Innovation Institution and Spatial Transfer of Energy Industry: The Case of Jiangsu Province, China

This study aims to explore the effect of innovation institution on spatial transfer of energy industry in Jiangsu, China. We focus on the disparity of innovation and energy industry, and analyze the spatial transfer difference in different types of energy industry, rather than view energy industry as a whole. The study demonstrates the spatial change of energy industry at regional level and maps the spatial pattern at city level. The study chooses intellectual property rights (IPRs) protection intensity, authorization patents and local research and development (R&D) investment as the proxy of innovation. Using official data and employing panel fixed-effect model at city-industry level, we conclude (a) innovation abilities significantly influence the spatial transfer of energy industry in Jiangsu. Especially, due to the different time, IPRs protection, patent counts, and R&D investment have different effects on different regions in Jiangsu; (b) 2010 is an important turning point for energy industry development in Jiangsu, and after 2010, the energy industry begins to shift to the middle and northern Jiangsu, whereas the spatial pattern of energy industry in coastal cities is basically unchanged; (c) there is a great difference between the regions in Jiangsu Province, and industrial upgrading has not been achieved in northern Jiangsu

Computational intelligent hybrid model for detecting disruptive trading activity

The term “disruptive trading behaviour” was first proposed by the U.S. Commodity Futures Trading Commission and is now widely used by US and EU regulation (MiFID II) to describe activities that create a misleading appearance of market liquidity or depth or an artificial price movement upward or downward according to their own purposes. Such activities, identified as a new form of financial fraud in EU regulations, damage the proper functioning and integrity of capital markets and are hence extremely harmful. While existing studies have explored this issue, they have, in most cases, either focused on empirical analysis of such cases or proposed detection models based on certain assumptions of the market. Effective methods that can analyse and detect such disruptive activities based on direct studies of trading behaviours have not been studied to date. There exists, accordingly, a knowledge gap in the literature. This paper seeks to address that gap and provides a hybrid model composed of two data-mining-based detection modules that effectively identify disruptive trading behaviours. The hybrid model is designed to work in an on-line scheme. The limit order stream is transformed, calculated and extracted as a feature stream. One detection module, “Single Order Detection,” detects disruptive behaviours by identifying abnormal patterns of every single trading order. Another module, “Order Sequence Detection,” approaches the problem by examining the contextual relationships of a sequence of trading orders using an extended hidden Markov model, which identifies whether sequential changes from the extracted features are manipulative activities (or not). Both models were evaluated using huge volumes of real tick data from the NASDAQ, which demonstrated that both are able to identify a range of disruptive trading behaviours and, furthermore, that they outperform the selected traditional benchmark models. Thus, this hybrid model is shown to make a substantial contribution to the literature on financial market surveillance and to offer a practical and effective approach for the identification of disruptive trading behaviour

Coarse and fine identification of collusive clique in financial market

Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally manipulate the market. They do this by increasing specific trading volumes, thus creating a false impression that a market is more active than it actually is. The traders involved in the collusive transactions are termed as collusive clique. The collusive clique and its activities can cause substantial damage to the market's integrity and attract much attention of the regulators around the world in recent years. Much of the current research focused on the detection based on a number of assumptions of how a normal market behaves. There is, clearly, a lack of effective decision-support tools with which to identify potential collusive clique in a real-life setting. The study in this paper examined the structures of the traders in all transactions, and proposed two approaches to detect potential collusive clique with their activities. The first approach targeted on the overall collusive trend of the traders. This is particularly useful when regulators seek a general overview of how traders gather together for their transactions. The second approach accurately detected the parcel-passing style collusive transactions on the market through analyzing the relations of the traders and transacted volumes. The proposed two approaches, on one hand, provided a complete cover for collusive transaction identifications, which can fulfill the different types of requirements of the regulation, i.e. MiFID II, on the other hand, showed a novel application of well known computational algorithms on solving real and complex financial problem. The proposed two approaches are evaluated using real financial data drawn from the NYSE and CME group. Experimental results suggested that those approaches successfully identified all primary collusive clique scenarios in all selected datasets and thus showed the effectiveness and stableness of the novel application

Studies on antioxidant capacity of anthocyanin extract from purple sweet potato (Ipomoea batatas L.)

