Skip to main content
Banner [Small]

Test out our new Bento Search

test area
x
# results
shortcut
Sections
HTML elements
Section Tiles
expand
Tile Cover
Mouse
Math Lab
Space
Tile Short Summary
Math Lab Rooms located in the Main Library in rooms 300X and 300Y
expand
Tile Cover
coffee
CC's Coffee House
Space
Tile Short Summary
Located at the first floor of the LSU Main Library.
expand
Tile Cover
People troubleshooting on a computer
Ask Us
Service
Tile Short Summary
Check our FAQs, submit a question using our form, or launch the chat widget to find help.

Website

207

Gear

44

FAQ

169

Database Listing

376

Archive Records

41199

Staff

101

Discovery

1263760
Classification of set-net fish catch volumes in Iwate Prefecture, Japan using machine learning with water temperature and current distribution images at migration depth
Efficient management of set-net fisheries can be achieved by predicting fish catches in advance. We aimed to estimate fish catch volumes using machine learning based on the data obtained from past set-net fisheries. We also suggested that the estimation can be conducted without the installation of special sensors, such as fish finders, sonars, and cameras, in the set nets. Therefore, a special feature of this study is the construction of a machine learning model using marine environmental images around the set net. In particular, the distribution images of water temperature and current at a certain water depth were applied to the model. In set-nets where migratory fish are caught, ocean data related to migration routes can be useful for estimation. The images used in this study included the water temperature at a depth of 100 m, absolute velocity at a depth of 50 m, and sea surface temperature for comparison. We then classified the fish catch into three classes as follows: chum salmon, yellowtail, and Japanese common squid in Iwate Prefecture, Japan. Consequently, our results achieved an accuracy of approximately 70–80% compared with the validation and test dataset with the water temperature at depths of 100 m. They showed higher accuracy than the sea surface temperature and absolute velocity at a depth of 50 m. This study indicates the usefulness of image-based classification for predicting the volumes of fish caught by set nets.
Association between weekend catch-up sleep and depression of the United States population from 2017 to 2018: A cross-sectional study
Insufficient sleep on weekdays has become a societal norm, and studies have shown that sleep deprivation increases the risk of depression. Although individuals often resort to weekend catch-up sleep (CUS) as a compensatory measure, the present evidence supporting its efficacy in mitigating the risk of depression is limited. This article attempts to explore the relationship between CUS and depression. In this study, a total of 5510 participants were included, characterized into two groups: nondepressed (n = 5051) and depressed (n = 459), with data extracted from the National Health and Nutrition Examination Survey (NHANES). Compared with people without CUS, those practicing CUS exhibited a significantly lower risk of depression (OR = 0.81, P = 0.048). In subgroup analysis, this reduction effect was only observed in males (OR = 0.70, 95 % CI 0.05 to 0.99, P = 0.04), middle-aged (>40, ≤60) (OR: 0.57, 95 % CI: 0.40 to 0.81, P = 0.002), married or living with parents (OR: 0.61, 95 % CI: 0.44 to 0.86, P = 0.004), groups with three or more family members (OR: 0.69, 95 % CI: 0.52 to 0.93, P = 0.01), and individuals without alcohol intake (OR: 0.24,95 % CI: 0.09 to 0.67, P = 0.006). Therefore, in the realm of depression treatment, doctors may consider advising patients to get adequate sleep on weekends as part of their overall treatment plan. At the same time, individuals can also choose weekend sleep as a proactive strategy for regulating their psychological status.