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Waste cooking oil (WCO) is converted into methyl ester using transesterification. Various mixtures of biodiesel and diesel oil in ratios of 25, 50, 75, and 100% were created and approved by ASTM. Several experiments are conducted to investigate engine performance and emissions of biodiesel blends. Two mathematical models as extreme learning machine (ELM) and quadratic regression are developed to predict engine parameters and emissions at different engine speeds and biodiesel concentrations. At peak output power and engine speed of 1500 rpm, the lowest output power of the 100% methyl ester mixture is 25% lower than crude diesel. Largest increases of exhaust gas temperature and specific fuel consumption about diesel oil for biodiesel were 28 and 23 %, respectively. Biodiesel has equivalence ratio and volumetric efficiency as 15% and 4% lower than diesel. At 1500 rpm engine speed, the 100% biodiesel blend reduces CO, CO 2 , HC, and smoke concentrations by 12, 13, 44 and 48% respectively about diesel oil, while resulting in 23% more NOx emission. Quadratic regression is favorable about ELM in predicting engine performance and emissions for most variables achieving lower root-mean square and mean absolute percentage errors. Biodiesel from WCO is a promising substitute fuel in diesel engines. • WCO biodiesel were prepared and mixed with diesel as 25, 50, 75 and 100%. • ELM and QM predict engine performance and emissions. • Output power of biodiesel is 25% lower than crude diesel at full load. • Reductions in CO, CO 2 , HC, and smoke were 12, 13, 44 and 48%, respectively. • QM is favorable than ELM in predicting engine performance and emissions. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, known as diabetes, is a chronic disease that affects the control of blood glucose concentration levels, it is a disease that mostly affects adults (type 2 diabetes), but it can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the diseases with the highest prevalence and high mortality worldwide. Diabetes has no cure, but continuous monitoring to maintain blood glucose levels in normal ranges reduces the possibility of suffering from gastrointestinal problems, vision loss, limb amputations (such as diabetic foot) and damage to vital organs such as the heart and kidneys, among other associated complications. This article compares the results in glucose estimation by using a linear, quadratic and cubic regression considering the electrical characteristics generated in the cardiac conduction (HR, HRV, T-wave peak, and QT interval) recorded on a single-lead electrocardiogram (VII), used as a non-invasive blood glucose estimation model. The best estimate was obtained using a cubic regression. The validation was performed using the Clarke grid having 77.78 % of data in the A zone and 22.22 % in the B zone and a Pearson correlation value of 0.94103 in the cubic regression.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky–Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

Accurately monitoring river discharge is paramount for effective water resource management, and the utilization of remote sensing has emerged as a promising approach. However, estimating river discharge from a single pixel is limited in its ability across various river sizes: suffering from the applicability of single-pixel size to different river scales. Here, we develop an innovative regional gauging method to help overcome these barriers and improve the accuracy of estimates. Our method focuses on periodically flooded areas that are acutely sensitive to alterations in river discharge, denoted discharge-sensitive areas (SAs). For a river reach, we achieved automated extraction of these SAs using Sentinel-1 data and obtained water fraction within SAs from Sentinel-2 images. Water fraction is used to establish a quadratic relationship with in-situ daily mean discharge observations for enabling satellite-based river discharge estimates. The method was rigorously evaluated on fourteen rivers worldwide, encompassing mean widths from approximately 50 m to 3000 m. The relative root mean square error observed in our estimates ranged from 7% to 50%, with the Nash-Sutcliffe efficiency ranging from 0.69 to 0.81 for smaller rivers (< 200 m wide), 0.86–0.98 for medium rivers (200–500 m wide) and 0.81–0.99 for large rivers (> 1 km wide). For the estimated discharge time series, the mean time sampling is 9 days, and the maximum time gap ranges from 2 to 10 months. For some stations, no high river flow could be retrieved. Nevertheless, for most rivers, the proportion of time gaps >30 days is <10%. Consequently, we argue that SA-based regional gauging is a promising method to enhance discharge monitoring across different river widths and discharge scales and provide reliable data for hydrological research and policy-making. • Regional gauging method for discharge estimation across various river sizes. • SAs automatically extracted using satellite-derived water occurrence frequency. • Water fraction within SAs served as a critical component for discharge estimation. • Tukey's test was used to remove outliers from water fraction series. [ABSTRACT FROM AUTHOR]

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses.

