Search

As a common technology product in life, display screen has been deeply embedded in various fields, and the display performance is the key indicator to determine the quality of display products. The evaluation of screen flicker is one of the key control parameters to evaluate the optical performance of the screen. As the paper introduced, commonly there are two methods of testing flicker: FMA method and JEITA method. This paper makes a research about the above two evaluation methods in time and frequency domain. Particularly, by using nonlinear regression of the mathematical statistics, this paper deduces the mathematical relationship of the two methods for the evaluating in the normal data field. Also, this paper gives accurate conversion formula and the precision analysis. By using the empirical formula, the complexity of existing test equipment can be simplified, the cost and test time can be saved greatly. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently. [ABSTRACT FROM AUTHOR]

Text regression is an important task in natural language processing (NLP), which aims to predict continuous numerical values associated with text. Previous work focused on linear text regression requiring manual feature selection for financial prediction. Recently, non‐linear text regression through neural network models has become a trend. However, most models rely only on convolutional neural networks (CNN) and suffer from insufficient interpretability. In this paper, we propose a deep neural network model named EM‐CBA for text regression and further interpret the model. The proposed model is powered by word EMbedding, CNN, Bidirectional long short‐term memory (Bi‐LSTM) and Attention mechanism. The proposed EM‐CBA takes financial report texts as input and predicts a financial metric named return on assets (ROA). We conduct comprehensive experiments on a dataset about the reports of enterprises. Experimental results show that the proposed model provides more accurate predictions of enterprises' metrics than previous convolutional neural network models and other classical models. The validity of each module of the model is also verified. Finally, we demonstrate a way of performing analysis in words change and results errors to intuitively interpret the effect of different text inputs on the model. The analysis demonstrates that the model is able to use information about sentiment words to analyse their associated contexts to revise the predictions. [ABSTRACT FROM AUTHOR]

The article offers brief information on several papers to be published in forthcoming issues of the periodical "Australian & New Zealand Journal of Statistics" on topics including nonlinear regression, longitudinal data and linear regression models.

In order to adapt to the development of modern education and provide education practitioners with decision‐making suggestions, it is necessary to understand the relationship between the public's attention to basic chemistry education (BCE) and academic attention. However, many existing research is based on a single platform to study social hot information, such as Google Index, Baidu Index, Web of Science, and so on. But they ignore the relationship that exists between the Baidu Index and related academic platforms, and ignore the common information reflected between them. This paper takes advantage of the big data method, through the big data of Baidu Index and the big data obtained from China National Knowledge Internet (CNKI) database, to study the network attention and academic attention of BCE, and propose a CVS‐LSP‐GP framework. It first selects keywords through correlation analysis, secondly uses the data obtained from the first step to construct a nonlinear regression model, and finally combines the results of gray prediction to predict the academic attention of CNKI related to BCE. The research results show that the BCE is mainly affected by the micro‐lecture teaching mode, and relevant education practitioners should integrate the micro‐lecture mode into teaching for more further research and practice. [ABSTRACT FROM AUTHOR]

The article lists several articles to be published in the "Australian & New Zealand Journal of Statistics" including "Confidence intervals for linear combinations of Poisson means," "A quantile survival model for censored data," and "Weighted Wilcoxon Estimators in Nonlinear Regression."

Predicting the arrival aircraft's flight time plays a critical role in effectively optimizing and scheduling spatial‐temporal resources in the terminal airspace. This paper focuses on a data‐driven method for predicting the arrival flight time. First, based on the existing research, a feature set is constructed from four aspects: initial state, arrival pressure, sequencing pressure, and wind information, which are believed to affect arrival flight time significantly. Second, eight widely used models are developed to predict flight time, including linear regression models, nonlinear regression models, and tree‐based ensemble models. Furthermore, the stacking technique is adopted to improve the prediction performance. Finally, take Guangzhou Baiyun International Airport as a study case to verify the proposed method's effectiveness. The results indicate that the arrival pressure (describing the arrival traffic demand) and the sequencing pressure (sketching the arrival traffic distribution) could effectively improve the prediction accuracy. The mean absolute percentage error of the predicted flight time via ATAGA and IGONO can be increased by 1%. Besides, the proposed method of extracting wind data could also improve the prediction performance. The mean absolute error of the predicted flight time via GYA can be reduced by 4.85 s. [ABSTRACT FROM AUTHOR]

Lifespan face age transformation aims to generate facial images that accurately depict an individual's appearance at different age stages. This task is highly challenging due to the need for reasonable changes in facial features while preserving identity characteristics. Existing methods tend to synthesize unsatisfactory results, such as entangled facial attributes and low identity preservation, especially when dealing with large age gaps. Furthermore, over‐manipulating the style vector may deviate it from the latent space and damage image quality. To address these issues, this paper introduces a novel nonlinear regression model‐Disentangled Lifespan face Aging (DL‐Aging) to achieve high‐quality age transformation images. Specifically, we propose an age modulation encoder to extract age‐related multi‐scale facial features as key and value, and use the reconstructed style vector of the image as the query. The multi‐head cross‐attention in the W+ space is utilized to update the query for aging image reconstruction iteratively. This nonlinear transformation enables the model to learn a more disentangled mode of transformation, which is crucial for alleviating facial attribute entanglement. Additionally, we introduce a W+ space age regularization term to prevent excessive manipulation of the style vector and ensure it remains within the W+ space during transformation, thereby improving generation quality and aging accuracy. Extensive qualitative and quantitative experiments demonstrate that the proposed DL‐Aging outperforms state‐of‐the‐art methods regarding aging accuracy, image quality, attribute disentanglement, and identity preservation, especially for large age gaps. [ABSTRACT FROM AUTHOR]

