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The use of laser cladding technology to prepare coatings of AlCoCrFeNi high-entropy alloy holds enormous potential for application. However, the cladding quality will have a considerable effect on the properties of the coatings. In this study, considering the complex coupling relationship between cladding quality and the process parameters, an orthogonal experimental design was employed, with laser power, scanning speed, and powder feed rate as correlation factor variables, and microhardness, dilution rate, and aspect ratio as characteristic variables. The experimental data underwent gray correlation analysis to determine the effect of various process parameters on the quality of cladding. Then, the NSGA-II algorithm was used to establish a multi-objective optimization model of process parameters. Finally, the ANSYS Workbench simulation model was employed to conduct numerical simulations on a group of optimized process parameters and analyze the change rule of the temperature field. The results demonstrate that the laser cladding coating of AlCoCrFeNi high-entropy alloy with the single pass is of high quality within the determined orthogonal experimental parameters. The powder feed rate exerts the most significant influence on microhardness, while laser power has the greatest impact on dilution rate, and scanning speed predominantly affects aspect ratio. The designed third-order polynomial nonlinear regression model exhibits a high fitting accuracy, and the NSGA-II algorithm can be used for multi-objective optimization to obtain the Pareto front solution set. The numerical simulation results demonstrate that the temperature field of AlCoCrFeNi high-entropy alloy laser cladding exhibits a "comet tail" phenomenon, where the highest temperature of the molten pool is close to 3000 °C. The temperature variations in the molten pool align with the features of laser cladding technology. This study lays the groundwork for the widespread application of laser cladding AlCoCrFeNi high-entropy alloy in surface engineering, additive manufacturing, and remanufacturing. Researchers and engineering practitioners can utilize the findings from this research to judiciously manage processing parameters based on the results of gray correlation analysis. Furthermore, the outcomes of multi-objective optimization can assist in the selection of appropriate process parameters aligned with specific application requirements. Additionally, the methodological approach adopted in this study offers valuable insights applicable to the exploration of various materials and diverse additive manufacturing techniques. [ABSTRACT FROM AUTHOR]

The separation of silybin A (SilA) and B (SilB) diastereomers in optically pure compounds is challenging due to their very similar physical and chemical properties. However, such separation is crucial for evaluating the biological activity of the diasteroisomers SilA and SilB, which show very different performance in pharmacological applications like treating prostate cancer, liver diseases, and Alzheimer's disease. The most common isolation method is based on high-performance liquid chromatography, but it is slow and has a yield in pure SilB of hundreds of milligrams per day. An alternative chemo-enzymatic separation method, utilizing an immobilized lipase CALB catalyst to stereoselectively acetylate silybin B (1b), offers advantages in terms of higher productivity, selectivity, and scalability, particularly when applied in flow reactors. This study delves into the kinetics of Sil acetylation catalyzed by Novozym 435 in a continuous flow milli-reactor, investigated at various temperatures, volumetric flow rates, and Sil initial concentrations. It is noteworthy that, at the current state of the art, there is a lack of kinetic studies on this reaction, emphasizing the novelty and significance of this work. The kinetic and fluid dynamic parameters were estimated using a non-linear regression analysis of experimental data. The examined reaction showed a null apparent activation energy, explaining the temperature insensitivity of the final acetylated silybin B (1b) concentration. Furthermore, the decrease in steady-state concentrations of the acetylated products with increasing volumetric flow rates indicated that the reaction was occurring in a kinetic regime. Interestingly, a maximum starting Sil concentration was identified, above which there was no favorable impact on conversion. Highlights: Silybin B was more selectively acetylated compared to silybin A. Reaction rate was insensitive to temperature due to exothermic adsorption. Kinetics and adsorption parameters were obtained by non-linear regression analysis. Good prediction achieved with axial dispersion model and Eley-Rideal mechanism. Flow chemistry boosts silibinin enzymatic resolution over traditional methods. [ABSTRACT FROM AUTHOR]

Summary: The aim of this study is to clarify the essential means of describing the characteristics of foods represented by texture terms using a model based on the mechanical properties of foods. Seven texture terms were identified, and the mechanical properties of gel‐like samples were determined using elasticity and rupture tests. The Blatz, Sharda and Tschoegl (BST) equation was applied to the stress‐strain curve of the samples, and the elastic parameter E and the nonlinear index n were calculated. The evaluation model of the score obtained from the sensory test for each texture term was developed using E and n, rupture stress, and rupture depth. The model demonstrated the contribution of each variable to the texture term. Moreover, a single correlation was investigated between each specific term for the texture and each mechanical parameter, respectively. These results confirmed that some of the impressions given by the texture terms did not match the actual mechanical properties of foods. [ABSTRACT FROM AUTHOR]

Functionally gradient material (FGM) in service often experience temperature variations that can affect the propagation characteristics of guided waves. This investigation aims to study the propagation of thermoelastic guided waves in the FGM plate. A computational method for the state vector and Legendre polynomials hybrid approach, which is proposed based on the Green–Nagdhi theory of thermoelasticity. The heat conduction equation is introduced into the governing equations, and optimized using univariate nonlinear regression for arbitrary gradient distributions of the material components. To study their dispersion characteristics, a non-hierarchical calculation for the dispersion curves of FGM plates versus temperature is realized. In addition, a frequency domain simulation model is developed and compared with theoretical data to evaluate the accuracy and feasibility of the proposed theory. Then, the influence of Legendre orthogonal polynomial cut-off order on dispersion curve convergence is investigated. Subsequently, the shift of the gradient index and temperature variation on the fundamental mode in dispersion curve is analyzed. The results indicate that changes in both gradient index and temperature lead to a systematic shift in the phase velocity of fundamental modes in the low frequency range. Meanwhile, anti-symmetric modes exhibit higher sensitivity. On this basis, the study can provide theoretical support for the acoustic non-destructive characterization of FGM plates versus temperature. [ABSTRACT FROM AUTHOR]

Chloroprene rubber (CR) is an expensive and frequently used material in many industries. Thus, a blend of natural rubber (NR) and CR can be used to balance cost and product performance. In this research, the primary objective is to achieve the ideal blend of CR/NR rubber for automotive industry by simultaneously optimizing various response variables, including hardness, tensile strength (TS), vulcanization index (CRI), torque difference and Tan δ. This optimization process is carried out using response surface methodology (RSM) and desirability functions. The study delves into examining the influence of accelerators, retarders, curatives, and the ratio of NR in the final batch on both curing characteristics and mechanical properties. The investigation is conducted through the application of analysis of variance (ANOVA) and linear/nonlinear regression models with the assistance of Design Expert 11. When the quantities of the fillers, TMTM80, DP80, S80, and CTPI80, are at their optimum levels of 1.08, 1.78, 3.5, and 0.96 PHR, respectively, and the NR ratio in the final masterbatch is around 27%, the estimated values for Tan δ, hardness, and TS are approximately 0.144, 55.183 Shore A, and 21.085 MPa, respectively. The observations from the validation experiments align with the predicted outcomes, as all response variables fall within the 95% prediction interval. It is noteworthy to mention that prior research has not attempted simultaneous optimization for CR/NR blend, incorporating these fillers. [ABSTRACT FROM AUTHOR]

