Author: IMC

Getting in sync can be exhilarating when you’re dancing in rhythm with other people or clapping along in an audience. Fireflies too know the joy of synchronization, timing their flashes together to create a larger display to attract mates.

Synchronization is important at a more basic level in our bodies, too. Our heart cells all beat together (at least when things are going well) and synchronized electrical waves can help coordinate brain regions—but too much synchronization of brain cells is what happens in an epileptic seizure.

Sync most often emerges spontaneously rather than through following the lead of some central timekeeper. How does this happen? What is it about a system that determines whether sync will emerge, and how strong it will be?

In new research published in Proceedings of the National Academy of Sciences, we show how the strength of synchronization in a network depends on the structure of the connections between its members—whether they be brain cells, fireflies, or groups of dancers.

The science of sync

Scientists originally became interested in sync to understand the inner workings of natural systems. We have also become interested in designing sync as a desired behaviour in human-made systems such as power grids (to keep them in phase).

Mathematicians can analyse sync by treating the individuals in the system as “coupled oscillators.” An oscillator is something that periodically repeats the same pattern of activity, like the sequence of steps in a repetitive dance, and coupled oscillators are ones that can influence each other’s behaviour.

It can be useful to measure whether a system of oscillators can synchronize their actions, and how strong that synchronization would be. Strength of synchronization means how well the sync can recover from disturbances.

Take a group dance, for example. A disturbance might be one person starting to get some steps wrong. The person might quickly recover by watching their friends, they might throw their friends off for a few steps before everyone recovers, or in the worst case it might just cause chaos.

Synced systems are strong but hard to unravel

Two factors make it difficult to determine how strong the synchronization in a set of coupled oscillators could be.

First, it’s rare for a single oscillator to be in charge and telling everyone else what to do. In our dance example, that means there’s neither music nor lead dancers to set the tempo.

And second, usually each oscillator is only connected to a few others in the system. So each dancer can only see and react to a few others, and everyone is taking their cues from a completely different set of dancers.

This is the case in the brain, for example, where there is a complex network structure of connections between different regions.

Real complex systems like this, where there is no central guiding signal and oscillators are connected in a complex network, are very robust to damage and adaptable to change, and can more easily scale to different sizes.

Stronger sync comes from more wandering walks

One drawback of such complicated systems is for scientists, as they are mathematically difficult to come to grips with. However, our new research has made a significant advance on this front.

We have shown how the network structure connecting a set of oscillators controls how well they can synchronize. The quality of sync depends on “walks” on a network, which are sequences of hops between connected oscillators or nodes.

Our math examines what are called “paired walks.” If you start at one node and take two walks with randomly chosen next hops for a specific number of hops, the two walks might end up at the same node (these are convergent walks) or at different nodes (divergent walks).

We found that the more often paired walks on a network were convergent rather than divergent, the worse the synchronization on the network would be.

When more paired walks are convergent, disturbances tend to be reinforced.

In our dancing example, one person making the wrong steps might lead some neighbours astray, who may then lead some of their neighbours astray and so on.

These chains of potential disturbances are like walks on the network. When those disturbances propagate through multiple neighbours and then converge on one person, that person is going to be much more likely to copy the out-of-sync moves than if only one of their neighbours was offbeat.

Social networks, power grids and beyond

So networks with many convergent walks are prone to poorer synchronization. This is good news for the brain avoiding epilepsy, as its highly modular structure brings a high proportion of convergent walks.

We can see this reflected in the echo chamber phenomenon in social media. Tightly coupled subgroups reinforcing their own messages can synchronize themselves well, but may fall far out of step with the wider population.

Our results bring a new understanding to how synchronization functions in different natural network structures. It opens new opportunities in terms of designing network structures or interventions on networks, either to aid synchronization (in power grids, say) or to avoid synchronization (say in the brain).

More widely, it represents a major step forward in our understanding of how the structure of complex networks affects their behaviour and capabilities.

