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Journal articles on the topic 'Rate-based model'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Nugroho, Vina, Roy Sembel, Edison Hulu, and Gracia Ugut. "Interest rate spread determinant based on the interdependency relationship between a bank’s loan rate and time deposit rate." Banks and Bank Systems 17, no. 2 (May 17, 2022): 57–74. http://dx.doi.org/10.21511/bbs.17(2).2022.06.

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This study analyzes the factors responsible for the lower net interest rate at commercial banks located in Indonesia, Thailand and the Philippines. Data were collected from 35, 10 and 13 commercial banks in Indonesia, Thailand, and the Philippines, respectively, from 2012 to 2020 using the Fixed effect model. The Simultaneous Equation Model was used to analyze the macroeconomic factors and banks’ specific characteristics towards Loan and Time Deposit rates. The result showed that macroeconomic factors, such as the inflation rate, significantly affect loan and time deposit rates in these countries. In Indonesia, bank competition should be reduced and banks’ stability should be higher to minimize Net Interest Margin Spread (difference between Loan Rate and Deposit Rate). In the Philippines, banks should increase their capital and liquidity. So, they will be more confident and prudent in lowering their NIM. Thailand’s banking industry has unique characteristics with high monopoly power. The bigger and greater the market share, the larger the interest rate spread on customers. Therefore, regulators in each country need to consider these important variables when making decisions on lowering the net interest rates by banks to enhance social welfare.

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2

Zhongwei Zhang, Guizhong Liu, Hongliang Li, and Yongli Li. "A novel PDE-based rate-distortion model for rate control." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 11 (November 2005): 1354–64. http://dx.doi.org/10.1109/tcsvt.2005.856904.

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3

Solem, Kristian, Pablo Laguna, Juan Pablo MartÍnez, and Leif SÖrnmo. "Model-Based Detection of Heart Rate Turbulence." IEEE Transactions on Biomedical Engineering 55, no. 12 (December 2008): 2712–22. http://dx.doi.org/10.1109/tbme.2008.2002113.

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4

Zhang, Xili. "Modeling the Dynamics of Shanghai Interbank Offered Rate Based on Single-Factor Short Rate Processes." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/540803.

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Using the Shanghai Interbank Offered Rate data of overnight, 1 week, 2 week and 1 month, this paper provides a comparative analysis of some popular one-factor short rate models, including the Merton model, the geometric Brownian model, the Vasicek model, the Cox-Ingersoll-Ross model, and the mean-reversion jump-diffusion model. The parameter estimation and the model selection of these single-factor short interest rate models are investigated. We document that the most successful model in capturing the Shanghai Interbank Offered Rate is the mean-reversion jump-diffusion model.

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5

Ahlborn, B., F. Ahlborn, and S. Loewen. "A model for turbulence based on rate equations." Journal of Physics D: Applied Physics 18, no. 11 (November 14, 1985): 2127–41. http://dx.doi.org/10.1088/0022-3727/18/11/005.

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6

Liu, Jie, Wen-lu Chen, and Yu-quan Li. "Rate-equation-based VCSEL thermal model and simulation." Journal of Zhejiang University-SCIENCE A 7, no. 12 (December 2006): 1968–72. http://dx.doi.org/10.1631/jzus.2006.a1968.

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7

Luo, Minmin, and Kaibin Wu. "Heart rate prediction model based on neural network." IOP Conference Series: Materials Science and Engineering 715 (January 3, 2020): 012060. http://dx.doi.org/10.1088/1757-899x/715/1/012060.

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8

Mena, P. V., J. J. Morikuni, S. M. Kang, A. V. Harton, and K. W. Wyatt. "A simple rate-equation-based thermal VCSEL model." Journal of Lightwave Technology 17, no. 5 (May 1999): 865–72. http://dx.doi.org/10.1109/50.762905.

