Recovery of polyphenols from camu-camu (Myrciaria dubia H.B.K. McVaugh) depulping residue by cloud point extraction

In this study the potential of cloud point extraction formed by a non-ionic surfactant was used in order to separate polyphenols from industrial residues of camu-camu. The effects of operational conditions of the cloud point extraction (CPE) on the polyphenol recovery and volumetric ratio were investigated. The results showed a maximum recovery of 95.71% that was obtained using 7.0 wt% Triton X-114, native pH (3.25), and 80 wt% polyphenol extract at 30 °C. The use of cloud point extraction was successful to recover the polyphenols from agroindustrial residue since it is a simple as well as of low-cost technique.     

Measurement of Consumed Energy Due to the Two-Phase Pressure Drop in a Pulsed Semi-Industrial Column: Effect of Geometry

This article deals with the evaluation of pressure drop and consumption of energy for a steady-state solvent extraction in a horizontal pulsed sieve-plate column, which are important for the design and optimization of the periodic-flow processes for industrial applications. In this study, the pressure drop and the position of loading points are investigated. Moreover, a mathematical evaluation on the energy consumption in the case of a pulsed flow is conducted, and besides the influence of pulsation intensity, the effect of geometrical parameters including the plate spacing and plate-free area is investigated as well. The results of this study are helpful for optimization of column geometry targeted to higher performance and lower energy consumption.     

Applications of Diglycolamide Based Solvent Extraction Processes in Spent Nuclear Fuel Reprocessing,Part 1: TODGA

Over the last decade there has been much interest in the applications of diglycolamide (DGA) ligands for the extraction of the trivalent lanthanide and actinide ions from PUREX high active raffinates or dissolved spent nuclear fuel. Of the DGAs, the N,N,N’,N’-tetraoctyldiglycolamide (TODGA) is the best known and most widely studied. A number of new actinide separation processes have been proposed based on extraction with TODGA. This review covers TODGA-based processes and extraction data, specifically focusing on how phase modifiers have been used to increase metal loading and thus enhance the operating process envelopes. Effects of third phase formation and the organic phase speciation are reviewed in this context. Relevant aspects of the extraction chemistry of important solvents (TODGA-modifier-diluent combinations) are described and their performances demonstrated by a consideration of the published flowsheet tests. It is seen that modifiers are successfully enabling the use of TODGA in actinide separation processes but to date the identification and testing of suitable modifiers has been rather empirical. There is a growing understanding of the fundamental chemistry occurring in the organic phase and how that affects extractant speciation and metal loading capacity but studies are still needed if TODGA-based flowsheets are to become an industrially deployable option for minor actinide (MA) recovery processes.     

Seismic life-cycle cost analysis of ageing highway bridges under chloride exposure conditions: modelling and recommendations

Chloride-induced corrosion of highway bridges constitutes a critical form of environmental deterioration and may result in significant escalation of seismic life-cycle costs due to increased fragility during earthquake events. Most of existing literature tends to adopt simplistic uniform area loss assumptions in lieu of potentially complex, yet realistic and more detrimental, pitting corrosion models for seismic vulnerability analysis. Since the degree of deterioration depends on the severity and duration of exposure, there exists a need to investigate the influence of uniform vs. pitting corrosion assumption on seismic life-cycle costs for varied chloride exposure conditions. A case-study example of a highway bridge in Central and Southeastern US reveals consideration of pitting corrosion as critical for extreme exposures compared to relatively minor settings. Subsequently this study provides recommendations to aid bridge engineers and stakeholders to balance between computational cost and accuracy of results to aid prompt decisions on rehabilitation of ageing bridges in different exposure conditions. A framework is also included to compute seismic life-cycle costs from generic measures of corrosion, independent of assumed exposure scenario. This framework is particularly helpful for seismic loss assessment of highway bridges in chloride exposure zones with periodic field measurements to estimate the extent of structural deterioration.     

综合改进奇异谱分解和奇异值分解的齿轮故障特征提取方法

针对齿轮故障特征微弱，在强背景噪声下难以有效提取的问题，提出了一种改进奇异谱分解（ISSD）结合奇异值分解（SVD）的齿轮故障特征提取方法。针对奇异谱分解（SSD）算法中模态参数需凭经验选取的缺陷，基于散布熵优化算法对SSD算法进行了改进，在得到既定的一组奇异谱分量的基础上，根据峭度值最大准则筛选出了最佳奇异谱分量并进行了SVD处理，采用奇异值能量标准谱自适应地确定了信号重构阶数以还原信号和提高降噪效果。最后对信号进行包络解调以提取齿轮故障特征，将所提方法运用到仿真信号和齿轮实测信号中，并同传统包络谱、SSD包络谱以及经验模态分解结合SVD(EMD-SVD)方法进行了对比分析，结果表明，所提方法的降噪和特征提取效果更佳，能够更加有效地实现齿轮故障的判别。     

Extraction optimization and adsorption isotherm kinetics of polyphenols from blossoms of Citrus aurantium L. var. amara Engl

ABSTRACT

Crude extracts were extracted from blossoms of Citrus aurantium L. var. amara Engl. with 50% ethanol (CAVAP-E), 50% methanol (CAVAP-M), and hot water (CAVAP-W). CAVAP-W had the highest polyphenol yield of 3.7%. The optimum values of liquid/solid ratio, ultrasonic time, and ultrasonic power were 34.06 mL/g, 1 h, and 81.04 W, respectively. The adsorption characteristics of polyphenols were investigated using different macroporous resins. AB-8 and HP-20 resins offered higher adsorption and desorption capacities. Langmuir equation was more suitable to predict the adsorption of polyphenols. The pseudo second-order model was satisfactory to describe the kinetic data of polyphenols onto AB-8 and HP-20.     

Identifying cooperation for innovation―a comparison of data sources

ABSTRACT

The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.