This study developed and evaluated an online English speaking training approach that integrates corpora and artificial intelligence (AI) tools. The training integrated a self-developed spoken corpus, generative AI tools, and text-to-speech AI tools. Pre- and post-test results identified improvements in participants’ speaking performances. Participants attempted to use more positive linguistic features (e.g. producing complex sentences more frequently) and avoid using negative linguistic features (e.g. reducing the number of vowel errors) after receiving the training. Participants showed positive attitudes towards this corpus-based and AI-integrated English oral ability learning approach and affirmed the importance of integrating both tools. The corpus helped raise participants’ awareness of features that influence speaking performance and offered prompt engineering and feedback-checking functions, while the generative AI tools provided useful feedback and tailor-made sample responses. Additionally, text-to-speech AI tools offered learners with tailor-made native speaker samples for imitation and helped learners learn pausing. Results also revealed that this approach helped create an interactive oral ability learning environment, and the combination of corpora and AI tools provided more accurate feedback for each subskill of speaking.

The current study aims to assess associations between trimethylamine N-oxide (TMAO) levels and mortality and to investigate modification effects of genetics. A total of 500 participants from a family-based cohort study were enrolled from 2005 to 2017 and followed up until 2020 in Fangshan District, Beijing, China. Serum TMAO levels were measured using the ELISA kit. The primary outcomes were all-cause mortality and deaths from CVD and stroke. During a median follow-up time of 7·38 years, thirty-eight deaths were recorded, including twenty deaths due to CVD and nineteen deaths due to stroke. Compared with the lowest TMAO quartile group, the HR for all-cause mortality was 1·35 (95 % CI: 0·44, 4·15), 1·65 (95 % CI: 0·58, 4·64) and 2·45 (95 % CI: 0·91, 6·57), respectively, in higher groups. No association was observed between TMAO and CVD mortality. However, compared with the lowest TMAO concentration group, the HR for stroke mortality was 1·93 (95 % CI: 0·40, 9·39), 1·91 (95 % CI: 0·41, 8·96) and 4·16 (95 % CI: 0·94, 18·52), respectively, in higher groups (Pfor trend = 0·046). Furthermore, polygenic risk score (PRS) for longevity modified the association of TMAO with all-cause mortality (Pfor interaction = 0·008). The risk of mortality (HR = 2·20, 95 % CI: 1·06, 4·57) was higher among participants with lower PRS compared with higher PRS (HR = 1·00, 95 % CI: 0·71, 1·40). The study indicates that elevated serum TMAO levels are potentially associated with long-term mortality risk in rural areas of northern China, especially for stroke deaths. Additionally, it provides novel evidence that genetic variations might modify the association.

6D pose estimation can perceive an object’s position and orientation in 3D space, playing a critical role in robotic grasping. However, traditional sparse keypoint-based methods generally rely on a limited number of feature points, restricting their performance under occlusion and viewpoint variations. To address this issue, we propose a novel Neighborhood-aware Graph Aggregation Network (NGANet) for precise pose estimation, which combines fully convolutional networks and graph convolutional networks (GCNs) to establish dense correspondences between 2D–3D and 3D–3D spaces. The $K$-nearest neighbor algorithm is integrated to build neighborhood relationships within isolated point clouds, followed by GCNs to aggregate local geometric features. When combined with mesh data, both surface details and topological shapes can be modeled. A positional encoding attention mechanism is introduced to adaptively fuse these multimodal features into a unified, spatially coherent representation about pose-specific features. Extensive experiments indicate that our proposed NGANet achieves a higher estimation accuracy on LINEMOD and Occlusion-LINEMOD datasets. In addition, its effectiveness is also validated under real-world scenarios.

Epidemiologic evidence on the association between dietary choline, betaine and mortality risk remains limited, particularly among non-Western populations. We examined the association of dietary choline and betaine with all-cause mortality in Chinese adults using data from the China Health and Nutrition Survey 1991–2015. We included 9027 men and 8828 women without CVD and cancer at baseline. Dietary intake was assessed using 3-day 24-hour dietary recalls and household food inventories. Death was ascertained through household surveys in each wave. Time-dependent Cox proportional hazards regression models estimated multivariable-adjusted hazard ratios (HRs) and 95 % CIs. During a median follow-up of 9·1 years, 891 men and 687 women were deceased. Higher total choline intake was associated with lower all-cause mortality in both men (HRQ5 v. Q1 = 0·58 (95 % CI: 0·45, 0·74)) and women (HRQ5 v. Q1 = 0·59 (95 % CI: 0·44, 0·78)). The dose–response curve were reverse J-shaped in men and L-shaped in women (both P-nonlinear ≤ 0·005). Similarly, fat-soluble choline intake was inversely associated with mortality in both men (HRQ5 v. Q1 = 0·59 (95 % CI: 0·46, 0·75)) and women (HRQ5 v. Q1 = 0·53 (95 % CI: 0·40, 0·70)), showing reverse J-shaped patterns (both P-nonlinear < 0·001). A J-shaped association between water-soluble choline and mortality was observed in women (P-nonlinear < 0·001), but a null association was found in men. Betaine intake was not associated with all-cause mortality in either sex. Our findings suggest that adequate choline intake is linked to reduced all-cause mortality in Chinese adults with predominantly plant-based diets.

