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A large theoretical charge storage capacity along with a low discharge working potential renders silicon a promising anode material for high energy density lithium ion batteries. However, up to 400% volume expansion during charge-discharge cycling coupled with a low intrinsic electronic conductivity causes pulverization and fracture, thus inhibiting silicon's widespread use in practical applications. We report herein on a low cost approach to fabricate hybrid silicon nanowire (SiNW)/graphene nanostructures that exhibit enhanced cycle performance with the capability of retaining more than 90% of their initial capacity after 50 cycles. We also demonstrate the use of hot-pressing in the absence of any common polymer binder such as PVDF to bind the hybrid structure to the current collector. The applied heat and pressure ensure strong adhesion between the SiNW/graphene nano-composite and current collector. This facile yet strong binding method is expected to find use in the further development of polymer-binder free anodes for lithium ion batteries.

Study Design. Analytical biomechanical study using a finiteelement (FE) model. Objective. We investigated the effects of paraspinal muscle volume to the physiological loading on the lower lumbar vertebral column using a FE model. Summary of Background Data. The FE model analysis can measure the physiological load on the lumbar vertebral column. Which changes as the surrounding environment changes. In this study, our FE model consisted of the sacrum, lumbar spine (L3-L5), intervertebral discs, facet joints, and paraspinal muscles. Methods. Three-dimensional FE models of healthy lumbar spinal units were reconstructed. The physiological loads exerted on the lumbar vertebra column were evaluated by applying different paraspinal muscle volumes (without muscles, 50%, 80%, and 100% of healthy muscle volume).Results. As the paraspinal muscle volume increased, the loads exerted on the vertebral column decreased. The mean load on the intervertebral disc was 1.42 AE 0.75 MPa in the model without muscle, 1.393 AE 0.73 MPa in the 50% muscle volume model, 1.367 AE 0.71 MPa in the 80% muscle volume model, and 1.362 AE 0.71 MPa in the 100% muscle volume model. The mean loads exerted on the posterior column of lumbar spine were 1 1 . 7 9 AE 4 . 7 0 M P a i n t h e m o d e l w i t h o u t m u s c l e s , 11.57 AE 4.57 MPa in the model with 50% muscle volume, and 11.13 AE 4.51 MPa in the model with 80% muscle volume, and 10.92 AE 4.33 MPa in the model with 100% muscle volume. The mean pressure on the vertebral body in the model without paraspinal muscle, and with 50%, 80%, and 100% paraspinal muscle volume were 14.02 AE 2.82, 13.82 AE 2.62, 13.65 AE 2.61, and 13.59 AE 2.51 MPa, respectively. Conclusion.Using FEM, we observed that the paraspinal muscle volume decreases pressure exerted on the lumbar vertebral column. Based on these results, we believe that exercising to increase paraspinal muscle volume would be helpful for spinal pain management and preventing lumbar spine degeneration.

Massive multiple-input multiple-output (MIMO) can improve the overall system performance significantly. Massive MIMO systems, however, may require a large number of radio frequency (RF) chains that could cause high cost and power consumption issues. One of promising approaches to resolve these issues is using low-resolution analog-to-digital converters (ADCs) at base stations. Channel estimation becomes a difficult task by using low-resolution ADCs though. This paper addresses the channel estimation problem for massive MIMO systems using one-bit ADCs when the channels are spatially and temporally correlated. Based on the Bussgang decomposition, which reformulates a non-linear one-bit quantization to a statistically equivalent linear operator, the Kalman filter is used to estimate the spatially and temporally correlated channel by assuming the quantized noise follows a Gaussian distribution. Numerical results show that the proposed technique can improve the channel estimation quality significantly by properly exploiting the spatial and temporal correlations of channels.

This paper considers the channel estimation problem for massive multiple-input multiple-output (MIMO) systems that use one-bit analog-to-digital converters (ADCs). Previous channel estimation techniques for massive MIMO using one-bit ADCs are all based on single-shot estimation without exploiting the inherent temporal correlation in wireless channels. In this paper, we propose an adaptive channel estimation technique taking the spatial and temporal correlations into account for massive MIMO with one-bit ADCs. We first use the Bussgang decomposition to linearize the one-bit quantized received signals. Then, we adopt the Kalman filter to estimate the spatially and temporally correlated channels. Since the quantization noise is not Gaussian, we assume the effective noise as a Gaussian noise with the same statistics to apply the Kalman filtering. We also implement the truncated polynomial expansion-based low complexity channel estimator with negligible performance loss. Numerical results reveal that the proposed channel estimators can improve the estimation accuracy significantly by using the spatial and temporal correlations of channels.