Growth‐to‐standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth‐to‐standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of nonlinear quantile regression models to longitudinal student test score data. This paper demystifies AGPs by deriving them in the more familiar linear regression framework. It further shows that unlike SGPs, AGPs and on‐track classifications based on AGPs are strongly related to status. Lastly, AGPs are evaluated in terms of their classification accuracy. An empirical study and analytic derivations reveal AGPs can be problematic indicators of students’ future performance with previously not proficient students being more likely incorrectly flagged as not on‐track and previously proficient students as on track. These classification errors have equity implications at the individual and school levels. [ABSTRACT FROM AUTHOR]

Optimizing structural designs is crucial today, with additive manufacturing, particularly selective laser melting, gaining prominence. Thorough mechanical characterization of new materials remains vital. This paper investigates fatigue crack growth behavior in SLM 316L specimens under cyclic loading conditions. The study addresses result uncertainties by using CT specimens aligned along three building directions per ASTM E647 standards and a constant loading ratio (R = 0.1), necessitating mean value and confidence interval predictions. Departing from linear prediction models, innovative Bootstrap Polynomial and Power Regression Models and Bayesian Nonlinear Regression Model updated posterior distribution by Markov Chain Monte Carlo are employed. These approaches leverage bootstrapping to construct confidence intervals, offering robustness and flexibility in handling non‐normal data behavior and limited sample sizes. They provide tailored fits to data curvature, revealing limitations of linear prediction models in capturing observed nonlinear behavior, enhancing reliability in additive manufacturing applications, and advancing material science and engineering. Highlights: Fatigue crack growth in SLM316L is evaluated.Robust nonlinear regression techniques are utilized.Distribution approximation is done using Kernel estimator.Confidence intervals are estimated employing Bootstrap and Bayesian regression models. [ABSTRACT FROM AUTHOR]

Peak power load forecasting is a key part of the commercial operation of the energy industry. Although various load forecasting methods and technologies have been put forward and tested in practice, the growing subject of tolerance for abnormal accidents is to develop robust peak load forecasting models. In this paper, we propose a robust smooth non‐convex support vector regression method, which improves the robustness of the model by adjusting adaptive control loss values and adaptive robust parameters and by reducing the negative impact of outliers or noise on the decision function. A concave‐convex programming algorithm is used to solve the non‐convexity of the optimization problem. Good results are obtained in both linear regression model and nonlinear regression model and two real data sets. An experiment is carried out in a power company in Jiangxi Province, China, to evaluate the performance of the robust smooth non‐convex support vector regression model. The results show that the proposed method is superior to support vector regression and generalized quadratic non‐convex support vector regression in robustness and generalization ability. [ABSTRACT FROM AUTHOR]

The global economy's transition toward more sustainable development models is undoubtedly grounded on small and medium‐sized enterprises (SMEs). However, SMEs, individual entrepreneurs, and microenterprises have always encountered barriers to implementing social responsibility and sustainability concepts. The paper investigates the enabling role of formalized corporate networks to drive SMEs toward sustainable behaviors. A quantitative nonlinear regression approach is applied to a content analysis of a sample of network contracts coded. The content analysis is applied to analyze the declared objectives, the purpose of the contract, and sustainability areas. An ordered logistic regression is applied on variables related to the behavior of SMEs before entering in the contract and post‐adhesion phases. Data demonstrates how networks of SMEs can be used as enabling factors to boost sustainability among them. Specifically, the study is based on a sample of 96 formalized network contracts (FNCs), including 1486 Italian SMEs in that sustainability‐oriented networks. It offers an evidence‐based perspective on how networks of companies can play a fundamental role in the development of policies aimed at bringing small companies closer to the concept of sustainability (such as eco‐innovations, eco‐efficiency, environmental performance, and social innovations, among others) and its practical implementation. This paper has two significant strengths. The first is that it uses as a sample a set of 1486 companies, including individual entrepreneurs and microenterprises, whose data are usually difficult to collect. The second is that it demonstrates the efficacy of a contractual form that could be scalable to different countries. [ABSTRACT FROM AUTHOR]

The implementation of rainwater harvesting (RWH) has emerged as a key strategy to cope with the water crisis in urban areas. However, the required design parameters of the RWH system in different buildings vary widely because of the differences in architectural characteristics. In this paper, the relationships between the building characteristics parameters and the optimal tank (Tc) capacity are investigated by simulating the RWH system for more than 120 buildings at different locations in Guangzhou. Explicit expressions relating the optimal storage volume of RWH systems to building characteristics are derived based on nonlinear regression analysis. A set of bivariate exponential equations for estimating the optimal tank size under different building conditions was obtained. The model has a MER of less than 10% for buildings with a C/D ratio between 17.5 and 85. In Guangzhou, the unit cost of an RWH system can be reduced to CNY 2.62/m2 with optimally designed tanks. Highlight: A generalization equation is proposed to assist in the design of cisterns for RWH systems.The validity range of the equation is verified.The derived analytical equation is adequate to determine the cistern volume for medium‐sized buildings. [ABSTRACT FROM AUTHOR]

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within‐subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub. [ABSTRACT FROM AUTHOR]

