Environmental impact of mining activity on the pollution of the Medjerda River, north-west Tunisia

 In the Fej Hcine mining area of north-west Tunisia, the Medjerda River and its principal tributary, El Melah, are polluted by heavy metals and arsenic. The geochemical studies undertaken have indicated pronounced falls in the concentrations of metals since the mining activities ceased, although the river sediments still contain higher levels than the local host rocks, particularly arsenic and zinc. At the Tunisia/Algeria border, arsenic concentrations in the Medjerda waters can exceed 1 mg/l. Leaching tests on samples of ore, flotation tailings and an arseniferous mass indicate that whilst Zn, Pb, Cu and Cd concentrations do not exceed 1 mg/l in the leachate solutions, arsenic levels reach 5, 344 and 9,400 mg/l respectively. The study has shown that the pollution originates not only from the mining activities but also from the mineral occurrences present in the mining district and from another source within Algeria. Received: 18 May 1998 · Accepted: 24 June 1998     

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.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

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.     

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.

     

A direct algebraic method for eigensolution sensitivity computation of damped asymmetric systems

In general, the derivative of an eigenvector of a vibrating symmetric system is the solution of a singular problem. Further complications are encountered in dealing with asymmetric damped systems for which the left and right eigenvectors and their derivatives become distinct and complex. Several approaches have been proposed to overcome this singularity such as Nelson's method and the modal method. In the present work, a new approach is presented for calculating simultaneously the derivatives of the eigenvalues and their associated derivatives of the left and right eigenvectors for asymmetric damped systems. With the proposed method, the exact eigenderivatives can be obtained by solving a first‐order linear algebraic system of equations. The method is applied on a 104 DOF ventilator–rotor system, which is used as an example of an asymmetric damped system with distinct eigenvalues. The diameter of the shaft has been chosen as the design parameter. The comparison of the computational time shows that the proposed method is more efficient than both Nelson's approach and the modal method. Copyright © 2006 John Wiley & Sons, Ltd.     

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.     

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.     

Physical layer security for cooperative NOMA networks via Nakagami-m fading channels

Telecommunication Systems - In this paper, we investigate the physical layer security in cooperative non-orthogonal multiple access (NOMA) networks over Nakagami-m fading channels. Both...