Time-lagged Correlation Research Articles

The relationship between lightning flash rate and ice-related volume derived from dual-polarization radar

To conduct accurate lightning prediction and nowcasting, it is important to clarify the relationship between electrification and microphysical properties in clouds. Using the dual-polarization Doppler radar with the hydrometeor classification algorithm and lightning observation, radar-derived microphysical properties related to lightning flash rate were investigated in terms of 10 isolated thunderstorm cases over the Kanto Plain, Japan, with 351 radar volume scans conducted every 5 min during the summer period. In this study, the ice-related particles within the 35 dBZ volume (V35IC) showed the highest correlation coefficient (r = 0.75) and the lowest normalized root mean square error (NRMSE = 8.3%) in relation to cloud-to-ground lightning (CG), and also best to intra-cloud lightning (IC, r = 0.69, NRMSE = 8.1%). The correlation between V35IC and CG flash rate was better than that of the mixed-phase (between 0 °C and − 40 °C) 35 dBZ volume (r = 0.64), without considering the microphysical particles. The second-best radar-derived microphysical property was the ice mass consisting of the radar grids of all the ice-related particles (r = 0.72). These results suggest that an effective lightning indicator in terms of a radar-derived property is the ice-related particle volume, in addition to the traditionally used graupel and hail. The ice-related particle volume in the colder region of the thunderstorm (air temperature lower than −10 °C) showed a higher correlation with CG and IC than that in the warmer region, especially in terms of IC. These results are consistent with the non-inductive charging theory that has been well accepted in previous studies. Following time-lag correlation analysis from 0 to −30 min every 5 min before thunderstorm initiation, it was found that the ice-related index of V35IC demonstrated the slowest decrease (r = 0.75–0.65 in 30 min) in the correlation coefficient with time lapse and may be suitable as a lightning nowcast indicator.

Investigation of a seismic teleconnection model between Iran and Iceland regions during 1980–2018

In this study, a rapid statistical model was assumed to answer the research hypothesis, concerning a triggering relationship between Iran and Iceland earthquakes. For this purpose, a long-term analysis of earthquake data (1980–2018) was considered for both Iran and Iceland regions using the cross-correlation function (CCF) and the time-lagged correlation method. Results revealed that the frequency and maximum magnitude of earthquakes in Iran could lead the earthquake frequency and magnitude in Iceland within a period of 4 months, revealing a positive association through the time shift. On this basis, the lagged time of seismic teleconnection can be assumed as 4 months between predictor (Iran) and indicator (Iceland) zones. Particularly, the increasing earthquake occurrences in Iran can notify subsequent increasing earthquake events in Iceland within 78 and 18 day lagged-time, confirming the hypothesis of seismic teleconnection. Owing to the seismically active status of both Iran and Iceland regions, the crustal relationships, e.g., influenced by global scale divergence and convergence of plates, could describe the finding of this study.

Arrival of Long Ocean Waves and Hourly Sea Level Oscillations in Masan Bay, Korea on 19–22 March 2014

Park, S.J.; Choi, B.-J.; Sim, H.S., and Byun, D.-S., 2020. Arrival of long ocean waves and hourly sea level oscillations in Masan Bay, Korea on 19-22 March 2014. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1510–1514. Coconut Creek (Florida), ISSN 0749-0208.Calmness and predictability of the sea level in a harbor are essential for safe shipping and cargo-loading on vessels. Masan Bay is an inner bay located on the south coast of Korea where the mean spring tidal range is 174 cm. It has been reported that sea level often fluctuates hourly in Masan Bay in addition to the effect of tides. The cause of the hourly sea level fluctuations have not been explained. The fluctuation periods are close to the natural resonant periods which are determined by the harbor geometry and depth. To find the generating sources of the hourly sea level fluctuations and to examine their characteristics, sea level data measured minutely at Masan harbor and the neighboring 11 tidal stations were analyzed. There were 42 events of long-period sea level oscillations with a maximum height of more than 40 cm in Masan Harbor from 2013 to 2017. Power spectral analysis on the sea level data shows the presence of high energy density in the periods ranging from 48 to 125 min at Masan and neighboring tidal stations. It was found that long ocean waves frequently propagate from the southwestern region of Korea Strait (KS) and enter Masan Bay. Time-lagged correlation analysis between the time series of sea level data along the south coast of Korea in March 2014 suggests that the long waves propagated from the southwestern KS to the eastern KS and entered Masan Bay. It took 2 h 27 min for the waves to propagate from Jeju Island in the southwestern KS to Masan Harbor. The estimated traveling speeds of the long waves using time-lagged correlation analysis were similar to the shallow water wave speed in the region. Oceanic processes that generate the propagating long ocean waves might be tsunami, seismic waves, meteotsunami or internal waves in the open ocean. The long wave events were not related to local winds or air pressure variations.

Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis

ObjectivesTo assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe. MethodsWe performed a Google TrendsTM search for “Coronavirus” and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis. ResultsWorldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. The worldwide Google TrendsTM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of −11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of −7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April. ConclusionsPublic interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.

How Does Magnetic Reconnection Drive the Early-stage Evolution of Coronal Mass Ejections?

Abstract Theoretically, coronal mass ejection (CME) kinematics are related to magnetic reconnection processes in the solar corona. However, the current quantitative understanding of this relationship is based on the analysis of only a handful of events. Here we report a statistical study of 60 CME-flare events from 2010 August to 2013 December. We investigate kinematic properties of CMEs and magnetic reconnection in the low corona during the early phase of the eruptions, by combining limb observations from STEREO with simultaneous on-disk views from SDO. For a subset of 42 events with reconnection rate evaluated by the magnetic fluxes swept by the flare ribbons on the solar disk observed from SDO, we find a strong correlation between the peak CME acceleration and the peak reconnection rate. Also, the maximum velocities of relatively fast CMEs (≳600 km s−1) are positively correlated with the reconnection flux, but no such correlation is found for slow CMEs. A time-lagged correlation analysis suggests that the distribution of the time lag of CME acceleration relative to reconnection rate exhibits three peaks, approximately 10 minutes apart, and on average, acceleration-led events have smaller reconnection rates. We further compare the CME total mechanical energy with the estimated energy in the current sheet. The comparison suggests that, for small-flare events, reconnection in the current sheet alone is insufficient to fuel CMEs. Results from this study suggest that flare reconnection may dominate the acceleration of fast CMEs, but for events of slow CMEs and weak reconnection, other mechanisms may be more important.

The Great Markarian 421 Flare of 2010 February: Multiwavelength Variability and Correlation Studies

Abstract We report on variability and correlation studies using multiwavelength observations of the blazar Mrk 421 during the month of 2010 February, when an extraordinary flare reaching a level of ∼27 Crab Units above 1 TeV was measured in very high energy (VHE) γ-rays with the Very Energetic Radiation Imaging Telescope Array System (VERITAS) observatory. This is the highest flux state for Mrk 421 ever observed in VHE γ-rays. Data are analyzed from a coordinated campaign across multiple instruments, including VHE γ-ray (VERITAS, Major Atmospheric Gamma-ray Imaging Cherenkov), high-energy γ-ray (Fermi-LAT), X-ray (Swift, Rossi X-ray Timing Experiment, MAXI), optical (including the GASP-WEBT collaboration and polarization data), and radio (Metsähovi, Owens Valley Radio Observatory, University of Michigan Radio Astronomy Observatory). Light curves are produced spanning multiple days before and after the peak of the VHE flare, including over several flare “decline” epochs. The main flare statistics allow 2 minute time bins to be constructed in both the VHE and optical bands enabling a cross-correlation analysis that shows evidence for an optical lag of ∼25–55 minutes, the first time-lagged correlation between these bands reported on such short timescales. Limits on the Doppler factor (δ ≳ 33) and the size of the emission region ( ) are obtained from the fast variability observed by VERITAS during the main flare. Analysis of 10 minute binned VHE and X-ray data over the decline epochs shows an extraordinary range of behavior in the flux–flux relationship, from linear to quadratic to lack of correlation to anticorrelation. Taken together, these detailed observations of an unprecedented flare seen in Mrk 421 are difficult to explain with the classic single-zone synchrotron self-Compton model.

