Guthrie family'source' shares new version of events about how kidnapped Nancy was reported missing after failing to show up for church Bad Bunny smashes the record for the most watched Super Bowl Halftime Show in history with 135.4 MILLION views Bombshell secret that could DESTROY Turning Point USA: As Erika Kirk misses halftime show... whistleblowers tell all to KENNEDY Nancy Guthrie investigation takes dramatic new focus after missed ransom deadline: Brown bags of'evidence' and uncomfortable questions close to home Sad demise of 80s child star: No one in Hollywood even noticed when he died... now family reveal dark shame he'took to his early grave' Sweden's Princess Sofia breaks her silence on Jeffrey Epstein links and admits meeting him at'social events' Andrew's horrible stench is now engulfing William and Kate. THAT picture is just so damning... this could spell the end of the monarchy Wealthy banker's appalling act of greed outside stunning Maine beach house that ended generations-long family friendship Nancy Guthrie case at pivotal crossroads as Savannah laments'nightmare' and FBI takes central role while ransom letter's second deadline expires: 'Our mom is still out there' Irishman detained by ICE agents describes'torture' after five months in prison camp despite living in US 20 years, having an American wife and no criminal record Revealed: Lindsey Vonn's coach's chilling pre-race observation that predicted specific details of her'cursed' crash which broke her leg at Winter Olympics Secret behind how nepo baby, 27, REALLY became Pentagon's Karoline Leavitt... as ugly gossip about her'crass' husband swirls in DC Doctors took FIVE years to realize I had the same'taboo' cancer that killed Catherine O'Hara: Never let them ask you this question... The world's best pizza for 2026 has been revealed - and it's not in Italy Woke aide to NYC's socialist mayor boasted of her hatred for'white women behavior' after one gave her dirty look for taking 40 minute phone call on train The cure for baldness is finally here: Doctors hail'gamechanger' lotion with NO major side-effects or sex-drive impact that can regrow hair by more than 500pc. I dated 100 men but was STILL a virgin at 41. I thought I'd never find true love... then one word changed everything I'm a fitness coach and this is EXACTLY what I'd order from McDonald's on a weight-loss journey'House burping' trend sees people flinging open their windows to get rid of germ-filled air - and now scientists say it really works READ MORE: Gen Z are'rawdogging boredom' to fix their attention spans'House burping' is the latest trend taking over social media, with users flinging open the windows of their homes in the depths of winter, in the hopes of getting rid of germ-filled air.

In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecasting the daily average temperature over a ten-day horizon. However, advances in forecasting events related to the maximum temperature over short horizons remain a challenge for the community. A problem that is even more complex consists in making predictions of the maximum daily temperatures in the short, medium, and long term. In this work, we focus on forecasting events related to the maximum daily temperature over medium-term periods (90 days). Therefore, instead of addressing the problem from a meteorological point of view, this article tackles it from a climatological point of view. Due to the complexity of this problem, a common approach is to frame the study as a temporal classification problem with the classes: maximum temperature "above normal", "normal" or "below normal". From a practical point of view, we created a large historical dataset (from 1981 to 2018) collecting information from weather stations located in South America. In addition, we also integrated exogenous information from the Pacific, Atlantic, and Indian Ocean basins. We applied the AutoGluonTS platform to solve the above-mentioned problem. This AutoML tool shows competitive forecasting performance with respect to large operational platforms dedicated to tackling this climatological problem; but with a "relatively" low computational cost in terms of time and resources.

The increasing complexity of modern chemical processes, coupled with workforce shortages and intricate fault scenarios, demands novel automation paradigms that blend symbolic reasoning with adaptive control. In this work, we introduce a unified agentic framework that leverages large language models (LLMs) for both discrete fault-recovery planning and continuous process control within a single architecture. We adopt Finite State Machines (FSMs) as interpretable operating envelopes: an LLM-driven planning agent proposes recovery sequences through the FSM, a Simulation Agent executes and checks each transition, and a Validator-Reprompting loop iteratively refines invalid plans. In Case Study 1, across 180 randomly generated FSMs of varying sizes (4-25 states, 4-300 transitions), GPT-4o and GPT-4o-mini achieve 100% valid-path success within five reprompts-outperforming open-source LLMs in both accuracy and latency. In Case Study 2, the same framework modulates dual-heater inputs on a laboratory TCLab platform (and its digital twin) to maintain a target average temperature under persistent asymmetric disturbances. Compared to classical PID control, our LLM-based controller attains similar performance, while ablation of the prompting loop reveals its critical role in handling nonlinear dynamics. We analyze key failure modes-such as instruction following lapses and coarse ODE approximations. Our results demonstrate that, with structured feedback and modular agents, LLMs can unify high-level symbolic planningand low-level continuous control, paving the way towards resilient, language-driven automation in chemical engineering.

