Turbulent emulsions are ubiquitous in chemical engineering, food processing, pharmaceuticals and other fields. However, our experimental understanding of this area remains limited due to the multiscale nature of turbulent flow and the presence of extensive interfaces, which pose significant challenges to optical measurements. In this study, we address these challenges by precisely matching the refractive indices of the continuous and dispersed phases, enabling us to measure local velocity information at high volume fractions. The emulsion is generated in a turbulent Taylor–Couette flow, with velocity measured at two radial locations: near the inner cylinder (boundary layer) and in the middle gap (bulk region). Near the inner cylinder, the presence of droplets suppresses the emission of angular velocity plumes, which reduces the mean azimuthal velocity and its root mean squared fluctuation. The former effect leads to a higher angular velocity gradient in the boundary layer, resulting in greater global drag on the system. In the bulk region, although droplets suppress turbulence fluctuations, they enhance the cross-correlation between azimuthal and radial velocities, leaving the angular velocity flux contributed by the turbulent flow nearly unchanged. In both locations, droplets suppress turbulence at scales larger than the average droplet diameter and increase the intermittency of velocity increments. However, the effects of the droplets are more pronounced near the inner cylinder than in the bulk, likely because droplets fragment in the boundary layer but are less prone to break up in the bulk. Our study provides experimental insights into how dispersed droplets modulate global drag, coherent structures and the multiscale characteristics of turbulent flow.

This study employs direct numerical simulations to examine the effects of varying backpressure conditions on the turbulent atomisation of impinging liquid jets. Using the incompressible Navier–Stokes equations, and a volume-of-fluid approach enhanced by adaptive mesh refinement and an isoface-based interface reconstruction algorithm, we analyse spray characteristics in the environments with ambient gas densities ranging from 1 to 40 times the atmospheric pressure under five different backpressure scenarios. We investigate the behaviour of turbulent jets, incorporate realistic orifice geometries and identify significant variations in the atomisation patterns depending on backpressure. Two distinct atomisation types emerge, namely jet-sheet-ligament-droplet at lower backpressures and jet-sheet-fragment-droplet at higher ones, alongside a transition from dilute to dense spray patterns. This variation affects the droplet size distribution and spray dynamics, with increased backpressure reducing the spray's spreading angle and breakup length, while increasing the droplet size variation. Furthermore, these conditions promote distributions that induce rapid, nonlinear wavy motion in liquid sheets. Topological analysis of the atomisation field using velocity-gradient tensor invariants reveals significant variations in topology volume fractions across different regions. Downstream, the droplet Sauter mean diameter increases and then stabilises, reflecting the continuous breakup and coalescence processes, notably under higher backpressures. This research underscores the substantial impact of backpressure on impinging-jet atomisation and provides essential insights for nozzle design to optimise droplet distributions.

Survey questionnaires are commonly used by psychologists and social scientists to measure various latent traits of study subjects. Various causal inference methods such as the potential outcome framework and structural equation models have been used to infer causal effects. However, the majority of these methods assume the knowledge of true causal structure, which is unknown for many applications in psychological and social sciences. This calls for alternative causal approaches for analyzing such questionnaire data. Bayesian networks are a promising option as they do not require causal structure to be known a priori but learn it objectively from data. Although we have seen some recent successes of using Bayesian networks to discover causality for psychological questionnaire data, their techniques tend to suffer from causal non-identifiability with observational data. In this paper, we propose the use of a state-of-the-art Bayesian network that is proven to be fully identifiable for observational ordinal data. We develop a causal structure learning algorithm based on an asymptotically justified BIC score function, a hill-climbing search strategy, and the bootstrapping technique, which is able to not only identify a unique causal structure but also quantify the associated uncertainty. Using simulation studies, we demonstrate the power of the proposed learning algorithm by comparing it with alternative Bayesian network methods. For illustration, we consider a dataset from a psychological study of the functional relationships among the symptoms of obsessive-compulsive disorder and depression. Without any prior knowledge, the proposed algorithm reveals some plausible causal relationships. This paper is accompanied by a user-friendly open-source R package OrdCD on CRAN.

