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Abstract The flexibility and complexity of IPv6 extension headers allow attackers to create covert channels or bypass security mechanisms, leading to potential data breaches or system compromises. The mature development of machine learning has become the primary detection technology option used to mitigate covert communication threats. However, the complexity of detecting covert communication, evolving injection techniques, and scarcity of data make building machine-learning models challenging. In previous related research, machine learning has shown good performance in detecting covert communications, but oversimplified attack scenario assumptions cannot represent the complexity of modern covert technologies and make it easier for machine learning models to detect covert communications. To bridge this gap, in this study, we analyzed the packet structure and network traffic behavior of IPv6, used encryption algorithms, and performed covert communication injection without changing network packet behavior to get closer to real attack scenarios. In addition to analyzing and injecting methods for covert communications, this study also uses comprehensive machine learning techniques to train the model proposed in this study to detect threats, including traditional decision trees such as random forests and gradient boosting, as well as complex neural network architectures such as CNNs and LSTMs, to achieve detection accuracy of over 90%. This study details the methods used for dataset augmentation and the comparative performance of the applied models, reinforcing insights into the adaptability and resilience of the machine learning application in IPv6 covert communication. We further introduce a Generative AI-driven script refinement framework, leveraging prompt engineering as a preliminary exploration of how generative agents can assist in covert communication detection and model enhancement. 

Real-world observational datasets and machine learning have revolutionized data-driven decision-making, yet many models rely on empirical associations that may be misleading due to confounding and subgroup heterogeneity. Simpson’s paradox exemplifies this challenge, where aggregated and subgroup-level associations contradict each other, leading to misleading conclusions. Existing methods provide limited support for detecting and interpreting such paradoxical associations, especially for practitioners without deep causal expertise. We introduceDe-paradox Tree, an interpretable algorithm designed to uncover hidden subgroup patterns behind paradoxical associations under assumed causal structures involving confounders and effect heterogeneity. It employs novel split criteria and balancing-based procedures to adjust for confounders and homogenize heterogeneous effects through recursive partitioning. Compared to state-of-the-art methods,De-paradox Treebuilds simpler, more interpretable trees, selects relevant covariates, and identifies nested opposite effects while ensuring robust estimation of causal effects when causally admissible variables are provided. Our approach addresses the limitations of traditional causal inference and machine learning methods by introducing an interpretable framework that supports non-expert practitioners while explicitly acknowledging causal assumptions and scope limitations, enabling more reliable and informed decision-making in complex observational data environments. 

While existing works have emphasized how elites shape mass opinion, we ask whether the reverse also holds: do audience reactions on social media actively shape elite behavior? We examine this question through the lens of cross-partisan interactions (CPIs), which can either foster deliberation or deepen polarization. Using a dataset of over 1.1 million cross-party retweets, replies, and mentions between U.S. state legislators and their audiences on Twitter/X (2020–2021), we first establish baseline patterns of engagement: Democrats gain modest engagement in replies and mentions, while Republicans often face penalties in direct cross-party interactions. Building on this, we show that audience engagement produces a feedback loop that conditions future elite behavior. Following highly visible CPIs, legislators are not only more likely to engage again in cross-talk, but also shift their rhetorical strategies. Engagement consistently promotes causal reasoning, subjective language, and positive-emotion framing in subsequent CPIs. These findings suggest a positive association between audience engagement and constructive cross-party discourse among elites, challenging overly simplified interpretations in the literature that emphasize social media as a primary driver of rising or falling polarization. 

Abstract Cold protons play an important role in the Earth’s magnetosphere by modifying the dispersion relation of plasma waves. Their energy flux can be enhanced through a nonresonant response to electromagnetic ion cyclotron (EMIC) waves. This study combines Magnetospheric Multiscale (MMS) observations and a hybrid simulation to investigate cold proton dynamics during this nonresonant process. It is found that the energy flux of cold protons with kinetic energies below ~ 200 eV increases due to the bulk flow induced by EMIC waves, and this enhancement becomes stronger and extends to higher energies at higher magnetic latitudes. Despite the flux enhancement, the temperature and number density of cold protons remain constant. Moreover, we identify the formation mechanism of proton phase-bunching distribution in stationary gyrophase. During the nonresonant process, particles bunch in anti-phase with the wave magnetic fields, without exchanging energy with waves. In contrast, during the resonant process, particles bunch in anti-phase with the wave electric fields, facilitating energy transfer from particles to waves. We demonstrate that particle phase-bunching is a necessary but not sufficient condition for identifying resonant interactions with waves, and that the occurrence of energy transfer determines whether the response is nonresonant or resonant. Graphical Abstract 

