Exercise is recommended to increase physical health and performance. However, it is unclear how low-intensity exercise (LIE) of different durations may affect or improve recovery ability. This study aimed to investigate how LIE-duration with the same volume affects recovery ability in adults. Twenty healthy male adults participated in this study. Participants were randomly assigned to the 30-min (n = 10) or the 1-h LIE group (n = 10). The intervention included sixteen exercise sessions/four weeks with a 30-min LIE group, and eight exercise sessions/four weeks with a 1-h LIE group. Heart rate (HR) corresponding to <2 mmol∙L−1 blood lactate (La−) was controlled for LIE. Pre- and post-testing was conducted before and after 4-week LIE and tests included jogging/running speed (S), HR, and differences (delta; ∆) in HR and S between pre- and post-testing at 1.5, 2.0, and 4.0 mmol∙L−1 La−. Only the HR at 2.0 mmol∙L−1 La− of the 30-min LIE group was decreased in the post-test compared to the pre-test (p = 0.043). The jogging/running speed of the 1-h LIE group was improved in the post-test compared to the pre-test (p < 0.001, p = 0.006, p = 0.002, respectively). ∆HR at 2.0 and ∆S between the 30-min and 1-h LIE group at 1.5, 2.0, and 4.0 mmol∙L−1 La− were significantly different (p = 0.023, p < 0.001, p = 0.002, and p = 0.019, respectively). Furthermore, moderate to high positive correlations between ∆HR and ∆S of all subjects at 1.5 (r = 0.77), 2.0 (r = 0.77), and 4.0 (r = 0.64) mmol∙L−1 La− were observed. The 1-h LIE group showed improved endurance not only in the low-intensity exercise domain, but also in the beginning of the moderate to high-intensity exercise domain while the 30-min LIE group was not affected by the 4-week LIE intervention. Therefore, LIE (<2.0 mmol∙L−1) for at least 1-h, twice a week, for 4 weeks is suggested to improve recovery ability in adults.

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.

No abstract

No abstract

Long-term changes in air and water temperatures and the resulted stratification phenomena were observed for Soyang Lake (SY), Paldang Lake (PD), Chungju Lake (CJ), and Daecheong Lake (DC) in South Korea. Non-parametric seasonal Kendall and Mann-Kendall tests, Sen slope estimator, and potential energy anomaly (PEA) were applied. The lake surface water temperatures (LSWTs) of SY and DC increased at the same rate (0.125 °C/y), followed by those of CJ (0.071 °C/y) and PD (0.06 °C/y). Seasonally, the LSWT increase rates for all lakes, except PD, were 2–3 times higher than the air temperature increase rates. The lake stratification intensity order was similar to those of the LSWT increases and correlations. SY and DC displayed significant correlations between LSWT (0.99) and PEA (0.91). Thus, the LSWT significantly affected stratification when the water temperature increased. PD demonstrated the lowest correlation between LSWT and PEA. Inflow, outflow, rainfall, wind speed, and retention time were significantly correlated, which varied within and between lakes depending on lake topographical, hydraulic, and hydrological factors. Thus, hydraulic problems and nutrients should be managed to minimize their effects on lake water quality and aquatic ecosystems because lake cyanobacteria can increase as localized water temperatures increase.