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This study introduces an efficient computational framework designed to support scalable learning in complex data environments using deep neural networks. In many real-world settings, data are not only large in volume but also diverse in structure, noisy in quality, and constantly evolving. These conditions often make conventional deep learning pipelines difficult to scale and expensive to maintain, especially when computational resources are limited or when rapid model updates are required. To address these challenges, we propose a framework that integrates adaptive data preprocessing, modular neural network architectures, and resource-aware training strategies into a unified learning pipeline. The framework is built to balance learning performance with computational efficiency, allowing models to be trained and updated without excessive overhead. Experiments were conducted on multiple heterogeneous datasets representing different levels of data complexity and scale. The results show that the proposed approach consistently improves training stability and convergence speed while maintaining competitive predictive performance compared to standard deep learning setups. In addition, the framework demonstrates better adaptability when handling data distribution shifts, which are common in dynamic environments. These findings suggest that scalable learning does not necessarily require increasingly complex model designs, but rather thoughtful integration of computational strategies that align model behavior with data characteristics and system constraints. The proposed framework offers a practical pathway for deploying deep learning solutions in large-scale, real-world applications where efficiency, robustness, and scalability are equally important.
In an era of rapid information exchange and increasingly complex global challenges, scientific reasoning has become a vital skill in both education and research. This study aims to investigate the evolution and global influence of scientific reasoning ability from 2000 to 2024 through a bibliometric analysis. The research examines publication trends, leading authors, and international collaborations, while also exploring the relevance of scientific reasoning to pressing global issues such as climate change, public health, and technological innovation. The findings reveal a significant increase in scholarly output and cross-national partnerships, indicating a growing recognition of scientific reasoning as a core academic and practical competency. Moreover, the integration of scientific reasoning into educational frameworks and research agendas underscores its critical role in cultivating adaptive, critical thinkers. These insights highlight the strategic importance of fostering scientific reasoning skills to prepare future leaders capable of addressing global challenges.
The emergence of a new bee species, Tetragonula laeviceps, in Indonesia has attracted scientific attention that promoted further exploration. We aimed to analyze changes in the biochemical composition of T. laeviceps honey stored at different temperatures and to study the kinetics of hydroxymethylfurfural formation. T. laeviceps honey was stored at 25, 50, and 80 degrees C for 6 h. The pollen sources were identified using the melissopalynology method, followed by a biochemical analysis using UHPLC-DAD-ESI/MS. The kinetics of hydroxymethylfurfural formation were analyzed using the Arrhenius equation applied to zero-, first-, and second-order reactions. T. laeviceps honey was multifloral (three or more pollen types, each with < 16% frequency), with dominant Zea mays spp. Mays (L.) (40.24%) and Vigna unguiculate sesquipedalis (L.) (22.52%). Heating at 80 degrees C significantly (p < 0.05) increased phenolic acids, flavonoid acids, total phenolics, total flavonoids, and hydroxymethylfurfural, as well as significantly (p < 0.05) degraded diastase, invertase, glucose oxidase, and DPPH. Heating at 50 degrees C only had a significant impact on hydroxymethylfurfural and diastase. Ferulic acid and kaempferol compounds dominated in the phenolic and flavonoid acids in all the samples. The kinetics of hydroxymethylfurfural formation followed a first-order reaction, with specific rate constants of 0.1098/h (25 degrees C), 0.0597/h (50 degrees C), and 0.0053/h (80 degrees C), involving an activation energy of 69.23 KJ/mol. This study highlights the impact of storage and heating on the chemical composition of Klanceng honey. Our findings provide practical guidance for improving honey production and storage, while enhancing the commercial value of T. laeviceps.
This present research tended to analyze the teacher talk performed by the English teachers in a Senior High School in Palu, Central Sulawesi through the use of Self-Evaluation of Teacher Talk (SETT). This research employed a case study research design, in the nature of qualitative research. Random sampling technique was used in determining the research’s participants. From the results of the research, it was found that the English teachers implemented all of the fourteen interactional strate- gies of SETT. Besides, the teachers and students’ perceptions were also investigated.
Purpose: This quasi-experimental study examined the association between Indonesia's Kurikulum Merdeka, as a macro-level social intervention, and discrimination-related outcomes in schools, specifically its links to teacher attitudes toward students with disabilities and school tolerance climate. Method: Using nationally representative data from 333,489 elementary schools, a comparative static-group design was employed, with school socioeconomic status, school status, and regional location included as covariates. Results: Schools implementing the Kurikulum Merdeka were associated with significantly higher disability attitude scores (Cohen's d = 0.88) and tolerance climate scores (d = 1.05) compared to Kurikulum 2013 schools. Mediation analysis indicated that school tolerance climate significantly mediated the curriculum attitude relationship, accounting for 30.82% of the total association. Discussion: These findings provide evidence consistent with the view that values-driven curriculum reform is linked to meaningful reductions in school-based discrimination at scale, with implications for school social work practice in advancing inclusive education and social justice.
