Search Results (972)

Basonuclin 2 (BNC2) is a highly conserved cysteine–histidine (C2H2)-type zinc-finger nuclear regulatory protein characterized by three pairs of zinc-finger domains, a putative nuclear localization signal, a serine-rich region, broad tissue distribution, and remarkable transcript diversity generated through alternative promoter usage, alternative splicing, and polyadenylation. Increasing evidence from human genetics, animal models, functional genomics, and transcriptomic studies indicates that BNC2 links nuclear regulatory mechanisms to tissue-specific developmental and disease phenotypes. In the nervous system, BNC2-positive neuronal populations and BNC2-derived circular RNAs have been implicated in energy-balance circuits and neuroinflammatory regulation. In the skeletal system, BNC2 contributes to osteochondral development, periosteal stem-cell activation, chromatin remodeling, fracture repair, and genetic susceptibility to adolescent idiopathic scoliosis. BNC2 variants have also been associated with congenital lower urinary tract obstruction, whereas its expression and regulatory landscape are closely related to germ-cell development, epithelial ovarian cancer susceptibility, pigmentation traits, fibrosis, and several tumor contexts. Mechanistically, BNC2-associated phenotypes appear to involve cysteine–histidine zinc-finger-mediated transcriptional regulation, non-coding enhancer activity, epigenetic alterations, RNA-processing-associated nuclear functions, and chromatin-remodeling-dependent control of cell proliferation, differentiation, and stromal activation. This review integrates current evidence on the molecular architecture and regulatory functions of BNC2, critically discusses its context-dependent roles across development and disease, and highlights unresolved questions regarding isoform-specific activity, cell-type-specific regulation, downstream target networks, and clinical translation. A clearer understanding of these mechanisms may support the future evaluation of BNC2 as a biomarker, genetic susceptibility locus, molecular stratification factor, and potential therapeutic regulatory node. Full article

Diagnostic imaging is central to clinical decision-making across many care pathways, yet the expertise needed to use these images well is unevenly distributed across health systems, with workforce limitations identified as a major barrier to equitable access, particularly in low- and middle-income countries. Digital education has emerged as one response to this gap, offering scalability, asynchronous and just-in-time access, and the cost-efficiency required for global deployment. This paper examines the digital education portfolio of the International Atomic Energy Agency’s Nuclear Medicine and Diagnostic Imaging Section, hosted mainly on the open-access Human Health Campus, which in 2025 recorded approximately 45,800 active users and 150,000 views across 159 countries. The portfolio combines structured e-learning courses, interactive webinars, virtual conference access through the Livestream programme, and a broader repository of publications, teaching cases, and reference resources, supported by an internal e-learning framework and learning management system infrastructure. Partnerships with international scientific societies further extend the reach of expert knowledge and professional exchange. The paper argues that these initiatives are best understood not as content delivery alone but as a coordinated strategy to support diagnostic quality at the level of the practising physician, extending access to expertise and strengthening the conditions for better practice, while remaining a complement to, rather than a substitute for, supervised clinical training. Full article

Alpha/beta (α/β) pulse shape discrimination (PSD) in liquid scintillation counting (LSC) is fundamentally limited by the charge comparison method (CCM) at low energies, where the entire tritium (³H) beta spectrum resides (0–18.6 keVee). The CCM figure-of-merit drops below 0.6 in this region, rendering it inadequate for simultaneous tritium and natural uranium alpha monitoring in nuclear power plant (NPP) liquid effluents. We present a one-dimensional convolutional neural network (1D-CNN) trained on an 80,000-waveform physics-based simulation dataset using established scintillation parameters for Ultima Gold AB. The proposed network achieves 97.4% overall classification accuracy and an area under the receiver operating characteristic curve (AUC) of 0.9981 on the held-out test set, representing improvements of 13.8 percentage points and 0.046 AUC over CCM. In the critical 0–18.6 keVee region, CNN accuracy exceeds 95% compared to below 60% for CCM—a greater than 35 percentage point improvement. Pulse amplitude discrimination (PAD), evaluated as a preliminary screening method, exhibits a 6.3% alpha spillover rate into the beta window, exceeding the regulatory limit of 3%. Gradient-weighted class activation maps (Grad-CAM) confirm that the network exploits physically meaningful pulse features rather than simulation artefacts. A comprehensive background suppression strategy combining dual-SiPM coincidence (24× reduction), anti-coincidence guard detector (5.8× reduction), composite passive shielding (10× reduction), and CNN-assisted discrimination reduces the system equivalent background to 1.83 ± 0.12 cpm, yielding a tritium minimum detectable activity (MDA) of 0.21 Bq/mL (10 mL sample, 30 min count), which satisfies the GB 14587 reference limit of 0.5 Bq/mL. After 8-bit post-training quantisation, the model achieves sub-microsecond inference latency on an embedded Xilinx Artix-7 Field-programmable gate array(FPGA), enabling real-time deployment in portable online monitoring systems. Full article

