Development and Validation of the Perceived Psychological Capacity Scale as a Consideration for Determining Employee Productive Age Period

Purpose of Study : The changes over the age period especially in employee occur physically and psychologically. The employee psychological condition changes can be affecting the work activities, productivity and performance. Although WHO and another national regulation has been settled the retirement age limit for employee, but there always some psychological down turning happen. While, there has been little empirical research conducted in relation to diagnostic tool for perceived employee productive age period. This study aims to introduce and explore the factor structure of a new measure of Perceived Psychological Capacity Scale. Methodology : To validate this scale we used Exploratory Factor Analysis, Confirmatory Factor Analysis and Bivariate Correlation with Other Correlated Variable (Human Capital). The participants in this study were 267 employees in a company which provide railways transportation services in Indonesia, with an age range of 39-56 years old. Result : The analysis suggest that Perceived Psychological Capacity Scale is valid and reliable. It contains 25 items in 4 dimensions i.e. Cognitive, Motivation, Emotion, Social Interaction. This result indicates that Perceived Psychological Capacity Scale can measure the decrease of psychological capacity based on employees’ perceived perception particularly in service work scope on certain age. Implications/Applications : This study can be used as an alternative tool to diagnose the productive age of employee based on their perceived psychological capacity specifically in railways transportation service employees. We cannot generalize the findings of this study for every employee in other jobs and company due to the limitation of this study.

A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management

In this work we attempt is to locate and analyze via multivariate analysis techniques, highly correlated covariates (factors) which are interrelated with the Gross Domestic Product and therefore are affecting either on short-term or on long-term its shaping. For the analysis, feature selection techniques and model selection criteria are used. The case study focuses on annual data for Greece for the period 1980-2018.

Morphological structuring using principal component analysis of Rampur-Bushair sheep under transhumance production in western Himalayan region, India

Principal component analysis (PCA) was employed on 12 biometric traits of Rampur-Bushair sheep of Himachal Pradesh. Morphological and biometrical observations were recorded on 162 young and 566 adult animals. Multivariate statistics and principal component analysis revealed that body measurements except for peripheral traits were mostly positively and significantly correlated. The correlation among conformation traits ranged from -0.08 to 0.79 and “0.18 to 0.71 in young and adult sheep respectively. Three and four factors were extracted in young and adult sheep respectively, which accounted for 57% and 61% of variation. The principal component extracted contributed effectively to explain general body conformation. The regression analysis suggested that use of principal component was more appropriate than the use of original correlated variable in estimating body weights. Therefore, factor extracted could be helpful in breeding programme with sufficient reduction in the number of biometric traits to be recorded to explain the body conformation.

Modeling Maintenance of Long-Term Potentiation in Clustered Synapses: Long-Term Memory without Bistability

Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (>1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the “clustered plasticity hypothesis” which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.

The Forecast of Transportation Flows under Certain and Uncertain Trends: The Application of Combined Grey System Models

The complexity of modern economic system makes the forecast about correlated variable more difficult. Combined grey system models with general grey forecasting method and grey verhulst forecasting method are utilized to overcome the forecast issues under certain and uncertain changing trends. The results indicate that the forecast accuracy is high and short-term forecasts are available, which supports the methodological applicability and robustness.

The Application of PCA and SVM in Rolling Bearing Fault Diagnosis

The key to the fault diagnosis is feature extracting and fault pattern classifying. Principal components analysis (PCA) and support vector machine (SVM) method are introduced to recognize the fault pattern of the rolling bearing in this paper. Multidimensional correlated variable is converted into low dimensional independent eigenvector by means of PCA. The pattern recognition and the nonlinear regression are achieved by the method of SVM. In the light of the feature of vibrating signals, eigenvector is obtained using PCA, fault diagnosis of rolling bearing is recognized correspondingly using SVM fault classifier. Theory and experiment show that the recognition of fault diagnosis of rolling bearing based on PCA and SVM theory is available in the fault pattern recognition and provides a new approach to intelligent fault diagnosis.