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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]

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]

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]

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]

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 paper 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 (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) 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. [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]

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]

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]

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]

• 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]

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]

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]

AbstractThis 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]

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]

In order to explore the micro‐pore structure improvement mechanism of multi‐walled carbon nanotubes (MWCNT) on the frost resistance of concrete, five kinds of MWCNT concrete with different contents were prepared and the frost resistance test and mercury intrusion test were carried out. The results show that when the content of MWCNT is 0.1 wt%, the mass loss rate and compressive strength loss rate of concrete after freezing and thawing are 15.97% and 23.85% lower than that of ordinary concrete, respectively, indicating that this content is best amount to improve the frost resistance of concrete in this study. Moreover, 0.1 wt% MWCNT can significantly optimize the internal pore structure of concrete, and the average pore diameter growth rate and harmful pore ratio are 30.1% and 64.45%, respectively, after 200 freeze–thaw cycles. The proportion of more harmful pores has the greatest influence on compressive strength and relative elastic modulus, and the gray correlation degree is more than 0.8. Finally, the mathematical models of compressive strength‐pore parameters and relative dynamic elastic modulus‐pore parameters of MWCNT concrete after freeze–thaw cycle are established considering pore structure parameters and pore size distribution. [ABSTRACT FROM AUTHOR]

Reliable estimation of reference evapotranspiration (ETo), an essential component of optimal irrigation management, is challenging in many regions due to its complex dependence on meteorological factors. Alternative empirical models, often used to estimate ETo considering data limitations, provide highly unreliable estimates for Iraq. This study aimed to formulate simpler empirical models for accurate ETo estimation with fewer variables in different climate regions of Iraq. The metaheuristic Whale Optimization Algorithm (WOA) was used to finetune the coefficients of the nonlinear least square fitting regression (NLLSF) model during development. Two simpler models were developed based on (1) only mean air temperature (T) (NLLSF-T) and (2) solar radiation and T (NLLSF-R) as inputs. The performance of the models was validated using historical ground observations (2012–2021), and the ETo was estimated using the Penman–Monteith method from the reanalyzed (ERA5) datasets (1959–2021). The models' spatial, seasonal, and temporal performance in estimating daily ETo was rigorously evaluated using multiple statistical metrics and visual presentations. The Kling-Gupta Efficiency (KGE) and normalized root mean square error (NRMSE) of the NLLSF-T model were 0.95 and 0.30, respectively, compared to 0.75 and 0.40 for Kharrufa, the best-performing temperature-based models in Iraq. Similarly, NLLSF-R improved the KGE from 0.78 to 0.97 in KGE and NRMSE from 0.44 to 0.22 compared to Caprio, the best-performing radiation-based model in Iraq. The spatial assessment revealed both the models' excellent performance over most of Iraq, except in the far north, indicating their suitability in estimating ETo in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

In the present study, the authors have performed oil agglomeration of coal fines using castor oil and combination of castor oil and turpentine oil in various proportions. The coal samples were collected from three different mines of Mahanadi Coalfields Limited having high ash content (27–39%). From the post proximate analysis it was revealed that in case of Mine 1 coal, the moisture content reduced by 83%, for Mine 2 it was 94% and for Mine 3 the moisture reduced by 90% from the initial values. Grade achieved after oil agglomeration process was G5 grade for Mine 2 coal and G3 grade for Mine 1 & 2 coal. The effect of various parameters such as oil dosage, pulp density, pH, on yield, ash rejection and organic matter recovery were studied. The parameter with the highest relative importance was found to be oil dosage (normalized importance of 100%) whereas agitation time and pulp density were found to have normalized relative importance of 74% and 70%, respectively. A non-linear regression model in the form of quadratic equation has also been proposed for ash rejection (%) based on the independent variables such as pulp density, oil dosage and agitation time. [ABSTRACT FROM AUTHOR]

