Assumption University

The flower buds of Styphnolobium japonicum (FBSJ), a medicinal and food plant, exhibit broad application prospects and diverse therapeutic effects, including cooling the blood, stopping bleeding, lowering blood sugar, and exerting antioxidant and anti-inflammatory activities. To provide a reference for the identification, resource development, and utilization, a widely targeted metabolomics study was conducted on FBSJ samples from four production regions in China: Shandong (SJsd), Anhui (SJah), Hebei (SJhb), and Henan (SJhn). In this research, the metabolomic analysis provided the possibility of identifying metabolites to differentiate plants from different origin to broaden a limited metabolome coverage and unclear origin-metabolite-efficacy correlations. A UPLC-MS/MS-based metabolomics analysis was selected to investigate the metabolites of FBSJ from different geographic origins, utilizing the MWDB database and the multi-reaction monitoring mode of triple-quadrupole mass spectrometry (TQMS). A total of 1,559 metabolites were identified, of which 294 were annotated as key active ingredients in traditional Chinese medicine (TCM), and 193 as active pharmaceutical components against 11 diseases, including cancer and diabetes. Among the 708 differential metabolites identified across the four different origin groups, 176 were annotated by KEGG and distributed across 87 metabolic pathways. The most significantly altered metabolic pathway was isoflavonoid biosynthesis (p < 0.01), where SJsd samples exhibited an upregulation trend in key differential metabolites compared to the other groups. Among the 193 disease-active metabolites, 13 (e.g., 3'-Methoxydaidzein, Maackiain) were at least twofold more abundant in SJsd compared to any other origin, highlighting their critical role in explaining SJsd's superior pharmacological potential. This study provides a comprehensive molecular-level analysis of FBSJ metabolites, offering useful insights into its pharmacological analysis across different origins. These findings enhance our understanding of FBSJ metabolites annotated as anti-disease-related components in public databases and offer a valuable reference for investigating their potential in human health applications and the development of novel functional foods.

Artificial intelligence (AI) adoption is often hindered more by human factors than technical barriers. End-users frequently lack confidence in integrating AI tools, and adoption intentions vary across demographic groups. This study introduces an intervention-augmented Unified Theory of Acceptance and Use of Technology (UTAUT) model to explain and enhance $\mathbf{A I}$ adoption across generations. The study utilized the explanatory sequential mixed-methods design. A survey of $\mathbf{1, 0 3 8}$ respondents confirmed two adoption pathways: (1) performance expectancy influenced the intention to adopt AI through performance expectancy; and (2) performance expectancy influenced intention to adopt AI through social influence and facilitating conditions. Building on these findings, stakeholders representing academics, relevant government agencies, and industry experts co-created a national AI training program. Post-training results demonstrated an increase in AI adoption intention eliminating the generational difference in adoption. The study demonstrates stakeholder-co-created intervention as an antecedent in UTAUT and offers practical guidance for human-computer interaction and policy strategies to promote inclusive AI adoption.

IntroductionDue to its significant impact on performers’ psychological well-being and career development, music performance anxiety (MPA) has recently received growing public attention. The Kenny Music Performance Anxiety Inventory-Revised (K-MPAI-R) is one of the most widely focused MPA scales, often used to assess the manifestations and influencing factors of MPA in musicians across different genres and professional stages. However, most current K-MPAI-R validation studies did not distinguish between vocal and instrumental performers. As they differ significantly in performance settings and stressors, these variations may impact the validity of the scale and lead to biased results.MethodsWith Kenny’s permission, this study aims to re-examine the reliability and validity of the K-MPAI-R in the context of Chinese university students majoring in vocal music. 736 Chinese students participated in this study. The researchers divided the total sample into two equal subsamples, each consisting of 368 students. One subsample was subjected to exploratory factor analysis (EFA) and the other to confirmatory factor analysis (CFA).ResultsThrough EFA, the researchers obtained a four-factor model (F1: Psychological Vulnerability, F2: Proximal Somatic and Cognitive Anxiety, F3: Parental Support, F4: Memory Self-Efficacy); subsequently, CFA was employed to confirm that the model demonstrated a good fit and exhibited adequate reliability and validity.DiscussionThis study represents the first refined validation of the K-MPAI-R scale for Chinese university students majoring in vocal music, providing cross-cultural and discipline-specific evidence for optimizing measurement tools to assess MPA among vocal performers accurately.

