Output Voltage Response Improvement and Ripple Reduction Control for Input-parallel Output-parallel High-Power DC Supply

A three-phase isolated AC-DC-DC power supply is widely used in the industrial field due to its attractive features such as high-power density, modularity for easy expansion and electrical isolation. In high-power application scenarios, it can be realized by multiple AC-DC-DC modules with Input-Parallel Output-Parallel (IPOP) mode. However, it has the problems of slow output voltage response and large ripple in some special applications, such as electrophoresis and electroplating. This paper investigates an improved Adaptive Linear Active Disturbance Rejection Control (A-LADRC) with flexible adjustment capability of the bandwidth parameter value for the high-power DC supply to improve the output voltage response speed. To reduce the DC supply ripple, a control strategy is designed for a single module to adaptively adjust the duty cycle compensation according to the output feedback value. When multiple modules are connected in parallel, a Hierarchical Delay Current Sharing Control (HDCSC) strategy for centralized controllers is proposed to make the peaks and valleys of different modules offset each other. Finally, the proposed method is verified by designing a 42V/12000A high-power DC supply, and the results demonstrate that the proposed method is effective in improving the system output voltage response speed and reducing the voltage ripple, which has significant practical engineering application value.Comment: Accepted by IEEE Transactions on Power Electronic

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey.Comment: 32 pages, 3 figure

Maximizing Coverage Quality with Budget Constrained in Mobile Crowd-Sensing Network for Environmental Monitoring Applications

The Mobile Crowd-sensing Network is a novel cyber−physical−social network which has received great attention recently and can be used as a powerful tool to monitor the phenomenon of the field of interest. Due to the limited budget, how to choose appropriate participants to maximize the coverage quality is one of the most important issues when the mobile crowd-sensing network applies to practical application, such as air quality monitoring. In this paper, given the number of available participants, the traverse path and the reward of each participant, we investigate the problem of how to choose suitable participants to monitor an environment of a critical region by a crowd-sensing network, while the total rewards for all selected participants is not larger than the limited budget. In our solution, we first divide a big critical region such as a city into smaller regions of different size, and select some sampling points in the smaller region; the collected data of those sampling points represents the collected data of the whole smaller region. Then, we design a greedy algorithm to select participants to cover the maximum sampling points while the total rewards of selected participants does not exceed the limited budget. Finally, we evaluate the validity and efficiency of the proposed algorithm by conducting extensive simulations. The simulation results show that the greedy algorithm outperforms an existing scheme

Multi-state traction control of regional CCHP system

The existing optimization scheduling of regional CCHP system does not take into account the different control objectives and response characteristics of each unit under multiple operating states in the whole operation domain, so it is difficult to achieve the unified traction control of multiple operation stages of multiple units. To this end, this paper proposes a unified traction control strategy for multi-state operation of regional CCHP system. Based on the different control objectives and response characteristics CCHP unit under multiple states, a two-layer traction control strategy of the regional CCHP system and unit was established. The upper level takes into account the different control objectives and response characteristics of each unit under different operating states, and optimizes the target operating state of each traction unit. The lower level realizes the efficient traction control of each unit under the target operating state based on the optimization decision results. Finally, a simulation example is designed to verify that this paper can effectively consider the different control objectives and response characteristics of CCHP unit under different operating states to achieve the optimal traction control

Genome-Wide Identification and Expression Profiling of Monosaccharide Transporter Genes Associated with High Harvest Index Values in Rapeseed (Brassica napus L.)

Sugars are important throughout a plant’s lifecycle. Monosaccharide transporters (MST) are essential sugar transporters that have been identified in many plants, but little is known about the evolution or functions of MST genes in rapeseed (Brassica napus). In this study, we identified 175 MST genes in B. napus, 87 in Brassica oleracea, and 83 in Brassica rapa. These genes were separated into the sugar transport protein (STP), polyol transporter (PLT), vacuolar glucose transporter (VGT), tonoplast monosaccharide transporter (TMT), inositol transporter (INT), plastidic glucose transporter (pGlcT), and ERD6-like subfamilies, respectively. Phylogenetic and syntenic analysis indicated that gene redundancy and gene elimination have commonly occurred in Brassica species during polyploidization. Changes in exon-intron structures during evolution likely resulted in the differences in coding regions, expression patterns, and functions seen among BnMST genes. In total, 31 differentially expressed genes (DEGs) were identified through RNA-seq among materials with high and low harvest index (HI) values, which were divided into two categories based on the qRT-PCR results, expressed more highly in source or sink organs. We finally identified four genes, including BnSTP5, BnSTP13, BnPLT5, and BnERD6-like14, which might be involved in monosaccharide uptake or unloading and further affect the HI of rapeseed. These findings provide fundamental information about MST genes in Brassica and reveal the importance of BnMST genes to high HI in B. napus

Nathan P. WiederholdImprovement in the Bioavailability of Poorly Water-Soluble Drugs via Pulmonary Delivery of Nanoparticles

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DataSheet1_Design, synthesis and biological evaluation of 9-aryl-5H-pyrido[4,3-b]indole derivatives as potential tubulin polymerization inhibitors.pdf

A series of new 9-aryl-5H-pyrido[4,3-b]indole derivatives as tubulin polymerization inhibitors were designed, synthesized, and evaluated for antitumor activity. All newly prepared compounds were tested for their anti-proliferative activity in vitro against three different cancer cells (SGC-7901, HeLa, and MCF-7). Among the designed compounds, compound 7k displayed the strongest anti-proliferative activity against HeLa cells with IC50 values of 8.7 ± 1.3 μM. In addition, 7k could inhibit the polymerization of tubulin and disrupt the microtubule network of cells. Further mechanism studies revealed that 7k arrested cell cycle at the G2/M phase and induced apoptosis in a dose-dependent manner. Molecular docking analysis confirmed that 7k may bind to colchicine binding sites on microtubules. Our study aims to provide a new strategy for the development of antitumor drugs targeting tubulin.</p