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.

What Should Be in Your Emergency Kit Before Disaster Strikes? We consulted preparedness experts and WIRED's team of testers on the essential bug-out gear to keep your family safe during an unplanned exit. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You never know when you're going to have to bug out on short notice.

Air pollution forecasting is critical for proactive environmental management, yet data irregularities and scarcity remain major challenges in low-resource regions. Northern Nigeria faces high levels of air pollutants, but few studies have systematically compared the performance of advanced machine learning models under such constraints. This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants (CO, SO2, SO4) using monthly observational data from 2018 to 2023 across 19 states. Results show that Prophet often matches or exceeds LSTM's accuracy, particularly in series dominated by seasonal and long-term trends, while LSTM performs better in datasets with abrupt structural changes. These findings challenge the assumption that deep learning models inherently outperform simpler approaches, highlighting the importance of model-data alignment. For policymakers and practitioners in resource-constrained settings, this work supports adopting context-sensitive, computationally efficient forecasting methods over complexity for its own sake.

Air pollution remains one of the most pressing environmental challenges of the modern era, significantly impacting human health, ecosystems, and climate. While traditional air quality monitoring systems provide critical data, their high costs and limited spatial coverage hinder effective real-time pollutant identification. Recent advancements in micro-sensor technology have improved data collection but still lack efficient methods for source identification. This paper explores the innovative application of machine learning (ML) models to classify pollutants in real-time using only data from optical micro-sensors. We propose a novel classification framework capable of distinguishing between four pollutant scenarios: Background Pollution, Ash, Sand, and Candle. Three Machine Learning (ML) approaches - XGBoost, Long Short-Term Memory networks, and Hidden Markov Chains - are evaluated for their effectiveness in sequence modeling and pollutant identification. Our results demonstrate the potential of leveraging micro-sensors and ML techniques to enhance air quality monitoring, offering actionable insights for urban planning and environmental protection.

U.S. Navy veteran John Ryan shares how he beat lung cancer, which he believes is due to an immunotherapy clinical trial he underwent at Johns Hopkins. Cigarette smoking is by far the biggest risk factor for lung cancer, data shows -- but in a surprising turn of events, the most common form of the disease is primarily found in non-smokers. Researchers at the International Agency for Research on Cancer (IARC) analyzed global trends in four main lung cancer subtypes: adenocarcinoma, squamous cell carcinoma, small-cell carcinoma and large-cell carcinoma. They found that adenocarcinoma has been the most "predominant subtype" in recent years, according to a press release summarizing the study. Younger females were found to be at a particularly high risk.

In Nepal, air pollution is a serious public health concern, especially in cities like Kathmandu where particulate matter (PM2.5 and PM10) has a major influence on respiratory health and air quality. The Air Quality Index (AQI) is predicted in this work using a Random Forest Regressor, and the model's predictions are interpreted using SHAP (SHapley Additive exPlanations) analysis. With the lowest Testing RMSE (0.23) and flawless R2 scores (1.00), CatBoost performs better than other models, demonstrating its greater accuracy and generalization which is cross validated using a nested cross validation approach. NowCast Concentration and Raw Concentration are the most important elements influencing AQI values, according to SHAP research, which shows that the machine learning results are highly accurate. Their significance as major contributors to air pollution is highlighted by the fact that high values of these characteristics significantly raise the AQI. This study investigates the Hydrogen-Alpha (HA) biodegradable filter as a novel way to reduce the related health hazards. With removal efficiency of more than 98% for PM2.5 and 99.24% for PM10, the HA filter offers exceptional defense against dangerous airborne particles. These devices, which are biodegradable face masks and cigarette filters, address the environmental issues associated with traditional filters' non-biodegradable trash while also lowering exposure to air contaminants.

Air pollution, particularly airborne particulate matter (PM), poses a significant threat to public health globally. It is crucial to comprehend the association between PM-associated toxic components and their cellular targets in humans to understand the mechanisms by which air pollution impacts health and to establish causal relationships between air pollution and public health consequences. Current methods for modeling and analyzing these interactions are rudimentary, with experimental approaches offering limited throughput and comprehensiveness. Leveraging cutting-edge deep learning technologies, we developed tipFormer (toxin-protein interaction prediction based on transformer), a novel machine-learning approach for identifying toxic components capable of penetrating human cells and instigating pathogenic biological activities and signaling cascades. It incorporates dual pre-trained language models to derive encodings for protein sequences and chemicals. It employs a convolutional encoder to assimilate the sequential attributes of proteins and chemicals. It then introduces a novel learning module with a cross-attention mechanism to decode and elucidate the multifaceted interactions pivotal for the hotspots binding proteins and chemicals. Through thorough experimentation, tipFormer was shown to be proficient in capturing interactions between proteins and toxic components. This approach offers significant value to the air quality and toxicology research communities by enabling high-throughput, high-content identification and prioritization of hazards. Keywords: Air pollution, toxin-protein interaction, computational modeling, attention mechanisms 1. Introduction Air pollution has emerged as a critical global health concern, primarily driven by rapid economic, industrial and population growth and further exacerbated by climate change and other non-anthropogenic factors [1]. The World Health Organization estimates that approximately 7 million premature deaths occur every year due to air pollution exposure. The consequences of air pollution extend far beyond individual health implications and exacerbate the strain on societal and healthcare systems in numerous ways [2]. The health risks associated with airborne particulate matter (PM) are particularly concerning for public health [3].

Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset.

Urbanization enables economic growth but also harms the environment through degradation. Traditional methods of detecting environmental issues have proven inefficient. Machine learning has emerged as a promising tool for tracking environmental deterioration by identifying key predictive features. Recent research focused on developing a predictive model using pollutant levels and particulate matter as indicators of environmental state in order to outline challenges. Machine learning was employed to identify patterns linking areas with worse conditions. This research aims to assist governments in identifying intervention points, improving planning and conservation efforts, and ultimately contributing to sustainable development.

With a rising attention for the issue of PM2.5 or PM0.3, particulate matters have become not only a potential threat to both the environment and human, but also a harming existence to instruments onboard International Space Station (ISS). Our team is aiming to relate various concentration of particulate matters to magnetic fields, humidity, acceleration, temperature, pressure and CO2 concentration. Our goal is to establish an early warning system (EWS), which is able to forecast the levels of particulate matters and provides ample reaction time for astronauts to protect their instruments in some experiments or increase the accuracy of the measurements; In addition, the constructed model can be further developed into a prototype of a remote-sensing smoke alarm for applications related to fires. In this article, we will implement the Bi-GRU (Bidirectional Gated Recurrent Unit) algorithms that collect data for past 90 minutes and predict the levels of particulates which over 2.5 micrometer per 0.1 liter for the next 1 minute, which is classified as an early warning