ABSTRACT This study first examined the function of social security schemes in moderating the adverse effects on child education of unemployment among household members. Using data from the 2019 non‐crisis period and the 2021 crisis period from the Thailand Socio‐Economic Survey, two‐step estimation models were employed due to the presence of a large number of censored observations. In addition, the validity of the results was supported by utilizing an instrumental variable approach to address the possibility of an endogeneity problem. Of greater importance, the negative effects of unemployment were moderated when unemployed household members were covered by social security schemes. The results show that the negative impact of unemployment on educational spending was significantly larger during the 2021 crisis period compared to the 2019 non‐crisis period. To further verify the mitigating role of SSS, the robustness of the results was also checked by including an interaction term between unemployment and SSS in two‐stage decision models, along with additional control variables.

This study aims to develop and implement an integrated learning model combining Project-Based Learning (PjBL) and Guided Inquiry Learning (GIL) to enhance science literacy in students within the advanced science education course. The study was conducted to students from the Elementary School Teacher Education Program at the Catholic University of Santo Thomas, focusing on the topic of States of Matter. A quasi-experimental design with a pre-test – post-test control group design was employed, where the control group received conventional teaching methods, and the experimental group engaged in the integrated PjBL-GIL model. The results reveal a significant difference in science literacy improvement between the groups. The experimental group, using the integrated PjBL-GIL model, showed a greater improvement (22.4%) compared to the control group (7.7%). Statistical analysis using a two-sample t-test confirmed the significance of this difference, with a t-value of 5.16 and a p-value of 0.000, indicating a statistically significant improvement in the experimental group. The effect size, measured by Cohen's d, was 2.53, indicating a large effect. ANCOVA results showed that even after controlling for baseline differences, the experimental group still demonstrated significantly higher post-test scores (p-value = 0.000). Furthermore, Pearson’s correlation analysis revealed a significant positive relationship between collaboration and science literacy improvement (r = 0.62, p = 0.01), emphasising the importance of collaboration in enhancing learning outcomes. These findings suggest that the integrated PjBL-GIL model is effective in improving critical thinking, problem-solving skills, and the application of scientific concepts. And it is recommended that this model be expanded in science education in Indonesia to further improve the quality of learning and students' science literacy.

Background: The prevalence of sexually transmitted infections (STIs) is increasing globally, with a particularly alarming rise in Thailand, especially among young people. This trend poses significant repercussions for their health, academic performance, and overall quality of life. This integrative literature review endeavors to elucidate the multifaceted roles of university nurses in the realms of prevention, care, and education pertaining to sexual health, tailored to the distinct needs of students. Methods: A comprehensive systematic search was executed across PubMed, CINAHL, Scopus, and ThaiJO for scholarly publications spanning from 2015 and 2025, using the PICO framework. Studies were included if they concentrated on university health services focusing on prevention, care, and sexual health education for the student population. A total of 13 studies met the inclusion criteria and were analyzed. Results: Three major roles of university nurses were identified: (1) prevention strategies that increased service utilization, though male students were less engaged; (2) educational interventions that improved knowledge, attitudes, condom negotiation skills, and testing practices; and (3) supportive health services that reduced stigma and enhanced accessibility, especially when integrated with mental health and sexual violence care. Conclusion: This literature review underscores the efficacy of a nurse-led, student-centered paradigm within university health services. Fundamental components encompass screening, sexual education, provision of condoms, peer support, an inclusive clinic environment, and comprehensive follow-up care. Future research should explore the long-term ramifications, systemic capacity, and cost-effectiveness of these interventions.

