Asia Pacific International University

This study investigated the growth language mindset of undergraduate students in Thailand, focusing on their beliefs about intelligence, aptitude, and age sensitivity in English language learning. A total of 1,174 students from diverse academic disciplines participated in the survey, which employed the adapted Language Mindset Inventory (LMI). Descriptive statistics, multivariate analyses of variance (MANOVA), and confirmatory factor analysis (CFA) were conducted to address four research questions: (1) the overall level of growth mindset in English learning, (2) variations of mindset across disciplines, (3) differences based on the urban–rural divide, and (4) the construct validity of the proposed three-factor model. Results revealed that students generally hold strong growth-oriented beliefs, with the highest mean score for age sensitivity. MANOVA results indicated significant disciplinary differences in intelligence beliefs and age sensitivity beliefs, with students in telecommunication engineering, polymer engineering, manufacturing automation and robotics engineering, and mechanical engineering reporting the strongest growth mindsets. In contrast, bioscience and environmental engineering students showed weaker orientations. A separate MANOVA showed significant urban–rural differences across all three constructs, with urban students consistently reporting higher growth beliefs, particularly in age sensitivity. The CFA provided strong support for the three-factor model, and fit indices confirmed an excellent model fit. These findings highlight that while growth-oriented beliefs about language learning are widespread among Thai undergraduates, disciplinary and geographical disparities remain. The validated three-factor model confirms that intelligence, aptitude, and age sensitivity beliefs are distinct but interrelated dimensions of language mindset. The study highlights the need for tailored interventions to foster growth-oriented language learning environments.

Betacyanins are bioactive compounds found in Caryophyllales, including red beetroots (Beta vulgaris), amaranths (Amaranthus sp.), and red dragon fruits (Hylocereus polyrhizus). Their biosynthesis requires several enzymes, including tyrosinase, 4,5-DOPA-extradiol-dioxygenase (DOD), and 5-O-glucosyl transferase (5GT). Environmentally friendly extraction techniques, such as ultrasound-assisted, microwave-assisted, supercritical fluid, and pulsed electric field extraction, improve the recovery of betacyanins from natural resources. Betacyanins have commercial value as food coloring agents, for smart food packaging, and in the nutraceutical, pharmaceutical, and cosmetic industries. Industrial application is expanding as more innovative methods are discovered. Several factors affect the absorption of betacyanins, including gastrointestinal degradation, the nature of the ingested betacyanins, and the food matrix. Betanin, the main betacyanin, is excreted through urine when given intravenously. However, orally administered betanin showed limited urinary excretion, suggesting extensive modification or digestion in the gastrointestinal tract. Biologically, betacyanins are shown to have antioxidant, anti-inflammatory, antidiabetic, hypolipidemic, gut-microbiome-modulatory, antiproliferative, and antimicrobial properties. Recent molecular docking developments showed this compound group’s potential in modulating key target enzymes and proteins.

This paper conducts a numerical analysis based on computational fluid dynamics (CFD) of a double-pass solar air collector with the help of COMSOL Multiphysics. The collector will have a perforated corrugated absorbent plate and this allows increased turbulence and increased convective heat transfer. The five inlet holes are located below the absorber and air is admitted invrino with the vertical orientation followed by passing through the overheated absorber surface and finally out through the outlet. The simulation is provided with varying values of solar radiation that reflect the actual daytime spatial values which varies between 552-855 W/m 2 of solar irradiance. The influence of the velocity of inlet air (0.5 m/s) and inlet temperature (298.15 K) on the thermal performance is considered. Important performance factors including temperature distribution, convective heat transfer coefficient, useful energy gain and the overall thermal efficiency are numerically calculated. A double-pass design improved the outlet air temperature by 10-14 0 C, the convective heat transfer coefficient by 22-31 %, and thermal efficiency by 18.4 % over a conventional single-pass collector. The findings indicate that the double-pass system is highly effective in thermal performance than the conventional single-pass systems especially when exposed to changing solar conditions.

Creativity is a crucial skill for business entrepreneurs, particularly in the aftermath of the COVID-19 pandemic. Therefore, the objective of this research is to examine the influence of entrepreneurial creativity using the Cognitive Process Associated with Creativity (CPAC) scale model to examine its influence on business innovation and the success of small business entrepreneurs in the post COVID-19 context according to the concept of CPAC Model as a tool to study, together with, the variables on business innovation and the success of business entrepreneurs. The findings show that the CPAC model positively influences business innovation, which in turn contributes to the success of business entrepreneurs. Thus, the findings of this research suggest that business entrepreneurs need to use creativity so that their performance leads to business innovation and to business success. The results also suggest that the CPAC model be examined in other samples to explore the perspectives of individuals regarding the use of creativity to support the search for leveraging creativity for successful business operations. This research encompasses the CPAC model into entrepreneurship, showing how cognitive processes foster innovation and business success, with both theoretical and practical contributions.

Abstract Cassava represents a crucial staple crop in sub-Saharan Africa, whose productivity is severely threatened by viral and bacterial diseases. While deep learning approaches show promise for automated disease detection, they often operate under unrealistic assumptions and suffer from dataset limitations including class imbalance and label noise. This paper presents a novel hierarchical deep learning framework that addresses these challenges through a three-stage pipeline: crop verification, health assessment, and disease classification. Our approach achieves computational efficiency through conditional execution while maintaining diagnostic accuracy. On the Cassava Leaf Disease Classification dataset comprising 21,397 images, our framework achieves 91.8\% overall accuracy with statistically significant improvements over flat classification baselines (mean difference: 1.7\%, bootstrap 95\% CI: 1.2-2.2\%, p < 0.001, paired t-test on 5-fold cross-validation). The framework demonstrates robust performance across all disease classes, with particular strength in handling class imbalance through a multi-focal loss formulation. Comprehensive validation includes per-class metrics, 5-fold cross-validation, ablation studies, explainability analysis, and out-of-distribution evaluation. Our work provides a validated blueprint for hierarchical approaches in agricultural AI, with complete reproducibility through public code and dataset splits.

This study aims to develop and validate a questionnaire instrument based on Self-Determination Theory to measure breakfast habits among junior high school adolescents. Breakfast is a crucial factor in supporting adolescents' physical and cognitive health, yet the underlying psychological motivations for this habit remain underexplored. Self-Determination Theory emphasizes three basic psychological needs—autonomy, competence, and relatedness—which are believed to contribute to intrinsic motivation in maintaining healthy behaviors, such as breakfast consumption. This research used an explanatory survey design with a cross-sectional approach. The sample consisted of 119 students from UNKLAB Junior High School, Airmadidi, North Minahasa, selected using stratified random sampling. The research instrument was a modified self-determination questionnaire adapted to the local context. Validity and reliability tests showed that the questionnaire had strong construct validity and reliability. This study contributes to the development of a measurement tool that can be utilized in adolescent health promotion and nutritional interventions. Further validation is recommended to improve the cultural and social sensitivity of the instrument.