A Survey on Advanced Multiple Access Techniques for 5G and Beyond Wireless Communications

Wireless Personal Communications - This paper summarizes the ongoing research initiatives based on the advanced multiple access techniques towards the fifth generation (5G) wireless communication...     

CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

An adaptive descent method for non-linear viscoplasticity

A forming model based on a viscoplastic flow formulation is derived which includes the effects of small elastic strains. A significant feature of the formulation is its reliance on the dominant inelastic material characteristics in the formation of the stiffness matrix for large strain problems. The resultant non-linear system of equations is solved by an adaptive descent method which combines the rapid convergence of Newton's method near the solution with the robustness of a method of successive approximations. The use of the adaptive descent method effectively extends the viscoplastic flow formulations into the nearly rate-insensitive range of behaviours exhibited, for example, by metals at low temperature, where slow convergence of the non-linear solution algorithm has traditionally hampered their use.     

Crystallization of amorphous Ni60 Nb40−x Cr x alloys

Crystallization behaviour of amorphous Ni₆₀ Nb_40-x Cr _x (x = 0, 5, 10 and 13 at%) alloys was studied by differential Scanning calorimetry and X-ray diffraction measurements. It is shown that the addition of chromium reduces the crystallization temperature, stages of crystallization and activation energies associated with the crystallization sages of the Ni₆₀Nb₄₀ glass. Crystallization of the Ni₆₀Nb₄₀ glass occurred in three stages; in the initial stage a metastable M-phase formed in the amorphous matrix as reported earlier [1] . However, contrary to earlier observation [1], M -phase was not very stable and transformed together with some amorphous phase to the equilibrium Ni₃Nb phase in the second stage of crystallization. In the third stage, the remaining amorphous matrix transformed to the equilibrium NiNb phase. On addition of chromium the formation/stability of the M-phase was found to be suppressed and equilibrium NbCr₂ phase precipitated preferentially in the first stage. The second stage, corresponding to the formation of Ni₃Nb phase, remained almost unaltered. The third stage corresponding to the crystallization of NiNb phase disappeared completely at 13 at% Cr. In the fully crystallized samples the proportion of the NiNb phase decreased and that of NbCr₂ phase increased continuously with chromium concentration.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.     

A behavioral medicine intervention in persons with HIV

This study used a pretest-posttest preexperimental design to examine the effect of a 10-week behavioral medicine support group intervention in a sample of persons with HIV. Using Solomon's psychoneuroimmunologic framework, the 10-week behavioral medicine program focused on the mind/body interaction, the relaxation response, coping with illness, hardiness, and nutrition. Pearson correlation coefficients and t tests were performed on the pre- and postintervention measures of hardiness, social support, immune function, and perceived health status. Results of the study indicated that hardiness (preintervention) and CD4 counts (pre- and postintervention) were significantly correlated with health status; however, CD4 counts decreased over the course of the behavioral medicine program. Implications for nursing and recommendations for further research are discussed.