The thermal decomposition and sintering kinetics of microfine magnesite were investigated to inaugurate a new One-step process for preparing high-density sintered magnesia. The optimal parameters for the discharging particle size of magnesite through ball milling were determined using an orthogonal experiment and Three-Way Measures ANOVA. The model for the thermal decomposition process of microfine magnesite was established through TGA-DSC and nonlinear regression analysis. Meanwhile, by describing the sintering process of microfine magnesite in detail through microstructure and CTE, the kinetic mechanisms of grain growth at different sintering stages were obtained. The results revealed that increasing the ratio of ball to powder can quickly obtain microfine magnesite which the bulk density reaches 3.45 g·cm−3 after One-step sintering. The thermal decomposition process required an activation energy of only 124.98 kJ·mol−1, which is much lower than existing technology and followed the Two-dimensional phase boundary reaction mechanism R 2. Additionally, the densification process was dominated alternatively by grain boundary diffusion and volume diffusion. This paper provided a detailed theoretical basis and leads a new direction in the One-step preparation of high-density sintered magnesia. [ABSTRACT FROM AUTHOR]

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

The paper considers the problem of nonparametric estimation of nonlinear characteristic in the FIR Wiener system. Methods proposed so far have suffered from the so-called 'curse of dimensionality' and their practical applications have been limited to the very short impulse responses of the linear component. In the proposed approach, the class of (stochastically initialised) repeated exponential input excitations is introduced, and a modified kernel-based estimator is presented. It is shown that the rate of convergence to the genuine system characteristic is significantly improved and does not depend on the length of the impulse response of the linear dynamic block. [ABSTRACT FROM AUTHOR]

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Nonlinear regression models are extensively applied across various scientific disciplines. It is vital to accurately fit the optimal nonlinear model while considering the biases of the Bayesian optimal design. We present a Bayesian optimal design by utilising the Dirichlet process as a prior. The Dirichlet process serves as a fundamental tool in the exploration of Nonparametric Bayesian inference, offering multiple representations that are well-suited for application. This research paper introduces a novel one-parameter model, referred to as the 'Unit-Exponential distribution', specifically designed for the unit interval. Additionally, we employ a stick-breaking representation to approximate the D-optimality criterion considering the Dirichlet process as a functional tool. Through this approach, we aim to identify a Nonparametric Bayesian optimal design. [ABSTRACT FROM AUTHOR]

This research models and simulates the development of transmission capacity considering electricity supply and demand dynamics. After presenting a causal loop diagram and designing a stock and flow diagram, the model's validity is confirmed using validation methods for system dynamics models. The analysis then proceeds to scenarios for the total electricity demand in Iran. Firstly, the country's electricity demand structure is broken into industrial, household, agricultural, and other sectors. By studying consumption trends in each sector, linear and nonlinear regression are used to predict total electricity demand. Next, three scenarios - optimistic, moderate, and pessimistic - are defined in terms of electricity demand, and the required transmission capacity is calculated and designed for 400, 230, 132, and 63-66 kilovolt substations to cover and meet future electricity demand over twenty years. The research findings over a 20-year horizon indicate that in the moderate scenario, where electricity demand increases by 90 percent, the transmission capacity needs to increase by 106 percent to meet the demand. In the optimistic scenario, where electricity demand increases by 71 percent, the transmission capacity needs to increase by 85 percent. In the pessimistic scenario, where electricity demand increases by 110 percent, the transmission capacity needs to increase by 126 percent. [ABSTRACT FROM AUTHOR]

This laboratory study employed synthesized urea-formaldehyde (UF) microcapsules and polyvinyl alcohol (PVA) microfibers as a self-healing system to improve the durability of concrete in cold climates. The resistance of concrete specimens to rapid freezingand- thawing (F/T) cycles was evaluated by measuring the change of relative dynamic modulus of elasticity (RDM) with respect to the number of F/T cycles. The control specimens (either with or without PVA microfibers) approached the failure state with a reduction of 38% in RDM after being subjected to 54 F/T cycles, whereas the self-healing specimens (either with or without PVA microfibers) remained in a good state with a reduction of approximately 10 to 15% in RDM after 732 F/T cycles. A polynomial regression model was developed to establish the relationship between the RDM and number of F/T cycles, and a three-parameter Weibull distribution model was employed to conduct the probabilistic damage analysis and characterize the relationship between the number of F/T cycles (N) and the damage level (D) with various reliabilities. The results revealed that the benefits of UF microcapsules and PVA microfibers to the frost durability of concrete diminish once the damage level exceeds a certain high level. Based on the Weibull distribution model, the relationships were established and validated between N and D by comparing the experimental data, the predicted data based on the nonlinear polynomial regression model, and the predicted data based on N-D relationships. The field service life of the self-healing concrete was then predictable at any given reliability. [ABSTRACT FROM AUTHOR]

This paper presents a finite difference-based analysis of the cone penetration test (CPT) in saturated intact clays. The modified Cam clay (MCC) model was used to capture the undrained behaviour of the saturated clays. The cone tip resistance values obtained from the numerical analyses were validated against those obtained from field and calibration chamber tests, as well as numerical analyses reported in the literature. The effect of the MCC parameters on tip resistance was investigated through sensitivity analyses. Based on a large number of numerical analyses of CPT with different MCC parameters performed in this study, a relatively comprehensive database of CPT results was obtained. Relative weight analysis was then used to determine the relative effect of each MCC parameter on the tip resistance. Using nonlinear regression analysis of the obtained database, a relationship between tip resistance and soil properties was developed. Finally, the application of the proposed equation in estimating the overconsolidation ratio and effective friction angle is illustrated with useful charts, numerical examples, and comparisons with different case studies. [ABSTRACT FROM AUTHOR]

In this study, the static E50 and υ parameters of rock materials were investigated using P-S wave velocities and Shore hardness (SH), using non-destructive measurement methods. In this study, the multiple linear regression (MLR), multiple non-linear regression (MNLR), and artificial neural network (ANN) models were used to estimate and determine the static E50 and υ parameters. When comparing the models defined by MLR, MNLR, and ANN to the R2 values, it was found that the ANN models, which estimate the E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, ρd, and SH), achieved higher accuracy than the MLR and MNLR models. The originality of this study is rooted in the fact that ores such as galena, chromite, and barite were studied for the first time from a rock mechanics perspective, providing an innovative viewpoint. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the importance of the study findings. [ABSTRACT FROM AUTHOR]

Objective: to identify correlations between quality and quantity of health care resources, national economic indicators, and postoperative in-hospital mortality as reported in the EUSOS study. Methods: Different variables were identified from a series of publicly available database. Postoperative in-hospital mortality was identified as reported by EUSOS study. Spearman non-parametric and Coefficients of non-linear regression were calculated. Results: Quality of health care resources was strongly and negatively correlated to postoperative in-hospital mortality. Quantity of health care resources were negatively and moderately correlated to postoperative in-hospital mortality. National economic indicators were moderately and negatively correlated to postoperative in-hospital mortality. General mortality, as reported by WHO, was positively but very moderately correlated with postoperative in-hospital mortality. Conclusions: Postoperative in-hospital mortality is strongly determined by quality of health care instead of quantity of health resources and health expenditures. We suggest that improving the quality of health care system might reduce postoperative in-hospital mortality. [ABSTRACT FROM AUTHOR]

