488 research outputs found
Census 2010 Demographic Profile: Madison County
This demographic profile describes characteristics of the local and state population based on results from the 2010 Census.
The decennial census is an official enumeration, or count, of all residents on April 1st of the census year. The results of the census provide us with information about basic demographic characteristics of the population, including age, race, ethnicity, household composition, housing occupancy, and housing tenure
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RFID localization using special antenna technique
In this paper, a RFID localization method using special antenna technique is presented. By using an active RFID system with external dipole antenna the angle and the distance from the antenna to the RFID tag can be found based on the principle of null steering. Compared with other techniques, this method has a number of advantages such as simple design, easy to implement, low cost and high reliability
市民社会のプレーヤー交代劇と政治-仁平典宏の<贈与のパラドックス>論を読み解く-
千葉大学大学院人文社会科学研究科研究プロジェクト報告書第257集『都市コミュニティにおける相互扶助と次世代育成』水島治郎 編Sustainable Urban Communities: Communality and Generativity Report on the Research Projects No.25
Probing the Hyperconjugative Aromaticity of Cyclopentadiene and Pyrroliums Containing Group 7 Transition Metal Substituents
Aromaticity and hyperconjugation are two fundamental
concepts in
chemistry. Combining them together led to the proposal of the concept
of hyperconjugative aromaticity by Mulliken in 1939. Now, it has been
attracting considerable attention from both theoretical and experimental
chemists. Recently, the concept of hyperconjugative aromaticity has
been extended from main-group substituents to transition metal systems
including groups 9, 10, and 11 transition metal substituents. Here,
we report the hyperconjugative aromaticity in cyclopentadienes and
pyrroliums containing group 7 transition metal substituents through
density functional theory (DFT) calculations. It is found that the
metal–metal bonding interaction can significantly reduce the
aromaticity in cyclopentadienes, whereas the stronger σ-donor
ligand, bridged hydride, and carbonyls can enhance aromaticity. All
these findings expand the scope of the concept of hyperconjugative
aromaticity, enriching aromatic organometallic chemistry
Probing the Hyperconjugative Aromaticity of Cyclopentadiene and Pyrroliums Containing Group 7 Transition Metal Substituents
Aromaticity and hyperconjugation are two fundamental
concepts in
chemistry. Combining them together led to the proposal of the concept
of hyperconjugative aromaticity by Mulliken in 1939. Now, it has been
attracting considerable attention from both theoretical and experimental
chemists. Recently, the concept of hyperconjugative aromaticity has
been extended from main-group substituents to transition metal systems
including groups 9, 10, and 11 transition metal substituents. Here,
we report the hyperconjugative aromaticity in cyclopentadienes and
pyrroliums containing group 7 transition metal substituents through
density functional theory (DFT) calculations. It is found that the
metal–metal bonding interaction can significantly reduce the
aromaticity in cyclopentadienes, whereas the stronger σ-donor
ligand, bridged hydride, and carbonyls can enhance aromaticity. All
these findings expand the scope of the concept of hyperconjugative
aromaticity, enriching aromatic organometallic chemistry
Why Does Activation of the Weaker CS Bond in CS<sub>2</sub> by P/N-Based Frustrated Lewis Pairs Require More Energy Than That of the CO Bond in CO<sub>2</sub>? A DFT Study
The sequestration
of carbon disulfide (CS<sub>2</sub>), a common
pollutant in environmental systems, is of great importance due to
its physical harm to human beings. Compared with CO<sub>2</sub> capture,
that of CS<sub>2</sub> is much less developed. The use of P/N-based
frustrated Lewis pairs (FLPs) has been proven, both experimentally
and theoretically, to be an alternative strategy to efficiently sequestrate
CO<sub>2</sub>. Therefore, we pose the question of whether the analogue
CS<sub>2</sub> could also be sequestrated by the same FLPs, given
that the CS bond in CS<sub>2</sub> is weaker than the CO
bond in CO<sub>2</sub>. Herein, we carry out a thorough DFT study
to theoretically examine this hypothesis for a series of P/N-based
FLPs. Our results reveal unexpectedly higher reaction barriers in
CS<sub>2</sub> capture by most of the P/N-based FLPs, although the
bond dissociation energy of the CS bond in CS<sub>2</sub> (105.3
kcal mol<sup>–1</sup>) is smaller than that of the CO
bond in CO<sub>2</sub> (127.2 kcal mol<sup>–1</sup>). The unexpected
higher energy required for CS<sub>2</sub> activation can be rationalized
by its larger bond distortion and its reverse bond polarization, as
revealed by energy decomposition analysis and natural bond orbital
analysis, respectively. Our findings could be helpful for experimentalists
investigating the sequestration of CS<sub>2</sub> with P/N-based FLPs
Supplement 1. Annotated computer code in Matlab and SAS for performing the simulations and analyses in examples 1–4.