In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self‐organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with top p% winner‐take‐all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state‐of‐the‐art methods, even on datasets with a large percentage of missing values. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Simple isotherm models, such as the Langmuir and Freundlich equations, are frequently used to correlate water contaminant adsorption data. In the conventional data fitting approach, the Langmuir and Freundlich equations are fitted to experimental data by minimizing the difference between the measured and calculated adsorbed phase concentrations. However, some isotherm equations are implicit in adsorbed phase concentration. Such isotherms must be converted to explicit forms to allow the use of standard nonlinear regression methods in data correlation. This paper explores the use of the Lambert W function to invert the loading‐implicit isotherms of Elovich, Volmer, and Jossens to solve for adsorbed phase concentration as a function of liquid phase concentration. Results: The inverted Elovich, Volmer, and Jossens isotherms were fitted to literature adsorption data of water contaminants using the standard nonlinear regression procedure in Excel. Data fitting was accomplished by making use of a freely available Excel add‐in and a highly accurate analytical approximation to evaluate the Lambert W function. The inverted isotherms were tested against the experimental isotherms of cobalt, uranium, and cephalosporin C. In all cases studied, satisfactory fits were obtained, validating the practicability of the proposed data fitting approach. Conclusions: The loading‐implicit Elovich, Volmer, and Jossens isotherms can be formulated as loading‐explicit equations in terms of the Lambert W function, thus allowing the use of conventional least‐squares fitting procedures in data correlation. The practical implementation of the Lambert W function in Excel provides a novel method to fit loading‐implicit isotherms to experimental data of water contaminants. © 2022 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Missing values are ubiquitous in the nonlinear regression research, and may lead to bias and a loss of efficiency. Even in a large dataset, values drop‐out can substantially reduce the available information for analysis. In this paper, we propose an improved hybrid model to solve the nonlinear regression problem under missing data scenarios, consisting of two parts: an overcomplete winner‐take‐all (WTA) autoencoder and a multilayer gated linear network. The WTA autoencoder is trained in an adversarial training process by taking advantage of gradually renewed teacher signals and the discrimination of missing values and observed values, and is designed to play two roles: (1) to impute missing components conditioned on observed samples; (2) to generate gate control sequences. On the other hand, the multilayer gated linear network with the generated gate control sequences implements a powerful piecewise linear regression model, whose parameters are optimized by formulating a support vector regression (SVR) with a deep quasi‐linear kernel. Experimental results based on different real‐world datasets demonstrate the effectiveness of our proposed hybrid model. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between public investment and private investment in a sample of 21 Organization for Economic Cooperation and Development (OECD) countries between 2000 and 2019. Using panel data nonlinear threshold regression models, the empirical results show that there exist threshold levels for the share of public investment in private investment, the real Gross Domestic Product (GDP) growth rate and the real interest rate that affect the relationship between public and private investment. All estimates support a crowding‐in effect of public investment on private investment. In terms of policy prescriptions, by increasing public investment, OECD governments can expect positive spillovers to private investment. [ABSTRACT FROM AUTHOR]

BACKGROUND: Respiration is an important physiological activity of eggs and is closely related to their freshness. To further observe the diffusion of carbon dioxide released by egg respiration, we used a respirometer to measure the respiration parameters of eggs stored at room temperature and performed a respiration simulation using Fluent software. This paper also explores the relationship between respiratory intensity, freshness, and storage period. RESULTS: The results demonstrate that the diffusion of carbon dioxide released from the respiration of eggs is related to the characteristics of heavy gas diffusion. By comparison, the simulated value (0.0199 m s−1) is close to the experimental value (0.0208 m s−1), which indicates that the simulation and analysis results are valid. In addition, the logarithmic model was used to assess the relationship between respiration intensity, Haugh unit, and the yolk index (R2 values 0.89 and 0.87). The R2 of the relationship between the real and the predicted Haugh unit value and the yolk index are 0.9 and 0.84 respectively, indicating that the model is a good fit. The equivalent egg age model was established using nonlinear regression, where the correlation coefficient R was 0.888 and P < 0.01, indicating it was both stable and reliable. CONCLUSION: The standard k–ε model is suitable for egg respiration simulation analysis. Respiratory intensity can be used as a potential index for nondestructive testing of egg freshness, which is a new method for nondestructive testing of egg freshness and storage period. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Radial anomalous diffusion coefficients are among the largest sources of uncertainty in mean‐field plasma‐edge codes such as SOLPS‐ITER. These coefficients are machine, scenario, and space‐dependent, thus hampering the predictive and interpretive capability of these codes. In fact, modellers usually adjust these coefficients manually, based on expert judgement or on large, computationally expensive, parameter scans. Matching data from various diagnostics, each with their own experimental uncertainties, additionally complicates the problem of finding a good parameter set. In addition, standard non‐linear regression techniques have shown to become prohibitive for expensive plasma‐edge simulations with many unknown parameters, as gradient calculation was based on finite differences. In this paper, we apply algorithmic differentiation (AD) as an efficient and more accurate alternative for gradient calculation in large, continuously developed codes as SOLPS‐ITER. In addition, the lack of uncertainty information and danger of data overfitting, key limitations of regression techniques, are overcome by combining data and uncertainties from different diagnostics in a Bayesian framework. We implement for the first time such a Bayesian inference framework into SOLPS‐ITER, using gradient‐based optimization with gradients obtained through tangent AD to find the maximum a posteriori (MAP) values of the parameters. The recently developed κ turbulence model is employed to limit the number of unknown parameters compared to full spatial profiles of diffusion coefficients. The Bayesian MAP‐estimation is compared to standard regression techniques on a small‐scale tokamak. We adopt fictitious experimental data obtained from a reference SOLPS‐ITER solution with artificial measurement noise. [ABSTRACT FROM AUTHOR]

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income. [ABSTRACT FROM AUTHOR]

This paper presents the results of pull‐off and twist‐off tests on bond strength between fiber reinforced polymer (FRP) and cement‐based mortar substrate and is carried out to investigate the effect of moisture and temperature at the time of installation. In the field of fracture mechanics, it is common to classify the pull‐off and twist‐off tests as mode‐I and mode‐II fractures, respectively. A total of 27 specimens were prepared and 243 tests were conducted. In order to evaluate the significance of each factor and their interactions, analysis of variance (ANOVA) was performed. Results revealed that surface moisture content and temperature significantly affect the adhesion of the two materials and bond strength. ANOVA confirmed that these two parameters have great influence on bond strength between FRP and cement‐based mortar, although compressive strength and total porosity of substrate were not statistically significant. To estimate the bond strength by considering the effective parameters and based on the collected experimental data, new and more accurate relations were proposed using nonlinear regression techniques. [ABSTRACT FROM AUTHOR]

