Heat gain reduction by means of thermoelectric roof solar collector

This paper presents a numerical investigation on attic heat gain reduction by using thermoelectric modules integrated in a conventional roof solar collector (RSC). This system, called thermoelectric roof solar collector (TE-RSC), is composed of a transparent glass, air gap, a copper plate, thermoelectric modules (TE) and rectangular fin heat sink. Due to the incident solar radiation, a temperature difference is created between the hot and cold sides of TE modules that generates a direct current. This current is used to drive a ventilating fan for cooling the TE-RSC and enhancing attic ventilation that reduces ceiling heat gain. The system performance was simulated using TRNSYS program with new TE and DC fan components developed by our team and compared to a common house.Simulation results using real house configuration showed that a TE-RSC unit of 0.0525 m² surface area can generate about 9 W under 972 W/m² global solar radiation and 35 °C ambient temperature. The induced air change varied between 20 and 40 and the corresponding ceiling heat transfer rate reduction is about 3–5 W/m². The annual electrical energy saving was about 362 kWh. Finally, economical calculations indicated that the payback period of the TE-RSC is 4.36 years and the internal rate of return is 22.05%.     

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.     

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.     

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     

Detecting communities of triangles in complex networks using spectral optimization

The study of the sub-structure of complex networks is of major importance to relate topology and functionality. Many efforts have been devoted to the analysis of the modular structure of networks using the quality function known as modularity. However, generally speaking, the relation between topological modules and functional groups is still unknown, and depends on the semantic of the links. Sometimes, we know in advance that many connections are transitive, and as a consequence, triangles have a specific meaning. Here we propose the study of the modular structure of networks considering triangles as the building blocks of modules. The method generalizes the standard modularity and uses spectral optimization to find its maximum. We compare the partitions obtained with those resulting from the optimization of the standard modularity in several real networks. The results show that the information reported by the analysis of modules of triangles complements the information of the classical modularity analysis.     

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     

A momentum imaging microscope for dissociative electron attachment

We describe an experimental approach to image the three-dimensional (3D) momentum distribution of the negative ions arising from dissociative electron attachment (DEA). The experimental apparatus employs a low energy pulsed electron gun, an effusive gas source and a 4π solid-angle ion momentum imaging spectrometer consisting of a pulsed ion extraction field, an electrostatic lens, and a time- and position-sensitive detector. The time-of-flight and impact position of each negative ion are measured event by event in order to image the full 3D ion momentum sphere. The system performance is tested by measuring the anion momentum distributions from two DEA resonances, namely H(-) from H(2)O(-) ((2)B(1)) and O(-) from O(2)(-) ((2)Π(u)). The results are compared with existing experimental and theoretical data.