Twisted vertex operators and unitary Lie algebras

A representation of the central extension of the unitary Lie algebra coordinated with a skew Laurent polynomial ring is constructed using vertex operators over an integral Z_2-lattice. The irreducible decomposition of the representation is explicitly computed and described. As a by-product, some fundamental representations of affine Kac-Moody Lie algebra of type $A_n^{(2)}$ are recovered by the new method.Comment: 26 page

Comparison of ARIMA and ANN models used in electricity price forecasting for power market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model

A short-term electricity price forecasting scheme for power market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July 7th 2010

OpenGCD: Assisting Open World Recognition with Generalized Category Discovery

A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classes) online; (2) Grouping and labeling these unknown as novel known classes; (3) Incremental learning (IL), i.e., continual learning these novel classes and retaining the memory of old classes. Ideally, all of these steps should be automated. However, existing methods mostly assume that the second task is completely done manually. To bridge this gap, we propose OpenGCD that combines three key ideas to solve the above problems sequentially: (a) We score the origin of instances (unknown or specifically known) based on the uncertainty of the classifier's prediction; (b) For the first time, we introduce generalized category discovery (GCD) techniques in OWR to assist humans in grouping unlabeled data; (c) For the smooth execution of IL and GCD, we retain an equal number of informative exemplars for each class with diversity as the goal. Moreover, we present a new performance evaluation metric for GCD called harmonic clustering accuracy. Experiments on two standard classification benchmarks and a challenging dataset demonstrate that OpenGCD not only offers excellent compatibility but also substantially outperforms other baselines. Code: https://github.com/Fulin-Gao/OpenGCD

Assessing the application of miscible CO2 flooding in oil reservoirs: a case study from Pakistan

Miscible carbon dioxide (CO2) flooding has been recognized as a promising approach to enhance the recovery of oil reservoirs. However, depending on the injection strategy and rock/fluid characteristics, efficiency of the miscible CO2flooding varies from reservoir to reservoir. Although, many studies have been carried out to evaluate the performance of the miscible CO2flooding, a specific strategy which can be strictly followed for a hydrocarbon reservoir has not been established yet. The aim of this study is to assess one of Pakistan’s oil reservoirs for miscible CO2flooding by applying a modified screening criterion and numerical modeling. As such, the most recent miscible CO2screening criteria were modified, and a numerical modeling was applied on the prospective reservoir. Based on the results obtained, South oil reservoir (S3) is chosen for a detailed assessment of miscible CO2flooding. It was also found that implementation of CO2water-alternating gas (CO2-WAG) injection at early stages of production can increase the production life of the reservoir

Retinal status analysis method based on feature extraction and quantitative grading in OCT images

Background: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. Methods: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. Results: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. Conclusions: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosi

Enhanced Oil Production Through a Combined Application of Gel Treatment and Surfactant Huff\u27n\u27puff Technology

Surfactant huff-puff for production wells has become a recent interest, especially for extremely heterogeneous reservoirs. However, injected surfactant will preferentially move into fractures in fractured reservoirs or higher permeability zones in unfractured reservoirs and will bypass much of the reservoir. In addition, the surfactant will be produced immediately after the production well resumes to production, so the soaking time is limited. This paper introduces a novel process which couples gel treatment and surfactant huff-puff in one EOR process in which surfactant solution is first injected into the production well, then followed by gel treatment. The proposed method has been applied for 10 production wells in a polymer flooding unit in Daqing oilfield. The best compatible surfactant and gel were screened out for the given reservoir conditions before the field application. The mechanisms of the proposed method were studied using core flooding experiments. The field application design process is presented and the application results have been analyzed. Application results show pressure drawdown test can be an important criterion to select candidate for the combined method

Robust and Efficient CPU-Based RGB-D Scene Reconstruction

3D scene reconstruction is an important topic in computer vision. A complete scene is reconstructed from views acquired along the camera trajectory, each view containing a small part of the scene. Tracking in textureless scenes is well known to be a Gordian knot of camera tracking, and how to obtain accurate 3D models quickly is a major challenge for existing systems. For the application of robotics, we propose a robust CPU-based approach to reconstruct indoor scenes efficiently with a consumer RGB-D camera. The proposed approach bridges feature-based camera tracking and volumetric-based data integration together and has a good reconstruction performance in terms of both robustness and efficiency. The key points in our approach include: (i) a robust and fast camera tracking method combining points and edges, which improves tracking stability in textureless scenes; (ii) an efficient data fusion strategy to select camera views and integrate RGB-D images on multiple scales, which enhances the efficiency of volumetric integration; (iii) a novel RGB-D scene reconstruction system, which can be quickly implemented on a standard CPU. Experimental results demonstrate that our approach reconstructs scenes with higher robustness and efficiency compared to state-of-the-art reconstruction systems

