This study was designed to explore the mediating role of serum 25-hydroxyvitamin D (25(OH) D) in Triglyceride–glucose (TyG) index and hypertension (HTN). Study participants were selected from the 2001 to 2018 National Health and Nutrition Examination Survey. Firstly, we estimated the association between TyG index and serum 25(OH)D with HTN using a weighted multivariable logistic regression model and restricted cubic spline. Secondly, we used a generalised additive model to investigate the correlation between TyG index and serum 25(OH)D. Lastly, serum 25(OH)D was investigated as a mediator in the association between TyG index and HTN. There were 14 099 subjects in total. TyG index was positively and linearly associated with HTN risk, while serum 25(OH)D had a U-shaped relationship with the prevalence of HTN. When the serum 25(OH)D levels were lower than 57·464 mmol/l, the prevalence of HTN decreased with the increase of serum 25(OH)D levels. When serum 25(OH)D levels rise above 57·464 mmol/l, the risk of HTN increases rapidly. Based on the U-shaped curve, serum 25(OH)D concentrations were divided into two groups: < 57·464 and ≥57·464 mmol/l. According to the mediation analysis, when serum 25(OH)D levels reached < 57·464 mmol/l, the positive association between the TyG index and incident HTN was increased by 25(OH)D. When serum 25(OH)D levels reached ≥ 57·464 mmol/l, the negative association between the TyG index and incident HTN was increased by 25(OH)D. There was a mediation effect between the TyG index and HTN, which was mediated by 25(OH)D. Therefore, we found that the association between serum 25(OH)D levels and TyG index may influence the prevalence of HTN.

This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave-h-out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of predictors is relatively large, we propose a factor TVCSA method to reduce the computational burden by first reducing the dimension of predictors by extracting a few factors using principal component analysis and then obtaining the TVCSA forecasts from time-varying models with the generated factors. We show that the TVCSA estimator remains asymptotically optimal in the presence of generated factors. Monte Carlo simulation studies have provided favorable evidence for the TVCSA methods relative to the popular model-averaging methods in the literature. Empirical applications to equity premiums and inflation forecasting highlight the practical merits of the proposed methods.

When an oblate droplet translates through a viscous fluid under linear shear, it experiences a lateral lift force whose direction and magnitude are influenced by the Reynolds number, the droplet’s viscosity and its aspect ratio. Using a recently developed sharp interface method, we perform three-dimensional direct numerical simulations to explore the evolution of lift forces on oblate droplets across a broad range of these parameters. Our findings reveal that in the low-but-finite Reynolds number regime, the Saffman mechanism consistently governs the lift force. The lift increases with the droplet’s viscosity, aligning with the analytical solution derived by Legendre & Magnaudet (Phys. Fluids, vol. 9, 1997, p. 3572), and also rises with the droplet’s aspect ratio. We propose a semi-analytical correlation to predict this lift force. In the moderate- to high-Reynolds-number regime, distinct behaviours emerge: the $L\hbox{-}$ and $S\hbox{-}$mechanisms, arising from the vorticity contained in the upstream shear flow and the vorticity produced at the droplet surface, dominate for weakly and highly viscous droplets, respectively. Both mechanisms generate counter-rotating streamwise vortices of opposite signs, leading to observed lift reversals with increasing droplet viscosity. Detailed force decomposition based on vorticity moments indicates that in the $L\hbox{-}$mechanism-dominated regime for weakly to moderately viscous droplets, the streamwise vorticity-induced lift approximates the total lift. Conversely, in the $S\hbox{-}$mechanism-dominated regime, for moderately to highly viscous droplets, the streamwise vorticity-induced lift constitutes only a portion of the total lift, with the asymmetric advection of azimuthal vorticity at the droplet interface contributing additional positive lift to counterbalance the $S\hbox{-}$mechanism’s effects. These insights bridge the understanding between inviscid bubbles and rigid particles, enhancing our comprehension of the lift force experienced by droplets in different flow regimes.