Neonicotinoids are known as effective pesticides against various insect species. They can harm useful insects including honeybees, with a relatively low threat to nontarget organisms and the environment. This paper presents combined methods to explore the insecticidal activity of neonicotinoids with diverse scaffolds, active against Aphis craccivora. Pharmacophore, molecular docking into the active site of nicotinic acetylcholine receptor homology model, and linear and non‐linear regression approaches were used to find new insecticide candidates. The potential toxic effects against honeybees were evaluated using the molecular docking in the active site of the new Aphis mellifera homology model. Four new untested compounds were assigned as insecticide candidates, active against Aphis craccivora with less potential toxic effects for honeybees. This approach may be an effective strategy to design environmentally friendly insecticides against the cowpea aphid. [ABSTRACT FROM AUTHOR]

Component‐based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen–Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component‐based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model. [ABSTRACT FROM AUTHOR]

Missing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper studies the effects of five parameters, including fiber diameter, crystallinity, thickness, packing density, and surface roughness, on the transparency of electrospun polymethyl methacrylate/polydimethylsiloxane (PMMA/PDMS) nanofibrous membranes after heat treatment. The PMMA/PDMS membranes produced at an electrospinning voltage of 20 kV reaches 54.85% transparency with the diameter 0.759 µm, crystallinity 14.81%, thickness 9.2 µm, roughness 0.659 µm, and packing density a 0.0081% after the heat treatment from 80 to 200°C. In general, the PMMA/PDMS membranes present higher transparency with the decrease of diameter, crystallinity, thickness, roughness, and packing density. The influencing parameters on transparency are first quantified by multiple nonlinear regressions with a R2 of the model equals 0.869. A composite scaled sensitivity (CSSj) analysis shows that the most influential factor is the nanofibrous membrane thickness with a CSSj value of 1, followed by fiber diameter with a CSSj value of 0.515. The CSSj values for packing density, membrane roughness and crystallinity are 0.385, 0.361, and 0.173, respectively. Finally, the nanofibrous membrane transparency is a function of nanofiber material density, fiber diameter, and nanofibrous membrane thickness based on the Chandrasekhar radiative transfer equation and the Rayleigh's Scattering Theory. [ABSTRACT FROM AUTHOR]

Objective: To contribute to the debate regarding the minimum volume of radical cystectomies (RCs) that a hospital should perform by evaluating the association between hospital volume (HV) and postoperative mortality. Patients and Methods: Patients who underwent RC for bladder cancer between 1 January 2008 and 31 December 2018 were retrospectively identified from the Netherlands Cancer Registry. To create a calendar‐year independent measure, the HV of RCs was calculated per patient by counting the RCs performed in the same hospital in the 12 months preceding surgery. The relationship of HV with 30‐ and 90‐day mortality was assessed by logistic regression with a non‐linear spline function for HV as a continuous variable, which was adjusted for age, tumour, node and metastasis (TNM) stage, and neoadjuvant treatment. Results: The median (interquartile range; range) HV among the 9287 RC‐treated patients was 19 (12–27; 1–75). Of all the included patients, 208 (2.2%) and 518 (5.6%) died within 30 and 90 days after RC, respectively. After adjustment for age, TNM stage and neoadjuvant therapy, postoperative mortality slightly increased between an HV of 0 and an HV of 25 RCs and steadily decreased from an HV of 30 onwards. The lowest risks of postoperative mortality were observed for the highest volumes. Conclusion: This paper, based on high‐quality data from a large nationwide population‐based cohort, suggests that increasing the RC volume criteria beyond 30 RCs annually could further decrease postoperative mortality. Based on these results, the volume criterion of 20 RCs annually, as recently recommended by the European Association of Urology Guideline Panel, might therefore be reconsidered. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between sentiment‐apt investors and UK stock returns at industry level over the period January 1988 to December 2017. Using two new sentiment proxies (laggards to leaders and growth opportunity index) for 10 discrete sector groupings, we provide novel evidence on how returns in the UK stock market react to the activities of sentiment‐disposed investors. First, using threshold nonlinear regression, we document a significant relationship between the laggards to leaders sentiment proxy and sectoral returns. Our findings reveal that aggregate returns in the sector are affected by activities of investors who embark on profit‐taking when there is an increase in the proportion of lagging to leading stocks beyond the threshold value. Secondly, when using the growth opportunity sentiment proxy, we report that the increase in growth above the growth threshold value has a significant impact on sector returns. This study further confirms significant impact of non‐threshold variables on sector groupings. Our findings are robust, having been subjected to a range of robustness checks. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach in examining the conditional relationship between beta and returns for stocks traded on S&P 500 for the period from July 2001 to June 2011. We challenge other competitive models with portfolios formed based on the book value per share and betas using monthly data. A novel approach for capturing time variation in betas whose pattern is treated as a function of market returns is developed and presented. The estimated coefficients of a nonlinear regression constitute the basis of creating a two factor model. Our results indicate that the proposed specification surpasses alternative models in explaining the cross‐section of returns. The implications of this study show that the proposed new risk factors that found to be significant both in time series and cross‐section analyses provide valuable information in better understanding the characteristics of returns, targeting the reinforcement of stock market efficiency, and the capital allocation procedure. [ABSTRACT FROM AUTHOR]

Summary: Strength‐reduction factors that reduce ordinates of floor spectra acceleration due to nonlinearity in the secondary system are investigated. In exchange for permitting some inelastic deformation to occur in the secondary system or its supports, these strength reduction factors allow to design the nonstructural elements or their supports for lateral forces that are smaller than those that would be required to maintain them elastically during earthquakes. This paper presents the results of a statistical analysis on component strength‐reduction factors that were computed considering floor motions recorded on instrumented buildings in California during various earthquakes. The effect of yielding in the component or its anchorage/bracing in offering protection against excessive component acceleration demands is investigated. It is shown that strength‐reduction factors computed from floor motions are significantly different from those computed from ground motions recorded on rock or on firm soils. In particular, they exhibit much larger reductions for periods tuned or nearly tuned to the dominant modal periods of the building response. This is due to the large differences in frequency content of ground motions and floor motions, with the former typically characterized by wide‐band spectra whereas the latter are characterized by narrow‐band spectra near periods of dominant modes in the response of the building. Finally, the study provides approximate equations to estimate component strength‐reduction factors computed through nonlinear regression analyses. [ABSTRACT FROM AUTHOR]

