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The main problems restricting the development of solar heating systems are the low energy density and instability of solar energy. The problems of seasonal mismatch between energy supply with demand and the instability can be solved effectively, through the application of seasonal thermal energy storage technology in the solar space heating system. In this paper, a solar district heating system model (basically composed of a solar collector’s array, a seasonal thermal energy storage, a buffer tank, an auxiliary natural gas-fired boiler and controllers) has been established based on the dynamic simulations platform TRNSYS. The variation law of the key parameters and dynamic operation characteristics of the system are obtained, which provides an important reference for the popularization and application of solar space heating system with seasonal thermal energy storage. And the solar fraction of the system can be reached as much as 96.7 % with the low temperature district heating technology.

The Markowitz mean-variance (MV) model is the basis of modern portfolio theory, the goal of which is to choose an optimal set of weights with the maximum expected return for a given level of risk. However, the MV model assumes that returns are normally distributed, which ignores the asymmetry of returns in real life. The mean-variance-skewness (MVS) portfolio framework introduces the skewness of the return as an extension of the classical MV model. Because of the increasing conflicting objective functions, the multi-objective portfolio optimization problem is more difficult to solve. This paper investigates the use of a multi-objective evolutionary algorithm based on decomposition (MOEA/D) for solving the portfolio optimization problem in the stock market. The results show that MOEA/D can produce Pareto fronts efficiently with three objectives to be optimized.

Background subtraction is one of the most popular methods to detect moving objects in videos. In this paper, we propose an efficient hierarchical background subtraction method with block-based and pixel-based codebooks (CBs) using haar-like features for foreground detection. In the block-based stage, four haar-like features and a block average value, which can be calculated rapidly using integral image and are not sensitive to dynamic background, are used to represent a block. Through the block-based stage we can remove most of the background without reducing the true positive rate. To overcome the low precision problem in the block-based stage, the pixel-based stage is adopted to increase the precision. Experiment results show that our approach can provide faster computation speed compared with that of the present related approaches meanwhile, ensure a high correct detection rate.

Peer-to-peer networks offer advantages over traditional client-server networking models, such as the lack of a need for connectivity to trusted intermediary hosts or servers and the use of less costly communication links. While they have become popular in the wired broadband environment, they have not yet been effectively adapted to the resource-constrained mobile network environment. They promise significant potential in applications such as the sharing of files like multimedia and operating system updates between mobile devices. However, the peer-to-peer model faces unique challenges in the mobile context, such as limitations on processing power, on-board device memory, wireless data bandwidth, and available battery energy. We propose a high-level framework for a peer-to-peer protocol with these specific constraints addressed. In addition, we investigate the feasibility of a practical implementation of a peer-to-peer file sharing model on smartphones, including an analysis of how performance is impacted by various variables that can be dynamically controlled in the protocol. Through experimentation on leading smartphones, we have found various optimal strategies, including minimizing the upload-to-download ratio to conserve battery life, using larger file segments to increase throughput, and using sockets to decrease memory overhead.

Propose: Idiopathic orbital inflammatory pseudotumors (IOIP) are inflammatory pseudotumors (IPT) of unknown etiology that develop in the orbit. Due to the lack of well-defined pathogenic mechanisms and diagnostic markers, diagnosis and treatment of this disease remain a significant challenge. Therefore, the aims of this study are to define the etiological factors of IOIP and to identify potential gene targets for the treatment of IOIP. Methods: Eleven IOIP tissue samples were obtained from patients during surgical resection; as control tissue, four normal tissue samples were collected from surgery removal of eye due to trauma. Total RNA was extracted and the gene expression profile of both diseased and normal tissues was analyzed using human oligonucleotide microarrays, which contained 32,050 well-characterized human genes. The expression patterns of selected genes were further confirmed by Real-time RT-PCR. Results: Microarray analyses identified 744 genes differentially expressed in IOIP and non-IOIP samples, with at least 21.5-fold changes and P-value < .005, including 552 downregulated and 192 upregulated genes in IOIPs. Principal Component Analysis (PCA) and Hierarchical Clustering of microarray data successfully distinguished the IOIP group from the non-IOIP control group. Functional annotation using the Molecule Annotation System categorized the 744 genes into immune functions, inflammatory responses, signaling molecules in the PI3K and NF-κB pathways, extracellular matrix degradation, and unknown functions. Conclusions: Immune and inflammatory responses and activation of the PI3K and NF-κB pathways are likely associated with IOIPs. Drugs that would suppress these two responses and two signal pathways may offer new therapeutics of IOIP treatment.

Purpose: The etiology of idiopathic orbital inflammatory pseudotumors (IOIP) is unknown. Epstein-Barr virus (EBV) infection, which causes significant lymphocyte infiltration, has been proposed to be involved in IOIP. This study tries to validate the relationship between EBV infection and the development of IOIP. Methods: Eleven surgical samples from IOIP patients and four non-IOIP control samples from surgery removal of eye due to trauma were analyzed by microarray. The data were confirmed by real time RT-PCR. The existence of EBV-DNA segments in IOIP samples was determined by PCR. Results: Hierarchical clustering of microarray data successfully distinguished the IOIP group and the non-IOIP control group. The gene EBI2, usually induced by EBV infection, was significantly up-regulated in the IOIP group (max = 11.7-fold over control). EBV-DNA was detected in 9/10 (90%) of the IOIP samples and in 0/4 of the control samples. Up-regulation of EB12, the immunoglobulin kappa light chain (IGKC) and the immunoglobulin heavy chain gene 1(IGHA1) were detected in the IOIP group compared to the control group. Conclusion: Collectively, these findings suggest that IOIP may be related to EBV infection and IGKC and IGHA1 activation. Furthermore, we hypothesize that the expression of the EBI2 gene may serve as a molecular marker for IOIP. More IOIP patient samples and detection of EBV related genes ( EBER1, EBNA2 and LMP1) expression need to be investigated.