Search Results (1,608)

In disciplines such as epidemiology, economics, and public health, inference and estimation of heterogeneous treatment effects (HTE) are critical. This approach helps reveal differences in treatment effect estimates between subgroups, which supports personalized decision-making processes. While a variety of meta-learners (e.g., S-, T-, X-learners) have been proposed for estimating HTE, there is a lack of consensus on their relative strengths and weaknesses under different data conditions. To address this gap and provide actionable guidance for applied researchers, this study conducts a comprehensive simulation-based comparison of these methods. We first introduce the causal inference framework and review the underlying principles of the methods used to estimate these effects. We then simulate different data generating processes (DGPs) and compare the performance of S-, T-, X-, DR-, and R-learners with the causal forest, highlighting the potential of meta-learners for HTE estimation. Our evaluation reveals that each learner excels under distinct conditions: the S-learner yields the least bias and is most robust when the conditional average treatment effect (CATE) is approximately zero; the T-learner provides accurate estimates when the response functions differ significantly between the treatment and control groups, resulting in a complex CATE structure, and the X-learner can accurately estimate the HTE in imbalanced data.Additionally, by integrating Z-bias—a bias that may arise when adjusting the covariate only affects the treatment variable—with a specific sensitivity analysis, this study demonstrates its effectiveness in reducing the bias of causal effect estimates. Finally, through an empirical analysis of the Trends in International Mathematics and Science Study (TIMSS) 2019 data, we illustrate how to implement these insights in practice, showcasing a workflow for HTE assessment within the meta-learner framework. Full article

This study presents the one-year follow-up in grade 3 of two randomized controlled interventions, addressing phonics and numeracy targeting children at-risk for early reading or mathematics difficulties. The participants (n = 130) had been part of two intervention studies including 21 Swedish elementary schools. Results show that the post-test advantage of the intervention groups had faded for most outcome measures. A significant difference between groups sustained only for conceptual knowledge in the mathematics intervention group and for the speeded reading test in the reading intervention group. These results raise questions about the integration of rigorous interventions into mainstream education. Furthermore, the study pinpoints the importance of longitudinal intervention research and conditions to make special education interventions sustainable. Full article

Understanding how emotions and psychological states influence both individual and collective actions is critical for expressing the real complexity of biosocial and ecological systems. Recent breakthroughs in mathematical modeling have created new opportunities for systematically integrating these emotion-specific elements into dynamic frameworks ranging from human health to animal ecology and socio-technical systems. This review builds on mathematical modeling approaches by bringing together insights from neuroscience, psychology, epidemiology, ecology, and artificial intelligence to investigate how psychological effects such as fear, stress, and perception, as well as memory, motivation, and adaptation, can be integrated into modeling efforts. This article begins by examining the influence of psychological factors on brain networks, mental illness, and chronic physical diseases (CPDs), followed by a comparative discussion of model structures in human and animal psychology. It then turns to ecological systems, focusing on predator–prey interactions, and investigates how behavioral responses such as prey refuge, inducible defense, cooperative hunting, group behavior, etc., modulate population dynamics. Further sections investigate psychological impacts in epidemiological models, in which risk perception and fear-driven behavior greatly affect disease spread. This review article also covers newly developing uses in artificial intelligence, economics, and decision-making, where psychological realism improves model accuracy. Through combining these several strands, this paper argues for a more subtle, emotionally conscious way to replicate intricate adaptive systems. In fact, this study emphasizes the need to include emotion and cognition in quantitative models to improve their descriptive and predictive ability in many biosocial and environmental contexts. Full article

