In the fields of meal-assisting robotics and human–robot interaction (HRI), real-time and accurate mouth pose estimation is critical for ensuring interaction safety and improving user experience. The complexity arises from the diverse opening degrees of mouths, variations in orientation, and external factors such as lighting conditions and occlusions, which pose significant challenges for real-time and accurate posture estimation of mouths. In response to the above-mentioned issues, this paper proposes a novel method for point cloud fitting and posture estimation of mouth opening degrees (FP-MODs). The proposed method leverages both RGB and depth images captured from a single viewpoint, integrating geometric modeling with advanced point cloud processing techniques to achieve robust and accurate mouth posture estimation. The innovation of this work lies in the hypothesis that different states of mouth openings can be effectively described by distinct geometric shapes: closed mouths are modeled by spatial quadratic surfaces, half-open mouths by spatial ellipses, and fully open mouths by spatial circles. Then, based on these hypotheses, we developed algorithms for fitting geometric models to point clouds obtained from mouth regions, respectively. Specifically, for the closed mouth state, we employ an algorithm based on least squares optimization to fit a spatial quadratic surface to the point cloud data. For the half-open or fully open mouth states, we combine inverse projection methods with least squares fitting to model the contour as a spatial ellipse and circle, respectively. Finally, to evaluate the effectiveness of the proposed FP-MODs method, extensive actual experiments were conducted under varying conditions, including different orientations and various types of mouths. The results demonstrate that the proposed FP-MODs method achieves high accuracy and robustness. This study can provide a theoretical foundation and technical support for improving HRI and food delivery safety in the field of robotics.

The Hector Galaxy Survey is a new optical integral field spectroscopy (IFS) survey currently using the Anglo-Australian Telescope (AAT) to observe up to 15,000 galaxies at low redshift (z < 0.1). The Hector instrument employs 21 optical fibre bundles feeding into two double-beam spectrographs, AAOmega and the new Spector spectrograph, to enable wide-field multi-object IFS observations of galaxies. To efficiently process the survey data, we adopt the data reduction pipeline developed for the SAMI Galaxy Survey, with significant updates to accommodate Hector’s dual-spectrograph system. These enhancements address key differences in spectral resolution and other instrumental characteristics relative to SAMI, and are specifically optimised for Hector’s unique configuration. We introduce a two-dimensional arc fitting approach that reduces the root-mean-square (RMS) velocity scatter by a factor of 1.2–3.4 compared to fitting arc lines independently for each fibre. The pipeline also incorporates detailed modelling of chromatic optical distortion in the wide-field corrector, to account for wavelength-dependent spatial shifts across the focal plane. We assess data quality through a series of validation tests, including wavelength solution accuracy (1.2–2.7 km s–1 RMS), spectral resolution (FWHM of 1.2–1.4 Å for Spector), throughput characterisation, astrometric precision (≲ 0.03 arcsec median offset), sky subtraction residuals (1–1.6% median continuum residual), and flux calibration stability (4% systematic offset when compared to Legacy Survey fluxes). We demonstrate that Hector delivers high-fidelity, science-ready datasets, supporting robust measurements of galaxy kinematics, stellar populations, and emission-line properties, and provide examples. Additionally, we address systematic uncertainties identified during the data processing and propose future improvements to enhance the precision and reliability of upcoming data releases. This work establishes a robust data reduction framework for Hector, delivering high-quality data products that support a broad range of extragalactic studies.

Soft robots have emerged as a transformative technology with widespread applications across diverse fields. Among various actuation mechanisms, fluid-based actuation remains predominant in soft robotics, where precise fluid regulation is fundamental to system performance. This review aims to provide a comprehensive reference for researchers interested in fluid regulation strategies in soft robots by outlining the current state of research in this field and discussing innovations in valve designs to inspire future advancements. The fluid regulation strategies discussed in this review are systematically categorized into three main approaches: valve-based, smart fluid-based, and pressure source-based strategies, with each type systematically classified and discussed in detail. Building upon this analysis, a Task-to-Fluidic Regulation System mapping framework is proposed, integrating the V-model principles from systems engineering to provide a structured, requirements-driven methodology that links task objectives to concrete regulation system configurations through sequential design and multi-level verification. Finally, the latest advancements in fluid regulation methods in soft robotics are summarized, along with emerging trends and directions for future development.

