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Fulltext Analysis of the Association between Teachers' Classroom Teaching Behaviors and Students' Knowledge Acceptance Based on Psychological Data Analysis

Yao T, Yang X

Occup Ther Int, 2022;2022:2661398.
PMID: 35814354 DOI: 10.1155/2022/2661398

This paper adopts the method of psychological data analysis to conduct in-depth research and analysis on the correlation between teachers' classroom teaching behaviors and students' knowledge acceptance. Firstly, this paper proposes a health factor prediction model, which is specifically divided into clustering and then classification model and a clustering and classification synthesis model. The classroom learning process is coded, sampled, and quantified to obtain data on students' learning behaviors, and a visualization system based on classroom students' learning behaviors is designed and developed to record and analyze students' behaviors in the classroom learning process and grasp students' classroom learning. These two models use algorithms to fine-grained divide the dataset from the perspective of subject users and mental health factors, respectively, and then use decision tree algorithms to classify and predict the mental health factor information by the subject user base information. Second, based on the collected datasets, we designed comparison experiments to validate the clustering-then-classification model and the integrated clustering-classification model and selected the optimal model for comparison. Teachers should increase effective praise and encouragement behaviors; teachers should increase meaningful teacher-student interaction behaviors; teachers should be proficient in teaching media technology to reduce unnecessary time wastage. Strategies to enhance teachers' TPACK include enriching teachers' knowledge base of CK, TK, and PK; developing teachers' integration thinking; and enriching teachers' types of activities for integrating technology.
Fulltext The origin of cultivated mangosteen (Garcinia mangostana L. var. mangostana): Critical assessments and an evolutionary-ecological perspective

Yao TL, Nazre M, McKey D, Jalonen R, Duminil J

Ecol Evol, 2023 Mar;13(3):e9792.
PMID: 36937066 DOI: 10.1002/ece3.9792

Mangosteen (Garcinia mangostana var. mangostana) is a popular tropical fruit, yet many aspects of its biology and evolutionary history are little known. Its origin remains contentious, although recent findings suggest G. mangostana L. var. malaccensis (Hook. f.) Nazre (synonym: G. malaccensis Hook. f.) as the sole progenitor. We review hypotheses on the origin of mangosteen and clarify points that have been affected by errors of fact and interpretation. The narrow focus and lack of detail in published results make their interpretation difficult. When possible, we support our interpretations with field observations and examination of herbarium specimens. We outline the main biological traits (e.g., dioecy, facultative apomixis, and polyploidy) of mangosteen and its wild relatives to infer traits that might have evolved during domestication of mangosteen. We find no clear indication that apomixis and polyploidy evolved during domestication. Polyploidy is known in the wild relatives, but apomixis has not yet been demonstrated. Also, we propose a testable new evolutionary-ecological framework that we call "Forest-Dusun Interface" to infer processes in the origin of mangosteen. Dusun (Malay) refers to subsistence orchards in this context. Lastly, we propose future studies to address identified knowledge gaps.
Fulltext SDS screen-positive prediction from structured questionnaires in university students: a comparative evaluation of large language model prompting strategies

Yang X, Cao Y, Wang H, Li H, Jiang X, Xu H, et al.

Front Psychiatry, 2026;17:1942164.
PMID: 42973452 DOI: 10.3389/fpsyt.2026.1942164

