The heating effect of electromagnetic waves in ion cyclotron range of frequencies (ICRFs) in magnetic confinement fusion device is different in different plasma conditions. In order to evaluate the ICRF heating effect in different plasma conditions, we conducted a series of experiments and corresponding TRANSP simulations on the EAST tokamak. Both simulation and experimental results show that the effect of ICRF heating is poor at low core electron density. The decrease in electron density changes the left-handed electric field near the resonant layer, resulting in a significant decrease in the power absorbed by the hydrogen fundamental resonance. However, quite a few experiments must be performed in plasma conditions with low electron density. It is necessary to study how to make ICRF heating best in low electron density plasma. Through a series of simulation scans of the parallel refractive index (n//) of the ICRF antenna, it is concluded that the change of the ICRF antenna n// will lead to the change of the left-handed electric field, which will change the fundamental absorption of ICRF power by the hydrogen minority ions. Fully considering the coupling of ion cyclotron wave at the tokamak boundary and the absorption in the plasma core, optimizing the ICRF antenna structure and selecting appropriate parameters such as parallel refractive index, minority ion concentration, resonance layer position, plasma current and core electron temperature can ensure better heating effect in the ICRF heating experiments in the future EAST upgrade. These results have important implications for the enhancement of the auxiliary heating effect of EAST and other tokamaks.

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.

We investigate the dynamics of circular self-propelled particles in channel flow, modelled as squirmers using a two-dimensional lattice Boltzmann method. The simulations explore a wide range of parameters, including channel Reynolds numbers ($\textit{Re}_c$), squirmer Reynolds numbers ($\textit{Re}_s$) and squirmer-type factors ($\beta$). For a single squirmer, four motion regimes are identified: oscillatory motion confined to one side of the channel, oscillatory crossing of the channel centreline, stabilisation at a lateral equilibrium position with the squirmer tilted and stable upstream swimming near the channel centreline. For two squirmers, interactions produce not only these four corresponding regimes but also three additional ones: continuous collisions with repeated position exchanges, progressive separation and drifting apart and, most notably, the formation of a stable wedge-like conformation (regime D). A key finding is the emergence of regime D, which predominantly occurs for weak pullers ($\beta = 1$) and at moderate to high $\textit{Re}_c$ values. Hydrodynamic interactions align the squirmers with streamline bifurcations near the channel centreline, enabling stability despite transient oscillations. Additionally, the channel blockage ratio critically affects the range of $\textit{Re}_s$ values over which this regime occurs, highlighting the influence of geometric confinement. This study extends the understanding of squirmer dynamics, revealing how hydrodynamic interactions drive collective behaviours. The findings also offer insights into the design of self-propelled particles for biomedical applications and contribute to the theoretical framework for active matter systems. Future work will investigate three-dimensional effects and the stability conditions for spherical squirmers forming stable wedge-like conformations, further generalising these results.

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.

This paper presents an efficient trajectory planning method for a 4-DOF robotic arm designed for pick-and-place manipulation tasks. The method addresses several challenges, where traditional optimization approaches struggle with high dimensionality, and data-driven methods are costly to collect enough data. The proposed approach leverages Bézier curves for computationally efficient, smooth trajectory generation, minimizing abrupt changes in motion. When continuous solutions for the end-effector angle are unavailable, joint angles are interpolated using Bézier or Hermite interpolation. Additionally, we use custom metrics to evaluate deviation between the interpolated trajectory and the original trajectory, as well as the overall smoothness of the path. When a continuous solution exists, the trajectory is treated as a Gaussian process, where a prior factor is generated using the centerline. This prior is then combined with a smoothness factor to optimize the trajectory, ensuring it remains as smooth as possible within the feasible solution space through stochastic gradient descent. The method is evaluated through simulations in Nvidia Isaac Sim; results highlight the method’s suitability, and future work will explore enhancements in prior trajectory integration and smoothing techniques.

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.

Amino acids are fundamental to sustaining life. They are crucial for intracellular processes, such as energy metabolism, biosynthesis of nucleotides, and maintenance of oxidative homeostasis. These processes ensure the proper functioning of cells (including immune cells) and organs. Many studies have demonstrated that immune cells, as key players in immune regulation, have distinct amino acid demands, and their rapid growth and activation are shaped by amino acid availability in their microenvironment. In particular, the proliferation, maturation, and functional responses of innate immune cells are closely linked to amino acid metabolism. The transport, sensing, and mobilization of amino acids drive metabolic reprogramming to support these processes. Therefore, this review focuses on the influence of amino acids on the fate and function of immune cells across development, homeostasis, activation, and effector phases, highlighting the underlying mechanisms. It provides a scientific basis for improving disease resistance and production efficiency in animals.

