scholarly journals Shoreline changes in Barren Island, India

2020 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Time Series ◽  
Active Volcano ◽  
Vital Role ◽  
Tectonic Activity ◽  
Analysis Software ◽  
Landsat Images ◽  
The Mean ◽  
Barren Island ◽  
End Point Rate ◽  
Extended Area

Barren Island is the only active volcano in the Indian sub-continent. This study was conducted to understand the changes in the shoreline, which were extracted using the historic after map of [1] and from the time series Landsat images of 1976, 2003 and 2018. End Point Rate (EPR) and Net Shoreline Movement (NSM) were computed using the Digital Shoreline Analysis Software (DSAS) and the ArcMap 10.3 software. The results of mean EPR (9.95m/year) and mean NSM (149.27m) were maximum during volcanic eruption (2003-2018). During dormancy (1885-1976), the mean EPR and NSM values were calculated to be 0.97m/year and 87.89m, respectively. Tropical monsoonal rains, waves and tidal action played a vital role in shaping the shoreline of the island during quiescence apart from volcanic, seismic, and tectonic activity. The estimated values of the extended area of this Island were 827.35 ha and 860.67 ha, respectively, for the years 1885 and 2018. The accretion of lava as a delta in the coastal frontier had dramatically changed the shoreline of the island.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

Effect of the Interaction Between Hypertension and Cerebral White Matter Changes on the Progression of Alzheimer Disease

2018 ◽  
Vol 15 (14) ◽  
pp. 1354-1360 ◽  
Author(s):  
Ping-Song Chou ◽  
Yi-Hui Kao ◽  
Meng-Ni Wu ◽  
Mei-Chuan Chou ◽  
Chun-Hung Chen ◽  
...  
Keyword(s):  
White Matter ◽  
Alzheimer Disease ◽  
Rating Scale ◽  
Vital Role ◽  
Independent Effect ◽  
Cerebral White Matter ◽  
Clinical Dementia Rating ◽  
White Matter Changes ◽  
Age Related ◽  
The Mean

Background: Cerebrovascular pathologies and hypertension could play a vital role in Alzheimer disease (AD) progression. However, whether cerebrovascular pathologies and hypertension accelerate the AD progression through an independent or interaction effect is unknown. Objective: To investigate the effect of the interactions of cerebrovascular pathologies and hypertension on AD progression. Method: A retrospective longitudinal study was conducted to compare AD courses in patients with different severities of cerebral White Matter Changes (WMCs) in relation to hypertension. Annual comprehensive psychometrics were performed. WMCs were rated using a rating scale for Age-related WMCs (ARWMC). Results: In total, 278 patients with sporadic AD were enrolled in this study. The mean age of the patients was 76.6 ± 7.4 years, and 166 patients had hypertension. Among AD patients with hypertension, those with deterioration in clinical dementia rating-sum of box (CDR-SB) and CDR had significantly severe baseline ARWMC scales in total (CDR-SB: 5.8 vs. 3.6, adjusted P = 0.04; CDR: 6.4 vs. 4.4, adjusted P = 0.04) and frontal area (CDR-SB: 2.4 vs. 1.2, adjusted P = 0.01; CDR: 2.4 vs. 1.7, adjusted P < 0.01) compared with those with no deterioration in psychometrics after adjustment for confounders. By contrast, among AD patients without hypertension, no significant differences in ARWMC scales were observed between patients with and without deterioration. Conclusion: The effect of cerebrovascular pathologies on AD progression between those with and without hypertension might differ. An interaction but not independent effect of hypertension and WMCs on the progression of AD is possible.

scholarly journals The influence of solar wind on extratropical cyclones – Part 1: Wilcox effect revisited

2009 ◽  
Vol 27 (1) ◽  
pp. 1-30 ◽  
Author(s):  
P. Prikryl ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Northern Hemisphere ◽  
High Speed ◽  
Coronal Holes ◽  
Extratropical Cyclones ◽  
Sector Boundary ◽  
Speed Solar Wind ◽  
The Mean ◽  
High Speed Solar Wind

