Socioeconomic Development Aspects of Democratic Governance across Selected Countries

Abstract. This study is focused on the effect of good governance on eeconomic growth in the number of oil producing countries of MENA (Algeria, Egypt, Iran, Syria, Tunisia, United Arabic Emirates and Yemen) during 1996-2010. The used method is panel data also was used of cross section data for examining. Based on the results of the investigation also estimation of the model we concluded that oil revenues have a negative effect on economic growth also democracy has a significant and positive effect on the economic growth. So with increasing of public participation the economic growth will increase in these countries. Indicators of good governance have a significant and positive effect on the economic growth, which with improvement of the institutions then the economic growth will also increase. Economic freedom has not significant impact on the economic growth in these countries.

Human development is one of the indicators of the success of economic growth. The good quality of human resources goes hand in hand with economic growth, this is because resources as part of the factor of production are the most valuable assets in the economic activities of a country. This study aims to determine and analyze the causality of economic growth towards the quality of human resources. The method used in this study was using the Granger Causality method. Granger Causality Approach is used to measure the strength of the relationship between two or more variables, or it can also be used as a tool to see the direction of the relationship between independent variables and the dependent variable. The variable Human Development Index (HDI) as the dependent variable and per capita income variable as the independent variable. From this study it was concluded that the granger causality test on the proposed research model states that there is no causality relationship between two independent variables and the dependent variable, but significantly the Indonesian economic growth variable has a one-way relationship with the human development index (HDI).Human development is one of the indicators of the success of economic growth. The good quality of human resources goes hand in hand with economic growth, this is because resources as part of the factor of production are the most valuable assets in the economic activities of a country. This study aims to determine and analyze the causality of economic growth towards the quality of human resources. The method used in this study was using the Granger Causality method. Granger Causality Approach is used to measure the strength of the relationship between two or more variables, or it can also be used as a tool to see the direction of the relationship between independent variables and the dependent variable. The variable Human Development Index (HDI) as the dependent variable and per capita income variable as the independent variable. From this study it was concluded that the granger causality test on the proposed research model states that there is no causality relationship between two independent variables and the dependent variable, but significantly the Indonesian economic growth variable has a one-way relationship with the human development index (HDI).

This article explores the political economy of Islamic banking by examining the impact of political regime types, institutional environment, government and political risk on the development of Islamic banking proxied by financing or loan growth in the case of 16 Muslims majority countries with autocratic and democratic regimes over the period of 2000–2013. The performance of Islamic banking loan growth is examined from three different perspectives—political regime and institutions, governance and political risks in both regime settings. Results suggest that loan growth is positive and significant in democratic regimes where political and civil rights are predominant. In addition, loan growths are slower during election years but higher throughout pre‐election year in democratic regimes, suggesting the opportunistic behaviour of incumbent government artificially boosting the economy in preparation for the upcoming election. It is also found that the good quality of public services, policy formulation and implementation, and credibility of government's commitment to realizing the policies are vital in ensuring positive loan growth. The lack of differences in the reaction of loan growth to the governance in democratic and autocratic regimes shows some convergence in the regimes despite having significantly different political perspective. This implies that political commitment of the ruling government is vital to set forth a strong and robust Islamic banking standing regardless of its political standpoint.

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.

بررسی اثر اندازه دولت بر فساد؛ با تأکید بر نقش دموکراسی در منطقه منا نظر دهمرده دانشیار دانشکده اقتصاد، دانشگاه سیستان و بلوچستان محمد علیزاده استادیار دانشکده علوم اقتصادی و اداری دانشگاه لرستان سمیرا زیدی زاده دانش آموخته کارشناسی ارشد اقتصاد تاریخ دریافت: 11/6/1391 تاریخ پذیرش: 28/11/1391 چکیده فساد یک پدیده ناهنجاراقتصادی، سیاسی و فرهنگی است؛ پدیده ای که کم و بیش، درکشورهای توسعه یافته و در حال توسعه وجود دارد. از آن جا که این پدیده از مهم ترین موانع پیشرفت و رشد اقتصادی کشورهای درحال توسعه شمرده می‌شود،‌ ریشه‌یابی و برخورد با آن به طور جدی احساس می-گردد. هدف اصلی این مقاله، شناخت پدیده فساد و شناسایی وابستگی فساد با برخی از متغیرهای اقتصادی و سیاسی از جمله دموکراسی است. رابطه بین اندازه دولت با فساد یک موضوع مهم در اقتصاد بخش عمومی است، که تحقیق مذکور با استفاده از رویکرد داده‌های تابلویی، طی سال-های(2003-2010) تأثیر این عامل مهم بر فساد را در 16 کشور منتخب منطقه منا ، با توجه به نقش دموکراسی مورد بررسی قرار می‌دهد. نتایج نشان می دهد که گسترش اندازه دولت، یا پایین بودن سطح دموکراسی می تواند سطح فساد در منطقه را افزایش دهد. همچنین اثر تعاملی اندازه دولت و دموکراسی بر فساد، گویای این مطلب است که با کاهش یافتن میزان دموکراسی، اندازه بزرگ تر دولت، میزان فساد را کاهش می دهد؛ این نتیجه بازخورد این مطلب است که در سطح دموکراسی پایین، سیستم نظارت و کنترل نیز کمتر است، در نتیجه وقوع فساد کمتر نشان داده می شودکه الزاما به معنای کاهش فساد نیست. بنابراین به منظور کاهش سطح فساد در دولت های منطقه، گسترش دموکراسی و به ویژه آزادی مطبوعات و رسانه و تقویت سیستم نظارت و پاسخ گویی پیشنهاد می‌گردد. طبقه بندی C23,D73,H11 : JEL کلیدواژه ها: فساد، اندازه دولت، دموکراسی، منطقه منا، داده های تابلویی

An optimized principal component analysis (PCA) framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP) in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data. In this study, we first compare how Principal Component Analysis (PCA) and an autoencoder on poses (Pose-AE) transform human kinematics data during flat ground and stair walking. Second, we compare the performance of PCA, Pose-AE and a new autoencoder trained on full human movement trajectories (Move-AE) in order to capture the time varying properties of gait. We compare these methods for both movement classification and identifying the individual. These are key capabilities for identifying useful data representations for prosthetic control. We first find that Pose-AE outperforms PCA on dimensionality reduction by achieving a higher Variance Accounted For (VAF) across flat ground walking data, stairs data, and undirected natural movements. We then find in our second task that Move-AE significantly outperforms both PCA and Pose-AE on movement classification and individual identification tasks. This suggests the autoencoder is more suitable than PCA for dimensionality reduction of human gait, and can be used to encode useful representations of entire movements to facilitate prosthetic control tasks.

In many medical applications, feature selection is obvious; but in medical domains, selecting features and creating a feature vector may require more effort. The wavelet transform (WT) technique is used to identify the characteristic points of an electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high-frequency and low-frequency noise. Principal component analysis (PCA) is a suitable technique for ECG data analysis, feature extraction, and image processing — an important technique that is not based upon a probability model. The aim of the paper is to derive better diagnostic parameters for reducing the size of ECG data while preserving morphology, which can be done by PCA. In this analysis, PCA is used for decorrelation of ECG signals, noise, and artifacts from various raw ECG data sets. The aim of this paper is twofold: first, to describe an elegant algorithm that uses WT alone to identify the characteristic points of an ECG signal; and second, to use a composite WT-based PCA method for redundant data reduction and better feature extraction. PCA scatter plots can be observed as a good basis for feature selection to account for cardiac abnormalities. The study is analyzed with higher-order statistics, in contrast to the conventional methods that use only geometric characteristics of feature waves and lower-order statistics. A new algorithm — viz. PCA variance estimator — is developed for this analysis, and the results are also obtained for different combinations of leads to find correlations for feature classification and useful diagnostic information. PCA scatter plots of various chest and augmented ECG leads are obtained to examine the varying orientations of the ECG data in different quadrants, indicating the cardiac events and abnormalities. The efficacy of the PCA algorithm is tested on different leads of 12-channel ECG data; file no. 01 of the Common Standards for Electrocardiography (CSE) database is used for this study. Better feature extraction is obtained for some specific combinations of leads, and significant improvement in signal quality is achieved by identifying the noise and artifact components. The quadrant analysis discussed in this paper highlights the filtering requirements for further ECG processing after performing PCA, as a primary step for decorrelation and dimensionality reduction. The values of the parameters obtained from the results of PCA are also compared with those of wavelet methods.

