Research in Crops and Soils: A Progress Report

The Experiment Station Agronomy Farm, located 1 mile east of Brookings, is representative of a large area of land in eastern South Dakota. It consists of 160 acres, 150 of which are laid out in various soil and crop experiments. The soil, commonly called loam and classified as Barnes Loam, is in a good state of fertility. Results of the experiments on this farm will indicate what may be expected from similar soil management, cropping systems, and crop varieties on the same type of soil and under comparable climatic conditions. Numerous experiments are in progress on this farm. The information in this circular is a progress report on those experiments for which results can now be evaluated. Further results will be published at intervals as the experiments progress

Progress Report of Research in Crops and Soils

The Experiment Station Agronomy Farm, located one mile east of Brookings, is representative of a large area of land in eastern South Dakota. It consists of 160 acres, of which about 130 acres are now laid out in various soil and crop experiments. The soil, commonly called loam and classified as Barnes Loam, is in a good state of fertility. Results of the experiments on this farm will indicate closely what may be expected from similar soil management, cropping systems and crop varieties on the same type of soil and under comparable climatic conditions. Numerous experiments are now in progress on this farm. The information given in this circular represents a progress report on only those experiments for which results can now be evaluated. Further results will be published at intervals as the experiments progress

Efficient Bayesian inference for natural time series using ARFIMA processes

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory

Exploring the relationship between EMG feature space characteristics and control performance in machine learning myoelectric control

In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It has been suggested that the increase in performance originates from changes in characteristics of the Electromyography (EMG) patterns, such as separability or repeatability. However, the relation between these EMG metrics and control performance has hardly been studied. We assessed the relation between three common EMG feature space metrics (separability, variability and repeatability) in 20 able bodied participants who learned ML myoelectric control in a virtual task over 15 training blocks on 5 days. We assessed the change in offline and real-time performance, as well as the change of each EMG metric over the training. Subsequently, we assessed the relation between individual EMG metrics and offline and real-time performance via correlation analysis. Last, we tried to predict real-time performance from all EMG metrics via L2-regularized linear regression. Results showed that real-time performance improved with training, but there was no change in offline performance or in any of the EMG metrics. Furthermore, we only found a very low correlation between separability and real-time performance and no correlation between any other EMG metric and real-time performance. Finally, real-time performance could not be successfully predicted from all EMG metrics employing L2-regularized linear regression. We concluded that the three EMG metrics and real-time performance appear to be unrelated

Present and Future Experiments with Stored Exotic Nuclei at Relativistic Energies

Recent progress is presented from experiments on masses and lifetimes of bare and few-electron exotic nuclei at GSI.Comment: Proceedings of International Conference on "Frontiers in Nuclear Structure, Astrophysics and Reactions", Kos, Greece, September 12-17, 200

Direct observation of long-lived isomers in 212^{212}Bi

Long-lived isomers in 212Bi have been studied following 238U projectile fragmentation at 670 MeV per nucleon. The fragmentation products were injected as highly charged ions into the GSI storage ring, giving access to masses and half-lives. While the excitation energy of the first isomer of 212Bi was confirmed, the second isomer was observed at 1478(30) keV, in contrast to the previously accepted value of >1910 keV. It was also found to have an extended Lorentz-corrected in-ring halflife >30 min, compared to 7.0(3) min for the neutral atom. Both the energy and half-life differences can be understood as being due a substantial, though previously unrecognised, internal decay branch for neutral atoms. Earlier shell-model calculations are now found to give good agreement with the isomer excitation energy. Furthermore, these and new calculations predict the existence of states at slightly higher energy that could facilitate isomer de-excitation studies.Comment: published in PRL 110, 12250

The price of rapid exit in venture capital-backed IPOs

This paper proposes an explanation for two empirical puzzles surrounding initial public offerings (IPOs). Firstly, it is well documented that IPO underpricing increases during “hot issue” periods. Secondly, venture capital (VC) backed IPOs are less underpriced than non-venture capital backed IPOs during normal periods of activity, but the reverse is true during hot issue periods: VC backed IPOs are more underpriced than non-VC backed ones. This paper shows that when IPOs are driven by the initial investor’s desire to exit from an existing investment in order to finance a new venture, both the value of the new venture and the value of the existing firm to be sold in the IPO drive the investor’s choice of price and fraction of shares sold in the IPO. When this is the case, the availability of attractive new ventures increases equilibrium underpricing, which is what we observe during hot issue periods. Moreover, I show that underpricing is affected by the severity of the moral hazard problem between an investor and the firm’s manager. In the presence of a moral hazard problem the degree of equilibrium underpricing is more sensitive to changes in the value of the new venture. This can explain why venture capitalists, who often finance firms with more severe moral hazard problems, underprice IPOs less in normal periods, but underprice more strongly during hot issue periods. Further empirical implications relating the fraction of shares sold and the degree of underpricing are presented

Electron Loss from 1.4-MeV / u U\u3csup\u3e4,6,10+\u3c/sup\u3e Ions Colliding with Ne, N₂, and Ar Targets

Absolute, total, single- and multiple-electron-loss cross sections are measured for 1.4-MeV / u U4,6,10+ ions colliding with neon and argon atoms and nitrogen molecules. It is found that the cross sections all have the same dependence on the number of electrons lost and that multiplying the cross sections by the initial number of electrons in the 6s, 6p, and 5f shells yields good agreement between the different projectiles. By combining the present data with previous measurements made at the same velocity, it is shown that the scaled cross sections slowly decrease in magnitude for incoming charge states between 1 and 10, whereas the cross sections for higher-charge-state ions fall off much more rapidly

Relativistic quantum dynamics in strong fields: Photon emission from heavy, few-electron ions

Recent progress in the study of the photon emission from highly-charged heavy ions is reviewed. These investigations show that high-$Z$ ions provide a unique tool for improving the understanding of the electron-electron and electron-photon interaction in the presence of strong fields. Apart from the bound-state transitions, which are accurately described in the framework of Quantum Electrodynamics, much information has been obtained also from the radiative capture of (quasi-) free electrons by high-$Z$ ions. Many features in the observed spectra hereby confirm the inherently relativistic behavior of even the simplest compound quantum systems in Nature.Comment: Version 18/11/0

A novel method to improve temperature simulations of general circulation models based on ensemble empirical mode decomposition and its application to multi-model ensembles

A novel method based on the ensemble empirical mode decomposition (EEMD) method was developed to improve model performance. This method was evaluated by applying it to global surface air temperatures, which were simulated by eight general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The temperature simulations of the eight models were separated into their different components by EEMD. The model's performance improved after the first high-frequency component was removed from the original simulations by EEMD for each model, on both the global and continental scale. Moreover, EEMD was more effective in improving the model's performance compared to the wavelet transform method. The multi-model ensembles (MMEs) were calculated based on the EEMD-improved model simulations using the Average Ensemble Mean, Multiple Linear Regression, Singular Value Decomposition and Bayesian Model Averaging methods. The results showed that the MME forecasts performed better when the calculations were based on the EEMD-improved simulations as opposed to the original simulations on both the global and continental scale. Therefore, the results of the MME were further improved by using the EEMD-improved model simulations. This new method provides a simple way to improve model performance and can be easily applied to further improve MME simulations