Kernel methods are popular in nonlinear and nonparametric regression due to their solid mathematical foundations and optimal statistical properties. However, scalability remains the primary bottleneck in applying kernel methods to large-scale data regression analysis. This paper aims to improve the scalability of kernel methods. We combine Nyström subsampling and the preconditioned conjugate gradient method to solve regularized kernel regression. Our theoretical analysis indicates that achieving optimal convergence rates requires only (n) memory and (n n) time (up to logarithmic factors). Numerical experiments show that our algorithm outperforms existing methods in time efficiency and prediction accuracy on large-scale datasets. Notably, compared to the FALKON algorithm [A. Rudi, L. Carratino and L. Rosasco, Falkon: An optimal large scale kernel method, in Advances in Neural Information Processing Systems (Curran Associates, 2017), pp. 3891–3901], which is known as the optimal large-scale kernel method, our method is more flexible (applicable to non-positive definite kernel functions) and has a lower algorithmic complexity. Additionally, our established theoretical analysis further relaxes the restrictive conditions on hyperparameters previously imposed in convergence analyses. [ABSTRACT FROM AUTHOR]

Confidence bands are used to estimate the likely range of the true parameter, whereas prediction bands provide an idea of the possible values of the parameter after applying a regression model. This study proposes an equation to construct confidence and prediction bands applicable to any linear or nonlinear chemometric regression model; that is, statistical methods used in analytical chemistry to establish relationships between two or more variables. The proposed equation is developed and presented as a useful tool to represent the uncertainty in estimates based on a set of data in chemical measurements. Once the set of data is adjusted as a regression model (linear, polynomial, or multivariable), the proposed equation can be used to construct confidence and prediction bands based on the adjustment equation. The proposed equation operates if the adjustment equation is continuous and derivable. The plot bands on any regression model provide a solid understanding of uncertainty assessment and have the advantage of total control and independence of algorithms and numeral approximations. [ABSTRACT FROM AUTHOR]

Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high‐dimensional datasets, such as omics and medical image data. However, the literature on nonlinear regression algorithms and variable selection techniques for interval‐censoring is either limited or nonexistent, particularly in the context of neural networks. Our objective is to introduce a novel predictive framework tailored for interval‐censored regression tasks, rooted in Accelerated Failure Time (AFT) models. Our strategy comprises two key components: (i) a variable selection phase leveraging recent advances on sparse neural network architectures; (ii) a regression model targeting prediction of the interval‐censored response. To assess the performance of our novel algorithm, we conducted a comprehensive evaluation through both numerical experiments and real‐world applications that encompass scenarios related to diabetes and physical activity. Our results outperform traditional AFT algorithms, particularly in scenarios featuring nonlinear relationships. [ABSTRACT FROM AUTHOR]

The energy market operates in a highly deregulated and competitive environment, where electricity price plays a crucial role. Forecasting electricity prices presents a significant challenge due to the influence of complex factors such as weather patterns, fuel costs, and the advancement of renewable energy technologies. This study focuses on monthly electricity prices in four neighboring European countries: Bulgaria, Greece, Hungary, and Romania, which share similar weather conditions and economic characteristics. The research investigates the efficacy of four forecasting methods: Grey Verhulst Model (GVM), Nonlinear Regression, Feedforward Neural Network, and Support Vector Regression (SVR). These methods are applied to both short-term (1 month-ahead) and long-term (up to 7 months) electricity price forecasting in the aforementioned countries. The findings reveal that GVM proves suitable for short-term predictions. However, when it comes to long-term forecasting, SVR accurately captures the trends and turning points in electricity prices, albeit with unsatisfactory error rates. To address this issue, a modified version of SVR, referred to as Modified SVR (MSVR), is proposed to mitigate the errors. The results demonstrate that MSVR is an effective approach for long-term electricity price prediction. [ABSTRACT FROM AUTHOR]

While the world recovers from the COVID-19 pandemic, another outbreak of contagious disease remains the most likely future risk to public safety. Now is therefore the time to equip health authorities with effective tools to ensure they are operationally prepared for future events. We propose a direct approach to obtain reliable nearly instantaneous time-varying reproduction numbers for contagious diseases, using only the number of infected individuals as input and utilising the dynamics of the susceptible–infected–recovered (SIR) model. Our approach is based on a multivariate nonlinear regression model simultaneously assessing parameters describing the transmission and recovery rate as a function of the SIR model. Shortly after start of a pandemic, our approach enables estimation of daily reproduction numbers. It avoids numerous sources of additional variation and provides a generic tool for monitoring the instantaneous reproduction numbers. We use Norwegian COVID-19 data as case study and demonstrate that our results are well aligned with changes in the number of infected individuals and the change points following policy interventions. Our estimated reproduction numbers are notably less volatile, provide more credible short-time predictions for the number of infected individuals, and are thus clearly favorable compared with the results obtained by two other popular approaches used for monitoring a pandemic. The proposed approach contributes to increased preparedness to future pandemics of contagious diseases, as it can be used as a simple yet powerful tool to monitor the pandemics, provide short-term predictions, and thus support decision making regarding timely and targeted control measures. [ABSTRACT FROM AUTHOR]

