STEM-Zine Issue 2

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A science, technology, engineering, and mathematics zine by students for students

The Shipwrecks That Sustain the Abyssopelagic Amelia Topology: Everyday Math Michelle

Test Taking and Science Hana

Alzheimer’s Disease: A Research Paper Kayla

Psychology as a tool for mitigating climate change Alexandra

How Machine Learning Can Predict Chaos Samiha

BY AMELIA KIRK

Deep in the abyssal zone, far below the reach of the sun’s rays, the hull of a different kind of ship settles in its final resting place. A rich oasis in the desert of the ocean floor, the shell of a leviathan. The skeleton of a sunken cetacean has the propensity to sustain the creatures of the deep for decades at a time. The delayed decomposition of its giant carcass creates a localised and unique ecosystem, attracting a variety of intriguing marine organisms to feed on the remains and seek shelter within the cage of bones.

This phenomenon is commonly referred to as “whale fall”. When a whale reaches the end of its life far out at sea, it’s body will sink like a stone to the bathyal or abyssal zone. These zones extend deeper than 1000m below the surface, and the chilling temperatures combined with high hydrostatic pressures slows the decomposition rates of organic matter so that a falling carcass stays intact during its descent (Allison, Smith, Kukert, Deming, & Bennett, 1991). This cannot happen in shallow waters, where the carcass of such a large animal would be quickly stripped of flesh by scavengers within weeks. The extension of the decomposition process at depths of thousands of metres is key in allowing the formation of a rich and local ecosystem.

Many species of deep-sea marine organisms have been observed to visit these carcasses, which go through three definite successional stages (Amano & Little, 2005). Mobile-scavengers such as sleeper

Scavenger stage

Opportunist stage

Sulphophilic stage

Up to

2 years

Up to

2 years

Up to

50 years

sharks and hagfish feed on the soft tissue for as long as two years (Little, 2010), which defines the first stage of a whale’s afterlife. After the consumption of the soft tissue, an enrichment opportunist stage follows, defined by colonisation of the newly exposed bones. Polychaetes, marine annelid worms, are a large proportion of colonisers.

They strip the carcass clean of any remaining tissue and blubber, until all that’s left are the bones. For mysterious reasons, whale bones are a rich source of lipids, which makes them particularly attractive for anaerobic microbial decomposition.

At this stage, the sulphophilic stage, hydrogen sulphide as waste is produced by anaerobic bacteria, and oxidised by chemosynthetic bacteria often found living as symbionts with other

Sources Allison, P. A., Smith, C. R., Kukert, H., Deming, J. W., & Bennett, B. A. (1991). Deep-water taphonomy of vertebrate carcasses: a whale skeleton in the bathyal Santa Catalina Basin.

Paleobiology, 17(01), 78–89. https://doi.org/10.1017/S0094837300010368 Amano, K., & Little, C. T. S. (2005). Miocene whale-fall community from Hokkaido, northern Japan. Palaeogeography, Palaeoclimatology, Palaeoecology, 215(3–4), 345–356. https://doi.org/10.1016/J.PALAEO.2004.10.003 Goffredi, S. K., Paull, C. K., Fulton-Bennett, K., Hurtado, L. A., & Vrijenhoek, R. C. (2004).

Unusual benthic fauna associated with a whale fall in Monterey Canyon, California. Deep Sea Research Part I: Oceanographic Research Papers, 51(10), 1295–1306. https://doi.org/10.1016/J.DSR.2004.05.009 Little, C. T. S. (2010). The Prolific Afterlife of Whales. Scientific American, 302(2), 78–84. https://doi.org/10.1038/scientificamerican0210 -78

Topology

BY ANDREA MICHELLE

"IF X IS A SET AND T IS A SET OF SUBSETS OF X; THEN T IS A TOPOLOGY."

WHAT IS TOPOLOGY? Topology makes simple complicated and complicated simple. It is widely used to understand unusual states of matter that have unique physical characteristics. These days, it has become more and more popular to talk about applied topology. But, before jumping right in, it’s first important to define what exactly is topology is.