Accurate mortality forecasting is crucial for actuarial pricing, reserving, and capital planning, yet the traditional Lee-Carter model struggles with non-linear age and cohort patterns, coherent multi-population forecasting, and quantifying prediction uncertainties. Recent advances in deep learning provide a range of tools that can address these limitations, but actuarial surveys have not kept pace. This paper provides the first concise view of deep learning in mortality forecasting. We cover six deep network architectures, namely Recurrent Neural Networks, Convolutional Neural Networks, Transformers, Autoencoders, Locally Connected Networks, and Multi-Task Feed-Forward Networks. We discuss how these architectures tackle cohort effects, population coherence, interpretability, and uncertainty in mortality forecasting. Evidence from the literature shows that carefully calibrated deep learning models can consistently outperform the Lee-Carter baselines; however, no single architecture resolves every challenge, and open issues remain with data scarcity, interpretability, uncertainty quantification, and keeping pace with the advances of deep learning. This review is also intended to provide actuaries with a practical roadmap for adopting deep learning models in mortality forecasting.

Despite general public support, efforts to build affordable housing often encounter stiff resistance due to “not in my backyard” (NIMBY) attitudes, which are often rooted in false or unsupported beliefs about affordable housing and its impacts on surrounding communities. Would correcting these misperceptions increase support for building affordable housing? To answer this question, we conducted a preregistered survey experiment measuring how support for affordable housing in the U.S. varies at different distances from where respondents live (one-eighth of a mile away, two miles away, or in their state). Our results indicate that correcting stereotypes about affordable housing and misperceptions about its effects increase support for affordable housing. Contrary to expectations, these effects are often larger for affordable housing near the respondent’s home (rather than at the state level), suggesting that debunking myths about affordable housing may help to counter NIMBY attitudes.

Four-wheeled, 25–50 horsepower tractors imported to China from other socialist countries in the 1950s were a symbol of modernity and a source of problems. They were introduced to North China to increase multiple cropping. No significant increase in multiple cropping occurred in that region. The cost of tractor services far outweighed what could be earned with the labour they displaced in the 1950s and early 1960s. However, the government remained committed to them, even as it promoted cheaper five horsepower two-wheeled tractors. Greater use of four-wheeled tractors was sustained by the rapid downgrading of the hitherto privileged role of the tractor driver, alongside an ad hoc system of tacit subsidies. These changes meant deviation from the original vision for tractors. The dire fate of draught livestock during the era of rural collectivisation was an important reason for persevering with four-wheeled tractors even as the country turned away from Soviet development models.

In this study, the statistical properties and formation mechanisms of particle clusters that consider the influence of particle–wall interactions in particle-laden wall turbulence are systematically investigated through wind tunnel experiments. In the experiments, two particle release modes, including particle top-releasing mode (Case 1) and particle locally laying mode (Case 2), were adopted to establish varying conditions with different particle–wall interaction strengths. The Voronoï diagram method was employed to identify the particle clusters, and the impact of particle–wall interactions on the characteristics of the clusters was analysed. The results indicate that particle–wall interaction is the predominant factor in the formation of particle clusters in the near-wall region. Under Case 1 and Case 2, the maximum concentration of particles in the clusters could reach nearly five times the average particle concentration; however, the clusters with large particle numbers ($N_C\gt 5$) in Case 1 tended to form near the wall and the vertical velocities of these clusters were greater than the average velocities of all particles. In contrast, under Case 2, clusters with large particle numbers exhibited a higher probability of occurrence further from the wall and the vertical velocities of these clusters were lower than the average velocity of all particles. Furthermore, this study found that the presence of particle clusters in these flows significantly alters the flow field properties surrounding them, implying that a region of high strain and low vorticity constitutes an essential but non-sufficient condition for the generation of particle clusters in wall turbulence.

Tuberculosis (TB) remains a significant public health concern in China. Using data from the Global Burden of Disease (GBD) study 2021, we analyzed trends in age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life years (DALYs) for TB from 1990 to 2021. Over this period, HIV-negative TB showed a marked decline in ASIR (AAPC = −2.34%, 95% CI: −2.39, −2.28) and ASMR (AAPC = −0.56%, 95% CI: −0.62, −0.59). Specifically, drug-susceptible TB (DS-TB) showed reductions in both ASIR and ASMR, while multidrug-resistant TB (MDR-TB) showed slight decreases. Conversely, extensively drug-resistant TB (XDR-TB) exhibited upward trends in both ASIR and ASMR. TB co-infected with HIV (HIV-DS-TB, HIV-MDR-TB, HIV-XDR-TB) showed increasing trends in recent years. The analysis also found an inverse correlation between ASIRs and ASMRs for HIV-negative TB and the Socio-Demographic Index (SDI). Projections from 2022 to 2035 suggest continued increases in ASIR and ASMR for XDR-TB, HIV-DS-TB, HIV-MDR-TB, and HIV-XDR-TB. The rising burden of XDR-TB and HIV-TB co-infections presents ongoing challenges for TB control in China. Targeted prevention and control strategies are urgently needed to mitigate this burden and further reduce TB-related morbidity and mortality.