Neutron capture reactions provide essential nuclear physics input for modeling the synthesis of heavy elements in stars. The growing precision of stellar spectroscopy and isotopic measurements in presolar SiC grains now demands cross sections with improved accuracy over the full energy range, and access to unstable nuclei relevant to slow (s-) process branchings and the intermediate (i-) process. This article reviews recent progress in direct neutron capture measurements, focusing on time-of-flight (TOF) experiments at CERN n_TOF and complementary activation techniques. Substantial advances have been achieved for stable s-only and bottleneck isotopes, significantly improving constraints on s-process models. In parallel, the combination of high instantaneous neutron fluxes and advanced detector systems has facilitated first-time neutron capture measurements on several radioactive branching-point nuclei. Feasibility studies, however, reveal current limitations related to sample availability, background conditions, and restricted energy coverage. In this context, the complementarity between TOF and activation emerges as a central strategy. Future developments, including high-flux facilities and novel inverse kinematics experiments in ion storage rings, are expected to extend the boundaries of neutron capture measurements, overcoming current limitations and helping unlock new frontiers in our understanding of stellar nucleosynthesis. Full article

It has been observed in prior research that high thermal impact—resulting from a large temperature difference between hot water vapor and cold liquid water—can enhance the thermal performance of cavitation-induced low-energy nuclear reactions (LENRs) in water, with an estimated increase in the coefficient of performance (COP) of approximately 50% for every 100 °C temperature rise. The temperature of the hot water vapor is primarily determined by the boiler output, which typically represents the highest temperature source and plays a dominant role in reactor performance. In this study, a flow oscillator was designed as an thermal conditioning component for these potential LENR reactor systems using linear flow network analysis (LFNA) to generate flow resonance that elevates the hot vapor temperature, thereby increasing thermal impact and improving LENR performance. LFNA is based on the linearization of the fluid flow equations governing mass and momentum transport and utilizes a fluid-electric circuit analogy. For a fluid flow system, various components can be modeled using analogs of electrical resistance, capacitance, and inductance (R, C, and L), allowing the system behavior to be analyzed similarly to an RLC circuit. Through this analogy, flow resonance phenomena can be predicted, potentially enabling the generation of high-temperature and high-pressure responses that are beneficial to LENR processes. The analytical model was experimentally validated and subsequently applied in the LENR reactor design. The analytical result shows that an output temperature difference exceeding 350 °C can be achieved using a 0.5 m pulse tube at a 46 Hz triggering frequency with 20 kPa perturbation, which indicates a potential COP enhancement of 175% based on prior studies. The result provides a potential mechanism to significantly enhance the thermal impact conditions and promote LENR performance in water-based reactor systems. Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

Sample return missions are among the most difficult tasks for robotic spacecraft in exploring our solar system. However, the samples they return to Earth have significantly high value for the planetary science community. Thus far, we have only acquired samples from the Moon, three asteroids, a comet’s tail, and the solar wind at the Earth–Sun Lagrange Points. The National Academy’s most recent decadal survey of planetary science at NASA emphasized the value of samples returned to Earth for analysis and called for NASA to prioritize samples returned from Mars, the Moon’s South Pole, a Jupiter-family comet, and Ceres. Currently available rockets and propulsion technology impose severe, and possibly insurmountable, limits to where we can send robot explorers and return samples within a reasonable timescale. Now, the advent of large new rockets offers the potential for very high C₃ (characteristic energy) Earth escape trajectories. Parallel developments in Nuclear Propulsion yield much higher I_SP than chemical propulsion and can operate far away from the Sun. Our novel trajectory modeling results and mission architecture analysis show that, by combining these technologies, sample return from across the solar system becomes feasible within the career lifetime of a planetary scientist. Full article

