Search Results (62)

The quasielastic neutron scattering technique is an indispensable tool for probing microscopic dynamics in condensed matter at the nanoscale. However, the lack of a high-resolution spectrometer has restricted comprehensive studies of dynamics in China. To address this gap, a new time-of-flight neutron backscattering spectrometer, NuBS, is currently under construction at the China Spallation Neutron Source. This review systematically introduces basic principles, instrumental design, and scientific opportunities of NuBS. NuBS is engineered to deliver high energy resolution with a broad dynamic range, based on the moderator pulse structure. Given the wide time window of NuBS, we highlight its research prospects in frontier areas, including energy storage materials and heterogeneous catalysis. NuBS is expected to provide new opportunities for studies of complex molecular dynamics and in situ investigations, significantly elevating the capabilities of the quasielastic neutron scattering community upon its scheduled completion. Full article

White neutron Single Event Effect (SEE) cross-sections were measured for two Static Random Access Memories (SRAMs) on the Atmospheric Neutron Irradiation Spectrometer at the China Spallation Neutron Source, and we applied the unfolding technique using a particle swarm optimization algorithm to extract the neutron energy dependence of SEE cross-sections from the experimental data. The calculated results agree well with the experimental data, validating this method. This method demonstrates how white neutron SEE experimental data can be effectively used with unfolding techniques to obtain the neutron energy dependence of SEE cross-sections for microelectronic devices. Full article

This research investigates atmospheric neutron-induced single event effects (SEEs) on advanced artificial intelligence (AI) chips during natural environment operation. The RK3588 neural processing unit (NPU) is the evaluated target chip, and its SEE is assessed through a combination of irradiation testing and software fault injection. During the irradiation test, the chip was exposed to a spectrum neutron at the China Spallation Neutron Source. Upon reaching a cumulative fluence of 8.25 × 10⁹ n·cm², a total of 14,018 soft errors were detected, of which 99.97% manifested as variations in target recognition accuracy and network inference latency. Among these variations, both detrimental effects (reduced target recognition accuracy or prolonged network inference time) and beneficial effects (enhanced target recognition accuracy or shortened network inference time) caused by single event effects were observed. In addition, atmospheric neutron single event effects were found to cause NPU operation suspension and system crashes. Based on the irradiation test results, failure predictions for neural processing units in real-world environments were estimated, and mitigation recommendations were proposed. Furthermore, software fault injections were employed to conduct in-depth analysis of detected soft errors during irradiation testing. This research provides support and references for the reliable application of artificial intelligence chips in natural environments. Full article

The effect of grain boundaries on small-angle neutron scattering (SANS) was investigated for pure nickel. A series of annealed cold-rolled nickel samples were characterized by SANS, electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The experimental results indicate that the proportion of low-angle grain boundaries (LAGBs) has a noticeable influence on the scattering intensity. Cold-rolled samples exhibited a similar proportion of LAGBs, leading to only slight differences in scattering intensity. The scattering intensity was found to be dependent on annealing time and temperature. For samples with low degrees of recrystallization, a large number of LAGBs remained, resulting in high scattering intensity at low q. In contrast, samples with a high degree of recrystallization showed a significant reduction in LAGBs, which caused a noticeable decrease in SANS intensity. Full article

Periodic structures that may exist within the neutron imaging detector can introduce periodic noise into the imaging results, directly degrading image quality and further affecting the performance of deconvolution. This periodic noise appears as four-pointed star-shaped peaks in the amplitude spectrum of the frequency domain. However, the distribution of honeycomb-like noise structures in neutron imaging results makes it difficult to detect using conventional thresholding methods. We propose a method that applies a dilation operation before threshold detection to enhance the contrast between peaks and the surrounding areas. Then, a notch filter is used to smooth the peaks containing noise information, thereby removing the periodic noise structure. This approach effectively eliminates honeycomb structures of approximately 40 micrometers and improves the image quality after deconvolution processing. Full article

The global shortage of ³He resources has created an urgent need for alternative neutron detection technologies in applications such as national security, neutron scattering, and nuclear energy. This study designed and developed a zero-dimensional planar high-efficiency thermal neutron detector based on a boron-lined multi-wire proportional chamber (MWPC) employing two distinct efficiency-enhancement approaches: a multilayer structure and grazing-incidence geometry. For ease of use, a sealed detector has been developed, eliminating the need for gas cylinders. Geant4 simulations were utilized to optimize the ${\mathrm{B}}_4\mathrm{C}$ thickness of conversion layer and evaluate $\gamma$-ray sensitivity. Prototype detectors were fabricated and experimentally validated at the 20th beamline (BL20) of China Spallation Neutron Source (CSNS). Simulation results indicate that the optimal ${\mathrm{B}}_4\mathrm{C}$ thickness varies with layer count and neutron wavelength, measuring approximately 2.0 µm at 1.8 Å and 1.5 µm at 4 Å for a 10-layer structure, with $\gamma$-ray sensitivity below $5\times {10}^{-6}$. Experimental measurements demonstrate that a five-layer detector achieved neutron detection efficiencies of 28.0 ± 1.5% at 4.78 Å and 17.8 ± 1.8% at 2.87 Å, while a two-layer detector at 11.5° incidence attained 19.2% and 11.7%. This research lays the groundwork for developing large-area, high-efficiency, position-sensitive neutron detectors. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

