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

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

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

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

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

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

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

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]

This article discusses the chemical composition and potential health benefits of essential oils derived from different parts of the Merremia tuberosa plant. The study focuses on identifying the chemical constituents and biological properties of the essential oil, which is commonly used in folk medicine. The essential oils obtained from the leaves, flowers, and stems of the plant were analyzed, revealing different compounds in each part. The flower oil showed greater antioxidant activity and inhibition of acetylcholinesterase compared to the leaf oil. These findings suggest that the essential oil of Merremia tuberosa may have potential therapeutic applications. [Extracted from the article]

This research analyzes the thermal and kinetic behavior of the combustion of hydrochar obtained industrially from green waste biomass. The combustion kinetic analysis was studied using thermogravimetry and applying complementary methods such as Friedman's isoconversional analysis, mathematical deconvolution, combined kinetic analysis, and optimization by nonlinear regression of the kinetic curves. The process involves two overlapping steps: the organic compounds devolatilization and the oxidation of fixed carbon. For steps 1 and 2, the apparent activation energies were 178.5 and 183.1 kJ mol−1, while the apparent ln (A / s - 1) were 31.2 and 30.0, respectively. The truncated Šesták–Berggren model was used as a surrogate for distributed reactivity, resulting in n 1 = 1.37 and m 1 = −0.54 for step 1, and n 2 = 3.31 and m 2 = 0.496 for step 2. In step 1, the m < 0 serves as a surrogate for an activation energy distribution, while in step 2, the value of n > 1 would substitute a gamma distribution of frequency factors. Finally, the obtained kinetic model allows successful predictions of thermal programs outside the range of the analysis. [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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In this work, we introduce the random weighting method to the nonlinear regression model and study the asymptotic properties for the randomly weighted least squares estimator with dependent errors. The results reveal that this new estimator is consistent. Moreover, some simulations are also carried out to show the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

The Sand Hypoplastic (SH) constitutive law by von Wolffersdorff (1996) is a widely used hypoplastic model for soil mechanics. This model includes eight parameters, usually calibrated using the oedometric (OE) and the drained isotropically consolidated triaxial tests (CD). However, previous studies show that the SH models calibration in the CD test has conflicting requirements in predicting the evolution of stresses and strains. In this work, we study the SH model calibration over a wide range of testing conditions using 12 OE and 25 CD tests by Wichtmann and Triantafyllidis (2016) on the Karlsruhe sands. The parameter space is extensively explored via genetic algorithm optimization (GA) using the recently developed open-source software GA-cal (available at https://github.com/FraJoMen/GA-cal). This exploration allowed us to study the SH model's predictive limits and to identify, using a multi-objective analysis, the main parameters governing the compromise between the accurate prediction of stresses versus strain in the CD tests. [ABSTRACT FROM AUTHOR]

This study aims to achieve the densest possible state of soil for constructing dams and roads. This requires assessing the compaction characteristics, Optimum Moisture Content (OMC), and Maximum Dry Density (MDD) to determine the soil's suitability for earthworks. However, this process is resource-intensive and time-consuming. To streamline the assessment, the study incorporates six parameters: gravel (G), sand (S), fine (F) contents, plastic limit (PL), liquid limit (LL), and plasticity index (PI). Four different models are used to predict compaction characteristics: artificial neural network (ANN), nonlinear regression (NLR), linear regression (LR), and multilinear regression (MLR). The study utilized a substantial dataset of 2162 entries, considering various soil gradation and plasticity properties as input variables. To evaluate the models' effectiveness, several statistical measures, including coefficient of determination (R2), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), a20-index, and Objective (OBJ) value, were employed. The ANN model outperformed other models in predicting OMC, with RMSE, MAE, OBJ, SI, a20-index, and R2 values of 3.51, 2.31, 4.26, 0.202, 0.7, and 0.92%, respectively. However, for predicting MDD, the ANN model had the highest R2 value (R2 = 0.87), but the minimum RMSE (1.01), MAE (0.8), a20-index (0.998), and OBJ (1.07) were obtained from the MLR and LR models. Furthermore, sensitivity analyses revealed that the plastic limit significantly influences the OMC, while the gravel content plays a dominant role in predicting MDD. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) signals are susceptible to ionospheric plasma irregularities and associated scintillations, causing large deviations in the positioning solutions. This study aims to develop statistical regression models to estimate kinematic three-dimensional (3D) precise point positioning errors associated with ionospheric plasma irregularities based on the Rate Of Total electron content Index (ROTI). By assuming that the positioning errors follow the Laplace distribution, we perform nonlinear regression using the Levenberg–Marquardt algorithm on a collection of experimental data from 700 + Trimble receivers deployed in the NOAA Continuously Operating Reference Stations (CORS) Network. Three ROTI-based regression models are identified by curve fitting with nonlinear functions, i.e., third-degree polynomial (Poly3), two-term exponential (Exp2) and two-term power (Power2) models. A goodness-of-fit test suggests the models fit well into the relationship between ROTI and the 3D positioning errors with the adjusted coefficient of determination above 0.97. The regression models are subsequently employed to predict the 3D positioning errors with a given set of ROTI. Evaluation analysis using the observations from four CORS networks across different geographical regions indicate that the Exp2 model demonstrates encouraging prediction performance, with bias and root mean square error within − 0.14 m and 0.34 m, respectively, and the correct prediction ratio consistently surpasses 60.3%. The ROTI-based regression models have great potential in predictions of the degradation in GNSS positioning due to ionospheric space weather effects. [ABSTRACT FROM AUTHOR]

Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage, the current design is augmented by adding p design points where p is the dimension of the parameter of the model. The augmenting p points are such that, at the current parameter estimate, they constitute the locally D-optimal design within the set of all saturated designs. Two relevant subclasses of nonlinear regression models are focused on, which were considered in previous work of the authors on the adaptive Wynn algorithm: firstly, regression models satisfying the 'saturated identifiability condition' and, secondly, generalized linear models. Adaptive least squares estimators and adaptive maximum likelihood estimators in the algorithm are shown to be strongly consistent and asymptotically normal, under appropriate assumptions. For both model classes, if a condition of 'saturated D-optimality' is satisfied, the almost sure asymptotic D-optimality of the generated design sequence is implied by the strong consistency of the adaptive estimators employed by the algorithm. The condition states that there is a saturated design which is locally D-optimal at the true parameter point (in the class of all designs). [ABSTRACT FROM AUTHOR]

Produced water re-injection (PWRI) is one of the most important management methods to dispose fluid associated with oil and natural gas production because it is economic and environmentally friendly method. However, several formation damage mechanisms are associated with the re-injection, and the most important damage is clogging of pore throats phenomenon due to the suspended particles, which will reduce the permeability of formation. Therefore, it is necessary to build mathematical model to predict the growth and extend of the formation damage in reservoir to help surveillance PWRI operations. Empirical correlation is one of the Algebraic formation damage models. Empirical model becomes important when there is a loss in detailed information about process of damage creation. In this paper, an empirical model was built to describe growth and extent of the damage in Zubair formation caused by the operation of unfiltered produced water re-injection (UPWRI) in North-Rumaila oilfield under matrix condition by using nonlinear regression tools. From growth and extent of the damage, we conclude that reinjected unfiltered produce water under matrix pressure led to large damage in Main Pay formation near and round wellbore injector because of increased volume of produced water injection. In matrix injection, the quality of the injected water must meet stringent requirements. The damage is start from maximum value (75%) at near wellbore and gradually decreases away from it. The damage zone expands symmetrical around axial wellbore injector formed circular dish that increase-by-increase flow rate and duration injection. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990–2000 to 80.86 km2 in the period 2000–2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization. [ABSTRACT FROM AUTHOR]