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the conditional independence assumption underlying the latent variable model. The second stage then uses mixture modeling treating the class membership as known. The proposed approach is theoretically justifiable, directly checks the conditional independence assumption, and converges much faster than the full likelihood approach when analyzing high-dimensional data. This paper also develops a new classification rule based on latent variable models. The proposed classification procedure reduces the dimensionality of measured data and explicitly recognizes the heterogeneous nature of the complex disease, which makes it perfect for analyzing high-throughput genomic data. Simulation studies and real data analysis demonstrate the advantages of the proposed method.

The presence of dispersed-phase droplets can result in a notable increase in a system's drag. However, our understanding of the mechanism underlying this phenomenon remains limited. In this study, we use three-dimensional direct numerical simulations with a modified multi-marker volume-of-fluid method to investigate liquid–liquid two-phase turbulence in a Taylor–Couette geometry. The dispersed phase has the same density and viscosity as the continuous phase. The Reynolds number $Re\equiv r_i\omega _i d/\nu$ is fixed at 5200, the volume fraction of the dispersed phase is up to $40\,\%$, and the Weber number $We\equiv \rho u^2_\tau d/\sigma$ is approximately 8. It is found that the increase in the system's drag originates from the contribution of interfacial tension. Specifically, droplets experience significant deformation and stretching in the streamwise direction due to shear near the inner cylinder. Consequently, the rear end of the droplets lags behind the fore head. This causes opposing interfacial tension effects on the fore head and rear end of the droplets. For the fore head of the droplets, the effect of interfacial tension appears to act against the flow direction. For the rear end, the effect appears to act in the flow direction. The increase in the system's drag is attributed primarily to the effect of interfacial tension on the fore head of the droplets which leads to the hindering effect of the droplets on the surrounding continuous phase. This hindering effect disrupts the formation of high-speed streaks, favouring the formation of low-speed ones, which are generally associated with higher viscous stress and drag of the system. This study provides new insights into the mechanism of drag enhancement reported in our previous experiments.

We investigate the coupling effects of the two-phase interface, viscosity ratio and density ratio of the dispersed phase to the continuous phase on the flow statistics in two-phase Taylor–Couette turbulence at a system Reynolds number of $6\times 10^3$ and a system Weber number of 10 using interface-resolved three-dimensional direct numerical simulations with the volume-of-fluid method. Our study focuses on four different scenarios: neutral droplets, low-viscosity droplets, light droplets and low-viscosity light droplets. We find that neutral droplets and low-viscosity droplets primarily contribute to drag enhancement through the two-phase interface, whereas light droplets reduce the system's drag by explicitly reducing Reynolds stress due to the density dependence of Reynolds stress. In addition, low-viscosity light droplets contribute to greater drag reduction by further reducing momentum transport near the inner cylinder and implicitly reducing Reynolds stress. While interfacial tension enhances turbulent kinetic energy (TKE) transport, drag enhancement is not strongly correlated with TKE transport for both neutral droplets and low-viscosity droplets. Light droplets primarily reduce the production term by diminishing Reynolds stress, whereas the density contrast between the phases boosts TKE transport near the inner wall. Therefore, the reduction in the dissipation rate is predominantly attributed to decreased turbulence production, causing drag reduction. For low-viscosity light droplets, the production term diminishes further, primarily due to their greater reduction in Reynolds stress, while reduced viscosity weakens the density difference's contribution to TKE transport near the inner cylinder, resulting in a more pronounced reduction in the dissipation rate and consequently stronger drag reduction. Our findings provide new insights into the physics of turbulence modulation by the dispersed phase in two-phase turbulence systems.