The distinct solidification behavior of additively manufactured (AM) Inconel 718 (IN718) produces a unique microstructure and precipitation response compared with its conventionally forged counterpart, leading to fundamentally different responses to heat treatment and intermediate-temperature deformation behaviors. In this work, the intermediate-temperature (450–750 °C) deformation mechanisms of laser powder bed fusion (LPBF)-fabricated and forged IN718 alloys were systematically compared under various heat-treatment conditions. Overall, under solution treatment state, the LPBF alloy exhibited fine columnar grains, a high dislocation density, and retained δ phases along the grain boundaries, whereas the forged alloy showed coarse equiaxed γ grains without the δ phase. Under solution + aging (STA) treatment, the δ phase in the LPBF alloy effectively pinned grain boundaries and enhanced flow stress, while in the forged alloy, strengthening was dominated by the uniform precipitation of γ″ and γ′ phases. Owing to Nb consumption by δ-phase formation, the STA-treated LPBF alloy contained fewer γ″/γ′ precipitates and exhibited slightly lower strength than the STA-treated forged alloy. This study demonstrates that the inherent δ phase retention and Nb segregation in LPBF-built IN718 critically influence its precipitation behavior and deformation resistance, distinguishing it from conventionally processed alloys and providing valuable insights for microstructure design in AM-built high-temperature superalloys. Full article

During the heat treatment of nickel-based superalloy (for instance Inconel 718 alloy), the non-isothermal heating and cooling processes significantly influenced precipitation behaviors as well as the final mechanical properties. This study compared the precipitation behaviors and the resulting hardening and softening behaviors of additively manufactured and conventionally forged Inconel 718 alloys under non-isothermal heat treatment processes. The results indicated that additively manufactured Inconel 718 alloy accelerated aging precipitation behavior due to the fine dendritic structure during both heating and cooling processes. As a result, the additively manufactured alloy reached peak hardness of ~480 HV at ~650 °C (~100 °C earlier than the forged alloy’s peak hardness of ~460 HV at ~750 °C) during heating and gained almost constant hardness during cooling. Further, the heating rate significantly affected the precipitation behaviors of γ″ and γ′ phases in both alloys. Slower heating rates provided sufficient time for phase transformation, leading to a more pronounced precipitation., e.g., the volume fraction of precipitates in the SLM alloy increased from 1.5% at 5 °C/min to 5.9% at 0.5 °C/min when heated to 850 °C. During cooling process, the twisted grain boundaries of additively manufactured alloy facilitated the precipitation of δ-phase, which in turn inhibited the formation of γ/γ′/γ″ phase. Both alloys exhibited minimum hardness of ~380–390 HV at 1000 °C due to complete dissolution of strengthening phases. This study provides a comparative understanding of non-isothermal phase evolution in AM and forged Inconel 718, which is critical for optimizing heat treatment in aerospace applications. Full article

Industrial control systems (ICSs) face escalating security challenges due to evolving cyber threats and the inherent limitations of traditional intrusion detection methods, which fail to adequately model spatiotemporal dependencies or interpret complex protocol semantics. To address these gaps, this paper proposes DLG–IDS—a lightweight intrusion detection framework that innovatively integrates dynamic graph construction for capturing real–time device interactions and logical control relationships from traffic, LLM–driven semantic enhancement to extract fine–grained embeddings from graphs, and a spatio–temporal graph neural network (STGNN) optimized via sparse attention and local window Transformers to minimize computational overhead. Evaluations on SWaT and SBFF datasets demonstrate the framework’s superiority, achieving a state–of–the–art accuracy of 0.986 while reducing latency by 53.2% compared to baseline models. Ablation studies further validate the critical contributions of semantic fusion, sparse topology modeling, and localized temporal attention. The proposed solution establishes a robust, real–time detection mechanism tailored for resource–constrained industrial environments, effectively balancing high accuracy with operational efficiency. Full article