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Credit of the article given to Joseph Lizier, The Conversation

Knots are used in all sorts of ways, every day. They ensure safety both indoors and for outdoor activities such as boating or sailing, are used as surgical sutures, as decorations, and they can even be found at nanoscales in nature, for example in DNA molecules.

Elastic knots are those that bounce back into their original shape in the absence of friction. There are open elastic knots tied with a single length of wire with two ends, which revert to being a straight line, and closed elastic knots in which the ends of the wire used to tie them have been attached together. These tend to spring back into a curved shape.

With a focus on closed knots, researchers in the Ecole Polytechnique Federale de Lausanne Geometric Computing Laboratory, led by Professor Mark Pauly, along with colleagues in Canada and the United States, have discovered thousands of new transformable knots including three novel shapes that the humble figure-eight knot can assume, doubling the number documented to date in scientific literature.

The findings are published in the journal ACM Transactions on Graphics.

To make these discoveries, the team first developed a computational pipeline that combines randomized spatial sampling and physics simulation to efficiently find the stable equilibrium states of elastic knots. Leveraging results from knot theory, they ran their pipeline on thousands of different topological knot types to create an extensive data set of multistable knots.

“By applying a series of filters to this data, we discovered new transformable knots with interesting physical properties and beautiful geometric forms,” explained doctoral assistant Michele Vidulis, the lead author of the paper “Computational Exploration of Multistable Elastic Knots.”

“This rich set of fascinating shapes can be created simply by knotting an elastic wire, and we noticed how such seemingly simple objects can sometimes exhibit tens or even hundreds of different stable shapes. The novel geometric patterns we identified were at times surprising. For example, we found that most—but not all—the preferred shapes of elastic knots are flat and planar, while few of them assume three-dimensional shapes,” Vidulis continued.

The team conducted further analysis across knot types that revealed new geometric and topological patterns with constructive principles not seen in previously tabulated knot types, showing how multistable elastic knots might be used to design new structures.

“As a result of our research, we can see elastic knots being used in the design process of self-deployable structures, like pop-up tents or lightweight emergency shelters. New metamaterials can be designed that combine several elastic knotted elements to build a network with complex mechanical behaviour,” Vidulis explained.

The team also created engaging recreational puzzles with the challenge to deform an elastic knot and manually find some of the interesting geometric shapes that they have computed with their algorithms.

As satisfying as these new discoveries are, Vidulis and the team believe that the work opens the way to several other potential new research directions.

“We want to explore the design of self-deployable structures and consider coupling elastic rods with fabric materials. As well, despite simulating thousands of different knots, our exploration only scratched the surface of the millions of knots that are known. We also plan to study more complex ensembles of knotted systems, in which new mechanical properties might emerge from the way in which the individual components are intertwined to each other,” he concluded.

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Credit of the article given to anya Petersen, Ecole Polytechnique Federale de Lausanne

Exciting a brain region using electrical noise stimulation can help improve mathematical learning in those who struggle with the subject, according to a new study from the Universities of Surrey and Oxford, Loughborough University, and Radboud University in The Netherlands.

During this unique study, published in PLOS Biology, researchers investigated the impact of neurostimulation on learning. Despite the growing interest in this non-invasive technique, little is known about the neurophysiological changes induced and the effect it has on learning.

Researchers found that electrical noise stimulation over the frontal part of the brain improved the mathematical ability of people whose brain was less excited (by mathematics) before the application of stimulation. No improvement in mathematical scores was identified in those who had a high level of brain excitation during the initial assessment or in the placebo groups. Researchers believe that electrical noise stimulation acts on the sodium channels in the brain, interfering with the cell membrane of the neurons, which increases cortical excitability.

Professor Roi Cohen Kadosh, Professor of Cognitive Neuroscience and Head of the School of Psychology at the University of Surrey who led this project, said, “Learning is key to everything we do in life—from developing new skills, such as driving a car, to learning how to code. Our brains are constantly absorbing and acquiring new knowledge.

“Previously, we have shown that a person’s ability to learn is associated with neuronal excitation in their brains. What we wanted to discover in this case is if our novel stimulation protocol could boost, in other words excite, this activity and improve mathematical skills.”