TOPOLOGICAL SPACE What is topological space? We can say that if X is a set and T is a set of subsets of X; then T is a topology. This is the usual or ordinary topology, which is also called Euclidean Topology.

To understand this set theory: imagine all the numbers, this will be the set X; then a subset of X can be all the numbers under 100, another subset can be all the even numbers,the combinations of these subsets is a topology. All these math sounds really abstract, and it is. Topology is mostly abstract but it can have amazing applications in real life.

Knot Theory. Knots are an everyday application of topology. You may think that you usually aren't surrounded by knots, but think again. Your hair may have knots on it when you wake up.

Your earphones are surely knotted when you take them out of your bag. And then again there are more examples of knots in your everyday life.

But what do you think when you see a knot? You don't think that your earphones transformed to a circular matter form, you just think they are disorganized- that they aren't in their original form. The form they are seen is different, but they are the same earphones you bought.

Origami.

Most of us have tried doing origami at least once in our lives. To be able to transform a single page of paper to a swan or a dragon - that's Topology.

Hot chocolate. When you mix your hot chocolate or cocoa, did you know there's always a point that doesn't move, a point that stays the same. At any given time t, there is always at least one point that is in its original position. However not that there does not necessarily have to be a single point invariant for all times t, there can be multiple point that do not move at all at different times.

By the river. If you enjoy nature and mathematics, you might have seen a river and see how the stream goes. A stream is a topological space. It has consistent preorders on the open sets, we can call it circulation. When we say consistent, it means that the preorder on an open set is equal to the transitive closure of the preorders on its open subsets Folding 2D. A piece of square cloth is basically a two-dimension space. However, you can manipulate it folding it in pleats. Now the cloth has become a threedimensional.

The Banach-Tarski paradox. If you cut a grapefruit into 5 pieces and reassemble them by rigid motions to form 2 grapefruits.

Homeomorphisms. A doughnut and coffee cup are indistinguishable to a topologist. You just have to manipulate one enough to have the other one.

These are just some examples of what topology looks like in the real life, but I'm sure that if you look close enough you will find that we are all surrounded by this branch of mathematics. In what other ways do you see topology in your daily life?

BIBLIOGRAPHY Ghrist, R. (2014). Elementary applied topology.

CreateSpace.

Raussen, M. (2014). Contributions to directed algebraic topology. Aalborg: Department of Mathematical Sciences, Aalborg University.

Taherifar, A. (2013). I have seen some paper about applications of topology. But I'm not convinced. So I have the following question. What are the applications of toplogy?. Retrieved from https://www.researchgate.net/post/I_have_seen_some_ paper_about_applications_of_topology_But_Im_not_con vinced_So_I_have_the_following_question_What_are_th e_applications_of_toplogy

STEM ZINE ISSUE 2

TEST TAKING AND SCIENCE THE EFFECTS OF MULTIPLE FACTORS ON TEST TAKING ABILITY WRITTEN BY: H.F.

Practice Tests.

According to a study by Tufts University, people under stress usually have more trouble remembering information. However using a strategy called retrieval practice, which is just another way of saying practice testing, people can pull information from their memory even when they’re stressed. This is important since most students find test taking to be stressful, which could impair their memory retrieval abilities. However, taking practice tests may help their memory and performance on an exam.

According to a study by Kent State University, taking practice tests improves learning through supporting the use of more effective (memory) encoding strategies. The researchers found that when people were tested, they came up with better mediators or hints, than when they were only restudying the material. This method could be effective when trying to learn foreign language vocabulary for an exam since practice testing supports the generation of better hints to remember the words correctly.

Stress.

According to a study by the University of Chicago, stress can affect student test performance differently depending on how it is perceived and the student’s overall mindset during the test. The researchers had a category of students that had large working memory and split them into two groups: students who were confident about math and students who were anxious about math. Saliva samples were taken from the students before and after a stressful math test to measure their cortisol (stress hormone) levels. For students confident about math, cortisol increase led to a better performance. However, for students who were apprehensive about math, cortisol increase led to a poorer performance. The study shows that a person’s viewpoint of an event can change the way stress affects their performance. When taking a test, students should try to change their perspective on the test. The study suggests writing down what they feel anxious about before the test, similar to a brain dump. Another similar study conducted by the British Psychological Society found that students under stress who had a large working memory were more confident, which boosted their exam scores. Following a similar pattern as the first study, students with a poor working memory were more prone to anxiety and had lower exam scores as a result when under stress.

