Network Intrusion Detection and Prevention Systems (NIDPSs) play a crucial role in identifying and blocking malicious traffic. In-kernel NIDPSs leveraging extended Berkeley Packet Filter (eBPF) have achieved high detection performance and throughput by operating directly in kernel space. However, eBPF-based NIDPSs face portability challenges across diverse runtime environments, limiting their deployment on kernel-bypassing frameworks such as AF_XDP and the Data Plane Development Kit (DPDK). In this paper, we propose a userspace BPF (uBPF)-based NIDPS empowered by machine learning (ML) algorithms and integrate it with kernel-bypassing frameworks to achieve portability across diverse runtime environments while accurately and efficiently detecting and preventing Distributed Denial of Service (DDoS) attacks. We evaluate the proposed NIDPSs from both DDoS detection performance and packet processing performance perspectives. Evaluation results demonstrate that the proposed NIDPS achieves high classification performance for DDoS detection, while enabling the same uBPF-based detection logic to operate across different packet processing frameworks. Moreover, the uBPF-based NIDPSs running on the DPDK data plane achieve packet processing rates 3.38–8.60× higher than the evaluated in-kernel NIDPS implementations.
While ferromagnetism and antiferromagnetism are well-established classes of magnetic order, a third class of collinear magnetic order, termed altermagnetism, has recently attracted scientific interest. We measured magnetic circular dichroism (MCD) in core-level photoemission (XPS) at the Ru 2p3/2 and 2p1/2 core levels in epitaxial RuO2(110)/TiO2(110) films using circularly polarized x rays at 6 keV, as well as x-ray magnetic circular dichroism (XMCD) in resonant x-ray absorption at the Ru M3,2 (3p3/2 and 3p1/2) edges. Charge transfer multiplet calculations show that the MCD-XPS and the XMCD can be explained by an altermagnetic locking of Ru magnetic moments and a distorted crystal field orientation. The distortion is caused by the epitaxial strain. The collinear magnetic moments in RuO2 occupy sublattice sites with distorted octahedral crystal fields that are rotated by 90 degrees with respect to each other. A change in the sign of the MCD-XPS at different sample positions indicates the presence of altermagnetic domains with the size of around hundreds of micrometers.
Although azobenzenes have the potential to possess both aggregation-induced emission (AIE) and photoresponsivity, examples are still limited due to their low emission efficiency. Herein, we report a new supramolecular strategy utilizing a large molecular dipole moment to enhance emission efficiency in the aggregate state and to induce controllable crystal polymorphism, thereby demonstrating the first observation of azobenzene-based whispering gallery mode (WGM) emission with multiple sharp peaks. While dipolar azobenzene monomers in toluene are non-emissive, the drop-cast film exhibits red emission under photoexcitation due to the AIE effect. Microscopic photoluminescence (PL) measurements revealed that the film comprises a mixture of crystals, with major red and minor near-infrared (NIR) emissions. By tuning the recrystallization temperature in toluene, we successfully controlled the crystal polymorphism and revealed the PL properties of each form using time-resolved PL spectroscopy and theoretical approaches. A direct transition from the NIR-emissive crystal to the red-emissive one was observed upon thermal annealing. Remarkably, the micro-sized particles exhibited multiple sharp PL peaks due to the microresonator effect, attributed to WGM emission, and the WGM peak positions shifted upon photoirradiation. This work provides a fundamental principle for the creation of new photoresponsive WGM-based materials.
Zwitterionic ring-opening polymerization (ZROP) is widely used synthetic methods for cyclic polymer synthesis. While various ZROP studies utilizing organic base catalysts have been explored, reports involving 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) are rare, leaving the specific ZROP mechanism unclear. In this study, we investigate the polymerization mechanism using TBD and trimethylene carbonate (TMC). Polymerization kinetics studies reveal an irreversible formation of initiation species at the initiation reaction, showing deviation from known ZROP mechanisms using N-heterocyclic carbene as organic base catalysts. Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF–MS) results indicate that polymerization attempts using polar solvents like THF stabilize active zwitterionic species and prevent side reactions. Based on our kinetics study, we quantitatively demonstrate that TBD-catalyzed ZROP of TMC proceeds via an effectively irreversible initiation step, exhibiting first-order dependence on TBD concentration. Furthermore, detailed MALDI-TOF–MS analysis, including minor polymer populations (L2 and L3), reveals solvent-dependent suppression of intermolecular exchange reactions. These findings provide mechanistic insights beyond previous reports and clarify the distinct behavior of TBD in ZROP of TMC. We investigated the polymerization mechanism of trimethylene carbonate monomer using TBD in the absence of alcohol. Our findings revealed that despite the polymerization progressing via ZROP, the initiation reaction is irreversible. We hypothesize that the deprotonation of TBD leads to the dissolution of zwitterion, resulting in the formation of a mixture of cyclic and linear polymers.
The widespread adoption of generative AI (GenAI) tools such as GitHub Copilot and ChatGPT is transforming software development. Since generated source code is virtually impossible to distinguish from manually written code, their real-world usage and impact on opensource software (OSS) development remain poorly understood. In this paper, we introduce the concept of self-admitted GenAI usage, that is, developers explicitly referring to the use of GenAI tools for content creation in software artifacts. Using this concept as a lens to study how GenAI tools are integrated into OSS projects, we analyze a curated sample of more than 200,000 GitHub repositories, identifying 1,292 such self-admissions across 156 repositories in commit messages, code comments, and project documentation. Using a mixed methods approach, we derive a taxonomy of 32 tasks, 10 content types, and 11 purposes associated with GenAI usage based on 1,292 qualitatively coded mentions. We then analyze 13 documents with policies and usage guidelines for GenAI tools and conduct a developer survey to uncover the ethical, legal, and practical concerns behind them. Our findings reveal that developers actively manage how GenAI is used in their projects, highlighting the need for project-level transparency, attribution, and quality control practices in AI-assisted software development. Finally, we examine the longitudinal impact of GenAI adoption on code churn in 151 repositories with self-admitted GenAI usage and find no general increase, contradicting popular narratives on the impact of GenAI on software development.