Background: Health disparities research increasingly relies on complex survey data to understand survival differences between population subgroups. While Peters–Belson decomposition provides a principled framework for distinguishing disparities explained by measured covariates from unexplained residual differences, traditional approaches face challenges with complex data patterns and model validation for counterfactual estimation. Objective: To develop validated Peters–Belson decomposition methods for survival analysis that integrate ensemble machine learning with transfer learning while ensuring logical validity of counterfactual estimates through comprehensive model validation. Methods: We extend the traditional Peters–Belson framework through ensemble machine learning that combines Cox proportional hazards models, cross-validated random survival forests, and regularized gradient boosting approaches. Our framework incorporates a transfer learning component via principal component analysis (PCA) to discover shared latent factors between majority and minority groups. We note that this “transfer learning” differs from the standard machine learning definition (pre-trained models or domain adaptation); here, we use the term in its statistical sense to describe the transfer of covariate structure information from the pooled population to identify group-level latent factors. We develop a comprehensive validation framework that ensures Peters–Belson logical bounds compliance, preventing mathematical violations in counterfactual estimates. The approach is evaluated through simulation studies across five realistic health disparity scenarios using stratified complex survey designs. Results: Simulation studies demonstrate that validated ensemble methods achieve superior performance compared to individual models (proportion explained: 0.352 vs. 0.310 for individual Cox, 0.325 for individual random forests), with validation framework reducing logical violations from 34.7% to 2.1% of cases. Transfer learning provides additional 16.1% average improvement in explanation of unexplained disparity when significant unmeasured confounding exists, with 90.1% overall validation success rate. The validation framework ensures explanation proportions remain within realistic bounds while maintaining computational efficiency with 31% overhead for validation procedures. Conclusions: Validated ensemble machine learning provides substantial advantages for Peters–Belson decomposition when combined with proper model validation. Transfer learning offers conditional benefits for capturing unmeasured group-level factors while preventing mathematical violations common in standard approaches. The framework demonstrates that realistic health disparity patterns show 25–35% of differences explained by measured factors, providing actionable targets for reducing health inequities. Full article

This study proposes a model for a wet string grid dust removal system based on gas–droplet–particle turbulent Eulerian–Lagrangian simulation, providing in-depth insights into the dust removal mechanism of droplet groups and its impact on dust collection efficiency. Through numerical simulations and theoretical derivation, we systematically introduce the mathematical expression of the droplet group dust removal efficiency and validate its applicability in wet string grid dust removal processes. The study reveals that the dust removal efficiency of the wet string grid system is influenced by multiple factors, including airflow velocity, droplet distribution, and the interaction between droplets and dust particles. By adjusting spray volume, wind speed, and the geometric parameters of the water mist zone, the dust removal process was optimized. The results show that increasing the wind speed enhances dust removal efficiency, but excessive wind speed reduces the dust capture efficiency of droplets. Additionally, based on simulation results of the flow field, the study identifies key factors influencing the dust removal efficiency of droplet groups and provides valuable insights for optimizing wet string grid dust removal systems in practical engineering. Full article

STEM (Science, Technology, Engineering, and Mathematics) is essential for the development of 21st-century skills, particularly in a world driven by technological innovation. However, in vulnerable school contexts, access to meaningful STEM experiences remains limited. This study addresses this issue through the design and implementation of a didactic strategy in a public high school in Bogotá, Colombia, based on two educational resources: the BioSen electronic board, which is compatible with Arduino technology and designed to acquire physiological signals such as electrocardiography (ECG), electromyography (EMG), electrooculography (EOG), and body temperature; and the Space Exploration instructional guide, which is organized around contextualized learning missions. This study employed a quasi-experimental mixed-methods design that combined pre–post perception questionnaires, unstructured classroom observations, and a contextualized knowledge test administered to three student groups. Findings demonstrate that after eight weeks of implementation with upper secondary students, the strategy had a positive impact on the development of 21st-century skills, such as creativity, computational thinking, and critical thinking. These skills were assessed through a mixed quasi-experimental design combining perception questionnaires, qualitative observations, and knowledge evaluations. Unlike the control groups, students who participated in the intervention adjusted their self-perceptions when facing real-world challenges and showed progress in the application of key competencies. Overall, the results support the effectiveness of integrating low-cost biomedical tools with gamified STEM instruction to enhance higher-order thinking skills and student engagement in vulnerable educational contexts. Full article

