Using a nationally representative longitudinal database of Korean students (N = 6908), we investigated the temporal relationships between self-efficacy and interest across Grades 7 through 11, and ...between the two constructs and Grade 12 achievement in math, English, and Korean. To extend previous findings, we focused on the potential moderation of these relationships by individual differences such as gender and perceptions of domain importance. Autoregressive cross-lagged modeling revealed significant reciprocal relationships between self-efficacy and interest in all three subjects, with interest being a stronger predictor of self-efficacy than vice versa in math and Korean. In English, self-efficacy and interest were equally predictive of each other. However, boys in English and students who considered the domain unimportant in math did not follow this general pattern in the respective domain. These findings underscore the necessity to approach student motivation based on the subject matter and individual student characteristics.
Academic self-efficacy and interest are among the most important motivation constructs that influence various decisions students make at school, including their task choice, effort expenditure, persistence, strategy use, and achievement. Although it would be ideal that self-efficacy and interest in the subject are developed concurrently, educators and researchers alike have debated which of the two should receive the limited educational resources first. The present results based on a nationally representative sample of Korean adolescents surveyed annually over 5 years show that the answer to this question depends on the subject matter and student characteristics. In math and Korean, feeling interested in the subject better predicted how self-efficacious in the subject students would later become than vice versa, although prior self-efficacy and interest in math were equally predictive of each other among students who did not perceive math to be as important. In English as a foreign language, the prior self-efficacy and interest in the subject were equally predictive of each other in general, which was not applied to boys as their prior interest in English better predicted their subsequent self-efficacy in learning the foreign language than their prior self-efficacy in English predicted their subsequent interest in the subject. Attending to these differences will help design instructional activities that bring greater motivational benefits to learners.
•Longitudinal relations between self-efficacy and interest were tested over 5 waves.•Overall, interest was a stronger predictor of self-efficacy than vice versa.•The paths from prior interest to later self-efficacy were stronger for boys.•The paths were also stronger for students who perceived the domain to be important.•The results offer implications on how best to intervene in student motivation.
Transgender adolescents often categorize themselves in the same way that cisgender adolescents do-that is, as girls/women and boys/men. Potential differences in the extent to which these ...self-categorizations matter to transgender and cisgender adolescents, however, have yet to be explored, as has the relative importance transgender adolescents place on their gender compared to their transgender self-categorization. In the current study, we explored self-reported identity importance in a sample of 392 primarily White (70%) and multiracial/ethnic (20%) 12-18-year-old (M = 15.02) binary transgender (n = 130), binary cisgender (n = 236), and nonbinary (n = 26) adolescents in the United States and Canada. Results revealed that binary transgender adolescents considered their gender self-categorization to be more important to them than both binary cisgender and nonbinary adolescents did. Most binary transgender adolescents rated their gender self-categorization as maximally important to them. Additionally, transgender adolescents considered their gender self-categorization to be more important to them than their transgender self-categorization (that is, their identification with the label "transgender"). These findings demonstrate that the identities that are often denied to binary transgender adolescents may be the very identities that are most important to them. Results also suggest that gender diverse adolescents with different gender identities may differ in the importance they place on these identities.
Many species of fungi and oomycetes are plant pathogens of great economic importance. Over the past 7 years, the genomes of more than 30 of these filamentous plant pathogens have been sequenced, ...revealing remarkable diversity in genome size and architecture. Whereas the genomes of many parasites and bacterial symbionts have been reduced over time, the genomes of several lineages of filamentous plant pathogens have been shaped by repeat-driven expansions. In these lineages, the genes encoding proteins involved in host interactions are frequently polymorphic and reside within repeat-rich regions of the genome. Here, we review the properties of these adaptable genome regions and the mechanisms underlying their plasticity, and we illustrate cases in which genome plasticity has contributed to the emergence of new virulence traits. We also discuss how genome expansions may have had an impact on the co-evolutionary conflict between these filamentous plant pathogens and their hosts.
•Turbulence creators in absorber tubes impacts more heat absorption.•Twisted tapes increase the wall heat flux in the absorber of flat plate collector.•Hybrid nanofluid is effective to increase ...exergy efficiency of solar water heaters.•Solar collectors integrated with PCM minimizes the nonuniform heat flux distribution.•Hybrid PV/T solar system produces higher overall efficiency.
