Variations in the phytosterol and tocopherol compositions and the oxidative stability in seed oils from four safflower (Carthamus tinctorius L) varieties grown in north‐eastern Morocco

Phytosterol and tocopherol contents and oxidative stability were evaluated from seeds oils of four safflower (Carthamus tinctorius L) varieties originating from Spain (Rancho), India (Sharda) and Morocco (Cartamar and Cartafri), which were cultivated at the experimental station in Oujda (a semi‐arid region of eastern Morocco). Total phytosterols ranged from 3640 to 4140 mg kg^?1. GC analysis allowed the identification of nine compounds, of which β‐sitosterol was the major component. Total tocopherols ranged from 461.56 to 499.68 mg kg^?1. HPLC analysis allowed the identification of three compounds, α‐tocopherol (99.45%–98.84%), β‐tocopherol (0.94%–0.5%) and γ‐tocopherol (0.21%–0.01%). Oxidative stability study showed that Sharda had the lowest induction period of 2.3 h compared with 7.18, 7 and 6.67 h for Cartafri, Rancho and Cartamar, respectively. Likewise, we established a positive correlation between the oxidative stability and γ‐tocopherol; however, this difference was not significant.     

Dempster–Shafer theory-based human activity recognition in smart home environments

Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.     

A decomposition approach for the solution of the unit loading problem in hydroplants

This paper presents a decomposition approach for the solution of the dynamic programming formulation of the unit loading problem in hydroplant management. This decomposition approach allows the consideration of network and canal constraints without additional computational effort.     

Simulation study of view angle effects on thermal infrared measurements over heterogeneous surfaces

The issue of deriving cross-scale aggregation rules has been extensively investigated over the last two decades. A widely used approach consists of formulating grid-scale surface radiances using the same equations that govern the patch-scale behavior but whose arguments are the aggregate expressions of those at the patch-scale. This approach derives the area-averaged or effective radiative surface temperature as might be observed using low spatial resolution satellite data. The problem however is that such satellite data exhibit large directional effects and no study has addressed this issue. The present work tackles this problem in the thermal infrared domain. The directional effects are studied by modeling. Thus, an infrared sensor observing a two-dimensional (2-D) heterogeneous plane surface is modeled. The 2-D heterogeneous plane surface is simulated by a grid with two homogeneous elements (vegetation-bare soil). The angular properties of the local surfaces, assumed homogeneous, are calculated by a multiple scattering model. The equivalent angular radiance of the complete heterogeneous scene is then determined by applying the aggregation method. This radiance is very sensitive to the surface heterogeneity, especially when the spatial variation of the surface temperature is significant and when the directional behavior of the surface is non-Lambertian. As a result, an angular variation of 6% on radiance was obtained on a heterogenous surface between a zenith angle of 70/spl deg/ and on-nadir measurements.     

The control of myopic behavior in semi-heterarchical production systems: A holonic framework

Heterarchical control architectures are essentially founded on cooperation and full local autonomy, resulting in high reactivity, no master/slave relationships and local information retention. Consequently, these architectures experience myopic decision-making, bringing entities towards local optimality rather than the system’s overall performance. Although this issue has been identified as an important problem within heterarchical control architectures, it has not been formally studied. The aim of this paper is to identify the dimensions of myopic behavior and propose mechanisms to control this behavior. This study focuses on myopic behavior found in manufacturing control. For this particular study, we propose a holonic framework and a holonic organization that integrates specific mechanisms to control the temporal and social myopia. Our proposal was validated using simulations designed for solving the allocation problem in flexible manufacturing systems. These simulations were conducted to show the improvement by integrating the new mechanisms. These simulation results indicate that the myopic control mechanisms achieve better performance than the reactive strategies, because not only they introduce a planning horizon, but also because they balance local and global objectives, seeking a consensus.     

Sensitivity derivatives for flexible sensorimotor learning

To learn effectively, an adaptive controller needs to know its sensitivity derivatives--the variables that quantify how system performance depends on the commands from the controller. In the case of biological sensorimotor control, no one has explained how those derivatives themselves might be learned, and some authors suggest they are not learned at all but are known innately. Here we show that this knowledge cannot be solely innate, given the adaptive flexibility of neural systems. And we show how it could be learned using forms of information transport that are available in the brain. The mechanism, which we call implicit supervision, helps explain the flexibility and speed of sensorimotor learning and our ability to cope with high-dimensional work spaces and tools.     

Surface plasmon resonance and impedance spectroscopy on gold electrode for biosensor application

This paper describes new-label free immunoassays for the detection of atrazine using optical Surface Plasmon Resonance (SPR) and Electrochemical Impedance Spectroscopy (EIS) by employing gold functionalised with self-assembled mixed monolayer. The mixed monolayer, formed by 16-mercaptohexadecanoic acid (MHDA) and biotinyl–PE, is used as the basic layer in the fabrication of the immunoprobe. The interfacial interaction of a biotinyl–Fab fragment K47 antibody with the mixed monolayer is based on a biotin–avidin system using the neutravidin molecule. Thus, in this study the multilayer engineering is mentored by SPR and EIS methods. In addition, the specificity and sensitivity of this self-assembled multilayer system to the presence of atrazine are investigated. The limit detection of Atrazine obtained with EIS technique is equal to 20 ng/ml and with SPR technique is equal to 50 ng/ml.     

Under flow impedimetric measurements using magnetic particles for label-free detection affinity target

The necessity to adapt sensors based on electrochemical techniques for high throughput analysis control increases the interest to develop new analytical systems able to perform measurements under buffer flow.

In this report we explored the possibility of employing a new system to make impedimetric measurements to detect the interaction between proteins and small molecules. The well-known biotin–streptavidin interaction was adopted to evaluate the proposed assembly. This system allows us to perform experiments under flow. Magnetic beads functionalized with streptavidin were used and first characterized using AFM and FTIR.

Non-faradic impedance spectroscopy allowed the detection of the biotin–streptavidin interaction. Using our new system and under a flow of PBS buffer, 5 10^− 5 M of biotin was detected with a stable signal.