The Public Administration Compared To Public Management And Other Sciences
In this paper we try to analyze the meaning of the public administration term. In our opinion it is necessary to be able to define exactly what public administration means but at the same time to clearly distinguish it from the other sciences. This is due to the fact that very often confusion is made between public administration and public management, public administration and bureaucracy or public administration and policy. Throughout this paper we hope to clarify some of the aspects related to what exactly distinguishes public administration from the above mentioned sciences and what are its own particularities.
Handbook of Public Administration, Third Edition
Municipal Shared Services and Consolidation: A Public Solutions Handbook Teaching Resources Guide for Public Affairs and Administration (3rd Edition). The third edition of the Handbook of Public Administration provides much-needed coverage of the theory and practice to help address these contemporary. The SAGE Handbook of Public Administration, Second Edition The SAGE Handbook of Strategic Handbook of Marketing Scales, Third Edition Handbook.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.