According to another study by the University of Chicago, anxious students who write about what makes the worried before the exam improve their scores. Since anxiety does lead to poor test performance, the researcher decided to see what would happen if writing was used as a way to relieve worry and excessive anxiety before the exam and how that could affect test performance in the end. Students that wrote expressively about their feelings right before a math test had better accuracy, with a 5% improvement.

Water Intake.

According to the British Psychological Society, bringing water when taking a test correlated with better performance in student grades. 25% of students brought in water when taking the exam and they scored about 5% better than students who did not bring in water. Dr. Pawson from the University of East London said that drinking water could "alleviate anxiety" and could affect the brain's thinking function, which may make sense since the brain is 73% water. Although more research is needed to fully understand the science behind this, students should probably stay hydrated and bring a water bottle when they take a test. It may help them feel more comfortable and prepared and contribute to a slightly better score.

Amount of Sleep.

According to Dr. Philip Alapat, the medical director at Harris Health System, students that pull all-nighters and lose sleep could do more harm than good to their memory. He says, "Memory recall and ability to maintain concentration are much improved when an individual is rested."

Sleep deprivation affects a person's energy and ability to focus. Sleep-deprived students are unable to properly recall information, and may lead to poor performances on an exams.

External Messages.

According to a study conducted by the American Psychological Association, the messages a teacher gave their students also affected their scores based on how they perceived it. When a teacher gave a negative message focusing on failure, highlighting that the students had to do well on the exam or they would not have a secure future, students had a lower score on the exam. When a teacher gave a message that had similar content but was more positive, for example, a message that focused on the success of the student if they did well on the exam, students ended up doing better on the exam.

This is quite important to think about since it suggests that the type of environment that a person is in can affect motivation and outcome of a test. Perhaps students should try to distance themselves from toxic environments and surround themselves with more positive authority figures and peers to boost their motivation before an important exam.

Memory.

According to a study by Baylor University, students who actively regenerated information by telling someone about it after learning could recall the information relatively easily later and are able to remember it longer. This study also found that people are able to recall details from a memory better, when presented with a cue. Applying this study technique could improve the recall ability of students, which could improve test scores. I've been using this method for a while and I find that remembering information is easier when I am able to explain it to other people. For me, I am able to organize the information in my head in a way that makes sense and in a way that I can also explain it to others easily and concisely, so my brain can learn which details are important to know and understand.

PSYCHOLOGY Our current approach as we mention climate change is doing the opposite of what it is supposed to be done. Scientists, often with good intentions, scare people into ignoring the problem by using “We need to change the guilt and fear as ethos to support their argument, emotions that way we talk about climate change” humans tend to tune out. Our goal is supposed to be engaging – Conservation scientist citizens into caring for the environment, adapting a more ecoDr. M. Sanjayan friendly lifestyle, demanding action from our governments, educating ourselves into what we are doing to our only home.

The current train of thought is: “S omeb o dy els e c an de a l w it h it”.

Climate change is seen as a distant problem with no real relation to the flood and the wildfires and the reason snow doesn’t come as often as it used to. Distant in times, as if the consequences will be happening years from now when you’ll now longer be here to face them. Distant in space, climate change is

not coming get you, it just affects developing countries or some polar bear in the Antarctic.

Global warming is by far the biggest issue of our time. That sentence makes seem every personal action somewhat insignificant. What does it matter if I use reusable bottles instead of plastic

P S Y CHOLO GY

ones, if thousands still end up in oceans and landfills every day? Does any of this make a difference when it comes to climate change? Humans erect a series of psychological barriers to justify why they should not act either individually or through collective institutions to mitigate climate change.

We need to change strategies and aim to turn human apathy to action.

AS A TOOL FOR M I T I G AT I N G C L I M AT E C H A N G E

by Alexandra Dalmau “What we need is a chorus, a diversity of many voices to deliver this message, and to deliver it in a way that gets their community to sit up and listen. You are a messenger too.”

