Coordination control and analysis of TCSC devices to protect electrical power systems against disruptive disturbances

summary:In this work, we study coordination control and effective deployment of thyristor-controlled series compensation (TCSC) to protect power grids against disruptive disturbances. The power grid consists of flexible alternate current transmission systems (FACTS) devices for regulating power flow, phasor measurement units (PMUs) for detecting system states, and control station for generating the regulation signals. We propose a novel coordination control approach of TCSC devices to change branch impedance and regulate the power flow against unexpected disturbances on buses or branches. More significantly, a numerical method is developed to estimate a gradient vector for generating regulation signals of TCSC devices and reducing computational costs. To describe the degree of power system stress, a performance index is designed based on the error between the desired power flow and actual values. Moreover, technical analysis is presented to ensure the convergence of the proposed coordination control algorithm. Numerical simulations are implemented to substantiate that the coordination control approach can effectively alleviate the stress caused by contingencies on IEEE 24 bus system, as compared to the classic PID control. It is also demonstrated that the deployment of TCSCs can alleviate the system stress greatly by considering both impedance magnitude and active power on branches

Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.Comment: OpenBG. Work in Progres

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. The ModelScope-Agent library\footnote{https://github.com/modelscope/modelscope-agent} and online demo\footnote{https://modelscope.cn/studios/damo/ModelScopeGPT/summary} are now publicly available

Direct enzymatic ethanolysis of potential Nannochloropsis biomass for co-production of sustainable biodiesel and nutraceutical eicosapentaenoic acid

Abstract Background Marine microalga Nannochloropsis is a promising source for the production of renewable and sustainable biodiesel in replacement of depleting petroleum. Other than biodiesel, Nannochloropsis is a green and potential resource for the commercial production of nutraceutical eicosapentaenoic acid (EPA, C20:5). In recent studies, low-value biodiesel can be achieved by transesterification of Nannochloropsis biomass. However, it is undoubtedly wasteful to produce microalgal biodiesel containing EPA from nutritional and economical aspects. A new strategy was addressed and exploited to produce low-value bulky biodiesel along with EPA enrichment via enzymatic ethanolysis of Nannochloropsis biomass with a specific lipase. Results Cellulase pretreatment on Nannochloropsis sp. biomass significantly improved the biodiesel conversion by direct ethanolysis with five enzymes from Candida antarctica (CALA and CALB), Thermomyces lanuginosus (TL), Rhizomucor miehei (RM), and Aspergillus oryzae (PLA). Among these five biocatalysts, CALA was the best suitable enzyme to yield high biodiesel conversion and effectively enrich EPA. After optimization, the maximum biodiesel conversion (46.53–48.57%) was attained by CALA at 8:1 ethanol/biomass ratio (v/w) in 10–15% water content with 10% lipase weight at 35 °C for 72 h. Meanwhile, EPA (60.81%) was highly enriched in microalgae NPLs (neutral lipids and polar lipids), increasing original EPA levels by 1.51-fold. Moreover, this process was re-evaluated with two Nannochloropsis species (IMET1 and Salina 537). Under the optimized conditions, the biodiesel conversions of IMET1 and Salina 537 by CALA were 63.41% and 54.33%, respectively. EPA contents of microalgal NPLs were 50.06% for IMET1 and 53.73% for Salina 537. Conclusion CALA was the potential biocatalyst to discriminate against EPA in the ethanolysis of Nannochloropsis biomass. The biodiesel conversion and EPA enrich efficiency of CALA were greatly dependent on lipidic class and fatty acid compositions of Nannochloropsis biomass. CALA-catalyzed ethanolysis with Nannochloropsis biomass was a promising approach for co-production of low-value biodiesel and high-value microalgae products rich in EPA

A Rice Receptor-like Protein Negatively Regulates Rice Resistance to Southern Rice Black-Streaked Dwarf Virus Infection

Plants rely on various receptor-like proteins and receptor-like kinases to recognize and defend against invading pathogens. However, research on the role of receptor-like proteins in plant antiviral defense, particularly in rice–virus interactions, is limited. In this study, we identified a receptor-like gene, OsBAP1, which was significantly induced upon infection with southern rice black-streaked dwarf virus (SRBSDV) infection. A viral inoculation assay showed that the OsBAP1 knockout mutant exhibited enhanced resistance to SRBSDV infection, indicating that OsBAP1 plays a negatively regulated role in rice resistance to viral infection. Transcriptome analysis revealed that the genes involved in plant–pathogen interactions, plant hormone signal transduction, oxidation–reduction reactions, and protein phosphorylation pathways were significantly enriched in OsBAP1 mutant plants (osbap1-cas). Quantitative real-time PCR (RT-qPCR) analysis further demonstrated that some defense-related genes were significantly induced during SRBSDV infection in osbap1-cas mutants. Our findings provide new insights into the role of receptor-like proteins in plant immune signaling pathways, and demonstrate that OsBAP1 negatively regulates rice resistance to SRBSDV infection