Abstract. A sun-weather correlation, namely the link between solar magnetic sector boundary passage (SBP) by the Earth and upper-level tropospheric vorticity area index (VAI), that was found by Wilcox et al. (1974) and shown to be statistically significant by Hines and Halevy (1977) is revisited. A minimum in the VAI one day after SBP followed by an increase a few days later was observed. Using the ECMWF ERA-40 re-analysis dataset for the original period from 1963 to 1973 and extending it to 2002, we have verified what has become known as the "Wilcox effect" for the Northern as well as the Southern Hemisphere winters. The effect persists through years of high and low volcanic aerosol loading except for the Northern Hemisphere at 500 mb, when the VAI minimum is weak during the low aerosol years after 1973, particularly for sector boundaries associated with south-to-north reversals of the interplanetary magnetic field (IMF) BZ component. The "disappearance" of the Wilcox effect was found previously by Tinsley et al. (1994) who suggested that enhanced stratospheric volcanic aerosols and changes in air-earth current density are necessary conditions for the effect. The present results indicate that the Wilcox effect does not require high aerosol loading to be detected. The results are corroborated by a correlation with coronal holes where the fast solar wind originates. Ground-based measurements of the green coronal emission line (Fe XIV, 530.3 nm) are used in the superposed epoch analysis keyed by the times of sector boundary passage to show a one-to-one correspondence between the mean VAI variations and coronal holes. The VAI is modulated by high-speed solar wind streams with a delay of 1–2 days. The Fourier spectra of VAI time series show peaks at periods similar to those found in the solar corona and solar wind time series. In the modulation of VAI by solar wind the IMF BZ seems to control the phase of the Wilcox effect and the depth of the VAI minimum. The mean VAI response to SBP associated with the north-to-south reversal of BZ is leading by up to 2 days the mean VAI response to SBP associated with the south-to-north reversal of BZ. For the latter, less geoeffective events, the VAI minimum deepens (with the above exception of the Northern Hemisphere low-aerosol 500-mb VAI) and the VAI maximum is delayed. The phase shift between the mean VAI responses obtained for these two subsets of SBP events may explain the reduced amplitude of the overall Wilcox effect. In a companion paper, Prikryl et al. (2009) propose a new mechanism to explain the Wilcox effect, namely that solar-wind-generated auroral atmospheric gravity waves (AGWs) influence the growth of extratropical cyclones. It is also observed that severe extratropical storms, explosive cyclogenesis and significant sea level pressure deepenings of extratropical storms tend to occur within a few days of the arrival of high-speed solar wind. These observations are discussed in the context of the proposed AGW mechanism as well as the previously suggested atmospheric electrical current (AEC) model (Tinsley et al., 1994), which requires the presence of stratospheric aerosols for a significant (Wilcox) effect.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

Bước đầu đánh giá hiệu quả hệ thống theo dõi SpO2 từ xa ở bệnh nhân Covid-19 mức độ nhẹ - trung bình

2022 ◽  
Author(s):  
An Vinh Bui-Duc
Keyword(s):  
Monitoring System ◽  
Clinical Symptoms ◽  
Health Care Systems ◽  
Care Center ◽  
Vital Role ◽  
Blood Oxygen Saturation ◽  
Runny Nose ◽  
Care Systems ◽  
The Mean ◽  
D Dimer

