This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

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.

Recently, the regression extension of latent class analysis (RLCA) model has received much attention in the field of medical research. The basic RLCA model summarizes shared features of measured multiple indicators as an underlying categorical variable and incorporates the covariate information in modeling both latent class membership and multiple indicators themselves. To reduce complexity and enhance interpretability, one usually fixes the number of classes in a given RLCA. Often, goodness of fit methods comparing various estimated models are used as a criterion to select the number of classes. In this paper, we propose a new method that is based on an analogous method used in factor analysis and does not require repeated fitting. Two ideas with application to many settings other than ours are synthesized in deriving the method: a connection between latent class models and factor analysis, and techniques of covariate marginalization and elimination. A Monte Carlo simulation study is presented to evaluate the behavior of the selection procedure and compare to alternative approaches. Data from a study of how measured visual impairments affect older persons’ functioning are used for illustration.

This paper focuses on analyzing data collected in situations where investigators use multiple discrete indicators as surrogates, for example, a set of questionnaires. A very flexible latent class model is used for analysis. We propose a Bayesian framework to perform the joint estimation of the number of latent classes and model parameters. The proposed approach applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions. In the paper, we also develop a procedure for the unique labeling of the classes. We have carried out a detailed sensitivity analysis for various hyperparameter specifications, which leads us to make standard default recommendations for the choice of priors. The usefulness of the proposed method is demonstrated through computer simulations and a study on subtypes of schizophrenia using the Positive and Negative Syndrome Scale (PANSS).

In recent years, latent class models have proven useful for analyzing relationships between measured multiple indicators and covariates of interest. Such models summarize shared features of the multiple indicators as an underlying categorical variable, and the indicators' substantive associations with predictors are built directly and indirectly in unique model parameters. In this paper, we provide a detailed study on the theory and application of building models that allow mediated relationships between primary predictors and latent class membership, but that also allow direct effects of secondary covariates on the indicators themselves. Theory for model identification is developed. We detail an Expectation-Maximization algorithm for parameter estimation, standard error calculation, and convergent properties. Comparison of the proposed model with models underlying existing latent class modeling software is provided. A detailed analysis of how visual impairments affect older persons' functioning requiring distance vision is used for illustration.

Escherichia albertii is an emerging foodborne enteropathogen associated with infectious diarrhoea in humans. In February 2023, an outbreak of acute gastroenteric cases was reported in a junior high school located in Hangzhou, Zhejiang province, China. Twenty-two investigated patients presented diarrhoea (22/22, 100%), abdominal pain (21/22, 95.5%), nausea (6/22, 27.3%), and vomiting (3/22, 13.6%). E. albertii strains were successfully isolated from anal swabs collected from six patients. Each isolate was classified as sequence type ST2686, harboured eae-β gene, and carried both cdtB-I and cdtB-II subtypes, being serotyped as EAOg32:EAHg4 serotype. A comprehensive whole-genome phylogenetic analysis revealed that the six isolates formed a distinct cluster, separate from other strains. These isolates exhibited minimal genetic variation, differing from one another by 0 to 1 single nucleotide polymorphism, suggesting a common origin from a single clone. To the best of our knowledge, this represented the first reported outbreak of gastroenteritis attributed to E. albertii outside of Japan on a global scale.

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.

Bilinguals may choose to speak a language either at their own will or in response to an external demand, but the underlying neural mechanisms in the two contexts is poorly understood. In the present study, Chinese–English bilinguals named pairs of pictures in three conditions: during forced-switch, the naming language altered between pictures 1 and 2. During non-switch, the naming language used was the same. During free-naming, either the same or different languages were used at participants' own will. While behavioural switching costs were observed during free-naming and forced-switching, neuroimaging results showed that forced language selection (i.e., forced-switch and non-switch) is associated with left-lateralized frontal activations, which have been implicated in inhibitory control. Free language selection (i.e., free-naming), however, was associated with fronto-parietal activations, which have been implicated in self-initiated behaviours. These findings offer new insights into the neural differentiation of language control in forced and free language selection contexts.