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at  U E  and  B S , this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only  10 %  of the beams from the  U E  and  B S  codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at  U E  and  B S  vary from  128 × 128  to  1024 × 1024 . [ABSTRACT FROM AUTHOR]

This study investigates the effect of non-plastic fines content of sand–silt mixtures on excess pore water pressure generation using an energy-based approach. For this purpose, an experimental program was designed and conducted on mixtures of Firoozkuh sand No. 161 and Firoozkuh silt. A total of 35 undrained strain-controlled cyclic triaxial tests were performed on the reconstituted specimens of sand–silt mixtures, with a wide range of effective parameters such as relative density, effective confining stress, and fines content. Also, to present a more generalized and precise model, 37 undrained cyclic tests were collected from previous studies. Then, using nonlinear regression analysis and the results of these 72 tests, a model, relating residual excess pore water pressure ratio ( r u ) to the ratio of dissipated energy to capacity energy ( W / W liq ), is proposed. The results indicated that initial effective confining stress has a negligible effect on the r u - W / W liq trend and so, the calibration parameter of the model only depends on the relative density and fines content. Convincingly, the accuracy of the proposed model was verified using the results of a series of centrifuge tests, reported by others, and the recorded data of wildlife downhole array site during the Superstition Hills 1987 earthquake. The proposed model is practical, applicable to various sands and sand–silt mixtures with nonplastic fines contents, and able to be calibrated using convenient parameters (i.e., relative density and fines content). Finally, the model can be easily implemented in site response analysis for energy-based liquefaction potential evaluation. [ABSTRACT FROM AUTHOR]

Open caissons are commonly used in the construction of various underground structures, such as launch and reception shafts for tunnel-boring machines, storage or attenuation tanks, and cofferdams. During the sinking phase, the cutting edge of a caisson wall with a cutting face encounters soil and is subjected to loading to facilitate and control the sinking process. In this regard, the bearing capacity factor (N) of the cutting face of a circular open caisson in heterogeneous clay is evaluated via finite element limit analysis, which accounts for the increase in the undrained shear strength with depth. The parameters considered in this study cover practical aspects, including the excavation geometry, soil strength profile, and caisson geometry. This investigation also explored the impacts of the cutting face angle (β), roughness (α), ratio of the internal embedment depth to the embedment width (H/B), ratio of the internal radius to the embedment width (R/B), and strength gradient ratio (ρB/su0). In particular, when the H/B > R/B ratio, the N value tends to stabilize. Crucially, when H/B > 4, an increasing trend in R/B leads to a rise in N until R/B exceeds 10, i.e., large diameter caissons, stabilizing the N value. Furthermore, the results reveal the significant dependency of the cutting face roughness and strength gradient ratio of clay on N. The artificial neural network model, which is a soft computing-based model, is also developed to present the undrained bearing capacity forecasting equation. Compared with conventional regression, including the multiple linear regression model and the multiple nonlinear regression model, it has excellent performance, as measured by eight indices. In addition, ANOVA and Z-tests can support the research hypothesis and reject the null hypothesis. [ABSTRACT FROM AUTHOR]

The high-temperature particles produced by air gouging processes are common pollutants in cleaning workshops, which could endanger workers' health. In this research, a two-way coupled Eulerian-Lagrangian method based on discrete phase modelling (DPM) was used to investigate the transport characteristics of high-temperature particles generated from air gouging processes. The particle group in the movement was divided into core zone and boundary zone, and the kinetic mechanism of particles in different zones was analysed. The results show particles in the boundary zone are more susceptible to the vortex entrainment effect than particles in the core zone, and some particles could be moved from the vortex diffusion zone to the vortex recirculation zone. The smaller the particle size, the better the followability to the hot airflow. The slower the temperature decay of particles, the more significant the thermophoresis effect, which could enhance the diffusion behaviour of the small particles. By analysing the average residence time of particles in the breathing zone, the influence of different factors on the average vertical diffusion height h p ¯ of particles is summarized, and a nonlinear regression prediction model of h p ¯ is established. The results can contribute to health risk assessment and industrial ventilation design. [ABSTRACT FROM AUTHOR]

