Predictive modelling using machine learning has become very popular for spatial mapping of the environment. Models are often applied to make predictions far beyond sampling locations where new geographic locations might considerably differ from the training data in their environmental properties. However, areas in the predictor space without support of training data are problematic. Since the model has no knowledge about these environments, predictions have to be considered uncertain. Estimating the area to which a prediction model can be reliably applied is required. Here, we suggest a methodology that delineates the "area of applicability" (AOA) that we define as the area, for which the cross-validation error of the model applies. We first propose a "dissimilarity index" (DI) that is based on the minimum distance to the training data in the predictor space, with predictors being weighted by their respective importance in the model. The AOA is then derived by applying a threshold based on the DI of the training data where the DI is calculated with respect to the cross-validation strategy used for model training. We test for the ideal threshold by using simulated data and compare the prediction error within the AOA with the cross-validation error of the model. We illustrate the approach using a simulated case study. Our simulation study suggests a threshold on DI to define the AOA at the .95 quantile of the DI in the training data. Using this threshold, the prediction error within the AOA is comparable to the cross-validation RMSE of the model, while the cross-validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross-validation is applied. We suggest to report the AOA alongside predictions, complementary to validation measures.

Robotics, machine learning, data science, and mathematical modelling are just some of the tools that a group of researchers will use to forecast environmental changes across Antarctica as part of a seven-year research project. To be led by Monash University, the Securing Antarctica's Environmental Future (SAEF) project will involve 30 Australian and overseas organisations, including Queensland University of Technology (QUT), University of Wollongong, University of New South Wales, James Cook University, University of Adelaide, the South Australian Museum, and the Western Australian Museum. According to QUT Institute for Future Environments executive director Kerrie Wilson, who will form part of the program's leadership team, the research aims to "bring new perspectives to Antarctic conservation". "Antarctica is facing unprecedented threats from climate change, fishing, visitation, and other human activities. Safeguarding its future will require new ideas, and collaborations between different fields of science," she told ZDNet.

It may sound like a plot from a science fiction film, but NASA and the world governments are concerned about alien viruses contaminating Earth. As the first humans prepare for the Mars mission, experts warn that protocols are necessary to keep extraterrestrial pollutants from hitchhiking on space ships and astronauts when returning home from the Red Planet. Stanford professor of aeronautics and astronautics Scott Hubbard said in an interview that the solution is'planetary protection'. Mechanical systems will have to undergo a combination of chemical cleaning and heat sterilization, while the tubes containing samples from Mars need to be treated'as though they are the Ebola virus until proven safe.' Hubbard also suggests that astronauts must be quarantine once they touch down on our planet, as the first men who visited the moon in the Apollo mission did. As the first humans prepare for the Mars mission, experts warn that protocols need to be created to keep extraterrestrial pollutants from hitchhiking on space ships and astronauts when returning home.

Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment. Non-adherence exacerbates health risks and drives significant increases in treatment costs. In order to address these challenges, the importance of predicting patients' adherence has been recognised. In other words, it is important to improve the efficiency of interventions of the current healthcare system by prioritizing resources to the patients who are most likely to be non-adherent. Our objective in this work is to make predictions regarding individual patients' behaviour in terms of taking their medication on time during their next scheduled medication opportunity. We do this by leveraging a number of machine learning models. In particular, we demonstrate the use of a connected IoT device; a "Smart Sharps Bin", invented by HealthBeacon Ltd.; to monitor and track injection disposal of patients in their home environment. Using extensive data collected from these devices, five machine learning models, namely Extra Trees Classifier, Random Forest, XGBoost, Gradient Boosting and Multilayer Perception were trained and evaluated on a large dataset comprising 165,223 historic injection disposal records collected from 5,915 HealthBeacon units over the course of 3 years. The testing work was conducted on real-time data generated by the smart device over a time period after the model training was complete, i.e. true future data. The proposed machine learning approach demonstrated very good predictive performance exhibiting an Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 0.86.