Er:CaF2 crystals are crucial gain media for producing 3 μm mid-infrared (MIR) lasers pumped by 976 nm continuous-wave (CW) lasers owing to their low phonon energy and high conversion efficiency. This study investigated the damage characteristics and mechanism of Er:CaF2 crystals irradiated with a 976 nm CW laser. The laser-induced damage threshold of Er:CaF2 crystals with different Er3+ doping levels was tested; the damage morphology consists of a series of regular 70° cracks related to the angle of the crystal slip system on the surface. A finite-element model was used to calculate the temperature and stress fields of the crystals. The results indicated that the damage can be attributed to surface tensile stresses caused by the temperature gradient, and crystals with higher doping concentrations were more susceptible to damage owing to stronger light absorption. These findings provide valuable insights into the development of high-power MIR lasers.

Emerging evidence has shown a strong correlation between serum TAG levels, the inflammatory response and Parkinson’s disease (PD) onset. However, the causal relationship between TAG levels and PD has not been well established. We aimed to investigate the relationship between serum TAG levels and risk of PD and explore the potential mediating role of circulating immune cells and inflammatory proteins. We utilised genotype data from the GeneRISK cohort, and summary data from genome-wide association studies investigating PD, circulating immune cells, inflammatory proteins and plasma lipidomes. Using Mendelian randomisation (MR) and multivariate MR (MVMR) analysis, we further adjusted for phosphatidylcholine (17:0_18:1) and TAG (58:7). Our results suggested a robust causal link between higher serum TAG (51:4) levels and a decreased risk of PD, with 1 sd genetically instrumented higher serum TAG (51:4) level leading to a 21 per cent (95 % CI 0·66, 0·96) reduction in the risk of PD (P= 0·015). Additionally, the results of the mediation analysis suggested a possible role for mediation through circulating immune cells (including IgD-CD38-B cells and resting CD4 regulatory T cells), but not circulating inflammatory proteins, in the causal relationship between the plasma lipidomes and PD. Our study confirms a causal relationship between higher serum TAG (51:4) levels and a lower risk of PD and clarifies a possible role for mediation through circulating immune cells, but not inflammatory proteins. These findings indicate that serum TAG (51:4) regulates immunity to effectively lower the risk of PD.

Revealing the impact of forest succession processes on changes in plant diversity is crucial for understanding the mechanisms that maintain plant diversity across various succession stages. While previous research has predominantly focused on the influence of environmental factors or management strategies on plant diversity within rubber plantation understories, there is a scarcity of studies examining the effects of forest succession processes on plant diversity. This study focuses on the plant diversity of the understory herbaceous layer within the rubber forest of the Yinggeling area, located in National Park of Hainan Tropical Rainforest. It employs a spatial analysis approach, rather than a temporal one, to examine the characteristics of the understory herbaceous community. The findings revealed that (1) The understory of Yinggeling rubber plantations harbors 175 plant species from 149 genera and 75 families, with Gramineae and Rubiaceae representing 46.45% of total species. And the dominant families are Rubiaceae, Gramineae, and Moraceae, with Ficus and Pteris being the dominant genera. (2) The dominant species vary with succession duration, with Tetrastigma pachyphyllum dominating in 0-year succession, Paspalum conjugatum in 3-year succession, and Microstegium fasciculatum in 7-year succession. (3) Diversity indices such as the Shannon–Wiener index, Simpson index, and Pielou index peak at 7 years of natural succession, while the species richness is highest at 3 years. (4) The similarity coefficient between understory herbaceous plant communities in rubber plantations undergoing 0 and 3 years of natural succession is highest 0.56, indicating a significant similarity, while similarity is lowest between 0 and 7 years of succession. This research shows that natural restoration helps increase species diversity in the understory herb layer of rubber forests. Succession leads to changes in the dominant families, genera, and species of the herbaceous layer. This change can be attributed to the intraspecific competition and ecological competition that occur during the succession process, leading to changes in biological and resource allocation.

