A Novel Power Flow Method for Long Term Frequency Stability Analysis

This thesis presents a novel approach for a power system to find a practical power flow solution when all the generators in the system have hit their real power output limits, such as some generator units shutting down or load outages. The approach assumes the frequency of the system is unable to be kept at the rated value (usually 60 or 50 Hz) and accordingly, the generator real power outputs are affected by the system frequency deviation. The modification aims to include the system frequency deviation as a new state variable in the power flow so that the power system can be described in a more precise way when the generation limits are hit and the whole system is not operated under the normal condition. A new mathematical formulation for power flow is given by modified the conventional power flow mismatch equation and Jacobian matrix. The Newton – Raphson method is particularly chose to be modified because Newton – Raphson method is most widely used and it is a fast convergent and accurate method. The Jacobian matrix will be augmented by adding a column and a row. Matlab is used as a programming tool to implement the Power Flow for Long Term Frequency Stability (PFLTFS) method for a simple 4-bus system and the IEEE 118-bus system. And PSS/E Dynamic simulation is used to verify the steady state solution from PFLTFS is reasonable. The PSS/E Dynamic Simulation plots are used to analyze the long term frequency response. The PFLTFS method provides a technique for solving an abnormal state system power flow. From the results we can conclude that the PFLTFS method is reasonable for solving power flow of a real power unbalanced system

Assessment of putative protein targets derived from the SARS genome

AbstractThe ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas™ pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest

Oxygen dissociation on the C3N monolayer: A first-principles study

The oxygen dissociation and the oxidized structure on the pristine C3N monolayer in exposure to air are the inevitably critical issues for the C3N engineering and surface functionalization yet have not been revealed in detail. Using the first-principles calculations, we have systematically investigated the possible O2 adsorption sites, various O2 dissociation pathways and the oxidized structures. It is demonstrated that the pristine C3N monolayer shows more O2 physisorption sites and exhibits stronger O2 adsorption than the pristine graphene. Among various dissociation pathways, the most preferable one is a two-step process involving an intermediate state with the chemisorbed O2 and the barrier is lower than that on the pristine graphene, indicating that the pristine C3N monolayer is more susceptible to oxidation than the pristine graphene. Furthermore, we found that the most stable oxidized structure is not produced by the most preferable dissociation pathway but generated from a direct dissociation process. These results can be generalized into a wide range of temperatures and pressures using ab initio atomistic thermodynamics. Our findings deepen the understanding of the chemical stability of 2D crystalline carbon nitrides under ambient conditions, and could provide insights into the tailoring of the surface chemical structures via doping and oxidation.Comment: 23 pages,8 figure

Development of high performance catalysts for CO oxidation using data-based modeling

Abstract This paper presents a model-aided approach to the development of catalysts for CO oxidation. This is in contrast to the traditional methodology whereby experiments are guided based on experience and intuition of chemists. The proposed approach operates in two stages. To screen a promising combination of active phase, promoter and support material, a powerful "space-filling" experimental design (specifically, Hammersley sequence sampling) was adopted. The screening stage identified Au-ZnO/Al 2 O 3 as a promising recipe for further optimization. In the second stage, the loadings of Au and ZnO were adjusted to optimize the conversion of CO through the integration of a Gaussian process regression (GPR) model and the technique of maximizing expected improvement. Considering that Au constitutes the main cost of the catalyst, we further attempted to reduce the loading of Au with the aid of GPR, while keeping the low-temperature conversion to a high level. Finally we obtained 2.3%Au-5.0%ZnO/Al 2 O 3 with 21 experiments. Infrared reflection absorption spectroscopy and hydrogen temperature-programmed reduction confirmed that ZnO significantly promotes the catalytic activity of Au

Widespread subsonic turbulence in Ophiuchus North 1

Supersonic motions are common in molecular clouds. (Sub)sonic turbulence is usually detected toward dense cores and filaments. However, it remains unknown whether (sub)sonic motions at larger scales ($\gtrsim$1~pc) can be present in different environments or not. Located at a distance of about 110 pc, Ophiuchus North 1 (Oph N1) is one of the nearest molecular clouds that allows in-depth investigation of its turbulence properties by large-scale mapping observations of single-dish telescopes. We carried out the $^{12}$CO ($J=1-0$) and C$^{18}$O ($J=1-0$) imaging observations toward Oph N1 with the Purple Mountain Observatory 13.7 m telescope. The observations have an angular resolution of $\sim$55\arcsec (i.e., 0.03~pc). Most of the whole C$^{18}$O emitting regions have Mach numbers of $\lesssim$1, demonstrating the large-scale (sub)sonic turbulence across Oph N1. Based on the polarization measurements, we estimate the magnetic field strength of the plane-of-sky component to be $\gtrsim$9~$\mu$G. We infer that Oph N1 is globally sub-Alfv{\'e}nic, and is supported against gravity mainly by the magnetic field. The steep velocity structure function can be caused by the expansion of the Sh~2-27 H{\scriptsize II} region or the dissipative range of incompressible turbulence. Our observations reveal a surprising case of clouds characterised by widespread subsonic turbulence and steep size-linewidth relationship. This cloud is magnetized where ion-neutral friction should play an important role.Comment: 16 pages, 12 figures, accepted for publication in A&

pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning

Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we developed a tool called pLMFPPred (Protein Language Model-based Functional Peptide Predictor) for predicting functional peptides and identifying toxic peptides. We also introduced SMOTE-TOMEK data synthesis sampling and Shapley value-based feature selection techniques to relieve data imbalance issues and reduce computational costs. On a validated independent test set, pLMFPPred achieved accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score values of 0.974, 0.99, and 0.974, respectively. Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides.The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods. pLMFPPred has achieved good performance in predicting functional peptides and represents a new computational method for predicting functional peptides.Comment: 20 pages, 5 figures,under revie

Hierarchical Multi-scale Attention Networks for action recognition

Recurrent Neural Networks (RNNs) have been widely used in natural language processing and computer vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the computer vision task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the vision task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN