CeO<Subscript>2</Subscript> and Co<Subscript>3</Subscript>O<Subscript>4</Subscript>–CeO<Subscript>2</Subscript> nanoparticles: effect of the synthesis method on the structure and catalytic properties in COPrOx and methanation reactions

CeO₂ and Co₃O₄–CeO₂ nanoparticles were synthesized, thoroughly characterized, and evaluated in the COPrOx reaction. The CeO₂ nanoparticles were synthesized by the diffusion-controlled precipitation method with ethylene glycol. A notably higher yield was obtained when H₂O₂ was used in the synthesis procedure. For comparison, two commercial samples of CeO₂ nanoparticles (Nyacol^®)—one calcined and the other sintered—were also studied. Catalytic results of bare CeO₂ calcined at 500 °C showed a strong influence of the method of synthesis. Despite having similar BET area values, the CeO₂ synthesized without H₂O₂ was the most active sample. Co₃O₄–CeO₂ catalysts with three different Co/(Co + Ce) atomic ratios, 0.1, 0.3, and 0.5, were prepared by the wet impregnation of the CeO₂ nanoparticles. TEM and STEM observations showed that impregnation produced mixed oxides composed of small CeO₂ nanoparticles located both over the surface and inside the Co₃O₄ crystals. The mixed oxide catalysts prepared with a cobalt atomic ratio of 0.5 showed methane formation, which started at 200 °C due to the reaction between CO₂ and H₂. However, above 250 °C, the reaction between CO and H₂ became important, thus contributing to CO elimination with a small H₂ loss. As a result, CO could be totally eliminated in a wide temperature range, from 200 to 400 °C. The methanation reaction was favored by the reduction of the cobalt oxide, as suggested by the TPR experiments. This result is probably originated in Ce–Co interactions, related to the method of synthesis and the surface area of the mixed oxides obtained.     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

Anti-cloning protocol suitable to EPCglobal Class-1 Generation-2 RFID systems

Radio frequency Identification (RFID) systems are used to identify remote objects equipped with RFID tags by wireless scanning without manual intervention. Recently, EPCglobal proposed the Electronic Product Code (EPC) that is a coding scheme considered to be a possible successor to bar-code with added functionalities. In RFID-based applications where RFID tags are used to identify and track tagged objects, an RFID tag emits its EPC in plaintext. This makes the tag inevitably vulnerable to cloning attacks as well as information leakage and password disclosure. In this paper, we propose a novel anti-cloning method in accordance with the EPCglobal Class-1 Generation-2 (C1G2) standard. Our method only uses functions that can be supported by the standard and abides by the communication flow of the standard. The method is also secure against threats such as information leakage and password disclosure.     

Relational Learning with GPUs: Accelerating Rule Coverage

Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.     

Modeling the benefit of e-recruiting process integration

While e-recruiting has been widely adopted as one of the most successful e-business applications, it constitutes an under-researched area in e-business research. This study reviews the integration issues in e-recruiting and presents an e-recruiting integration decision model. The benefits of the investment in e-recruiting process integration are discussed in comparison to separate e-recruiting investments. We show that the optimal investment in the e-recruiting process integration results in a lower total cost than the separate e-recruiting investments. In addition, in light of the widely practiced resource constrained investments, we present the method of Lagrange multipliers which is used to find the optimal investment under a budget constraint.     

A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.     

ICP-AES法测定废水中七种元素

取废水 5 0ml,加入 1mlHNO3 ,在电炉上消化之后 ,过滤进 5 0ml比色管中 ,直接用ICP -AES法对Cu、Pb、Fe、Mn、As、Be等元素进行定量分析     

Treatment of High Nitrate-Containing Wastewaters by Sequential Heterotrophic and Autotrophic Denitrification

The treatment performance of sequential heterotrophic and autotrophic denitrification process was evaluated using synthetic wastewaters containing high nitrate concentrations. The effluents from two sequentially connected reactors, for heterotrophic denitrification and sulfur-based autotrophic denitrification, were analyzed for more than 200 days. Experimental results indicated that higher than 95% of the nitrate removal could be achieved with volumetric nitrate loading rates of 2.16, 3.24, and 4.32 kg/m3?d. The maximum denitrification rates, with 1,000 mgN/l influent nitrate concentrations, for the heterotrophic and sulfur-packed autotrophic reactors, were found to be 2.47 and 3.61 kg/m3?d, respectively. A sequential heterotrophic and autotrophic denitrification process is considered a good alternative for the sole autotrophic denitrification process, providing excellent nitrate removal, especially for nitrate-rich wastewaters with very low organic contents.