Boosting gLite with cloud augmented volunteer computing

The paper details the result of the EU FP7 EDGI project focusing on the cloud developments and usability improvements. Volunteer desktop grids, like BOINC, are designed to handle millions of parameter sweep type jobs and millions of desktop machines as worker nodes. Seamless transfer of gLite jobs to desktop grids was already implemented by EDGI; however this huge number of DG resources could not be utilized efficiently due to slow completion time caused by unpredictable behavior of the volunteer resources. The paper details how clouds have been utilized to shorten completion time on the EDGeS@home volunteer desktop grid to boost the performance of the supported gLite VO and also details how this service can be exploited by the gLite user communities of EGI (European Grid Initiative) all over Europe.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Competitive closed-loop supply chain network design under uncertainty

This paper studies the competition between two closed-loop supply chains including manufacturers, retailers and recyclers in an uncertain environment. The competition factors are the retail prices of new products and incentives paid to consumers for taking back the used products. Market demands are price sensitive and also the amount of returned products is sensitive to incentives. The primary goal of this paper is to investigate the impact of simultaneous and Stackelberg competitions between two closed-loop supply chains on their profits, demands and returns. A game theoretic approach which is empowered by possibility theory is applied to obtain the optimal solutions under uncertain condition. Finally the theoretical results are analyzed using sample data inspired by a real industrial case.     

Use of micellar extraction and cloud point preconcentration for valorization of saponins from sisal (Agave sisalana) waste

Sisal (Agave sisalana) is the main hard fiber produced worldwide, with an estimated generation of 400 thousands t in 2011. From its leaves, only the hard fibers, which represent 3–5% of their weight, are removed. The remaining 95–97% is referred to as sisal waste and contains steroidal saponins that can be potentially used in foods, cosmetics and pharmaceuticals formulations, as well as for soil bioremediation. The present work aimed at to evaluate strategies for the extraction and concentration of saponins from sisal waste, focused on the use of clean solvents, such as water and ethanol. For this purpose, it was firstly performed a central composite rotatable design for the optimization of the extraction conditions followed by a comparison of this strategy with other methods (Soxhlet, ultrasound-assisted extraction and micellar extraction). Cloud point preconcentration was then tested, using several types and concentrations of salts. The use of orbital shaker extraction (200 rpm) with an ethanolic solution (30%, v/v) at 50 °C, a mass/volume ratio sisal/solvent of 0.17 (g/mL) for 4 h allowed a recovery of 38.6% of the saponins. When a micellar extraction strategy using 7.5% (v/v) of Triton X-100, under the above-mentioned conditions was performed, saponins recovery raised to 98.4%. In a subsequent step, the addition of 20% (m/v) sodium carbonate led to a preconcentration factor of 20.3. The best adsorbent for Triton removal from the preconcentrated solution was Amberlite FPX-66. The process strategy proposed in the present study showed to be efficient for saponins extraction and preconcentration from a low-cost, highly available agricultural waste.     

基于Spider卷积的三维点云分类与分割网络

针对传统的卷积神经网络（CNN）不能直接处理点云数据，需先将点云数据转换为多视图或者体素化网格，导致过程复杂且点云识别精度低的问题，提出一种新型的点云分类与分割网络Linked-Spider CNN。首先，在Spider CNN基础上通过增加Spider卷积层数以获取点云深层次特征；其次，引入残差网络的思想在每层Spider卷积增加短连接构成残差块；然后，将每层残差块的输出特征进行拼接融合形成点云特征；最后，使用三层全连接层对点云特征进行分类或者利用多层卷积层对点云特征进行分割。在ModelNet40和ShapeNet Parts数据集上将所提网络与PointNet、PointNet++和Spider CNN等网络进行对比实验，实验结果表明，所提网络可以提高点云的分类精度和分割效果，说明该网络具有更快的收敛速度和更强的鲁棒性。     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

SRS: Spatial-tagged radio-mapping system combining LiDAR and mobile-phone data for indoor location-based services

