Agentic large language model systems increasingly automate tasks by retrieving URLs and calling external tools. We show that this workflow gives rise to implicit prompt injection: adversarial instructions embedded in automatically generated URL previews, including titles, metadata, and snippets, can introduce a system-level risk that we refer to as silent egress. Using a fully local and reproducible testbed, we demonstrate that a malicious web page can induce an agent to issue outbound requests that exfiltrate sensitive runtime context, even when the final response shown to the user appears harmless. In 480 experimental runs with a qwen2.5:7b-based agent, the attack succeeds with high probability (P (egress) =0.89), and 95
This paper introduces a methodology for estimating mayfly populations by utilizing radar data from the US National Weather Service's NEXRAD system. The research emphasizes the integration of Python-based tools, including PyEphem, Boto3, and Py-ART, to efficiently retrieve, process, and filter radar data, thereby improving the accuracy of mayfly emergence monitoring. The filtration process leverages advanced polarimetric radar techniques, allowing for the effective distinction between meteorological and biological targets, such as insects. The method estimates mayfly population density by calculating the total backscattering area from radar reflectivity data, which is then used to derive the number of mayflies in the surveyed region. This approach provides a robust and reliable technique for ecological monitoring and can be applied to studies of insect migrations and environmental shifts within the Upper Mississippi River Region (UMRR).
Smart motor controllers usually provide plentiful information that makes it possible to model the various mechanisms they control. One such mechanism, a flywheel, is one of the simplest models to implement, yet has various characteristics that need to be identified for the model to be accurate. To ensure accurate control of the flywheel, not only is real-time state information necessary such as position, velocity, and acceleration, but also system characteristics such as inertia, friction, and drag. This paper investigates the use of extended Kalman filter to simultaneously estimate the system state and parameters, providing rich information that the user could use to optimally control the end mechanism.
As a social media platform, Twitter significantly influences stock market movement in the contemporary digital era. Twitter users can share real-time events and opinions about financial news instantly among the mass population. However, tweets sentiments on financial news have complexity considering sentiment ambiguity, financial jargon, and lack of context and linguistic style. Here, we employed machine learning models (Random Forest, XGBoostGradient Boosting, support vector Machine, Logistic Regression), pre-trained models (RoBERTa, DistilBERT, FinBERT, XLNet, and Vedar), and deep learning models (LSTM, GRU, and a combination of CNN and LSTM) to analyze stock market sentiment utilizing the Twitter dataset. We also deployed a word cloud analysis to identify investors' sentiments. This research utilized a Twitter stock news dataset[7] [binary sentiment labeled as positive (1) or negative (-1)] from Kaggle containing 5971 tweets. Furthermore, we reported the effectiveness of the multiple computational methods in classifying financial sentiment. The study findings demonstrated that DistilBERT, the pre-trained model, outperformed all the machine learning, deep learning, and other pre-trained models. It had the highest accuracy score of 80%, the highest F-1 score of 84.10%, and RoBERTa had the highest area under the curve score of 86.43%. As per word cloud analysis, market sentiment of tweets are well represented by the resulting cloud word (e.g, “short”, “volume”, “low”, “bullish”, “earning”, “target”, “breakout”). This research benefits investors, financial analysts, AI researchers, and hedge funds by providing a robust sentiment-driven stock price prediction framework. Future work should focus on multi-source sentiment incorporation and enhance domain-specific knowledge to enhance predictive accuracy and reliability.
Cloud services are helping with the management and organizational placement of organizational IT resources by providing products that are malleable, versatile, and inexpensive. But as people start using cloud-based applications in areas that cannot afford any disruption in healthcare, finance, public safety and defense, the need for sturdy and reliable systems becomes critical. For critical business processes, performance disruption, availability problem or failure conditions, risk exposure that causes loss, brand deterioration, or death is not acceptable. Durability architectures are built to avoid, accommodate and restore disruptions resulting from equipment breakdown, cyber- attacks, calamities or surge in workload. This paper focuses on fault tolerance, replication of data, disaster recovery, and distributed systems, as methods that can be used to construct reliable systems. It also explores newer trends like Artificial Intelligence, Machine Learning, and Edge Computing that allow the continuous monitoring, analytics, and automated recovery. The issues are presented and discussed in detail, and the trends that include on-premise and cloud environments, compliance issues and evolving threat landscape are highlighted. Specific steps to increase business productivity when using business intelligence, credibility of information, and adaptability are described. This work positions resilience as a competitive advantage underpinning the capabilities of cloud-oriented organisations to navigate the challenges of current technology environments cohesively.