The radical scavenging effects by α,α-diphenyl-β-picrylhydrazyl (DPPH) and superoxide anions of anthocyanin extract from purple sweet potato were investigated. The antioxidation experiments showed that the reducing power of the anthocyanin extract reduced 0.572 at 0.5 mg/ml, while those of Lascorbic acid (L-AA) and butylated hydroxytoluene (BHT) reduced 0.460 and 0.121, respectively. They also displayed potent antioxidant effects against the DPPH radical and superoxide anions radical, showing the IC50 values of 6.94 and 3.68 μg/ml, respectively. Moreover, this anthocyanin extract also could significantly inhibit the formation of lipid peroxidation compound. Sixteen kinds of anthocyanins in purple sweet potato were detected by high-performance liquid chromatography with diode-array detection (HPLC-DAD), and most of the anthocyanins were acylated.Key words: Antioxidant activity, anthocyanins, purple sweet potato

Data analytics enhanced component volatility model

Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass filter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatility across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons

Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images

The current deep convolutional neural networks for very-high-resolution (VHR) remote-sensing image land-cover classification often suffer from two challenges. First, the feature maps extracted by network encoders based on vanilla convolution usually contain a lot of redundant information, which easily causes misclassification of land cover. Moreover, these encoders usually require a large number of parameters and high computational costs. Second, as remote-sensing images are complex and contain many objects with large-scale variances, it is difficult to use the popular feature fusion modules to improve the representation ability of networks. To address the above issues, we propose a dynamic convolution self-attention network (DCSA-Net) for VHR remote-sensing image land-cover classification. The proposed network has two advantages. On one hand, we designed a lightweight dynamic convolution module (LDCM) by using dynamic convolution and a self-attention mechanism. This module can extract more useful image features than vanilla convolution, avoiding the negative effect of useless feature maps on land-cover classification. On the other hand, we designed a context information aggregation module (CIAM) with a ladder structure to enlarge the receptive field. This module can aggregate multi-scale contexture information from feature maps with different resolutions using a dense connection. Experiment results show that the proposed DCSA-Net is superior to state-of-the-art networks due to higher accuracy of land-cover classification, fewer parameters, and lower computational cost. The source code is made public available.National Natural Science Foundation of China (Program No. 61871259, 62271296, 61861024), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2021JC-47), in part by Key Research and Development Program of Shaanxi (Program No. 2022GY-436, 2021ZDLGY08-07), in part by Natural Science Basic Research Program of Shaanxi (Program No. 2022JQ-634, 2022JQ-018), and in part by Shaanxi Joint Laboratory of Artificial Intelligence (No. 2020SS-03)

An improved calcium chloride method preparation and transformation of competent cells

Transformation is one of the fundamental and essential molecular cloning techniques. In this paper, we have reported a modified method for preparation and transformation of competent cells. This modifiedmethod, improved from a classical protocol, has made some modifications on the concentration of calcium chloride and competent bacteria solution, rotation speed in centrifugation and centrifugationtime. It was found that the optimal transformation efficiency were obtained when the concentration of CaCl2 was 75 mmol/l, OD600 of the culture meets 0.35 to 0.45, the temperature of rotation was 4°C , rotation speed was 1000 g and rotation time was 5 min. Even more, we also found out that the transformation efficiency would increase about 10 to 30 times when adding 15% glycerine into CaCl2 solution. The transformation efficiency, using our new method, reaches 108cf u/μg and higher than ultra-competent Escherichia coli method. This method will improve the efficiency in the molecular cloning and the construction of gene library.Keywords: Competent cells, CaCl2, improved method, transformation, glycerine, transformation efficienc

Towards Operational Hedging for Logistics Uncertainty Management in Prefabrication Construction

published_or_final_version15th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2015), Ottawa, Canada, 11-13 May 2015. In IFAC-PapersOnLine, 2015, v. 48, p. 1128-113