Structure Model Research Articles

H_2-H_2O demixing in Uranus and Neptune: Adiabatic structure models

Demixing properties of major planetary constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests that phase separation between these two components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between the water-poor outer envelope and the water-rich deep interior in Uranus and Neptune. We constructed seven H$_ $-H$_ $O phase diagrams from the available experimental and computational data. We computed interior adiabatic structure models and compared these to the phase diagrams to infer whether demixing occurred. We obtain a strong water depletion in the top layer due to the rain-out of water and find upper limits on the atmospheric water-mass fraction Zatm of 0.21 for Uranus and 0.16 for Neptune. The transition from the water-poor to the water-rich layer is sharp and occurs at pressures $P_Z$ between 4 and 11 GPa. Using these constraints on Zatm and $P_Z$, we find that the observed gravitational harmonics $J_2$ and $J_4$ can be reproduced if $P_Z 10$ GPa in Uranus and $ 5$ GPa in Neptune, and if the deep interior has a high primordial water-mass fraction of 0.8, unless rocks are also present. The agreement with $J_4$ is improved if rocks are confined deeper than $P_Z$, for instance, below a rock cloud level at 2000 K (20--30 GPa). These findings confirm classical few-layer models and suggest that a layered structure may result from a combination of primordial mass accretion and subsequent phase separation. Reduced observational uncertainty in $J_4$ and its dynamic contribution, atmospheric water abundance measurements from the Uranus Orbiter and Probe (UOP) or a Neptune mission, and better understanding of the mixing behaviour of constituents are needed to constrain the interiors of ice giants.

Identification and in vivo functional analysis of a novel missense mutation in GATA3 causing hypoparathyroidism, sensorineural deafness and renal dysplasia syndrome in a Chinese family.

Hypoparathyroidism, sensorineural deafness, and renal dysplasia (HDR) syndrome is a rare autosomal dominant genetic disease associated with mutations in the GATA3 gene, which encodes GATA3 that plays essential roles in vertebrate development. This study aimed to identify and report the pathogenic mutation in GATA3 in a Chinese family diagnosed with HDR syndrome and determine its functional impacts in vivo. The clinical features of a 25-year-old male patient with HDR syndrome and his parents were collected. GATA3 gene exome sequencing and Sanger sequencing were performed on the proband and his family, respectively. Functional analyses of GATA3 were performed using bioinformatics tools and zebrafish assays to determine pathogenicity and phenotype spectrum. A novel, heterozygous, missense mutation in exon 4 of the GATA3 gene, c.863 G > A, p.Cys288Tyr, in the proband and his mother who presented the complete HDR triad, was predicted to be deleterious by in silico tools. 3D structure modeling showed that the variant caused significant structural changes. In vivo studies using a zebrafish animal model revealed the deleterious impact of the variant on the gill buds, otoliths, and pronephros. We identified a novel missense mutation, GATA3 p.Cys288Tyr, within a family with HDR syndrome and delineated it as a loss-of-function variant in vivo. This expands the spectrum of GATA3 mutations associated with HDR syndrome in the Chinese population and mimics HDR-related changes in vivo.

Numerical simulation study on discharge characteristics of the Dual hollow cathode plasma source using magnetic mirror field

The Dual hollow cathode plasma source uses hollow cathode cylindrical sputtering and ionizing metal elements, which has a high material utilization rate and has potential application in the field of material modification. Its performance index requires the cooperation of electrode discharge parameters and the rational utilization of the inner surface of the hollow cathode. In this paper, a two-dimensional fluid model of a Dual hollow cathode structure is developed to simulate the basic distribution of the Dual hollow cathode discharge, which fills the central region of the Dual hollow cathode structure and the electrons oscillate on the axis. The effects of electrode parameters and hollow cathode radius on the discharge were investigated. The results show that the radial electron density of the hollow cathode increases with increasing heated cathode voltage, but as the hot cathode voltage continues to expand beyond 1200 V this trend becomes less obvious. The enhancement of the wall ion current by the heated cathode voltage is very pronounced, and the hollow cathode discharge is the result of the mutual enhancement of the heated cathode voltage and the hollow cathode voltage. Hollow cathode sputtering needs to reach a voltage threshold of 300 V, and the existence of an optimal radius (r = 4 mm−6 mm) to achieve a high plasma density and at the same time favor hollow cathode sputtering.

