As my extra credit project, I propose to examine what factors drive energy use at office buildings in Washington DC.Washington is one of a small number of cities that have energy disclosure laws. While the dataset was introduced later than New York's Local Law 84, it provides rich data with which to analyze and benchmark energy use.Motivation:Washington DC has a significant number of large apartment complexes with more than 100 residential units. However, these differ in age and design characteristics: some date from 1900-1945; many others were built in 1945-1975; and a new generation of large, expensive apartment blocks is going up.Should the city government focus on the older residential buildings - which may have poor build quality and lack good insulation - or should it focus on the fancy new buildings, which are ostensibly high-tech and energy efficient, but also have swimming pools and large floor layouts? The project should provide an answer by prioritizing which set of buildings to focus on based on their relative energy consumption.Data availability:The Washington DC Department of Energy and Environment (DOEE) publishes annual data including EUI / sq foot, and weather normalized EUI.City planning data makes available floorspace, building age, and amount of the lot covered by the building.Approach:The project would merge and clean the relevant datasets.A linear regression model would be constructed with building EUI as the dependent variable.During the exploratory phase, additional data columns may be discovered and incorporated into the model.The validity of the regression model would be tested against a training set of Washington DC buildings, and the model would be optimized through feature selection, as well as checking and correcting for multi-collinearity.Expected outputs:A simple predictive model for apartment building energy consumption in Washington DC together with its results.Identification of the top large apartment blocks whose EUI differs from the predicted value, visualized on a map.Identification of features associated with high energy use (such as the energy use tendencies for 1950s, 1970s and 2000s buildings).

Blockchain technology has great potential to revolutionize healthcare data management. The technology is sufficiently complex, however, making it essential that a large number of people with a broad range of skills will be required to implement the technology. Innovation clusters will be the primary means of producing blockchain breakthroughs in healthcare by bringing together computer scientists, medical experts, and business people in the pursuit of a common goal. Healthcare innovation clusters are most likely to be centered around medical universities, but will also include in close geographic proximity technology, business, and medical insurance organizations.

Space is all that there is, just not the space as we live in it every day. The true space has position but not as we know it. Yet from it we can find all the forces that make our world.

We use picture-taking-boxes with a very large make-bigger-power to take pictures of the very small building blocks that make up humans and other animals. We can also see the tiny things that do important stuff inside these building blocks. These tiny things are interesting because we need to understand them to see why people get sick and how to make them better. If there isn't a box that does what we need, we build a new one that does! Sometimes we even make boxes that go inside other boxes so we can take different kinds of pictures all at once to tell us different things.The boxes make lots of pictures so we need good ways to understand them quickly with computers. To do this we need to carefully give the computer lots of orders and numbers to control what they do so they do good work. At the moment, even with the best computers this is too hard and takes too long, so we need to think of better ways to train the computer to do good work more quickly. Often we have to ask a person to do the work as they still do it best, but slowly.We have started to ask tens of hundreds of people around the world to help us as they can do good work together more quickly than one person. We can also get them to help us train the computers to do better work in up-coming times. The people that help us are aged from school children all the way up to older people who don't have to go to work any more, and everything in between. 

We describe a design that uses 3D printed parts and off-the-shelf components to create an inexpensive system for automated storage and retrieval of microplates. In this way, a robotic system for storage of 24 plates can be built for less than $400.

My work is about two types of studies. In one study we look at cells from a normal person and a sick person. We find out what is different between them. In the other study we look at many possible ways to make a person better again. You can not buy most of these in a shop as they have not been studied in all possible ways to be sure that they really do what they should. In a next step we use a computer to match what we know about sick people with the best possible way to make them better again. This best possible match becomes the starting point for coming up with a new way to get sick people better again. It does not happen very often at all, but sometimes we can even find something that you can already buy. In those cases we can help people very quickly \cite{Lamb_2006}.

big body is beautiful. It feels happy. It is made of small beautiful parts. when one part turns bad, it stops being beautiful. more parts start to go bad, the big body does not feel happy. so the beautiful parts make the body happy and the not beautiful parts make the body feel bad.

