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.     

Background subtraction model based on color and depth cues

Background subtraction consists of segmenting objects in movement in a video captured by a static camera. This is typically performed using color information, but it leads to wrong estimations due to perspective and illumination issues. We show that multimodal approaches based on the integrated use of color and depth cues produce more accurate and robust results than using either data source independently. Depth is less affected by issues such as shadows or foreground objects similar to background. However, objects close to the background may not be detected when using only range information, being color information complementary in those cases. We propose an extension of a well-known background subtraction technique which fuses range and color information, as well as a post-processing mask fusion stage to get the best of each feature. We have evaluated the method proposed using a well-defined dataset and different disparity estimation algorithms, showing the benefits of our method for fusion color and depth cues.     

Evaluation of realtime people tracking for indoor environments using ubiquitous motion sensors and limited wireless network infrastructure

We present the development and evaluation of a realtime indoor localisation system for tracking people. Our aim was to track a person’s indoor position using dead-reckoning, while limiting position error without depending on extensive wireless network infrastructure. The Indoor People Tracker used wearable motion sensors, a floor-plan map and a limited wireless sensor network for proximity ranging. We evaluated how the position accuracy of the Indoor People Tracker was affected by floor-plan map features, wireless proximity range and motion information. The advantage of the Indoor People Tracker was found; it was able to achieve accurate position resolution with minimal error, while not depending on wireless proximity.