This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

Natural infection by Trichinella sp. has been reported in humans and more than 150 species of animals, especially carnivorous and omnivorous mammals. Although the presence of Trichinella sp. infection in wild boars (Sus scrofa) has been documented worldwide, limited information is known about Trichinella circulation in farmed wild boars in China. This study intends to investigate the prevalence of Trichinella sp. in farmed wild boars in China. Seven hundred and sixty-one (761) muscle samples from farmed wild boars were collected in Jilin Province of China from 2017 to 2020. The diaphragm muscles were examined by artificial digestion method. The overall prevalence of Trichinella in farmed wild boars was 0.53% [95% confidence interval (CI): 0.51–0.55]. The average parasite loading was 0.076 ± 0.025 larvae per gram (lpg), and the highest burden was 0.21 lpg in a wild boar from Fusong city. Trichinella spiralis was the only species identified by multiplex polymerase chain reaction. The 5S rDNA inter-genic spacer region of Trichinella was amplified and sequenced. The results showed that the obtained sequence (GenBank accession number: OQ725583) shared 100% identity with the T. spiralis HLJ isolate (GenBank accession number: MH289505). Since the consumption of farmed wild boars is expected to increase in the future, these findings highlight the significance of developing exclusive guidelines for the processing of slaughtered farmed wild boar meat in China.

During the automatic docking assembly of aircraft wing-fuselage, using monocular camera or dual-camera to monitor the docking stage of the fork-ear will result in an incomplete identification of the fork-ear pose-position and an inaccurate description of the deviation in the intersection holes’ position coordinates. To address this, a quality inspection and error correction method is proposed for the fork-ear docking assembly based on multi-camera stereo vision. Initially, a multi-camera stereo vision detection system is established to inspect the quality of fork-ear docking assembly. Subsequently, a spatial position solution mathematical model of the fork-ear feature points is developed, and a spatial pose determination mathematical model of fork-ear is established by utilised the elliptical cone. Finally, an enhanced artificial fish swarm particle filter algorithm is proposed to track and estimate the coordinate of the fork-ear feature points. An adaptive weighted fusion algorithm is employed to fuse the detection data from the multi-camera and the laser tracker, and a wing pose-position fine-tuning error correction model is constructed. Experimental results demonstrate that the method enhances the effect of the assembly quality inspection and effectively improves the wing-fuselage docking assembly accuracy of the fork-ear type aircraft.

The motion of a sphere freely rising or falling in a 5d (d is the diameter of the sphere) square tube was numerically studied for the sphere-to-fluid density ratio ranging from 0.1 to 2.3 (0.1 ≤ ρs/ρ ≤ 2.3, ρs is the density of spheres and ρ the fluid density) and Galileo number from 140 to 230 (140 ≤ Ga ≤ 230). We report that Hopf bifurcation occurs at Gacrit ≈ 157, where both the heavy and light spheres lose stability. The helical motion is widely seen for all spheres at Ga > 160 resulting from a double-threaded vortex interacting with the tube walls, which becomes irregular at Ga ≥ 190 where heavy spheres act differently from their counterparts; that is, heavy spheres change their helical directions alternately while light spheres exhibit helical trajectories with jaggedness in connection with the shedding of the double-threaded vortices. This is because of the difference in inertia between the heavy and light spheres. We also checked the oscillation periods for the helical motion of the spheres. They show opposite variations with ρs/ρ for the two types of spheres. Light spheres (ρs/ρ ≤ 0.7) reach a zigzagging regime at Ga ≥ 200 where a vortex loop (hairpin-like vortical structure) is formed which may develop into a vortex ring downstream at small ρs/ρ. This might be the first time a transition from the helical motion to the zigzagging motion for heavy spheres (ρs/ρ ≥ 1.8) has been reported. Finally, we examined the dependence of both the terminal Reynolds number and the drag coefficient of the spheres on the Galileo number.

