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.

Research on the association between the Chinese visceral adiposity index (CVAI) and hyperuricemia (HUA) is scarce, and whether the association differs by sex is unclear. This research aimed to explore sex-specific associations between CVAI and HUA and to compare CVAI’s predictive performance with other adiposity indices using data from 22,171 adults (30–79 years) in the China Multi-Ethnic Cohort (CMEC) study (Chongqing region). The prevalence of HUA was 20.9% in men and 9.7% in women. Multivariable logistic regression analyses were utilized to assess the adjusted odds ratios (ORs) and 95% confidence intervals (CIs). After multivariable adjustment, CVAI was associated with HUA in men (OR Q4 vs. Q1 = 3.31, 95% CI 2.73, 4.03) and women (OR Q4 vs. Q1 = 7.20, 95% CI 5.12, 10.12). Moreover, Significant interactions were observed between BMI and CVAI on HUA in both sexes (all Pinteraction <0.001), with the strongest associations in those with BMI <24.0 kg/m2. The ORs (95% CI) across different BMI groups (<24.0, 24.0–27.9, ≥28.0 kg/m²) were 1.87 (1.63, 2.13), 1.65 (1.48, 1.85), and 1.30 (1.14, 1.49) for men, and 2.76 (2.18, 3.51), 2.46 (1.98, 3.07), and 1.87 (1.47, 2.39) for women, respectively. Additionally, CVAI showed satisfactory predictive performance for HUA in women, with the largest area under the receiver operating characteristic curve (AUROC) of 0.735, but not in men (0.660). These findings suggest a close association between CVAI and HUA, particularly pronounced in those with BMI <24.0 kg/m², and a stronger association in women than in men.

Let $(X_t)_{t \geqslant 0}$ be the solution of the stochastic differential equation

\[\mathrm{d} X_t = b(X_t) \,\mathrm{d} t+A\,\mathrm{d} Z_t, \quad X_{0}=x,\]

$b\,:\, \mathbb{R}^d \rightarrow \mathbb{R}^d$

$A \in \mathbb{R}^{d \times d}$

$(Z_t)_{t\geqslant 0}$

$\alpha$

$\alpha \in (1,2)$

$x\in\mathbb{R}^{d}$

$\Gamma = (\gamma_n)_{n\in \mathbb{N}}$

$(X_t)_{t \geqslant 0}$

$\alpha$

$\gamma^{\frac{1}{\alpha}}_n$

$\gamma^{1+\frac {1}{\alpha}-\frac{1}{\kappa}}_n$

$\kappa \in [1,\alpha)$

$\gamma^{\frac{2-\alpha}{\alpha}}_n$

$\alpha$

In this study, we use a novel design to test for directional behavioral spillover and cognitive load effects in a set of multiple repeated games. Specifically, in our experiment, each subject plays a common historical game with two different matches for 100 rounds. After 100 rounds, the subject switches to a new game with one match and continues playing the historical game with the other match. This design allows us to identify the direction of any behavioral spillover. Our results show that participants exhibit both behavioral spillover and cognitive load effects. First, for pairs of Prisoners’ Dilemma and Alternation games, we find that subjects apply strategies from the historical game when playing the new game. Second, we find that those who participate in a Self Interest game as either their historical or new game achieve Pareto efficient outcomes more often in the Prisoners’ Dilemma and Alternation games compared to their control counterparts. Overall, our results show that, when faced with a new game, participants use strategies that reflect both behavioral spillover and cognitive load effects.

Silicified microfossils are reported from nine stratigraphic sections of the Ediacaran Doushantuo Formation deposited in shelf margin, slope, and basin environments in Hunan Province of South China. These microfossils include sphaeromorphic and acanthomorphic acritarchs (15 genera and 29 species, including three new acanthomorph species, Bullatosphaera? colliformis n. sp., Eotylotopalla inflata n. sp., and Verrucosphaera? undulata n. sp.), multicellular algae, tubular microfossils, and other problematic forms, representing major fossil groups similar to those from the Doushantuo Formation in more proximal facies (e.g., inner shelf and shelf lagoon). A database of the abundance and occurrences of Doushantuo acanthomorphs is assembled and analyzed using quantitative and data-visualization methods (e.g., rarefaction analysis, non-parametric multidimensional scaling, and network analysis). The results show that, at the genus and species levels, taxonomic richness of Doushantuo acanthomorphs exhibits considerable variation among facies, but this variation is largely due to sampling and taphonomic biases. The results also show that numerous acanthomorph taxa have broad facies distribution, affirming their biostratigraphic value. The analysis confirms that acanthomorphs in the Weng'an biota of shelf margin facies are composed of a mixture of Member II and Member III assemblages of shelf-lagoon facies in the Yangtze Gorges area. The study shows the biostratigraphic potential of acanthomorphs in the establishment of regional biozones using the first appearance datum of widely distributed taxa, highlighting the importance of continuing exploration of under-sampled Doushantuo sections in slope and basinal facies.