Quantitative Assessment for the Dynamics of the Main Ecosystem Services and their Interactions in the Northwestern Arid Area, China

Evaluating changes in the spatial–temporal patterns of ecosystem services (ESs) and their interrelationships provide an opportunity to understand the links among them, as well as to inform ecosystem-service-based decision making and governance. To research the development trajectory of ecosystem services over time and space in the northwestern arid area of China, three main ecosystem services (carbon sequestration, soil retention, and sand fixation) and their relationships were analyzed using Pearson’s coefficient and a time-lagged cross-correlation analysis based on the mountain–oasis–desert zonal scale. The results of this study were as follows: (1) The carbon sequestration of most subregions improved, and that of the oasis continuously increased from 1990 to 2015. Sand fixation decreased in the whole region except for in the Alxa Plateau–Hexi Corridor area, which experienced an “increase–decrease” trend before 2000 and after 2000; (2) the synergies and trade-off relationships of the ES pairs varied with time and space, but carbon sequestration and soil retention in the most arid area had a synergistic relationship, and most oases retained their synergies between carbon sequestration and sand fixation over time. Most regional relationships of sand fixation and soil retention with a weak trade-off during the before-2000 stage turned into weak synergies at the after-2000 stage; (3) the ES pairs of carbon sequestration and sand fixation had significant time-lagged trade-off relationships in most arid areas, and these time trade-offs were generally maintained for 2–6 years, where a higher absolute value of the maximum time-lagged correlation coefficient corresponded to a longer time-lag. This study could assist us in understanding the trade-off and synergy of ESs and will provide quantitative zonal information for the ecosystem-services-based ecomanagement of arid areas.

Short-Term Truckload Spot Rates’ Prediction in Consideration of Temporal and Between-Route Correlations

Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking industry. As such, this paper aims to leverage the high-resolution trucking data from an OFEX platform to forecast short-term TSR. Specifically, a lagged coefficient weighted matrix-based multiple linear regression modeling (Lag-WMR) is proposed, and exogenous variables are selected by the light gradient boosting (LGB) method. This model simultaneously incorporates the dependency between historical and current TSR (temporal correlation) and correlations between the rates on alternative routes (between-route correlation). In addition, the effects of incorporating temporal and between-route correlations, time-lagged correlation and exogenous variable selection in modeling are emphasized and assessed through a case study on short-term TSR in Southwest China. The comparative results show that the proposed Lag-WMR model outperforms autoregressive integrated moving average (ARIMA) model and LGB in terms of model fitting and the quality and stability of predictions. Further research could focus on rates' standardization, to define a practical freight index for the trucking industry. Although our results are specific to the Chinese trucking market, the method of analysis serves as a general model for similar international studies.

The correlation of google trends as an alternative information source in the early stages of covid-19 outbreak in Indonesia

This study conducted correlation of google trends as an alternative information source in the early stages of COVID-19 outbreak in Indonesia. Time series approach was used in this study. We sought to compare the official COVID-19 report in Indonesia accessible on a daily basis as well as information searches in Google Trends regarding COVID-19. Data analysis was performed using moving average in Minitab version 18.0. Correlation were calculated using pearson correlation and Time lag. R value ≥ 0.7 (p ≤ 0.05) was defined strong correlation. Moving average analysis showed a linear time series pattern between COVID-19 search trends and the official COVID-19 report. Pearson correlation analysis indicated strong correlation with R value ranging from 0.870-0.927 (p ≤ 0,05) among the four keywords used in Google trends with the official report of COVID-19 in Indonesia. Time lag correlation inference COVID-19 search trends data could possibly be utilized for an early identification of public reaction against the increasing cases of COVID-19. In the early stages of COVID-19 outbreak found the correlations and similarity of linear time series patterns are shown between COVID-19 search trends and the official COVID-19 report. Public behavior in information seeking is useful in early identification of disease outbreaks in Indonesia. How to Cite this Article Pubmed Style Usman E, Nindrea RD. Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. SRP. 2020; 11(9): 431-438. doi:10.31838/srp.2020.9.61 Web Style Usman E, Nindrea RD. Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. http://www.sysrevpharm.org/?mno=8780 [Access: March 30, 2021]. doi:10.31838/srp.2020.9.61 AMA (American Medical Association) Style Usman E, Nindrea RD. Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. SRP. 2020; 11(9): 431-438. doi:10.31838/srp.2020.9.61 Vancouver/ICMJE Style Usman E, Nindrea RD. Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. SRP. (2020), [cited March 30, 2021]; 11(9): 431-438. doi:10.31838/srp.2020.9.61 Harvard Style Usman, E. & Nindrea, . R. D. (2020) Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. SRP, 11 (9), 431-438. doi:10.31838/srp.2020.9.61 Turabian Style Usman, Elly, and Ricvan Dana Nindrea. 2020. Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Reviews in Pharmacy, 11 (9), 431-438. doi:10.31838/srp.2020.9.61 Chicago Style Usman, Elly, and Ricvan Dana Nindrea. The Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. Systematic Reviews in Pharmacy 11 (2020), 431-438. doi:10.31838/srp.2020.9.61 MLA (The Modern Language Association) Style Usman, Elly, and Ricvan Dana Nindrea. The Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Indonesia. Systematic Reviews in Pharmacy 11.9 (2020), 431-438. Print. doi:10.31838/srp.2020.9.61 APA (American Psychological Association) Style Usman, E. & Nindrea, . R. D. (2020) Correlation of Google Trends as an Alternative Information Source in the Early Stages of COVID-19 Outbreak in Reviews in Pharmacy, 11 (9), 431-438. doi:10.31838/srp.2020.9.61