The effects of the evolution of vortices on the aeroacoustics generated by a hovering wing are numerically investigated by using a hybrid method of an immersed boundary–finite difference method for the three-dimensional incompressible flows and a simplified model based on the Ffowcs Williams-Hawkings acoustic analogy. A low-aspect-ratio ($AR=1.5$) rectangular wing at low Reynolds ($Re=1000$) and Mach ($M=0.04$) numbers is investigated. Based on the simplified model, the far-field acoustics is shown to be dominated by the time derivative of the pressure on the wing surface. Results show that vortical structure evolution in the flow fields, which is described by the divergence of the convection term of the incompressible Navier–Stokes equations in a body-fixed reference frame, determines the time derivative of the surface pressure and effectively the far-field acoustics. It dominates over the centrifugal acceleration and Coriolis acceleration terms in determining the time derivative of the surface pressure. The position of the vortex is also found to affect the time derivative of the surface pressure. A scaling analysis reveals that the vortex acoustic source is scaled with the cube of the flapping frequency.

This work investigates the spatio-temporal evolution of coherent structures in the wake of a generic high-speed train, based on a three-dimensional database from large eddy simulation. Spectral proper orthogonal decomposition (SPOD) is used to extract energy spectra and energy ranked empirical modes for both symmetric and antisymmetric components of the fluctuating flow field. The spectrum of the symmetric component shows overall higher energy and more pronounced low-rank behaviour compared with the antisymmetric one. The most dominant symmetric mode features periodic vortex shedding in the near wake, and wave-like structures with constant streamwise wavenumber in the far wake. The mode bispectrum further reveals the dominant role of self-interaction of the symmetric component, leading to first harmonic and subharmonic triads of the fundamental frequency, with remarkable deformation of the mean field. Then, the stability of the three-dimensional wake flow is analysed based on two-dimensional local linear stability analysis combined with a non-parallelism approximation approach. Temporal stability analysis is first performed for both the near-wake and the far-wake regions, showing a more unstable condition in the near-wake region. The absolute frequency of the near-wake eigenmode is determined based on spatio-temporal analysis, then tracked along the streamwise direction to find out the global mode growth rate and frequency, which indicate a marginally stable global mode oscillating at a frequency very close to the most dominant SPOD mode. The global mode wavemaker is then located, and the structural sensitivity is calculated based on the direct and adjoint modes derived from a local spatial analysis, with the maximum value localized within the recirculation region close to the train tail. Finally, the global mode shape is computed by tracking the most spatially unstable eigenmode in the far wake, and the alignment with the SPOD mode is computed as a function of streamwise location. By combining data-driven and theoretical approaches, the mechanisms of coherent structures in complex wake flows are well identified and isolated.

In this study, we examine how local government debt responds to environmental policies in China. We show that when an environmental policy impacts the economy, local governments are likely to increase debt issuance, with this effect becoming stronger when local officials have greater career incentives within the Chinese bureaucratic system. Over-accumulation of local government debt, which leads to social welfare losses, is closely tied to the urgency local officials feel to secure promotions. Our analysis offers valuable insights for better coordination between fiscal and environmental policies.

Let $L=-\Delta +V$ be a Schrödinger operator in ${\mathbb R}^n$ with $n\geq 3$, where $\Delta $ is the Laplace operator denoted by $\Delta =\sum ^{n}_{i=1}({\partial ^{2}}/{\partial x_{i}^{2}})$ and the nonnegative potential V belongs to the reverse Hölder class $(RH)_{q}$ with $q>n/2$. For $\alpha \in (0,1)$, we define the operator

$$ \begin{align*} T_N^{L^{\alpha}} f(x) =\sum_{j=N_1}^{N_2} v_j(e^{-a_{j+1}L^\alpha} f(x)-e^{-a_{j}L^\alpha} f(x)) \quad \mbox{for all }x\in \mathbb R^n, \end{align*} $$

where $\{e^{-tL^\alpha } \}_{t>0}$ is the fractional heat semigroup of the operator L, $\{v_j\}_{j\in \mathbb Z}$ is a bounded real sequence and $\{a_j\}_{j\in \mathbb Z}$ is an increasing real sequence.

