The Spectrum of GRB 930131 (``Superbowl Burst'') from 20 keV to 200 MeV

We have constructed a broad-band spectrum for GRB 930131 (the ``Superbowl Burst''), ranging from 20 keV to 200 MeV, by combining spectral information from the Gamma Ray Observatory's BATSE, COMPTEL and EGRET instruments. We present general methods for combining spectra from different time intervals obtained by the same instrument as well as for combining spectra from the same time interval taken by different instruments. The resulting spectrum is remarkably flat (in nu F_nu-space) up to high energies. We find that the spectral shape can be successfully fitted by the shocked synchrotron emission model of Tavani. We present evidence that the flatness of the spectrum at high energies is not due to spectral time-variability.Comment: ApJ accepted, 10 pages, 1 table, 3 figure

Laboratory Quality Control Report: Why is it Important?

The Arkansas Water Resources Center (AWRC) maintains a fee-based water quality lab that is certified through the Arkansas Department of Environmental Quality (ADEQ). The AWRC Water Quality Lab analyzes water samples for a variety of constituents, using standard methods for the analysis of water samples (APHA 2012). Whether you have one or several water samples tested, the lab generates a report of values for each parameter that you have analyzed, which is provided to the client. Included with every water quality report is a Lab Quality Control (QC) report for each of the parameters analyzed within the package. The Lab QC report provides important information about the performance of the methods used to test your water sample(s)

Mumford dendrograms and discrete p-adic symmetries

In this article, we present an effective encoding of dendrograms by embedding them into the Bruhat-Tits trees associated to $p$-adic number fields. As an application, we show how strings over a finite alphabet can be encoded in cyclotomic extensions of $\mathbb{Q}_p$ and discuss $p$-adic DNA encoding. The application leads to fast $p$-adic agglomerative hierarchic algorithms similar to the ones recently used e.g. by A. Khrennikov and others. From the viewpoint of $p$-adic geometry, to encode a dendrogram $X$ in a $p$-adic field $K$ means to fix a set $S$ of $K$-rational punctures on the $p$-adic projective line $\mathbb{P}^1$. To $\mathbb{P}^1\setminus S$ is associated in a natural way a subtree inside the Bruhat-Tits tree which recovers $X$, a method first used by F. Kato in 1999 in the classification of discrete subgroups of $\textrm{PGL}_2(K)$. Next, we show how the $p$-adic moduli space $\mathfrak{M}_{0,n}$ of $\mathbb{P}^1$ with $n$ punctures can be applied to the study of time series of dendrograms and those symmetries arising from hyperbolic actions on $\mathbb{P}^1$. In this way, we can associate to certain classes of dynamical systems a Mumford curve, i.e. a $p$-adic algebraic curve with totally degenerate reduction modulo $p$. Finally, we indicate some of our results in the study of general discrete actions on $\mathbb{P}^1$, and their relation to $p$-adic Hurwitz spaces.Comment: 14 pages, 6 figure

A pp-adic RanSaC algorithm for stereo vision using Hensel lifting

A $p$-adic variation of the Ran(dom) Sa(mple) C(onsensus) method for solving the relative pose problem in stereo vision is developped. From two 2-adically encoded images a random sample of five pairs of corresponding points is taken, and the equations for the essential matrix are solved by lifting solutions modulo 2 to the 2-adic integers. A recently devised $p$-adic hierarchical classification algorithm imitating the known LBG quantisation method classifies the solutions for all the samples after having determined the number of clusters using the known intra-inter validity of clusterings. In the successful case, a cluster ranking will determine the cluster containing a 2-adic approximation to the "true" solution of the problem.Comment: 15 pages; typos removed, abstract changed, computation error remove

Water Quality Reporting Limits, Method Detection Limits, and Censored Values: What Does It All Mean?

The Arkansas Water Resources Center (AWRC) maintains a fee-based water-quality lab that is certified by the Arkansas Department of Environmental Quality (ADEQ). The AWRC Water Quality Lab analyzes water samples for a variety of constituents, using standard methods for the analysis of water samples (APHA 2012). The lab generates a report on the analysis, which is provided to clientele, and reports the concentrations or values as measured. Often times the concentrations or values might be very small, even zero as reported by the lab – what does this mean? How should we use this information? This document is intended to help our clientele understand the analytical report, the values, and how one might interpret information near the lower analytical limits. Every client wants the analysis of their water sample(s) to be accurate and precise, but what do we really mean when we say those two words? These words are often used synonymously or thought of as being the same, but the two words mean two different things. Both are equally important when analyzing water samples for constituent concentrations

Stream Water Quality to Support HUC 12 Prioritization in the Lake Wister Watershed, Oklahoma: August 2017 through May 2019

