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This paper considers the novel fractional-order operator developed by Atangana-Baleanu for transmission dynamics of the SARS-CoV-2 epidemic. Assuming the importance of the non-local Atangana-Baleanu fractional-order approach, the transmission mechanism of SARS-CoV-2 has been investigated while taking into account different phases of infection and various transmission routes of the disease. To conduct the proposed study, first of all, we shall formulate the model by using the classical operator of ordinary derivatives. We utilize the fractional order derivative and the model will be extended to a model containing fractional order derivatives. The operator being used is the fractional differential operator and has fractional order Φ1. The model is analyzed further and some basic aspects of the model are investigated besides calculating the basic reproduction number and the possible equilibria of the proposed model. The equilibria of the model are examined for stability purposes and necessary conditions for stability are obtained. Stability is also necessary in terms of numerical setup. The theory of non-linear functional analysis is employed and Ulam-Hyers’s stability of the model is presented. The approach of newton’s polynomial is considered and a new numerical scheme is developed which helped in presenting an iterative process for the proposed ABC system. Based on this scheme, sample curves are obtained for various values of Φ1 and a pattern is derived between the dynamics of the infection and the order of the derivative. Further simulations are presented which show the cruciality and importance of various parameters and the impact of such parameters on the dynamics and control of the disease is presented. The findings of this study will also provide strong conceptual insights into the mechanisms of contagious diseases, assisting global professionals in developing control policies.

In this paper, we consider the dynamical behaviour of a stochastic coronavirus (COVID-19) susceptible-infected-removed epidemic model with the inclusion of the influence of information intervention and Lévy noise. The existence and uniqueness of the model positive solution are proved. Then, we establish a stochastic threshold as a sufficient condition for the extinction and persistence in mean of the disease. Based on the available COVID-19 data, the parameters of the model were estimated and we fit the model with real statistics. Finally, numerical simulations are presented to support our theoretical results.

Purple tea, renowned for its anthocyanin content and distinctive purple hue, has gained prominence. The anthocyanin content in purple tea can exceed three times that of traditional green-leaf tea. Purple tea harbors various anthocyanins, implicating intricate pathways of biosynthesis and transcriptional regulation. Concurrently, owing to its distinctive chemical composition, the processing of purple tea may be constrained, potentially influencing the sensory attributes and flavor profile of the tea. The richness of anthocyanins in purple tea has yielded potential health benefits, including antioxidative and anti-cancer properties, rendering purple tea a sought-after commodity in the tea market. However, current research on purple tea remains incomplete, including indistinct networks of anthocyanin biosynthesis and regulatory mechanisms, incomplete chemical characterization, and a need for comprehensive investigations into its biological activities. The limited research foundation has greatly reduced the popularity and consumption of purple tea. This paper aims to provide an overview of recent advancements in the biosynthesis and regulation of anthocyanins, as well as the chemical compositions, processing, and health benefits of purple tea. This review will provide the groundwork for future efforts in the selection and innovation of purple tea germplasm, purple tea processing, and the expansion of the market for purple tea consumption.

White tea is a unique kind of micro-fermented tea in China. In order to explore the quality differences of different types of white tea, ten samples including Baihaoyinzhen, Baimudan, Gongmei, Shoumei and New White tea were selected for high performance liquid chromatography (HPLC) detection of catechins, gas chromatography-mass spectrometry (GC-MS) detection of aroma components and sensory evaluation, combining with multivariate statistical analysis such as analysis of variance and OPLS-DA. The results showed that the content of catechins in different types of white tea varied greatly, the total catechin content of Gongmei was the highest and that of Shoumei was the lowest, which were 66.84 and 32.66 mg/g respectively, and a total of fifty aroma substances were detected in all white tea, the main aroma components consisted of hexanal, (E)-2-hexenal, benzaldehyde, linalool and its oxides (I, II). The aroma composition and content of Baihaoyinzhen, Baimudan, Gongmei and Shoumei were different from those of the New White tea. Among them, eight aroma components such as (E)-2-pentenoaldehyde and 2-heptanone existed in a large number in the four traditional white tea, but they were not detected in the New White tea. Through multi-component chemometric analysis, it was found that there were significant differences in catechins and aroma metabolites between Baihaoyinzhen, Baimudan, Gongmei and New White tea, but there was little difference between Shoumei and New White tea. This study preliminarily proved the quality differences of different types of white tea, which provided a theoretical basis for the regulation of white tea flavor quality.

Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three step-wise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties.

Image-based refined 3D reconstruction relies on high-resolution and multi-angle images of scenes. The assistance of multi-rotor drones and gimbal provides great convenience for image acquisition. However, capturing images with manual control generally takes a long time. It could easily lead to redundant or insufficient local area coverage, resulting in poor quality of the reconstructed model. We propose a surface geometric primitive-guided UAV path planning method (SGP-G) that aims to automatically and quickly plan a collision-free path to capture fewer images, based on which high-quality models can be obtained. The geometric primitives are extracted by plane segmentation on the proxy, which performs three main functions. First, a more representative evaluation of the reconstructability of the whole scene is realized. Second, two optimization strategies for different geometric primitives are executed to quickly generate a near-global optimized set of viewpoints. Third, regularly arranged viewpoints are generated to improve the efficiency of image acquisition. Experiments on virtual and real scenes demonstrate the remarkable performance of our method. Compared with the state of the art, we accomplish the planning of the photographic path with higher efficiency in a relatively simple way, achieving equivalent and even higher quality of the reconstructed model with fewer images.

Epigallocatechin gallate (EGCG) is an important contributor to bitterness and astringency in summer tea leaves; however, the transcriptional regulatory mechanisms of EGCG biosynthesis remain unclear. In this study, EGCG content was significantly decreased after foliar spraying with nano-Se fertilizers in tea leaves. A WRKY transcription factor (TF), CsWRKY70, was found to be positively related to EGCG content. The open reading frame of CsWRKY70 was 891 bp encoding 296 amino acids. CsWRKY70 is localized to the nucleus and has transcriptional activation activity. The electrophoretic mobility shift assay indicated that CsWRKY70 can directly bind to the promoters of CsLAR and CsUGT84A containing W-box (5′-C/TTGACT/C-3′) sequences. Dual-luciferase reporter experiment verified that CsWRKY70 activated CsLAR and CsUGT84A expressions in tobacco leaves. In summary, these results demonstrated that CsWRKY70 may reduce EGCG biosynthesis by inhibiting the CsLAR and CsUGT84A expressions under nano-Se treatment. Our findings provide new insight into the regulatory mechanism of WRKY TFs involved in catechin biosynthesis and offer a theoretical basis for breeding low or high EGCG content tea cultivars.

Polyphenol oxidase (PPO) plays a role in stress response, secondary metabolism, and other physiological processes during plant growth and development, and is also a critical enzyme in black tea production. However, the regulatory mechanisms of PPO genes and their activity in tea plants are still unclear. In this study, we measured PPO activity in two different tea cultivars, Taoyuandaye (TYDY) and Bixiangzao (BXZ), which are commonly used to produce black tea and green tea, respectively. The expression pattern of CsPPO1 was assessed and validated via transcriptomics and quantitative polymerase chain reaction in both tea varieties. In addition, we isolated and identified an R2R3-MYB transcription factor CsMYB59 that may regulate CsPPO1 expression. CsMYB59 was found to be a nuclear protein, and its expression in tea leaves was positively correlated with CsPPO1 expression and PPO activity. Transcriptional activity analysis showed that CsMYB59 was a transcriptional activator, and the dual-luciferase assay indicated that CsMYB59 could activate the expression of CsPPO1 in tobacco leaves. In summary, our study demonstrates that CsMYB59 represents a transcriptional activator in tea plants and may mediate the regulation of PPO activity by activating CsPPO1 expression. These findings provide novel insights into the regulatory mechanism of PPO gene in Camellia sinensis, which might help to breed tea cultivars with high PPO activity.

