Hydrodynamics and mass transfer in a bubble column with an organic liquid

Hydrodynamic and mass transfer data in Soltrol-130 (a mixture of C₉+ iso-paraffins) were measured in a 0.305 m diameter bubble column. The gas holdup structure (i.e., the contributions to holdup from the small and large bubble fractions of the dispersion) for this hydrocarbon liquid in the churn turbulent flow regime was analyzed using the dynamic gas disengagement technique. The validity of the assumption of axially uniform gas holdup structure was checked. Literature correlations were found inadequate to explain the observed gas holdup and the volumetric mass transfer coefficients for Soltrol-130. The volumetric mass transfer coefficient per unit volume of large bubbles is shown to be independent of superficial gas velocity for the fully developed churn turbulent regime. The present hydrodynamic and mass transfer data in the churn turbulent regime should be useful in the design and scale-up of bubble columns used in organic process industries.     

The role of lanthana loading on the catalytic properties of Pd/Al2O3-La2O3 in the NO reduction with H2

The role of La₂O₃ loading in Pd/Al₂O₃-La₂O₃ prepared by sol–gel on the catalytic properties in the NO reduction with H₂ was studied. The catalysts were characterized by N₂ physisorption, temperature-programmed reduction, differential thermal analysis, temperature-programmed oxidation and temperature-programmed desorption of NO.

The physicochemical properties of Pd catalysts as well as the catalytic activity and selectivity are modified by La₂O₃ inclusion. The selectivity depends on the NO/H₂ molar ratio (GHSV = 72,000 h⁻¹) and the extent of interaction between Pd and La₂O₃. At NO/H₂ = 0.5, the catalysts show high N₂ selectivity (60–75%) at temperatures lower than 250 °C. For NO/H₂ = 1, the N₂ selectivity is almost 100% mainly for high temperatures, and even in the presence of 10% H₂O vapor. The high N₂ selectivity indicates a high capability of the catalysts to dissociate NO upon adsorption. This property is attributed to the creation of new adsorption sites through the formation of a surface PdO_x phase interacting with La₂O₃. The formation of this phase is favored by the spreading of PdO promoted by La₂O₃. DTA shows that the phase transformation takes place at temperatures of 280–350 °C, while TPO indicates that this phase transformation is related to the oxidation process of PdO: in the case of Pd/Al₂O₃ the O₂ uptake is consistent with the oxidation of PdO to PdO₂, and when La₂O₃ is present the O₂ uptake exceeds that amount (1.5 times). La₂O₃ in Pd catalysts promotes also the oxidation of Pd and dissociative adsorption of NO mainly at low temperatures (<250 °C) favoring the formation of N₂.     

Impact damage detection in carbon fibre composites using HTS SQUIDs and neural networks

A neural network-based data analysis tool, developed to speed the damage detection process for the NDE of impact damaged carbon fibre composites, is discussed. A feature extraction method utilising a gradient threshold search function and a feed forward neural network for pattern recognition were used to develop the system. Impact damaged carbon composite sample plates were scanned with an eddy current-based NDE setup using HTS SQUID gradiometers and double-D excitation coils. Detection of damage sites in data affected by noise spikes caused by environmental disturbances is demonstrated. Finally, a possible design for a future entirely automated scanning system is also introduced.     

Fuzzy Structure of Complex Belief Systems: Fuzzy Relations and Fuzzy Belief Sets

In previous work, Nescolarde-Selva and Usó-Doménech (2014a Nescolarde-Selva, J. and J. L. Usó-Doménech. “Semiotic Vision of Ideologies.” Found Sci 19 (2014a): 263–282.[Crossref], [Web of Science ®] , [Google Scholar], b) discussed the theory that complex belief systems have a topological structure. In this article it is suggested that this structure is also fuzzy. We introduce the concepts of fuzzy sets in the context of beliefs (substantive and derived), and between derived beliefs themselves. Also introduced are the concepts of fuzzy covering and fuzzy invariance and the relationships between them.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Understanding student participation in undergraduate course communities: A case study

Participation is the cornerstone of any community. Promoting, understanding and properly managing it allows not only keeping the community sustainable, but also providing personalized services to its members and managers. This article presents a case study in which student participation in a course community was motivated using two different extrinsic mechanisms, and mediated by a software platform. The results were compared with a baseline community of the same course, in which participation was not motivated by external means. The analysis of these results indicates that managing a partially virtual course community requires the introduction of monitoring services, community managers and extrinsic mechanisms to motivate participation. These findings allow community managers to improve their capability for promoting participation and keeping the community sustainable. The findings also raise several implications that should be considered in the design of software supporting this kind of community, when managing the participation of its members.     

Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service

This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this model, customers have the choice between different service levels. Each service level is associated with a price per unit of job execution time, and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding quality of service for all accepted jobs. Since we do not consider any optimistic scheduling approach, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. In this article, we analyze several scheduling algorithms with different cloud configurations and workloads, considering the maximization of the provider income and minimization of the total power consumption of a schedule. We distinguish algorithms depending on the type and amount of information they require: knowledge free, energy-aware, and speed-aware. First, to provide effective guidance in choosing a good strategy, we present a joint analysis of two conflicting goals based on the degradation in performance. The study addresses the behavior of each strategy under each metric. We assess the performance of different scheduling algorithms by determining a set of non-dominated solutions that approximate the Pareto optimal set. We use a set coverage metric to compare the scheduling algorithms in terms of Pareto dominance. We claim that a rather simple scheduling approach can provide the best energy and income trade-offs. This scheduling algorithm performs well in different scenarios with a variety of workloads and cloud configurations.     

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.