Search Results (513)

This paper presents a design for a high-speed digital prototype system for discriminating fast neutrons, thermal neutrons, and γ-rays. The system uses a stilbene–⁶Li glass composite scintillator with excellent pulse shape discrimination (PSD) properties as the neutron detector. The PSD performance was investigated at different sampling rates, revealing stable performance at rates above 250 MSPS. The system core is a high-speed acquisition board based on the AD9434 analog-to-digital converter (ADC) and the ZYNQ7020 field-programmable gate array (FPGA), which acquires detector signals and implements real-time algorithms. The system was energy-calibrated with ²²Na, ¹³⁷Cs, and ⁶⁰Co γ-ray sources and evaluated in a n–γ mixed field. Under an ²⁴¹Am–Be neutron source, the system achieved Figure of Merit (FOM) values of 1.26 for fast neutron/γ, 2.18 for fast neutron/thermal neutron, and 3.25 for γ/thermal neutron discrimination above the 50 keVee electron equivalent energy threshold. These results are consistent with the analysis of down-sampled data from a DT-5730 digitizer, confirming that the system meets its design objectives. Additionally, the measured false alarm rates (FAR) were 0.33% for ⁶⁰Co, 0.34% for ¹³⁷Cs, and 0.26% for ²²Na. This system integrates waveform discrimination and energy spectrum measurement capabilities, providing a high-performance, cost-effective electronic solution for high-speed signal acquisition and real-time processing in novel composite scintillator neutron detectors. Full article

Total organic carbon (TOC) is a critical parameter for evaluating shale source rock quality and hydrocarbon generation potential. However, accurate TOC estimation from conventional well logs remains challenging, especially in data-limited geological settings. This study proposes an optimized XGBoost model for TOC prediction using conventional logging data from the Shahejie Formation in the Dongying Depression, Bohai Bay Basin, China. We systematically transform four standard logs—resistivity, acoustic transit time, density, and neutron porosity—into 165 candidate features through multi-scale smoothing, statistical derivation, interaction term creation, and spectral transformation. A two-stage feature selection process, combining univariate filtering and recursive feature elimination and further refined by principal component analysis, identifies ten optimal predictors. The model hyperparameters are optimized via Bayesian search within the Optuna framework to minimize cross-validation error. The optimized model achieves an R² of 0.9395, with a Mean Absolute Error (MAE) of 0.3392, a Root Mean Squared Error (RMSE) of 0.4259, and a Normalized Root Mean Squared Error (NRMSE) of 0.0604 on the test set, demonstrating excellent predictive accuracy and generalization capability. This study provides a reliable and interpretable methodology for TOC characterization, offering a valuable reference for source rock evaluation in analogous shale formations and sedimentary basins. 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

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments. Full article

Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic transients. Observational strategies for these early EM searches are therefore critical, yet current practice remains suboptimal, motivating improved, coordination-aware approaches. We propose and evaluate the Two-Step Localization strategy, a coordinated observational protocol in which one wide-field auxiliary telescope and one narrow-field main telescope monitor the evolving GW sky localization in real time. The auxiliary telescope, by virtue of its large field of view, has a higher probability of detecting early EM emission. Upon registering a candidate signal, it triggers the main telescope to slew to the inferred location for prompt, high-resolution follow-up. We assess the performance of Two-Step Localization using large-scale simulations that incorporate dynamic sky-map updates, realistic telescope parameters, and signal-to-noise ratio (SNR)-weighted localization contours. For context, we compare Two-Step Localization to two benchmark strategies lacking coordination. Our results demonstrate that Two-Step Localization significantly reduces the median detection latency, highlighting the effectiveness of targeted cooperation in the early-time discovery of EM counterparts. Our results point to the most impactful next step: next-generation faster telescopes that deliver drastically higher slew rates and shorter scan times, reducing the number of required tiles; a deeper, truly wide-field auxiliary improves coverage more than simply adding more telescopes. Full article