The performance of horizontal wells in shale gas is controlled by multiple factors, and there is a difference in gas production rate between different fracturing stages. However, the mechanism of the nonuniform gas production rate is still not clear. In this paper, a typical shale gas well in the Longmaxi formation was selected to conduct the performance evaluation based on the nonradioactive gas tracer technology. The gas production profile was obtained, and the production feature of each stage was analyzed. On this basis, the production differences between fracturing stages were compared in four aspects: reservoir physical properties, distribution of natural fracture, parameters of fracturing operation, and technology of temporary plugging re‐orientation. The effect of various factors on the contribution of each stage was analyzed. And then, the mechanism of the nonuniform gas production rate was clarified. Finally, relevance analysis was performed to determine the main controlling factors, and a mathematical model for characterizing the nonuniform gas production rate feature was established by adopting the nonlinear regression method. This study provides a reliable basis for optimal fracturing designing and reasonable production planning. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error. Starting with the modal regression procedure developed for the measurement error‐free case, a non‐trivial modification is made so that the modified version can effectively correct the potential bias caused by measurement error. Large sample properties of the proposed estimate, such as the convergence rate and the asymptotic normality, are thoroughly investigated. A simulation study and real data application are conducted to illustrate the satisfying finite sample performance of the proposed estimation procedure. A modal estimation procedure is proposed for nonlinear regression models when the covariates are measured with Laplace error. It enjoys the merits of both robustness and bias reduction. [ABSTRACT FROM AUTHOR]

In this paper, we investigate Bayesian generalized nonlinear mixed‐effects (NLME) regression models for zero‐inflated longitudinal count data. The methodology is motivated by and applied to colony forming unit (CFU) counts in extended bactericidal activity tuberculosis (TB) trials. Furthermore, for model comparisons, we present a generalized method for calculating the marginal likelihoods required to determine Bayes factors. A simulation study shows that the proposed zero‐inflated negative binomial regression model has good accuracy, precision, and credibility interval coverage. In contrast, conventional normal NLME regression models applied to log‐transformed count data, which handle zero counts as left censored values, may yield credibility intervals that undercover the true bactericidal activity of anti‐TB drugs. We therefore recommend that zero‐inflated NLME regression models should be fitted to CFU count on the original scale, as an alternative to conventional normal NLME regression models on the logarithmic scale. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the new features and model updates in the upcoming release of the NASA Analogy Software Cost Tool (ASCoT). ASCoT, hosted within the Online NASA Space Estimation Tools (ONSET) on the One NASA Cost Engineering (ONCE) Database, is a web‐based tool that provides a suite of estimation tools to support early lifecycle NASA flight software cost analysis. In addition to the traditional parametric flight software costing method CO‐COMO II, ASCoT contains a Bayesian linear regression to predict total flight software development cost as a function of total spacecraft cost, as well as four analogic methods: k‐Nearest Neighbors (kNN) and Clustering models to predict Effort (in work‐months) and total source lines of code (SLOC). These methods are designed to work primarily with system‐level inputs such as mission type (orbiter, lander, etc.), mission destination (Earth, Inner Planetary, etc.), and the number of instruments and deployables. Nonlinear principal components analysis (NLPCA) is performed to find the principal features of the data composed of both categorical and numerical variables and is necessary prior to defining our analogic methods. Sensitivity analyses and in‐ and out‐of‐sample model performance results are presented for the Bayesian CER and the analogic models. [ABSTRACT FROM AUTHOR]

The S-shaped growth curves such as Compertz, logistic, normal and Weibull are widely used for forecasting technological substitutions. A family of data-based transformed (DBT) models, which are linear in the regression parameters, including the above-mentioned four models as special cases has been shown to be quite useful for short-term forecasts. This paper explores modeling the technology penetration data directly with assumed S-shaped growth curves. The resulting models, which are nonlinear in the regression parameters, also incorporate proper dependence structure and power transformation. It appears that the nonlinear modeling is a viable alternative to the DBT and other conventional forecasting models in forecasting technological substitutions. Hence, an appropriate strategy is to consider the nonlinear modeling approaches as possible alternatives and use the data at hand to select, via pseudo-cross-validation, the best model for forecasting purposes. [ABSTRACT FROM AUTHOR]

At the start of a moderate‐intensity square‐wave exercise, after a short delay, breath‐by‐breath O2 uptake at the mouth is approximated to a mono‐exponential function, whose time constant is considered matched to that of the O2 uptake of the working muscles. We compared the kinetic parameters obtained from the breath‐by‐breath gas exchange data yielded by the 'Independent‐breath' algorithm (IND), which accounts for the changes in lung gas stores, with those obtained with the classical 'Expiration‐only' algorithm (EXP). The two algorithms were applied on the same flow and gas fraction traces acquired on 10 healthy volunteers, performing 10 times the same moderate‐intensity exercise transition. Repeated O2 uptake responses were stacked together and the kinetic parameters of a mono‐exponential function were estimated by non‐linear regression, removing the data pertaining to 1‐s progressively longer initial periods (ΔTr). Independently of ΔTr, the mean response time (time constant + time delay) obtained for the IND data was faster compared to the EXP data (∼43 s vs. ∼47 s, P < 0.001), essentially because of shorter time delays. Between ΔTr = 16 s and ΔTr = 29s, the time constants of the IND data decreased (30.7 s vs. 28.0 s, P < 0.05; drop = 10%), but less than those of the EXP data (32.2 s vs. 26.2 s, P < 0.001; drop = 23%); with the same ΔTr, the time constants of the two algorithms' data were not different (P > 0.07). The different decrease in the time constant, together with the different mean response time, suggests that the data yielded by the two algorithms provide a different picture of the phenomena occurring at the beginning of the exercise. What is the central question of this study?Is the kinetics of breath‐by‐breath gas exchange at the start of a square‐wave moderate intensity exercise faster when accounting for the changes in lung gas stores?What is the main finding and its importance?Accounting for the changes in lung gas stores, the time constant of the V̇O2${\dot V_{{{\mathrm{O}}_{\mathrm{2}}}}}$ kinetics changed less when the data pertaining to the initial period were progressively removed; time delays and mean response times were faster. Consequently, using different gas exchange calculation algorithms, the physiological phenomena occurring at the beginning of moderate‐intensity exercise are characterized differently. [ABSTRACT FROM AUTHOR]

