Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated.

In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.

Measures of agreement are used in a wide range of behavioral, biomedical, psychosocial, and health-care related research to assess reliability of diagnostic test, psychometric properties of instrument, fidelity of psychosocial intervention, and accuracy of proxy outcome. The concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. In modern-day applications, data are often clustered, making inference difficult to perform using existing methods. In addition, as longitudinal study designs become increasingly popular, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data and other related issues for performing CCC analysis within a longitudinal data setting. The approach is illustrated with both real and simulated data.

As avionics systems become increasingly complex, traditional fault prediction methods are no longer sufficient to meet modern demands. This paper introduces four advanced fault prediction methods for avionics components, utilising a multi-step prediction strategy combined with a stacking regressor. By selecting various standard regression models as base regressors, these base regressors are first trained on the original data, and their predictions are subsequently used as input features for training a meta-regressor. Additionally, the Tree-structured Parzen Estimator (TPE) algorithm is employed for hyperparameter optimisation. The experimental results demonstrate that the proposed stacking regression methods exhibit superior accuracy in fault prediction compared to traditional single-model approaches.

Aircraft ground taxiing contributes significantly to carbon emissions and engine wear. The electric towing tractor (ETT) addresses these issues by towing the aircraft to the runway end, thereby minimising ground taxiing. As the complexity of ETT towing operations increases, both the towing distance and time increase significantly, and the original method for estimating the number of ETTs is no longer applicable. Due to the substantial acquisition cost of ETT and the need to reduce waste while ensuring operational efficiency, this paper introduces for the first time an ETT quantity estimation model that combines simulation and vehicle scheduling models. The simulation model simulates the impact of ETT on apron operations, taxiing on taxiways and takeoffs and landings on runways. Key timing points for ETT usage by each aircraft are identified through simulation, forming the basis for determining the minimum number of vehicles required for airport operations using a hard-time window vehicle scheduling model. To ensure the validity of the model, simulation model verification is conducted. Furthermore, the study explores the influence of vehicle speed and airport scale on the required number of ETTs. The results demonstrate the effective representation of real-airport operations by the simulation model. ETT speed, airport runway and taxiway configurations, takeoff and landing frequencies and imbalances during peak periods all impact the required quantity of ETTs. A comprehensive approach considering these factors is necessary to determine the optimal number of ETTs.

An onboard three-dimensional (3D) trajectory generation approach based on the reinforcement learning (RL) algorithm and deep neural network (DNN) is proposed for hypersonic vehicles in glide phase. Multiple trajectory samples are generated offline through the convex optimisation method. The deep learning (DL) is employed to pre-train the DNN for initialising the actor network and accelerating the RL process. Based on the offline deep policy deterministic actor-critic algorithm, a flight target-oriented reward function with path constraints is designed. The actor network is optimised by the end-to-end RL and policy gradients of the critic network until the reward function converges to the maximum. The actor network is considered as the onboard trajectory generator to compute optimal control values online based on the real-time motion states. The simulation results show that the single-step online planning time meets the real-time requirements of onboard trajectory generation. The significant improvement in terminal accuracy of the online trajectory and the better generalisation under biased initial states for hypersonic vehicles in glide phase is observed.

We report the experimental results of the commissioning phase in the 10 PW laser beamline of the Shanghai Superintense Ultrafast Laser Facility (SULF). The peak power reaches 2.4 PW on target without the last amplifying during the experiment. The laser energy of 72 ± 9 J is directed to a focal spot of approximately 6 μm diameter (full width at half maximum) in 30 fs pulse duration, yielding a focused peak intensity around 2.0 × 1021 W/cm2. The first laser-proton acceleration experiment is performed using plain copper and plastic targets. High-energy proton beams with maximum cut-off energy up to 62.5 MeV are achieved using copper foils at the optimum target thickness of 4 μm via target normal sheath acceleration. For plastic targets of tens of nanometers thick, the proton cut-off energy is approximately 20 MeV, showing ring-like or filamented density distributions. These experimental results reflect the capabilities of the SULF-10 PW beamline, for example, both ultrahigh intensity and relatively good beam contrast. Further optimization for these key parameters is underway, where peak laser intensities of 1022–1023 W/cm2 are anticipated to support various experiments on extreme field physics.