- Conservation scientist, Dr. M. Sanjayan

So then, what types of message could motivate people to change their behavior? It is money? Health? Impact on the environment? Not surprisingly, given our human nature, the answer lies in social pressure and competition.

In 2016, using this technique, a company called Viget was able to generate the equivalent of two terawatthours of electricity saving.

That’s enough to power every home in Miami for more than a year. If we apply profound importance in individual actions the combination of numerous will generate a measurable difference.

Even with severe drought, heat waves, floods, sea level rise and many others, some people still don’t “believe” in climate change. Isn’t the science enough?

When scientist present their data the audience feels “bullied”, and are unable relate to scientist sharing their data. What if pop culture celebrities were to advocate and educate their audience about environmental issues and individual actions?

What impact could they create?

The answer lies in the Francis effect, where, when the pope gave his famous speech a

couple years back, about how we should be better steward towards the earth, it caused 35% of Catholics to change their personal views on climate change.

Simply by switching the messenger from a scientist to a religious figure, the audience listened. Everyone with a platform, big or small, should take a stance in the mitigation of climate change, from artists to lawyers, doctors and business women, policemen and firefighters.

By Kayla Woods

before the first signs of memory loss are recorded.

The brain has 100 billion neurons which connect to other nerve cells that form communication networks.

If one part of the neuron system begins to cause problems, then soon the damage spreads - cells begin to lose the ability to function, and eventually, the destruction leads to death. Shown by many brain scans of individuals with Alzheimer’s disease (AD), a large number of plaques (protein fragment deposits that build in between nerve cells) and tangles (twisted fibers of protein that build up inside cells) are present. Research shows that although most individuals develop plaques and tangles as they age, individuals with Alzheimer’s tend to develop more and “in a predictable pattern, beginning in the areas important for memory before spreading to other regions”. It is the belief that the plaques and tangles block communication amidst neurons and disrupts a cell’s process which results in the eradication of nerve cells.

The epidemiology of Alzheimer's disease is identified by risk factors that increase the likelihood of developing AD. Age is the greatest known risk factor for Alzheimer’s as most individuals with the neurodegenerative disease are around the age of 65 and older. According to the Alzheimer’s Association, “one in nine people in this age group and nearly one-third of people age 85 and older have

have Alzheimer’s”. Genetics and heredity is another risk factor. Research shows that those who have Alzheimer’s in their family history are more likely to develop the disease than those who do not have in it their family history although, the risk only seems to increase if more than one family member has AD. In general, two categories of genes have a direct influence on whether a disease is developed in the human body: risk genes and deterministic genes.

The former increases the possibility of developing a disease, however, there is no guarantee that the person will have it. The latter is a direct result of inherited genetic mutation disorders, meaning that there is a guarantee that the disorder will develop.

Several genes, such as APOE-e4, APOE-e2, and APOE-e3, increase the risk of Alzheimer’s as found by research. Furthermore, APOE-e4 is the “first risk gene identified and remains the one with the strongest impact”; people who inherit one copy of APOE-e4 have an increased risk of developing AD. These deterministic genes have been found in a few hundred families worldwide, begetting in Alzheimer’s.

In 2010, the US Census Bureau reported that about 20 percent of the US population between the ages of 65 and older, were a minority - whether racially or ethnic wise. Research shows that older Latinos and

“first risk gene identified and remains the one with the strongest impact�

Furthermore, other signs unrelated to memory loss have also been noticed, like changes in appearance, changes in vision, frequent falling, and lapses in judgment.

Although symptoms of Alzheimer’s disease varies from patient to patient, there is a general idea of the course the disease takes. This system created by Dr. Barry Reisberg is outlined as 7 stages, ranging from no impairment to very severe decline.

Beginning with stage 1 (no impairment), the disease is not detectable and no symptoms of dementia are evident. Next comes the very mild decline stage, in which the disease is unlikely to be detected by physicians and some memory problems occur, though the issues aren't distinguishable from normal age-related memory loss. Stage 3 of AD follows, also known as mild decline. At this stage, cognitive decline may be noticeable. Memory and cognitive test performances are affected and a physician can detect impaired cognitive function. Difficulty in some areas such as remembering where possessions are located, planning and organizing, etc. are noticed.

