Co-occurrence patterns of Korean and Japanese adverbs expressing speculation

2018 ◽  
Vol 115 ◽  
pp. 149-172
Author(s):  
Kun Soo Jang ◽  
Keyword(s):  
Occurrence Patterns

scholarly journals Global earthworm distribution and activity windows based on soil hydromechanical constraints

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Siul A. Ruiz ◽  
Samuel Bickel ◽  
Dani Or
Keyword(s):  
Soil Structure ◽  
Mechanistic Model ◽  
Biophysical Model ◽  
Ecosystem Functions ◽  
Soil Conditions ◽  
Agricultural Systems ◽  
Occurrence Patterns ◽  
Additional Constraints ◽  
Earthworm Activity ◽  
Good Agreement

AbstractEarthworm activity modifies soil structure and promotes important hydrological ecosystem functions for agricultural systems. Earthworms use their flexible hydroskeleton to burrow and expand biopores. Hence, their activity is constrained by soil hydromechanical conditions that permit deformation at earthworm’s maximal hydroskeletal pressure (≈200kPa). A mechanistic biophysical model is developed here to link the biomechanical limits of earthworm burrowing with soil moisture and texture to predict soil conditions that permit bioturbation across biomes. We include additional constraints that exclude earthworm activity such as freezing temperatures, low soil pH, and high sand content to develop the first predictive global map of earthworm habitats in good agreement with observed earthworm occurrence patterns. Earthworm activity is strongly constrained by seasonal dynamics that vary across latitudes largely due to soil hydromechanical status. The mechanistic model delineates the potential for earthworm migration via connectivity of hospitable sites and highlights regions sensitive to climate.

scholarly journals A Time-Series Data Generation Method to Predict Remaining Useful Life

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1115
Author(s):  
Gilseung Ahn ◽  
Hyungseok Yun ◽  
Sun Hur ◽  
Si-Yeong Lim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Generation Model ◽  
Data Generation ◽  
Training Time ◽  
Symbolic Aggregate Approximation ◽  
Useful Life ◽  
Occurrence Patterns

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

Occurrence, patterns, and sources of hazardous organic chemicals in edible insects and insect-based food from the Japanese market

2021 ◽  
pp. 112311
Author(s):  
Giulia Poma ◽  
Yukiko Fujii ◽  
Siebe Lievens ◽  
Jasper Bombeke ◽  
Beibei Gao ◽  
...  
Keyword(s):  
Organic Chemicals ◽  
Edible Insects ◽  
Japanese Market ◽  
Occurrence Patterns

Mining Co-Occurrence Patterns among Deep Road Distresses Using Association Rule Analysis

2022 ◽  
Vol 148 (1) ◽  
Author(s):  
Qian Gao ◽  
Chenglong Liu ◽  
Yishun Li ◽  
Yuchuan Du ◽  
Guanghua Yue ◽  
...  
Keyword(s):  
Association Rule ◽  
Rule Analysis ◽  
Occurrence Patterns

Family Caregiver Experience of Resistance to Care: Occurrence Patterns, Context, and Impact on Caregiver

2021 ◽  
pp. 104973232110426
Author(s):  
Yumi Shirai ◽  
Susan Silverberg Koerner ◽  
Shuo Xu
Keyword(s):  
Family Caregiver ◽  
Qualitative Content Analysis ◽  
Well Being ◽  
Intervention Strategies ◽  
Specific Intervention ◽  
Structured Interviews ◽  
Support Intervention ◽  
Stress Theory ◽  
Potential Impact ◽  
Occurrence Patterns

Although the extant literature identifies resistance to care (RTC) as one of the most frequently reported and impactful caregiver (CG) stressors, typical studies that rely on quantitative measures of RTC do not fully explain how and why RTC negatively influences CGs’ well-being. As such, it is difficult to develop specific intervention strategies to support CGs in dealing with RTC. Informed by existing literature and tenets from Stress Theory, the current study includes semi-structured interviews with 19 family CGs of community-dependent (non-institutionalized) elders, regarding their RTC experiences. Through a directed qualitative content analysis, we explored occurrence patterns, contextual factors of when and how RTC occurs, how CGs respond to RTC, and its potential impact on CGs’ subjective stress. The results revealed distinguishable characteristics of four types of RTC: Frequent-Pervasive, Frequent-Delimited, Transition-Activated, and Shock-to-Unfamiliar/Unexpected. We discuss how recognition of those types of RTC can be integrated into CG support intervention strategies.

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