HistoryI decided to be a scientist when I was 7 years old after watching Carl Sagan on TV talk about neutron stars. By 16 I discovered the protein folding problem: a disarmingly simply stated problem whose solution lay at the heart of everything in biology. How hard could it be?So a degree in Biochemistry later, I did a PhD in the lab of a computational molecular modeller (Mike Sutcliffe) and got started on the problem of clustering protein structures from NMR. I wrote my first program in C and built my first web site with my characteristically stupid pun in the name - OLDERADO: On-Line Database of Ensemble Representatives and Domains [1] - to look at the results. Each morning I would go on the internet as it then was to check the dozen or so new sites that had sprung up the night before and which had been manually added to a list known as the ‘internet directory’ by someone ‘out there’ on the new web. Google appeared about a month later. Websites were going to be a big deal. Being an early adopter of web-based science seemed like a good bet.Then a move to London to be with Mike Sternberg, a world expert on protein structure prediction. I inherited an early protein fold recognition program (Foldfit [2]) written by Rob Russell, Mansoor Saqi, Paul Bates and Roger Sayle (of Rasmol fame) and had to improve it. The result was 3D-PSSM [3] and I made a ‘fancy’ website (read: 1990’s black background with gold, animated logo) for other people to use the program and for me to figure out where the bugs were. It worked great - we won the automated section of the international CASP competition in structure prediction, and hundreds of people started using the site. It was one of the first of its kind, which made it easier to stand out. Throughout I was mainly making the site to help me understand how my software worked in various situations. With so many different types of data to look at, I needed a way to sensibly display this info just to understand what was going on internally. This remains a critical focus.New techniques arose in the field and I sat on my laurels with 3D-PSSM. I neglected the site and realised only later how valuable a large internet audience is and how I had squandered it at its peak. So I vowed to try again and rebuild it from the ground up, this time properly. Using newfound skills in web development I created Phyre [4]. This did well in both scientific recognition and number of users so I was very pleased. But soon funding looked iffy and was about to run out. Funding I wondered if any users could help. So I asked on the Phyre page if anyone could write a letter of support for us that we could include with our application for funding. We received over 1,000 letters of support which was amazing and had a huge effect on our application. But I think it placed the funding bodies in a difficult position. Making web sites that help scientists use state-of-the-art tools is undoubtedly a good thing. These site are continuously working, 24 hrs/day reliably doing some work to help science. They seem like safe and sensible places to direct funding - IF they are widely used. IF they are widely used, the funding body would look deliberately negligent if they failed to fund it. But at the same time they are justified in thinking this is outside their remit. They decide on science funding, not tools. So new arms of the funding bodies started to form, dedicated to tools and resources. This is where we are today. But deciding how to handle funding for tools is a new challenge we are all still grappling with. The quest for funding is ever-present. It is critical that tools are maintained. How do you justify the maintenance cost? It is easy to fall through the funding cracks - it’s neither proper science, nor pure infrastructure. I get very paranoid about funding living in this limbo. But if user numbers and citations continue to increase, we are safe. These are the only ways to prove your value in this game. But this hopefully reflects real-world utility of the tools you create. So what’s the best way to maximise the utility of these tools to researchers?Build it, and they will comeGreat tools based on state-of-the-art algorithms often go unused and unnoticed - great science gets ‘hidden’ in the hands of a few people competing intensely over small absolute changes in accuracy using programs designed for no one but the developers. The gains to society and science by enabling more people to use these tools are far greater. But then they need to be far easier to use if more people are to use them. Hence my focus on user interfaces. The best way to learn what works for user interfaces is by using lots of them. And thinking about the types of question a user may have. Simply BE a user and every time you want to do something and CAN’T, write a program to do it. Accumulate these programs in a web site. This empowers me and anyone else who cares, with these new tools - a virtuous circle.