The input variable selection problem has recently garnered much interest in the time series modeling community, especially within water resources applications, demonstrating that information theoretic (nonlinear)-based input variable selection algorithms such as partial mutual information (PMI) selection (PMIS) provide an improved representation of the modeled process when compared to linear alternatives such as partial correlation input selection (PCIS). PMIS is a popular algorithm for water resources modeling problems considering nonlinear input variable selection; however, this method requires the specification of two nonlinear regression models, each with parametric settings that greatly influence the selected input variables. Other attempts to develop input variable selection methods using conditional mutual information (CMI) (an analog to PMI) have been formulated under different parametric pretenses such as k nearest-neighbor (KNN) statistics or kernel density estimates (KDE). In this paper, we introduce a new input variable selection method based on CMI that uses a nonparametric multivariate continuous probability estimator based on Edgeworth approximations (EA). We improve the EA method by considering the uncertainty in the input variable selection procedure by introducing a bootstrap resampling procedure that uses rank statistics to order the selected input sets; we name our proposed method bootstrap rank-ordered CMI (broCMI). We demonstrate the superior performance of broCMI when compared to CMI-based alternatives (EA, KDE, and KNN), PMIS, and PCIS input variable selection algorithms on a set of seven synthetic test problems and a real-world urban water demand (UWD) forecasting experiment in Ottawa, Canada. [ABSTRACT FROM AUTHOR]

A companion paper introduced the rERP framework, which recasts traditional event-related potential ( ERP) averaging as a special case of a more flexible regression-based approach to estimating ERP waveforms. Here, we build on this foundation, showing how rERP analysis can also handle the estimation of nonlinear effects (a generalization of both the well-known approach of dichotomizing continuous covariates, and also of the ERP image technique), and can disentangle overlapping ERPs to temporally adjacent stimuli. We then consider how the use of rERPs impacts on other parts of the EEG analysis pipeline, including baselining, filtering, significance testing, and artifact rejection, and provide practical recommendations. Free software implementing these techniques is available. [ABSTRACT FROM AUTHOR]

Systems of whole‐stand yield models facilitate projections of forest attributes, but their inputs may be difficult to measure accurately. This study conducted sensitivity analyses to examine the effect of systematic and stochastic measurement errors on outputs from a representative system of equations. Simulated error was added to explanatory variables stand age, site index, or both. Results showed that large systematic error in one variable tended to produce moderate to large percent changes in all models, particularly the height and volume equations (often >50% change). Systematic error in both variables amplified this effect, especially for young, less productive stands. Stochastic error dramatically increased estimate variability (some relative standard errors >50%), particularly in the height and volume models at young ages and low site indices. These results suggest that measurement error may considerably alter projections and increase uncertainty when using whole‐stand yield models, highlighting the need for careful crew training. Recommendations for Resource Managers: Measurement error in input variables has the potential to significantly affect estimates from systems of whole‐stand yield models.Effects can be over‐ or underestimation of volume yields and financial returns or other forest attributes of interest.Careful crew training is paramount to ensure forest measurements remain consistent with the quality of the data used to fit the models. [ABSTRACT FROM AUTHOR]

In this paper, we consider the estimation of parameters of a general near regression model. An estimator that minimises the weighted Wilcoxon dispersion function is considered and its asymptotic properties established under mild regularity conditions similar to those used in least squares and least absolute deviations estimation. As in linear models, the procedure provides estimators that are robust and highly efficient. The estimates depend on the choice of a weight function and diagnostics which differentiate between nonlinear fits are provided along with appropriate benchmarks. The behavior of these estimates is discussed on a real data set. A simulation study verifies the robustness, efficiency and validity of these estimates over several error distributions including the normal and a family of contaminated normal distributions. [ABSTRACT FROM AUTHOR]

A corrugated box's compression strength can easily be affected by how the box is used, including pallet overhang which reduces a box's effective compression strength (BCT). The specific impact of a given amount of overhang on BCT remains poorly defined. In the current study, a range of box sizes and constructions were examined in over a dozen single‐side overhang configurations and five adjacent‐side overhang scenarios to identify key factors contributing to loss of strength, producing an average reduction in BCT of up to 40%. These results indicated that common safety factors fit well with adjacent overhang scenarios but overestimate single‐side overhang scenarios. We developed a range of multiple linear and nonlinear regression models, estimating the change in a box's compression strength due to overhang compared with a no‐overhang scenario. The impact of overhang on the short and long sides, whether overhang exists on a single side or adjacent side, box size, and board type were all statistically significant. This work also indicates the need for further research refining the first‐order model and extending it to other materials, box sizes, and box aspect ratios. [ABSTRACT FROM AUTHOR]

Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non‐parametric machine learning algorithm that automatizes the selection of non‐linear terms in regression models. In this study, we propose using MARS in a spatial regression framework to account for potential non‐linearities and spatial effects in spatial regression models. Using a relatively large data set of 17,000 dwellings in St. Petersburg (Russia), we examine how this algorithm works. The empirical evidence shows that most explanatory variables in the spatial regression model—including the spatial lag of the dependent variable—have a non‐linear impact on the asking prices of dwellings. [ABSTRACT FROM AUTHOR]

