Real Interest Rates, Bubbles and Monetary Policy in the GCC countries

The Gulf Cooperation Council countries (GCC) include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE. Their monetary policy objective is to stabilize the foreign price, i.e., exchange rate instead of the domestic price level, where the nominal interest rate is equalized with the US federal fund rate, but the inflation rates are independent. High oil prices and the depreciating US dollar caused inflation to rise and real interest rates to be persistently negative in the UAE and Qatar. Asset prices bubbles formed then burst creating large loses. They could have moderated the effect of, or avoided, the bubble had they floated the currency and stabilized domestic prices.Inflation, real interest rate, bubbles.

ANALISIS PENGARUH PROFITABILITAS DAN KEBIJAKAN UTANG TERHADAP NILAI PERUSHAAN (studi kasus pada perusahaan subsektor makanan dan minuman yang terdaftar di bursa efek indonesia periode 2013-2019)

Abstrak Penelitian ini bertujuan untuk menganalisis Pengaruh Profitabilitas dan Kebijakan Utang Terhadap Nilai Perusahaan (Studi Kasus pada Perusahaan Subsector Makanan dan Minuman yang terdaftar di Bursa Efek Indonesia periode 2013-2019), populasi dalam penelitian ini adalah perusahaan subsector makanan dan minuman yang terdaftar di bursa efek Indonesia periode 2013-2019. Sampel diambil sampel perusahaan purposive sampling yang didasarkan pada kriteria tertentu. dengan menggunakan metode mengumpulkan, menganalisis dan menginterpretasikan, data yang didapat, kemudian membuat kesimpulan dari hasil penelitian tersebut. Metode pengolahan data yang digunakan adalah analisis deskriptif, uji asumsi klasik, analisis regresi berganda, uji koefisien determinasi, uji f ,dan uji t. Hasil penelitian menunjukkan bahwa profitabilitas memiliki pengaruh positif tidak signifikan terhadap nilai perusahaan sedangkan untuk kebijakan utang memiliki pengaruh signifikan terhadap nilai perusahaan dan secara simultan profitabilitas dan kebijakan utang berpengaruh signifikan terhadap nilai perusahaan. Abstract This study aims to analyze the Effect of Profitability and Debt Policy on Corporate Value (Case Study of Food and Beverage Subsector Companies listed on the Indonesia Stock Exchange 2013-2019 period ), the population in this study are food and beverage subsector companies listed on the Indonesia stock exchange in the 2013-2019 period. Samples were taken by purposive sampling company samples based on certain criteria. by using methods of collecting, analyzing and interpreting, the data obtained, then make conclusions from the results of the research. Data processing methods used are descriptive analysis, classic assumption test, multiple regression analysis, coefficient of determination test, f test, and t test. The results showed that profitability had a significant positive effect on firm value while debt policy had a significant effect on firm value and simultaneously profitability and debt policy had a significant effect on firm value

Computer-Aided Detection, Pulmonary Embolism, Computerized Tomography Pulmonary Angiography: Current Status

Angiography (mostly computed tomography, but in some cases, conventional) is still the gold diagnostic standard in the clinical diagnosis of pulmonary embolism (PE). Computer-aided detection (CAD) is software that alerts radiologists the presence of PE during computerized tomography pulmonary angiography (CTPA) examinations. Interpreting CTPA scans with the aid of commercially available CTPA-CAD has improved the detectability of PE patients. This chapter aims to complete the scope of this book by explaining the clinical evidences of PE, the CTPA technology, the role of CTPA-CAD software in improving the diagnostic abilities of CTPA and the role of conventional pulmonary angiography in daily clinical practice. The reader will be introduced to the performance of diagnosing PE with or without the aid of CTPA-CAD algorithms. Differences among CTPA-CAD’s output will be compared and tabled according to “per patient,” “per clot,” “first reader,” and “second reader” basis. This includes, but not limited to, the CTPA-CAD’s sensitivity and specificity in comparison to human observer performance (i.e., radiologist). These topics cover the current status practice at the pulmonary angiography clinic

The Transitional Dynamic of Finance Led Growth

We depart from the empirical literature on testing the finance led growth. Instead of regression analysis, we use a semi-endogenous growth model, which identifies two productivity growth paths: a steady state and a transitional path. Steady state growth is anchored by population growth. In the transitional dynamic, productivity growth depends on the typical factors growth rates, and excess knowledge, which is the deviation of TFP in the financial sector from steady state growth. TFP is endogenous. It is an increasing function of global research efforts, which is driven by the proportion of population in developed countries that is engaged in research in finance, and the stock of human capital. We find positive evidence for this theory of TFP in the data of ten developed European countries and the United States. We also found some evidence for finance-led-growth, albeit weaker after the past Global Financial Crisis

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification

© 2001-2011 IEEE. Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications

Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks

© 2013 IEEE. Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

A π-CLCL Type Immittance Converter for Constant Current and Dynamic Load Applications

Impedance-admittance converter is shortly termed as immittance converter. In this converter, the output current is proportional to the input voltage and the output voltage is proportional to the input current. The output current is thus independent of the load. This research evaluates the characteristics of a proposed π-CLCL immittance converter, which is a combination of the typical π- and T-type configurations, for constant current and dynamic load applications. The input-output characteristics and efficiency characteristics are analyzed and simulated. The characteristics are compared to that of the typical π- and T-type converters. The input-output characteristics and efficiency characteristics are then examined experimentally. It is observed that the experimental results agree with those of the simulation ones, and confirm that the π-CLCL configuration is more efficient than the typical π- and T-type immittance converters while maintaining a nearly constant output current and thus applicable for dynamic loads.DOI:http://dx.doi.org/10.11591/ijece.v4i5.595

Robust 2D Joint Sparse Principal Component Analysis with F-Norm Minimization for Sparse Modelling: 2D-RJSPCA

© 2018 IEEE. Principal component analysis (PCA) is widely used methods for dimensionality reduction and Lots of variants have been proposed to improve the robustness of algorithm, however, these methods suffer from the fact that PCA is linear combination which makes it difficult to interpret complex nonlinear data, and sensitive to outliers or cannot extract features consistently, i.e., collectively; PCA may still require measuring all input features. 2DPCA based on 1-norm has been recently used for robust dimensionality reduction in the image domain but still sensitive to noise. In this paper, we introduce robust formation of 2DPCA by centering the data using the optimized mean for two-dimensional joint sparse as well as effectively combining the robustness of 2DPCA and the sparsity-inducing lasso regularization. Optimal mean helps to improve the robustness of joint sparse PCA further. The distance in spatial dimension is measure in F-norm and sum of different datapoint uses 1-norm. 2DR-JSPCA imposes joint sparse constraints on its objective function whereas additional plenty term help to deal with outliers efficiently. Both theoretical and empirical results on six publicly available benchmark datasets shows that Optimal mean 2DR-JSPCA provides better performance for dimensionality reduction as compare to non-sparse (2DPCA and 2DPCA-L1) and sparse (SPCA, JSPCA)