Trustworthy deployment of artificial intelligence in safety-critical systems requires accurate diagnosis of anticipated scenarios and reliable rejection of out-of-distribution (OOD) inputs that fall outside the modeled operational scope. Existing data-driven diagnostic models typically assume that test inputs are drawn from the training distribution or rely on heuristically tuned thresholds that lack enforceable safety guarantees. This article presents SCOPE (Safety-Calibrated Out-of-distribution Prediction via Contrastive Embeddings), a framework integrating supervised contrastive learning with split-conformal prediction to provide statistically grounded OOD rejection with finite-sample false-alarm control. SCOPE employs a causal residual convolutional encoder to map multivariate sensor streams into a hyperspherical embedding space with a compact, class-specific structure. A k-nearest-neighbor density nonconformity score, computed in the encoder embedding space, flags transients that occupy low-density regions relative to known accident manifolds; an ablation shows that this density score outperforms prototype distance, entropy, and conservative maximum fusion as well as a panel of standard OOD baselines (MSP, ODIN, energy, Mahalanobis, OpenMax, MC-dropout, and a reconstruction autoencoder). To support temporally evolving trajectories, SCOPE aggregates window-level scores under a monotone decision policy and performs trajectory-level conformal calibration, yielding distribution-free guarantees that bound the probability of falsely rejecting a known accident run. SCOPE is evaluated on the Nuclear Power Plant Accident Data (NPPAD) benchmark using high-openness splits that withhold entire accident families as unknowns, and all metrics are reported as mean ± standard deviation across multiple random seeds. Results demonstrate strong diagnostic accuracy on accepted trajectories, conservative false-alarm rates satisfying user-specified safety constraints across multiple operating points, and timely rejection of unseen accident mechanisms, making SCOPE suitable for deployment in safety-critical monitoring applications. Full article

This study examines the temporal evolution of low-carbon energy production in the European Union (EU27) using annual data for 1990–2022, focusing on the dynamic interaction between nuclear, renewable, and biofuel production at the EU aggregate level. After evaluating stochastic properties via Augmented Dickey–Fuller (ADF) tests and assessing long-run cointegration through the Johansen framework, short-run interactions are modeled using a Vector Autoregression (VAR) of order one. Dynamic responses and innovation variances are analyzed using impulse response functions (IRFs) and forecast error variance decomposition (FEVD). The Augmented Dickey–Fuller (ADF) results suggest both series are I(1). The Johansen test fails to reject the null of no cointegration, implying that there is no stable long-run equilibrium relationship between the two series over 1990–2022. VAR-based IRFs show small, short-lived cross-responses that dissipate within a few years. FEVD results indicate that variance shares are horizon-dependent and sensitive to the Cholesky ordering. Granger causality tests provide limited evidence of short-run directional predictability. A Zivot–Andrews test does not reject the unit-root-with-break null. These findings suggest that nuclear and renewables follow largely independent dynamics in the EU27 aggregate. A key limitation is that EU27 aggregation masks cross-country heterogeneity (e.g., Germany vs. France) and excludes policy variables, prices, and demand-side drivers. The estimated VAR(1) satisfies the stability condition: all eigenvalues of the companion matrix lie inside the unit circle (modulus < 1), confirming that the system is dynamically stable. Full article

