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The scanning electron microscope (SEM) recordings of dynamic nano-electromechanical systems (NEMS) are difficult to analyze due to the noise caused by low frame rate, insufficient resolution, and blurriness induced by applied electric potentials. Here, we develop an image processing platform enhanced by the physics of the underlying system to track the motion of buckling NEMS structures in the presence of high noise levels. The algorithm is composed of an image filter, two data filters, and a nonlinear regression model, which utilizes the expected form of the physical solution. The method was applied to the recordings of a NEMS beam about 150 nm wide, undergoing intra- and inter-well post-buckling states with a transition rate of approximately 0.5 Hz. The algorithm can track the dynamical motion of the NEMS and capture the dependency of deflection amplitude on the compressive force on the beam. With the help of the proposed algorithm, the transition from inter-well to intra-well motion is clearly resolved for buckling NEMS imaged under SEM. [ABSTRACT FROM AUTHOR]

The widely used log-linear transformation for fitting scattered positive observations to power models can be generalized to zero and negative values by embedding shape parameters into an inverse hyperbolic sine (IHS) function. The most common IHS-type of functions are analyzed and compared for log-linear modeling. In addition, a novel parametrized log-linear (ParLo) transformation is introduced that generalizes not only the ordinary linear and log-linear regressions but as well the most popular IHS type of transformation, since it allows to reduce the regression's residual for the case of exclusively positive values. Residual computations for a single regressor are used to benchmark the IHS-type of transformations. An optimization of the shape parameter may lead to residuals lower than those that are achievable by nonlinear least-squares (NLLS) regression for mono-exponential models since reverse IHS transformations are bi-exponential. Thus, the NLLS regression and the covariance-invariant mapping are applied to deterministic bi-exponential models for further benchmarking. [ABSTRACT FROM AUTHOR]

Introduction: Gestational diabetes mellitus (GDM) is associated with an increased risk of developing type 2 diabetes mellitus (T2DM). The inflammatory potential of diet is crucial in GDM development. This study compares dietary inflammatory indices (DII) in females with and without a history of GDM and constructs a predictive model for prediabetes risk. Methods: Cross-sectional data from NHANES cycles (2011–2014) were analyzed using the DII. Independent t tests, chi-square test, and Mann-Whitney U test examined DII scores in relation to GDM history. Multivariate logistic regression assessed DII's association with prediabetes in females with GDM history. Restricted cubic spline (RCS) and LASSO regression modeled non-linear relationships and predicted prediabetes risk. Results: 971 female participants were included. Those with GDM history had lower DII scores (1.62 (0.58, 2.93) vs. 2.05 (0.91, 2.93)). Higher DII scores in females with GDM were linked to prediabetes, remaining significant after adjusting for confounders. RCS analysis found no non-linear correlation (non-linear p = 0.617). The prediabetes model for GDM history had strong predictive performance (AUC = 88.6%, 95% CI: 79.9-97.4%). Conclusion: Females with GDM history show lower DII levels, potentially reflecting improved diet and health awareness. Higher DII scores correlate with increased prediabetes risk in this group, emphasizing diet's role in diabetes risk. Further studies are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Given their lower socioeconomic background and achievement, immigrants and their descendants have been shown to make more ambitious decisions regarding educational transitions. While previous research has extensively analysed transitions within and after compulsory education, research on late transitions is scarce. This study focuses on the secondary effect of ethnic origin in the transition after upper secondary education in Germany, where school leavers decide between vocational education and training and higher education. Based on longitudinal data from the National Educational Panel Study, we first analyse differences by ethnic origin (Turkey, former Soviet Union, Poland, other countries) and immigrant generation (first, second, third generation) net of socioeconomic background and achievement. Compared to those of the ethnic majority, we find increased probabilities of enrolment in higher education for most ethnic groups of the first and second immigrant generation, net of differences in socioeconomic background and achievement. Second, we test four explanatory approaches for secondary ethnic effects (immigrant optimism, relative status maintenance, anticipated discrimination, and information deficits). By employing decomposition analyses for nonlinear regression models, we find that, taken together, the four explanations account for up to half of the ethnic differences in educational choice after upper secondary education. Specifically, increased levels of immigrant optimism and information deficits regarding vocational education explain a substantial share of the ethnic differences among all origin groups. For Turkish immigrants, the motive to maintain relative status in the origin country also contributes to increased enrolment rates in higher education. [ABSTRACT FROM AUTHOR]

Shallow foundations in the vicinity of industrial zones are often subjected to machine induced cyclic loads. One of the key problems associated with these kinds of foundation is the determination of amount of settlement under cyclic load. A close observation of the existing studies reveals lack of studies explaining the influence of depth of embedment and frequency of loading on the cyclic load induced settlement of the foundation. An attempt has been made to fill these voids in this study. In the present study, the soil structure interface is modeled using nonlinear elasto-plastic springs and settlement is observed by varying different parameters like the relative density of soil, embedment ratio, factor of safety, intensity and frequency of cyclic loading. It is found that the settlement becomes negligible after certain number of load cycles, i.e. the critical number of load cycles. An empirical expression is developed to estimate the non-dimensional settlement of the foundation using nonlinear regression. It is found that the static load on the foundation is the most important factor influencing the combined static and cyclic settlement response of the foundation. Also, the ultimate settlement is attained earlier for higher values of frequency of loading. [ABSTRACT FROM AUTHOR]

This study proposes a new method for the rapid and non‐contact measurement of cable forces in cable‐stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton‐based cable force solving method is proposed. Furthermore, terrestrial laser scanning technology is used to collect geometric data of the cables. Meanwhile, automatic segmentation, noise reduction, and centerline extraction algorithms for the cable point cloud are proposed to accurately and efficiently obtain the cable shape. The correctness of the proposed cable force calculation method is verified in a well‐designed experiment, with the measurement error of cable forces for 15 test samples being less than 1%. Finally, the proposed point cloud automation processing algorithm and cable force measurement method are fully tested on a cable‐stayed bridge with a span of 460 m. The measurement accuracy of the proposed method for actual bridge cable tension is comparable to that of the frequency method, but the detection efficiency on site is nine times higher than that of the traditional frequency method. Overall, this study provides a new measurement method for construction control, health monitoring, intelligent detection, and other fields of cable‐stayed bridges. [ABSTRACT FROM AUTHOR]