Investigating the contribution of climate variables to estimates of net primary productivity in a tropical deciduous forest in India.

Investigating the impact of climate variables on net primary productivity is crucial to evaluate the ecosystem health and the status of forest type response to climate change. The objective of this paper is (1) to estimate spatio-temporal patterns of net primary productivity (NPP) during 2001 to 2010 in a tropical deciduous forest based on the input variable dataset (i.e.meteorological and biophysical) derived from the remote sensing and other sources and (2) to investigate the effects of climate variables on NPP during 2001 to 2010. The study was carried out in Katerniaghat Wildlife Sanctuary that forms a part of a tropical forest and is situated in Uttar Pradesh, India, along the Indo-Nepal border. Mean annual NPP was observed to be highest during 2007 with a value of 878gCm-2year-1 and 781.25gCm-2year-1 for sal and teak respectively. A decline in mean NPP during 2002-2003, 2005 and 2008-2010 could be attributed to drought, increased temperature and vapour pressure deficit (VPD). The time lag correlation analysis revealed precipitation as the major variables affecting NPP, whereas combination of temperature and VPD showed dominant effect on NPP as revealed by generalized linear modelling. The carbon gain in NPP in sal forest was observed to be marginal higher than that of teak plantation throughout the study period. The decrease in NPP was observed during 2010, pertaining to increased VPD. Contribution of different climatic variables through some link process was revealed in statistical analysis and clearly indicated the co-dominance of all the variables in explaining NPP.

Deciphering well connectivity with diagnostic signal processing techniques

Data mining has become increasingly crucial in deciphering reservoir dynamics, given that operators currently acquire an enormous amount of data. These data contain valuable information about subsurface processes. Interwell connectivity is one of the most significant aspects of subsurface characterization that can impact a project's success. In this study, we present novel techniques to quantify and monitor interwell communication by applying signal processing methods to observe and derive well-based measurements. A large dataset from the Permian Basin constituted the primary content of this investigation.We constructed a suite of realistic reservoir models under varying conditions involving multiple producing and water injection wells. More than 40 static and dynamic parameters including permeability, porosity, water saturation, fluid properties, and rock-fluid interaction terms are varied using experimental designs to capture realistic uncertainty. Waterflood scenarios are modeled using streamline simulation to infer injector-producer pair connectivity, pattern allocations, drainage efficiency, and their evolution period. These injector-producer variables are analyzed using numerous signal processing methods, including cross-correlation, time-lag correlation coefficient, coherence, and periodogram, among others. Well variables examined involve pressure, rate, and their derivative functions.The objectives of this study include: (1) Examining and devising several signal-processing techniques; (2) Identifying informative well data to provide information of communication between injectors and producers over time; (3) Deducing connectivity measures and systematically evaluate their efficacy in characterizing subsurface connectivity picture.The approaches reveal new insights into determining reservoir communication metrics. The proposed methods have the potential for implementation in the automated virtual flow-metering system. These methods also helped identifying the predominant injector-producer pairs and making operational decisions at any stage of any waterflood and enhanced recovery projects.

Nonpooling Convolutional Neural Network Forecasting for Seasonal Time Series With Trends.

