Motivational visualization for resources-sharing online communities

As online applications such as online newsgroups, internet game-rooms, online chat-rooms, and peer-to-peer (P2P) resources-sharing systems become popular, online community visualization became a hot research topic. Different forms and metaphors of visualizations focused on various aspects of online communities have been proposed. In this thesis, I propose one prototype of online community visualization which is designed to motivate user contributions in various aspects and stimulate users to participate in the online community more actively. The uneven participation is a well known problem in human society; according to the 80-20 rule, 20% of the people make 80% of contributions, for example, 20% of the employees in a company do 80% of the work. This problem exits in all kinds of online communities, e.g. newsgroups, chat-rooms, but it is particularly crucial for P2P online resources-sharing communities. Such communities do not have a central server and rely solely on the peers not just to provide contributions, but also to ensure the infrastructure. Large P2P file-sharing communities like KaZaA and Limewire can provide the redundancy of peers and resources needed to support the infrastructure and availability of resources. However, when an online community is small, for example, the students in a class, a research group, a department, or a school, the problem of lack of users it is hard to reach a “critical mass” of user participation, leading to poor service and resource availability, which reduces users’ interest in participating in the system. To attract users and motivate them to make more contributions into an online resources-sharing community, I propose to use motivational visualization of the community and the contributions of its members. The motivational effect of the visualization is grounded on two theories in social psychology which explain how individuals align their behaviour with each other and with their group (community). In this thesis, I discuss three stages in the design of the visualization and the subsequent redesigns following results from evaluation and user feedback

ENERGY & STORAGE SHARING STRATEGIES IN AN ELECTRICITY MARKET ENVIRONMENT

The rapid growth of renewable energy generation (REG) and energy storage systems (ESS) has created a need to further develop the electricity market for distributed energy, to stimulate the technology and application of REG and battery energy storage systems (BESS). Considering that the investment cost is still high at this stage, a window of opportunity exists for the development of a sharing economy. In light of this, this thesis focuses on energy and storage sharing strategies in an electricity market environment. A distributed energy sharing strategy is proposed for a peer-to-peer (P2P) model on a microgrid. In addition, the pricing model for users in this proposed strategy has been optimised using game theory—with the Bayesian Nash Equilibrium (GM-BNE) algorithm. Based on the basic call auction trading model, the energy trading mechanism has been modified. Meanwhile, an energy sharing cloud service is proposed based on a decentralised approach, in which the cloud energy management strategy can be customised for each participant. Rigorous proofs are also given. A detailed energy storage sharing strategy of the hybrid electricity and gas energy is proposed in the distribution network, which considers the energy operation of BESS and thermal energy storage system (TESS). The techno-economic analysis based on the BESS and TESS sizing model is conducted for storage sharing between users. When considering the battery firm in the joint storage sharing strategy, a novel sharing model is proposed based on the classic per-use sharing economy business model. Rigorous mathematical proofs are given for the application of the sharing economy model to BESS, in which the sharing pricing model is validated for technical feasibility and accuracy. The proposed energy and storage sharing strategies are applicable to distributed users, in the cases of the hospitality industry and smart home. The proposed sharing strategies are also beneficial for investors, as demonstrated in the case for a battery firm. In the case of the battery firm, this per-use rental service can open new benefits. The case studies results show that the proposed energy and storage sharing strategies provide a 'win-win' situation for customers, the battery sales firm and energy networks

Development of a Rapid and Confirmatory Method to Identify Ganoderic Acids in Ganoderma Mushrooms

To examine the composition of lanostanoids in Ganoderma lucidum, we have developed a liquid chromatography–mass spectrometry (LC–MS) method by using the ganoderic acids isolated in our laboratory as reference standards. The identity of 14 peaks in the high performance liquid chromatogram (HPLC) of G. lucidum has been confirmed. By using the HPLC retention times of these ganoderic acids and their mass fragmentation patterns established in this paper, one can use LC–MS to analyze G. lucidum without requiring the reference standards of these 14 ganoderic acids. Subsequently, only the HPLC–UV method would be needed to analyze routine samples of G. lucidum

Human beta defensin 2 selectively inhibits HIV-1 in highly permissive CCR6+CD4+ T cells

Chemokine receptor type 6 (CCR6)+CD4+ T cells are preferentially infected and depleted during HIV disease progression, but are preserved in non-progressors. CCR6 is expressed on a heterogeneous population of memory CD4+ T cells that are critical to mucosal immunity. Preferential infection of these cells is associated, in part, with high surface expression of CCR5, CXCR4, and α4β7. In addition, CCR6+CD4+ T cells harbor elevated levels of integrated viral DNA and high levels of proliferation markers. We have previously shown that the CCR6 ligands MIP-3α and human beta defensins inhibit HIV replication. The inhibition required CCR6 and the induction of APOBEC3G. Here, we further characterize the induction of apolipoprotein B mRNA editing enzyme (APOBEC3G) by human beta defensin 2. Human beta defensin 2 rapidly induces transcriptional induction of APOBEC3G that involves extracellular signal-regulated kinases 1/2 (ERK1/2) activation and the transcription factors NFATc2, NFATc1, and IRF4. We demonstrate that human beta defensin 2 selectively protects primary CCR6+CD4+ T cells infected with HIV-1. The selective protection of CCR6+CD4+ T cell subsets may be critical in maintaining mucosal immune function and preventing disease progression

Preparation of resistant sweet potato starch by steam explosion technology using response surface methodology

Purpose: To obtain the optimal conditions and analyze the structure, gelatinization, and digestion characteristics of resistant sweet potato starch prepared by steam explosion (SE) technology.Methods: A response surface method was used to investigate the effects of explosion pressure, pressure-holding time and autoclaving time on digestion resistance of sweet potato starch. The resulting resistant sweet potato starch was identified by Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and for in vitro starch digestion rate.Results: The optimum preparation conditions for resistant sweet potato starch were explosion pressure, 2.1 MPa; pressure-holding time, 56 s; and autoclaving time, 26 min. Under these conditions, digestion resistance of sweet potato starch of up to 37.73 ± 0.86 % was obtained. Infra-red spectra indicate that no new chemical groups appeared in the structure of the resistant starch. Furthermore, a gelatinisation induced endothermic peak was observed in the DSC thermogram of potato starch at about 160 °C. The in vitro digestion data showed that the in vitro digestion rate had undergone a significant decrease.Conclusion: Sweet potato starch treated by SE and autoclaving has lower digestibility and therefore, can potentially be used in food or medicine for diabetic patients.Keywords: Resistant sweet potato starch, Steam explosion, Digestion resistance, Starch digestion rate, Response surface methodolog

Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data information and so on. In this paper, a complex-valued generative adversarial network (GAN) is proposed for the first time to address these issues. The complex number form of model complies with the physical mechanism of PolSAR data and in favor of utilizing and retaining amplitude and phase information of PolSAR data. GAN architecture and semi-supervised learning are combined to handle deficiency of labeled data. GAN expands training data and semi-supervised learning is used to train network with generated, labeled and unlabeled data. Experimental results on two benchmark data sets show that our model outperforms existing state-of-the-art models, especially for conditions with fewer labeled data