<h2>File List</h2><blockquote>
<p><b><i>Text file</i></b></p>
<blockquote>
<p><a href="fullV.txt">fullV.txt</a></p>
</blockquote>
<p><b><i>Matlab executable files</i></b></p>
<blockquote>
<p><a href="IZ_GLSeg.m">IZ_GLSeg.m</a></p>
<p><a href="IZ_SPACEeg.m">IZ_SPACEeg.m</a></p>
<p><a href="Space_MLfunct.m">Space_MLfunct.m</a></p>
<p><a href="IZ_TIMEeg.m">IZ_TIMEeg.m</a></p>
<p><a href="Time_MLfunct.m">Time_MLfunct.m</a></p>
<p><a href="IZ_MIXEDeg.m">IZ_MIXEDeg.m</a></p>
<p><a href="IZ_MIXEDegRep.m">IZ_MIXEDegRep.m</a></p>
</blockquote>
<p><b><i>SAS executable files</i></b></p>
<blockquote>
<p><a href="SASMIXEDeg.sas">SASMIXEDeg.sas</a></p>
<p><a href="SASMIXEDegREG.sas">SASMIXEDegREG.sas</a></p>
</blockquote>
<p><b><i>File descriptions</i></b></p>
<blockquote>
<p>IZ_GLSeg.m –
Matlab code simulating and analyzing phylogenetic data as in example #1</p>
<p>fullV.txt –
Text file containing covariance matrix needed for IZ_GLSeg.m</p>
<p>IZ_SPACEeg.m –
Matlab code simulating and analyzing spatial data as in example #2</p>
<p>Space_MLfunct.m –
Matlab function giving the likelihood function called by IZ_SPACEeg.m</p>
<p>IZ_TIMEeg.m –
Matlab code simulating and analyzing time-series data as in example #3</p>
<p>Time_MLfunct.m –
Matlab function giving the likelihood function called by IZ_TIMEeg.m</p>
<p>IZ_MIXEDeg.m –
Matlab code simulating a single data set with spatial and temporal corelations
as in example #4</p>
<p>IZ_MIXEDegRep.m –
Matlab code simulating multiple data sets with spatial and temporal corelations
as in example #4</p>
<p>SASMIXEDeg.sas –
SAS code using REML to analyze a single data set produced by IZ_MIXEDeg.m</p>
<p>SASMIXEDegREG.sas –
SAS code using REML to analyze multiple data sets produced by IZ_MIXEDegRep.m</p>
</blockquote>
<p>
</p></blockquote><h2>Description</h2><blockquote>
<p>Matlab ".m" files are
text files that should run under Matlab version 5.0 and 6.5 (MathWorks 1996).
The two programs used for example #4, IZ_MIXEDeg.m
and IZ_MIXEDegRep.m, generate
text files called 'mixedeg.txt'and 'mixedegrep.txt', respecitvely. These text
files are then analyzed by the two SAS programs, SASMIXEDeg.sas
and SASMIXEDegREG.sas, respectively,
which are written in SAS version 6.12 (SAS 1996). To run the SAS programs,
the infile line will have to be changed to set the path to the files mixedeg.txt
and mixedegrep.txt. The current
infile line in SASMIXEDeg.sas
is</p>
<p>infile 'Macintosh
HD:mixedeg.txt';</p>
<p>which finds the file mixedeg.txt
on the hard drive 'Macintosh HD'.</p>
<p>All files should run under Macintosh,
Windows, and Unix operating systems.</p>
</blockquote>
<p>
</p
Table_6_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC
<p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p
Table_5_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC
<p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p
Table_7_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC
<p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p
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