Item factor analysis (IFA), also known as Multidimensional Item Response Theory (MIRT), is a general framework for specifying the functional relationship between respondents’ multiple latent traits and their responses to assessment items. The key element in MIRT is the relationship between the items and the latent traits, so-called item factor loading structure. The correct specification of this loading structure is crucial for accurate calibration of item parameters and recovery of individual latent traits. This paper proposes a regularized Gaussian Variational Expectation Maximization (GVEM) algorithm to efficiently infer item factor loading structure directly from data. The main idea is to impose an adaptive $L_1$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_1$$\end{document}-type penalty to the variational lower bound of the likelihood to shrink certain loadings to 0. This new algorithm takes advantage of the computational efficiency of GVEM algorithm and is suitable for high-dimensional MIRT applications. Simulation studies show that the proposed method accurately recovers the loading structure and is computationally efficient. The new method is also illustrated using the National Education Longitudinal Study of 1988 (NELS:88) mathematics and science assessment data.

A high-energy pulsed vacuum ultraviolet (VUV) solid-state laser at 177 nm with high peak power by the sixth harmonic of a neodymium-doped yttrium aluminum garnet (Nd:YAG) amplifier in a KBe2BO3F2 prism-coupled device was demonstrated. The ultraviolet (UV) pump laser is a 352 ps pulsed, spatial top-hat super-Gaussian beam at 355 nm. A high energy of a 7.12 mJ VUV laser at 177 nm is obtained with a pulse width of 255 ps, indicating a peak power of 28 MW, and the conversion efficiency is 9.42% from 355 to 177 nm. The measured results fitted well with the theoretical prediction. It is the highest pulse energy and highest peak power ever reported in the VUV range for any solid-state lasers. The high-energy, high-peak-power, and high-spatial-uniformity VUV laser is of great interest for ultra-fine machining and particle-size measurements using UV in-line Fraunhofer holography diagnostics.

The influence of outer large-scale motions (LSMs) on near-wall structures in compressible turbulent channel flows is investigated. To separate the compressibility effects, velocity fluctuations are decomposed into solenoidal and dilatational components using the Helmholtz decomposition method. Solenoidal velocity fluctuations manifest as near-wall streaks and outer large-scale structures. The spanwise drifting of near-wall solenoidal streaks is found to be driven by the outer LSMs, while LSMs have a trivial influence on the spanwise density of solenoidal streaks, consistent with the outer LSM impacts found in incompressible flows (Zhou et al., J. Fluid Mech., vol. 940, 2022, p. A23). Dilatational motions are characterized by the near-wall small-scale travelling-wave packets and the large-scale parts in the outer region. The streamwise advection velocity of the near-wall structures remains at $16 \sim 18u_{\tau }$, hardly influenced by Mach numbers, Reynolds numbers and wall temperatures. The spanwise drifting of near-wall dilatational structures, quantified by the particle image velocimetry method, follows a mechanism distinct from solenoidal streaks. This drifting velocity is notably larger than those of the solenoidal streaks, and the influence of outer LSMs is not the primary trigger for this drifting.

The practical implementation of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained with the data of the restricted nonlinear (RNL) model is used to predict the normal velocity on a detection plane at $y^+=10$ in a turbulent channel flow, and the predicted velocity is used as wall blowing and suction for drag reduction. An active control test is carried out by using the well-trained CNN in direct numerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall shear stresses, respectively. Furthermore, we explore the online control of wall turbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its observation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchronously in a RNL flow field. The control strategy discovered by RL has similar drag reduction rates with those obtained previously by the established method. Also, the training cost decreases by over thirty times at $Re_{\tau }=950$ compared with the DNS-RL model. The present results provide a perspective that combining the RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical. Also, this approach can be easily extended to flows at higher Reynolds numbers.