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9

Derezinski, Stephen J. "A Melting Rate Model Based on Extrusion Data." Journal of Reinforced Plastics and Composites 19, no. 17 (November 2000): 1428–42. http://dx.doi.org/10.1177/073168400772678527.

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10

Zhang, Linxi, Jing Li, Zhouting Jiang, and Agen Xia. "Folding rate prediction based on neural network model." Polymer 44, no. 5 (March 2003): 1751–56. http://dx.doi.org/10.1016/s0032-3861(03)00021-1.

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11

Leng, Fei, and Gao Lin. "Dissipation-based consistent rate-dependent model for concrete." Acta Mechanica Solida Sinica 23, no. 2 (April 2010): 147–55. http://dx.doi.org/10.1016/s0894-9166(10)60016-x.

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12

Yang, Nie, Ge Hua, and Jing Li-li. "Model-Based Design Methodology for Sampling Rate Converter." International Journal of Multimedia and Ubiquitous Engineering 11, no. 5 (May 31, 2016): 83–92. http://dx.doi.org/10.14257/ijmue.2016.11.5.09.

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13

Huang, Mike, Hayato Nakada, Srinivas Polavarapu, Ken Butts, and Ilya Kolmanovsky. "Rate-Based Model Predictive Control of Diesel Engines." IFAC Proceedings Volumes 46, no. 21 (2013): 177–82. http://dx.doi.org/10.3182/20130904-4-jp-2042.00094.

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14

Choi, I. J., C. K. Un, and N. S. Kim. "Speech recognition based on variable information rate model." Electronics Letters 33, no. 9 (1997): 749. http://dx.doi.org/10.1049/el:19970520.

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15

von Rosenberg, Wilhelm, Marc-Oscar Hoting, and Danilo P. Mandic. "A physiology based model of heart rate variability." Biomedical Engineering Letters 9, no. 4 (August 19, 2019): 425–34. http://dx.doi.org/10.1007/s13534-019-00124-w.

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16

Kreul, L. U., A. Górak, and P. I. Barton. "Dynamic rate-based model for multicomponent batch distillation." AIChE Journal 45, no. 9 (September 1999): 1953–62. http://dx.doi.org/10.1002/aic.690450912.

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17

Qing Yang, Qing Yang, Ning Li Qing Yang, Shiyan Hu Ning Li, Heyong Li Shiyan Hu, and Jingwei Zhang Heyong Li. "Click-Through Rate Prediction Algorithm Based on Modeling of Implicit High-Order Feature Importance." 網際網路技術學刊 23, no. 5 (September 2022): 1077–86. http://dx.doi.org/10.53106/160792642022092305016.

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<p>Click-through rate (CTR) prediction plays a central role in online advertising and recommendation systems. In recent years, with the successful application of deep neural networks (DNNs) in many fields, researchers have integrated deep learning into CTR prediction algorithms to model implicit high-order features. However, most of these existing methods unify the weights of implicit higher-order features to predict user behaviors. The importance of such features of different dimensions for predicting user click behaviors are different. Base on this, we propose a prediction method that dynamically learns the importance of implicit high-order features. Specifically, we integrate the output features of deep and shallow components, and adaptively learn the weights of implicit high-order features from among all features through the designed attention network, which effectively capturing the deep interests of users. In addition, this framework has strong versatility and can be combined with shallow models such as Logistic Regression (LR) and Factorization Machines (FMs) to form different models and achieve optimal performance. The extended experiment is conducted on two large-scale datasets, AVAZU and SafeDrive, and the experimental results show that the performance of the proposed model is superior to that of existing baseline models.</p> <p> </p>

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18

Shriki, Oren, David Hansel, and Haim Sompolinsky. "Rate Models for Conductance-Based Cortical Neuronal Networks." Neural Computation 15, no. 8 (August 1, 2003): 1809–41. http://dx.doi.org/10.1162/08997660360675053.

