Search Results (866)

Reliable performance of TRISO (tristructural isotropic) nuclear fuel depends on the interplay between its multilayer architecture and the buffer-layer microstructure, which are difficult to isolate experimentally. We implement a multiscale, multiphysics model in the open-source MOOSE (Multiphysics Object-Oriented Simulation Environment) framework that couples particle-scale thermo-mechanical finite-element analysis with mesoscale phase-field fracture to link microstructure to effective stiffness and strength. The model resolves the combined influence of pore volume fraction, size, and aspect ratio and explicitly separates the effects of reduced load-bearing capacity from stress concentrations in the porous buffer. Simulations reveal substantial hoop stresses across coating layers under nominal thermal conditions due to material property mismatches and temperature gradients. In the buffer, stiffness and strength decrease with porosity; morphology is decisive: as aspect ratio decreases, strength degrades far more rapidly than stiffness, consistent with crack-like pores that amplify local stresses. The framework reproduces logarithmic trends with aspect ratio and explains the higher sensitivity of strength, providing parameters that can inform design and acceptance criteria (e.g., limits on pore elongation and porosity gradients). Implemented within MOOSE, the approach is readily extensible to irradiation-dependent kinetics, interface debonding, and uncertainty-quantified 3D analyses to support risk-informed TRISO fuel development. Full article

Soil microbial carbon use efficiency (CUE) is the core of the soil carbon (C) cycle that captures a dual microbial control point between soil organic C (SOC) accumulation and loss. The interpretation of these patterns and drivers of microbial CUE after long-term afforestation remains, however, a major scientific challenge. In particular, there are major uncertainties about the role of microbial traits in driving CUE. Here, we compared sites along a 45-year afforestation chronosequence and combined the novel ¹⁸O-H₂O tracer method with metagenomic analysis to quantify CUE and explore the mechanisms underlying microbe-mediated C dynamics. The results showed that soil microbial CUE significantly increased following afforestation and showed a positive relationship with SOC, which suggested that microbial CUE could promote C accumulation in afforested ecosystems. We further found the critical role of microbial traits in the regulation of CUE through altering microbial life history strategies: microbial CUE was positively and significantly correlated with resource acquisition (A) genes, but showed a negative and significant correlation with stress tolerance (S) strategy genes. These results suggested that soil microbes reduce investment in S strategies and shift to A and high yield (Y) strategies, thereby increasing CUE. This knowledge is important because it advances our understanding of the microbial physiological and evolutionary tradeoffs mediating soil C cycling in the context of human-induced land use change. Full article

The wildland fire management system is increasingly complex and uncertain, which challenges suppression actions and increases stress on an already strained system. Researchers and managers have called for the use of strategic, risk-informed decision making and decision support tools (DSTs) in wildfire management to manage complexity and mitigate uncertainty. This paper evaluated the use of an emerging wildfire DST, the Risk Management Assistance (RMA) dashboard, during the 2021 and 2022 wildfire seasons. We used a mixed-method approach, consisting of an online survey and in-depth interviews with fire managers. Our objectives were the following: (1) to determine what factors at multiple scales facilitated and frustrated the adoption of RMA; and (2) to identify actionable recommendations to facilitate uptake of RMA. We situate our findings within the diffusion of innovations literature and use-inspired research. Most respondents indicated RMA tools were easy to use, accurate, and relevant to decision-making processes. We found evidence that the tools were used throughout the fire management cycle. Previous experience with RMA and training in risk management, trust in models, leadership support, and perceptions of current and future fire risk affected RMA adoption. Recommendations to improve RMA included articulating how the tools integrate with existing wildland fire DSTs, new tools that consider dynamic forecasting of risk, and both formal and informal learning opportunities in the pre-season, during incidents, and in post-fire reviews. We conclude with research and management considerations to increase the use of RMA and other DSTs in support of safe, effective, and informed wildfire decision making. Full article

