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This study investigated (a) differences between males and females for changes in serum, tear, and urine osmolality, hematocrit, and urine specific gravity following acute passive dehydration and (b) assessed the reliability of these biomarkers separately for each sex. Fifteen males (age: 26.3 ± 3.5 years, body mass: 76 ± 7 kg) and 15 females (age: 28.8 ± 6.4 years, body mass: 63 ± 7 kg) completed a sauna protocol twice (5–28 days apart), aiming for 4% body mass loss (BML). Urine, blood, and tear markers were collected pre- and postdehydration, and change scores were calculated. Male BML was significantly greater than that of females in Trial 1 (3.53% ± 0.55% vs. 2.53% ± 0.43%, p <.001) and Trial 2 (3.36% ± 0.66% vs. 2.53% ± 0.44%, p =.01). Despite significant differences in BML, change in hematocrit was the only change marker that displayed a significant difference in Trial 1 (males: 3% ± 1%, females: 2% ± 1%, p =.004) and Trial 2 (males: 3% ± 1%, females: 1% ± 1%, p =.008). Regression analysis showed a significant effect for sex (male) predicting change in hematocrit (β = 0.8, p =.032) and change in serum osmolality (β = −3.3, p =.005) when controlling for BML but not for urinary or tear measures. The intraclass correlation coefficients for females (ICC 2, 1) were highest for change in urine specific gravity (ICC =.62, p =.006) and lowest for change in tear osmolarity (ICC = −.14, p =.689), whereas for males, it was posthematocrit (ICC =.65, p =.003) and post tear osmolarity (ICC =.18, p =.256). Generally, biomarkers showed lower test–retest reliability in males compared with females but, overall, were classified as poor–moderate in both sexes. These findings suggest that the response and reliability of hydration biomarkers are sex specific and highlight the importance of accounting for BML differences. [ABSTRACT FROM AUTHOR]

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications. [ABSTRACT FROM AUTHOR]

In this Methods Showcase Article, we illustrate mixed‐effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future‐time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step‐by‐step procedures for multinomial logistic regression with fixed and random effects, and we discuss the interpretation of the model and its advantages and limitations. [ABSTRACT FROM AUTHOR]

We analyzed images of every northern hemisphere Sd galaxy listed in the Third Reference Catalogue of Bright Galaxies with a relatively face-on inclination (θ ≤ 30°). Specifically, we measured the spiral arms' winding angle, ϕ, in 85 galaxies. We applied a novel black hole mass planar scaling relation involving the rotational velocities (from the literature) and pitch angles of each galaxy to predict central black hole masses. This yielded 23 galaxies, each having at least a 50% chance of hosting a central intermediate-mass black hole (IMBH), 102 < M • ≤ 105 M ☉. These 23 nearby (≲50 Mpc) targets may be suitable for an array of follow-up observations to check for active nuclei. Based on our full sample of 85 Sd galaxies, we estimate that the typical Sd galaxy (which tends to be bulgeless) harbors a black hole with log (M • / M ☉) = 6.00 ± 0.14 , but with a 27.7% chance of hosting an IMBH, making this morphological type of galaxy fertile ground for hunting elusive IMBHs. Thus, we find that a ∼106 M ☉ black hole corresponds roughly to the onset of bulge development and serves as a conspicuous waypoint along the galaxy–supermassive black hole coevolution journey. Our survey suggests that >1.22% of bright galaxies (B T ≲ 15.5 mag) in the local Universe host an IMBH (i.e., the "occupation fraction"), which implies a number density >4.96 × 10−6 Mpc−3 for central IMBHs. Finally, we observe that Sd galaxies exhibit an unexpected diversity of properties that resemble the general population of spiral galaxies, albeit with an enhanced signature of the eponymous prototypical traits (i.e., low masses, loosely wound spiral arms, and smaller rotational velocities). [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is the third most common tumour entity in the world and up to 50% of the patients develop liver metastases (CRLM) within five years. To improve and personalize therapeutic strategies, new diagnostic tools are urgently needed. For instance, biomechanical tumour properties measured by magnetic resonance elastography (MRE) could be implemented as such a diagnostic tool. We postulate that ex vivo MRE combined with histological and radiological evaluation of CRLM could provide biomechanics-based diagnostic markers for cell viability in tumours. Methods: 34 CRLM specimens from patients who had undergone hepatic resection were studied using ex vivo MRE in a frequency range from 500 Hz to 5300 Hz with increments of 400 Hz. Single frequency evaluation of shear wave speed and wave penetration rate as proxies for stiffness and viscosity was performed, along with rheological model fitting based on the spring-pot model and powerlaw exponent α, ranging between 0 (complete solid behaviour) and 1 (complete fluid behaviour). For histological analysis, samples were stained with H&E and categorized according to the degree of regression. Quantitative histologic analysis was performed to analyse nucleus size, aspect ratio, and density. Radiological response was assessed according to RECIST-criteria. Results: Five samples showed major response to chemotherapy, six samples partial response and 23 samples no response. For higher frequencies (> 2100 Hz), shear wave speed correlated significantly with the degree of regression (p ≤ 0.05) indicating stiffer properties with less viable tumour cells. Correspondingly, rheological analysis of α revealed more elastic-solid tissue properties at low cell viability and major response (α = 0.43 IQR 0.36, 0.47) than at higher cell viability and no response (α = 0.51 IQR 0.48, 0.55; p = 0.03). Quantitative histological analysis showed a decreased nuclear area and density as well as a higher nuclear aspect ratio in patients with major response to treatment compared to patients with no response (all p < 0.05). Discussion: Our results suggest that MRE could be useful in the characterization of biomechanical property changes associated with cell viability in CRLM. In the future, MRE could be applied in clinical diagnosis to support individually tailored therapy plans for patients with CRLM. [ABSTRACT FROM AUTHOR]

