Search

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]

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]

In this study, we employed a novel machine learning (ML) methodology to predict water levels (WLs) from their constituent components at both entrances of a sea strait, namely the Bosphorus. The principal components of WLs in the strait are mean sea level pressure (MSLP), wind speeds (W, U, V), discharges from the Danube River (Q), and tidal conditions (T). Following the application of the t-test, SFS, PCA, and VIF analyses, and the consideration of a range of ML techniques (including Linear Regression (LR), Regression Trees (RT), Support Vector Machine Regression (SVMR), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANNs)), the number of predictors was reduced in order to obtain the most flexible and accurate regression model. As a consequence of this process, MSLP, W, and Q were retained, while the remaining variables (tide) were excluded. Furthermore, the order of importance for the optimal regression model was identified as Q_lagged, MSLP, V_lagged, and U at the north entrance model, while at the south entrance model, the order was MSLP, Q_lagged, U, and V. The models were trained using 80%, 50%, and 33% of the data, respectively. The model trained on 80% of the data yielded the most accurate predictions, with a correlation coefficient of R ≅ 0.95 and a root mean square error (RMSE) of 0.02 m. The model demonstrated a markedly superior predictive capacity compared to previous studies in the region, which is attributed to two factors that are regarded as the novelty of the study. The first factor was the random selection of training data from each month of the year, which allowed for the representation of the general pattern of water level (WL) behaviours. The second factor was the selection of the physically most meaningful inputs, which were selected according to the results of the significance and multicollinearity check. Furthermore, the predicted and measured WLs were employed as boundary conditions in a hydrodynamic model to evaluate the predictive capacity of the predicted WLs on the current results in the strait in comparison to the use of observed WLs. The 80% data-trained model exhibited similar current velocities to the observed WL model used, whereas the 50% and 30% data-trained models yielded slightly different results. [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]

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]

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]

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]

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]

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]

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]

Statistics on manifolds attracts more and more attentions because a variety of observations or responses have manifold structures. In this paper, we address the regression task on Grassmannian manifolds, which is arisen from the statistical shape analysis. Particularly, we develop an intrinsically semi-parametric model instead of the geodesic regression for better approximating shapes. Concretely, first we introduce a nonparametric term on Grassmannian manifolds to further depict the relationships that cannot be well described by the geodesic model on Grassmannian manifolds. Second, we utilize an alternatively iterative strategy to update parametric and nonparametric parts to form a solution. Finally, we testify the effectiveness of our regression model on synthetic data on Gr (n , m) with multiple combinations of n and m, as well as a real data of corpus callosum shapes. The experimental results validate that our proposed model outperforms the conventional geodesic regression model and shows the advantage of approaches driven doubly by model and data. [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]

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]

The experiment was conducted during kharif season ( June-October, 2022) at Rice Research Station, Kaul, CCSHAU, Hisar using 48 rice genotypes to evaluate the genetic variability and character association under aerobic conditions. Analysis of variance revealed that the mean sum of squares was highly significant for all the traits examined. The magnitude of phenotypic coefficient of variation exceeded the genotypic coefficient of variation for all the traits analysed in the study. High genotypic coefficient of variation and phenotypic coefficient of variation were observed for root dry weight plant-1 followed by number of grains panicle-1, grain yield plant-1, biological yield plant-1, number of effective tillers plant-1 and flag leaf length. High heritability coupled with high genetic advance as per cent of mean was observed for grain yield plant-1, number of grains panicle-1, biological yield plant-1, root fresh weight plant-1 and root dry weight plant-1 indicating the presence of additive gene action. Grain yield plant-1 showed a highly significant and positive association with biological yield plant-1, harvest index percentage, root fresh weight plant-1, number of effective tillers plant-1 and 1000 seed weight. Path analysis revealed that biological yield plant-1 and harvest index percentage showed highest positive direct effect on grain yield plant-1. Furthermore, regression analysis revealed that biological yield plant-1, harvest index percentage, numbers effective of tillers plant-1, number of grains panicle-1, plant height and head rice recovery % as important traits contributing to grain yield plant-1. Therefore, phenotypic selection could be a viable approach for genetically improving these traits to obtain promising genotypes for future rice breeding programs. [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]

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]

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]