From Snowpiercer to The Day After Tomorrow, countless movies and series have put forward their vision of how climate change might reshape the world. Worryingly, scientists predict that the reality might be far more shocking than anything imagined by a Hollywood studio. Now, artificial intelligence (AI) reveals what this might look like. With Google's ImageFX AI image generator, MailOnline has used the latest scientific research to predict how the world will be in 2100. As greenhouse gas levels continue to increase, scientists predict that entire cities will be plunged under water.

Brits largely endured frigid temperatures in September – but globally, the story was quite different. Last month was the second-hottest September on record, the EU's climate change programme has revealed. The global average air temperature for September 2024 was 61.1 F (16.17 C), which is 1.31 F (0.73 C) above the September average. What's more, it's just shy of the record set by September 2023 – 61.4 F (16.38 C). Worryingly, experts point to human-cased greenhouse gas emissions as the cause for this latest temperature'anomaly'.

This study investigates the long-term effects of temperature variations on economic growth using a data-driven approach. Leveraging machine learning techniques, we analyze global land surface temperature data from Berkeley Earth and economic indicators, including GDP and population data, from the World Bank. Our analysis reveals a significant relationship between average temperature and GDP growth, suggesting that climate variations can substantially impact economic performance. This research underscores the importance of incorporating climate factors into economic planning and policymaking, and it demonstrates the utility of machine learning in uncovering complex relationships in climate-economy studies.

The critical heat flux (CHF) represents the maximum heat flux in the nucleate boiling process, marking an abrupt increase in surface temperature. As a crucial factor in high heat-flux systems to ensure safe operation and prevent system damage, CHF diagnosis has been extensively researched, leading to the development of various mechanistic models explaining the triggering mechanisms of CHF [1][2][3][4]. Among these models -- such as the hydrodynamic instability model, macrolayer dryout model, and interfacial lift-off model -- the hot/dry spot model suggests that irreversible dry patch formation leads to increasing temperature, resulting in the postulation that the development of the irreversible dry spot's temperature hinders the wetting of the heated surface by the supplied liquid [5]. The dry patch is first generated at high heat flux, then coalesces and expands again under the remnant bubble to trigger CHF [6]. To validate and improve such models, visual observation methods have been developed [7][8]. Total reflection visualization and (TR) infrared thermometry (IR) are arguably the most important techniques for visualizing the formation of dry patches while measuring the coincidental temperature evolution of the liquid-vapor system [9][10][11]. Through the methods, the behavior of the bubble structure and dry patch under flow boiling has been observed, and the hydrodynamic mechanism of the irreversible dry patch have been analyzed. Also, there have been attempts to determine CHF based on the temperature of the dry patch periphery [6][12]. Besides, following recent advancements in Convolutional Neural Networks (CNNs), which excel in capturing visual information characteristics, neural networks are expected to have the potential to simplify infrared thermal imaging, as the process typically involves tedious experimental setups and extensive data reduction [13].

In many developing nations, a lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises. Currently, socioeconomic surveys are the only method implemented on a large scale for organizations and researchers to measure and track poverty. However, the inability to collect survey data efficiently and inexpensively leads to significant temporal gaps in poverty data; these gaps severely limit the ability of organizational entities to address poverty at its root cause. We propose a transfer learning model based on surface temperature change and remote sensing data to extract features useful for predicting poverty rates. Machine learning, supported by data sources of poverty indicators, has the potential to estimate poverty rates accurately and within strict time constraints. Higher temperatures, as a result of climate change, have caused numerous agricultural obstacles, socioeconomic issues, and environmental disruptions, trapping families in developing countries in cycles of poverty. To find patterns of poverty relating to temperature that have the highest influence on spatial poverty rates, we use remote sensing data. The two-step transfer model predicts the temperature delta from high resolution satellite imagery and then extracts image features useful for predicting poverty. The resulting model achieved 80% accuracy on temperature prediction. This method takes advantage of abundant satellite and temperature data to measure poverty in a manner comparable to the existing survey methods and exceeds similar models of poverty prediction.

Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares four different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. The four proposed models investigate the different type of input representations (i.e. tensorial unistream vs. multi-stream matrices) as well as the combination of convolutional neural networks and LSTM (i.e. cascaded vs. ConvLSTM). In particular, we show that a model that uses a multi-stream input representation and that processes each lag individually combined with a cascaded convolution and LSTM is capable of better forecasting than the other compared models. In addition, we show that visualization techniques such as occlusion analysis and score maximization can give an additional insight on the most important features and cities for predicting a particular target feature and city.

Climate change is seen as the biggest challenge to the future of human life on Earth, and understanding the scientific language used to describe it can sometimes feel just as difficult. But help is at hand. Use our translator tool to find out what some of the words and phrases relating to climate change mean. Keeping the rise in global average temperature below 1.5 degrees Celsius will avoid the worst impacts of climate change, scientists say.