Secant pile walls (SPWs) are frequently employed as temporary excavation support systems in urban areas; thus, they are useless once permanent foundations and basements have been constructed. Recently, attempts have been made to employ these walls to bear axial loads and lateral earth pressure, which may have significant potential benefits. Unfortunately, the shortage of research that recognizes them as bearing elements, especially numerical studies, has prevented this approach from reaching the final design and implementation stage. Thus, the primary objective of this research was to calibrate a finite element model to depict the performance of axially loaded SPW and extend the finite element analysis by including key factors and additional trials that may impact the performance of the axially loaded SPW. This research investigated various aspects to describe SPW behavior, including the maximum axial capacity of the SPW (Pmax), normalized horizontal deflection (δh/Ht%), normalized ground settlement (δvg/Ht%), the normalized vertical deflection of the SPW (δvw/Ht%), the normalized pivot point location (ε′/He) and settlement influence zone (Do). Several parameters, such as normalized penetration depth (He/Hc), sand relative density (Dr), surcharge load density (Wsur), and wall rigidity (Ht/Dp), were studied and examined in relation to these aspects. Additionally, 224 axially loaded SPW trails were performed as part of a parametric study. The findings demonstrated that the proposed FEM could depict the performance of the experimental model with fair accuracy under the effect of the axial and lateral loads. The effects of the investigated aspects on the Pmax were also discussed in detail. Also, using linear and non-linear regression analysis, the data from the parametric analysis was utilized to create rational relations between the various parameters. The best-achieved mathematical models for predicting the Pmax were also represented in graphical forms. Moreover, these graphs can be a useful and fast tool for civil and geotechnical engineers to predict the maximum axial capacity of an SPW according to the different variables. [ABSTRACT FROM AUTHOR]

In this study, Nernst growth model equations are used to explain the anodic biofilm (ABF) modeling, linear sweep voltammetry (LSV) at various growth stages of biofilm, and polarization curve modeling for its electron generation behavior in a miniaturized single-chambered microbial fuel cell (SMFC). Kinetic constants of various growth model equations were determined using non-linear regression analysis. Maximum specific growth rate (μmax) at anodic surface is observed 0.016 h−1 at a glucose concentration of 12 g L−1, whereas retardation in μmax is observed 14 g L−1 or more in SMFC. LSV results showed maximum current density of 6720.56 mA m−2. Anode performance in SMFC is examined through polarization curve resulting maximum open-circuit voltage (OCV), minimum charge transfer loss, and ohmic loss for NWG (NiWO4 impregnated on rGO), NiWO4, rGO, and plain CC (carbon cloth) anode. These results demonstrate significant enhancement in performance of MFC to lead towards model-based process controlling for significant scale-up in future. [ABSTRACT FROM AUTHOR]

A biomechanical model often requires parameter estimation and selection in a known but complicated nonlinear function. Motivated by observing that the data from a head-neck position tracking system, one of biomechanical models, show multiplicative time-dependent errors, we develop a modified penalized weighted least squares estimator. The proposed method can be also applied to a model with possible non-zero mean time-dependent additive errors. Asymptotic properties of the proposed estimator are investigated under mild conditions on a weight matrix and the error process. A simulation study demonstrates that the proposed estimation works well in both parameter estimation and selection with time-dependent error. The analysis and comparison with an existing method for head-neck position tracking data show better performance of the proposed method in terms of the variance accounted for. [ABSTRACT FROM AUTHOR]

Kernel methods are often used for nonlinear regression and classification in statistics and machine learning because they are computationally cheap and accurate. The wavelet kernel functions based on wavelet analysis can efficiently approximate any nonlinear functions. In this article, we construct a novel wavelet kernel function in terms of random wavelet bases and define a linear vector space that captures nonlinear structures in reproducing kernel Hilbert spaces (RKHS). Based on the wavelet transform, the data are mapped into a low-dimensional randomized feature space and convert kernel function into operations of a linear machine. We then propose a new Bayesian approximate kernel model with the random wavelet expansion and use the Gibbs sampler to compute the model's parameters. Finally, some simulation studies and two real datasets analyses are carried out to demonstrate that the proposed method displays good stability, prediction performance compared to some other existing methods. [ABSTRACT FROM AUTHOR]

The excavation of rock, whether in mining, petroleum, or civil engineering projects, predominantly relies on traditional drilling techniques. Across these applications, drilling bit wear considered as a primary factor impacting the overall cost of rock excavation projects. This wear of drill bits is directly linked to the properties of the rock being drilled. In this study, an investigated relations between drilling bit wear and geomechanical properties have been investigated. To measure drill bit wear, a laboratory-scale drilling rig was employed, based on 30 selected rock units. A comprehensive laboratory testing plan was executed on these rock units, encompassing various rock characteristics such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Cerchar abrasivity index (CAI), equivalent quartz content (EQC), grain size of minerals (GS), and Schmidt rebound number (SRN). Nonlinear regression techniques were employed to predict bit wear based on geomechanical rock properties. Performance evaluation criteria were used to validate the regression models. The results revealed an exponential increase in bit wear values with rising UCS, BTS, CAI, EQC, GS, and SRN. The statistical analysis indicated a strong correlation between rock characteristics and drill bit wear, with CAI emerging as the most influential parameter, having a correlation coefficient of R2 = 0.954. The regression models developed in this study are primarily intended for rock engineers engaged in rock drilling projects. [ABSTRACT FROM AUTHOR]