L'arrivée inattendue du cancer chez l'adolescent et le jeune adulte (AJA) constitue une expérience difficile et complexe qui entraîne des répercussions profondes et durables sur la famille et son fonctionnement. Dans ce contexte, la famille est amenée à modifier son fonctionnement habituel pour s'adapter à la maladie et à ses thérapeutiques. Notre objectif est de décrire les interactions entre le jeune et ses proches – parents et fratrie – pour appréhender l'impact du cancer sur la famille et son fonctionnement. Nous avons réalisé une étude qualitative, proposant un entretien de recherche de manière individuelle aux AJA, aux mères, aux pères et à la fratrie. Les entretiens (AJA = 13 ; parents = 11 ; fratrie = 2) ont été soumis à une analyse thématique. Dix thèmes majeur composés de sous-thèmes auprès des AJA, 10 thèmes majeurs composés de sous-thèmes auprès des parents et 6 thèmes majeurs avec des sous-thèmes auprès de la fratrie ont été identifiés. L'analyse montre que l'AJA malade, à la frontière entre l'infantile et l'âge adulte, régresse vers des positions infantiles devenant dépendant de l'entourage familial (les parents notamment et la fratrie) pour les soins nécessaires. L'entourage familial a également tendance à valoriser cette dépendance par les soins apportés au jeune malade. Les interactions qui se mettent en place dans la famille ont tendance à l'acculer dans un fonctionnement régressif pour assurer sa survie physique et psychique. Le fonctionnement régressif comme un processus permet à la famille de faire face au cancer, et de ce fait l'évolution des liens familiaux est remise à plus tard. Un accompagnement davantage centré sur la famille est nécessaire, en mettant l'accent sur les besoins spécifiques des AJA, en termes d'individuation et d'autonomisation. Adolescent and young adult (AYA) cancer is a difficult and complex experience, which has major and ongoing consequences for the family and its functioning. In this context, the family need change its usual functioning to cope with the disease and its treatments. The aim of this study is to describe the interactions between the young patient and the family members – parents and siblings – in order to understand the impact of cancer on his/her family system and its functioning. We conducted a qualitative study, with individual research interviews with AYAs, mothers, fathers and siblings. The interviews (AJA = 13; parents = 11; siblings = 2) were subjected to thematic analysis. Ten major themes with sub-themes for AYAs, 10 major themes with sub-themes for parents and 6 major themes with sub-themes for siblings were identified. The results show that the patient AYA, transitioning from childhood to adulthood, can easily regress to childhood positions, becoming dependent on his family (parents and siblings), for daily care. Relatives also tend to promote this dependency by providing care for the young patient. The interactions, which develop in the family, tend to push him into a regressive functioning in order to maintain his psychological and physical survival. The regressive functioning helps families to cope with cancer, and as a result the evolution of family bonds are postpone. More family-centered support is needed, focusing on the specific needs of AYAs, particularly in terms of individuation and autonomization. [ABSTRACT FROM AUTHOR]

With increasing pressures on big businesses to expand performance objectives beyond financial metrics and to include social and environmental objectives, business organizations experience rising tension in balancing these various objectives. Oftentimes, subjective narratives can weigh in on the relative importance of competing objectives. This subjectivity is a contributing factor to findings of inconsistent and mixed results for the financial impact of an organization's environmental performance in the prior literature. Our research effort seeks to provide a positivist perspective on the relationship between environmental performance and financial performance of companies. Also, given the importance of efficient operations for corporate success, we examine the influence of operational productivity on the environmental and financial performance relationship. Using a global dataset compiled from reputable sources, including 1738 unique firms spanning between the years 2011 and 2020, we find statistically significant results that indicate that lower carbon emissions are associated with higher profitability when a firm has competitively high operational productivity. Companies with operational productivity that is competitively low do not perform well financially when carbon emissions are low. Thus, our study fills a research gap in this domain by relying exclusively on a broad set of purely objective data and illuminating the importance of operational efficiency on the relationship between the environmental performance and financial performance of firms. [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]

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]

