Dynamical modelling of an activated sludge system of a petrochemical plant operating at high temperatures.

The Mexican petrochemical industry, Morelos S.A. de C.V., is one of the biggest and more important petroleum industries in Mexico and Latin America. It has an activated sludge system to treat its wastewater flow, which is approximately 7,000 m3/d. The wastewater contains volatile organic carbon substances classified as toxics. The old surface aeration system was changed for fine bubble diffusers; however, one major drawback of the new aeration system is that the temperature in the bioreactor has increased due to the compression of the air, which at the compressor exit reaches 85 degrees C. This effect results in the temperature in the bioreactor attaining 32 degrees C during the fall, whereas in the spring and summer, the bioreactor temperature reaches higher values than 40 degrees C. The high temperatures reduce the microorganism activity and cause a higher volatilisation rate of volatile compounds, among other effects, which affect the performance of the biological treatment. This work was performed to obtain a better modelling of the wastewater treatment from the petrochemical industry. The model describes the effect of the temperature on the performance of the biological treatment. The model was obtained from tests that were carried out in laboratory reactors with 14 L capacity, which were operated at different temperatures (from 30 to 45 degrees C), with the same wastewater and conditions as the actual system.     

Effect of pretreatments and freezing rate on the firmness of potato tissue after a freeze–thaw cycle

The texture of potato tissue after a freeze–thaw process using different freezing rates and different pretreatments was analysed, in order to select the best strategy for optimum preservation of the textural characteristics of pre‐frozen potato. Ten blanching conditions were tested and a two‐step blanching process with calcium chloride (0.07 g mL^?1) proved the most effective in protecting the tissue after a freeze–thaw process (maximum load force around 10–55% of the raw tissue, depending on potato batch, for air‐blast freezing and 20–60% for immersion freezing). Vacuum impregnation at 100 and 400 mbar, even when followed by different pre‐drying treatments to remove excess water, was very detrimental to resistance to a freeze–thaw process (maximum load force below 10% of the raw tissue for air‐blast freezing and below 20% for immersion freezing). Microstructure analysis confirmed better tissue integrity retention with ethyleneglycol immersion freezing instead of air‐freezing. Differences were found between batches with a 6‐month difference in storage time, indicating that the fresher batch was more suitable for freezing.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Chattering-Free Sliding Mode Altitude Control for a Quad-Rotor Aircraft: Real-Time Application

Nowadays, the chattering problem in sliding mode control is one of the most important points to consider in real-time applications. To address this problem, a real-time robust altitude control scheme is proposed for the efficient performance of a Quad-rotor aircraft system using a continuous sliding mode control. The sensing of altitude measurement sensing is performed by a pressure sensor in order to obtain a robust altitude control of the vehicle in hovering mode both indoor and outdoor. The altitude measurement has the advantage of introducing this state information directly in the closed loop control which should be very useful for achieving robust stabilization of the altitude control. Accordingly, we propose a sliding mode control strategy without chattering. The sliding mode control proposed removes the chattering phenomenon by replacing a sign function with a high-slope saturation function. The control algorithm is derived from the Lyapunov stability theorem. Moreover, we have assumed that the actuators are able to respond quickly and accurately and we have not enforced limits on the control signals for a real-time application. Finally, to verify the satisfactory performance of proposed nonlinear control law, several simulations and experimental results of the Chattering-free sliding mode control for the Quad-rotor aircraft in the presence of bounded disturbances are presented.     

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.