Genome-Wide Identification and Gene Expression Analysis of the OTU DUB Family in <i>Oryza sativa</i>

Ovarian tumor domain (OTU)-containing deubiquitinating enzymes (DUBs) are an essential DUB to maintain protein stability in plants and play important roles in plant growth development and stress response. However, there is little genome-wide identification and analysis of the OTU gene family in rice. In this study, we identified 20 genes of the OTU family in rice genome, which were classified into four groups based on the phylogenetic analysis. Their gene structures, conserved motifs and domains, chromosomal distribution, and cis elements in promoters were further studied. In addition, OTU gene expression patterns in response to plant hormone treatments, including SA, MeJA, NAA, BL, and ABA, were investigated by RT-qPCR analysis. The results showed that the expression profile of OsOTU genes exhibited plant hormone-specific expression. Expression levels of most of the rice OTU genes were significantly changed in response to rice stripe virus (RSV), rice black-streaked dwarf virus (RBSDV), Southern rice black-streaked dwarf virus (SRBSDV), and Rice stripe mosaic virus (RSMV). These results suggest that the rice OTU genes are involved in diverse hormone signaling pathways and in varied responses to virus infection, providing new insights for further functional study of OsOTU genes

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from the problems of low computational efficiency and information asymmetry brought by the long visual sequence in cross-modal alignment. To address these problems, mPLUG introduces an effective and efficient vision-language architecture with novel cross-modal skip-connections, which creates inter-layer shortcuts that skip a certain number of layers for time-consuming full self-attention on the vision side. mPLUG is pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering. mPLUG also demonstrates strong zero-shot transferability when directly transferred to multiple video-language tasks

Combined Genomics and Experimental Analyses of Respiratory Characteristics of Shewanella putrefaciens W3-18-1

It has previously been shown that the Shewanella putrefaciens W3-18-1 strain produces remarkably high current in microbial fuel cells (MFCs) and can form magnetite at 0 degrees C. To explore the underlying mechanisms, we developed a genetic manipulation method by deleting the restriction-modification system genes of the SGI1 (Salmonella genome island 1)-like prophage and analyzed the key genes involved in bacterial respiration. W3-18-1 has less respiratory flexibility than the well-characterized S. oneidensis MR-1 strain, as it possesses fewer cytochrome c genes and lacks the ability to oxidize sulfite or reduce dimethyl sulfoxide (DMSO) and timethylamine oxide (TMAO). W3-18-1 lacks the hydrogen-producing Fe-only hydrogenase, and the hydrogen-oxidizing Ni-Fe hydrogenase genes were split into two separate clusters. Two periplasmic nitrate reductases (NapDAGHB and NapDABC) were functionally redundant in anaerobic growth of W3-18-1 with nitrate as the electron acceptor, though napDABC was not regulated by Crp. Moreover, nitrate respiration started earlier in W3-18-1 than in MR-1 (with NapDAGHB only) under microoxic conditions. These results indicate that Shewanella putrefaciens W3-18-1 is well adapted to habitats with higher oxygen levels. Taken together, the results of this study provide valuable insights into bacterial genome evolution.It has previously been shown that the Shewanella putrefaciens W3-18-1 strain produces remarkably high current in microbial fuel cells (MFCs) and can form magnetite at 0 degrees C. To explore the underlying mechanisms, we developed a genetic manipulation method by deleting the restriction-modification system genes of the SGI1 (Salmonella genome island 1)-like prophage and analyzed the key genes involved in bacterial respiration. W3-18-1 has less respiratory flexibility than the well-characterized S. oneidensis MR-1 strain, as it possesses fewer cytochrome c genes and lacks the ability to oxidize sulfite or reduce dimethyl sulfoxide (DMSO) and timethylamine oxide (TMAO). W3-18-1 lacks the hydrogen-producing Fe-only hydrogenase, and the hydrogen-oxidizing Ni-Fe hydrogenase genes were split into two separate clusters. Two periplasmic nitrate reductases (NapDAGHB and NapDABC) were functionally redundant in anaerobic growth of W3-18-1 with nitrate as the electron acceptor, though napDABC was not regulated by Crp. Moreover, nitrate respiration started earlier in W3-18-1 than in MR-1 (with NapDAGHB only) under microoxic conditions. These results indicate that Shewanella putrefaciens W3-18-1 is well adapted to habitats with higher oxygen levels. Taken together, the results of this study provide valuable insights into bacterial genome evolution