Mechanical properties of graphene films enhanced by homo-telechelic functionalized polymer fillers via π–π stacking interactions

Most researches on graphene/polymer composites are focusing on improving the mechanical and electrical properties of polymers at low graphene content instead of paying attention to constructing graphene’s macroscopic structures. In current study the homo-telechelic functionalized polyethylene glycols (FPEGs) were tailored with π-orbital-rich groups (namely phenyl, pyrene and di-pyrene) via esterification reactions, which enhanced the interaction between polyethylene glycol (PEG) molecules and chemical reduced graphene oxide (RGO) sheets. The π–π stacking interactions between graphene sheets and π-orbital-rich groups endowed the composite films with enhanced tensile strength and tunable electrical conductivity. The formation of graphene network structure mediated by the FPEGs fillers via π–π stacking non-covalent interactions should account for the experimental results. The experimental investigations were also complemented with theoretical calculation using a density functional theory. Atomic force microscope (AFM), scanning electron microscope (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), thermal gravimetric analysis (TGA), UV–vis and fluorescence spectroscopy were used to monitor the step-wise preparation of graphene composite films.     

Design and analysis of a self-tuning feedback controller for the Internet

Active queue management (AQM) is an effective method used in Internet routers for congestion avoidance, and to achieve a tradeoff between link utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. This paper proposes a novel packet dropping scheme, called self-tuning proportional and integral RED (SPI-RED), as an extension of RED. SPI-RED is based on a self-tuning proportional and Integral feedback controller, which considers not only the average queue length at the current time point, but also the past queue lengths during a round-trip time to smooth the impact caused by short-lived traffic dynamics. Furthermore, we give theoretical analysis of the system stability and give guidelines for selection of feedback gains for the TCP/RED system to stabilize the average queue length at a desirable level. The proposed method can also be applied to the other variants of RED. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed SPI-RED algorithm outperforms the existing AQM schemes in terms of drop probability and stability.     

Investigation of Ant System parameter interactions by using design of experiments for job-shop scheduling problems

In recent years, one of the most important and promising research fields has been metaheuristics to find optimal or near-optimal solutions for NP-hard combinatorial optimization problems. Improving the quality of the solution or the solution time is basic research area on metaheuristics. Modifications of the existing ones or creation of hybrid approaches are the focus of these efforts. Another area of improving the solution quality of metaheuristics is finding the optimal combination of algorithm control parameters. This is usually done by design of experiments or one-at-a-time approach in genetic algorithms, simulated annealing and similar metaheuristics. We observe that, in studies which use Ant Colonies Optimization (ACO) as an optimization technique; the levels of control parameters are determined by some non-systematic initial experiments and the interactions of the parameters are not studied yet.In this study, the parameters of Ant System have been investigated on different sized and randomly generated job-shop scheduling problems by using design of experiments. The effects and interactions of the parameters have been interpreted with the outputs of the experiments. Referring to the statistical analysis it is observed that none of the interactions between the Ant System parameters has a significant effect on makespan value. A specific fractional experimental design is suggested instead of the full factorial design. Depending on the findings from the benchmark problems it will be a reliable approach to use the suggested design for saving time and effort in experiments without sacrificing the solution quality.     

PSO-based single multiplicative neuron model for time series prediction

Single multiplicative neuron model is a novel neural network model introduced recently, which has been used for time series prediction and function approximation. The model is based on a polynomial architecture that is the product of linear functions in different dimensions of the space. Particle swarm optimization (PSO), a global optimization method, is proposed to train the single neuron model in this paper. An improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. The proposed CRPSO, PSO, back-propagation algorithm and genetic algorithm are employed to train the model for three well-known time series prediction problems. The experimental results demonstrate the superiority of CRPSO-based neuron model in efficiency and robustness over the other three algorithms.     

