The Convention of the American Society of Agricultural Engineers

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On optimal inventory policies using Markovian structured linear programming techniques

The linear programming formulation to determine optimum inventory policies when the stochastic demand is Markovian is reviewed. Conventional linear programming models under stochastic demand result in convex objective functions which must be approximated by piecewise linear functions

Heat conductivity of DNA double helix

Thermal conductivity of isolated single molecule DNA fragments is of importance for nanotechnology, but has not yet been measured experimentally. Theoretical estimates based on simplified (1D) models predict anomalously high thermal conductivity. To investigate thermal properties of single molecule DNA we have developed a 3D coarse-grained (CG) model that retains the realism of the full all-atom description, but is significantly more efficient. Within the proposed model each nucleotide is represented by 6 particles or grains; the grains interact via effective potentials inferred from classical molecular dynamics (MD) trajectories based on a well-established all-atom potential function. Comparisons of 10 ns long MD trajectories between the CG and the corresponding all-atom model show similar root-mean-square deviations from the canonical B-form DNA, and similar structural fluctuations. At the same time, the CG model is 10 to 100 times faster depending on the length of the DNA fragment in the simulation. Analysis of dispersion curves derived from the CG model yields longitudinal sound velocity and torsional stiffness in close agreement with existing experiments. The computational efficiency of the CG model makes it possible to calculate thermal conductivity of a single DNA molecule not yet available experimentally. For a uniform (polyG-polyC) DNA, the estimated conductivity coefficient is 0.3 W/mK which is half the value of thermal conductivity for water. This result is in stark contrast with estimates of thermal conductivity for simplified, effectively 1D chains ("beads on a spring") that predict anomalous (infinite) thermal conductivity. Thus, full 3D character of DNA double-helix retained in the proposed model appears to be essential for describing its thermal properties at a single molecule level.Comment: 16 pages, 12 figure

A Preliminary Investigation of the Cr3Si-Mo Pseudo-Binary Phase Diagram

An investigation was undertaken to study the phase relations in Cr3Si alloyed with Mo varying from 10 to 83.5 wt. % of the material. Specimens were prepared from arc-melted buttons that were subsequently heat treated at 1673 K for 200 h and air quenched to room temperature to structures. Alloys containing more than 20 wt. % MO were primarily two-phase materials of M3Si and M5Si3, where M is (Cr,Mo). Three alloys contained less than 5% of a third phase, which also had the M5Si3 crystal structure. Differential thermal analysis (DTA) was performed on several specimens at temperatures up to 2073 K in order to determine a solidus curve for the M3Si phase. Since only one DTA peak was observed in each alloy, the M5Si3 phase must melt above 2073 K, the maximum temperature examined. A preliminary pseudo-binary phase diagram for (Cr,Mo)3Si and a portion of the 1673 K isothermal section of the Cr-Mo-Si ternary phase diagram are presented

Allocation in Practice

How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on Artificial Intelligence (KI 2014), Springer LNC

Peripheral Calcitonin Gene-Related Peptide Receptor Activation and Mechanical Sensitization of the Joint in Rat Models of Osteoarthritis Pain

OBJECTIVE: To investigate the role of the sensory neuropeptide calcitonin gene-related peptide (CGRP) in peripheral sensitization in experimental models of osteoarthritis (OA) pain. METHODS: Experimental knee OA was induced in rats by intraarticular injection of monosodium iodoacetate (MIA) or by transection of the medial meniscus (MMT). Single-unit recordings of joint-innervating nociceptors were obtained in MIA- and saline-treated rats following administration of CGRP or the CGRP receptor antagonist CGRP 8-37. Effects of CGRP 8-37 were also examined in rats that underwent MMT and sham operations. Protein and messenger RNA (mRNA) levels of CGRP receptor components in the L3-L4 dorsal root ganglion (DRG) were investigated following MIA treatment. RESULTS: In both the MIA and MMT groups, the mechanical sensitivity of joint nociceptors was enhanced compared to that in the control groups. Exogenous CGRP increased mechanical sensitivity in a greater proportion of joint nociceptors in the MIA-treated rats than in the saline-treated rats. Local blockade of endogenous CGRP by CGRP 8-37 reversed both the MIA- and MMT-induced enhancement of joint nociceptor responses. Joint afferent cell bodies coexpressed the receptor for CGRP, called the calcitonin-like receptor (CLR), and the intracellular accessory CGRP receptor component protein. MIA treatment increased the levels of mRNA for CLR in the L3-L4 DRG and the levels of CLR protein in medium and large joint afferent neurons. CONCLUSION: Our findings provide new and compelling evidence implicating a role of CGRP in peripheral sensitization in experimental OA. Our novel finding of CGRP-mediated control of joint nociceptor mechanosensitivity suggests that the CGRP receptor system may be an important target for the modulation of pain during OA. CGRP receptor antagonists recently developed for migraine pain should be investigated for their efficacy against pain in OA

SiC (SCS-6) Fiber Reinforced-Reaction Formed SiC Matrix Composites: Microstructure and Interfacial Properties

Microstructural and interfacial characterization of unidirectional SiC (SCS-6) fiber reinforced-reaction formed SiC (RFSC) composites has been carried out. Silicon-1.7 at.% molybdenum alloy was used as the melt infiltrant, instead of pure silicon, to reduce the activity of silicon in the melt as well as to reduce the amount of free silicon in the matrix. Electron microprobe analysis was used to evaluate the microstructure and phase distribution in these composites. The matrix is SiC with a bi-modal grain-size distribution and small amounts of MoSi2, silicon, and carbon. Fiber push-outs tests on these composites showed that a desirably low interfacial shear strength was achieved. The average debond shear stress at room temperature varied with specimen thickness from 29 to 64 MPa, with higher values observed for thinner specimens. Initial frictional sliding stresses showed little thickness dependence with values generally close to 30 MPa. Push-out test results showed very little change when the test temperature was increased to 800 C from room temperature, indicating an absence of significant residual stresses in the composite

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)