Effect of ratio of corn silage to grass-legume silage with high concentrate during dry period on milk production and health of dairy cows

Four corn silage: gross legume silage: concentrate totally mixed diets 1) 75:25:0; 2) 48.75:16.25:35; 3) 50:50:0; and 4) 32.5:32.5:5.35 were fed to Holsteins in second and third lactation for two complete lactations. Cows fed diets 1 or 2 during dry period were fed diet 2 during lactation. Diets did not affect length of dry period or lactation, calf weight, milk protein percent, or milk fat percent. Feeding concentrate during dry period increased gain from .63 to 1.11 kg/day. Feeding higher grass-legume silage diets 3 and 4 increased gain during lactation from .25 to .35 kg/day. On a mature equivalent basis, cows fed high corn silage diets 1 and 2 produced more milk (7,105 versus 6,663 kg), protein (236 versus 216 kg), fat (267 versus 245 kg), solids-not-fat (612 versus 564 kg), and a higher solids-not-fat percent (8.62 versus 8.50%) than diets 3 and 4. Diets did not alter health or reproduction. Production and weight gains favored feeding diet 1 during dry period and diet 2 during lactation.     

Long‐Range,Entangled Carbon Nanotube Networks in Polycarbonate

A method is presented for dispersing ropes or bundles of single‐walled carbon nanotubes (RCNTs) in a polycarbonate (PC) matrix. Films of PC/RCNT composites are produced, with thicknesses ranging from 10 to 60 μm, and containing small concentrations (0.06–0.25 wt.‐%) of RCNT. Our process is based on a unique method of hot casting, annealing, and drying from dichlorobenzene solution. A wet annealing prior to complete drying yields a uniform and transparent film. Despite the low RCNT loading, scanning electron microscopy (SEM) analysis of the films after fracture reveals that the RCNTs form an entangled network throughout the film, which is a key requirement for enhanced properties. An increase of up to 30 % in the Young's modulus, as compared to PC, results with this method of composite fabrication.     

Three-dimensional subsurface analysis of electromagnetic scatteringfrom penetrable/PEC objects buried under rough surfaces: use of thesteepest descent fast multipole method

The electromagnetic scattering from a three-dimensional (3D) shallow object buried under a two-dimensional (2D) random rough dielectric surface is analyzed. The buried object can be a perfect electric conductor (PEC) or can be a penetrable dielectric with size and burial depth comparable to the free-space wavelength. The random rough ground surface is characterized with Gaussian statistics for surface height and for surface autocorrelation function. The Poggio, Miller, Chang, Harrington, and Wu (PMCHW) integral equations are implemented and extended. The integral equation-based steepest descent fast multipole method (SDFMM), that was originally developed at UIUC, has been used and the computer code based on this algorithm has been successfully modified to handle the current application. The significant potential of the SDFMM code is that it calculates the unknown moment method surface electric and magnetic currents on the scatterer in a dramatically fast, efficient, and accurate manner. Interactions between the rough surface interface and the buried object are fully taken into account with this new formulation. Ten incident Gaussian beams with the same elevation angle and different azimuth angles are generated for excitation as one possible way of having multiple views of a given target. The scattered electric fields due to these ten incident beams are calculated in the near zone and their complex vector average over the multiple views is computed. The target signature is obtained by subtracting the electric fields scattered from the rough ground only from those scattered from the ground with the hurled anti-personnel mine     

Effect of added water, sodium erythorbate and storage time on the functional properties of prerigor beef preblends in a model system

Singular and combined effects of added water, sodium erythorbate and storage time on salt soluble protein extractability, bacteriological and chemical characteristics of preblended hot-boned beef were evaluated. Waterholding and gel forming capacities of preblended hot-boned beef containing either 0, 10 or 20% added water were determined. Significant interactions between added water level and storage time on microbial counts and between sodium erythorbate level and storage time on thiobarbituric acid values and residual nitrite levels were noted. The presence of sodium erythorbate resulted in a more (P < 0·05) rapid rate of nitrite disappearance, but it did not affect (P > 0·05) microbial counts. Salt-soluble protein extractability was not affected (P > 0·05) by added water, but more protein could be extracted with increasing storage time. A trend existed to suggest that the presence of added water in the meat preblends slightly improved the gel formation and waterholding capacities.     

Nonvolatile Memory Effect in Indium Gallium Arsenide-Based Metal–Oxide–Semiconductor Devices Using II–VI Tunnel Insulators

This paper reports the successful use of ZnSe/ZnS/ZnMgS/ZnS/ZnSe as a gate insulator stack for an InGaAs-based metal–oxide–semiconductor (MOS) device, and demonstrates the threshold voltage shift required in nonvolatile memory devices using a floating gate quantum dot layer. An InGaAs-based nonvolatile memory MOS device was fabricated using a high-κ II–VI tunnel insulator stack and self-assembled GeO _x -cladded Ge quantum dots as the charge storage units. A Si₃N₄ layer was used as the control gate insulator. Capacitance–voltage data showed that, after applying a positive voltage to the gate of a MOS device, charges were being stored in the quantum dots. This was shown by the shift in the flat-band/threshold voltage, simulating the write process of a nonvolatile memory device.     

A Distortion-Sensitive Seam Carving Algorithm for Content-Aware Image Resizing

As displays become less expensive and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed (Avidan and Shamir, ACM Trans Graphics 26(3), 2007; Guo et al., IEEE Trans Multimedia 11(5):856–867, 2009; Shamir and Avidan, Commun ACM 52(1), 2009; Simakov et al. 2008; Wolf et al. 2007), which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose a distortion-sensitive seam carving algorithm for content-aware image resizing that improves edge preservation and decreases aliasing artifacts. In the proposed approach, we use local gradient information along with a thresholding technique to guide the seam selection process and provide a mechanism to halt seam carving when further processing would introduce unacceptable visual distortion in the resized image. Furthermore, anti-aliasing filter is used to reduce the aliasing artifacts caused by seam removal. Experiments have demonstrated superior performance over the current seam carving methods.     

Compact digitally tunable laser

We present a new design for a digitally tunable laser using two waveguide grating routers with different free spectral range. For an N-channel device, both the number of required amplifiers and the number of required grating arms scale as the square root of N, resulting in a more compact device than previously demonstrated and better scaleability. Contrary to previously demonstrated devices with a reduced number of amplifiers, all wavelength channels are multiplexed together in one common channel, allowing for a higher output power. Tuning speeds below 10 ns are envisioned. Experimental results are presented.     

Principal component based diffeomorphic surface mapping

We present a new diffeomorphic surface mapping algorithm under the framework of large deformation diffeomorphic metric mapping (LDDMM). Unlike existing LDDMM approaches, this new algorithm reduces the complexity of the estimation of diffeomorphic transformations by incorporating a shape prior in which a nonlinear diffeomorphic shape space is represented by a linear space of initial momenta of diffeomorphic geodesic flows from a fixed template. In addition, for the first time, the diffeomorphic mapping is formulated within a decision-theoretic scheme based on Bayesian modeling in which an empirical shape prior is characterized by a low dimensional Gaussian distribution on initial momentum. This is achieved using principal component analysis (PCA) to construct the eigenspace of the initial momentum. A likelihood function is formulated as the conditional probability of observing surfaces given any particular value of the initial momentum, which is modeled as a random field of vector-valued measures characterizing the geometry of surfaces. We define the diffeomorphic mapping as a problem that maximizes a posterior distribution of the initial momentum given observable surfaces over the eigenspace of the initial momentum. We demonstrate the stability of the initial momentum eigenspace when altering training samples using a bootstrapping method. We then validate the mapping accuracy and show robustness to outliers whose shape variation is not incorporated into the shape prior.