The power quality problem refers to the issues caused by the sudden rise of nonstandard voltage, current, or frequency. The problems that emerge from poor power quality due to non-linear loads are voltage sag, swell, interruptions, harmonics, and transients in distribution systems. Various compensation devices are used nowadays to improve power quality. The advances in power electronic technologies improve the reliability and functionality of power electronic based controllers, resulting in increased applications of FACTS devices like DSTATCOM and Dynamic Voltage Restorer (DVR) which are fast, flexible, and efficient solutions to the power quality problems. These devices are used to restore the source, load voltage, and current disturbances caused by different loads and faults. These devices were tested in a standard IEEE 14-bus system for Total Harmonic Distortion (THD) minimization while utilizing PI-based Artificial Neural Networks (ANNs) and Linear Regression (LR). The results were analyzed and compared. [ABSTRACT FROM AUTHOR]

The combination of typhoon-induced storm surges and astronomical tides can result in extreme seawater levels and disastrous effects on coastal socioeconomic systems. The construction of an appropriate wind field has consistently been a challenge in storm tide forecasting and disaster warning. In this study, we optimized a nonlinear regression formula based on the C15 model to determine the maximum wind radius. The simulation based on the improvement showed good accuracy for storm tides during super typhoon Mangkhut (WP262018), Saola (WP092023), and severe typhoon Hato (WP152017). The correlation coefficients were in the 0.94-0.98 range, and the peak bias was less than 5cm. The trough errors were significantly reduced compared to other wind fields. Owing to the importance and lack of the maximum wind radius (Rmax), we attempted to predict Rmax using an LSTM (Long Short-Term Memory) neural network for forecasting storm tides during strong typhoons. Constrained LSTM showed good performance in hours 6-48, and effectively enhanced the forecasting capability of storm tides during strong typhoons. The workflows and methods used herein have broad applications in improving the forecasting accuracy of strong typhoon-induced storm tides. [ABSTRACT FROM AUTHOR]

Mathematical growth models are useful in describing the growth of livestock. The study was done to assess the predictive ability and accuracy of four three-parameter nonlinear mathematical models (namely: Gompertz, Gompertz-Laird, Logistic, and von Bertalanffy) and one four-parameter (namely: Richards) nonlinear mathematical model. Models were used to predict the body weight (BW) of commercial Ross broiler chickens adapted to tropical conditions (n = 1,286). Age-weight data were collected once every week for 6 weeks. The Gauss-Newton iterative process of the nonlinear procedure in SAS was employed to obtain the parameters for each model. In addition, each model's goodnessof-fit, residuals, and computational difficulty were estimated. Model parameters were evaluated using Akaike's information criterion (AIC), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR²) and root mean square error (RMSE). The AdjR² value for all five models was high; however, the highest value was observed in the Gompertz and Gompertz-Laird models. Furthermore, the lowest AIC, BIC and RMSE values were observed in the Gompertz models. Using a complimentary method (involving a subjective pairwise comparison of the observed and predicted BWs), the Logistic, Gompertz-Laird, von Bertalanffy, and Richards models fitted well for the data used. However, the best fitting was obtained in the Gompertz model. Some similarities were observed between the Logistic and Richards models. In conclusion, all five nonlinear mathematical models fitted the age weight data used in this study well, with the Gompertz model being the best. [ABSTRACT FROM AUTHOR]

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations. [ABSTRACT FROM AUTHOR]

The saturate, aromatic, resin, and asphaltene content in petroleum (SARA composition) provides valuable information about the chemical nature of oils, oil compatibility, colloidal stability, fouling potential, and other important aspects in petroleum chemistry and processing. For that reason, SARA composition data are important for petroleum engineering research and practice. Unfortunately, the results of SARA composition measurements reported by diverse laboratories are frequently very dissimilar and the development of a method to assign SARA composition from oil bulk properties is a question that deserves attention. Petroleum fluids with great variability of SARA composition were employed in this study to model their SARA fraction contents from their density, flash point, sulfur content, and simulated distillation characteristics. Three data mining techniques: intercriteria analysis, regression, and artificial neural networks (ANNs) were applied. It was found that the ANN models predicted with higher accuracy the contents of resins and asphaltenes, whereas the non-linear regression model predicted most accurately the saturate fraction content but with an accuracy that was lower than that reported in the literature regarding uncertainty of measurement. The aromatic content was poorly predicted by all investigated techniques, although the prediction of aromatic content was within the uncertainty of measurement. The performed study suggests that as well as the investigated properties, additional characteristics need to be explored to account for complex petroleum chemistry in order to improve the accuracy of SARA composition prognosis. [ABSTRACT FROM AUTHOR]

