Green Finance and Sustainable Development

Green finance is currently gaining importance with the growing global resistance to climate change. However, there is limited empirical evidence supporting green finance and economic development nexus considering environmental issues. Despite the fastest growing economy in Asia, Bangladesh still has ambiguity about the role of green finance on sustainable economic growth, though it is already initiated in Bangladesh. Therefore, applying correlation matrix and Granger causality test, this chapter aims to overview the present scenario and identify the role of green finance on sustainable development in Bangladesh from 2014-2019. Hence, it considers GDP growth and CO2 emissions for economic development and climate change issues, respectively, and green finance as the proxy of greening. This study finds that renewable energy consumption and power generation from renewable and waste contribute to green growth. Hence, this study suggests green finance for sustainable development not only in Bangladesh but also in other emerging economies.

Risk of Business Bankruptcy

Recently, the demand of business owners to ensure the sustainability of their businesses has come to the fore. It results in a focus on identifying the risks of businesses' financial failure. Several prediction models can be applied in a given area. Which of these models is most suitable for Slovak companies? The aim of this chapter was to point out the possibility of applying the DEA method in measuring the financial health of companies and predicting the risk of their possible bankruptcy. The research was carried out on a sample of companies operating in the field of heat supply. The indicators were selected using related empirical studies, a univariate Logit model, and a correlation matrix. In this chapter, two main models were applied: the DEA model and the Logit model. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing businesses' financial health.

Thresholding Approach for Low-Rank Correlation Matrix based on MM algorithm

Background: Low-rank approximation is a very useful approach for interpreting the features of a correlation matrix; however, a low-rank approximation may result in estimation far from zero even if the corresponding original value was far from zero. In this case, the results lead to misinterpretation. Methods: To overcome these problems, we propose a new approach to estimate a sparse low-rank correlation matrix based on threshold values combined with cross-validation. In the proposed approach, the MM algorithm was used to estimate the sparse low-rank correlation matrix, and a grid search was performed to select the threshold values related to sparse estimation. Results: Through numerical simulation, we found that the FPR and average relative error of the proposed method were superior to those of the tandem approach. For the application of microarray gene expression, the FPRs of the proposed approach with d=2,3, and 5 were 0.128, 0.139, and 0.197, respectively, while FPR of the tandem approach was 0.285. Conclusions: We propose a novel approach to estimate sparse low-rank correlation matrix. The advantage of the proposed method is that it provides results that are easy to interpret and avoid misunderstandings. We demonstrated the superiority of the proposed method through both numerical simulations and real examples.

Charged and neutral $$ {\overline{B}}_{u,d,s} $$ → γ form factors from light cone sum rules at NLO

We present the first analytic $$ \mathcal{O}\left({\alpha}_s\right) $$ O α s -computation at twist-1,2 of the $$ {\overline{B}}_{u,d,s} $$ B ¯ u , d , s → γ form factors within the framework of sum rules on the light-cone. These form factors describe the charged decay $$ {\overline{B}}_u\to \gamma {\mathrm{\ell}}^{-}\overline{v} $$ B ¯ u → γ ℓ − v ¯ , contribute to the flavour changing neutral currents $$ {\overline{B}}_{d,s}\to \gamma {\mathrm{\ell}}^{+}{\mathrm{\ell}}^{-} $$ B ¯ d , s → γ ℓ + ℓ − and serve as inputs to more complicated processes. We provide a fit in terms of a z-expansion with correlation matrix and extrapolate the form factors to the kinematic endpoint by using the gBB*γ couplings as a constraint. Analytic results are available in terms of multiple polylogarithms in the supplementary material. We give binned predictions for the $$ {\overline{B}}_u\to \gamma {\mathrm{\ell}}^{-}\overline{v} $$ B ¯ u → γ ℓ − v ¯ branching ratio along with the associated correlation matrix. By comparing with three SCET-computations we extract the inverse moment B-meson distribution amplitude parameter λB = 360(110) MeV. The uncertainty thereof could be improved by a more dedicated analysis. In passing, we extend the photon distribution amplitude to include quark mass corrections with a prescription for the magnetic vacuum susceptibility, χq, compatible with the twist-expansion. The values χq = 3.21(15) GeV−2 and χs = 3.79(17) GeV−2 are obtained.

Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM

KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detection method based on correlation analysis and HMM. Correlation analysis is used to obtain the correlation between abnormal KPIs in the system, thereby reducing the false alarm rate of anomaly detection. The HMM (Hidden Markov Model) is used for anomaly detection by finding the close relationship between abnormal KPIs. In our correlation analysis of abnormal KPIs, firstly, the time series prediction model (1D-CNN-TCN) is proposed. The residual sequence is obtained by calculating the residual between the predicted value and the actual value. The residual sequence can highlight the abnormal segment in each data point and improve the accuracy of anomaly screening. According to the obtained residual sequence, these abnormal KPIs are preliminarily screened out from the historical data. Next, KPI correlation analysis is performed, and the correlation score is obtained by adding a sliding window onto the obtained anomaly index residual sequence. The correlation analysis based on the residual sequence can eliminate the interference of the original data fluctuation itself. Then, a correlation matrix of abnormal KPIs is constructed using the obtained correlation scores. In anomaly detection, the constructed correlation matrix is processed to obtain the adaptive parameters of the HMM model, and the trained HMM is used to quickly discover the abnormal KPI that may cause a KPI anomaly. Experiments on public data sets show that the method obtains good results.

Alias Structures and Sequential Experimentation for Mixed-Level Designs

Alias structures for two-level fractional designs are commonly used to describe the correlations between different terms. The concept of alias structures can be extended to other types of designs such as fractional mixed-level designs. This paper proposes an algorithm that uses the Pearson’s correlation coefficient and the correlation matrix to construct alias structures for these designs, which can help experimenters to more easily visualize which terms are correlated (or confounded) in the mixed-level fraction and constitute the basis for efficient sequential experimentation.