Realizing selenium-rich fruit production in selenium-poor area is one of the subjects of functional fruit production. In order to master the production method of selenium-rich grapes, in this study, greenhouse-planted Shine Muscat was chosen as the experimental object, and selenium-rich nutrient solution was selected as selenium source, and two methods including foliar spraying and grape ear dipping were applied for supplement of selenium. Based on quadratic regression universal rotary combination test design, by detecting the selenium content, sugar content, and hardness of grapes at harvest time, the regression equations between the indexes and foliar spraying concentration, ear dipping concentration, and growth period were calculated, clarifying the effect of the two methods of selenium supplement on grapes. The results showed that there was no significant correlation between sugar content and hardness of grapes and the supplementing selenium concentrations and the period. There was a significant correlation between the selenium content and the three factors containing foliar spraying concentration, ear dipping concentration, and supplementing selenium period. The same concentration of foliar spraying and ear dipping had the equal effect of selenium supplement. The effect of selenium supplement was significantly affected by the supplementing selenium period. There was a linear correlation between the selenium content and the supplementing concentrations, and a quadratic correlation between the content and the supplementing selenium period. The best period was about within 60 days before grape maturity, and the closer to maturity, the higher the supplementing selenium efficiency was. Therefore, selenium content in grape fruits could be significantly increased by foliar spraying and ear dipping applying selenium-rich nutrient solution. Selenium-rich grape production could be realized in selenium-poor soil by selecting suitable the concentration and the period. [ABSTRACT FROM AUTHOR]

We consider a new estimator for the quadratic errors-in-variables model that exploits higher-order moment conditions under the assumption that the distribution of the measurement error is symmetric and free of excess kurtosis. Our approach contributes to the literature by not requiring any side information and by straightforwardly allowing for one or more error-free control variables. We propose a Wald-type statistical test, based on an auxiliary method-of-moments estimator, to verify a necessary condition for our estimator’s consistency. We derive the asymptotic properties of the estimator and the statistical test and illustrate their finite-sample properties by means of a simulation study and an empirical application to existing data from the literature. Our simulations show that the method-of moments estimator performs well in terms of bias and variance and even exhibits a certain degree of robustness to the distributional assumptions about the measurement error. In the simulation experiments where such robustness is not present, our statistical test already has high power for relatively small samples. [ABSTRACT FROM AUTHOR]

Ti-6Al-4V ELI (Grade 23) is highly recommended for bio-materials and due to its low thermal conductivity and chemically reactive properties, machinability is poor. Thus the current work emphasized on the selection of appropriate cooling technique and optimal cutting parameters for machining of Ti-6Al-4V ELI alloy with sustainability analysis for surface roughness, flank wear and cutting power. Initially, the cutting performances under dry, flood and MQL environments are compared and MQL is observed to better performed. At lower speed (70 m/min), MQL exhibited 26.38% and 19.69% lesser surface roughness relative to dry and flood cooling individually. At the same cutting condition, MQL assisted cutting resulted in lower flank wear relative to dry (157. 33%) and flood cooling (40%). Further, a detailed investigation has been made under MQL through Taguchi L18 design of experiments. The major mechanisms for flank wear are found to be abrasion, chipping and notch wear. Optimal data set through Grey relational analysis is found to be v1 (70 m/min), f1 (0.1 mm/rev) and d1 (0.1 mm) and improved. Quadratic regression model is found to be significant for prediction of responses. Sustainability Pugh matrix assessment revealed that MQL environment enhanced the economical, technological as well as environmental and operator health aspects. Reduction of energy consumption by 53.96% and savings of carbon footprints by 68.46 kg of CO2 observed under MQL at optimal conditions and thus saves manufacturing cost. [ABSTRACT FROM AUTHOR]

Many economic theories suggest that the relation between two variables y and x follow a function forming a convex or concave curve. In the classical linear model (CLM) framework, this function is usually modeled using a quadratic regression model, with the interest being to find the extremum value or turning point of this function. In the CLM framework, this point is estimated from the ratio of ordinary least squares (OLS) estimators of coefficients in the quadratic regression model. We derive an analytical formula for the expected value of this estimator, from which formulas for its variance and bias follow easily. It is shown that the estimator is biased without the assumption of normality of the error term, and if the normality assumption is strictly applied, the bias does not exist. A simulation study of the performance of this estimator for small samples show that the bias decreases as the sample size increases. [ABSTRACT FROM AUTHOR]