The dimensions of tenons in solid wood furniture significantly influence the mechanical performance of mortise and tenon joints. While previous studies have primarily focused on tenon length, width, and thickness, they often overlooked the impact of clearance between the mortise and tenon. This study investigates the effects of tenon length, tenon width, and clearance on the mechanical performance of mortise and tenon joints, aiming to enhance their bending moment capacity (BMC) and stiffness. A three-factor, three-level orthogonal test was conducted, utilizing range analysis and variance analysis to assess the effects of each factor on BMC and stiffness. The LSD post hoc test was employed to identify significant differences between levels of the same factor, and nonlinear regression analysis was used to fit the experimental results. Based on orthogonal experiment outcomes, a grey relational theory-based evaluation system was developed to assess the comprehensive performance of joints, including both moment capacity and stiffness. Results indicate that tenon length has the most significant effect on BMC, followed by clearance and tenon width, while clearance has the greatest impact on stiffness, followed by tenon length and tenon width. These findings are consistent with those obtained from grey relational analysis. When considering both BMC and stiffness as a comprehensive evaluation, the optimal combination is a tenon length of 40 mm, a tenon width of 35 mm, and a clearance of −0.1 mm. This study offers valuable insights for the rational design of mortise and tenon joints, contributing to improved performance and reduced manufacturing costs. [ABSTRACT FROM AUTHOR]

One technique that is widely used in various fields, including nonlinear target tracking, is the extended Kalman filter (EKF). The well-known minimum mean square error (MMSE) criterion, which performs magnificently under the assumption of Gaussian noise, is the optimization criterion that is frequently employed in EKF. Further, if the noises are loud (or heavy-tailed), its performance can drastically suffer. To overcome the problem, this paper suggests a new technique for maximum correntropy EKF with nonlinear regression (MCCEKF-NR) by using the maximum correntropy criterion (MCC) instead of the MMSE criterion to calculate the effectiveness and vitality. The preliminary estimates of the state and covariance matrix in MCKF are provided via the state mean vector and covariance matrix propagation equations, just like in the conventional Kalman filter. In addition, a newly designed fixed-point technique is used to update the posterior estimates of each filter in a regression model. To show the practicality of the proposed strategy, we propose an effective implementation for positioning enhancement in GPS navigation and radar measurement systems. [ABSTRACT FROM AUTHOR]

Gauge length influences the biomechanical properties of herbaceous roots such as tensile resistance, tensile strength and Young's modulus. However, the extent to which and how these biomechanical properties of herbaceous roots are influenced remain unknown. To better understand the behavior of roots in tension under different conditions and to illustrate these behaviors, uniaxial tensile tests were conducted on the Poa araratica roots as the gauge length increased from 20 mm to 80 mm. Subsequently, ANOVA was used to test the impact of the significant influences of gauge length on the biomechanical properties, nonlinear regression was applied to establish the variation in the biomechanical properties with gauge length to answer the question of the extent to which the biomechanical properties are influenced, and Weibull models were subsequently introduced to illustrate how the biomechanical properties are influenced by gauge length. The results reveal that (1) the variation in biomechanical properties with root diameter depends on both the gauge length and the properties themselves; (2) the gauge length significantly impacts most of the biomechanical properties; (3) the tensile resistance, tensile strength, and tensile strain at cracks decrease as the gauge length increases, with values decreasing by 20%–300%, while Young's modulus exhibits the opposite trend, with a corresponding increase of 30%; and (4) the Weibull distribution is suitable for describing the probability distribution of these biomechanical properties; the Weibull modulus for both tensile resistance and tensile strain at cracks linearly decrease with gauge length, whereas those for tensile strength and Young's modulus exhibit the opposite trend. The tensile resistance, tensile strength, and tensile strain at the cracks linearly decrease with increasing gauge length, while the tensile strength and Young's modulus linearly increase with increasing gauge length. [ABSTRACT FROM AUTHOR]

This study aims to predict the undrained shear strength of remolded soil samples using nonlinear regression analyses, fuzzy logic, and artificial neural network modeling. A total of 1306 undrained shear strength results from 230 different remolded soil test settings reported in 21 publications were collected, utilizing six different measurement devices. Although water content, plastic limit, and liquid limit were used as input parameters for fuzzy logic and artificial neural network modeling, liquidity index or water content ratio was considered as an input parameter for nonlinear regression analyses. In non-linear regression analyses, 12 different regression equations were derived for the prediction of undrained shear strength of remolded soil. Feed-Forward backpropagation and the TANSIG transfer function were used for artificial neural network modeling, while the Mamdani inference system was preferred with trapezoidal and triangular membership functions for fuzzy logic modeling. The experimental results of 914 tests were used for training of the artificial neural network models, 196 for validation and 196 for testing. It was observed that the accuracy of the artificial neural network and fuzzy logic modeling was higher than that of the non-linear regression analyses. Furthermore, a simple and reliable regression equation was proposed for assessments of undrained shear strength values with higher coefficients of determination. [ABSTRACT FROM AUTHOR]

The light weight and moderate stiffness of multistorey timber buildings make them susceptible to increased lateral displacements and accelerations under service-level wind loading. Therefore, the fulfilment of serviceability requirements is a major challenge. In this study, linear elastic finite element analysis was used to perform a parametric study of moment-resisting timber frames combined with cross laminated timber walls. In the parametric study, various mechanical and geometrical parameters were varied within practical ranges. The results of the parametric study were used to derive simplified analytical expressions and to train artificial neural networks which can be used to estimate fundamental frequency, mode shape, top floor displacement, maximum inter-storey drift, and wind-induced acceleration. The analytical expressions and the artificial neural networks can be used for the preliminary assessment of serviceability performance of moment-resisting timber frames with and without cross laminated timber walls, under service-level wind loading. [ABSTRACT FROM AUTHOR]

The extract of the plant species Satureja hortensis L. (often used as traditional ethno-therapy and in food processing) was prepared using the ultrasonic extraction technique, and contains a large quantity of secondary metabolites, with scientific evidence for antioxidant, antimicrobial, sedative, antispastic and antidiarrheal activities. Process optimization was carried out using a mathematical–statistical method (response surface methodology—RSM), which models and examines the effects of three levels and three variables on the observed response. The investigated responses were the content of total phenolic components (TPC) and total flavonoids (TFC), as well as tests of antioxidant activity at the level of radicals. The independent variables were ethanol concentration (40–80%), temperature (40–80 °C) and the liquid–solid ratio (10–30 mL/g). Results from this study were entered into a second-degree polynomial with multiple non-linear regression. Analysis of variance (ANOVA) was applied to find the most favorable environment for assessing the model's performance and effectiveness with an ethanol concentration of 20%, temperature of 80 °C and LSR of 21.4 mL/g. ANOVA assessed the model's significance, and a second-order polynomial model described the relationships between variables and responses. Diagnostic plots confirmed the model's adequacy and reliability. The estimated values were 4.11 mg chlorogenic acid equivalents per gram of dry weight (CEA/g), 2.18 mg of rutin equivalents per gram of dry weight (RE/g), and 0.030 mg/mL and 0.030 mg/mL for TPC, TFC, IC50 and EC50, respectively. High-Performance Liquid Chromatography with diode array detection (HPLC-DAD) examination revealed that the prominent substance in the tested extract is rosmarinic acid (46,172 µg/mL), followed by chlorogenic acid (1519 µg/mL). [ABSTRACT FROM AUTHOR]