Nonpoint source pollution associated with human land use (agriculture and urbanization) is one of the leading causes of impairment to waterways in the United States (EPA 2000). The primary pollutants associated with agricultural and urban land use are sediment and nutrients which enter nearby streams during rain events and are then carried downstream. These sediments and nutrients may result in water quality issues in the downstream water bodies like increased algal growth or decreased water clarity (e.g. Smith et al., 1999). Best management practices (BMPs) are often used to mitigate the effects of nonpoint source pollution in the watershed. Practices such as riparian buffers installed along the edge of field and conservation tillage (e.g., no-till, spring-till, and cover crops) slow overland flow, reducing erosion and nutrient loss from the landscape (Schoumans et al. 2014). Installing BMPs throughout the entire watershed would have the greatest effect at reducing nonpoint source pollution; however, this is not socially or economically feasible. Targeting critical source areas or priority watersheds for BMPs installation, optimizes the benefits while reducing the overall (Sharpley et al. 2000)

Watershed Investigative Support to the Poteau Valley Improvement Authority: Stream Water Quality to Support HUC 12 Prioritization in the Lake Wister Watershed, Oklahoma

Nonpoint source pollution associated with human land use (agriculture and urbanization) is one of the leading causes of impairment to waterways in the United States (EPA, 2000). The primary pollutants associated with agricultural and urban land use are sediment and nutrients which enter nearby streams during rain events and are then carried downstream. These sediments and nutrients may result in water quality issues in the downstream water bodies like increased algal growth or decreased water clarity (e.g. Smith et al., 1999)

2-16 mu m spectroscopy of micron-sized enstatite (Mg,Fe)(2)Si2O6 silicates from primitive chondritic meteorites

We present mid-infrared spectra from individual enstatite silicate grains separated from primitive type 3 chondritic meteorites. The 2-16 mu m transmission spectra were taken with microspectroscopic Fourier-transform infrared (FT-IR) techniques as part of a project to produce a data base of infrared spectra from minerals of primitive meteorites for comparison with astronomical spectra. In general, the wavelength of enstatite bands increases with the proportion of Fe. However, the wavelengths of the strong En(100) bands at 10.67 and 11.67 decrease with increasing Fe content. The 11.67-mu m band exhibits the largest compositional wavelength shift (twice as large as any other). Our fits of the linear dependence of the pyroxene peaks indicate that crystalline silicate peaks in the 10-mu m spectra of Herbig AeBe stars, HD 179218 and 104237, are matched by pyroxenes of En(90-92) and En(78-80), respectively. If these simplistic comparisons with the astronomical grains are correct, then the enstatite pyroxenes seen in these environments are more Fe-rich than are the forsterite (Fo(100)) grains identified in the far-infrared which are found to be Mg end-member grains. This differs from the general composition of type 3 chondritic meteoritic grains in which the pyroxenes are more Mg-rich than are the olivines from the same meteorite

Gamma Ray Burst Host Galaxies Have `Normal' Luminosities

The galactic environment of Gamma Ray Bursts can provide good evidence about the nature of the progenitor system, with two old arguments implying that the burst host galaxies are significantly subluminous. New data and new analysis have now reversed this picture: (A) Even though the first two known host galaxies are indeed greatly subluminous, the next eight hosts have absolute magnitudes typical for a population of field galaxies. A detailed analysis of the 16 known hosts (ten with red shifts) shows them to be consistent with a Schechter luminosity function with $R^{*} = -21.8 \pm 1.0$ as expected for normal galaxies. (B) Bright bursts from the Interplanetary Network are typically 18 times brighter than the faint bursts with red shifts, however the bright bursts do not have galaxies inside their error boxes to limits deeper than expected based on the luminosities for the two samples being identical. A new solution to this dilemma is that a broad burst luminosity function along with a burst number density varying as the star formation rate will require the average luminosity of the bright sample ($>$$6 \times 10^{58} ph \cdot s^{-1}$ or $>$$1.7 \times 10^{52} \cdot erg \cdot s^{-1}$) to be much greater than the average luminosity of the faint sample ($\sim 10^{58} ph \cdot s^{-1}$ or $\sim 3 \times 10^{51} erg \cdot s^{-1}$). This places the bright bursts at distances for which host galaxies with a normal luminosity will not violate the observed limits. In conclusion, all current evidence points to GRB host galaxies being normal in luminosity.Comment: 18 pages, 3 figures, Submitted to ApJLet

How to Collect your Water Sample and Interpret the Results for the Poultry Analytical Package

Rapidly growing birds may consume up to twice as much water as feed (Scantling and Watkins 2013), which means a plentiful supply of clean water is crucial for poultry health and productivity. To determine the quality of your poultry’s water resources, periodic sampling and analysis is needed. Analyzing water supplies can also be a crucial tool in identifying existing or potential challenges. The Arkansas Water Resources Center (AWRC) in cooperation with the UA Cooperative Extension Service offers several analytical packages to assess the quality of your water resources. This document is intended to provide guidance to poultry producers on collecting water samples for analysis and understanding the “Poultry Water Report Form” provided by the AWRC’s Water Quality Laboratory (Lab). The information contained within this fact sheet should be used as general guidance, and the reader is encouraged to seek advice from Extension specialists regarding the interpretation of individual reports and water testing results that may be of concern