Curve Model Research Articles

Gender Stratification, Racial Disparities, and Student Debt Trajectories in the United States

In the United States, post-secondary educational outcomes are stratified by gender, with women attending college and completing their degrees at higher rates. At the same time, student loan debt has become an ubiquitous part of the college process for many pursuing higher education, increasing the financial risks associated with the pursuit of higher education while lessening the financial rewards. Using data from the National Longitudinal Survey of Youth 1997 (NLSY97) of young adults who ever attended a post-secondary institution and growth curve models, we estimate gender disparities in student debt trajectories throughout young adulthood. Building on work on the racialization of the student debt landscape (Houle & Addo 2022), we also investigate the intersection of race and gender, and include both within race gender analyses and within gender racial analyses. Gender differences in student debt at baseline, or at the age of first college enrollment, are large and the gender debt gap grows at 1.2% per year. We document evidence of a curvilinear relationship in student debt in young adulthood, with student debt accumulating at the beginning of the college enrollment period and the rate of increase declining over time as adults age. Women, who start the period with larger amounts of student debt, experience faster growth in their debt accumulation followed by lower declines relative to young adult men. These patterns are magnified among Black young adults, with the gender debt disparity between Black women and Black men growing 3.4% annually. We find no association between gender and student debt trajectories among Latinx and White young adults. Within-gender models highlight the prevalence of the student debt crisis among Black women and Black men. Additional analyses suggest gender differences in debt trajectories are a function of postsecondary and young adult socioeconomic characteristics, reflecting broader societal inequalities that stem from structural elements related to post-secondary schooling and economic status. These patterns may inhibit wealth-building among women and Black adults in the United States.

In Vitro Antifungal Activity of White Thyme, Oregano, and Savory Oils Against Five Monilinia fructicola Isolates from the Southeastern United States

Monilinia fructicola is a primary pathogen causing fruit brown rot of peach during pre- and postharvest in the southeast of the United States. In this in vitro study, we assessed the antifungal activity of four plant essential oil treatments against five isolates of M. fructicola obtained from naturally infected peaches in Florida, Georgia, and South Carolina. The tested essential oils were white thyme oil, oregano oil, summer savory oil, and a 1:1 mixture of thyme and oregano oils. We evaluated isolates’ growth using a Gompertz growth curve model. All tested essential oils exhibited antifungal activity against all isolates, with thyme, oregano, and the 1:1 mixture of thyme and oregano oils showing no significant differences and no evidence of synergy. Savory oil showed the least effectiveness and stability of the four essential oil treatments. We observed diverse sensitivities of different M. fructicola isolates to the essential oils. The minimum inhibitory concentration values for the essential oils ranged from 200 to 500 μl/liter, and the minimum fungicidal concentration values varied from 500 to 750 μl/liter, depending on the fungal isolate and essential oil treatment. All isolates were inhibited by the essential oils at 500 μl/liter and killed at 750 μl/liter. Our findings provide a basis for future in vivo research on the use of these essential oils for controlling fruit brown rot caused by M. fructicola in peach postharvest handling. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

TESS light curve modeling and period study of four eclipsing binaries

Abstract We reported the light curves and orbital period variations of four short-period eclipsing binaries in the Southern Hemisphere. From short-cadence time-series observations of the TESS, the intrinsic light changes rapidly with time. Δmmax and Δmmin exhibit random wave-like variations or quasi-periodicities. We deduced 14 sets of photometric solutions from the truncated light curves using the W-D program. The results imply that V757 Cen and BC Gru are near-contact binaries, whose one or two components almost fill the Roche lobes with $f>98{{\%}}$. Meanwhile, V535 Ara and AQ Tuc are A-subtype contact binaries. The asymmetric light curves were modelled by a cool spot assumed on a more massive component, which was used to track the stellar longitudes of starspots. Based on all available eclipsing times (ETs), including 1495 ones from TESS light curves (LCs), the orbital period variations of four binaries were analysed. The (O − C) curves are coincidentally demonstrated by the light-time effect (LITE) via the presence of the third bodies. The modulation periods approximate to 50 ∼ 60 years. In the coplanar orbit with the central binaries, the third companions’ masses are 0.105 M⊙ for V757 Cen, 0.340 M⊙ for BC Gru, 0.238 M⊙ for V535 Ara, and 0.343 M⊙ for AQ Tuc, whose systems are stable according to Harrington’s criterion. Therefore, the four short-period eclipsing binaries, V757 Cen, BC Gru, V535 Ara and AQ Tuc are hierarchical triple stellar systems.

