Biofiltration of toluene in the absence and the presence of ethyl acetate under continuous and intermittent loading

BACKGROUND: Two peat biofilters were used for the removal of toluene from air for one year. One biofilter was fed with pure toluene and the other received 1:1 (by weight) ethyl acetate:toluene mixture. RESULTS: The biofilters were operated under continuous loading: the toluene inlet load (IL) at which 80% removal occurred was 116 g m^?3 h^?1 at 57 s gas residence time. Maximum elimination capacity of 360 g m^?3 h^?1 was obtained at an IL of 745 g m^?3 h^?1. The elimination of toluene was inhibited by the presence of ethyl acetate. Intermittent loading, with pollutants supplied for 16 h/day, 5 days/week, did not significantly affect the removal efficiency (RE). Biomass was fully activated in 2 h after night closures, but 6 h were required to recover RE after weekend closures. Live cell density remained relatively constant over the operational period, while the dead cell fraction increased. Finally, a 15 day starvation period was applied and operation then re‐started. Performance was restored with similar re‐acclimatization period to that after weekend closures, and a reduction in dead cell fraction was observed. CONCLUSION: This study demonstrates the capacity of the system to handle intermittent loading conditions that are common in industrial practices, including long‐term starvation. Copyright © 2008 Society of Chemical Industry     

Substance use escalation and substance use restraint among adolescent children of alcoholics.

Assessed the escalations in substance use over 3 yrs among 246 adolescent children of alcoholics (COAs) and 208 controls (aged 10.5–15.5 yrs). Older COAs showed the steepest escalations in drug use. Younger COAs whose fathers had continuing alcohol-related consequences showed the greatest escalations in alcohol use. Ss' beliefs about drinking restraint were related to their alcohol and drug use. Those whose alcoholic fathers had no continuing alcohol-related consequences showed the strongest relations between substance use and self-control reasons for limiting drinking, perceived risk for future drug problems, and seeing the negative effects of alcohol on someone else. These adolescents may be deterred from substance use escalations because of particular parental characteristics (e.g., mild forms of paternal alcoholism) or because of their beliefs about substance use. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Renal osteodystrophy in diabetic patients

To assess the effects of diabetes mellitus on renal osteodystrophy, we examined the database of 256 patients (45% on hemodialysis and 55% on peritoneal dialysis) who were prospectively studied in three Toronto dialysis centers between October of 1987 and 1989. All patients had serial documentation of their clinical, laboratory and risk parameters of bone disease, and completed a series of investigations that included the deferoxamine test, measurement of intact 1-84 PTH levels, and an iliac crest bone biopsy. Twenty-five percent of these patients were diabetic. When compared to non-diabetic patients, they were on dialysis for a shorter duration (2.4 +/- 0.3 vs. 4.7 +/- 0.3 years; P < 0.0002), used calcium carbonate as the only phosphate binder more frequently (40 vs. 25%; P < 0.007), and had lower parathyroid hormone levels (12 +/- 1.4 vs. 24 +/- 2.3 pmol/liter; P < 0.002). High-turnover bone disorders (that is, osteitis fibrosa and mixed disorder) were distinctly uncommon (8 vs. 33%; P < 0.01 by Fisher's exact test), while the mild (19 vs. 9%; P = NS) and the aplastic disorders (with mean stainable bone surface aluminum of 6.5 +/- 0.7%) (46 vs. 31%; P = NS) tended to be more common in diabetic patients. The prevalence of aluminum bone disease was the same in both groups (27%). Diabetic patients ingested a smaller cumulative dose of aluminum gels (3.7 +/- 0.6 vs. 9.3 +/- 1.1 kg; P < 0.005), yet had a higher rate of aluminium accumulation on bone surfaces than non-diabetic patients (1.5 +/- 0.19 vs. 0.96 +/- 0.10% per month on dialysis; P < 0.015).(ABSTRACT TRUNCATED AT 250 WORDS)     

Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times

In this article we study thetabu search (TS) method in an application for solving an important class of scheduling problems. Tabu search is characterized by integrating artificial intelligence and optimization principles, with particular emphasis on exploiting flexible memory structures, to yield a highly effective solution procedure. We first discuss the problem of minimizing the sum of the setup costs and linear delay penalties when N jobs, arriving at time zero, are to be scheduled for sequential processing on a continuously available machine. A prototype TS method is developed for this problem using the common approach of exchanging the position of two jobs to transform one schedule into another. A more powerful method is then developed that employs insert moves in combination with swap moves to search the solution space. This method and the best parameters found for it during the preliminary experimentation with the prototype procedure are used to obtain solutions to a more complex problem that considers setup times in addition to setup costs. In this case, our procedure succeeded in finding optimal solutions to all problems for which these solutions are known and a better solution to a larger problem for which optimizing procedures exceeded a specified time limit (branch and bound) or reached a memory overflow (branch and bound/dynamic programming) before normal termination. These experiments confirm not only the effectiveness but also the robustness of the TS method, in terms of the solution quality obtained with a common set of parameter choices for two related but different problems.     

Image segmentation towards new image representation methods

Very low bit-rate video coding has recently become one of the most important areas of image communication and a large variety of applications have already been identified. Since conventional approaches are reaching a saturation point, in terms of coding efficiency, a new generation of video coding techniques, aiming at a deeper “understanding” of the image, is being studied. In this context, image analysis, particularly the identification of objects or regions in images (segmentation), is a very important step. This paper describes a segmentation algorithm based on split and merge. Images are first simplified using mathematical morphology operators, which eliminate perceptually less relevant details. The simplified image is then split according to a quad tree structure and the resulting regions are finally merged in three steps: merge, elimination of small regions and control of the number of regions.     

Facilitating the learning process in parallel computing by using instant messaging

Parallel Programming skills may require a long time to acquire. “Think in parallel” is a skill that requires time, effort, and experience. In this work, we propose to facilitate the students’ learning process in parallel programming by using instant messaging. Our aim was to find out whether students’ interaction through instant messaging tools is beneficial for the learning process. In order to do so, we asked several students of an HPC course of the Master’s degree in Computer Science of the University of León to develop a specific parallel application, each of them using a different application program interface: OpenMP, MPI, CUDA, or OpenCL. Even though the used APIs are different, there are common points in the design process. We encouraged students to interact with each other by using Gitter, an instant messaging tool for GitHub users. Our analysis of the communications and results demonstrate that the direct interaction of students through the Gitter tool has a positive impact on the learning process.

     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

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.