Biosynthesis of zinc oxide nanoparticles using Euphorbia milii leaf constituents: Characterization and improved photocatalytic degradation of methylene blue dye under natural sunlight

A facile biosynthesis route was followed to prepare zinc oxide nanoparticles (ZnO NPs) using Euphorbia milii (E. milii) leaf constituents. The SEM images exhibited presence of spherical ZnO NPs and the corresponding TEM images disclosed monodisperse nature of the ZnO NPs with diameter ranges between 12 and 20 nm. The Brunauer–Emmett–Teller (BET) analysis revealed that the ZnO NPs have specific surface area of 20.46 m²/g with pore diameter of 2 nm–10 nm and pore volume of 0.908 cm³/g. The EDAX spectrum exemplified the existence of Zn and O elements and non-appearance of impurities that confirmed pristine nature of the ZnO NPs. The XRD pattern indicated crystalline peaks corresponding to hexagonal wurtzite structured ZnO with an average crystallite size of 16.11 nm. The FTIR spectrum displayed strong absorption bands at 512 and 534 cm^?1 related to ZnO. The photocatalytic action of ZnO NPs exhibited noteworthy degradation of methylene blue dye under natural sunlight illumination. The maximum degradation efficiency achieved was 98.17% at an illumination period of 50 min. The reusability study proved considerable photostability of the ZnO NPs during photocatalytic experiments. These findings suggest that the E. milii leaf constituents can be utilized as suitable biological source to synthesis ZnO NPs for photocatalytic applications.     

Concurrent multiresponse non-linear screening: Robust profiling of webpage performance

Profiling engineered data with robust mining methods continues attracting attention in knowledge engineering systems. The purpose of this article is to propose a simple technique that deals with non-linear multi-factorial multi-characteristic screening suitable for knowledge discovery studies. The method is designed to proactively seek and quantify significant information content in engineered mini-datasets. This is achieved by deploying replicated fractional-factorial sampling schemes. Compiled multi-response data are converted to a single master-response effectuated by a series of distribution-free transformations and multi-compressed data fusions. The resulting amalgamated master response is deciphered by non-linear multi-factorial stealth stochastics intended for saturated schemes. The stealth properties of our method target processing datasets which might be overwhelmed by a lack of knowledge about the nature of reference distributions at play. Stealth features are triggered to overcome restrictions regarding the data normality conformance, the effect sparsity assumption and the inherent collapse of the ‘unexplainable error’ connotation in saturated arrays. The technique is showcased by profiling four ordinary controlling factors that influence webpage content performance by collecting data from a commercial browser monitoring service on a large scale web host. The examined effects are: (1) the number of Cascading Style Sheets files, (2) the number of JavaScript files, (3) the number of Image files, and (4) the Domain Name System Aliasing. The webpage performance level was screened against three popular characteristics: (1) the time to first visual, (2) the total loading time, and (3) the customer satisfaction. Our robust multi-response data mining technique is elucidated for a ten-replicate run study dictated by an L₉(3⁴) orthogonal array scheme where any uncontrolled noise embedded contribution has not been necessarily excluded.     

Path to achieving molecular dispersion in a dense reactive mixture

A uniform dispersion of reactants is necessary to achieve a complete reaction involving multicomponents. In this study, we have examined the role of plasticizer in the reaction of two seemingly unlikely reactants: a highly crystalline hexamethylenetetramine (HMTA) and a strongly hydrogen bonded phenol formaldehyde resin. By combining information from NMR, infrared spectroscopy and differential scanning calorimetry, we were able to determine the role of specific intermolecular interactions necessary for the plasticizer to dissolve the highly crystalline HMTA and to plasticize the phenol formaldehyde resin in this crosslinking reaction. The presence of the plasticizer increased the segmental mobility, disrupted the hydrogen bonded matrix, and freed the hydroxyl units, which further increased the solubility of the HMTA. Both the endothermic and exothermic transitions are accounted for in the calorimetric data obtained. For the first time, it is possible to obtain the effective molar ratio of each component needed to complete the crosslinking reaction efficiently. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2015 , 53, 1519–1526     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.     

