Metal-Free Catalytic Wet Oxidation: From Powder to Structured Catalyst Using N-Doped Carbon Nanotubes

This work shows a promising N-doped carbon catalyst for the oxidation of oxalic acid by catalytic wet oxidation, which is able to compete with the traditional noble metal and metal oxide catalysts used in the process. After preliminary studies conducted in batch mode, the catalytic performance of the metal-free carbon nanotubes, both in powder form and supported on a macrostructured carrier (a cordierite monolith), was evaluated under continuous operating conditions. The ability of the N-functionalities to promote activation and chemisorption of oxygen led not only to fast oxalic acid mineralization under batch mode (5 min of reaction to reach full mineralization), but also to good performance under continuous operation (more than 90% conversion of oxalic acid in the steady state, using the powder and around 55% using the catalyst immobilized on a honeycomb cordierite monolith).     

Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.     

Selective harvesting over networks

Active search on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights (encoding pairwise similarities) under a query budget constraint. However, in most current networks, nodes, network topology, network size, and edge weights are all initially unknown. In this work we introduce selective harvesting, a variant of active search where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario can suffer from what we call a tunnel vision effect: without any recourse to independent sampling, the urge to only query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of active search methods and standard classifiers. We demonstrate that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as a weighted ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried in the future. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. Based on these observations we propose D\(^3\)TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, which outperforms all competing methods on five real network datasets in our evaluation and exhibits comparable performance on the other two.     

NoSQL real-time database performance comparison

The amount of data being produced is increasing constantly, as the number and variety of connected devices are growing and the advances in data storage and mining are supporting this evolution. However, storing and handling high quantities of data is challenging the current Relational Database Management Systems. Big Data and its related products came to help in this matter, and the NoSQL databases arise with the purpose to offer better solutions and features to handle massive amounts of data with higher performance, sometimes near real-time. The present study presents the NoSQL databases scenario and background, and elaborates a detailed study with the characteristics, a features comparison and a performance evaluation of three different NoSQL databases extensively used in the market nowadays: Couchbase, MongoDB and RethinkDB. Tests were performed in two different scenarios: single thread and multiple threads. The results reveal that Couchbase had a better performance at most of the operations, except for retrieving multiple documents and inserting documents with multiple threads, operations in which MongoDB scored better.     

Daily Reference Evapotranspiration for Hyper-Arid to Moist Sub-Humid Climates in Inner Mongolia,China: I. Assessing Temperature Methods and Spatial Variability

When weather data sets available for computing the reference evapotranspiration are incomplete or of questionable quality, there is the need to replace the FAO Penman-Monteith (PM-ET_o) method by approaches requiring reduced sets only, particularly maximum and minimum temperature. The Hargreaves-Samani (HS) equation and the PM-ET_o using only temperature data (PMT) are considered in this study and their results are compared with those of the PM-ET_o using full datasets. Daily data sets refer to the period 1981–2012 and to a network of 50 meteorological stations covering the wide range of climates of Inner Mongolia. For both the PMT and HS methods, the solar radiation coefficients k_Rs were calibrated and have shown to be similar for both methods and to vary with climate aridity. For the PMT, the estimation of the dew point temperature (T_dew) was performed using the minimum temperature corrected for site aridity or, for humid climates, from a value near the average temperature. This improved estimation of T_dew was essential for a good performance of the PMT method in arid conditions and when temperatures are extremely low. RMSE <1 mm day^?1 was obtained for both HS and PMT methods, and the modeling efficiency generally exceeded 0.85. The worse results correspond to windy and arid locations. The principal components analysis (PCA) in R-Mode have shown that the spatial variability of ET_o computed with PM-ET_o or with the HS and PMT methods were coherent. PCA supported the interpretation of ET_o results. Overall, PMT performed better than HS for most locations.     