Consider the nonlinear pseudo-monotone equations over a nonempty closed convex set. A spectral conjugate gradient projection method with the inertial factor is proposed for solving the problem under discussion. Following the projection strategy, we prove that the sequence of spectral parameters is bounded. The search direction generated by the algorithm satisfies the sufficient descent condition and possesses trust region property at each iteration. Under some mild conditions, the global convergence of the proposed method is established without the Lipschitz continuity assumption. Under some standard assumptions, we also establish the linear convergence rate of our method. Preliminary numerical results on constrained nonlinear monotone and pseudo-monotone equations demonstrate the efficiency of the proposed method. Furthermore, to highlight its applicability, we extend our method to deal with logistic regression problems. [ABSTRACT FROM AUTHOR]

We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression nonparametrically. A penalized estimation and the corresponding algorithm are proposed. Our theoretical investigation, which allows the dimensions of the tensor covariate to diverge, indicates that the proposed estimation yields a desirable convergence rate. We also provide a minimax lower bound, which characterizes the optimality of the proposed estimator for a wide range of scenarios. Numerical experiments are conducted to confirm the theoretical findings, and they show that the proposed model has advantages over its existing linear counterparts. [ABSTRACT FROM AUTHOR]

In this study, we investigate assessing the oxygen transfer efficiency of a rectangular venturi flume (VFOTF20), employing machine learning ensemble models. Our investigation revolves around how models like gradient boosting machine (GBM), stacked ensemble (SE), extreme gradient boosting (XRT), M5 (Pruned and Unpruned), and random forest (RF) estimate the oxygen transfer efficiency of a rectangular venturi flume. Key input parameters, including discharge per unit width (q), throat width (W), throat length (F), and water depth (Ha), are scrutinized. Laboratory experiments furnish the datasets essential for our analysis. During the testing phase, these modelling ensembles are rigorously evaluated. The findings unequivocally demonstrate that the GBM model surpasses all other contenders in performance. Nevertheless, it is noteworthy that all proposed ensemble models, alongside conventional models like linear regression (LR) and nonlinear regression (NLR), exhibit commendable performance, albeit with variations. The correlation diagram and sensitivity analysis underscore the pivotal role of discharge per unit width (q) as the most significant variable impacting the VFOTF20. Additionally, we conduct a one-way analysis of variance (ANOVA) and an uncertainty study to enrich our understanding of the subject matter further. [ABSTRACT FROM AUTHOR]

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BACKGROUND: Industrialization continues to increase in the 21st century, as industries start moving towards automation. Manufacturing of a component/item requires a lot of resources and energy. Usage of water in industries has increased in recent years, as it is used as both a resource and an energy generating source. Neutralization of a processed liquid is carried out by addition of solution through final control elements and most industries are utilizing pneumatic‐based control valves, which have a disadvantage in the regulation of additives. RESULTS: A motorized valve system is introduced, which is compared with the pneumatic system in terms of their performance. The proposed system is implemented with different controllers such as proportional–integral controller tuned using linear matrix inequalities, modified internal model controller, model predictive controller and nonlinear autoregressive (NAR) model, where the final control elements are tested for pH neutralization. On implementation of the proposed system the NAR model has proven to be better under the motorized control valve system for the neutralization process, with a peak value of 7.08 (pH), settling time of 75.16 s, rise time of 5.96 s, maximum overshoot of 10.1%, slew rate of 0.21 per second, integral square error of 0.95, integral absolute error of 0.48 and integral time absolute error of 1.88. CONCLUSION: Based on the results obtained it is observed that the NAR controller utilized with motorized control valve has better functionality in neutralizing a solution. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

The common method for estimating nonlinear regression coefficients with Gaussian errors is nonlinear least squares. It is not robust for regression with heavy-tailed errors. The nonlinear least trimmed squares method is an efficient procedure for regression with heavy-tailed errors. We are introducing nonlinear least trimmed squares for the nonlinear regression with non-Gaussian stable errors and estimate the parameters of the stable distributions based on the order statistics. The traditional least trimmed squares regression algorithm is adopted for errors with infinite variance by modifying the trimming procedure. This paper estimated the nonlinear regression coefficients with stable errors through the proposed nonlinear least trimmed squares algorithm based on a new trimming procedure based on the properties of stable order statistics moments. The proposed algorithm was applied to real and simulated datasets and evaluated using a bootstrap standard deviation estimator. [ABSTRACT FROM AUTHOR]

Objective: To identify trends in physical fitness test scores of college freshmen and their physical fitness from 2018 to 2021, and to analyze the relationship between college students’ Body Mass Index (BMI) and Physical Fitness Index (PFI). Methods: This study obtained physical fitness test data from 7,541 freshmen at a university in Beijing, China, from 2018 to 2021. Analysis of variance (ANOVA) was used to compare the physical fitness indicators among different BMI levels by gender. A nonlinear quadratic regression model was used to evaluate the relationship between BMI and each indicator within gender groups. Results: The BMI of freshmen in China was generally increased over the study period, and BMI levels influenced students’ physical fitness indexes to varying degrees. BMI was significantly correlated with the physical fitness indexes and PFI. The increase in BMI had a greater influence on the PFI of males than females. Conclusion: Students with a normal BMI show better physical fitness. A BMI below or above the normal range may result in poor physical fitness. The relationship between BMI and PFI has an inverted u-shaped curve. Physical education programs should be tailored to students with different fitness levels and fundamentals, including but not limited to the development of strength, speed, and other qualities. [ABSTRACT FROM AUTHOR]