The treatment of pharmaceutical wastewater is a critical environmental challenge, necessitating efficient removal methods. This study investigates the adsorption of a synthetic multicomponent pharmaceutical wastewater (SPWW) containing methanol, benzene, methylene chloride, 4-aminophenol, aniline, and sulfanilic acid onto charcoal-based activated carbon (AC). Batch experiments were conducted to study the effects of pH, contact time, and initial concentrations of the adsorbates. The results show that longer contact time and higher initial concentrations increase the adsorption capacity, whereas pH shows no significant effect on the adsorption capacity at a value of less than 10, eliminating the need for pH adjustment and reducing process costs. The pseudo-second order (PSO) kinetic model best describes the adsorption process, with intraparticle diffusion playing a key role, as confirmed by the Weber and Morris (W-M) model. Six models describing the adsorption at equilibrium are applied to experimental data, and their parameters are estimated with a nonlinear regression model. Among isotherm models, the Langmuir-Freundlich model provides the best fit, suggesting multilayer adsorption on a heterogeneous granular activated carbon (GAC) surface. The maximum adsorption capacity is estimated to be 522.3 mgC/gAC. Experimental results confirm that GAC could effectively treat highly concentrated pharmaceutical wastewater, achieving up to 52% removal efficiency. [ABSTRACT FROM AUTHOR]

Accurate assessment and prediction of mandible shape are fundamental prerequisites for successful orthognathic surgery. Previous studies have predominantly used linear models to predict lower facial structures from facial landmarks or measurements; the prediction errors for this did not meet clinical tolerances. This paper compared non-linear models, namely a Multilayer Perceptron (MLP), a Mixture Density Network (MDN), and a Random Forest (RF) model, with a Linear Regression (LR) model in an attempt to improve prediction accuracy. The models were fitted to a dataset of measurements from 155 subjects. The test-set mean absolute errors (MAEs) for distance-based target features for the MLP, MDN, RF, and LR models were respectively 2.77 mm, 2.79 mm, 2.95 mm, and 2.91 mm. Similarly, the MAEs for angle-based features were 3.09°, 3.11°, 3.07°, and 3.12° for each model, respectively. All models had comparable performance, with neural network-based methods having marginally fewer errors outside of clinical specifications. Therefore, while non-linear methods have the potential to outperform linear models in the prediction of lower facial measurements from upper facial measurements, current results suggest that further refinement is necessary prior to clinical use. [ABSTRACT FROM AUTHOR]

The optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L16 (45) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water. [ABSTRACT FROM AUTHOR]

Introduction: Recent outbreaks of monkeypox (Mpox) have occurred in countries outside of Africa, with large numbers of cases spreading rapidly to almost every continent. We aimed to analyze the correlation between the Global Health Security (GHS) Index (categories and indicators) and the Mpox case rate in different regions and globally. Methods: In this cross-sectional study, we used data from Mpox cases from the WHO, and the GHS categories and indicators for detection, prevention, reporting, health system, rapid response, international norms compliance, and risk environment. Outcome measures were the relationship between GHS index (categories and indicators) and Mpox case rate using crude and adjusted non-linear regression models. Results: After performing adjusted analyses, only risk environment and detection and reporting index were associated with Mpox case rates in the 99 countries and the Region of the Americas, respectively. Antimicrobial resistance (AMR) indicators of the prevention category, risk communication of the rapid response category, the joint external evaluation and performance veterinary services of the norms category, and the infrastructure adequacy of the risk environment category were associated with Mpox case rates in the 99 countries (p<0.05). Laboratory systems strength and quality indicator of the detection and reporting category, and emergency response operation indicator of the response rapid category were associated with Mpox case rates in the countries of the region of the Americas (p<0.05). AMR indicator of the prevention of the emergence category, and the infrastructure adequacy of the risk environment category were associated with Mpox case rates in the countries of the European Region (p<0.05). In the countries of the other regions, only the trade and travel restrictions indicator of the rapid response category was associated with Mpox case rates (p<0.05). Conclusions: Countries, particularly in the Americas region, with high levels of infrastructure adequacy and laboratory system strength and quality as measured by the GHS index are better equipped to detect more Mpox cases. Therefore, they have higher Mpox case detection rates and can successfully respond to Mpox outbreaks. [ABSTRACT FROM AUTHOR]