Coccidian parasites in the endangered Forest Musk Deer (

We examined 674 fresh fecal samples from forest musk deer (Moschus berezovskii Flerov) in Sichuan and Shaanxi Provinces, China, for coccidian oocysts and 65% were infected with Eimeria spp. Previously, only four Eimeria species were known from Moschus spp. Here we describe six new Eimeria species. Eimeria aquae n. sp., in 38% deer, has ovoidal oocysts, 32.0 × 23.0 μm, micropyle (M) and scattered polar granules (PGs) of various sizes are present, sometimes oocyst residuum (OR) is present; ovoidal sporocysts, 14.1 × 7.5 μm, with Stieda body (SB) and sporocyst residuum (SR). Eimeria dolichocystis n. sp., in 11% deer; cylindroidal oocysts, 36.6 × 18.9, with a M, 1 PG and OR; ovoidal sporocysts, 13.9 × 7.7, with SB and SR. Eimeria fengxianensis n. sp., in 7% deer; ovoidal oocysts, 36.3 × 25.2, a M and PGs present but OR absent; ovoidal sporocysts, 13.9 × 7.3, with SB and SR. Eimeria helini n. sp. in 24% deer; subspheroidal oocysts, 27.0 × 24.1, OR and PGs often present, but M absent; ovoidal sporocysts, 13.5 × 7.7, with SB and SR. Eimeria kaii n. sp. in 26% deer; ovoidal oocysts, 33.2 × 20.7, M and PGs present, but OR absent; ovoidal sporocysts, 14.4 × 7.5, with SB and SR. Eimeria oocylindrica n. sp., in 17% deer; cylindroidal oocysts, 36.0 × 21.4, M and 1-2 PGs present but OR absent; ovoidal sporocysts, 13.8 × 7.7, with SB and SR. Eimeria dujiangyanensis n. nom. is proposed to replace E. moschus Sha, Zhang, Cai, Wang & Liu, 1994, a junior homonym of E. moschus Matschoulsky, 1947

H-1-NMR-based metabonomic analysis of metabolic profiling in diabetic nephropathy rats induced by streptozotocin

National Natural Science Foundation of China [20705037, 30730026]; Chinese Academy of Sciences [KSCX2-YW-R-118, KSCX2-YW-R-104]; Program of Shanghai [09XD1405100]Zhao L, Gao H, Lian F, Liu X, Zhao Y, Lin D. H-1-NMR-based metabonomic analysis of metabolic profiling in diabetic nephropathy rats induced by streptozotocin. Am J Physiol Renal Physiol 300: F947-F956, 2011. First published January 12, 2011; doi:10.1152/ajprenal.00551.2010.-Elucidation of the metabolic profiling in diabetic nephropathy (DN) rats is of great assistance for understanding the pathogenesis of DN. In this study, H-1-nuclear magnetic resonance (NMR)-based metabonomics combined with HPLC measurements was used to quantitatively analyze the metabolic changes in urine and kidney extracts from diabetic 2-wk and 8-wk rats induced by streptozotocin (STZ). Pattern recognition analysis of either urine or kidney extracts indicated that the two diabetic groups were separated obviously from the control group, suggesting that the metabolic profiles of the diabetic groups were markedly different from the control. The diabetic 8-wk rats showed lower levels of creatine, dimethylamine, and higher levels of ascorbate, succinate, lactate, citrate, allantoin, 2-ketoglutarate, and 3-hydrobutyrate (3-HB) in the urine samples. Moreover, the diabetic 8-wk rats displayed lower levels of succinate, creatine, myo-inositol, alanine, lactate, and ATP, and higher levels of 3-HB and glucose in the kidney extracts. The observed metabolic changes imply the enhanced pathways of either lipid or ketone body synthesis and decreased pathways of either tricarboxylic acid cycle or glycolysis in DN rats compared with the control. Our results suggest that the energy metabolic changes are associated with the pathogenic process of DN