AbstractThe present study used low-cost, abundant, and nontoxic kaolin and kaolin-based PVA/alginate composite (KPA) as adsorbents to remove U(VI) and Sr(II) ions from aqueous solutions. The characterization of the adsorbents was performed by several techniques. The results showed that PVA/alginate enhanced the functional groups and changed the surface morphology. Adsorption isotherm parameters were determined through both linear and non-linear regression analyses, from which it was inferred that the Langmuir model provides a better fit than other ones. The uranium removal capacity of KPA significantly improved as compared to that of kaolin, reaching 104.17 mg/g under the conditions of 30 min, pH 4.0, 298 K, and a solid-to-liquid ratio of 1.2 g/L. The strontium removal capacity of kaolin was significantly high as compared to that of KPA, reaching 68.96 mg/g under the optimum conditions. Removal of the radionuclides on both adsorbents was better described by the pseudo-second-order kinetic model, showing the ions to be immobilized on the adsorbents through chemical adsorption. Kaolin was not affected by interfering metals for U(VI) and Sr(II) adsorption, but KPA composite was influenced by the other metals. The adsorbents display potential as candidates to remove uranium and strontium from wastewater. [ABSTRACT FROM AUTHOR]

Pectinases are commonly industrially synthesized by molds. This study aimed to optimize pectin lyase synthesis by a bacterium, Pseudomonas fluorescens, using both the one‐factor‐at‐a‐time (OFAT) method and response surface methodology. First, on optimization of pectin lyase fermentation by the OFAT method, the effects of pectin, peptone, yeast extract, (NH4)2SO4, pH, and salts were investigated. The highest pectin lyase activity was determined to be 28.63 U/mL at pH 8, 30°C, with 1% (w/v) pectin and 0.14% (w/v) (NH4)2SO4 concentration at the 90th hour. The effect of substrate inhibition on the microbial growth was also investigated, and the results showed that the process can be described by noncompetitive inhibition model. The values of kinetic constants were determined as µm = 0.175 h−1, KS = 6.931 g/L, and, KI = 6.932 g/L by nonlinear regression analysis. It was reported that pectin lyase enzymes exhibited peak activity at 50°C and pH 8. Finally, response surface methodology (RSM) was utilized to optimize pH, concentrations of ammonium sulfate, and pectin, which were chosen as independent variables. The interactions between these variables were also examined. According to RSM, the optimum values of the parameters to achieve a maximum pectin lyase activity of 35.62 U/mL were determined to be pH 7.97, 1.25% (w/v) pectin concentration, and 0.25% (w/v) (NH4)2SO4 concentration. [ABSTRACT FROM AUTHOR]

The study aimed to optimise post-activation potentiation (PAP) strategies for Rider 1 in elite team sprints to improve performance over 250 m (opening lap), with a focus on female cyclists. Eight national-level track cyclists participated in this study, undergoing four sets of activation strategies: control (CON), dynamic high inertia (DYN, 4 × 4 pedal strokes), isometric contraction (ISO, 4 × 4 s, 4 angles), and back squat activation (BSQ, 4 × 4 rep, 80%1RM). The tests were divided into pre-activation and post-activation phases, including measurements at 4 min, 8 min, and 12 min after activation. The tests included a 250 m time trial (TT) and segment timing, with measurements of peak torque, peak power, average power, and cadence. The mean cadence, torque, and power for the first 62.5 m of pedal revolutions were collected. Paired-sample t-tests were used to assess activation differences. Multiple group comparisons were conducted using analysis of variance (ANOVA). The Bonferroni correction was used to control Type I errors. For significant activation strategies, linear or non-linear regression was applied to extrapolate the torque-cadence and power-cadence profiles, and the parameter differences were examined to investigate profile changes. Cohen's d and Cohen's f were used as effect sizes. After DYN activation, the 250 m TT significantly improved (p = 0.018), primarily through a reduction in the 62.5 m time (p = 0.006) and an increase in peak torque (p = 0.018). After 12 min of ISO activation, the 250 m TT showed a large effect but did not reach the significance level under Bonferroni correction (p = 0.135, d = 0.860), with a notable reduction in the 62.5 m time (p = 0.003). PAP can be strategically employed to enhance the performance of elite female Rider 1 in team sprints. [ABSTRACT FROM AUTHOR]

The automotive industry is increasingly focused on developing more energy-efficient and eco-friendly air-conditioning systems. In this context, CO2 microchannel gas coolers (MCGCs) have emerged as promising alternatives due to their low global warming potential (GWP) and environmental benefits. This paper explores the application of machine learning (ML) algorithms to predict the thermohydraulic performance of MCGCs in automotive air-conditioning systems. Using data generated from an experimentally validated numerical model, this study compares various ML techniques, including both linear and nonlinear regression models, to forecast key performance metrics such as refrigerant outlet temperature, pressure drop, and heat transfer rate. Spearman's correlation was employed to develop performance maps, whereas the R2 and MSE metrics were used to evaluate the models' predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas cooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy ranging from 71% to 99%. This implies that nonlinear regression generally performs better than linear regression models in assessing the overall thermohydraulic performance of microchannel gas coolers. This research brings forth new ideas on how ML methods can be applied to enhance efficiency and effectiveness in gas coolers, contributing to the development of more eco-friendly automotive air-conditioning systems. [ABSTRACT FROM AUTHOR]

Residuals are essential in regression analysis for evaluating model adequacy, validating assumptions, and detecting outliers or influential data. While traditional residuals perform well in linear regression, they face limitations in exponential family models, such as those based on the binomial and Poisson distributions, due to heteroscedasticity and dependence among observations. This article introduces a novel standardized combined residual for linear and nonlinear regression models within the exponential family. By integrating information from both the mean and dispersion sub-models, the new residual provides a unified diagnostic tool that enhances computational efficiency and eliminates the need for projection matrices. Simulation studies and real-world applications demonstrate its advantages in efficiency and interpretability over traditional residuals. [ABSTRACT FROM AUTHOR]

Low serum albumin levels increase coronary morbidity, mortality, and postoperative cardiovascular risk. Therefore, this study investigated the relationship between these levels and the length of hospital stay in patients undergoing percutaneous coronary intervention (PCI) for acute coronary syndrome. A total of 350 patients were divided into quartiles according to serum albumin levels. Univariate and multivariate analyses were performed to identify factors associated with the length of hospital stay. A non-linear regression analysis of serum albumin and length of hospital stay was also performed. The results of the multifactorial analysis revealed low serum albumin levels as an independent predictor of longer hospital stay, even in the fully adjusted model. In the segmented linear regression model, serum albumin level showed a U-shaped relationship with the length of hospital stay. In conclusion, low serum albumin level was an independent predictor of longer hospital stay in patients undergoing PCI for acute coronary syndrome, with shorter stays observed for increasing serum albumin levels. Low serum albumin can be used to identify patients who require longer hospitalization and may need additional nutritional support or interventions to improve their prognosis. [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]

Most of the scientific research is focused on KOH-based alkaline electrolysers, while NaOH-based ones are unexplored although they present interesting features. This paper presents a semi-empirical model developed in the Python environment to predict a NaOH-based alkaline electrolyser's performance to cover such a research gap and perform an optimisation procedure of electrochemical parameters. A sensitivity analysis has been carried out to study how its performance changes while varying the: i) NaOH content, ii) pressure, and iii) both. Separately, the best result has been obtained with a NaOH content and an operating pressure of 8% and 6.5 bar, respectively. Furthermore, the same values have been recorded even by varying both the NaOH content and the operating pressure. Specifically, a maximum average efficiency increase of 3.57% at 35 ° C , 0.17% at 40 ° C , and 3.74% at 35 ° C in the case of NaOH content, pressure, and both, respectively. [Display omitted] • A semi-empirical model of a NaOH-based alkaline electrolyser is developed. • Optimised electrochemical coefficients are obtained using non-linear regression. • A sensitivity analysis of a NaOH-based alkaline electrolyser is performed. • Optimal operating conditions, based on the cell temperature, are achieved. • Efficiency increase of 3.74% is obtained with NaOH content and pressure variation. [ABSTRACT FROM AUTHOR]

Oyster reef living shorelines are popular solutions to mitigate coastal hazards while providing ecosystem services, and their hydrodynamic characteristics deserve further examination. This paper reports an experimental and numerical study of the hydrodynamic behavior of a bagged oyster shell (BOS) reef under regular waves. The dependence of the pore pressure within the reef and the hydrodynamic coefficients on the reef height, length, porosity, and wave parameters are investigated. The results show that the normalized maximum pore pressure p s _ m ∗ decreases from the seaside of the BOS reef to its leeside and decreases with increasing dimensionless reef length. With increasing wave steepness, p s _ m ∗ fluctuates with an overall increasing trend. With increasing BOS reef porosity, p s _ m ∗ decreases at seaside locations and increases at leeside locations; simultaneously, the wave reflection coefficient Kr and the wave dissipation coefficient Kd decrease, and the wave transmission coefficient Kt increases. With increasing wave steepness for a given wave period, Kr and Kt tend to increase, but Kd exhibits the opposite trend. As the dimensionless reef height and length increase, Kt decreases and Kd increases. Multivariate nonlinear regression and genetic programming-based symbolic regression methods are individually used to derive two formulas for predicting the wave transmission coefficient, with the formula from the latter method being found to give a higher prediction accuracy. [ABSTRACT FROM AUTHOR]

We propose sparse regression as an alternative to neural networks for the discovery of parsimonious constitutive models (CMs) from oscillatory shear experiments. Symmetry and frame invariance are strictly imposed by using tensor basis functions to isolate and describe unknown nonlinear terms in the CMs. We generate synthetic experimental data using the Giesekus and Phan-Thien Tanner CMs and consider two different scenarios. In the complete information scenario, we assume that the shear stress, along with the first and second normal stress differences, is measured. This leads to a sparse linear regression problem that can be solved efficiently using l1 regularization. In the partial information scenario, we assume that only shear stress data are available. This leads to a more challenging sparse nonlinear regression problem, for which we propose a greedy two-stage algorithm. In both scenarios, the proposed methods fit and interpolate the training data remarkably well. Predictions of the inferred CMs extrapolate satisfactorily beyond the range of training data for oscillatory shear. They also extrapolate reasonably well to flow conditions like startup of steady and uniaxial extension that are not used in the identification of CMs. We discuss ramifications for experimental design, potential algorithmic improvements, and implications of the non-uniqueness of CMs inferred from partial information. [ABSTRACT FROM AUTHOR]

Polytetrafluoroethylene/aluminum (PTFE/AL) projectiles can effectively enhance the damage efficiency of traditional projectiles. Numerical simulation via ANSYS AUTODYN and data analysis via Origin are effective means to help predict the damage efficiency of PTFE/AL projectiles. In order to obtain a suitable damage prediction model to describe the explosive damage behavior, the Smoothed Particle Hydrodynamics (SPH) algorithm was used to describe the deflagration behavior of the core. In addition, core reactive models under different conditions of projectile caliber, impact velocity, Rolled Homogeneous Armor (RHA) steel target thickness, and head metal block thickness were established. The reactive behaviors under the influence of multiple factors were analyzed on the basis of the single-factor model. In addition, the multiple nonlinear regression damage prediction model was obtained, and the effectiveness of the model was verified. The results show that the multiple relationship index model has the highest reliability, and the overall error can be controlled within 20%. The research method has a certain guiding significance for the damage evaluation of the PTFE/AL projectile. [ABSTRACT FROM AUTHOR]