How does pre-service teachers’ general pedagogical knowledge develop during university teacher education? Examining the impact of learning opportunities and entry characteristics over five time points using longitudinal models

ABSTRACT General pedagogical knowledge (GPK) is regarded as a central component of the competence that teachers acquire during university teacher education. However, existing research on GPK typically uses only one or two measurement points to assess development and identify influencing factors. The present study draws on an annual survey of pre-service teachers’ GPK over 5 measurement points during the course of university teacher education (i.e. over the standard five-year study period). Latent growth curve models were used to assess knowledge development and explore potential influences. Knowledge development was best described as an increasing trend that flattens out over time. While students’ initial knowledge levels differed, their knowledge increase showed little interindividual variance. Initial knowledge level was related to the use of learning opportunities at the beginning of the study programme, but no significant association was identified between knowledge growth and increased learning opportunities over time. Moreover, initial knowledge level was predicted by the grade point average achieved in secondary school examinations but not by intrinsic motivation. Overall, the findings indicate that university teacher education effectively promotes GPK, with the entry phase being of particular significance.

Can We Differentiate a Latent Growth Curve Model from Competitors? Evidence Based on Individual Case Residuals

In latent growth curve modeling (LGCM), overall fit indices have garnered increased disputation for model selection, and model fit evaluation based on the mean structure has becoming popularity. The present study developed a versatile fit index, named Weighted Root Mean Squared Errors (WRMSE), based on individual case residuals (ICRs) with the aim of facilitating model selection. Through comparing to various fit indices, this study demonstrated that WRMSE could provide effective model fit information beyond previous indices. Due to higher sensitivity to misspecification in the mean structure, WRMSE was suggested to be utilized as a supplement for the traditional overall fit indices in the process of model selection. The limitation of WRMSE was that for complex models beyond the commonly used polynomial models, an increased number of repeated measurement occasions was required to ensure the precision of WRMSE.

Social Isolation, Loneliness, and Depressive Symptoms among Older Adults: The Moderating Effect of Resilience.

Social isolation has been recognized as a social problem with negative health consequences. Using data from three waves of the Health and Retirement Study, this study aimed to examine the long-term impact of social isolation on loneliness and depressive symptoms and to explore the moderating effect of resilience. This study comprised 3,681 U.S. adults aged 60 and older at the baseline wave. Social isolation index was constructed using five indicators, including not married or cohabitating with a partner, no social participation, and less than monthly contacts with children, family members, or friends. Loneliness was measured by the UCLA loneliness scale and depressive symptoms were measured by the Center for Epidemiological Studies-Depression scale (CES-D). The moderator of resilience was assessed by the simplified resilience score (SRS). Latent growth curve models with robust standard errors were estimated. The results of latent growth curve models showed that social isolation was significantly associated with more initial loneliness and depressive symptoms. However, social isolation was associated with a slower increasing rate of loneliness, but no significant relationship with the change rate of depressive symptoms. Furthermore, resilience significantly buffered the negative effect of social isolation on the initial level of depressive symptoms. The findings underscore the importance of enacting strategies and interventions targeting resilience to address social isolation and its negative consequences among older adults.

