A Study of the Effect of Aluminium on the Mechanochemical Reduction of Hematite by Graphite

The effect of addition of small amounts of aluminium on mechano‐chemical reduction of hematite by graphite was studied. Various amounts of aluminium (0 to 10%) were added to a hematite‐graphite mixture, in which C/O ratio was 1:1. The hematite‐graphite‐aluminium mixtures were then subjected to ball milling followed by heating up reduction. The heating up reduction was carried out in Ar atmosphere, using TG‐DTA device. In TG‐DTA experiments, samples were heated by a constant heating rate of 10 °C/min from room temperature up to 1100 °C and maintained for 30 minutes at this temperature. To clarify the reactions which took place during milling and heating up reduction, the samples were subjected to XRD examinations. It was found that the heat generated during exothermic reaction of aluminothermic reduction of hematite promoted the endothermic reaction of carbothermic reduction. In the course of heating up reduction, the carbothermic reaction occurred just after aluminothermic reaction. Increasing of aluminum content from 0 to 10% in 2 hours ball milled samples decreased the temperature of carbothermic reaction from 1020 °C to about 860 °C. The further ball milling of the samples up to 5 and 10 hours, for the samples containing 10 and 5% aluminium respectively, caused the decrease of the temperature of aluminothermic and carbothermic reactions to around the melting point of aluminium.     

Hyaluronic Acid Scaffolds for Loco-Regional Therapy in Nervous System Related Disorders

Hyaluronic acid (HA) is a Glycosaminoglycan made of disaccharide units containing N-acetyl-D-glucosamine and glucuronic acid. Its molecular mass can reach 10 MDa and its physiological properties depend on its polymeric property, polyelectrolyte feature and viscous nature. HA is a ubiquitous compound found in almost all biological tissues and fluids. So far, HA grades are produced by biotechnology processes, while in the human organism it is a major component of the extracellular matrix (ECM) in brain tissue, synovial fluid, vitreous humor, cartilage and skin. Indeed, HA is capable of forming hydrogels, polymer crosslinked networks that are very hygroscopic. Based on these considerations, we propose an overview of HA-based scaffolds developed for brain cancer treatment, central and peripheral nervous systems, discuss their relevance and identify the most successful developed systems.     

In Vitro Rescue of the Bile Acid Transport Function of ABCB11 Variants by CFTR Potentiators

ABCB11 is responsible for biliary bile acid secretion at the canalicular membrane of hepatocytes. Variations in the ABCB11 gene cause a spectrum of rare liver diseases. The most severe form is progressive familial intrahepatic cholestasis type 2 (PFIC2). Current medical treatments have limited efficacy. Here, we report the in vitro study of Abcb11 missense variants identified in PFIC2 patients and their functional rescue using cystic fibrosis transmembrane conductance regulator potentiators. Three ABCB11 disease-causing variations identified in PFIC2 patients (i.e., A257V, T463I and G562D) were reproduced in a plasmid encoding an Abcb11-green fluorescent protein. After transfection, the expression and localization of the variants were studied in HepG2 cells. Taurocholate transport activity and the effect of potentiators were studied in Madin–Darby canine kidney (MDCK) clones coexpressing Abcb11 and the sodium taurocholate cotransporting polypeptide (Ntcp/Slc10A1). As predicted using three-dimensional structure analysis, the three variants were expressed at the canalicular membrane but showed a defective function. Ivacaftor, GLP1837, SBC040 and SBC219 potentiators increased the bile acid transport of A257V and T463I and to a lesser extent, of G562D Abcb11 missense variants. In addition, a synergic effect was observed when ivacaftor was combined with SBC040 or SBC219. Such potentiators could represent new pharmacological approaches for improving the condition of patients with ABCB11 deficiency due to missense variations affecting the function of the transporter.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

CFD modeling of flow and heat transfer in a thermosyphon

In the present study a gas/liquid two-phase flow and the simultaneous evaporation and condensation phenomena in a thermosyphon was modeled. The volume of fluid (VOF) technique was used to model the interaction between these phases. Experiments in a thermosyphon were carried out at different operating conditions. The CFD predicted temperature profile in the thermosyphon was compared with experimental measurements and a good agreement was observed. It was concluded that CFD is a useful tool to model and explain the complex flow and heat transfer in a thermosyphon.     

考虑电容器电压特性的配电网无功规划

传统的配电网无功规划中,补偿电容器的容量往往被当作固定值,事实上,电容器的实际补偿容量和其接入点的电压水平有着很大的关系。考虑电容器的电压特性,以年综合费用最小为目标,建立了配电网无功规划模型。针对该模型的特点,提出采用自适应遗传算法进行求解。通过33节点和69节点配电网的仿真分析对模型进行验证。     

基于电网分区的负荷恢复智能优化策略

针对电力系统大停电后的负荷快速恢复问题,提出了一种基于电网分区的负荷恢复智能优化策略。提出了一种大停电事故后系统恢复的最优分区策略,并在所建立的优化模型中考虑了为恢复发电机而引入的架空线路充电无功以及为恢复负荷所引入的线路合闸操作次数等影响因素。在完成对大规模系统优化分区后,对各分区建立了一个统一的并计及网络重构因素的负荷恢复优化模型,实现各分区负荷的并行恢复。针对上述所提出的优化模型,通过结合传统图论理论和遗传算法,实现基于电网分区的负荷恢复优化问题的求解。解算中,对遗传算法进行了改进,并针对应用遗传算法时所产生的大量不可行解问题,提出了随机甩负荷和最短路径修补策略的处理方法,进一步提高了算法的寻优效率和全局优化能力。以IEEE30节点系统为算例进行仿真分析,验证了所提模型与方法的正确性和有效性。     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.