Direct numerical simulations (DNSs) are performed to investigate the roughness effects on the statistical properties and the large-scale coherent structures in the turbulent channel flow over three-dimensional sinusoidal rough walls. The outer-layer similarities of mean streamwise velocity and Reynolds stresses are examined by systematically varying the roughness Reynolds number $k^{+}$ and the ratio of the roughness height to the half-channel height $k / \delta$. The energy transfer mechanism of turbulent motions in the presence of roughness elements with different sizes is explored through spectral analysis of the transport equation of the two-point velocity correlation and the scale-energy path display of the generalized Kolmogorov equation. The results show that, with increasing $k^+$, the downward shift of the mean streamwise velocity profile in the logarithmic region increases and the peak intensities of turbulent Reynolds stresses decrease. At an intermediate Reynolds number ($Re_{\tau }= 1080$), the length scale and intensity of the large-scale coherent structures increase for a small roughness ($k^{+}=10$), which leads to failure of the outer-layer similarity in rough-wall turbulence, and decrease for a large roughness ($k^{+}=60$), as compared with the smooth-wall case. The existence of the small roughness ($k^{+}=10$) enhances the mechanism of inverse energy cascade from the inner-layer small-scale structures to the outer-layer large-scale structures. Correspondingly, the self-sustaining processes of the outer-layer large-scale coherent structures, including turbulent production, interscale transport, pressure transport and spatial turbulent transport, are all enhanced, whereas the large roughness ($k^{+}=60$) weakens the energy transfer between the inner and outer regions.

Insect response to cold stress is often associated with adaptive strategies and chemical variation. However, low-temperature domestication to promote the cold tolerance potential of Bactrocera dorsalis and transformation of main internal substances are not clear. Here, we use a series of low-temperature exposure experiments, supercooling point (SCP) measurement, physiological substances and cryoprotectants detection to reveal that pre-cooling with milder low temperatures (5 and 10°C) for several hours (rapid cold hardening) and days (cold acclimation) can dramatically improve the survival rate of adults and pupae under an extremely low temperature (−6.5°C). Besides, the effect of rapid cold hardening for adults could be maintained even 4 h later with 25°C exposures, and SCP was significantly declined after cold acclimation. Furthermore, content of water, fat, protein, glycogen, sorbitol, glycerol and trehalose in bodies were measured. Results showed that water content was reduced and increased content of proteins, glycogen, glycerol and trehalose after two cold domestications. Our findings suggest that rapid cold hardening and cold acclimation could enhance cold tolerance of B. dorsalis by increasing proteins, glycerol, trehalose and decreasing water content. Conclusively, identifying a physiological variation will be useful for predicting the occurrence and migration trend of B. dorsalis populations.

The interactions between the near-wall streaks and the large-scale motions (LSMs) of the outer region of wall-bounded turbulent flows are investigated. The co-supporting hypothesis of Toh & Itano (J. Fluid Mech., vol. 524, 2005, pp. 249–262) is checked in full-scale channels at low to moderate Reynolds numbers, from two points of view. To study the top-down influence of the outer structures on the spanwise motion of the near-wall streaks, a method inspired by particle-image velocimetry is used to track the spanwise position of the streaks. Their spanwise advection velocity is found to be affected by the hierarchy of large-scale circulations in the logarithmic layer, but their spanwise streak density is only weakly related to the LSMs. The evidence suggests that a top-down influence exists and drives the drift of the streaks in the spanwise direction, as suggested by Toh & Itano (J. Fluid Mech., vol. 524, 2005, pp. 249–262), but that the hypothesised streak accumulation rarely occurs. Numerical experiments at $Re_{\tau }\thickapprox 535$ are then performed to clarify the role of the near-wall streaks in the generation and preservation of the outer LSMs. The results show that the merger of the near-wall streaks is only weakly correlated with the generation of the LSMs, and that removing the near-wall roots of the LSMs does not affect the evolution of their outer region. It is concluded that the bottom-up influence from the near-wall streaks is not essential for the LSM generation and preservation, also weakening the evidence for the co-supporting hypothesis.