Diffuse radio sky models using large-scale shapelets

Sky models used in radio interferometric data-processing primarily consist of compact and discrete radio sources. When there is a need to model large-scale diffuse structure such as the Galaxy, specialized source models are sought after for the sake of simplicity and computational efficiency. We propose the use of shapelet basis functions for modeling the large-scale diffuse structure in various radio interferometric data-processing pipelines. The conventional source model construction using shapelet basis functions is restricted to using images of smaller size due to limitations in computational resources such as memory. We propose a novel shapelet decomposition method to lift this restriction, enabling the use of images of millions of pixels (as well as a wide spectral bandwidth) for building models of large-scale diffuse structure. Furthermore, the application of direction-dependent errors onto diffuse sky models is an expensive operation that is often performed as a convolution. We propose using some specific properties of shapelet basis functions to apply these direction-dependent errors as a product of the model coefficients, which avoids the need for convolution. We provide results based on simulations and real observations. In order to measure the efficacy of our proposed method in modeling large-scale diffuse structure, we considered the direction-dependent calibration of simulated as well as real LOFAR observations that have a significant number of diffuse large-scale structure. The results show that by including large-scale shapelet models of the diffuse sky, we are able to overcome a major problem of existing calibration techniques, which do not model this large-scale diffuse structure, that is, the suppression of this large-scale diffuse structure because the model is incomplete.

Ensemble Monte Carlo transport studies of zinc-blende cuprous halides

We present a comprehensive theoretical analysis of intrinsic high-field transport in cuprous halides: CuCl, CuBr, and CuI. Ensemble Monte Carlo transport simulations were performed based on analytical approximations of the multi-valley and three-band electronic structure model. The deformation potentials, extracted from carrier–phonon interaction matrix elements calculated via the Wannier function approach, were employed to determine scattering rates. Our detailed analysis uncovers intriguing transport characteristics based on the complex valence band structures, resulting from spin–orbit coupling and band inversion. Remarkably, the hole mobility in CuI was exceptionally high, reaching up to 193 cm2/V s, while CuCl exhibited unusual temperature dependencies in hole transport. Additionally, the electron mobility in CuI was found to be 254 cm2/V s, indicating a minimal disparity between carrier mobilities, which is advantageous for optoelectronic applications.

SIMULATION OF GEOELECTRIC STRUCTURE OF NORTHERN VIETNAM BY 3D INVERSION OF MAGNETOVARIATIONAL TIPPERS

This paper presents the results of constructing a model of the geoelectric structure of Northern Vietnam obtained by 3D inversion of magnetovariational tippers calculated for 13 values of variation periods in a range of 40–10047 s at 12 points where geomagnetic variations are recorded. Inversion is performed using the ModEM software, which makes it possible to construct a model in a 400 × 400 × 200-km spatial region with the center at the Hanoi Observatory (PHU). The resulting model of the geoelectric structure contains two regional blocks separated by the Red River fault region. A conductive block is located in the southwest of the fault region, and a high-resistivity block is located in the northeast. The boundary of the blocks, inclined to the northeast at an angle of about 45°, is visible to a depth of 150 km. The conductive block occupies the region between the Red River and Song Ma faults. Its western boundary could not be localized due to insufficient data in this region. Highly conductive local blocks stand out against the background of the regional conductive block. They usually gravitate toward the faults and are located in a depth range of 10–20 km with a slight inclination to the west on latitudinal profiles and to the south on meridional ones. In a depth range of 12–14 km, they merge into one highly conductive band extending in the northwest and marking the Red River fault system connected to the Gulf of Tonkin waters. Also, there are more massive highly conductive blocks in depth intervals of 20–50 km, which are often associated with upper crustal ones. There is a highly conductive block observed on the latitudinal profile, passing through the central region of the Hanoi Basin, and steeply dipping to the east (75°) to depths of more than 100 km. The deep geoelectric features of the Red River fault system are compared with the geoelectric section under their continuation in Southern Tibet in the adjacent territory in China.