For the study, 102 participants were recruited, and their mathematical skills were assessed through a series of multiplication problems. Participants were then split into four groups including a learning group exposed to high-frequency random electrical noise stimulation and an overlearning group in which participants practiced the multiplication beyond the point of mastery with high-frequency random electrical noise stimulation.

The remaining two groups consisted of a learning and overlearning group but they were exposed to a sham (i.e., placebo) condition, an experience akin to real stimulation without applying significant electrical currents. EEG recordings were taken at the beginning and at the end of the stimulation to measure brain activity.

Dr. Nienke van Bueren, from Radboud University, who led this work under Professor Cohen Kadosh’s supervision, said, “These findings highlight that individuals with lower brain excitability may be more receptive to noise stimulation, leading to enhanced learning outcomes, while those with high brain excitability might not experience the same benefits in their mathematical abilities.”

Professor Cohen Kadosh adds, “What we have found is how this promising neurostimulation works and under which conditions the stimulation protocol is most effective. This discovery could not only pave the way for a more tailored approach in a person’s learning journey but also shed light on the optimal timing and duration of its application.”

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Credit of the article given to University of Surrey

Statistical methods can evaluate whether pivotal military events, like the Battle of Jutland, American involvement in the Vietnam war or the nuclear arms race, could’ve turned out otherwise, according to a new book.

Military historical narratives and statistical modeling bring fresh perspectives to the fore in a ground-breaking text, “Quantifying Counterfactual Military History,” by Brennen Fagan, Ian Horwood, Niall MacKay, Christopher Price and Jamie Wood, a team of historians and mathematicians.

The authors explain, “In writing history, it must always be remembered that a historical fact is simply one of numberless possibilities until the historical actor moves or an event occurs, at which point it becomes real. To understand the one-time possibility that became evidence we must also understand the possibilities that remained unrealized.”

Re-examining the battlefield

Midway through the First World War, Britain and Germany were locked in a technologically driven arms race which culminated in the Battle of Jutland in 1916. By that time, both nations had built over 50 dreadnoughts—speedy, heavily-armored, turbine-powered “all-big-gun” warships.

In the context of a battle that Britain couldn’t afford to lose, Winston Churchill was quoted as saying that the British commander John Jellicoe was “the only man on either side who could lose the war in an afternoon.”

Both nations, at various points in time, have claimed victory in the Battle of Jutland, and there is no consensus on who “won.” Using mathematical modeling, “Quantifying Counterfactual Military History” probes whether the Germans could have achieved a decisive victory.

The five scholars note, “This reconstructive battling enables us to put some level of statistical insight into multiple realizations of a key phase of Jutland. The model is crude and laden with assumptions—as are all wargames—but, unlike in a wargame, our goal is simply to understand what is plausible and what is not.”

Understanding nuclear deterrence

Counterfactual reasoning is positioned centrally when it comes to the fraught history of nuclear deterrence. By the 1980s, the nuclear arms race between the US and the Soviet Union had already spanned three decades and 1983 would bring a crisis less well-known than the Cuban missile crisis of 1962.

The authors draw attention to the peak of intensity in November 1983 during the so-called “Second Cold War.” A NATO “command post” exercise in Western Europe—known as Able Archer—was created to test communications in the event of nuclear war.

The Soviets, however—likely the result of faulty intelligence gathering—believed that an attack was imminent with the NATO exercise interpreted as the first phase. “Quantifying Counterfactual Military History” highlights the example as one where each side placed themselves in dangerous counterfactual mindsets.

“Mutual misapprehension in 1983 continued a long tradition of misunderstanding which had always created catastrophic potential for war, now based consciously and unconsciously on game theory and its erroneous assumption that rational actors were guided by accurate information,” the authors explain. “In this way they stumbled towards a war that neither had willed.”

Embrace the alternative

“Quantifying Counterfactual Military History” uses case studies of Jutland, Able Archer, the Battle of Britain and the Vietnam War to appraise long-established narratives around military events and examine the probabilities of the events that took place alongside the potential for alternative outcomes.