Perfluorooctane sulfonate (PFOS), a persistent and bioaccumulative perfluoroalkyl substance, poses significant environmental and human health risks due to the extraordinary stability of its C–F bonds. Conventional remediation strategies largely fail to achieve mineralization, instead transferring contamination or producing secondary waste streams. In this study, we investigate neutron irradiation as a potential destructive approach for PFOS remediation in both solid and aqueous matrices. Samples were exposed to thermal neutrons (flux: 3.2 × 10⁹ n·cm⁻²·s⁻¹, 0.0025 eV) at the Argonauta reactor for 6 h. Raman and FTIR spectroscopy revealed that PFOS in powder form remained largely resistant to degradation, with only minor structural perturbations observed. In contrast, aqueous PFOS solutions exhibited pronounced spectral changes, including attenuation of C–F and S–O vibrational signatures, the emergence of carboxylate and carbonyl functionalities, and enhanced O–H stretching, consistent with radiolytic oxidation and partial defluorination. Notably, clear peak shifts were predominantly observed for PFOS in aqueous solution after irradiation (overall displacement toward higher wavenumbers), whereas in powdered PFOS the main spectral signature of irradiation was the attenuation of CF₂ and S–O related bands with comparatively limited band relocation. To evaluate the biological relevance of these structural alterations, cell viability assays (MTT) were performed using human umbilical vein endothelial cells. Non-irradiated PFOS induced marked cytotoxicity at 100 and 50 μg/mL (p < 0.0001), whereas neutron-irradiated PFOS no longer exhibited significant toxicity, with cell viability comparable to the control. These findings indicate a matrix-dependent response: neutron scattering in solids yields negligible molecular breakdown, whereas radiolysis-driven pathways in water facilitate measurable PFOS transformation. The cytotoxicity assay demonstrates that neutron irradiation promotes sufficient molecular degradation of PFOS in aqueous media to suppress its cytotoxic effects. Although complete mineralization was not achieved under the tested conditions, the combined spectroscopic and biological evidence supports neutron-induced radiolysis as a promising pathway for perfluoroalkyl detoxification. Future optimization of neutron flux, irradiation duration, and synergistic catalytic systems may enhance mineralization efficiency. Because PFOS concentration, fluoride release (F⁻), and TOC were not quantified in this study, remediation was assessed through spectroscopic fingerprints of transformation and the suppression of cytotoxicity, rather than by mass-balance mineralization metrics. This study highlights neutron irradiation as a promising strategy for perfluoroalkyl destruction in contaminated water sources. Full article

The growing use of radiation technologies has increased the need for shielding materials that are lightweight, safe, and adaptable to complex geometries. While lead remains highly effective, its toxicity and weight limit its suitability, driving interest in alternative materials. The process of 3D printing enables the rapid fabrication of customized shielding geometries; however, only limited research has focused on 3D-printed polymer composites formulated specifically for mixed photon–neutron fields. In this study, we developed a series of 3D-printable ABS-based composites incorporating tungsten (W), bismuth oxide (Bi₂O₃), gadolinium oxide (Gd₂O₃), and boron nitride (BN). Composite filaments were produced using a controlled extrusion process, and all materials were 3D printed under identical conditions to enable consistent comparison across formulations. Photon attenuation at 120 kVp and neutron attenuation using a broad-spectrum Pu–Be source (activity 4.5 × 10⁷ n/s), providing a mixed neutron field with a central flux of ~7 × 10⁴ n·cm⁻²·s⁻¹ (predominantly thermal with epithermal and fast components), were evaluated for both individual composite samples and layered (sandwich) configurations. Among single-material prints, the 30 wt% Bi₂O₃ composite achieved a mass attenuation coefficient of 2.30 cm²/g, approximately 68% of that of lead. Layered structures combining high-Z and neutron-absorbing fillers further improved performance, achieving up to ~95% attenuation of diagnostic X-rays and ~40% attenuation of neutrons. The developed materials provided a promising balance between 3D-printability and dual-field shielding effectiveness, highlighting their potential as lightweight, lead-free shielding components for diverse applications. Full article

Boron Neutron Capture Therapy (BNCT) is a radiotherapeutic modality which couples selective pharmacological delivery of ¹⁰B with irradiation by low-energy neutrons to achieve highly localized tumor cell killing. The BNCT therapeutic approach is undergoing rapid evolution driven primarily by advances in compact accelerator-driven neutron-source and associated facility-level nuclear infrastructure. This review examines the key physical and radiobiological principles of BNCT, with emphasis on the current engineering and operational aspects, such as neutron production and moderation, spectral shaping, beam optimization and dosimetric quantification, that critically influence clinical translation. Recent progress in ¹⁰B production and enrichment, as well as in strategies for efficient ¹⁰B delivery, is also briefly addressed. By tracing the pathway from neutron source to clinical target, this review defines the state of the art in BNCT technology, identifies the main physical and infrastructural challenges, and delineates the multidisciplinary advances needed to support widespread clinical implementation of next-generation BNCT systems. Full article

At a spallation neutron source, neutron pulses of varying energies are generated, and the detection of neutrons by instrument detectors is recorded as time-of-flight from the emission of the neutron pulse to its arrival at specific detector pixels with high time resolution. The flight path of neutrons from the moderator to the sample and then to the detector must be precisely calibrated at the detector-pixel level using standard powders, so the neutron events from all pixels can be time-focused to produce high-resolution diffraction patterns. Modern time-of-flight neutron diffractometers at spallation neutron sources are equipped with two-dimensional detectors with millimeter-scale pixelations. The number of pixels in a diffraction instrument can reach millions, which makes a single-pixel-level calibration process time-consuming or even impossible with conventional refinement or fitting approaches. Here we present a machine-learning-aided calibration process using a train-and-predict approach, in which machine learning models are trained on the relationship between an individual pixel time-of-flight diffraction pattern and its diffraction constant. These models use a portion of the available pixels for training, and a good model then predicts the diffraction constants precisely and rapidly for large sets of pixel diffraction patterns. Full article