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Population rate models provide powerful tools for investigating the principles that underlie the cooperative function of large neuronal systems. However, biophysical interpretations of these models have been ambiguous. Hence, their applicability to real neuronal systems and their experimental validation have been severely limited. In this work, we show that conductance-based models of large cortical neuronal networks can be described by simplified rate models, provided that the network state does not possess a high degree of synchrony. We first derive a precise mapping between the parameters of the rate equations and those of the conductance-based network models for time-independent inputs. This mapping is based on the assumption that the effect of increasing the cell's input conductance on its f-I curve is mainly subtractive. This assumption is confirmed by a single compartment Hodgkin-Huxley type model with a transient potassium A-current. This approach is applied to the study of a network model of a hypercolumn in primary visual cortex. We also explore extensions of the rate model to the dynamic domain by studying the firing-rate response of our conductance-based neuron to time-dependent noisy inputs. We show that the dynamics of this response can be approximated by a time-dependent second-order differential equation. This phenomenological single-cell rate model is used to calculate the response of a conductance-based network to time-dependent inputs.

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19

Xuan Jing, Lap-Pui Chau, and Wan-Chi Siu. "Frame Complexity-Based Rate-Quantization Model for H.264/AVC Intraframe Rate Control." IEEE Signal Processing Letters 15 (2008): 373–76. http://dx.doi.org/10.1109/lsp.2008.920010.

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20

Yang, Wei Ming, Jian Zhang, and Jin Xiang Peng. "Binomial Bit-Rate Computation Model Based on Wireless Channel." Applied Mechanics and Materials 241-244 (December 2012): 2482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2482.

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For the encoding bit-rate problem in H.264 wireless video communication, the bit-rate computation model and the standard deviation distortion model were analyzed to establish the relation between the quantization parameter of encoding bit-rate and the intra-frame refresh rate of macroblocks, a new proposal of the coding rate thus put forward based on the general binomial computation model theory. Furthermore, this method not only can adaptively adjust the bit allocation and quantization parameters to prevent buffer from overflowing downward or upward under given network bandwidth, but also can apply the rate-distortion to perfect the solution method, control the encoding bits accurately and optimize the allocation between the inter-frame encoding macroblocks.

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21

Sobolev, Yuri Vladimirovich. "Exchange-Rate-Based Stabilization: A Model of Financial Fragility." IMF Working Papers 00, no. 122 (2000): 1. http://dx.doi.org/10.5089/9781451854480.001.

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22

Hanagal, David D., and Arvind Pandey. "Gamma shared frailty model based on reversed hazard rate." Communications in Statistics - Theory and Methods 45, no. 7 (January 31, 2014): 2071–88. http://dx.doi.org/10.1080/03610926.2013.870204.

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23

Vass, Sándor, and Máté Zöldy. "A model based new method for injection rate determination." Thermal Science, no. 00 (2020): 159. http://dx.doi.org/10.2298/tsci190417159v.

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This paper presents a detailed model of a Common Rail Diesel injector and its validation using injection rate measurement. A new method is described for injector nozzle flowrate determination using simulation and measurement tools. The injector model contains fluid dynamic, mechanic and electro-magnetic systems, describing all-important internal processes and also includes the injection rate meter model. Injection rate measurements were made using the W. Bosch method, based on recording the pressure traces in a length of fuel during injections. Comparing the results of the simulated injection rate meter, simulated injector orifice flow and injection rate measurements, the simulated and measured injection rates showed good conformity. In addition to this, the difference between nozzle flow rate and the measured flow rate is pointed out in different operating points, proving, that the results of a Bosch type injection rate measurements cannot be directly used for model validation. However, combining injector, injection rate meter simulation and measurement data, the accurate nozzle flow rate can be determined, and the model validated.

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24

DeCastro, Jonathan A. "Rate-Based Model Predictive Control of Turbofan Engine Clearance." Journal of Propulsion and Power 23, no. 4 (July 2007): 804–13. http://dx.doi.org/10.2514/1.25846.