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

Abstract Photosynthetic light response curves serve as powerful mathematical tools for quantitatively describing the rate of photosynthesis of plants in response to changes in irradiance. However, in practical applications, the daunting task of selecting an appropriate nonlinear model to accurately fit these curves persists as a significant challenge. Thus, there arises a need for a method to systematically evaluate the efficacy of such models. In the present study, four distinct nonlinear models, namely Exponential Model (EM), Rectangular Hyperbola Model (RHM), Nonrectangular Hyperbola Model (NHM), and Modified Rectangular Hyperbola Model (MRHM), were used to fit the relationship between light intensity and the rate of photosynthesis across 42 empirical datasets. The goodness of fit for each model was assessed using the root-mean-square error, and relative curvature measures of nonlinearity were employed to assess the nonlinear behavior of the models. In terms of goodness of fit, pairwise difference tests of the root-mean-square error revealed that there was little to choose among the four models, although RHM gave a marginally poorer fit. However, in terms of nonlinear behavior, EM not only provided the most favorable linear approximation performance at the global level, but also exhibited the best close-to-linear behavior at the individual parameter level among the four models across the 42 datasets. Consequently, the results strongly advocate for EM as the most suitable mathematical framework for fitting photosynthetic light response curves. These findings provide insights into the model assessment for nonlinear regression in describing the relationship between the photosynthetic rate and light intensity.

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Using non-destructive and low-cost methods to determine leaf area has gained important applications. The research objectives were (i) to propose a non-destructive method to estimate the leaf area of castor bean crops and (ii) to build equations that accurately and quickly estimate the leaf area of specie. One thousand healthy and expanded leaves of five castor bean cultivars (New Zealand Purple, Sipeal, Carmencita, Amarelo de Irecê, and IAC-80) were collected, and 200 leaves were collected from each. The maximum length, maximum width, and leaf area were calculated for each leaf. The product between length and width (LW) were calculated. We performed tests with different linear and non-linear regression models between leaf area and linear leaf dimensions of each cultivar. The models used were linear, linear without intercept, and power. The criteria for choosing the best models to estimate the leaf area of castor beans were a higher coefficient of determination, more elevated Pearson’s linear correlation coefficient, lower Akaike information criterion, higher Willmott agreement index, and smallest root mean square error. The equations that presented the best criteria for estimating the leaf area of castor bean cultivars were those that used the product between length and width, compared to equations that used only one leaf dimension. The model ŷ = 0.439 × LW can be used to accurately and quickly estimate the castor bean leaf area through linear measurements of the leaves, using the product between length and width (LW), regardless of the cultivar chosen. [ABSTRACT FROM AUTHOR]

Double-arch tunnels, as one of the popular forms of tunnels, might be exposed to boiling liquid expanding vapour explosions (BLEVEs) associated with transported liquified petroleum gas (LPG), which could cause damage to the tunnel and even catastrophic collapse of the tunnel in extreme cases. However, very limited study has investigated the performance of double-arch tunnels when exposed to internal BLEVEs and in most analyses of tunnel responses to accidental explosions. The TNT-equivalence method was used to approximate the explosion load, which may lead to inaccurate tunnel response predictions. This study numerically investigates the response of typical double-arch tunnels to an internal BLEVE resulting from the instantaneous rupture of a 20 m3 LPG tank. Effects of various factors, including in-situ stresses, BLEVE locations, and lining configurations on tunnel responses are examined. The results show that the double-arch tunnels at their early-operation ages are more vulnerable to severe damage when exposed to the BLEVE due to the low action of in-situ stress of rock mass on the response of early-age tunnels. It is also found that directing the LPG tank to different driving lanes inside tunnels can affect the BLEVE-induced tunnel response more significantly than varying the configurations of tunnel lining. Moreover, installing section-steel arches in the mid-wall can effectively improve the blast resistance of the double-arch tunnels against the internal BLEVE. In addition, the prediction models based on multi-variate nonlinear regressions and machine learning methods are developed to predict the BLEVE-induced damage levels of the double-arch tunnels without and with section-steel arches. [ABSTRACT FROM AUTHOR]

In this research paper, thin-layer convective drying characteristics for Jamun (Indian Blackberry) slices were analyzed at 50 to 80°C air drying temperatures with an interval of 10°C and an air velocity of 0.5 m/s. During the drying investigation, it was found that it occurred only in a range of falling periods of drying. With drying air temperature increment, the duration of drying was significantly reduced. Moisture removal took place from initially at 83.68 (% wb) to finally around 2.88 (% wb) in the tray dryer. The experimental observations were verified with various drying models of thin-layer. By nonlinear regression analysis, the selected drying model parameters were determined. Between the experimental and estimated moisture ratios, the selected drying models were examined with the help of r-squared (r2), Mean Absolute Percentage Error (MAPE), Chi-square (χ2) and Root Mean Squared Deviations (RMSD). As a result, it was found that the Page model was fitted satisfactorily at 50 and 60°C air drying temperatures while the model developed by Midilli et al was fitted satisfactorily at 70 and 80°C air drying temperatures to characterize Jamun slices with convective drying curves. The effective diffusion of water and activation energy values were estimated as 2.79–7.71×10−9 m2/min and 30.82 kJ/mol respectively for Jamun slices. [ABSTRACT FROM AUTHOR]