Drawing on Martin's Multilevel Framework for Analyzing Mathematics Socialization and Identity Among African Americans this study explores the mechanisms that influence Black students' decision-making processes related to math course taking in high school. Three years of student transcript data for 1561 Black seniors in a Southern California school district were analyzed using descriptive statistics and logistic regression analyses in STATA. The findings reveal factors that impact students' ability and desire to complete a fourth year of math in high school, interconnected with institutional, structural, identity, and parental factors. One such finding is that accelerated 8th-grade math placement significantly increases the likelihood that a student would enroll in upper-level math coursework, such as "Beyond IM3" courses in high school. The researcher also investigated the influence of the concentration of Black math teachers in a school on the likelihood of a Black student enrolling in a Beyond IM3 math course. The results of this study contribute to an understanding of the limited racial diversity in STEM fields, highlighting the role of math as a major deterrent for Black students' interest and persistence in STEM. The findings suggest the need for policy and curriculum changes to promote equitable access to advanced math coursework for Black students, especially in the 8th grade. This study also emphasizes the need to address the structural and institutional factors that influence Black students' decision-making processes related to math course taking in high school. [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]

Since the 1970s, arid and semi-arid zones, as one of the most vulnerable ecosystems, have attracted considerable attention. The the study of the influences of climate variations and human activities in this zone have become, and being the hot topics in the field of ecological environment research. A better understanding of the evolution of land use patterns and plant cover is a major concern for countries whose ecosystems are undergoing severe degradation. Indeed, land use dynamics have direct implications on the availability of natural resources. spatial remote sensing provides an extremely effective tool for locating problems and monitoring developments, our study carried out on the Aures Mountains in eastern Algeria, using the normalized difference vegetation index (NDVI), highlights the extent of changes in vegetation cover between 2000 and 2023, 80815,25 ha of the vegetal cover witnessed a regression between 2000 and 2023, or the area lost 3513,706 ha of vegetation the per year. A pronounced regressive evolution of the plant cover is observed in the southwest and northeast of the Aures mountains, particularly in the "forest of Beni Oudjana, Beni Melloul and Ouled Yaakoub", in the municipalities of Chelia, Bouhmama and M'sara. The main factors contributing to the decline in plant cover are: deforestation, overgrazing, forest fires and the resulting water erosion. [ABSTRACT FROM AUTHOR]

Objective: This cross-sectional and descriptive study was conducted to evaluate the attitudes of healthcare workers towards the safe use of needle-stick and sharp medical instruments. Methods: The study was conducted cross-sectionally on 248 healthcare workers. Data were collected face-to-face using the "Healthcare Worker Identification Form" and the "Healthcare Workers' Attitude Scale Towards Safe Use of Needle-Stick and Sharp Medical Instruments". Results: The total score and cognitive and affective subscale mean scores of the Attitudes Toward Safe Use of Needle-Stick and Sharp Medical Instruments Scale were statistically significantly higher in women than in men (p<0.05). The mean scores of healthcare workers who were having night shift were lower than those who were not having night shift (p<0.05). According to the results of linear regression analysis, gender, educational status, occupation, and exposure to injury were determined as factors independently affecting attitudes towards safe use of needle-stick and sharp medical instruments (p<0.05). Conclusion: In this study, it was found that the attitudes of male healthcare workers, those who were having night shift, those who experienced sharps injuries, midwives and health officers towards safe use of needle-stick and sharp medical instruments were low. In line with these results, it is recommended to organize in-service training programs for the prevention of sharps injuries, to pay attention to standard prevention practices and to actively use the reporting system, taking into account the personal (gender) and professional characteristics of healthcare workers (occupation, having a needle-stick and sharp injury, postgraduate education status, having night shift, etc.). [ABSTRACT FROM AUTHOR]

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

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]

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]

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]

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]

This study focuses on optimizing carbon/epoxy laminate configurations to maximize the flutter speed of composite structures using a Fast Tree Regression algorithm. Initially, a seed dataset was created, using finite element method (FEM) modal analysis for common stack-ups used in composite fins and UAV components. The FEM analysis, based on the Lanczos algorithm for extracting modal frequencies in bending and torsion, was verified through experimental modal analysis using an AS-4/3501-6 composite system. Custom software was developed to interface with the FEA modal software, enabling the generation and augmentation of laminate dataset scenarios. The seed dataset was expanded until the coefficient of determination (R2) reached at least 0.95. Various regression algorithms, including Fast Forest Regression, Fast Tree Regression, Sdca Regression, and Lbfgs Poisson Regression, were evaluated. The Fast Tree Regression algorithm was selected for further analysis due to its superior performance. This algorithm was applied to a design space of nearly 2000 potential laminate candidates, focusing on symmetric lay-ups to avoid undesirable coupling between bending and torsion in UAV and missile control surfaces. The final optimized lay-ups, exhibit the highest Delta function values (the squared difference of modal frequencies in torsion and bending), indicating the expected highest flutter speeds. The results demonstrate the efficacy of tailored composite materials in achieving specific aerodynamic performance goals. [ABSTRACT FROM AUTHOR]