The book’s authors, however, take a restrained approach to counterfactual theory, one that acknowledges and considers why some events—including the actions of individuals or the rise of institutions—are more important than others and can be considered “critical junctures.” They understand this as very different from the arbitrary, loosely substantiated suppositions made by “exuberant” counterfactuals.

They say, “We can never be certain of the existence of critical junctures, or of the grounds of their criticality, but ‘restrained’ counterfactuals, if done with multiple perspectives and sufficient thoroughness, can surely make a distinctive contribution to the literature.”

The book is underpinned by an inter-disciplinary method which combines historical narrative and statistical data and analysis, offering both quantitative and qualitative rigor.

They explain, “This study has taken us in directions which are not common in academic collaboration, but which we hope demonstrate that collaborative research exploring what had been dead ground between the sciences and the humanities is long overdue.”

Rather than attempt to merely reinvent the past, “Quantifying Counterfactual Military History” calls attention to the dynamism inherent in historical practice and offers another tool for understanding historical actors, the decisions they made and the futures they shaped.

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Credit of the article given to Taylor & Francis

Calculus is the study of change. Calculus teaching methods, however, have changed little in recent decades. Now, FIU research shows a new model could improve calculus instruction nationwide.

A study published in Science shows a reimagined, innovative active learning approach to calculus instruction benefits all students. The model, developed at FIU, focuses on mastering different ways of thinking and solving problems—skills that are important beyond the classroom.

Rote memorization and large lecture halls have been replaced by active learning classrooms where students work collaboratively to solve problems. The result is greater learning outcomes and an understanding of calculus concepts, as well as better grades than their peers in traditional, lecture-based classes, according to the research.

“This large-scale study shows us what we’ve been seeing at FIU: If you put students in an interactive, active learning environment, they can and do learn significantly more, developing the ‘habits of mind’ they’ll use for a long time and throughout their careers,” said Laird Kramer, the study’s lead author and founding director of FIU’s STEM Transformation Institute.

Kramer and a team from the STEM Transformation Institute followed 811 FIU undergraduates enrolled in different sections of the same Calculus I course with two very different teaching methods—half of the sections were traditional lecture-based classes and the other half employed the evidence-based active learning model developed at FIU.

To see which group retained more information and better understood calculus concepts, the students were tested at the end of the course. Active learning classes had a higher average pass rate of 11%. Apply that to the roughly 300,000 students taking calculus each year in the U.S. and it could mean an additional 33,000 students passing calculus and getting closer to a STEM degree and career.

The active learning group’s learning gains cut across majors and academic paths and included underrepresented groups in STEM. This finding is significant since less than half of students entering universities as STEM majors actually graduate with a STEM degree. Failing calculus is a major reason.

“Calculus remains a critical step on the pathway to numerous STEM careers in engineering and the sciences,” said Michael J. Ferrara, Program Director at NSF Directorate of STEM Education. “This study makes a rigorous and compelling argument that active, student-centered calculus courses result in significantly greater learning and success outcomes when compared to more traditional approaches.

“These benefits are particularly profound for students from populations that have traditionally been underrepresented in the STEM workforce, which underscores how a more modern approach to teaching mathematics is critical as we look to nurture the full spectrum of STEM talent across the nation.”

Improving teaching methods in calculus means students are more likely to stay on track and stick with a STEM program. That, in turn, helps graduate more STEM professionals.

“Student success is FIU’s priority, as demonstrated by the development and successful implementation of active learning strategies in our calculus courses,” FIU Executive Vice President and Provost Elizabeth M. Béjar said. “This research builds on years of studying the positive impacts of active learning in the classroom to ensure students have the knowledge and skills they need to move through their STEM courses with confidence.”

FIU has led collaborative initiatives through its nationally recognized STEM Transformation Institute to improve learning. Research has informed the development and introduction of innovative instructional strategies for calculus, mathematics and other sciences and engineering. That’s led to significant increases in four-year graduation rates for STEM majors at FIU.

