Agroforestry with a sustainable model of production is considered to be an effective solution to the unsustainability of the existing model in agricultural production, and it is also an important topic for ecosystem services and sustainable development goals to improve human well-being. In addition, existing literatures confirm that the importance of forest functions in increasing agricultural production and maintaining agro-ecological sustainability. The "rice-fish-duck-forest" is an important representative of the agroforestry complex ecosystem because of its unique management mechanism and characteristic social culture. As a result, "rice-fish-duck-forest" ecosystem services are beginning to be studied. In the absence of a systematic scientific understanding of the ecosystem services of "rice-fish-duck-forest", there are potential challenges to its sustainable development. This study thoroughly analyzed the current literature on rice-fish-duck-forest ecosystem services in order to have a more thorough grasp of it and to be more sustainable. This research found that research on the ecosystem services of "rice-fish-duck-forest" involves four themes: "regulational", "ecology", "economy", and "socio-culture". Deforestation, socio-cultural marginalization, and Low community management participation are the three main issues facing the "rice-fish-duck-forest" ecosystem service. To address these issues, this paper builds a framework for the sustainable development of rice-fish-duck-forest ecosystem services within the context of current management frameworks for agriculture and forestry, and further discusses its relevance to the Sustainable Development Goals. This study will provide a theoretical decision-making guide for the transformation of agriculture to agroforestry and the sustainable development of agroforestry.

The complexity of modern power grids, exacerbated by integrating diverse energy sources, espe-cially inverter-based resources (IBRs), presents a significant challenge to grid operation and plan-ning since linear models fail to capture the intricate IBR dynamics. This study employs the Sparse Identification of Nonlinear Dynamics (SINDy) method to bridge the gap between theoretical un-derstanding and practical implementation in power system analysis. It introduces the novel Volterra-based Nonlinearity Index (VNI) to examine system-level nonlinearity comprehensively. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise elucidates the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to IBRs, revealing system-level nonlinearity compared to module-level nonlinearity in conventional syn-chronous generators. The VNI quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a versatile tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the proposed VIN profile.

By shifting the focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can contribute to a more comprehensive understanding of driving behavior. Combing DAD with a conflict identification (CIM) model can potentially enhance the effectiveness of Advanced Driver Assistance Systems (ADAS) in terms of crash evasion capabilities. This paper is part of our research titled Automatic Safety Diagnosis in Connected Vehicle Environment, which received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.

ORB (Oriented FAST and Rotated Brief) features are the most commonly used features in visual SLAM and visual odometry that have high computational speed, and rotation scale invariance. However, due to the homogenization of ORB features, the corner properties of specific features are poor in some artificial environments, and it can lead to poor matching performance when the environmental texture is not rich. The underlying reason is that the pixel grayscale changes in the less-richly textured regions on the image are not obvious, leading to a certain degree of mis-matching in the matching process. At the same time, when the camera motion speed is low, there is much overlap between adjacent frames. This results in minimal or almost no changes in the projection of feature points, and the system is highly sensitive to errors, In this case, even small errors can cause significant fluctuations in the calculation results. In response to the above issues, an improved feature point method for stereo vision mileage calculation is proposed to solve the problem of system stability deterioration when cameras move at low speeds in artificial envi-ronments. Firstly, weight calculation based on different texture regions is used to solve the problem of low corner properties of feature points caused by environmental texture differences. Then, keyframes are used for motion model estimation to improve the system's stability under low-speed motion. The experimental results of the dataset showed that the key frame rate was optimal between 10% and 12%, and the positioning accuracy was higher than these open-source systems compared with three common open-source VO systems.

Azo dyes are the most widely used synthetic dyes in the printing and dyeing process. However, the discharge of untreated azo dyes poses potential threat for human health and aqueous ecosystem. Herein, we fabricated a novel heterogenous catalyst - activated carbon fiber-supported ferric alginate (FeAlg-ACF) . Together with peroxymonosulfate (PMS) and visible light, this photocatalytic oxidation system was used to remove an azo dye - azophloxine. The results indicated that the proposed catalytic oxidation system can remove 100% azophloxine within 24 min, while under the same system, the removal rate was only 92 % and 84 % when ferric alginate was replaced with ferric citrate and ferric oxalate respectively, which showed the superiority of activated carbon fiber-supported ferric alginate. The degradation of azophloxine is achieved by the active radicals (SO4•− and •OH) released from PMS and persistent free radicals from activated carbon fiber. After treating for 24 min, the total organic carbon of azophloxine solution (50 μmol/L) decreased from 1.82 mg/L to 79.3 μg/L and the nitrate concentration of ions increased from 0.3 mg/L to 8.6 mg/L. That is, up to 93.5% azophloxine molecules were completely degraded into inorganic compounds. Consequently, potential secondary contamination by intermediate organic products during catalytic degradation was prohibited. The azophloxine removal ratio was kept almost constant after seven cycles, indicating the recyclability and longevity of this system. Furthermore, the azophloxine removal was still promising at high concentrations of Cl-, HCO3-, CO32-. Therefore, our proposed system is potentially effective to remove dye pollutants from seawater. It provides a feasible method for the development of efficient and environmental friendly PMS activation technology combined with FeAlg-ACF, has significant academic and application value.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

In this paper, four lithological parameters, i.e. thermal conductivity, particle size, porosity and saturation, are investigated by combining experimental observations with numerical simulations to study the influence laws and mechanisms of action on the combustion process of methane hydrate sediments. The variation of combustion characteristics parameters such as flame height, effective combustion ratio and dimensionless discharge water mass with the lithological parameters are studied. In addition, the combustion mechanism of lithologic parameters on methane hydrate deposits is revealed. Combining the experimental results and simulation calculations, to optimize the combustion of methane hydrate sediments, it is recommended to use methane hydrate sediment samples with high saturation and low thermal conductivity, while the oxidant concentration and porosity of methane hydrate sediment samples should be increased.

Ginkgolic acids (GA) have been reported to exhibit anticancer properties, however, the mechanisms remain unclear. This study aims to investigate the mechanisms of GA C13:0 that was isolated from Ginkgo biloba exocarp (GBE) for anti-proliferation, pro-apoptosis and anti-migration effects in human MCF-7 and mouse 4T-1 breast cancer cells. The cytotoxic effect, apoptosis induction and migration inhibition were measured using MTT, TUNEL and Wound healing assays. The expression of mRNA and protein were determined using qPCR and Western blot. Our results showed that no cytotoxicity was found at concentrations of C13:0 below 100µM. The effects of GA C13:0 was further demonstrated by up-regulation of the Bax/Bcl-2 apoptosis pathway and the expression of Apaf-1 protein in the mitochondria. In addition, GA C13:0 also suppressed cell migration and epithelial to mesenchymal transition (EMT) with the increase of E-cadherin expression accompanied by the decrease of Snail, MMP-2, MMP-9 and Vimentin expression. Moreover, GA C13:0 induced cytochrome P450 (CYP) 1B1 expression in aryl hydrocarbon receptor (AhR) pathway. Notably, the up-regulation of CYP1B1 also might play a pivotal regulatory role in mitochondrial and EMT pathways in MCF-7 and 4T-1 cells. Our results may have implications for the development of anticancer agents containing GA as functional additives.