UUID: http://zoobank.org/6fc92858-4054-4117-8043-1f06cfe77155

The Early-Middle Jurassic impression/compression macroflora and the palynoflora from the Qaidam Basin in the northeastern Qinghai-Xizang (Tibetan) Plateau have been well studied; however, fossil wood from this region has not been previously documented systematically. Here, we describe an anatomically well-preserved fossil wood specimen from the Lower Jurassic Huoshaoshan Formation at the Dameigou section in northern Qinghai Province, northwestern China. This fossil exhibits typical Metapodocarpoxylon Dupéron-Laudoueneix et Pons anatomy with usually araucarian radial tracheid pits and variable cross-field pits, representing a new record for Metapodocarpoxylon in the Qaidam Basin. This discovery indicates that trees with this type of wood anatomy were not confined to northern Gondwana but also grew in more northerly regions in Laurasia. The wood displays distinct growth rings, with abundant, well-formed earlywood and narrow latewood. This observation, along with previous interpretations based on macroflora, palynoflora and sedimentological data, suggests that a warm and humid climate with mild seasonality prevailed in the region during the Early Jurassic.

The Asian corn borer, Ostrinia furnacalis (Guenée), emerges as a significant threat to maize cultivation, inflicting substantial damage upon the crops. Particularly, its larval stage represents a critical point characterised by significant economic consequences on maize yield. To manage the infestation of this pest effectively, timely and precise identification of its larval stages is required. Currently, the absence of techniques capable of addressing this urgent need poses a formidable challenge to agricultural practitioners. To mitigate this issue, the current study aims to establish models conducive to the identification of larval stages. Furthermore, this study aims to devise predictive models for estimating larval weights, thereby enhancing the precision and efficacy of pest management strategies. For this, 9 classification and 11 regression models were established using four feature datasets based on the following features geometry, colour, and texture. Effectiveness of the models was determined by comparing metrics such as accuracy, precision, recall, F1-score, coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, Shapley Additive exPlanations analysis was employed to analyse the importance of features. Our results revealed that for instar identification, the DecisionTreeClassifier model exhibited the best performance with an accuracy of 84%. For larval weight, the SupportVectorRegressor model performed best with R2 of 0.9742. Overall, these findings present a novel and accurate approach to identify instar and predict the weight of O. furnacalis larvae, offering valuable insights for the implementation of management strategies against this key pest.

Substantial changes resulting from the interaction of environmental and dietary factors contribute to an increased risk of obesity, while their specific associations with obesity remain unclear. Identify inflammation-related dietary patterns (DPs) and explore their associations with obesity among urbanized Tibetan adults under significant environmental and dietary changes.Totally, 1826 subjects from the suburbs of Golmud City were enrolled in an open cohort study, of which 514 were followed up. Height, weight, and waist circumference were used to define overweight and obesity. DPs were derived using reduced rank regression with 41 food groups as predictors and hs-CRP and prognostic nutritional index as inflammatory response variables. Altitude was classified as high or ultra-high. Two DPs were extracted. DP-1 was characterized by having high consumptions of sugar-sweetened beverages, savory snacks, and poultry, and a low intake of tsamba. DP-2 had high intakes of poultry, pork, animal offal, and fruits, and a low intake of butter tea. Participants in the highest tertiles (T3) of DPs had increased risks of overweight and obesity (DP-1: OR=1.37, 95% CI: 1.07, 1.77; DP-2: OR=1.48, 95% CI: 1.18, 1.85) than those in the lowest tertiles (T1). Participants in T3 of DP-2 had an increased risk of central obesity (OR=2.25, 95% CI: 1.49, 3.39) than those in T1. The positive association of DP-1 with overweight and obesity was only significant at high altitudes, while no similar effect was observed for DP-2. Inflammation-related DPs were associated with increased risks of overweight and/or obesity.

The dynamic behaviour of helicopter during water impact, considering variations in initial downward velocity and pitching angle, have been investigated numerically and theoretically in the present study. The air-water two-phase flows are simulated by solving unsteady Reynolds-averaged Navier-Stokes equations enclosed by standard $k - \omega $ turbulence model. A treatment for computational domain in combination with a global dynamic mesh technique is applied to deal with the relative motion between the helicopter and water. Results indicate that the initial downward velocity of helicopter exhibits behaviour similar to that of a V-shaped body impacting on water, as does the initial pitching angle. To extend the theoretical approach for predicting the kinematic parameters during helicopter ditching, a shape factor capturing the combined effect of various attributes and an average deadrise angle for asymmetric wedges are also introduced.

The immediate priorities for high-power delivery employing solid-core fibers are balancing the nonlinear effect and beam deterioration. Here, the scheme of tapered multimode fiber is experimentally realized. The tapered multimode fiber, featuring a 15 m (24/200 μm)–10 m (tapered region)–80 m (48/400 μm) profile, guides the laser with a weakly coupled condition. With the input power of 1035 W, the maximum output power over the 105 m delivery is 962 W, corresponding to a high efficiency of over 93% and a nonlinear suppression ratio of over 50 dB. Mode resolving results show high-order-mode contents of less than –30 dB in the whole delivery path, resulting in a high-fidelity delivery with M2 factors of 1.20 and 1.23 for the input and output lasers, respectively. Furthermore, the ultimate limits of delivery lengths for solid-core weakly coupled fibers are discussed. This work provides a valuable reference to reconsider the future boom of high-power laser delivery based on solid-core fibers.

Identification of the origins of maritime-traded porcelain, though key to unravelling ancient production and trade dynamics, remains challenging. The authors present a pioneering micro-provenance analysis of Dehua-style porcelain from the late-twelfth-century Nanhai I shipwreck, recovered from the South China Sea. By pinpointing the origins of porcelain subtypes, including those bearing ink inscriptions, this study provides greater nuance in understanding spatial patterns of production and the impact of buyer/seller choice in maritime trade. The findings further highlight the effectiveness of portable x-ray fluorescence as a high-precision provenancing analysis and offer insights into porcelain production timelines in south-east China.

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.