Importance of retronasal and orthonasal olfaction for odor aversion memory in rats.

The role of odors in food memory formation, especially for aversions, has long been considered secondary to taste. However, the importance of odor ingestion in conditioned odor aversion (COA) has recently challenged this assumption (B. M. Slotnick, F. Westbrook, & F. M. C. Darling, 1997). The aim of the present study was to evaluate the respective role of orthonasal and retronasal olfactory experience in COA acquisition, long-term retention, extinction, and spontaneous recovery. To this end, the odor was presented either close to the drinking spout (orthonasal stimulation) or close to and mixed with the drinking water (eliciting both orthonasal and retronasal stimulation). The authors brought evidence that odor ingestion was crucial for COA acquisition, especially when odor presentation and gastric malaise were separated by long delays. On the contrary, once formed, a distal (orthonasal) odor recognition was sufficient for COA to be retrieved. COA was odor specific and long lasting (more than 50 days). Moreover, results brought evidence for a spontaneous recovery of odor aversion tested 57 days after its extinction. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

SKWN: Smart and dynamic key management scheme for wireless sensor networks

In the era of the Internet of Things (IoT), we are witnessing to an unprecedented data production because of the massive deployment of wireless sensor networks (WSNs). Typically, a network of several hundred sensors is created to ensure the interactions between the cyber world and the physical world. Unfortunately, the intensive use of this kind of networks has raised several security issues. Indeed, many WSN‐based applications require secure communication in order to protect collected data. This security is generally ensured by encryption of communication between sensors, which requires the establishment of many cryptographic keys. Managing these keys, within a protocol, is an important task that guarantees the effectiveness of the security mechanism. The protocol should be intelligently adaptable not only to intrusion events but also to the security level needed by some applications. An efficient protocol optimizes also sensors energy and consequently increases the network life cycle. In this paper, we propose, a smart and dynamic key management scheme for hierarchical wireless sensor networks (SKWN). Our protocol offers three subschemes to deal with key establishment, key renewal, and new node integration. Regarding existing schemes, SKWN does not only provide reliable security mechanisms, but it also optimizes energy consumption and overheads related to the communication and memory usage. Furthermore, our approach relies on a machine learning approach to monitor the state of the network and decide the appropriate security level. We provide a formal approach and its implementation, together with simulations allowing to compare resources usage with respect to existing approaches.     

Hybrid polyaniline/nanomagnetic particles composites: High performance materials for EMI shielding

In this work, two types of hybrid composite materials were elaborated. The first based on polyaniline (PANI) doped Camphor Sulfonic acid (CSA), Carbon‐Coated Cobalt (CCo), and FeNi nanoparticles dispersed in polyurethane. A value of 10⁴ S/m of conductivity and a 90 dB of shielding effectiveness in multilayer structure were obtained over the 8–18 GHz frequency band. The second type, based on PANI doped para‐toluene sulfonic acid (PTSA), dispersed in epoxy resin with FeNi nanoparticles. A thick material with moderate conductivity and high attenuation of electromagnetic waves was obtained. It was found that a PANI‐PTSA/FeNi/epoxy resin composite with thicknesses of 9.7 and 6.5 mm had, respectively, reflection loss values of ?22 dB at 9.52 GHz, and ?20.7 dB at 14.7 GHz. The electromagnetic properties of the elaborated structure hybrid materials can be optimized to increase the electromagnetic reflection–absorption properties. Thus, the obtained structure can be used in shielding and radar absorbing materials applications. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Simulation of hydrodynamic behaviour and mass transfer efficiency of reduced pressure distillation

A random packing hydrodynamic simulator is designed specially to carry out experiments under reduced pressures with a counter-current flow air/water system. The simulator results (air/water system) compared to those obtained in a real packed distillation column (benzylchloride/ ethylbenzene system) show a good agreement between hydrodynamic parameters like pressure drop, flooding flow rate, and total liquid hold-up. Empirical relationships derived from the simulator experimental results are proposed allowing the calculation of the pressure drop in a packed distillation column, operating in the pressure range between 10 kPa and atmospheric pressure. The influence of reduced pressure on the HETP in the distillation column and interfacial area in the simulator was also investigated.     

Non‐constraining Online Signature Reconstruction System for Persons with Handwriting Problems

This paper presents a new non‐constraining online optical handwritten signature reconstruction system that, in the main, makes use of a transparent glass pad placed in front of a color camera. The reconstruction approach allows efficient exploitation of hand activity during a signing process; thus, the system as a whole can be seen as a viable alternative to other similar acquisition tools. This proposed system allows people with physical or emotional problems to carry out their own signatures without having to use a pen or sophisticated acquisition system. Moreover, the developed reconstruction signature algorithms have low computational complexity and are therefore well suited for a hardware implementation on a dedicated smart system.     

User’s behavior study for smart houses occupant prediction

This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by nonintrusive sensors and devices, our approach use supervised learning technics to predict the house occupant. We applied support vector machines classifier to build a behavior classification model and learn the users’ habits when they perform activities for predicting and identifying the house occupant among a group of inhabitants. We analyzed the publicly available dataset from the Washington State University smart apartment tesbed. We particulary studied the grooming, having breakfast and bed to toilet activities. The results showed a hight prediction precision and demonstrated that each user has his own manner to perform his daily activities and can be easily identified by just learning his habit.     

Fire retardancy effect of phosphorus‐modified halloysite on polyamide‐11 nanocomposites

Halloysite nanotubes (HNTs) were successfully incorporated as flame retardants in polyamide‐11 (PA11) after their modification with methyl phosphonic acid. Fourier transform infrared spectroscopy, thermal gravimetric analysis (TGA) and pyrolysis–gas chromatography–mass spectrometry were used to evidence the functionalization of the clay. Raw and modified HNTs were then incorporated by melt mixing in PA11 at 20 wt%. Compositions containing both ammonium polyphosphate (APP) and HNTs were also prepared. TGA and pyrolysis combustion flow calorimeter exhibited enhancement in thermal stability upon incorporation of both raw and modified halloysite nanotubes while APP causes degradation at lower temperature. Cone calorimeter data showed that modified halloysite acts by forming an insulating barrier during the combustion, which limits heat and mass transfers. Moreover, elemental analysis of sample residues after cone test evidenced that a part of the phosphorus of the modified halloysite was transferred to the gaseous phase. These results suggest the full potential of halloysite as fire retardant agent for polyamides. POLYM. ENG. SCI., 59:526–534, 2019. © 2018 Society of Plastics Engineers