Large capacitance and fast polarization response of thin electrolyte dielectrics by spin coating for two-dimensional MoS2 devices

Nano Research - A spin-coating method was applied to obtain thinner and smoother PEO/LiClO4 polymer electrolyte films (EFs) with a lower level of crystallization than those obtained using a...     

Regulation of Polymer Configurations Enables Green Solvent-Processed Large-Area Binary All-Polymer Solar Cells With Breakthrough Performance and High Efficiency Stretchability Factor

With the great potential of the all-polymer solar cells for large-area wearable devices, both large-area device efficiency and mechanical flexibility are very critical but attract limited attention. In this work, from the perspective of the polymer configurations, two types of terpolymer acceptors PYTX-A and PYTX-B (X = Cl or H) are developed. The configuration difference caused by the replacement of non-conjugated units results in distinct photovoltaic performance and mechanical flexibility. Benefiting from a good match between the intrinsically slow film-forming of the active materials and the technically slow film-forming of the blade-coating process, the toluene-processed large-area (1.21 cm²) binary device achieves a record efficiency of 14.70%. More importantly, a new parameter of efficiency stretchability factor (ESF) is proposed for the first time to comprehensively evaluate the overall device performance. PM6:PYTCl-A and PM6:PYTCl-B yield significantly higher ESF than PM6:PY-IT. Further blending with non-conjugated polymer donor PM6-A, the best ESF of 3.12% is achieved for PM6-A:PYTCl-A, which is among the highest comprehensive performances.     

基于蚁群算法的网格资源发现模型研究

本文通过对传统的网格资源发现存在的问题进行分析,针对其不足,引入蚁群算法,提出基于蚁群算法的网格资源发现模型（AA_GRRM）,设计并分析AA_GRRM的体系结构,并对其关键模块分析设计,以提高网格资源发现效率。     

Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method

Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements – a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster–Shafer theory (DST) to estimate the “risk” of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes “soft” computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method.     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

Image compression-encryption scheme combining 2D compressive sensing with discrete fractional random transform

Multimedia Tools and Applications - Most of the existing image encryption algorithms based on compressive sensing are too complex to operate. An image compression-encryption scheme with simple...     

基于工作任务的“4-5-6”情景化项目驱动教学模式的设计与实践

目前我国高职教育正处于改革的重要阶段,创新课程的教学模式,是高职教育的要求,也是课程建设与职业岗位有效接轨的要求。基于工作任务的"4-5-6"情景化项目驱动教学模式,是以职业能力培养为目标,围绕"岗位工作任务"这一主题,以"4"个阶段来划分能力培养过程,以"5"化教学作为教学设计,以"6"个步骤作为教学实施的教学法。通过在Linux操作系统课程教学中的改革实践表明,该教学模式培养了学生的自我学习能力和创造力,提高了学生的职业能力。     

Temporal scale effect of loading data on instream nitrate-nitrogen load computation

The temporal scale effect of loading data on nitrate-nitrogen load computation was examined using outputs of watershed modeling tool Hydrologic Simulation Program-FORTRAN (HSPF) for the Amite River in Louisiana, USA. The daily nitrate-nitrogen concentrations simulated using the HSPF were employed first to obtain daily, weekly, bi-weekly, and monthly average data and then to develop load duration curves for the data with four different temporal scales. The duration curves exhibited high variability in the load estimated using daily data as compared with those based on bi-weekly and monthly data. According to daily data, the nitrate-nitrogen load in the winter was found to be 2,780 kg. The nitrate-nitrogen load decreased with increasing temporal (daily, weekly, bi-weekly, and monthly) scale (commonly used in water quality monitoring) of the data. The coefficient of variation, used to quantify the effect of temporal scale on the load, was found to be linearly and inversely correlated with the logarithm of the time scale. Based on the finding, empirical equations were proposed to extrapolate near real-time data for flow and nitrate-nitrogen, greatly simplifying nutrient monitoring and reducing the cost involved in water quality monitoring.     

Least squares recursive projection twin support vector machine for classification

In this paper we formulate a least squares version of the recently proposed projection twin support vector machine (PTSVM) for binary classification. This formulation leads to extremely simple and fast algorithm, called least squares projection twin support vector machine (LSPTSVM) for generating binary classifiers. Different from PTSVM, we add a regularization term, ensuring the optimization problems in our LSPTSVM are positive definite and resulting better generalization ability. Instead of usually solving two dual problems, we solve two modified primal problems by solving two systems of linear equations whereas PTSVM need to solve two quadratic programming problems along with two systems of linear equations. Our experiments on publicly available datasets indicate that our LSPTSVM has comparable classification accuracy to that of PTSVM but with remarkably less computational time.