Zinc in potato production

August 1966.Covers not scanned.Includes bibliographical references.Zinc deficiencies have been reported from the states of Washington, Idaho, North Dakota and Texas. The deficiencies are reported to be localized in areas where the parent material of soil is low in zinc content or where the topsoil has been removed by landleveling or lost through erosion. There is evidence that zinc plays an important role in the synthesis of tryptophane, an amino acid that is necessary for the formation of the growth hormone, auxin. Other data indicate that zinc is essential for the metabolic activities of some enzymes. No difficulty has been reported in correcting the deficiency. Foliage and soil applications of zinc sulfate have given a yield response. Some workers indicate that a broadcast application and incorporation with a disc will produce a yield response where other methods will not. Foliage symptoms and response to method of application seems to differ with varieties

An Energy-Aware Protocol for Periodical Data Collection in Wireless Sensor Networks

In this paper we propose a protocol for periodical data collection applications in wireless sensor networks. The protocol is energy-aware in the sense that the way of energy consumption used here is evenly distributed. The protocol is chain-oriented and uses data fusion at every sensor node. Compare to other data collection protocols, this protocol shows better performance with respect to both latency and energy. It has been found that the proposed protocol outperforms PEGASIS with respect to latency in data delivery and performs better than that of LEACH with respect to energy. Furthermore, our protocol performs higher number rounds than that of PEGASIS in the case when the first node dies in the network. In a word the protocol shows an outstanding time-energy compromise

Biocompatibility of cross-linked hyaluronate (Gel-200) for the treatment of knee osteoarthritis

SummaryObjectiveTo compare the biocompatibility and immunogenicity of two intra-articular hyaluronan formulations, Gel-200 (Gel-One®) and hylan G-F 20 (Synvisc® series).Experimental designA comparison of the biocompatibility of Gel-200 and hylan G-F 20 was made using a rat subcutaneous air pouch model and the knee joint of normal rabbits. Immunogenicity was evaluated using a homologous passive cutaneous anaphylaxis (PCA) assay in guinea pigs.ResultsIn the air pouch model in rats, characteristic fibrous belts formed in the subcutaneous tissue. Injection of hylan G-F 20 into the air pouch induced granulomatous nodules primarily composed of macrophages, multinucleated giant cells, and eosinophils accompanied with the test material in the center of the nodules in the fibrous belt. Furthermore, the thickness of the fibrous belt in the hylan G-F 20 group increased significantly compared to the saline group. Injection of Gel-200 into the air pouch induced neither granulomatous inflammation nor significant thickening of fibrous belt, while foamy macrophages containing the test material were observed. Intra-articular injection of hylan G-F 20 into the rabbit knee joints induced granulomatous inflammation, eosinophil infiltration, and significant increase in the number of cells in the synovial fluid, while these findings were absent in the Gel-200 group. In the immunogenicity assay, hylan G-F 20 induced a positive PCA reaction, but the Gel-200 did not.ConclusionGel-200 showed more favorable biocompatibility and less immunogenicity compared to hylan G-F 20. Gel-200 is expected to be a single injection hyaluronan product with less safety concerns for the treatment of knee osteoarthritis (OA) pain

Thermal and Electrical Properties of gamma-NaxCoO2 (0.70 < x < 0.78)

We have performed specific heat and electric resistivity measurements of Na$_{x}$CoO$_{2}$ ($x=0.70$-0.78). Two anomalies have been observed in the specific heat data for $x=0.78$, corresponding to magnetic transitions at $T_{c}=22$ K and $T_{k}\simeq 9$ K reported previously. In the electrical resistivity, a steep decrease at $T_{c}$ and a bending-like variation at $T_{b}$(=120K for $x=0.78$) have been observed. Moreover, we have investigated the $x$-dependence of these parameters in detail. The physical properties of this system are very sensitive to $x$, and the inconsistent results of previous reports can be explained by a small difference in $x$. Furthermore, for a higher $x$ value, a phase separation into Na-rich and Na-poor domains occurs as we previously proposed, while for a lower $x$ value, from characteristic behaviors of the specific heat and the electrical resistivity at the low-temperature region, the system is expected to be in the vicinity of the magnetic instability which virtually exists below $x=0.70$.Comment: 4 pages (3 figures included) and an extra figure (gif), to be published in J. Phys. Soc. Jpn. 73 (9) with possible minor revision