In the current study, we explored the performance of steel slag ballast (SSB), granite ballast (GB), and a slag–granite ballast mixture under cyclic loading conditions using a large-scale track simulation apparatus. In addition, the single-particle crushing strength and toxicity characteristics of steel slag were evaluated. The test results indicate that the SSB particles exhibited higher crushing strength (σo) than conventional GB. In addition, the toxicity characteristics leaching procedure (TCLP) test results indicate that the leaching concentrations of all the heavy metals in the slag were below the recommended limit, except for lead, which exceeded the drinking water limit. The cyclic test results indicate that the SSB exhibited lower vertical (Sv) and lateral deformation (ld) than the GB, and that, with the increase in the proportion of SSB (PSSB) in the GB, both the Sv and ld were reduced. It was additionally found that, with an increase in PSSB in the GB, the resilient modulus (Mr), damping ratio (D), track stiffness (k), and track modulus (u) increased, whereas particle breakage (Bg/BBI) decreased. It was further observed that the extent of the sleeper (avs) and ground acceleration was lower in the SSB samples than the GB samples. Nonlinear regression models were developed to predict the vertical strain (ε1), lateral strain (ε3), Mr, and D of the slag–granite ballast mixture. A detailed analysis of the performance of the slag–granite ballast mixture, in terms of the toxicity characteristics, volumetric strain (εv), shear strain (εs), Mr, D, avs, Bg/BBI, and k, revealed that a blend of 58% SSB and 42% GB (SSB58) can be used for optimal performance. Practical Applications: The problem of the excessive demand for conventional ballast is addressed in the current study by utilizing steel slag as railway ballast. The benefits of utilizing steel slag ballast (SSB) over conventional granite ballast (GB), in terms of lesser vertical deformation and particle degradation, are highlighted. The lower degradation exhibited by the SSB helped in reducing the frequency of ballast replacement operations, and hence the track maintenance costs. Moreover, owing to the higher specific gravity of the SSB, its lateral displacement was found to be lower compared with conventional GB, and thereby the lateral stability of the track is improved when using steel slag as railway ballast. In addition, the SSB exhibited a higher resilient modulus compared with conventional GB, implying better passenger riding comfort where the track has steel slag as the railway ballast. In addition, in bridge approaches, SSB can be mixed with conventional ballast to allow for a gradual transition from soft to stiff and stiff to soft zones. [ABSTRACT FROM AUTHOR]

This paper was conducted to model the drying kinetics of Capparis spinosa buds using dragonfly swarm optimized nonlinear regression and to compare the impact of drying treatments on their quality. Experiments included hot-air convective drying (HACD) from 40 to 120 °C, vacuum drying (VD) at 40, 60, and 80 °C, microwave drying (MD) ranging from 200 to 1000 W. Out of 30 models tested, the drying kinetics fitted best with Jena Das model in both HACD and VD and modified Midilli for MD. The comparison between the drying treatments used indicated that when taking into account the quality of the dried caper, VD at 80 °C was the most effective, resulting in a high-quality caper with a well-preserved color (ΔE = 7.47), high bioactives (TPC = 30.18mgGAE/gDW and TFC = 10.27 mg QE/gDW) and radical scavenging abilities (DPPH = 0.249 and ABTS = 4.448 mg/mL) at the expense of long duration (6h) and high specific energy consumption (50.667 kWh/kg). On the other hand, when considering the drying behavior MD ranked best with samples dried at 1000 W showing high diffusivities 2.04e−8 m2s−1 low energy consumption (MER 0.1216 kg/h), and short drying times (12 mn). This research is critical in selecting better drying conditions for increased usage in the culinary, cosmetic, and pharmaceutical sectors. [ABSTRACT FROM AUTHOR]

This study aims to investigate the efficiency of Bailey method of optimizing the fatigue life of asphalt mixtures when natural sand is included in the mix at two coarseness levels of aggregate gradations: Fine-graded (FG) and coarse-graded (CG). Three mixes which varied with the percentage of the natural river sand were prepared at each coarseness level, namely: Coarse-graded with quarry sand only (CG-QS), coarse-graded with natural sands only (CG-NS), coarse-graded with quarry and natural sands (CG-QNS), fine-graded with quarry sand only (FG-QS), fine-graded with natural sands only (FG-NS), and fine-graded with quarry and natural sands (FG-QNS). The portions of the natural sand either in CG-QNS and FG-QNS mixes were minimized as possible without violating the Bailey ratios. The beam fatigue (BF) test was used to evaluate the performance of each mixture at strain levels of 800, 1000, and 1200 microstrain. Also, to further investigate the air voids influence on the performance of the asphalt mixes, each mix was prepared at two air void levels: 7 and 4%. The sensitivities of the volumetric measures with the number of cycles to failure (Nf) were evaluated. The study's findings indicate that Nf was generally decreasing with the increase of the natural sand in the mix at any strain level. Also, the 7% air voids mixes provided a lower Nf when compared to the 4% air voids mixes. The dust proportion (DP) was the most significant volumetric. The Bailey gradation method successfully provided a similar gradation coarseness for CG-QNS compared to CG-QS, which resulted in comparable Nf and indicates a similar aggregate interlock. The percentage decrease of CG-QNS containing 4% air voids was minimal, ranging from 1.92 to 5.46%, and a little higher at 7% air voids where Nf ranged from 11.48 to 13.23%. [ABSTRACT FROM AUTHOR]

A straightforward, dependable, and quick RP-LC method for the analysis of abiraterone acetate in its dose form and human urine has been devised. With DAD detection, sensitivity was reported to be high. The LOD and LOQ of the procedure were deemed adequate. The suggested approach was exhaustively validated in accordance with ICH requirements, and the findings demonstrated that it was exact, accurate, selective, and sensitive for the analysis of this pharmaceutical. The chromatographic separation was realized using a X-Terra RP-18 (150 × 4.60 mm i.d. × 5 μm) column and a UV detector set at 255 and 267 nm. In addition, p K a values were calculated based on the relationship between the retention factor and the pH of the mobile phase. The influence of the composition of the mobile phase on the ionization constant was investigated by measuring the p K a at various acetonitrile–water combinations ranging from 50 to 70% (v/v). [ABSTRACT FROM AUTHOR]

Even though many experimental studies have evidenced that fiber-reinforced polymer (FRP)-confined concrete columns under compression might exhibit a postpeak strain-softening response, followed by a hardening behavior (a stress reduction–recovery response), most existing models are only applicable to confined columns developing full hardening behavior. Additionally, their applicability is limited to a certain column cross-sectional shape (circular or noncircular). Therefore, the present study is dedicated to the establishment of a new design-oriented stress–strain model, unified for FRP-confined circular/noncircular concrete columns. For this purpose, first, a new nondimensional confinement stiffness-based index is developed, below which the column response is transformed from a full-hardening behavior (Type A) to a postpeak strain softening–hardening one (Type B). A parabolic-linear stress–strain formulation is proposed for Type A columns by developing a new expression for the calculation of the linear hardening branch's slope. For the case of Type B, a new methodology is introduced for the simulation of the stress reduction–recovery response beyond the transition zone, whose main elements are calibrated by a large test data set. The dominance degree of the noncircularity effect of the cross section on both Type A and Type B cases was assessed and reflected in the key components of the proposed stress–strain models, based on nonlinear regression analysis. With these considerations, this model could accurately simulate the impact of the noncircularity effect on the stress–strain relationship of Type A and Type B columns, whose predictive performance is validated through a comparative assessment with existing models based on large test stress–strain results. [ABSTRACT FROM AUTHOR]

