PREDICTION OF FORMATION OF SAND SPIT ON COAST WITH SUDDEN CHANGE IN COASTLINE USING IMPROVED BG MODEL

2011 ◽  
Author(s):  
MASUMI SERIZAWA ◽  
TAKAAKI UDA
Keyword(s):  
Sudden Change ◽  
Sand Spit

The value of Electron Microscope studies of imperfect natural diamonds

1990 ◽  
Vol 48 (4) ◽  
pp. 194-195
Author(s):  
J C Walmsley ◽  
A R Lang
Keyword(s):  
Electron Microscope ◽  
Classification Scheme ◽  
Natural Diamond ◽  
Sudden Change ◽  
Growth Conditions ◽  
Diamond Growth ◽  
Major Constituent ◽  
Natural Diamonds ◽  
Type Ia ◽  
Platelet Defects

Interest in the defects and impurities in natural diamond, which are found in even the most perfect stone, is driven by the fact that diamond growth occurs at a depth of over 120Km. They display characteristics associated with their origin and their journey through the mantle to the surface of the Earth. An optical classification scheme for diamond exists based largely on the presence and segregation of nitrogen. For example type Ia, which includes 98% of all natural diamonds, contain nitrogen aggregated into small non-paramagnetic clusters and usually contain sub-micrometre platelet defects on {100} planes. Numerous transmission electron microscope (TEM) studies of these platelets and associated features have been made e.g. . Some diamonds, however, contain imperfections and impurities that place them outside this main classification scheme. Two such types are described.First, coated-diamonds which possess gem quality cores enclosed by a rind that is rich in submicrometre sized mineral inclusions. The transition from core to coat is quite sharp indicating a sudden change in growth conditions, Figure 1. As part of a TEM study of the inclusions apatite has been identified as a major constituent of the impurity present in many inclusion cavities, Figure 2.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

Characterization of Microcrystalline Transition from Amorphous Silicon as a Function of Hydrogen Dilution and Substrate Temperature of Hot-wire CVD

2002 ◽  
Vol 715 ◽  
Author(s):  
Keda Wang ◽  
Haoyue Zhang ◽  
Jian Zhang ◽  
Jessica M. Owens ◽  
Jennifer Weinberg-Wolf ◽  
...  
Keyword(s):  
Substrate Temperature ◽  
Sudden Change ◽  
Chemical Vapor ◽  
Threshold Value ◽  
Ir Absorption ◽  
Sudden Increase ◽  
Hot Wire ◽  
Hydrogen Dilution ◽  
Integral Absorption ◽  
Two Peaks

Abstracta-Si:H films were prepared by hot wire chemical vapor deposition. One group was deposited at a substrate temperature of Ts=250°C with varied hydrogen-dilution ratio, 0<R<10; the other group was deposited with fixed R=3 but a varied Ts from 150 to 550°C. IR, Raman and PL spectra were studied. The Raman results indicate that there is a threshold value for the microstructure transition from a- to μc-Si. The threshold is found to be R ≈ 2 at Ts = 250°C and Ts ≈ 200°C at R=3. The IR absorption of Si-H at 640 cm-1 was used to calculate the hydrogen content, CH. CH decreased monotonically when either R or Ts increased. The Si-H stretching mode contains two peaks at 2000 and 2090 cm-1. The ratio of the integral absorption peaks I2090/(I2090+I2090) showed a sudden increase at the threshold of microcrystallinity. At the same threshold, the PL features also indicate a sudden change from a- to μc-Si., i.e. the low energy PL band becomes dominant and the PL total intensity decreases. We attribute the above IR and PL changes to the contribution of microcrystallinity, especially the c-Si gain-boundaries.

Discovery of a direct sea route to India: open-sea travel and the Bab el-Mandeb barrier

2020 ◽  
Author(s):  
Mateusz Lisak
Keyword(s):  
Sudden Change ◽  
Process Conditions ◽  
Direct Connection ◽  
The Troubles ◽  
Trade Relations ◽  
Open Sea

The issue of discovery of a sea route to India is one of the most important questions about Indo-Roman trade relations and it has yet to be resolved. Historians tend to focus on who and when made the first open-sea journey, and whether it was a sudden change or a process. Conditions essential for discovery of a new route are not considered (not clear – are not considered here, in this paper?), nor are the circumstances that would have made this journey possible. Another issue (of what?) is the case of the Arabia Eudaimon port. The 1st-century AD Periplus Maris Erythraei states that the port had been ransacked and there was no direct connection between India and Egypt, but that all ships were forced to stop there. Thus the resumption of active trade with India necessitated the lifting of the tentative blockade of Arabia Eudaimon and discovering the trans-oceanic route. The nautical guide, however, does not describe the new repute in the context of the troubles in Bab el-Mandeb, but can we be really sure that these two events were not related? What were the circumstances and conditions that had to be met for it to be possible to discover a new route?

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

INFLUENCE OF DEVELOPMENTAL CONDITION OF SAND SPIT ON INTRUSION CHARACTERISTICS OF WAVES INTO AN ESTUARY

2019 ◽  
Vol 75 (2) ◽  
pp. I_253-I_258
Author(s):  
Yuki KAJIKAWA ◽  
Natsumi WADA ◽  
Masamitsu KUROIWA ◽  
Takashi KATAYAMA
Keyword(s):  
Sand Spit ◽  
Developmental Condition

scholarly journals A global analysis of the impact of COVID-19 stay-at-home restrictions on crime

2021 ◽  
Author(s):  
Amy E. Nivette ◽  
Renee Zahnow ◽  
Raul Aguilar ◽  
Andri Ahven ◽  
Shai Amram ◽  
...  
Keyword(s):  
Public Space ◽  
Global Analysis ◽  
Sudden Change ◽  
Interrupted Time Series ◽  
Urban Crime ◽  
Substantial Variation ◽  
Different Types ◽  
Time Series Analyses ◽  
The Impact ◽  
At Home

AbstractThe stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.

scholarly journals Investigation of Pressure Oscillation and Cavitation Characteristics for Submerged Self-Resonating Waterjet

2021 ◽  
Vol 11 (15) ◽  
pp. 6972
Author(s):  
Lihua Cui ◽  
Fei Ma ◽  
Tengfei Cai
Keyword(s):  
Sudden Change ◽  
Three Dimensional ◽  
Pressure Oscillation ◽  
Experimental Tests ◽  
Low Pressure ◽  
The Self ◽  
Two Phase ◽  
Cavitation Phenomenon ◽  
Pressure Zone ◽  
Unsteady Characteristics

The cavitation phenomenon of the self-resonating waterjet for the modulation of erosion characteristics is investigated in this paper. A three-dimensional computational fluid dynamics (CFD) model was developed to analyze the unsteady characteristics of the self-resonating jet. The numerical model employs the mixture two-phase model, coupling the realizable turbulence model and Schnerr–Sauer cavitation model. Collected data from experimental tests were used to validate the model. Results of numerical simulations and experimental data frequency bands obtained by the Fast Fourier transform (FFT) method were in very good agreement. For better understanding the physical phenomena, the velocity, the pressure distributions, and the cavitation characteristics were investigated. The obtained results show that the sudden change of the flow velocity at the outlet of the nozzle leads to the forms of the low-pressure zone. When the pressure at the low-pressure zone is lower than the vapor pressure, the cavitation occurs. The flow field structure of the waterjet can be directly perceived through simulation, which can provide theoretical support for realizing the modulation of the erosion characteristics, optimizing nozzle structure.

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