AHMED SHAKER

How unrealistic are the limitations and assumptions on which the management model are based, by nominate one module ( Baumol model and Pricing or Marris model on dividends and grow), i will examine the extent to the model’s value to modern management today.

There are few evidence-based recommendations on the most effective methods for diagnosing prosthetic joint infections (PJIs), and the potency of tests in relation to each other also remains vague. This systematic review aimed to (1) identify systematic reviews reporting accuracies of available approaches for diagnosing PJI, (2) critically appraise their quality and bias, and (3) compare the available approaches in terms of accuracy for diagnosing PJI. PubMed and EMBASE databases were searched for meta-analyses reporting accuracies of different diagnostic modalities for PJIs. Thirteen systematic reviews met the inclusion and exclusion criteria, and their data were extracted and tabulated by two reviewers in duplicate and independent manners. The 13 articles reported diagnostic accuracy from 278 clinical studies comprising 27,754 patients and evaluating 13 diagnostic tests grouped into 7 broad categories. Implant sonication had the highest positive likelihood ratio (17.2), followed by bacteriology (15.3) and synovial fluid differentiated cytology (13.3). The highest negative likelihood ratio was for interleukin (IL)-6 serum marker (0.03) followed by synovial fluid cytology and differentiation (0.12 and 0.13, respectively). The diagnostic tests that are most likely to rule out PJI include serum IL-6, serum C-reactive protein, and synovial fluid cytology. On the other hand, the diagnostic test that is most likely to confirm PJI is implant sonication. Nuclear imaging showed low overall accuracy as diagnostic tests for PJI. The findings of this study could enable clinicians to confirm or rule out PJIs using the most accurate, rapid, least invasive, and cost-effective tools available, thereby enabling fast treatment before formation of resistant biofilms and degradation of patient conditions. Systematic review, Level IV.

The herpes virus enters into latency after symptomatic or asymptomatic herpetic infection. During latency, the virus has no impact on infected cells. However, internal or external stimuli, including certain lasers, can induce virus reactivation. The aim was to study the reactivation power of the low-intensity diode and CO(2) lasers on the latent herpes virus. The bovine herpesvirus 1 (BHV-1) was inoculated in either the nasal cavity or the lacrimal film of an animal model. Once the virus entered into latency, the trigeminal ganglia of animals were exposed to either a low-intensity diode or CO(2) laser. The reactivation of the virus was then explored by PCR, RT-PCR, and dot-blot hybridization on nasal or lacrimal swabs. The accuracy, sensitivity, and specificity of the aforementioned techniques were compared. The low-intensity diode laser reactivated the herpes virus less than the CO(2) laser. The nasally inoculated virus was more liable for reactivation by both lasers. PCR was considered as the standard method for the detection of the reactivated virus. Low-intensity diode and CO(2) lasers can induce herpes virus reactivation, with the diode laser less likely to reactivate the virus than the CO(2) laser.

To evaluate the clinical efficacy, and safety of newly customized natural oral mucoadhesive gels, containing either aloe vera or myrrh as active ingredients, in the management of minor recurrent aphthous stomatitis (MiRAS). Ninety subjects with MiRAS were recruited from Oral Medicine Clinic, at Faculty of Dentistry, King Abdulaziz University, Saudi Arabia, for this randomized, double-blind, placebo-controlled study. Two new natural gels, containing aloe vera and myrrh, were prepared in a concentration of (0.5% w/w), in addition to a plain mucoadhesive gel used as a placebo. Patients with fresh ulcers (<48-h duration) were instructed to apply either one of the three gels four times a day for a period of 5 days. Clinical efficacy was investigated in the form of changes in ulcer size, pain intensity, erythema, and exudation at days 4 and 6 of study entry. Participants were interviewed for the emergence of any side effects. 76.6% of patients using aloe gel showed complete ulcer healing, 86.7%, and 80% of them revealed subsidence of erythema and exudation, respectively, especially at day 6 visit, whereas 76.7% of myrrh-treated patients revealed almost absence of pain at day 6. No side effects were encountered with the use of any of the three gels. The new formulated aloe- and myrrh-based gels proved to be effective in topical management of MiRAS. Aloe was superior in decreasing ulcer size, erythema, and exudation; whereas myrrh resulted in more pain reduction.