State-owned enterprises (SOEs) in China play a critical role in national economic development and the country's positioning on the global stage. Chinese SOEs have undergone substantial transformations from traditional government-run entities to a variety of corporate forms exhibiting different levels of state involvement. Despite their substantial influence, the internal diversity of SOEs – from wholly state-owned to mixed-ownership – has not been thoroughly examined. This paper provides an overview of SOEs' critical roles in the Chinese economy, the relationship between SOEs and privately owned enterprises (POEs), and the challenges of SOEs in different stages of Chinese economic development. It then introduces five research papers that explore the institutional, strategic, and organizational perspectives on how SOEs manage the dual pressures of state and market logic, respond to policy adjustments, tackle leadership challenges, and navigate current global trends such as digital transformation, technological innovation, and environmental sustainability. In this paper, we provide important implications for policy and managerial practices and highlight a future research agenda for the heterogeneity of Chinese SOEs, and how SOEs respond to these challenges in the evolving geopolitical landscape, adapt their strategies, and manage relationships with foreign governments and enterprises under such conditions.

When a word is being translated, its immediately adjacent lexical items may impact the translation of the target word. However, the impact of adjacent lexical items on the oral translation of a target word situated in central vision remains unexplored. This behavioral study used a bilingual version of the flanker paradigm to examine the impact of within- and cross-language semantic effects on oral word translation. Unbalanced bilinguals were presented with a central target word that was flanked by two flanking words on either side. The target-flanker relations were manipulated as a function of semantic relatedness (identical, related and unrelated) and language congruency (congruent and incongruent). The task was to orally translate the target word from L1 to L2 (forward translation) in one session and from L2 to L1 (backward translation) in the other while ignoring the flanker words. Results showed faster responses for forward compared to backward translation. Moreover, in within-language (congruent) but not in cross-language (incongruent) contexts, semantic priming effects were observed in both directions of translation, with the effects being larger for backward than forward translation. Additionally, substantial cross-language semantic repetition priming effects were obtained. The findings are discussed within the framework of a two-process account for oral word translation.

In this paper, we propose a novel and highly effective variational Bayesian expectation maximization-maximization (VBEM-M) inference method for log-linear cognitive diagnostic model (CDM). In the implementation of the variational Bayesian approach for the saturated log-linear CDM, the conditional variational posteriors of the parameters that need to be derived are in the same distributional family as the priors, the VBEM-M algorithm overcomes this problem. Our algorithm can directly estimate the item parameters and the latent attribute-mastery pattern simultaneously. In contrast, Yamaguchi and Okada’s (2020a) variational Bayesian algorithm requires a transformation step to obtain the item parameters for the log-linear cognitive diagnostic model (LCDM). We conducted multiple simulation studies to assess the performance of the VBEM-M algorithm in terms of parameter recovery, execution time, and convergence rate. Furthermore, we conducted a series of comparative studies on the accuracy of parameter estimation for the DINA model and the saturated LCDM, focusing on the VBEM-M, VB, expectation-maximization, and Markov chain Monte Carlo algorithms. The results indicated that our method can obtain more stable and accurate estimates, especially for the small sample sizes. Finally, we demonstrated the utility of the proposed algorithm using two real datasets.

This study aimed to investigate the effects of physical multimorbidity on the trajectory of cognitive decline over 17 years and whether vary across wealth status. The study was conducted in 9035 respondents aged 50+ at baseline from nine waves (2002–2019) of the English Longitudinal Study of Aging. A latent class analysis was used to identify patterns of physical multimorbidity, and mixed multilevel models were performed to determine the association between physical multimorbidity and trajectories of cognitive decline. Joint analyses were conducted to further verify the influence of wealth status. Four patterns of physical multimorbidity were identified. Mixed multilevel models with quadratic terms of time and status/patterns indicated significant non-linear trajectories of multimorbidity on cognitive function. The magnitude of the association between complex multisystem patterns and cognitive decline increased the most as follow-up progressed. Individuals with high wealth and hypertension/diabetes patterns have significantly lower composite global cognitive z scores over time as compared with respiratory/osteoporosis patterns. Physical multimorbidity at baseline is associated with the trajectory of cognitive decline, and the magnitude of the association increased over time. The trend of cognitive decline differed in specific combinations of wealth status and physical multimorbidity.

