A computer program for randomizing patients with near-even distribution of important parameters.

In clinical trials, the methods for achieving an event or near-even distribution of important patient parameters have been tedious and often disappointing. In this paper, a computer program for minimizing differences between experimental groups with respect to such important parameters, based on a method described by Traves (Clin. Pharmacol. Ther. London 15, 443, 1974) is presented. The program is available from the author.     

Ultra-broadband reflectionless Brewster absorber protected by reciprocity

The Brewster's law predicts zero reflection of p-polarization on a dielectric surface at a particular angle. However, when loss is introduced into the permittiv...     

Comparison of interwell connectivity predictions using percolation, geometrical, and Monte Carlo models

Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity controls waterflood sweep efficiency and it affects decisions concerning well placement and spacing. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior to estimate interwell connectivity.This study applies a percolation method to estimate interwell connectivity. Using results derived by Andrade, King, and others for fluid travel time between locations in a percolation model, we develop a method to estimate interwell connectivity. Four parameters are needed to use this approach: the net-to-gross ratio p_sand, the typical sandbody size, reservoir length and well spacing. To evaluate this new percolation method, the results are compared to results from geometrical models, Monte Carlo, and reservoir simulation.These methods were applied to estimate interwell connectivity for three non-communicating stratigraphic intervals in Monument Butte oil field, Utah. The results suggest that the percolation method can estimate the probability of interwell connectivity reliably for thin intervals for any values of p_sand, well spacing, and reservoir length. The geometrical model also performs well, but can only be applied in fields where the well spacing is less than one-half of the sandbody size.The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new method to 3D cases.     

Synthesis and thermal decomposition of potassium tetraamidoboranealuminate,K[Al(NH2BH3)4]

A new potassium tetraamidoboranealuminate, K[Al(NH₂BH₃)₄], has been synthesized by a mechanochemical reaction between KAlH₄ and NH₃BH₃. The compound, K[Al(NH₂BH₃)₄], crystallizes in a triclinic unit cell with space group symmetry P?1. The crystal structure consists of [K(NH₂BH₃)₆]^5? octahedra which facilitate the bridging between K⁺ in 1D chains, while also bridging K⁺ to Al³⁺ to connect the 1D chains in a 3D network. Thermal analysis reveals that K[Al(NH₂BH₃)₄] decomposes in two exothermic steps at T ～ 94 and 138 °C and releases primarily hydrogen. The total gas release amounts to ～6.0 wt% H₂. The decomposition products are investigated ex situ by powder X-ray diffraction, infrared spectroscopy, and ¹¹B and ²⁷Al NMR and identified as KBH₄ and amorphous phases, possibly BN₃, N₂BH, and/or NBH₂ whereas aluminum is found in four-, five-, and six-fold coordination. Unfortunately, the decomposed sample shows no hydrogen absorption at T = 260 °C and p(H₂) = 110 bar.     

Dynamic forecasting of individual cow milk yield in automatic milking systems

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.     

A multiscale approach to network event identification using geolocated twitter data

The large volume of data associated with social networks hinders the unaided user from interpreting network content in real time. This problem is compounded by the fact that there are limited tools available for enabling robust visual social network exploration. We present a network activity visualization using a novel aggregation glyph called the clyph. The clyph intuitively combines spatial, temporal, and quantity data about multiple network events. We also present several case studies where major network events were easily identified using clyphs, establishing them as a powerful aid for network users and owners.     

Effect of initial joint position on nerve-cuff recordings of muscleafferents in rabbits

The objective was to characterize nerve-cuff recordings of muscle afferents to joint rotation over a large part of the physiological joint range. This information is needed to develop control strategies for functional electrical stimulation (FES) systems using muscle afferent signals for sensory feedback. Five acute rabbit experiments were performed. Tripolar cuff electrodes were implanted around the tibial and peroneal divisions of the sciatic nerve in the rabbit's left leg. The electroneurograms (ENG) were recorded during passive ankle rotation, using a ramp-and-hold profile starting at seven different joint positions (excursion=5°, velocity=10°/s, initial positions 60°, 70°, 80°, 90°, 100°, 110°, and 120°). The amplitude of the afferent activity was dependent on the initial joint position. The steady-state sensitivity of both nerve responses increased with increasing joint flexion, whereas the dynamic sensitivity increased initially but then decreased. The results indicate that recordings of the muscle afferents may provide reliable information over only a part of the physiological joint range, Despite this limitation, muscle afferent activity may be useful for motion feedback if the movement to be controlled is within a narrow joint range such as postural sway     

Energy demand in the Norwegian building stock: Scenarios on potential reduction

A model has been developed for studying the effect of three hypothetical approaches in reducing electricity and energy demand in the Norwegian building stock: wide diffusion of thermal carriers, heat pumps and conservation measures, respectively. Combinations of these are also considered. The model has a demand side perspective, considers both residential and service sectors, and calculates energy flows from net to delivered energy. Energy demand is given by the product of activity and intensity matrices. The activity levels are defined for the stock and the new construction, renovation and demolition flows. The intensity properties are defined in archetypes, and are the result of different energy class and heating carriers share options. The scenarios are shaped by combining the activity flows with different archetypes. The results show that adopting conservation measures on a large scale does allow reducing both electricity and total energy demand from present day levels while the building stock keeps growing. The results also highlight the importance of making a clear distinction between the assumptions on intensity and activity levels.     

Measuring spatial dispersion: exact results on the variance of random spatial distributions

Measuring the spatial distribution of locations of many entities (trees, atoms, economic activities, etc.), and, more precisely, the deviations from purely random configurations, is a powerful method to unravel their underlying interactions. Several coefficients have been developed in the past to quantify the possible deviations. It is important to quantify the variances of the coefficients for random distributions, to ascertain the statistical significance of an empirical deviation. By lack of a proper analytical expression, the significance is usually obtained by simulating many random configurations by Monte Carlo simulations. In the present paper, we present an exact analytical expression for the variance of several spatial coefficients for random distributions, and we rigorously show that these distributions asymptotically follow a Normal law. These two results eliminate the need for cumbersome Monte Carlo simulations. They also allow to understand qualitatively the main factors that may change the variance: number of sites, spatial inhomogeneity, etc.