FIU’s active learning model is just as challenging and rigorous as a traditional lecture style, but more effective for the often incredibly complex process of learning and provides opportunities for different parts of the brain to engage and store information. Students learn by doing. Class time is collaboration time. In small groups, students work face-to-face developing and testing hypotheses. The goal is to learn to ask the right questions and look at problems in new ways—meaning, they think and act like mathematicians, engineers, scientists, doctors.

“This research began as an experiment to see if we could identify new ways of teaching coursework and give students an alternative way of learning the rigorous content in calculus,” said Mike Heithaus, executive dean of the College of Arts, Sciences & Education. “Based on prior research, we felt confident the active learning method would be effective. But even we were surprised at how much better students did in the active learning sections versus the traditional. As soon as we got these results, we began implementing these methods throughout our entire math curriculum.”

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Credit of the article given to Angela Nicoletti, Florida International University

There is more to where maths came from than the ancient Greeks. From calculus to the theorem we credit to Pythagoras, so much of our knowledge comes from other places, including ancient China, India and the Arabian peninsula, says Kate Kitagawa.

The history of mathematics has an image problem. It is often presented as a meeting of minds among ancient Greeks who became masters of logic. Pythagoras, Euclid and their pals honed the tools for proving theorems and that led them to the biggest results of ancient times. Eventually, other European greats like Leonhard Euler and Isaac Newton came along and made maths modern, which is how we got to where we are today.

But, of course, this telling is greatly distorted. The history of maths is far richer, more chaotic and more diverse than it is given credit for. So much of what is now incorporated into our global knowledge comes from other places, including ancient China, India and the Arabian peninsula.

Take “Pythagoras’s” theorem. This is the one that says that in right-angled triangles, the square of the longest side is the sum of the square of the other two sides. The ancient Greeks certainly knew about this theorem, but so too did mathematicians in ancient Babylonia, Egypt, India and China.

In fact, in the 3rd century AD, Chinese mathematician Liu Hui added a proof of the theorem to the already old and influential book The Nine Chapters on the Mathematical Art. His version includes the earliest written statement of the theorem that we know of. So perhaps we should really call it Liu’s theorem or the gougu theorem as it was known in China.

The history of maths is filled with tales like this. Ideas have sprung up in multiple places at multiple times, leaving room for interpretation as to who should get the credit. As if credit is something that can’t be split.

As a researcher on the history of maths, I had come across examples of distorted views, but it was only when working on a new book, The Secret Lives of Numbers, that I found out just how pervasive they are. Along with my co-author, New Scientist‘s Timothy Revell, we found that the further we dug, the more of the true history of maths there was to uncover.

Another example is the origins of calculus. This is often presented as a battle between Newton and Gottfried Wilhelm Leibniz, two great 17th-century European mathematicians. They both independently developed extensive theories of calculus, but missing from the story is how an incredible school in Kerala, India, led by the mathematician Mādhava, hit upon some of the same ideas 300 years before.

The idea that the European way of doing things is superior didn’t originate in maths – it came from centuries of Western imperialism – but it has infiltrated it. Maths outside ancient Greece has often been put to one side as “ethnomathematics”, as if it were a side story to the real history.

In some cases, history has also distorted legacies. Sophie Kowalevski, who was born in Moscow in 1850, is now a relatively well-known figure. She was a fantastic mathematician, known for tackling a problem she dubbed a “mathematical mermaid” for its allure. The challenge was to describe mathematically how a spinning top moves, and she made breakthroughs where others had faltered.

During her life, she was constantly discouraged from pursuing maths and often had to work for free, collecting tuition money from her students in order to survive. After her death, biographers then tainted her life, painting her as a femme fatale who relied on her looks, implying she effectively passed off others’ work as her own. There is next to no evidence this is true.

