scholarly journals Wavelet analysis for seasonal precipitation variations of Yuanmou dry-hot valley in recent 50 years

MAUSAM ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 663-672
Author(s):  
L. N. SUN ◽  
J. Y. WANG ◽  
B. ZHANG
Keyword(s):  
Wavelet Analysis ◽  
Time Scales ◽  
Periodic Oscillation ◽  
Landscape Patterns ◽  
Annual Precipitation ◽  
Seasonal Precipitation ◽  
Short Term ◽  
Time Frequency ◽  
Precipitation Variation ◽  
Meyer Wavelet

The dry-hot valley is a special kind of degradation ecosystem region in Hengduan Mountains. Variations of seasonal precipitation have important influnces on its landscape patterns and agricultural activities. Based on the monthly and annual precipitation data from 1956 to 2006, the multi-time scales characteristics of seasonal and annual variations of precipitation in the past 50a in the Yuanmou County had been analyzed using Meyer wavelet analysis in this paper. The periodic oscillation of precipitation variation and the points of abrupt change at different time scales along the time series are discovered and the main periods of every serial are confirmed. It was showed that the periodic oscillation of 8-12a and 4-6a for the seasonal and annual precipitation variation are obvious. The time-frequency local change characteristic of Meyer wavelet analysis can demonstrate the fine structures of precipitation and the method provides a new way in analyzing climate multi-time scales characteristics and forecasting short-term climate. The localization characteristics of time -frequency for wavelet analysis can demonstrate the detailed structures of rainfall. The wavelet analysis can be an alternative approach to analyze climate multi-time scales characteristics and forecast short-term climate variations. The research on the regularity of seasonal precipitation variation in the dry-hot valley region has a great guidance meaning to the agriculture production and resilience in flood prevention.  

Wavelet analysis of the complex precipitation series in the Northern Jiansanjiang Administration of the Heilongjiang land reclamation, China

2016 ◽  
Vol 7 (4) ◽  
pp. 796-809 ◽  
Author(s):  
Dong Liu ◽  
Qiang Fu ◽  
Tianxiao Li ◽  
Yuxiang Hu ◽  
Khan M. Imran ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Land Reclamation ◽  
Southern Oscillation ◽  
Annual Precipitation ◽  
Major Influence ◽  
Precipitation Series ◽  
Analysis Method ◽  
Variation Characteristics ◽  
Precipitation Variation ◽  
Jiansanjiang Administration

Due to interference from natural factors and the intensity of human activities, the complex characteristics of the regional precipitation process have become increasingly evident, which creates a challenge for the rational development and utilisation of precipitation resources. In this perspective of complexity diagnosis, the multi-timescale variation characteristics of precipitation were analysed in the Northern Jiansanjiang Administration of Heilongjiang land reclamation, China, by the wavelet analysis method. The results showed that the most complex precipitation series was at Qinglongshan Farm. There are five significant main periods of approximately 2, 3, 4, 9 and 12 years in the seasonal and annual precipitation of Qinglongshan Farm; these periodic variation characteristics are almost identical to the periods of the EI Niño-Southern Oscillation phenomena and sunspot activity, which illustrates that climate change has a major influence on the local precipitation variation characteristics. At the same time, precipitation in summer and autumn has similar periods and a similar variation trend to the annual precipitation at Qinglongshan Farm, which indicates that the local annual precipitation variation characteristics are mainly affected by summer and autumn precipitation variation. In contrast with the harmonic analysis method based on Fourier transform, wavelet analysis has a significant advantage in terms of accurately identifying the main cycle of the hydrological time series.

scholarly journals Characteristics of Annual and Seasonal Precipitation Variation in the Upstream of Minjiang River, Southwestern China

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Shuqi Liang ◽  
Wensheng Wang ◽  
Dan Zhang
Keyword(s):  
Summer Precipitation ◽  
Annual Precipitation ◽  
Southwestern China ◽  
Linear Regression Method ◽  
Seasonal Precipitation ◽  
Spring Precipitation ◽  
Minjiang River ◽  
Increasing Trend ◽  
Precipitation Variation ◽  
Upstream Of Minjiang River

Characteristics of annual and seasonal precipitation variation are explored in the upstream of Minjiang River (UMR), Southwestern China, spanning from 1960 to 2015. The moment of method (MOM), linear regression method, Mann–Kendall test, sequential cluster analysis, and Morlet wavelet analysis were utilized. The results clearly show the following: (1) Distribution of precipitation is uneven in space, with more in the south and less in the southeast. Decade average of annual precipitation reached the lowest in the 2000s and increased during 2010–2015 at all gauging stations and UMR. (2) Areal annual precipitation exhibited an insignificant decreasing trend with a rate of 4.47 mm/10a, which was mainly attributable to decreased summer precipitation. Spring precipitation exhibited an insignificant increasing trend and winter precipitation remained unchanged. (3) The change points mainly appeared in the 1980s and 1990s. And the almost periods of study area were generally 2–5 years, 7–11 years, and 15–20 years. (4) The increasing trend of annual precipitation is relatively obvious at higher altitudes, while the decreasing trend is more significant at low altitude stations.

scholarly journals Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Image Analysis ◽  
Short Term Memory ◽  
Disease Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Time Frequency ◽  
Time Space ◽  
Histopathological Images ◽  
Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

scholarly journals Responses of groundwater to precipitation variability and ENSO in the Vietnamese Mekong Delta

2021 ◽  
Author(s):  
Phong V. V. Le ◽  
Hai V. Pham ◽  
Luyen K. Bui ◽  
Anh N. Tran ◽  
Chien V. Pham ◽  
...  
Keyword(s):  
Water Resources ◽  
Climate Variability ◽  
Time Scales ◽  
Groundwater Level ◽  
Southern Oscillation ◽  
Mekong Delta ◽  
Precipitation Variability ◽  
Annual Precipitation ◽  
Aquifer Recharge ◽  
Deep Aquifers

Abstract Groundwater is a critical component of water resources and has become the primary water supply for agricultural and domestic uses in the Vietnamese Mekong Delta (VMD). Widespread groundwater level declines have occurred in the VMD over recent decades, reflecting that extraction rates exceed aquifer recharge in the region. However, the impacts of climate variability on groundwater system dynamics in the VMD remain poorly understood. Here, we explore recent changes in groundwater levels in shallow and deep aquifers from observed wells in the VMD and investigate their relations to the annual precipitation variability and El Niño–Southern Oscillation (ENSO). We show that groundwater level responds to changes in annual precipitation at time scales of approximately 1 year. Moreover, shallow (deep) groundwater in the VMD appears to correlate with the ENSO over intra-annual (inter-annual) time scales. Our findings reveal a critical linkage between groundwater level changes and climate variability, suggesting the need to develop an understanding of the impacts of climate variability across time scales on water resources in the VMD.

scholarly journals Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter H. Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
High Frequency ◽  
Prediction Models ◽  
Severe Brain Injury ◽  
Short Term ◽  
Time Frequency ◽  
Yield Information ◽  
Functional States

AbstractOur goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

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