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Summary: In order to improve the security and reliability of power industry control systems, research proposes the design of intelligent industrial control systems based on digital twins. This design combines the digital twin multilevel reconstruction method to construct an intelligent power control system structure, and proposes a system safety verification range design and evaluation indicators. The experimental data illustrates that the production time of the deep learning digital twin system reconstruction algorithm is 19.1% less than that of the reconstruction algorithm based on differential evolution; and when the number of nodes and the type and number of twins change, the algorithm proposed in the study takes less time and is more in line with the actual needs of the situation. The intelligent power industry control system based on digital twins has a score of no less than 9.5 in four aspects, indicating that the overall effectiveness of the system is good. The results indicate that this method can improve the safety and reliability of the system, ensure the stable operation of the power system, and provide technical support for the widespread application of intelligent power industry control systems. [ABSTRACT FROM AUTHOR]

In today's highly advanced information technology environment, modern network and communication technologies are widely used in monitoring and controlling power systems. These technologies have evolved significantly. They now form a high-performance digital system known as the cyber–physical power system. However, vulnerabilities in communication networks present growing threats to these systems. This paper seeks to enhance the accurate assessment of the security posture of cyber-physical power systems by inferring attackers' intentions. A threat modeling approach based on Bayesian attack graphs is presented, employing Bayesian networks to define and evaluate potential threats that attackers could pose to different system infrastructures. The paper initially conducts a qualitative analysis of the system's threats, constructing a directed graph structure and establishing conditional probability tables among nodes based on prior knowledge. Subsequently, methods are developed to compute the threat levels at different system nodes using real-time detected attack events. Further analysis methods and security assessment metrics are also developed to identify attack paths and quantify system security. Finally, a Bayesian attack graph is constructed in accordance with the system's structure. In practical scenarios, the attack path analysis method can predict the most vulnerable attack paths, while the absolute values of the security assessment metrics indicate the overall risk level of the system. [ABSTRACT FROM AUTHOR]

"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed. [ABSTRACT FROM AUTHOR]

Natural bioactive compounds from plants exhibit substantial pharmacological potency and therapeutic value. However, the development of most plant bioactive compounds is hindered by low solubility and instability. Conventional pharmaceutical forms, such as tablets and capsules, only partially overcome these limitations, restricting their efficacy. With the recent development of nanotechnology, nanocarriers can enhance the bioavailability, stability, and precise intracellular transport of plant bioactive compounds. Researchers are increasingly integrating nanocarrier‐based drug delivery systems (NDDS) into the development of natural plant compounds with significant success. Moreover, natural products benefit from nanotechnological enhancement and contribute to the innovation and optimization of nanocarriers via self‐assembly, grafting modifications, and biomimetic designs. This review aims to elucidate the collaborative and reciprocal advancement achieved by integrating nanocarriers with botanical products, such as bioactive compounds, polysaccharides, proteins, and extracellular vesicles. This review underscores the salient challenges in nanomedicine, encompassing long‐term safety evaluations of nanomedicine formulations, precise targeting mechanisms, biodistribution complexities, and hurdles in clinical translation. Further, this study provides new perspectives to leverage nanotechnology in promoting the development and optimization of natural plant products for nanomedical applications and guiding the progression of NDDS toward enhanced efficiency, precision, and safety. This article is categorized under:Therapeutic Approaches and Drug Discovery > Emerging TechnologiesNanotechnology Approaches to Biology > Nanoscale Systems in Biology [ABSTRACT FROM AUTHOR]

Only a few calli regeneration systems of bamboos were successfully established, which limited the research on the physiological mechanism of callus differentiation. In this study, we successfully established the callus differentiation systems of Dendrocalamus brandisii (Munro) via seeds. The results showed that the best medium for the callus induction of D. brandisii seeds was basal Murashige and Skoog (1962) (MS) media amended with 5.0 mg l−1 2,4-D and 0.5 mg l−1 kinetin (KT), and the optimal medium for shoot differentiation was the basal MS media supplemented with 4.0 mg l−1 6-benzylaminopurine (6-BA) and 0.5 mg l−1 1-Naphthaleneacetic acid (NAA). Callus tissues had apparent polarity in cell arrangement and developed their own meristematic cell layers. Alpha-amylase (α-amylase), starch phosphorylase (STP) and sucrose synthase (SUSY) played a dominant role in carbohydrate degradation in callus during shoot differentiation. The pentose phosphate pathway (PPP) and TCA pathways were up-regulated in the shoot-differentiated calli. The dynamics of 6-BA and KT contents in calli were consistent with their concentrations applied in medium. Indoleacetic acid (IAA) synthesis and the related signal transduction were down-regulated, whereas the endogenous CTK contents were up-regulated by the exogenous cytokinin (CTK) application in shoot-differentiated calli, and their related synthesis, transport and signal transduction pathways were also up-regulated. The down-regulated signal transduction pathways of IAA and abscisic acid (ABA) revealed that they did not play the key role in the shoot differentiation of bamboos. Gibberellins (GAs) also played a role in shoot differentiation based on the down-regulation of DELLA and the up-regulation of PIF4 genes. The overexpression of DbSNRK2 and DbFIF4 genes further confirmed the negative role of ABA and the positive role of GAs in shoot differentiation. [ABSTRACT FROM AUTHOR]

The basic work of power data research is anomaly detection. It is necessary to find a method suitable for processing current power system data. Research proposes an algorithm of fast density peak clustering with Local Outlier Factor (LOF). The algorithm has poor performance in processing datasets with irregular shapes and significant local density changes, and has the disadvantage of strong dependence on truncation distance. This study provides the decision rules for outliers incorporating the idea of LOF. The improved algorithm can fully consider the characteristics of power data and reduce the dependence on truncation distance. In anomaly detection based on the simulation of real power data, the classification accuracy of the improved CFSFDP algorithm is 4.87% higher than that of the traditional algorithm, and the accuracy rate is 97.41%. The missed and false detection rates of the LOF-CFSFDP algorithm are decreased by 2.23% and 2.64%, respectively, compared to the traditional algorithm, and it is ultimately able to reach rates of 1.26% and 1.33%. These results indicate that the algorithm proposed in this study can better describe the characteristics of power data, making the features of outliers and cluster center points more obvious. [ABSTRACT FROM AUTHOR]