Cold tolerance was measured midwinter in a New Hampshire, USA, orchard collection of 33 peach and nectarine cultivars over 3 years using natural and artificial freezing methods. Flower bud survival and xylem and cambium hardiness were based on the inflection point from nonlinear regression, as well as comparisons of injury at about 220 (C. Flower bud hardiness was greatest in ‘BuenOs’, ‘Contender’, ‘Cresthaven’, ‘Redhaven’, and ‘Scarlet Rose’. The lowest hardiness occurred in ‘Brigantine’, ‘Desiree’, ‘Emeraude’, ‘Evelynn’, ‘Galaxy’, ‘Glenglo’, ‘Jade’, ‘PF5D’, ‘PF23’, ‘Silverglo’, ‘Spring Snow’, and ‘Sugar May’. Other cultivars had either intermediate or inconsistent year-to-year bud hardiness. Bud hardiness in 2021 was weakly correlated with bud hardiness in 2023, but neither were correlated with bud mortality in 2022 after a severe freeze. Cambial hardiness in one year was not correlated with hardiness in other years. ‘August Rose’, ‘BuenOs’, ‘Cresthaven’, ‘Desiree’, and ‘Silverglo’ had the hardiest cambium which was consistent from year to year. Xylem hardiness was greatest in ‘BuenOs’, ‘Contender’, ‘Desiree’, ‘John Boy’, ‘PF17’, ‘Redhaven’, ‘Silvergem’, and ‘TangOs’. Xylem injury was significantly correlated across years indicating that this tissue responds more consistently to midwinter freezing temperatures than flower buds and cambium. [ABSTRACT FROM AUTHOR]

Background: The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest. Methods: Alternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit). Results: The beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches. Conclusion: When analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure. Trials registration: ORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014. [ABSTRACT FROM AUTHOR]

Background: Gunshots affect those directly involved in an incident and those in the surrounding community. The community-level impact of nighttime gunshots, which may be particularly disruptive to the sleep of nearby community members, is unknown. Objective: Our aim is to estimate the number of people potentially affected by nighttime gunshots and the relationship between nighttime gunshots and median household income in the USA. Design: We collected publicly available data on the timing and location of gunshots in six U.S. cities (Baltimore, MD; Boston, MA; Washington, D.C.; New York, NY; Philadelphia, PA; and Portland, OR) from 2015 to 2021. We then analyzed the data by computing rate ratios (RRs) to compare the frequency of gunshots during nighttime hours (6:00 pm to 5:59 am) versus daytime hours (6:00 am to 5:59 pm). Additionally, we used geospatial mapping to create choropleth maps to visualize the variation in nighttime gunshot density across cities. We estimated, using city-wide population, person-nights potentially impacted by the sound of gunshots within areas of 0.2- (low) and 0.5-mile (high) radius. Finally, for five of six cities where data on median household income were available by census tract, we built nonlinear regression models to estimate the relationship between the number of nighttime gunshots and median household income. Key Results: We analyzed 72,236 gunshots. Gunshots were more common during the nighttime than daytime (overall RR = 2.5). Analyses demonstrated that the low estimates for the mean annual number of person-nights impacted by nighttime gunshots were 0.4 million in Baltimore and Portland, 1.3 million in Philadelphia, 1.6 million in Boston, 2.9 million in New York City, and 5.9 million in Washington. The number of nighttime gunshots was inversely related to median household income. Conclusions: Nighttime gunshots are prevalent, particularly in low-income neighborhoods, and may have under-recognized effects on the surrounding community. [ABSTRACT FROM AUTHOR]

The petroleum fraction of crude oil is laden with notorious nitrogen compounds, whose removal facilitates fuel oil transport and enhances the longevity of hydrodesulfurization catalysts. Petroleum refining operation thus demands the adoption of unconventional techniques, such as coupled oxidative–extractive denitrogenation. Kinetic models can provide insights into the reaction mechanism and the design and performance of reactors. In this work, nine different kinetic models were developed by applying the Langmuir–Hinshelwood and Eley–Rideal approaches to various reaction pathways. The screening of the kinetic models was done based on the values of the rate law parameters and corresponding R2 and adjusted R2 values calculated through a multi-parametric, non-linear regression based on the Levenberg–Marquardt algorithm. Out of all the models, the Eley–Rideal model corresponding to the rate expression rs9 was found to be the best fit, reflecting that hydrogen peroxide remained in bulk while pyridine alone was adsorbed on the catalyst. The selection of suitable extractants for the extractive removal of oxidation products was made based on their extraction efficiency and partition coefficients. The ternary liquid–liquid equilibrium (LLE) data for the (acidulated water-pyridine-isooctane) and (acidulated water-pyridine N-oxide-isooctane) systems were plotted, and Othmer–Tobias, Hand, and Bachman correlations explained the consistency of the experimental LLE data. [ABSTRACT FROM AUTHOR]

Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets. • Adaptive soft sensor combines unsupervised domain adaptation and dynamic PLS for variable conditions. • Leverages time-delay data augmentation to capture dynamics and constructs a nonlinear regression model in a latent space. • Introduces regular terms for distribution alignment and first-order differences, and incorporates Laplace regularity for geometric structure preservation. • Optimization within partial least squares framework, hyperparameters tuned by Bayesian optimization. [ABSTRACT FROM AUTHOR]

This research aims to analyze the threshold effect (nonlinear regression) of the impact of economic growth, foreign direct investment inflow, and trade on the environmental degradation in China from 2007 to 2019 by using panel data at the province level. The results of threshold effect regression show that China's economic growth and trade have a single threshold effect on carbon dioxide emissions, but foreign direct investment does not have any. The results reveal that when economic development increases and reaches a threshold value, its impact on carbon dioxide emissions weakens. When trade volume increases to a threshold value, its negative impact on carbon dioxide emissions strengthens. Both methods find that foreign direct investment and trade have a slightly negative relation with carbon dioxide emissions, which may indicate that in recent years of economic development, China's technological and industrial upgrading has been decoupling foreign investment and trade from carbon dioxide emissions. Moreover, this research finds that China's eastern provinces have reached the threshold, yet its middle and western provinces have not. Based on our empirical results, it is suggested that the Chinese government needs to pay more attention to the unbalanced economic development and environment governance between east and middle & western areas, and take measures to prevent the transfer of polluting industries from China's developed provinces to undeveloped middle and western provinces. [ABSTRACT FROM AUTHOR]