Sensitivity analysis of thermal performances of flat plate solar air heaters

Sensitivity analysis is a mathematical tool, first developed for optimization methods, which aim is to characterize a system response through the variations of its output parameters following modifications imposed on the input parameters of the system. Such an analysis may quickly become laborious when the thermal model under consideration is complex or the number of input parameters is high. In this paper, we develop a mathematical model to analyse the heat exchanges in four different types of solar air collectors. When building this thermal model we show that for each collector, at quasi-steady state, the energy balance equations of the components of the collector cascade into a single first-order non-linear differential equation that is able to predict the thermal behaviour of the collector. Our heat transfer model clearly demonstrates the existence of an important dimensionless parameter, referred to as the thermal performance factor of the collector, that compares the useful thermal energy which can be extracted from the heater to the overall thermal losses of that collector for a given set of input parameters. A sensitivity analysis of our thermal model has been performed for the most significant input parameters such as the incident solar irradiation, the inlet fluid temperature, the air mass flow rate, the depth of the fluid channel, the number and nature of the transparent covers in order to measure the impact of each of these parameters on our model. An important result which can be drawn from this study is that the heat transfer model developed is robust enough to be used for thermal design studies of most known flat plate solar air heaters, but also of flat plate solar water collectors and linear solar concentrators.     

可重构制造系统重组过程中设备选择模型的研究

根据生产任务选择加工设备进行制造资源重组是实现可重构制造系统的关键问题之一，由于设备的选择涉及到多种因素，既有定量指标，又有定性指标，传统的依靠人工经验的方法显得力不从心。本文首先结合实际情况，提出了一套设备选择评价体系，通过对模糊判断矩阵采用最小对数二乘法确定各评价因素的权重系数，针对定性指标和定量指标采用不同的方法确定其性能指标值，通过模糊积分对评判指标进行综合评判，最后进行了实例研究。所提出的方法有效地简化了决策过程，为可重构制造系统设备选择提供了一套行之有效的方法。     

托卡马克工程试验混合堆等离子体性能的等值线图分析

本文简要叙述托卡马克工程试验混合堆等离子体概念设计的物理基础,对等离子体性能进行了等值线图(Plasma Operation Contour)分析。根据工程试验混合堆的要求,得出一组等离子体参数。     

具有模糊数的模糊多目标群体决策优选模型与方法

多目标群体决策问题是运筹学的一个重要研究领域，目前已经提出了一些有效的决策方法。但对目标值和权重均为模糊数的模糊多目标群体决策问题却研究不多，本对此类模糊多目标群体决策问题进行了探讨，利用相对正理想方案与相对负理想方案概念定义了相对差异距离，进而建立了模糊多目标群体决策优选模型与方法，并通过战役决心方案的评价说明了该方法是可行、有效的，可作为军事决策与决策支持系统的备选方法。     

测定麦芽糖转糖苷反应体系组成的高效液相色谱法

建立了分析低聚异麦芽糖组成的高效液相色谱法，采用Spherisorb-NH2色谱柱，示差折光检测器，乙腈－水（体积比70：30）为流动相，外标法定量测定；结果显示各糖质量浓度在0．1－10g/L范围内与峰面积呈良好线性关系，相关系数为0．999 0－0．999 7；应用法跟踪了pH5．0的柠檬酸－柠檬酸钠缓冲溶液中以α－葡萄糖转苷酶为催化剂在58℃温度下的麦芽糖转糖苷反应，分析了反应体系组成随时间的变化，得到了上述反应条件下麦芽糖最大限度地转化为低聚异麦芽糖的最佳反应时间为24h；该法快捷、简单、准确，可用于低聚异麦芽糖生产的质量控制。     

热耦合型二级高频脉冲管制冷机实验研究

降低制冷温度至4 K温区是高频脉冲管制冷机的最新发展要求。本文介绍了一套热耦合型二级高频脉冲管制冷机的性能特性,它可以达到低于15 K以下的最低温度,是目前报道的高频二级脉冲管制冷机能获得的最低温度．通过对制冷机一级预冷、二级输入功率、二级冷量、室温条件等影响的分析,从实验角度揭示了该二级制冷系统的复杂影响,为进一步的深入分析和改进提供了基础。