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25

张, 骁. "Conversion Rate Prediction Based on Combined Response Prediction Model." Advances in Applied Mathematics 09, no. 05 (2020): 791–97. http://dx.doi.org/10.12677/aam.2020.95094.

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26

Reetz, Henning. "Pitch perception based on a temporal energy rate model." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3272. http://dx.doi.org/10.1121/1.411609.

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27

Lai, John, and Jason J. Ford. "Relative Entropy Rate Based Multiple Hidden Markov Model Approximation." IEEE Transactions on Signal Processing 58, no. 1 (January 2010): 165–74. http://dx.doi.org/10.1109/tsp.2009.2028115.

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28

Wu, Chunqing. "The Optimal Vaccination Rate Based on Structured SI Model." Procedia Engineering 29 (2012): 1713–17. http://dx.doi.org/10.1016/j.proeng.2012.01.200.

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29

Mahesh, Sivasambu. "A binary-tree based model for rate-independent polycrystals." International Journal of Plasticity 26, no. 1 (January 2010): 42–64. http://dx.doi.org/10.1016/j.ijplas.2009.05.002.

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30

Hansen, B. L., I. J. Beyerlein, C. A. Bronkhorst, E. K. Cerreta, and D. Dennis-Koller. "A dislocation-based multi-rate single crystal plasticity model." International Journal of Plasticity 44 (May 2013): 129–46. http://dx.doi.org/10.1016/j.ijplas.2012.12.006.

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31

Huo Yan, Jing Tao, and Li Sheng-Hong. "Rate distortion model based on dual parameters Weibull distribution." Acta Physica Sinica 59, no. 2 (2010): 859. http://dx.doi.org/10.7498/aps.59.859.

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32

Ishikawa, Satoshi, Yuichi Yoshida, Atsuko Shimosaka, Yoshiyuki Shirakawa, and Jusuke Hidaka. "Estimation of Sieving Rate Based on Particle Percolation Model." KAGAKU KOGAKU RONBUNSHU 32, no. 3 (2006): 227–35. http://dx.doi.org/10.1252/kakoronbunshu.32.227.

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33

Aytac, Ozlem. "A model of a heterodox exchange rate based stabilization." Economic Modelling 46 (April 2015): 100–117. http://dx.doi.org/10.1016/j.econmod.2014.10.061.

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34

Winkelman, J. G. M., F. Gambardella, and H. J. Heeres. "A rate based reactor model for BiodeNOx absorber units." Chemical Engineering Journal 133, no. 1-3 (September 2007): 165–72. http://dx.doi.org/10.1016/j.cej.2007.03.002.

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35

Sanpui, Debjit, Manish K. Singh, and Ashok Khanna. "Rate-based nonisothermal LLX model and its experimental validation." AIChE Journal 50, no. 2 (2004): 368–81. http://dx.doi.org/10.1002/aic.10033.

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36

LU, Debiao, Dezhang TANG, and Dirk SPIEGEL. "Hazard Rate Estimation for GNSS-Based Train Localization Using Model-Based Approach." Chinese Journal of Electronics 29, no. 1 (January 1, 2020): 49–56. http://dx.doi.org/10.1049/cje.2019.09.006.

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37

Li, Wenqiang, and Ning Hou. "Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model." Scientific Programming 2022 (June 24, 2022): 1–19. http://dx.doi.org/10.1155/2022/8455629.

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Accurate prediction of aircraft failure rate can improve flight safety and spare parts supply efficiency and effectively provide good maintenance and maintenance decisions and health management guidance. In order to achieve accurate prediction of non-linear and non-stationary aircraft failure rate, an aircraft failure rate prediction method based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and combined model is proposed. Firstly, the complementary set empirical mode is used to decompose the failure rate into multiple components with different frequencies, then the integrated moving average autoregressive model (ARIMA) model and grey Verhulst model are selected to predict different components, the entropy weight method is used to solve the coefficients of the combined model, and finally the prediction results of each prediction model are multiplied by their respective weight coefficients to obtain the final prediction results. The experiment was carried out by taking the actual case application of the failure rate data of the aircraft fuel control system as an example. Seven evaluation functions are used as evaluation criteria to evaluate the performance of the combined model. Experimental results show that the developed combined model is better than other models such as sum of squared error (SSE) and mean absolute error (MAE), which can significantly improve the prediction accuracy of aircraft failure rate. It is proved that the model can improve the accuracy and effectiveness of aircraft failure rate prediction. At the same time, the stability of the model has certain advantages over other models and has a good application prospect.