Thermal protection performance (TPP) is an important index to evaluate the performance of firefighting clothing. The purpose of this work is to build a model to predict the TPP values of fabrics with fewer variables. Two properties of flame-retardant cotton were tested with TPP values under different air gaps, and the correlations between these properties were also analyzed. A combined model was established by integrating multivariate nonlinear regression model and gradient boosting regression tree model. Then the combined model was compared with these two single models. The results showed that the correlation coefficients between gram weight and thickness of fabric and TPP value were 0.833 and 0.837, respectively, indicating a strong correlation. The correlation coefficient between air gap and TPP value was 0.304, indicating a weak correlation. In predicting the thermal protective performance of flameretardant cotton, this study employed a multivariate nonlinear regression model, a Gradient Boosting Regression Tree (GBRT) model, and a combined model. After comparing various evaluation metrics, it was finally decided to adopt the combined model for predicting the thermal protective performance values of flame-retardant cotton. This method improved the prediction accuracy of thermal protective performance, facilitating the promotion and application of the combined model. Furthermore, when exploring the thermal protective performance of flame-retardant cotton, the use of fewer variables to establish the prediction model can not only significantly simplify the complex structure of the model but also greatly enhance the analysis efficiency, ensuring the efficiency and precision of the research process. [ABSTRACT FROM AUTHOR]

We introduced DrYFiT (drying data fitting tool), a Microsoft Excel freeware tool to be used for modeling thin‐layer drying of foods, which is available at https://drive.google.com/drive/folders/1ouompmNkmdmw1KMTJUnY0t8dJqSJ9iKv?usp=drive%5flink. There are 12 models in the tool and it can be used without any modeling and programming skills. Time and moisture ratio data can be entered and one of models available (one at a time) can be selected to describe the drying data. Parameter values, standard error of the parameters, p value and a statement that indicates whether the parameter is statistically significant or not (α = 0.05) are reported. Moreover, R2, adjusted R2 and root mean square error values are calculated for each model. Users can instantaneously observe the experimental data and the model fit on the same graph. Residual plot is given next to this graph. It is possible for the users to have the results of all models applied to drying data within a couple of minutes. The results of DrYFiT were compared with some popular software programs used for nonlinear regression and identical values (parameters, standard errors, p values, goodness‐of‐fit statistics) were obtained for 40 datasets. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental 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.)

Deformation plays an important role in dam monitoring, especially in judging potential damage. Excessive deformation serves as an indicator of the abnormal evolution of dam structures, but the pivotal issue lies in definition for "excessive" precisely. Deformation prediction serves as reference for detection, but it is challenged by multiple factors. Sequence decomposition is an approach to select related factors and predict precisely, but the mechanism in application is a critical problem. This study proposes a decomposition Long short-term memory (DLSTM) model for the deformation prediction of arch dams. Segmented correlation feature extraction method balances feature correlation and consistency. Multiple linear regression is introduced for the integration, and the periodic extension is used to simplify unnecessary predicting operations. The proposed DLSTM model is evaluated using monitoring data from the LYX arch dam. The results show an 18% improvement in the accuracy, as well as better stability of the prediction model. The DLSTM-based multiple linear regression outperforms nonlinear neural network methods in terms of both physical interpretation and performance. The superiority of the proposed model over BP neural network, wavelet neural network, and LSTM model demonstrates that the DLSTM model is a promising approach for practical applications in dam deformation monitoring. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNA) exert regulatory actions via base pairing with their binding sites on target mRNAs. Cooperative binding, i.e., synergism, among binding sites on an mRNA is biochemically well characterized. We studied whether this synergism is reflected in the global relationship between miRNA-mediated regulatory activity and miRNA binding site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, we found that, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extraordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA-targeted mRNAs in translationally less active polysomes relative to more active polysomes. In these four studies, we qualitatively observed obvious positive correlation between the extent to which an mRNA is miRNA-regulated and its binding site count. This paper summarizes the datasets used. We also quantitatively analyzed the correlation by comparative linear and non-linear regression analyses. Non-linear relationships, i.e., accelerating rise of regulatory activity as binding site count increases, fit the data much better, conceivably a transcriptome-level reflection of cooperative binding among miRNA binding sites on a target mRNA. This observation is potentially a guide for integrative quantitative modeling of the miRNA regulatory system. [ABSTRACT FROM AUTHOR]

This paper presents the differential evolution algorithm with adaptive mutation and crossover strategies (DEAMC) for solving nonlinear regression problems. The DEAMC algorithm adaptively uses two mutation strategies and two ranges of crossover rate. We evaluate its performance on the National Institute of Standards and Technology (NIST) nonlinear-regression benchmark containing many models of varying levels of difficulty and compare it with classic differential evolution (DE), enhanced differential evolution algorithm with an adaptation of switching crossover strategy (DEASC), and controlled random search methods (CRS4HC, CRS4HCe). We also apply the proposed method to solve parameter identification applications and compare it with enhanced chaotic grasshopper optimization algorithms (ECGOA), self-adaptive differential evolution with dynamic mutation and pheromone strategy (SDE-FMP), and JAYA and its variant methods. The experimental results show that DEAMC is more reliable and gives more accurate results than the compared methods. [ABSTRACT FROM AUTHOR]

