Author Archives: guestpost

Choosing Between Chemical Engineering and Bioengineering

Chemical engineering and bioengineering, also called biomedical engineering, overlap in some areas because they both create new technology and innovations for the healthcare industry. However, the two disciplines are very different. Here is a comparison of the two careers to help you choose the one that would be best for you.

What Does a Chemical Engineer Do?

A chemical engineer uses science to find solutions to problems, such as manufacturing issues for a food company. They can also work for pharmaceutical, chemical, science, petroleum, coal, oil, gas, trade, manufacturing and other companies.

They usually work in a laboratory or office setting. Sometimes they have to work in an industrial or chemical plant. Some chemical engineers work in the field, such as a refinery. The daily tasks of a chemical engineer can vary, but they usually include research and testing. They may develop new chemicals products, or they may create and test equipment.

photo of a chemical engineering lab setup

Sometimes chemical engineers can solve important problems that affect different aspects of people’s lives. For example, Líney Árnadóttir is a chemical engineering associate professor who studies chemical processes on different surfaces to try to uncover how and why materials degrade.

Árnadóttir and other researchers used supercomputers to study chloride’s role in corrosion. Chemical engineers sometimes use technology, such as the supercomputers at the San Diego Supercomputer Center and the Texas Advanced Computing Center, to do their work and solve problems. By understanding how chloride affects materials like steel, the researchers can help companies, manufacturers and the environment deal with corrosion better.

What Is Bioengineering?

Bioengineering is a field that uses engineering to study and design biomedical technology and systems. A bioengineer usually works in healthcare. They frequently make new medical devices, equipment, software, computer systems and other products to help people.

Bioengineers can create new laboratory machines to diagnose medical problems or artificial organs to replace the ones in a person. It is possible for a bioengineer to find work in a laboratory, research center, manufacturing facility, hospital or university. Some bioengineers work for large companies and help them develop new products.

Every time you go to a doctor’s office or hospital you are seeing examples of bioengineering. When you need an MRI or CT scan, you are using technology built by bioengineers. If you need a hip replacement or a new knee, you are also benefiting from the designs created by bioengineers.

What Type of Qualifications Does Each Require?

In addition to studying engineering and chemistry, a chemical engineer must study math, biology and physics. As a student, you may have to study science topics like engineering computation or chemical engineering thermodynamics. A strong science and math background is important for becoming a chemical engineer. Many pursue a master’s degree after their bachelor’s degree.

A chemical engineer has to be a good problem solver. They have to look at a process or design and figure out how to make it work. They also have to fix it and figure out why it is not working when problems develop. Creativity is essential for this career.

A bioengineer must study engineering, biology and medical science. Additional topics studied by bioengineers include: genetics, computational biology and cell biology. Bioengineers will also must study math and other subjects during college. Many choose to pursue a master’s in biomedical engineering after earning their bachelor’s.

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I Just Finished Statistics for Experimenters and I Cannot Praise it Enough

Guest post by Michael Betancourt.

I just finished Box, Hunter, and Hunter (Statistics for Experimenters) and I cannot praise it enough. There were multiple passages where I literally giggled. In fact I may have been a bit too enthusiastic about tagging quotes beyond “all models are wrong but some are useful” that I can’t share them all.

I wish someone had shared this with me when I was first learning statistics instead of the usual statistics textbooks that treat model development as an irrelevant detail. So many of the elements that make this book are extremely relevant to statistics today. Some examples:

The perspective of learning from data only through the lens of the statistical model. The emphasis on sequential modeling, using previous fits to direct better models, and sequential experiments, using past fits to direct better targeted experiments.
The fixation on checking model assumptions, especially with interpretable visual diagnostics that capture not only residuals but also meaningful scales of deviation. Proto visual predictive checks as I use them today.
The distinction between empirical models and mechanistic models, and the treatment of empirical linear models as Taylor expansions of mechanistic models with covariates as _deviations_ around some nominal value. Those who have taken my course know how important I think this is.
The emphasis that every model, even mechanistic models, are approximations and should be treated as such.
The reframing of frequentist statistical tests as measures of signal to noise ratios.
The importance of process drift and autocorrelation in data when experimental configurations are not or cannot be arbitrarily randomized.
The diversity of examples and exercises using real data from real applications with detailed contexts, including units everywhere.

