Residential Energy Consumption in Urban China

Residential energy consumption (REC) is the second largest energy use category (10%) in China and urban residents account for most of the REC. Understanding the underlying drivers of variations of urban REC thus helps to identify challenges and opportunities and provide advices for future policy measures. This paper applies the logarithmic mean Divisia index (LMDI) to a decomposition of China’s urban REC during the period of 1998-2007 at disaggregated product/activity level using data collected from a wide range of sources. Our results have shown an extensive structure change towards a more energy-intensive household consumption structure as well as an intensive structure change towards high-quality and cleaner energy such as electricity, oil, and natural gas, which reflects a changing life style and consumption mode in pursuit of a higher level of comfort, convenience and environmental protection. We have also found that China’s price reforms in the energy sector have contributed to a reduction of REC while scale factors including increased urban population and income levels have played a key role in the rapid growth of REC. We suggest that further deregulation in energy prices and regulatory as well as voluntary energy efficiency and conservation policies in the residential sector should be promoted.Residential Energy Consumption, Index Decomposition Analysis (IDA), China, Consumer/Household Economics, Resource /Energy Economics and Policy, Q32, Q43,

Object Tracking with Multiple Instance Learning and Gaussian Mixture Model

Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

LncRNA HOTAIR promotes MPP+-induced neuronal injury in Parkinson’s disease by regulating the miR-874-5p/ATG10 axis

Parkinson’s disease (PD) is a neurodegenerative disease caused by the loss of dopaminergic neurons. Long non-coding RNAs (lncRNAs) play an important role in many neurological diseases, including PD. This study aimed to investigate the role of lncRNA HOX transcript antisense RNA (HOTAIR) in PD pathogenesis and its potential mechanism. SK-N-SH cells were exposed to 1-methyl-4-phenylpyridinium (MPP+) to mimic PD model in vitro. The levels of HOTAIR, miR-874-5p and autophagy-related 10 (ATG10) were determined by quantitative real-time polymerase chain reaction (qRT-PCR) or western blot assay. Cell viability and apoptosis were assessed by Cell Counting Kit-8 (CCK-8) assay and flow cytometry. The expression of apoptosis-related proteins was measured by western blot. The levels of neuroinflammation-related factors were detected by enzyme-linked immunosorbent assay (ELISA). Commercial kits was used to monitor lactate dehydrogenase (LDH) activity, reactive oxygen (ROS) generation and superoxide dismutase (SOD) activity. The interaction among HOTAIR, miR-874-5p and ATG10 were verified by dual-luciferase reporter assay or RNA immunoprecipitation (RIP) assay. HOTAIR and ATG10 were up-regulated, and miR-874-5p was down-regulated in dose- and time-dependent manners in MPP+-treated SK-N-SH cells. HOTAIR knockdown reduced MPP+-induced neuronal damage. HOTAIR aggrandized MPP+-triggered neuronal injury by sponging miR-874-5p. Also, miR-874-5p attenuated MPP+-triggered neuronal damage by targeting ATG10. Moreover, HOTAIR regulated ATG10 expression via sponging miR-874-5p. HOTAIR promoted MPP+-induced neuronal injury via modulating the miR-874-5p/ATG10 axis in SK-N-SH cells

Exploration and practice of talent training mode in Sino-foreign cooperative schools -- taking Software engineering as an example

With the deepening of globalization, Sino-foreign cooperation in running schools plays an important role in China’s higher education. Sino-foreign cooperation in running schools refers to the degree education program set up by Chinese higher education institutions and foreign higher education institutions. This mode of cooperation can not only provide Chinese students with broader learning opportunities and international educational resources, but also promote educational exchanges and cooperation between China and foreign countries. As an important subject in the field of information technology, software engineering has broad employment prospects and development potential, so it has been widely used and promoted in Chinese-foreign cooperative education. This paper will take the software engineering major as an example to explore and practice the talent training model of Sino-foreign cooperation in running schools, aiming to provide reference for other majors and Chinese-foreign cooperation in running schools