To address the increasing complexity of web user behavior anomaly detection and the issue of missing semantic information caused by relying solely on features like request semantics or request sequences, this study proposes a multi-angle semantic feature fusion approach for user behavior anomaly detection. The research is based on user sessions. Firstly, by analyzing the access sequence behavior within user sessions and utilizing an improved SimHash algorithm, sequence features are extracted to model browsing patterns. Secondly, combining the semantic content contained in user sessions, a multi-attention Transformer model is employed to extract semantic features, representing user visit semantics. Finally, an end-to-end model is constructed to fuse sequence and semantic features, enabling effective detection of user behavior anomalies. Experimental results demonstrate that the proposed model exhibits excellent performance and stability in detection accuracy, with significant effects in real-world anomaly user identification. As the proportion of anomalous sessions increases, precision, recall, and F1-score also improve, all reaching 99%. Even when anomalous sessions are scarce in the dataset, the model still achieves satisfactory detection results. Full article

In many engineering applications, multi-objective optimization problems can be reformulated as single-objective problems with multiple constraints to improve computational efficiency. This paper discusses the characteristics and challenges of RF accelerating structure optimizations and proposes an enhanced single-objective optimization strategy based on progressive exploration method to find the global optimal solution within a large solution space characterized by a continuous and confined distribution of feasible solutions. It begins from an arbitrary feasible solution and progressively slides and expands the solution space fragment along the distribution path of feasible solutions to rapidly explore the entire space. By incorporating a re-initialization mechanism to enhance swarm diversity and introducing penalty factors in place of constraints to increase the number of feasible solutions, the algorithm significantly improves its ability to escape local optima traps. The proposed algorithm is applied to optimize a DAA structure, yielding satisfactory results and convergence speed. These results highlight the method’s effectiveness and its potential applicability to other complex constrained optimization problems. Full article

With the continuous growth of network threats, intrusion detection systems need to have robustness and adaptability to effectively identify malicious behaviors. However, factors such as noise interference, class imbalance, and complex attack pattern recognition have posed significant challenges to traditional systems. To address these issues, this paper proposes a dynamic preprocessing-enhanced DyP-CNX framework. The framework designs a sliding window dynamic interquartile range (IQR) standardization mechanism to effectively suppress the temporal non-stationarity interference of network traffic. It also combines a random undersampling strategy to mitigate the class imbalance problem. The model architecture adopts a CNN-XGBoost collaborative learning framework, combining a dual-channel convolutional neural network (CNN) and two-stage extreme gradient boosting (XGBoost) to integrate the original statistical features and deep semantic features. On the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the method achieved F1 values of 91.57% and 99.34%, respectively. The experimental results show that the DyP-CNX method has the potential to handle the feature drift and pattern confusion problems in complex network environments, providing a new technical solution for adaptive intrusion detection systems. Full article

Geometrically frustrated magnets exhibit exotic excitations due to competing interactions between spins. The spinel compound MgCr₂O₄, a three-dimensional Heisenberg antiferromagnet, hosts both spin-wave and spin-resonance modes, but the origin of its resonant excitations remains debated. Suppressing magnetic order via non-magnetic doping can help isolate these modes in neutron scattering studies. We synthesized Ga³⁺ and Cd²⁺-doped MgCr₂O₄ via solid-state reaction and analyzed their structure and magnetism. Ga³⁺ doping (0–20%) causes anomalous lattice shrinkage due to site disorder from Ga³⁺ occupying both Mg²⁺ and Cr³⁺ sites. Magnetically, Ga³⁺ doping drives the system from the antiferromagnetic order to a spin-glass state, fully suppressing magnetic ordering at 20% doping. In contrast, Cd²⁺ replaces only Mg²⁺, expanding the lattice and meantime inducing strong spin-glass behavior. At 10% Cd²⁺, long-range antiferromagnetic order is entirely suppressed. Thus, 10% Cd-doped MgCr₂O₄ offers an ideal platform to study the resonant magnetic excitations without any spin-wave interference. Full article

This study examined the comprehensive properties of Mg-Zn-Nd-Zr alloys in order to achieve both high strength and thermal conductivity simultaneously. The impact of rolling on the microstructure, mechanical properties, and thermal conductivity was analyzed for Mg-5Zn-xNd-0.4Zr alloys (x = 1, 2). The results indicate that the addition of Nd promotes the formation of the W phase (Mg₃Zn₃RE₂), which contributes to grain boundary strengthening and enhances the overall strength. Moreover, dynamic precipitation during the rolling process leads to the formation of nanoscale MgZn₂ and Zn₂Zr phases, significantly improving both the strength and thermal conductivity. After rolling, both the Mg-5Zn-1Nd-0.4Zr (ZNK510) and Mg-5Zn-2Nd-0.4Zr (ZNK520) alloys exhibited a notable enhancement in thermal conductivity, with ZNK520 demonstrating superior properties due to its higher Nd content. This study highlights that optimizing alloy composition and phase evolution through rolling can markedly enhance both the mechanical and thermal properties, offering a promising strategy for the development of high-performance magnesium alloys. Full article