‘BE the user’ sounds great. But it gets harder as you spend more time with computer development and less time working on a biological problem with real proteins. I don’t mean wet lab. Just computational analysis problems with specific proteins faced by real scientists. That’s the hardest part to remain connected to. For now I just imagine scenarios. I imagine all the combinations we can make by connecting our tools in different ways, and build some that seem most promising. I meet users at workshops to find out what they want. But this is where the next development needs to happen. How can I communicate effectively with 50,000 users about what they want and what I can deliver? This is the biggest challenge for me right now.Despite the funding complexities, we got funding for Phyre2 [5]. THIS time I can do it properly, I thought. Use the most up to date facilities in the browser to make it look nicer, easier to use, and add those new ideas I had or were suggested to me by users at workshops (PhyreAlarm, BackPhyre, One-2-one threading). I also tried to improve how a user could look both at sequence and structure at the same time in the browser to analyse a range of features. This led to the development of Phyre Investigator (one module in Phyre2) and honed my skills at javascript for the future. This most recent paper has again done well in the citation game which I hope reflects its usefulness to researchers ‘out there’ on the web.FoldingIt’s important to remember the reason I’ve done any of this, and that reason is the mystery of protein folding. The person that solves the protein folding problem (at least I hope it’s a person) won’t do it with pen and paper. It will be someone extremely adept at using a variety of tools that probably already exist individually, and who will put them together in the right way. That is generic problem solving. Make this easier for people, and problems in general get solved faster. With folding my main fear is this being one of the first major science problems where artificial intelligence gets there before us. Google DeepMind is after it, and we’ve seen what they’ve done to the game Go.Protein folding is the molecular biology equivalent of the Goldbach conjecture: The impossible task that no one would sensibly pay you to work on but you want to work on because of its elegant simplicity and deep importance. So you need to do something useful while you ponder the impossible. You find the most useful thing you can do for everyone else and for you. Keep it as close as you can to folding whilst maintaining an audience size that justifies your existence - i.e. homology modelling. Problems that no one has been able to solve for decades typically don’t fall over from expected directions of attack. To be a long-standing problem means most lines of attack that occur to people have been tried. So new lines must be found by looking further afield, in other areas of science where an analogous problem may have been thwarted. But to see the mapping between a problem in say physics or economics or mathematics, to the folding problem requires a decent understanding of both areas. So that means trying to learn physics, signal processing, quantum mechanics, maths, computer science, AI, etc. All in the hope of seeing a new angle from which to attack the problem. And it’s fun and fascinating. But you need to make yourself useful to the world and justify your salary. My most recent direct stab at folding involved eigen decomposition of protein contact maps. As you may surmise, I have not yet been contacted by Stockholm.Where to publish?Well it’s not really science is it? Its tools to do science. So it doesn’t sit easily with most journals. You can submit an ‘Application Note’ which is about a page describing your web server. This doesn’t really count as a proper scientific paper in many eyes. Or often authors will use their tool to do some rapidly thrown together analysis that makes what would otherwise be an application note pass for a normal scientific article. Neither of these scenarios is ideal. The way I see it, when I’ve made a tool I haven’t answered a biological question, but I’ve made it easier to answer many biological questions. In the end we went for Nature Protocols which is aimed at step by step instruction for how to use previously published tools. It fits, albeit somewhat uncomfortably.FutureSo Phyre2 has done very well in attracting users and now I’m on to Phyre3 which will be out shortly (end of 2017). Again it’s a full redesign, this time with two people on it, me on the front end and my colleague Stefans Mezulis on the back-end, using the browser to its best, new web tech, polished with a new engine (the PhyreEngine, naturally) and more hardware for faster processing of bigger genomes. Also an entirely new tool called PhyreRisk is being developed: a portal for analysing disease, mutations, structures and complexes. But the primary focus is still the same: utility. What do people want or need to do and what can be done computationally to help them. The better we can match what can be done computationally with what researchers want to do, the faster we will make progress in all of science. And maybe we’ll beat DeepMind to the answer to protein folding. ReferencesOLDERADO: On‐line database of ensemble representatives and domains. LA Kelley, MJ Sutcliffe, Protein science 6 (12), 2628-2630 (1997)Recognition of analogous and homologous protein folds - assessment of prediction success and associated alignment accuracy using empirical substitution matrices. Russell RB, Saqi MA, Bates PA, Sayle RA, Sternberg MJ. Protein Eng. Jan;11(1):1-9. (1998)Enhanced genome annotation using structural profiles in the program 3D-PSSM. LA Kelley, RM MacCallum, MJE Sternberg. Journal of molecular biology 299 (2), 501-522. (2000)Protein structure prediction on the Web: a case study using the Phyre server. LA Kelley, MJE Sternberg. Nature protocols 4 (3), 363-371. (2009)The Phyre2 web portal for protein modeling, prediction and analysis. LA Kelley, S Mezulis, CM Yates, MN Wass, MJE Sternberg. Nature protocols 10 (6), 845-858 (2015)