Background and Aims: Language education in ethnic minority regions faces challenges such as limited resources for Mandarin teaching and students’ relatively weak language foundation. Mobile-Assisted Language Learning (MALL) has been proposed as a potential solution. This study aimed to investigate whether a MALL intervention—combining Automatic Speech Recognition (ASR) feedback with structured practice modules—could effectively improve the accuracy of Mandarin pronunciation and the fluency of oral expression among this area students. Research Methodology: Using a post-test control experimental design, 90 students were divided into an experimental group (MALL intervention for 8 weeks) and a control group (traditional teaching). Their performance was evaluated using the National Mandarin Proficiency Test (PSC) indicators, and the results were analyzed with Jamovi. Results: The experimental group significantly outperformed the control group in monosyllabic and polysyllabic reading accuracy (p &lt; .001) and demonstrated moderate improvement in impromptu speaking (p &lt; .05). No statistically significant differences were observed in extended text reading fluency, although the mean improvement on each proficiency test ranged from 3.04 to 4.13. Conclusion: MALL technology can serve as an effective supplement to traditional instruction in resource-limited settings by improving students' Mandarin pronunciation and accuracy. Future research could provide a more comprehensive understanding of the impact of MALL by expanding the scope and duration of the survey to include variables such as motivation and self-efficacy.

This study explored the experiences, challenges, coping mechanisms, and insights of teachers at Sta. Josefa Central Elementary School: Under the SPED Program for School Year 2024-2025. Results suggested that the management of ancillary tasks is a two-fold battle. Key experiences for teachers were handling extra duties as homeroom teacher, helping out the school nurse, attending seminars and workshops, gathering data, carrying out school protocols and backing up fellow-teachers. Still others faced difficulties managing their roles at work and home, at times forgetting about their parenting duties and battling scheduling issues. In regard to ancillary responsibilities, these benefits included receipt of promotional certificates, refined organizational, professional, and managerial skills, increased levels of industry and patience, and enhanced understanding of the processes involved in undertaking tasks. A number of teachers also mentioned some ecological benefits, like the conservation of some natural resources and the creation of some recyclable waste materials. On the other hand, some of the disadvantages included the less teaching and contact time, greater administrative burden with no increase in teaching support, lack of workload balance, difficulties with time management, and the necessity of drawing on personal resources. Challenges faced were technical issues, conflicting duties, waiting for required data, poor disposal of waste, and school needs maintenance. Coping mechanisms that were efficient involved self-motivation, staying positive, prioritization, seeking assistance from peers, reminding pupils of regulations, and getting enough rest. Teachers' realizations regarding ancillary task management indicated the need to put effort, lead a balanced life, and accept the positive as well as negative sides of these responsibilities. A few persons think that teachers are not supposed to be given such assignments, yet some recognize their great influence. In order to assist colleagues in the same position, recommendations were looking for assistance, proper time management, being optimistic, and accepting assigned work with commitment. Keywords: Educational Administration Ancillary Tasks, Teachers’ Experiences, Challenges, Coping Mechanisms, Insight, Phenomenological Study

Previous studies have shown that utilizing data sources such as historical or technical data along with deep learning approaches can increase the accuracy of forex price prediction. However, relying on a single data source, such as historical data alone, limits predictive performance as it fails to capture the entire complexity of the market. This study aims to demonstrate that merging financial data from four different sources can significantly improve prediction accuracy. This study proposes a hybrid LSTM-GRU-Attention deep learning model, leveraging historical, fundamental, technical, and sentiment data, to predict the closing price of the GBP/USD currency pair, which is actively traded on a global scale. QuantManager, Forexfactory, and DailyFX data were collected over three timeframes: 30-minute, 1-hour, and 1-day, from January 1, 2013 to December 31, 2023, respectively. The model’s performance was evaluated using MSE, RMSE, and MAE metrics. The proposed model with multi-source data demonstrated substantial error reductions compared to historical data only models from previous studies, achieving RMSE decreases from 17% to 89% and MAE reductions from 22% to 94% across 30-minute, 1-hour, and 1-day timeframes. The integrated multi-source data model outperformed models that integrated only historical data, across all timeframes, highlighting the benefits of improved forex prediction accuracy.