Thankfully, historians of mathematics are re-examining and correcting the biases and stereotypes that have plagued the field. This is an ongoing process, but by embracing its diverse and chaotic roots, the next chapters for maths could be the best yet.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Kate Kitagawa *

With mass shootings happening randomly every year in the United States, it may seem that there is no way to predict where the next horrific event is most likely to occur. In a new study published by the journal Risk Analysis, scientists at Iowa State University calculate the annual probability of a mass shooting in every state and at public places such as shopping malls and schools.

Their new method for quantifying the risk of a mass shooting in specific places could help security officials make informed decisions when planning for emergency events.

For their analysis, Iowa State associate professor Cameron MacKenzie and his doctoral student Xue Lei applied statistical methods and computer simulations to a database of mass shootings recorded from 1966 to 2020 by the Violence Project. The Violence Project defines a mass shooting as an incident with four or more victims killed by a firearm in a public place.

According to the Violence Project, the U.S. has experienced 173 public mass shootings from 1966 to 2020—with at least one mass shooting every year since 1966.

After they generated a probability distribution of annual mass shootings in the U.S., the scientists used two different models to simulate the annual number of mass shootings in each state. The results were used to calculate the expected number of mass shootings and the probability that at least one mass shooting would occur in each state in one year.

The Violence Project also provides the percentage of mass shootings in different types of locations. MacKenzie and Lei used that data to calculate the probability of a mass shooting in nine different types of public locations (including a restaurant, school, workplace, or house of worship) in the states of California and Iowa and also at the two largest high schools in each of those states.

Their findings include the following:

• The states with the greatest risk of a mass shooting are the most populous states: California, Texas, Florida, New York, and Pennsylvania. Together they account for almost 50% of all mass shootings.
• Some states, such as Iowa and Delaware, have never experienced a mass shooting.
• The annual risk of a mass shooting at the largest California high school is about 10 times greater than the risk at the largest Iowa high school.
• The number of mass shootings in the U.S. has increased by about one shooting every 10 years since the 1970s.

Importantly, MacKenzie points out that the probability of a mass shooting at a specific location depends on the definition of a mass shooting. In contrast to the Violence Project, the Gun Violence Archive defines a mass shooting as four or more individuals shot, injured or killed, in any location, not necessarily a public location. As a result, The Gun Violence Archive has collected data on shootings that occur in both public and private locations as well as targeted shootings (i.e., a gang shooting).

When the researchers applied data from The Gun Violence Archive to their models, the predicted number of annual mass shootings was nearly 100 times greater than the forecast based on The Violence Project’s data. The models predicted 639 mass shootings in 2022 with a 95% chance that the U.S. would experience between 567 and 722 mass shootings in that same year.

MacKenzie points out that “most media appear to use this broader definition of mass shootings.” Because of this, he urges that journalists explain how they are defining a mass shooting when reporting the statistical data.

With regard to the danger posed to children at school, MacKenzie explains, that “our results show that it is very, very unlikely that a specific student will attend a K-12 school and experience a mass shooting. But to parents of a child at a school that has experienced a mass shooting, explaining that the school was extremely unlucky provides no comfort.”

While it is important to take precautions, he adds that “we should not live in fear that our children will experience such a horrific event. Mass shootings are very low probability but very high consequence events.”

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Credit of the article given to Society for Risk Analysis

Researchers are using mathematical models to better understand the effects of disruptions like daylight savings time, working night shifts, jet lag or even late-night phone scrolling on the body’s circadian rhythms.

The University of Waterloo and the University of Oxford researchers have developed a new model to help scientists better understand the resilience of the brain’s master clock: the cluster of neurons in the brain that coordinates the body’s other internal rhythms. They also hope to suggest ways to help improve this resilience in individuals with weak or impaired circadian rhythms. The study, “Can the Clocks Tick Together Despite the Noise? Stochastic Simulations and Analysis,” appears in the SIAM Journal on Applied Dynamical Systems.

Sustained disruptions to circadian rhythm have been linked to diabetes, memory loss, and many other disorders.

“Current society is experiencing a rapid increase in demand for work outside of traditional daylight hours,” said Stéphanie Abo, a Ph.D. student in applied mathematics and the study’s lead author. “This greatly disrupts how we are exposed to light, as well as other habits such as eating and sleeping patterns.”

