Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.     

Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration

This paper describes algorithms that use demand-side management to address large-scale integration of wind power. In particular, demand response (DR) is used to manage wind power intermittency by shifting the time that electrical power system loads occur in response to real-time prices and wind availability. An economic dispatch with transmission, DR capacity and operational constraints is used to model the operation of a transmission constrained system with a high penetration of wind power. This optimization model is used to determine the optimal sizing and distribution of DR given a fixed budget for customer incentives and the installation of enabling technology. We demonstrate the effectiveness of the operational model based on a simple PJM 5-bus system and an IEEE 118-bus system. Simulation results show that transmission constraints have a greater effect on sizing of DR capacity than the location of wind power, which means that buses electrically close to congested lines tend to have higher incentives to deploy DR resources than other buses. The second part of the work examines optimal siting of technology that enables DR based on the frequency of DR based load changes, which are generally a function of the network location.     

高校实验室计算机网络故障诊断技术研究

为了提升高校实验数据处理水平,高校实验室的相关建设工作已经步入正轨,但层出不穷的信息安全问题也向高校实验室计算机网络维护工作提出了新的挑战。文章从现阶段高校实验室计算机网络的相关建设工作出发,简要论述引发网络故障的具体原因,并对其诊断技术进行探讨。     

川西彭州地区雷口坡组潮坪相薄储层辨识机理研究

川西彭州地区三叠系雷口坡组雷四上亚段潮坪相薄储层识别难度极大。围绕如何从复合地震强反射中区分并识别上、下两套储层面临的地球物理难题,采用先“分”后“合”的研究思路,基于实际地层结构及不同储层叠加样式建立正演模型,利用全波场波动方程正演模拟技术,剖析了不同主频条件下薄储层的地震响应特征,通过波形差异化分析,从复合地震响应中“剥离”出了两套储层所引起的地震响应特征及变化规律,明确了两套储层在不同频带下的地震识别标志和识别方法,为该区强反射界面干扰下两套薄互层储层辨识机理分析及精准预测奠定了基础。基于不同频带下薄储层辨识机理的分析结果,定性预测了薄储层平面展布,提出了深层潮坪相薄储层识别和预测难题的解决方案,为该区地震资料品质评价、面向薄储层的地震采集技术设计、地震资料处理及薄储层预测提供了依据和指导。     

Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents,modeling, and optimization techniques

In this work, the effects of solid/solvent ratio (0.10–0.25?g/ml), extraction time (3–8?h), and solvent type (n-hexane, ethyl acetate, and acetone) together with their shared interactions on Kariya seed oil (KSO) yield were investigated. The oil extraction process was modeled via response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) while the optimization of the three input variables essential to the oil extraction process was carried out by genetic algorithm (GA) and RSM methods. The low mean relative percent deviation (MRPD) of 0.94–4.69% and high coefficient of determination (R²) > 0.98 for the models developed demonstrate that they describe the solvent extraction process with high accuracy in this order: ANFIS, ANN, and RSM. The best operating condition (solid/solvent ratio of 0.1?g/ml, extraction time of 8?h, and acetone as solvent of extraction) that gave the highest KSO yield (32.52?wt.%) was obtained using GA-ANFIS and GA-ANN. Solvent extraction efficiency evaluation showed that ethyl acetate, n-hexane, and acetone gave maximum experimental oil yields of 19.20?±?0.28, 25.11?±?0.01, and 32.33?±?0.04?wt.%, respectively. Properties of the KSO varied based on the type of solvent used. The results of this work showed that KSO could function as raw material in both food and chemical industries.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.     

Research on phase shift characteristics of electromagnetic wave in plasma

The phase shift characteristics reflect the state change of electromagnetic wave in plasma sheath and can be used to reveal deeply the action mechanism between electromagnetic wave and plasma sheath. In this paper, the phase shift characteristics of electromagnetic wave propagation in plasma were investigated. Firstly, the impact factors of phase shift including electron density,collision frequency and incident frequency were discussed. Then, the plasma with different electron density distribution profiles were employed to investigate the influence on the phase shift characteristics. In a real case, the plasma sheath around the hypersonic vehicle will affect and even break down the communication. Based on the hypersonic vehicle model, we studied the electromagnetic wave phase shift under different flight altitude, speed, and attack angle. The results indicate that the phase shift is inversely proportional to the flight altitude and positively proportional to the flight speed and attack angle. Our work provides a theoretical guidance for the further research of phase shift characteristics and parameters inversion in plasma.     

含齿形裂隙类岩石材料单轴压缩试验研究

为研究含齿形裂隙岩石在单轴压缩下的破坏特征及强度特性，制作了含不同裂隙倾角和起伏角的齿形裂隙类岩石材料试件，并采用岩石力学伺服试验机进行单轴压缩试验。试验结果表明：（1）试件主要产生拉伸、剪切和拉剪复合裂纹，且根据裂纹的扩展路径可划分为A型（拉伸破坏）、B型（剪切破坏）、C型（复合破坏）3种破坏模式，裂隙倾角对试件最终破坏模式影响显著；（2）当裂隙倾角较小时，试件应力—应变曲线为多峰曲线，随着裂隙倾角的增大，曲线呈单峰形式，表现为延性减弱，脆性增强，而裂隙倾角相同但起伏角不同的试件应力—应变曲线大致相同；（3）当裂隙起伏角相同时，试件当量峰值强度随裂隙倾角的增大呈先减小后增大的规律，且裂隙起伏角对试件当量峰值强度的影响小于裂隙倾角。     

Screening of frozen-thawed conditions for keeping nutritive compositions and physicochemical characteristics of goat milk

Frozen milk can help producers overcome the seasonality of goat milk production, low goat production and short lactation periods, and avoid discarding milk during some special periods. We investigated effects of combination between freezing (cryogenic refrigerator of ?16 to ?20°C or ultra-cryogenic refrigerator of ?76 to ?80°C) and thawing (homeothermy of 20 to 25°C or refrigeration of 2 to 4°C) on nutritive compositions and physicochemical characteristics of raw goat milk during storage period (80 d). Compared with fresh goat milk, the frozen-thawed milk decreased contents of fat, protein, and lactose, as well as surface tension and stability coefficient, whereas increased effective diameter and polydispersity index. The average values of color values (L*, a*, and b*) in 4 group samples changed from 83.01 to 82.25, ?1.40 to ?1.54, 3.51 to 3.81, respectively, and the ΔE of most samples did not exceed 2. In contrast to the other 3 frozen-thawed treatments, goat milk treated with ultra-cryogenic freezing-homeothermic thawing (UFHT) possessed higher fat (5.20 g/100 g), smaller effective particle diameter (0.32 µm), and the lowest polydispersity index value (0.26). The color and confocal laser scanning microscopy images of UFHT were similar to those of fresh goat milk, illustrating UFHT was the optimal approach to maintain the natural quality of goat milk. Our finding provides a theoretical basis for producers to freeze surplus milk.