Lentil (Lens culinaris Medik. subsp. culinaris) is a Mediterranean legume crop of high value due to nutritional quality and adaptability; however, its cultivation is increasingly threatened due to climate uncertainty and reduction in genetic diversity in modern cultivars. The present research study evaluated 31 Greek lentil accessions (twenty-two landraces and nine commercial cultivars of both small and large seed types) in a semi-arid environment of Central Greece, over two cropping seasons, focusing on phenological, morphological, yield, and quality traits. The great diversity observed at the morpho-phenological and qualitative levels implies the high genotypic diversity of these genetic resources. Small-seeded landraces performed better in seed and biological yield, harvest index, and protein content, having greater phenological stability and tolerance to the Mediterranean environments. In particular, the highest seed yield was observed in LAX small-seeded landrace (1930 kg ha⁻¹), followed by TSO (1559 kg ha⁻¹), DIG (1449 kg ha⁻¹), and EGL (1437 kg ha⁻¹) small-seeded landraces. As for the regression analysis, seed yield was positively correlated with days to flowering (TF: r = 0.076, p < 0.01), plant height (PH: r = 0.143, p < 0.05), number of pods per plant (NPP: r = 0.941, p < 0.001), number of seeds per pod (NPP: r = 0.432, p < 0.001), number of branches (NPB: r = 0.234, p < 0.01), biological yield (BY: r = 0.683, p < 0.001), and harvest index (HI: r = 0.650, p < 0.001). Principal component analysis (PCA) distinguished small-seeded landraces associated with adaptive and yield traits from large-seeded cultivars associated with seed size. Greek lentil landraces, especially the small-seeded genotypes (e.g., LAX and DIG), have great potential for use in the development of climate-tolerant and high-yielding lentil varieties adapted for sustainable Mediterranean production. Breeding programs can target the crossing of landraces with large-seeded cultivars (e.g., IKAm and THEm) to develop varieties that combine stress tolerance, adaptation, and high productivity with adaptation to different seed sizes. Subsequent studies on drought tolerance and heat resistance are still important for continued improvement in lentil productivity in a changing climate. Full article

We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with a GARCH–MIDAS volatility model to link short-run transmission to long-run behavioural effects. We construct a regional investor-sentiment (IS) index from Google search data on five macro-financial topics using principal component analysis and analyse it together with global benchmarks (MSCI EM, S&P 500), gold, clean-energy equities, and macro-uncertainty indicators. The TVP–VAR maps dynamic spillovers among the ASEAN-5 and external nodes, while the GARCH–MIDAS relates the slow component of variance to investor attention. The evidence indicates that connectedness tightens in stress regimes, with global benchmarks and policy uncertainty acting as transmitters and ASEAN equities absorbing incoming shocks. In the volatility block, the Google-based IS factor exerts a negative and economically meaningful influence on the long-run component over and above global uncertainty, supporting the view that attention and uncertainty function as complementary channels of risk propagation. The integrated framework is parsimonious and replicable, and it offers actionable insights for regime-aware risk management, policy communication, and the timing of green-finance issuance in emerging markets. Full article

Destructive testing evaluates material strength but prohibits repeated measurements on the same specimen. Finite element simulations, such as those using ANSYS Mechanical, provide a cost-effective alternative by delivering deterministic solutions under defined conditions. To incorporate input variability, the Monte Carlo method applies assigned probability distributions to parameters like magnitude and angle of the applied force and geometric tolerances of the specimen. Thus, input variability yields distributions for output such as stress and deformation, enabling uncertainty quantification. In this study, an example of static force on a concrete cube was modeled in ANSYS, and uncertainty was propagated using the Monte Carlo method, as described in JCGM 101:2008. This approach enables the identification of critical factors affecting the outcome and provides confidence intervals that might be used as decision rules to support comparisons of numerical simulations with experimental data and of results of different models and to calculate the limits of quality control charts of a testing laboratory. Full article

The construction sector is traditionally known for a harsh working culture characterized by uncertainty, frequent crises, and long working hours, which increase stress among employees at all organizational levels. The primary objective of the present research is to examine the extent to which workplace stress factors grouped under organisational/interpersonal, task and physical stressor categories influence professionals within the construction industry. A cross-sectional online survey of 185 construction professionals in Turkey was conducted between October 2024 and February 2025. Findings reveal that the task stressors category associated with increased workload exerts the most significant adverse effect on employees, while organisational/interpersonal stressors are the least impactful. The analysis also shows significant variations in perceptions across different demographic and workplace contexts. An overwhelming majority of significant differences between male and female employees occur within the “Organizational/Interpersonal Stressors”, with six out of the eight significant factors falling under this category. In particular, the most pronounced gender-based differences are observed in specific areas such as gender and age discrimination in promotion and development opportunities at work, fear of failure at the job/job insecurity and insufficient encouragement/support from managers. In addition, young professionals and employees of medium sized firms are found to be more severely influenced by task stressors. By highlighting the differing perceptions of stress factors among employees based on their demographic profiles, these findings provide insights for managers in formulating effective organizational policies. Establishing goals about streamlining tasks, reviewing hiring policies to reduce workloads, providing guidance and training for employees’ task prioritization skills, and implementing workload redistribution strategies are some of the strategies that may be considered by organisations in the construction industry. Full article