Extremist communities increasingly rely on social media to sustain and amplify divisive discourse. However, the relationship between their internal participation structures, audience engagement, and narrative expression remains underexplored. This study analyzes ten years of Facebook activity by hate groups related to the Israel–Palestine conflict, focusing on anti-Semitic and Islamophobic ideologies. Consistent with prior work, we find that higher participation centralization in online hate groups is associated with greater user engagement across hate ideologies, suggesting the role of key actors in sustaining group activity over time. Meanwhile, our narrative frame detection models—based on an eight-frame extremist taxonomy (e.g., dehumanization, violence justification)—reveal a clear contrast across hate ideologies: centralized Islamophobic groups employ more uniform messaging, while centralized anti-Semitic groups demonstrate greater framing diversity and topical breadth, potentially reflecting distinct historical trajectories and leader coordination patterns. Analysis of the inter-group network indicates that, although centralization and homophily are not clearly linked, ideological distinctions emerge: Islamophobic groups cluster tightly, whereas anti-Semitic groups remain more evenly connected. Overall, these findings clarify how participation structure may shape the dissemination pattern and resonance of extremist narratives online and provide a foundation for tailored strategies to disrupt or mitigate such discourse. 

Abstract We introduce the Digitally Accountable Public Representation (DAPR) Database, an innovative archive that systematically tracks and analyzes the online communication of federal, state, and local elected officials in the U.S. Focusing on X/Twitter and Facebook, the current database includes 28,834 public officials, their demographic information, and 5,769,904 X/Twitter posts along with 450,972 Facebook posts, dating from January 2020 to December 2024. The database integrates three interconnected datasets: metadata on elected officials, weekly aggregated X data, and weekly aggregated Facebook data. These weekly aggregated datasets provide detailed insights into platform activity, capturing officials’ posting volumes, engagement metrics, and content trends. Our framework ensures ongoing database expansion by incorporating new officials and platforms, maintaining its relevance and research utility for analyzing officials’ digital communication. 

Abstract Solar wind directional discontinuities, such as rotational discontinuities (RDs), significantly influence energy and transport processes in the Earth's magnetosphere. A recent observational study identified a long‐lasting double cusp precipitation event associated with RD in solar wind on 10 April 2015. To understand the magnetosphere‐ionosphere response to the solar wind RD, a global hybrid simulation of the magnetosphere was conducted, with solar wind conditions based on the observation event. The simulation results show significant variations in the magnetopause and cusp regions caused by the passing RD. After the RD propagates to the magnetopause, ion precipitation intensifies, and a double cusp structure at varying latitudes and longitudes forms near noon in the northern hemisphere, which is consistent with the satellite observations by Wing et al. (2023,https://doi.org/10.1029/2023gl103194). Regarding dayside magnetopause reconnection, the simulation reveals that the high‐latitude reconnection process persists during the RD passing, regardless of whether the interplanetary magnetic field (IMF) with a highB_y/B_zratio has a positive or negativeB_zcomponent, and low‐latitude reconnection occurs after the RD reaches the magnetopause at noon when the IMF turns southward. By examining the ion sources along the magnetic field lines, a connection is found between the single‐ or double‐cusp ion precipitation and the solar wind ions entering from both high‐latitude and low‐latitude reconnection sites. This result suggests that the double‐cusp structure can be triggered by magnetic reconnection occurring at both low latitudes and high latitudes in the opposite hemispheres, associated with a largeB_y/B_zratio of the IMF around the RD. 

This paper investigates deploying connected and automated vehicle (CAV) lanes in transportation networks with a focus on measuring and preserving equity among travelers. A new metric is proposed to characterize equity based on (1) generalized travel cost per unit origin-destination (OD) distance for travelers on each OD pair and using each vehicle type and (2) maximum deviation of the standardized unit generalized travel cost from system average. A bi-level bi-objective program is developed to simultaneously minimize system travel cost and inequity while deploying CAV lanes. A solution algorithm that combines nondominated sorting genetic algorithm II and variable neighborhood search is designed. Through extensive numerical experiments, we find (1) inequity is more prominent when travel demand is high; (2) human-driven vehicle travelers become more disadvantageous with lower CAV price and higher CAV automation; and (3) subsidy is effective in mitigating inequity, but a fee for using CAV lanes is less promising.