Beam-column connections are vulnerable parts of a frame pier under lateral earthquake. This paper proposes a novel double-stage yielding beam-column connection (DYC) for seismic damage control. The main feature is the use of two kinked steel plates (KSP) in the upper and lower edges of the DYC to tolerate the deformation induced by the beam-column rotation. Hence, the major RC column and beams could be protected from plasticity development. Five KSP samples and one DYC specimen were designed and tested. The experimental results indicated that the DYC had a secondary yielding mechanism due to the straightening of the kinked segment. Benefitting from the deformation redistribution, the DYC specimen experienced slighter plasticity in the major RC components than did the traditional RC connection. Based on the tests, a multiscale numerical model was developed, which yielded good agreement with the test results. Dynamic analysis of the benchmark bridge was performed to evaluate the seismic performance. The overall response of the whole bridge and local behavior of the connection were compared. The influence of the springing height and angle is discussed. To effectively control the seismic damage under earthquakes of varying intensities, the design method of DYC is proposed.
This article explores the integration of Artificial Intelligence (AI) with Zero Trust Architecture (ZTA) in cloud environments, presenting a comprehensive framework for enhancing cybersecurity in modern digital ecosystems. It begins by examining the core principles of Zero Trust Architecture, including micro-segmentation, identity-based access controls, and continuous verification. The role of AI in cybersecurity is then discussed, focusing on its capabilities in analyzing large-scale datasets, identifying anomalous behaviors, and predictive threat detection. The synergy between AI and ZTA is explored in depth, highlighting how this combination enables real-time threat analysis, advanced behavior pattern recognition, and improved threat intelligence parsing. A case study illustrates the practical implementation of AI-enhanced ZTA, demonstrating significant improvements in threat detection, response times, and overall security posture. The article also addresses key challenges and considerations, including AI bias, resource requirements, and data governance issues. Finally, it provides a roadmap for organizations looking to implement AI-enhanced ZTA, covering assessment, tool selection, performance optimization, and regulatory compliance. This comprehensive exploration offers valuable insights for security professionals and researchers, bridging the gap between theoretical advancements and practical applications in the rapidly evolving field of cybersecurity.
The spread of false information and counterfeit news on the Internet has become an urgent issue on the international level with serious consequences in the political, health, and social trust sectors. Traditional methods of detection, relying either on natural language processing (NLP) strategies or on machine learning models, do not consider multi-relational and multi-contextual scaffolds on which misinformation spreads. Recent advances in Graph Neural Networks (GNNs) offer a promising paradigm to learn such complicated relationships by modelling information ecosystems as graphs of users, posts and promotion paths. GNNs offer strong information-detecting strengths at scale through their use of structural and contextual dependencies in social networks. In this paper, we have critically revised the GNN-based misinformation and fake news detecting models. It talks about how the use of graph representations (including content graphs, social graphs, heterogeneous networks, etc.) can enhance detection accuracy when it combines textual, visual and relational information. The article gives an overview of popular GNNs, such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and heterogeneous GNNs, and identifies them as applied to rumour detection, credibility assessment, and fake news detection early in its evolution. The implementation concerns such as scalability, graph-based on-the-fly construction, and the interpretability are discussed too. Its outcome is that the GNNs prove to be more useful than the old models because of the fact that it can produce those features that are network related, yet its computation is too complex, and that it can be adversarial is not an attribute of the real world. Future research directions also describe explainable GNNs, why they are necessary in combination with multimodal learning, and privacy-preserving detection systems. Overall, GNN-based solution is an important step forward in combating fake information since it provides a deeper insight into the functionality of interactions within the online ecosystem.
Sources of de-correlated returns permit reducing portfolios' left-tail exposure. "Risk premia" strategies have become more widely included within multi-asset portfolios over the past decade, given the diversification benefit offered by the alternative strategies over their traditional, long-only counterparts. The number of liquid assets within the options space is scarcer and diversification requires looking for orthogonal sources of PnL drivers across the tradeable assets. This task typically involves maximizing the number of volatility parameters that allow a direct mapping via a viable trading strategy.
Accurate detection and classification of seizures from electroencephalography (EEG) data can potentially enable timely interventions and treatments for neurological diseases. Currently, EEG recordings are exclusively reviewed by human experts, namely neurologists with specialized training. While indispensable, this time-consuming workflow represents a major bottleneck. Review of EEG records is laborious, time-consuming, expensive, prone to fatigue-induced errors, and suffers from inter-rater reliability even among expert reviewers. This paper introduces a new deep neural network (DNN) with interpretable layers for the classification of seizures and other pathologic brain activities such as periodic discharges, rhythmic delta waves and miscellaneous activities. The DNN architecture uses interpretable layers that allow clinicians to evaluate the model’s decision-making pipeline and build trust in the model and support clinical decision making. The combination of deep learning and interpretability layers is novel and addresses the limitations of existing methods. We demonstrate the usefulness of the proposed approach on a publicly available EEG dataset. Our method achieves state-of-the-art performance and provides classification decisions that are interpretable, useful for clinical experts. This paper contributes to the existing body of literature on EEG-based seizure detection and addresses the gap between DNN-based methods and clinical interpretability, leading to accurate and clinically meaningful predictions.