Thomas F Heston, MD, FAAFP1 1Department of Medical Education and Clinical Sciences, Elson S Floyd College of Medicine, Washington State University, Spokane, Washington USA Citation: Heston TF. Sauna bathing and the cardiovascular system. Int J Sci Res (Ahmedabad). 2017 Nov; 6(11) 569-570.

PUI2017 Extra Credit Project ProposalResidential Building Permits Issuance and Housing Violationsby: Dana Chermesh (dcr346)  https://github.com/danachermesh/PUI2017_dcr346Instructor: Dr. Federica Bianco Problem Description:My motivation for this assignment is to analyze how does the number of residential permits issuance correlate  with housing violations number, if at all, and by this to identify areas that should get more attention regarding building codes and building use validation. I relate to permit issuance as an indicator of urban renewal, due to the fact that the majority of construction in New York City requires a Department of Buildings permit to make sure that the plans are in compliance with Building Code (from nyc.gov). I pre assume that an area with a very low number of residential permits issued over a year (meaning, an area that is less developing / renewing) will also have a relatively large number of building violation (illegal conversion). I also guess there are highly renewing areas with large number of permits issued and a large number of building violation complaints. Additionally, I am interested in assessing the roll of income in the predicting of housing violations. 

Healthcare complexity and costs can be decreased through the application of blockchain technology to medical records and insurance companies. Estonia has taken a leadership role in blockchain based services both in the commercial sector and in government. The Estonian government’s innovation strategy was to create GovTech partnerships to implement blockchain based technologies throughout the country, and become a global leader in the technology. Starting in 2011, just 3 years after Satoshi Nakamoto published the first description of distributed ledgers and blockchain technology, the Estonian Government started partnering with the private technology startup company Guardtime to use blockchains to secure public and internal records. Then in 2016, Estonia once again reinforced its global leadership in blockchain technology when it announced it would use blockchain technology to secure the health records of over a million citizens. Estonia’s systematic method of applying blockchain technologies through GovTech partnerships demnostrates how innovation is a process. Estonia also identified early the value of the blockchain as a disruptive platform innovation. The application of blockchain technology to healthcare is a radical innovation given that nearly all previous applications have been in the financial and legal sectors.

Healthcare complexity and costs can be decreased through the application of blockchain technology to medical records and insurance companies. Estonia has taken a leadership role in blockchain based services both in the commercial sector and in government. The Estonian government’s innovation strategy was to create GovTech partnerships to implement blockchain based technologies throughout the country, and become a global leader in the technology. Starting in 2011, just 3 years after Satoshi Nakamoto published the first description of distributed ledgers and blockchain technology, the Estonian Government started partnering with the private technology startup company Guardtime to use blockchains to secure public and internal records. Then in 2016, Estonia once again reinforced its global leadership in blockchain technology when it announced it would use blockchain technology to secure the health records of over a million citizens. Estonia’s systematic method of applying blockchain technologies through GovTech partnerships demnostrates how innovation is a process. Estonia also identified early the value of the blockchain as a disruptive platform innovation. The application of blockchain technology to healthcare is a radical innovation given that nearly all previous applications have been in the financial and legal sectors.

Blockchain technology can be utilized to improve gun control without changing existing laws. Firearm related mortality is at epidemic levels in the United States and not only has a significant impact upon public health, it also creates a large financial burden. Suicide is the most common way guns kill. Through better gun tracking and improved screening of high risk individuals, this technological advance in distributed ledger technology will improve background checks on individuals and tracing of guns used in crimes.

Blockchain technology enables the creation of immutable, publicly available data. Initial applications have been primarily in the fields of finance (Bitcoin) and law (smart contracts), yet it can also help advance science by reducing human bias.  The blockchain can ensure that hypotheses are not altered; that methods of data collection are transparent; that results are publicly available for independent analyses; and that conclusions are less biased. Our current system of medical research suffers from too much bias. Blockchain technologies, by creating immutable data, will lead to an increased confidence in evidence based medicine.

Early in the course of Parkinson disease (PD), treatment usually goes well. However, after five to ten years, things start to change as treatment requires higher doses of medications and side effects become more problematic. One of the most difficult problems is the development of hallucinations or delusions. Throughout the 20th century, treatment options were unproven and unsatisfactory, but the past 20 years have brought important changes. Two medications that are well tolerated in PD have now proved efficacious in randomized, controlled trials, and others are in development. Here I summarize this history briefly and provide a general plan for treating the patient with PD complicated by psychotic symptoms.

