In small-plot experiments, weed scientists have traditionally estimated herbicide efficacy through visual assessments or manual counts with wooden frames—methods that are time-consuming, labor-intensive, and error-prone. This study introduces a novel mobile application (app) powered by convolutional neural networks (CNNs) to automate the evaluation of weed coverage in turfgrass. The mobile app automatically segments input images into 10 by 10 grid cells. A comparative analysis of EfficientNet, MobileNetV3, MobileOne, ResNet, ResNeXt, ShuffleNetV1, and ShuffleNetV2 was conducted to identify weed-infested grid cells and calculate weed coverage in bahiagrass (Paspalum notatum Flueggé), dormant bermudagrass [Cynodon dactylon (L.) Pers.], and perennial ryegrass (Lolium perenne L.). Results showed that EfficientNet and MobileOne outperformed other models in detecting weeds growing in bahiagrass, achieving an F1 score of 0.988. For dormant bermudagrass, ResNet performed best, with an F1 score of 0.996. Additionally, app-based coverage estimates (11%) were highly consistent with manual assessments (11%), showing no significant difference (P = 0.3560). Similarly, ResNeXt achieved the highest F1 score of 0.996 for detecting weeds growing in perennial ryegrass, with app-based and manual coverage estimates also closely aligned at 10% (P = 0.1340). High F1 scores across all turfgrass types demonstrate the models’ ability to accurately replicate manual assessments, which is essential for herbicide efficacy trials requiring precise weed coverage data. Moreover, the time for weed assessment was compared, revealing that manual counting with 10 by 10 wooden frames took an average of 39.25, 37.25, and 42.25 s per instance for bahiagrass, dormant bermudagrass, and perennial ryegrass, respectively, whereas the app-based approach reduced the assessment times to 8.23, 7.75, and 14.96 s, respectively. These results highlight the potential of deep learning–based mobile tools for fast, accurate, scalable weed coverage assessments, enabling efficient herbicide trials and offering labor and cost savings for researchers and turfgrass managers.

Yiyang Dahegu rice (YyDHG) is an important agricultural specialty of Yiyang County, Jiangxi Province, and it is also a significant component of the local cultural and economic development. In this experiment, 89 samples of Dahegu rice (DHG) were collected from Jiangxi Province, including 52 samples of YyDHG and 37 samples of DHG from other regions within Jiangxi Province (oDHG). Comprehensive analysis was conducted using polyacrylamide gel electrophoresis, field phenotypic observation, population structure analysis and quality analysis. The results of variety identification indicated that the 89 samples actually comprised 52 distinct varieties, including 19 varieties of YyDHG. Population analysis has revealed rich genetic diversity among DHG varieties within Jiangxi Province, yet no significant subpopulation differentiation was observed between YyDHG and oDHG. Quality experiments demonstrated that YyDHG exhibits significant differences in appearance quality from oDHG, but no notable differences in milling quality or cooked taste and flavour. This suggests that the competitiveness of YyDHG in the market may not entirely depend on its unique quality characteristics, but rather more on its cultural value and brand effect. This experiment conducted a comprehensive analysis of the variety characteristics, genetic diversity and quality traits of YyDHG. Not only does it provide a scientific basis for the breeding and germplasm resource conservation of YyDHG, but it also holds positive implications for promoting the development of its industry.

Tuberculosis (TB) remains a significant public health concern in China. Using data from the Global Burden of Disease (GBD) study 2021, we analyzed trends in age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life years (DALYs) for TB from 1990 to 2021. Over this period, HIV-negative TB showed a marked decline in ASIR (AAPC = −2.34%, 95% CI: −2.39, −2.28) and ASMR (AAPC = −0.56%, 95% CI: −0.62, −0.59). Specifically, drug-susceptible TB (DS-TB) showed reductions in both ASIR and ASMR, while multidrug-resistant TB (MDR-TB) showed slight decreases. Conversely, extensively drug-resistant TB (XDR-TB) exhibited upward trends in both ASIR and ASMR. TB co-infected with HIV (HIV-DS-TB, HIV-MDR-TB, HIV-XDR-TB) showed increasing trends in recent years. The analysis also found an inverse correlation between ASIRs and ASMRs for HIV-negative TB and the Socio-Demographic Index (SDI). Projections from 2022 to 2035 suggest continued increases in ASIR and ASMR for XDR-TB, HIV-DS-TB, HIV-MDR-TB, and HIV-XDR-TB. The rising burden of XDR-TB and HIV-TB co-infections presents ongoing challenges for TB control in China. Targeted prevention and control strategies are urgently needed to mitigate this burden and further reduce TB-related morbidity and mortality.