Itinerant-Electron Magnet of the Pyrochlore Lattice: Indium-Doped YMn2Zn20

We report on a ternary intermetallic compound, "YMn2Zn20", comprising a pyrochlore lattice made of Mn atoms. A series of In-doped single crystals undergo no magnetic long-range order down to 0.4 K, in spite of the fact that the Mn atom carries a local magnetic moment at high temperatures, showing Curie-Weiss magnetism. However, In-rich crystals exhibit spin-glass transitions at approximately 10 K due to a disorder arising from the substitution, while, with decreasing In content, the spin-glass transition temperature is reduced to 1 K. Then, heat capacity divided by temperature approaches a large value of 280 mJ K-2 mol-1, suggesting a significantly large mass enhancement for conduction electrons. This heavy-fermion-like behavior is not induced by the Kondo effect as in ordinary f-electron compounds, but by an alternative mechanism related to the geometrical frustration on the pyrochlore lattice, as in (Y,Sc)Mn2 and LiV2O4, which may allow spin entropy to survive down to low temperatures and to couple with conduction electrons.Comment: 5 pages, 4 figures, J. Phys. Soc. Jpn., in pres

Cycle-to-cycle combustion variability modelling in spark ignited engines for control purposes

This is the author's version of a work that was accepted for publication in International Journal of Engine Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as https://doi.org/10.1177/1468087419885754.[EN] A control-oriented model of spark ignition combustion is presented. The model makes use of avaliable signals, such as spark advance, air mass, intake pressure, and lambda, to characterize not only the average combustion evolution but also the cycle-to-cycle variability. The conventional turbulent flame propagation model with two states, namely entrained mass and burnt mass, is improved by look-up tables at some parameters, and the cycle-to-cycle variability is estimated by propagation of an exogenous noise with a normal probabilistic distribution at the turbulent and laminar flame speed, which intends to simulate the unknowns at turbulent flow, temperature distribution, or initial kernel distribution. The model is able to estimate which is the expected variability during the combustion evolution and might be used online for characterizing the time response of closed-loop control actions or it can be used offline to improve the control strategies without large experimental test campaigns. Experimental data from a four-stroke commercial engine was used for calibration and validation purposes, demonstrating the capabilities of the model in steady and transient conditions.The authors appreciate the technical support and the clues given by J. Israel Sanchez for the model development and also acknowledge the support of Spanish Ministerio de Economia, Industria y Competitividad through project TRA2016-78717-R.Pla Moreno, B.; De La Morena, J.; Bares-Moreno, P.; Jimenez, IA. (2020). Cycle-to-cycle combustion variability modelling in spark ignited engines for control purposes. International Journal of Engine Research. 21(8):1398-1411. https://doi.org/10.1177/1468087419885754S13981411218Wang, S., Prucka, R., Zhu, Q., Prucka, M., & Dourra, H. (2016). A Real-Time Model for Spark Ignition Engine Combustion Phasing Prediction. SAE International Journal of Engines, 9(2), 1180-1190. doi:10.4271/2016-01-0819Kim, N., Ko, I., & Min, K. (2018). Development of a zero-dimensional turbulence model for a spark ignition engine. International Journal of Engine Research, 20(4), 441-451. doi:10.1177/1468087418760406Wang, S., Zhu, Q., Prucka, R., Prucka, M., & Dourra, H. (2015). Input Adaptation for Control Oriented Physics-Based SI Engine Combustion Models Based on Cylinder Pressure Feedback. SAE International Journal of Engines, 8(4), 1463-1471. doi:10.4271/2015-01-0877Zhen, X., Wang, Y., Xu, S., Zhu, Y., Tao, C., Xu, T., & Song, M. (2012). The engine knock analysis – An overview. Applied Energy, 92, 628-636. doi:10.1016/j.apenergy.2011.11.079Bares, P., Selmanaj, D., Guardiola, C., & Onder, C. (2018). Knock probability estimation through an in-cylinder temperature model with exogenous noise. Mechanical Systems and Signal Processing, 98, 756-769. doi:10.1016/j.ymssp.2017.05.033Zhang, Y., Shen, X., Wu, Y., & Shen, T. (2019). On-board knock probability map learning–based spark advance control for combustion engines. International Journal of Engine Research, 20(10), 1073-1088. doi:10.1177/1468087419858026Spelina, J. M., Peyton Jones, J. C., & Frey, J. (2014). Stochastic simulation and analysis of a classical knock controller. International Journal of Engine Research, 16(3), 