The National Oceanic and Atmospheric Administration has shared what it says are the first images and video captured inside a hurricane by a surface drone. The agency placed the Saildrone Explorer SD 1045 in the path of the category-four Hurricane Sam. The saildrone overcame 50-foot waves and winds at speeds topping 120 miles per hour to capture data from the hurricane and offer a new perspective into such storms. The device has a special "hurricane wing" to help it survive the intense wind conditions. The SD 1045 is one of five saildrones that have been in the Atlantic Ocean during hurricane season.

NOAA and Saildrone Inc. are piloting five specially designed surface drones in the Atlantic Ocean to gather data around the clock to help understand the physical processes of hurricanes. NOAA and Saildrone Inc. are piloting five specially designed surface drones in the Atlantic Ocean to gather data around the clock to help understand the physical processes of hurricanes. Researchers for the National Oceanic and Atmospheric Administration have dispatched a surface drone inside Hurricane Sam as it barrels toward the Caribbean, giving scientists a new perspective of what it's like inside such a storm. The video and images shared were the first of their kind gathered by an "uncrewed surface vehicle" from inside a major hurricane as it moved across the Atlantic Ocean. The onboard camera shows eerily gray skies and turbulent ocean waters.

This specific morphology gives rise to several catchments with steep slopes and limited extension [1]. Autumn events, when deep Atlantic troughs more easily enter the Mediterranean area and activate very moist and unstable flow lifted by the mountain range, may determine catastrophic flood on these coastal areas characterized by a high population density (see [2, 3] for a review of climatology and typical atmospheric configurations of extreme precipitations over the Mediterranean area). Just as an example, the November 4th 2011 flood in Genoa determined six deaths and economic damages up to 100 million euros [4, 5, 6, 7]). A common feature in these extreme events are the presence of a quasi-stationary convective system with a spatial extension of few kilometers [8, 9, 10, 11, 12] Medium and long range either deterministic or ensemble Numerical Weather Prediction (NWP) models still struggle to correctly predict both the intensity and the location of these events, which can be triggered and enhanced by very small-scale features. High resolution convection-permitting NWP models manage to partly return a more realistic description of the dynamics of severe thunderstorms. Many studies addressed the role played by different components or settings of NWP models in order to better describe severe convective systems over the Liguria area, such as model resolution, initial conditions, microphysics schemes or small-scale patterns of the sea surface temperature ([6, 13, 14, 15, 16, 17, 18, 17, 19]).

NASA wants to push the concept of Swarm intelligence to new heights. Swarms of drones have gotten a lot of time in the spotlight lately, mostly for their use in potential military operations. The U.S. military is testing out swarm operations in simulations, while the British Army is using live drones operating in swarms during actual training operations. Other militaries are also interested in deploying swarms. One of the biggest advantages a swarm of drones has when performing military operations is its resiliency.

Many mathematical algorithms that we use in data science and machine learning require numeric data. And many algorithms tend to be very complex to implement (such as Support Vector Machines or Local Linear Embedding, which we previously discussed). But, association rule mining is perfect for categorical (non-numeric) data and it involves nothing more than simple counting! What we have here is a simple algorithm with not so simplistic results! The ratio of actionable insights discovery potential (high) to algorithm complexity (low) is quite large and atypical, IMHO.

Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.

Deep learning is on the frontline in a new age of piracy, outwitting attacks with pre-emptive tech, explains Yarden Gross, CEO and co-founder of Orca AI. Almost a decade has passed since piracy raged off Somalia, and yet the danger posed by maritime hijackings is as present as ever. The global pandemic last year sparked a resurgence of attacks, with piracy incidents doubling across Asia, in a worrying uptick also seen in the Gulf of Mexico and West Africa. The fallout from coronavirus, including the loss of key security personnel, turned quarantined vessels into easy targets. This wave has since receded a little, with the International Maritime Bureau reporting a 44 per cent YoY dip in piracy and armed robbery incidents in 2021.

The prediction of behavior in dynamical systems, is frequently subject to the design of models. When a time series obtained from observing the system is available, the task can be performed by designing the model from these observations without additional assumptions or by assuming a preconceived structure in the model, with the help of additional information about the system. In the second case, it is a question of adequately combining theory with observations and subsequently optimizing the mixture. In this work, we proposes the design of time-continuous models of dynamical systems as solutions of differential equations, from non-uniform sampled or noisy observations, using machine learning techniques. The performance of strategy is shown with both, several simulated data sets and experimental data from Hare-Lynx population and Coronavirus 2019 outbreack. Our results suggest that this approach to the modeling systems, can be an useful technique in the case of synthetic or experimental data.

Swarms of drones have gotten a lot of time in the spotlight lately, mostly for their use in potential military operations. The U.S. military is testing out swarm operations in simulations, while the British Army is using live drones operating in swarms during actual training operations. Other militaries are also interested in deploying swarms. One of the biggest advantages a swarm of drones has when performing military operations is its resiliency. If a swarm enters combat and several individual drones get shot down or otherwise incapacitated, it really doesn't reduce the combat effectiveness of the swarm, nor the tactics that it uses.

Recently, Github and OpenAI released one of the most anticipated AI-based tools for developers -- Github Copilot. The Artificial Intelligence (AI) tool is advertised as a pair programming assistant that does much more than usual code autocomplete tools out there. By no means is Copilot a tool intended to substitute developers in any way. Instead, the tool is meant to be used as an assistant that can facilitate many of the "boring" and "repetitive" parts of programming and lets coders worry about parts of the process that require human thinking and reasoning. It is important to note that GitHub Copilot is based on a recent deep learning model published by OpenAI in a paper called "Evaluating Large Language Models Trained on Code". This research paper introduces Codex, a GPT-like language model fine-tuned on publicly available code from GitHub.