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.

Studies on the evolution of characteristics and dynamic mechanisms of dry/wet status in global arid regions are contradictory. We systematically assessed the evolution and drivers of dry/wet status in global arid regions from a paleoclimate perspective using observational datasets, paleoclimate records, and climate model simulations from Paleoclimate Model Intercomparison Project Phase 4 (PMIP4)-Coupled Model Intercomparison Project Phase 6 (CMIP6) and PMIP3-CMIP5. Our results show that climate change during the last glacial maximum (LGM) provides a reverse analog for the near-future climate in global arid regions. The notable migration of the subtropical high during the LGM profoundly altered the atmospheric circulation and influenced dry/wet status in global arid regions. The multimodel ensembles project that under the shared socioeconomic pathway (SSP) 8.5 scenario, nonuniform heating induced by polar-amplified warming will introduce northward migration of the subtropical high. The resulting reduction in subtropical precipitation will lead to expansion of global arid regions under global warming, which is consistent with previous studies based on atmospheric aridity.

Insufficient sleep’s impact on cognitive and emotional function is well-documented, but its effects on social functioning remain understudied. This research investigates the influence of depressive symptoms on the relationship between sleep deprivation (SD) and social decision-making. Forty-two young adults were randomly assigned to either the SD or sleep control (SC) group. The SD group stayed awake in the laboratory, while the SC group had a normal night’s sleep at home. During the subsequent morning, participants completed a Trust Game (TG) in which a higher monetary offer distributed by them indicated more trust toward their partners. They also completed an Ultimatum Game (UG) in which a higher acceptance rate indicated more rational decision-making. The results revealed that depressive symptoms significantly moderated the effect of SD on trust in the TG. However, there was no interaction between group and depressive symptoms found in predicting acceptance rates in the UG. This study demonstrates that individuals with higher levels of depressive symptoms display less trust after SD, highlighting the role of depressive symptoms in modulating the impact of SD on social decision-making. Future research should explore sleep-related interventions targeting the psychosocial dysfunctions of individuals with depression.

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.

This paper is focused on the existence and uniqueness of nonconstant steady states in a reaction–diffusion–ODE system, which models the predator–prey interaction with Holling-II functional response. Firstly, we aim to study the occurrence of regular stationary solutions through the application of bifurcation theory. Subsequently, by a generalized mountain pass lemma, we successfully demonstrate the existence of steady states with jump discontinuity. Furthermore, the structure of stationary solutions within a one-dimensional domain is investigated and a variety of steady-state solutions are built, which may exhibit monotonicity or symmetry. In the end, we create heterogeneous equilibrium states close to a constant equilibrium state using bifurcation theory and examine their stability.

In response to the requirements for assessing the impact safety of aero-engines, a high-fidelity numerical simulation method based on overset mesh technology for six-degree-of-freedom rigid body motion is proposed. A gas-solid two-phase flow model is established, coupling two types of ice-debris (externally ingested ice and internally delaminated ice) with air, to analyse their behaviour in a dorsal S-shaped inlet with a diffusion ratio of 1.3. Results indicate that the ice-debris entering from the upper region of the entrance section exerts the most significant distortion on the total-pressure at the engine inlet. Additionally, the behaviour of ice-debris is determined by its angle with respect to the incoming flow direction and the shape of ice. Furthermore, although the ice-debris detached from the entrance section poses no immediate threat to the engine, the prolonged acceleration by high-speed airflow, with velocity increments exceeding 45 m/s, results in a higher kinetic energy carried upon impact with the inlet walls. Regarding externally ingested ice-debris, a smaller initial velocity corresponds to a higher probability of impacting the engine, accompanied by a significant increase in velocity. For instance, the irregular ice-debris ingested at an initial velocity of 6 m/s can experience velocity amplification exceeding 590%.