Automatic precision herbicide application offers significant potential for reducing herbicide use in turfgrass weed management. However, developing accurate and reliable neural network models is crucial for achieving optimal precision weed control. The reported neural network models in previous research have been limited by specific geographic regions, weed species, and turfgrass management practices, restricting their broader applicability. The objective of this research was to evaluate the feasibility of deploying a single, robust model for weed classification across a diverse range of weed species, considering variations in species, ecotypes, densities, and growth stages in bermudagrass turfgrass systems across different regions in both China and the United States. Among the models tested, ResNeXt152 emerged as the top performer, demonstrating strong weed detection capabilities across 24 geographic locations and effectively identifying 14 weed species under varied conditions. Notably, the ResNeXt152 model achieved an F1 score and recall exceeding 0.99 across multiple testing scenarios, with a Matthews correlation coefficient (MCC) value surpassing 0.98, indicating its high effectiveness and reliability. These findings suggest that a single neural network model can reliably detect a wide range of weed species in diverse turf regimes, significantly reducing the costs associated with model training and confirming the feasibility of using one model for precision weed control across different turf settings and broad geographic regions.

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.

Brown dwarfs are failed stars with very low mass (13–75 Jupiter mass) and an effective temperature lower than 2 500 K. Their mass range is between Jupiter and red dwarfs. Thus, they play a key role in understanding the gap in the mass function between stars and planets. However, due to their faint nature, previous searches are inevitably limited to the solar neighbourhood (20 pc). To improve our knowledge of the low mass part of the initial stellar mass function and the star formation history of the Milky Way, it is crucial to find more distant brown dwarfs. Using James Webb Space Telescope (JWST) COSMOS-Web data, this study seeks to enhance our comprehension of the physical characteristics of brown dwarfs situated at a distance of kpc scale. The exceptional sensitivity of the JWST enables the detection of brown dwarfs that are up to 100 times more distant than those discovered in the earlier all-sky infrared surveys. The large area coverage of the JWST COSMOS-Web survey allows us to find more distant brown dwarfs than earlier JWST studies with smaller area coverages. To capture prominent water absorption features around 2.7 ${\unicode{x03BC}}$m, we apply two colour criteria, $\text{F115W}-\text{F277W}+1\lt\text{F277W}-\text{F444W}$ and $\text{F277W}-\text{F444W}\gt\,0.9$. We then select point sources by CLASS_STAR, FLUX_RADIUS, and SPREAD_MODEL criteria. Faint sources are visually checked to exclude possibly extended sources. We conduct SED fitting and MCMC simulations to determine their physical properties and associated uncertainties. Our search reveals 25 T-dwarf candidates and 2 Y-dwarf candidates, more than any previous JWST brown dwarf searches. They are located from 0.3 to 4 kpc away from the Earth. The spatial number density of 900–1 050 K dwarf is $(2.0\pm0.9) \times10^{-6}\text{ pc}^{-3}$, 1 050–1 200 K dwarf is $(1.2\pm0.7) \times10^{-6}\text{ pc}^{-3}$, and 1 200–1 350 K dwarf is $(4.4\pm1.3) \times10^{-6}\text{ pc}^{-3}$. The cumulative number count of our brown dwarf candidates is consistent with the prediction from a standard double exponential model. Three of our brown dwarf candidates were detected by HST, with transverse velocities $12\pm5$, $12\pm4$, and $17\pm6$ km s$^{-1}$. Along with earlier studies, the JWST has opened a new window of brown dwarf research in the Milky Way thick disk and halo.