Principal Component Analysis (PCA) is a widely used dimensional reduction method that aims to find a low dimension sub space of highly correlated data for its major information to be used in further analysis. Machine learning methods based on PCA are popular in high dimensional data analysis, such as video and image processing. In video processing, the Robust PCA (RPCA), which is a modified method of the traditional PCA, has good properties in separating moving objects from the background, but it may have difficulties in separating those when light intensity of the background varies significantly in time. To overcome the difficulties, a modified PCA method, called Regression PCA (RegPCA), is proposed. The method is developed by combining the traditional PCA and regression approaches together, and it can be easily combined with RPCA for video processing. We focus the presentation of RegPCA with the combination of RPCA on video processing and find that it is more reliable than RPCA only. We use RegPCA to separate moving object from the background in a color video and get a better result than that given by RPCA. In the implementation, we first derive the explanatory variables by the background information. we then process a number of frames of the video and use those as a set of response variables. We remove the impact of the background by regressing the response against the explanatory variables by a regression model. The regression model provides a set of residuals, which can be further analyzed by RPCA. We compare the results of RegRPCA against those of RPCA only. It is evident that the moving objects can be completely removed from the background using our method but not in RPCA. Note that our result is based on a combination of RegPCA with RPCA. Our proposed method provides a new implementation of RPCA under the framework of regression approaches, which can be used to account for the impact of risk factors. This problem cannot be addressed by the application of RPCA only.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use profile. Principal Component Analysis (PCA), is the classical way of dimensional reduction. However, traditional PCA coincides with maximum likelihood interpretation only when data follows Gaussian distribution. The Bregman Divergence was introduced to extend PCA with maximum likelihood in exponential family distribution. In this work, we study this generalized form PCA for Bernoulli variables, which is called Logistic PCA (LPCA). We extend the batch-mode LPCA to a sequential version (SLPCA). The convergence property of this algorithm is discussed compared to the batch version (BLPCA), as well as its performance in reducing the dimension for multivariate binary-state systems. Its application in building energy end-use profile modeling is also investigated.

Recently, many dimensionality reduction algorithms, including local methods and global methods, have been presented. The representative local linear methods are locally linear embedding (LLE) and linear preserving projections (LPP), which seek to find an embedding space that preserves local information to explore the intrinsic characteristics of high dimensional data. However, both of them still fail to nicely deal with the sparsely sampled or noise contaminated datasets, where the local neighborhood structure is critically distorted. On the contrary, principal component analysis (PCA), the most frequently used global method, preserves the total variance by maximizing the trace of feature variance matrix. But PCA cannot preserve local information due to pursuing maximal variance. In order to integrate the locality and globality together and avoid the drawback in LLE and PCA, in this paper, inspired by the dimensionality reduction methods of LLE and PCA, we propose a new dimensionality reduction method for face recognition, namely, unsupervised linear difference projection (ULDP). This approach can be regarded as the integration of a local approach (LLE) and a global approach (PCA), so that it has better performance and robustness in applications. Experimental results on the ORL, YALE and AR face databases show the effectiveness of the proposed method on face recognition.

Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591-1598.

PurposeThe aims of the current study were twofold: first, to rank the world’s emerging economies based on a novel National Intellectual Capital Index (NICI) and its components; and second, to examine the impact of NICI and its components on economic growth, measured in terms of real GDP per capita.Design/methodology/approachWe employed principal component analysis (PCA) to construct the novel NICI based on five key socio-economic indicators including (1) national human capital, (2) national structural capital, (3) national relational capital, (4) national informational capital and (5) national innovational capital. These indicators are publicly available for many countries. The index was generated by considering the most appropriate socio-economic indicators as precise measures of NIC from the Penn world table (version 10.0), the World Bank’s database of world governance and development indicators and the KOF globalization across the selected emerging economies.FindingsThe empirical findings revealed that national human capital is a significant driver of NIC, corresponding to higher economic growth. This is followed by national informational capital, national relational capital, national innovation capital and national structural capital. Furthermore, results indicate that the contribution of national structural capital is marginal compared to other critical strands of NIC.Practical implicationsNIC is generally considered the most valuable strategic resource for driving knowledge economies, especially in the Industry 5.0 revolution. Ranking emerging economies based on the NICI sheds light on the accumulated stock of NIC and how it contributes to and improves the economic growth of these economies. The stock of NIC is considered a critical success factor for measuring both current and future economic prosperity. Therefore, using the socio-economic indicators of KOFGI as accurate measures of NICI will assist policymakers in formulating and implementing relevant policies to enhance the accumulation of knowledge-based capital, which are critical components of NIC.Originality/valueTo the best of the authors' knowledge, this is the first study of its kind, both theoretically and empirically, to measure the National Intellectual Capital Index (NICI) using the most nascent socio-economic indicators of NIC. Moving forward, this study evaluates the impact of NICI and its components on economic growth, which is a relatively sparse area of research in the context of emerging knowledge economies.

The study contributes the literature by examining the impact of life expectancy and governance indicators to achieve sustainable objectives in the US. For empirical estimations, we have used the dynamic autoregressive distributed lagged model. We have used three base models with economic growth, carbon emission and human development as dependent variables. Life expectancy reports positive coefficients for carbon emission and human development. The results mention that higher life expectancy causes environmental degradation. However, it enhances the human development indicators. As far as sustainable economic growth, life expectancy has confirmed the insignificant relationship. In light of governance indicators, governance effectiveness helps to reduce carbon emission. Regulatory quality has mentioned positive coefficients, indicating the significance of improving human development indicators. In concise, life expectancy and governance indicators influence the sustainable environment and human development, while sustainable economic growth is insignificant. The findings propose important policy implications to enhance the environmental and human quality.