The behavior and possible contamination risk due to the presence of potentially harmful metals (PHM) were studied based on 2250 soil samples that were collected in a 5-year period (2013–2017) from the plain of Thessaly (prefectures of Karditsa, Trikala, and Larissa). The vertical distribution of metals was also investigated from sample profiles at three depths 0–30, 30–60, and 60–90cm. The soils of the sampling belong to four taxonomy soil orders that are dominant in the studied area (Alfisols, Inceptisols, Endisols, and Vertisols). In a novel approach, robust quadratic regression analysis on multiple variables was used to define prediction models of the concentrations of two metals: Fe which is an essential metal and the toxic Cd. Linear and quadratic regression formulae were estimated based on the iteratively reweighted least squares robust regression approach in an effort to eliminate the impact of the outliers. These formulae define how several soil properties affect the distribution of the considered metals in each soil order. The evaluation of the estimated regression equations based on the R2 metric indicates that they constitute a useful, reliable, and valuable tool for managing, describing, and predicting the pollution in the studied area. [ABSTRACT FROM AUTHOR]

Multiscale methods have become progressively valuable in geomorphometric analysis as data have become increasingly detailed. This paper evaluates the theoretical and empirical properties of several common scaling approaches in geomorphometry. Direct interpolation (DI), cubic convolution resampling (RES), mean aggregation (MA), local quadratic regression (LQR), and an efficiency optimized Gaussian scale-space implementation (fGSS) method were tested. The results showed that when manipulating resolution, the choice of interpolator had a negligible impact relative to the effects of manipulating scale. The LQR method was not ideal for rigorous multiscale analyses due to the inherently non-linear processing time of the algorithm and an increasingly poor fit with the surface. The fGSS method combined several desirable properties and was identified as an optimal scaling method for geomorphometric analysis. The results support the efficacy of Gaussian scale-space as a general scaling framework for geomorphometric analyses. [ABSTRACT FROM AUTHOR]

This study proposed to explore real estate trends in the high-rise housing market in Belém, Northern Brazil. We based the theoretical approach on the macroeconomic, territorial, and social elements of the high-rise segment both in national and international contexts. The sample comprised 137 high-rise projects launched throughout the 2005–2017 period. A quadratic regression model confirmed real estate susceptibility to macroeconomic oscillation. A geographically weighted regression model revealed that larger dwellings were launched more in upper-income, lower formal employment rate, and lower infrastructure coverage regions of Belém. A multinomial logit regression model established differences in the launching likelihood between different categories of housing size projects. The outcomes were compared with worldwide tendencies, seeking both common and unique patterns of real estate exploitation. The methodological forms of exploring real estate trends in developing countries—which are scarcer—add significant scientific value for the urban planning community, as they assist public policy by identifying the susceptibility of urban areas to land exploitation, in addition to enabling replication of such real estate diagnostics in other cities of emerging countries. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the changes in the number of Angora goat and the amount of mohair obtained from Angora goats, which is one of the Turkey's important gene sources from Central Asia to present day, between 1991-2019 in Turkey via different regression models and to evaluate the results. For this purpose, simple linear, quadratic, cubic, inverse, and logarithmic regression models are used in the study. In order to compare the regression models and determine the most suitable model, the square root of the mean squares (Root Mean Square error-RMSE), the determination coefficient (R2) and the adjusted determination coefficient (AdjR2) were used as comparison criteria. Accordingly, R2 values obtained from simple linear, quadratic, cubic, inverse and logarithmic regression models for the number of Ankara goats shorn are 0.628, 0.99, 0.99, 0.74, 0.90, and RMSE are 135288.27, 25651.46, 18966.20, 114681.75, 71592.54, respectively. For Mohair production, R2 values are 0.61, 0.98, 0.99, 0.75, 0.88, while RMSE is 208.99, 41.84, 32.64, 167.85 and 114.32 respectively. In the number of angora goats, R2 values are 0.70, 0.99, 0.99, 0.73, 0.93, while RMSE is 165264.22, 32818.49, 23410.64, 155421.63 and 79544.79, respectively. In parameter estimates, the most appropriate model according to the highest R2 value and the lowest RMSE value is the cubic regression model. According to the cubic regression model, the estimated number of Angora goats that will be shorn, the number of Angora (mohair) goat, and mohair production in Turkey will be 254307 and 275431 heads, 268321 and 287846 heads, and 439 and 474 tons in 2020 and 2021, respectively. Although in the coming years, an increase in mohair production is projected in Turkey, this is much lower than expected.

