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Silver sillago (Sillago sihama) is a commercially important marine fish species in East Asia. In this study, we compared the transcriptome response to hypoxia stress in the gill tissue of S. sihama. The fish were divided into four groups, such as 1 h of hypoxia (hypoxia1h, DO = 1.5 ± 0.1 mg/L), 4 h of hypoxia (hypoxia4h, DO = 1.5 ± 0.1 mg/L), 4 h of reoxygen (reoxygen4h, DO = 8.0 ± 0.2 mg/L) after 4 h of hypoxia (DO = 1.5 mg/L), and normoxia or control (DO = 8.0 ± 0.2 mg/L) groups. Compared to the normoxia group, a total of 3550 genes were identified as differentially expressed genes (DEGs) (log2foldchange > 1 and padj < 0.05), including 1103, 1451 and 996 genes in hypoxia1h, hypoxia4h and reoxygen4h groups, respectively. Only 247 DEGs were differentially co-expressed in all treatment groups. According to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DEGs were significantly enriched in steroid biosynthesis, biosynthesis of amino acids, glutathione metabolism and metabolism of xenobiotics by cytochrome P450, ferroptosis and drug metabolism—cytochrome P450 pathways. Of these, the cytochrome P450 (CYP) and glutathione S-transferase (GST) gene families were widely expressed. Our study represents the insights into the underlying molecular mechanisms of hypoxia stress.

This study aimed to enhance visitor engagement and interactions with historical textile exhibits in small museums by implementing a Hand-i-Craft Bot. Our research aligns with the broader trend of leveraging technology to improve museum experiences. This research addressed the challenge of increasing engagement and understanding of historical textiles in small museums. We employed an experimental research methodology grounded in a design thinking approach, which involved developing the Hand-i-Craft Bot and evaluating its usability. This research utilized the Namuensri Textile Museum as a case study. The assessment used the System Usability Scale (SUS), Net Promoter Score (NPS), and user satisfaction, robustly measuring the chatbot’s effectiveness. The results showed that the chatbot had a SUS score of 74.92. The most common response was ‘excellent’, indicating good usability. The average grade of a B was another positive usability experience indicator. The NPS score of ‘promoter’ showed that most participants would likely recommend the chatbot to others. User satisfaction results indicate that the Hand-I-Craft Bot is an effective tool for enhancing visitors’ museum experiences. By providing information, being consistent and informative, and making the visit more interesting, the Hand-I-Craft Bot increased user engagement and enhanced visitors’ experiences.

Digital literacy is important capacity for students’ learning in the world of rapid changes in technology. This article aimed to study digital literacy among non-STEM undergraduate students. The study covered the component of 4 skills, i.e., operation skills, thinking skills, collaborative skills, and awareness skills. The hypothesis set in the study was academic major in non-STEM has different digital literacy. When each component was separately studied, digital literacy among non-STEM students should be different as well. The participants in the study were 378 non-STEM undergraduate students of Prince of Songkla University, Surat Thani Campus. The research instrument was digital literacy measurement. Inferential statistics were used in the data analysis to compare two independent groups. The results support hypotheses that the sample in non-STEM undergraduate students with different academic majors had different levels of digital literacy. Students major in a language had higher mean scores than students major in management. When each component was separately studied, students with different academic majors had different levels of digital literacy in each component. Students major in language had higher mean scores than students major in management in all components. This article mentioned an important point about digital literacy of non-STEM undergraduate students so as to be a guideline for stakeholders to determine strategies, set policies and make plans to develop digital skills among non-STEM students, which is highly important to students in the era of rapid changing technology.

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.