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

The cosmic evolution of the chemical elements from the Big Bang to the present time is driven by nuclear fusion reactions inside stars and stellar explosions. A cycle of matter recurrently re-processes metal-enriched stellar ejecta into the next generation of stars. The study of cosmic nucleosynthesis and this matter cycle requires the understanding of the physics of nuclear reactions, of the conditions at which the nuclear reactions are activated inside the stars and stellar explosions, of the stellar ejection mechanisms through winds and explosions, and of the transport of the ejecta towards the next cycle, from hot plasma to cold, star-forming gas. Due to the long timescales of stellar evolution, and because of the infrequent occurrence of stellar explosions, observational studies are challenging, as they have biases in time and space as well as different sensitivities related to the various astronomical methods. Here, we describe in detail the astrophysical and nuclear-physical processes involved in creating two radioactive isotopes useful in such studies, $^{26}\mathrm{Al}$ and $^{60}\mathrm{Fe}$ . Due to their radioactive lifetime of the order of a million years, these isotopes are suitable to characterise simultaneously the processes of nuclear fusion reactions and of interstellar transport. We describe and discuss the nuclear reactions involved in the production and destruction of $^{26}\mathrm{Al}$ and $^{60}\mathrm{Fe}$ , the key characteristics of the stellar sites of their nucleosynthesis and their interstellar journey after ejection from the nucleosynthesis sites. This allows us to connect the theoretical astrophysical aspects to the variety of astronomical messengers presented here, from stardust and cosmic-ray composition measurements, through observation of $\gamma$ rays produced by radioactivity, to material deposited in deep-sea ocean crusts and to the inferred composition of the first solids that have formed in the Solar System. We show that considering measurements of the isotopic ratio of $^{26}\mathrm{Al}$ to $^{60}\mathrm{Fe}$ eliminate some of the unknowns when interpreting astronomical results, and discuss the lessons learned from these two isotopes on cosmic chemical evolution. This review paper has emerged from an ISSI-BJ Team project in 2017–2019, bringing together nuclear physicists, astronomers, and astrophysicists in this inter-disciplinary discussion.

The Straw-headed Bulbul Pycnonotus zeylanicus is one of South-East Asia’s most threatened songbirds due to relentless demand for the regional cage-bird trade. The species was recently uplisted from ‘Endangered’ to ‘Critically Endangered’ only two years after its previous uplisting. Intriguingly, populations in highly urbanised Singapore appear relatively secure. However, the last Singaporean density estimates, derived from traditional census methods, were obtained nearly two decades ago in 2001. A recent population estimate in 2016 was derived from the census work in 2001 coupled with relative abundance indices from population trends. We thus performed systematic field surveys using the distance sampling method, estimating 573 ± 185 individuals nation-wide, with a break-down of 217 ± 81 on the main island of Singapore and 356 ± 104 birds on the satellite of Pulau Ubin. Taken together, the total population estimate reported here comprises 22.9–57.3% of the global wild population, underscoring the importance of Singapore as a stronghold for the species. In spite of its apparently secure status in Singapore, the species remains susceptible to local and foreign trapping pressures. Based on our assessment, we propose a number of local and regional conservation measures to ensure the continued survival of populations in Singapore.

Stratospheric airships are promising aircraft, usually designed as a non-rigid airship. As an essential part of the non-rigid airship, the envelope plays a significant role in maintaining its shape and bearing the external force load. Generally, the envelope material of a flexible airship consists of plain-weave fabric, composed of warp and weft fibre yarn. At present, biaxial tensile experiments are the primary method used to study the stress–strain characteristics of such flexible airship materials. In this work, biaxial tensile testing of UN-5100 material was carried out. The strain on the material under unusual stress and the stress ratio were obtained using Digital Image Correlation (DIC) technology. Also, the stress–strain curve was corrected by polynomial fitting. The slope of the stress–strain curve at different points, the Membrane Structures Association of Japan (MSAJ) standard and the Radial Basis Function (RBF) model were compared to identify the stress–strain characteristics of the materials. Some conclusions on the mechanical properties of the flexible airship material can be drawn and will play a significant role in the design of such envelopes.