Ostrinia furnacalis Guenée (Lepidoptera: Crambidae) is a key lepidopteran pest affecting maize production across Asia. While its general biology has been well studied, the phenomenon of pupal ring formation remains poorly understood. This study examined the factors influencing pupal ring formation under controlled laboratory conditions. Results showed that pupal rings were formed exclusively when larvae were reared on an artificial diet, with no ring formation observed on corn-stalks. Females exhibited a significantly higher tendency to participate in ring formation than males. Additionally, male participation increased proportionally with the number of rings formed, a pattern not observed in females. The size of the rearing arena significantly influenced ring formation, with smaller arenas (6 cm diameter) promoting more frequent pairing, particularly among females. Temperature also played a significant role: lower participation rates were recorded at 22 °C compared to 25 °C and 28 °C, although the number of rings formed did not differ significantly across temperatures. Developmental stage and sex composition further influenced pairing behaviour; pupal rings formed only among individuals of similar maturity, and male participation was significantly reduced in all-male groups compared to mixed-sex groups. These findings suggest that pupal ring formation in O. furnacalis is modulated by dietary substrate, larval sex, environmental conditions, and developmental synchrony, offering new insights into the behavioural ecology of this pest.

Asian corn borer, Ostrinia furnacalis Guenée (Lepidoptera: Crambidae), is a major pest in corn production, and its management remains a significant challenge. Current control methods, which rely heavily on synthetic chemical pesticides, are environmentally detrimental and unsustainable, necessitating the development of eco-friendly alternatives. This study investigates the potential of the entomopathogenic nematode Steinernema carpocapsae as a biological control agent for O. furnacalis pupae, focusing on its infection efficacy and the factors influencing its performance. We conducted a series of laboratory experiments to evaluate the effects of distance, pupal developmental stage, soil depth, and light conditions on nematode attraction, pupal mortality and sublethal impacts on pupal longevity and oviposition. Results demonstrated that S. carpocapsae exhibited the highest attraction to pupae at a 3 cm distance, with infection declining significantly at greater distances. Younger pupae (<12 h old), were more attractive to nematodes than older pupae, and female pupae were preferred over males. Nematode infection was highest on the head and thorax of pupae, with a significant reduction in infection observed after 24 h. Infection caused 100% mortality in pupae within 2 cm soil depth, though efficacy was reduced under light conditions. Sublethal effects included a significant reduction in the longevity of infected adults and a decrease in the number of eggs laid by infected females compared to controls. These findings underscore the potential of S. carpocapsae as an effective biocontrol agent for sustainable pest management in corn production, offering a viable alternative to chemical pesticides.

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.

Direct numerical simulations of temporally developing compressible mixing layers have been performed to investigate the effects of large-scale structures (LSSs) on turbulent kinetic energy (TKE) budgets at convective Mach numbers ranging from $M_c=0.2$ to $1.8$ and at Taylor Reynolds numbers up to 290. In the core region of mixing layers, the volume fraction of low-speed LSSs decreases linearly with respect to the vertical distance at a Mach-number-independent rate. The contributions of low-speed LSSs to TKE, and its budget, including production, dissipation, pressure-strain and spatial diffusion terms, are primarily concentrated in the upper region of mixing layer. The streamwise and vertical mass flux coupling terms mainly transport TKE downwards in low-speed LSSs, and their magnitudes are comparable to the other dominant terms. Near the edges of LSSs, the sources and losses of all three components of TKE are completely different to each other, and dominated by turbulent diffusion, pressure diffusion, pressure-strain and dissipation terms. The TKE, their total variation and dissipation are significantly amplified at edges of low-speed LSSs, especially at the upper edge. This observation supports the existence of amplitude modulation exerted by the LSSs onto the near-edge small-scale structures in mixing layers. The level of amplitude modulation is strongest for the vertical velocity, followed by the streamwise velocity, and weakest for the spanwise velocity. Additionally, the amplitude modulation effect decreases significantly with increasing convective Mach number. The results on the amplitude modulation effect is helpful for developing predictive models of budget terms of TKE in mixing layers.

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.