Introduction: Intermittent exotropia (IXT) is commonly seen in areas with more sunlight, females and Asians. Surgical alignment is recommended to improve binocular function, but a high recurrence rate has been reported. This study aimed to assess the stability of ocular alignment and factors contributing to exodrift post-surgery for IXT over 6-month follow-up. Methods: We retrospectively reviewed the medical records of patients with IXT who underwent unilateral recess resect procedures and attended follow-up examinations for at least six months post-surgery. A significant exodrift was defined as exodrift of more than 10PD at distance and near. Ocular deviation, binocularity, and stereopsis at one and six-month post-operative visits were studied to assess the incidence of exodrift post-surgery and the probable factors causing such drift. Results: Of the 50 patients studied, 26 (52%) were males, 42 (84%) were <20 years. The median age at surgery was 10 years (range 6–14), and the median preoperative angle of deviation was 37 PD. Majority of patients (62%) had exodrift within six months of surgery, and about a quarter of the study population had significant exodrift >10 PD. No clinical factors were found to contribute significantly to exodrift in this study.16 (61.5%) patients attained near and distance BSV post-surgery (p-value: <0.001). Six (12%) and 10 (20%) patients with subnormal near and distance stereopsis, respectively, attained normal stereopsis post-surgery. Conclusion: Over half of the patients operated for IXT had some Exodrift, and one-fourth had significant exodrift within six months post-surgery. Despite improved BSV, many patients failed to attain normal stereopsis with just motor alignment. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TWSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TWSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with, we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects. [ABSTRACT FROM AUTHOR]

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models. [ABSTRACT FROM AUTHOR]

Feature selection is one of the most relevant processes in any methodology for creating a statistical learning model. Usually, existing algorithms establish some criterion to select the most influential variables, discarding those that do not contribute to the model with any relevant information. This methodology makes sense in a static situation where the joint distribution of the data does not vary over time. However, when dealing with real data, it is common to encounter the problem of the dataset shift and, specifically, changes in the relationships between variables (concept shift). In this case, the influence of a variable cannot be the only indicator of its quality as a regressor of the model, since the relationship learned in the training phase may not correspond to the current situation. In tackling this problem, our approach establishes a direct relationship between the Shapley values and prediction errors, operating at a more local level to effectively detect the individual biases introduced by each variable. The proposed methodology is evaluated through various examples, including synthetic scenarios mimicking sudden and incremental shift situations, as well as two real-world cases characterized by concept shifts. Additionally, we perform three analyses of standard situations to assess the algorithm's robustness in the absence of shifts. The results demonstrate that our proposed algorithm significantly outperforms state-of-the-art feature selection methods in concept shift scenarios, while matching the performance of existing methodologies in static situations. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values. [ABSTRACT FROM AUTHOR]

This paper proposes a new probability model called as Maxwell-Boltzmann-Exponential (MBE) distribution. The MBE distribution arises as a mixture distribution of the Maxwell-Boltzmann and exponential distributions. The statistical properties of the distributions are studied and obtained in closed-form expressions. Three methodologies are assessed and compared for the estimation of parameters in the MBE distribution. The MBE regression model is defined, with the proposed regression model being an alternative to the gamma regression model for response variables that are extremely right-skewed and bimodal. Two real data sets are used to demonstrate the applicability of the proposed models against the existing models. [ABSTRACT FROM AUTHOR]