We investigate the boundedness of the operator $T_N^{L^{\alpha }}$ and the related maximal operator $T^*_{L^{\alpha }}f(x):=\sup _N \vert T_N^{L^{\alpha }} f(x)\vert $ on the spaces $L^{p}(\mathbb {R}^{n})$ and $BMO_{L}(\mathbb {R}^{n})$, respectively. As extensions of $L^{p}(\mathbb {R}^{n})$, the boundedness of the operators $T_N^{L^{\alpha }}$ and $T^*_{L^{\alpha }}$ on the Morrey space $L^{\rho ,\theta }_{p,\kappa }(\mathbb {R}^{n})$ and the weak Morrey space $WL^{\rho ,\theta }_{1,\kappa }(\mathbb {R}^{n})$ has also been proved.

Since the outbreak of the COVID-19 epidemic, it has posed a great crisis to the health and economy of the world. The objective is to provide a simple deep-learning approach for predicting, modelling, and evaluating the time evolutions of the COVID-19 epidemic. The Dove Swarm Search (DSS) algorithm is integrated with the echo state network (ESN) to optimize the weight. The ESN-DSS model is constructed to predict the evolution of the COVID-19 time series. Specifically, the self-driven ESN-DSS is created to form a closed feedback loop by replacing the input with the output. The prediction results, which involve COVID-19 temporal evolutions of multiple countries worldwide, indicate the excellent prediction performances of our model compared with several artificial intelligence prediction methods from the literature (e.g., recurrent neural network, long short-term memory, gated recurrent units, variational auto encoder) at the same time scale. Moreover, the model parameters of the self-driven ESN-DSS are determined which acts as a significant impact on the prediction performance. As a result, the network parameters are adjusted to improve the prediction accuracy. The prediction results can be used as proposals to help governments and medical institutions formulate pertinent precautionary measures to prevent further spread. In addition, this study is not only limited to COVID-19 time series forecasting but also applicable to other nonlinear time series prediction problems.

We consider linear-fractional branching processes (one-type and two-type) with immigration in varying environments. For $n\ge0$, let $Z_n$ count the number of individuals of the nth generation, which excludes the immigrant who enters the system at time n. We call n a regeneration time if $Z_n=0$. For both the one-type and two-type cases, we give criteria for the finiteness or infiniteness of the number of regeneration times. We then construct some concrete examples to exhibit the strange phenomena caused by the so-called varying environments. For example, it may happen that the process is extinct, but there are only finitely many regeneration times. We also study the asymptotics of the number of regeneration times of the model in the example.

In order to improve the regional applicability of existing evaporative misting systems, a new evaporative misting system based on photovoltaic/thermal and ventilation heat exchange is proposed for sow farms. Compared to traditional systems, the proposed system can help to improve their regional applicability and reduce their energy consumption. Meanwhile, its simulation model is constructed and the reliability is verified for its core equipment. The maximum error is less than 14.5% for the above models. Through simulation, the optimal regulation variable ranges in the proposed system are determined for mass flow rate of working fluid (MFWF) and misting power by analysing its operating characteristics. The results show that its optimal ranges of MFWF and misting power are 7245–9245 kg/h and 40–45 kW, respectively. The annual performance is further quantified and analysed under different load ratios for the proposed system in Guangzhou. It can be found that the annual exergy loss, heat exchange coefficient and solar energy benefits of the proposed system in Guangzhou are negatively correlated with load ratio, but its annual energy consumption and energy efficiency ratio are positively correlated. Meanwhile, the system performance and benefits are not significantly improved by increasing device investment and sow density when load ratio exceeds 120% in sow farms. The above conclusion can contribute to improving the existing evaporative misting systems in sow farms and guiding the operating regulation of the proposed system.