Stage 4 (moderate decline) of Alzheimer’s disease presents concise symptoms that AD is present. Poor short-term memory, difficulty with simple arithmetic and calculations, and the inability to

manage finance are examples of symptoms at this stage.

During the fifth stage of Alzheimer’s, the moderate-severe decline is presented. Although patients at this stage can function, they may need help with day to day activities. They are able to remember things like their relatives and family history. Stage 6 of AD is when severe decline is present, causing the patient to become more dependent on those around them. Patients need constant supervision and frequent professional and medicinal care. Symptoms include major personality changes, behavioral problems, apathy, loss of bowel and bladder control, and wandering.

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is produced less and less as the neurodegenerative disease progresses. Antipsychotics, anti-anxiety drugs, sleep aids and anticonvulsant are other examples of medications used to treat the symptoms of Alzheimer’s disease.

Research for this disease has progressed to the point in which scientists have begun to look at the underlying processes of the disease. In clinical trials, willing patients aid in developing possible interventions and treatments. For example, cognitive training, physical therapy, and even immunization therapy are all developed and tested options for Alzheimer’s. The hope is that through these trials, we can find a way to improve treatments and therapies for Alzheimer's disease and hopefully, one day, find a cure.

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Sources "The “Invisible Patients:” Alzheimer's and Its Effects on the Family." Impressions Memory Care, Bryn Mawr, www.impressionsmemorycare.org/news/1494358089-“invisi ble-patients”-alzheimers-and-its-effects-family, 2017. Accessed 26 Jan 2018.

"Treatment of Alzheimer's Disease." National Institue on Aging, U.S. Department of Health & Human Services, www.nia.nih.gov/health/how-alzheimers-disease-treated . Accessed 29 Jan 2018.

"Seven Stages of Alzheimer's." Alzheimer's Association, alz.org, m.alz.org/stages-of-alzheimers.asp, 2018. Accessed 26 Jan 2018.

Hamilton, Jon. "Stress And Poverty May Explain High Rates Of Dementia In African-Americans." NPR, Shots, www.npr.org/sections/health-shots/2017/07/16/53693595 7/stress-and-poverty-may-explain-high-rates-of-dement ia-in-african-americans, 2017. Accessed 23 Jan 2018.

Duthey, Ph.D., Béatrice. "Background Paper 6.11 Alzheimer Disease and other Dementias." "A Public Health Approach to Innovation", Priority Medicines for Europe and the World, 2013. Accessed 22 Jan 2018.

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SAMIHA ARULSHANKAR

MACHINE LEARNING: PPREDICTING UNPREDICTABILITY While many are entranced with the unpredictability of chaos theory, a team of scientists from the University of Maryland set about predicting unpredictability.

THE BUTTERFLY EFFECT While many are entranced with the unpredictability of chaos theory, a team of scientists from the University of Maryland set about predicting unpredictability.

Chaos theory is the science of the unpredictable and nonlinear.

Nonlinearity, in essence, is when cause and effect are unproportionate. A principle of chaos theory publicized in modern culture is the notorious butterfly effect.

The butterfly effect is the concept that a small action in the initial stages of an event can cause a highly unpredictable and large change. For example, a butterfly flapping its wings can cause a hurricane. It was through this that a conclusion that long-term prediction is not possible in full was formed.

HOWEVER, THINGS ARE ONLY IMPOSSIBLE UNTIL THEY ARE DONE. The aforementioned team accomplished just that. It was done through utilising a machine learning algorithm capable of predicting the dynamics of a chaotic system based on the Kuramoto-Sivashinsky equation. The equation is a model of a flame front.

This algorithm, after training, can accurately predict states in a system eight Lyapunov times, which is eight times farther than current systems.

Lyapunov time is a unit of measurement for the amount of time it takes for a state in a system to become wholly unpredictable. Thus, it varies depending on the different systems.

A point to consider is that the algorithm used is trained using data instead of an equation or a model and so has applications in other chaotic systems such as the weather and, in some instances, in healthcare.