This study was conducted to identify the dendrometric variables highly correlating with above-ground (AGB), below-ground (BGB) and total (TB) biomass of tea grown in Uva and Up Country Sri Lanka, develop allometric models to estimate AGB, BGB and TB of tea plants, and to investigate the applicability of already developed allometric models in other countries to Sri Lanka. Twelve experimental plots from TRIORCON field experiment at Tea Research Institute of Sri Lanka, and 18 farmer fields in Uva region, Sri Lanka were selected as the study sites. Representative tea plants were uprooted from the selected sites and seven dendrometric variables were measured. Fresh and dry AGB and BGB of the tea plants were determined. The best allometric models to predict AGB, BGB and TB using dendrometric variables were developed using multiple linear and non-linear regression analyses. Non-linear allometric models fitted best when predicting AGB and TB. Over 64% of total variability of AGB and TB was explained by these models using collar diameter at the soil level. Linear allometric fitted best when predicting BGB. Over 92% of total variability of BGB was explained by linear regression models using collar circumference, mainstem circumference, mean length and circumference at end of the primary branches. Leave-one-out cross validation showed strong positive (AGB and TB) and moderate positive (BGB) correlations between observed and predicted biomass values. Developed linear allometric models in comparison to those in literature showed better fit to data collected under Sri Lankan conditions and precisely estimated AGB, BGB and TB. [ABSTRACT FROM AUTHOR]

This paper employs the Finite Element Method (FEM) to simulate and analyze the effects of corrosion defect parameters on the stress and failure pressure of pipelines. It investigates how the boundary conditions of the pipeline model influence stress and examines the sensitivity of failure pressure to corrosion defect parameters. A nonlinear regression equation has been developed from a dataset obtained through simulation experiments to predict the failure pressure of corroded pipelines. To validate the effects of corrosion defect parameters on failure pressure, a hydrostatic test platform for an API 5L X65 pipeline with corrosion defects was established to measure stress levels and failure pressures across varying corrosion defects. This study reveals that failure pressure is negatively correlated with corrosion length and depth, while positively correlated with corrosion width. Among these parameters, corrosion depth exerts a more significant influence on the pipeline's failure pressure than corrosion length and width. Within the range of corrosion defect parameters examined, the maximum deviation of the prediction equation's results from the simulation results is 8.71%, with an average deviation of 5.81%. The standard deviation of the fitted residuals is 0.01837. Additionally, the maximum deviation between the predicted results and experimental measurements is 8.39%, with an average deviation of 7.77%. The strong agreement between the predicted results from the equation and the actual measured data underscores the effectiveness of the nonlinear regression equation. [ABSTRACT FROM AUTHOR]

We investigate whether COVID-19 vaccination is monotonically associated with political and religious orientations. Conspiracy theories among politically conservative groups and Evangelical Protestants have drawn considerable attention to COVID-19 vaccination. Previous research indicates that political and religious orientations can have either positive or negative effects on vaccination. We employ non-linear regression with data on Republican turnout and Protestantism in over 2,000 US counties. Our findings reveal that the relationships between COVID-19 vaccination and Republican and Evangelical Protestant inclinations follow an inverted-U shape. Furthermore, Protestant inclination has a less negative impact on COVID-19 vaccination when infection rates are high. For vaccination promotion, targeting segmented groups is preferable to solely targeting based on political and religious affiliations. [ABSTRACT FROM AUTHOR]

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models. [ABSTRACT FROM AUTHOR]

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusions\nKEY POINTSTherapeutic drug monitoring (TDM) is an important tool for treatment optimisation. Its usefulness has recently been demonstrated for some first-line antidepressants; however, few studies have been reported on the relationship between blood levels of mirtazapine and its antidepressant effects. The aim of this study was to investigate the association between blood concentration of mirtazapine and antidepressant response.59 outpatients treated with mirtazapine for depression were recruited and followed up for three months in a naturalistic setting. Hamilton Depression Rating Scale-21 (HAMD-21) was administered at baseline, month 1, and month 3 to assess antidepressant response. Mirtazapine serum concentration was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between serum concentration of mirtazapine and antidepressant response.Our results showed no overall association between serum concentration of mirtazapine and symptom improvement at month 1 and month 3. A marginally significantly higher serum concentration of mirtazapine was found in responders vs non-responders at month 3.The study suggests that serum concentration of mirtazapine is not strongly associated with the antidepressant efficacy of mirtazapine. This is probably attributed to its pharmacodynamic profile, even though higher blood levels seem to be marginally more effective.Mirtazapine plasma levels association with response is mild and do not follow the same curve of other antidepressantsMirtazapine higher plasma levels may show some benefit in a subgroup of patientsTherapeutic drug monitoring may help during antidepressant treatment [ABSTRACT FROM AUTHOR]

AbstractFlorida sugarcane production is mostly on organic soils, but the cultivation area on mineral soils is also expanding. Silicon (Si) is beneficial for sugarcane growth and development on weathered soil. However, the low solubility and availability of soluble Si is an issue in Florida’s mineral soils. This study aimed to assess soil Si extraction methods based on sugarcane yield response to calcium silicate application. It also examined the relationship between sugarcane yield with extracted plant-available soil Si, alongside plant Si concentrations. The experiment followed a randomized complete block design involving 2 fields, with 24 plots each, and 6 replications. Four rates of calcium silicate (0 control, 2.24, 4.48, and 6.67 Mg ha−1) were applied before planting sugarcane. Soil samples were collected prior to planting and after each harvest. Soil Si was extracted using 0.5 M acetic acid, 0.7 N NH4OAc, Mehlich 3, and 0.01 M CaCl2 extractants. Leaf samples were collected over a three-year sugarcane life cycle (from 2019 to 2022) to evaluate the relationship between soil Si and leaf Si concentrations. The relationships between parameters were analyzed using linear and nonlinear regression models. Results indicated that higher Si application rates increased Si concentrations in sugarcane leaves in both fields. However, only one field showed a yield response to Si, likely due to high pre-plant soil Si in the other field. The best extractant should correlate strongly with either yield or leaf Si content, but no single extractant showed a strong correlation with either, making it infeasible to identify the best extractant. [ABSTRACT FROM AUTHOR]