This paper deals with the optimal integration of power plants, including a storage device. For such systems, numerous structures are possible, involving different numbers of heat exchangers, and for each of them, optimal operating temperatures need to be found. Moreover, the heat-storage system can be located at different temperature levels, offering another degree of freedom when optimizing the whole system. If process simulators are presently very powerful tools for optimizing complex processes, they need to propose a primary design before any optimization steps. Finite-Dimension Thermodynamics (FDT) could help engineers to propose this primary design, close to the optimal one. To this aim, the FDT method is generalized for power-generation systems including a storage device and any number of heat exchangers. The optimization step consists of maximizing the power generation submitted to the thermodynamics constraints (first and second laws) related to each heat exchanger, power block, and thermal storage system. Two types of heat transfer law are studied and compared: Newton’s law $\left(K\times ∆T\right)$ and phenomenological law issued from thermodynamics of irreversible processes $\left(L\times ∆\left(1/T\right)\right)$). Remarkable results have been found: (i) all the studied structures lead to the Curzon–Ahlborn efficiency when optimized with Newton’s law, (ii) for the same driving source (same high temperature and same power), and without any storage system, the output power production varies as N⁻², N being the number of the heat exchangers, (iii) Charge and discharge times scenarios have a big impact on the optimal operating temperatures and on the resulting daily energy production. Efficiencies of operational plants, including nuclear or solar plants and ORC, are finally compared with the theoretical efficiency found at the maximum power point. This shows that FDT provides a good assessment of the actual efficiency of existing power plants. Full article

The sustainable development of nuclear energy requires a secure long-term uranium supply. Seawater uranium extraction offers a nearly inexhaustible resource; however, its commercialization is limited due to high costs. To improve economic viability, this study proposes a synergistic strategy for simultaneously recovering uranium and vanadium using amidoxime-based adsorbents, with vanadium as a valuable co-product. Herein, a porous silica-supported poly(amidoxime) adsorbent was synthesized and characterized. The material possesses a well-developed porous structure with a specific surface area of 49.8 m² g⁻¹. Spectroscopic analyses confirmed the successful grafting of amidoxime groups onto the silica framework, whereas X-ray photoelectron spectroscopy revealed that uranium adsorption occurs via coordination with nitrogen and oxygen donor atoms. Batch experiments demonstrated rapid adsorption equilibrium within 2 h and a maximum Langmuir uranium capacity of 48.5 mg g⁻¹ at 45 °C. The adsorbent exhibited high selectivity toward uranium over vanadium and competing ions at near-neutral pH. Dynamic column experiments demonstrated efficient stepwise separation using 0.1 mol L⁻¹ HNO₃ for uranium and a Na₂CO₃–H₂O₂ system for vanadium, even in simulated seawater containing high concentrations of competing ions. Under the controlled model conditions employed, this study demonstrates a promising adsorbent and a feasible co-recovery strategy that may contribute to enhancing the economic feasibility of seawater uranium extraction, warranting further validation in natural seawater. Full article

In fecal sludges (FSs) from non-sewered sanitation systems, bound moisture constituted 46–67% of total moisture across all sanitation types investigated, yet the energetic basis for its resistance to removal has not previously been characterized. Existing classifications of moisture fractions lack quantitative binding energy data, leaving the thermodynamic limits of solid–liquid separation undefined for FS. This study investigates the distribution and binding energies of bound moisture fractions in FS obtained from ventilated pit latrines, urine-diverting dehydrating toilets, and septic tank systems. Bound moisture fractions were determined using moisture sorption isotherms, low-temperature convective drying, nuclear magnetic resonance, and thermogravimetric–differential scanning calorimetry analyses. Results show that interstitial moisture constituted 37–50% of total moisture, followed by vicinal (6–14%) and intracellular (3–9%) fractions, with net isosteric heat rising sharply below 20–30% moisture content (w.b.). Evaporation enthalpy exceeded that of bulk water at moisture contents below ~30% (w.b.), consistent with EPS-mediated adsorption and capillary confinement contributing to increased energy requirements for moisture removal and indicating a transition from capillary-controlled to structure-influenced retention. These findings provide a thermodynamic basis for interpreting why conventional mechanical dewatering stalls at a residual moisture content that differs systematically between VIP, UDDT, and septic tank sludges. These insights are relevant for improving FS treatment strategies, particularly in selecting appropriate combinations of dewatering, drying, and pre-treatment processes. Full article

