Insidious secret life of promiscuous neurosurgeon found dead in his $2.5m mansion America's best and worst states to retire revealed - and why Florida is no longer the obvious winner Texas Gov. Abbott warns ICE'losing respect' as Minneapolis shooting scandal rocks Trump Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Young single mother's selfless final act after finding out she had just weeks to live Seven dead in private jet crash as audio reveals voice said'Let there be light' seconds before tragedy at snowy Maine airport Defiant Trump dismisses Alzheimer's fears as he struggles to recall name of disease in interview NFL's'scripted' conspiracy theory resurfaces as fans find five-month old post hinting at Super Bowl 60 matchup Stunning twist of fate that saw Brittany leave Patrick Mahomes weeks after he was drafted by the Chiefs... Kate Hudson's Oscar nomination torched as an'abomination' amid toxic family feud over Song Sung Blue Mystery of Egypt's Giza pyramids deepens as hidden megastructure 4,000 feet below is revealed Prophecy from apocalyptic'messiah' warns of death so widespread'even birds won't escape' A poem written over 120 years ago by a revered religious figure has resurfaced as some fear its prediction of an apocalyptic event could be coming true today. Hazrat Mirza Ghulam Ahmad, also known as the Promised Messiah and the Imam Mahdi, wrote a 1905 poem describing massive earthquakes and destruction across the world, which some have now interpreted as a warning of World War III . In the poem, published around the time of his death in 1908, Ahmad predicted streams of blood flowing from widespread death, entire regions being wiped out, a massive earthquake, and even strange sky events beyond scientific explanation. It mentions of calamity befalling the Czar of Russia has been seen by some as foreshadowing modern conflicts involving Russia, such as the war in Ukraine and continued tensions with the US and NATO .

INFRASTRUCTURE SENSOR-ENABLED VEHICLE DA T A GENERA TION USING MUL TI-SENSOR FUSION FOR PROACTIVE SAFETY APPLICA TIONS A T WORK ZONE Suhala Rabab Saba Department of Civil, Construction & Environmental Engineering, The University of Alabama Smart Communities and Innovation Building (SCIB), 28 Kirkbride Lane, Tuscaloosa, AL 35487-0288 Email: ssaba@crimson.ua.edu Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 3 ABSTRACT Infrastructure-based sensing and real-time trajectory generation hold significant promise for improving safety in high-risk roadway segments like work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by (i) integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and (ii) employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70% compared to individual sensors while preserving lateral accuracy within 1-3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments. Keywords: work zone, fusion, lidar, camera, localization, safety Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 4 INTRODUCTION Work zone crashes do not necessarily impact only the vehicles and people directly involved; instead, they have cascading effects that cause operational delays for passing vehicles and project completion delays for work zone contractors. The Federal Motor Carrier Safety Administration (FMCSA) report indicates that commercial motor vehicles (CMVs) are involved in one-third of work zone fatal crashes, although they represent only 5% of all vehicular traffic (1). In addition, speed is a contributing factor in 26% of all fatal work zone crashes (2). According to Jiao (2022) (3), 13% of CMV drivers are fatigued when they are involved in crashes.

Proactive safety systems aim to mitigate risks by anticipating potential conflicts between vehicles and enabling early intervention to prevent work zone-related crashes. This study presents an infrastructure-enabled proactive work zone safety warning system that leverages a Digital Twin environment, integrating real-time multi-sensor data, detailed High-Definition (HD) maps, and a historical prediction attention mechanism-based trajectory prediction model. Using a co-simulation environment that combines Simulation of Urban MObility (SUMO) and CAR Learning to Act (CARLA) simulators, along with Lanelet2 HD maps and the Historical Prediction Network (HPNet) model, we demonstrate effective trajectory prediction and early warning generation for vehicle interactions in freeway work zones. To evaluate the accuracy of predicted trajectories, we use two standard metrics: Joint Average Displacement Error (ADE) and Joint Final Displacement Error (FDE). Specifically, the infrastructure-enabled HPNet model demonstrates superior performance on the work-zone datasets generated from the co-simulation environment, achieving a minimum Joint FDE of 0.3228 meters and a minimum Joint ADE of 0.1327 meters, lower than the benchmarks on the Argoverse (minJointFDE: 1.0986 m, minJointADE: 0.7612 m) and Interaction (minJointFDE: 0.8231 m, minJointADE: 0.2548 m) datasets. In addition, our proactive safety warning generation application, utilizing vehicle bounding boxes and probabilistic conflict modeling, demonstrates its capability to issue alerts for potential vehicle conflicts.

Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications. Traditional research methods face limitations, whereas social media offers real-time insights into self-reported substance use and overdose symptoms. This study proposes an AI-driven NLP framework trained on annotated social media data to detect commonly used drugs and associated overdose symptoms. Using a hybrid annotation strategy with LLMs and human annotators, we applied traditional ML models, neural networks, and advanced transformer-based models. Our framework achieved 98% accuracy in multi-class and 97% in multi-label classification, outperforming baseline models by up to 8%. These findings highlight the potential of AI for supporting public health surveillance and personalized intervention strategies.