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties. [ABSTRACT FROM AUTHOR]

Previous studies have validated a performance equation (PE) and its generalized version (GPE) in describing the rotated and right-shifted Lorenz curves of organ size (e.g., leaf area and fruit volume) distributions of herbaceous plants. Nevertheless, there are still two questions that have not been adequately addressed by prior work: (i) whether the PE and GPE apply to woody plant species and (ii) how do the PE and GPE perform in comparison with other Lorenz equations when fitting data. To address these deficiencies, we measured the lamina length and width of each leaf on 60 Alangium chinense saplings to compare the performance of the PE and GPE with three other Lorenz equations in quantifying the inequality of leaf area distributions across individual trees. Leaf area is shown to be the product of a proportionality coefficient (k) and leaf length and width. To determine the numerical value of k, we scanned 540 leaves to obtain the leaf area empirically. Using the estimated k, the leaf areas of 60 A. chinense saplings were calculated. Using these data, the two performance equations and three other Lorenz equations were then compared and assessed using the root-mean-square error (RMSE) and Akaike information criterion (AIC). The PE and GPE were found to be valid in describing the rotated and right-shifted Lorenz curves of the A. chinense leaf area distributions, and GPE has the lowest RMSE and AIC values. This work validates the GPE as the best model in gauging variations in leaf area of the woody species. [ABSTRACT FROM AUTHOR]

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

Buried pipelines, as the most common method of natural gas transportation, are prone to pipeline leakage accidents and are difficult to detect due to their harsh and concealed environment. This paper focused on the problem regarding the free dissipation of residual gas in buried gas pipelines and soil after closing the gas supply end valve after a period of leakage by numerical simulation. A multiple non-linear regression model was established based on the least squares method and multiple regression theory, and MATLAB 2016b mathematical calculation software was used to solve the problem. The research results indicated that compared to the gas leakage diffusion stage, the pressure and velocity distribution during the free dissipation stage were significantly reduced. The increase in leakage time, pipeline pressure, leakage size, and pipeline burial depth led to a large accumulation of natural gas, which increased the concentration and distribution range of gas on the same free dissipation stage monitoring line. A prediction model for natural gas concentration in the free dissipation stage was established with an average error of 7.88%. A calculation model for the safety repair time of buried gas pipeline leakage accidents was further derived to determine the safety repair time of leakage accidents. [ABSTRACT FROM AUTHOR]

Recent research has made significant progress in automated unmanned systems utilizing Artificial Intelligence (AI)-based image processing to optimize the rebar manufacturing process and minimize defects such as twisting during production. Despite various studies, including those employing data augmentation through Generative Adversarial Networks (GANs), the performance of rebar twist prediction has been limited due to image quality degradation caused by environmental noise, such as insufficient image quality and inconsistent lighting conditions in rebar processing environments. To address these challenges, we propose a novel approach for real-time rebar twist prediction in manufacturing processes. Our method involves restoring low-quality grayscale images to high resolution and employing an object detection model to identify and track rebar endpoints. We then apply regression analysis to the coordinates obtained from the bounding boxes to estimate the error rate of the rebar endpoint positions, thereby determining the occurrence of twisting. To achieve this, we first developed a Unified-Channel Attention (UCA) module that is robust to changes in intensity and contrast for grayscale images. The UCA can be integrated into image restoration models to more accurately detect rebar endpoint characteristics in object detection models. Furthermore, we introduce a method for predicting the future positions of rebar endpoints using various linear and non-linear regression models. The predicted positions are used to calculate the error rate in rebar endpoint locations, determined by the distance between the actual and predicted positions, which is then used to classify the presence of rebar twisting. Our experimental results demonstrate that integrating the UCA module with our image restoration model significantly improved existing models in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Moreover, employing regression models to predict future rebar endpoint positions enhances the F1 score for twist prediction. As a result, our approach offers a practical solution for rapid defect detection in rebar manufacturing processes. [ABSTRACT FROM AUTHOR]