One of the most challenging issues in radio received signal strength (RSS)-based localization systems is the generation and distribution of a radio map with a coordinate system linked with spatial information in a large indoor space. This study proposes a novel spatial-tagged radio-mapping system (SRS) that effectively combines the heterogeneous properties of LiDAR and mobile phones to simultaneously perform both spatial and radio mappings. The SRS consists of synchronization, localization, and map building processes, and enables real-time spatial and radio mapping. In the synchronization process, the distance range, motion data, and radio signals obtained through the LiDAR and mobile phone are collected in nodal units according to the sensing time. In the localization process, a feature variance filter is used to control the number of features generated from LiDAR and estimate the positions at which the nodes are generated in real time according to the motion data and radio signals. In map building, the estimated positions of the nodes are used to extract spatial and radio maps by using a unified location coordinate system. To ensure mobility, the SRS is manufactured in the form of a backpack supporting LiDAR and a mobile phone; the usefulness of the system is experimentally verified. The experiments are performed in a large indoor shopping mall with a complex structure. The experimental results demonstrated that a common coordinate system could be used to build spatial and radio maps with high accuracy and efficiency in real time. In addition, the field applicability of the SRS to location-based services is experimentally verified by applying the constructed radio map to well-known fingerprinting algorithms using the heterogeneous mobile phones.     

Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach

The rapid development of Additive Manufacturing (AM) has been conspicuous and appealing towards manufacturing end-use products and components over the past decade. The continual advancement of AM has brought many advantages such as personalization and customization, reduction of material waste, cutting off the existence of special tooling during fabrication, etc. However, the AM approach has its limitations, such as a lack of knowledge of AM process activities and the progressive industrialization of AM, which makes the design process activities unstable, unpredictable, and have a limited effect. The concept of “design for AM (DFAM)” is increasing, which means we have the opportunity to concentrate almost totally on product functioning. Therefore, the entire design paradigm must be revised to accommodate new production capabilities, geometries, and parameters to avoid molding or machine tooling technology constraints. Few studies have attempted to provide systematic and quantitative knowledge of the relationship between these elements and the feasibility of the design process, making it difficult for designers to assess and control AM industrialization. For this reason, DFAM is needed to reform AM from rapid manufacturing to a mainstream manufacturing method. This paper put forward a framework based on the Fuzzy Bayesian Network (FBN) for DFAM decision-making. Twenty impact factors were encapsulated from experts’ experience and existing literature to investigate the potential adaptability of DFAM. The proposed approach uses expert knowledge and Fuzzy Set Theory (FST) presented with Triangular Fuzzy Numbers (FFN) to perceive the uncertainties. The Bayesian Network (BN) captures the causal relationships and dependencies among the impact components and analyzes the DFAM adaptability for robust probabilistic reasoning. A robot arm claw was used to show the effectiveness of our approach. The results showed that FBN could be used to guide DFAM adaptability in the manufacturing industry.     

Ni-based catalysts with coke resistance enhance by radio frequency discharge plasma for CH4/CO2 reforming

Coke deposition has been considered to be one of the most important reasons hindering the stability of the catalyst during CH₄/CO₂ reforming. In this study, after the addition of P123 (PEG-PPG-PEG triblock copolymer), Ni²⁺ can be well-dispersed on the mesoporous molecular sieve MCM-41. And then, the catalysts were prepared by using N₂ radio frequency (RF) discharge plasma for different treatment times to reduce the size of Ni particles, improve the anti-coking performance, and thereby improve the stability of the catalyst. The results showed that the catalyst NM-P123-PN2h exhibits superior catalytic properties in the CH₄/CO₂ reforming. The initial conversions of CH₄ and CO₂ were 90.80% and 89.60% at 750 °C, respectively. The catalyst NM-P123-PN2h showed highly coke resistance with less carbon deposition (1.12%) at 750 °C after 10 h of continuous reaction, while the carbon deposition of the catalyst NM-C was 37.32%. Compared with the traditional calcination method, the catalyst prepared by plasma treatment has a smaller particle size and better dispersibility of nickel. In particular, the nickel particle size of the catalyst NM-C was 8.37 nm, however, that of the catalyst NM-P123-PN2h was only 1.70 nm, and the nickel particle size was reduced by 5 times. Therefore, it can be concluded that the catalyst prepared under the combined action of P123 and RF plasma-treated can effectively improve the coke resistance of the catalyst and the stability of the CH₄/CO₂ reforming.     

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.