Salinity is a major environmental factor that influences the population dynamics of fish and shellfish along coasts and estuaries, yet empirical methods for hindcasting salinity at specific sampling stations are not widely available. The specific aim of this research was to predict the salinity experienced by juvenile and adult oysters (Crassostrea virginica) collected at sampling stations in Delaware Bay. To do so, empirical relationships were created to predict salinity at five oyster bed stations using observing systems data. These relationships were then applied to construct indices of salinity exposure over an oyster's lifetime. Three independent salinity data sources were used in conjunction with observing systems data to construct and validate the predictive relationships. The root mean square error (RMSE) of the models ranged from 0.5 to 1.6 psu when model predictions were compared with the three independent data sets. Results demonstrated that data from an observing system near the head of Delaware Bay could be used to predict salinity within ± 2 psu at oyster bed stations as far down-estuary as 39 km. When these models were applied to estimate low salinity exposure of 2-year-old oysters via the metric of consecutive days below 5 psu, the indices suggested that there could be as much as a 42-day difference in low salinity exposure for oysters at stations just 31 km apart. The approach of using observing systems data to hindcast salinity could be applied to advance understanding of salt distribution and the effect of low salinity exposure on organisms in other estuaries, especially bottom-associated species. [ABSTRACT FROM AUTHOR]

Machine learning algorithms have achieved great success due to their excellent predictive performance, but their applicability is limited in areas where there is a high demand for model interpretability. Aiming at the weakness of lacking interpretability of machine learning algorithms, a new interpretable machine learning algorithm called ensemble trees penalized logistic rule regression is proposed based on the idea of rules ensemble, which can achieve comparable predictive performance to the ensemble trees algorithm with less structural complexity and retains the interpretive effect of logistic regression. Firstly, it extracts branches from ensemble trees such as random forest and XGBoost, and converts them into logic rules. Then, the rule set is pruned and deduplicated to obtain a streamlined rule set. Finally, the rules are incorporated into logistic regression as variables and complexity control is performed with Lasso algorithm. Taking the enterprise risk warning as an example, it is compared with multiple machine learning algorithms. The results show that this algorithm not only inherits the default discrimination ability of the ensemble trees well and exceeds most of the machine learning algorithms in various classification indices, but also can give the thresholds of the enterprise risk indices through the rules, which is convenient for enterprises to carry out risk management. Further, the enterprise credit score is produced according to this algorithm, which verifies its wide applicability. The obtained score conforms to the objective law and is discriminative, and the robustness of the model’s prediction performance is verified by three public datasets.

AbstractData transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This article introduces a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach directly targets the posterior distribution of the transformation by linking it with the marginal distributions of the independent and dependent variables, and then deploys a Bayesian nonparametric model via the Bayesian bootstrap. Crucially, this approach delivers (a) joint posterior consistency under general conditions, including multiple model misspecifications, and (b) efficient Monte Carlo (not Markov chain Monte Carlo) inference for the transformation and all parameters for important special cases. These tools apply across a variety of data domains, including real-valued, positive, and compactly-supported data. Simulation studies and an empirical application demonstrate the effectiveness and efficiency of this strategy for semiparametric Bayesian analysis with linear models, quantile regression, and Gaussian processes. The R package SeBR is available on CRAN. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Selection interviews have long been integral to medical school admissions, yet their limited predictive validity and susceptibility to bias raise concerns. This study delves into potential interviewer bias within the dynamics of interviewee and interviewer gender. We analyze a dataset of 5,200 applicants and over 370 selection committees engaged in semi-structured interviews from 2006 to 2019 at a large German medical school with multiple linear and non-linear regression analyses. Our findings reveal that all-female committees tended to award male candidates, on average, one point more than their female counterparts, significantly enhancing the chances of submission for male applicants despite lower academic grades, which constituted 51% of the selection process points. All-male and mixed-gender committees exhibited similar ratings for both genders. The role of valuing voluntary services emerged prominently: all-male and mixed committees acknowledged women's volunteer work but not men's, while all-female committees demonstrated the opposite pattern. Our results attribute variations in interview outcomes to the absence of standardization, such as insufficient interviewer training, divergent rating strategies, variations in interviewer experience, and imbalances in candidate allocation to selection committees, rather than to a "gender bias", for example by favoritism of males because of their gender. [ABSTRACT FROM AUTHOR]

The incorporation of conformal cooling systems has significantly enhanced the efficiency and quality of injection molding process. While several automated methods have been developed for creating conformal cooling channels in injection molds, the current optimization process for conformal cooling design parameters is hindered by labor-intensive iterative thermal simulation processes and the substantial reliance on empirical human knowledge. This paper presents an innovative machine learning method to assess the thermal performance of conformal cooling systems by employing a combination of a non-linear regression model and a neural network. By employing a logarithmic regression model describing the temperature graph and a neural network predicting the coefficients of the logarithmic regression model, the thermal performance of specified conformal cooling systems can be assessed and predicted precisely. This methodology empowers designers to evaluate the thermal efficiency of conformal cooling systems efficiently and effectively to further optimize the conformal cooling design parameters without relying on tedious manual thermal and fluid simulation processes. [ABSTRACT FROM AUTHOR]

This study examines the impact of canopy density, side wind speed, nozzle tilt angle, and droplet size on droplet penetration during plant protection spraying operations. Experiments conducted in citrus orchards evaluated how side wind speed and nozzle tilt angle influence droplet penetration across various canopy densities. A Phase Doppler Analyzer (PDA) was used to assess droplet size variations under different nozzle tilt angles and side wind speeds, yielding a multiple linear regression equation (R2 = 0.866) that links nozzle tilt angle and side wind speed with droplet size. Results showed that droplet size decreases with increasing nozzle tilt angle at a constant crosswind speed. Further experiments investigated the effects of droplet size and canopy leaf area density on droplet penetration, involving three canopy leaf area densities, four wind speeds, and six nozzle tilt angles. Droplet deposition and canopy coverage were measured under various spraying parameters, with conventional operations (0° nozzle tilt and orthogonal wind speeds) serving as controls. The study found that adjusting nozzle tilt angle and wind speed enhances droplet penetration in different canopy structures. Optimal parameters varied with leaf area density (LAD): an 18° tilt angle and 3 m/s wind speed for a LAD of 5.94 m3/m3, a 45° tilt angle and 2 m/s wind speed for a LAD of 8.47m2/m3, and a 36° tilt angle and 3 m/s wind speed for a LAD of 11.12m2/m3. At 1 m/s, droplet deposition followed a downward parabolic trend with changes in nozzle tilt angle, whereas at 2 m/s, deposition followed an upward parabolic trend. At a side wind speed of 3 m/s, droplet deposition remained unchanged with nozzle tilt angle but decreased with increasing canopy density. Nonlinear regression analysis indicated that leaf area density had a greater impact on deposition differences than droplet size, with droplet penetration decreasing as leaf area density increased. This study provides a reference for enhancing fog droplet penetration techniques in plant protection operations, offering practical guidelines for optimizing spraying conditions and improving pesticide use efficiency in different canopy structures. [ABSTRACT FROM AUTHOR]

