More on fairy lights and volume decomposition (with ice cream included)

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Explanation in textLast June, I wrote about representing five-dimensional data using a three-dimensional stack of transparent cubes containing fairy lights whose brightness varied with time and also using feature vectors in which the data are compressed into a relatively short string of numbers [see ‘Fairy lights and decomposing multi-dimensional datasets’ on June 14th, 2023].  After many iterations, we have finally had an article published describing our method of orthogonally decomposing multi-dimensional data arrays using Chebyshev polynomials.  In this context, orthogonal means that components of the resultant feature vector are statistically independent of one another.  The decomposition process consists of fitting a particular form of polynomials, or equations, to the data by varying the coefficients in the polynomials.  The values of the coefficients become the components of the feature vector.  This is what we do when we fit a straight line of the form y=mx+c to set of values of x and y and the coefficients are m and c which can be used to compare data from different sources, instead of the datasets themselves.  For example, x and y might be the daily sales of ice cream and the daily average temperature with different datasets relating to different locations.  Of course, it is much harder for data that is non-linear and varying with w, x, y and z, such as the intensity of light in the stack of transparent cubes with fairy lights inside.  In our article, we did not use fairy lights or icecream sales, instead we compared the measurements and predictions in two case studies: the internal stresses in a simple composite specimen and the time-varying surface displacements of a vibrating panel.

The image shows the normalised out-of-plane displacements as the colour as a function of time in the z-direction for the surface of a panel represented by the xy-plane.

Source:

Amjad KH, Christian WJ, Dvurecenska KS, Mollenhauer D, Przybyla CP, Patterson EA. Quantitative Comparisons of Volumetric Datasets from Experiments and Computational Models. IEEE Access. 11: 123401-123417, 2023.

Evolutionary model of knowledge management

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Towards the end of last year, I wrote about the challenges in deploying digital technologies in holistic approaches to knowledge management in order to gain organizational value and competitive advantage [see ‘Opportunities lost in knowledge management using digital technology’ on October 25th, 2023].  Almost on the last working day of 2023, we had an article published in PLOS ONE (my first in the journal) in which we explored ‘The impact of digital technologies on knowledge networks in two engineering organizations’.  We used social network analysis and semi-structured interviews to investigate the culture around knowledge management, and the deployment of digital technologies in support of it, in an engineering consultancy and an electricity generator.  The two organizations had different cultures and levels of deployment of digital technologies.  We proposed a new evolutionary model of the culture of knowledge management based on Hudson’s evolutional model of safety culture that is widely used in industry. Our new model is illustrated in the figure from our article, starting from ‘Ignored: we have no knowledge management and no plans for knowledge management’ through to ‘Embedded: knowledge management is integrated naturally into the daily workflow’.  We also proposed that social networks could be used as an indicator of the stage of evolution of knowledge management with low network density and dispersed networks representing higher stages of evolution, based on our findings for the two engineering organizations.

Sources:

Hudson, P.T.W., 2001. Safety management and safety culture: the long, hard and winding road. Occupational health and safety management systems, pp.3-32, 2001

Patterson EA, Taylor RJ, Yao Y. The impact of digital technologies on knowledge networks in two engineering organisations. PLoS ONE 18(12): e0295250, 2023.

 

Sleeping on the job

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Decorative image onlyAt the end of 2023, following my visit to IBM [see ‘Chirping while calculating probabilities‘ on November 22nd, 2023], I spent a significant amount of time trying to understand quantum computing and exploring its potential applications in my research.  It was really challenging because, as one article I read stated, quantum-mechanical phenomena appear to be weird and the mathematical tools used to model them are complex and abstract.  Just to make it harder you have to learn a new language or at least new terminology and mathematical notation.  I have always found that my unconscious mind is capable of solving mathematical problems given sufficient time and sleep.  However, the mathematics of quantum computing took many nights of unconscious thought to assemble into some sort of understanding and left me with mild headaches.  Around the same time I was reading one of Cormac McCarthy’s new novels, Stella Maris, which consists entirely of a psychologist interviewing a mathematician who is a patient in a hospital. They discuss that mathematical work is performed mostly in the unconscious mind and we have no notion as to how the mind goes about it.  They find it hard to avoid the conclusion that the unconscious mind does not use numbers.  I suspect that it does not use mathematical notation either; perhaps it is more a form of synaesthesia using three-dimensional shapes [see ‘Engineering synaesthesia‘ on September 21st, 2016].  A couple of pages before discussing the unconscious mind’s mathematical work, one of the protagonists comments that ‘If we were constructed with a continual awareness of how we worked we wouldn’t work’.  So, perhaps I should not probe too deeply into how I have acquired a rudimentary understanding of quantum computing.

BTW in case you missed my last post at the start of January [‘600th post and time for a change‘ on January 3rd 2024] and have been wondering what has happened to my weekly post – I have decided to switch to posting monthly on the first Wednesday of each month.

Source:

Cormac McCarthy, Stella Maris, Picador, 2022.

600th post and time for a change

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Decorative photograph of a wind-shaped tree on a hillside in fogSimplism is the ideology of simple answers for complex problems and it appears to be gaining popularity as high-level reading skills decline around the world.  People without high-level reading skills also tend to lack high-level thinking skills and their need for simplicity is met by simplism delivered from a range of sources, including politicians. However, complex problems by definition can be viewed from multiple competing perspectives and have multiple possible solutions; so, simple answers are unlikely to be informative or represent reality.  While trying to provide intelligible clear explanations in this blog [see ‘When less is more from describing digital twins to protoplasm‘ on February 22nd, 2023], I have always tried to avoid over-simplification or any drift towards simplism.  I fear that my uncompromising approach to complex issues and the decline in high-level readers globally has led to a steady decline in the readership of this blog over the past twelve months (to about half the number in 2022 and the lowest level since 2015).  Or perhaps I have just run out of interesting original topics to share in posts.  In either case, my decision to stop writing regularly posts announced in September [see ‘Reflecting on the future of RealizeEngineering‘ on September 20th, 2023] seems appropriate.  This is the 600th post and represents 11 years of weekly posting (for those readers working out the mathematics: there were 21 posts before I started weekly posting), which seems an appropriate moment to change the pattern to monthly posts, on the first Wednesday of each month.

Source: Simon Kuper, The end of reading and the rise of simplism, FT Weekend Magazine, October 21/22, 2023.