Tag Archives: Einstein

Blind to complexity

fruit fly nervous system Albert Cardona HHMI Janelia Research Campus Welcome Image Awards 2015When faced with complexity, we tend to seek order and simplicity.  Most of us respond negatively to the uncertainty associated with complex systems and their apparent unpredictability.  Complex systems can be characterised as large networks operating using simple rules but without central control which results in self-organising behaviour and non-trivial emergent behaviour.  Emergent behaviour is the behaviour of the system that is not apparent or expected from the behaviour of its constituent parts [see ‘Emergent properties‘ on September 16th, 2015].

The philosopher, William Wimsatt observed that we tend to ignore phenomena whose complexity exceeds our predictive capability and our detection apparatus.  This is problematic because we try to over-simplify our descriptions of complex systems.  Occam’s razor is often mis-interpreted to mean that simple explanations are better ones, whereas in reality ‘everything should be made as simple as possible, but not simpler’, (which is often attributed to Einstein).  This implies that our explanation and any mathematical model of a complex system, such as the nervous system in the image, will need to be complex.  In mathematical terms, this will probably mean a non-linear dynamic model with a solution in the form of a phase portrait.  ‘Non-linear’ because the response of the system not proportional to the stimulus inducing the response; ‘dynamic’ because the system changes with time; and a ‘phase portrait’ because the system can exist in many states, some stable and some unstable, dependent on its prior history; so, for instance for a pendulum, its phase portrait is a plot of all of its possible positions and velocities.

If all this sounds too hard, then you see why people shy away from using complex models to describe a complex system even when it is obvious that the system is complex and extremely unlikely to be adequately described by a linear model, such as for the nervous system in the image.

In other words, if we can’t see it and its too hard to think about it, then we pretend it’s not happening!


The thumbnail shows an image of a fruit-fly’s nervous system taken by Albert Cardona from HHMI Janelia Research Campus.  The image won a Wellcome Image Award in 2015.

William C. Wimsatt, Randomness and perceived randomness in evolutionary biology, Synthese, 43(2):287-329, 1980.

For more on this topic see: ‘Is the world comprehensible?‘ on March 15th, 2017.


Everyday examples contribute to successful learning

Some weeks ago I quoted Adams and Felder [2008] who said that the ‘educational role of faculty [academic staff] is not to impart knowledge; but to design learning environments that support…knowledge acquisition’ [see ‘Creating an evolving learning environment’ on February 21st, 2018].  A correspondent asked how I create a learning environment and, in response, this is the first in a series of posts on the topic that will appear every third week.  The material is taken from a one-day workshop that Pat Campbell [of Campbell-Kibler Associates] and I have given periodically in the USA [supported by NSF ] and UK [supported by HEA] for engineering academics.

Albert Einstein is reputed to have said that ‘knowledge is experience, everything else is just information’.  I believe that a key task for a university teacher of engineering is to find the common experiences of their students and use them to illustrate engineering principles.  This is relatively straightforward for senior students because they will have taken courses or modules delivered by your colleagues; however, it is more of a challenge for students entering the first year of an engineering programme.  Everyone is unique and a product of their formative conditions, which makes it tricky to identify common experiences that can be used to explain engineering concepts.  The Everyday Engineering Examples, which feature on a page of this blog [https://realizeengineering.blog/everyday-engineering-examples/], were developed to address the need for illustrative situations that would fall into the experience of most, if not all, students.  Two popular examples are using the splits in sausages when you cook them to illustrate two-dimensional stress systems in pressure vessels [see lesson plan S11] and using a glass to extinguish a birthday candle on a cup cake to explain combustion processes [see lesson plan T11].

Everyday Engineering Examples were developed as part of an educational research project, which was funded by the US National Science Foundation [see ENGAGE] and demonstrated that this approach to teaching works.  The project found that significantly more students rated their learning with Everyday Engineering Examples as high or significant than in the control classes independent of the level of difficult involved [Campbell et al. 2008].  So, this is one way in which I create a learning environment that supports knowledge acquisition.  More in future posts…


Adams RS & Felder RM, Reframing professional development: A systems approach to preparing engineering educators to educate tomorrow’s engineers. J. Engineering Education, 97(3):230-240, 2008

Campbell PB, Patterson EA, Busch Vishniac I & Kibler T, Integrating Applications in the Teaching of Fundamental Concepts, Proc. 2008 ASEE Annual Conference and Exposition, (AC 2008-499), 2008


CALE #1 [Creating A Learning Environment: a series of posts based on a workshop given periodically by Pat Campbell and Eann Patterson in the USA supported by NSF and the UK supported by HEA]

Slow moving nanoparticles

Random track of a nanoparticle superimposed on its image generated in the microscope using a pin-hole and narrowband filter.

A couple of weeks ago I bragged about research from my group being included in a press release from the Royal Society [see post entitled ‘Press Release!‘ on November 15th, 2017].  I hate to be boring but it’s happened again.  Some research that we have been performing with the European Union’s Joint Research Centre in Ispra [see my post entitled ‘Toxic nanoparticles‘ on November 13th, 2013] has been published this morning by the Royal Society Open Science.

Our experimental measurements of the free motion of small nanoparticles in a fluid have shown that they move slower than expected.  At low concentrations, unexpectedly large groups of molecules in the form of nanoparticles up to 150-300nm in diameter behave more like an individual molecule than a particle.  Our experiments support predictions from computer simulations by other researchers, which suggest that at low concentrations the motion of small nanoparticles in a fluid might be dominated by van der Waals forces rather the thermal motion of the surrounding molecules.  At the nanoscale there is still much that we do not understand and so these findings will have potential implications for predicting nanoparticle transport, for instance in drug delivery [e.g., via the nasal passage to the central nervous system], and for understanding enhanced heat transfer in nanofluids, which is important in designing systems such as cooling for electronics, solar collectors and nuclear reactors.

Our article’s title is ‘Transition from fractional to classical Stokes-Einstein behaviour in simple fluids‘ which does not reveal much unless you are familiar with the behaviour of particles and molecules.  So, here’s a quick explanation: Robert Brown gave his name to the motion of particles suspended in a fluid after reporting the random motion or diffusion of pollen particles in water in 1828.  In 1906, Einstein postulated that the motion of a suspended particle is generated by the thermal motion of the surrounding fluid molecules.  While Stokes law relates the drag force on the particle to its size and fluid viscosity.  Hence, the Brownian motion of a particle can be described by the combined Stokes-Einstein relationship.  However, at the molecular scale, the motion of individual molecules in a fluid is dominated by van der Waals forces, which results in the size of the molecule being unimportant and the diffusion of the molecule being inversely proportional to a fractional power of the fluid viscosity; hence the term fractional Stokes-Einstein behaviour.  Nanoparticles that approach the size of large molecules are not visible in an optical microscope and so we have tracked them using a special technique based on imaging their shadow [see my post ‘Seeing the invisible‘ on October 29th, 2014].


Coglitore D, Edwardson SP, Macko P, Patterson EA, Whelan MP, Transition from fractional to classical Stokes-Einstein behaviour in simple fluids, Royal Society Open Science, 4:170507, 2017. doi: