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Current Events & History

Should I Eat That? Food Safety with Wolfram Language

Foodborne illness, or food poisoning, is something many of us have experienced. According to the World Health Organization, almost 1 in 10 people in the world fall ill each year after eating contaminated food. Luckily, by following recommended food safety practices, we can do our best to avoid getting sick.

September is Food Safety Education Month. To highlight the importance of food safety, we have introduced two new properties in Wolfram Language that can help users make smart choices about food storage:

Isabel Skidmore, Food Data Consultant
Current Events & History

Cheers! A Computational Exploration of Alcoholic Beverages with the Wolfram Language

For 10 thousand years, humans have been using fermentation to produce beverages for pleasure, rituals and healing. In ancient Greece, honey was fermented to produce mead. Today, popular sources of beverage fermentation are grains, grapes, berries and rice. The science of fermentation—known as zymology (or zymurgy)—is a fascinating blend of chemistry, biology, history and geography. The Wolfram Language now brings a new dimension to the study of alcoholic beverages through an extensive dataset ready to be explored and analyzed.
Isabel Skidmore, Food Data Consultant
Current Events & History

John Snow & the Birth of Epidemiology Data Analysis & Visualization

In 1854, there was a major cholera outbreak in Soho, a neighborhood in London that Judith Summers described as full of “cow-sheds, animal droppings, slaughterhouses, grease-boiling dens and primitive, decaying sewers.” At the time, the cause of the outbreak was unknown because germ theory was still being developed and disease transmission was not well understood. Miasma theory was the dominant hypothesis, and it proposed that diseases, including cholera and the plague, were spread by foul gasses emitted from decomposing organic matter.
Roman Parker, Community Advancement Mads Bahrami, Academic Innovation Support
Current Events & History

Florida Spring Break 2021: February COVID-19 Data Forecasts the March of the Variants

It is widely believed that students and others spending their 2020 spring break in Florida helped spread COVID-19 far and wide, in the US and elsewhere (see also this study). The picture in 2021 is quite different in several ways. For one, the disease has been in the US for over a year, and an approximated 30% of the population has antibodies from prior exposure. Also, several vaccines are now in use, and close to 20% have received at least one inoculation at the time of this writing. (Since those two groups overlap, the total is believed to be in the ballpark of 45% of the total population.) We now know that children under the age of 16 do not get the disease in large numbers and are not a major vector for its spread. Social distancing practices are in use to varying degrees, and infection numbers are currently falling across the country. This is believed to be due to a combination of increased immunity and non-pharmacological interventions (NPIs) such as social distancing and mask use.
Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D
Announcements & Events

Crop Production Forecasts and Groundwater Trends Based on the Predator–Prey Model

Ever since Thomas Robert Malthus’s book An Essay on the Principle of Population, scientists have sought to determine the limit to the growth of human population due to finite resources. One such resource is groundwater. About 40% of global food production ultimately depends on irrigation and, increasingly, the source of irrigation water is groundwater aquifers. Groundwater irrigation allows farmers to increase crop yields, maintain them in dry spells and overcome temporal mismatches between growing seasons and seasonal rain. In many parts of the world, groundwater withdrawal (or pumping from wells) exceeds recharge, leading to groundwater depletion. In these regions, the “lifespan” of groundwater aquifers is limited, putting a bound on the amount of irrigation per year and the sustainability of groundwater-based agriculture. The goal of this study was to propose a dynamical systems framework capable of explaining past trends in groundwater-based irrigation and providing forecasts of food production.

Assaad Mrad, Guest Author
Computation & Analysis

Distinguishing Risks of Modes of Cardiac Death in Heart Failure with Machine Learning

In medical fields like cardiology, the Wolfram Language continues to help researchers make discoveries and predictions. I recently coauthored a study that uses the machine learning functionality of the Wolfram Language to predict risks of deaths due to heart failure. In it, we aimed to build a classifier that is capable of distinguishing the probabilities of cardiac death caused by end-stage heart failure (HFD) and severe arrhythmic events/sudden death (ArE). What follows is a summary of the paper we published earlier this year.

Koji Maruyama, Sales Engineer
Computation & Analysis

Accessing Monarch Biodiversity Data with New Wolfram Language Functions

Earth has experienced five major extinctions since life first appeared almost four billion years ago. The sixth is happening right now; the current extinction rate is between one hundred and one thousand times greater than what it was before 1800.

Despite the alarming extinction rate, it’s easier than ever to document biodiversity with the help of the Wolfram Language. Using the monarch butterfly as an example, I will explore the new biodiversity data access functions in the Wolfram Function Repository and how they can help you join a community of thousands of citizen scientists from iNaturalist in preserving biodiversity.

Jofre Espigulé-Pons, Machine Learning
Announcements & Events

AI and the Wolfram Language Work toward Partial Automation in the Search for Cancer

NOTE: The following post contains real medical images.

As more technology is folded into medical environments all over the world, Wolfram’s European branch has taken on work with the United Kingdom’s National Health Service (NHS) in an effort to partially automate the process of cancer diagnosis. The task is to use machine learning to avoid checking thousands of similar-looking images of people’s insides by hand for signs of cancer.

Sjoerd Smit, Wolfram Europe
Current Events & History

Tackling a Pandemic: A Computer-Based Maths Approach

How did the Department of Health and Social Care (DHSC) come up with their multi-phase response to tackle COVID-19? In this post, I investigate how the UK government's original plan against the coronavirus aligns with the four-step computational thinking process. Teachers are welcome to use this post as a free resource.

Please note: where possible, I have taken data from before the DHSC's plan was published.

The Computational Thinking Process

What is the computational thinking process? Simply put, it is a sequence of four steps that you can take in order to solve a problem. The aim is not just to obtain a solution, but to ensure that the right choices were made, the right tools were used and the right outcomes were achieved along the way. The steps are as follows: you define explicitly the problem you wish to solve, abstract it to a computational form, compute an answer, then interpret the result:

Daniel Robinson, CBM Content Author, European Sales
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