Named Entity Recognition (NER) plays a pivotal role in various Natural Language Processing (NLP) tasks by identifying and classifying named entities (NEs) from unstructured data into predefined categories such as person, organization, location, date, and time. While extensive research exists for high-resource languages and general domains, NER in Urdu particularly within domain-specific contexts like education remains significantly underexplored. This is Due to lack of annotated datasets for educational content which limits the ability of existing models to accurately identify entities such as academic roles, course names, and institutional terms, underscoring the urgent need for targeted resources in this domain. To the best of our knowledge, no dataset exists in the domain of the Urdu language for this purpose. To achieve this objective this study makes three key contributions. Firstly, we created a manually annotated dataset in the education domain, named EDU-NER-2025, which contains 13 unique most important entities related to education domain. Second, we describe our annotation process and guidelines in detail and discuss the challenges of labelling EDU-NER-2025 dataset. Third, we addressed and analyzed key linguistic challenges, such as morphological complexity and ambiguity, which are prevalent in formal Urdu texts.

The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second paper presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails. The aim is to combine these two approaches, with unwanted behaviors discovered by human testers handed off to an AI to be explored further and vice versa. Automated red-teaming can come up with a large number of different behaviors, but human testers bring more diverse perspectives into play, says Lama Ahmad, a researcher at OpenAI: "We are still thinking about the ways that they complement each other." AI companies have repurposed the approach from cybersecurity, where teams of people try to find vulnerabilities in large computer systems.

Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. To fully explore the potential of social media content in disaster informatics, access to relevant content and the correct geo-location information is very critical. In this paper, we propose a three-step solution to tackling these challenges. Firstly, the proposed solution aims to classify social media posts into relevant and irrelevant posts followed by the automatic extraction of location information from the posts' text through Named Entity Recognition (NER) analysis. Finally, to quickly analyze the topics covered in large volumes of social media posts, we perform topic modeling resulting in a list of top keywords, that highlight the issues discussed in the tweet. For the Relevant Classification of Twitter Posts (RCTP), we proposed a merit-based fusion framework combining the capabilities of four different models namely BERT, RoBERTa, Distil BERT, and ALBERT obtaining the highest F1-score of 0.933 on a benchmark dataset. For the Location Extraction from Twitter Text (LETT), we evaluated four models namely BERT, RoBERTa, Distil BERTA, and Electra in an NER framework obtaining the highest F1-score of 0.960. For topic modeling, we used the BERTopic library to discover the hidden topic patterns in the relevant tweets. The experimental results of all the components of the proposed end-to-end solution are very encouraging and hint at the potential of social media content and NLP in disaster management.

This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and online surveys, are used. However, these surveys have several limitations, such as the limited number of participants and low frequency due to the labor involved in conducting such surveys. In this paper, we propose a Natural Language Processing (NLP) framework to automatically collect and analyze water-related posts from social media for data-driven decisions. The proposed framework is composed of two components, namely (i) text classification, and (ii) topic modeling. For text classification, we propose a merit-fusion-based framework incorporating several Large Language Models (LLMs) where different weight selection and optimization methods are employed to assign weights to the LLMs. In topic modeling, we employed the BERTopic library to discover the hidden topic patterns in the water-related tweets. We also analyzed relevant tweets originating from different regions and countries to explore global, regional, and country-specific issues and water-related concerns. We also collected and manually annotated a large-scale dataset, which is expected to facilitate future research on the topic.

The beds are full at the National Centre for the Rehabilitation of Addicts (NCRA), one of only two public addiction rehabilitation facilities in Jordan. In the midst of the busy centre, Ahmad*, 34, takes a breath in the facility's garden. The young man is on his eighth day of treatment for addiction to crystal methamphetamine. Cases of crystal meth abuse are rising throughout Jordan – according to doctors and scientists, the drug is even more addictive and dangerous than the now widely-available and also highly-addictive amphetamine, captagon. "On crystal [meth], I felt I was a different person," he told Al Jazeera, glancing down at the tattoo sleeves that envelop his arms, his brothers' names inscribed around each bicep.

Artificial intelligence research startup OpenAI on Tuesday introduced a tool that's designed to figure out if text is human-generated or written by a computer. The release comes two months after OpenAI captured the public's attention when it introduced ChatGPT, a chatbot that generates text that might seem to have been written by a person in response to a person's prompt. Following the wave of attention, last week Microsoft announced a multibillion-dollar investment in OpenAI and said it would incorporate the startup's AI models into its products for consumers and businesses. Schools were quick to limit ChatGPT's use over concerns the software could hurt learning. Sam Altman, OpenAI's CEO, said education has changed in the past after technology such as calculators has emerged, but he also said there could be ways for the company to help teachers spot text written by AI.