Accurate prediction of core-scale conjugate heat transfer remains challenging due to the prohibitive computational cost of fully resolved three-dimensional simulations, particularly in complex systems with densely distributed coolant channels. To address this limitation, this study develops a hybrid coarse-fine mesh CFD framework for full-core CHT analysis within a single three-dimensional solver. The proposed approach employs a coarse-mesh representation of the coolant channels while retaining a fine-resolution three-dimensional model for the solid structures. Coolant channels are axially segmented to extract segment-averaged bulk flow quantities, which are used to construct convective heat-transfer boundary conditions on the solid surfaces. To ensure strict energy conservation and avoid artificial heat localization caused by under-resolved near-wall transport, the solid-to-fluid heat exchange is introduced into the fluid energy equation as a segment-consistent volumetric source term. The effective heat-transfer coefficients and viscosity correction factors are obtained through calibration against fine-mesh CFD simulations or experimental data for the same channel geometry. The methodology is validated against a single heated-tube benchmark, experimental data from the High Temperature Engineering Test Reactor (HTTR), and numerical reference results for the High Temperature Test Facility (HTTF). The results demonstrate that the proposed framework predicts coolant bulk temperature, wall temperature, and pressure drop with deviations generally within +/- 5%, while the maximum deviation in axially averaged coolant temperature remains below 0.5%. The hybrid framework significantly reduces the coolant-side degrees of freedom while preserving key safety-relevant thermal features, providing a practical and computationally efficient tool for core-scale thermal-hydraulic analysis and design optimization of prismatic HTGRs.

The U-Mo/Al dispersion fuel has emerged as a highly promising candidate for high-density Low Enriched Uranium (LEU) fuel in high-performance research reactors. Understanding the irradiation behavior of U-Mo/Al dispersion fuel plates is crucial for ensuring the safety and performance of these reactors. In this study, we propose a homogenization-based approach to analyze the irradiation performance of U-Mo/Al dispersion fuel plates. At the mesoscopic scale, the Representative Volume Element (RVE) approach, combined with the Finite Element Method (FEM), is employed to determine the equivalent thermomechanical properties of the dispersion fuel meat. To account for the significant influence of the U-Mo/Al interaction layer (IL), the empirical model of the growth of this layer under irradiation is seamlessly integrated into the RVE model. Subsequently, based on the obtained equivalent thermomechanical property models, irradiation performance analysis of U-Mo/Al fuel plate at the macroscopic scale is conducted. Both the meso- and macroscale models have been validated against publicly available experimental data. By informing macroscopic performance analysis with mesoscopic homogenization, this study provides a reliable and efficient tool for optimizing the design and enhancing the performance of advanced dispersion fuels in research reactor applications.

With increasing heat dissipation demands in advanced thermal management systems, enhancing flow boiling efficiency in microchannel has become crucial. This study systematically investigates microchannel flow boiling enhancement using copper nanowires (Cu NWs) coated surfaces. Flow visualization reveals that the Cu NWs coating maintains a stable annular flow with a persistent liquid film, resulting in wall temperature fluctuations within +0.45 degrees C, whereas the plain-wall microchannel exhibits unstable vapor-liquid interfaces and dry patches, leading to temperature oscillations exceeding 2 degrees C. Heat transfer measurements demonstrate a 26.91% improvement in heat transfer coefficient and a 19.99%-26.17% increase in critical heat flux, with a 5 degrees C wall superheat delay compared to plain-wall microchannel. Pressure drop analysis shows regular oscillations (0.6-1.2 kPa, 2-4 s) in the Cu NWs coated microchannel, contrasting with irregular fluctuations (0.3-0.8 kPa, 4-8 s) in the plain-wall microchannel. A modified Friedel-based correlation is developed and predicts the two-phase pressure drop well, with all data within +20% error. The nanostructure enhances boiling via three capillary-dominated mechanisms: increased nucleation site density, capillary-driven liquid replenishment and interfacial stabilization, which reduces axial velocity components and weakens destabilizing forces. The Cu NWs coating thus provides an effective and scalable solution for high heat flux cooling applications.

The critical heat flux (CHF) represents the thermal safety limit in two-phase systems. Exceeding this threshold can lead to a boiling crisis. Despite advancements in mechanistic understanding, the intricate nature of bubble dynamics continues to impede the development of reliable and robust CHF prediction tools. Recent progress in machine learning has shown its potential as a powerful tool for modeling nonlinear phenomena. This study presents a transfer learning framework that encodes knowledge from a prior model directly into the parameter space of a neural network. Diverging from conventional hybrid frameworks which learn the deviation from a prior model's predictions, our method establishes a pretrained initial state, which is then fine-tuned with application specific data. The proposed framework was rigorously benchmarked against a hybrid framework and a standard multilayer perceptron (MLP) on a consolidated database of 1637 tube-flow CHF data points from six independent sources and an additional 768 proprietary measurements from 5 x 5 fuel assemblies. Our framework demonstrated accelerated convergence, accurate interpolation, and robust pressure-extrapolation. Interpretability analysis indicates that the enhanced generalization is attributable to the pretrained weights preserving physically meaningful patterns, thereby establishing a robust initial foundation for learning. Furthermore, window-type extrapolation mapping identifies the optimal experimental domains for future data acquisition. This work proposes a paradigm shift from deviation-correction to a pretraining framework, establishing a foundational and generalizable tool for next-generation digital twins in thermal-hydraulic safety analysis.

The efficacy of doxorubicin (DOX) in treating various cancers is hindered by its associated cardiotoxicity, limiting its clinical utility. Mitophagy dysfunction and impaired mitochondrial function may be significantly involved in the development of DOX-induced cardiotoxicity (DIC). Tax1 (T-cell leukemia virus type I)-binding protein 1 (TAX1BP1) is an autophagic receptor; however, its role in DIC remains elusive. Therefore, the objective of this study was to assess the influence of TAX1BP1 overexpression on DIC and its molecular mechanisms, focusing specifically on mPTP opening and mitophagy in cardiomyocytes. Male C57BL/6J mice were intravenously administered recombinant adeno-associated virus serotype-9 (AAV9) carrying Flag-TAX1BP1 and cardiac troponin T (cTNT) promoter (AAV9-cTNT-TAX1BP1). Mice were randomly subjected to a DOX challenge with weekly administration of 4 mg/kg body weight for 5 weeks. TAX1BP1 levels were notably decreased in the cardiac tissues of DIC mice. Cardiac TAX1BP1 overexpression alleviated cardiac remodeling and dysfunction in mice with DIC, with little effect from TAX1BP1 overexpression alone. TAX1BP1 overexpression improved mitochondrial dysfunction and promoted mitophagy in DIC cardiomyocytes. Furthermore, TAX1BP1 interacted with mTOR through its SKICH domain and facilitated the assembly of the mTORC2 complex, thereby initiating SGK1 phosphorylation, limiting the aggregation of VDAC1 and preventing the opening of the mitochondrial permeability transition pore (mPTP) in the cardiomyocytes facing DOX challenge. Collectively, our study highlights the significant role of TAX1BP1 in preventing mPTP opening and enhancing mitophagy, which contributes to the mitigation of mitochondrial and cardiac abnormalities associated with DIC. Our findings highlighted the potential therapeutic benefits of targeting TAX1BP1 in patients with DIC.

OBJECTIVE:Histone deacetylase 1 (HDAC1) exacerbates ventricular remodeling and heart failure by promoting myocardial peroxidative damage. Thus, this study aimed to investigate whether the HDAC1 inhibitor mocetinostat alleviates pathological cardiac hypertrophy via suppressing ferroptosis and to elucidate the potential mechanisms involving the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. METHODS:Primary cardiomyocytes were stimulated with phenylephrine (PE) to induce hypertrophy and ferroptosis in vitro, with or without mocetinostat treatment. The Nrf2 inhibitor ML385 was used to verify pathway specificity. In vivo, a mouse model of pressure-overload-induced cardiac hypertrophy was established using a transverse aortic constriction (TAC)-induced approach. Mocetinostat was administered to evaluate its therapeutic effects. Ferroptosis markers including lipid peroxidation, iron accumulation, and levels of ferroptosis-related proteins, were assessed. The acetylation and nuclear translocation of Nrf2, as well as the expression of the associated downstream targets (Solute carrier family 7 member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), ferroportin, ferritin heavy chain 1 (FTH1), heme oxygenase-1 (HO-1)), were analyzed. RESULTS:Mocetinostat treatment significantly ameliorated PE-induced cardiomyocyte hypertrophy and ferroptosis in vitro and attenuated TAC-induced cardiac hypertrophy and fibrosis in vivo. Mechanistically, mocetinostat facilitated Nrf2 acetylation and promoted Nrf2 nuclear translocation, leading to the transcriptional activation of downstream antioxidant targets and subsequent inhibition of lipid peroxidation. The protective effects of mocetinostat were abrogated by the Nrf2 inhibitor ML385. CONCLUSION:This study demonstrates that mocetinostat attenuates pathological cardiac hypertrophy by inhibiting ferroptosis through activation of the Nrf2 pathway . These findings indicate the potential of mocetinostat as a therapeutic strategy for delaying heart failure progression.

In pressurized water reactor fuel assemblies, mixing vanes are essential to promote coolant crossflow and mitigate thermal hotspots. To efficiently optimize their complex geometry, this research proposes a novel design framework integrating a surrogate-assisted evolutionary algorithm (SAEA) with a swarm-intelligence neural network (SI-NN). A customized toolchain developed via Fluent secondary scripting automates parameterized geometric updates, meshing, and numerical solving, substantially accelerating training dataset generation. The Grasshopper Optimization Algorithm (GOA) then globally optimizes the Backpropagation Neural Network (BPNN) hyperparameters. Evaluations on the test set show that the introduction of GOA keeps the pressure drop prediction stable while increasing the coefficient of determination (R2) of the vortex mixing rate prediction model by 1.03%. By using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize a 3×3 subchannel model, a new mixing-vane structure is obtained. Simulations demonstrate the new design significantly enhances mixing, particularly in the mid-to-near-field regions, boosting vortex mixing rates at the 4Dh, 7Dh, and 10Dh planes by 21.2%, 22.8%, and 17.0%, respectively, while limiting the overall pressure drop increment to 2.03%. Further scalability validation in a 5×5 layout confirms the optimized structure improves the downstream average vortex-induced mixing rate (1Dh-10Dh planes) by 14.7% (from 0.1660 to 0.1904), with the pressure drop increasing slightly from 4320.7 Pa to 4393.5 Pa, introducing a marginal 1.66% pressure penalty.

Thermal management technology is an indispensable key technology in the development of modern highprecision electronic devices, aiming to control the temperature of target objects within an allowable range. In nuclear reactors, characterized by high power density and long operating duration, represent a sustainable and clean energy source, favored by countries around the world. During normal operation, it is necessary to remove heat from the reactor core and heat-generating equipment to ensure the core operates safely and stably; in accident scenarios, the core of accident response failure in removing residual heat from the core to prevent temperatures from exceeding safety limits and causing more severe consequences. As the most complex system in a reactor, researching efficient thermal management systems is a necessary condition for ensuring the safety and efficient operation of DCS(Distributed control system) systems. DCS systems require the development of efficient thermal management systems to ensure their safe and efficient operation. The success or failure of thermal management in DCS systems directly impacts their performance, reliability, environmental comfort, and energy efficiency. In the industrial sector, thermal management technology has evolved from traditional air-cooling to more efficient cooling methods such as liquid cooling and phase-change cooling, significantly improving thermal management efficiency while enabling miniaturization and compactness of thermal management systems. Currently, DCS systems typically employ air-cooling for heat dissipation, resulting in large system sizes, high operational noise, and poor temperature control performance. With the increasing demands on DCS systems for miniaturization, modularization, digitization, and intelligence in nuclear power, coupled with the significant improvement in chip performance leading to a substantial increase in heat flux density, the air-cooling method with its low efficiency has become inadequate to meet the higher heat dissipation requirements of DCS systems. This paper conducts research on the application of thermal management technology in DCS thermal control design. A modular DCS board module model is established, and both air-cooling and liquid loop cooling methods are modeled, calculated, analyzed, and compared. Sensitivity analyses are also performed to provide references for the next step in designing efficient thermal management systems for DCS systems.

As a most common structure in nuclear reactor core, rod bundle fuel assemblies are widely adopted. With the continuous development of ocean nuclear power platforms, the influence of the marine environment on the transient characteristics of thermal and hydraulic performance in rod bundle should be considered in detail. In this study, the multi-scale coupling method between RELAP5 and Fluent is adopted to obtain both the systematic behavior and the detailed local phenomena for the 3 x 3 rod bundles in the natural circulation loop with asymmetrical inclination. The coupling method is validated against the experimental results of subcooled boiling and natural circulation under inclination. Then, the single-phase and two-phase thermal-hydraulic characteristics of 3 x 3 rod bundles are investigated under asymmetrical inclination of the natural circulation loop. The results show that under single-phase operation the increase of inclination angle makes the gravitational driving force and the system flow rate in the loop decrease accordingly. Due to the asymmetrical inclination, the system flow rate during forward inclination is smaller than that during reverse inclination. In the rod bundle, the inclination leads to the flow mixing enhancement of low-temperature fluid at the positive side of inclination. Under two-phase operation, the loop characteristics of natural circulation under inclined condition are similar to those under single-phase condition with more drastic change. In the rod bundle, the secondary flow on the crosssection is intensified by the increase of inclination angle. The vapor phase migrates and aggregates towards the wall on the opposite side of the inclination, which may lead to heat transfer deterioration in the reactor core.

High-performance fuels are essential for raising reactor power density, safety margins and economic efficiency. While cylindrical fuel rods are prevalent due to their structural simplicity, irregularly shaped fuels can offer superior thermal-hydraulic performance but present manufacturing and analytical challenges. Advances in additive manufacturing (AM) enable practical implementation of these complex geometries, yet conventional analytical methods remain inadequate. In this work, a neutronics (N) and thermal-hydraulics (T-H) coupling analysis framework is developed for irregularly shaped fuels by integrating the CAD-based Monte Carlo neutron-transport code RMC with the computational fluid dynamics (CFD) solver ANSYS Fluent. A 7-pin hexagonal arranged assembly of helical cruciform fuel (HCF) is analyzed with this method. Compared with the uncoupled case, the coupled N/T-H analysis leads to a decrease in keff (-0.10349) and power peaking factor (-0.152), along with an increase in peak fuel temperature (+40 K) and peak wall temperature (+14 K). The results indicate the inherent safety feedback, more uniform power distribution, and the necessity for conservative thermal design. The traditional cylindrical and cruciform (non-helical) fuels are also analyzed with the coupling method. The results show that the helical cruciform geometry leads to a decrease in reactivity by 768 pcm and an increase in pressure drop by 11%, but it can improve thermal performance with a decrease in peak fuel temperature by 11 K. These results demonstrate both the analytical capability of the proposed methodology and the thermal-hydraulic advantages and trade-offs of helical cruciform fuel geometry.

Electrocatalytic reduction of nitrates plays a crucial role in ammonia (NH3) production. In this study, a novel cuprous oxide/graphdiyne (Cu2O/GDY) electrocatalyst was synthesized by growing Cu2O/GDY on a Cu substrate with a porous architecture capable of increasing the number of active sites and enhancing mass transfer ability. The sp-C-Cu bonds between Cu2O and GDY facilitate rapid charge transfer and promote direct electron transport from active sites to reaction intermediates. Consequently, the electrocatalyst exhibits high NH3 production performance with a yield rate (YNH3) of 652.82 mu mol h-1 cm- 2 and Faradaic efficiency of 82.98% at -1.8 V (vs. SCE) under ambient conditions in an aqueous solution. This work introduces a novel and efficient approach for the in situ fabrication of self-supported heterostructures, thereby enabling high-performance ammonia production under ambient conditions.

Optimizing coolant channel designs in fuel elements is essential for ensuring safe and efficient operation. However, existing optimization approaches primarily focused on thermal performance, ignoring the impact of thermo-mechanical coupling on fuel element integrity. To address this limitation, a thermo-mechanical coupling optimization method is introduced in this study to simultaneously achieving lower peak temperature and thermal stress. The coolant channel shape is parameterized using a Non-Uniform Rational B-Spline (NURBS) curve, and response surface methodology (RSM) is employed to construct efficient surrogate models for the objective and constraint functions, guiding an iterative optimization process. Applied to a gas-cooled microreactor fuel element, the key achievement is an optimized snowflake-like coolant channel geometry that demonstrably reduces peak fuel temperature by 1082.1 K and maximum Von Mises stress by 35.1 MPa, compared to a conventional circular channel. These results show that the proposed method can be utilized effectively for enhancing microreactor performance and safety.

The electrocatalytic nitrate reduction reaction (NitRR) is a promising dual-functional strategy for carbon-free ammonia synthesis and sustainable wastewater treatment. The complexity of the eight-electron/nine-proton transfer process in the NitRR highlights the need for improved catalysts to optimize reaction pathways and suppress competitive side reactions. Herein, the successful growth of CuCo2Ox nanowires with tailored defect structures is reported with the assistance of graphdiyne (GDY) through an atomic-level heterointerface engineering strategy. The synergistic interactions between GDY electron-rich sp-C atoms and electron-deficient bimetallic atoms induce self-optimized planar defects along nanowires and accelerate interfacial charge transfer via metal‒carbon covalent hybridization. These properties significantly facilitate dynamic NO3 - adsorption-activation and completely suppress byproduct formation via intermediate stabilization. As a result, CuCo2Ox/GDY exhibites a remarkable NitRR performance of 100% Faradaic efficiency (FE), a record-high NH3 yield rate (YNH3, 3332 µg cm-2 h-1) at an ultralow operational potential (-0.132 V vs RHE), no side reactions, and long-term durability. This work pioneers atomic-level interface engineering in GDY-based systems, establishing a general method for synthesizing high-performance electrocatalysts in sustainable nitrogen cycles.

Fully Ceramic Microencapsulated (FCM) fuel, fabricated using 3D printing technology, incorporates traditional Tristructural-Isotropic (TRISO) fuel particles embedded within a 3D-printed SiC matrix. This fuel type shows great potential for compact, high-temperature nuclear reactors. However, comprehensive performance analysis remains challenging due to the multilayered structure, complex material properties, and the irradiation behavior of numerous TRISO particles within the matrix. To address these challenges, this study aims to apply finite element (FE)-based computational homogenization to evaluate the fuel's thermomechanical properties. First, a detailed finite element analysis (FEA) of a single TRISO particle's performance was conducted, focusing primarily on the Buffer-IPyC gap size change during irradiation, a critical factor influencing the fuel's thermal performance. Second, the homogenization of a single TRISO particle was performed using FEA, accounting for the changes in the Buffer-IPyC gap size. Finally, homogenized TRISO particles were randomly distributed and perfectly bonded within a 3D-printed SiC matrix to form a Representative Volume Element (RVE). The FE homogenization of the RVE was then conducted to derive the effective thermomechanical properties of the FCM fuel. The packing fraction of TRISO particles in the matrix ranged from 10 % to 50 %, with temperature and burnup conditions spanning 800-1600 K and 0-16 % FIMA, respectively. Results show that irradiation significantly affects the effective properties of the fuel, though this impact diminishes as burnup increases. Additionally, the effective thermal conductivity of the FCM fuel decreases with increasing TRISO packing fraction, assuming the thermal conductivity of the SiC matrix exceeds that of TRISO particles. This study provides a valuable reference for the design and optimization of FCM fuel for future nuclear applications.

The application of mini-channels provides many practical benefits in several industries. Surface modification by chromium coating has the advantage of easy production and corrosion resistance to enhance heat transfer. Based on the method of electroplating and anodizing, the chromium coating layer on stainless steel surface is prepared with lower contact angle and higher number of activated vaporization cores. A comparative experimental study is conducted in mini-channels for stainless steel surfaces and anodized-chromium surfaces to investigate the subcooled flow boiling behavior. Based on the visual image and measurement analysis, the bubble behavior and heat transfer characteristics with increasing heat flux are investigated. The onset of initial boiling and reverse flow from anodized-chromium surface are earlier than that from stainless steel surface. The average and the maximum heat transfer coefficient are increased by 11.8% and 14.1% on anodized-chromium surface respectively.

Helical cruciform fuels are novel in nuclear reactors, potential to increase reactor's power density. However, the geometry is complicated so influence of it on the fuel performance is not identified yet. To evaluate flow and heat transfer performance of the fuel, thermal and mechanical characteristics of fuel used in Fluoride-Salt-cooled high-Temperature Advanced Reactor are analyzed. Impact of power density, cross-section parameters, and twist pitch on the fuel is discussed separately based on fluid-thermal-mechanical coupling. In general, twist pitch is vital to helical cruciform fuel while others have few effects considering thermal-mechanical features. For thermal features, increase in twist pitch leads to temperature rise owing to weaker mixing effects. Fuel center temperature at middle plane of 300 mm-pitch rod is 829.58 degrees C, 54.66 degrees C higher than that of 100 mm-pitch rod. Besides, axial temperature of cladding outer surface increases wavelike due to complex geometry. For mechanical features, not geometry sizes but temperature affects stress distribution. Maximum Von-Mises stress appears at the elbow, where maximum temperature exists, 116.8 MPa under normal conditions, lower than tensile strength of the material. After identifying the influence of these factors, five dimensionless parameters are proposed to evaluate and rate fuel performance based on Technique for Order Preference by Similarity to an Ideal Solution. As a result, an optimization comes up, scoring 0.310, twice more than the original design, owing to the thermal uniformity and mechanical safety. This study provides a reference for identifying the performance of helical structure and a new fuel design in Fluoride-Salt-cooled high-Temperature Reactor.

Financial statement frauds by listed firms pose significant challenges to public investors and jeopardize the stability of financial markets. Previous studies have identified deceptive verbal and vocal cues from earnings conference calls as indicators of financial statement fraud. However, these studies only extracted managers' verbal and vocal cues separately over the entire call, neglecting the utterance-level fusion between verbal and vocal cues as well as the multi-turn interaction between analysts and managers. To fill this gap, we develop a novel end-to-end contrastive multimodal dialogue network (CMMD) that considers both verbal-vocal fusion and multi-role interactions to uncover hidden deceptive cues in earnings conference calls. The proposed model comprises two core modules, namely, the Multimodal Fusion Learning module and the Dialogue Interaction Learning module. Building on Vrij's verbal-nonverbal complementary mechanisms in deception detection, the designed Multimodal Fusion Learning employs contrastive learning to align verbal and vocal cues and a co-attention mechanism to learn cross-modal interaction. Inspired by the Interpersonal Deception Theory that emphasizes the dynamic interaction process between deceivers and targets, the Dialogue Interaction Learning utilizes a dialogue-aware co-attention mechanism to model multi-turn analyst-manager interaction and uses contrastive learning to improve dialogue representations. Our extensive empirical results show that CMMD achieves 8.64 % improvement in detecting fraudulent cases compared to the best baseline model. As such, our study advances the research frontier in fraud detection and contributes an innovative IT artifact in practice.

Digital Twin(DT) technology offers a transformative pathway to mitigate the fundamental trade-off in nuclear energy between uncompromising safety and economic competitiveness. This review synthesizes the state of the field, revealing a critical insight: despite progress, implementations of nuclear DTs globally remain nascent, predominantly confined to low-to-mid maturity levels. This developmental lag stems from unique, sector-specific challenges: severe data scarcity due to extreme in-vessel conditions, an irreconcilable trade-off between highfidelity multi-physics model accuracy and real-time computational demands, and the "black-box" nature of data-driven artificial intelligence conflicting with nuclear safety demand for interpretability and verifiable trust. A comparative analysis against aerospace, power grid, maritime, and healthcare sectors confirms that nuclear applications face exceptionally stringent regulatory requirements and uniquely high technical barriers. To overcome these hurdles, this work establishes a comprehensive DT application framework and introduces a novel five-tier maturity hierarchy for nuclear reactors. This model provides a standardized, actionable roadmap for technological evolution-from basic simulation guidance to fully symbiotic autonomy-thereby positioning the DT as the indispensable engine for the future of safe, efficient, and intelligent nuclear power.