Seismic stability of the famous telemly bridge-building located in Algiers

This work focuses on the static and dynamic stability of the historical irregularly shaped bridge-building located in a rocky valley at Telemly, Algiers. This work focuses on the static and dynamic stability of the famous building-bridge located in Telemely district, Algiers. The Telemly building-bridge is a structure that combines the functions of a residential building and a road/pedestrian bridge. It is located in the Telemly district of Alger-Centre, Algeria. This building-bridge of Telemely is one of two kinds in the world. This structure is composed of eight floors used for residential habitation, and the last slab supports a roadway and serves for automobile traffic. The stability study is performed simultaneously based on the Algerian seismic code for buildings RPA99 Version 2003 and the Algerian seismic code for bridges RPOA. This structure is located in a high seismic zone according to Algerian seismic codes. A three-dimensional finite elements model of this famous structure is realized using a finite elements code. Static and dynamic analyses were conducted using this finite elements model under static loads, seismic excitations and moving loads on the last floor. The responses of the different components of Telemly building-bridge were calculated and illustrated. The results of this study, represented in terms of internal efforts and displacements, demonstrate that the structure of Telemly building-bridge presents good performances under moving loads and seismic excitations.

Well-connectedness and community detection

Community detection methods help reveal the meso-scale structure of complex networks. Integral to detecting communities is the expectation that communities in a network are edge-dense and “well-connected”. Surprisingly, we find that five different community detection methods–the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Infomap, Markov Cluster (MCL), and Iterative k-core (IKC)–identify communities that fail even a mild requirement for well-connectedness. To address this issue, we have developed the Connectivity Modifier (CM), which iteratively removes small edge cuts and re-clusters until communities are well-connected according to a user-specified criterion. We tested CM on real-world networks ranging in size from approximately 35,000 to 75,000,000 nodes. Post-processing of the output of community detection methods by CM resulted in a reduction in node coverage. Results on synthetic networks show that the CM algorithm generally maintains or improves accuracy in recovering true communities. This study underscores the importance of network clusterability–the fraction of a network that exhibits community structure–and the need for more models of community structure where networks contain nodes that are not assigned to communities. In summary, we address well-connectedness as an important aspect of clustering and present a scalable open-source tool for well-connected clusters.

A tied-weight autoencoder for the linear dimensionality reduction of sample data

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than nonlinear methods, they can provide a linear relationship between the original data and the dimensionality-reduced representation, leading to better interpretability. In this research, we present a tied-weight autoencoder as a dimensionality reduction model with the merit of both linear and nonlinear methods. Although the tied-weight autoencoder is a nonlinear dimensionality reduction model, we approximate it to function as a linear model. This is achieved by removing the hidden layer units that are largely inactivated by the input data, while preserving the model’s effectiveness. We evaluate the proposed model by comparing its performance with other linear and nonlinear models using benchmark datasets. Our results show that the proposed model performs comparably to the nonlinear model of a similar autoencoder structure to the proposed model. More importantly, we show that the proposed model outperforms the linear models in various metrics, including the mean square error, data reconstruction, and the classification of low-dimensional projections of the input data. Thus, our study provides general recommendations for best practices in dimensionality reduction.

Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars

Massive main-sequence stars have convective cores and radiative envelopes, but can also have sub-surface convection zones caused by partial ionisation zones. However, the convective properties of such regions strongly depend on opacity and therefore a star's metallicity. Non-rotating 1D evolution models of main-sequence stars between $7 M odot $ and metallicity of the Small Magellanic Cloud (SMC) galaxy suggest tenuous (if any) sub-surface convection zones when using the Rayleigh number as a criterion for convection owing to their substantially lower metallicity compared to Galactic massive stars. We test if massive stars of different metallicities both inside and outside of asteroseismically calibrated stability windows for sub-surface convection exhibit different properties in stochastic low-frequency (SLF) variability. Thus we aim to constrain the metallicity dependence of the physical mechanism responsible for SLF variability commonly found in light curves of massive stars. We extract customised light curves from the ongoing NASA Transiting Exoplanet Survey Satellite (TESS) mission for a sample of massive stars using an effective point spread function (ePSF) method, and compare their morphologies in terms of characteristic frequency, $ char $, and amplitude using a Gaussian process (GP) regression methodology. We demonstrate that the properties of SLF variability observed in time-series photometry of massive stars are generally consistent across the metallicity range from the Milky Way down to the SMC galaxy, for stars both inside and outside of the sub-surface stability windows based on the Rayleigh number as a criterion for convection. We conclude that non-rotating 1D stellar structure models of sub-surface convection cannot alone be used to explain the mechanism giving rise to SLF variability in light curves of massive stars. Additionally, the similar properties of SLF variability across a large range in metallicity, which follow the same trends in mass and age in the Hertzsprung--Russell (HR) diagram at both high and low metallicity, support a transition in the dominant mechanism causing SLF variability from younger to more evolved stars. Specifically, core-excited internal gravity waves (IGWs) are favoured for younger stars lacking sub-surface convection zones, especially at low-metallicity, and sub-surface convection zones are favoured for more evolved massive stars.

Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines

To effectively mitigate unsafe events and accident symptoms stemming from flight dispatchers’ human factors, this paper proposes a novel risk evaluation model to accurately identify and evaluate potential human risks associated with flight dispatchers. First, the HFACS (Human Factors Analysis and Classification System, HFACS) model is employed to construct a human risk assessment indicator system for flight dispatchers. Second, the hesitant fuzzy set is introduced to represent the uncertainty during experts’ evaluation, and the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) method is applied within a hesitant fuzzy environment to obtain rankings of human factors. Third, the hesitant fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory, DEMATEL)-ISM (Interpretive Structural Modeling, ISM) approach is constructed to analyze the correlation among human factors, leading to the establishment of a multi-level hierarchical structure model. Finally, a case study of risk assessment for human factors of flight dispatchers in Sichuan Airlines is conducted to demonstrate the effectiveness of the proposed method. The results revealed the flight dispatchers’ human factors associated with higher risks and identified the key factors with a larger impact on other factors in Sichuan Airlines. Subsequently, a multi-level hierarchical structure model comprising five layers is developed to investigate the internal correlations among human factors, facilitating the formulation of targeted improvement suggestions for the higher risk indicators and key influencing factors.

A fast prediction method of fatigue life for crane structure based on Stacking ensemble learning model

To quickly obtain the fatigue life of cranes in service, the metal structure that determines the crane life is anchored. Meanwhile, the fast prediction method of fatigue life of crane metal structures based on the Stacking ensemble learning model is proposed. Firstly, in line with the structural stress method, the global rough model of the metal structure is established by the co-simulation technology to obtain the fatigue damage regions of the structure. The local fine model is constructed by local cutting and boundary condition transplantation to determine the critical weld at the failure regions. Secondly, through weld definition, equivalent structural stress acquisition, and fatigue life calculation, the sample data set with lifting load and trolley running position as input and fatigue life cycle times as output is constructed. Then, the Stacking integrated learning model combining gradient boosting, ridge regression, Extra Trees, and linear is built. On this basis, combined with the Miner theory, the rapid prediction of crane fatigue life is realized. Finally, the proposed method is applied to the QD40t × 22.5 m × 9 m general bridge crane. The results show that the life sample set constructed by the structural stress method is more accurate and reasonable than the nominal, hot spot, and fracture mechanics methods. The life prediction results of the Stacking integration model were improved by 6.3 to 49.2% compared to the single model. The method has theoretical and practical significance in reducing accidents and ensuring the safe operation of cranes.