With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational speed. This study introduces a novel “divide- and-conquer” parallel algorithm built on the Wasserstein posterior approximation concept, aiming to enhance computational speed while maintaining accurate parameter estimation. This algorithm enables drawing parameters from segmented data subsets in parallel, followed by an amalgamation of these parameters via Wasserstein posterior approximation. Theoretical support for the algorithm is established through asymptotic optimality under certain regularity assumptions. Practical validation is demonstrated using real-world data from the Programme for International Student Assessment. Ultimately, this research proposes a transformative approach to managing educational big data, offering a scalable, efficient, and precise alternative that promises to redefine traditional practices in educational assessments.

Soft robots show an advantage when conducting tasks in complex environments due to their enormous flexibility and adaptability. However, soft robots suffer interactions and nonlinear deformation when interacting with soft and fluid materials. The reason behind is the free boundary interactions, which refers to undetermined contact between soft materials, specifically containing nonlinear deformation in air and nonlinear interactions in fluid for soft robot simulation. Therefore, we propose a new approach using material point method (MPM), which can solve the free boundary interactions problem, to simulate soft robots under such environments. The proposed approach can autonomously predict the flexible and versatile behaviors of soft robots. Our approach entails incorporating automatic differentiation into the algorithm of MPM to simplify the computation and implement an efficient implicit time integration algorithm. We perform two groups of experiments with an ordinary pneumatic soft finger in different free boundary interactions. The results indicate that it is possible to simulate soft robots with nonlinear interactions and deformation, and such environmental effects on soft robots can be restored.

In the present paper, we consider the following fractional double phase problem with nonlocal reaction:

\begin{align*}\qquad \quad \left\{\begin{array}{lll}(- \Delta)_p^s u + (- \Delta)_q ^s u + V(\varepsilon x) (|u|^{p - 2} u + |u|^{q - 2} u) = \left(\frac{1}{|x|^\mu} * F(u)\right) f(u) & \ \text{in}\ \mathbb{R}^N, \\u \in W^{s,p}(\mathbb{R}^N) \cap W^{s,q}(\mathbb{R}^N) ,\ u \gt 0 & \ \text{in}\ \mathbb{R}^N,\end{array}\right\}\end{align*}

where ɛ is apositive parameter, $0 \lt s \lt 1$, $2 \leqslant p \lt q \lt \min\{2p, N / s\}$, $0 \lt \mu \lt sp$, $(- \Delta)_t^s$ $(t \in \left\{p,q\right\})$ is the fractional t-Laplace operator, the reaction term $f : \mathbb{R} \mapsto \mathbb{R}$ is continuous, and the potential $V \in C (\mathbb{R}^N , \mathbb{R})$ satisfying a local condition. Using a variational approach and topological tools (the non-standard C1-Nehari manifold analysis and the abstract category theory), multiplicity of positive solutions and concentration properties for the above problem are established. Our results extend and complement some previous contributions related to double phase variational integrals.

Folate metabolism is involved in the development and progression of various cancers. We investigated the association of single nucleotide polymorphisms (SNP) in folate-metabolising genes and their interactions with serum folate concentrations with overall survival (OS) and liver cancer-specific survival (LCSS) of newly diagnosed hepatocellular carcinoma (HCC) patients. We detected the genotypes of six SNP in three genes related to folate metabolism: methylenetetrahydrofolate reductase (MTHFR), 5-methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR) and 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR). Cox proportional hazard models were used to calculate multivariable-adjusted hazard ratios (HR) and 95 % CI. This analysis included 970 HCC patients with genotypes of six SNP, and 864 of them had serum folate measurements. During a median follow-up of 722 d, 393 deaths occurred, with 360 attributed to HCC. In the fully-adjusted models, the MTRR rs1801394 polymorphism was significantly associated with OS in additive (per G allele: HR = 0·84, 95 % CI: 0·71, 0·99), co-dominant (AG v. AA: HR = 0·77; 95 % CI: 0·62, 0·96) and dominant (AG + GG v. AA: HR = 0·78; 95 % CI: 0·63, 0·96) models. Carrying increasing numbers of protective alleles was linked to better LCSS (HR10–12 v. 2–6 = 0·70; 95 % CI: 0·49, 1·00) and OS (HR10–12 v. 2–6 = 0·67; 95 % CI: 0·47, 0·95). Furthermore, we observed significant interactions on both multiplicative and additive scales between serum folate levels and MTRR rs1801394 polymorphism. Carrying the variant G allele of the MTRR rs1801394 is associated with better HCC prognosis and may enhance the favourable association between higher serum folate levels and improved survival among HCC patients.