461-473. doi:10.1177/1468087414551073Neumann, D., Jörg, C., Peschke, N., Schaub, J., & Schnorbus, T. (2017). Real-time capable simulation of diesel combustion processes for HiL applications. International Journal of Engine Research, 19(2), 214-229. doi:10.1177/1468087417726226Pipitone, E. (2008). A Comparison Between Combustion Phase Indicators for Optimal Spark Timing. Journal of Engineering for Gas Turbines and Power, 130(5). doi:10.1115/1.2939012Bares, P., Selmanaj, D., Guardiola, C., & Onder, C. (2018). A new knock event definition for knock detection and control optimization. Applied Thermal Engineering, 131, 80-88. doi:10.1016/j.applthermaleng.2017.11.138Peyton Jones, J. C., Spelina, J. M., & Frey, J. (2013). Optimizing knock thresholds for improved knock control. International Journal of Engine Research, 15(1), 123-132. doi:10.1177/1468087413482321Emiliano, P. (2014). Spark Ignition Feedback Control by Means of Combustion Phase Indicators on Steady and Transient Operation. Journal of Dynamic Systems, Measurement, and Control, 136(5). doi:10.1115/1.4026966Zhu, Q., Prucka, R., Wang, S., Prucka, M., & Dourra, H. (2016). Model-Based Optimal Combustion Phasing Control Strategy for Spark Ignition Engines. SAE International Journal of Engines, 9(2), 1170-1179. doi:10.4271/2016-01-0818Zhang, Y., & Shen, T. (2017). Cylinder pressure based combustion phase optimization and control in spark-ignited engines. Control Theory and Technology, 15(2), 83-91. doi:10.1007/s11768-017-6175-1Zhang, Y., Shen, X., & Shen, T. (2018). A survey on online learning and optimization for spark advance control of SI engines. Science China Information Sciences, 61(7). doi:10.1007/s11432-017-9377-7Corti, E., Forte, C., Mancini, G., & Moro, D. (2014). Automatic Combustion Phase Calibration With Extremum Seeking Approach. Journal of Engineering for Gas Turbines and Power, 136(9). doi:10.1115/1.4027188Corti, E., Cerofolini, A., Cavina, N., Forte, C., Mancini, G., Moro, D., … Ravaglioli, V. (2014). Automatic Calibration of Control Parameters based on Merit Function Spectral Analysis. Energy Procedia, 45, 919-928. doi:10.1016/j.egypro.2014.01.097Popovic, D., Jankovic, M., Magner, S., & Teel, A. R. (2006). Extremum seeking methods for optimization of variable cam timing engine operation. IEEE Transactions on Control Systems Technology, 14(3), 398-407. doi:10.1109/tcst.2005.863660Hellstrom, E., Lee, D., Jiang, L., Stefanopoulou, A. G., & Yilmaz, H. (2013). On-Board Calibration of Spark Timing by Extremum Seeking for Flex-Fuel Engines. IEEE Transactions on Control Systems Technology, 21(6), 2273-2279. doi:10.1109/tcst.2012.2236093Pera, C., Chevillard, S., & Reveillon, J. (2013). Effects of residual burnt gas heterogeneity on early flame propagation and on cyclic variability in spark-ignited engines. Combustion and Flame, 160(6), 1020-1032. doi:10.1016/j.combustflame.2013.01.009Zhao, L., Moiz, A. A., Som, S., Fogla, N., Bybee, M., Wahiduzzaman, S., … Kodavasal, J. (2017). Examining the role of flame topologies and in-cylinder flow fields on cyclic variability in spark-ignited engines using large-eddy simulation. International Journal of Engine Research, 19(8), 886-904. doi:10.1177/1468087417732447Pera, C., Knop, V., & Reveillon, J. (2015). Influence of flow and ignition fluctuations on cycle-to-cycle variations in early flame kernel growth. Proceedings of the Combustion Institute, 35(3), 2897-2905. doi:10.1016/j.proci.2014.07.037Schiffmann, P., Reuss, D. L., & Sick, V. (2017). Empirical investigation of spark-ignited flame-initiation cycle-to-cycle variability in a homogeneous charge reciprocating engine. International Journal of Engine Research, 19(5), 491-508. doi:10.1177/1468087417720558Galloni, E. (2009). Analyses about parameters that affect cyclic variation in a spark ignition engine. Applied Thermal Engineering, 29(5-6), 1131-1137. doi:10.1016/j.applthermaleng.2008.06.001Tamaki, S., Sakayanagi, Y., Sekiguchi, K., Ibuki, T., Tahara, K., & Sampei, M. (2014). On-line Feedforward Map Generation for Engine Ignition Timing Control. IFAC Proceedings Volumes, 47(3), 5691-5696. doi:10.3182/20140824-6-za-1003.01886Zhang, Y., & Shen, T. (2018). Combustion Variation Feedback Control Approach for Multi-cylinder Spark Ignition Engines. IFAC-PapersOnLine, 51(31), 105-110. doi:10.1016/j.ifacol.2018.10.020Corti, E., & Forte, C. (2011). Spark Advance Real-Time Optimization Based on Combustion Analysis. Journal of Engineering for Gas Turbines and Power, 133(9). doi:10.1115/1.4002919Gao, J., Wu, Y., & Shen, T. (2016). Experimental comparisons of hypothesis test and moving average based combustion phase controllers. 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Non-generality of the Kadowaki-Woods ratio in correlated oxides