Abstract–This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the environment or in synthetic mixtures and as a result exposure levels can be highly correlated. We propose a latent factor joint model, which includes shared factors in both the predictor and response components while assuming conditional independence. By including a quadratic regression in the latent variables in the response component, we induce flexible dimension reduction in characterizing main effects and interactions. We propose a Bayesian approach to inference under this factor analysis for interactions (FIN) framework. Through appropriate modifications of the factor modeling structure, FIN can accommodate higher order interactions. We evaluate the performance using a simulation study and data from the National Health and Nutrition Examination Survey. Code is available on GitHub. for this article are available online. [ABSTRACT FROM AUTHOR]

In this paper, synthesized organic compound of the family of ketene dithioacetal was studied as corrosion inhibitors for mild steel in 1.0 M hydrochloric acid by gravimetric measurement. The aim of this work is to study the effect of inhibitor concentration and the temperature on the resistance, corrosion and to compare the experimental results with mathematical models. The structural properties were performed using scanning electron microscopy technique. It has been found that the inhibition efficiency, increases with increasing inhibitor concentration. The adsorption of studied compounds on mild steel surface follows Langmuir’s isotherm. Obtained data were analyzed by suggesting two mathematical models based on linear and quadratic regression, taking into account the influence of concentration and temperature on the inhibition efficiency. Obtained experimental data were in good agreement with those predicated by the quadratic model

Quadratic regressions extend linear models by simultaneously including the main effects and the interactions between the covariates. As such, estimating interactions in high-dimensional quadratic regressions has received extensive attention. Here, we introduce a novel method that allows us to estimate the main effects and the interactions separately. Unlike existing methods for ultrahigh-dimensional quadratic regressions, our proposal does not require the widely used heredity assumption. In addition, our proposed estimates have explicit formulae and obey the invariance principle at the population level. We estimate the interactions in matrix form under a penalized convex loss function. The resulting estimates are shown to be consistent, even when the covariate dimension is an exponential order of the sample size. We develop an efficient alternating direction method of multipliers algorithm to implement the penalized estimation. This algorithm fully exploits the cheap computational cost of the matrix multiplication and is much more efficient than existing penalized methods, such as the all-pairs LASSO. We demonstrate the promising performance of the proposed method using extensive numerical studies. [ABSTRACT FROM AUTHOR]

Pressure swing adsorption (PSA) is an important technology for mixture gas separation and purification. In this work, a dynamic model for a layered adsorption bed packed with activated carbon and zeolite 5A was developed and validated to study the PSA process. The model was validated by calculating breakthrough curves of a five-component gas mixture (H 2 /CH 4 /CO/N 2 /CO 2 = 56.4/26.6/8.4/5.5/3.1 mol%) and comparing the results with available experimental data. The purification performance of six-step layered bed PSA cycle was studied using the model. In order to optimize the cycle, the Box-Behnken design (BBD) method was used, as implemented in Design Expert™. The parametric study showed that, for adsorption step durations ranging from 160 to 200 s, as the adsorption time increased, the purity decreased, whereas the recovery and productivity increased. During the pressure equalization step, the purity increased as the pressure equalization time increased, but the recovery and productivity decreased for step durations ranging from 10 to 30 s. As the P/F ratio (hydrogen used in purge step to hydrogen fed in adsorption step) increased from 0.05 to 0.125, the purity increased, whereas the recovery and productivity decreased. The optimization of the layered bed PSA process by the BBD method was then performed. In addition to the adsorption time, the pressure equalization time and the P/F ratio were considered as independent optimization parameters. Quadratic regression equations were then obtained for three responses of the system, namely purity, recovery, and productivity. When purity is set as the main performance indicator, the following values were obtained for the optimization parameters: an adsorption time of 168 s, a pressure equalization time of 14 s, and a P/F ratio of 0.11. Under those conditions, the system achieved a purity of 99.99%, a recovery of 57.76%, and a productivity of 6.41 mol/(kg·h). • A layered bed pressure swing adsorption (PSA) model is developed and well validated. • Effects of adsorption time, pressure equalization time, P/F ratio are investigated. • Optimization based on Box-Behnken design (BBD) method for PSA cycle are studied. • Quadratic regression equations are obtained for purity, recovery, and productivity. • Optimal design parameters and performances are obtained under different strategies. [ABSTRACT FROM AUTHOR]

It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations. [ABSTRACT FROM AUTHOR]

