Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the net-work after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct Recursive Hybrid LSTM, CNNLSTM, and Attention LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting we are aware of, this study demonstrates that matrix profile data can enhance attention-based models however, given the hardware constraints, hybrid models outperformed attention and CNNLSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1 day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy outperforms other models. Hybrid models have favourable performance up to a 20 min lookahead with performance being comparable to attention models when forecasting 25/50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.

This paper proposes a novel lexicon-based unsupervised sentimental analysis method to measure the $``\textit{hope}"$ and $``\textit{fear}"$ for the 2022 Ukrainian-Russian Conflict. $\textit{Reddit.com}$ is utilised as the main source of human reactions to daily events during nearly the first three months of the conflict. The top 50 $``hot"$ posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, UkraineWarReports) and their relative comments are scraped and a data set is created. On this corpus, multiple analyses such as (1) public interest, (2) hope/fear score, (3) stock price interaction are employed. We promote using a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modelling is also utilised to understand the main issues raised by users and what are the key talking points. Experimental analysis shows that the hope strongly decreases after the symbolic and strategic losses of Azovstal (Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and negatives, are present after important battles, but also some non-military events, such as Eurovision and football games.

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Many market models implement this principle to predict electricity prices but typically require certain assumptions and simplifications. In this article, we present an explainable machine learning model for the prices on the German day-ahead market, which substantially outperforms a benchmark model based on the merit order principle. Our model is designed for the ex-post analysis of prices and thus builds on various external features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle the role of the different features and quantify their importance from empiric data. Load, wind and solar generation are most important, as expected, but wind power appears to affect prices stronger than solar power does. Fuel prices also rank highly and show nontrivial dependencies, including strong interactions with other features revealed by a SHAP interaction analysis. Large generation ramps are correlated with high prices, again with strong feature interactions, due to the limited flexibility of nuclear and lignite plants. Our results further contribute to model development by providing quantitative insights directly from data.

'Watters' World' host lists the many domestic issues President Biden faces This is a rush transcript from "Watters' World," December 4, 2021. This copy may not be in its final form and may be updated. JESSE WATTERS, FOX NEWS HOST: Welcome to WATTERS' WORLD, I'm Jesse Watters. The Annual White House Christmas Tree lighting is always such a special event, except Joe Biden, the President seemingly forgot he was supposed to light it. Maybe he thought Barack was going to light it. These things just keep happening every single week. I kind of feel bad for LL, they needed to do a second take. Now, President Biden and First Lady, Dr. Jill Biden [CHEERING AND APPLAUSE] (END VIDEO CLIP) WATTERS: So, how are we supposed to feel confident the President can crush the virus when he can't even get it together for a Christmas Tree lighting? I'm not worried about the new variant. I'm worried about how the government is going to overreact to the new variant. Biden has got a new plan. More masks, more testing, but unvaxxed illegals can just pour across the Southern border without testing, without quarantining. And then Joe packs them onto planes and buses and sends them to your neighborhood. Does that make sense to anybody? (BEGIN VIDEO CLIP) PETER DOOCY, FOX NEWS CHANNEL WHITE HOUSE CORRESPONDENT: Dr. Fauci, as you advised the President about the possibility of new testing requirements for people coming into this country? ANTHONY FAUCI, DIRECTOR, NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES: Everybody who is coming into the country needs to get a test within 24 hours of getting on the plane to come here. DOOCY: But what about people who don't take a plane and just these border crossers coming in in huge numbers?

When we talk about Machine Learning and Deep Learning, terms that we frequently come across is linear and non linear functions. Although, we would have studied this in our high school math, there will be at least a handful of them like me who would like to brush up on these mathematical terminologies. We'll cover how they look on a graph, and how you can tell them apart when they're written as equations. Algebraic tools allow us to express functional relationships very efficiently; to find the value of one thing (such as the gas price) when we know the value of the other (say number of gallons); and display a relationship visually in a way that allows us to quickly grasp the direction, magnitude, and rate of change in one variable over a range of values of the other. For simple problems such as determining gas prices, existing knowledge of multiplication will usually allow us to calculate the cost for a specific amount of gas, once we have the price per gallon (say $2).

New York – When Toyota aired a Super Bowl television ad featuring a surprisingly quick Prius gas-electric hybrid eluding police, it marked a turning point for the auto industry. For years, automakers pushed fuel efficiency to sell hybrid and electric vehicles. Now, in an era of cheap gasoline, the message is: These cars are faster and quieter than their gas-powered counterparts. And, yes, you still save on fuel. "They've graduated out of the class of something that's a bit of an oddity to drive," says Mike O'Brien, vice president of product planning for Hyundai.

From 2008-2010 we built an experimental personalized news system where readers subscribe to organized channels of topical information that are curated by experts. AI technology was employed to efficiently present the right information to each reader and to radically reduce the workload of curators. The system went through three implementation cycles and processed over 20 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system.