An explicit expression for the Kadowaki-Woods ratio in correlated metals is derived by invoking saturation of the (high-frequency) Fermi-liquid scattering rate at the Mott-Ioffe-Regel limit. Significant deviations observed in a number of oxides are quantitatively explained due to variations in carrier density, dimensionality, unit cell volume and the number of individual sheets in the Brillouin zone. A generic re-scaling of the original Kadowaki-Woods plot is also presented.Comment: 9 pages of text, 1 table, 2 figure

Effect of toroidal field ripple on plasma rotation in JET

Dedicated experiments on TF ripple effects on the performance of tokamak plasmas have been carried out at JET. The TF ripple was found to have a profound effect on the plasma rotation. The central Mach number, M, defined as the ratio of the rotation velocity and the thermal velocity, was found to drop as a function of TF ripple amplitude (3) from an average value of M = 0.40-0.55 for operations at the standard JET ripple of 6 = 0.08% to M = 0.25-0.40 for 6 = 0.5% and M = 0.1-0.3 for delta = 1%. TF ripple effects should be considered when estimating the plasma rotation in ITER. With standard co-current injection of neutral beam injection (NBI), plasmas were found to rotate in the co-current direction. However, for higher TF ripple amplitudes (delta similar to 1%) an area of counter rotation developed at the edge of the plasma, while the core kept its co-rotation. The edge counter rotation was found to depend, besides on the TF ripple amplitude, on the edge temperature. The observed reduction of toroidal plasma rotation with increasing TF ripple could partly be explained by TF ripple induced losses of energetic ions, injected by NBI. However, the calculated torque due to these losses was insufficient to explain the observed counter rotation and its scaling with edge parameters. It is suggested that additional TF ripple induced losses of thermal ions contribute to this effect