Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.     

Cheaper membrane materials for microalgae dewatering

Among different strategies to reduce costs in microalgae dewatering process via cross-flow filtration, the one related to membrane material was investigated in order to be decreased. Several materials were tested, starting with the ones commonly used in membrane technology [ceramic, polysulfone (PSf) and polyacrylonitrile (PAN)] to the ones generally employed in packaging industry [acrylonitrile butadiene styrene (ABS), glycol-modified polyethylene terephthalate (PETG) and polylactic acid (PLA)], the latter being considerably cheaper. Experiments carried out showed promising results in terms of permeabilities for PSf–Pluronic^® F127 blended membranes and PAN membranes (11 ± 1 L/h/m²/bar and 22 ± 1 L/h/m²/bar, respectively, instead of 2 ± 2 L/h/m²/bar of PSf membranes), but with high related costs. PLA membranes showed good mechanical properties, biodegradability and price, but low permeability values (5 ± 1 L/h/m²/bar). PETG membranes showed attractive results in terms of costs and permeability, but poor mechanical properties. The polymer that offered the best results was the ABS that reached membrane permeabilities of 19 ± 1 L/h/m²/bar, maintaining good mechanical properties while filtering the microalgae Phaeodactylum tricornutum Bohlin. Thus, a novel functionality was found for these not so common polymers in microalgae dewatering. This indicates that use of these materials could also be considered in other aqueous micro/ultrafiltration applications. In addition, the biodegradable PLA polymer introduces a new concept of cheap and environmental friendly membrane in this application.     

Characterization of effluent water qualities from satellite membrane bioreactor facilities

Membrane bioreactors (MBRs) are often a preferred treatment technology for satellite water recycling facilities since they produce consistent effluent water quality with a small footprint and require little or no supervision. While the water quality produced from centralized MBRs has been widely reported, there is no study in the literature addressing the effluent quality from a broad range of satellite facilities. Thus, a study was conducted to characterize effluent water qualities produced by satellite MBRs with respect to organic, inorganic, physical and microbial parameters. Results from sampling 38 satellite MBR facilities across the U.S. demonstrated that 90% of these facilities produced nitrified (NH₄-N <0.4 mg/L-N) effluents that have low organic carbon (TOC <8.1 mg/L), turbidities of <0.7 NTU, total coliform bacterial concentrations <100 CFU/100 mL and indigenous MS-2 bacteriophage concentrations <21 PFU/100 mL. Multiple sampling events from selected satellite facilities demonstrated process capability to consistently produce effluent with low concentrations of ammonia, TOC and turbidity. UV-254 transmittance values varied substantially during multiple sampling events indicating a need for attention in designing downstream UV disinfection systems. Although enteroviruses, rotaviruses and hepatitis A viruses (HAV) were absent in all samples, adenoviruses were detected in effluents of all nine MBR facilities sampled. The presence of Giardia cysts in filtrate samples of two of nine MBR facilities sampled demonstrated the need for an appropriate disinfection process at these facilities.     

Toward the prediction of porous membrane permeability from morphological data

One of the challenges in membrane technology is predicting permeability in porous membranes for liquid applications in an easy and inexpensive way. This is the aim of this work. To achieve this objective, several techniques can be considered. In this study, a morphological approach from two‐dimensional scanning electron micrographs is proposed. First, numerical membrane morphological parameters have been determined from micrographs by using the QUANTS tool, which applies a texture recognition process. Second, the obtained data have been fit to the Darcy's and Hagen–Poiseuille models to calculate permeations. The QUANTS results have also been compared with the ones obtained through a mercury porosimeter, which is a classic and well‐known methodology. Each parameter of the Hagen–Poiseuille model has been analyzed. A comparison between experimentally measured permeations and calculated ones has been performed. An even easier approach is proposed to predict flow rate with the only knowledge of membrane surface mean pore size. This method is based on cross‐section pore size interpolation by using function fits from surface mean pore sizes. The obtained results show a reasonable agreement between measured and computed results, making this technique a valid approach for predicting membrane permeability. POLYM. ENG. SCI., 56:118–124, 2016. © 2015 Society of Plastics Engineers     

A genetic algorithm for energy-efficiency in job-shop scheduling

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.     

Functional mesoporous poly(ionic liquid)-based copolymer monoliths: From synthesis to catalysis and microporous carbon production

Ionic liquid-functionalized mesoporous polymeric networks with specific surface area up to 935 m²/g have been successfully synthesized one pot by solvothermal copolymerization of divinylbenzene and monomeric ionic liquids. The as-obtained polymers exhibit a monolithic structure featuring large pore volumes, an abundant mesoporosity and an adjustable content of ionic liquids. The effect of the reaction conditions on the pore structure has been studied in detail. These poly(ionic liquid)-based porous networks (PILPNs) have then been employed as precursors in two distinct applications, namely organocatalysis and production of microporous carbon monoliths. Selected organocatalyzed reactions, including carbonatation of propylene oxide by cycloaddition with carbon dioxide, benzoin condensation, and cyanosilylation of benzaldehyde have been readily triggered by PILPNs acting as crosslinked polymer-supported (pre)catalysts. The two latter reactions required the prior deprotonation of the imidazolium salt units with a strong base to successfully generate polymer-supported N-heterocyclic carbenes, referred to as poly(NHC)s. Facile recycling and reuse of polymer-supported (pre)catalysts was achieved by simple filtration owing to the heterogeneous reaction conditions. Furthermore, PILPNs could be easily converted into microporous carbon monoliths via CO₂ activation.     

Learning curve analysis of concentrated photovoltaic systems

Price declines and volume growth of concentrated photovoltaic (CPV) systems are analysed using the learning curve methodology and compared with other forms of solar electricity generation. Logarithmic regression analysis determines a learning rate of 18% for CPV systems with 90% confidence of that rate being between 14 and 22%, which is higher than the learning rates of other solar generation systems (11% for CSP and 12 to 14% for PV). Current CPV system prices are competitive with PV and CSP, which, when combined with the higher learning rate, indicates that CPV is likely to further improve its marketability. A target price of 1 $/W in 2020 could be achieved with a compound growth rate of 67% for the total deployed volume between 2014 and 2020, which would realize a cumulative deployed volume of 7900 MW. Other projections of deployment volumes from commercial sources are converted using the learning rate into future price scenarios, resulting in predicted prices in the range of 1.1 to 1.3 $/W in 2020. © 2014 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.