TÓM TẮT Đặt vấn đề: Đại dịch COVID-19 (coronavirus disease of 2019) do chủng vi rút Corona mới SARS-CoV-2 vẫn đang bùng phát trên toàn thế giới, gây gia tăng gánh nặng lên Hệ thống chăm sóc Y Tế các quốc gia. Chính vì vậy, việc phát triển hệ thống giúp hỗ trợ chẩn đoán và theo dõi bệnh nhân COVID-19 từ xa được xem là vấn đề cấp thiết hiện nay. Trong đó, chỉ số SpO2 có vai trò quan trọng đối với bệnh COVID-19 và được lựa chọn để theo dõi bệnh nhân tại các Cơ sở Y tế cũng như tại nhà. Nghiên cứu này được chúng tôi thực hiện với mục đích đánh giá hiệu quả ban đầu của hệ thống theo dõi SpO2từ xa trên các bệnh nhân COVID-19 mức độ nhẹ - trung bình. Đối tượng, phương pháp: Nghiên cứu cắt ngang, theo dõi dọc ngắn hạn các bệnh nhân COVID-19 mức độ nhẹ - trung bình điều trị tại Trung tâm Hồi sức Tích cực điều trị bệnh nhân COVID-19 trực thuộc Bệnh viện Trung Ương Huế tại TP. Hồ Chí Minh. Kết quả: Trong giai đoạn từ 8/2021 - 10/2021, 32 bệnh nhân COVID-19 được gắn thiết bị theo dõi chỉ số SpO2, trung bình là 34,2 ± 12,0 tuổi. Các yếu tố nguy cơ bao gồm: BMI xếp loại béo phì 25%, hút thuốc lá (18,8%), tăng huyết áp (15,6%) và đái tháo đường (12,5%). Phần lớn bệnh nhân vào viện do khó thở (71,9%) và chuyển từ tuyến dưới (62,5%). Triệu chứng lâm sàng chủ yếu là ho, hắt hơi, chảy mũi nước (40,6%), theo sau đó là giảm hoặc mất khứu giác, vị giác (25%). 81,3% có D-Dimer ≤ 500ng/mL. 62,5% bệnh nhân được phân độ COVID-19 mức trung bình. Tổng cộng 3.161 lượt đo SpO2, trong đó có 8 lượt cảnh báo SpO2 < 93%. SpO2 trung bình 98,1 ± 0,2 %. Tất cả bệnh nhân xuất viện thành công. Kết luận: Hệ thống theo dõi SpO2 từ xa bước đầu có hiệu quả giúp theo dõi các bệnh nhân COVID-19 mức độ nhẹ - trung bình. ABSTRACT INITIAL EFFECTIVENESS EVALUATION OF THE REMOTE SPO2 MONITORING SYSTEM IN PATIENTS WITH MILD - TO - MODERATE COVID-19 DISEASE Background: The COVID-19 pandemic affected by the new Coronavirus SARS-CoV-2 continues to spread globally, increasing the burden on countries’ Health Care systems. Therefore, generating a platform to help diagnose and monitor COVID-19 patients remotely is considered an essential issue today. In particular, the SpO2 index plays a vital role in COVID-19 disease and is selected to monitor patients at health facilities and homes. This study aimed to evaluate the initial effectiveness of the remote SpO2 monitoring system in patients with mild - to - moderate COVID-19 diseases. Methods: This cross - section study was conducted on mild - to - moderate COVID-19 patients treated at the COVID-19 Intensive Care Center operated by Hue Central Hospital in Ho Chi Minh City, Vietnam Results: From August 2021 to October 2021, 32 COVID-19 patients were applied with SpO2 monitoring smartwatches. The mean age was 34.2 ± 12.0. Risk factors including obesity (25%), smoking (18.8%), hypertension (15.6%), and diabetes (12.5%). Most patients were admitted to the center due to shortness of breath (71.9%) and transferred from lower - level hospitals (62.5%). The main clinical symptoms are coughing, sneezing, runny nose (40.6%), followed by a decrease or loss of smell and taste (25%). 81.3% of patients had D-Dimer ≤ 500 ng/mL. 62.5% of patients had moderate COVID-19 grades. A total of 3,161 SpO2 measurements, including 8 alarms < 93%. The average SpO2 was 98.1 ± 0.2 %. All patients were discharged successfully. Conclusion: A remote SpO2 monitoring system is considered to have preliminary effectiveness in monitoring mild - to - moderate COVID-19 patients. Keywords: COVID-19, blood oxygen saturation, smartwatch, health monitoring system.

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