Weedy rice (Oryza sativa f. spontanea Auct. ex Backer) is a troublesome annual weed from the Gramineae family and infests rice (Oryza sativa L.) fields globally, with a notable presence in China. However, limited information is available regarding the effects of diverse environmental factors on its germination and emergence. A better understanding of the seed biology and ecology of weedy rice is crucial for developing effective weed management strategies. Experiments were conducted to evaluate the effects of temperature, light, soil burial depth, wheat (Triticum aestivum L.) crop residue amount, salt stress, osmotic stress, and radiant heat on the germination and seedling emergence of weedy rice. Weedy rice exhibited robust germination (>98%) when exposed to varying day/night temperatures (20/15 to 35/30 C) and remained unaffected by light conditions. Seedling emergence was not influenced within the top 5-cm soil layer, where 100% of the seedlings emerged. However, emergence decreased as the soil burial depth increased, eventually resulting in no emergence from a burial depth of 11 cm. The soil burial depth required for 50% of the maximum emergence was 8.3 cm. Seedling emergence ranged from 97% to 100% across different amounts of the wheat straw residue cover (0 to 10,000 kg ha−1). The sodium chloride concentration and osmotic potential required for 50% were 230.8 mM and −0.5 MPa, respectively. No germination was observed when weedy rice seeds were exposed to >110 C pretreatment (radiant heat for 5 min), indicating that residue burning could reduce infestation of weedy rice. The insights gained from this study contribute valuable knowledge to enhance the integrated management of weedy rice in China.

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.

This paper retrospectively analysed the prevalence of macrolide-resistant Mycoplasma pneumoniae (MRMP) in some parts of China. Between January 2013 and December 2019, we collected 4,145 respiratory samples, including pharyngeal swabs and alveolar lavage fluid. The highest PCR-positive rate of M. pneumoniae was 74.5% in Beijing, the highest resistance rate was 100% in Shanghai, and Gansu was the lowest with 20%. The highest PCR-positive rate of M. pneumoniae was 74.5% in 2013, and the highest MRMP was 97.4% in 2019; the PCR-positive rate of M. pneumoniae for adults in Beijing was 17.9% and the MRMP was 10.48%. Among the children diagnosed with community-acquired pneumonia (CAP), the PCR-positive and macrolide-resistant rates of M. pneumoniae were both higher in the severe ones. A2063G in domain V of 23S rRNA was the major macrolide-resistant mutation, accounting for more than 90%. The MIC values of all MRMP to erythromycin and azithromycin were ≥ 64 μg/ml, and the MICs of tetracycline and levofloxacin were ≤ 0.5 μg/ml and ≤ 1 μg/ml, respectively. The macrolide resistance varied in different regions and years. Among inpatients, the macrolide-resistant rate was higher in severe pneumonia. A2063G was the common mutation, and we found no resistance to tetracycline and levofloxacin.

The integration of camera and LiDAR technologies has the potential to significantly enhance construction robots’ perception capabilities by providing complementary construction information. Structured light cameras (SLCs) are a desirable alternative as they provide comprehensive information on construction defects. However, fusing these two types of information depends largely on the sensors’ relative positions, which can only be established through extrinsic calibration. This paper introduces a novel calibration algorithm considering a customized board for SLCs and repetitive LiDARs, which are designed to facilitate the automation of construction robots. The calibration board is equipped with four symmetrically distributed hemispheres, whose centers are obtained by fitting the spheres and adoption with the geometric constraints. Subsequently, the spherical centers serve as reference features to estimate the relationship between the sensors. These distinctive features enable our proposed method to only require one calibration board pose and minimize human intervention. We conducted both simulation and real-world experiments to assess the performance of our algorithm. And the results demonstrate that our method exhibits enhanced accuracy and robustness.