Water is weird

Source: Wikimedia Commons. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

What’s new?

Two researchers at the University of Southern California (USC) have shown that when a graphene electrode is placed into water the molecules of water closest to the electrode “align in a completely different way than the rest of the water molecules,” a result that was not anticipated. The findings may have implications in many fields, especially in methods proposed for desalinization of water.  

What does it mean?

Water has many strange properties, according to Alok Jha, author of The Water Book. Unlike most other liquids, water expands when it freezes; thus, ice floats in water, insulating life under the ice. Water in rocks expands in the cold and cracks the rocks open, an important fact in the creation of soil. Water, even though made from two gases, is a liquid. It has a surprisingly large surface tension, enabling insects to walk on it. The attraction between water molecules leads to capillary action, important to all life. Almost anything dissolves in water. I could go on and on since Rachel Brazil claims that water “has at least 66 properties that differ from most liquids – high surface tension, high heat capacity, high melting and boiling points and low compressibility.”

The electrode the researchers used is made from graphene, a very interesting form of carbon in which the carbon atoms are arranged in a single layer honeycomb lattice. It has promise to improve battery performance, hence its use as an electrode in this experiment.

The result they observed occurs at the surface of the electrode, where the water and electrode meet. Many interesting chemical and physical effects occur at surfaces. One of the researchers at USC concentrates on the molecular structure and physics of surfaces,” as explained at this web page about the Benderskii Research Group.

What does it mean for you?

I draw two lessons from these facts about water and from the newly reported research. First, science has increased our understanding of the world amazingly, but some simple parts of our world still defy our understanding; at least sometimes engineers can use the natural world in ways that science does not actually understand well. The fact that discoveries continue to be made about water – water! – amazes me.

Second, discoveries continue to be made that will improve our ability to generate, distribute, and use electrical energy. While not mentioned in the article, this result has implications for the development of batteries. I believe that we must move much more quickly than we are doing now to reduce the emissions of greenhouse gases to head off the climate changes that are occurring, but I also believe that surprises await us in science, engineering, and technology that will help us along this path. Stay tuned for more news.

Where can you learn more?

As you might expect, the US Geological Services (USGS) has a great page about the properties of water. This BBC animation, narrated by author Alok Jha, explores some of the strange properties of water. Some of those properties are listed here.


Source: https://rossetti.github.io/RossettiArenaBook/modeling-a-simple-discrete-event-dynamic-system.html  This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

What’s new?

In a 10 June 2021 posting on IISE Connect, Professor Manuel Rossetti of the University of Arkansas announced the availability of the third edition of his textbook Simulation Modeling and Arena as an open text. Previous editions were published in book form by Wiley, and Professor Rossetti decided to make this edition available for free, saying:

With technology constantly changing, I thought that it would be useful to get this book on-line so that I can more readily keep it up to date. It is my hope to provide updated versions when there are significant changes made within Arena.

What does it mean?

Humans are makers, including makers of tools. An important tool for humans and especially for humans who are engineers is a model, that is, a representation, sometimes conceptual or physical but often mathematical, of a real-world system.

Mathematical models can be analyzed using mathematics; that is, we can use mathematics to elicit results concerning the behavior of the model. For example, the differential equation that describes a pendulum swinging in small angles can be solved to give a mathematical description of the pendulum’s motion over time, but the differential equation is an approximation and does not hold if the pendulum swings in a large arc. Also, the more accurate differential equation that describes large swings cannot be solved to give an equation of motion over time, but rather must be solved using numerical methods, again giving an approximate solution.

The real-world systems of our organizations are complicated, so the models of them must be complicated. Also, unlike a pendulum whose motion can be predicted with certainty, we are inherently uncertain about what will happen in the future with many real-world systems. Professor Rossetti starts his book by describing the behavior of an emergency room in a hospital. Randomness abounds in such a system, so any model must use the mathematics of uncertainty. Probability is the field that enables us, indeed requires us, to talk precisely about our uncertainty.

Having good data makes your model a more accurate representation of the real-world system. But even without data, methods can elicit the knowledge of experts and express them in a way that can be used in a simulation.

Professor Rossetti uses the simulation computer program Arena throughout his book.  The graphical user interface of Arena supports a drag-and-drop approach to select model components from menus. If you want to get a feel for what kinds of systems can be modeled, what the necessary steps are, how Arena is programmed, and what the resulting model looks like, section 4.5 of the book has an extended example. Depending on your background, you may not understand all of what is shown there, but you will get a feel for simulation models and Arena. The book has other examples that will help you understand simulation more.

The book is not about the simulation of all systems. “This book primarily examines stochastic, dynamic, discrete systems.” “Stochastic” means that the system has randomness, “dynamic” means that it changes over time, and “discrete” means that changes in the system occur at specific times (for example, when a patient arrives at the emergency department) not continuously (for example, when water flows out of a reservoir). Many of our organizations have systems that can be modeled in such a way.

What does it mean for you?