Fine particulate matter (PM2·5) is a known risk factor for heart failure (HF), while plant-based dietary patterns may help reduce HF risk. This study examined the combined impact of PM2·5 exposure and a plant-based diet on HF incidence. A total of 190 092 participants from the UK Biobank were included in this study. HF cases were identified through linkage to the UK National Health Services register, with follow-up lasting until October 2022 in England, August 2022 in Scotland and May 2022 in Wales. Annual mean PM2·5 concentration was obtained using a land use regression model, while the healthful plant-based diet index (hPDI) was calculated using the Oxford WebQ tool based on two or more 24-hour dietary assessments of seventeen major food groups. Cox proportional hazard models assessed the associations of PM2·5 and hPDI with HF risk, and interactions were evaluated on additive and multiplicative scales. During a median of 13·4-year follow-up, 4351 HF cases were recorded. Participants in the highest PM2·5 tertile had a 23 % increased HF risk (hazard ratio: 1·23, 95 % CI: 1·14, 1·32) compared with those in the lowest tertile. Moderate or high hPDI was associated with reduced HF risk relative to low hPDI. The lowest HF risk was observed in individuals with high hPDI and low PM2·5 exposure, underscoring the protective role of a plant-based diet, particularly in areas with lower PM2·5 levels. A healthy plant-based diet may mitigate HF risk, especially in populations exposed to lower PM2·5 levels.

Steady-state Bloch wave systems at resonance with fixed frequencies and amplitudes are investigated using the homotopy analysis method. Nonlinear waves propagate over a stationary undulating bottom topography of infinite extent, modelled as a superposition of two waveforms. The wave systems are classified as type 1 if the primary transmitted and resonant wave components have equal energies, and type 2 if the energy distribution is unequal. Two subtypes of type 2 are identified, distinguished by their responses to frequency detuning and bottom topography: the wave steepness in subtype 1 shows monotonic variations with detuning, while in subtype 2 it exhibits a peak at a particular detuning value, indicating downward resonance that intensifies with greater wave steepness. A pair of peaks in wave steepness arises in each subtype at certain values of the angle $\theta$ between the waveforms of the bottom topography. In both subtypes, the peaks are slightly affected by the ratio $k_{{b}1}/k_{{b}2}$ of the two bottom wave vectors, and significantly affected by the propagation angle $\alpha$ of the primary transmitted wave, but remain stable under changes to other topographic parameters. As the topography amplitude and $\theta$ vary, significant additional contributions to the total energy of the wave system appear from components other than resonant and primary transmitted waves. The most pronounced effects occur with changes in $\theta$, with the additional components accounting for up to 12 % of the total energy. This study provides an enriched understanding of resonant Bloch wave systems and a basis for improving the effectiveness of wave energy converters.

Industrial robots are widely utilized in the machining of complex parts because of their flexibility. However, their low positioning accuracy and spatial geometric error characteristics significantly limit the contour precision of robot machined parts. Therefore, in the robot machining procedure, an in situ measurement system is typically required. This study aims to enhance the trajectory accuracy of robotic machining through robotic in situ measurement and meta-heuristic optimization. In this study, a measurement-machining dual-robot system for measurement and machining is established, consisting of a measurement robot with a laser sensor mounted at the robot end and a machining robot equipped with a machining tool. In the measuring process, high-precision standard spheres are set on the edge of the machining area, and the high-precision standard geometry is measured by the measurement robot. According to measured geometry information in the local area, the trajectory accuracy for the machining robot is improved. By utilizing the standard radius of the standard spheres and adopting a meta-heuristic optimization algorithm, this study addresses the complexity of the robot kinematics model, while also overcoming local optima commonly introduced by gradient-based iterative methods. The results of the experiments in this study confirm that the proposed method markedly refines the precision of the robot machining trajectory.

Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk interactions. This paper introduces various types of contribution ratio measures based on the multivariate conditional value-at-risk (MCoVaR), multivariate conditional expected shortfall (MCoES), and multivariate marginal mean excess (MMME) studied in [34] (Ortega-Jiménez, P., Sordo, M., & Suárez-Llorens, A. (2021). Stochastic orders and multivariate measures of risk contagion. Insurance: Mathematics and Economics, vol. 96, 199–207) and [11] (Das, B., & Fasen-Hartmann, V. (2018). Risk contagion under regular variation and asymptotic tail independence. Journal of Multivariate Analysis 165(1), 194–215) to assess the relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and statistically dependent notions. Numerical examples are presented to validate these conditions. Finally, a real dataset from the cryptocurrency market is used to analyze the spillover effects through our proposed contribution measures.