Reinforced concrete structures are typically exposed to a combination of aggressive substances and mechanical stresses, which contribute to fast degradation. The present research was conducted to evaluate five time-dependent parameters from several different tests, namely compressive strength, static modulus, dynamic modulus, surface, and bulk electrical resistance. Some parameters were obtained using destructive testing (DT) and some using non-destructive testing (NDT). Due to the correlation and calculation of regression curves, it was possible to compare the correlation of parameters important for estimating the durability of reinforced concrete structures in relation to degradation and corrosion. Concrete of C40/50 grade was examined in several time periods, and the parameter relationships were analysed. At the same time, a statistical evaluation was carried out, and therefore the study contains the average values and standard deviations of all measured parameters. The results show that the compressive strength and the electrical resistivity of the surface and bulk have a high correlation. In contrast, the dynamic modulus and electrical resistivity have low linear correlation, but it was possible to apply a quadratic curve with a high degree of fit. For the comparison of static elastic modulus and electrical resistance, the quality of the quadratic regression model was low but sufficient. The results show that, for structural concrete, the presented NDT methods can be used to estimate other parameters obtained from the DT methods.

Sodium selenite was added to basal diet at five levels (0.10, 0.42, 0.67, 1.06 and 1.46 mg Se/kg) and fed fish for 8 weeks. The dietary selenium requirement of juvenile blunt snout bream (Megalobrama amblycephala) was quantified. Dietaryseleniums at 0.67–1.06 mg Se/kg improved weight gain rate, specific growth rate and feed efficiency. The optimal amount was 0.96 mg/kg, for which the specific growth rate was 1.798%/day and the weight gain rate was 173.852% (p < 0.05). Se deposition in muscle was increased (p < 0.05) at ≥0.67 mg/kg, but moisture, protein, lipid and ash content were not affected. Physiological status and lipid metabolism were improved by 1.06–1.46 mg/kg dietary selenium based on total protein and albumin in plasma, and total cholesterol and triglycerides (p < 0.05). Activities of hepatic anti-oxidant enzymes catalase, total superoxide dismutase, glutathione peroxidase and reduced glutathione were enhanced at Se1.06 (p < 0.05). However, malondialdehyde content was lowered at Se1.06 (p < 0.05). Expression of anti-inflammatory cytokines, nuclear factor erythroid 2-related factor 2 (Nrf2) and kelch-like ECH-associated protein 1 (Keap-1) in liver were elevated at Se1.06 (p < 0.05), as were mRNA levels of glutathione peroxidase, copper zinc superoxide dismutase and catalase. Expression of pro-inflammatory cytokines, interleukin 8, tumour necrosis factor-α and transforming growth factor-β were inhibited at 0.67–1.46 mg/kg (p < 0.05). In general, 0.96 mg/kg was optimal, and optimal selenium enhanced antioxidant stress tolerance and anti-inflammatory ability. • Growth performance, plasma biochemical parameters were affected by dietary selenium levels. • The dietary selenium requirement was estimated to be 0.96 mg/kg of diet. • The optimal dietary selenium level could reduce plasma cholesterol, then improve the hepatic antioxidant capacity. • The optimal dietary selenium level could enhance the hepatic antioxidant and anti-inflammatory ability. [ABSTRACT FROM AUTHOR]

Feed intake changes as animals age and grow. A constraint of most functional forms used to describe this relationship is that intake is maximum only once an animal reaches its mature weight. Often such is not the case and maximum intake is achieved earlier. Our aim was to describe a form unburdened by such a constraint and to determine its utility to describe the relationship between feed intake and liveweight across multiple species. Twelve data sets representing seven domestic animal species (cattle, chicken, dog, pig, rat, sheep, and turkey) with a wide range of mature weights were used. Average daily ad libitum feed intakes and liveweights were available on either a weekly or fortnightly basis. Rates of intake were scaled to mature intake. Within each set, the quadratic regression of scaled intake on the degree of maturity in weight was fitted. This form provided a very good description of the relationship between these variables (R2 > 0.86) and, for all but one case, a realistic prediction of mature intake. With one exception, intake reached its maximum value at a liveweight below its mature value. Furthermore, by appropriately scaling the relationship between intake and liveweight, the data could be described by a function with a single parameter with general relevance across species. By expressing the rate of intake as a function of its value at maturity, a quadratic form provides a robust and general description of the relationship between feed intake scaled to mature intake and degree of maturity in weight. [ABSTRACT FROM AUTHOR]

