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Zearalenone (ZEN) is a mycotoxin produced by Fusarium spp., which commonly and severely contaminate food/feed. ZEN severely affects food/feed safety and reduces economic losses owing to its carcinogenicity, genotoxicity, reproductive toxicity, endocrine effects, and immunotoxicity. To explore efficient methods to detoxify ZEN, we identified and characterized an efficient ZEN-detoxifying microbiota from the culturable microbiome of Pseudostellaria heterophylla rhizosphere soil, designated Bacillus amyloliquefaciens D-1. Its highest ZEN degradation rate reached 96.13% under the optimal condition. And, D-1 can almost completely remove ZEN (90 μg·g−1) from coix semen in 24 h. Then, the D-1 strain can detoxify ZEN to ZEM, which is a new structural metabolite, through hydrolyzation and decarboxylation at the ester group in the lactone ring and amino acid esterification at C2 and C4 hydroxy. Notably, ZEM has reduced the impact on viability, and the damage of cell membrane and nucleus DNA and can significantly decrease the cell apoptosis in the HepG2 cell and TM4 cell. In addition, it was found that the D-1 strain has no adverse effect on the HepG2 and TM4 cells. Our findings can provide an efficient microbial resource and a reliable reference strategy for the biological detoxification of ZEN. [ABSTRACT FROM AUTHOR]

Risk classification assessment and early warning systems are indispensable tools and technologies in the realm of regulatory control. Evaluating and issuing early warnings regarding heavy metal contaminants in meat products play a pivotal role in ensuring public safety and maintaining societal stability. In this study, we focused on heavy metal pollutants such as lead, cadmium, chromium, and arsenic. We collected national inspection data for meat products from 20 provinces in 2020. Combining dietary structure data, toxicology information, and dietary exposure assessment methods, we constructed a risk assessment model for heavy metal contaminants in food. Furthermore, we employed an entropy weight-based analytic hierarchy process (AHP-EW) to classify the results of the risk assessment for heavy metal contaminants in food. This involved determining risk rating levels and thresholds. Finally, we constructed a multi-step food contaminant risk prediction model based on the Transformer framework. To validate the model's performance, comparative assessments were conducted across 20 datasets using various models. The results clearly indicate that the Transformer model outperformed the others in 14 datasets, excelling in its ability to provide advanced warnings for heavy metal risks in meat products. This empowers relevant authorities to strengthen their regulatory oversight of meat products based on the procedures and models proposed in this study, ultimately enhancing the efficiency of food safety risk management. [ABSTRACT FROM AUTHOR]

This paper investigates a novel adaptive fixed-time disturbance observer (AFXDO)-based approximate optimal tracking control architecture for nonlinear systems with partially unknown dynamic drift and perturbation under an adaptive dynamic programming (ADP) scheme. To attenuate the impact of disturbance, a novel AFXDO was designed based on the principle of a fixed-time stable system without prior information of disturbance, making disturbance observer errors converge to zero in a fixed time independent of initial estimation error. Additionally, approximate optimal control is conducted by incorporating the real-time estimation of AFXDO into a critic-only ADP framework to stabilize the dynamics of tracking errors and strike a balance between consumption and performance. In particular, to address the heavy calculation burden and oscillation phenomenon in the traditional actor–critic structure, an improved adaptive update law with a variable learning rate was developed to update the weight for adjusting the optimal cost function and optimal control policy simultaneously, avoiding the initial chattering phenomenon and achieving a prescribed convergence without resorting to dual networks. With the efforts of AFXDO and a weight law with a variable learning rate, the track errors were achieved with fast transient performance and low control consumptions in a fixed time. By revisiting Lyapunov stability, the tracking error and weight estimation error were proven to be uniformly ultimately bounded, and the designed control tended to optimal control. The simulations were carried out on quadrotor tracking to demonstrate the effectiveness of the developed control scheme, which achieves rapid convergence by lower control consumption in 4 s, where the cost function is reduced by 19.13%. [ABSTRACT FROM AUTHOR]

A critic-only learning-based tracking control with prescribed performance was proposed for a class of uncertain nonlinear systems. Based on an estimator and an optimal controller, a novel controller was designed to make tracking errors uniformly ultimately bounded and limited in a prescribed region. First, an unknown system dynamic estimator was employed online to approximate the uncertainty with an invariant manifold. Subsequently, by running a novel cost function, an optimal controller was derived by online learning with a critic-only neural network, which ensured that tracking errors can evolve within a prescribed area while minimizing the cost function. Specifically, weight update can be driven by weight estimation error, avoiding introducing an actor-critic architecture with a complicated law. At last, the stability of a closed-loop system was analyzed by Lyapunov theorem, and tracking errors evolved within prescribed performance with the optimal controller. The effectiveness of the proposed control can be demonstrated by two examples. [ABSTRACT FROM AUTHOR]