结合SURF算子和极线约束的柑橘立体图像对匹配

提出一种结合SURF算子和极线约束的立体匹配方法。对采集的双目视觉柑橘图像进行R-B分量的计算,在该分量上,采用快速hessian检测子进行特征点检测,并使用SURF描述子对检测到的特征点进行64维的特征描述。采用欧式距离和极线约束进行特征点匹配。实验表明,该方法对一幅图像对的平均处理时间为293ms,在果实被遮挡或光线变化的情况下均能较好地进行特征点提取和匹配。该方法为后续的深度信息计算提供了基础。     

硅基负载全氟丁基磺酰亚胺催化纤维素的水解

为了寻求一种有效的纤维素绿色化转化途径,以获取可再生能源或高附加值化工产品,采用新的耐水性固体酸,二氧化硅负载全氟丁基磺酰亚胺催化离子液体体系中纤维素水解,考察了时间、温度和催化剂用量等对反应产物的影响,结果表明:在120℃下,1.5~2h内,主要产物5-羟甲基呋喃甲醛(HMF)和还原糖(TRS)分别获得29%和67%的产率,纤维素完全转化为水溶性产物.13 C CP/MAS检测表明,这类固体酸酸性强于一般的固载磺酸,这有利于有效促进纤维素水解,以及进一步降解为5-羟甲基呋喃甲醛.固体酸循环使用三次,还原糖产率没有明显降低,表明该类催化剂具有一定的可循环使用性能,是一种高效、对环境友好的催化剂,对纤维素的绿色转化,有良好的应用前景.     

Global sensitivity analysis of outputs over rice-growth process in ORYZA model

Dynamic crop models usually have a complex structure and a large number of parameters. Those parameter values usually cannot be directly measured, and they vary with crop cultivars, environmental conditions and managements. Thus, parameter estimation and model calibration are always difficult issues for crop models. Therefore, the quantification of parameter sensitivity and the identification of influential parameters are very important and useful. In this work, late-season rice was simulated with meteorological data in Nanchang, China. Furthermore, we conducted a sensitivity analysis of 20 selected parameters in ORYZA_V3 using the Extended FAST method. We presented the sensitivity results for four model outputs (LAI, WAGT, WST and WSO) at four development stages and the results for yield. Meanwhile, we compared the differences among the sensitivity results for the model outputs simulated in cold, normal and hot years. The uncertainty of output variables derived from parameter variation and weather conditions were also quantified. We found that the development rates, RGRLMN and FLV0.5 had strong effects on all model outputs in all conditions, and parameters WGRMX and SPGF had relative high effects on yield in cold year. Only LAI was sensitive to ASLA. Those influential parameters had unequal effects on different outputs, and they had different effects at four development stages. With the interaction effects of parameter variation and different weather conditions, the uncertainty of model outputs varied significantly. However, the weather conditions had negligible effects on the identification of influential parameters, although they had slight effects on the ranks of the parameters' sensitivity for outputs in the panicle-formation phase and the grain-filling phase, including yield at maturity. The results suggested that the influential parameters should be recalibrated in priority and fine-tuned with higher accuracy during model calibration.     

Logistics cost analysis of rice straw pellets for feasible production capacity and spatial scale in heat utilization systems: A case study in Nanporo town,Hokkaido, Japan

Rice straw is a promising renewable energy source because it is abundantly available in Asia. This study conducted a case study of logistics cost analysis for rice straw pellets by considering all stages in the supply chain to define the main factors affecting the selling price of rice straw pellets: collection (job-commission or employment of part-time workers), transportation, storage (vinyl greenhouses or storage buildings with larger capacity), pelletizing, and delivery to users with biomass boilers. The selling price was found to be strongly dependent on the production capacity because the investment cost for the pellet production facility had a significant effect of economies of scale. A production capacity of larger than 1500 t y⁻¹ is required for rice straw pellets to compete with wood pellets and fossil fuels in the studied Japanese context if the subsidy rate for the investment is 50%, part-time workers conduct the collection, and rice straw is stored in the storage buildings. Our sensitivity analysis also showed an economically feasible spatial scale: for example, rice straw should be collected within a 20 km radius and the users should be within a 38 km radius when the production capacity is 1500 t y⁻¹. In addition, other critical factors related to the collection of rice straw from the paddy fields and transportation of rice straw rolls to storage were identified as planning factors to further reduce the total logistics cost of rice straw pellets.