From Armageddon to the Day After Tomorrow, there have been plenty of Hollywood movies about how our world might end. But if there is to be a global apocalypse, what might be to blame for wiping out all life on Earth? A wandering black hole, giant asteroid impact and nuclear war could all trigger such disaster, as could the rise of killer robots or the reversal of our planet's magnetic field. Many of these might seem far-fetched but with the Doomsday Clock being placed at a record 90 seconds to midnight this year – and scientists warning that humanity's continued existence is at greater risk than ever before – the threat is now all to real. So how exactly would these devastating possibilities come about? End of days: Ff there is to be a global apocalypse, what might be to blame for wiping out all life on Earth?

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models LLMs GPT 3.5, GPT 4, and GoogleBARD in the context of predicting and verifying historical events based on given data. A novel metric, Distance to Reality (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT 4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.

In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean. The technique utilizes the dynamics and modes identified in ESMs to improve the accuracy of buoy measurements while still preserving features such as seasonality. Using this technique, errors in localized temperature predictions made by the MPAS-O model can be corrected. We demonstrate that our approach improves accuracy compared to other interpolation and data assimilation methods. We apply our method to assimilate the Model for Prediction Across Scales Ocean component (MPAS-O) with the Global Drifter Program's in-situ ocean buoy dataset.

In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoning and present a method which further expands the capabilities of an LLM by embedding it within an algorithm or program. To demonstrate the benefits of this approach, we present an illustrative example of evidence-supported question-answering. We obtain a 6.4\% improvement over the chain of thought baseline through a more algorithmic approach without any finetuning. Furthermore, we highlight recent work from this perspective and discuss the advantages and disadvantages in comparison to the standard approaches.

An apparent drone attack on the Kremlin this week has sparked fears of an escalation in Russia's brutal war in Ukraine. On Wednesday night, two remotely-operated devices flew towards the domed roof of the Kremlin before being shot down by Russian air defences, exploding but harming no one. After the incident, Moscow Mayor Sergey Sobyanin declared that flying drones by private citizens was now banned in Moscow. Russia said the United States masterminded the attack, claiming Ukraine carried it out. Washington and Kyiv have denied responsibility, insisting that Ukraine's war efforts are purely defensive.

Air raid sirens sounded in Ukraine's Kyiv after residents were subjected to drone attacks, spasms of gunfire and explosions during the fourth attack on the capital in as many days, according to officials. Officials said at least one drone was downed after anti-aircraft units went into action during the raid on Thursday evening, which began just after 8pm (17:00 GMT) and lasted about 20 minutes. Kyiv Mayor Vitali Klitschko said there had been two impacts from downed drones. "During the last air alert, an unmanned aerial vehicle was spotted over Kyiv. The object was shot down by air defence forces," Kyiv city military administration head Serhiy Popko said on Telegram.

Drones, missiles and cross-border artillery took centre stage during the 62nd week of Russia's war in Ukraine, as the 63rd began with a dramatic allegation from Russia – that Ukraine made an attempt on President Vladimir Putin's life. Ukraine may have targeted Russian fuel depots – a possible preamble to its expected counteroffensive. Russia, meanwhile, sharply intensified strikes against Ukrainian civilians, claiming dozens of lives. Ukraine was likely responsible for explosions in Kozacha Bay, near Sevastopol on the Crimean Peninsula, where the Russian Black Sea Fleet has a base, on April 29. Footage showed a massive black mushroom cloud rising from a fuel tank park.

Russia has blamed Ukraine for setting ablaze one of its oil refineries, while Kyiv has accused Moscow of launching dozens of overnight strikes by unmanned aerial vehicles for the second day running. The targeting of the fuel facility on Thursday occurred at the Ilsky refinery near the Black Sea port of Novorossiysk in the Krasnodar region, Russia's TASS news agency reported citing local emergency services. A fuel reservoir was on fire, it said, but gave no further details. A day earlier, a fuel depot further to the west caught fire near a bridge linking Russia's mainland with the occupied Crimean Peninsula. "A second turbulent night for our emergency services," Krasnodar Governor Veniamin Kondratyev wrote on Telegram, confirming tanks with oil products were set ablaze.

Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9,300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Ukrainian President Volodymyr Zelenskyy traveled to Helsinki for talks with the prime ministers of four Nordic countries Wednesday as part of his effort to secure greater firepower for his country's armed forces as they figure out how to dislodge Russian troops from occupied areas of Ukraine. The Nordic countries -- Finland, Sweden, Norway and Denmark -- have been among Kyiv's strongest backers since Russia's full-scale invasion of Ukraine in February 2022. Before the meeting with Zelenskyy in Finland's capital, Nordic officials appeared ready to provide more aid as the war stretches into its 15th month.