ALBERT

Description: This paper presents a novel control strategy to force a vertical take-off and landing (VTOL) aircraft to accomplish the pinpoint landing task. The control development is based on the image-based visual servoing method and the back-stepping technique; its design differs from the existing methods because the controller maps the image errors onto the actuator space via a visual model which does not contain the depth information of the feature point. The novelty of the proposed method is to extend the image-based visual servoing technique to the VTOL aircraft control. In addition, the Lyapunov theory is used to prove the asymptotic stability of the VTOL aircraft visual servoing system, while the image error can converge to zero. Furthermore, simulations have been also conducted to demonstrate the performances of the proposed method.

Description: Chromatin-associated nonhistone proteins (CHRAPs) are readily collected from the DNaseI digested crude chromatin preparation. In this study, we show that the absolute abundance-based label-free quantitative proteomic analysis fail to identify potential CHRAPs from the CHRAP-prep. This is because that the most-highly abundant cytoplasmic proteins such as ribosomal proteins are not effectively depleted in the CHRAP-prep. Ribosomal proteins remain the top-ranked abundant proteins in the CHRAP-prep. On the other hand, we show that relative abundance-based SILAC-mediated quantitative proteomic analysis is capable of discovering the potential CHRAPs in the CHRAP-prep when compared to the whole-cell-extract. Ribosomal proteins are depleted from the top SILAC ratio-ranked proteins. In contrast, nucleus-localized proteins or potential CHRAPs are enriched in the top SILAC-ranked proteins. Consistent with this, gene-ontology analysis indicates that CHRAP-associated functions such as transcription, regulation of chromatin structures, and DNA replication and repair are significantly overrepresented in the top SILAC-ranked proteins. Some of the novel CHRAPs are confirmed using the traditional method. Notably, phenotypic assessment reveals that the top SILAC-ranked proteins exhibit the high likelihood of requirement for growth fitness under DNA damage stress. Taken together, our results indicate that the SILAC-mediated proteomic approach is capable of determining CHRAPs without prior knowledge.

Description: Background. The mechanisms by which plasminogen activator inhibitor-1 (PAI-1) regulates inflammation, especially in acute respiratory distress syndrome (ARDS), are largely unknown.Objective. To assess the relationship between PAI-1 and autophagy in inflammatory reactions induced by LPS in rat NR8383 cells.Methods. ELISA was used to assess the amounts of TNF-α, IL-1β, and PAI-1 in cell culture supernatants; TLR4, MyD88, PAI-1, LC3, Beclin1, and mTOR protein and mRNA levels were determined by western blot and quantitative RT-PCR, respectively; western blot was used to determine NF-κB protein levels. To further evaluate the role of PAI-1, the PAI-1 gene was downregulated and overexpressed using the siRNA transfection technology and the pCDH-PAI-1, respectively. Finally, the GFP Positive Expression Rate Method was used to determine the rate of GFP-LC3 positive NR8383 cells.Results. In LPS-induced NR8383 cells, TNF-α, IL-1β, and PAI-1 expression levels increased remarkably. Upon PAI-1 knockdown, TNF-α, IL-1β, PAI-1, TLR4, MyD88, NF-κB, LC3, and Beclin1 levels were decreased, while mTOR increased. Conversely, overexpression of PAI-1 resulted in increased amounts of TNF-α, IL-1β, PAI-1, TLR4, MyD88, NF-κB, LC3, and Beclin1. However, no significant change was observed in mTOR expression.Conclusions.In NR8383 cells, PAI-1 contributes in the regulation of LPS-induced inflammation, likely by promoting autophagy.

