The latest research study released by HTF MI evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support. The market Study is segmented by key a region that is accelerating the marketization. The oil and gas (O&G) industry faces many severe challenges. The shortage of easily accessible hydrocarbon reserves forces companies to use remote reserves that are hard to discover, costly, and risky. Moreover, sustainability concerns are shifting demand away from O&G toward cleaner sources, and COVID-19 has further suppressed the demand.

The green energy revolution promised by nuclear fusion is now a step closer, thanks to the first successful use of a cutting-edge artificial intelligence system to shape the superheated hydrogen plasmas inside a fusion reactor. The successful trial indicates that the use of AI could be a breakthrough in the long-running search for electricity generated from nuclear fusion -- bringing its introduction to replace fossil fuels and nuclear fission on modern power grids tantalizingly closer. "I think AI will play a very big role in the future control of tokamaks and in fusion science in general," Federico Felici, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL) and one of the leaders on the project, told Live Science. "There's a huge potential to unleash AI to get better control and to figure out how to operate such devices in a more effective way." Felici is a lead author of a new study describing the project published in the journal Nature.

To be future-ready, companies must start combining AI, human skills, and trusted partnerships now. After all, climate change is happening now Rising sea levels, and intensifying wildfires, storms, droughts and floods hammer home that message every day. The damage is undeniable, and the clock is ticking. Human and artificial, the energy challenge requires intelligence Let’s be clear: clean energy and efficient energy management are key to attacking the climate crisis. And the true value of Artificial Intelligence in energy management springs to life when technology meets human expertise. When you equip energy market experts with data-based insights and digital technologies,

Autonomous ships that can deploy uncrewed underwater vehicles could be used to inspect pipelines, cables and seabed drilling activity

Recently, we have shown that quantum statistics, and more specifically the Bose-Einstein statistics, is also prominently and convincingly present in human cognition, and more specifically in the structure of human language (Aerts & Beltran, 2020, 2022). The presence of the Bose-Einstein statistics in quantum mechanics is associated with the'identity' and'indistinguishability' of quantum particles, and is probably one of the most still poorly understood aspects of quantum reality (French & Redhead, 1988; Saunders, 2003; Muller & Seevinck, 2009; Krause, 2010; Dieks & Lubberdink, 2020). Although there are connections to entanglement, and in linear quantum optics there is now effective experimental use of the'indistinguishability' of photons to fabricate qubits, and thus'indistinguishability' is considered a'resource' for quantum computing, it remains one of the most mysterious quantum properties, also structurally different from entanglement (Franco & Compagno, 2018). The original interest of one of us in identifying in human cognition and language an equivalent of this situation of'indistinguishability' in quantum mechanics, leading to the Bose-Einstein statistics, was motivated by working on a specific interpretation of quantum mechanics, called the'conceptuality interpretation' (Aerts, 2009b). Thus, this original motivation was aimed more at increasing the understanding and explanation of what'identical' and'indistinguishable' quantum particles really are, rather than intended to introduce an additional rationale for research in quantum cognition. With a focus still primarily on this original motivation, work continued on the identification of a Bose-Einstein-like statistics by one of us, with a PhD student and collaborator, and more and better experimental evidence was collected for the superiority of Bose-Einstein statistics in modeling specific situations in human language as compared to Maxwell-Boltzmann statistics (Aerts, Sozzo & Veloz, 2015b).

We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to verify a simulated (MPC) and a synthetic (LASSO) optimization problems solved on a reduced-precision machine in combination with an inaccurate proximal operator. We also show how the probabilistic bounds are more robust for algorithm verification and more accurate for application performance guarantees. Under some statistical assumptions, we also prove that some cumulative error terms follow a martingale property. And conforming to observations, e.g., in \cite{schmidt2011convergence}, we also show how the acceleration of the algorithm amplifies the gradient and proximal computational errors.

The distributed, granular nature of edge computing – where an "edge device" could mean anything from an iPhone to a hyper-specialized IoT sensor on an oil rig in the middle of an ocean – is reflected in the variety of its enterprise use cases. There are some visible common denominators powering edge implementations: Containers and other cloud-native technologies come to mind, as does machine learning. But the specific applications of edge built on top of those foundations quickly diversify. "Telco applications often have little in common with industrial IoT use cases, which in turn differ from those in the automotive industry," says Gordon Haff, technology evangelist, Red Hat. This reflects the diversity of broader edge computing trends he sees expanding in 2022.

Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. Such methods can be employed to obtain classification forecasts in many time-critical applications. However, available techniques are not equally suitable for every problem, since differentiations in the data characteristics can impact algorithm performance in terms of earliness, accuracy, F1-score, and training time. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets originating from the life sciences and maritime domains. Our goal is to provide a framework for the evaluation and comparison of ETSC algorithms and to obtain intuition on how such approaches perform on real-life applications. The presented framework may also serve as a benchmark for new related techniques.

Today, nearly everyone accepts that in order to slow the damage we are doing to our planet and environment, humans must transition away from the use of fossil fuels. This has led to many science and business innovations as we search for new sustainable or renewable alternatives to coal, oil, and gas. Although it would be nice to think everyone wants to do their part in order to save the world, there are strong financial incentives too. The value of the renewable energy market is set to grow from $880 billion to nearly $2 trillion by 2030. And the growing awareness of the importance of environmental and social governance (ESG) issues means there are tremendous political incentives, too.

Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE). However, their application to multiphysics problem, governed by several coupled PDEs, present unique challenges that have hindered the robustness and widespread applicability of this approach. Here we investigate the application of PINNs to the forward solution of problems involving thermo-hydro-mechanical (THM) processes in porous media, which exhibit disparate spatial and temporal scales in thermal conductivity, hydraulic permeability, and elasticity. In addition, PINNs are faced with the challenges of the multi-objective and non-convex nature of the optimization problem. To address these fundamental issues, we: (1)~rewrite the THM governing equations in dimensionless form that is best suited for deep-learning algorithms; (2)~propose a sequential training strategy that circumvents the need for a simultaneous solution of the multiphysics problem and facilitates the task of optimizers in the solution search; and (3)~leverage adaptive weight strategies to overcome the stiffness in the gradient flow of the multi-objective optimization problem. Finally, we apply this framework to the solution of several synthetic problems in 1D and~2D.