To mitigate the effects of climate change, the world must significantly reduce its reliance on fossil fuels to lower greenhouse gas emissions. The nuclear power and renewable energy sources, such as solar, wind, water, waste, and geothermal energy, emit minimal to no greenhouse gases or pollutants during operation. These sources are considered crucial for combating climate change and supporting sustainable development. However, the production of electricity, like most industries, generates waste. Comparisons show clear differences: fossil fuel plants produce the largest total waste mass (primarily combustion ash, flue gas desulfurization residues, and wastewater sludge), while nuclear facilities generate a minimal volume but high-activity spent fuel and long-lived radioactive materials. Solar PV systems generate significant end-of-life electronic waste and glass encapsulant, and wind turbines yield moderate composite blade residues. Hydropower sediment management and geothermal scaling contribute unique waste streams of local concern. Regardless of the energy source, responsible waste management is critical to minimize environmental impacts. This article explores the sustainability of low-carbon energy sources, specifically focusing on waste management with the aim of highlighting the need of implementing targeted strategies such as advanced recycling and material substitution in order to minimize environmental impacts and enhance the circularity of low-carbon energy systems. Full article

Space energy supply is critical for human space exploration, serving as the foundation to support long-term space missions and future permanent settlement beyond Earth. To date, humanity has developed a variety of technologies for space energy supply. However, due to the constraints of the space environment and the diversity of energy sources, the energy supply technologies adopted by space exploration missions mainly depend on the feasibility of energy acquisition. This review presents a systematic review of the technical principles, power supply devices, and practical applications of space energy supply systems. First, this review summarizes the technologies for space-based solar power generation and energy storage, as well as strategies for improving the efficiency of solar power generation in space. Next, an overview of dynamic power generation technologies and static power systems for space thermal energy is investigated, along with a performance evaluation comparing these two types of systems. Subsequently, the work reviews space nuclear power systems based on thermoelectric generation technology, discusses recent advancements in nuclear fusion research, and analyzes the feasibility of utilizing helium-3 (³He) fusion technology on the Moon. Finally, to address the challenges associated with the storage and transportation of space energy, the review also introduces the applications of battery and fuel cell technologies in space. This review also discusses the technical challenges faced by space energy supply systems and explores future development prospects, aiming to provide a reference for the comprehensive development and utilization of space energy in the future. Full article

T-2 toxin is widely present in agricultural products and poses a significant neurotoxicity threat. Betulinic acid (BA), a natural triterpenoid, exhibits strong antioxidant and anti-inflammatory properties. However, its protective role against T-2 toxin-induced neuroinflammation remains poorly understood. This study aimed to elucidate the mechanisms underlying T-2 toxin-induced neurotoxicity and evaluate the therapeutic potential of BA. Our results demonstrated that T-2 toxin (1 mg/kg/bw) exposure caused significant pathological damage in the hippocampus and cerebral cortex. T-2 toxin also induced marked oxidative stress, reflected by elevated reactive oxygen species (ROS) accumulation. At the inflammatory level, T-2 toxin upregulated the mRNA expression of pro-inflammatory cytokines (Interleukin-1 beta (IL-1β), Interleukin-6 (IL-6)) and altered anti-inflammatory IL-10 expression. In addition, T-2 toxin exhibited strong binding affinity for the tight junction proteins Occludin and Claudin-1 (docking energies of −4.41 and −5.53 kcal/mol, respectively), and molecular dynamics simulations confirmed stable protein–ligand interactions. At the molecular level, T-2 toxin suppressed Nuclear factor erythroid 2-related factor 2 (Nrf2) protein expression, increased Kelch-like ECH-associated protein 1 (Keap1) expression, and activated the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome pathway. Furthermore, molecular docking analysis revealed that BA displayed strong binding affinity to proteins associated with the blood–brain barrier and the Nrf2/NLRP3 signaling pathway. Collectively, these findings indicate that BA mitigates T-2 toxin-induced neuroinflammation through regulating the Nrf2/NLRP3 signaling pathway in mice. Not only do these results clarify a key mechanism of T-2 toxin-induced central nervous system injury, but they also highlight BA as a promising candidate for developing interventions targeting mycotoxin-related neurological disorders. Full article