scholarly journals Study of the Structure of FeOx-CaO-SiO2-MgO and FeOx-CaO-SiO2-MgO-Cu2O-PdO Slags Relevant to Urban Ores Processing through Cu Smelting

Metals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 78 ◽  
Author(s):  
Mohammad Mehedi Hasan ◽  
M. Akbar Rhamdhani ◽  
M. Al Hossaini Shuva ◽  
Geoffrey A. Brooks
Keyword(s):  
Frequency Band ◽  
Slag Phase ◽  
Low Frequency ◽  
Degree Of Polymerization ◽  
Copper Smelting ◽  
Valuable Metal ◽  
Metal Recycling ◽  
Ftir Spectrometry ◽  
Secondary Copper Smelting ◽  
Semi Empirical

Ferrous-calcium-silicate (commonly known as FCS) slags are used in the valuable metal recycling from urban ores through both primary and secondary copper smelting processes. In the present study, the structure of selected FCS-MgO (FCSM) and FCS-MgO-Cu2O-PdO (FCSM-Cu2O-PdO) slags, relevant to the processes, were investigated using Fourier-transform infrared (FTIR) spectrometry. Deconvolution of the FTIR spectra was carried out to calculate the relative abundance of different silicate structural units (Qn), the overall degree of polymerization (DOP) of the slags and the oxygen speciation in the FCS slags. It was observed that, for the slag investigated, the relative intensity of both the high-frequency band ≈ 1100 cm−1 (Q3) and low-frequency band ≈ 850 cm−1 (Q0) were affected by Fe/SiO2 ratio, basicity, temperature (T) and oxygen partial pressure (pO2). The DOP and the average number of bridging oxygen (BO) were found to decrease with increasing both Fe/SiO2 ratio and basicity. Improved semi-empirical equations were developed to relate the DOP of the slags with chemistry, process parameters and partitioning ratio (i.e., the ratio of the amount of element in the slag phase to metal phase, also known as distribution ratio) of Pd and Ge. Possible reactions, expressed as reactions between metal cations and silicate species, as a way to evaluate thermodynamic properties, are presented herein.

Contact Current Density Analysis Inside Human Body in Low-Frequency Band Using Geometric Multi-Grid Solver

2020 ◽  
Vol E103.C (11) ◽  
pp. 588-596
Author(s):  
Masamune NOMURA ◽  
Yuki NAKAMURA ◽  
Hiroo TARAO ◽  
Amane TAKEI
Keyword(s):  
Frequency Band ◽  
Current Density ◽  
Human Body ◽  
Low Frequency ◽  
Density Analysis

scholarly journals Spillovers of Stock Markets among the BRICS: New Evidence in Time and Frequency Domains before the Outbreak of COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 112
Author(s):  
Kai Shi
Keyword(s):  
Stock Market ◽  
Frequency Band ◽  
Stock Markets ◽  
Emerging Market ◽  
Low Frequency ◽  
Systematic Risk ◽  
Error Variance ◽  
Frequency Bands ◽  
Volatility Spillovers ◽  
Risk Sources

We attempted to comprehensively decode the connectedness among the abbreviation of five emerging market countries (BRICS) stock markets between 1 August 2002 and 31 December 2019 not only in time domain but also in frequency domain. A continuously varying spillover index based on forecasting error variance decomposition within a generalized abbreviation of vector-autoregression (VAR) framework was computed. With the help of spectral representation, heterogeneous frequency responses to shocks were separated into frequency-specific spillovers in five different frequency bands to reveal differentiated linkages among BRICS markets. Rolling sample analyses were introduced to allow for multiple changes during the sample period. It is found that return spillovers dominated by the high frequency band (within 1 week) part declined with the drop of frequencies, while volatility spillovers dominated by the low frequency band (above 1 quarter) part grew with the decline in frequencies; the dynamics of spillovers were influenced by crucial systematic risk events, and some similarities implied in the spillover dynamics in different frequency bands were found. From the perspective of identifying systematic risk sources, China’s stock market and Russia’s stock market, respectively, played an influential role for return spillover and volatility spillover across BRICS markets.

Locally resonant periodic structures with low-frequency band gaps

2013 ◽  
Vol 114 (3) ◽  
pp. 033532 ◽  
Author(s):  
Zhibao Cheng ◽  
Zhifei Shi ◽  
Y. L. Mo ◽  
Hongjun Xiang
Keyword(s):  
Frequency Band ◽  
Periodic Structures ◽  
Low Frequency ◽  
Band Gaps

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

2021 ◽  
Vol 18 ◽  
Author(s):  
Luoyu Wang ◽  
Qi Feng ◽  
Mei Wang ◽  
Tingting Zhu ◽  
Enyan Yu ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Frequency Band ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Angular Gyrus ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Frequency Bands ◽  
Local Functional ◽  
Left Angular Gyrus

Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

Design of High Precision Power Supply for Low-Frequency Vibration Cutting

2004 ◽  
Vol 471-472 ◽  
pp. 494-497
Author(s):  
X.G. Jiang ◽  
D.Y. Zhang
Keyword(s):  
Power Amplifier ◽  
High Precision ◽  
Frequency Band ◽  
Power Supply ◽  
High Stability ◽  
Low Frequency ◽  
Sine Wave ◽  
Vibration Cutting ◽  
Low Frequency Vibration ◽  
Small Band

The frequency of piezoelectric transducer requires high stability and can also be continuously changed. The voltage requires smooth and stable sine wave. To the two problems, a high precision power supply for vibration cutting is designed. It divides the whole frequency band into several small bands. By means of CPLD, the sine wave is digitally fitted individually at each small band. So the sine wave can be always suitable at a wide frequency band. At the power output, OCL power amplifier is adopted. The output sine voltage becomes smooth and stable by adding voltage negative feedback to the power amplifier. The experiment results show its feasibility.

scholarly journals Acoustic Emission Wavelet Analysis of Filament Wound Composite Tube Fatigue Damage Process

2015 ◽  
Vol 9 (1) ◽  
pp. 214-219 ◽  
Author(s):  
Su Hua ◽  
Chang Cheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fatigue Damage ◽  
Frequency Band ◽  
Low Frequency ◽  
High Signal ◽  
Wave Form ◽  
Radial Compression ◽  
Time Frequency ◽  
Emission Signal

This paper performed a radial compression fatigue test on glass fiber winding composite tubes, collected acoustic emission signals at different fatigue damages stages, used time frequency analysis techniques for modern wavelet transform, and analyzed the wave form and frequency characteristics of fatigue damaged acoustic emission signals. Three main frequency bands of acoustic emission signal had been identified: 80-160 kHz (low frequency band), 160-300 kHz (middle frequency band), and over 300kHz (high frequency band), corresponding to the three basic damage modes: the fragmentation of matrix resin, the layered damage of fiber and matrix, and the fracture of cellosilk respectively. The usage of wavelet transform enabled the separation of fatigue damaged acoustic emission signals from interference wave, and the access to characteristics of high signal-noise-ratio fatigue damage.

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