Cathepsin B (CTSB) is a cysteine protease that is widely found in eukaryotes and plays a role in insect growth, development, digestion, metamorphosis, and immunity. In the present study, we examined the role of CTSB in response to environmental stresses in Myzus persicae Sulzer (Hemiptera: Aphididae). Six MpCTSB genes, namely MpCTSB-N, MpCTSB-16D1, MpCTSB-3098, MpCTSB-10270, MpCTSB-mp2, and MpCTSB-16, were identified and cloned from M. persicae. The putative proteins encoded by these genes contained three conserved active site residues, i.e. Cys, His, and Asn. A phylogenetic tree analysis revealed that the six MpCTSB proteins of M. persicae were highly homologous to other Hemipteran insects. Real-time polymerase chain reaction revealed that the MpCTSB genes were expressed at different stages of M. persicae and highly expressed in winged adults or first-instar nymphs. The expression of nearly all MpCTSB genes was significantly upregulated under different environmental stresses (38°C, 4°C, and ultraviolet-B). This study shows that MpCTSB plays an important role in the growth and development of M. persicae and its resistance to environmental stress.

Phylogenetic analysis demonstrates that Kuamaia lata, a helmetiid euarthropod from the lower Cambrian (Series 2, Stage 3) Chengjiang Konservat-Lagerstätte, nests robustly within Artiopoda, the euarthropod clade including trilobitomorphs. Microtomography of new specimens of K. lata reveals details of morphology, notably a six-segmented head and raptorial frontal appendages, the latter contrasting with filiform antennae considered to be a diagnostic character of Artiopoda. Phylogenetic analyses demonstrate that a raptorial frontal appendage is a symplesiomorphy for upper stem-group euarthropods, retained across a swathe of tree space, but evolved secondarily in K. lata from an antenna within Artiopoda. The phylogenetic position of K. lata adds support to a six-segmented head being an ancestral state for upper stem- and crown-group euarthropods.

Robot pick-and-place for unknown objects is still a very challenging research topic. This paper proposes a multi-modal learning method for robot one-shot imitation of pick-and-place tasks. This method aims to enhance the generality of industrial robots while reducing the amount of data and training costs the one-shot imitation method relies on. The method first categorizes human demonstration videos into different tasks, and these tasks are classified into six types to symbolize as many types of pick-and-place tasks as possible. Second, the method generates multi-modal prompts and finally predicts the action of the robot and completes the symbolic pick-and-place task in industrial production. A carefully curated dataset is created to complement the method. The dataset consists of human demonstration videos and instance images focused on real-world scenes and industrial tasks, which fosters adaptable and efficient learning. Experimental results demonstrate favorable success rates and loss results both in simulation environments and real-world experiments, confirming its effectiveness and practicality.

The large number of patients with ankle injuries and the high incidence make ankle rehabilitation an urgent health problem. However, there is a certain degree of difference between the motion of most ankle rehabilitation robots and the actual axis of the human ankle. To achieve more precise ankle joint rehabilitation training, this paper proposes a novel 3-PUU/R parallel ankle rehabilitation mechanism that integrates with the human ankle joint axis. Moreover, it provides comprehensive ankle joint motion necessary for effective rehabilitation. The mechanism has four degrees of freedom (DOFs), enabling plantarflexion/dorsiflexion, eversion/inversion, internal rotation/external rotation, and dorsal extension of the ankle joint. First, based on the DOFs of the human ankle joint and the variation pattern of the joint axes, a 3-PUU/R parallel ankle joint rehabilitation mechanism is designed. Based on the screw theory, the inverse kinematics inverse, complete Jacobian matrix, singular characteristics, and workspace analysis of the mechanism are conducted. Subsequently, the motion performance of the mechanism is analyzed based on the motion/force transmission indices and the constraint indices. Then, the performance of the mechanism is optimized according to human physiological characteristics, with the motion/force transmission ratio and workspace range as optimization objectives. Finally, a physical prototype of the proposed robot was developed, and experimental tests were performed to evaluate the above performance of the proposed robot. This study provides a good prospect for improving the comfort and safety of ankle joint rehabilitation from the perspective of human-machine axis matching.