Transcultural adaptation and validation of the Eating Self-Efficacy Brief Scale (ESEBS): the Brazilian version

PurposeThis study aimed to develop and validate the Brief Eating Self-Efficacy Scale (ESEBS-BR) in Brazilian Portuguese, addressing the lack of tools to assess eating self-efficacy beliefs in Portuguese-speaking populations.MethodThe study sought to cross-culturally adapt the scale, evaluate its internal structure, validate its reliability and validity, and explore potential associations between eating self-efficacy and eating behaviors. The cross-cultural adaptation process involved translation and back-translation, expert committees, and pre-testing. Scale validation was conducted with 228 participants, including reliability analyses, confirmatory factor analysis, and correlations with established measures of eating behavior.ResultsThe ESEBS-BR, maintaining all 8 original items, demonstrated high reliability, with a two-factor structure model confirmed by confirmatory factor analysis. Significant correlations were found between ESEBS-BR scores and measures of binge eating and eating behaviors, validating its criterion validity.ConclusionsThe development and validation of the ESEBS-BR represent a significant contribution to the assessment of eating self-efficacy in the Brazilian population. The scale proved to be sensitive, reliable, and valid, offering an important tool for research and clinical interventions related to eating behavior.Level of evidence: V, descriptive cross-sectional study.

Improving mechanical properties of 3D printed products through honeycomb structure and deep learning optimization

The use of three-dimensional (3D) Printing has rapidly grown due to its various applications in daily life. However, products made with this technology are not widely adopted because their quality is not yet seen as equal to that of products made with traditional materials. To address this issue, a honeycomb structure model was developed to improve the mechanical properties of the items created using 3D printing technology. This model not only saves materials, but also reduces printing time compared to previous models. The honeycomb structure was designed using a theoretical model, and its components were modified to ensure compatibility with the 3D printing extrusion method. Moreover, deep learning was used to optimise the honeycomb structure model’s boundary conditions by generating contour curves through linear interpolation. This process significantly improved the mechanical strength of the honeycomb structure model compared to structures made using 3D printing technology. The theoretical findings were validated through various material tensile tests conducted under different scenarios. As a result, this model can be used in various industries and products that use 3D printing technology, thanks to the critical role that deep learning in improving its mechanical properties.

Iron(III) Monoglycerolate as a New Biocompatible Precursor in the Synthesis of Bioactive Nanocomposite Glycerohydrogels.

Nanocomposite glycerohydrogels based on biocompatible elementcontaining glycerolates are of practicular interest for biomedical applications. Using two biocompatible precursors, silicon and iron glycerolates, a new bioactive nanocomposite silicon‒iron glycerolates hydrogel was obtained by sol-gel method. The composition and structural features of the hydrogel were studied using a complex of modern analytical techniques, including TEM, XRD, and AES. On the example of experimental animals hemostatic activity of the hydrogel was studied, as well as primary toxicological studies were carried out. The composition of dispersed phase and dispersion medium of silicon‒iron glycerolates hydrogel was determined. The structural features of hydrogel were revealed and its structure model was proposed. It was shown that silcon-iron glycerolates hydrogel is nontoxic, and exhibits pronounced hemostatic activity. Silicon-iron glycerolates hydrogel is a potential hemostatic agent for topical application in medical and veterinary practice.