Electronic-cigarette smoke (eCS) has been shown to cause a degree of oxidative stress and inflammatory damage in lung tissue. The aim of this study was to evaluate the repair mechanism of Eurotium cristatum fermented loose dark tea (ECT) and Eurotium cristatum particle metabolites (ECP) sifted from ECT after eCS-induced injury in mice. Sixty C57BL/6 mice were randomly divided into a blank control group, an eCS model group, an eCS + 600 mg/kg ECP treatment group, an eCS + 600 mg/kg ECT treatment group, an eCS + 600 mg/kg ECP prevention group, and an eCS + 600 mg/kg ECT prevention group. The results show that ECP and ECT significantly reduced the eCS-induced oxidative stress and inflammation and improved histopathological changes in the lungs in mice with eCS-induced liver injury. Western blot analysis further revealed that ECP and ECT significantly inhibited the eCS-induced upregulation of the phosphorylation levels of the extracellular Regulated protein Kinases (ERK), c-Jun N-terminal kinase (JNK) and p38mitogen-activated protein kinases (p38MAPK) proteins, and significantly increased the eCS-induced downregulation of the expression levels of the pregnane X receptor (PXR) and aryl hydrocarbon receptor (AhR) proteins. Conclusively, these findings show that ECP and ECT have a significant repairing effect on the damage caused by eCS exposure through the MAPK and PXR/AhR signaling pathways; ECT has a better effect on preventing eCS-induced injury and is suitable as a daily healthcare drink; ECP has a better therapeutic effect after eCS-induced injury, and might be a potential therapeutic candidate for the treatment of eCS-induced injury.

The shadow-mapping and ray-tracing algorithms are the two popular approaches used in visibility handling for multi-view based texture reconstruction. Visibility testing based on the two algorithms needs a user-defined bias to reduce computation error. However, a constant bias does not work for every part of a geometry. Therefore, the accuracy of the two algorithms is limited. In this paper, we propose a high-precision graphics pipeline-based visibility classification (GPVC) method without introducing a bias. The method consists of two stages. In the first stage, a shader-based rendering is designed in the fixed graphics pipeline to generate initial visibility maps (IVMs). In the second stage, two algorithms, namely, lazy-projection coverage correction (LPCC) and hierarchical iterative vertex-edge-region sampling (HIVERS), are proposed to classify visible primitives into fully visible or partially visible primitives. The proposed method can be easily implemented in the graphics pipeline to achieve parallel acceleration. With respect to efficiency, the proposed method outperforms the bias-based methods. With respect to accuracy, the proposed method can theoretically reach a value of 100%. Compared with available libraries and software, the textured model based on our method is smoother with less distortion and dislocation.

This paper introduces a probability criterion for two-person zerosum stochastic games, and focuses on the probability that the payoff before the first passage time to some target state set exceeds a level formulated by both players, which shows the security for player 1 and the risk for player 2. For the game model based on discrete-time Markov chains, under suitable conditions on the game's primitive data, we establish the Shapley equation, from which the existences of the value of the game and a pair of optimal policies are ensured. We also provide a recursive way of computing (or at least approximating) the value of the game. At last, the application of our main result is exhibited via an inventory system. [ABSTRACT FROM AUTHOR]

This paper deals with a first passage mean-variance problem for semi-Markov decision processes in Borel spaces. The goal is to minimize the variance of a total discounted reward up to the system's first entry to some target set, where the optimization is over a class of policies with a prescribed expected first passage reward. The reward rates are assumed to be possibly unbounded, while the discount factor may vary with states of the system and controls. We first develop some suitable conditions for the existence of first passage mean-variance optimal policies and provide a policy improvement algorithm for computing an optimal policy. Then, two examples are included to illustrate our results. At last, we show how the results here are reduced to the cases of discrete-time Markov decision processes and continuous-time Markov decision processes. [ABSTRACT FROM AUTHOR]

In this paper we focus on the finite-horizon optimality for denumerable continuous-time Markov decision processes, in which the transition and reward/cost rates are allowed to be unbounded, and the optimality is over the class of all randomized history-dependent policies. Under mild reasonable conditions, we first establish the existence of a solution to the finite-horizon optimality equation by designing a technique of approximations from the bounded transition rates to unbounded ones. Then we prove the existence of ε (≥ 0)- optimal Markov policies and verify that the value function is the unique solution to the optimality equation by establishing the analog of the Itô-Dynkin formula. Finally, we provide an example in which the transition rates and the value function are all unbounded and, thus, obtain solutions to some of the unsolved problems by Yushkevich (1978). [ABSTRACT FROM AUTHOR]