To assess the effect of silicon content (SC) and test parameters on the lubricated wear of Zn-30 wt.% Al-3 wt.% Cu-(0.5–5) wt.% Si alloys, six quaternary alloys were produced by casting. Their wear properties were investigated at different ranges of contact pressure (CP) of 1–8 MPa and sliding velocity (SV) of 1–3 ms−1 for two different sliding distances (SD) of 20 km and 108 km. It was observed that Si addition (SA) improved the tribological properties of Zn-30Al-3Cu alloy, but an increase in the SC above a certain percentage (0.5%) adversely affected these properties. Therefore, Zn-30Al-3Cu-0.5Si alloy demonstrated the lowest friction coefficient (FC) and wear volume (WV) among the alloys tested. The correlations between the lubricated wear properties (FC and WV) of the alloys and test conditions (CP and SV) were made by non-linear regression analysis using the wear test data. Finally, the equations for the relationship between the wear properties of the alloys and the test parameters were determined. [ABSTRACT FROM AUTHOR]

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation. In this study, a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite (CWG) from a fault zone, considering with height-diameter (h/d) ratio, dry densities (ρd) and moisture contents (ω). Based on the experimental results, a regression mathematical model of unconfined compressive strength (UCS) for CWG was developed using the Multiple Nonlinear Regression method (MNLR). The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase of ω. When the h/d ratio increased to 1.0, the UCS increased ω with up to 10.5% and then decreased. Increasing ρd is conducive to the improvement of the UCS at any ω. The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio, ρd and ω, and the h/d ratio is the dominant factor affecting the final failure mode, followed by ω and ρd. The specimens with different h/d ratio exhibited completely different fracture mode, i.e., typical splitting failure (h/d=0.6) and shear failure (h/d=1.0). By comparing the experimental results, this regression model for predicting UCS is accurate and reliable, and the h/d ratio is the dominant factor affecting the UCS of CWG, followed by ρd and then ω. These findings provide important references for maintenance of the tunnel crossing other fault fractured zones, especially at low confining pressure or unconfined condition. [ABSTRACT FROM AUTHOR]

Competencies related to the evaluation of own cognitive processes, called metacognitive monitoring, are crucial as they help decide whether to persist in or desist from cognitive efforts. One of the most well-known phenomena in this context—the Dunning–Kruger effect—is that less skilled people tend to overestimate their performance. This effect has been reported for various kinds of performance including creativity. More recently, however, it has been suggested that this phenomenon could be a statistical artifact caused by the better-than-average effect and by regression toward the mean. Therefore, we examined the Dunning–Kruger effect in the context of creative thinking performance (i.e., divergent thinking ability) across two studies (Study 1: N = 425; Study 2: N = 317) and applied the classical quartile-based analysis as well as newly recommended, advanced statistical approaches: the Glejser test of heteroscedasticity and nonlinear quadratic regression. We found that the results indeed depended on the employed statistical method: While classical analyses supported the Dunning–Kruger effect across all conditions, it was not consistently supported by the more advanced statistical methods. These findings are in line with recent work challenging certain assumptions of the Dunning–Kruger effect and we discuss factors that undermine accurate self-assessments, especially in the context of creative performance. [ABSTRACT FROM AUTHOR]

