Quantum Mechanics of Spin Transfer in Coupled Electron-Spin Chains

The manner in which spin-polarized electrons interact with a magnetized thin film is currently described by a semi-classical approach. This in turn provides our present understanding of the spin transfer, or spin torque phenomenon. However, spin is an intrinsically quantum-mechanical quantity. Here, we make the first strides towards a fully quantum-mechanical description of spin transfer through spin currents interacting with a Heisenberg-coupled spin chain. Because of quantum entanglement, this requires a formalism based on the density matrix approach. Our description illustrates how individual spins in the chain time-evolve as a result of spin transfer

Evapotranspiration Prediction Using M5T Data Mining Method

Evapotranspiration (ET) estimation takes an important role in hydraulic designs and irrigation management. Even these imperative importance ET estimation methods are not clear and easily employable enough. This study focused on M5T data mining method to estimate ET due this method is in use for nonlinear physical cases. 1543 daily Solar Radiation (SR), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (U) meteorological parameters are used to create a M5T model. 1153 daily data is used for training the model and 385 left data is used for testing model results. Data set is taken from St. Johns, Florida, USA weather station.The correlation coefficient (R) is calculated as 0.983 for the M5T. Model results are compared with Turc empirical formula and it is found that M5T data mining method has better performance than Turc empirical formula

Stopping Light All-Optically

We show that light pulses can be stopped and stored all-optically, with a process that involves an adiabatic and reversible pulse bandwidth compression occurring entirely in the optical domain. Such a process overcomes the fundamental bandwidth-delay constraint in optics, and can generate arbitrarily small group velocities for light pulses with a given bandwidth, without the use of any coherent or resonant light-matter interactions. We exhibit this process in optical resonator systems, where the pulse bandwidth compression is accomplished only by small refractive index modulations performed at moderate speeds. (Accepted for publication in Phys. Rev. Lett. Submitted on Sept. 10th 2003)Comment: 18 pages including 3 figures. Accepted for publication in Phys. Rev. Let

10 x 112Gb/s PDM-QPSK transmission over 5032 km in few-mode fibers

Few-mode fibers (FMFs) are used for the first time to transmit over 5000 km. Ten WDM channels with 50GHz channel spacing at 112 Gb/s per channel using PDM-QPSK are launched into the fundamental mode of the FMFs by splicing single-mode fibers directly to the FMFs. Even though few-mode fibers can support an additional spatial mode LP11 at 1550 nm, the signal remains in the fundamental mode and does not experience mode coupling throughout fiber transmission. After each span the signal is collected by a second single-mode fiber which is also spliced to the FMF. Span loss is compensated by single-mode EDFAs before it is launched to the next FMF span. The lack of mode coupling ensures that the signal does not suffer any impairments that may result from differential mode delay or excess loss. Therefore the FMFs used in this single-mode operation have the same bandwidth as single-mode fibers. Experimental results verified that FMFs have the significant advantage of large core size which reduces the nonlinear impairments suffered by the signal. It is shown that FMFs with an effective area of 130 mu m(2), have an optimum launch power 2 dB higher compared to standard single-mode fibers and as a result a 1.1 dB improvement in the Q-factor is obtained after 3000 km

Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach

Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods