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The growing concerns over nuclear power plant safety in the wake of extreme impact events have highlighted the need for containment structures with superior resistance to large commercial aircraft strikes. Conventional reinforced concrete containment has shown limitations in withstanding high-mass and high-velocity impacts, posing potential risks to structural integrity and operational safety. Addressing this challenge, this study focuses on the dynamic impact resistance and vibration behavior of steel–ultra-high-performance concrete (S-UHPC) composite containment, aiming to enhance nuclear facility resilience under beyond-design-basis aircraft impact scenarios. Validated finite element models in LS-DYNA were developed to simulate impacts from four representative large commercial aircraft types, considering variations in wall and steel plate thicknesses, UHPC grades, and soil–structure interaction conditions. Unlike existing studies that often focus on isolated parameters, this work conducts a systematic parametric analysis integrating multiple aircraft types, structural configurations, and foundation conditions, providing comprehensive insights into both global deformation and high-frequency vibration behavior. Comparative analyses with conventional reinforced concrete containment were performed, and floor response spectra were evaluated to quantify high-frequency vibration characteristics under different site conditions. The results show that S-UHPC containment reduces peak displacement by up to ~24% compared to reinforced concrete of the same thickness while effectively localizing core damage without through-thickness failure. In addition, aircraft impacts predominantly excite 90–125 Hz vibrations, with soft soil conditions amplifying acceleration responses by more than four times, underscoring the necessity of site-specific dynamic analysis in nuclear containment and equipment design. Full article

This study presents long-term observations of tritium (³H) concentrations in the Neris River at monitoring sites located near the Belarus–Lithuania border and in the city of Vilnius. Since the commissioning of the Belarusian Nuclear Power Plant (BelNPP), ³H levels in the river have consistently exceeded natural background values, with pronounced temporal variations. These fluctuations are attributed to routine ³H releases from the BelNPP, with increased concentrations observed during scheduled maintenance periods. A ³H transport model was developed to estimate the downstream propagation of releases and to assess the time lag between upstream discharge events and their detection at downstream locations. The model reliably simulates ³H behavior in flowing water and can be adapted to future scenarios and other water-soluble radionuclides, provided that isotope-specific and hydrological data are available. These findings highlight the importance of continued monitoring and further research on the fate and transport of radioactive substances in transboundary river systems. Full article

The rapid increase in global warming requires that sustainable energy choices aimed at achieving net-zero greenhouse gas emissions be implemented as soon as possible. This objective, emerging from the European Green Deal and the UN Climate Action, could be achieved by using clean and efficient energy sources such as hydrogen produced from nuclear power. “Renewable” hydrogen plays a fundamental role in decarbonizing both the energy-intensive industrial and transport sectors while addressing the global increase in energy consumption. In recent years, several strategies for hydrogen production have been proposed; however, nuclear energy seems to be the most promising for applications that could go beyond the sole production of electricity. In particular, nuclear advanced reactors that operate at very high temperatures (VHTR) and are characterized by coolant outlet temperatures ranging between 550 and 1000 °C seem the most suitable for this purpose. This paper describes the potential use of nuclear energy in coordinated and coupled configurations to support clean hydrogen production. Operating conditions, energy requirements, and thermodynamic performance are described. Moreover, gaps that require additional technology and regulatory developments are outlined. The intermediate heat exchanger, which is the key component for the integration of nuclear hybrid energy systems, was studied by varying the thermal power to determine physical parameters needed for the feasibility study. The latter, consisting of the comparative cost evaluation of some nuclear hydrogen production methods, was carried out using the HEEP code developed by the IAEA. Preliminary results are presented and discussed. Full article

The potential for hydrogen to reshape energy systems has been recognized for over a century. Yet, as decarbonization priorities have sharpened in many regions, three distinct frontier areas are critical to consider: hydrogen produced from wind; hydrogen produced from nuclear power; and the development of natural hydrogen. These pathways reflect technology and policy changes, including a 54% increase in the globally installed wind capacity since 2020, plus new signs of potential emerging in nuclear energy and natural hydrogen. Broadly speaking, there are a considerable number of studies covering hydrogen production from electrolysis, yet none systematically examine wind- and nuclear-derived hydrogen, natural hydrogen, or the policies that enable their adoption in key countries. This article highlights international policy and technology developments, with a focus on prime movers: Germany, China, the US, and Russia. Full article

As severe selection and declining population numbers in many breeds have resulted in losses in the worldwide livestock genetic biodiversity, human concern about the situation of genetic variety in livestock breeds and their conservation has grown. In this context, genomic techniques now allow for more exact monitoring of adaptive traits and inbreeding, while reproductive techniques such as somatic cell nuclear transfer and IVF (In Vitro Fertilization) help to preserve and recover rare genetic lines. AI-powered (Artifficial Inteligence) risk assessment models and digital herdbooks contribute to data-driven reproductive strategies, particularly in smallholder settings. Nonetheless, these advances face persistent hurdles, including a lack of legislative frameworks, high costs, limited accessibility in low-resource settings, and unresolved ethical problems. The findings highlight the importance of a balanced, interdisciplinary strategy that combines new biotechnologies with traditional knowledge, collaborative practices, and strong policy to assist in preserving the long-term viability of livestock genetic resources. This review intends to assess modern and traditional methods for the preservation of livestock breeds, analyzing references published between 2019 and the present. Full article

The Akkuyu Nuclear Power Plant (NPP) is framed as a flagship of Türkiye’s national low-carbon transition. This study examines how domestic economic actors perceive the project’s socio-economic and environmental impacts, and how those perceptions align with—or diverge from—official assessments and the United Nations Sustainable Development Goals. Using a qualitative phenomenological approach, the research draws on 28 semi-structured interviews with members of the Silifke Chamber of Commerce and Industry Council. This lens captures how locally embedded businesses read the project’s risks and rewards in real time. Four themes stand out. First, respondents see a clear economic uptick—but one that feels time-bound and vulnerable to the project cycle. Second, many feel excluded from decision-making; as a result, their support remains conditional rather than open-ended. Third, participants describe environmental signals as ambiguous, paired with genuine ecological concern. Fourth, skepticism about governance intertwines with sovereignty anxieties, particularly around foreign ownership and control. Overall, while short-term economic benefits are widely acknowledged, support is tempered by procedural exclusion, environmental worry, and distrust of foreign control. Conceptually, the study contributes to energy-justice scholarship by elevating sovereignty as an additional dimension of justice and by highlighting the link between being shut out of processes and perceiving higher environmental risk. Policy implications follow directly: create robust, domestic communication channels; strengthen participatory governance so local actors have a real voice; and embed nuclear projects within regional development strategies so economic gains are durable and broadly shared. Full article

Nuclear power continues to be a great promise in the green revolution, as it is a cost-effective, low-emission, and safer alternative to fossil fuels that is capable of continuous operation. A preliminary design evaluation is presented for a submersible nuclear power station capable of operating under its own power during emergencies and routine maintenance. Because it is stationed at sea, it offers a resilient solution to natural disasters such as earthquakes and tsunamis, giving it the capability to disengage and sail to deeper waters in less than a half of an hour. In the present evaluation, the hull dimensions of a very large existing submarine and the turbomachinery layout of a Pebble Bed Modular Reactor cycle were used as baselines. The conceptual design of the submersible nuclear power station includes reactor and turbomachinery integration, preliminary sizing (4 pressure hull design; total length of 57.74 m), and propulsion system analysis, demonstrating the technical viability of the proposed submersible power station. Full article

This paper presents a hybrid diagnostic framework that integrates Knowledge Graphs (KGs) with Large Language Models (LLMs) to support fault diagnosis in complex, high-reliability systems such as nuclear power plants. The framework is based on the Dynamic Master Logic (DML) model, which organizes system functions, components, and dependencies into a hierarchical KG for logic-based reasoning. LLMs act as high-level facilitators by automating the extraction of DML logic from unstructured technical documentation, linking functional models with language-based reasoning, and interpreting user queries in natural language. For diagnostic queries, the LLM agent selects and invokes predefined tools that perform upward or downward propagation in the KG using DML logic, while explanatory queries retrieve and contextualize relevant KG segments to generate user-friendly interpretations. This ensures that reasoning remains transparent and grounded in the system structure. This approach reduces the manual effort needed to construct functional models and enables natural language queries to deliver diagnostic insights. In a case study on an auxiliary feedwater system used in the nuclear pressurized water reactors, the framework achieved over 90 percent accuracy in model element extraction and consistently interpreted both diagnostic and explanatory queries. The results validate the effectiveness of LLMs in automating model construction and delivering explainable AI-assisted health monitoring. Full article

Control valves are the main throttling resistance components in industries such as chemical engineering, nuclear power, aerospace, hydrogen energy, natural gas transportation, marine engineering, and energy systems. Flow-induced vibration fatigue failure is a common failure mode. To provide engineers and researchers with a reference for reliable design analysis of control valves and to predict and prevent potential failures, this article reviews and categorizes vibration-induced failure in control valves by integrating numerous engineering cases and research articles. The vibration failures of control valves are mainly divided into categories such as jet flow, vortex flow, cavitation, and acoustic cavity resonance. This paper reviews control valve vibration research from three aspects: theoretical models, numerical simulations, and experimental methods. It highlights the mechanisms by which internal unstable flow, jet flow, vortex shedding, cavitation, and acoustic resonance lead to vibration-induced fractures in valve components. Additionally, it examines the influence of valve geometry, component constraints, and damping on flow-induced valve failures and summarizes research on vibration and noise reduction in control valves. This paper aims to serve as a reference for the analysis of vibration-induced failures in control valves, helping identify failure mechanisms under different operating conditions and proposing effective solutions to enhance structural reliability and reduce the occurrence of vibration failures. Full article

This study assessed ten alternatives, comprising nine power generation technologies and Battery Energy Storage Systems (BESS), using a combined hybrid approach based on group Multi-Criteria Decision-Making (MCDM) methods. Specifically, AHP was employed for determining criteria weights, while fuzzy VIKOR was utilised for ranking the alternatives. Six electricity sector experts evaluated each technology, organised within a hierarchical decision model that included four main criteria: economic, environmental, technical, and social, along with 13 subcriteria. To mitigate subjectivity in criteria weights stemming from diverse expert backgrounds, a consensus technique was implemented post-AHP. Fuzzy VIKOR was employed to address uncertainty in expert ratings. The findings revealed a significant preference towards renewable technologies, with Photovoltaic (PV) and Wind at the forefront, whereas Coal occupied the lowest position. A validation process was conducted using BWM for criteria weights and fuzzy TOPSIS for ranking alternatives. This hybrid soft computing method’s key contributions include its modular design, allowing for the sequential determination of criteria weights, followed by the calculation of alternative rankings, fostering interactive and collaborative evaluations of various energy mixes by expert groups. Additionally, the study evaluated three emerging energy technologies: BESS, Small Modular Nuclear Reactors (SMRs), and Hydrogen, highlighting their potential in the evolving energy landscape. Full article

Using high-frequency financial data, this study investigates volatility spillovers between five renewable energy subsectors (wind, solar, geothermal, bioenergy, and fuel cells), five conventional energy markets (oil, gas, coal, uranium, and gasoline), and carbon emissions for five industrial sectors (power, industry, ground transportation, domestic aviation, and residential) based on a Diebold–Yilmaz VAR-based spillover framework. The results document that the industry and power sectors are the key players in the transmission effects of carbon shocks. In contrast, the reverse is true for the residential and aviation sectors. For renewable energy, fuel cells, and geothermal power, strong forward linkages appear to significantly reduce carbon emissions, while reverse linkages that increase carbon emissions in response to shocks in clean-energy and carbon-intensive industries are relatively high for coal and oil. We also find that the total volatility connectedness exceeds 84%, indicating significant systemic risk transmission. The clean-energy subsectors, particularly wind and solar, now compete in fossil-fuel markets during geopolitical crises. Applying the DCC-GARCH t-copula method to assess portfolio hedging strategies, we find that fuel cell and geothermal assets are the most effective in hedging against volatility in fossil-fuel prices. In contrast, nuclear and gas assets provide benefits from diversification. These results underscore the growing strategic importance of clean energy in mitigating sector-specific emission risks and fostering resilient energy systems in alignment with the United States’ net-zero carbon goals. Full article

Magnetic resonance techniques are powerful, nondestructive, non-invasive tools with broad applications in radiation dosimetry. Electron paramagnetic resonance (EPR) enables direct quantification of dose-dependent radiation-induced paramagnetic defects, while nuclear magnetic resonance (NMR) reflects the influence of such defects through changes in line width and nuclear spin relaxation. To date, these methods have typically been applied independently. Their combined use to probe radiation damage in the same material offers new opportunities for comprehensive characterization and preferred dosimetry techniques. In this work, we apply both EPR and NMR to investigate radiation damage in lithium carbonate (Li₂CO₃). A detailed EPR analysis of γ-irradiated samples shows that the concentration of paramagnetic defects increases with dose, following two distinct linear regimes: 10–100 Gy and 100–1000 Gy. A gradual decay of the EPR signal was observed over 40 days, even under cold storage. In contrast, ⁷Li NMR spectra and spin–lattice relaxation times in Li₂CO₃ exhibit negligible sensitivity to radiation doses up to 1000 Gy, while ¹H NMR results remain inconclusive. Possible mechanisms underlying these contrasting behaviors are discussed. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies. Full article

Timely detection of anomalies in nuclear power plants (NPPs) is essential for operational safety, especially under conditions where process signals deviate gradually or abruptly from nominal patterns. Traditional detection methods often struggle to adapt under transient conditions or in the absence of well-labeled fault data. To address this challenge, we propose KD-ADWIN, an adaptive concept drift-detection framework designed for unsupervised anomaly detection in dynamic industrial environments. The method integrates three core components: a Kalman-based prediction module to extract smoothed signal trends, a multi-channel detection strategy combining statistical and derivative-based drift indicators, and an adaptive thresholding mechanism that tunes detection sensitivity based on local signal variability. Evaluations on a synthetic dataset show that KD-ADWIN accurately detects both abrupt and gradual drifts, outperforming classical baselines. Further validation using full-scope simulation data from a modular high-temperature gas-cooled reactor (MHTGR) demonstrates its effectiveness in identifying concept drifts under realistic actuator and sensor fault conditions. Full article