On Empirical System Gramians

Currently investment and construction activities are based on the implementation of development projects. As any project’s success heavily depends on joint efforts of a project team members, there is an urgent need for a motivation system able to stimulate team members’ result-orientation and satisfy their individual needs. The Project Management Body of Knowledge (PMBOK) methodology is suggested as a basis for building a sound development team motivation system, with its motivational tools correlating to stages of project management. The purpose of this article is to build methodical approach to system of motivation of the development project team. The methodological approach is formed taking into account the correspondence of the goal and the type of motivation depending on the stage of project management, as well as on the basis of the principles of forming the motivation system of the project team. The result is a constructed conceptual model for the development of a motivation system for the development project team based on the principles of PMBOK. Methods of comparative, empirical, system and economic analysis were used to substantiate the propositions put forward in the article.

Download Full-text

A Model for Run-In and Other Transitions in Sliding Friction

Journal of Tribology ◽

10.1115/1.3261499 ◽

1987 ◽

Vol 109 (3) ◽

pp. 537-543 ◽

Cited By ~ 25

Author(s):

Peter J. Blau

Keyword(s):

Sliding Friction ◽

Friction Model ◽

Mathematical Framework ◽

Semiempirical Model ◽

System Parameters ◽

Debris Accumulation ◽

Scaling Parameters ◽

Coefficient Versus ◽

Specific Sliding ◽

Empirical System

The mathematical framework for a sliding friction model for run-in and other tribological transitions is presented. The semiempirical model was developed to portray the commonly observed shapes, durations, and variations in kinetic friction coefficient versus sliding time curves. Terms in the model involve material properties and physical interface conditions such as transfer, debris accumulation, and surface roughness. The forms of individual terms are adjustable through the use of systemspecific scaling parameters in order to provide enough modeling flexibility to treat a variety of possible tribological conditions. Effects of such conditions as lubrication efficiency loss over time, and temperature build-up can be incorporated by modification of appropriate terms. Illustrative plots using the framework with several combined contributions are compared with experimental data from previous work. The basic framework of the model can be further developed to incorporate sub-models for specific sliding friction contributions and, in so doing, reduce the number of empirical system parameters required to model actual tribosystem behavior.

Download Full-text

An Improved Method to Estimate Empirical System Parallel Resonant Frequencies Using Capacitor Switching Transient Data

IEEE Transactions on Power Delivery ◽

10.1109/tpwrd.2006.874559 ◽

2006 ◽

Vol 21 (3) ◽

pp. 1751-1753 ◽

Cited By ~ 6

Author(s):

K. Hur ◽

S. Santoso

Keyword(s):

Improved Method ◽

Resonant Frequencies ◽

Capacitor Switching ◽

Transient Data ◽

Empirical System

Download Full-text

Probing the binding of ligands to estrogen receptor using an empirical system

International Journal of Quantum Chemistry ◽

10.1002/qua.20811 ◽

2005 ◽

Vol 106 (3) ◽

pp. 641-646 ◽

Cited By ~ 3

Author(s):

T. Dottorini ◽

P. Cozzini

Keyword(s):

Estrogen Receptor ◽

Empirical System

Download Full-text

A global empirical system for probabilistic seasonal climate prediction

Geoscientific Model Development ◽

10.5194/gmd-8-3947-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 3947-3973 ◽

Cited By ~ 9

Author(s):

J. M. Eden ◽

G. J. van Oldenborgh ◽

E. Hawkins ◽

E. B. Suckling

Keyword(s):

Air Temperature ◽

Large Scale ◽

Southern Oscillation ◽

Surface Air Temperature ◽

Climate Change Signal ◽

Seasonal Forecasts ◽

Physical Relationship ◽

Skill Scores ◽

Probabilistic Forecasts ◽

Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Download Full-text

What Was Kant’s Contribution to the Understanding of Biology?

Kant Yearbook ◽

10.1515/kantyb-2017-0008 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Idan Shimony

Keyword(s):

Theoretical Framework ◽

The Other ◽

Critical Philosophy ◽

Modern Biology ◽

Biological Phenomena ◽

Conceptual Problems ◽

The One ◽

Proper Science ◽

Philosophical Underpinning ◽

Empirical System

AbstractKant’s theory of biology in the Critique of the Power of Judgment may be rejected as obsolete and attacked from two opposite perspectives. In light of recent advances in biology one can claim contra Kant, on the one hand, that biological phenomena, which Kant held could only be explicated with the help of teleological principles, can in fact be explained in an entirely mechanical manner, or on the other, that despite the irreducibility of biology to physico-mechanical explanations, it is nonetheless proper science. I argue in response that Kant’s analysis of organisms is by no means obsolete. It reveals biology’s uniqueness in much the same way as several current theorists do. It brings to the fore the unique purposive characteristics of living phenomena, which are encapsulated in Kant’s concept of “natural end” and which must be explicated in natural terms in order for biology to become a science. I maintain that Kant’s reluctance to consider biology proper science is not a consequence of his critical philosophy but rather of his inability to complete this task. Kant lacked an appropriate theoretical framework, such as provided later by modern biology, which would enable the integration of the unique features of biology in an empirical system. Nevertheless, as I show in this paper, the conceptual problems with which Kant struggled attest more to the relevance and depth of his insights than to the shortcomings of his view. His contribution to the biological thought consists in insisting on an empirical approach to biology and in providing the essential philosophical underpinning of the autonomous status of biology.

Download Full-text

Dynamically vs. empirically downscaled medium-range precipitation forecasts

Hydrology and Earth System Sciences ◽

10.5194/hess-13-1649-2009 ◽

2009 ◽

Vol 13 (9) ◽

pp. 1649-1658 ◽

Cited By ~ 5

Author(s):

G. Bürger

Keyword(s):

Dynamical System ◽

Lead Time ◽

Regional Model ◽

The Other ◽

Medium Range Forecast ◽

Mountainous Catchments ◽

Skill Scores ◽

Medium Range ◽

Ecmwf Model ◽

Empirical System

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

Download Full-text

A global empirical system for probabilistic seasonal climate prediction

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3941-2015 ◽

2015 ◽

Vol 8 (5) ◽

pp. 3941-3970 ◽

Cited By ~ 4

Author(s):

J. M. Eden ◽

G. J. van Oldenborgh ◽

E. Hawkins ◽

E. B. Suckling

Keyword(s):

Air Temperature ◽

Large Scale ◽

Surface Air Temperature ◽

Climate Change Signal ◽

Seasonal Forecasts ◽

Monthly Basis ◽

Physical Relationship ◽

Skill Scores ◽

Probabilistic Forecasts ◽

Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, NGOs and companies and relies on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated using correlation and skill scores. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known ENSO teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Download Full-text