The start of the growing season (SOS) is essential to track the responses of vegetation to climate change. However, recent findings on whether the SOS in the middle high latitudes of the Northern Hemisphere (NH) conti...
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The start of the growing season (SOS) is essential to track the responses of vegetation to climate change. However, recent findings on whether the SOS in the middle high latitudes of the Northern Hemisphere (NH) continued to advance or reversed during the global warming hiatus were not consistent. It is necessary to investigate the causes of this controversy and to examine the relationship between the SOS and preseason temperature trends. To this end, we first applied four widely used phenology extraction methods to derive the SOS from the GIMMS NDVI3g dataset and then used the ensembleempirical modal decomposition (EEMD) method to extract the nonlinear trends of the SOS and preseason temperature. Our results clarify, for the first time, that the limitations of the linear assumption based trend analysis methods are an important but overlooked cause of the discrepancies among existing studies on whether the SOS was advanced or delayed in the NH (> 30 degrees N) during the global warming hiatus. We further revealed the range of the mismatches between the SOS and preseason temperature trends at the latitude, altitude and biome levels. Specifically, we discovered that the SOS in the NH (> 30 degrees N) obtained by the four phenology extraction methods showed a significant reversal from advance to delay during the global warming hiatus, and the corresponding average rate of change was very small. The area showing increasing preseason temperatures decreased during the global warming hiatus, but it always occupied most of the NH (> 30 degrees N). However, delayed SOS trends were dominant in the NH from 50 degrees N to 60 degrees N, above 3000 m and in biomes other than TBMF and BF. Accordingly, using an EEMD- like approach to evaluate the changes in the SOS and preseason temperature is necessary for improving our understanding of the changes in the SOS and their association with climate.
Respiratory rate is an essential indicator of serious illness. Compared to heart rate or systolic blood pressure, its change is more evident, hence is a better means to discern stable patients from those at risks. In ...
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Respiratory rate is an essential indicator of serious illness. Compared to heart rate or systolic blood pressure, its change is more evident, hence is a better means to discern stable patients from those at risks. In addition, the respiration rate is a crucial indication of sleep quality associated with sleep disorders such as obstructive sleep apnea. Oronasal pressure, as a clinical respiratory signal for sleep analysis, can be used in polysomnography both in labs of hospital and homes. Besides, it is often taken as a reference signal in research as opposed to the estimated respiration rate. This study aims to provide an automated respiratory rate estimation system with signals taken from oronasal pressure transducer that can cope with noises and is adaptive to various respiratory frequencies. A robust approach is presented here that employs ensemble empirical mode decomposition method to remove signal noise, together with Butterworth band-pass filter to obtain the breathing frequency by means of zero-crossing. Among 97.6% of the test data, the study yields a root mean square error of 1.031. Compared to other methods, the current approach provides a more accurate respiration rate estimation in the application of orinasal pressure to sleep analysis.
Dynamics of fires in Africa are of critical importance for understanding changes in ecosystem properties and effects on the global carbon cycle. Given increasing fire risk from projected warming on the one hand and a ...
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Dynamics of fires in Africa are of critical importance for understanding changes in ecosystem properties and effects on the global carbon cycle. Given increasing fire risk from projected warming on the one hand and a documented human-driven decline in fires on the other, it is still unknown how the complex interplay between climate and human factors affects recent changes of fires in Africa. Moreover, the impact of recent strong El Nino events on fire dynamics is not yet known. By applying an ensemble empirical mode decomposition method to satellite-derived fire burned area, we investigated the spatio-temporal evolution of fires in Africa over 2001-2016 and identified the potential dominant drivers. Our results show an overall decline of fire rates, which is continuous over the time period and mainly caused by cropland expansion in northern sub-Saharan Africa. However, we also find that years of high precipitation have caused an initial increase in fire rates in southern Africa, which reversed to a decline in later years. This decline is caused by a high frequency of dry years leading to very low fuel loads, suggesting that recent drought causes a general reduction of burned areas, in particular in xeric savannas. In some mesic regions (10 degrees-15 degrees S), solar radiation and increased temperature caused increase in fires. These findings show that climate change overrules the impact of human expansion on fire rates at the continental scale in Africa, reducing the fire risk.
In the paper, based on the combination of sample entropy and complexity-invariant distance, a new synchrony-measured method, called the composite complexity synchronization (CCS), is proposed to measure the degree of ...
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In the paper, based on the combination of sample entropy and complexity-invariant distance, a new synchrony-measured method, called the composite complexity synchronization (CCS), is proposed to measure the degree of synchrony of two time series with same data length. Implementing the multiscale cross-sample entropy and multiscale composite complexity synchronization (MCCS) analysis for seven representative stock market indexes, multiscale coupling behaviors of logarithmic returns are compared. Furthermore, the selective data of different sampling frequency within the same time period are applied to analyze the effect of sampling rate in the data on the MCCS behaviors. And we apply the ensembleempiricalmodedecomposition to decompose the stock logarithmic returns into the intrinsic mode functions and investigate the extent that they have inherited the coupling behaviors of original returns. empirical results demonstrate the feasibility and effectiveness of the proposed method, and exhibit its superiority in distinguishing the very subtle synchrony behaviors among the time series.
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