Establishing the invariance property of an instrument (e.g., a questionnaire or test) is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item functioning (DIF) analysis, i.e., detecting DIF items whose response distribution depends not only on the latent trait measured by the instrument but also on the group membership. DIF analysis is confounded by the group difference in the latent trait distributions. Many DIF analyses require knowing several anchor items that are DIF-free in order to draw inferences on whether each of the rest is a DIF item, where the anchor items are used to identify the latent trait distributions. When no prior information on anchor items is available, or some anchor items are misspecified, item purification methods and regularized estimation methods can be used. The former iteratively purifies the anchor set by a stepwise model selection procedure, and the latter selects the DIF-free items by a LASSO-type regularization approach. Unfortunately, unlike the methods based on a correctly specified anchor set, these methods are not guaranteed to provide valid statistical inference (e.g., confidence intervals and p-values). In this paper, we propose a new method for DIF analysis under a multiple indicators and multiple causes (MIMIC) model for DIF. This method adopts a minimal $L_1$\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} norm condition for identifying the latent trait distributions. Without requiring prior knowledge about an anchor set, it can accurately estimate the DIF effects of individual items and further draw valid statistical inferences for quantifying the uncertainty. Specifically, the inference results allow us to control the type-I error for DIF detection, which may not be possible with item purification and regularized estimation methods. We conduct simulation studies to evaluate the performance of the proposed method and compare it with the anchor-set-based likelihood ratio test approach and the LASSO approach. The proposed method is applied to analysing the three personality scales of the Eysenck personality questionnaire-revised (EPQ-R).

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

The breaking and energy distribution of mode-1 depression internal solitary wave interactions with Gaussian ridges are examined through laboratory experiments. A series of processes, such as shoaling, breaking, transmission and reflection, are captured completely by measuring the velocity field in a large region. It is found that the maximum interface descent ($a_{max}$) during wave shoaling is an important parameter for diagnosing the type of wave–ridge interaction and energy distribution. The wave breaking on the ridge depends on the modified blockage parameter $\zeta _m$, the ratio of the sum of the upper layer depth and $a_{max}$ to the water depth at the top of the ridge. As $\zeta _m$ increases, the interaction type transitions from no breaking to plunging and mixed plunging–collapsing breaking. Within the scope of this experiment, the energy distribution can be characterized solely by $\zeta _m$. The transmission energy decreases monotonically with increasing $\zeta _m$, and there is a linear relationship between $\zeta _m^2$ and the reflection coefficient. The value of $a_{max}$ can be determined from the basic initial parameters of the experiment. Based on the incident wave parameters, the depth of the upper and lower layers, and the topographic parameters, two new simple methods for predicting $a_{max}$ on the ridge are proposed.

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.

Nano-silicon has been regarded as the most promising anode material for next-generation lithium-ion batteries (LIBs). However, the preparation of nano-silicon suffers from high cost, complex procedures, and low yield, which hinders its commercial application. In this study, porous nano-silicon with particle sizes in the range of 50–100 nm was prepared through molten salt-assisted magnesiothermic reduction using porous nano-silica derived from clay minerals as the precursor. Through combining ball milling and acid activation, the synthesised nano-silica derived from montmorillonite exhibited smaller particle sizes (below 50 nm), higher specific surface area (647 m2 g–1), and total pore volume (0.71 cm3 g–1). This unique structure greatly facilitated the conversion efficiency of silica into nano-silicon by maximising the contact area between silica and magnesium powder and optimising the diffusion kinetics of magnesium atoms. When used as anodes in LIBs, the synthesised nano-silicon materials demonstrated a high specific capacity of up to 1222 mAh g–1 and an excellent capacity retention rate of 79% after 150 cycles at a current density of 0.5 A g–1. This method provides a novel approach for the cost-effective and large-scale production of nano-silicon materials for high-performance anodes.

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.

One species-general life history (LH) principle posits that challenging childhood environments are coupled with a fast or faster LH strategy and associated behaviors, while secure and stable childhood environments foster behaviors conducive to a slow or slower LH strategy. This coupling between environments and LH strategies is based on the assumption that individuals’ internal traits and states are independent of their external surroundings. In reality, individuals respond to external environmental conditions in alignment with their intrinsic vitality, encompassing both physical and mental states. The present study investigated attachment as an internal mental state, examining its role in mediating and moderating the association between external environmental adversity and fast LH strategies. A sample of 1169 adolescents (51% girls) from 9 countries was tracked over 10 years, starting from age 8. The results confirm both mediation and moderation and, for moderation, secure attachment nullified and insecure attachment maintained the environment-LH coupling. These findings suggest that attachment could act as an internal regulator, disrupting the contingent coupling between environmental adversity and a faster pace of life, consequently decelerating human LH.

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.