On-line strength assessment of distribution systems with distributed energy resources

To enable the online strength assessment of distribution systems integrated with Distributed Energy Resources (DERs), a novel hybrid model and data-driven approach is proposed. Based on the IEC-60909 standard, a new short-circuit calculation method is developed, allowing inverter-based DERs (IBDERs) to be represented as either voltage or current sources with controllable internal impedance. This method also accounts for the impact of distant generators by introducing a site-dependent Short Circuit Ratio (SCR) index to evaluate system strength. An adaptive sampling strategy is employed to generate synthetic data for real-time assessment. To predict the strength of distribution systems under various conditions, a rectified linear unit (ReLU) neural network is trained and further reformulated as a mixed-integer linear programming (MILP) problem to verify its robustness and input stability. The proposed method is validated through case studies on modified IEEE-33 and IEEE-69 bus systems, demonstrating its effectiveness regarding the varying operating conditions within the system

Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition

Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios. Classic end-to-end models fused with extra language models perform well, but mainly in data matching scenarios and are gradually approaching a bottleneck. In this work, we introduce Seed-ASR, a large language model (LLM) based speech recognition model. Seed-ASR is developed based on the framework of audio conditioned LLM (AcLLM), leveraging the capabilities of LLMs by inputting continuous speech representations together with contextual information into the LLM. Through stage-wise large-scale training and the elicitation of context-aware capabilities in LLM, Seed-ASR demonstrates significant improvement over end-to-end models on comprehensive evaluation sets, including multiple domains, accents/dialects and languages. Additionally, Seed-ASR can be further deployed to support specific needs in various scenarios without requiring extra language models. Compared to recently released large ASR models, Seed-ASR achieves 10%-40% reduction in word (or character, for Chinese) error rates on Chinese and English public test sets, further demonstrating its powerful performance

N, S-co-doped carbon/Co1-xS nanocomposite with dual-enzyme activities for a smartphone-based colorimetric assay of total cholesterol in human serum.

We fabricated a novel N,S-co-doped carbon/Co1-xS nanocomposite (NSC/Co1-xS) using a facile sol-gel approach, which featured a multiporous structure, abundant S vacancies and Co-S nanoparticles filling the carbon-layer pores. When the Co1-xS nanoparticles were anchored onto the surface of N,S-co-doped carbon, a synergistic catalysis action occurred. The NSC/Co1-xS nanocomposites possessed both peroxidase-like and oxidase-mimetic dual-enzyme activities, in which the oxidase-mimetic activity dominated. By scavenger capture tests, the nanozyme was demonstrated to catalyze H2O2 to produce h+, •OH and •O2-, among which the strongest and weakest signals were h+ and •OH, respectively. The multi-valence states of Co atoms in the NSC/Co1-xS structure facilitated electronic transfer that enhanced redox reactions, thereby improving the resultant color reaction. Based on the NSC/Co1-xS's enzyme-mimetic catalytic reaction, a visual colorimetric assay and Android "Thing Identify" application (app), installed on a smartphone, offered detection limits of 1.93 and 2.51 mg/dl, respectively, in human serum samples. The selectivity/interference experiments, using fortified macromolecules and metal ions, demonstrated that this sensor had high selectivity and low interference potential for cholesterol analysis. Compared to standard assay kits and previously reported visual detection, the Android smartphone-based assays provided higher accuracy (recoveries up to 93.6-104.1%), feasibility for trace-level detection, and more convenient on-site application for cholesterol assay due to the superior enzymatic activity of NSC/Co1-xS. These compelling performance metrics lead us to posit that the NSC/Co1-xS-based nanozymic sensor offers a promising methodology for several practical applications, such as point-of-care diagnosis and workplace health evaluations

Simultaneous determination of two flavonoids based on disulfide linked β-cyclodextrin dimer and Pd cluster functionalized graphene-modified electrode

Illustration of the SS-β-CD–Pd@RGO nanohybrids simultaneously sensing baicalin and luteolin by an electrochemical strategy.</p