ABSTRACT Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid damages and failures. Thus, considering these storms' potential effects on human activities, accurate forecasts of the geoeffectiveness of CMEs are paramount. This work focuses on experimenting with different machine learning methods trained on white-light coronagraph datasets of close to sun CMEs, to estimate whether such a newly erupting ejection has the potential to induce geomagnetic activity. We developed binary classification models using logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward artificial neural networks, as well as ensemble models. At this time, we limited our forecast to exclusively use solar onset parameters, to ensure extended warning times. We discuss the main challenges of this task, namely the extreme imbalance between the number of geoeffective and ineffective events in our dataset, along with their numerous similarities and the limited number of available variables. We show that even in such conditions, adequate hit rates can be achieved with these models. INTRODUCTION The purpose of this work is to develop a machine learning (ML) based model that can predict whether a coronal mass ejection (CME) will be geoeffective, using only numerical solar parameters as input. Coronal mass ejections are solar eruptive events whose magnetically charged particles can, directly or indirectly, under certain circumstances, reach Earth and cause geomagnetic storms (GSs), i.e., be geoeffective. These storms represent perturbations in the Earth's magnetic field, which have the potential to lead to electrical systems and grids failure and/or damage, power outages, navigation errors, radio signal perturbations, significant exposure to dangerous radiations for astronauts during space missions, etc. Given the potential negative impacts of such storms, predicting their occurrence is paramount for enabling safeguarding of human technology (Schwenn 2006; Pulkkinen 2007; Council 2013; Vourlidas et al. 2019; Temmer 2021). The intensity of the storms can be measured by various geomagnetic indices such as Ap, Kp, AE, PC or Dst (see Lockwood 2013, and references therein). Herein, we have chosen to use the values of the Dst index (Sugiura 1964) to establish whether the magnetic field perturbations do, in fact, manifest as storms. This is an index that is calculated using four geomagnetic stations situated at low latitudes. Depending on the value of this index, it can be established whether these perturbations are associated with geomagnetic storms or not. In terms of storm intensity, one of the most popular classifications that takes into consideration the minimum value of the Dst index is that of Gonzalez et al. (1994).

With a GDP of AED 1.5 trillion in 2020, the UAE's economy is the fifth-largest in the Middle East. The UAE economy, which was once reliant on oil exports, is now increasingly dependent on earnings from petroleum and natural gas. Economic diversification has occurred in recent years, particularly in Dubai. According to studies, the worldwide number of internet-connected devices is predicted to reach 1 trillion by 2030, with the UAE alone expected to achieve this amount by 2050. As a transit country between the East and the West with a pro-business environment, the UAE has become a technology powerhouse for the Internet of Things in all fields, enabling digital transformation in airports, freight, and logistics.

Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands, while violating privacy. Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices, which are typically low-power. This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning (FL). The proposed framework quantifies both the energy footprints and the carbon equivalent emissions for vanilla FL methods and consensus-based fully decentralized approaches. We discuss optimal bounds and operational points that support green FL designs and underpin their sustainability assessment. Two case studies from emerging 5G industry verticals are analyzed: these quantify the environmental footprints of continual and reinforcement learning setups, where the training process is repeated periodically for continuous improvements. For all cases, sustainability of distributed learning relies on the fulfillment of specific requirements on communication efficiency and learner population size. Energy and test accuracy should be also traded off considering the model and the data footprints for the targeted industrial applications. Training deep Machine Learning (ML) models at the network edge has reached notable gains in terms of accuracy across many tasks, applications and scenarios. However, such improvements have been acquired at the cost of large computational and communication resources, as well as significant energy footprints which are currently overlooked. Vanilla ML requires all training procedures to be conducted inside data centers [1] that collect data from producers, such as sensors, machines and personal devices.

Millions of shipments of nuclear and other radiological materials are moved in the U.S. every year for good reasons, including health care, power generation, research and manufacturing. But there remains the threat that bad actors in possession of stolen or illegally produced nuclear materials or weapons will try to smuggle them across borders for nefarious purposes. Texas A&M University researchers are making it harder for them to succeed. If border agents intercept illicit nuclear materials, investigators need to know who produced them and where they came from. Fortunately, nuclear materials carry certain forensic markers that can reveal valuable information, much like fingerprints can identify criminals.