Overnutrition during before and pregnancy can cause maternal obesity and raise the risk of maternal metabolic diseases during pregnancy, and in offspring. Lentinus edodes may prevent or reduce obesity. This study aimed to to assess Lentinus edodes fermented products effects on insulin sensitivity, glucose and lipid metabolism in maternal and offspring, and explore its action mechanism. A model of overnutrition during pregnancy and lactation was developed using a 60 % kcal high-fat diet in C57BL6/J female mice. Fermented Lentinus edodes (FLE) was added to the diet at concentrations of 1 %, 3 %, and 5 %. The results demonstrated that FLE to the gestation diet significantly reduced serum insulin levels and homeostatic model assessment for insulin resistance (HOMA-IR) in pregnant mice. FLE can regulate maternal lipid metabolism and reduce fat deposition. Meanwhile, the hepatic phosphoinositide-3-kinase-protein kinase (PI3K/AKT) signaling pathway was significantly activated in the maternal mice. There is a significant negative correlation between maternal FLE supplementation doses and offspring body fat percentage and visceral fat content. Furthermore, FLE supplementation significantly increased offspring weaning litter weight, significantly reduced fasting glucose level, serum insulin level, HOMA-IR and serum glucose level, significantly activated liver PI3K/AKT signaling pathway in offspring, and upregulated the expression of liver lipolytic genes adipose triglyceride lipase, hormone-sensitive lipase and carnitine palmitoyltransferase 1 mRNA. Overall, FLE supplementation can regulate maternal lipid metabolism and reduce fat deposition during pregnancy and lactation, and it may improve insulin sensitivity in pregnant mothers and offspring at weaning through activation of the PI3K/AKT signaling pathway.

This study aimed to compare the clinical outcomes of using refrigerated versus pre-warmed media for preparing time-lapse dishes in in vitro fertilization (IVF). Patients undergoing their first IVF/ICSI cycle were divided into two groups. The control group used pre-warmed culture media, while the experimental group used refrigerated culture media. The osmotic pressure of the culture droplets in both groups was tested. No statistical differences were found between the two groups’ basic data. The proportion of air microbubbles affecting imaging significantly decreased (4.55% vs. 37.97%, P < 0.001) when using pre-warmed media. However, the blastocyst formation rate (56.62% vs. 49.70%, P = 0.046) and total high-quality embryo rate (22.26% vs. 17.06%, P = 0.047) were significantly higher in the refrigerated media group compared to the pre-warmed media group. The higher rate of high-quality embryos in the refrigerated media group might result in a higher single embryo transfer rate (45.10% vs. 18.52%, P = 0.020) and implantation rate (58.23% vs. 34.69%, P = 0.010). From day –1 to day 1, osmolality increased, with the P-3.5 group showing a significant elevation compared to the other three groups. After 5 days of incubation, the osmotic pressure of group R-4.0 was significantly lower than that of groups P-3.5, P-4.0 and P-3.5. In conclusion, refrigerated culture media dishes helped stabilize the osmotic pressure of the culture microenvironment and reduce water evaporation. The refrigerated group showed a higher rate of high-quality embryos and live births, although pre-warmed culture media effectively reduced the occurrence of air microbubbles that affect embryo imaging in the next day’s dishes.

The fat deposition and lipid composition directly influence meat quality and feed efficiency during pork production. Glutamate (GLU) is a major component of proteins and has been widely utilized in livestock production. However, the role of GLU in regulating lipid deposition and the lipo-nutritional quality of porcine fat remains unclear. This study aimed to investigate the effects of GLU on fatty acid composition and lipid profiles in subcutaneous fat (SF) and perirenal fat (PF) from Shaziling pigs. Forty-eight finishing pigs aged 150 days (31.56 ± 0.95 kg) were divided into the control (CON) group and the GLU-supplemented group (1% GLU), each consisting of 6 pens (4 pigs per pen). After 51 days, 6 pigs (1 pig/pen) from each group were slaughtered for analysis. Fatty acid analysis detected 46 species in SF and 40 in PF. In SF, 1% GLU significantly increased the content of C18:3n3 (P < 0.05), which was accompanied by an increase in n3 PUFA deposition (P < 0.05) and a decreased n6/n3 ratio (P = 0.06). In PF, GLU supplementation reduced the levels of C18:1n9t, C24:1, C22:6n3, and others (P < 0.05). The content of monounsaturated fatty acids (MUFAs) and n9 unsaturated fatty acids (UFAs) was significantly decreased in the GLU group (P < 0.05). Similarly, GLU significantly reduced the n6/n3 PUFA ratio in PF (P < 0.05). Lipidomics profiling identified 2264 unique lipids in fat tissues. GLU had minimal effects on lipid composition in SF but significantly reduced ceramides (Cer), phosphatidylserine (PS), and phosphatidylinositol (PIP) contents in PF (P < 0.05) compared to the CON group. Additionally, GLU influenced the acyl chain saturation degree, fatty acyl chain length, and individual acyl chain composition in glycerophospholipid (GP) pools of PF. These results demonstrate the regulatory role of GLU on lipid dynamics in porcine fat and provide insights into regulating fat deposition and liponutritional quality in indigenous Chinese pig breeds.