Description: A tremendous amount of work has been conducted in content-based image retrieval (CBIR) on designing effective index structure to accelerate the retrieval process. Most of them improve the retrieval efficiency via complex index structures, and few take into account the parallel implementation of them on underlying hardware, making the existing index structures suffer from low-degree of parallelism. In this paper, a novel graphics processing unit (GPU) adaptive index structure, termed as plane semantic ball (PSB), is proposed to simultaneously reduce the work of retrieval process and exploit the parallel acceleration of underlying hardware. In PSB, semantics are embedded into the generation of representative pivots and multiple balls are selected to cover more informative reference features. With PSB, the online retrieval of CBIR is factorized into independent components that are implemented on GPU efficiently. Comparative experiments with GPU-based brute force approach demonstrate that the proposed approach can achieve high speedup with little information loss. Furthermore, PSB is compared with the state-of-the-art approach, random ball cover (RBC), on two standard image datasets, Corel 10 K and GIST 1 M. Experimental results show that our approach achieves higher speedup than RBC on the same accuracy level.

Description: The dual hesitant fuzzy sets (DHFSs) were proposed by Zhu et al. (2012), which encompass fuzzy sets, intuitionistic fuzzy sets, hesitant fuzzy sets, and fuzzy multisets as special cases. Correlation measures analysis is an important research topic. In this paper, we define the correlation measures for dual hesitant fuzzy information and then discuss their properties in detail. One numerical example is provided to illustrate these correlation measures. Then we present a direct transfer algorithm with respect to the problem of complex operation of matrix synthesis when reconstructing an equivalent correlation matrix for clustering DHFSs. Furthermore, we prove that the direct transfer algorithm is equivalent to transfer closure algorithm, but its asymptotic time complexity and space complexity are superior to the latter. Another real world example, that is, diamond evaluation and classification, is employed to show the effectiveness of the association coefficient and the algorithm for clustering DHFSs.

Description: As an important issue in the public-private partnership (PPP) projects, the profit distribution has a critical impact on both the public and private sectors. Moreover, the influence of the private sector on the profit distribution of PPP projects cannot be ignored because the private sector are the implementers of PPP projects and responsible for the life-cycle performance and management of PPP projects. Therefore, this study aims at (1) investigating the influencing factors of the profit distribution of PPP projects from the private sector’s perspective and (2) analyzing the relationships between the factors and the profit distribution by the qualitative comparative analysis (QCA). The results first showed that the five key influencing factors on the profit distribution from the private sector’s perspective were the risk sharing, financing ability, investment, management ability, and effort level. Moreover, the results indicated that the risk sharing was the most important factor that had a positive impact on the profit distribution. Furthermore, the strong management ability and the high ratio of investment were identified as critical factors that led to a larger proportion of profits distributed to the private sector. In addition, the financing ability and the effort level of the private sector should also be considered in the profit distribution plan. The findings first contributed to the body of knowledge on the influencing factors of the profit distribution in PPP projects. In addition, this study is the first attempt of exploring the characteristics of the private sector under the context of profit distribution of PPP projects and using the QCA method to enrich the theoretical research. Thus, the findings would help the private companies improve their abilities and ensure their profits. Besides, the public and private sectors can make appropriate profit distribution proposals in practice based on the conclusion of this study.

Description: Public-private partnership (PPP) projects employ a high leverage in terms of debt finance needed by the private consortium. Debt finance providers need to know the bankability-related issues of PPP projects to make the timely arrangement of debt financing and avoid funding problems. However, there is still a lack of a systematic research on the bankability of PPP projects despite the worsening debt arrangement situation for PPP projects after the credit crisis and economic recession from 2008. To bridge this knowledge gap and enhance the practical debt arrangement of PPP projects, this study aims to identify and prioritize the critical bankability criteria of PPP projects. To achieve the objectives, 41 bankability-related criteria were first identified from a comprehensive literature. A structured questionnaire survey was then conducted with 31 industry experts in China who worked in financial institutes and had experienced PPP projects to investigate the relative importance of each criterion. To handle the uncertainty and vagueness of the subjective evaluation from surveys, this study analyzed the relative importance weight of each criterion using a proposed fuzzy analytical hierarchy process-based approach. The results showed that political environment, economic environment, shareholders’ credibility, financial market, legal system, public sector’s reliability, financial structure, and regulatory framework were ranked as the top eight critical bankability criteria. The findings of this study first contributed to the project finance body of knowledge for the bankability of PPP projects. Moreover, the outputs of this study would provide valuable information to the private and public sectors for formulating strategies on improving the bankability of PPP projects.

Description: Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.