Background: Knee instability in midflexion may contribute to patient dissatisfaction following total knee arthroplasty (TKA). Midflexion instability involves abnormal motions and tissue loading in multiple planes. Therefore, we quantified and compared the tensions carried by the medial and lateral collateral ligaments (MCL and LCL) following posterior-stabilized (PS) TKA through knee flexion, and then compared these tensions with those carried by the native knee. Finally, we examined the relationships between collateral ligament tensions and anterior tibial translation (ATT). Methods: Eight cadaveric knees (from 5 male and 3 female donors with a mean age of 62.6 years and standard deviation of 10.9 years) underwent PS TKA. Each specimen was mounted to a robotic manipulator and flexed to 90°. ATT was quantified by applying 30 N of anterior force to the tibia. Tensions carried by the collateral ligaments were determined via serial sectioning. Robotic testing was also conducted on a cohort of 15 healthy native cadaveric knees (from 9 male and 6 female donors with a mean age of 36 years and standard deviation of 11 years). Relationships between collateral ligament tensions during passive flexion and ATT were assessed via linear and nonlinear regressions. Results: MCL tensions were greater following PS TKA than in the native knee at 15° and 30° of passive flexion, by a median of ≥27 N (p = 0.002), while the LCL tensions did not differ. Median tensions following PS TKA were greater in the MCL than in the LCL at 15°, 30°, and 90° of flexion, by ≥4 N (p ≤ 0.02). Median tensions in the MCL of the native knee were small (≤11 N) and did not exceed those in the LCL (p ≥ 0.25). A logarithmic relationship was identified between MCL tension and ATT following TKA. Conclusions: MCL tensions were greater following PS TKA with this typical nonconforming PS implant than in the native knee. Anterior laxity at 30° of flexion was highly sensitive to MCL tension during passive flexion following PS TKA but not in the native knee. Clinical Relevance: Surgeons face competing objectives when performing PS TKA: they can either impart supraphysiological MCL tension to reduce anterior-posterior laxity or maintain native MCL tensions that lead to heightened anteriorposterior laxity, as shown in this study. [ABSTRACT FROM AUTHOR]

ABSTRACT This tutorial describes single‐step low‐dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t‐distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT‐test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different‐scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly‐k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models. [ABSTRACT FROM AUTHOR]

Background: Recent studies have implicated remnant cholesterol (RC) in the etiology, progression, and prognosis of cancer. However, very few of them concentrated on the study of the precise relationship between serum RC levels and cancer risk, leaving this subject unexplored. Consequently, this study aims to investigate the association between serum RC levels and 4 site-specific cancers, employing a dual approach that combines observational and mendelian randomization (MR) analysis. Methods: Based on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020, this study collected data from18,067 participants. To rule out confounders, this study utilized weighted multivariable logistic regression and assessed non-linear associations using restricted cubic spline (RCS) regression, followed by two-piecewise linear regression. Sensitivity analysis conducted in this study included subgroup analysis, multiple imputation, outlier removal, and propensity score matching. To strengthen causal inference, this study employed univariable and multivariable MR analysis. The robustness and reliability of the findings were estimated by the application of replication and meta-analysis. Results: The results of multivariable logistic regression analysis demonstrated a significant association between serum RC levels and breast cancer, showing that individuals in the higher logRC category had a higher risk of breast cancer compared to those in the lower category (Q3 vs. Q1: OR = 1.71, 95% CI: 1.01–2.88, P = 0.044). Weighted RCS revealed an inverted L-shape association between RC and the risk of breast cancer (P-nonlinear = 0.0386, P-overall = 0.010). Primary MR analysis provided evidence for an increased risk of breast (IVW: OR = 1.08, 95% CI: 1.03–1.12, P = 0.000951) and colorectal cancer (IVW: OR = 1.12, 95% CI: 1.00-1.24, P = 0.0476) associated with RC. However, the results of replication and meta-analysis did not support a significant causal association of RC with the risk of breast cancer (OR = 1.04, 95% CI: 0.95–1.13), lung cancer (OR = 0.95, 95% CI: 0.88–1.03), colorectal cancer (OR = 1.05, 95% CI: 0.92–1.19), and prostate cancer (OR = 1.01, 95% CI: 0.95–1.08). Conclusion: Although a non-linear relationship was observed in the cross-sectional study between remnant cholesterol levels and breast cancer risk, MR analyses failed to provide any causal evidence. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) techniques were pioneered to analyze non-linear relationships. • Algorithm's predictive power validated ML models, preventing spurious relations. • Recursive feature elimination explored predictor importance. • Presence of non-linear relationships is suggested, advising further investigation. • Acknowledgment of skepticism stresses rigorous analysis for long-term correlations. The Earth's climate is a complex system that can be influenced by various internal and external factors. Previous research has explored the relationship between climate and external forcings such as solar activity and GCR. However, the relations are intricate and intertwined into almost chaotic meteorological measurements. This paper delves deeper into understanding their interactions. GCR flux, Sunspot number (SSN), total solar irradiance (TSI), UV irradiance (UVI), and the Oceanic Niño Index (ONI) are the predictor variables, while total cloud amount (TCA) and low cloud amount (LCA) are the response variables. The analysis begins with standard statistical techniques and continues with multiple regression models including random forest, which is a machine learning (ML) method that can be used for non-linear regression. Subsequently, Recursive Feature Elimination (RFE) is employed to scrutinize the correlation among the predictor parameters. In the ML model, the final 25% of the dataset is held out and tested for validation, so, the predictive power of the algorithm is measured. Both geographical and temporal patterns have been investigated. This study suggests that a non-linear relationship might exist between the parameters, and should be investigated further, particularly in specific regions of the world. [ABSTRACT FROM AUTHOR]

Two types of grounding systems are recommended for use in the international standard IEC 62305-3, Part 3: Physical damage to structures and life hazard. One of these is a radial-based grounding system (type-A), which is used in soil resistivities of up to 3000 Ω m and is considered in this paper. It is a well-known fact that during lightning strikes, only a part of the grounding wire contributes to dissipating the lightning current into the surrounding soil. This effective part of the grounding system depends on several features, such as soil resistivity, burial depth, and rise time of the dissipated lightning current. The effect of all of these features on the effective length of the type-A grounding system is explored in this paper. A suitable supervised machine learning regression model is developed, which will enable readers to accurately approximate the effective length of the type-A grounding system for realistic values of input features. The trained model in the paper yielded an R 2 value of 0.99998 on the test set. In addition, two simple mathematical formulas are also provided, which produce similar but less accurate results ( R 2 values of 0.989883 and 0.998557, respectively). [ABSTRACT FROM AUTHOR]

The anthropogenic climate change may impact pregnancy outcomes. Rather than ambient temperature, we aimed to use a composite bioclimatic metric (Universal Thermal Climate Index, UTCI) to identify critical susceptible windows for the associations between bioclimatic exposure and hypertensive disorders of pregnancy (HDPs) risk. Daily UTCI exposure from 12 weeks of preconception through pregnancy was linked to 415,091 singleton pregnancies between 1st January 2000 and 31st December 2015 in Western Australia. Adjusted weekly-specific and cumulative odds ratios (ORs) and 95% confidence intervals (CIs) of gestational hypertension and preeclampsia were estimated with distributed lag non-linear and standard non-linear logistic regressions. Exposures from early pregnancy to week 30 were associated with greater odds of HDPs with critical susceptible windows, particularly elevated at the 1st (10.2 °C) and 99th (26.0 °C) exposure centiles as compared to the median (14.2 °C). The most elevated ORs were 1.07 (95% CI 1.06, 1.08) in weeks 8–18 for gestational hypertension and 1.10 (95% CI 1.08, 1.11) in weeks 11–16 for preeclampsia for the 99th exposure centile. Cumulative exposures associated with HDPs with relatively higher but less precise ORs. The effects of high exposure to HDPs indicated sociodemographic inequalities. The identified critical periods and subpopulations could benefit from climate-related interventions. [ABSTRACT FROM AUTHOR]