#88 Establishment of machine learning-based risk prediction model for acute kidney injury after acute myocardial infarction

Abstract Background and Aims Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) with high morbidity, mortality and lack of effective treatment, so prevention is particularly important. This study intends to use machine learning to establish a precise prediction model for AKI after AMI. Method AMI patients were consecutively collected from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University. First, SelectFromModel and Lasso regression model were used to select features. A predictive model (Model A) was then created using a multivariate logistic regression model in the training set and validated in the test set. At the same time, the prediction model (Model B) is created by machine learning algorithm in the training set. The process of model construction and evaluation is implemented by Python. The algorithm includes MLP, SVM, KNN and SimpleRNN, from which the model with the largest area under the ROC curve (Model B) is selected and verified in the test set. DeLong method was used to compare the model B with model A to compare whether the area under the ROC curve was better than multivariate logistic regression model and select the best model. Results A total of 6014 AMI patients were included in this study, 70% were randomly selected as the training set, and the remaining 30% were used as the test set. Males comprised 80.5% of the total population with a mean age of 58.4 ± 11.7 years. The incidence of AKI in the overall population was 11.2% (674/6014) (Fig. 1). A total of 12 important characteristics were included in the model, including the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third-degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine value, admission urea nitrogen value, admission CK-MB peak value, whether diuretics were used, maximum daily dose of diuretics, days of diuretic use, and whether statins were used. Logistic regression analysis resulted in an area under the ROC curve of 0.80 (95% CI, 0.76-0.84) in the test set (Fig. 2). The models were constructed by MLP, SVM, KNN and SimpleRNN algorithms, respectively, and validated in the test set to calculate the area under the ROC curve of each model in the test set (Fig. 3), and finally the model constructed by MLP algorithm was selected as model B, and its area under the ROC curve was 0.82 (95% CI, 0.78–0.85). The area under the ROC curve of the two models was compared at p=0.363, but the machine learning algorithm constructed models with higher absolute values (Fig. 4). Conclusion The prediction model of AKI risk after AMI constructed based on machine learning is similar to that constructed by logistic regression analysis in terms of prediction ability, but the model constructed by machine learning algorithm has a better trend, indicating that the machine learning algorithm may improve the prediction ability of prediction model and provide an effective tool for early identification of these high-risk patients in clinical practice, early preventive measures, and reduction of morbidity.

Parenting self-efficacy in relation to the family check-up's effect on elementary school children's behavior.

Prior research points to the promotion of parenting self-efficacy (PSE) as an important component of parenting interventions; however, few studies have tested PSE as a mediator or moderator of the effects of parenting programs on child behavior. In the present study, we examined the efficacy of the family check-up (FCU), a brief, strengths-based parenting intervention adapted for kindergarten school entry. We tested the FCU's effects on reducing growth in parent-reported child conduct problems (CP) from kindergarten to fifth grade and whether PSE functioned as a mediator or moderator of intervention effects, using a latent growth curve model and intent-to-treat approach. Participants were parents of 321 children from five elementary schools in a northwestern U.S. city. Although we did not find a main effect of the FCU in reducing growth in CP from kindergarten through fifth grade, we found a significant indirect effect of the FCU on reducing CP growth via improving PSE in second grade and that the indirect effect was moderated by baseline levels of PSE. Together, our findings suggest that the FCU is effective in promoting PSE, which is subsequently associated with reduced CP growth, particularly for parents with initially low PSE. Our findings bolster existing work on the relationship between PSE and child CP in the context of a preventive parenting intervention and emphasize the importance of PSE as an agent of change. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Impact of stroke on cognition in old age: Comparison of two population-based cohorts, born up to 30 years apart and followed from age 70 to 85.