Pulsed dye lasers are used effectively in the treatment of psoriasis with long remission time and limited side effects. It is, however, not completely understood which biological processes underlie its favorable outcome. Pulsed dye laser treatment at 585-595 nm targets hemoglobin in the blood, inducing local hyperthermia in surrounding blood vessels and adjacent tissues. While the impact of destructive temperatures on blood vessels has been well studied, the effects of lower temperatures on the function of several cell types within the blood vessel wall and its periphery are not known. The aim of our study is to assess the functionality of isolated blood vessels after exposure to moderate hyperthermia (45 to 60°C) by evaluating the function of endothelial cells, smooth muscle cells, and vascular nerves. We measured blood vessel functionality of rat mesenteric arteries (n=19) by measuring vascular contraction and relaxation before and after heating vessels in a wire myograph. To this end, we elicited vascular contraction by addition of either high potassium solution or the thromboxane analogue U46619 to stimulate smooth muscle cells, and electrical field stimulation (EFS) to stimulate nerves. For measurement of endothelium-dependent relaxation, we used methacholine. Each vessel was exposed to one temperature in the range of 45-60°C for 30 seconds and a relative change in functional response after hyperthermia was determined by comparison with the response per stimulus before heating. Non-linear regression was used to fit our dataset to obtain the temperature needed to reduce blood vessel function by 50% (Half maximal effective temperature, ET50). Our findings demonstrate a substantial decrease in relative functional response for all three cell types following exposure to 55°C-60°C. There was no significant difference between the ET50 values of the different cell types, which was between 55.9°C and 56.9°C (P>0.05). Our data show that blood vessel functionality decreases significantly when exposed to temperatures between 55°C-60°C for 30 seconds. The results show functionality of endothelial cells, smooth muscle cells, and vascular nerves is similarly impaired. These results help to understand the biological effects of hyperthermia and may aid in tailoring laser and light strategies for selective photothermolysis that contribute to disease modification of psoriasis after pulsed dye laser treatment. [ABSTRACT FROM AUTHOR]

Purpose: Numerous methods have been proposed to characterise tubercle lateralisation. However, their normal values and related changes remain unclear. Accordingly, it was aimed to determine the potential sex and age effects and determined the optimal individualised method of diagnosing lateralisation of the tibial tubercle in patients with recurrent patellar dislocation (RPD). Methods: Measurements included the tibial tubercle–trochlear groove (TT‐TG) distance, tibial tubercle‐posterior cruciate ligament (TT‐PCL) distance and tibial tubercle lateralisation (TTL); and the proximal tibial width (PTW), trochlear width (TW) and trochlear dysplasia index (TDI), for adjustment. A two‐way analysis of variance was used to determine the effect of age, sex and their interaction within the normal group. When the age effect was statistically significant, a nonlinear regression was created. Areas under the receiver‐operating characteristic curve (AUCs) were calculated to assess diagnostic accuracy. Results: A total of 277 normal participants (mean [SD] age, 13.5 [2.6] years; 125 [45.1%] female) and 227 patients with RPD (mean [SD] age, 13.5 [2.6] years; 161 [58.1%] female) were analysed. It was found that in the normal group, in patients aged 7–10, TT‐PCL distance (p = 0.006), TTL (p = 0.007) and TT‐PCL/PTW (p < 0.001) were significantly larger in females than in males. A significant sex effect was also detected on TT‐TG/TW (p = 0.014). TT‐TG distance, TT‐PCL distance, TTL and TT‐PCL/PTW (in male patients) approached an established normal adult value of 12.3 mm, 20.9 mm, 0.64 and 0.28, respectively, with increasing age (p < 0.001). The AUC was greater for TT‐TG/TDI and TT‐TG/TW (p ≤ 0.01) and TT‐TG/TDI outperformed TT‐TG/TW in patients aged 15–18 (p = 0.004). Conclusions: Tubercle lateralisation increased with age and was affected by sex, with the exception of TT‐TG distance and TT‐TG/TDI. TT‐TG/TDI is the optimal method of diagnosing a lateralized tibial tubercle in patients with RPD. These findings assist with the evaluation of tubercle lateralisation in that they provide a proper protocol for paediatric and adolescent populations with RPD; and thus, will help determine whether medial tubercle transfer should be included among the tailored surgical procedures considered for the treatment of patients with RPD. Level of Evidence: Level III. [ABSTRACT FROM AUTHOR]

In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses decision trees constructed with multiple projections as the "base-learners", which we call "functional multi-index trees". We establish identifiability conditions for these trees and introduce two algorithms to compute them. We use numerical experiments to investigate the performance of our method and compare it with several linear and nonlinear regression estimators, including recently proposed nonparametric and semiparametric functional additive estimators. Simulation studies show that the proposed method is consistently among the top performers, whereas the performance of existing alternatives can vary substantially across different settings. In a real example, we apply our method to predict electricity demand using price curves and show that our estimator provides better predictions compared to its competitors, especially when one adjusts for seasonality. [ABSTRACT FROM AUTHOR]