• Incorporate battery degradation into EV network-level energy consumption estimation. • Estimate unit energy consumption functions using quadratic regression models. • Propose an easy-to-implement trajectory-based energy consumption estimation method. • Validate the proposed method based on real taxi trajectories on Singapore's road network. As a cost-effective and environmental-friendly transport means, electric vehicle (EV) has received widespread attention in the recent decade. The increasing market share and wider adoption of EV in transportation systems also bring about the energy use issues. In this paper, we propose a method to estimate network-wide EV energy consumption by taking into account battery degradation. We first derive unit energy consumption functions with respect to the battery degradation rate by quadratic regression method using EV operational data in the US. We then put forward an easy-to-implement and tangible method to estimate EV network energy consumption based on EV trajectories and the derived unit energy consumption function. In detail, EVs' battery degradation rates are assumed to follow a given and known probability distribution and daily travel mileage of each EV is derived from EV trajectories. Based on these, we can get the expected total energy consumption in the network. We also propose a prorated assignment approach to determine the expected energy consumed by EVs through each EV charging point based on the estimated temporal-spatial charging demand distribution from EVs' trajectories. A case study in Singapore is demonstrated in the end and the importance of incorporating battery degradation is highlighted. The results reveal that overlooking battery degradation would lead to more than 10% of estimation error in energy consumption estimation. [ABSTRACT FROM AUTHOR]

Camera zoom operation and fast approaching/receding target causes scaling of acquired target in video frames. Fast moving target manifests in large inter-frame motion. In general, non-uniform background degrades performance of tracking algorithms. Fast Fourier transform (FFT)-based Correlation algorithms improve tracking in this scenario, but their applications is limited to small inter-frame motion. Increasing search region has implication on execution speed of the algorithms. Rapid target scaling, non-uniform background and large inter-frame motion of target hinder accurate and long term visual tracking. These challenges have been addressed for extended target tracking by augmenting fast discriminative scale space tracking (fDSST) algorithm with probable target location prediction and target detection. Localisation of fast motion has been achieved by applying fused outputs of Kalman filter and quadratic regression based prediction before applying fDSST. It has helped in accurate localisation of fast motion without increasing search region. In each frame, target location and size have been estimated using fDSST and further refined by target detection near this location. Smoothing and limiting of trajectory and size of detected target has enhanced tracking performance. Experimental results show considerable improvement of precision, success rate and centre location error tracking performance against state-of-the-art trackers in stringent conditions. [ABSTRACT FROM AUTHOR]

Research linking functional traits to competitive ability of coexisting species has largely relied on rectilinear correlations, yielding inconsistent results. Based on concepts borrowed from natural selection theory, we propose that trait–competition relationships can generally correspond to three univariate selection modes: directional (a rectilinear relationship), stabilising (an n‐shaped relationship), and disruptive (a u‐shaped relationship). Moreover, correlational selection occurs when two traits interact in determining competitive ability and lead to an optimum trait combination (i.e., a bivariate nonlinear selection mode).We tested our ideas using two independent datasets, each one characterising a group of species according to (a) their competitive effect on a target species in a pot experiment and (b) species‐level values of well‐known functional traits extracted from existing databases. The first dataset comprised 10 annual plant species frequent in a summer‐rainfall desert in Argentina, while the second consisted of 37 herbaceous species from cool temperate vegetation types in Canada. Both experiments had a replacement design where the identity of neighbours was manipulated holding total plant density in pots constant. We modelled the competitive ability of neighbours (i.e., the log inverse of target plant biomass) as a function of traits using normal multiple regression.Leaf dry matter content (LDMC) was consistently subjected to negative directional selection in both experiments as well as to stabilising selection among temperate species. Leaf size was subjected to stabilising selection among desert species while among temperate species, leaf size underwent correlational selection in combination with specific leaf area (SLA): selection on SLA was negative directional for large‐leaved species, while it was slightly positive for small‐leaved species.Synthesis. Multiple quadratic regression adds functional flexibility to trait‐based community ecology while providing a standardised basis for comparison among traits and environments. Our analyses of two datasets from contrasting environmental conditions indicate (a) that leaf dry matter content can capture an important component of plant competitive ability not accounted for by widely used competitive traits, such as specific leaf area, leaf size, and plant height and (b) that optimum relationships (either univariate or bivariate) between competitive ability and plant traits may be more common than previously realised. [ABSTRACT FROM AUTHOR]

The intense discussions on how inflation affects the growth have triggered many studies to empirically investigate it. This paper is not an exception. The previous research in Indonesia focusing on how inflation and growth are correlated, and various thresholds of inflation suggested have provided puzzling indicators. Therefore, this paper aims to examine the impact of inflation on the growth in Indonesia by employing Autoregressive Distributive Lag (ARDL) technique, Threshold Regression (TR), and quadratic regression. ARDL is applied to reveal the nexus in the short-run and long-run whereas the TR and quadratic regression are employed to find the threshold of inflation. In the short-run and long-run time frame, the relationships are found to be a negative interaction between inflation and growth. Moreover, this paper empirically proves that the inflation threshold exists at the level of 14.31% after comparing two different approaches' results. Eventually, the finding of this paper suggests a room for less tight monetary policy that can be applied. [ABSTRACT FROM AUTHOR]

