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The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) "The greatest truths are concise" is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture. [ABSTRACT FROM AUTHOR]

Citrocin is an anti-microbial peptide that holds great potential in animal feed. This study evaluates the anti-microbial and anti-biofilm properties of Citrocin and explores the mechanism of action of Citrocin on the biofilm of P. aeruginosa. The results showed that Citrocin had a significant inhibitory effect on the growth of P. aeruginosa with a minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of 0.3 mg/mL. All five concentrations (1/4MIC, 1/2MIC, MIC, 2MIC, and 4MIC) of Citrocin inhibited P. aeruginosa biofilm formation. Citrocin at the MIC, 2MIC and 4MIC removed 42.7%, 76.0% and 83.2% of mature biofilms, respectively, and suppressed the swarming motility, biofilm metabolic activity and extracellular polysaccharide production of P. aeruginosa. Metabolomics analysis indicated that 0.3 mg/mL of Citrocin up- regulated 26 and down-regulated 83 metabolites, mainly comprising amino acids, fatty acids, organic acids and sugars. Glucose and amino acid metabolic pathways, including starch and sucrose metabolism as well as arginine and proline metabolism, were highly enriched by Citrocin. In summary, our research reveals the anti-biofilm mechanism of Citrocin at the metabolic level, which provides theoretical support for the development of novel anti-biofilm strategies for combatting P. aeruginosa. [ABSTRACT FROM AUTHOR]

The sorghum–sudangrass hybrid is the main high-quality forage grass in Southwest China, but, in recent years, it has suffered from leaf spot disease, with a prevalence of 88% in Bazhong, Sichuan, China, seriously affecting yield and quality. The causal agents were obtained from symptomatic leaves by tissue isolation and verified by pathogenicity assays. A combination of morphological characterization and sequence analysis revealed that strains SCBZSL1, SCBZSX5, and SCBZSW6 were Nigrospora sphaerica, Colletotrichum boninense, and Didymella corylicola, respectively, and the latter two were the first instance to be reported on sorghum–sudangrass hybrids in the world. SCBZSX5 significantly affected the growth of the plants, which can reduce plant height by 25%. The biological characteristics of SCBZSX5 were found to be less sensitive to the change in light and pH, and its most suitable culture medium was Potato Dextrose Agar (PDA), with the optimal temperature of 25 °C and lethal temperature of 35 °C. To clarify the interactions between the pathogen SCBZSX5 and plants, metabolomics analyses revealed that 211 differential metabolites were mainly enriched in amino acid metabolism and flavonoid metabolism. C. boninense disrupted the osmotic balance of the plant by decreasing the content of acetyl proline and caffeic acid in the plant, resulting in disease occurrence, whereas the sorghum–sudangrass hybrids improved tolerance and antioxidant properties through the accumulation of tyrosine, tryptophan, glutamic acid, leucine, glycitein, naringenin, and apigetrin to resist the damage caused by C. boninense. This study revealed the mutualistic relationship between sorghum–sudangrass hybrids and C. boninense, which provided a reference for the control of the disease. [ABSTRACT FROM AUTHOR]

Pennisetum sinese, a versatile and adaptable plant, plays an essential role in phytoremediation, soil reclamation, and fodder production. From 2018 to 2021, the occurrence of Fusarium wilt, with symptoms of foliar blight and internal discoloration of the stem, was observed in Chongqing, China. Pathogens were isolated from the symptomatic leaves. Based on morphological characteristics as well as DNA sequences of the 18S ribosomal RNA (SSU), translation elongation factor 1-α (EF1-α), RNA polymerase II subunit 1 (rpb1), and RNA polymerase II second largest subunit (rpb2) genes, the causal agents were identified as Fusarium oxysporum. Phylogenetic analysis of the combined dataset of EF1-α, rpb1 and rpb2 showed that pathogenic isolates clustered with F. oxysporum strains. The pathogen was reisolated from inoculated and diseased tissues; thus, Koch's postulates were fulfilled. This is the first report of F. oxysporum causing Fusarium wilt on P. sinese in China and worldwide. [ABSTRACT FROM AUTHOR]

In this article, we investigated changes in public firms' attitudes towards environmental protection in 2018–2021 in China. We crawled the firm–investor Q&A record on the website of East Money, extracted the carbon- and environment-related corpus, and then applied the sentiment analysis method of NLP (natural language processing) to calculate the sentiment weight of each firm-level record to estimate the attitude before and after towards carbon reduction. We found that there were significant changes in firms' attitudes towards carbon reduction and environmental protection after the COVID-19 pandemic and the implementation of environment-related policies. We also found a heterogeneous effect of the attitude in different industries. In addition, we built several models to examine the relationship between a firm's carbon reduction attitude and its financial performance. We found that: A goal with consequent specific policies can raise the positive attitudes of firms toward carbon reduction topics; firms' attitudes toward ecological topics are different from industry to industry, which means that there are different needs and situations in the trend of carbon reduction from industry to industry. COVID-19 influenced firms' attitudes toward carbon reduction and environmental protection, calling back the classic dilemma or trilemma of economic growth, carbon reduction, and energy consumption or, perhaps, epidemic control today. The stock situation also influenced the attitude toward environmental protection. [ABSTRACT FROM AUTHOR]

