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Spanish mackerel species are migratory species, also are highly commercially valuable in Taiwan. However, in recent years, climate change and variability have influenced oceanographic conditions, substantially impacting the population dynamics of pelagic species. This study developed weighted habitat suitability index models for three crucial Scomberomorus species—Scomberomorus commerson, Scomberomorus niphonius, and Scomberomorus guttatus—in the waters off Taiwan. Remote sensing data including sea surface temperature, sea surface chlorophyll-a, sea surface salinity, and sea surface height, along with voyage data records that included detailed catch data from Taiwanese drift gillnet fisheries were collected during 2011–2019. The primary fishing grounds for S. commerson are located in the central to southwestern Taiwan Strait, while S. niphonius and S. guttatus are mainly found in the coastal waters off western Taiwan. In autumn, the oceanographic factor preferences are relatively similar among Scomberomorus species, with sea surface temperature being the primary influencing factor. Sea surface height turns to the main influencing factor in winter. Based on the results of the weighted habitat suitability model, we found seasonal differences in habitat preferences and ranges. There is more pronounced overlap in potential habitat in autumn and partial separate condition in potential habitat in winter among Scomberomorus species. Further exploration of the spatiotemporal distribution dynamics of Scomberomorus species under different ENSO periods revealed that during La Niña periods, the potential habitat ranges are broader and extend further south. In contrast, during El Niño periods, and the potential habitat ranges contract northward. Habitat suitable index model may be a useful approach for evaluating possible changes in habitat suitability resulting from climate change or other oceanographic phenomena and in offering suggestions for fishery scientific management.

This study aimed to improve the attendance of 36 Grade 8 Santan students in Values Education classes during Catch-Up Fridays through Collaborative Game-Based Learning. Using a mixed-method design, the study evaluated attendance trends before and after the intervention. Quantitative data were collected from School Form 2 (SF2) to measure attendance, and a paired sample t-test revealed a significant increase in mean attendance values, from 3.47 to 4.47, with a p-value of less than 0.05, confirming the intervention's effectiveness. Qualitative data were gathered through a focus group discussion (FGD) and a thematic analysis following Braun and Clarke's (2006) framework was used to examine how students find Collaborative Game-Based Learning as an intervention to improve attendance. Students’ responses from the FGD showed that Collaborative Game-Based fosters student engagement, enhances participation, and creates a fun and dynamic learning environment.

Abstract Background Depression is a common global mental health issue, affecting around 3.8% of the population. It significantly impacts quality of life and social functioning, posing a major public health challenge. Sleep is a key factor influencing depression, with both sleep quality and quantity linked to mental health. However, sleep deprivation is widespread, and many people compensate by “weekend sleep recovery.” The effects of sleep deprivation and weekend recovery on depression risk are unclear, as irregular sleep patterns may worsen depressive symptoms. This study introduces the “Weekend Catch-up Sleep Ratio” (CUS ratio) to better understand the relationship between sleep patterns and depression. Methods Cross-sectional data were obtained from individuals who participated in the 2021–2023 National Health and Nutrition Examination Survey (NHANES) and had complete data on CUS and the Patient Health Questionnaire (PHQ-9). Multivariable logistic regression was performed to assess the potential independent association between depression and the CUS ratio. Additionally, smoothing curve fitting, threshold effect analysis, subgroup analysis, and interaction tests were conducted. Results A total of 4,656 individuals were analyzed, categorized by depression symptoms (PHQ-9 score of 10 or higher), with an overall depression risk of 12.4%. In the adjusted model, the CUS ratio was significantly positively associated with depression risk (AOR = 1.75, 95% CI: 1.25–2.45), exhibiting a nonlinear threshold effect (inflection point at 1.11). When the CUS ratio ≤ 1.11, an increase in the ratio was associated with a reduced depression risk (AOR = 0.34, 95% CI: 0.13–0.89), whereas when the CUS ratio > 1.11, each unit increase in the ratio significantly increased depression risk by 187% (AOR = 2.87, 95% CI: 1.84–4.50). Individuals with education levels of less than 9th grade, some college or an Associate of Arts (AA) degree, those who are overweight (25 ≤ BMI 1.11, each unit increase in the ratio significantly increased depression severity (Aβ = 0.36, 95% CI: 0.24–0.49). In particular, individuals without diabetes appeared more sensitive to fluctuations in sleep patterns. Conclusions This study suggests that maintaining a balanced sleep pattern, with a CUS ratio between 1 and 1.11, may help reduce depression risk and promote better mental health.