Aiming at the complex and cumbersome problems of current bread staling detection technology, a food constitutive modeling method based on the multiobjective particle swarm optimization (MOPSO) was proposed, which can quickly and efficiently identify the creep test parameters for bread, and predict the viscoelastic parameters of bread staling using the analyzed viscoelastic parameters, resulting in convenient and efficient detection of bread staling. Firstly, airflow‐laser detection technology was used to carry out rapid, efficient, and non‐destructive bread rheological tests to obtain bread creep test data. The MOPSO based on the Pareto set was then used to identify the generalized Kelvin model, and the discrimination accuracy was evaluated by using the inversion results established by the viscoelastic parameters, which resulted in efficient discrimination of creep test data of starch‐based products represented by bread. Finally, using extreme learning machine regression (ELM), a prediction model between the analysis results and the moisture content of bread staling was established, and the prediction effect of the analysis results on bread staling was verified. The experimental results show that, when compared to finite element analysis (FEA) and non‐linear regression (NLR) to identify creep parameters, the MOPSO overcomes the shortcomings of easy falling into the local optimal solution, is easy to implement, has strong global search ability, and is suitable for the analysis of high‐dimensional viscoelastic models of complex foods. The correlation coefficient (R) of the prediction set established by the 12‐membered viscoelastic parameters in the prediction model established by multi‐element viscoelastic parameters and bread moisture content was 0.847, and the root mean square error (RMSE) was 0.021. This demonstrated that, when combined with MOPSO, airflow‐laser detection technology could effectively identify the viscoelastic parameters of bread and establish a method suitable for monitoring bread staling in industrial production. The results of this study provide a reference for the identification of viscoelastic parameters of complex foods and to detect bread staling quickly and efficiently. [ABSTRACT FROM AUTHOR]

This study establishes a multi‐hazard probabilistic assessment framework for assessing the integrity of monopile offshore wind turbines (OWT) under the stochastic coupled effect of wind, wave and earthquake loading. The procedure deals with the entire operational range of inflow wind speed (i.e., 3–25 m/s), for which the probability of failure under multi‐hazard excitations is found to be non‐negligible. Numerical analysis is performed by implementing nonlinear finite‐element models of the OWT developed in OpenSees. The dynamic response of the OWT system under wind‐ and wave‐load combinations is individually validated against those obtained from the aero‐hydro‐servo‐elastic simulator OpenFAST. Following the Latin‐hypercube approach, a cloud‐based assessment procedure is then performed with an ensemble of 300 earthquake ground motions, from which the multi‐hazard performance of the OWT regarding the serviceability limit state (SLS) and the ultimate limit state (ULS) can be evaluated. The epistemic uncertainty associated with various loads, structural properties, and soil conditions is also accounted for. Based on this probabilistic assessment framework, the sensitivity of the resulting OWT fragility surfaces to different statistical regression methods and wind—ground motion intensity measure pairs (IM‐pairs) is further scrutinised. Regression methods are comparatively evaluated. The efficiency, practicality, proficiency and sufficiency of various IM‐pairs are examined for the purpose of assessing operating OWT multi‐hazard fragility functions. The optimum IM‐pair is then employed in a trained Gaussian Process Regression (GPR) scheme for cloud data regression to assess the multi‐hazard fragility of the system. The derived multi‐hazard fragility function shows that the contribution of seismic forces in structural demand for a design‐level earthquake is comparable to those caused by operational‐level wind and wave loads. [ABSTRACT FROM AUTHOR]

Lemonwood‐derived biochar was created by biomass pyrolysis (600°C) and modified with zeolite, sodium alginate, and magnetic nanoparticles to design an efficient adsorbent. The characterizations of the nanocomposite were studied employing X‐ray diffraction, field emission scanning electron microscope, EDAX, Brunauer–Emmett–Teller, and Fourier transform infrared spectrophotometer. The sorption of Zn(II), Cu(II), and Cd(II) ions from an aqueous single metal solution was evaluated for the influence of contact time, adsorbent dose, solution pH, initial concentration, and individual adsorption. Four isotherm models (Langmuir, Freundlich, Temkin, and Dubinin‐Radushkevich) were employed to analyze the equilibrium data by nonlinear regression. The capacity for adsorption of Zn(II), Cu(II), and Cd(II) at pH = 4, contact time 90 min, and 293°K from wastewater is as high as 98.22, 109.37, and 80.15 mg/g, respectively. The kinetics analysis demonstrated that the adsorption mechanism agreed with the pseudo‐second‐order rate law. In addition, the adsorption of metal ions onto engineered adsorbent exhibited that the process is exothermic, and with increasing the temperature, the adsorption capacity decreased, showing the spontaneous nature. The adsorbent was regenerated and reused three consecutive in adsorption–desorption without considerable loss of its adsorption capacity. The results indicated that it could be employed as an effective adsorbent for eliminating metal ions from an aqueous media. [ABSTRACT FROM AUTHOR]

Introducing nanotechnology in concrete is one of the most significant successes in enhancing the mechanical properties of concrete. It affects the quality of the microstructure of the concrete due to its extremely small nanoparticles leading to a faster reaction due to its large surface area. The nanoparticles fill the concrete's pores, resulting in considerably improved strengths in the early ages of the concrete. This research used six models; linear regression (LR), multilinear regression (MLR), nonlinear regression (NLR), pure quadratic (PQ), interaction (IA), and full quadratic (FQ), to predict the compressive strength of the concrete modified with different Nanosilica contents for various mix proportions. The models were applied to three datasets, with 420 collected from several research studies. The ranges of the concrete mix proportions used in this study were as follows, water/cement ratio (w/c) ranged between 0.1 and 1, cement content (C) ranged between 153.81 and 1200 kg/m3, fine aggregate content (FA) ranged between 492 and 2270 kg/m3, coarse aggregate content (CA) ranged between 617 and 2900 kg/m3, superplasticizer (SP) ranged between 0% and 6.7%, coarse aggregate size (CAS) ranged between 6 and 50.8 mm, fine aggregate size (FAS) ranged between 0.025 and 10 mm, Nanosilica content (NS%) ranged between 0% and 15%, w/c of 0.4–0.6 and the curing time (days) ranged between 3 and 180. The compressive strength of the collected datasets ranged from 3 to 120 MPa. The parameters that have improved the analyzed datasets of the compressive strength were the content of Nanosilica and the SP. The models were assessed for their accuracy using multiple assessment criteria; the most evident ones are; the objective function (OBJ), root mean square error (RMSE), Scatter Index (SI), and the maximum absolute error (MAE). The analytical models and the model comparison operations will be shown in detail in this research, indicating that the most effective and accurate model is the FQ model; where coefficient of determination (R2) of 0.96, RMSE of 3.49 MPa, MAE of 2.96 MPa, and SI of 0.08 were conducted from the analytical studies. From the developed models, the compressive strength of the concrete up to a high compressive strength level with high accurately can be predicted. Also, the compressive strength of the concrete can be accurately predicted for various sizes of aggregate and specimens. [ABSTRACT FROM AUTHOR]