To persist (without immigration) in habitats with unpredictable environmental conditions, annuals must produce seeds each year or have a seed bank. Thus, we predicted that compared to perennials, annuals have a wider germination temperature range (GTR, the difference in temperature between the week with the highest and the week with the lowest germination during the natural germination season). We determined the GTR via germination phenology data for 350 herbaceous species in 59 families from the eastern USA: summer annuals (SA), 63; winter annuals (WA), 83; monocarpic perennials (MP), 28; and polycarpic perennials (PP), 176. There was no significant phylogenetic signal for the GTR. The width of the GTR during the first spring germination season was 9.6, 8.7 and 8.8°C for MP, PP and SA, respectively, and during the first autumn germination season 12.8, 11.8 and 12.4°C for MP, PP and WA, respectively. Annuals did not have a wider GTR than perennials in either the spring or the autumn germination season. Our data suggest that selection for early germination in either spring or autumn has resulted in only small differences in the GTR. We predict that global warming will have little or no effect on reshaping the germination phenology of herbaceous species of temperate eastern North America.

A new crocodylian, Dongnanosuchus hsui n. gen. n. sp., is described mainly based on four well-preserved skulls from the Eocene Youganwo Formation of the Maoming Basin, southeastern China. It is an alligatoroid and phylogenetically nested within the Orientalosuchina. Dongnanosuchus hsui n. gen. n. sp. differs from all other orientalosuchines primarily in certain features of the skull: (1) a preorbital ridge connects posteriorly with the dorsal orbital rim and a ridge around the anteroventral margin of the orbit, (2) the palatine is short and does not reach the posterior border of the suborbital fenestra, (3) the external mandibular fenestra is closed, and (4) the splenial joins the formation of the mandibular symphysis. The discovery of Dongnanosuchus hsui n. gen. n. sp. confirms the monophyly and North American origin of the Orientalosuchina but suggests that the clade dispersed to Asia via Beringia after diverging from the mainline rather than a sub-lineage of the Alligatoroidea in the Late Cretaceous.

UUID: http://zoobank.org/f8ca22f1-6323-462e-bf32-03c6a164c82e.

No relevant studies have yet been conducted to explore which measurement can best predict the survival time of patients with cancer cachexia. This study aimed to identify an anthropometric measurement that could predict the 1-year survival of patients with cancer cachexia. We conducted a nested case–control study using data from a multicentre clinical investigation of cancer from 2013 to 2020. Cachexia was defined using the Fearon criteria. A total of 262 patients who survived less than 1 year and 262 patients who survived more than 1 year were included in this study. Six candidate variables were selected based on clinical experience and previous studies. Five variables, BMI, mid-arm circumference, mid-arm muscle circumference, calf circumference and triceps skin fold (TSF), were selected for inclusion in the multivariable model. In the conditional logistic regression analysis, TSF (P = 0·014) was identified as a significant independent protective factor. A similar result was observed in all patients with cancer cachexia (n 3084). In addition, a significantly stronger positive association between TSF and the 1-year survival of patients with cancer cachexia was observed in participants aged > 65 years (OR: 0·94; 95 % CI 0·89, 0·99) than in those aged ≤ 65 years (OR: 0·96; 95 % CI 0·93, 0·99; Pinteraction = 0·013) and in participants with no chronic disease (OR: 0·92; 95 % CI 0·87, 0·97) than in those with chronic disease (OR: 0·97; 95 % CI 0·94, 1·00; Pinteraction = 0·049). According to this study, TSF might be a good anthropometric measurement for predicting 1-year survival in patients with cancer cachexia.