Really the only reason why I wouldn’t recommend this as an absolute must read is that the focus on linear models and use of frequentist methods does limit the relevance of the text to contemporary Bayesian applications a bit.

Texts like these make me even more frustrated by the desire to frame movements like data science as revolutions that give people the justification to ignore the accumulated knowledge of applied statisticians.

Academic statistics has no doubt largely withdrawn into theory with increasingly smaller overlap with applications, but there is so much relevant wisdom in older applied statistics texts like these that doesn’t need to be rediscovered just reframed in a contemporary context.

Oh, I forgot perhaps the best part! BHH continuously emphasizes the importance of working with domain experts in the design and through the entire analysis with lots of anecdotal examples demonstrating how powerful that collaboration can be.

I felt so much less alone every time they talked about experimental designs not being implemented properly andthe subtle effects that can have in the data, and serious effects in the resulting inferences, if not taken into account.

Michael Betancourt, PhD, Applied Statistician – long story short, I am a once and future physicist currently masquerading as a statistician in order to expose the secrets of inference that statisticians have long kept from scientists. More seriously, my research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that bridge statistical theory and practice and enable scientists to make the most out of their data.
Twitter: @betanalpha
Website: betanalpha
Patreon: Michael Betancourt

Building a Network of Tunnels Underground to Ease the Flow of Traffic

Guest post by Aron Alba

“Roads must go 3D” – Elon Musk

The Boring company plans to build the network of tunnels under the ground in order to combat traffic congestions all over United States. As seen in their presentation video, the idea is to construct a system of tunnels in which electric vehicles autonomously zip around cars, people and cargo transport in high speed under the surface (like a scene from a science fiction movie).

The ride would begin with the lift that lowers the vehicles from the surface into the tunnel system. These lifts could be a possible bottleneck for the entire system, but it may be the best solution. To secure the vehicle to the autonomous pod and possibly select the end destination would take some time anyways, so this transition into the tunnel system could go unnoticed. Pods could travel at higher speeds than those allowed for the human driver, since the system is autonomous and completely monitored. The scenery wouldn’t be much though, so probably not the most interesting ride, but certainly fast.

Why build a tunnel network in the first place?
Traffic congestion is a very common nuisance in american lives. With the problem just getting worse. In order to solve this problem you have to build more roads or have fewer cars on them with arranging a better public transport. The land for the roads is scarce. The alternative of going up using drones to fly people around may not become possible due to safety concerns in a long time. Where to go then? Underground.

This has not been done before for obvious reasons, it is really expensive. The most expensive roads to build are tunnels and bridges. Tunnels have even more problems the larger they get. With people driving inside of them there needs to be proper ventilation to get rid of the carbon-monoxide. Resting stops for people. Great deal of risk with so many people driving inside a closed tunnel. The subway system is one solution to many of these problems. Except subways lack the flexibility and require substantially more infrastructure.

Elon Musk’s big plan is to use the technology that his other company Tesla already has developed. Instead of trains like in a subway system, Musk plans to have autonomous pods that run on battery power to zip along the tunnels. This has several advantages. First the battery powered pods to not require power lines to be continuously run through the tunnel like the train does, this saves on the costs of the tunnel. Also since the pods will be autonomous, this saves on personnel needed to operate the system. But probably the smartest idea behind the Boring company’s plans is to build a tunnel with a smaller bore diameter. Probably large enough to fit a pod with a largest planned Tesla vehicle but certainly smaller than the current tunnels for trains.

The Boring company plans to build the tunnel network using a tunnel boring machines. These machines are massive systems build to bore tunnels with circular cross section. They consist of cutting head system, a system for removing earth, systems for advancing the cutting head, systems for laying the concrete walls around the bore. At the end these machines leave a tunnel pretty much ready to use.

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3D Printing at Home: Today, Challenges and Opportunities

Guest post by Noah Hornberger

The State of 3D Printing at Home

Rapid prototyping is very rewarding. Moving from an idea that you had during breakfast to an object you can hold in your hands by lunchtime feels like magic or science fiction.

Modeling tools are getting easier to use, making the actual process of designing 3D objects fairly intuitive and dare I say . . . easy. I suspect home 3D printing is empowering a silent revolution that will be more and more apparent in the coming years.

Taco Shell Holder, a recent idea I had during breakfast was ready to test the next day.