Abstract In this paper, we present a new and simple model for the band gap narrowing (BGN) that could be applied to any semiconductor material and implemented easily in device simulators. We here apply the model to Silicon. The model is based on treating the ...

In the absence of sensor calibration and satellite orbit information for Ikonos satellite imagery, empirical methods have to be adopted for the geometric correction of the images. This paper addresses two major terrain related issues. First, the paper focuses on the effects of ...

Image‐to‐image registration is a prerequisite step for many important applications in different disciplines. In the field of remote sensing, the launch of new high‐resolution satellites raises the need for a fast and dynamic technique for image‐to‐image registration ...

For various satellite imagery applications, geo-referencing through rectification is a common operation. Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the image space and object space. With government funded ...

City/regional authorities are responsible to design and structure the urban morphology based on the desired land-use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as Gross Domestic Product (GDP). In Canada, the gross national income mainly relies on mining and manufacturing industries. In order to facilitate new city development, this study aims to utilize remote sensing and GIS techniques to assess the relationship between the industrial area and the reported GDP in nine major cities in Canada. Free archive multi-temporal Landsat TM images and land use vector data were obtained for year 2005 and 2010 during the summer season, where the socio-economic data, such as GDP, population, and total employment are obtained from Metropolitan Housing Outlook for the same duration. The Landsat TM images were first atmospherically corrected and the built-up values were computed using the Normalized Difference Built-up Index (NDBI) a...

ABSTRACT In this paper the idea is to combine classifiers with different error types based on Fuzzy Majority Voting (FMV). Four study areas with different sensors and scene characteristics were used to assess the performance of the model. First, the lidar point clouds were filtered to generate a Digital Terrain Model (DTM), and then a Digital Surface Model (DSM) and the Normalized Digital Surface Model (nDSM) were generated. A total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar intensity image, DSM and nDSM. Three different classification algorithms were used to classify buildings, trees, roads and ground from aerial images, lidar data and the generated attributes. The used classifiers include: Self-Organizing Map (SOM); Classification Trees (CTs); and Support Vector Machines (SVMs) with average classification accuracies of 96.8%, 95.9% and 93.7% obtained for SVMs, SOM, and CTs respectively. FMV was then applied for combining the class memberships from the three classifiers. The main aim is to reduce overlapping regions of different classes for minimizing misclassification errors. The outcomes demonstrate that the overall accuracy as well as commission and omission errors have been improved compared to the best single classifier.

ABSTRACT This project aims to investigate the use of stereo high-resolution satellite imagery in retrieving highway 2D and 3D geometric parameters that facilitate different transportation applications. A stereo-pair of very high resolution (VHR) IKONOS satellite images are acquired for a number of highway intersections nearby the Pearson International Airport in Toronto. Measurements of the static global positioning system (GPS) are taken to establish ground control points (GCP) for satellite senor modeling, while kinematic GPS measurements are obtained by driving along the highways to assess the accuracy. The parameters of the highway alignments, such as ramp radius and vertical curvature, are extracted semi-automatically using the stereoscopic measurements. Finally, the results obtained from the high-resolution satellite imagery are compared to those derived from the kinematic GPS measurements in terms of root mean square (RMS) errors. Based on this study, several aspects related to the weakness and strength of the proposed method will be discussed with emphasizes on the potential use of satellite remote sensing techniques in transportation applications.

MOSFET-like CNTFETs suffer from band-to-band tunnelling, which in turn causes ambipolar conduction. A new structure based on the gate dielectric constant engineering is proposed. It is observed that the dielectric constant (k) plays an important role in modifying the energy band diagram. Therefore, by selecting suitable values of k over the source, channel and drain regions the tunnelling path at the source–channel interface could be eliminated. Consequently, the ambipolar conduction is suppressed.

In this paper, a coaxial gated SB-CNTFET with double gates heterojunction DGHJ is proposed. By using a non-equilibrium Green's function (NEGF) method, the transport characteristics of a DGHJ-SB-CNTFET is compared to those of conventional SB-CNTFET with homogenous channel. According to simulation results, DGHJ-SB-CNTFET demonstrates much less leakage tunnelling current, larger ON-OFF current ratio (I ON / I OFF), suppression of ambipolar characteristics and better switching parameters. According to these advantages, the proposed structure could be a suitable candidate for low-power and high speed applications. [M. Ossaimee, A. Shaker, M. El-Banna and M. Abouelatta. A computational study on electrical characteristics of a proposed double gate heterojunction SB-CNTFET. J Am Sci 2014;10(12):301-303]. (ISSN: 1545-1003). http://www.jofamericanscience.org. 38 1.Introduction: Carbon nanotube transistors have excellent electrical performance that is as good as or better than state-of-the...