Flower color not only determines the quality and commercial value of ornamental plants, but it also plays a vital role in ecological processes such as pollinator attraction. This study aimed to investigate the relationship between flower color and the cellular physicochemical factors of Bletilla striata. The color space values of 21 samples were initially determined, followed by a selection of five samples with significant color differences for testing cell shape, total flavonoid content (ranging from 1.86 to 5.42 mg/g), total anthocyanin content (ranging from 0.52 to 292.62 (A530 − 0.25 ∗ A657)/g), cell pH (varying between 5.03 and 5.74), and metal ion content (including Al3+, Ca2+, Fe3+, K+, Mg2+, Na+, P5+, Zn2+, Mo6+, Cu2+, and Mn2+). The flowers of Bletilla were predominantly purple, pink, and yellow; and distributed in quadrants I, II, and IV on the a* and b* rectangular coordinate. The a* value was identified as the primary color indicator for this species. Total anthocyanin content and Zn2+ showed a significant positive correlation with a*, while Al3+, Ca2+, Fe3+, Mg2+, Na+, and Mn2+ demonstrated negative correlations. Cell shape, flavonoid content, and pH had nonsignificant correlations with a*. In conclusion, the total anthocyanin content and metal ions play crucial roles in determining the flower color of B. striata, which can have implications for pollinator attraction. Future research should focus on understanding the complex interactions between these factors to develop novel ornamental plant varieties with desirable flower colors and enhanced ecological functions. [ABSTRACT FROM AUTHOR]

This paper studies the consensus of first-order discrete-time multi-agent systems with fixed and switching topology, and there exists cooperative and antagonistic interactions among agents. A signed graph is used to model the interactions among agents, and some sufficient conditions for consensus are obtained by analyzing the eigenvalues of a Laplacian matrix in the case of fixed topology. The results indicate that having a spanning tree is only a necessary condition for the consensus of multi-agent systems with signed graphs, which is also affected by edge weights. Consensus is further discussed in the case of switching topology, and the results reveal that consensus can be reached if the controller gain and the union graphs among some consecutive time intervals satisfy some conditions. Finally, several simulation examples further confirm the theoretical results. [ABSTRACT FROM AUTHOR]

Semen coicis is not only a traditional Chinese medicine (TCM), but also a typical food in China, with significant medical and healthcare value. Because semen coicis is rich in starch and oil, it can be easily contaminated with Aspergillus flavus and its aflatoxins (AFs). Preventing and controlling the contamination of semen coicis with Aspergillus flavus and its aflatoxins is vital to ensuring its safety as a drug and as a food. In this study, the endosphere bacteria Pseudomonas palleroniana strain B-BH16-1 produced volatiles that strongly inhibited the mycelial growth and spore formation activity of A. flavus. Gas chromatography–mass spectrometry profiling revealed three volatiles emitted from B-BH16-1, of which 1-undecene was the most abundant. We obtained authentic reference standards for these three volatiles; these significantly reduced mycelial growth and sporulation in Aspergillus, with dimethyl disulfide showing the most robust inhibitory activity. Strain B-BH16-1 was able to completely inhibit the biosynthesis of aflatoxins in semen coicis samples during storage by emitting volatile bioactive components. The microscope revealed severely damaged mycelia and a complete lack of sporulation. This newly identified plant endophyte bacterium was able to strongly inhibit the sporulation and growth of Aspergillus and the synthesis of associated mycotoxins, thus not only providing valuable information regarding an efficient potential strategy for the prevention of A. flavus contamination in TCM and food, but potentially also serving as a reference in the control of toxic fungi. [ABSTRACT FROM AUTHOR]

As the concept of green building is increasingly promoted worldwide, the use of green building materials has also attracted great attention. In order to improve the utilization rate of green building materials in construction projects, this paper tries to explore influencing factors based on the method of agent-based modeling (ABM), which is well-suited to the study of complex systems and their emergent behaviors. There are two types of agents being set, named, respectively, greedy construction enterprises and selective construction enterprises. Meanwhile, the construction unit's intention to use green building materials is treated as the criterion. Two types of construction enterprises compete for projects to obtain profits. In order to reveal the mechanism of the evolutionary path of the macro system, the movement step, project unit profit, project cost and expansion cost are adopted as the independent variables and the number of enterprises is the dependent variable. Then, the experiment was performed with the NetLogo simulation platform. The simulation results show that, it is not beneficial for the selective construction enterprises when the movement step in selecting projects is too long and the project units are too profitable. However, when the project cost and expansion cost become higher, there is a significant advantage for selective construction enterprises to go for a long-term development. Therefore, after a comprehensive evaluation of project costs and unit profits, an optimal strategy can be formulated to ensure the quantitative scale of construction enterprises in the construction industry. This behavior can indirectly improve the intention of construction units to use green building materials and further promote the utilization rate of green building materials. [ABSTRACT FROM AUTHOR]