In this lecture we will discuss some basic properties of the Helium atom. We will introduce first some useful notations for the specific Hamiltonian at hand. Then we will focus on the important consequences played by the electron-electron interaction on the spin structure and the level scheme of the system. Finally, we will introduce the variational method for the estimation of the ground state energy.

Blockchain technology is a system of creating an immutable, secure, distributed database of transactions. Blockchains were initially created to provide a distributed ledger of financial transactions that did not rely upon a central bank, credit company, or other financial institution. The technological breakthrough, however, has been extended to transactions involving legal matters, medical records, insurance billing, and smart contracts. One primary way that blockchain technology is important to healthcare professionals in that it can revolutionize medical database interoperability. This greater interoperability can help improve access to medical records, imaging archives, prescription databases. Given that a patient’s medical history is a primary cornerstone of good medicine, blockchain technology has the potential to dramatically improve medical care.

A document by Mazen A. Afif. Click on the document to view its contents.

My favorite flower is a dandelion. Why? Because it pops up everywhere in the Spring. There are whole fields full of bright yellow dandelions. And guess what? They’re free. You can pick as many as you want. You can make them into bouquets, mix them with a few daisies. You can give them to friends, keep them for yourself. Later on, when they go to seed, you can blow on them and relish in the knowledge that you’re contributing to a whole new Spring full of extra dandelions. The joy moves on.

Engineering is a field that touches nearly every aspect of our lives. Almost anything human-made that you come into contact with on a day-to-day basis was worked on by an engineer during at least one phase of its development before coming to fruition. Whether it was in the initial design, the selection of materials, or the manufacturing method, engineering is deeply woven into the fabric of our lives as well as our economic, technological, and social development as a society. Despite the importance of engineering to society, there remains a large portion of our global population that are not able to participate as equal partners in the engineering practice. This includes participating both as consumers and contributors of engineering knowledge. Accessing engineering knowledgeDespite the recent surge of interest in open access (OA) publishing models, engineering work remains, for the most part, locked away in traditional academic journals \cite{fleischfresser2016}. A recent study found that only ~17% of published manuscripts in engineering can be accessed by the public for free \cite{s2017}. This results in a disadvantage for those who have limited ability to purchase access to these resources, which is particularly hard felt in regions of the world where engineering solutions are needed to solve some of our most pressing problems such as access to clean drinking water and reliable electricity.The need for access to engineering knowledge does not reside solely in the realm of academic publishing. Other areas where inequity in access exists include educational resources and engineering tools such as software and standards. Open educational resources (OERs) is another area that has gained momentum in recently especially as people examine the costs of higher education. The growth of OERs for engineering doesn't seem to have taken off as quickly as in other fields such as computer science or mathematics. Solutions are still needed here for the development of engineering OERs that would provide alternative options for both the engineering student at university and the individual trying to learn on their own. Production of engineering knowledgeThe practice of engineering can include diverse array of work products. The products can include code, multimedia, 3D models, sketches, physical objects, writing, and everything in between. As such, it can be very difficult to find a one-size-fits-all model to prescribe when promoting the addition of open practices to the engineer's workflow. In terms of the tools available to enable an engineer's work, many of the standard tools that an engineering student is trained on and that they will later utilize in the workplace are closed source, proprietary technologies. Common examples include solid modeling software such as Solidworks or PTC Creo and computational tools such as Matlab. Fortunately there is much ongoing work occurring in the development of open source alternatives to these packages such as FreeCAD, BRL-CAD, and GNU Octave. While the maturity of these projects varies and their adoption in the work place is minimal, they show promise for the future availability and usefulness of these tools. The real test is if more widespread adoption can be facilitated such that continued development of these tools could be incentivized. 