Organo-montmorillonite (OMnt) has wide applications in paints, clay-polymer nanocomposites, biomaterials, etc. In most cases, the dispersibility and swellability of OMnt dictate the performance of OMnt in the target products. Previous studies have revealed that the properties can be improved when multiple organic species are co-introduced into the interlayer space of montmorillonite (Mnt). In the present study, single surfactant erucylamide (EA), dual-surfactants cetyltrimethyl ammonium bromide (CTAB) and octadecyltrimethyl ammonium chloride (OTAC), and ternary-surfactants EA, CTAB, and OTAC were co-introduced into Mnt by solution intercalation. The resulting OMnts were characterized by powder X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, thermogravimetry-differential thermogravimetry (TG-DTG), water contact-angle tests, scanning electronic microscopy (SEM), laser particle-size analysis, and swelling indices. Mnt co-modified by ternary CTAB, OTAC, and EA led to a large d001 value (4.20 nm), surface hydrophobicity with a contact angle of 95.6°, swellability (50 mL/g) with small average particle sizes (2.1−2.8 μm) in xylene, and >99% of the OMnt particles were kept as <5 μm in deionized water. The formation of EA-modified-Mnt was proposed according to hydrophobic affinity, hydrogen bonding, and van der Waals forces. The nanoplatelets of the CTA+, OTA+, and EA co-modified OMnts in xylene were assembled into a house-of-cards structure by face-to-edge and edge-to-edge associations. The electrostatic attractions, electrostatic and steric repulsions, and hydrophobic interactions were responsible for the good dispersibility of OMnt in xylene. The ternary surfactant co-modified OMnt with high dispersion and swellability will make OMnt better suited for real-world applications.

Montmorillonite (Mnt)-based solid acids have a wide range of applications in catalysis and adsorption of pollutants. For such solid acids, the acidic characteristic often plays a significant role in these applications. The objective of the current study was to examine the effects of H3PO4-activation and supporting WO3 on the textural structure and surface acidic properties of Mnt. The Mnt-based solid acid materials were prepared by H3PO4 treatment and an impregnation method with a solution of ammonium metatungstate (AMT) and were examined as catalysts in the dehydration of glycerol to acrolein. The catalysts were characterized by nitrogen adsorption-desorption, powder X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, scanning electronic microscopy (SEM), X-ray photoelectron spectroscopy (XPS), diffuse reflectance ultraviolet-visible (DR UV-Vis) spectroscopy, temperature programmed desorption of NH3 (NH3-TPD), diffuse reflectance Fourier-transform infrared (DR FTIR) spectroscopy of adsorbed pyridine, and thermogravimetric (TG) analyses. The phosphoric acid treatment of Mnt created Brönsted and Lewis acid sites and led to increases in specific surface areas, porosity, and acidity. WO3 species influenced total acidity, acid strength, the numbers of Brönsted and Lewis acid sites, and catalytic performances. A high turnover frequency (TOF) value (31.2 h−1) based on a maximal 60.7% yield of acrolein was reached. The correlation of acrolein yield with acidic properties indicated that the cooperative role of Brönsted and Lewis acid sites was beneficial to the formation of acrolein and a little coke deposition (<3.3 wt.%). This work provides a new idea for the design of solid acid catalysts with cooperative Brönsted and Lewis acidity for the dehydration of glycerol.

Radio frequency (RF) breakdown can result in pulse shortening and seriously degrade the stability and reliability of relativistic backward wave oscillators (RBWOs). This paper discusses the energy range of electrons causing breakdown traces in slow-wave structures (SWSs) through particle-in-cell (PIC) simulation, numerical calculation, and experimental verification. The PIC simulation and numerical calculation results reveal that the energy of the majority of the field-induced electrons bombarding the SWS surfaces after being accelerated is less than 120 keV. Furthermore, the micro appearances of the breakdown traces in SWSs and the witness targets bombarded directly by electrons of various energy levels have been analyzed. Scanning electron microscope (SEM) shows that the breakdown traces are featured with corrugated morphologies with a wide range and a shallow depth. A mass of craters emerge in the vicinity of the corrugated morphologies. These appearances are quite similar to destructive traces impacted directly by low-energy electrons (around 160 keV). Thus, it is confirmed that the breakdown traces result from the bombardment of low-energy electrons. Therefore, the breakdown mechanism of field-emitted electrons impacting on the structure surfaces in RBWOs has been further improved.

