had residual valves and needs to repeated ablation. The ANN accuracy, sensitivity, and specificity for predicting the residual valve were 90.75%, 92.73%, and 73.19%, respectively. Younger age at surgery, hyperechogenicity of the renal parenchyma, presence of vesicoureteral reflux (VUR), and grade of reflux before surgery were among the most significant characteristics that affected postoperative outcome variables, the need for repeated ablation, and were given the highest relative weight by the ANN system. Conclusions: The ANN is an integrated data-gathering tool for analyzing and finding relationships among variables as a complex non-linear statistical model. The results indicate that ANN is a valuable tool for outcome prediction of the residual valve after endoscopic valve ablation in patients with PUV. [ABSTRACT FROM AUTHOR]

In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available. [ABSTRACT FROM AUTHOR]

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits. [ABSTRACT FROM AUTHOR]

The article discusses the implementation of independent component analysis (ICA) in the brain and its potential for modeling brain computations. The author suggests that while modeling the brain on statistical and objective levels is promising, modeling the algorithmic level is more challenging due to the difficulty of measuring single neurons and their learning. The author also discusses the interpretation of nonlinear ICA as defining an exponential model and the use of self-supervised learning methods in machine learning. Overall, the article highlights the complexities and challenges of nonlinear ICA as a statistical model and its potential applications in understanding brain computations. [Extracted from the article]

In this paper, we present a novel nonlinear model predictive control (NMPC) algorithm based on the Laguerre function for dynamic positioning ships to solve the problems of input saturation, unknown time-varying disturbances, and heavy computation. The nonlinear model of a dynamic positioning ship is presented as a linear model, transformed from a standard affine nonlinear state-space model by precise feedback linearization. The environmental disturbance is overcome using an integrator. The time cost of the proposed nonlinear control algorithm is decreased by inducing the Laguerre function to describe the feedback-linearization system input increments. The Laguerre function reduces the matrix dimensions of the nonlinear optimization problem. The simulation results for a DP supply vessel showed that the novel algorithm maintained the effective control performance of the original nonlinear model predictive control algorithm and had a reduced computation load to satisfy the requirements of real-time operation. [ABSTRACT FROM AUTHOR]

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov–Barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi‐linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled‐data MPC framework to deal with states and inputs constraints, and to analyze stability of closed‐loop systems. Moreover, event‐triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example. [ABSTRACT FROM AUTHOR]

We construct a new class of nonlinear coherent states for the isotonic oscillator by replacing the factorial of the coefficients z n / n ! of the canonical coherent states by the factorial x n γ ! = x 1 γ. x 2 γ ... x n γ with x 0 γ = 0 , where x n γ is a sequence of positive numbers and γ is a positive real parameter. This also leads to the construction of a Bargmann-type integral transform which will allow us to find some integral transforms for orthogonal polynomials. The statistics of our coherent states will also be considered by the calculus of one called Mandel parameter. The squeezing phenomenon was also discussed. [ABSTRACT FROM AUTHOR]

Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, obtaining precise load statistics often involves computationally intensive simulations. Furthermore, the estimation of statistics using current practices is subject to ongoing discussion due to the inherent uncertainty involved. To address these challenges, we present a novel machine learning framework that leverages data‐driven surrogate modeling to predict hydrodynamic loads on monopile foundations while reducing reliance on costly simulations and facilitate the load statistics reconstruction. The primary advantage of our approach is the significant reduction in evaluation time compared to traditional modeling methods. The novelty of our framework lies in its efficient construction of the surrogate model, utilizing the Gaussian process regression machine learning technique and a Bayesian active learning method to sequentially sample wave episodes that contribute to accurate predictions of extreme hydrodynamic forces. Additionally, a spectrum transfer technique combines computational fluid dynamics (CFD) results from both quiescent and extreme waves, further reducing data requirements. This study focuses on reducing the dimensionality of stochastic irregular wave episodes and their associated hydrodynamic force time series. Although the dimensionality reduction is linear, Gaussian process regression successfully captures high‐order correlations. Furthermore, our framework incorporates built‐in uncertainty quantification capabilities, facilitating efficient parameter sampling using traditional CFD tools. This paper provides comprehensive implementation details and demonstrates the effectiveness of our approach in delivering reliable statistics for hydrodynamic loads while overcoming the computational cost constraints associated with classical modeling methods. [ABSTRACT FROM AUTHOR]

The present study aimed to apply a sinusoidal model to duck body weight records in order to introduce it to the field of poultry science. Using 8 traditional growth functions as a guide (Bridges, Janoschek, logistic, Gompertz, Von Bertalanffy, Richards, Schumacher, and Morgan), this study looked at how well the sinusoidal equation described the growth patterns of ducks. By evaluating statistical performance and examining model behavior during nonlinear regression curve fitting, models were compared. The data used in this study came from 3 published articles reporting 1) body weight records of Kuzi ducks aged 1 to 70 d, 2) body weight records for Polish Peking ducks aged 1 to 70 d, and 3) average body weight of Peking ducks aged 1 to 42 d belonging to 5 different breeds. The general goodness-of-fit of each model to the various data profiles was assessed using the adjusted coefficient of determination, root mean square error, Akaike's information criterion (AIC), and Bayesian information criterion. All of the models had adjusted coefficient of determination values that were generally high, indicating that the models generally fit the data well. Duck growth dynamics are accurately described by the chosen sinusoidal equation. The sinusoidal equation was found to be one of the best functions for describing the age-related changes in body weight in ducks when the growth functions were compared using the goodness-of-fit criteria. To date, no research has been conducted on the use of sinusoidal equations to describe duck growth development. To describe the growth curves for a variety of duck strains/lines, the sinusoidal function employed in this study serves as a suitable substitute for conventional growth functions. [ABSTRACT FROM AUTHOR]