Compared to conventional solid-fueled reactors, the liquid fuel transport in molten salt reactors (MSRs) leads to a strong coupling between thermal-hydraulics and neutronics. To enable system-level analysis of MSR, this study focuses on the main loop of the Molten Salt Reactor Experiment (MSRE). A system model is developed using the open-source, multiphysics modeling platform Modelica/TRANSFORM. The model is validated against ORNL experimental data under various conditions, including zero-power pump start/stop, natural circulation. In addition, the xenon transport behavior is compared with predictions from a two-region analytical model. Results indicate that the number of discretized core nodes significantly influences the estimation of delayed neutron precursor (DNP) losses due to fuel circulation. The applicability of the ANSI/ANS-5.1 decay heat model, originally developed for light water reactors, is confirmed to be conservative when applied to MSRE conditions. Finally, natural circulation behavior with decay heat transport is further analyzed. Full article

This review highlights the significance of modern quantum-beam techniques, particularly neutron and synchrotron radiation sources, for advanced microstructural characterization of metallic systems. Following a brief introduction to neutron and synchrotron diffraction, selected studies demonstrate their application in probing thermally induced structural evolution in plastically deformed metals. Additively manufactured CoCrFeNi alloys and 316L stainless steels subjected to high-pressure torsion (HPT) were investigated by in situ neutron diffraction during heating, revealing the sequential regimes of recovery, recrystallization, and grain growth. Coupled with mechanical measurements, the results show that HPT followed by controlled thermal treatment improves the mechanical performance, offering strategies for designing engineering materials with enhanced properties. The thermal anisotropy behavior of Ti-45Al-7.5Nb alloys under in situ neutron diffraction is defined as anisotropic ordering upon heating, while the HPT-processed alloy displayed isotropic recovery of order at earlier temperatures. Complementary in situ synchrotron studies in rolled-sheet magnesium alloys unveiled microstructural rearrangement, grain rotation, recovery, and precipitate dissolution during annealing. And phase transformation, recovery, and recrystallization processes were detected in steel using HEXRD. This work emphasizes the complementary strengths of the neutron and synchrotron methods and recommends their broader application as powerful tools to unravel microstructure–property relationships in plastically deformed metals. Full article

We present the concept of an ultracold neutron (UCN) source with a superfluid He-4 (SF ⁴He) converter located in the thermal column of the WWR-K research reactor at the Institute of Nuclear Physics (INP) in Almaty, Kazakhstan. The conceptual design is based on the proposal of accumulating UCNs in the source and effectively transporting them to experimental setups. We propose to improve the UCN density in the source by separating the heat and UCN transport from the production volume and decreasing the temperature of the SF ⁴He converter to below about 1 K. To obtain operation temperatures below 1 K, we plan to use a He-3 pumping cryogenic system and minimize the thermal load on the UCN accumulation trap walls. Additional gain in the total number of accumulated UCNs can be achieved through the use of a material with a high critical velocity for the walls of the accumulation trap. The implementation of such a design critically depends on the availability of materials with specific UCN and cryogenic properties. This paper describes the conceptual design of the source, discusses its implementation methods and material requirements, and plans for material testing studies. Full article

OpenStack is a popular open-source cloud platform that orchestrates virtualized compute, storage, and networking resources. In such virtualized environments, packet traceability refers to the ability to track the path and transformations of network packets as they traverse virtual switches, routers, and interfaces. This paper presents a comprehensive overview of packet traceability in OpenStack cloud environments. We provide an introduction to the OpenStack architecture with a focus on the Networking component (Neutron) and discuss how packets flow through virtual networking elements. We examine the routing and interface mechanisms that enable communication within and across nodes, and compare single-node versus multi-node OpenStack deployments from a packet tracing perspective. Furthermore, we survey tools and techniques for packet tracing (such as monitoring interfaces, using tcpdump, and Open vSwitch tracing), and highlight the challenges faced (multi-tenancy, overlay networks, etc.) in tracing packets. We offer recommendations for improving traceability, including leveraging built-in OpenStack features and advanced kernel-level tracing technologies. Our goal is to aid cloud administrators and researchers in understanding and improving network packet observability in OpenStack clouds, thereby enhancing troubleshooting and security analysis capabilities. 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

Neutron Resonance Transmission Analysis (NRTA) is a non-destructive technique allowing the elemental and isotopic characterization of materials and objects. This study represents a first step toward understanding the NRTA technique and developing a novel compact system adapted for industrial applications. The industrial feasibility of the NRTA was assessed by simulating a compact system using the Monte Carlo code MCNP 6.1. Neutron transmission spectra were generated for various metallic samples, ranging from 0.1 mm to 1 cm in thickness, and analyzed using a home-developed quantification method that incorporates nuclear cross sections from the ENDF/B-VIII.0 library and accounts for instrumental resolution. For this first study, an idealized configuration was considered, with a 0 µs pulsed neutron source and a Gaussian resolution function, to validate the methodology under a simple controlled condition. The results demonstrate that the areal densities of isotopes of Uranium and Plutonium can be determined with relative deviations below 10%, even under compact measurement conditions. This study validates the characterization method and represents a first step toward the continued development of an industrial NRTA prototype for rapid, non-destructive isotopic control of nuclear materials. Full article