Semi-blind amplify-and-forward in two-way relaying networks

This paper addresses the analysis of a two-way semi-blind amplify-and-forward (AF) relay network, in which the relay node requires partial instantaneous channel state information (CSI) to amplify the received signals. First, we derive the expression of end-to-end signal-to-noise ratio in dual-hop transmissions over independent and not necessarily identically distributed Rayleigh fading channels. Based on the opportunistic AF selection relaying, we define bounds of some metrics such as average sum-rate and outage probability. Furthermore, we provide exact and approximate expressions for the average symbol error rate. The obtained results show that the performance of the proposed system decreases slightly while the processing complexity is reduced significantly compared to those of CSI-assisted AF relay schemes. In order to prove the exactness of the proposed analysis, a selection of numerical results is provided.     

Comparative study of conductometric glucose biosensor based on gold and on magnetic nanoparticles

The aim of this study was to show the feasibility and the performances of nanoparticle biosensing. A glucose conductometric biosensor was developed using two types of nanoparticles (gold and magnetic), glucose oxidase (GOD) being adsorbed on PAH (poly(allylamine hydrochloride)) modified nanoparticles, deposited on a planar interdigitated electrode (IDEs). The best sensitivities for glucose detection were obtained with magnetic nanoparticles (70 μM/mM and 3 μM of detection limit) compared to 45 μM/mM and 9 μM with gold nanoparticles and 30 μM/mM and 50 μM with GOD directly cross-linked on IDEs. When stored in phosphate buffer (20 mM, pH 7.3) at 4 °C, the biosensor showed good stability for more than 12 days.     

Safe Nanoparticles: Are We There Yet?

The field of nanotechnology has grown over the last two decades and made the transition from the benchtop to applied technologies. Nanoscale-sized particles, or nanoparticles, have emerged as promising tools with broad applications in drug delivery, diagnostics, cosmetics and several other biological and non-biological areas. These advances lead to questions about nanoparticle safety. Despite considerable efforts to understand the toxicity and safety of these nanoparticles, many of these questions are not yet fully answered. Nevertheless, these efforts have identified several approaches to minimize and prevent nanoparticle toxicity to promote safer nanotechnology. This review summarizes our current knowledge on nanoparticles, their toxic effects, their interactions with mammalian cells and finally current approaches to minimizing their toxicity.     

Real-time scene background initialization based on spatio-temporal neighborhood exploration

In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods.

     

SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.     

Liposomes Targeting P21 Activated Kinase-1 (PAK-1) and Selective for Secretory Phospholipase A2 (sPLA2) Decrease Cell Viability and Induce Apoptosis in Metastatic Triple-Negative Breast Cancer Cells

P21 activated kinases (or group I PAKs) are serine/threonine kinases whose expression is altered in prostate and breast cancers. PAK-1 activity is inhibited by the small molecule “Inhibitor targeting PAK-1 activation-3” (IPA-3), which has selectivity for PAK-1 but is metabolically unstable. Secretory Group IIA phospholipase A₂ (sPLA₂) expression correlates to increased metastasis and decreased survival in many cancers. We previously designed novel liposomal formulations targeting both PAK-1 and sPLA_2, called Secretory Phospholipase Responsive liposomes or SPRL-IPA-3, and demonstrated their ability to alter prostate cancer growth. The efficacy of SPRL against other types of cancers is not well understood. We addressed this limitation by determining the ability of SPRL to induce cell death in a diverse panel of cells representing different stages of breast cancer, including the invasive but non-metastatic MCF-7 cells, and metastatic triple-negative breast cancer (TNBC) cells such as MDA-MB-231, MDA-MB-468, and MDA-MB-435. We investigated the role of sPLA₂ in the disposition of these liposomes by comparing the efficacy of SPRL-IPA-3 to IPA-3 encapsulated in sterically stabilized liposomes (SSL-IPA-3), a formulation shown to be less sensitive to sPLA₂. Both SSL-IPA-3 and SPRL-IPA-3 induced time- and dose-dependent decreases in MTT staining in all cell lines tested, but SPRL-IPA-3-induced effects in metastatic TNBC cell lines were superior over SSL-IPA-3. The reduction in MTT staining induced by SPRL-IPA-3 correlated to the expression of Group IIA sPLA₂. sPLA₂ expression also correlated to increased induction of apoptosis in TNBC cell lines by SPRL-IPA-3. These data suggest that SPRL-IPA-3 is selective for metastatic TNBC cells and that the efficacy of SPRL-IPA-3 is mediated, in part, by the expression of Group IIA sPLA₂.     

Use of Electrocoagulation with Aluminum Electrodes to Reduce Hardness in Tunisian Phosphate Mining Process Water

This study evaluated electrocoagulation using Al electrodes for removal of non-carbonate hardness in phosphate mining process water. Examination of process parameters identified optimal conditions for hardness removal at pH 7, a NaCl concentration of 4 g/L, a current density of 22.2 mA/cm², an inter-electrode distance of 2 cm, a stirring speed of 450 revolutions per minute, and a treatment time of 30 min. Production of primary coagulant during electrolysis improved removal of both calcite and magnesium hydroxide. The optimized hardness removal was highly efficient, with an overall removal rate of 83.8 % at 30 min of retention time.     

Domain framework for implementation of open IoT ecosystems

The current Internet-of-things (IoT) hype, pushed by the unprecedented rate of the technological enablers’ innovation, is threatening to leave behind some major, not so obvious, unresolved issues. IoT platforms will extend existing enterprise information systems (EIS) infrastructures to encompass cross-domain sensing and actuating capabilities, thus introducing additional complexity and major risks to the implementation. Furthermore, IoT platforms are typically driven by models of the trivial complexity; they support very simple data structures and almost no business logic implementation. Finally, IoT systems are today managed centrally, which often means less openness, less flexibility and greater change management costs. In this article, we provide the overview of the scientific disciplines which could contribute to the resolution of the IoT implementation problem, namely requirements engineering, change management/continuous improvement, model-based systems engineering, system architecture design, interoperability and policy and regulatory aspects. Then, we identify the challenges of these contributions in the context of IoT and finally make an attempt to identify research directions which could have a significant impact. The discussion of the challenges and opportunities is illustrated by the proposed domain framework for implementation of open IoT ecosystems.     

Does Curcuma longa root powder have an effect against CCl4-induced hepatotoxicity in rats: a protective and curative approach

Food Science and Biotechnology - This study was conducted to investigate potentially protective and curative effects of Curcuma longa root (turmeric) powder on CCl4-induced hepatotoxicity in rats....     

Joint depth-motion dense estimation for multiview video coding

The multiview video coding (MVC) extension of H.264/MPEG-4 AVC [1] is one of the most promising visual encoders for three-dimensional television and free viewpoint video applications. In this paper, we propose a joint dense motion/disparity estimation algorithm, designed to replace the classical temporal/inter-view unit within MVC, which uses a block-based motion/disparity estimation. The motion vector fields and the disparity vector fields are therefore simultaneously derived using the stereo-motion consistency constraint in a set theoretic convex optimization framework. The obtained displacement vector fields are then jointly segmented by minimizing a rate-distortion cost function, in line with the multiple reference frame strategy used in H.264/MPEG-4 AVC. Experimental results demonstrate the benefits of the proposed method compared to the separated dense estimation scheme or the block-based estimation technique.