Why some religious individuals are not emotionally smart? The explanatory role of spirituality

<p align="left">In two studies, we intend to investigate whether spirituality can explain the relationship between intrinsic religious orientation (IRO) and emotional intelligence (EI). Seventy-three worshipping houses-going adults, aged 18-56, had participated in the study. Data was collected by employing Intrinsic Scale of Religious Orientation Scale, Spirituality Assessment Scale, and the Schutte Self-Report Emotional Intelligence Test. Our first study discovered that IRO is not a significant predictor of EI. Nevertheless, Bootstrap analysis with 5000 samples and 95% interval confidence indicated that spirituality fully mediated the link between intrinsic religious orientation and emotional intelligence in our second study. In other words, without high level of spirituality one’s religious orientation does not significantly predict their emotional intelligence. Limitation and suggestion are discussed at the end of the paper. </p>

The Religious Orientation Scale Revised among Spanish Catholic People: Structural Validity and Internal Consistency of a 21-item Model

The personal religious orientation understood as the motivation behind religious behaviors must be considered as the process that manages and organizes the behavior of those who are religious. Thus, identifying the dimensionality of religiosity is important (Francis, 2007; Kirkpatrick & Hood, 1990). This paper analyzed the structural validity and internal consistency of the 31-item Batson and Ventis Religious Orientation Scale. Participants were 529 Spanish Catholic undergraduates aged between 18 and 55 years, M = 21.55, SD = 4.39. A Principal Component Analysis with Equamax rotation method was performed on the ROS-31 with the randomized 50% of the sample, obtaining a 21-item three-component model (intrinsic, extrinsic, and quest religious orientations). Then, a CFA carried out with the other 50% of the sample showed an adequate fit of the obtained model, SBχ2(186) = 352.45, p < 0.01, CFI = 0.93, IFI = 0.93, RMSEA = 0.059 (CI 90% [0.049, 0.067]). The intrinsic scale showed an excellent internal consistency, the quest scale showed good internal consistency, and the extrinsic scale showed an acceptable internal consistency. Future lines of research are suggested in order to clarify the relationship between the religious orientation scales and some psychosocial variables.

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

Degradation feature extraction of hydraulic pump based on LCD-DCS fusion algorithm

Hydraulic pump degradation feature extraction is a key step of condition-based maintenance. In this article, a novel method based on local characteristic-scale decomposition (LCD) and discrete cosine transform–composite spectrum (DCS) fusion algorithm is proposed. In order to reduce noises and other disturbances, vibration signals are first processed by LCD with the high frequency harmonic. Detail components with sensitive information are achieved by the selection of intrinsic scale components. Furthermore, on the basis of the earlier composite spectrum (CS), the DCS fusion algorithm is proposed to make fusion of the obtained detail components for improving the feature performance. The DCS entropy is extracted as the fault degradation feature. Analysis of the hydraulic pump degradation experiment demonstrates that the proposed algorithm is feasible and effective to indicate the performance degradation of the hydraulic pump.

Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.

Assessment of Relative Efficiency of Countries in Attaining Human Development Sub Indexes

Competitiveness and human development are two major aspects of nations’ performance. However, the main objective of competitiveness should be to improve human development. In the current study, we aimed to examine the relative efficiency of countries in achieving the aforementioned target. In other words, the question is whether competitiveness has led to human development. To this end, we selected 31 countries with the same category in human development (high human development) and also with available data on competitiveness and its components. Due to the nature of the study, we used Data Envelopment Analysis (DEA) method. The model used in this study employed three subindexes of global competitiveness including basic requirements, efficiency enhancers, and innovation and sophistication factors as input variables and three subindexes of human development including life expectancy at birth, mean years of schooling, and per capita national income as output variables. Since, as noted, the goal of countries (DMUs) is to improve human development; this study employed an output-oriented DEA model. Though, a DEA model with either constant or variable return to scale could be used, this paper employs DEA with constant return to scale because variable case has extended to accommodate scale effects while in our case (where countries in the role of units under assessment are large enough) intrinsic scale effects do not exist and also CRS models have higher separable power for differentiating efficient and inefficient units. And finally after running the model we found that 9 out of 31 assessed countries are technically efficient which implies that these 9 countries have used competitiveness subindexes to attain expected values of human development sub-indexes. In 2012 Iran is an inefficient unit, having a technical efficiency rank of 19th among the assessed countries. As in this paper there are two kinds of variables, i.e. input and output variables, the most effective subindex which have lowered Iran’s rank are life expectancy at birth for input variables and efficiency enhancers for output variables. Moreover, Albania and Venezuela have been introduced as reference set for Iran in this year.

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.