INTRODUCTION: Large language models (LLMs) can render structured questionnaire variables as natural-language profiles, but their value in mental-health screening cannot be judged from a single fixed-threshold score. We evaluated LLM prompt conditions in a cross-questionnaire benchmark for predicting screen-positive status on the Self-Rating Depression Scale (SDS) among undergraduates. The intended application was an offline methodological benchmark using previously collected questionnaire data, not a shorter alternative to direct SDS administration.
METHODS: De-identified records were assigned to Development (n=1,951), Test (n=2,186), and Holdout (n=2,258) grade cohorts. We evaluated logistic regression, XGBoost, and 12 model-prompt conditions at the fixed 0.5 threshold and at thresholds selected only in Development-validation. We also examined paired bootstrap differences, ranking stability, scale correlations, grouped ablations, cohort shift, calibration intercepts and slopes, alternative expected calibration error specifications, representative repeated calls at temperatures 0 and 0.1, sex-stratified performance, and per-1,000 screening scenarios.
RESULTS: At the fixed 0.5 threshold on Test, GPT E1, Gemini E2, and XGBoost achieved macro-F1 values of 0.732, 0.730, and 0.674. At Development-derived thresholds of 0.38, 0.50, and 0.174, the corresponding macro-F1 values were 0.702, 0.730, and 0.660. At the Development-selected specificity target, XGBoost achieved a Test macro-F1 of 0.738. Pairwise intervals showed no clear fixed-0.5 macro-F1 difference among the three highest-ranked LLM conditions. XGBoost yielded the lowest Brier scores. The combined-sample Spearman correlation between SDS and SAS was 0.60. Together with the ablation results, this correlation indicated predictive information distributed across overlapping questionnaire groups rather than dependence on a single measured group. In repeated calls across the complete Holdout cohort, inconsistent-case rates were 23.6% for GPT E1 and 1.5% for Gemini E2. Temperature 0 did not clearly eliminate GPT E1 instability in the representative 400-case subset.
DISCUSSION: Model conclusions depended on both the threshold and the metric. Certain LLM prompt conditions were competitive at specific operating points, but the evidence did not establish a stable or general advantage over structured-data models. Nor did it establish external validity, clinical diagnostic utility, or reduced questionnaire burden. Threshold choice, calibration, output validity, and repeated-call stability therefore require separate evaluation before prospective use.
Fulltext Interactions between all pairs of neighboring trees in 16 forests worldwide reveal details of unique ecological processes in each forest, and provide windows into their evolutionary histories

Wills C, Wang B, Fang S, Wang Y, Jin Y, Lutz J, et al.

PLoS Comput Biol, 2021 Apr;17(4):e1008853.
PMID: 33914731 DOI: 10.1371/journal.pcbi.1008853

When Darwin visited the Galapagos archipelago, he observed that, in spite of the islands' physical similarity, members of species that had dispersed to them recently were beginning to diverge from each other. He postulated that these divergences must have resulted primarily from interactions with sets of other species that had also diverged across these otherwise similar islands. By extrapolation, if Darwin is correct, such complex interactions must be driving species divergences across all ecosystems. However, many current general ecological theories that predict observed distributions of species in ecosystems do not take the details of between-species interactions into account. Here we quantify, in sixteen forest diversity plots (FDPs) worldwide, highly significant negative density-dependent (NDD) components of both conspecific and heterospecific between-tree interactions that affect the trees' distributions, growth, recruitment, and mortality. These interactions decline smoothly in significance with increasing physical distance between trees. They also tend to decline in significance with increasing phylogenetic distance between the trees, but each FDP exhibits its own unique pattern of exceptions to this overall decline. Unique patterns of between-species interactions in ecosystems, of the general type that Darwin postulated, are likely to have contributed to the exceptions. We test the power of our null-model method by using a deliberately modified data set, and show that the method easily identifies the modifications. We examine how some of the exceptions, at the Wind River (USA) FDP, reveal new details of a known allelopathic effect of one of the Wind River gymnosperm species. Finally, we explore how similar analyses can be used to investigate details of many types of interactions in these complex ecosystems, and can provide clues to the evolution of these interactions.
Demographic composition, not demographic diversity, predicts biomass and turnover across temperate and tropical forests

Needham JF, Johnson DJ, Anderson-Teixeira KJ, Bourg N, Bunyavejchewin S, Butt N, et al.

Glob Chang Biol, 2022 Jan 25.
PMID: 35080088 DOI: 10.1111/gcb.16100

The growth and survival of individual trees determine the physical structure of a forest with important consequences for forest function. However, given the diversity of tree species and forest biomes, quantifying the multitude of demographic strategies within and across forests and the way that they translate into forest structure and function remains a significant challenge. Here, we quantify the demographic rates of 1,961 tree species from temperate and tropical forests and evaluate how demographic diversity (DD) and demographic composition (DC) differ across forests, and how these differences in demography relate to species richness, aboveground biomass, and carbon residence time. We find wide variation in DD and DC across forest plots, patterns that are not explained by species richness or climate variables alone. There is no evidence that DD has an effect on either aboveground biomass or carbon residence time. Rather, the DC of forests, specifically the relative abundance of large statured species, predicted both biomass and carbon residence time. Our results demonstrate the distinct demographic compositions of globally distributed forests, reflecting biogeography, recent history, and current plot conditions. Linking the demographic composition of forests to resilience or vulnerability to climate change, will improve the precision and accuracy of predictions of future forest composition, structure and function.