Effect of short-term tempering by high-frequency currents on the structure and properties of solid rolled products

1984 ◽  
Vol 26 (9) ◽  
pp. 688-691
Author(s):  
V. M. Ivashchenko ◽  
M. G. Erlikh
Keyword(s):  
High Frequency ◽  
Structure And Properties ◽  
Short Term

Prior Return Effect in Indian Stock Market: An Intra-day Analysis

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vanita Tripathi ◽  
Shalini Aggarwal
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
High Frequency ◽  
Good News ◽  
High Frequency Data ◽  
Short Term ◽  
Fund Managers ◽  
News Events ◽  
Return Effect ◽  
Indian Stock Market

In a first of this kind, this paper examines the issue of prior return effect in Indian stock market in intra-day analysis using high frequency data. We document that in Indian stock market, security returns exhibit a reversal in their direction within few minutes of extreme price rises as well as price falls. However the speed with which the correction takes place is slightly different for good news events and bad news events. Indian investors tend to be optimistic as they immediately bring stock prices up following unjustified price falls but take time to bring stock prices down following unjustified price rises. These findings lend a further support to short-term overreaction literature. More importantly, these findings serve as a proof of predictability of the direction of future stock prices and consequent returns on an intra-day basis. It forwards important investment implications for traders, fund managers, and investors at large.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

scholarly journals Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Luyi Li ◽  
Dayu Hu ◽  
Wenlou Zhang ◽  
Liyan Cui ◽  
Xu Jia ◽  
...  
Keyword(s):  
Heart Rate ◽  
Air Pollution ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Normal Weight ◽  
High Frequency Power ◽  
Short Term ◽  
Particulate Air Pollution ◽  
Short Term Exposure ◽  
Frequency Power

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p <  0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. Conclusions Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied. Graphical abstract

Cibenzoline for High-Frequency Ventricular Arrhythmias: A Short-Term Comparison With Quinidine and a Long-Term Follow-up

1987 ◽  
Vol 27 (4) ◽  
pp. 283-287 ◽  
Author(s):  
Prasad R. Palakurthy ◽  
Claudio Maldonado ◽  
Gurbachan Sohi ◽  
Nancy C. Flowers
Keyword(s):  
High Frequency ◽  
Ventricular Arrhythmias ◽  
Short Term ◽  
Long Term Follow Up

scholarly journals Multi-Scale Spatiotemporal Change Characteristics Analysis of High-Frequency Disturbance Forest Ecosystem Based on Improved Spatiotemporal Cube Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2537
Author(s):  
Yangcen Zhang ◽  
Xiangnan Liu ◽  
Meiling Liu ◽  
Xinyu Zou ◽  
Qian Zhang ◽  
...  
Keyword(s):  
High Frequency ◽  
Forest Ecosystems ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Anthropogenic Factors ◽  
Residential Areas ◽  
Short Term ◽  
Spatiotemporal Changes ◽  
Spatiotemporal Scales ◽  
The Impact

High-frequency disturbance forest ecosystems undergo complex and frequent changes at various spatiotemporal scales owing to natural and anthropogenic factors. Effectively capturing the characteristics of these spatiotemporal changes from satellite image time series is a powerful and practical means for determining their causes and predicting their trends. Herein, we combined the spatiotemporal cube and vegetation indices to develop the improved spatiotemporal cube (IST-cube) model. We used this to acquire the spatiotemporal dynamics of forest ecosystems from 1987 to 2020 in the study area and then classified it into four spatiotemporal scales. The results showed that the cube-core only exists in the increasing IST-cubes, which are distributed in residential areas and forests. The length of the IST-cube implies the duration of triggers. Human activities result in long-term small-scope IST-cubes, and the impact in the vicinity of residential areas is increasing while there is no change within. Meteorological disasters cause short-term, large scope, and irregular impacts. Land use type change causes short-term small scope IST-cubes and a regular impact. Overall, we report the robustness and strength of the IST-cube model in capturing spatiotemporal changes in forest ecosystems, providing a novel method to examine complex changes in forest ecosystems via remote sensing.

scholarly journals Middle to Upper Jurassic Saudi Arabian carbonate petroleum reservoirs: biostratigraphy, micropalaeontology and palaeoenvironments

GeoArabia ◽  
2004 ◽  
Vol 9 (3) ◽  
pp. 79-114 ◽  
Author(s):  
Geraint Wyn ap Gwilym Hughes
Keyword(s):  
High Frequency ◽  
Petroleum Reservoirs ◽  
Upper Jurassic ◽  
Sediment Thickness ◽  
Fair Weather ◽  
Short Term ◽  
Shallow Marine ◽  
Potential Reservoir ◽  
Intertidal Assemblage ◽  
Depth Reduction

ABSTRACT Recent work has improved understanding of the benthic foraminiferal stratigraphic and palaeoenvironmental ranges of the Middle to Upper Jurassic reservoir-containing carbonates of Saudi Arabia. The entire Jurassic succession includes the Marrat, Dhruma, Tuwaiq Mountain, Hanifa, Jubaila and Arab formations that terminate with a succession of evaporites, the final, thickest unit of which is termed the Hith Formation. This study focuses on selected carbonate members studied from the Dhruma Formation and above, and includes the Lower Fadhili, Upper Fadhili, Hanifa and Arab-D reservoirs. The Hadriya and Manifa reservoirs are not considered. An ascending order of tiered deep-to shallow-marine foraminiferal assemblages has been determined for each formation and applied to distinguish both long- and short-term palaeobathymetric variations. The Lenticulina-Nodosaria-spicule dominated assemblage characterises the deepest mud-dominated successions in all formations. The consistent presence of Kurnubia and Nautiloculina species suggests only moderately deep conditions, considered to be below fair-weather wave base and shelfal. A foraminiferally-depleted succession then follows that is characterised by encrusting and domed sclerosponges, including Burgundia species, in the Tuwaiq Mountain, Hanifa and Jubaila formations. This assemblage is followed, in the Hanifa and upper Jubaila formations, by a biofacies dominated by fragments of the branched sclerosponge Cladocoropsis mirabilis, together with Kurnubia and Nautiloculina species and a variety of indeterminate simple miliolids. Pseudocyclammina lituus, Alveosepta powersi/jacardi and Redmondoides lugeoni are present within this assemblage. A slightly shallower, possibly lagoon-influenced assemblage is developed in the Hanifa and Arab formations that include Cladocoropsis mirabilis, Kurnubia and Nautiloculina species and the dasyclad algae Clypeina sulcata and Heteroporella jaffrezoi. A further shallower assemblage, found only in the upper Arab-D Member, is characterised by the presence of Mangashtia viennoti, Clypeina sulcata and Cladocoropsis mirabilis. This assemblage is gradually supplemented by “Pfenderina salernitana” and is interpreted as slightly shallower conditions in the upper Arab-D. A very shallow assemblage in the uppermost Arab-D is characterised by the presence of Trocholina alpina, which is then followed by an intertidal assemblage of cerithid gastropods and felted calcareous algae in which foraminifera are typically absent. These various microbiofacies have provided depositional and potential reservoir stratification. A phenomenon termed “palaeobathymetric compression” has been observed in which depositional cycles are enhanced by rapidly shallowing upwards tiered biofacies that encompass less than 3m of sediment thickness but represent in excess of 20m of water depth reduction. This is attributed to short-term rapid lowering of sea level, and may be considered as the microfaunal signals of high frequency forced regressions.

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.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

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