Resistance switching of electrodeposited cuprous oxide

In this work, the resistance switching behavior of electrodeposited cuprous oxide (Cu2O) thin films in Au/Cu2O/top electrode (Pt, Au-Pd, Al) cells was studied. After an initial FORMING process, the fabricated cells show reversible switching between a low resistance state (16.6 Ω) and a high resistance state (0.4 x 106 Ω). Changing the resistance states in cuprous oxide films depends on the magnitude of the applied voltage which corresponds to unipolar resistance switching behavior of this material. The endurance and retention tests indicate a potential application of the fabricated cells for nonvolatile resistance switching random access memory (RRAM). The results suggest formation and rupture of one or several nanoscale copper filaments as the resistance switching mechanism in the cuprous oxide films. At high electric voltage in the as-deposited state of Au/Cu2O/Au-Pd cell structure, the conduction behavior follows Poole-Frenkel emission. Various parameters, such as the compliance current, the cuprous oxide microstructure, the cuprous oxide thickness, top electrode area, and top electrode material, affect the resistance switching characteristics. The required FORMING voltage is higher for Au/Cu2O/Al cell compared with the Au/Cu2O/Pt which is related to the Schottky behavior of Al contact with Cu2O. Cu2O nanowires in Au-Pt/ Cu2O/Au-Pt cell also show resistance switching behavior, indicating scalable potential of this cell for usage as RRAM. After an initial FORMING process under an electric field of 3 x 106 V/m, the Cu2O nanowire is switched to the LRS. During the FORMING process physical damages are observed in the cell, which may be caused by Joule heating and gas evolution --Abstract, page iii

The Effect of Culture on Consumers’ Attitude Towards Online Shopping

Consumers\u27 attitude towards online shopping is the key to survival and profitability of online retailers in today\u27s competitive market. The purpose of this exploratory research is to provide a deeper understanding of the role of culture on the adoption of online shopping. To this end, the Technology Acceptance Model(TAM) is adopted and then extended by examining the effect of trust and perceived e-vendors\u27 reputation on consumers\u27 attitude toward online shopping using US and non-US samples. The results indicate that culture plays a moderating role in the relations among antecedents and consequences of attitude toward online shopping. It can be concluded that the influential factors on attitude toward online shopping differ for consumers from collectivist cultures and individualist cultures

COMMUNITY DETECTION IN COMPLEX NETWORKS AND APPLICATION TO DENSE WIRELESS SENSOR NETWORKS LOCALIZATION

Complex network analysis is applied in numerous researches. Features and characteristics of complex networks provide information associated with a network feature called community structure. Naturally, nodes with similar attributes will be more likely to form a community. Community detection is described as the process by which complex network data are analyzed to uncover organizational properties, and structure; and ultimately to enable extraction of useful information. Analysis of Wireless Sensor Networks (WSN) is considered as one of the most important categories of network analysis due to their enormous and emerging applications. Most WSN applications are location-aware, which entails precise localization of the deployed sensor nodes. However, localization of sensor nodes in very dense network is a challenging task. Among various challenges associated with localization of dense WSNs, anchor node selection is shown as a prominent open problem. Optimum anchor selection impacts overall sensor node localization in terms of accuracy and consumed energy. In this thesis, various approaches are developed to address both overlapping and non-overlapping community detection. The proposed approaches target small-size to very large-size networks in near linear time, which is important for very large, densely-connected networks. Performance of the proposed techniques are evaluated over real-world data-sets with up to 106 nodes and syntactic networks via Newman\u27s Modularity and Normalized Mutual Information (NMI). Moreover, the proposed community detection approaches are extended to develop a novel criterion for range-free anchor selection in WSNs. Our approach uses novel objective functions based on nodes\u27 community memberships to reveal a set of anchors among all available permutations of anchors-selection sets. The performance---the mean and variance of the localization error---of the proposed approach is evaluated for a variety of node deployment scenarios and compared with random anchor selection and the full-ranging approach. In order to study the effectiveness of our algorithm, the performance is evaluated over several simulations that randomly generate network configurations. By incorporating our proposed criteria, the accuracy of the position estimate is improved significantly relative to random anchor selection localization methods. Simulation results show that the proposed technique significantly improves both the accuracy and the precision of the location estimation

Investigating the competitive advantage of medicinal plants and related products

Background and aims: The main objective of this study was to investigate the factors affecting the achievement of the competitive advantage in the international markets for medicinal plants and related products in Zagros forests. Methods: This is an applied descriptive/correlational research, because in addition to describing the current situation, its tests hypotheses are based on multiple regression method and determines the impact of variables using the inferential statistics method. In addition, it is an applied research since its expected results can be used to improve the international market of medicinal plants and related products in the Zagros forests. The study population included all customers of medicinal plants and related products in Zagros forests. Considering the fact that the population size is unlimited and undefined, the sample size formula was used. The method used in this research is the cluster sampling and available non-probability sampling. Initially, the Zone was divided into four parts: north, south, east and west. Then, the questionnaires were distributed. The sample size was calculated as 384 people utilizing sample size formula for unlimited population. In this research, the variables of interest were measured using researcher made questionnaires which were distributed among selected subjects whose views will be assessed on the variables. Finally, the collected data was analyzed using SPSS software. Results: The results showed that medicinal plants, diversity of medicinal plants, and industrialization of medicinal plants and production of the most essential drugs even certain drugs would lead to the achievement of competitive advantage in the international market of medicinal plants and related products in the Zagros forests. Conclusion: Paving the way for investment in the country will make great contribution to the export of medicinal plants products

The effect of microRNA-375 overexpression, an inhibitor of Helicobacter pylori-induced carcinogenesis, on lncRNA SOX2OT

