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.

The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research.

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.

Controller synthesis offers a correct-by-construction methodology to ensure the correctness and reliability of safety-critical cyber-physical systems (CPS). Controllers are classified based on the types of controls they employ, which include reset controllers, feedback controllers and switching logic controllers. Reset controllers steer the behavior of a CPS to achieve system objectives by restricting its initial set and redefining its reset map associated with discrete jumps. Although the synthesis of feedback controllers and switching logic controllers has received considerable attention, research on reset controller synthesis is still in its early stages, despite its theoretical and practical significance. This paper outlines our recent efforts to address this gap. Our approach reduces the problem to computing differential invariants and reach-avoid sets. For polynomial CPS, the resulting problems can be solved by further reduction to convex optimizations. Moreover, considering the inevitable presence of time delays in CPS design, we further consider synthesizing reset controllers for CPS that incorporate delays.

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.

In this paper, we consider the problem of contact parameters (slippage and sinkage) estimation for multi-modal robot locomotion on granular terrains. To describe the contact events in the same framework for robots operated at different modes (e.g., wheel, leg), we propose a unified description of contact parameters for multi-modal robots. We also provide a parameter estimation method for multi-modal robots based on CNN and DWT (discrete wavelet transformation) techniques and verify its effectiveness over different types of granular terrains. Besides motion modes, this paper also considers the influence of slope angles and the robot’s handing angles over contact parameters. Through comparison and analysis of the prediction results, our method can not only effectively predict the contact parameters of multi-modal robot locomotion on a granular medium (better than $96\%$ accuracy) but also achieves the same or better performance when compared to other (direct) contact measurement methods designed for individual motion modes, that is, single-modal robots such as quadruped robots and mars rovers. Our proposed unified contact parameter estimation method can be useful for studying the interaction mechanics between multi-modal robots and granular terrains as well as terrain classification tasks due to its superior sensitivity which is analyzed in the experiments.

Nitrate is linked to chronic human illness and to a variety of environmental problems, and continues to be a contaminant of concern in soils and natural waters. Improved methods for nitrate abatement, thus, are still needed. The purpose of this study was to assess the potential for redox-modified, iron-bearing clay minerals to act as nitrate decontamination agents in natural environments. The model clay mineral tested was ferruginous smectite (sample SWa-1) exchanged with either sodium (Na+) or polydiallyldimethylammonium chloride (poly-DADMAC). Structural iron (Fe) in SWa-1 was in either the oxidized or reduced state. Little nitrate uptake was observed in the Na+-SWa-1, which was attributed to coulombic repulsion between the basal surfaces of the smectite and the nitrate anion. The addition of the DADMAC to the SWa-1 reversed the electrostatic charge manifested at the smectite surface from negative to positive, as measured by the zeta (ζ) potential. The positively charged poly-DADMAC-SWa-1 yielded high nitrate uptake due to coulombic attraction in both the oxidized and reduced states of the Fe in the SWa-1. The presence of reduced structural Fe(II) in the positively charged poly-DADMAC-SWa-1 enabled a chemical reduction reaction with the nitrate to produce nitrite. The amounts of nitrite found in solution, however, failed to account for all of the Fe(II) oxidized, so other N reduction products may also have formed or perhaps nitrite was also present in the adsorbed phase. The effects of other complexities, such as polymer configuration at the surface, also need further investigation. The results do clearly establish abiotic nitrate reduction to nitrite and possibly other reduction products. The combination of bacterial activity in soils and sediments, which is known to reduce structural Fe in smectites, and the abundance of organic cations in soil organic matter creates an environment where reversed-charge smectite could exist in nature. This represents a potentially effective system for mitigating harmful effects of nitrate in soils, sediments, groundwater, and surface water.