Highlights • The algorithm is a quick calculation method for binomial peak-tracking methods. • The algorithm can be fulfilled completely with fixed-point operations. • The algorithm works nearly ten times faster than conventional method in MCUs. • A hardware module built with FPGA can handle a data rate of approximately 70 MS/s. Abstract A simple algorithm is proposed in this paper for the implementation of second-order-polynomial-based peak-tracking methods used in FBG sensors. By assuming that wavelengths samples are evenly distributed in the acquired reflective spectrum, which is always desired for most interrogation systems, we decomposed the Moore-Penrose of the coefficient matrix into the multiplication of an upper triangular matrix and a symmetric matrix, and consequently derived an equation nearly as simple as that of the centroid method for the determination of peak wavelength. The algorithm does not involve matrix multiplication and inverse matrix solution, so it can be easily fulfilled with solely fixed-point operations and consumes only one tenth of the calculation time needed by conventional method. Besides, the symmetric feature of the factorization makes it possible to facilitate the calculation with an existing FIR core. By using a piecewise linear fitting to the wavelength samples, the algorithm is also applicable in cases where wavelength sampling interval is not constant. An interrogation system was built up based on FPGA and a commercial spectrometer with a wavelength nonlinearity of 3 nm. It was able to interrogate over 50 FBG sensors at the same time at a measurement frequency of 17 kHz by using the proposed algorithm. [ABSTRACT FROM AUTHOR]

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It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations.

Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future date requires the determination of an annualized matrix through eigendecomposition followed by matrix powering. However, the technique yields multiple solutions, commonly with imaginary parts and negative transitions, and possibly with no non-negative real stochastic matrix solution. In addition, the computational burden of the procedure makes it infeasible for practical use with large problems. This paper describes a Regression-Based Markov (RBM) approximation technique based on quadratic regression of individual transitions that is shown to always yield stochastic matrices, with very low error characteristics. Using land cover data for the 48 conterminous US states, median errors in probability for the five states with the highest rates of transition were found to be less than 0.00001 and the maximum error of 0.006 was of the same order of magnitude experienced by the commonly used compromise of forcing small negative transitions estimated by eigendecomposition to 0. Additionally, the technique can solve land change modeling problems of any size with extremely high computational efficiency.

Using 38 years (January 1973-December 2010) of hourly wind records, the present paper aims at drawing the possible long-term trends of winds and ten surface wave parameters over the west region of Scotland using the quadratic regression approach. Four dominant wind components were determined: the southern, the western, the south-western and the north-western. Two opposite groups of oscillations were proven: one for the southern groups and one for the western groups.The examined wave parameters were: the wave frequency, the wave angular frequency, the peak angular frequency, the wave spectral density, the significant wave height, the peak period, both the peak and group velocities and lastly the wave energy and the wave power. Results revealed that every examined parameter tended to have a cyclic behaviour except the wave spectral density, which appeared to be linearly decreasing. All wave frequencies were in an inverse correlation to the mean monthly wind speed. All other wave parameters appeared to be highly correlated to the mean monthly wind speed with correlation factors exceeding 0.95 except the wave power, which had a correlation factor of 0.89.In conclusion, the general behaviours of the dominant wind components over the west region of Scotland, and of the different wave parameters tend to be cyclic. A longer time series, than that presently used, will be advantageous in order to strengthen this outcome with more robust investigation. This concluded cyclic behaviour may positively impact on the engineering work within the wave energy resource off the western coasts of Scotland.

