Effects of commercial canning on SDS-PAGE patterns of the albumin fraction of four pea sizes

Size has been related with pea seed growth and maturation [1], and it is also considered an important parameter when peas are chosen for canning [2]. Some changes occur in the composition of vegetables during blanching, which are the partial loss of major components [3], that are further reduced during canning, mainly due to leaching into the water used for processing [4]. This leaching can be significant if the brine is discarded and not used as a part of the food consumed, as it is often done with most of canned vegetables [5]. Pea seed is an important source of protein for humans, and it has been suggested [6] that when considering pea seeds protein quality more emphasis should be given to the study of the albumin fraction, because it is the fraction rich in the sulphur amino acids. Although electrophoretic analysis of heat-treated proteins can be considered as time consuming it has been used to monitor changes in specific proteins and mixtures of protein subjected to a variety of heat treatments [7]. In general, heat treatments result in a drastic change in polyacrylamide gel electrophoresis (PAGE) patterns in the presence of the detergent sodium dodecyl sulfate (SDS) by reducing the number of bands compared to untreated samples [8]. The purpose of this study was to monitor changes in the albumin fraction of the protein of 4 sizes of peas in the raw, blanched and canned products, and the can brine solutions, using SDS-PAGE.     

Specific consumption of liquid biofuels in gasoline fuelled engines

Oxygenated fuels increase fuel consumption due to their low enthalpy of combustion; however, their high antiknock index renders them suitable for use in engines with a high compression rate, increasing their thermal yield. This study evaluated the performance of biorenewable oxygenated fuels (ethanol and isoamyl alcohol) and partially renewable fuels (ETBE: ethyl tert-butyl ether, TAEE: tert-amyl ethyl ether and di-TAE: di-tert-amyl ether) with high degree of purity and in mixtures with automotive gasoline, based on tests with Otto cycle engines. Among the oxygenated fuels evaluated here, di-TAE was found to present the best characteristics of performance, both individually and in mixtures with gasoline.     

Carbonaceous adsorbents from sewage sludge and their application in a combined activated sludge-powdered activated carbon (AS-PAC) treatment

In this study, waste biological sludge is converted to an adsorbent by chemical activation with sulphuric acid. The adsorbent obtained is then applied to the aerated vessel of an activated sludge process treating glucose and phenol to improve the quality of the treated effluent. The sludge-based carbonaceous adsorbent was found to be mesoporous in nature, with a good adsorption capacity for large molecular weight compounds and limited removal efficiency for smaller molecules such as phenol. The addition of carbon, either sludge-based or commercial, enhanced phenol removal from 58% to 98.7% and from 87% to 93% the organic matter removal as measured by the chemical oxygen demand (COD) when operated with feed concentrations of 100 mg phenol/l and 2500 mg COD/l. No differences were found between the activated sludge-activated carbon bench scale continuous reactors operating with either commercial or sludge-based adsorbents in spite of the higher adsorption capacity of the former. It is suggested that powdered adsorbent bioregeneration in the combined AS-PAC system may be impaired by the obstruction of pores due to bacterial growth, the effect being more important for the commercial activated carbon with a narrower pore size distribution.     

Low-cost sensor to detect overtaking based on optical flow

The automotive industry invests substantial amounts of money in driver-security and driver-assistance systems. We propose an overtaking detection system based on visual motion cues that combines feature extraction, optical flow, solid-objects segmentation and geometry filtering, working with a low-cost compact architecture based on one focal plane and an on-chip embedded processor. The processing is divided into two stages: firstly analog processing on the focal plane processor dedicated to image conditioning and relevant image-structure selection, and secondly, vehicle tracking and warning-signal generation by optical flow, using a simple digital microcontroller. Our model can detect an approaching vehicle (multiple-lane overtaking scenarios) and warn the driver about the risk of changing lanes. Thanks to the use of tightly coupled analog and digital processors, the system is able to perform this complex task in real time with very constrained computing resources. The proposed method has been validated with a sequence of more than 15,000 frames (90 overtaking maneuvers) and is effective under different traffic situations, as well as weather and illumination conditions.     

Two proposals for the inclusion of directory information in the last-level private caches of glueless shared-memory multiprocessors

In glueless shared-memory multiprocessors where cache coherence is usually maintained using a directory-based protocol, the fast access to the on-chip components (caches and network router, among others) contrasts with the much slower main memory. Unfortunately, directory-based protocols need to obtain the sharing status of every memory block before coherence actions can be performed. This information has traditionally been stored in main memory, and therefore these cache coherence protocols are far from being optimal. In this work, we propose two alternative designs for the last-level private cache of glueless shared-memory multiprocessors: the lightweight directory and the SGluM cache. Our proposals completely remove directory information from main memory and store it in the home node’s L2 cache, thus reducing both the number of accesses to main memory and the directory memory overhead. The main characteristics of the lightweight directory are its simplicity and the significant improvement in the execution time for most applications. Its drawback, however, is that the performance of some particular applications could be degraded. On the other hand, the SGluM cache offers more modest improvements in execution time for all the applications by adding some extra structures that cope with the cases in which the lightweight directory fails.     

An industrial portrait background removal solution based on knowledge infusion

Background removal of an identity (ID) picture consists in separating the foreground (face, body, hair and clothes) from the background of the image. It is a necessary groundwork for all modern identity documents that also has many benefits for improving ID security. State of the art image processing techniques encountered several segmentation issues and offer only partial solutions. It is due to the presence of erratic components like hairs, poor contrast, luminosity variation, shadow, color overlap between clothes and background. In this paper, a knowledge infused approach is proposed that hybridizes smart image processing tasks and prior knowledge. The research is based on a divide and conquer strategy aiming at simulating the sequential attention of human when performing a manual segmentation. Knowledge is infused by considering the spatial relation between anatomic elements of the ID image (face feature, forehead, body and hair) as well as their “signal properties”. The process consists in first determining a convex hull around the person’s body including all the foreground while keeping very close to the contour between the background and the foreground. Then, a body map generated from biometric analysis associated to an automatic grab cut process is applied to reach a finer segmentation. Finally, a heuristic-based post-processing step consisting in correcting potential hair and fine boundary issues leads to the final segmentation. Experimental results show that the newly proposed architecture achieves better performances than tested current state-of-the-art methodologies including active contours, generalist popular deep learning techniques, and also two other ones considered as the smartest for portrait segmentation. This new technology has been adopted by an international company as its industrial ID foreground solution.