Climate change is causing more frequent and intense droughts, storm, heat waves, rising sea levels and melting glaciers and to stop this destruction, MIT researchers proposes'Space Bubbles' to shield Earth from the sun's rays to combat the devastation. This geoengineering idea would feature inflatable bubbles, organized in a circular shape the size of Brazil, which would sit between the Earth and the sun, blocking radiation from hitting our planet. 'We believe that inflating thin-film spheres directly in space from a homogeneous molten material–such as silicon can provide the variation in thickness that refracts a broader wave spectrum and allows us to avoid the necessity of launching large structural film elements,' the team share in a press release. Although Space Bubbles could reduce the amount of radiation hitting Earth, those involved with the work stress the innovation is designed to supplement and not replace current efforts to combat climate change. MIT researchers proposes'Space Bubbles' to shield Earth from the sun's rays to combat the devastation According to the team at MIT's Senseable City Lab, bubbles have been tested in outer space conditions that they believe could one day be used to deflect solar radiation.

How can robots learn to interact with and reason about themselves and the world without an intuitive feel for either? Communication is at the heart of biological and robotic systems. Inspired by control theory, information theory, and neuroscience, early work in artificial intelligence (AI) and robotics focused on a class of dynamical system known as feedback systems. These systems are characterized by recurrent mechanisms or feedback loops that govern, regulate, or'steer' the behaviour of the system toward desirable stable states in the presence of disturbance in diverse environments. Feedback between sensation, prediction, decision, action, and back is a critical component of sensorimotor learning needed to realize robust intelligent robotic systems in the wild, a grand challenge of the field. Existing robots are fundamentally numb to the world, limiting their ability to sense themselves and their environment. This problem will only increase as robots grow in complexity, dexterity, and maneuverability, guided by biomimicry. Feedback control systems such as the proportional integral derivative (PID), reinforcement learning (RL), and model predictive control (MPC) are now common in robotics, as is (optimal, Bayesian) Kálmán filtering of point-based IMU-GPS signals. Lacking are the distributed multi-modal, high-dimensional sensations needed to realize general intelligent behaviour, executing complex action sequences through high-level abstractions built up from an intuitive feel or understanding of physics.While the central nervous system and biological neural networks are quantum parallel distributed processing (PDP) engines, most digital artificial neural networks are fully decoupled from sensors and provide only a passive image of the world. We are working to change that by coupling parallel distributed sensing and data processing through a neural paradigm. This involves innovations in hardware, software, and datasets. At Nervosys, we aim to make this dream a reality by building the first nervous system and platform for general robotic intelligence.

New building regulations aimed at improving energy efficiency are set to increase the price of new homes, as well as those of extensions and loft conversions on existing ones. The rules, which came into effect on Wednesday in England, are part of government plans to reduce the UK's carbon emissions to net zero by 2050. They set new standards for ventilation, energy efficiency and heating, and state that new residential buildings must have charging points for electric vehicles. The moves are the most significant change to building regulations in years, and industry experts say they will inevitably lead to higher prices at a time when a shortage of materials and high labour costs is already driving up bills. Brian Berry, chief executive of the Federation of Master Builders, a trade group for small and medium-sized builders, says the measures will require new materials, testing methods, products and systems to be installed.

The Energy Stocks Package is based on the I Know First algorithm and is designed for investors and analysts who need recommendations for the best performing stocks for the whole Energy Industry. Package Name: Energy Stocks Forecast Recommended Positions: Long Forecast Length: 1 Year (6/18/21 – 6/19/22) I Know First Average: 48.05% I Know First's State of the Art Algorithm accurately forecasted 8 out of 10 trades for the 1 Year time period. The highest trade return came from BPT, at 283.06%. Further notable returns came from MRO and SSL at 87.36% and 44.86%, respectively.

In response to the continuously changing feedstock supply and market demand for products with different specifications, the processes need to be operated at time-varying operating conditions and targets (e.g., setpoints) to improve the process economy, in contrast to traditional process operations around predetermined equilibriums. In this paper, a contraction theory-based control approach using neural networks is developed for nonlinear chemical processes to achieve time-varying reference tracking. This approach leverages the universal approximation characteristics of neural networks with discrete-time contraction analysis and control. It involves training a neural network to learn a contraction metric and differential feedback gain, that is embedded in a contraction-based controller. A second, separate neural network is also incorporated into the control-loop to perform online learning of uncertain system model parameters. The resulting control scheme is capable of achieving efficient offset-free tracking of time-varying references, with a full range of model uncertainty, without the need for controller structure redesign as the reference changes. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial (chemical) processes. This approach also ensures the process stability during online simultaneous learning and control. Simulation examples are provided to illustrate the above approach.

Spectral Indices (SIs) are mathematical equations applied to each pixel image to highlight a specific phenomenon on the ground. Most SIs are computed from the reflectance data produced after some pre-processing stages of multispectral remote sensing images. In which bx and by are the reflectance values of a pixel in bands x and y. If we calculate the value of a SI for each pixel, we can generate an image from SI. In this post, I want to talk about the two most important spectral indices and how to calculate them for a case study in the center of Rome, Italy, using the Sentinel-hub cloud platform.