In 2015, I got obsessed with the idea of driverless trucks and started Starsky Robotics. In 2016, we became the first street-legal vehicle to be paid to do real work without a person behind the wheel. In 2018, we became the first street-legal truck to do a fully unmanned run, albeit on a closed road. In 2019, our truck became the first fully-unmanned truck to drive on a live highway. I remain incredibly proud of the product, team, and organization we were able to build; one where PhDs and truck drivers worked side by side, where generational challenges were solved by people with more smarts than pedigree, and where we discovered how the future of logistics will work.

Antarctica's Thwaites glacier has warm water from three directions well under it threatening to destroy the ice sheet and raise global sea levels by up to two feet. A team of scientists from Oregon State University made the most of ice free waters in West Antarctica to look under the glacier - which is about the size of Great Britain. Warm water from the deep ocean is welling up under the glacier from three different directions and mixing under the ice, the researchers discovered. If it collapses it could take other parts of the ice shelf with it and lead to the single largest driver of sea-level rise this century, lead researcher Erin Pettit told Nature. The £39million study involving UK and US scientists was launched after concerns the increasingly unstable glacier may have already started to collapse.

It seems like every day, another animal is at risk due to climate change. Antarctica's emperor penguin population is the latest to be in trouble. Their numbers have been dwindling thanks to breeding difficulties which are most likely caused by changing temperatures. According to a 2019 study by the British Antarctic Survey, the emperor penguins could completely disappear by 2100. To help save these beautiful creatures, scientists are turning to AI.

As the nations of Earth attempt to invent and implement solutions to the growing threat of climate change, just about every option is on the table. Investing in renewable sources of energy and dropping emissions around the globe are the dominant strategies, but utilizing artificial intelligence can help reduce the damage done by climate change. As reported by Live Mint, artificial intelligence algorithms can help conservationists limit deforestation, protect vulnerable species of animals from climate change, fight poaching, and monitor air pollution. The data science company Gramener has employed machine learning to help get estimates of the number of penguin colonies across Antarctica by analyzing images taken by camera traps. The size of penguin colonies in Antarctica has decreased dramatically over the course of the past decade, impacted by climate change.

Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (A WD) method developed by Lichtenberger (2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by A WD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to A WD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset. 1 Introduction Lightning strokes create powerful electromagnetic pulses that result in Very Low Frequency (VLF) waves propagating along the magnetic field lines of the earth. Due to the dipole shape of the geomagnetic field, these waves travel upward from the stroke location out through portions of the plasmasphere and back to the Earth's surface at the field line foot point in the opposite hemisphere. VLF antenna receivers set up at various high and middle latitude locations can detect whistler waves generated by these lightning strokes. The propagation time delay of these waves is dependent on the plasma density along the propagation path. This enables the use of whistler wave observations for characterising the plasmasphere in terms of particle number and energy density. The dynamics of energetic particle populations in the plasmasphere are an important factor in characterising the risk to spacecraft in orbit around Earth. Annual global lightning flash rates are on the order of 45 flash/s [2].

With coyote attacks on humans in cities and suburbs making headlines – coyotes injured two people in Chicago earlier this month – officials could tap into a data repository to get a better handle on what's bringing the area's animals into such close proximity to humans. Called eMammal, the tool has been around for several years in one form or another and has helped researchers manage camera-trapping projects. It uses a data pipeline that takes images and metadata from the field through a cloud-based review processes and into SIdora, a Smithsonian Institution data repository. To date, eMammal has data on more than 1 million detections of wildlife worldwide, including in cities. Smithsonian researchers collaborated with others at the North Carolina Museum of Natural Sciences, Conservation International and the Wildlife Conservation Society to develop an open standard for camera trap metadata -- the Camera Trap Metadata Standard -- as part of the eMammal project. Camera traps are ruggedized cameras that researchers place in forests, jungles, grasslands, cities and elsewhere to capture images of mammals.