Network meta-analysis (NMA) enables simultaneous assessment of multiple treatments by combining both direct and indirect evidence. While NMAs are increasingly important in healthcare decision-making, challenges remain due to limited direct comparisons between treatments. This data sparsity complicates the accurate estimation of correlations among treatments in arm-based NMA (AB-NMA). To address these challenges, we introduce a novel sensitivity analysis tool tailored for AB-NMA. This study pioneers a tipping point analysis within a Bayesian framework, specifically targeting correlation parameters to assess their influence on the robustness of conclusions about relative treatment effects. The analysis explores changes in the conclusion based on whether the 95% credible interval includes the null value (referred to as the interval conclusion) and the magnitude of point estimates. Applying this approach to multiple NMA datasets, including 112 treatment pairs, we identified tipping points in 13 pairs (11.6%) for interval conclusion change and in 29 pairs (25.9%) for magnitude change with a threshold at 15%. These findings underscore potential commonality in tipping points and emphasize the importance of our proposed analysis, especially in networks with sparse direct comparisons or wide credible intervals for correlation estimates. A case study provides a visual illustration and interpretation of the tipping point analysis. We recommend integrating this tipping point analysis as a standard practice in AB-NMA.

As a novel type of catalytic Janus micromotor (JM), a double-bubble-powered Janus micromotor has a distinct propulsion mechanism that is closely associated with the bubble coalescence in viscous liquids and corresponding flow physics. Based on high-speed camera and microscopic observation, we provide the first experimental results of the coalescence of two microbubbles near a JM. By performing experiments with a wide range of Ohnesorge numbers, we identify a universal scaling law of bubble coalescence, which shows a cross-over at dimensionless time $\tilde{t}$ = 1 from an inertially limited viscous regime with linear scaling to an inertial regime with 1/2 scaling. Due to the confinement from the nearby solid JM, we observe asymmetric neck growth and find the combined effect of the surface tension and viscosity. The bubble coalescence and detachment can result in a high propulsion speed of ∼0.25 m s−1 for the JM. We further characterise two contributions to the JM’s displacement propelled by the coalescing bubble: the counteraction from the liquid due to bubble deformation and the momentum transfer during bubble detachment. Our findings provide a better understanding of the flow dynamics and transport mechanism in micro- and nano-scale devices like the swimming microrobot and bubble-powered microrocket.

Two-dimensional simulations incorporating detailed chemistry are conducted for detonation initiation induced by dual hot spots in a hydrogen/oxygen/argon mixture. The objective is to examine the transient behaviour of detonation initiation as facilitated by dual hot spots, and to elucidate the underlying mechanisms. Effects of hot spot pressure and distance on the detonation initiation process are assessed; and five typical initiation modes are identified. It is found that increasing the hot spot pressure promotes detonation initiation, but the impact of the distance between dual hot spots on detonation initiation is non-monotonic. During the initiation process, the initial hot spot autoignites, and forms the cylindrical shock waves. Then, the triple-shock structure, which is caused by wave collisions and consists of the longitudinal detonation wave, transverse detonation wave and cylindrical shock wave, dominates the detonation initiation behaviour. A simplified theoretical model is proposed to predict the triple-point path, whose curvature quantitatively indicates the diffraction intensity of transient detonation waves. The longitudinal detonation wave significantly diffracts when the curvature of the triple-point path is large, resulting in the failed detonation initiation. Conversely, when the curvature is small, slight diffraction effects fail to prevent the transient detonation wave from developing. The propagation of the transverse detonation wave is affected not only by the diffraction effects but also by the mixture reactivity. When the curvature of the triple-point trajectory is large, a strong cylindrical shock wave is required to compress the mixture, enhancing its reactivity to ensure the transverse detonation wave can propagate without decoupling.

Simultaneous interpreting (SI) is an intensive multitasking activity that requires coordination of a variety of linguistic and cognitive control mechanisms. Research has shown that interpreters perform better in tasks that require domain-general executive functions (EF), but the question remains whether such cognitive alternation is a result of interpreting experience or it reflects a selection bias that only cognitively capable people are recruited and trained to be interpreters. We examined the cognitive changes experienced by beginner-level students engaged in an intensive, two-week interpreting training programme. Our findings show that: (a) only cognitive flexibility was enhanced by training, together with improvement in SI performance; (b) the three EF subcomponents in their pre-existing forms negatively correlated with training gains; and (c) only pre-existing cognitive flexibility was positively associated with improvement in SI performance. Findings were discussed regarding the relationship between cognitive abilities and the early-stage acquisition of interpreting.