Spherical robots face significant challenges in motion control on non-horizontal terrains, such as slopes, due to their unique spherical structure. This paper systematically investigates the motion stability of spherical robots on inclined surfaces through modeling, control algorithm design, and experimental validation. Precise Equilibrium Modeling: Using the virtual displacement method, the precise equilibrium equation for spherical robots on slopes is derived, addressing the issue of insufficient accuracy in describing the actual center of gravity in existing studies. Control Algorithm Design: For known slope conditions, a Backstepping Control (BSC) algorithm is designed, demonstrating excellent tracking performance. For unknown slope conditions, an Adaptive Backstepping Control (ABSC) algorithm is proposed, which significantly reduces tracking errors and enhances system robustness through parameter adaptation. Simulation and Physical Validation: Simulations confirm the effectiveness of the algorithms: BSC achieves high-precision control under known slopes, while ABSC exhibits strong adaptability under unknown slopes. Physical experiments validate the stability of the algorithms in a $5^\circ$ slope environment, demonstrating reliable performance across different control angles.

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.

This study presents a novel investigation into the vortex dynamics of flow around a near-wall rectangular cylinder based on direct numerical simulation at $Re=1000$, marking the first in-depth exploration of these phenomena. By varying aspect ratios ($L/D = 5$, $10$, $15$) and gap ratios ($G/D = 0.1$, $0.3$, $0.9$), the study reveals the vortex dynamics influenced by the near-wall effect, considering the incoming laminar boundary layer flow. Both $L/D$ and $G/D$ significantly influence vortex dynamics, leading to behaviours not observed in previous bluff body flows. As $G/D$ increases, the streamwise scale of the upper leading edge (ULE) recirculation grows, delaying flow reattachment. At smaller $G/D$, lower leading edge (LLE) recirculation is suppressed, with upper Kelvin–Helmholtz vortices merging to form the ULE vortex, followed by instability, differing from conventional flow dynamics. Larger $G/D$ promotes the formation of an LLE shear layer. An intriguing finding at $L/D = 5$ and $G/D = 0.1$ is the backward flow of fluid from the downstream region to the upper side of the cylinder. At $G/D = 0.3$, double-trailing-edge vortices emerge for larger $L/D$, with two distinct flow behaviours associated with two interactions between gap flow and wall recirculation. These interactions lead to different multiple flow separations. For $G/D = 0.9$, the secondary vortex (SV) from the plate wall induces the formation of a tertiary vortex from the lower side of the cylinder. Double-SVs are observed at $L/D = 5$. Frequency locking is observed in most cases, but is suppressed at $L/D = 10$ and $G/D = 0.9$, where competing shedding modes lead to two distinct evolutions of the SV.

In this study, the propagation behaviour of detonation waves in a channel filled with stratified media is analysed using a detailed chemical reaction model. Two symmetrical layers of non-reactive gas are introduced near the upper and lower walls to encapsulate a stoichiometric premixed H2–air mixture. The effects of gas temperature and molecular weight of the non-reactive layers on the detonation wave’s propagation mode and velocity are examined thoroughly. The results reveal that as the non-reactive gas temperature increases, the detonation wave front transitions from a ‘convex’ to a ‘concave’ shape, accompanied by an increase in wave velocity. Notably, the concave wave front comprises detached shocks, oblique shocks and detonation waves, with the overall wave system propagating at a velocity exceeding the theoretical Chapman–Jouguet speed, indicating the emergence of a strong detonation wave. Furthermore, when the molecular weight of non-reactive layers varies, the results qualitatively align with those obtained from temperature variations. To elucidate the formation mechanism of different detonation wave front shapes, a dimensionless parameter $\eta$ (defined as a function of the specific heat ratio and sound speed) is proposed. This parameter unifies the effects of temperature and molecular weight, confirming that the specific heat ratio and sound speed of non-reactive layers are the primary factors governing the detonation wave propagation mode. Additionally, considering the effect of mixture inhomogeneity on the detonation reaction zone, the stream tube contraction theory is proposed, successfully explaining why strong detonation waves form in stratified mixtures. Numerical results show good agreement with theoretical predictions, validating the proposed model.

The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods are labour-intensive and prone to errors. To address these limitations, this study investigates the use of machine learning for automated and precise identification of M. separata larval instars. A total of 1577 larval images representing different instar were analysed for geometric, colour, and texture features. Additionally, larval weight was predicted using 13 regression models. Instar identification was conducted using Support Vector Classifier (SVC), Random Forest, and Multi-Layer Perceptron. Key feature contributing to classification accuracy were subsequently identified through permutation feature importance analysis. The results demonstrated the potential of machine learning for automating instar identification with high efficiency and accuracy. Predicted larval weight emerged as a key feature, significantly enhancing the performance of all identification models. Among the tested approaches, BaggingRegressor exhibited the best performance for larval weight prediction (R2 = 98.20%, RMSE = 0.2313), while SVC achieved the highest instar identification accuracy (94%). Overall, the integration of larval weight with other image-derived features proved to be a highly effective strategy. This study demonstrates the efficacy of machine learning in enhancing pest monitoring systems by providing a scalable and reliable framework for precise pest management. The proposed methodology significantly improves larval instar identification accuracy and efficiency, offering actionable insights for implementing targeted biological and chemical control strategies.