Estimates of (convective core) masses, radii, and relative ages for ∼14 000 Gaia-discovered gravity-mode pulsators monitored by TESS

Context. Gravito-inertial asteroseismology saw its birth from the 4-year-long light curves of rotating main-sequence stars assembled by the Kepler space telescope. High-precision measurements of internal rotation and mixing are available for about 600 stars of intermediate mass so far that are used to challenge the state-of-the-art stellar structure and evolution models. Aims. Our aim is to prepare for future large ensemble modelling of gravity-mode pulsators by relying on a new sample of such stars recently discovered from the third Data Release of the Gaia space mission and confirmed by space photometry from the TESS mission. This sample of potential asteroseismic targets is about 23 times larger than the Kepler sample. Methods. We use the effective temperature and luminosity inferred from Gaia to deduce evolutionary masses, convective core masses, radii, and ages for ∼14 000 gravity-mode pulsators classified as such from their nominal TESS light curves. We do so by constructing two dedicated grids of evolutionary models for rotating stars with input physics from the asteroseismic calibrations of Keplerγ Dor pulsators. These two grids consider the distribution of initial rotation velocities at the zero-age main sequence deduced from gravito-inertial asteroseismology, for two extreme values found for the metallicity of γ Dor stars deduced from spectroscopy ([M/H]=0.0 and −0.5). Results. We find the new gravity-mode pulsators to cover an extended observational instability region covering masses from about 1.3 M⊙ to about 9 M⊙. We provide their mass-luminosity and mass-radius relations, as well as convective core masses. Our results suggest that oscillations excited by the opacity mechanism occur uninterruptedly for the mass range above about 2 M⊙, where stars have a radiative envelope aside from thin convection zones in their excitation layers. Conclusions. Our evolutionary parameters for the sample of Gaia-discovered gravity-mode pulsators with confirmed modes by TESS offer a fruitful starting point for future TESS ensemble asteroseismology once a sufficient number of modes is identified in terms of the geometrical wave numbers and overtone for each of the pulsators.

Budget-Aware Local Influence Iterative Algorithm for Efficient Influence Maximization in Social Networks

The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user’s one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%–255% and reaches the state-of-the-art simulation-based approach by 96%. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems.

Variational inequalities of multilayer elastic contact systems with interlayer friction: Existence and uniqueness of solution and convergence of numerical solution

Inspired by the layered structure models in pavement mechanics research, in this study, a class of multilayer elastic contact systems with interlayer frictional contact conditions and deformable supporting frictional contact conditions on the foundation has been constructed. Based on the nonlinear elastic constitutive equations, the corresponding system of partial differential equations and variational inequalities are respectively introduced. Under the framework of variational inequalities, the existence and uniqueness of solutions for such models, along with the approximation properties of finite element numerical solutions, are proven and analyzed. The aforementioned conclusions provide fundamental and broadly applicable theoretical support for addressing mechanical problems in multilayer elastic contact systems within the framework of variational inequalities. Finally, the numerical experimental results based on the mixed finite element method also substantiate our theoretical conclusions.

Unraveling the interaction between a glycolytic regulator protein EhPpdk and an anaphase promoting complex protein EhApc10: yeast two hybrid screening, in vitro binding assays and molecular simulation study.

The anaphase promoting complex (APC or cyclosome) is a major ubiquitin ligase that coordinates mitotic and G1 progression, acting as a major regulator of chromosome segregation. While the human APC contains fourteen subunits, it is yet to be explored in the pathogen Entamoeba histolytica. Our study reveals the existence of a single functional Apc10 homolog in E. histolytica, which acts as a processivity factor of ubiquitin ligase activity in human. A cDNA library generated from HM1:IMSS strain of E. histolytica was screened for interaction partners of EhApc10 in yeast two hybrid study. The novel interactor, a glycolytic enzyme, pyruvate phosphate dikinase (Ppdk) was found to interact with EhApc10 and further validated by in vitro assay. A comprehensive in silico study has emphasized the structural and functional aspects, encompassing physicochemical traits, predictive 3D structure modelling, validation of EhApc10-EhPpdk interaction through molecular docking and simulation. The interplay between a cell cycle protein and a glycolytic enzyme highlights the connection between cellular metabolism and the cell cycle regulatory mechanism. The study serves as the groundwork for future research on the non-mitotic role of APC beyond cell cycle.

Oil price shocks and bond risk premia: Evidence from a Panel of 15 Countries

We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.