Background: Phakic lenses (PIOLs, the most common and only disclosed type being the implantable collamer lens, ICL) are used in patients with large or excessive ametropia in cases where laser refractive surgery is contraindicated. The purpose of this study was to present a strategy based on anterior segment OCT data for calculating the refraction correction (REF) and the change in lateral magnification (ΔM) with ICL implantation. Methods: Based on a dataset (N = 3659) containing Casia 2 measurements, we developed a vergence-based calculation scheme to derive the REF and gain or loss in ΔM on implantation of a PIOL having power PIOLP. The calculation concept is based on either a thick or thin lens model for the cornea and the PIOL. In a Monte-Carlo simulation considering, all PIOL steps listed in the US patent 5,913,898, nonlinear regression models for REF and ΔM were defined for each PIOL datapoint. Results: The calculation shows that simplifying the PIOL to a thin lens could cause some inaccuracies in REF (up to ½ dpt) and ΔM for PIOLs with high positive power. The full range of listed ICL powers (− 17 to 17 dpt) could correct REF in a range from − 17 to 12 dpt with a change in ΔM from 17 to − 25%. The linear regression considering anterior segment biometric data and the PIOLP was not capable of properly characterizing REF and ΔM, whereas the nonlinear model with a quadratic term for the PIOLP showed a good performance for both REF and ΔM prediction. Conclusion: Where PIOL design data are available, the calculation concept should consider the PIOL as thick lens model. For daily use, a nonlinear regression model can properly predict REF and ΔM for the entire range of PIOL steps if a vergence calculation is unavailable. [ABSTRACT FROM AUTHOR]

Machine learning methods have been widely used control and information systems. Robust learning is an important issue in machine learning field. In this work, we propose a novel robust regression framework. Specifically, we propose a robust similarity measure induced by correntropy, and its important properties are demonstrated theoretically such as symmetry, boundedness, nonnegativity, consistency, smoothness and approximation behaviors. Moreover, the proposed robust metric extends the traditional metrics such as the l 2 -norm and l 0 -norm as the kernel parameter approaches different values. Then with the proposed similarity metric and ϵ -insensitive pinball loss, a new robust twin support vector regression framework (RTSVR) is proposed to handle robust regression problems. The linear RTSVR model is first built, and a kernelled RTSVR version is developed to deal with nonlinear regressions. To handle the nonconvexity of the proposed RTSVR, we use DC (different of convex function) programming algorithm to iteratively solve the problem, and the resulting algorithm converges linearly. To test the proposed RTSVR, numerical experiments are implemented on two databases including a public benchmark database and a practical application database. Experiments on benchmark data with different types of noise illustrate that the proposed methods achieve better performance than the traditional methods in most cases. Experiments on the application database, the proposed RTSVR is combined with near-infrared (NIR) spectral technique to analyze the hardness of licorice seeds in low frequency,intermediate frequency and high frequency spectral regions respectively. Experiments on different spectral regions show that the performance of the RTSVR is better than that of the traditional methods in all spectral regions. [ABSTRACT FROM AUTHOR]

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The Slide2 model was used to estimate seepage losses from canals after validation considering different canal geometries, lining thicknesses, and lining materials. SPSS was used to develop three models: NLR, MLP-ANN, and RBF-ANN. MATLAB software was used to write down the script code for the ANNs. Results showed that seepage losses were highly increased when the liner had high hydraulic conductivity, while with the increase of lining thickness, a noticeable reduction in seepage losses was obtained. The canal's side slope had a minimal effect on the seepage losses. Moreover, the MLP-ANN and RBF-ANN models performed better than the NLR model with determination coefficient (R2) of 0.996 and 0.965; Root-Mean-Square-Error (RMSE) of 1.172 and 0.699; Mean-Absolute-Error (MAE) of 0.139 and 0.528; index of agreement (d) = 0.999 and 0.991, respectively. The NLR model had lower values of R2 = 0.906, RMSE = 1.198, MAE = 0.942, and d = 0.971. Thus, ANNs are recommended as a robust, easy, simple, and rapid tool for estimating seepage losses from lined trapezoidal irrigation canals. [ABSTRACT FROM AUTHOR]