As individuals age, changes in their health status and functional level may begin to require caregiving from family caregivers. The role of caregiver can be taxing, a well-established phenomenon known as caregiver burden. While many studies have examined the factors within the caregiver that can be predictive of caregiver burden (or burnout), relatively few have examined the factors of the care recipient that predict caregiver burden. Using a sample of individuals enrolled in the Department of Veteran's Affairs Home Based Primary Care program, we collected data on demographic, psychological (i.e. mood and cognitive) and nursing (e.g. ADL/IADL, fall risk, etc.) factors. Using stepwise regression modeling, we discovered that only care recipient's age, IADL impairment, and gender predicted caregiver burden. We discuss possible causes of the relationships and implications for nursing and homecare practice. [ABSTRACT FROM AUTHOR]

Background: Down Syndrome Regression Disorder (DSRD) is a rare and poorly understood disorder of the central nervous system, characterized by acute or subacute neuropsychiatric symptoms in previously healthy individuals with Down syndrome (DS). Many patients exhibit immunotherapy-responsiveness, indicative of immune dysregulation as a potential underlying etiology. While hypotheses are emerging regarding the role of interferon signaling in DSRD and other autoimmune conditions associated with DS, it is unclear why a small subset of individuals with DS develop DSRD. The aim of this study was to investigate genes of immune regulation in persons with DSRD. Methods: This study included individuals with DSRD aged 10–30 years with trio exome sequencing performed during the diagnostic work up. Descriptive statistics and univariate analysis (Chi-square and Fisher's exact test) were used to describe and compare the characteristics of individuals with and without variants. Results: Forty-one individuals with DSRD had trio exome sequencing results. Eight (20%) had heterozygous de novo variants of immune regulatory genes, with four variants being pathogenic or likely pathogenic (UNC13D, XIAP, RNASEH2A, and DNASE1L3). All genes harboring pathogenic variants were associated with interferon type-1 inflammatory response. Individuals harboring variants were more likely to have a preceding trigger (p = 0.03, 95% CI 1.21–97.06), rapid clinical decline in less than 1 month (p = 0.01, 95% CI 1.67–52.06), and MRI abnormalities (p < 0.001, 95% CI 4.89–527.71). Discussion: A distinct subset of individuals with DSRD exhibited pathogenic variants in immune regulation genes associated with interferon-mediated inflammatory response, coinciding with previously established links between these genes and interferonopathies such as Aicardi-Goutieres syndrome. Our observations suggest that these variants might potentially contribute to the development of DSRD in individuals with DS. [ABSTRACT FROM AUTHOR]

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available. [ABSTRACT FROM AUTHOR]

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower's vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R 2 = 0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R 2 = 0.99647 , but is still considered a competitive model. A reduced model was introduced trading off some accuracy ( R 2 = 0.9916 ) for significantly reduced data collection requirements and effort. [ABSTRACT FROM AUTHOR]

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS's blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. [ABSTRACT FROM AUTHOR]

We study linear models for the prediction of the initial guess for the nonlinear Newton-Raphson solver. These models use one or more of the previous simulation steps for prediction, and their parameters are estimated by the ordinary least-squares method. A key feature of the approach is that the parameter estimation is performed using data obtained directly during the simulation and the models are updated in real time. Thus we avoid the expensive process of dataset generation and the need for pre-trained models. We validate the workflow on a standard benchmark Egg dataset of two-phase flow in porous media and compare it to standard approaches for the estimation of initial guess. We demonstrate that the proposed approach leads to reduction in the number of iterations in the Newton-Raphson algorithm and speeds up simulation time. In particular, for the Egg dataset, we obtained a 30% reduction in the number of nonlinear iterations and a 20% reduction in the simulation time. [ABSTRACT FROM AUTHOR]

Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this "crash course" accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models. [ABSTRACT FROM AUTHOR]

Dynamical numerical weather prediction has remarkably improved over the last decades. Yet postprocessing techniques are needed to calibrate forecasts which are based on statistical and machine learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on least absolute shrinkage and selection operator (LASSO) regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching mean-square-error skill improvements of up to 3% (day ahead) or 1% (week ahead). Only considering land surface improvements in Europe, improvements of 4%–6% for day-ahead forecasts and 1%–5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques. [ABSTRACT FROM AUTHOR]

Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed. [ABSTRACT FROM AUTHOR]

Foster homes promote optimal outcomes among youth who experience difficulties in their original families. However, foster caregivers often face various challenges and difficulties. Guided by ecological framework, the current study examined how multiple factors from foster caregivers' surrounding environments impact satisfaction and retention among 462 foster caregivers in the United States. Regression analyses suggested that certain demographics, parenting characteristics, and agency characteristics were associated with foster care retention, levels of satisfaction, and overall foster care experience. To probe these findings further, results from latent profile analyses suggested that foster caregivers fell into two profiles—those who experienced a large amount of challenges or few challenges during their time as foster caregivers. On average, those who experienced few challenges reported significantly more satisfaction with foster caregiving, greater intention to continue fostering, and a better overall fostering experience compared with those who experienced more challenges. Implications and limitations are discussed. [ABSTRACT FROM AUTHOR]