Iron deficiency anemia continues to pose a significant global health burden, necessitating the development of improved therapeutic delivery systems. This study investigates novel composite materials composed of organosilicon hydrogels and cross-linked sulfobutyl ether beta-cyclodextrin (SBECD) nanoparticles for the oral delivery of iron compounds. Two types of cross-linked SBECD nanoparticles were synthesized using 1,6-hexamethylene diisocyanate. These nanoparticles were characterized by DLS, NTA, and FTIR and possess size around 200–300 nm and negative zeta-potential around −35 mV with molecular weight 150–200 kDa. Various hydrogel matrices, including plain PMSSO hydrogels and modified versions with amino groups or silicate cross-links, are also described. The hydrogels were evaluated for their iron sorption capacity (up to 44% loading efficiency) and release kinetics for 3 h. The results demonstrate that cross-linked SBECD nanoparticles significantly enhance iron sorption and provide sustained release under simulated physiological conditions. Mathematical modeling indicated that the Higuchi model best describes the iron release kinetics. The findings suggest that the proposed composite materials hold considerable promise for the treatment of iron deficiency anemia, offering an innovative approach to enhance therapeutic efficacy and minimize adverse effects. Full article

Classical game theory provides powerful tools for modeling strategic interaction, but often overlooks the social, cultural, and institutional dimensions of human behavior. To address this gap, Tom Burns and collaborators developed generalized game theory (GGT) and later sociological game theory (SGT). These frameworks extend classical game theory by embedding rules, norms, values, beliefs, roles, and institutional structures into formal models of interaction. This review synthesizes thirty key contributions to this research program, organizing the literature into eight thematic areas and providing an integrated overview of the field. The originality of this work lies in its comprehensive approach, which advances conceptual and formal foundations while exploring practical applications and outlining directions for future research. GGT/SGT develops rule-based modeling, the analysis of norms and values, multiple modalities of action determination, and various equilibrium types, offering a rigorous framework for understanding strategic behavior in complex social contexts. In application, these approaches provide insights into organizational processes, negotiation, legitimacy, distributive justice, and institutionalized procedures, while integrating interactionist and group-theoretical perspectives. By linking formal modeling with normative and institutional analysis, GGT/SGT offers innovative socio-economic decision models that capture uncertainty, fairness, legitimacy, and institutional transformation. It extends classical game theory by bridging mathematics, economics, and sociology, providing a versatile theoretical tool for understanding complex socio-economic systems and improving strategic decision-making in contemporary society. Full article

The rapid advancements in high-throughput technologies have led to a dramatic increase in diverse -omics data types, enabling comprehensive analyses, especially for complex diseases like cancer. Despite the development of multi-omics approaches, the challenges of scaling integration to massive, heterogeneous -omics datasets suggest that novel computational tools need to be designed. In this study, we propose an approach for integrating microRNA (miRNA) and messenger RNA (mRNA) expression data, incorporating prior biological knowledge (PBK). This approach scores and ranks groups of miRNAs and their associated genes using cross-validation iterations. The proposed method incorporates a Pattern detection (P) component to identify molecular motifs unique to each biological group. The analysis also facilitates the visualization of the groups, facilitating the identification of co-occurring groups and their characteristic features across iterations. Furthermore, the groups are scored using an over-representation analysis through a new Enrichment (E) component in each iteration. The clusters of the groups based on the Enrichment Scores (ESs) are visualized in a heatmap to obtain novel insights into the collective behavior and dependencies of the groups, aiming to understand the molecular mechanisms of complex diseases. The developed G-S-M-E tool not only provides performance metrics and biological scores at the group level but also offers comprehensive insights into intricate multi-omics interactions. In summary, our study emphasizes the importance of mathematical and data science methodologies in elucidating intricate multi-omics integration, yielding a formalized approach that deepens our comprehension of complex diseases. Full article

Despite growing global interest in improving mathematics outcomes, there has been limited empirical research in Nigeria that has rigorously evaluated the impact of structured after-school intervention programs on low-achieving students, particularly through a gender-based lens. This study addresses this gap by examining the effectiveness of after-school mathematics instruction on the performance of senior secondary school students in Oyo State, Nigeria. The researchers adopted a quasi-experimental pretest–posttest control group design with a 2 × 2 factorial structure. The sample consisted of 92 purposively selected low-achieving students (47 males and 45 females) from eight public, co-educational secondary schools, who were randomly assigned to experimental and control groups. Over the course of six weeks, the experimental group received structured after-school mathematics lessons that targeted foundational skills, while the control group continued with conventional classroom instruction. Data was collected using a researcher-developed Mathematics Achievement Test (MAT), which was validated by mathematics education experts and yielded a Cronbach’s alpha of 0.82. Analysis of Covariance (ANCOVA) revealed a statistically significant improvement in the mathematics achievement of students in the intervention group (F(1, 87) = 114.88, p < 0.05), with a large effect size (Partial η² = 0.569). Although no significant interaction effect between gender and treatment was observed (F(1, 87) = 0.208, p > 0.05). This study contributes to the limited literature on gender-responsive after-school interventions in sub-Saharan African contexts. Findings support the implementation of targeted support programs to enhance mathematics outcomes for struggling learners, regardless of gender. Full article

Homework is a key learning activity that promotes students’ self-regulation, motivation, and academic achievement. Previous studies highlight the importance of parental and teacher autonomy support in fostering these outcomes, but the mechanisms underlying these relationships require further investigation. This study investigates the effects of parental and teacher autonomy support on students’ self-regulation skills, mathematics homework completion, and academic achievement. Additionally, it examines whether gender moderates these relationships. The research was conducted with 530 middle school students from five public schools in Istanbul, covering 5th, 6th, and 7th grades. Data were collected on teachers’ and parents’ autonomy support in homework, students’ self-regulation strategies, homework behaviors, and academic performance. Analyses were performed using SPSS 25 and AMOS 25 software, employing structural equation modeling (SEM) with mediation paths, multi-group path analysis, and correlation tests. The results indicate that both parental and teacher autonomy support positively influence students’ use of self-regulation strategies, which in turn enhances homework completion and academic success. Self-regulation was found to mediate these relationships, confirming its crucial role in academic outcomes. However, gender did not significantly moderate these associations. This study advances the understanding of how parental and teacher autonomy support influence self-regulation, homework behavior, and academic achievement, contributing to the existing literature. By examining the mediating role of self-regulation and the moderating effect of gender, it provides in-depth insights into variations in homework engagement and academic outcomes. Findings highlight the importance of autonomy-supportive practices by parents and teachers to foster students’ independent study skills. Future studies could extend these findings by examining subject-specific differences and longitudinal effect. Full article

This study introduces a Fuzzy–AHP–based analytical model for the quantitative assessment of authenticity loss in adaptive reuse practices, addressing a persistent gap in heritage research—the lack of reproducible mathematical frameworks capable of linking authenticity evaluation with sustainability indicators. Unlike previous studies that approach authenticity conceptually or qualitatively, this research develops a hybrid decision-support system that translates both intangible and tangible heritage attributes into measurable linguistic variables, enabling systematic and comparable authenticity assessments. The model was applied to ten traditional houses in Alanya, Türkiye, representing different adaptive reuse types (residential, cultural, commercial, and touristic). A total of 17 experts contributed to the Analytic Hierarchy Process (AHP) weighting stage, producing a Consistency Ratio of 0.0156 (<0.10), and 8 experts provided scoring inputs for the fuzzy system. The fuzzy inference system was implemented in MATLAB R2023a, incorporating seven main criteria and three subcriteria, nine input variables, five linguistic categories, and a rule base of 3400 fuzzy rules. Membership functions were defined within the 0–100 numerical range, and the centroid defuzzification method was used to compute final authenticity values. Model reliability was confirmed through Kendall’s W = 0.87, demonstrating strong inter-rater agreement. Results show that buildings retaining their original residential function achieved the highest authenticity scores (Final Score ≈ 86), while structures converted into boutique hotels or restaurants exhibited substantial authenticity losses (Final Score range: 25–45), especially within Group 2 criteria (environment, function, spirit, and intangible cultural heritage). This divergence illustrates a sustainability paradox: although adaptive reuse prolongs building life cycles and reduces embodied carbon, it may simultaneously undermine cultural sustainability when authenticity is significantly compromised. The proposed Fuzzy–AHP authenticity model provides a replicable, transparent, and empirically validated tool for evaluating the effects of functional transformation within a sustainability framework. By quantifying the relationship between adaptive reuse types and authenticity retention, the study contributes to sustainable heritage management research and supports the implementation of SDG 11—Sustainable Cities and Communities. Full article