The present review focusses on the recent developments in the thermo-economic performance of solar water heating systems regarding the design, modification of thermo-physical properties of heat transfer fluids, integrated thermal energy storage and hybrid systems of flat plate solar collectors (FPSC). Enhancement of design factors and convective heat transfer coefficient between the fluid and absorber tubes are the most desirable factors to improve the overall performance of solar collectors. In addition to various performance parameters, numerical heat transfer relations for the evaluation of energy/exergy efficiency enlisted to determine the performance of the different enhancement methods. At lower twist ratios of absorber flow inserts, the heat transfer enhancement achieved from 13.03% to 25.25% more than that of the larger twist ratios. The use of nanofluids increased the energy and exergy efficiency of FPSC up to 24% and 30%, respectively. The review article provides an eagle’s view of the recent developments, practical techniques, economic importance, requirement of solar water heating, and barriers in utilizing solar energy for water heating applications to all the stage holders of solar energy. The hybrid solar photovoltaic/thermal system (PV/T) is a useful technology for economic benefits and higher efficiency of the heating system. The research gap and areas requiring attention are discussed towards the improvement of deployment of FPSC.
•An updated database consisting of 532 UHPC beams that failed by shear is established.•Ten ML models with different algorithms are developed to predict the shear strength of UHPC beams.•The ...performance of the ML models is evaluated and compared to empirical models.•The ML models are interpreted using the SHAP methods.•The impact of critical features on the shear strength of UHPC beams is identified.
To provide more accurate and reliable predictions of the shear strength of ultrahigh-performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches were employed to develop the data-driven models, and the ML models were interpreted using the Shapley additive explanations (SHAP) method. It was found that the ensemble models, particularly CatBoost, outperform individual ML models and traditional empirical models. The geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength of UHPC beams, followed by the parameters of reinforcement and material properties of the UHPC.
Existence of delays is an inseparable part of projects and subject of disagreements among stakeholders in all countries. There are four valid techniques in the Society of Construction Law global ...protocol about project delays calculation that without having the ability to control the risk of delays incidence only calculate the prolongation of contract prolongation and consider it as project delays. Researcher believes: first, risk of delays should be managed, minimized, shared, transferred, or accepted, but it cannot be ignored; therefore, it must be predicted, covered, managed, and optimized; and second, prolongation time of project is not necessarily equal to increased delay amount, and many other factors that themselves are the basis of emergence of project risks are effective in calculation of delays. Therefore, it is necessary to identify these factors and develop the strategy for their control and management before starting the project. So, in this article, two researcher-made combinative techniques are presented in order to resolve this deficiency: first, introduction of importance degree factor model instead of weight factor; and second, introduction of a model in the field of claim management in order to controlling risk of delays, analyzing result-oriented delays, and determining the share of stakeholders' failures, based on variance and importance degree.
Live-line maintenance is a high risk activity. Hence, lineworkers require effective and safe training. Virtual Reality Training Systems (VRTS) provide an affordable and safe alternative for training ...in such high risk environments. However, their effectiveness relies mainly on having meaningful activities for supporting learning and on their ability to detect untrained students. This study builds a student model based on Learning Analytics (LA), using data collected from 1399 students that used a VRTS for the maintenance training of lineworkers in 329 courses carried out from 2008 to 2016. By employing several classifiers, the model allows discriminating between trained and untrained students in different maneuvers using three minimum evaluation proficiency scores. Using the best classifier, a Feature Importance Analysis is carried out to understand the impact of the variables regarding the trainees’ final performances. The model also involves the exploration of the trainees’ trace data through a visualization tool to pose non-observable behavioral variables related to displayed errors. The results show that the model can discriminate between trained and untrained students, the Random Forest algorithm standing out. The feature importance analysis revealed that the most relevant features regarding the trainees’ final performance were profile and course variables along with specific maneuver steps. Finally, using the visual tool, and with human expert aid, several error patterns in trace data associated with misconceptions and confusion were identified. In the light of these, LA enables disassembling the data jigsaw quandary from VRTS to enhance the human-in-the-loop evaluation.
•Learning analytics is used to examine training in high-risk virtual training system.•Trainees final performance is predicted using several classifiers.•Increasing evaluation strictness improves final performance prediction.•Feature analysis shows variables importance and contribution to final performance.•A visualization tool is used to unveil non-observable behavioral variables.