Little is known about birth cohort differences in the impact of stroke on cognitive aging. Given improved poststroke rehabilitation and better treatments for vascular health risk, we may expect a reduction in the stroke impact in later-born cohorts. We tested this prediction using data from two cohorts, born in 1901-1907 (n = 1,155) and 1930 (n = 919), identified from the same city population at the same age of 70 and subsequently measured on the same cognitive outcomes (i.e., spatial ability, perceptual-motor speed, and reasoning) at ages 70, 75, 79, and 85. We fitted multiple-group second-order latent growth curve models to the data, regressing the first-order cognitive factor on the time-varying stroke variable and controlling for relevant covariates. Findings revealed moderate to large average cognitive decline (d = -.45) following stroke, and the impact was relatively similar across cohorts (1901-1907: d = -.52; 1930: d = -.39). However, there was a stroke by age by cohort interaction, implying that the stroke impact increased with age in the 1901-1907 cohort (dage ≤ 75 = -.42; dage ≥ 79 = -.70) but decreased in the 1930 cohort (dage ≤ 75 = -.53; dage ≥ 79 = -.17). We found no evidence for lagged effect of stroke beyond the impact on measures following the incidence. Our hypothesis was only partially supported, as the impact of stroke was reduced in the later-born cohort but solely at higher ages. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice

AbstractIntroductionMachine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects of implementing therapist feedback via a deep‐learning model (DLM) tool that predicts client treatment response (i.e., reliable improvement on the Patient Health Questionnaire‐9 [PHQ‐9] or Generalized Anxiety Disorder‐7 [GAD‐7]) to internet‐delivered cognitive behavioural therapy (iCBT) in routine clinical care and (2) therapist acceptability of this prediction tool.MethodsFifty‐one therapists were randomly assigned to access the DLM tool (vs. treatment as usual [TAU]) and oversaw the care of 2394 clients who completed repeated PHQ‐9 and GAD‐7 assessments.ResultsMultilevel growth curve models revealed no overall differences between the DLM tool vs. TAU conditions in client clinical outcomes. However, clients of therapists with the DLM tool used more tools, completed more activities and visited more platform pages. In subgroup analyses, clients predicted to be ‘not‐on‐track’ were statistically significantly more likely to have reliable improvement on the PHQ‐9 in the DLM vs. TAU group. Therapists with access to the DLM tool reported that it was acceptable for use, they had positive attitudes towards it, and reported it prompted greater examination and discussion of clients, particularly those predicted not to improve.ConclusionAltogether, the DLM tool was acceptable for therapists, and clients engaged more with the platform, with clinical benefits specific to reliable improvement on the PHQ‐9 for not‐on‐track clients. Future applications and considerations for implementing machine learning predictions as feedback tools within iCBT are discussed.

The first low-mass eclipsing binary within the fully convective zone from TMTS

Abstract We present a comprehensive photometric and spectroscopic analysis of the short-period (∼5.32 hours) and low-mass eclipsing binary TMTSJ0803 discovered by Tsinghua-Ma Huateng Telescope for Survey (TMTS). By fitting the light curves and radial velocity data with the Wilson–Devinney code, we find that the binary is composed of two late spotted active M dwarfs below the fully convective boundary. This is supported by the discovery of a significant Balmer emission lines in the LAMOST spectrum and prominent coronal X-ray emission. In comparison with the typical luminosity of rapidly rotating fully convective stars, the much brighter X-ray luminosity (LX/Lbol = 0.0159 ± 0.0059) suggests the stellar magnetic activity of fully convective stars could be enhanced in such a close binary system. Given the metallicity of [M/H] = − 0.35 dex as inferred from the LAMOST spectrum, we measure the masses and radii of both stars to be M1 = 0.169 ± 0.010 M⊙, M2 = 0.162 ± 0.016 M⊙, R1 = 0.170 ± 0.006 R⊙, and R2 = 0.156 ± 0.006 R⊙, respectively. Based on the luminosity ratio from the light curve modeling, the effective temperatures of two components are also estimated. In comparison with the stellar evolution models, the radii and effective temperatures of two components are all below the isochrones. The radius deflation might be mainly biased by a small radial velocity (RV) data or (and) a simple correction on RVs, while the discrepancy in effective temperature might be due to the enhanced magnetic activity in this binary.

Hippocampal gray matter volume in young adulthood varies with adolescent alcohol use.

