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A drone delivery system, characterized by its low energy consumption, high efficiency, and extensive coverage capability, has been adopted as an effective solution to overcome the limitations of traditional ground transportation. However, due to strong interactions between urban structures and wind, the wind environment in the low-altitude airspace of urban areas poses significant safety risks for drone operations, a challenge that remains unresolved. To mitigate these risks, this study presents a methodology for precisely defining the no-fly zones (NFZs) for drone operations using computational fluid dynamics (CFD) simulations. Three hazardous indices—safe, deviation, and unsafe—are proposed to indicate the drone operation status. High-resolution CFD models of urban wind environments in a real city area are coupled with meteorological wind data to provide statistical results for the three indices. The Reynolds-averaged Navier–Stokes turbulence model is employed to simulate two wind environments, standard wind and strong wind, under 36 incoming flow directions. Considering eight flight orientations of drone operating in horizontal planes at various heights, a set of maps for the occurrence probability of the three hazardous indices is provided. These maps can be utilized to determine safe areas, identify no-fly zones corresponding to high occurrence probabilities of deviation and unsafe indices, and establish efficient flight paths for drone operations. [ABSTRACT FROM AUTHOR]

In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model's outputs as supervision, and guides the learning of agents in new tasks. The proposed method does not require the pre-design or training of complex state-action mappings, thereby reducing the cost of transfer. Experimental results in scenarios such as UAV surveillance and UAV cooperative target occupation, robot cooperative box pushing, UAV cooperative target strike, and multi-agent cooperative resource recovery in a particle simulation environment demonstrate that the DSN-KD transfer method effectively enhances the learning speed of new task policies and improves the proximity of the policy model to the theoretically optimal policy in practical tasks. [ABSTRACT FROM AUTHOR]

Investigating the intricate pathways through which FH06 fermentation broth exerts lipid-lowering and weight-loss effects is pivotal for advancing our comprehension of metabolic regulation and therapeutic interventions. Ultrahigh-performance liquid chromatography quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) detection and the ChEMBL database were used to determine the effective compounds in the FH06 fermentation broth and predict their targets. The TTD database and DisGeNET database were used to query obesity-related targets. The STRING database was used to construct protein interaction information. The Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used to perform biological function annotation (GO) and KEGG pathway enrichment analyses of the targets. Results: A total of 85 effective compounds were screened from the fermentation broth of FH06; these compounds may act on TP53, PPARG, TNF, and other targets through 10 signaling pathways, such as the chemical carcinogenesis-receptor activation and lipid and atherosclerosis pathways, and exert pharmacological effects, such as hypoglycemic effects and weight loss. They also have anti-inflammatory, antioxidant, antitumor, and immunoregulatory effects. These findings reveal the active ingredients of FH06 fermentation broth and its multi-target and multi-channel characteristics in lipid lowering and weight loss. This study has positive implications for the clinical treatment of obesity using FH06, providing a theoretical and scientific basis for further developing of FH06-assisted lipid-lowering products. [ABSTRACT FROM AUTHOR]

As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO2 flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO2 flux. Results indicate that throughout the observation period, the range of CO2 flux in the paddy field was −0.1 to −38.4 μmol/(m2·s), with a mean of −12.9 μmol/(m2·s). The highest CO2 flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO2 absorption. Diurnal variation in CO2 flux exhibited a "U"-shaped curve, with flux reaching its peak absorption at 11:30. The CO2 flux was notably higher in the morning than in the afternoon. The nocturnal CO2 flux remained relatively stable, primarily originating from respiratory CO2 emissions. The rice canopy CO2 flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO2 flux. The simulation of CO2 flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO2 flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors. [ABSTRACT FROM AUTHOR]

In this paper, we consider the general mean-field backward doubly stochastic differential equations (mean-field BDSDEs) whose generator f can be discontinuous in y. We prove the existence theorem of solutions under stochastic linear growth conditions and also obtain the related comparison theorem. Naturally, we present those results under the linear growth condition, which is a special case of the stochastic condition. Finally, a financial claim sale problem is discussed, which demonstrates the application of the general mean-field BDSDEs in finance. [ABSTRACT FROM AUTHOR]

The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China. [ABSTRACT FROM AUTHOR]

A novel method, known as the vector potential random flow generation (VPRFG) method, is introduced for synthesizing divergence-free homogeneous isotropic turbulence with arbitrary spectra. First, the proposed approach employs the random-flow-generation-based method to create a vector potential field. Subsequent application of the curl operation to this field produces a turbulent flow that inherently satisfies the divergence-free condition. In the formulas of the proposed method, we explicitly impose arbitrary homogeneous isotropic three-dimensional spatial cross-spectral density (CSD) and Taylor's frozen hypothesis. This ensures that the generated turbulence conforms to prescribed statistical characteristics, including energy spectra, one-dimensional spatial power spectral density (PSD), temporal PSD, spatial coherence function, turbulent kinetic energy, and Reynolds stress. Additionally, the theoretical accuracy of the proposed method is validated through numerical examples, employing the von Kármán energy spectrum as the target value. Finally, large eddy simulations of homogeneous isotropic temporal-decaying box turbulence generated by the VPRFG method demonstrate a close alignment with the experimental results. [ABSTRACT FROM AUTHOR]

Gallbladder cancer (GBC) is one of the most lethal malignancies worldwide, with extremely poor prognosis. Recently, forkhead box k1 (FOXK1), a member of the FOX transcription factor family, has been reported to be correlated with tumor progression in multiple malignancies. However, the role of FOXK1 in GBC has not been elucidated. In this study, we demonstrated that the expression level of FOXK1 was elevated in human GBC tissues and associated with increased liver metastasis, poor histological differentiation, advanced TNM stage, and shorter overall survival. Knockdown of FOXK1 expression inhibited GBC cells proliferation and metastasis. Consistently, overexpression of FOXK1 promoted GBC cells progression. Mechanical investigations verified that knockdown of FOXK1 could lead to G1/S cell cycle arrest through downregulating CDK4, CDK6, cyclin D1, and cyclin E1. And FOXK1 could regulate the expression of epithelial–mesenchymal transition (EMT) related proteins E-cad, N-cad, and Vimentin. Moreover, we found that FOXK1 could regulate the activation of Akt/mTOR signaling pathway. In addition, AKT special inhibitor MK-2206 could abolish the proliferation and metastasis discrepancy between FOXK1 overexpression GBC cells and control cells, which suggested the tumorpromoting effect of FOXK1 may be partially related with the activations of Akt/mTOR signaling pathway. Collectively, our results suggested that FOXK1 promotes GBC cells progression and represent a novel prognostic biomarker and potential therapeutic target in GBC.