The Peer Scholars Program is a new series of workshops sponsored and hosted by the NCSU Libraries, and led by postdoctoral scholars and graduate students with specific research skills, such as design, programming, analytics, immersive technologies, visualization, and data analytics.  For more information, read the press release. My name is Mira Waller and I am here today with Jami Jackson Mulgrave, a doctoral candidate in statistics at NC State, to talk about her experience in being the first graduate student to teach a two part workshop on Git and GitHub in the Peer Scholars Program. Can you tell us a little about yourself - why you decided to pursue a doctorate in Statistics and what your specific areas of interest and research are? My background is a little different from most statistics graduate students. I graduated from Columbia University with a Bachelor’s of Arts in Psychology with a concentration in the Pre-medical sciences. In the beginning of college, I thought I wanted to become a doctor, but towards the end of college, I started to think about alternative careers. I worked for five years at Memorial Sloan-Kettering Cancer Center (MSKCC) in clinical research and while working there, MSKCC partnered with IBM Watson to use the technology to help oncologists make better treatment choices. They started an Analytics group to use machine learning and statistics for cancer research and I really wanted to be a part of the group because it sounded like an awesome opportunity to learn something cutting edge. However, I needed to obtain at least a Master’s degree in a technical field such as Math or Statistics, so I decided to attend a graduate program in statistics because I enjoyed AP Statistics in high school. I ultimately decided to attend a PhD program because I could obtain a Master’s degree along the way while also trying out statistical research. It turns out that I love doing statistical research! My specific areas of interest and research are Bayesian nonparametrics, graphical models, and machine learning. 2. What made you want to teach a workshop with the NCSU Libraries? I knew of a couple of other people who led workshops with the NCSU Libraries (Denae Ford and Brittany Johnson) and I thought that it was really neat that they were able to get involved and teach to the broader NCSU community. I also wanted the opportunity to practice my public speaking skills. 3. How did you decide what you wanted to teach and why? I am interested in open source software and try to attend workshops and conferences related to the open source community. I use Git and GitHub for my own research projects, so I thought it would be natural for me to teach those platforms to other people. It was also a good opportunity for me to learn more about Git and GitHub while I was putting the workshop materials together. 4. I really enjoyed working with you in shaping the two workshops. How was the process for you? Would you do anything different if you could do it over again? The process was perfect! I had a lot of time to plan for the workshops, which was what I needed. I also appreciated the control that I had over the content of the workshops and that it was a low-pressure process. You gave me a lot of great advice as well! You helped me to make the slides clearer and you suggested a lot of good ideas, such as creating GIFs for the demonstrations. I didn’t end up making GIFs after all, but I will keep ideas such as those in mind in the future. I wouldn’t do anything different. 5. How did you feel about the actual workshops? How different was the actual experience from what you thought it would be like? I thought the workshops turned out well. I didn’t know how the attendees would respond to my slides and exercises, but after going through the workshops, it seemed like they appreciated the content and learned the basics. I received good feedback at the end of each workshop that will help me shape future workshops. The actual experience wasn’t too different from what I thought it would be. I thought that I would get some difficult questions that I wouldn’t be able to answer and that the workshops would end too early, but I ended up getting questions that I could answer and the workshops ended on time. 6. Is there anything else you would like to share? I think teaching a workshop with the NCSU Libraries is a great opportunity for graduate students and postdocs to hone their public speaking and teaching skills while also providing a service to the greater NCSU community. I am glad that I was able to be a part of such a great experience.

This project investigated the usage of the Citibike bikeshare system in New York City. Through analysis of large-scale data on rider demographics and characteristics of individual rides, it was possible to explore how uptake of the Citibike system differs by demographic group. We tested for difference in ride length between male and female users. The study found a statistically significant different in mean ride length, with men taking longer rides than women on average during the test period June-July 2017. The finding is of policy interest given New York City's commitment to equal opportunities, gender empowerment and mobility for all, and may support the need for outreach and awareness campaigns aimed at narrowing the gender gap in Citibike usage.

Presenting a unified description of high energy particle physics and gravitational physics. This model is both fully relativistic and fully quantum field theoretical. A space of Lagrangians is constructed. In this space of Lagrangians a differential equation is defined which will give the specific Lagrangians for gravity and elementary particle physics in various universes. From this a unified Lagrangian for the laws of physics in our universe is derived. Then the action S and generating functional for field interactions Z is computed. The integrals are done analytically and shown to not lead to infinities at high energies. With the generating functional Z the interaction between a gravitational wave and a simple particle physics experiment are calculated. It is shown that the laws of physics we know are recoverable from this model. It also makes robust predictions about higher energy physics. It is shown that this model applied to the big bang itself can give us a CMB as we currently observe it. A further experiment to test this model is proposed. This model is presented in hopes our colleagues in experimental physics will put it to the test.

Citi Bike is one of the most commonly preferred options for public transportation in New York. As such, it continuously collects millions of rows of data about its customers and subscribers, that contain significant insights reflecting various behavioural patterns of Citi Bike’s users. Particularly, considering that men in average are physically more developed, do they cycle longer distances than women? In this article, a research of the platform’s data has been conducted to discuss the issue and the results have been discussed.