The results of studies of the regional structure of Solenovian deposits of the Early Oligocene Rupelian Stage in the Cisсaucasian–Caspian region, which represented the northern slope of Eastern Paratethys in the Cenozoic, are presented. The geological structure and paleogeographic conditions in the basin during the Solenovian (second half of Early Oligocene) were refined based on a comprehensive interpretation of geological and geophysical materials. The spatial consequences of the sea level drop in Eastern Paratethys during the Oligocene regressions are traced. The periodic draining of shelf areas is reflected in the structure of erosional unconformities and buried river incisions. It has been established that the most significant was the late Solenovian regression with a relative sea level drop by more than 500 m. Consequently, a prominent unconformity surface, complicated by river incisions, was formed in the sedimentary sequence in the shelf part of the paleobasin (Maikop seismic sequence). Erosion ledges and landslide bodies formed on the slopes, and basin-infill complexes were accumulated in depressions. [ABSTRACT FROM AUTHOR]

Deep Learning-based Side-Channel Analysis (DL-SCA) has emerged as a powerful method in the field of side-channel analysis. Current works on DL-SCA primarily rely on publicly available datasets, which typically consist of well-organized and well-aligned training and attack sets. However, this disregards the challenges faced in real-world attacks, where the attack traces are not well-aligned with the training traces as attackers have different levels of control over profiling and attack devices. A network that is capable of identifying areas of leakage and subsequently predicting the leaked values can bypass such difficulty. Therefore, we proposed Arbitrary Trace Attacks, which are placed under the flexible scenario that provides training traces and attack traces with arbitrary sizes. To implement such attacks, we present the Arbitrary Convolutional Neural Network (ACNN), which scans the input trace of arbitrary sizes for leakage area identification and leakage value prediction using a sliding window. Experimental evaluation is conducted on two datasets DPAv4.2 and ASCAD to verify the effectiveness of our approach on unprotected and masked implementation respectively. As a result, the target leakage areas are detected with a significant frequency and the key recovery performance is on par with state-of-the-art. Moreover, the trained model shows the potential for detecting leakage in a general context, that is, detecting leakage of key bytes other than the target one. [ABSTRACT FROM AUTHOR]

This paper considers the problem of kernel regression and classification with possibly unobservable response variables in the data, where the mechanism that causes the absence of information can depend on both predictors and the response variables. Our proposed approach involves two steps: First we construct a family of models (possibly infinite dimensional) indexed by the unknown parameter of the missing probability mechanism. In the second step, a search is carried out to find the empirically optimal member of an appropriate cover (or subclass) of the underlying family in the sense of minimizing the mean squared prediction error. The main focus of the paper is to look into some of the theoretical properties of these estimators. The issue of identifiability is also addressed. Our methods use a data-splitting approach which is quite easy to implement. We also derive exponential bounds on the performance of the resulting estimators in terms of their deviations from the true regression curve in general L p norms, where we allow the size of the cover or subclass to diverge as the sample size n increases. These bounds immediately yield various strong convergence results for the proposed estimators. As an application of our findings, we consider the problem of statistical classification based on the proposed regression estimators and also look into their rates of convergence under different settings. Although this work is mainly stated for kernel-type estimators, it can also be extended to other popular local-averaging methods such as nearest-neighbor and histogram estimators. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms. Despite the documented use of various ML techniques, a thorough comparison and performance analysis under different operating conditions were lacking. This study fills this gap by conducting a rigorous evaluation of nine ML models using TCAD-generated data of Pseudomorphic AlGaN/InGaN/GaN HEMT. The research focuses on Small Signal Behavioral Modeling and examines regression techniques such as Multiple Linear Regression (MLR), Multivariate Linear Regression (MVLR), Ridge Regression (L2), Lasso Regression (L1), Elastic Net Regression (ENR), Decision Trees (DT), Random Forest (RF), Gradient Boosting Regression(GBR), and Support Vector Regression (SVR). These methods use biases, frequency, and device geometry as independent variables, with S-parameters being the dependent variables. K-fold cross-validation was employed to ensure model reliability and accuracy across diverse operating conditions. Results reveal that the RF, coupled with 10-fold cross-validation, exhibits superior performance giving 99.7% accurate results, with a Mean Squared Error (MSE) of 4.6375×10-5, and a coefficient of determination (R²) of 0.9977. Conversely, SVR, L1, and ENR fall short of expectations. This study underscores the significance of methodological advancements in ML-based GaN HEMT modeling and provides valuable insights for future research in this domain. [ABSTRACT FROM AUTHOR]

PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally robust optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written as Wasserstein-based DRO problems. Inspired by those results we propose a hyperparameter-free ambiguity set that explores the polyhedral structure of data-driven convex hulls, generating computationally tractable regression and classification methods for any convex loss function. Numerical results based on 100 real-world databases and an extensive experiment with synthetically generated data show that our methods consistently outperform their traditional counterparts. [ABSTRACT FROM AUTHOR]

Large watercourses are characterised by significant spatial and temporal changes in water quality due to both natural and anthropogenic impacts. The paper analyses changes in the Danube's spatial and seasonal water quality in its middle part at five monitoring stations for the period 2018–2022. Examined water quality parameters include oxygen saturation (Os), ammonium (N-NH4), pH, 5-day biochemical oxygen demand (BOD), nitrate nitrogen (N-NO3), orthophosphates (P-PO4), suspended solids (SS), electrical conductivity (EC), and water temperature (WT). Furthermore, the analysis took into account the influence of two additional factors: air temperature (AT) and discharge (D). Throughout the entire period under study, all parameters were characterised by low concentration values, which met the environmental objective of good ecological status. The use of multivariate statistical methods allowed for the identification of EC, N-NH4, WT, Os, SS, and N-NO3 as determining the greatest spatio-seasonal variability of water quality in a selected section of the Danube. Regression models determined WT, EC and nitrogen nitrate changes as depending on AT, flow, and exposure time. Knowing models not only gives a better understanding of the dynamics of changes in water quality in the stretch of the Danube under study but potentially allows the prediction of these parameters based on easily measurable environmental variables. [ABSTRACT FROM AUTHOR]

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain. [ABSTRACT FROM AUTHOR]

Sarcopenia, the age-related loss of skeletal muscle mass, is a core component of frailty that is associated with functional decline and adverse health events in older adults. Unfortunately, the available tools to diagnose sarcopenia are often inaccessible or not user-friendly for clinicians. Point-of-care ultrasound (US) is a promising tool that has been used to image the quadriceps muscle and measure its thickness (QMT) as a diagnostic criterion for sarcopenia. This measurement can be challenging for clinicians, especially when performed at the bedside using handheld systems or phased-array probes not designed for this use case. In this paper, we sought to automate this measurement using deep learning methods to improve its accuracy, reliability, and speed in the hands of untrained clinicians. In the proposed framework, which aids in better training, particularly when limited data are available, convolutional and transformer-based deep learning models with generic or data-driven pre-trained weights were compared. We evaluated regression (QMT as a continuous output in cm) and classification (QMT as an ordinal output in 0.5 cm bins) approaches, and in the latter, activation maps were generated to interpret the anatomical landmarks driving the model predictions. Finally, we evaluated a segmentation approach to derive QMT. The results showed that both transformer-based models and convolutional neural networks benefit from the proposed framework in estimating QMT. Additionally, the activation maps highlighted the interface between the femur bone and the quadriceps muscle as a key anatomical landmark for accurate predictions. The proposed framework is a pivotal step to enable the application of US-based measurement of QMT in large-scale clinical studies seeking to validate its diagnostic performance for sarcopenia, alone or with ancillary criteria assessing muscle quality or strength. We believe that implementing the proposed framework will empower clinicians to conveniently diagnose sarcopenia in clinical settings and accordingly personalize the care of older patients, leading to improved patient outcomes and a more efficient allocation of healthcare resources. [ABSTRACT FROM AUTHOR]

There is a correlative relationship between mediatization and social interaction. Communication scholars have paid attention to this relationship. We argue that mediatization involves the incorporation of various patterns of interaction among users of social media platforms. We used two theoretical approaches, namely mediatization and symbolic interaction. We surveyed a purposive sample that we selected according to certain criteria. Regression analysis revealed the correlative relationship between the dimensions of mediatization over social media platforms and the patterns of symbolic interaction among users. [ABSTRACT FROM AUTHOR]

Principal components analysis (PCA) and partial least squares (PLS) have been used for the construction of socioeconomic status (SES) indices to use as a predictor of the well-being status in targeted programs. Generally, these indicators are constructed as a linear combination of the first component. Due to the characteristics of the socioeconomic data, different extensions of PCA and PLS for non-metric variables have been proposed for these applications. In this paper, we compare the predictive performance of SES indices constructed using more than one component. Additionally, for the inclusion of non-metric variables, a variant of the normal mean coding is proposed that takes into account the multivariate nature of the variables, which we call multivariate normal mean coding (MNMC). Using simulations and real data, we found that PLS using MNMC as well as the classical dummy encoding method give the best predictive results with a more parsimonious SES index, both in regression and classification problems. [ABSTRACT FROM AUTHOR]