Early mathematical learning predicts later academic achievement, and creativity within mathematics plays a central role in higher-order thinking. This study examined the effects of linguistically responsive mathematics enrichment programs for nurturing mathematical creativity. Participants were 250 high-potential kindergarten English Learners across six urban schools in New York, Texas, and California. A linguistically responsive enrichment intervention adapted from the Mentoring Young Mathematicians (M²) math curriculum was implemented for 80 h across seven months. Using the Tangram Creativity Assessment, fluency, flexibility, and originality were measured in students’ tangram problem solving. Additional predictors included Tangram Problem Solving Speed (TPSS), general reasoning (CogAT), and mathematical achievement (NWEA MAP Math). ANCOVA showed significant post-test differences favoring the intervention group across all creativity components. Two-group structural equation modeling analysis supported measurement invariance and explained 55–60% of posttest creativity variance. TPSS emerged as the strongest predictor, with greater effects for the intervention group. These findings highlight the potential of enrichment programs and language-accessible geometry tasks to cultivate creativity in young gifted ELs by strengthening their mathematical foundation while supporting flexible and original problem solving. Full article

The trajectory accuracy of equipment with complex motion paths presents a critical engineering challenge. Targeting the precision issues in the operating trajectory of a carbon-free car, this paper proposes an optimization method for complex mechanical trajectories. Firstly, this study investigates gear backlash-induced return error on the steering precision of a carbon-free cam mechanism of cars. Secondly, considering the cumulative return error of gear transmission between gear groups, a comprehensive mathematical model was established to guide the optimization of cam structure. Finally, the steering accuracy before and after optimization is quantitatively evaluated by trajectory calculation. In addition, the optimized structure was tested and compared with the numerical calculation. The experimental and numerical calculation results are highly consistent. The numerical calculation results show that by adjusting the transmission ratio of the gear set and optimizing the cam profile, the cam deflection angle error is reduced by 24.74% and 27.15%, respectively, and the comprehensive cumulative deflection error of the car is significantly reduced by 45.31%. More importantly, the research provides crucial technical support and guidance for achieving precise control and planning complex paths in automated production lines. Full article

Complex motor tasks that integrate cognitive demands may particularly enhance executive functions, which support school success. Yet few school-based trials have tested structured interventions combining motor complexity and cognitive challenge in early adolescence. Purpose: This study examined the effects of a gamified “Dual-Challenge Circuit” (DCC), integrating motor patterns with cognitive tasks, on executive functions, academic performance, motor skills, and physical fitness among middle school students. Secondary aims were to explore whether executive functions mediated academic gains and whether a dose–response relationship emerged. Method: A cluster-randomized controlled trial was conducted in four middle schools in Southern Italy with sixth- and seventh-grade students. Participants were assigned to either the DCC program or traditional physical education. The 12-week intervention included two weekly 60 min sessions. Outcomes were executive functions (Stroop, Digit Span backward, Trail Making Test-B), academic achievement (grades, MT tests), motor coordination (KTK), physical fitness (PACER, long jump, sit-and-reach), and adherence/fidelity. Results: The DCC group showed significantly greater improvements in all executive function measures and in mathematics and language grades (medium-to-large effects). Mediation analyses confirmed executive functions predicted academic improvements. Motor coordination and fitness also improved, with large effects in aerobic capacity and strength. Conclusions: The DCC effectively enhanced executive functions, academic outcomes, and fitness. Gamified, cognitively demanding physical education formats appear feasible and beneficial in real-world school settings. Full article