Plant-growth-promoting endophytes (PGPE) are a kind of beneficial microorganisms which could inhabit plant tissues to antagonize certain plant pathogens and promote the host plant's growth and development. At present, many studies have confirmed the mutualistic effect of endophytes with plants, but there are few systematic studies on beneficial roles between endophytes and Dactylis glomerata, especially on the PGPE characteristics of the forage and environmental restoration plant. This study aimed to isolate PGPE from D. glomerata, evaluate their effects on plant growth, and ultimately acquire desirable microbial inoculants for agricultural use. First, endophytes were isolated from D. glomerata by plant re-inoculation experiment, and identified by morphological and molecular analyses. Fixation medium and methods were carried out to assess the nitrogen fixation ability of the strains. Then, the ability to dissolve phosphorus was determined by the Olsen and silicate medium methods; secretory IAA was measured by Salkowski colorimetric method; and the inhibitive effects on phytopathogen were observed by confrontation culture. Twenty-one strains were isolated from four varieties of D. glomerata, among which 14 strains with plant-growth-promoting characteristics were obtained by re-inoculation experiments, including seven endophytic bacteria and seven endophytic fungi. Further evaluation of three selected strains with the most significant PGP attributes were performed by using the pot re-inoculation experiment which revealed that TG2-B derived from Myroides odoratimimus was the most effective plant-growth-promoting agent due to its ability to produce high levels of IAA; the strain Bacillus cereus exhibited the most robust ability in dissolving inorganic phosphorus; and Trichoderma harzianum manifested a conspicuously antagonistic effect against a variety of plant pathogens. For the first time, this study reports the identification of D. glomerata endophytes that were able to promote plant growth and have a considerable antagonistic effects on plant pathogens, which could be considered as microbial inoculants for using in improving crop production and plant disease control. [ABSTRACT FROM AUTHOR]

Metal nanostructured materials, with many excellent and unique physical and mechanical properties compared to macroscopic bulk materials, have been widely used in the fields of electronics, bioimaging, sensing, photonics, biomimetic biology, information, and energy storage. It is worthy of noting that most of these applications require the use of nanostructured metals with specific controlled properties, which are significantly dependent on a series of physical parameters of its characteristic size, geometry, composition, and structure. Therefore, research on low-cost preparation of metal nanostructures and controlling of their characteristic sizes and geometric shapes are the keys to their development in different application fields. The preparation methods, physical and chemical properties, and application progress of metallic nanostructures are reviewed, and the methods for characterizing metal nanostructures are summarized. Finally, the future development of metallic nanostructure materials is explored. [ABSTRACT FROM AUTHOR]

Dietary intake of potato starch could induce a dramatic increase in blood glucose and is positively associated with chronic metabolic diseases (type II diabetes, cardiovascular disease, etc.). Grape seed proanthocyanidins (GSP) are known to decrease starch digestion by inhibiting digestive enzymes or changing the physicochemical properties of starch. In the present study, GSP were complexed with potato starch to prepare polyphenol–starch complexes. The physiochemical properties and digestibility of complexes were investigated by in vitro digestion model, X-ray diffraction, differential scanning calorimetry, rapid visco analyzer, Fourier transform infrared spectroscopy as well as texture profile analysis. Results indicated that the peak viscosity, breakdown, trough, and setback of the complexes disappeared, replaced by a special continuous increase in paste viscosity. The complexes showed a lower final viscosity and higher thermal stability with the increasing binding amount of GSP. GSP decreased the hardness of the complexes' gel significantly. FT-IR indicated that GSP might interact with potato starch through noncovalent forces. Additionally, the complexes also showed a higher content of slowly digestible starch and resistant starch than that of the native starch. Thus, we inferred that the addition of GSP could modify the digestibility of potato starch and be an optional way to modify the starch with lower digestion. [ABSTRACT FROM AUTHOR]

To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process. [ABSTRACT FROM AUTHOR]

The power system is a nonlinear complicated system. For power system analysis problems, they are mainly based on nonlinear equations. In practical systems, the calculation speed of a specific problem is very important. For most classical power system analysis methods, their one-time calculation speed with enough accuracy is difficult to be further improved after decades of research. However, if we see many-time calculations of different analysis functions as a whole, the new breakout may be made. This paper aims to present a novel foundational technique to improve the many-time calculation performance of power system problems. With the technique in this paper, the analysis of grid topology, bus types and line parameters can be separated, and the speed of online calculation of some problems can be improved by more than 10 times faster without any sacrifice of accuracy. This paper points out why the holistic speed of many-time calculations has the potential to be largely improved. The concept of a linear relationship based nonlinear problem (LRBNP) is proposed, which is critical to this technique. The detailed theory derivations are carefully performed. The proposed technique also shows a new way to understand the power systems. Finally, the verification of the derived formulas is performed. [ABSTRACT FROM AUTHOR]