Fixed Rate Loan Commitments, Take-Down Risk, and the Dynamics of Hedging with Futures

1983 ◽  
Vol 18 (4) ◽  
pp. 499 ◽  
Author(s):  
Thomas S. Y. Ho ◽  
Anthony Saunders
Keyword(s):  
Fixed Rate ◽  
Loan Commitments

Fee-Based Pricing of Fixed Rate Bank Loan Commitments

1979 ◽  
Vol 8 (1) ◽  
pp. 13 ◽  
Author(s):  
Brit J. Bartter ◽  
Richard J. Rendleman
Keyword(s):  
Bank Loan ◽  
Fixed Rate ◽  
Loan Commitments

Loan Commitments

2015 ◽  
Author(s):  
Dan Galai ◽  
Zvi Wiener
Keyword(s):  
Loan Commitments

Option Theory and Floating-Rate Securities with a Comparison of Adjustable- and Fixed-Rate Mortgages

1993 ◽  
Vol 66 (4) ◽  
pp. 595 ◽  
Author(s):  
James B. Kau ◽  
Donald C. Keenan ◽  
Walter J. Muller III ◽  
James F. Epperson
Keyword(s):  
Fixed Rate ◽  
Floating Rate ◽  
Option Theory

scholarly journals Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1511
Author(s):  
Saeed Mian Qaisar ◽  
Alaeddine Mihoub ◽  
Moez Krichen ◽  
Humaira Nisar
Keyword(s):  
Machine Learning ◽  
Signal Reconstruction ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Features Selection ◽  
Frequency Content ◽  
Fixed Rate ◽  
Ecg Signals ◽  
Learning Techniques ◽  
Multirate Processing

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

scholarly journals Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield

2021 ◽  
Vol 13 (14) ◽  
pp. 8059
Author(s):  
Calogero Schillaci ◽  
Tommaso Tadiello ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Precision Agriculture ◽  
Stem Elongation ◽  
Maize Yield ◽  
Application Rate ◽  
N Fertilization ◽  
Economic Benefits ◽  
Growth Monitoring ◽  
Fixed Rate ◽  
Positioning Systems ◽  
Significant Difference

Proximal sensing represents a growing avenue for precision fertilization and crop growth monitoring. In the last decade, precision agriculture technology has become affordable in many countries; Global Positioning Systems for automatic guidance instruments and proximal sensors can be used to guide the distribution of nutrients such as nitrogen (N) fertilization using real-time applications. A two-year field experiment (2017–2018) was carried out to quantify maize yield in response to variable rate (VR) N distribution, which was determined with a proximal vigour sensor, as an alternative to a fixed rate (FR) in a cereal-livestock farm located in the Po valley (northern Italy). The amount of N distributed for the FR (140 kg N ha−1) was calculated according to the crop requirement and the regional regulation: ±30% of the FR rate was applied in the VR treatment according to the Vigour S-index calculated on-the-go from the CropSpec sensor. The two treatments of N fertilization did not result in a significant difference in yield in both years. The findings suggest that the application of VR is more economically profitable than the FR application rate, especially under the hypothesis of VR application at a farm scale. The outcome of the experiment suggests that VR is a viable and profitable technique that can be easily applied at the farm level by adopting proximal sensors to detect the actual crop N requirement prior to stem elongation. Besides the economic benefits, the VR approach can be regarded as a sustainable practice that meets the current European Common Agricultural Policy.

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