Control Charts for the Expected System Size of Markovian Queues under F-policy

Author(s):  
Chia-Huang Wu ◽  
Dong-Yuh Yang
Pflege ◽  
2013 ◽  
Vol 26 (2) ◽  
pp. 119-127 ◽  
Author(s):  
Jan Kottner ◽  
Armin Hauss
Keyword(s):  

Vergleichende Qualitätsmessungen und Beurteilungen spielen in der Pflege eine zunehmend wichtige Rolle. Qualitätskennzahlen sind von systematischen und zufälligen Fehlern beeinflusst. Eine Möglichkeit, mit zufälliger Variation in Kennzahlenvergleichen adäquat umzugehen, bietet die Theorie der Statistischen Prozesskontrolle (SPC). Im vorliegenden Beitrag werden Regelkarten (control charts) als Werkzeuge der SPC vorgestellt. Es handelt sich dabei um grafische Darstellungen von Qualitätskennzahlen im zeitlichen Verlauf. Attributive Merkmale können mithilfe von p-, u- und c-Regelkarten dargestellt werden. Es gibt eine Reihe von Regeln, mit denen spezielle Variationen (special cause variation) innerhalb des betrachteten Prozesses identifiziert werden können. Finden sich im Diagramm keine Hinweise auf nichtzufällige Variationen, geht man davon aus, dass sich der Prozess innerhalb «statistischer Kontrolle» befindet (common cause variation). Eine Abweichung eines Datenpunktes um mehr als drei Standardabweichungen vom Mittelwert aller vorliegenden Datenpunkte gilt als stärkstes Signal nicht zufallsbedingter Variation. Im Qualitätsmanagementkontext sind Regelkarten für die dynamische Messung von Prozessen und Ergebnissen und deren Beurteilungen traditionellen Mittelwerts- und Streuungsvergleichen überlegen.


2010 ◽  
Author(s):  
Thomas H. Stone ◽  
I. M. Jawahar ◽  
Ken Eastman ◽  
Gabi Eissa

2019 ◽  
Author(s):  
Brian Nguyen ◽  
Guo P Chen ◽  
Matthew M. Agee ◽  
Asbjörn M. Burow ◽  
Matthew Tang ◽  
...  

Prompted by recent reports of large errors in noncovalent interaction (NI) energies obtained from many-body perturbation theory (MBPT), we compare the performance of second-order Møller–Plesset MBPT (MP2), spin-scaled MP2, dispersion-corrected semilocal density functional approximations (DFA), and the post-Kohn–Sham random phase approximation (RPA) for predicting binding energies of supramolecular complexes contained in the S66, L7, and S30L benchmarks. All binding energies are extrapolated to the basis set limit, corrected for basis set superposition errors, and compared to reference results of the domain-based local pair-natural orbital coupled-cluster (DLPNO-CCSD(T)) or better quality. Our results confirm that MP2 severely overestimates binding energies of large complexes, producing relative errors of over 100% for several benchmark compounds. RPA relative errors consistently range between 5-10%, significantly less than reported previously using smaller basis sets, whereas spin-scaled MP2 methods show limitations similar to MP2, albeit less pronounced, and empirically dispersion-corrected DFAs perform almost as well as RPA. Regression analysis reveals a systematic increase of relative MP2 binding energy errors with the system size at a rate of approximately 1‰ per valence electron, whereas the RPA and dispersion-corrected DFA relative errors are virtually independent of the system size. These observations are corroborated by a comparison of computed rotational constants of organic molecules to gas-phase spectroscopy data contained in the ROT34 benchmark. To analyze these results, an asymptotic adiabatic connection symmetry-adapted perturbation theory (AC-SAPT) is developed which uses monomers at full coupling whose ground-state density is constrained to the ground-state density of the complex. Using the fluctuation–dissipation theorem, we obtain a nonperturbative “screened second-order” expression for the dispersion energy in terms of monomer quantities which is exact for non-overlapping subsystems and free of induction terms; a first-order RPA-like approximation to the Hartree, exchange, and correlation kernel recovers the macroscopic Lifshitz limit. The AC-SAPT expansion of the interaction energy is obtained from Taylor expansion of the coupling strength integrand. Explicit expressions for the convergence radius of the AC-SAPT series are derived within RPA and MBPT and numerically evaluated. Whereas the AC-SAPT expansion is always convergent for nondegenerate monomers when RPA is used, it is found to spuriously diverge for second-order MBPT, except for the smallest and least polarizable monomers. The divergence of the AC-SAPT series within MBPT is numerically confirmed within RPA; prior numerical results on the convergence of the SAPT expansion for MBPT methods are revisited and support this conclusion once sufficiently high orders are included. The cause of the failure of MBPT methods for NIs of large systems is missing or incomplete “electrodynamic” screening of the Coulomb interaction due to induced particle–hole pairs between electrons in different monomers, leaving the effective interaction too strong for AC-SAPT to converge. Hence, MBPT cannot be considered reliable for quantitative predictions of NIs, even in moderately polarizable molecules with a few tens of atoms. The failure to accurately account for electrodynamic polarization makes MBPT qualitatively unsuitable for applications such as NIs of nanostructures, macromolecules, and soft materials; more robust non-perturbative approaches such as RPA or coupled cluster methods should be used instead whenever possible.<br>


2019 ◽  
Author(s):  
Rebecca Lindsey ◽  
Nir Goldman ◽  
Laurence E. Fried ◽  
Sorin Bastea

<p>The interatomic Chebyshev Interaction Model for Efficient Simulation (ChIMES) is based on linear combinations of Chebyshev polynomials describing explicit two- and three-body interactions. Recently, the ChIMES model has been developed and applied to a molten metallic system of a single atom type (carbon), as well as a non-reactive molecular system of two atom types at ambient conditions (water). Here, we continue application of ChIMES to increasingly complex problems through extension to a reactive system. Specifically, we develop a ChIMES model for carbon monoxide under extreme conditions, with built-in transferability to nearby state points. We demonstrate that the resulting model recovers much of the accuracy of DFT while exhibiting a 10<sup>4</sup>increase in efficiency, linear system size scalability and the ability to overcome the significant system size effects exhibited by DFT.</p>


2018 ◽  
Vol 9 (12) ◽  
pp. 1890-1897
Author(s):  
K. Rosaiah ◽  
B. Srinivasa Rao ◽  
J. Pratapa Reddy ◽  
C. Chinnamamba

Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.


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