Expansive soils, known for their significant volume change with variations in moisture content, are widely distributed around the globe. Due to their swelling properties, expansive soils pose significant engineering challenges, especially in rapidly developing countries like China. This study aims to investigate the swelling mechanisms of expansive soils, focusing on the influence of crack characteristics on swelling behavior. The research methodology includes field investigations, laboratory experiments, and theoretical modeling. By comprehensively considering crack rate, dry density, initial moisture content, and overburden load, a nonlinear regression swelling model is proposed in this research. The degree of crack development in expansive soils is quantitatively characterized by the content of filling materials, leading to the establishment of a crack rate model for expansive soils. Swelling tests on expansive soils with different crack contents were conducted. The results show that the swelling rate is negatively correlated with the initial moisture content and positively correlated with dry density and crack rate. Additionally, the larger the crack rate, the more significant the change in the swelling rate. Furthermore, model validation confirms that this nonlinear regression model accurately describes the relationship between swelling rate and influencing factors. It offers a more precise prediction tool for infrastructure design and maintenance in expansive soil areas, advancing geotechnical engineering practices. [ABSTRACT FROM AUTHOR]

Tensile perpendicular to grain is an important mechanical property in the design of joints in timber structures. However, according to the standards, this strength can be determined using at least two different methods: uniaxial tensile and three-point static bending. In this context, the present paper aims to investigate the influence of these test methods on the determination of tensile strength perpendicular to grain of wood used in civil construction timber. Three wood species from Brazilian planted forests (Pinus spp., Eucalyptus saligna, and Corymbia citriodora) were used in this investigation. Twelve specimens of each species were used for each test method investigated. Moreover, a statistical analysis was performed to propose an adjustment to the equation of the Code of International Organization for Standardization 13910:2014 for the three-point bending test. Tensile strength values perpendicular to grain obtained from the uniaxial tensile test were significantly higher than those determined by the three-point bending test. It is proposed that the tensile strength perpendicular to grain can be determined more precisely with adoption of coefficient 5.233 in the term [(3.75·Fult)/b·Lh] of the equation specified by the Code of International Organization for Standardization 13910:2014 for the three-point bending test. [ABSTRACT FROM AUTHOR]

During the installation of a cable oil terminal, it is easy to leave scratches on the main insulation owing to uneven forces when removing the semi-conductive layer. Scratch defects cause field intensity distortion, which leads to partial discharge and insulation failure. This study attempts to establish a simulation model of a 220 kV cable terminal to determine the effect of the length, depth, and position of the scratch on the maximum field strength at the defect. The simulation and experiment demonstrate that the maximum field strength at the defect increases with greater length and decreases as the depth increases. Therefore, a prediction method for the terminal defect field strength based on a multivariate nonlinear regression model was proposed in this study. When the defect is located at 20mmfromthe root of the stress cone, the maximum field strength is 14.5 MV/ m when the length and depth are 2mmand 1mm, respectively. The maximum field strength at the defect was predicted based on the length, depth, and position of the scratch defect to evaluate the severity of the defect. [ABSTRACT FROM AUTHOR]

Due to their superior technical and hydraulic performance and minimal environmental impact, porous weirs have emerged as a feasible alternative to traditional impermeable solid weirs in water transmission and distribution systems. This research study assesses the hydraulic performance of porous broad-crested weirs (PBCWs) with sloping crests. To accomplish this, 30 PBCW models were designed and tested under various operating conditions, including free and submerged flow scenarios. A comparative analysis was conducted to evaluate the performance of PBCWs with and without sloping crests, with the results being compared against a solid broad-crested weir (SBCW) model. The findings revealed that PBCWs with an upstream crest slope exhibited an average increase of 38% in the discharge coefficient compared to similar SBCWs. Similarly, PBCWs with a downstream crest slope demonstrated an average increase of 28% in the discharge coefficient. Additionally, PBCWs with sloping crests showcased heightened sensitivity to changes in tailwater levels compared to PBCWs with horizontal crests, with PBCWs featuring an upstream crest slope being more responsive than those with a downstream crest slope. Finally, leveraging extensive experimental data, empirical equations were derived using nonlinear multi-variable regression and gene-expression programming techniques to accurately represent the free flow discharge coefficient and submerged flow discharge reduction factor. [ABSTRACT FROM AUTHOR]

A salient bottleneck to driving the wider use of polyvinylidene fluoride (PVDF) and its copolymers in smart applications is the requirement for a high-voltage poling process required to activate their $\beta $β-phase crystal structure before the realisation of the piezoelectricity response. In pursuit of enhancing the β-phase content of PVDF-based films, various approaches have been employed in recent years. Yet, a perfect solution to the challenge remains elusive. Here, we report a preliminary investigation into the production of unpoled poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) films reinforced with barium titanate (BaTiO3) and activated carbon (AC) to improve the piezoelectric property. Employing the mixture design of experiments (MDOE) method, various formulations of the composite’s constituents weight fractions were determined. Afterwards, series of composite films were produced via a toxic-free solution casting method and characterised by Fourier Transform Infrared Spectrum (FTIR) to determine the β-phase content. A non-linear polynomial regression model was established from the experimental FTIR data, and the adequacy of the model was confirmed using ANOVA. Subsequently, an MDOE-enabled optimisation was undertaken, generating a formulation of the composite to obtain the highest β-phase content to be $15$15 wt.% BaTiO3 and $0.74$0.74 wt.% AC. This combination produces a theoretically predicted β-phase content of $72.7$72.7%. A further experimental test employing the optimal formulation produced a β-phase content of $71.90$71.90%, achieved with neither mechanical stretching, thermal annealing, nor electrical poling. [ABSTRACT FROM AUTHOR]

This paper uses neural networks and machine learning to study the optimal choice of the interior penalty parameter of the discontinuous Galerkin finite element methods for both the elliptic problems and Biot’s systems. It is crucial to choose the optimal interior penalty parameter, which is not too small or too large for the stability, robustness, and efficiency of the approximated numerical solutions. Both linear regression and nonlinear artificial neural network methods are employed and compared using several numerical experiments to illustrate the capability of our proposed computational framework. This framework is integral to developing automated numerical simulation because it can automatically identify the optimal interior penalty parameter. Real-time feedback could also be implemented to update and improve model accuracy on the fly. [ABSTRACT FROM AUTHOR]