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to ...running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods which may require 103−6 runs cannot be used directly. Surrogate models such as quadratic response surfaces, polynomial chaos expansions or Kriging (which are built from a limited number of runs of the original model) are then introduced as a substitute for the original model to cope with the computational cost. In practice it is almost impossible to quantify the error made by this substitution though. In this paper we propose to use a Kriging surrogate for the performance function as a means to build a quasi-optimal importance sampling density. The probability of failure is eventually obtained as the product of an augmented probability computed by substituting the metamodel for the original performance function and a correction term which ensures that there is no bias in the estimation even if the metamodel is not fully accurate. The approach is applied to analytical and finite element reliability problems and proves efficient up to 100 basic random variables.
► The use of metamodels reduces the computational cost of reliability analyses. ► Surrogate-based reliability methods do not enable error quantification. ► Metamodel-based importance sampling enables error quantification. ► The failure probability is estimated onto the actual limit-state function. ► The accuracy of the estimate is measured in terms of a variance of estimation.
•The influence of 171 different kinds of features was analyzed using Random Forests.•Average energy use intensities of 1322 regions were set as the regression target.•The model built by Random Forest ...has lower MSE than Lasso and SVM.•An educational feature was found to be the most influential.•The study not only identifies the influential features, but also matches the areas.
Efficient and effective city planning in improving the energy performance of residential buildings requires a clear understanding of the influential features. Previous studies on modeling the relationships between influential features and the energy consumption have several gaps and limitations, such as the linear modeling methodology and insufficient consideration of particular features. This study therefore aims at investigating the influence of 171 possibly related features on the regional energy use intensity (EUI) of residential buildings using a non-linear regression algorithm, namely Random Forests (RF). The New York City (NYC) was focused on due to data availability. The 171 features covered seven different aspects, which are building, economy, education, environment, households, surrounding, and transportation. The average site EUI of the residential buildings in each Block Group (BG) was set as the dependent variable. The regression model was compared to the models using typical linear methods, such as Multiple Linear Regression and Lasso. The results show that the RF model achieved a lower mean square error. In addition, the top 20 influential features were identified based on the out-of-bag estimation in RF. Results show that less percentage of well-educated people, higher percentage of households heated by fuel oil, lower household income and more residential complaints per capita are correlated with higher average site EUI in NYC. Related suggestions on improving the energy performance in different regions are presented to the local government.
Display omitted
•The performance of four ML classification methods are compared for corrosion prediction.•Bagging classifier has the highest prediction accuracy (94.4%) for electrolyte-based ...datasets.•DT classifier has the best prediction accuracy (93.95%) for critical ions-based datasets.•Feature creation increased model generalizability.•The factors that contribute the most to corrosion are ranked using feature importance.
Corrosion behavior prediction of materials in any given environmental condition is important to minimize time-consuming experimental work to avoid failures and catastrophes in industry. Supervised machine learning (ML) techniques are recently explored to predict corrosion behavior. However, there is still a lack of research that proposes a model capable of predicting the corrosion behavior of a wide range of stainless steel grades in varying environments, including acids, bases, and salts. Moreover, conventional experimental approaches are often insufficient in identifying the most influential factors in the corrosion process due to its multivariate and non-linear nature.
This study presents the development and evaluation of multiple ML models in predicting the corrosion behavior of different types of stainless steel in varying environments. The prediction performance of four ML algorithms, decision tree (DT), support vector machine (SVM), random forest (RF), and bagging classifier, were compared. Initially, the algorithms were fitted to a dataset based on the type of electrolyte (Dataset No. 1) and then modeled on a modified dataset (Dataset No. 2) in which the types of electrolytes were replaced with their critical ions contributing to corrosion reactions. The Bagging classifier achieved the highest prediction accuracy of 94.4% for Dataset No. 1, while the DT model was the most suitable for Dataset No. 2 with a testing accuracy of 93.95%. The application-driven approach of confusion matrix analysis to select the model’s capacity to correctly identify severe and poor corrosion behavior confirmed that Bagging and DT classifiers are the most suitable ML algorithms for predicting corrosion behavior in Dataset No. 1 and No. 2, respectively. Furthermore, the feature importance analysis identified hydrogen and sulfide concentrations in corrosive environments, as well as the sum of the number of alloying elements, as the most influential factors, contributing up to 77.8% to the corrosion behavior. As a result, users of stainless steels can leverage this model to predict the corrosion behavior of specific materials in specific environments, facilitating informed material selection for various applications, without the need of lengthy and costly experiments.
PDF