Herein, the experimental results of discontinuous ferromagnetic‐fiber manipulation using magnetic‐field interaction are presented. High‐aspect‐ratio steel fibers are realigned in viscous polydimethylsiloxanes (PDMSs) with electromagnets. The influence of the matrix viscosity, magnetic‐field strength, and fiber‐aspect ratio on the dynamics of the realignment process is investigated. To predict the fiber behavior under static magnetic fields, two commonly used theoretical models (hereinafter referred to as the torque model and the exponential model) are compared to a nonlinear regression model proposed by the authors. The latter model focuses on the alignment rate of a fiber as a function of the physical process parameters. To indicate the best fitting model for the experimental results, the statistical root‐mean‐square error (RMSE) values of the fitted models are assessed. A total of 54 comparisons are made between the exponential model and the authors' nonlinear regression model. In 50 cases, the nonlinear regression model provides a better fit. [ABSTRACT FROM AUTHOR]

Correlations between response spectral ordinates at different periods are used in several seismic hazard computations, such as for the construction of conditional mean spectra and conditional spectra. Conventionally, these correlations have been computed and reported only for a damping ratio of 5%; however, structures may have damping ratios substantially lower or higher than 5%. Therefore, in those cases, one requires correlations of spectral ordinates at different periods but having the same damping ratios that are different than 5%, correlations of spectral ordinates at the same period but having different damping ratios, or the general case of correlation between spectral ordinates of two oscillators having different damping ratios and different periods. This work computes such damping‐dependent correlations by using the NGA‐West2 ground motion database. In general, it is found that correlations increase as the damping ratio of any of the two spectral ordinates increases and as the ratio of periods of vibration of the two oscillators departs from one. A nonlinear regression model is fitted to the resulting damping‐dependent correlations to simplify future computations. Finally, the use of the new damping‐dependent correlations is illustrated by computing example conditional spectra for damping ratios differing from 5%. The results show that using 5%‐damped correlations for the construction of condition mean spectra, overestimates spectral ordinates for damping ratios lower than 5% and underestimates spectral ordinates for damping ratios higher than 5%. [ABSTRACT FROM AUTHOR]

Aircraft cabins have high‐performance ventilation systems, yet typically hold many persons in close proximity for long durations. The current study estimated airborne virus exposure and infection reductions when middle seats are vacant compared to full occupancy and when passengers wear surgical masks in aircraft. Tracer particle data reported by U.S. Transportation Command (TRANSCOM) and CFD simulations reported by Boeing were used along with NIOSH data, to build nonlinear regression models with particle exposure and distance from particle source as variables. These models that estimate exposure at given distances from the viral source were applied to evaluate exposure reductions from vacant middle seats. Reductions averaged 54% for the seat row where an infectious passenger is located and 36% for a 24‐row cabin containing one infectious passenger, with middle seats vacant. Analysis of the TRANSCOM data showed that universal masking (surgical masks) reduced exposures by 62% and showed masking and physical distancing provide further reductions when practiced together. For a notional scenario involving 10 infectious passengers, compared with no intervention, masking, distancing, and both would prevent 6.2, 3.8, and 7.6 secondary infections, respectively, using the Wells–Riley equation. These results suggest distancing alone, masking alone, and these practiced together reduce SARS CoV‐2 exposure risk in increasing order of effectiveness, when an infectious passenger is present. [ABSTRACT FROM AUTHOR]

In this study, apple slices were dried using infrared (IR) and hot air techniques and comprehensively analyzed in terms of drying and product characteristics. The influence of IR power level (450–650 W) and hot air temperature (60–75°C) on mass transfer, color kinetics, product texture, microstructure, and rehydration characteristics was studied. The results indicated that drying time, color change, and energy requirement were lower in IR drying than in hot air drying. Moisture diffusivity was observed to increase with IR power (3.367 × 10−9–5.579 × 10−9 m2/s) and hot air temperature (1.288 × 10−9–2.387 × 10−9 m2/s). The activation energies of apple slices in IR and hot air drying were 11.94 and 21.90 kJ/mol, respectively. IR‐dried apple slices were more crispy, with a porous structure and higher rehydration ability. Experimental data were fitted to nine different thin‐layer drying and four‐color kinetic models using nonlinear regression analysis. The results of regression analysis indicated that the Midilli–Kucuk model is the best model to describe the drying behavior in both techniques. The color characteristics (L, a, and b) can be best explained by the modified color model and total color change by fraction conversion model for both IR and hot air drying of apple slices. This study revealed that IR drying of apple slices results in a better quality product in less time and energy as compared to hot air drying. Practical applications: Drying is a vital food processing and preservation technique based on the principle of reducing the water content of the product. Although several drying techniques are available, there have been continuous efforts to improve drying methods in terms of energy efficiency and product quality attributes. The present work has been carried out considering the dearth of information on the influence of infrared power/intensity on the drying behavior and product quality of apple slices. Mass and color kinetics have been studied for a better understanding of the process, along with texture, microstructure, and rehydration properties. Our results showed that the process is superior, in terms of energy and product quality, as compared to other published work. It is concluded that infrared drying can be effectively used in the dehydration of apple slices on an industrial scale and can be promoted as a healthy alternative to fried snacks. [ABSTRACT FROM AUTHOR]