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38

Wang, Qing, Yi Zhang, Guannan Chen, Zhihao Chen, and Hwan Ing Hee. "Assessment of Heart Rate and Respiratory Rate for Perioperative Infants Based on ELC Model." IEEE Sensors Journal 21, no. 12 (June 15, 2021): 13685–94. http://dx.doi.org/10.1109/jsen.2021.3071882.

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39

Dong, Xuhui. "Prediction of College Employment Rate Based on Big Data Analysis." Mathematical Problems in Engineering 2021 (December 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/1421356.

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This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students’ personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education.

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40

Tian, Ye, Hong Jiang, Quan Xin Ding, and Guo Wei Liang. "Turn Rate Estimation Based Adaptive IMM Algorithm for Maneuvering Target Tracking." Advanced Materials Research 383-390 (November 2011): 5609–14. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5609.

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A turn rate estimation based adaptive interactive multiple model algorithm is put forward to solve model-set mismatch problem of target tracking algorithm applying to high maneuvering target. By considering both the estimation and the estimated variance of target’s turn rate, model-set is selected according to a rule based on the coefficient of variance of turn rate estimation. When turn rate estimation is acceptable, model-set is constructed according to turn rate estimation to reduce competition among models. When turn rate estimation is unacceptable, standard IMM algorithm model-set is applied to increase coverage of model-set. Simulation shows this algorithm improves tracking performance especially for high maneuvering targets.

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41

Huang, Ding, Ming Zhong, and Xupeng Shi. "Prediction of Interbank Offered Rate Based on Time Series Model." E3S Web of Conferences 251 (2021): 01014. http://dx.doi.org/10.1051/e3sconf/202125101014.

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This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

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42

Zhao, Yang, Jun Fang Li, and Qiang Gao. "The Predication of the Adhesion Rate Based on NARX Model." Applied Mechanics and Materials 687-691 (November 2014): 1346–49. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1346.

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The scaling and corrosion of circulating cooling water have been a major concern in industry circulating cooling water system, severely affecting the production operation of the circulating cooling water system. In this paper, the circulating cooling water quality is investigated and analyzed. In addition, based on the water quality analysis of Petrochemical Industries Co, it is indicated that part of water quality parameters have great influence on the adhesion rate. In order to further forecast the adhesion rate, a NARX neural network prediction model is developed. Finally, the actual data sets are utilized to demonstrate the feasibility of proposed model.

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43

Jung, Jongyeon, and Yutaek Seo. "Onboard CO2 Capture Process Design using Rigorous Rate-based Model." Journal of Ocean Engineering and Technology 36, no. 3 (June 30, 2022): 168–80. http://dx.doi.org/10.26748/ksoe.2022.006.

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The IMO has decided to proceed with the early introduction of EEDI Phase 3, a CO<sub>2</sub> emission regulation to prevent global warming. Measures to reduce CO<sub>2</sub> emissions for ships that can be applied immediately are required to achieve CO<sub>2</sub> reduction. We set six different CO<sub>2</sub> emission scenarios according to the type of ship and fuel, and designed a monoethanolamine-based CO<sub>2</sub> capture process for ships using a rate-based model of Aspen Plus v10. The simulation model using Aspen Plus was validated using pilot plant operation data. A ship inevitably tilts during operation, and the performance of a tilted column decreases as its height increases. When configuring the conventional CO<sub>2</sub> capture process, we considered that the required column heights were so high that performance degradation was unavoidable when the process was implemented on a ship. We applied a parallel column concept to lower the column height and to enable easy installation and operation on a ship. Simulations of the parallel column confirmed that the required column height was lowered to less than 3 TEU (7.8 m).