A topological space has a domain model if it is homeomorphic to the maximal point space $\mbox{Max}(P)$ of a domain $P$. Lawson proved that every Polish space $X$ has an $\omega$-domain model $P$ and for such a model $P$, $\mbox{Max}(P)$ is a $G_{\delta }$-set of the Scott space of $P$. Martin (2003) then asked whether it is true that for every $\omega$-domain $Q$, $\mbox{Max}(Q)$ is $G_{\delta }$-set of the Scott space of $Q$. In this paper, we give a negative answer to Martin’s long-standing open problem by constructing a counterexample. The counterexample here actually shows that the answer is no even for $\omega$-algebraic domains. In addition, we also construct an $\omega$-ideal domain $\widetilde{Q}$ for the constructed $Q$ such that their maximal point spaces are homeomorphic. Therefore, $\textrm{Max}(Q)$ is a $G_\delta$-set of the Scott space of the new model $\widetilde{Q}$ .

Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Triceps skinfold thickness (TSF) is a surrogate marker of subcutaneous fat. Evidence is limited about the association of sex-specific TSF with the risk of all-cause mortality among maintenance hemodialysis (MHD) patients. We aimed to investigate the longitudinal relationship of TSF with all-cause mortality among MHD patients. A multicenter prospective cohort study was performed in 1034 patients undergoing MHD. The primary outcome was all-cause mortality. Multivariable Cox proportional hazards models were used to evaluate the association of TSF with the risk of mortality. The mean (standard deviation) age of the study population was 54.1 (15.1) years. 599 (57.9%) of the participants were male. The median (interquartile range) of TSF was 9.7 (6.3–13.3 mm) in males and 12.7 (10.0–18.0 mm) in females. Over a median follow up of 4.4 years (interquartile range, 2.4-7.9 years), there were 548 (53.0%) deaths. When TSF was assessed as sex-specific quartiles, compared with those in quartile 1, the adjusted HRs (95%CIs) of all-cause mortality in quartile 2, quartile 3 and quartile 4 were 0.93 (0.73, 1.19), 0.75 (0.58, 0.97) and 0.69 (0.52, 0.92), respectively (P for trend =0.005). Moreover, when analyzed by sex, increased TSF (≥9.7 mm for males and ≥18mm for females) was significantly associated with a reduced risk of all-cause mortality (quartile 3-4 vs. quartile 1-2; HR, 0.70; 95%CI: 0.55, 0.90 in males; quartile 4 vs. Quartile 1-3; HR, 0.69; 95%CI: 0.48, 1.00 in females). In conclusion, high TSF was significantly associated with lower risk of all-cause mortality in MHD patients.

Following a decade of the Belt and Road Initiative (BRI), how do Chinese state companies and governments react to international resistance to the initiative? Pushbacks against the BRI have been well documented, yet there is limited study on how China has responded to such resistance. Based on fieldwork in Kenya, Ethiopia, Zambia and China between 2014 and 2023, this paper presents two of the response mechanisms adopted by Chinese state actors in the face of institutional gaps and information deficits. The first is that Chinese state-owned enterprises (SOEs) innovate public relations strategies and then promote these practices to Beijing for dissemination. The second mechanism allows information to be directly transmitted to Beijing, via the internal reporting system (neican), so that Beijing can respond promptly to overseas incidents. On a theoretical level, this paper contributes to the adaptive governance literature by analysing the overseas practices of Chinese state actors and underlining that host country social actors are key drivers of these changes. On an empirical level, this article focuses on the feedback mechanism of the Belt and Road Initiative in an attempt to fill the gap in related research in this field.

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalized shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one. For high-frequency data contaminated by microstructure noise, we introduce a localized pre-averaging estimation method that reduces the effective magnitude of the noise. We then use the estimation tool developed in the noise-free scenario and derive the uniform convergence rates for the developed spot volatility matrix estimator. We further combine kernel smoothing with the shrinkage technique to estimate the time-varying volatility matrix of the high-dimensional noise vector. In addition, we consider large spot volatility matrix estimation in time-varying factor models with observable risk factors and derive the uniform convergence property. We provide numerical studies including simulation and empirical application to examine the performance of the proposed estimation methods in finite samples.