A disruption database characterizing the current quench of disruptions with ITER-like tungsten divertor has been developed on EAST. It provides a large number of plasma parameters describing the predisruptive plasma, current quench time, eddy current, and mitigation by massive impurity injection, which shows that the current quench time strongly depends on magnetic energy and post-disruption electron temperature. Further, the energy balance and magnetic energy dissipation during the current quench phase has been well analysed. Magnetic energy is also demonstrated to be dissipated mainly by ohmic reheating and inductive coupling, and both of the two channels have great effects on current quench time. Also, massive gas injection is an efficient method to speed up the current quench and increase the fraction of impurity radiation.

Leg weakness (LW) issues are a great concern for pig breeding industry. And it also has a serious impact on animal welfare. To dissect the genetic architecture of limb-and-hoof firmness in commercial pigs, a genome-wide association study was conducted on bone mineral density (BMD) in three sow populations, including Duroc, Landrace and Yorkshire. The BMD data were obtained by ultrasound technology from 812 pigs (including Duroc 115, Landrace 243 and Yorkshire 454). In addition, all pigs were genotyped using genome-by-sequencing and a total of 224 162 single-nucleotide polymorphisms (SNPs) were obtained. After quality control, 218 141 SNPs were used for subsequent genome-wide association analysis. Nine significant associations were identified on chromosomes 3, 5, 6, 7, 9, 10, 12 and 18 that passed Bonferroni correction threshold of 0.05/(total SNP numbers). The most significant locus that associated with BMD (P value = 1.92e−14) was detected at approximately 41.7 Mb on SSC6 (SSC stands for Sus scrofa chromosome). CUL7, PTK7, SRF, VEGFA, RHEB, PRKAR1A and TPO that are located near the lead SNP of significant loci were highlighted as functionally plausible candidate genes for sow limb-and-hoof firmness. Moreover, we also applied a new method to measure the BMD data of pigs by ultrasound technology. The results provide an insight into the genetic architecture of LW and can also help to improve animal welfare in pigs.

Reducing dietary CP content is an effective approach to reduce animal nitrogen excretion and save protein feed resources. However, it is not clear how reducing dietary CP content affects the nutrient digestion and absorption in the gut of ruminants, therefore it is difficult to accurately determine how much reduction in dietary CP content is appropriate. This study was conducted to investigate the effects of reduced dietary CP content on N balance, intestinal nutrient digestion and absorption, and rumen microbiota in growing goats. To determine N balance, 18 growing wether goats (25.0 ± 0.5 kg) were randomly assigned to one of three diets: 13.0% (control), 11.5% and 10.0% CP. Another 18 growing wether goats (25.0 ± 0.5 kg) were surgically fitted with ruminal, proximate duodenal, and terminal ileal fistulae and were randomly assigned to one of the three diets to investigate intestinal amino acid (AA) absorption and rumen microbiota. The results showed that fecal and urinary N excretion of goats fed diets containing 11.5% and 10.0% CP were lower than those of goats fed the control diet (P < 0.05). When compared with goats fed the control diet, N retention was decreased and apparent N digestibility in the entire gastrointestinal tract was increased in goats fed the 10% CP diet (P < 0.05). When compared with goats fed the control diet, the duodenal flow of lysine, tryptophan and phenylalanine was decreased in goats fed the 11.5% CP diet (P < 0.05) and that of lysine, methionine, tryptophan, phenylalanine, leucine, glutamic acid, tyrosine, essential AAs (EAAs) and total AAs (TAAs) was decreased in goats fed the 10.0% CP diet (P < 0.05). When compared with goats fed the control diet, the apparent absorption of TAAs in the small intestine was increased in goats fed the 11.5% CP diet (P < 0.05) and that of isoleucine, serine, cysteine, EAAs, non-essential AAs, and TAAs in the small intestine was increased in goats fed the 10.0% CP diet (P < 0.05). When compared with goats fed the control diet, the relative richness of Bacteroidetes and Fibrobacteres was increased and that of Proteobacteria and Synergistetes was decreased in the rumen of goats fed a diet with 10.0% CP. In conclusion, reducing dietary CP content reduced N excretion and increased nutrient utilization by improving rumen fermentation, enhancing nutrient digestion and absorption, and altering rumen microbiota in growing goats.