Recently, deep learning methods have achieved remarkable success in the Chinese word segmentation (CWS) task. Some of them enhance the CWS model by utilizing contextual features and external resources (e.g., sub-words, lexicon, and syntax). However, existing approaches fail to fully use the heterogeneous features and their structural information. Therefore, in this paper, we propose a heterogeneous information learning framework for CWS, named heterogeneous graph neural segmenter (HGNSeg), which exploits heterogeneous features with the graph convolutional networks and the pretrained language model. Experimental results on six benchmark datasets (e.g., SIGHAN 2005 and SIGHAN 2008) confirm that HGNSeg can effectively improve the performance of CWS. Importantly, HGNSeg also demonstrates an excellent ability to alleviate the out-of-vocabulary (OOV) issue in cross-domain scenarios.

Newborn calf diarrhea has led to widespread overuse of antibiotics. Therefore, it is crucial to find effective solutions for calf diarrhea. In this study, we aimed to evaluate the impact of the synthetic organic zinc-chelating-peptide glycine-glutamine-Zn (GQ-Zn) on the microbiota and metabolites in the gut of calves with diarrhea. The results showed that GQ-Zn alleviated diarrhea in calves. Additionally, 16S rDNA sequencing and metabolomic analysis revealed that GQ-Zn improved antioxidant capacity, relieved inflammation, altered the gut microbiota by decreasing the number of harmful bacteria Prevotella denticola, Fusobacterium necrophorum and influenced metabolomic profiles via the linoleic acid metabolic pathway in calves. In conclusion, GQ-Zn supplementation alleviated diarrhea through regulating the gut microbiota and metabolites in pre-weaning Holstein calves.

Based on a contingent valuation method survey on air quality improvement in northern China, we construct several subjective perception determinants of respondents' valuation uncertainty from both the demand and perceived supply sides. Using the individual-level uncertainty measurements initially proposed by Wang and He (2011) and their alternative transformations, we analyze how these factors of demand and perceived supply sides affect people's valuation uncertainty. Our results demonstrate the significant contribution of these determinants in explaining respondents' uncertainty. On the demand side, people who ‘don't know much’ about benefits-related factors have the highest level of uncertainty, and those claiming to ‘know nothing’ most often report the lowest level of uncertainty. On the supply side, people who either do not trust or are not satisfied with the control policies tend to be more certain of their valuation. The subsequent analyses also suggest that these results be interpreted as negative certainty, which is attributed to a lack of interest.

The turbulent boundary layer (TBL) is a widely existing flow phenomenon in nature and engineering applications. Its strong mixing effect can achieve more sufficient material mixing, heat transport, etc. The understanding of the entrainment process and mechanism of irrotational fluids entering the turbulent region can be promoted by studying the geometric and dynamic characteristics of turbulent${/}$non-turbulent interfaces (TNTI). In compressible flow, it is unclear whether the properties of TNTI will change and whether the entrainment will show different features due to the influence of compressibility. Based on the direct numerical simulation results of supersonic compressible plate TBLs with Mach number of 2.9, the geometric and dynamic characteristics of TNTI are investigated in this paper. The interface is identified by the enstrophy method, and the height, thickness, fractal dimension, enstrophy transportation and entrainment characteristics of the interface are investigated. It is found that for the enstrophy transportation in a TBL, the contribution of compressibility-related terms accounts for approximately 13.4 % of the total enstrophy transportation, which tends to transfer the enstrophy of turbulence near the interface to both directions vertical to the interface. This promotes the expansion of the turbulent region towards the non-turbulent region, and the mean height, thickness and entrainment velocity are increased by approximately 3.7 %, 7.0 % and 8.5 %, respectively, while the fractal dimension is basically unaffected. Different from the incompressible flow, the contribution of the compressibility-related terms to the entrainment velocity is independent of the local curvature, and the intense entrainment process is more likely to occur on a highly curved concave surface.