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Forearm fractures are common injuries in childhood. Median nerve entrapment is a rare complication of forearm fractures, but several cases have been reported in the literature. This case report discusses the diagnosis and management of median nerve entrapment in a 13-yearold male who presented acutely with a both-bone forearm fracture and numbness in the median nerve distribution. Following the delayed diagnosis, surgical exploration revealed complete nerve entrapment and a nerve graft was performed.
In this paper, we present a virtual laboratory platform (VLP) baptized Mercury allowing students to make practical work (PW) on different aspects of mobile wireless sensor networks (WSNs). Our choice of WSNs is motivated mainly by the use of real experiments needed in most courses about WSNs. These experiments require an expensive investment and a lot of nodes in the classroom. To illustrate our study, we propose a course related to energy efficient and safe weighted clustering algorithm. This algorithm which is coupled with suitable routing protocols, aims to maintain stable clustering structure, to prevent most routing attacks on sensor networks, to guaranty energy saving in order to extend the lifespan of the network. It also offers a better performance in terms of the number of re-affiliations. The platform presented here aims at showing the feasibility, the flexibility and the reduced cost of such a realization. We demonstrate the performance of the proposed algorithms that contribute to the familiarization of the learners in the field of WSNs.
In this paper, we have simulated a rectangular microstrip patch antenna for aerospace applications based on graphen as a conductor and a multilayer substrate .as a result of the use of the graphen patch we obtained a reconfigurable antenna on the frequency range (0.6-0.7 terahertz) with a gain up to 12 db. The simulation of this antenna has been performed by using CST Microwave Studio, which is a commercially available finite integral based electromagnetic simulator.
This paper presents a nonlocal shear deformation beam theory for bending, buckling, and vibration of functionally graded (FG) nanobeams using the nonlocal differential constitutive relations of Eringen. The developed theory account for higher-order variation of transverse shear strain through the depth of the nanobeam, and satisfy the stress-free boundary conditions on the top and bottom surfaces of the nanobeam. A shear correction factor, therefore, is not required. In addition, this nonlocal nanobeam model incorporates the length scale parameter which can capture the small scale effect and it has strong similarities with Euler–Bernoulli beam model in some aspects such as equations of motion, boundary conditions, and stress resultant expressions. The material properties of the FG nanobeam are assumed to vary in the thickness direction. The equations of motion are derived from Hamilton’s principle. Analytical solutions are presented for a simply supported FG nanobeam, and the obtained results compare well with those predicted by the nonlocal Timoshenko beam theory.
<P>The performance degradation of graphite/Li<SUB>1.1</SUB>[Ni<SUB>1/3</SUB>Mn<SUB>1/3</SUB>Co<SUB>1/3</SUB>]<SUB>0.9</SUB>O<SUB>2</SUB> lithium-ion cells at elevated temperature was investigated. The electrochemical data suggest that the migration of dissolved transition metals from the cathode to the anode is the key contributor to the performance degradation. With the help of density function theory calculations, lithium difluoro[oxalato] borate was tested to be an effective electrolyte additive to mitigate the performance degradation of lithium-ion cells. The application of this novel electrolyte additive was found to significantly improve both the life and safety characteristics of graphite/Li<SUB>1.1</SUB>[Ni<SUB>1/3</SUB>Mn<SUB>1/3</SUB>Co<SUB>1/3</SUB>]<SUB>0.9</SUB>O<SUB>2</SUB> lithium-ion cells.</P> <P>Graphic Abstract</P><P>The performance degradation of graphite/Li<SUB>1.1</SUB>[Ni<SUB>1/3</SUB>Mn<SUB>1/3</SUB>Co<SUB>1/3</SUB>]<SUB>0.9</SUB>O<SUB>2</SUB> lithium-ion cells at elevated temperature was investigated. <IMG SRC='http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=c1jm11584g'> </P>
Mobile IPv6 is the current IETF standard for end host mobility management in the Internet. In order to provide a transparent location management, Mobile IPv6 operates in two different modes. In the first mode, mobile node incoming packets are tunneled to the node current location via the home network. In the second mode, traffic is exchanged directly between the mobile node and its communicating peers. In this paper, we evaluate the performance of these two modes on a real test bed. We first analytically model the Mobile IPv6 handover. Afterwards, we empirically assess its impact on transport protocols in general and more specifically on TCP CUBIC, the default TCP implementation in the current Linux kernel since version 2.6.19. We demonstrate that this TCP implementation induces high handover latency as it was not designed to be deployed in a dynamic environment.
<B>Graphic Abstract</B> <P>A new MSNP-LTO anode is developed to enable a high-power battery system that provides three times more power than any existing battery system. It shows excellent cycle life and low-temperature performance, and exhibits unmatched safety characteristics. <img src='wiley_img_2010/09359648-2010-22-28-ADMA201000441-content.gif' alt='wiley_img_2010/09359648-2010-22-28-ADMA201000441-content'> </P>
The detection, counting, and precise segmentation of white blood cells in cytological images are vital steps in the eff ectivediagnosis of several cancers. This paper introduces an effi cient method for automatic recognition of white blood cells inperipheral blood and bone marrow images based on deep learning to alleviate tedious tasks for hematologists in clinicalpractice. First, input image pre-processing was proposed before applying a deep neural network model adapted to cellslocalization and segmentation. Then, model outputs were improved by using combined predictions and corrections. Finally,a new algorithm that uses the cooperation between model results and spatial information was implemented to improve thesegmentation quality. To implement our model, python language, Tensorfl ow, and Keras libraries were used. The calculationswere executed using NVIDIA GPU 1080, while the datasets used in our experiments came from patients in the Hemobiologyservice of Tlemcen Hospital (Algeria). The results were promising and showed the effi ciency, power, and speed of theproposed method compared to the state-of-the-art methods. In addition to its accuracy of 95.73%, the proposed approachprovided fast predictions (less than 1 s).
A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MIMamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.