Increasing plant density and improving N fertilizer rate along with the use of high density-tolerant genotypes would lead to maximizing maize (Zea mays L.) grain productivity per unit land area. The objective of this investigation was to match the functions of optimum plant density and adequate nitrogen fertilizer application to produce the highest possible yields per unit area with the greatest maize genotype efficiency. Six maize inbred lines differing in tolerance to low N and high density (D) [three tolerant (T); L-17, L-18, L-53, and three sensitive (S); L-29, L-54, L-55] were chosen for diallel crosses. Parents and crosses were evaluated in the 2012 and 2013 seasons under three plant densities: low (47,600), medium (71,400), and high (95,200) plants ha− 1 and three N fertilization rates: low (no N addition), medium (285 kg N ha− 1) and high (570 kg N ha− 1). The T × T crosses were superior to the S × S and T × S crosses under the low N–high D environment in most studied traits across seasons. The relationships between the nine environments and grain yield per hectare (GYPH) showed near-linear regression functions for inbreds L54, L29, and L55 and hybrids L18 × L53 and L18 × L55 with the highest GYPH at a density of 47,600 plants ha− 1 and N rate of 570 kg N ha− 1 and a curvilinear relationship for inbreds L17, L18, and L53 and the rest of the hybrids with the highest GYPH at a density of 95,200 plants ha− 1 combined with an N rate of 570 kg N ha− 1. Cross L17 × L54 gave the highest grain yield in this study under both high N–high-D (19.9 t ha− 1) and medium N–high-D environments (17.6 t ha− 1).

The analysis of frequencies of extreme hydrological data such as floods, droughts, winds and maximum daily precipitation, is based on accepting that the maximum annual data of the available sample are independent and come from a random process that is stationary. This means that their statistical properties do not vary with time. Due to changes in land use and impacts of global warming, the hydrological data series show trends, indicating that they are non-stationary. The objective of this study was to expose the generalization of the L moments method, to estimate the fit parameters of the probability distribution functions: General Extreme Values (GVE), Generalized Logistics (LOG) and Generalized Pareto (PAG) of non-stationary type, by varying with time (t) its location parameter (u) in a linear and quadratic way. The probabilistic models GVE1, LOG1 and PAG1 have four fitting parameters (δ1, δ2, a, k), since u = δ1 + δ2·t and their scale (a) and form (k) parameters are constant. The models GVE2, LOG2 and PAG2 have five fitting parameters (δ1, δ2, δ3, a, k), due to the fact that u = δ1 + δ2·t + δ3·t². In series with trend it is used as a covariate t in years, but indicators of regional or global climate variability can also be used, such as the southern oscillation index. By means of the four numerical applications that are described, the simplicity of the operating procedure was demonstrated and the utility of the use of non-stationary GVE, LOG and PAG models is highlighted through their predictions in series with trend, or using a climatic covariate. [ABSTRACT FROM AUTHOR]

A new strategy is described to optimize multiple closely related parameters that are involved in the degradation of lignocellulose. Exo-β-1,4- glucanase, endo-β-1,4-glucanase, and β-glucosidase contained in the broth of Trichoderma viride 3.3711 cultures were used as enzyme solution. Corn stover (CS) pretreated by a combination of H2O2 and lignin peroxidase was used as raw feedstock. A comprehensive hydrolysis index (CHI) of three enzymatic activities was constructed by principal component analysis (PCA). Corn stover (CS) was pretreated with a combination of H2O2 and lignin peroxidase. The accuracy of the CHI was demonstrated by a quadratic regression using the CHI as an independent variable and the yield of the total reducing sugar (Ytrs) as a dependent variable. The results showed that the CHI was closely post-correlated with Ytrs and could be used to optimize the fermentation medium components for T. viride cultures due to a highly significant correlation between CHI and Ytrs. Based on the CHI at 96 h, an optimal medium contained 0.6% fructose, 0.6% xylose, 0.3% bean pulp, 0.15% yeast extract, 0.12% KH2PO4, 0.004% CaCl2, 0.008% FeSO4, 0.006% ZnSO4, 0.012% glycine betaine, and 0.004% polyethylene glycol. The maximum actual Ytrs was very near to the theoretical Ytrs. [ABSTRACT FROM AUTHOR]

In this paper, we use the fundamental idea of the incremental model (IM) and develop the design framework. The design method of IM is composed of two steps. In the first step, we perform a linear regression (LR) as the global model. In the second step, the errors obtained by the global model are predicted by fuzzy if-then rules generated through a local linguistic model. Although the effectiveness of IM has been demonstrated in various prediction examples, we propose an improved incremental model (IIM) to deal with complex nonlinear characteristics. For this purpose, we employ adaptive neuro-fuzzy networks (ANFN) or radial basis function networks (RBFN) to create local granular networks in the design of IIM. Furthermore, we use quadratic regression (QR) as a global model, because linear relationship of LR may not hold in many settings. Numerical studies concern four datasets (automobile data, energy efficiency data, Boston housing data and computer hardware data). The experimental results demonstrate that IIM outperformed the previous models. [ABSTRACT FROM AUTHOR]