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44

Huang, Jin, Cheng Zhi Yang, and Ji Feng Wang. "Speed Model Predictive Control Based on the Track Error Rate." Applied Mechanics and Materials 336-338 (July 2013): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.839.

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In order to make the controlled object have better dynamical characteristics, through introducing the differential item of error into optimal performance index function of tracking error, an improved algorithm of model predictive control is discussed in this paper. The theoretical analysis and Matlab simulation results show that it has better controlled quality and stronger robustness for closed-loop system.

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45

Moon, Seongin, Kyungmo Kim, Gyeong-Geun Lee, Yongkyun Yu, and Dong-Jin Kim. "Pipeline wall thinning rate prediction model based on machine learning." Nuclear Engineering and Technology 53, no. 12 (December 2021): 4060–66. http://dx.doi.org/10.1016/j.net.2021.06.040.

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46

Omiya, Masaki, and Kikuo Kishimoto. "Damage-based Cohesive Zone Model for Rate-depend Interfacial Fracture." International Journal of Damage Mechanics 19, no. 4 (April 23, 2009): 397–420. http://dx.doi.org/10.1177/1056789509103643.

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47

Kim, Taeyoung, Dongbeom Ko, Sungjoo Kang, Ingeol Chun, and Jeongmin Park. "Method for Evaluating Goal Model based on Goal Achievement Rate." International Journal of Software Engineering and Its Applications 10, no. 11 (November 30, 2016): 171–80. http://dx.doi.org/10.14257/ijseia.2016.10.11.15.

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48

Li, Ting, Nathanael Van Vorst, and Jason Liu. "A rate-based TCP traffic model to accelerate network simulation." SIMULATION 89, no. 4 (February 19, 2013): 466–80. http://dx.doi.org/10.1177/0037549712469892.

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49

KANEKO, Takuya, and Hidetoshi NAKAGAWA. "A Bank loan pricing model based on recovery rate distribution." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2007 (May 5, 2007): 37–39. http://dx.doi.org/10.5687/sss.2007.37.

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50

Riedl, Maik, Peter van Leeuwen, Alexander Suhrbier, Hagen Malberg, Dietrich Grönemeyer, Jürgen Kurths, and Niels Wessel. "Testing foetal–maternal heart rate synchronization via model-based analyses." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1892 (February 27, 2009): 1407–21. http://dx.doi.org/10.1098/rsta.2008.0277.

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The investigation of foetal reaction to internal and external conditions and stimuli is an important tool in the characterization of the developing neural integration of the foetus. An interesting example of this is the study of the interrelationship between the foetal and the maternal heart rate. Recent studies have shown a certain likelihood of occasional heart rate synchronization between mother and foetus. In the case of respiratory-induced heart rate changes, the comparison with maternal surrogates suggests that the evidence for detected synchronization is largely statistical and does not result from physiological interaction. Rather, they simply reflect a stochastic, temporary stability of two independent oscillators with time-variant frequencies. We reanalysed three datasets from that study for a more local consideration. Epochs of assumed synchronization associated with short-term regulation of the foetal heart rate were selected and compared with synchronization resulting from white noise instead of the foetal signal. Using data-driven modelling analysis, it was possible to identify the consistent influence of the heartbeat duration of maternal beats preceding the foetal beats during epochs of synchronization. These maternal beats occurred approximately one maternal respiratory cycle prior to the affected foetal beat. A similar effect could not be found in the epochs without synchronization. Simulations based on the fitted models led to a higher likelihood of synchronization in the data segments with assumed foetal–maternal interaction than in the segment without such assumed interaction. We conclude that the data-driven model-based analysis can be a useful tool for the identification of synchronization.

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