As Professor Rossetti points out: “A simulation model can be used to predict future behavior through running what-if scenarios.” You can perform experiments in the simulation, experiments that you can’t perform on the real-world system. What if the number of people arriving at the emergency room spikes due to a pandemic? What if we added two more nurses at certain times of the day? You can ask – and answer – questions like these and determine the likely effects on important performance measures, such as the waiting time for patients, the number of patients in the hospital, and the health outcomes of the patients. The simulation operates, of course, much more rapidly than the real-world system so you can simulate years of operation in seconds. Thus, you can ask a lot of questions and a get a feel for how the simulation performs in many different scenarios. Section 8.1.1 shows the results of several what-if simulations of the model from section 4.5.

A key point to remember, however, is one of my favorite phrases, “it’s only a model.” A model gives you numerical predictions of the future, but because of the randomness in the real-world system and because a model does not represent all aspects of the real-world system, most people use simulations of models to gain insight rather than exact predictions. For example, what changes to the model have the biggest impact on performance measures?

If you haven’t considered using simulation in your organization, Professor Rossetti’s book is a place to start. I think you will get a good idea from the many examples in the book about whether this approach is worth trying for your organization.

Where can you learn more?

The major point of this post is that you can learn more in Professor Rossetti’s book – and it is free. His book has advice on how to continue your education in simulation.

A library of videos about Arena is available here, including videos showing the animation of Arena models, a very powerful feature for understanding a model.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Your craft

“Funky sculpture at the Dogfish Head Craft Brewery in Milton, Delaware.” Source: The Library of Congress, https://www.loc.gov/pictures/item/2018701329/, which says “No known restrictions on publication.”

What’s new?

I subscribe to the magazine American Craft. The Summer 2021 issue has articles on

  • Sylvie Rosenthal who makes sculptures that combine animals and architecture,
  • Detroit metal artist Tiff Massey,
  • Artists whose works educate and advocate for ocean life,
  • Research by Namita Gupta Wiggers on craft products as “social objects that are inscribed with histories and narratives that can tell us something about the world.”
  • The artist in residency program at the Kohler Company in Wisconsin, and
  • Textile florist Yi Hsuan Sung’s use of agar, a high-strength gel made from seaweed.

The theme of this issue is Flourish, about which the editor says:

A flourish is a bold, extravagant gesture. To flourish means to grow or develop in a vigorous way. Flourishing also speaks to having a strong sense of well-being and meaning. So, for this issue, we looked to the craft community to find stories about many ways of flourishing. One thing that became clear when we put this collection of stories together was that flourishing is deeply connected to community.

What does it mean?

The word “craft” is loaded with different meanings and uses. Craft can invite women in (arts and crafts) but can also shut them out (craft workers). Is a creation art or craft? Craft is generally low brow, not high brow. Craft may emphasize function while art emphasizes decoration.

Another article in the Summer 2021 issues,  titled “The Art of the Flourish,” points out that function and decoration can merge. Vintage radiators are designed with fins to maximize surface for radiation of heat to the room, and the result is pleasing to the eye. My partner and I have visited Shaker Village at Pleasant Hill several times and delight in the spare, functional designs there (Shaker Mother Ann Lee said, “Put your hands to work and give your hearts to God”).

Engineers say that their unique function is design, but I have had many delightful conversations with art professors about how the concept of design unites engineering and art, with various combinations of knowledge, skill, and theory required to design. When does someone transition from being an artist to an artisan to a manufacturer? Craft usually involves a handmade object, but all crafters use tools and many use machines.

Why do people craft? Rather than calling humans homo sapiens some prefer homo faber with making, not thinking, as the defining function of modern humans. Humans emerged with the first chip flaked off a stone 2.6 million years ago.  That act was the root of craft, art, and engineering.

What does it mean for you?

For me, one of the unifying concepts in craft is process – how is something made. Design matters, but then the design in the maker’s head has to be made. I enjoy reading American Craft to see what people have crafted but also to see how they crafted it. Another unifying concepts is materials. The article on agar has details on the properties of that material.  I enjoy reading about the materials the crafters use.

For you, I suggest that inspiration can come from many sources. You should be constantly scanning the horizon, in your trade and business publications, for innovations and ideas that will have impact in your industry, but publications far from your field can be inspiring too.

For example, the work of Namita Gupta Wiggers that I mentioned earlier includes her invented word “craftscape” to emphasize the cultural connections represented in a craft object. Her work may illuminate manufacturing work through her ideas about how labor and raw materials are transformed into usable objects. If you seek to create a corporate culture, aspects of her work may spark ideas for you about the deeper meanings of objects for the people in your organization. Culture is not just ideas but also objects.

For another example, the article on the Kohler Company mentions the not uncommon use of their bathtubs as shrines: upended and half buried to enclose a religious figure. Do you know how your products are actually being used?

Print magazines are not, of course, the only way for you to find inspiration. Certainly a deep dive in the rabbit holes of the Internet will uncover much good – and much that is a complete waste of your time. For me, magazines have the advantage of being curated carefully and of being in print. I  linger with a magazine – and coffee – knowing that people spent time thinking, planning, photographing, writing, rewriting, editing, and formatting. American Craft is a well crafted magazine.

Where can you learn more?

American Craft magazine is published by the American Craft Council.