Fatigue cracks in highly stressed regions of marine structures, caused primarily due to wave loading, are critical life-limiting factors that can lead to structural failure. Structural Health Monitoring (SHM) systems offer the ability to remotely monitor damage progression during its initial phases, enabling failure prevention. One diagnostic approach utilizes the strain redistribution in the vicinity of the crack tip, captured by sensor readings, to inversely calculate the corresponding crack length. This work addresses the challenge of accurately calculating the crack length under variable sources of uncertainty by employing the statistical framework of Maximum Likelihood Estimation (MLE). The method is demonstrated on a simplified test geometry using simulated strain data, registered at locations where structural response sensors may be placed. This approach enables the integration of multiple strain features at modest computational cost, facilitating the assessment of different sensor placement strategies under realistic noise conditions. Full article

Plant microRNAs (miRNAs) are endogenous non-coding RNAs (~20–24 nucleotides) that regulate gene expression post-transcriptionally, playing critical roles in plant growth, development, and stress responses. This review systematically examines AI applications in plant miRNA research, tracing evolution from traditional machine learning to deep learning architectures. Plant miRNAs exhibit distinctive features necessitating plant-specific computational approaches: nuclear-localized biogenesis, high target complementarity (>80%), and coding region targeting. These characteristics enable more accurate computational prediction and experimental validation than animal systems. Methodological advances have improved prediction accuracy from ~90% (early SVMs) to >99% (recent deep learning), though metrics reflect different evaluation contexts. We analyze applications across miRNA identification, target prediction with degradome validation, miRNA–lncRNA interactions, and ceRNA networks. Critical assessment reveals that degradome data capture mixed RNA fragments from multiple sources beyond miRNA cleavage, requiring stringent multi-evidence validation. Similarly, fundamental ambiguities in lncRNA definition compound prediction uncertainties. Major challenges include severe data imbalance (positive to negative ratios of 1:100 to 1:10,000), limited cross-species generalization, insufficient model interpretability, and experimental validation bottlenecks. Approximately 75% of plant miRNA families in miRBase v20 lack convincing evidence, underscoring the need for rigorous annotation standards. Future directions encompass multimodal deep learning, explainable AI, spatiotemporal graph neural networks, and ultimately AI-driven de novo miRNA design, though the latter requires substantial advances in both computation and high-throughput validation. This synthesis demonstrates that AI has become indispensable for plant miRNA research, providing essential support for crop improvement while acknowledging persistent challenges demanding continued innovation. Full article

Load Frequency Control (LFC) is essential for ensuring frequency stability in modern power systems subject to load fluctuations, uncertainties, and increasing renewable penetration. This paper introduces a novel hybrid control framework that unifies H∞ stability guarantees, H₂ performance, and pole placement for transient shaping. Its originality is threefold. First, it models load variation as a measurable disturbance (D₁₂ = 0, D₂₁ ≠ 0), departing from the standard assumption of an unknown input. This enables a low-order H∞ controller that improves transient response, enhances robustness, and reduces energy consumption. Second, the framework explicitly accounts for a wider spectrum of real-world uncertainties, including governor and turbine dynamics and the transmission-line synchronizing power coefficient. Third, it integrates explicit energy optimization to reduce mechanical stress and extend equipment lifespan. This strategy yields substantial energy savings by minimizing fuel use and operational costs. Simulation results confirm its superiority: the proposed H∞/H₂ pole placement controller with measured disturbances achieves a 98% reduction in control energy relative to a standard H∞ controller, along with a 70% reduction in overshoot and a drastic improvement in settling time—from 7 s to 0.2 s—compared to a conventional H∞/H₂ controller. These results establish the proposed framework as a new benchmark for robust, efficient, and high-performance LFC design. Full article

This preliminary study investigates a direct, non-delaminated route to valorize multilayer pharmaceutical sachet offcuts (comprising paper/plastic/aluminum) as partial substitutes for wood fiber in wood-based panels. Milled offcuts were incorporated at 10, 20, and 30 wt% (control: wood only). Laboratory mats were hot-pressed at 170 °C for 9 min under a staged pressure regime. Sampling and three-point bending were performed according to EN 326-1 and EN 310, respectively, with the density held essentially constant by controlling the mat mass and press stops. Bending stiffness (MOE) was maintained at 10–20 wt% (within experimental uncertainty of the reference), while 30 wt% showed a consistent downward trend (approximately 10%). Bending strength (MOR) peaked at 10 wt% (approximately 8% higher than the reference), then declined at 20% and 30%. Representative stress–strain curves corroborated these outcomes, indicating auxiliary bonding and crack-bridging effects at low waste loadings. Hygroscopic performance improved monotonically: 24 h water absorption and thickness swelling decreased progressively with increasing substitution, attributable to the hydrophobic polymer layers and aluminum fragments interrupting capillary pathways. Process observations identified opportunities to improve press-cycle efficiency at higher waste contents, and the dispersed foil imparted a subtle decorative sheen. Overall, the results establish the technical feasibility and a practical utilization window of approximately 10–20 wt% for furniture-grade applications. Limitations include the laboratory scale, a single resin/press schedule, and the absence of internal bond, density profile, emissions, and long-term durability tests—topics prioritized for future work (including TGA/DSC, EN 317 extensions, and scale-up). Full article

