TCP CERL+: revisiting TCP congestion control in wireless networks with random loss

Wireless Networks - In this paper, we analyze the performance of wireless networks subject to random loss. In this regard, we revisit our TCP Congestion Control Enhancement for Random Loss (CERL)...     

Wall Rock Alteration,Uranium Mineralization,and Potentiality of Uranium and Iron Dissolution from Mineralized Abu Rusheid Gneiss Rock,South Eastern Desert,Egypt

Radiochemistry - Ferrugination, sericitization, desulfitization, chloritization, and kaolinitization of hydrothermal origin are the main wall rock alterations occurring within the NNW shear zones...     

Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

Academic faculty practices: issues for viability in competitive managed care markets

This study compares the perspectives of eighteen managed care executives and twenty-four faculty practice executives on critical policy issues related to the managed care marketplace. Market sites studied in 1994 included four major metropolitan areas: Minneapolis-St. Paul, Los Angeles, Philadelphia, and Atlanta. These markets were selected as being representative of communities with descending degrees of managed care involvement, but with significant market activity. Study participants from both managed care systems and faculty practices examined five policy issues: (1) the importance of including academic medical centers in current and future health care plans for marketing purposes; (2) the provision of clinical services that are unique to the academic medical center, that is, unavailable elsewhere in the community; (3) the degree of financial supplement that employers might pay for including an academic medical center; (4) future restructuring of organizations to sustain the educational mission of academic faculty within a viable delivery system; (5) satisfaction of managed care providers with graduates of academic medical centers, as measured by the clinical skills of graduate physicians. The study findings showed little support among managed care plans for paying supplements to include faculty practices in a health care network. Most study participants from managed care systems and academic faculty practices identified limited competencies that are unique to academic centers. Moreover, managed care organizations were only willing to undertake limited restructuring at best to include faculty practices within their networks. General concern about the preparation of resident physicians (especially those in primary care disciplines) for practice within contemporary managed care organizations existed among managed care informants. The results of the study indicate that as traditional funding sources for medical education are reduced, schools require greater integration with managed care plans to enable academic medical centers and their faculties to continue promoting clinical enterprise.     

The CD40-CD154 system in anti-infective host defense

Research in the past few years has documented significant advances in our understanding of the CD40-CD40 ligand (CD154) system in diverse immune functions. This system influences many T cell mediated inflammatory immune responses and effector functions, unmasking a previously unexpected role for CD40-CD154 in cell mediated immunity. Manipulation of CD154 in animal models of infection by the use of CD154-deficient mice or anti-CD154 antibodies has shown the importance of this system in the initiation of the inflammatory response, in the activation of antigen-presenting cells and in resistance to infections.     

Tuning of MST neurons to spiral motions

Cells in the dorsal division of the medial superior temporal area (MSTd) have large receptive fields and respond to expansion/contraction, rotation, and translation motions. These same motions are generated as we move through the environment, leading investigators to suggest that area MSTd analyzes the optical flow. One influential idea suggests that navigation is achieved by decomposing the optical flow into the separate and discrete channels mentioned above, that is, expansion/contraction, rotation, and translation. We directly tested whether MSTd neurons perform such a decomposition by examining whether there are cells that are preferentially tuned to intermediate spiral motions, which combine both expansion/contraction and rotation components. The finding that many cells in MSTd are preferentially selective for spiral motions indicates that this simple three-channel decomposition hypothesis for MSTd does not appear to be correct. Instead, there is a continuum of patterns to which MSTd cells are selective. In addition, we find that MSTd cells maintain their selectivity when stimuli are moved to different locations in their large receptive fields. This position invariance indicates that MSTd cells selective for expansion cannot give precise information about the retinal location of the focus of expansion. Thus, individual MSTd neurons cannot code, in a precise fashion, the direction of heading by using the location of the focus of expansion. The only way this navigational information could be accurately derived from MSTd is through the use of a coarse, population encoding. Positional invariance and selectivity for a wide array of stimuli suggest that MSTd neurons encode patterns of motion per se, regardless of whether these motions are generated by moving objects or by motion induced by observer locomotion.     

Fuzzy Learning of Talbot Effect Guides Optimal Mask Design for Proximity Field Nanopatterning Lithography

Processing methods used in photonics and nanotechnology possess many limitations restricting their application areas such as high cost, inability to produce fine details, problems with scalability, and long processing time. Proximity field nanopatterning is a lithography method which surpasses these limitations. By using interference patterns produced by a two-dimensional phase mask, the technique is able to generate a submicron detailed exposure on a millimeter-size slab of light sensitive photopolymer, which is then developed like a photographic plate to reveal three-dimensional interference patterns from the phase mask. While it is possible to use simulations to obtain the interference patterns produced by a phase mask, realizing the mask dimensions necessary for producing a desired interference pattern is analytically challenging due to the intricacies of light interactions involved in producing the final interference pattern. An alternative method is to iteratively optimize the phase mask until the interference patterns obtained converge to the desired pattern. However, depending on the optimization technique used, one either risks a significant probability of failure or requires a prohibitive number of iterations. We argue that an optimization technique that is to take advantage of the physics of the problem using machine learning methods (here fuzzy learning) can lead to competent mask design. This technique is described in this letter.