You can search and browse among magazines at Magazines.com, Magazine.store, or Magzter.  The top 10 magazines in the US by circulation include one I had never heard of:  Game Informer Magazine, published by game retailer GameStop. Your local library probably has many magazines for you to dip into; I found American Craft through my library and read it there until I decided I had to have my own copies.

What magazines do you subscribe to? I subscribe to Make, New Scientist, and Smithsonian, among others, in addition to receiving magazines and journals from my professional societies, ASEE (American Society for Engineering Education),  IISE  (Institute of Industrial and Systems Engineers) and ASQ (American Society for Quality). I also receive several publications from genealogical societies, to support my genealogical hobby.

Inside the volcano

Diagram of a volcanic eruption. Source: Wikimedia. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

What’s new?

In the 22 May 2021 issue of New Scientist, Michael Roberts of the Cambridge Image Analysis group at the University of Cambridge reported on a study of attempts to use machine-learning (an artificial intelligence technique) to diagnose COVID-19 and to predict how patients would fare with the disease. He and his colleagues examined over 300 papers published between 1 January and 3 October 2020 and found that none had produced a useful tool.

What does it mean?

I have written about artificial intelligence (AI) before in this blog. On 15 May 2020, I noted that the inability of many artificial intelligence based techniques to explain in human terms how they reached a conclusion limits their usefulness in circumstances where an explanation is needed as part of the decision making process. On 13 June 2020 I wrote that the computer program Watson had been a great success on the TV game show Jeopardy but had failed to be useful in the medical field.  On 2 Jan 2021, I cautioned against the hype concerning AI, in this case regarding a study meant to increase our understanding of whale calls. In my recent (8 May 2021) review of my year of blogging, I noted that AI was the one technology about which I was not generally positive. In this blog post, I am again going to caution about AI hype and about the need for a model that humans can understand.

I am heavily influenced in my opinion about AI by the results of a PhD dissertation I advised at Ohio State in 2002, titled “Quantitative measurement of loyalty under principal-agent relationship.” Keiko (Kay) Yamakawa attempted to detect disloyal insurance agents for a large insurance company, that is, insurance agents who issued policies from several companies and whose behavior indicated they may be failing to recommend products from this particular insurance company. Dr Yamakawa had little success with AI approaches (such as a hidden Markov model) but found that a more traditional approach using control charts was successful. The former approach, hidden Markov models, is based on a search for a statistical model that reproduces the patterns in the data and, even if successful, is unable to be used to generate an explanation of what it did. The latter approach, control charts, is based on classical hypothesis testing (with all its benefits and faults) to detect if a process that has been behaving with statistical regularity has moved out of that state of statistical control; the method includes charts that visually display an explanation of the result. Indeed I was dismayed that it took Kay and me so long to decide to try control charts since the formulation of the problem was clearly the detection of a process that had moved out of control.

Nineteen years later AI techniques and computer capabilities for handling huge data bases have advanced greatly, but I believe that the general findings of that dissertation still apply. AI techniques are mesmerizing in their promise to detect patterns and apply them to practical situations without the need to understand the domain. Other techniques require more domain knowledge and more understanding of the particular problem being attacked. A tension always exists between those who have powerful techniques and seek to apply them in disparate areas, sometimes in areas where they have little domain knowledge, and those who have the domain knowledge and watch the masters of technique flounder.

Paul Lingenfelter cites political scientist Donald Stokes as describing the statistical technique of factor analysis as “seizing your data by the throat and demanding: Speak to me!” Researchers must always guard against uncovering spurious patterns that occur just by chance (for example, by reserving some data to test findings). Increasing access to big data sets and computing power and increasing use of sophisticated data analysis techniques have created many successes but have also created failures such as this one in COVID diagnosis.

In 1947, economist Tjallings Koopmans wrote a cautionary article titled “Measurement Without Theory.” In reviewing a book on business cycles, Koopmans lamented the authors’ attempt to measure and analyze data without the use of theory. Koopmans wrote:

Measurable effects of economic actions are scrutinized, to all appearance, in almost complete detachment from any knowledge we may have of the motives of such actions. The movements of economic variables are studied as if they were the eruptions of a mysterious volcano whose boiling caldron can never be penetrated. There is no explicit discussion at all of the problem of prediction, its possibilities and limitations, with or without structural change, although surely the history of the volcano is important primarily as a key to its future activities. There is no discussion whatever as to what bearing the methods used, and the provisional results reached, may have on questions of economic policy.

Almost 75 years later, those sentences still bite.

What does it mean for you?

In my 11 July 2020 blog on the topic of models, I cited the quote “the purpose of modeling is insight, not numbers.” In creating models of real world systems and in analyzing data, I urge you to focus on understanding the system, not just on finding empirical patterns.  A black box that takes input and gives output is less useful in the long run than a transparent model that promotes understanding. You should consciously be building models – mental or mathematical – of your organization and the environment in which it functions.

Where can you learn more?

Koopmans’s article was published in The Review of Economic Statistics, volume 29, number 3, August 1947, pages 161-172.

This work is licensed under a Creative Commons Attribution 4.0 International License.