Climate change strongly influences the phenology of temperate fruit species, yet its long-term effects on Romanian plum orchards (Prunus domestica L.) remain insufficiently quantified. This study analyzes projected changes in the start (SGS), end (EGS), and duration (GSL) of the growing season under two emission scenarios (RCP 4.5 and RCP 8.5) throughout the 21st century. Using temperature-based phenological thresholds, SGS and EGS were modeled for six orchard clusters representing distinct regional and altitudinal conditions across Romania. Results reveal a consistent advancement of SGS and a marked extension of GSL, particularly under RCP 8.5, where the growing season may lengthen by up to 60 days compared with early-century conditions. Under RCP 4.5, changes are more moderate but directionally similar, indicating a robust climatic signal across all clusters. These findings highlight that earlier and longer vegetation periods may enhance fruit development potential but also increase risks associated with late spring frosts, heat stress, and pollination mismatches. Despite inherent model uncertainties, the convergence of trends suggests reliable projections that can support adaptive orchard management and long-term strategies for sustainable fruit production under a changing climate. Full article

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action–outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk. Full article

Focusing on the West African Craton (WAC) as a test bed, this protocol-oriented scoping review synthesizes indicators for orogenic gold and translates them into an auditable, map-first checklist that separates Fertility and Preservation, while deliberately deferring any performance estimation to a blinded, out-of-sample evaluation. There is a need for a transparent, auditable, and field-ready framework that integrates geological, structural, geophysical, and geochemical evidence. We (i) synthesize the state of knowledge into a map-first, reproducible targeting checklist, (ii) formalize an indicator decision matrix that separates Fertility from Preservation factors, and (iii) specify a deferred, out-of-sample evaluation protocol to quantify performance. We conduct a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-style scoping review (2010–2025) and codify commonly used indicators (e.g., transpressional jogs, lineament density, proximity to tonalite-trondhjemite-granodiorite (TTG)/tonalite contacts, Sr/Y proxies). Indicators are operationalized as auditable pass/fail rules and assembled into a decision matrix with explicit uncertainty handling and risk logging. We further define a deferred evaluation protocol using classification and ranking metrics (receiver operating characteristic (ROC) and precision–recall (PR) curves, odds ratios), ablation/sensitivity tests, and district-level threshold calibration. We deliver (1) a unified, auditable checklist with default (tunable) thresholds; (2) an indicator decision matrix that disentangles Fertility vs. Preservation signals; and (3) a deferred evaluation protocol enabling a reproducible, out-of-sample assessment without inflating apparent performance. All numerical thresholds reported here are explicit placeholders that facilitate transparency and auditability; they are not optimized. A properly blocked train/validation/test scheme, operating-point selection criteria, null models, and uncertainty procedures are prespecified for future evaluation. By publishing the checklist, data lineage, and audit-log schema now—without performance claims—we enable reproducible adoption and stress-test the framework ahead of calibration. Full article

(1) Background: The structural integrity of key components in unmanned aerial vehicle (UAV) mission systems is crucial for achieving performance goals. The installation environment of military and civilian UAV wing pylons is very complex, as they are subject to various complex vibration excitations. Therefore, it is necessary to conduct vibration fatigue analysis on the wing pylons of UAVs to ensure structural integrity and safe operation. (2) Method: This study is based on the experience of vibration fatigue design for military and civilian aircraft, and flight test data of HH-100 UAV, a specific wing pylon for UAV, was taken as the research object, and the vibration evaluation modeling method was studied. A vibration fatigue assessment model for wing pylons was established, and relevant fatigue failure and strain data were collected through experimental data to validate the vibration fatigue analysis model. A fatigue analysis model was used to conduct fatigue analysis on the design details of the wing pylon structure under multi-source dynamic loads and to determine the structural vibration fatigue characteristics. (3) Result: Based on the finite element method and using the power spectral density (PSD) of the load spectrum, analyses and calculations were carried out to obtain the stress distribution of the connecting structure under vibration and impact loads. Based on this, the fatigue weaknesses of the structure have been clearly identified. Subsequently, dynamic fatigue analysis was conducted to calculate the fatigue life of the structure. Using Miner’s damage accumulation theory and considering the uncertainty of calculations or the sensitivity of results to geometric simplification and PSD spectra, the nominal fatigue life of the pylon structure was obtained through conversion. (4) Conclusions: Using a fatigue analysis model validated through experiments, a comprehensive damage accumulation evaluation was conducted on the fatigue life of the wing pylon under external multi-source dynamic loads, and the vibration fatigue life of the wing pylon was obtained, which meets the design requirements of UAVs. Full article