Even so, there is a lot of quirkiness to the 3D print technology that an average consumer is probably not ready to deal with. In this post I want to give inside information I have learned by running my own home-based 3D print business. I have been there in the trenches, with a queue of orders, a few 3D printers and the drive to make it happen. And let me tell you that without the drive to push past the obstacles, it really would not be possible to run a 3D print-on-demand business this way.

3D printers have enabled me to pull off an impossible task of distributing my own artistic products to an international market. I have shipped to USA, Spain, Australia, Norway, Canada, and the UK. And this May of 2015 marks my first year of owning a 3D printer.

small 3d printed planters, 1 with a plant growing in it

Mini Dodecahedron Planters, my first attempt at designing and printing an idea from scratch. I was hooked.

So there is some magic I would say in being able to move through iterations of your ideas so fast. And magic in being able to post photos of your products that people can understand to be real and tangible things.

I have had ideas for products for many years and even tried to launch them (unsuccessfully). But now things are different. I do not have to convince people that an idea is good, I can show them a real example of finished art they can own.

I would argue that 3D modeling is the easiest part of the process. Getting a spectacular print can take some work and patience, because it can involve re-starting the printer with small changes in settings each time. As an American trained artist, I have a tendency to want things to be fast and easy. I want to press a button and it just works. 3D printers can kind of promise this ability, but most often, I am stepping in to keep the machines on track.

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Controlled Experiments for Software Solutions

by Justin Hunter

Jeff Fry linked to a great webcast in Controlled Experiments To Test For Bugs In Our Mental Models.

I firmly believe that applied statistics-based experiments are under-appreciated by businesses (and, for that matter, business schools). Few people who understand them are as articulate and concise as Kohavi. Admittedly, I could be accused of being biased as: (a) I am the son of a prominent applied statistician and (b) I am the founder of a software testing tools company that uses applied statistics-based methods and algorithms to make our tool work.

Summary of the webcast, on Practical Guide to Controlled Experiments on the Web: Listen to Your Customers not to the HiPPO – a presentation by Ron Kohavi with Microsoft Research.

1:00 Amazon: in 2000, Greg Linden wanted to add recommendations in shopping cards during the check out process. The “HiPPO” (meaning the Highest Paid Person’s Opinion) was against it on the grounds that it would be a bad idea; recommendations would confuse and/or distract people. Amazon, a company with a good culture of experimentation, decided to run a small experiment anyway, “just to get the data” – It was wildly successful and is in widespread use today at Amazon and other firms.

3:00 Dr. Footcare example: Including a coupon code above the total price to be paid had a dramatic impact on abandonment rates.

4:00 “Was this answer useful?” Dramatic differences occur when Y/N is replaced with 5 Stars and whether an empty text box is initially shown with either (or whether it is triggered only after a user clicks to give their initial response)

6:00 Sewing machines: experimenting with a sales promotion strategy led to extremely counter-intuitive pricing choice

7:00 “We are really, really bad at understanding what is going to work with customers…”
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What is an Engineer?

Guest post: What is an engineer? by Chris Gammell

I’ve been having what some would call an identity crisis. How, you ask? I’ve been working on digital electronics.

*GASP*!

I found out that in the early 90s and even earlier, analog engineers routinely switched from working in the analog domain to the digital domain…because it was paying really great. Not only that, most analog engineers had the expertise to do what most early digital engineers were doing (basically stringing together a lot of digital gates in DIP packages). It wasn’t until later that digital engineers started acting more as programmers and VHDL/Verilog experts.

So why do I bring this up? Because I’ve been thinking about the versatility required from engineers in general, not just analog or digital engineers. Routinely engineers are asked to switch modes or tasks or careers in order to get a job done. It’s not that other professions are never asked this; it’s just that the chameleon-like requirement placed on engineers seems to define the profession. Allow me to explain.

What is an engineer?

An engineer puts theories into practice using available devices and elements. They create new products and pass on knowledge through design iterations and trial and error. Their work should be directly applicable to the real world (sometimes in the form of an end-product, sometimes not) and hopefully able to be reproduced successfully in the same form for multiple parties (mass manufacturing). Engineers are often rooted in math and science but require a wide range of skill-sets in order to properly construct an end product.

I think it is important to note that an engineer is different from a scientist, although the line can often be blurred (especially when looking back at the inventors of the early 20th century). In modern times a scientist is usually tasked with pushing the barrier and finding new theories and concepts. This means that the concept will not necessarily be available in product form right away (although this is not always the case), as the product form must be iterated upon and improved for production.
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