The Advanced Research and Invention Agency (ARIA) will announce today that it's seeking proposals to work on systems for two related climate tipping points. One is the accelerating melting of the Greenland Ice Sheet, which could raise sea levels dramatically. The other is the weakening of the North Atlantic Subpolar Gyre, a huge current rotating counterclockwise south of Greenland that may have played a role in triggering the Little Ice Age around the 14th century. The goal of the five-year program will be to reduce scientific uncertainty about when these events could occur, how they would affect the planet and the species on it, and over what period those effects might develop and persist. In the end, ARIA hopes to deliver a proof of concept demonstrating that early warning systems can be "affordable, sustainable, and justified." No such dedicated system exists today, though there's considerable research being done to better understand the likelihood and consequences of surpassing these and other climate tipping points.

Previously, using forward-flux sampling (FFS) and machine learning (ML), we developed multivariate alarm systems to counter rare un-postulated abnormal events. Our alarm systems utilized ML-based predictive models to quantify committer probabilities as functions of key process variables (e.g., temperature, concentrations, and the like), with these data obtained in FFS simulations. Herein, we introduce a novel and comprehensive benchmark framework for rare-event prediction, comparing ML algorithms of varying complexity, including Linear Support-Vector Regressor and k-Nearest Neighbors, to more sophisticated algorithms, such as Random Forests, XGBoost, LightGBM, CatBoost, Dense Neural Networks, and TabNet. This evaluation uses comprehensive performance metrics, such as: $\textit{RMSE}$, model training, testing, hyperparameter tuning and deployment times, and number and efficiency of alarms. These balance model accuracy, computational efficiency, and alarm-system efficiency, identifying optimal ML strategies for predicting abnormal rare events, enabling operators to obtain safer and more reliable plant operations.

Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.

As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalities for vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [4]. The focus of this work is to test different object detection models on the task of detecting camels on the road. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that CenterNet performed the best in terms of accuracy and was the most efficient in training. In the future, the plan is to expand on this work by developing a system to make countryside roads safer.

The most prominent applications of the Internet of Things (IoT) are driverless cars and home appliances. In the world of fire safety, however, its most exciting application may be sensors in buildings. The collection and application of atmospheric data could dramatically alter our approach to fire prevention and firefighting and ultimately help to save lives. IoT sensors form a major part of what are known as'smart buildings'. Smart buildings are properties controlled in part by autonomous computer software, known as Building Management Systems (BMS).

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why #driverlesscars should have ears with eyes? If driverless cars are to become a reality they will have to have the same sight and sound abilities as humans. Today's driverless cars see things via cameras, radar and Lidar so consequently objects need to be within the line of sight to be identified by the system. Smart cars need to hear things and hear them a lot better and sooner than us humans. We have heard plenty about technological breakthroughs regarding sight systems for the autonomous cars but Hearing is an extremely important factor when it comes to absorbing and analysing the surrounding information needed by us humans to make everyday driving decisions.

Smart watches and fitness trackers are only the first signs of a world that will enfold us in a subtle but ubiquitous web of connectivity. We have digital assistants that can make a restaurant reservation, lock the garage door, or check whether our laundry is dry when asked. We have tablets with cameras that automatically track us as we move around to make our video calls feel more like being there. We even have home automation hubs that use subsonic sound waves to locate us in a room and make their displays more readable from a distance. The next logical step will be not needing to interact deliberately with our devices at all.

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome.These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost-benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.

Since the microchip, machine learning and Artificial Learning (AI) is the next biggest innovation. Those who kept asking what is machine learning have now realized it isn't a fiction, but a reality instead. Technologies such as neural networks are paving the way towards artificial intelligence breakthroughs. People are now more productive, healthier and happier than before with machine learning technology tools. Industry leaders claim that machine learning is ushering in another phase of the industrial revolution.

Being a nurse is a highly demanding, but genuinely fulfilling job with the chance to touch many people's lives. As it requires the core of what makes us human – paying attention, being empathetic and caring -, it will never be replaced by technology. However, innovations can relieve nurses of the burden of many monotonous and repetitive tasks. Let's see how technology supports the future of nurses! Clarissa, S.S. visits newborn babies and their mothers every week in the second district of Budapest.