The effect of ceria content on the performance of Pt/CeO2/Al2O3 catalysts in the partial oxidation of methane

The effect of CeO₂ loading (1–20 wt.%) on the properties and catalytic behaviors of CeO₂–Al₂O₃-supported Pt catalysts on the partial oxidation of methane was studied. The catalysts were characterized by S_BET, X-ray diffraction (XRD), temperature-programmed reduction (TPR) and oxygen storage capacity (OSC). XRD and TPR results showed that the pretreatment temperature of the support influences on the amount of CeO₂ with fluorite structure. The pretreatment temperature of the support and CeO₂ loading influenced the morphology of Pt. OSC analysis showed a significant increase in the oxygen storage capacity per weight of CeO₂ for samples with high CeO₂ loading (12 and 20 wt.%). TPR analyses showed that the addition of Pt promotes the reduction of CeO₂. This effect was more significant for the catalysts with high CeO₂ loading (≥12 wt.%). The dispersion of Pt, measured by the rate of cyclohexane dehydrogenation, increases with increasing of the pretreatment temperature of the support. It was shown that the kind of the support is very important for obtaining of catalysts resistant to carbon formation. The catalysts with high CeO₂ loading (≥12 wt.%) showed the highest catalytic activity and stability in the reaction of partial oxidation of methane due to a higher Pt–CeO₂ interface.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a “swarm”-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.     

Abnormal behavior detection using dominant sets

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\) -Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.     

Blocking the Routes to Triviality with Depth Relevance

In Rogerson and Restall’s (J Philos Log 36, 2006, p. 435), the “class of implication formulas known to trivialize (NC)” (NC abbreviates “naïve comprehension”) is recorded. The aim of this paper is to show how to invalidate any member in this class by using “weak relevant model structures”. Weak relevant model structures verify deep relevant logics only.     

Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.     

Dynamics of liquid-liquid mixing: A 2-zone model

The development of multiphase liquid-liquid morphologies during mixing at small Reynolds numbers has been modeled. The mixing process is divided into (i) stretching of dispersed drops. (ii) breakup of the liquid threads formed, and (iii) coalescence of the final droplets upon collision. Rules and criteria of the distinct processes are presented and combined to a general 2-zone mixing model simplifying the flow field into a sequence of alternating “strong and weak zones.” In a “strong zone,” dispersed drops and threads are stretched unless their radius is too small; meanwhile, the stretching threads might break up into droplets. In the subsequent “weak zone,” the remaining threads may disintegrate while any drops present may coalesce. After passing a number of zones, stretching, breakup, and coalescence lead to a dynamic equilibrium that could be considered as the “final” morphology. Using the 2-zone mixing model, the influence of material parameters and processing conditions on the morphology has been studied. Interestingly, increasing either viscosity (dispersed or continuous phase) yields a finer morphology due to the delay of thread breakup, allowing for further stretching and suppression of coalescence.     

Cholesterol synthesis and esterification in isolated enterocytes: Regulation by cholesterol and cholestyramine feeding

The purpose of the present study was to investigate the physiological control of the main regulatory enzymes of cholesterol metabolism in isolated enterocytes obtained from chick duodenum, jejunum and ileum. Cholesterol feeding resulted in an inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase and mevalonate 5-pyrophosphate decarboxylase, while cholestyramine feeding increased reductase activity in all the regions studied and decarboxylase activity only in duodenum. Cholesterol feeding markedly increased acyl-CoA:cholesterol acyltransferase, but the effects of cholestyramine were less clear. The effects on transferase activity cannot be due to differences in the availability of acyl-CoA as exogenous substrate as no significant differences were found in acyl-CoA hydrolase activity after any of the dietary treatments. The effects of cholesterol feeding were related to changes in the cholesterol content of epithelial cells, whereas in the case of cholestyramine this relationship was less apparent.     

Modern Applications of Electron Microscopy to Catalysis

The active sites of ultra-dispersed Pt/-Al₂O₃ catalysts are studied using high-resolution electron microscopy, Z-contrast and dark field. In addition we have calculated using a method based in density functional theory the electrostatic potential and charge distribution of the active sites. It is conclude that the most likely Pt clusters that are formed correspond to Pt₁₃ and Pt with icosahedral and decahedral structure. It is shown that this is consistent with the electron microscopy data.     

CRUDi framework proposal: financial industry application

The alignment of information systems with the business goals of an organisation, although a topic of great importance, is not always properly valued or taken into consideration. In general, managers have different opinions to chief information officers (CIOs) in relation to IS, especially with regard to their importance and value to the business and also in terms of investment needs. Here, we discuss and study new approaches to methods and tools for assessing the relative importance of each information system to business, focusing on the financial sector including banks and insurance companies. We suggest the introduction of new key indicators for better decision support and to identify investment priorities, and present results regarding the relative importance of each process to support the business strategy. The primary goal for the inherent research project is to analyse the main problems and difficulties encountered by IS and IT managers, featuring different players and how they relate. The main contributions of this work are the CRUDi framework as a tool to improve alignment between business and IS strategies and the CRUDi survey and its results qualifying the financial sector's opinion regarding the relative importance of processes and investments.