This paper presents a predictive control strategy based on nonlinear model predictive control (NMPC) for a supercavitating vehicle system with high coupling nonlinearity and model uncertainty. By considering the cavity memory effect and introducing a cavity axis correction model, a nonlinear time-varying dynamic model of the vehicle is established to derive the time-varying tail efficiency and nonlinear planing force models. Based on the model, a predictive controller based on the nonlinear characteristics of the vehicle is designed. The controller can solve a finite horizon optical control problem (FHOCP) online for the updated system state in each system iteration to find the optimal solution at a given time. In addition, the controller introduces a Lyapunov terminal cost function and terminal constraint for the system state to reduce the system's conservativeness, which can improve the control performance of the system. The simulation results verify that the controller exhibits good stability and robustness against external interference and parameter perturbation. • An improved time-varying tail fin model is derived and established to replace constant tail fin efficiency. • Nonlinear model predictive control (NMPC) was introduced into the vehicle pitch motion control. The control algorithm can solve the finite time domain optimization control problem (FHOCP) online for the updated vehicle state in each system iteration, so as to directly deal with the nonlinear and time-varying terms of the vehicle. • In order to reduce the conservatism of the system, a terminal cost function based on Lyapunov theory was introduced into the quadratic cost function. [ABSTRACT FROM AUTHOR]

• Combining CBPT with GLMEs allows statistical analysis to match experimental design. • CBPT with GLMEs accounts for subject variability and hierarchical random effects. • The proposed method maintains control of type I error, type II error, and FWER. • CBPT with GLMEs can be applied to individual channels and pseudo-population data. Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear model predictive control to automatic train regulation in metro systems under dynamically disturbed environments. A coupled state-space model for the evolution of train traffic and passenger load dynamics is explicitly developed. With the safety constraints, a mixed-integer nonlinear optimal control model is formulated to generate train regulation strategies. An efficient solution algorithm, based on spatial branch and bound combined with generalized Benders decomposition, is particularly designed to obtain exact solutions quickly for the embedded applications. The original complex problem can be converted into several smaller convex quadratic subproblems with reduced domains to be solved quickly. The effectiveness of our train control model and approaches are verified in numerical simulations. Computational results illustrate that the proposed control method effectively improves the operational efficiency and meanwhile reduces energy consumption during operations. Besides, the efficient embedded solution algorithm performs satisfactory computational efficiency, facilitating a real-time implementation. [ABSTRACT FROM AUTHOR]

The work presented in this paper investigates the possibility of using statistical linearization approach to predict the stochastic dynamics of nonlinear floating offshore wind turbines (FOWT) system under coupled nonlinear wave excitation. OC3-Hywind Spar floating platform equipped with a 5 MW reference wind turbine offered by NREL is taken as the discussed FOWT of the study. A coupled nonlinear analytical model subjected to coupled irregular wave excitation is established to describe the dynamic characteristics of the FOWT, and further, the governing equation of the model is transformed into the equivalent linear one through the statistical linearization approach. The error caused in the linearization process is minimized through an iterative process. The accuracy, adaptability and efficiency of the model and the proposed method built in this paper are verified by numerical examples and comparison with other methods, such as Monte Carlo method and FAST etc. The research shows that the proposed method based on statistical linearization process to predict the stochastic dynamic response of nonlinear FOWT system under coupled nonlinear wave excitation has not only high accuracy but also high computational efficiency. It is about 4–5 orders of magnitude faster than the deterministic analysis method, and is of great significance for the design and research of the FOWT. An application example conducted in this paper indicates that this method is effective and can be used extensively, such as structural design optimization, reliability analysis, determination of the optimal parameters of vibration control and fatigue analysis of components, etc. • Coupled nonlinear analytical model to describe the dynamic characteristics of the floating offshore wind turbine. • Consideration of both system nonlinearity and hydrodynamic nonlinearity. • Application of the statistical linearization, and determination of an equivalent linear system. • Solution of the stochastic response via frequency domain method. • A sensitivity study is performed to investigate the second-order motion under different sea conditions. • Nonlinear model shows better agreement with FAST compare to linear model, especially in extreme sea conditions. [ABSTRACT FROM AUTHOR]

Exercise capacity (EC) is an important predictor of survival in the general population and in subjects with cardiopulmonary disease. Despite its relevance, considering the percent-predicted workload (%pWL) given by current equations may overestimate EC in older adults. Therefore, to improve the reporting of EC in clinical practice, our main objective was to develop workload reference equations (pWL) that better reflect the relation between workload and age. Using the Fitness Registry and the Importance of Exercise National Database (FRIEND), we analyzed a reference group of 6,966 apparently healthy participants and 1,060 participants with heart failure who underwent graded treadmill cardiopulmonary exercise testing. For the first group, the mean age was 44 years (18 to 79); 56.5% of participants were males and 15.4% had obesity. Peak oxygen consumption was 11.6 ± 3.0 METs in males and 8.5 ± 2.4 METs in females. After partition analysis, we first developed sex-specific pWL equations to allow comparisons to a healthy weight reference. For males, pWL (METs) = 14.1-0.9×10 -3 ×age 2 and 11.5-0.87×10 -3 ×age 2 for females. We used those equations as denominators of %pWL, and based on their distribution, we determined thresholds for EC classification, with average EC defined by the range corresponding to 85% to 115%pWL. Compared with %pWL using current equations, the new equations yielded better-calibrated %pWL across different age ranges. We also derived body mass index-adjusted pWL equations that better assessed EC in subjects with heart failure. In conclusion, the novel pWL equations have the potential to impact the report of EC in practice., Competing Interests: Declaration of competing interest The authors have no competing interests to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)