A roughness scaling behaviour is tested by performing the direct numerical simulation (DNS) of a turbulent channel flow over three-dimensional sinusoidal rough walls. By systematically varying the roughness height ${{k}^{+}}$ and the roughness steepness S, the results for three groups of cases are considered and compared with those for flat-wall turbulence. The results show that the mean velocity and Reynolds stresses are highly dependent on both ${{k}^{+}}$ and S. To describe these specific relationships, we define a coupling scale ${{k}^{+}} S$. With this coupling scale, all the simulated data for the roughness function (${\rm \Delta} {{U}^{+}}$), the ratio of the pressure drag to the total wall resistance (${{\gamma }_{p}}$), the normalized bulk mean velocity ($U_{b}^{+}$) and the peak of the streamwise turbulent velocity fluctuations ($\overline {u_{p}^{\prime +}}$) collapse onto single curves, which shows that there is a strong direct correlation between them, i.e. ${\rm \Delta} U^{+}, \gamma _{p}, U_{b}^{+}, \overline {u_{p}^{\prime +}} \propto f(k^{+} S)$. Furthermore, a model for the prediction of wall resistance based on the roughness function can be established by defining a drag increasing ratio (DI). Accordingly, the wall resistance coefficient ${{C}_{f}}$ can be estimated directly from ${{k}^{+}}S$ of a given rough surface. These results suggest that this coupling scale provides a useful alternative to the equivalent sand grain roughness ${{k}_{s}}$.

Phase-resolved wave simulation and direct numerical simulation of turbulence are performed to investigate the surface wave effects on the energy transfer in overlying turbulent flow. The JONSWAP spectrum is used to initialize a broadband wave field. The nonlinear wave field is simulated using a high-order spectral method, and the resultant wave surface provides the bottom boundary conditions for direct numerical simulation of the overlying turbulent flow. Two wave ages of $c_{p}/u_{\ast }=2$ and 25 are considered, corresponding to slow and fast wave fields, respectively, where $c_{p}$ denotes the celerity of the peak wave and $u_{\ast }$ denotes the friction velocity. The energy transfer of turbulent motions in the presence of surface waves is investigated through the spectral analysis of the two-point correlation transport equation. It is found that the production term has an extra peak at the dominant wavelength scale in the vicinity of the surface, and the energy transported to the surface via viscous and spatial turbulent transport is enhanced in the region of $y^{+}<10$. The presence of surface waves results in an inverse turbulent energy cascade in the near-surface region, where small-scale wave-related motions transfer energy back to the dominant wavelength scale. Pressure-related terms reflecting the spatial and inter-component energy transfer are strongly dependent on the wave age. Furthermore, triadic interaction analysis reveals that the energy influx at the dominant wavelength scale is due to the contribution of the neighbouring streamwise turbulent motions, and those at the harmonic wavelength scales contribute the most.

Single-crystalline gallium arsenide (GaAs) grown by various techniques can exhibit hillock defects on the surface when sub-optimal growth conditions are employed. The defects act as nonradiative recombination centers and limit solar cell performance. In this paper, we applied near-field transport imaging to study hillock defects in a GaAs thin film. On the same defects, we also performed near-field cathodoluminescence, standard cathodoluminescence, electron-backscattered diffraction, transmission electron microscopy, and energy-dispersive X-ray spectrometry. We found that the luminescence intensity around the hillock area is two orders of magnitude lower than on the area without hillock defects in the millimeter region, and the excess carrier diffusion length is degraded by at least a factor of five with significant local variation. The optical and transport properties are affected over a significantly larger region than the observed topography and crystallographic and chemical compositions associated with the defect.

In this study, a data-driven method for the construction of a reduced-order model (ROM) for complex flows is proposed. The method uses the proper orthogonal decomposition (POD) modes as the orthogonal basis and the dynamic mode decomposition method to obtain linear equations for the temporal evolution coefficients of the modes. This method eliminates the need for the governing equations of the flows involved, and therefore saves the effort of deriving the projected equations and proving their consistency, convergence and stability, as required by the conventional Galerkin projection method, which has been successfully applied to incompressible flows but is hard to extend to compressible flows. Using a sparsity-promoting algorithm, the dimensionality of the ROM is further reduced to a minimum. The ROMs of the natural and bypass transitions of supersonic boundary layers at $Ma=2.25$ are constructed by the proposed data-driven method. The temporal evolution of the POD modes shows good agreement with that obtained by direct numerical simulations in both cases.