This article focuses on a problem important to automatic machine learning: the automatic processing of a nonpreprocessed time series. The convolutional neural network (CNN) is one of the most popular neural network (NN) algorithms for pattern recognition. Seasonal time series with trends are the most common data sets used in forecasting. Both the convolutional layer and the pooling layer of a CNN can be used to extract important features and patterns that reflect the seasonality, trends, and time lag correlation coefficients in the data. The ability to identify such features and patterns makes CNN a good candidate algorithm for analyzing seasonal time-series data with trends. This article reports our experimental findings using a fully connected NN (FNN), a nonpooling CNN (NPCNN), and a CNN to study both simulated and real time-series data with seasonality and trends. We found that convolutional layers tend to improve the performance, while pooling layers tend to introduce too many negative effects. Therefore, we recommend using an NPCNN when processing seasonal time-series data with trends. Moreover, we suggest using the Adam optimizer and selecting either a rectified linear unit (ReLU) function or a linear activation function. Using an NN to analyze seasonal time series with trends has become popular in the NN community. This article provides an approach for building a network that fits time-series data with seasonality and trends automatically.

Estimating Travel Speed of a Road Section Through Sparse Crowdsensing Data

The average travel speed on certain road sections is an important piece of information for the intelligent transportation system. Traditional ways for estimating travel speed usually depend on dedicated sensors or infrastructures, which is financially costly. Alternatively, as an infrastructure-free way, vehicular crowdsensing can be used to collect data including real-time locations and velocities of vehicles, which is quite low cost and effective. This paper aims to produce a fully covered distribution of average travel speeds for road sections both in time and space domains based on vehicular crowdsensing data. However, due to the uneven spatial-temporal distribution of vehicles and the variation of their data-offering intervals, vehicular crowdsensing data are usually coarse grained. This coarseness leads to missing travel speed values of vehicles on some road sections. To handle this problem, we propose an approach that exploits the spatial-temporal causality among travel speeds of road sections by a time-lagged correlation coefficient function. We use a time-lagging factor to quantify the time consumption of vehicles traveling along road sections. Then, we utilize the local stationarity of correlation coefficient to estimate the travel speeds of road sections. Experiments based on real taxi trace data show that the proposed method performs better than some methods in use.

Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

ABSTRACTBackground: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia.Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report.Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data.Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak.Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants.

The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers. Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index, calculated from leaf wilting and environmental data. However, these studies have not addressed the specific role of leaf wilting in the estimation. Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method. In this paper, we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network. To clarify the role of leaf wilting, this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting, quantified by optical flow, and stem diameter variations as a water stress index. The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations, which were water stress responses in plants. As the results were consistent with known plant water transport mechanisms, it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations.

Anomalous holiday precipitation over southern China

Abstract. The Chinese Spring Festival (CSF, also known as the Chinese New Year or Lunar New Year) is the most important festival in China. Lunar New Year's Day (LNYD) is the first day of the Lunar New Year. Traditionally, the CSF holiday begins a couple of days before LNYD and ends on lantern day, lasting for approximately 2 weeks. In this paper, based on the long-term station observations from 1979 to 2012, the precipitation during the holiday over southern China (108–123° E and 21–33° N, 155 stations) tends to be lower than that before and after the holiday. The mean precipitation frequency anomaly from the fourth day to the sixth day after LNYD (i.e., days [+4, +6]) decreases by −7.4 %. Simultaneously, the daily precipitation amount experiences a reduction of −0.62 mm day−1 during days [+2, +5]. The holiday precipitation anomalies are strongly linked to the anomalies of relative humidity (ΔRH) and cloud cover. The station observations of the ΔRH show an evident decrease from day +2 to day +7, and a minimum appears on days [+4, +6], with a mean of −3.9 %. The ΔRH vertical profile displays significant drying below approximately 800 hPa. Between 800 and 1000 hPa, the mean ΔRH is −3.9 %. The observed station daytime low cloud cover (LCC) evidently decreases by −6.1 % during days [+4, +6]. Meanwhile, the ERA-Interim daily LCC also shows a comparable reduction of −5.0 %. The anomalous relative humidity is mainly caused by the decreased water vapor in the lower-middle troposphere. Evident negative specific humidity anomalies persist from day −3 to day +7 in the station observations. The average specific humidity anomaly for days [+4, +6] is −0.73 g kg−1. When the precipitation days are excluded, the anomaly remains significant at −0.46 g kg−1. A significant water vapor deficit is observed in the lower troposphere below 700 hPa. Between 800 and 1000 hPa, the mean specific humidity drops by −0.70 g kg−1. This drier lower-middle troposphere is due to anomalous northerly winds, which are closely related to the cyclonic circulation anomaly over the northwestern Pacific. The time-lag correlation demonstrates that approximately 1 week after a lower temperature occurs over eastern China, a stronger cyclone is observed over the western Pacific. The possible mechanism needs further clarification through elaborate observation and numerical modeling.