Adolescent substance use is linked with negative future outcomes (e.g., depression, anxiety, substance use disorder). Given that the brain undergoes significant maturation during adolescence, this developmental period may represent a time of particular vulnerability to substance use. Neuroimaging research has largely focused on heavy or binge patterns of substance use; thus, relatively less is known about the neural impact of a broader range of adolescent substance use. Characterizing the neural impact of a broader range of adolescent substance use may inform prevention and treatment efforts. The present study investigated relationships between adolescent substance use trajectories (i.e., alcohol, tobacco, and cannabis) and gray matter volume in young adulthood. Substance use was assessed in 1,594 participants at ages 11, 13, 16, and 19. Following the last assessment, 320 participants completed a single magnetic resonance imaging session to assess brain gray matter volume. Latent growth curve models were used to estimate growth parameters characterizing alcohol, tobacco, and cannabis use trajectories for each participant. These growth parameters (i.e., intercept, linear slope, and quadratic slope) were then used as predictors of gray matter volume. The gray matter volume of the hippocampus was positively associated with age 14 alcohol use (i.e., intercept) but not other trajectories (i.e., progression or acceleration) or substances (tobacco or cannabis). These results provide new insight into the neural impact of distinct adolescent alcohol, tobacco, and cannabis use trajectories, which may help to refine prevention and treatment efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Longitudinal Analysis and Latent Growth Modeling of the Modified Hip Dysfunction and Osteoarthritis Outcome Score for Joint Replacement (HOOS-JR).

The Hip Dysfunction and Osteoarthritis Outcome Score for Joint Replacement (HOOS-JR) was developed as a short-form survey to measure progress after total hip arthroplasty (THA). However, the longitudinal validity of the scale structure pertaining to the modified five-item HOOS-JR has not been assessed. Therefore, the purpose of this study was to evaluate the structural validity, longitudinal invariance properties, and latent growth curve (LGC) modeling of the modified five-item HOOS-JR in a large multi-site sample of patients who underwent a THA. A longitudinal study was conducted using data from the Surgical Outcome System (SOS) database. Confirmatory factor analyses (CFAs) were conducted to assess the structural validity and longitudinal invariance across five time points. Additionally, LGC modeling was performed to assess the heterogeneity of the recovery patterns for different subgroups of patients. The resulting CFAs met most of the goodness-of-fit indices (CFI = 0.964-0.982; IFI = 0.965-0.986; SRMR = 0.021-0.035). Longitudinal analysis did not meet full invariance, exceeding the scalar invariance model (CFIDIFF = 0.012; χ2DIFF test = 702.67). Partial invariance requirements were met upon release of the intercept constraint associated with item five (CFIDIFF test = 0.010; χ2DIFF = 1073.83). The equal means model did not pass the recommended goodness-of-fit indices (CFIDIFF = 0.133; χ2DIFF = 3962.49). Scores significantly changed over time, with the highest scores identified preoperatively and the lowest scores identified at 2- and 3-years postoperatively. Upon conclusion, partial scalar invariance was identified within our model. We identified that patients self-report most improvements in their scores within 6 months postoperatively. Females reported more hip disability at preoperative time points and had faster improvement as measured by the scores of the modified five-item HOOS-JR.

Finite element modeling of thermal‐hydro‐mechanical coupled processes in unsaturated freezing soils considering air‐water capillary pressure and cryosuction

AbstractThis paper presents a comprehensive computational model for analyzing thermo‐hydro‐mechanical coupled processes in unsaturated porous media under frost actions. The model employs the finite element method to simulate multiphase fluid flows, heat transfer, phase change, and deformation behaviors. A new soil freezing characteristic curve model is proposed to consider the suctions from air‐water capillary pressure and water‐ice cryosuction. A total pore pressure with components from liquid water pressure, air pressure, and ice pressure is used in the effective stress law. Vapor and dry air are considered miscible gases, utilizing the ideal gas law and Dalton's law. The governing equations encompass the linear momentum balance equation, the energy balance equation, and mass conservation equations for water species (ice, liquid, and vapor) and dry air. Weak forms are formulated based on primary variables of displacement, water pressure, air pressure, and temperature. The spatial discretization is achieved through the finite element method, while temporal discretization employs the fully implicit finite difference method, resulting in a system of fully coupled nonlinear equations. To verify the proposed computational model, a numerical implementation is developed and validated against a set of experimental data from the literature. The successful verification demonstrates the robustness of the model. A detailed discussion of the contributions from phase change strain and different sources of pore pressure is also addressed.