Drag reduction at the external surface of a cylinder in turbulent flows along the axial direction by circumferential wall motion is studied by direct numerical simulations. The circumferential wall oscillation can lead to drag reduction due to the formation of a Stokes layer, but it may also result in centrifugal instability, which can enhance turbulence and increase drag. In the present work, the Reynolds number based on the reference friction velocity and the nominal thickness of the boundary layer is 272. A map describing the relationship between the drag-reduction rate and the control parameters, namely, the angular frequency $\unicode[STIX]{x1D714}^{+}=\unicode[STIX]{x1D714}\unicode[STIX]{x1D708}/u_{\unicode[STIX]{x1D70F}0}^{2}$ and the streamwise wavenumber $k_{x}^{+}=k_{x}\unicode[STIX]{x1D708}/u_{\unicode[STIX]{x1D70F}0}$, is obtained at the oscillation amplitude of ${A^{+}=A/u}_{\unicode[STIX]{x1D70F}0}=16$, where $u_{\unicode[STIX]{x1D70F}0}$ is the friction velocity of the uncontrolled flow and $\unicode[STIX]{x1D708}$ is the kinematic viscosity of the fluid. The maximum drag-reduction rate and the maximum drag-increase rate are both approximately 48 %, which are respectively attained at $(\unicode[STIX]{x1D714}^{+},k_{x}^{+})=$ (0.0126, 0.0148) and (0.0246, 0.0018). The drag-reduction rate can be scaled well with the help of the effective thickness of the Stokes layer. The drag increase is observed in a narrow triangular region in the frequency–wavenumber plane. The vortices induced by the centrifugal instability become the primary coherent structure in the near-wall region, and they are closely correlated with the high skin friction. In these drag-increase cases, the effective control frequency or wavenumber is crucial in scaling the drag-increase rate. As the wall curvature normalised by the boundary layer thickness becomes larger, the drag-increase region in the $(\unicode[STIX]{x1D714}^{+},k_{x}^{+})$ plane as well as the maximum drag-increase rate also become larger. Net energy saving with a considerable drag-reduction rate is possible when reducing the oscillation amplitude. At $A^{+}=4$, a net energy saving of 18 % can be achieved with a drag-reduction rate of 25 % if only the power dissipation due to viscous stress is taken into account in an ideal actuation system.

Living Caprini are dominant bovids in the pan-Tibetan area that are strongly adapted to dry steppe and high-mountain meadow habitats. Some taxa with Holarctic distributions, e.g., Ovis Linnaeus, 1758, were thought to originate on the Tibetan Plateau and subsequently dispersed elsewhere, which was depicted as an ‘out of Tibet’ story. However, except for some information on a stem caprine assemblage from the Qaidam Basin, the early evolution of Caprini around the Tibetan Plateau is poorly known. Here, we report new material of Olonbulukia tsaidamensis Bohlin, 1937, which was a member of this stem caprine assemblage, from the Wuzhong region, northern China, confirming the similarity of the Wuzhong Fauna and ‘Qaidam Fauna.’ Based on a biometric study of horncores from the ‘Qaidam’ and Wuzhong faunas, we recognize six taxa from this stem caprine assemblage: O. tsaidamensis, O. sp., Qurliqnoria cheni Bohlin, 1937, Tossunnoria pseudibex Bohlin, 1937, ?Protoryx cf. P. enanus Köhler, 1987, and cf. Pachytragus sp. Among these taxa, Q. cheni and T. pseudibex are probably related to some extant Tibetan endemic species, e.g., the Tibetan antelope, Pantholops hodgsonii (Abel, 1826), and the Himalayan tahr, Hemitragus jemlahicus (Smith, 1826). Others might be ancestral to the Turolian caprine assemblages and even possibly gave rise to the extant Caprina. This work reveals an early radiation of stem caprines along the northern side of the rising Tibetan Plateau and indicates a mixed pattern of pan-Tibetan stem caprine evolution prior to their dispersal out of the Tibetan Plateau.