Accelerated microwave-assisted hydrothermal/solvothermal processing: Fundamentals,morphologies, and applications

This article is designed to serve as a roadmap for understanding the fundamentals, the key advantages and the potential applications of microwave-assisted hydrothermal/solvothermal (MAH/S) processing. MAH/S synthesis is a versatile chemical method for preparing a diversity of materials such as metals, semiconductors, electroceramics, graphene and their composites as bulk powders, thin films, or single crystals. The key to improve performance of these materials is achieving controlled morphologies (0 to 3D dimensionality) that favor desirable physical-chemical phenomena at the surface, and in the bulk of these advanced materials. The main features related to the improvement of the thermal and non-thermal effects associated with the use of microwave power concurrently with hydrothermal or solvothermal methods are discussed. Furthermore, the main crystal growth mechanisms (Ostwald ripening and oriented attachment) of these solids in solution under MAH/S treatment are described. Products synthesized by the MAH/S, particularly of interest in the development of gas sensors, batteries, fuel cells, solar cells and photocatalysts are emphasized. We conclude by envisaging new future directions for the use of this rapid and versatile processing approach.     

Conductivity study of a gelatin-based polymer electrolyte

Natural polymers are particularly interesting due to their richness in nature, very low cost and principally biodegradation properties. For these reasons different solid polymeric electrolytes (SPE) have been obtained using cellulose derivatives, starch, chitosan and rubber. This work presents the results of gelatin-based protonic SPEs, which were characterized by impedance spectroscopy, X-ray diffraction, UV-vis-NIR spectroscopy and scanning electron microscopy (SEM). The ionic conductivity results obtained for these SPEs were 4.5 × 10⁻⁵ S/cm and 3.6 × 10⁻⁴ S/cm at room temperature and 80 °C, respectively. Temperature-dependent ionic conductivity measurements were taken to analyze the mechanism of ionic conduction in polymer electrolytes. Good conductivity results combined with transparency and good adhesion to the electrodes have shown that gelatin-based SPEs are very promising materials to be used as solid electrolyte in electrochromic devices.     

Security issues in cloud environments: a survey

In the last few years, the appealing features of cloud computing have been fueling the integration of cloud environments in the industry, which has been consequently motivating the research on related technologies by both the industry and the academia. The possibility of paying-as-you-go mixed with an on-demand elastic operation is changing the enterprise computing model, shifting on-premises infrastructures to off-premises data centers, accessed over the Internet and managed by cloud hosting providers. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. Besides of the issues derived from Web technologies and the Internet, clouds introduce new issues that should be cleared out first in order to further allow the number of cloud deployments to increase. This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject. It addresses several key topics, namely vulnerabilities, threats, and attacks, proposing a taxonomy for their classification. It also contains a thorough review of the main concepts concerning the security state of cloud environments and discusses several open research topics.     

Improved xylitol production in media containing phenolic aldehydes: application of response surface methodology for optimization and modeling of bioprocess

BACKGROUND: The combined effects of vanillin and syringaldehyde on xylitol production by Candida guilliermondii using response surface methodology (RSM) have been studied. A 2² full‐factorial central composite design was employed for experimental design and analysis of the results. RESULTS: Maximum xylitol productivities (Q_P = 0.74 g L^?1h^?1) and yields (Y_P/S = 0.81 g g^?1) can be attained by adding only vanillin at 2.0 g L^?1 to the fermentation medium. These data were closely correlated with the experimental results obtained (0.69 ± 0.04 g L^?1 h^?1 and 0.77 ± 0.01 g g^?1) indicating a good agreement with the predicted value. C. guilliermondii was able to convert vanillin completely after 24 h of fermentation with 94% yield of vanillyl alcohol. CONCLUSIONS: The bioconversion of xylose into xylitol by C. guilliermondii is strongly dependent on the combination of aldehydes and phenolics in the fermentation medium. Vanillin is a source of phenolic compound able to improve xylitol production by yeast. The conversion of vanillin to alcohol vanilyl reveals the potential of this yeast for medium detoxification. Copyright © 2009 Society of Chemical Industry