Background: Helicobacter pylori is a major human pathogenic bacterium in gastric mucosa. Although the association between gastric cancer and H. pylori has been well-established, the molecular mechanisms underlying H. pylori-induced carcinogenesis are still under investigation. MicroRNAs (miRNAs) are small noncoding RNAs that modulate gene expression at the posttranscriptional level. Recently, studies have revealed that miRNAs are involved in immune response and host cell response to bacteria. Also, microRNA-375 (miR-375) is a key regulator of epithelial properties that are necessary for securing epithelium-immune system crosstalk. It has been recently reported that miR-375 acts as an inhibitor of H. pylori-induced gastric carcinogenesis. There are few reports on miRNA-mediated targeting long noncoding RNAs (lncRNAs). Objectives: This study aimed to examine the possible effect of miR-375 as an inhibitor of H. pylori-induced carcinogenesis on the expression of lncRNA SOX2 overlapping transcript (SOX2OT) and SOX2, a master regulator of pluripotency of cancer stem cells. Materials and Methods: In a model cell line, NT-2 was transfected with the constructed expression vector pEGFP-C1 contained miR- 375. The RNA isolations and cDNA synthesis were performed after 48 hours of transformation. Expression of miR-375 and SOX2OT and SOX2 were quantified using real-time polymerase chain reaction and compared with control cells transfected with pEGFP-C1-Mock clone. Cell cycle modification was also compared after transfections using the flow cytometry analysis. Results: Following ectopic expression of miR-375, SOX2OT and SOX2 expression analysis revealed a significant decrease in their expression level (P < 0.05) in NT-2 cells compared to the control. Cell cycle analysis following ectopic expression of miR-375 in the NT-2 cells using propidium iodine staining revealed significant extension in sub-G1 cell cycle. Conclusions: This is the first report to show down-regulation of SOX2OT and SOX2 following induced expression of miR-375. This findingmaysuggest expression regulation potential between different classes of ncRNAs, for example between miR-375andSOX2OT. This data not only extends our understanding of possible ncRNA interactions in cancers but also may open novel investigation lines towards elucidation of molecular mechanisms controlling H. pylori inflammation and carcinogenesis. © 2016, Ahvaz Jundishapur University of Medical Sciences

A Survey From Distributed Machine Learning to Distributed Deep Learning

Artificial intelligence has achieved significant success in handling complex tasks in recent years. This success is due to advances in machine learning algorithms and hardware acceleration. In order to obtain more accurate results and solve more complex problems, algorithms must be trained with more data. This huge amount of data could be time-consuming to process and require a great deal of computation. This solution could be achieved by distributing the data and algorithm across several machines, which is known as distributed machine learning. There has been considerable effort put into distributed machine learning algorithms, and different methods have been proposed so far. In this article, we present a comprehensive summary of the current state-of-the-art in the field through the review of these algorithms. We divide this algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of studies worked on this algorithms. As a result, most of the articles we discussed here belong to this category. Based on our investigation of algorithms, we highlight limitations that should be addressed in future research

Moral foundations and judgment:Conceptualizing boundaries

Purpose: Even though the definitions of morality may seem to provide straightforward criteria to assess the morality of individuals, moral judgments are challenging and less exact. This paper aims to advance extant work on morality and moral judgment by providing a conceptualization of boundary conditions in the relationship between moral judgments and consumer behavior. Design/methodology/approach: An interdisciplinary literature review is conducted to integrate extant knowledge on morality, moral judgment and consumer behavior to identify and conceptualize boundary conditions affecting moral judgments and decision-making. The research draws on moral foundation theory and norm activation model, and the proposed factors and relationships are grounded in construal level theory and regulatory focus theory. Findings: The research identifies cultural, individual and situational factors that influence moral judgments and decision-making and argues that moral judgments exhibit a similar pattern across types, but cultural factors determine the salience of each moral foundation type. Moreover, construal factors relevant to the situation (i.e. proximity vs distance) affect the extent and manner of moral judgments, and individual mindsets and their associated information processing styles (e.g. money vs time orientation and promotion vs prevention orientation) make moral judgments more malleable, adding a degree of variability to judgments within similar cultures and situations. Originality/value: The research makes a rather unique contribution to consumer morality literature by identifying and discussing three different groups of factors with the potential to impact individuals’ judgments of, and reactions to, moral foundation violation information

Arthroconidia production in Trichophyton rubrum and a new ex vivo model of onychomycosis

The dermatophyte fungus Trichophyton rubrum often produces arthroconidia in vivo, and these cells are thought to be involved in pathogenesis, and, in shed skin scales, to act as a source of infection. The purpose of this study was (i) to examine the environmental and iatrogenic factors which affect arthroconidiation in T. rubrum in vitro, (ii) to look at arthroconidia formation in a large number of clinical isolates of T. rubrum and (iii) to develop a new model for the study of arthroconidia formation in nail tissue. Arthroconidia production was studied in T. rubrum grown on a number of media and under conditions of varying pH, temperature and CO2 concentration. The effect of the presence of antifungals and steroids on arthroconidia formation was also examined. Nail powder from the healthy toenails of volunteers was used as a substrate for arthroconidial production. On Sabouraud dextrose agar in the presence of 10 CO2 plus air, arthroconidial formation occurred optimally at 37 °C and pH 7.5, and was maximal at 10 days. Most isolates of T. rubrum showed a similar level of arthroconidial production, and only two out of 50 strains were unable to produce arthroconidia. Subinhibitory levels of some antifungals and betamethasone resulted in the stimulation of arthroconidia formation. Arthroconidial production in ground nail material also occurred under the same optimal conditions, but took longer to reach maximal levels (14 days). These in vitro and ex vivo results provide a useful basis for the understanding of arthroconidium formation in vivo in infected tissues such as nails. © 2006 SGM