Aging plays a crucial role in the mechanisms of the impacts of genetic and environmental factors on blood pressure and serum lipids. However, to our knowledge, how the influence of genetic and environmental factors on the correlation between blood pressure and serum lipids changes with age remains to be determined. In this study, data from the Chinese National Twin Registry (CNTR) were used. Resting blood pressure, including systolic and diastolic blood pressure (SBP and DBP), and fasting serum lipids, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were measured in 2378 participants (1189 twin pairs). Univariate and bivariate structural equation models examined the genetic and environmental influences on blood pressure and serum lipids among three age groups. All phenotypes showed moderate to high heritability (0.37–0.59) and moderate unique environmental variance (0.30–0.44). The heritability of all phenotypes showed a decreasing trend with age. Among all phenotypes, SBP and DBP showed a significant monotonic decreasing trend. For phenotype-phenotype pairs, the phenotypic correlation (Rph) of each pair ranged from −0.04 to 0.23, and the additive genetic correlation (Ra) ranged from 0.00 to 0.36. For TC&SBP, TC&DBP, TG&SBP and TGs&DBP, both the Rph and Ra declined with age, and the Ra difference between the young group and the older adult group is statistically significant (p < .05). The unique environmental correlation (Re) of each pair did not follow any pattern with age and remained relatively stable with age. In summary, we observed that the heritability of blood pressure was affected by age. Moreover, blood pressure and serum lipids shared common genetic backgrounds, and age had an impact on the phenotypic correlation and genetic correlations.

We present the results of two population surveys conducted 10 years apart (December 2010–February 2011 and December 2020–January 2021) of the Critically Endangered white-headed langur Trachypithecus leucocephalus in the Chongzuo White-Headed Langur National Nature Reserve, Guangxi Province, China. In the first survey, we recorded 818 individuals in 105 groups and 16 solitary adult males. In the second survey, we recorded 1,183 individuals in 128 groups and one solitary adult male. As a result of government policies, poaching for food and traditional medicine is no longer a primary threat to these langurs. However, severe forest loss and fragmentation caused by human activities could limit any future increase of this langur population.

The Wood Snipe Gallinago nemoricola is one of the least known shorebird species, and its habitat associations are very poorly understood. Here we provide the first assessment of the habitat use of the Wood Snipe during the breeding season. Between May and July 2021 at a 4-km2 alpine meadow in Sichuan province, China, we conducted population surveys and behavioural observations to identify sites where breeding Wood Snipe occurred and foraged. We quantified the habitat characteristics and food resource availability of these sites and compared them with randomly selected “background” sites. Comparison between 34 occurrence sites and 25 background sites indicated that during the breeding season, Wood Snipes are not distributed evenly across alpine meadow habitats, but preferred habitats in the lower part (3,378–3,624 m) of the alpine meadow with intermediate levels of soil moisture. In addition, comparison between 17 foraging sites and 24 background sites showed that the Wood Snipe tended to forage at sites with higher soil fauna abundance. We found weak evidence for denser vegetation cover at its height and no evidence for other biotic habitat variables such as vegetation composition or other abiotic habitat variables such as slope, soil penetrability, or disturbance level to influence Wood Snipe habitat associations. Our results suggest that the actual distribution range of the Wood Snipe during the breeding season may be smaller than expected from the extent of apparently suitable habitat. We advise caution in evaluating the potential habitat availability and distribution of the Wood Snipe, and call for further research to better understand the ecology of this rare species to inform its conservation.

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.