The precision of scroll machining plays a crucial role in scroll compressor efficiency. However, the improvement in scroll surface quality usually comes at the cost of an increase in energy consumption, so a trade-off between surface quality and energy consumption is required. Here in, we concentrate on optimizing several scroll end milling parameters including depth of cut, feed rate, cutting speed, and radial depth of cut to enhance the efficiency of scroll compressors, which greatly influence manufacturing responses like cutting force, surface roughness, and machining time. First, a set of scroll milling experiments are conducted and analyzed by the Taguchi L25 orthogonal array. Then, a single response optimization is done by the Taguchi method to show the impact of the milling parameters on each single response. Furthermore, the Taguchi method associated with the desirability function is applied to optimize the multi-response outputs. According to the analysis of variance (ANOVA) for the single-response, feed rate is found the most significant factor, while the results of the multi-response analysis prove that depth of cut with 37.53% is the most significant factor for the entire system. Finally, a quadratic regression analysis is performed to verify the validation of optimized results. The results revealed a 44.04% improvement in machining time, accompanied by a 2.46% enhancement in composite desirability. This demonstrated a perfect fit between the measured and expected values, achieving a balance between surface quality and energy consumption. The optimization results provide a guidance for the high-surface quality machining of scrolls and could be directly applied in the manufacturing processes of scroll compressors. [ABSTRACT FROM AUTHOR]

AbstractA novel model for count data is proposed in this work. This new model is briefly called the Zero-Inflated Probit Bell (ZIPBell) model. The ZIPBell model can be used to simulate and analyze traditional count data and can be used well in the simulation and analysis of count data with the phenomenon that the frequency for zero occupies a very high percentage. In the scope of this study, we first provide the general formula, functions or related equations for the ZIPBell model. The results of asymptotic inferences for the ZIPBell model are also given in this work. Maximum likelihood estimator (MLE) is used to check its performance of the proposed model. According to the numerical illustrations via several simulation researches and the analysis of a practical data set, it has been seen that the MLE method has offered correct and reliable inferences. Finally, the inferences and conclusions of this study are mentioned. [ABSTRACT FROM AUTHOR]

Objective: This study is to evaluate the association between preoperative cholinesterase levels and all-cause mortality in geriatric hip fractures. Methods: Elderly patients with hip fractures were screened between Jan 2015 and Sep 2019. Demographic and clinical characteristics of patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between preoperative cholinesterase levels and mortality in these patients. Analyses were performed using EmpowerStats and the R software. Results: Two thousand three hundred eighty-seven patients were included in this study. The mean follow-up period was 37.64 months. Seven hundred eighty-seven (33.0%) patients died due to all-cause mortality. Preoperative cholinesterase levels were 5910 ± 1700 U/L. Linear multivariate Cox regression models showed that preoperative cholinesterase level was associated with mortality (HR = 0.83, 95% CI: 0.78–0.88), P < 0.0001) for every 1000 U/L. However, the linear association was unstable, and nonlinearity was identified. A cholinesterase concentration of 5940 U/L was an inflection point. When preoperative cholinesterase level < 5940 U/L, the mortality decreased by 28% for every 1000 U/L increase in cholinesterase (HR = 0.72, 95%CI: 0.66–0.79, P < 0.0001). When cholinesterase was > 5940 U/L, the mortality was no longer decreased with the rise of cholinesterase (HR = 1.01, 95%CI: 0.91–1.11, P = 0.9157). We found the nonlinear association was very stable in the propensity score-matching sensitive analysis. Conclusions: Preoperative cholinesterase levels were nonlinearly associated with mortality in elderly hip fractures, and cholinesterase was a risk indicator of all-cause mortality. Trial registration: This study is registered on the website of the Chinese Clinical Trial Registry (ChiCTR: ChiCTR2200057323) (08/03/2022). [ABSTRACT FROM AUTHOR]

This manuscript presents a new two-parameter unit stochastic distribution, obtained by transforming the Laplace distribution, using a generalized logistic map, into a unit interval. The distribution thus obtained is named the Laplace-logistic unit (abbreviated LLU) distribution, and its basic stochastic properties are examined in detail. Also, the procedure for estimating parameters based on quantiles is provided, along with the asymptotic properties of the obtained estimates and the appropriate numerical simulation study. Finally, the application of the LLU distribution in dynamic and regression analysis of real-world data with accentuated "peaks" and "fat" tails is also discussed. [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]