Missouri Ozark woodlands are a unique, but imperilled ecosystem type due to fragmentation, lack of proper management and a changing climate. The management, restoration, and conservation of Ozark woodlands is a conservation priority. The Ozark woodlands contribute to the sequestration of carbon and nutrients through their robust productivity, effectively removing carbon dioxide from the atmosphere and storing it in the biomass and soil while cycling essential nutrients to support the ecosystem's health and vitality. We have assessed the over-and mid-storey leaf production, collecting leaves in baskets every autumn in a 1 200-ha conservation area in southwest Missouri since 2000. The leaf production data from 2000 to 2021 were compared among sites; control (not burnt for over 80 years), burnt (fire resumed in March of 1999 and repeated in 2001, 2003, 2008, 2010, 2013, and 2021) and reference (fire resumed in 1980 and repeated every 2 years). The average oak leaf production was statistically higher in the burnt site than the reference site, but only marginally higher than the control site. The leaf production varies statistically between the years. We applied a regression analysis among the productivity, temperature, and precipitation to associate the temporal variability in the weather with the productivity. The reference woodland showed statistical significance with the precipitation, but not with the temperature, while the other sites did not show any statistical significance with the precipitation. No statistically significance difference was observed between the productivity and temperature across any of the woodland burn histories. The March-June, March-May, and June-August precipitation statistically predicted the productivity. The results indicate that long-term burning is predictably associated with woodland leaf production and precipitation, but the precipitation is uncoupled with the productivity in woodlands that were more recently burnt or where burning has been suppressed. Sassafras saplings of approximately 1 m in height have emerged as the dominant species in the understorey of burnt woodlands while being completely absent from the control and reference woodlands. However, the productivity is the highest in the woodlands where burning has been suppressed and 20 years of prescribed fire does not significantly reduce the productivity. Oak regeneration over 20 years of burning is being suppressed by competition with sassafras, which may result in a significant shift in the ecosystem variables. [ABSTRACT FROM AUTHOR]

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees. [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]

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]

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]

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]

In stock exchanges, buyers and sellers meet to trade shares in public companies. Stock exchanges encourage investment. Raising capital enables companies to grow, expand, and create jobs in the economy. These investments are critical enablers of trade, economic growth, and prosperity. Data preprocessing and cleaning are integral to machine learning research and studies. This paper highlights the common mistakes present among the various models for stock price prediction, and it also offers the solution to the problem and a procedure to prepare the data, which any model can use as input. Our objective is to comprehend various models and studies that have been normalizing the price data of the stock, which causes the price to be fixed in each range, which is not valid in real-world scenarios; hence, such models will produce inaccurate predictions over a period. To solve this problem, this paper discusses Daily Return % as an alternative to close prices. To the best of our knowledge, this is the first paper discussing the issue of scaling the parameter known as the price. To achieve this, we use exploratory data analysis techniques and data visualization that focus on modeling and knowledge discovery for predictive rather than just descriptive processing. It assures good data quality and consistency. This study examines the issue of data cleaning and pinpoints a possible inaccuracy in a TATA POWER dataset. A quick summary of the current data-cleaning approaches and a survey and assessment of the many perspectives on data cleaning are provided. [ABSTRACT FROM AUTHOR]

Association Rules Mining is the technique of Data Mining. Association Rule Mining finds interesting relationships among large set of data items. Market Basket Analysis is one of the method of ARM. User can use the variables say Dependent Variable and Independent Variable. Here, Association Rules define as Dependent Variable and Support and Confidence are define as Independent Variable. To verify the relationship between Rules, Support and Confidence, apply the Regression Statistics, Regression Coefficients, Multiple Correlation Coefficient and Multi-collinearity. To evaluates the Regression line, to estimate and predict the Rules and other calculation, R-Studio is applying for to evaluate the Regression Statistics. The R-packages arules and arulesviz is apply for the Association Rules and Visulizations. [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]

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]

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]