Humans’ circadian rhythms, or internal clocks, are the roughly 24-hour cycles many body systems follow, usually alternating between wakefulness and rest. Scientists are still working to understand the cluster of neurons known as suprachiasmatic nucleus (SCN) or master clock.

Using mathematical modeling techniques and differential equations, the team of applied mathematics researchers modeled the SCN as a macroscopic, or big-picture, system comprised of a seemingly infinite number of neurons. They were especially interested in understanding the system’s couplings—the connections between neurons in the SCN that allow it to achieve a shared rhythm.

Frequent and sustained disturbances to the body’s circadian rhythms eliminated the shared rhythm, implying a weakening of the signals transmitted between SCN neurons.

Abo said they were surprised to find that “a small enough disruption can actually make the connections between neurons stronger.”

“Mathematical models allow you to manipulate body systems with specificity that cannot be easily or ethically achieved in the body or a petri dish,” Abo said. “This allows us to do research and develop good hypotheses at a lower cost.”

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Credit of the article given to University of Waterloo

Graphic novels can help make math and physics more accessible for students, parents or teachers in training. Metamorworks/iStock via Getty Images

Post-pandemic, some educators are trying to reengage students with technology – like videos, computer gaming or artificial intelligence, just to name a few. But integrating these approaches in the classroom can be an uphill battle. Teachers using these tools often struggle to retain students’ attention, competing with the latest social media phenomenon, and can feel limited by using short video clips to get concepts across.

Graphic novels – offering visual information married with text – provide a means to engage students without losing all of the rigor of textbooks. As two educators in math and physics, we have found graphic novels to be effective at teaching students of all ability levels. We’ve used graphic novels in our own classes, and we’ve also inspired and encouraged other teachers to use them. And we’re not alone: Other teachers are rejuvenating this analog medium with a high level of success.

In addition to covering a wide range of topics and audiences, graphic novels can explain tough topics without alienating student averse to STEM – science, technology, engineering and math. Even for students who already like math and physics, graphic novels provide a way to dive into topics beyond what is possible in a time-constrained class. In our book “Using Graphic Novels in the STEM Classroom,” we discuss the many reasons why graphic novels have a unique place in math and physics education. Here are three of those reasons:

Explaining complex concepts with rigor and fun

Increasingly, schools are moving away from textbooks, even though studies show that students learn better using print rather than digital formats. Graphic novels offer the best of both worlds: a hybrid between modern and traditional media.

This integration of text with images and diagrams is especially useful in STEM disciplines that require quantitative reading and data analysis skills, like math and physics.

For example, our collaborator Jason Ho, an assistant professor at Dordt University, uses “Max the Demon Vs Entropy of Doom” to teach his physics students about entropy. This topic can be particularly difficult for students because it’s one of the first times when they can’t physically touch something in physics. Instead, students have to rely on math and diagrams to fill in their knowledge.

Rather than stressing over equations, Ho’s students focus on understanding the subject more conceptually. This approach helps build their intuition before diving into the algebra. They get a feeling for the fundamentals before they have to worry about equations.

After having taken Ho’s class, more than 85% of his students agreed that they would recommend using graphic novels in STEM classes, and 90% found this particular use of “Max the Demon” helpful for their learning. When strategically used, graphic novels can create a dynamic, engaging teaching environment even with nuanced, quantitative topics.

Combating quantitative anxiety

Students learning math and physics today are surrounded by math anxiety and trauma, which often lead to their own negative associations with math. A student’s perception of math can be influenced by the attitudes of the role models around them – whether it’s a parent who is “not a math person” or a teacher with a high level of math anxiety.

Graphic novels can help make math more accessible not only for students themselves, but also for parents or students learning to be teachers.

In a geometry course one of us (Sarah) teaches, secondary education students don’t memorize formulas and fill out problem sheets. Instead, students read “Who Killed Professor X?”, a murder mystery in which all of the suspects are famous mathematicians. The suspects’ alibis are justified through problems from geometry, algebra and pre-calculus.