ABSTRACT Radiometric correction of airborne LiDAR intensity data has been proposed based on the use of the radar (range) equation for removing the effects of attenuation due to system- and environmental-induced distortions. Although radiometric correction of airborne LiDAR intensity data has been recently investigated with results revealing improved accuracy of surface classification, there exist a few voids requiring further research effort. First, the variation of object surface characteristics (slope and aspect) plays a crucial role in modeling the recorded intensity data, and thus, the laser incidence angle is usually considered in the correction process. Nevertheless, the use of incidence angle would lead to the effects of overcorrection, particularly on those features located in steep slope. Second, line-stripping problems are usually appeared in the overlapping region of LiDAR data strips acquired by sensors configured with automatic gain control (AGC). Currently, the effects of AGC cannot be perfectly modeled due to the nondisclosure of information by the sensor manufacturers. In this paper, we attempt to fill these voids by: 1) proposing a correction mechanism using the surface slope as a threshold to select either using scan angle or incidence angle in the radar (range) equation; and 2) proposing a subhistogram matching technique to radiometrically normalize the overlapping intensity data. The proposed approaches were applied to three real airborne LiDAR data strips for experimental testing. The results showed that the coefficient of variation reached to the lowest value for most of the land-cover features with a slope threshold between 30$^{circ}$ and 40$^{circ}$. The variance-to-mean ratio of five land-cover features was significantly reduced by 70%–82% after applying the proposed correction mechanism. In addition, the systematic noises appeared in the overlapping region were significantly red- ced after radiometric correction and normalization, where the overall accuracies were improved by up to 16.5% in the results of intensity data classification. With the demonstrated improvement in intensity homogeneity, it is recommended that airborne LiDAR intensity data should be radiometrically preprocessed before performing any thematic applications.

ABSTRACT In this paper the idea is to combine classifiers with different error types based on Fuzzy Majority Voting (FMV). Four study areas with different sensors and scene characteristics were used to assess the performance of the model. First, the lidar point clouds were filtered to generate a Digital Terrain Model (DTM), and then a Digital Surface Model (DSM) and the Normalized Digital Surface Model (nDSM) were generated. A total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar intensity image, DSM and nDSM. Three different classification algorithms were used to classify buildings, trees, roads and ground from aerial images, lidar data and the generated attributes. The used classifiers include: Self-Organizing Map (SOM); Classification Trees (CTs); and Support Vector Machines (SVMs) with average classification accuracies of 96.8%, 95.9% and 93.7% obtained for SVMs, SOM, and CTs respectively. FMV was then applied for combining the class memberships from the three classifiers. The main aim is to reduce overlapping regions of different classes for minimizing misclassification errors. The outcomes demonstrate that the overall accuracy as well as commission and omission errors have been improved compared to the best single classifier.

ABSTRACT Distribution of land cover has a profound impact on the climate and environment; mapping the land cover patterns from global, regional to local scales are important for scientists and authorities to yield better monitoring of the changing world. Satellite remote sensing has been demonstrated as an efficient tool to monitor the land cover patterns for a large spatial extent. Nevertheless, the demand on land cover maps at a finer scale (especially in urban areas) has been raised with evidence by numerous biophysical and socio-economic studies. This paper reviews the small-footprint LiDAR sensor - one of the latest high resolution airborne remote sensing technologies, and its application on urban land cover classification. While most of the early researches focus on the analysis of geometric components of 3D LiDAR data point clouds, there has been an increasing interest in investigating the use of intensity data, waveform data and multi-sensor data to facilitate land cover classification and object recognition in urban environment. In this paper, the advancement of airborne LiDAR technology, including data configuration, feature spaces, classification techniques, and radiometric calibration/correction are reviewed and discussed. The review mainly focuses on the LiDAR studies conducted during the last decade with an emphasis on identification of the approach, analysis of pros and cons, investigating the overall accuracy of the technology, and how the classification results can serve as an input for different urban environmental analysis. Finally, several promising directions for future LiDAR research are highlighted, in hope that it will pave the way for the applications of urban environmental modeling and assessment at a finer scale and a greater extent.