Little is known about the association between iodine nutrition status and bone health. The present study aimed to explore the connection between iodine nutrition status, bone metabolism parameters, and bone disease (osteopenia and osteoporosis). A cross-sectional survey was conducted involving 391, 395, and 421 adults from iodine fortification areas (IFA), iodine adequate areas (IAA), and iodine excess areas (IEA) of China. Iodine nutrition status, bone metabolism parameters and BMD were measured. Our results showed that, in IEA, the urine iodine concentrations (UIC) and serum iodine concentrations (SIC) were significantly higher than in IAA. BMD and Ca2+ levels were significantly different under different iodine nutrition levels and the BMD were negatively correlated with UIC and SIC. Univariate linear regression showed that gender, age, BMI, menopausal status, smoking status, alcohol consumption, UIC, SIC, free thyroxine, TSH, and alkaline phosphatase were associated with BMD. The prevalence of osteopenia was significantly increased in IEA, UIC ≥ 300 µg/l and SIC > 90 µg/l groups. UIC ≥ 300 µg/l and SIC > 90 µg/l were risk factors for BMD T value < –1·0 sd. In conclusion, excess iodine can not only lead to changes in bone metabolism parameters and BMD, but is also a risk factor for osteopenia and osteoporosis.

The dissolution kinetics occurring on clay minerals are influenced by various factors, including pH, temperature and mineral lattice structure. However, the influence of the surfactant is rarely studied. In the present work, cationic surfactants were investigated in terms of the dissolution of clay minerals in acidic environments. Kaolinite was selected as the representative clay mineral. The cationic surfactant inhibited the dissolution of clay minerals because it limited the attack of H+ on the kaolinite surface and then inhibited the dissolution of kaolinite by modifying the hydrophilicity of the kaolinite surface towards hydrophobicity. The inhibition ability of the surfactant might be related to its molecular structure and the type of acid used in dissolution experiments.

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.

Cleavers, an annual or winter annual broadleaf weed in the Rubiaceae family, has become troublesome in the wheat fields of the Huang-Huai-Hai region in China due to its herbicide resistance. In North America the common name of the plant is stickwilly; in China it known as cleavers. Four populations of cleavers (JS-15, SD-10, JS-22, and AH-20) were collected from wheat fields in Jiangsu, Shandong, and Anhui provinces, where the plant was not being controlled with applications of florasulam. The aims of this study were to identify the herbicide resistance patterns and investigate the mechanism underlying florasulam resistance. Whole-plant dose-response experiments revealed a notable variation in the degree of resistance exhibited by three specific populations toward florasulam, in comparison to the most sensitive population (S and AH-9), with the highest resistance index reaching 841.4. A gene-sequencing assay for acetolactate synthase (ALS) found that plants that were resistant to ALS from the JS-15, JS-22, and AH-20 populations had a Trp-574-Leu mutation, while no known ALS resistance mutations were discovered in SD-10 plants. In vitro ALS enzyme activity assays also indicated that the extractable ALS from JS-15, JS-22, and AH-20 plants was greatly resistant to florasulam relative to plants that are susceptible. Additionally, according to the resistance rating system, all resistant populations were susceptible to carfentrazone-ethyl + MCPA-sodium and bipyrazone + fluroxypyr-methyl. AH-20, JS-15, and JS-22 exhibited resistance to selected ALS, 4-hydroxyphenylpyruvate dioxygenase (HPPD), and photosystem II (PS II) complex inhibitors, demonstrating RR and RRR resistance profiles, whereas AH-9 displayed sensitivity to virtually all tested agents. The SD-10 population, on the other hand, exhibited RR and RRR resistance to HPPD and PS II inhibitors, and sensitivity to tribenuron-methyl. These findings indicate that a target site–based mechanism drives resistance to the ALS inhibitor florasulam in populations of cleavers, but nontarget site resistance may also have contributed to resistance, but this was not investigated. Other herbicides with different sites of action were tested and were active against cleavers.

A modeling method of multi-objective optimization design for parallel mechanisms (PMs) is proposed, whose implementation is illustrated with 2RPU-RPS mechanism as an example. The orientation of biased output axis on moving platform is depicted by spherical attitude angles, and its kinematic model is deduced through vector method. With screw theory as mathematic tool, a comprehensive evaluation method of kinematic performance for PM is established. On this basis, the expensive constrained multi-objective optimization model of dimensional parameters for the discussed mechanism is constructed. The NSDE-II algorithm, formed by replacing the genetic algorithm operators in non-dominated sorting genetic algorithm II (NSGA-II) with DE operators, is utilized to solve this multi-objective optimization problem, thus obtaining multiple Pareto optimal solutions with engineering application significance, which proves the feasibility and effectiveness of the proposed modeling method and algorithm. Moreover, the normalization coverage space and the minimum adjacent vector angle are proposed to evaluate the computational performance of NSDE-II. Finally, the potential engineering application value for the optimized 2RPU-RPS PM is presented.

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.

In this paper, we consider the discrete Orlicz chord Minkowski problem and solve the existence of this problem, which is the nontrivial extension of the discrete $L_{p}$ chord Minkowski problem for ${0<p<1}$.