A National Longitudinal Study of Same-Sex Partnership and Self-Rated Health Trajectories

We know little about population-based patterns on same-sex partnership and health from a longitudinal perspective, largely due to data limitations. In this study, we analyzed nationally representative longitudinal data to compare changes in self-rated health trajectories for individuals in same-sex cohabitation, different-sex marriage, and different-sex cohabitation. Data were drawn from the 1996, 2001, 2004, and 2008 panels of the Survey of Income and Program Participation, with each panel including 2–4 years of follow-up information on self-rated health. The analytic sample included 103,995 respondents who contributed 294,944 person-period records. Results from growth curve models suggest that same-sex cohabitors experienced a slower rate of decline in self-rated health over time than both different-sex married and different-sex cohabiting respondents. Further analyses show that same-sex cohabitation is associated with a slower rate of health decline only for women but not for men. We discuss the implications of these findings in relation to the minority stress theory and the gender-as-relational perspective.

Prospective Outcomes of Microlaryngoscopy Versus Office Laser Photoangiolysis for Vocal Fold Polyps.

The objective of this study is to compare treatment outcomes for vocal fold polyps (VFPs) between operating room microlaryngoscopy and office-based photoangiolysis with the potassium titanyl phosphate (KTP) laser. Prospective nonrandomized cohort study of patients with VFPs undergoing microlaryngoscopy ("OR group") or KTP laser photoangiolysis ("KTP group"). Voice outcomes (patient-reported outcome measures [Voice Handicap Index-10 (VHI-10) and Singing VHI-10 (SVHI-10)], auditory-perceptual measures [Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V)], videostroboscopic characteristics [Voice-Vibratory Assessment of Laryngeal Imaging (VALI)], and acoustic and aerodynamic measures) were performed at baseline and regular intervals after intervention. Forty-four subjects (17 OR group, 27 KTP group) with VFPs were enrolled. Mean VHI-10 significantly improved from baseline to each follow-up interval in both groups, except for the 1-2-week interval in the OR group. Mean SVHI-10 improved for both groups at some intervals. Growth curve models and time-to-event analyses for patient-reported outcomes did not differ between groups. There were significant improvements in all categories of auditory-perceptual voice quality and some categories of videostroboscopic characteristics in both groups. No significant trends were identified in acoustic and aerodynamic measures. Improvements in most outcomes did not significantly differ between groups or based on polyp size. There were no major complications. Significant improvements in patient-reported voice outcomes measures, auditory-perceptual voice evaluation, and videostroboscopic characteristics occur following surgical treatment of vocal fold polyps with either microlaryngoscopy or office-based KTP laser. Long-term voice outcomes do not significantly differ between treatment modalities. Level 3 Laryngoscope, 2024.

Trajectories of Perceived Parental Psychological Control and the Longitudinal Associations with Chinese Adolescents' School Adjustment across High School Years.