There is a plethora of research attempting to contrast high- and low-velocity pitchers to identify traits to target for increasing velocity. However, pitch velocity exists on a continuum. Therefore, our purpose is to display the analytical discrepancies between creating velocity subgroups and leaving velocity as a continuous variable by examining the influence of ball velocity on elbow valgus torque. Motion capture data for 1315 actively competing pitchers were retrospectively extracted from a private database. We compared three analytic methods: (1) linear regression of valgus torque on ball velocity, (2) t-test between low- and high-velocity groups formed by a median split, and (3) t-test between very low- and very high-velocity groups formed by upper and lower velocity quartiles. Linear regression indicates ball velocity influenced valgus torque (p < 0.001, R2 = 0.280). Median splitting reduced the predictability of ball velocity on valgus torque (p < 0.001, R2 = 0.180). Conversely, extreme group splitting artificially inflated the effect size (p < 0.001, R2 = 0.347). We recommend sports biomechanics researchers not discretise a continuous variable to form subgroups for analysis because (1) it distorts the relationship between the variables of interest and (2) a regression equation can be used to estimate the dependent variable at any value of the independent variable, not just the group means. [ABSTRACT FROM AUTHOR]

Accurate speed estimation of surrounding vehicles is of paramount importance for autonomous driving to prevent potential hazards. This paper emphasizes the critical role of precise speed estimation and presents a novel real-time framework based on deep learning to achieve this from images captured by an onboard camera. The system detects and tracks vehicles using convolutional neural networks and analyzes their trajectories with a tracking algorithm. Vehicle speeds are then accurately estimated using a regression model based on random sample consensus. A synthetic dataset using the CARLA simulator has been generated to validate the presented methodology. The system can simultaneously estimate the speed of multiple vehicles and can be easily integrated into onboard computer systems, providing a cost-effective solution for real-time speed estimation. This technology holds significant potential for enhancing vehicle safety systems, driver assistance, and autonomous driving. [ABSTRACT FROM AUTHOR]

The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid's bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices. [ABSTRACT FROM AUTHOR]

AbstractIn this critical quantitative study, we used regression with moderation analysis to investigate the factors that exacerbated or mitigated plurisexual (bisexual and queer) student affairs educators’ workplace trauma responses during COVID-19. Using the concept of monosexism and critical trauma theory, we assessed distinctions amongst 914 monosexual and plurisexual student affairs educators’ experiences with workplace trauma, finding a significant moderation effect of plurisexuality when it came to institutional leadership support’s prediction of workplace trauma response. Implications for student affairs research and practice are also discussed. [ABSTRACT FROM AUTHOR]

The ability to accurately predict when a job on a supercomputer will leave the queue and start to run is not only beneficial for providing insights to users, but can also help enable non‐traditional HPC workloads that are not necessarily suited to the batch queue style‐approach that is ubiquitous on production HPC machines. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work, we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behavior resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600), and 4‐cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. By combining categorization and regression models, we demonstrate that our approach delivers the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within 1 min of the actual start time for around 65% of jobs on ARCHER2 and 4‐cabinet, and 76% of jobs on Cirrus. When compared against what Slurm can deliver, via the backfill plugin, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within 10 min of the actual start time on ARCHER2 and 4‐cabinet, and for 90% of jobs on Cirrus. Whilst the initial driver of this work was to better facilitate non‐traditional, interactive and urgent, workloads on HPC machines, the insights gained can also be used to provide wider benefits to users, enrich existing batch queue systems, and inform supercomputing center policy also. [ABSTRACT FROM AUTHOR]

Student socialisation into the norms and environment of their institution is a crucial process during their college experience. During the COVID-19 pandemic, these processes were disrupted, and little published research exists exploring comparisons of these student socialisation processes before and during the pandemic. Accordingly, we report on a quantitative study conducted at a large mid-western university in the United States, which compared survey data from 2018 to data collected in 2021. We operationalised student socialisation processes using Ashford and Black’s proactive behaviours scale to explore how these behaviours and relevant socialisation outcomes changed from before to during the pandemic. The primary socialisation outcomes of interest were engineering identity and social capital. During the COVID pandemic, students reported lower levels of social capital. However, we simultaneously observed higher ratings of students’ engineering identity. Next, using multivariate linear regression, we examined which proactive behaviours best predicted socialisation outcomes in both datasets. We found all proactive behaviours were significant predictors of social capital. Proactive behaviours did not influence student’s sense of engineering identity. This study contributes to the growing literature on the pandemic's impact on engineering undergraduate students and how proactive behaviours are related to their socialisation outcomes. [ABSTRACT FROM AUTHOR]

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research. [ABSTRACT FROM AUTHOR]