Aedes aegypti is an important vector of dengue virus and other arboviruses that affect human health. After being ingested in an infectious bloodmeal, but before being transmitted from mosquito to human, dengue virus must disseminate from the vector midgut into the hemocoel and then the salivary glands. This process, the extrinsic incubation period, typically takes 6–14 days. Since older mosquitoes are responsible for transmission, understanding the age structure of vector populations is important. Transcriptional profiling can facilitate predictions of the age structures of mosquito populations, critical for estimating their potential for pathogen transmission. In this study, we utilized a two-gene transcript model to assess the age structure and daily survival rates of three populations (Key West, Marathon, and Key Largo) of Ae. aegypti from the Florida Keys, United States, where repeated outbreaks of autochthonous dengue transmission have recently occurred. We found that Key Largo had the youngest Ae. aegypti population with the lowest daily survival rate, while Key West had the oldest population and highest survival rate. Across sites, 22.67% of Ae. aegypti females were likely old enough to transmit dengue virus (at least 15 days post emergence). Computed estimates of the daily survival rate (0.8364 using loglinear and 0.8660 using non-linear regression), indicate that dengue vectors in the region experienced relatively low daily mortality. Collectively, our data suggest that Ae. aegypti populations across the Florida Keys harbor large numbers of older individuals, which likely contributes to the high risk of dengue transmission in the area. Author summary: Within a mosquito population it is generally only the very old mosquitoes that are capable of spreading disease-causing pathogens, like dengue virus. Scientists have been developing fast, accurate, and cheap methods to estimate mosquito age. In this study, we used one of those methods to measure the activity of age-responsive genes in dengue virus-transmitting Aedes aegypti mosquitoes from the Florida Keys, a dengue hotspot in the United States. This method allowed us to estimate the percentage of older mosquitoes in this region. We found that a high percentage of Ae. aegypti mosquitoes in Key Largo, Marathon, and Key West (between 11–32%) were likely old enough to be able to spread dengue virus. We also observed that Ae. aegypti from the Florida Keys experienced relatively low daily mortality, as the daily probability of survival for any given mosquito was estimated at 86–88%. These findings suggest that conditions in the Florida Keys are very suitable for mosquitoes to thrive, reach old age, and potentially contribute to dengue outbreaks in the region. [ABSTRACT FROM AUTHOR]

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector. [ABSTRACT FROM AUTHOR]

This comprehensive study addresses the correlation between water levels and meteorological features, including air temperature, evaporation, and precipitation, to accurately predict water levels in lakes within the Great Lakes basin. Various models, namely multiple linear regression (MLR), nonlinear autoregressive network with exogenous inputs (NARX), Facebook Prophet (FB-Prophet), and long short-term memory (LSTM), are employed to enhance predictions of lake water levels. Results indicate that all models, except for FB-Prophet, perform well, particularly for Lakes Erie, Huron-Michigan, and Superior. However, MLR and LSTM show reduced performance for Lakes Ontario and St. Clair. NARX emerges as the top performer across all lakes, with Lakes Erie and Superior exhibiting the lowest error metrics—root mean square error (RMSE: 0.048 and 0.034), mean absolute error (MAE: 0.036 and 0.026), mean absolute percent error (MAPE: 0.021% and 0.014%), and alongside the highest R-squared value (R2: 0.977 and 0.968), respectively. Similarly, for Lake Huron-Michigan, NARX demonstrates exceptional predictive precision with an RMSE (0.029), MAE (0.022), MAPE (0.013%), and an outstanding R2 value of 0.995. Despite slightly higher error metrics, NARX consistently performs well for Lake Ontario. However, Lake St. Clair presents challenges for predictive performance across all models, with NARX maintaining relatively strong metrics with an RMSE (0.076), MAE (0.050), MAPE (0.029%), and R2 (0.953), reaffirming its position as the leading model for water level prediction in the Great Lakes basin. The findings of this study suggest that the NARX model accurately predicts water levels, providing insights for managing water resources in the Great Lakes region. [ABSTRACT FROM AUTHOR]

The reaction kinetics of lithotrophic ammonia-oxidizing bacteria (AOB) are strongly dependent on dissolved oxygen (DO) as their metabolism is an aerobic process. In this study, we estimate the kinetic parameters, including the oxygen affinity constant (K m[O2]) and the maximum oxygen consumption rate (V max[O2]), of different AOB species, by fitting the data to the Michaelis–Menten equation using nonlinear regression analysis. An example for three different species of Nitrosomonas bacteria (N. europaea, N. eutropha , and N. mobilis) in monoculture is given, finding a K m[O2] of 0.25 ± 0.05 mg l−1, 0.47 ± 0.09 mg l−1, and 0.28 ± 0.08 mg l−1, and a V max[O2] of 0.07 ± 0.04 pg h−1cell−1, 0.25 ± 0.06 pg h−1cell−1, and 0.02 ± 0.001 pg h−1cell−1 for N. europaea, N. eutropha , and N. mobilis , respectively. This study shows that of the analyzed AOB, N. europaea has the highest affinity towards oxygen and N. eutropha the lowest affinity towards oxygen, indicating that the former can convert ammonia even under low DO conditions. These results improve the understanding of the ecophysiology of AOB in the environment. The accuracy of mathematically modelled ammonia oxidation can be improved, allowing the implementation of better management practices to restore the nitrogen cycle in natural and engineered water systems. [ABSTRACT FROM AUTHOR]

The incorporation and application of SAGD rapid preheating technology effectively solve the problem of the long preheating cycle in the SAGD steam cycle. The thermal properties of reservoir rocks are an important factor affecting the heat transfer law governing their formation during the rapid preheating process of SAGD. During the rapid preheating process of SAGD, the expansion of the reservoir and the steam cycle process will cause changes in the pore permeability, oil-water saturation, and temperature of the reservoir rocks, which will inevitably lead to differences in the changes that occur in the thermal properties of the reservoir rocks, compared to those under the influence of a single factor. In this study, experiments were conducted to determine the thermal properties of reservoir rocks under the combined influence of pore permeability, oil-water saturation, and temperature, quantitatively characterizing the changes in the thermal properties of reservoir rocks. Using the orthogonal method to design and carry out experiments for determining the thermal properties of reservoir rocks, the main factors affecting the thermal properties of reservoir rocks and the significance of each factor's impact on the thermal properties of reservoir rocks were determined through intuitive analysis and variance analysis of the experimental results. Finally, a regression equation that can characterize changes in the thermal properties of reservoir rocks under the influence of multiple factors was obtained through multiple nonlinear regressions of the experimental results. [ABSTRACT FROM AUTHOR]