Past research supports the detrimental effects of parental psychological control on adolescent school adjustment in both emotional and academic domains. However, how psychological control changes during adolescence, and how such developmental course is related to adolescent psychological well-being and academic functioning are unclear. The direction of effects between parenting and child behaviors is also inconclusive. This 3-year longitudinal study addressed these research gaps by using five waves of survey data on 710 Chinese adolescents of high school ages (Mean age at T1 = 15.54 years, SD = 0.45, 50% males). Using latent growth curve models and latent class growth analysis, the majority of adolescents (about 63%) reported gradual increases of parental psychological control in the first 2 years of high school but a slight decline afterwards, while the other 37% perceived low and stable levels. Results from parallel latent growth modeling suggested that trajectories of psychological control were positively related to developmental trends of internalizing problems (i.e., depression and anxiety) and maladaptive academic functioning, but negatively associated with the trajectory of adaptive academic functioning, as indexed by intercept-intercept and slope-slope associations. The random-intercept cross-lagged models further revealed that psychological control was predictive of adolescent anxiety and lower adaptive academic functioning, and bidirectionally associated with maladaptive academic-related beliefs and behaviors at the within-person level. Taken together, these findings highlight the crucial role of parental psychological control on adolescent school adjustment in the Chinese cultural context and support the reciprocal model of parent-child interactions.

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation.

This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate data collection, by proposing a more accessible and accurate method using fewer variables. The research utilized data from various international track events from 1987 to 2019. Two machine learning models, Random Forest (RF) and Neural Network (NN), were employed to predict the velocity-time curve, focusing on the acceleration phase of the sprint. The models were evaluated against the traditional exponential speed model using Mean Squared Error (MSE), with the NN model demonstrating superior performance. Additionally, the study explored the correlation between maximum velocity, the time of maximum velocity occurrence, the duration of the maximum speed phase, and the overall 100m sprint time. The findings indicate a strong negative correlation between maximum velocity and final time, offering new insights into the dynamics of sprinting performance. This research contributes significantly to the field of sports science, particularly in optimizing training and performance analysis in sprinting.

Bond performance between hybrid fiber-reinforced concrete and BFRP bars under freeze-thaw cycle.

This study applied the pull-out test to examine the influence of freeze-thaw cycles and hybrid fiber incorporation on the bond performance between BFRP bars and hybrid fiber-reinforced concrete. The bond-slip curves were fitted by the existing bond-slip constitutive model, and then the bond strength was predicted by a BP neural network. The results indicated that the failure mode changed from pull-out to splitting for the BFRP bar ordinary concrete specimens when the freeze-thaw cycles exceeded 50, while only pull-out failure occurred for all BFRP bar hybrid fiber-reinforced concrete specimens. An increasing trend was shown on the peak slip, but a decreasing trend was shown on the bond stiffness and bond strength when freeze-thaw cycles increased. The bond strength could be increased significantly by the incorporation of basalt fiber (BF) and cellulose fiber (CF) under the same freezing and thawing conditions as compared to concrete specimens without fibers. The Malvar model and the Continuous Curve model performed best in fitting the ascending and descending sections of the bond-slip curves, respectively. The BP neural network also accurately predicted the bond strength, with relative errors of predicted bond strengths ranging from 3.75% to 13.7%, and 86% of them being less than 10%.

Language anxiety does not affect the growth of L2 reading achievement: The latent growth curve model approach

Abstract Second language (L2) anxiety has been proposed to play a causal role in L2 achievement. However, most studies have failed to acknowledge confounding variables that may be relevant to the study of anxiety and L2 achievement or to investigate the causal effect of L2 anxiety using longitudinal data. For these reasons, we investigated the effect of L1 reading achievement, L2 aptitude, and L2 anxiety as covariates on the growth of L2 reading achievement across three time points. We used the latent growth curve model (LGCM) to estimate the growth trajectory of US secondary school students’ L2 reading growth in Spanish (N = 307) over three school years. The findings showed that students’ L1 reading achievement and L2 aptitude strongly and significantly predicted L2 reading achievement growth. However, L2 anxiety did not predict L2 reading achievement growth. Findings suggest that growth in L2 reading achievement depends on the language-related skills used for L1 reading and the language skills that comprise L2 aptitude, but not on anxiety. Similar to past cross-sectional studies, L2 anxiety related only to initial levels of L2 reading achievement, suggesting that anxiety reflects students’ initial experience of L2 reading but not their L2 achievement.