Glucagon-like peptide 1 (GLP-1) drives postprandial hyperinsulinemic hypoglycemia in pregnant women with a history of Roux-en-Y gastric bypass operation

BackgroundThe influential role of incretin hormones on glucose metabolism in patients with a history of Roux-en-Y gastric bypass (RYGB) has been investigated thoroughly, but there has been little examination of the effect of incretins and ectopic lipids on altered glucose profiles, especially severe hypoglycemia in pregnant women with RYGB. MethodsIn this prospective clinical study, an oral glucose tolerance test (OGTT), an intravenous glucose tolerance test (IVGTT), and continuous glucose monitoring (CGM) were conducted in 25 women with RYGB during pregnancy, 19 of normal weight (NW) and 19 with obesity (OB) between the 24th and the 28th weeks of pregnancy, and 3 to 6 months post-partum. Post-partum, the ectopic lipid content in the liver, heart, and skeletal muscle was analyzed using 1H-magnetic resonance spectroscopy (1H-MRS). ResultsRYGB patients presented with major fluctuations in glucose profiles, including a high occurrence of postprandial hyperglycemic spikes and hypoglycemic events during the day, as well as a high risk of hypoglycemic periods during the night (2.9 ± 1.1% vs. 0.1 ± 0.2% in the OB and vs. 0.8 ± 0.6% in the NW groups, p < 0.001). During the extended OGTT, RYGB patients presented with exaggerated expression of GLP-1, which was the main driver of the exaggerated risk of postprandial hypoglycemia in a time-lagged correlation analysis. Basal and dynamic GLP-1 levels were not related to insulin sensitivity, insulin secretion, or beta cell function and did not differ between pregnant women with and without GDM. A lower amount of liver fat (2.34 ± 5.22% vs.5.68 ± 4.42%, p = 0.015), which was positively related to insulin resistance (homeostasis model assessment of insulin resistance, HOMA-IR: rho = 0.61, p = 0.002) and beta-cell function (insulinogenic index: rho = 0.65, p = 0.001), was observed in the RYGB group after delivery in comparison to the OB group. ConclusionGLP-1 is mainly involved in the regulation of postprandial glucose metabolism and therefore especially in the development of postprandial hypoglycemia in pregnant RYGB patients, who are characterized by major alterations in glucose profiles, and thus in long-term regulation, multiple organ-related mechanisms, such as the lipid content in the liver, must be involved.

Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio.

As America's opioid crisis has become an "epidemic of epidemics," Ohio has been identified as one of the high burden states regarding fentanyl-related overdose mortality. This study aims to examine changes in the availability of fentanyl, fentanyl analogs, and other non-pharmaceutical opioids on cryptomarkets and assess relationship with the trends in unintentional overdoses in Ohio to provide timely information for epidemiologic surveillance. Cryptomarket data were collected at two distinct periods of time: (1) Agora data covered June 2014-September 2015 and were obtained from Grams archive; (2) Dream Market data from March-April 2018 were extracted using a dedicated crawler. A Named Entity Recognition algorithm was developed to identify and categorize the type of fentanyl and other synthetic opioids advertised on cryptomarkets. Time-lagged correlations were used to assess the relationship between the fentanyl, fentanyl analog and other synthetic opioid-related ads from cryptomarkets and overdose data from the Cincinnati Fire Department Emergency Responses and Montgomery County Coroner's Office. Analysis from the cryptomarket data reveals increases in fentanyl-like drugs and changes in the types of fentanyl analogues and other synthetic opioids advertised in 2015 and 2018 with potent substances like carfentanil available during the second period. The time-lagged correlation was the largest when comparing Agora data to Cincinnati Emergency Responses 1 month later 0.84 (95% CI 0.45, 0.96). The time-lagged correlation between Agora data and Montgomery County drug overdoses was the largest when comparing synthetic opioid-related Agora ads to Montgomery County overdose deaths 7 months later 0.78 (95% CI 0.47, 0.92). Further investigations are required to establish the relationship between cryptomarket availability and unintentional overdose trends related to specific fentanyl analogs and/or other illicit synthetic opioids.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa.

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998–2002, 2004–2006, and 2010–2013 and a noticeable periodicity value of 256–512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.