Modified kaolinites possess excellent adsorption properties and, therefore, are regarded widely as potential catalytic components. The use of modified kaolinites as a catalytic component for Fischer–Tropsch synthesis (FTS) has remained unexplored, however. In the current study, delaminated and pit-rich nano-kaolinite was prepared via acid treatment of N-methylformamide (NMF)-intercalated kaolinite (intercalation-etching strategy), and was used as a support to prepare a cobalt-based FTS catalyst (denoted as 15%-Co-HNKln). Compared with other FTS catalysts, the supports for which were raw kaolinite or directly acid-treated kaolinite, the 15%-Co-HNKln showed several advantages such as large specific surface area, dispersed Co particles with small particle size, more new active sites, and significant surface acidity. Given the aforementioned advantages, the 15%-Co-HNKln catalyst demonstrated very good FTS performance. Compared with that of the raw kaolinite-supported FTS catalyst, the CO conversion rate and C5–C12 hydrocarbon selectivity of 15%-Co-HNKln increased by 20% and 15%, respectively.

Blastocystis sp. is a prevalent protistan parasite found globally in the gastrointestinal tract of humans and various animals. This review aims to elucidate the advancements in research on axenic isolation techniques for Blastocystis sp. and their diverse applications. Axenic isolation, involving the culture and isolation of Blastocystis sp. free from any other organisms, necessitates the application of specific media and a series of axenic treatment methods. These methods encompass antibiotic treatment, monoclonal culture, differential centrifugation, density gradient separation, micromanipulation and the combined use of culture media. Critical factors influencing axenic isolation effectiveness include medium composition, culture temperature, medium characteristics, antibiotic type and dosage and the subtype (ST) of Blastocystis sp. Applications of axenic isolation encompass exploring pathogenicity, karyotype and ST analysis, immunoassay, characterization of surface chemical structure and lipid composition and understanding drug treatment effects. This review serves as a valuable reference for clinicians and scientists in selecting appropriate axenic isolation methods.

Sichuan cuisine was previously fitted into the Chinese Heart-Healthy Diet (CHH) trial to verify the antihypertensive effect. Whether the modified Sichuan diet lessens cardiovascular disease (CVD) is not fully explored. We aimed to estimate the effects of the Sichuan version of CHH diet (CHH diet-SC) on the 10-year risk of CVD and vascular age. A single-blinded randomised controlled feeding trial was conducted. General CVD prediction model was used in manners of intention-to-treat and per-protocol set. After a 7-d run-in period, fifty-three participants with pre- and grade I hypertension from local communities were randomised and provided with either CHH diet-SC (n 27) or a control diet (n 26) for 4 weeks. Mean absolute and relative estimated CVD risks were reduced by 4·5 % and 27·9 % in the CHH diet-SC group, and the between-group relative risk reduction was 19·5 % (P < 0·001) using linear mixed-effects models. The sensitivity analysis with datasets and models showed consistent results, and pre-specified factors were not associated with the intervention effects. The vascular age of CHH-SC group was theoretically 4·4 years younger than that of the control group after intervention. Compared with a typical diet, adopting the CHH diet-SC over 1 month significantly reduced 10-year CVD risks and vascular ages among local adults with mild hypertension.

Blastocystis sp. is a common parasite in the intestinal tract of humans and animals. The clinical diagnosis of Blastocystis sp. mainly depends on the microscopic observation of parasite, which can lead to false-negative results. An accurate and convenient diagnostic approach for Blastocystis sp. infection is crucial for effectively preventing and controlling blastocystosis. Herein, we developed a recombinase polymerase amplification (RPA) method for detecting Blastocystis sp. The results showed that the DNA amplification by RPA established in this study could be performed within 5 min at 37°C, with maximum band intensity observed at 30 min. The minimum detection limit of RPA was 100 fg μL−1, consistent with conventional polymerase chain reaction (cPCR). Furthermore, the RPA method exhibited no cross-reactivity with 7 other non-target pathogens in the intestinal tract. Next, the newly established RPA method was used to analyse 40 fecal samples collected clinically, and the detection results were consistent with cPCR. These results corroborate that the newly developed RPA method has good sensitivity and specificity and offers the advantage of short detection times, which can be harnessed for differential diagnosis and rapid detection of Blastocystis sp.

This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough, unbiasedness comes at no cost at all. The approach does not require any knowledge of how many factors there are, or whether they are deterministic or stochastic. The order of integration of the factors is also treated as unknown, as is the order of integration of the regressors, which means that there is no need to pre-test for unit roots, or to decide on which deterministic terms to include in the model.