About the Predictability and Complexity of Complex Systems

Sitte, Renate

doi:10.1007/978-3-642-02199-2_2

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Emergent properties (or emergent phenomena) is one of the manifestations of systems complexity. Emergence refers to properties or behavior observable in arrangements of multi-agent systems but not present in the individual agents making up the system (Corning P., 2002;Sitte R., 2009). The complex behavior of bulk powders is also observable in other properties.…”

Section: Powder Complexitymentioning

confidence: 99%

“…However, the usefulness of the AOR is severely limited as a design, control or operating tool, because its value depends on a multitude of parameters (Wouters I. and Geldart D., 1996). The AOR is another manifestation of complexity in powders; it is also an emergent property, and as such, it is the result of many variables operating all at once (Sitte R., 2009), some of which are not even traceable to the individual particles.…”

Section: Powder Complexitymentioning

confidence: 99%

“…On the other hand, the surface energy of a powder is the property of a complex system. It is worth noting that the complexity of a system is not a single all-encompassing quality; a complex system is likely to have different types of complexity, depending on the property under consideration (e.g., density vs. surface energy) (Sitte R., 2009). Moreover, it is unlikely that all emergent effects play a role in every case (i.e., property under consideration), or that one emergent force has the controlling effect in all cases (McMillan J. et al, 2013).…”

Section: Powder Complexitymentioning

confidence: 99%

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Integrating Particle Microstructure, Surface and Mechanical Characterization with Bulk Powder Processing

Pinal

Carvajal

2020

KONA

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Multiple industrial applications, including pharmaceuticals, rely on the processing of powders. The current powder characterization framework is fragmented into two general areas. One deals with understanding powders from the standpoint of its constituting agents-particles. The other deals with understanding based on the bulkthe collective behavior of particles. While complementary, the two aspects provide distinct pieces of information. Whenever possible, experimental techniques should be used to predict powder behavior. However, it is equally important to recognize that because of the natural complexity of powders, existing predictive approaches will continue to be of limited success for predicting the collective behavior of particles. This article discusses the understanding of powder properties from two perspectives. One is the effect of surface energy at the bulk level (large collections of particles), which controls interactions between powders. This aspect is most useful if studied at the bulk-powder level, not at the single-particle level. Another perspective deals with the physico-mechanical properties of individual particles, responsible for the observed behavior of powders when subjected to mechanical stress from unit operations such as milling. This aspect, which controls the failure mechanism of powders subjected to milling, is most useful if assessed at the single-particle, not at the bulk level. Therefore, in order to fully understand, and eventually predict, or at least effectively model powder behavior, a good-judgement-based combination of microscopic and bulk-level analytical methods is necessary.

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Section: Powder Complexitymentioning

confidence: 99%

Section: Powder Complexitymentioning

confidence: 99%

Section: Powder Complexitymentioning

confidence: 99%

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Integrating Particle Microstructure, Surface and Mechanical Characterization with Bulk Powder Processing

Pinal

Carvajal

2020

KONA

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“…One often speaks of MEMS as complex devices. However, the structural complexity and the functional complexity of MEMS [5] can be very different. They can be made of a few simple components that produce sophisticated function (example: a movable mirror in an optical switch), or several components in a complex arrangement that do simple function (example: a microfuidic pump).…”

Section: Evolution and Rise Of Memsmentioning

confidence: 99%

MEMS design simplification with virtual prototyping

Sitte

2016

FACTA U EE

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MEMS design requires a good understanding of interactions in complex processes and highly specialized interdisciplinary skills. Traditional prototyping is not easy or cheap due to typically needing very expensive manufacturing facilities for its implementation. Progress towards faster, cheaper prototyping has been achieved but, it cannot be applied to MEMS fabrication in general. This paper analyzes the benefits of Virtual Prototyping for a simplification and aid in MEMS design and proposes the continuation of MEMS Animated Graphic Design Aid (MAGDA) project. Its purpose is to simplify preliminary design stages and make MEMS design more accessible to a wider audience.

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“…Multi-scale entropy analysis of temperature anomalies over Europe indicated increasing entropy for the periods over 12 months (Balzter et al 2015). In a work about predictability and complexity, Sitte (2009) has explained that quantification is an essential step in modeling to achieve prediction and also that quantification is the crucial step in complexity, since there is no single universal definition. In this paper, we quantified two complexity measures, i.e., the sample entropy (SampEn) and the Kolmogorov complexity (KC) for the three different time series, the maximum and minimum air temperature, and the precipitation.…”

Section: Introductionmentioning

confidence: 99%

Complexity analysis of the air temperature and the precipitation time series in Serbia

Mimić

Mihailoviĉ

Kapor

2015

Theor Appl Climatol

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In this paper, we have analyzed the time series of daily values for three meteorological elements, two continuous and a discontinuous one, i.e., the maximum and minimum air temperature and the precipitation. The analysis was done based on the observations from seven stations in Serbia from the period 1951-2010. The main aim of this paper was to quantify the complexity of the annual values for the mentioned time series and to calculate the rate of its change. For that purpose, we have used the sample entropy and the Kolmogorov complexity as the measures which can indicate the variability and irregularity of a given time series. Results obtained show that the maximum temperature has increasing trends in the given period which points out a warming, ranged in the interval 1-2°C. The increasing temperature indicates the higher internal energy of the atmosphere, changing the weather patterns, manifested in the time series. The Kolmogorov complexity of the maximum temperature time series has statistically significant increasing trends, while the sample entropy has increasing but statistically insignificant trend. The trends of complexity measures for the minimum temperature depend on the location. Both complexity measures for the precipitation time series have decreasing trends.

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About the Predictability and Complexity of Complex Systems

Cited by 5 publications

References 17 publications

Integrating Particle Microstructure, Surface and Mechanical Characterization with Bulk Powder Processing

Integrating Particle Microstructure, Surface and Mechanical Characterization with Bulk Powder Processing

MEMS design simplification with virtual prototyping

Complexity analysis of the air temperature and the precipitation time series in Serbia

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