While trying to understand the hidden geometry of suspect relationships, students often forget that they are doing math – focusing instead on poring over secret hints and notes needed to solve the mystery.

Although this is just one experience for these students, it can help change the narrative for students experiencing mathematical anxiety. It boosts their confidence and shows them how math can be fun – a lesson they can then impart to the next generation of students.

Helping students learn and readers dream big

In addition to being viewed favourably by students, graphic novels can enhance student learning by improving written communication skills, reading comprehension and critical literacy skills. And even outside the classroom, graphic novels support long-term memory for those who have diagnoses like dyslexia.

Pause and think about your own experience – how do you learn about something new in science?

If you’re handed a textbook, it’s extremely unlikely that you’d read it cover to cover. And although the internet offers an enormous amount of math and physics content, it can be overwhelming to sift through hours and hours of videos to find the perfect one to get the “aha!” moment in learning.

Graphic novels provide a starting point for such a broad range of niche topics that it’s impossible for anyone to be experts in them all. Want to learn about programming? Try the “Secret Coders” series. Want to understand more about quantum physics? Dive into “Suspended in Language: Niels Bohr’s life, discoveries, and the century he shaped.” Searching for more female role models in science? “Astronauts: Women on the Final Frontier” could be just what you’re looking for.

With all that they offer, graphic novels provide a compelling list of topics and narratives that can capture the attention of students today. We believe that the right set of graphic novels can inspire the next generation of scientists as much as any single individual can.

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From avoiding the number seven to picking numbers over 31, mathematician Peter Rowlett has a few psychological strategies for improving your chances when playing the lottery.

Would you think I was daft if I bought a lottery ticket for the numbers 1, 2, 3, 4, 5 and 6? There is no way those are going to be drawn, right? That feeling should – and, mathematically, does – actually apply to any set of six numbers you could pick.

Lotteries are ancient. Emperor Augustus, for example, organised one to fund repairs to Rome. Early lotteries involved selling tickets and drawing lots, but the idea of people guessing which numbers would be drawn from a machine comes from Renaissance Genoa. A common format is a game that draws six balls from 49, studied by mathematician Leonhard Euler in the 18th century.

The probabilities Euler investigated are found by counting the number of possible draws. There are 49 balls that could be drawn first. For each of these, there are 48 balls that can be drawn next, so there are 49×48 ways to draw two balls. This continues, so there are 49×48×47×46×45×44 ways to draw six balls. But this number counts all the different arrangements of any six balls as a unique solution.

How many ways can we rearrange six balls? Well, we have six choices for which to put first, then for each of these, five choices for which to put second, and so on. So the number of ways of arranging six balls is 6×5×4×3×2×1, a number called 6! (six factorial). We divide 49×48×47×46×45×44 by 6! to get 13,983,816, so the odds of a win are near 1 in 14 million.

Since all combinations of numbers are equally likely, how can you maximise your winnings? Here is where maths meets psychology: you win more if fewer people share the prize, so choose numbers others don’t. Because people often use dates, numbers over 31 are chosen less often, as well as “unlucky” numbers like 13. A lot of people think of 7 as their favourite number, so perhaps avoid it. People tend to avoid patterns so are less likely to pick consecutive or regularly spaced numbers as they feel less random.

In July, David Cushing and David Stewart at the University of Manchester, UK, published a list of 27 lottery tickets that guarantee a win in the UK National Lottery, which uses 59 balls and offers a prize for matching two or more. But a win doesn’t always mean a profit – for almost 99 per cent of possible draws, their tickets match at most three balls, earning prizes that may not exceed the cost of the tickets!

So, is a lottery worth playing? Since less than half the proceeds are given out in prizes, you would probably be better off saving your weekly ticket money. But a lecturer of mine made an interesting cost-benefit argument. He was paid enough that he could lose the cost of a ticket each week without really noticing. But if he won the jackpot, his life would be changed. So, given that lottery profit is often used to support charitable causes, it might just be worth splurging.

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*Credit for article given to Peter Rowlett*