Superhydrophobic (SHPo) surfaces can capture a thin layer of air called a plastron under water to reduce skin friction. Although a ~30 % drag reduction has been recently reported with longitudinal micro-trench SHPo surfaces under a boat and in a towing tank, the results lacked the consistency to establish a clear trend. Designed based on Yu et al. (J. Fluid Mech, vol. 962, 2023, A9), this work develops and tests a series of high-performance SHPo surface coupons that can sustain a pinned plastron underneath a passenger motorboat revamped to reach 14 knots. Importantly, plastrons in a pinned state, not just their existence, are confirmed during flow experiments for the first time. All the drag-reduction data measured on different longitudinal micro-trenches are found to collapse if plotted against slip length in wall units. In comparison, aligned posts and transverse trenches show less and little drag reduction, respectively, confirming the adverse effect of the spanwise slip in turbulent flows. This report not only verifies SHPo surfaces can provide a consistent drag reduction at high speeds in open sea but also shows that one may predict the amount of drag reduction in turbulent flows using the physical slip length obtained for Stokes flows.

Machine vision–based herbicide applications relying on object detection or image classification deep convolutional neural networks (DCNNs) demand high memory and computational resources, resulting in lengthy inference times. To tackle these challenges, this study assessed the effectiveness of three teacher models, each trained on datasets of varying sizes, including D-20k (comprising 10,000 true-positive and true-negative images) and D-10k (comprising 5,000 true-positive and true-negative images). Additionally, knowledge distillation was performed on their corresponding student models across a range of temperature settings. After the process of student–teacher learning, the parameters of all student models were reduced. ResNet18 not only achieved higher accuracy (ACC ≥ 0.989) but also maintained higher frames per second (FPS ≥ 742.9) under its optimal temperature condition (T = 1). Overall, the results suggest that employing knowledge distillation in the machine vision models enabled accurate and reliable weed detection in turf while reducing the need for extensive computational resources, thereby facilitating real-time weed detection and contributing to the development of smart, machine vision–based sprayers.

This study presents the first Korean records of two subtropical fish species, Pseudojuloides paradiseus and Diplogrammus xenicus, collected around Jeju-do Island, as well as one boreal fish species, Erilepis zonifer, collected in Busan (approximately 200 km away from Jeju-do Island). In this study, we discuss the implications of the species’ habitat range expansion. Previously, P. paradiseus was known as an endemic species of Japan, while D. xenicus was known to inhabit the Eastern Indian Ocean and the Pacific Ocean excluding around the equator, and E. zonifer was only known to inhabit the Pacific Ocean between eastern Japan and the western USA. Their habitat range expansions might be attributed to the expansion of the Tsushima Warm Current at the surface layer and/or the North Korean Cold Current at the bottom layer. Our findings may suggest that habitat of marine fish is being changed continuously by climate change or oceanic currents. Therefore, it needs to conduct integrated and systematic monitoring of fish fauna to response changing marine biodiversity.

An experimental investigation is conducted to study the flow patterns, spectral properties and energy fluxes in thin-layer turbulence with varying system sizes and damping rates. It is found that although a system-size vortex (an indicator of spectral condensation) occurs for small system sizes and does not for large ones, the spectra for different system sizes consistently exhibit a scaling close to $k^{-3}$ in inverse cascade (another indicator of spectral condensation). On the other hand, under a fixed system size larger than the friction-dominated length scale, the energy spectrum in the inverse cascade range changes from $k^{-3}$ to $k^{-5/3}$ as the damping rate increases, suggesting that the friction-dominated length scale may not be a suitable parameter for predicting spectral transition. At lower damping rates and large system sizes, turbulent structures grow larger via inverse cascade, manifesting as long streamers, and the small-scale vortices are suppressed. This suppression leads to a reduction of energy flux at intermediate scales and a change in the spectral shape. The dimensionless Taylor microscale is found to exhibit a monotonic dependence on the damping rate. With the reduction in the damping rate, the Taylor microscale increases to become comparable with the forcing scale, and the spectrum in inverse cascade transits to a steeper scaling, $k^{-3}$, indicating that the dimensionless Taylor microscale may be used as a diagnostic parameter for spectral transition.