To understand how this algorithm works, a basic understanding of artificial neural networks is essential.

AN ALGORITHM THAT PREDICTS CHAOS The data, for the sake of simplicity, let us say it is equal to ‘a’. It would be passed from the input block to a hidden layer block (called a node).

There, it would undergo modification by ‘adding weight’.

The weight is the strength of the connection; if the input is changed how much would that affect the output. If the weight is closer to 0, it does not change the output drastically.

The hidden layer node, as can be seen in the image, receives data from more than one input node. Hence, it adds up all this data. Let us say after weighting the data and adding up the received inputs, the hidden layer node is now ‘12a’.

Then, a bias would be added. A high bias would mean fewer assumptions whilst a low bias is equivalent to more assumptions. After the bias, let us say our node is now ‘24a’.

Subsequently, the node would have to pass threshold value, say ‘20a’ to proceed, otherwise, it would remain dormant. That is why there are fewer nodes in the output layer compared to the input layer.

Artificial neural networks (ANN), however, were slightly modified in order to predict the dynamics of a chaotic system. Here, reservoir algorithms were used. Reservoir algorithms are similar to ANNs however the weights are trained differently.

Training, in machine learning, refers to determine the best weights for higher accuracy and is done through automation. The main difference between ANNs and reservoir computing is the fact that in reservoir computing the random weights are, as they suggest - random, in contrast to in ANNs where they are trained.

In 2002, a published paper presented a network of randomly connected nodes that had learnt the dynamics of a very small chaotic system consisting of 3 related variables. This network could predict the values of those 3 variables, however, it was found that when there were more coevolving variables, computing became too complex.

THE STUDY The team of the University of Maryland, however, wanted to overcome this problem. To do so, they parallelized the computation methods. After noticing that variables in a certain section of the system are only influenced by their neighbours, they split the system into several smaller systems. When splitting the system, they including overlaps so the impact variables on the border of two systems were accounted for. Thus, with enough computing power, they could run all of the systems in parallel, combine them and would have the result.

The procedure, in a way, is rather simple. After measuring the height of the flame of 5 different points along the front continuously, this data is given to random nodes in the reservoir. The data activates the network and connects the nodes.

While the data is given to the nodes, monitor the weights of several random neurons in the neural reservoir and after combining them in 5 different ways, the result gave the the next 5 inputs. After using trial and error to train the weights the reservoir will be able to predict the dynamics of the system. The network simulates the equation itself, using its outputs as the new inputs and continuing the cycle of predictions of the 5 points of the flame.

Although reservoir algorithms are effective, they are not the only option. Some papers have suggested that deep learning could work just as well, if not better though it is a far more computationally rigorous and complicated system. Similar arguments on efficiency also rose due to combining data-based machine learning approaches with the traditional equationbased approach being proven to improve predictions.

Overall, we can conclude that reservoir computing has a knack for learning the dynamics of chaotic systems; however, we still don’t know why. Neural networks, including reservoir computing,

images sourced from https://www.quantamagazine.org/machine-learningsamazing-ability-to-predict-chaos-20180418/

are considered to be black boxes in science. We use them, but without understanding their inner workings due to their complexity. Perhaps understanding them will be the next impossible turned possible story.

BIBLIOGRAPHY https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos20180418/ https://fractalfoundation.org/resources/what-is-chaos-theory/ http://www.columbia.edu/cu/biology/courses/w4070/Reading_List_Yuste/haas_04.pdf https://www.reddit.com/r/MachineLearning/comments/8ddykv/r_machine_learnings_amazing_abi lity_to_predict/ https://datascience.stackexchange.com/questions/19099/what-is-weight-and-bias-in-deeplearning https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html

STEMZINE ISSUE 2

ISSUE 2 Credits head editor

head designer

ashima m. r.

hana f

head of marketing

isabelle zhu

design team

isabelle zhu sofia velasquez hana f

marketing team

ashima m.r. kayla woods isabelle zhu

authors

hana f amelia kirk andrea michelle hana f alexandra rodriguez dalmau kayla woods

See you in the next issue.

See you in the next issue.