A dose‐response experiment was conducted to evaluate the effects of graded levels of dietary digestible threonine (dThr) during the first laying cycle on productive and reproductive performance, egg quality and immune responses of Japanese quail breeders (Coturnix coturnix japonica). Also, dThr requirements were determined based on nutrient dose‐response data. A total of 450 (360 females and 90 males) 11‐week‐old breeders were allocated to five increment (+0.03%) levels of dThr (0.49%, 0.52%, 0.55%, 0.58%, 0.61% and 0.64%) with five replicates per treatment and 15 (12 females and three males) birds each. The experiment lasted for 12 weeks. In response to increasing dietary dThr levels, egg production, egg mass, feed efficiency, egg specific gravity, eggshell relative weight, eggshell thickness, egg fertility (EF) and immune response against sheep red blood cell (SRBC) inoculation were improved with quadratic trends and egg hatchability as set eggs was improved with linear trends. Japanese quail breeders fed a diet with 0.58% dThr concentration (threonine/lysine ratio of 59%) showed the productive performance traits, EF, eggshell quality and immune response against SRBC inoculation in the highest values. However, feed intake, egg weight, egg albumen and yolk relative weight, egg shape index, haugh unit and egg composition were not affected by increasing dietary dThr level. Based on the broken‐line regression model, the dThr requirements to optimize productive performance, eggshell quality, EF and immune response against SRBC inoculation were estimated at 159−188, 169−183, 175 and 178 mg/bird per day, respectively. It is concluded, in the Japanese quail breeders during the first laying phase a daily dThr intake of 188 mg/bird, dietary dThr concentration at 0.58% (threonine/lysine ratio of 59%) is adequate for optimized productive and reproductive performance, eggshell quality and immune responses. The estimated requirements depend on what production parameter is taken into considered for optimization. [ABSTRACT FROM AUTHOR]

Permutation techniques have been used extensively in machine learning algorithms for evaluating variable importance. In ordinary regression, however, variables are often removed to gauge their importance. In this paper, we compared the results for permuting variables to removing variables in regression to assess relations between these two methods. We compared permute-and-predict (PaP) methods with leave-one-covariate-out (LOCO) techniques. We also compared these results with conventional metrics such as regression coefficient estimates, t-statistics, and random forest out-of-bag (OOB) PaP importance. Our results indicate that permutation importance metrics are practically equivalent to those obtained from removing variables in a regression setting. We demonstrate a strong association between the PaP metrics, true coefficients, and regression-estimated coefficients. We also show a strong relation between the LOCO metrics and the regression t-statistics. Finally, we illustrate that manual PaP methods are not equivalent to the OOB PaP technique and suggest prioritizing the use of manual PaP methods on validation data. [ABSTRACT FROM AUTHOR]

This research addresses the gap in comprehensive predictive models for construction project success rating by exploring the potential of regression models to evaluate project success rating. By analyzing 130 datasets from the National Capital Region, the study utilizes Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Artificial Neural Network (ANN) with a 22-30-1 configuration (22 input neurons, 30 neurons in a single hidden layer, and 1 output neuron). The input variables represent critical success factors rated on a scale of 1-5, while the output variable represents the predicted project success percentage rating. Various statistical tools, including ANOVA, Lasso Regression, R², MAE, and MSE, are utilized for evaluation. The findings reveal that SVR achieved the highest accuracy (R² = 0.881, MAE = 2.172, MSE = 7.054), followed closely by MLR (R² = 0.874, MAE = 2.180, MSE = 7.470), while ANN (R² = 0.743, MAE = 3.076, MSE = 15.239) may require refinement. Lasso Regression identified 22 critical success factors, with Financial Condition, Effectiveness in Decision-Making, and Compliance to Quality Standards ranking as the top three. This research contributes to the advancement of construction predictive analytics, which can lead to improved decision-making and more efficient, effective, and ethical construction practices. [ABSTRACT FROM AUTHOR]

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms struggle with streaming data as they rely on static datasets for model construction. In contrast, Online Machine Learning (OML) adapts to changing environments by training the model with each new observation in real-time. However, developing OML algorithms introduces complexities such as bias and variance considerations, making the selection of suitable estimators challenging. In this challenging landscape, ensemble learning emerges as a promising approach, offering a strategic framework to navigate the bias-variance tradeoff and enhance prediction accuracy by amalgamating outputs from diverse ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge, thereby empowering IoT applications with robust decision-making capabilities. Our study addresses the critical challenges of ML in resource-constrained edge computing environments, offering practical insights for enhancing predictive accuracy and scalability in IoT applications. To validate our ensemble's efficacy, we conducted comprehensive experimental evaluations leveraging both synthetic and real-world datasets. The results indicate that our ensemble surpassed state-of-the-art data stream algorithms and ensemble regressors across a range of regression metrics, underlining its superior predictive prowess. Furthermore, we scrutinized the ensemble's performance within the realm of auto-scaling for Virtual Network Function (VNF)-based applications situated at the network's edge, thereby elucidating its applicability and scalability in real-world scenarios. [ABSTRACT FROM AUTHOR]