714 research outputs found
An Algorithm for Computing the Ratliff-Rush Closure
Let I\subset K[x,y] be a -primary monomial ideal where K is a field.
This paper produces an algorithm for computing the Ratliff-Rush closure I for
the ideal I= whenever m_{i} is contained in the integral closure
of the ideal . This generalizes of the work of Crispin
\cite{Cri}. Also, it provides generalizations and answers for some questions
given in \cite{HJLS}, and enables us to construct infinite families of
Ratliff-Rush ideals
Reduced Gr\"obner Bases of Certain Toric Varieties; A New Short Proof
Let K be a field and let m_0,...,m_{n} be an almost arithmetic sequence of
positive integers. Let C be a toric variety in the affine (n+1)-space, defined
parametrically by x_0=t^{m_0},...,x_{n}=t^{m_{n}}. In this paper we produce a
minimal Gr\"obner basis for the toric ideal which is the defining ideal of C
and give sufficient and necessary conditions for this basis to be the reduced
Gr\"obner basis of C, correcting a previous work of \cite{Sen} and giving a
much simpler proof than that of \cite{Ayy}
Normality of Monomial Ideals
Given the monomial ideal I=(x_1^{{\alpha}_1},...,x_{n}^{{\alpha}_{n}})\subset
K[x_1,...,x_{n}] where {\alpha}_{i} are positive integers and K a field and let
J be the integral closure of I . It is a challenging problem to translate the
question of the normality of J into a question about the exponent set
{\Gamma}(J) and the Newton polyhedron NP(J). A relaxed version of this problem
is to give necessary or sufficient conditions on {\alpha}_1,...,{\alpha}_{n}
for the normality of J. We show that if {\alpha}_{i}\epsilon{s,l} with s and l
arbitrary positive integers, then J is normal
Using Infographics to Report Research Results
This article feature the results of a nation-wide survey of over 100 academic law libraries in the US regarding their staffing and information technology management. The IT Staffing and Services Survey was originally created by Ann E. Pucket, former professor and director of the University of Georgia School of Law Alexander Campbell King Law Library. It was last updated by Professor Carol Watson in 2010. The 2015 survey results demonstrate how law school information technology management has shifted from being directly managed by law libraries to a more complicated model where collaboration is the key. However, law libraries remain deeply involved with end-user training and instructional technologies. The accompanying infographic features a geographic distribution of schools participating in the survey, average full-time employees dedicated to IT, number of schools in a shared services agreement, as well as responsibilities for various IT domains and services
Assessment: Anything, Anywhere, Anytime Using JotForm
One of the challenges of online education is the lack of real-time feedback. JotForm is a web-based form builder used to track and evaluate student progress through an entire class rather than at the end of it. Forms can be embedded into existing learning materials and learning management systems. I will demonstrate some JotForm features suitable for online education and assessment and share how the UMKC School of Law is using JotForm to assess, engage, and communicate with students. Hands-on training will be provided on how to build your first interactive forms
Wearable Technologies in Academic Libraries: Fact, Fiction and the Future
Chapter 7 of Canuel, R & Crischton, C (2017). Mobile Technology and Academic Libraries: Innovative Services for Research and Learning. Chicago, IL. ACRL. Nick Moline, a developer and early Google Glass Explorer, can still recall Google’s
mantra when he was first introduced to the wearable device: “If you can bring technology
closer to you, you can actually get it out of the way” (Moline, personal communication,
December 29, 2015). Similarly, Steve Mann, a researcher and inventor
widely known as the father of wearable computing once wrote that “miniaturization
of components has enabled systems that are wearable and nearly invisible, so
that individuals can move about and interact freely, supported by their personal information
domain” (Nichol, 2015). Today’s wearable devices are the continuation
and evolution of decades of research and development. This transition began with
devices designed to be worn as backpacks, such as the 6502 multimedia computer
designed by Steve Mann in 1981, evolved to a one-handed keyboard and mouse
connected to a head-mounted display produced in 1993, and then advanced further
into a wrist computer made available the next year. The first commercially available
wearable device, however, was the Trekker, a 120 MHz Pentium computer with
support for speech and a head-mounted display, which sold for $10,000 (Sultan,
2015). These early wearable devices, however, were characterized by limited functionality
and bulky design. By the mid 2010s, fitness tracker devices emerged with their attractive designs targeting sport and fitness enthusiasts. More recent fitness
trackers blend smartwatches with multiple other functionalities, combining health
and activity monitoring as well as networking capabilities.
There are many factors that contributed to the rapid proliferation of wearable
devices in the last five years. These factors include the advent of more reliable Internet
access; the ubiquity of smartphones; decline in cost of sensors, cameras, and processing
power; and finally, a flourishing app ecosystem (Mind Commerce, 2014)
Performance Analysis of Swarm Intelligence-Based Routing Protocol for Mobile Ad Hoc Network and Wireless Mesh Networks
Ant colonies reside in social insect societies and maintain distributed systems that
present a highly structured social organization despite of the simplicity of their
individuals. Ants’ algorithm belongs to the Swarm Intelligence (SI), which is
proposed to find the shortest path. Among various works inspired by ant colonies, the
Ant Colony Optimization (ACO) metaheuristic algorithms are the most successful
and popular, e.g., AntNet, Multiple Ant Colony Optimization (MACO) and
AntHocNet. But there are several shortcomings including the freezing problem of the
optimum path, traffic engineering, and to link failure due to nodes mobility in
wireless mobile networks.
The metaheuristic and distributed route discovery for data load management in
Wireless Mesh Networks (WMNs) and Mobile Ad-hoc Network (MANET) are
fundamental targets of this study. Also the main aim of this research is to solve the
freezing problem during optimum as well as sub-optimum path discovery process. In
this research, Intelligent AntNet based Routing Algorithm (IANRA) is presented for routing in WMNs and MANET to find optimum and near-optimum paths for data
packet routing. In IANRA, a source node reactively sets up a path to a destination
node at the beginning of each communication. This procedure uses ant-like agents to
discover optimum and alternative paths. The fundamental point in IANRA is to find
optimum and sub-optimum routes by the capability of breeding of ants. This ability
is continuation of route that was produced by the parent ants. The new generations of
ants inherit identifier of their family, the generation number, and the routing
information that their parents get during their routing procedure. By this procedure,
IANRA is able to prevent some of the existing difficulties in AntNet, MACO and Ad
hoc On Demand Distance Vector (AODV) routing algorithms.
OMNeT++ was used to simulate the IARNA algorithm for WMNs and MANET.
The results show that the IANRA routing algorithm improved the data packet
delivery ratio for both WMNs and MANET. Besides, it is able to decrease average
end-to-end packet delay compared to other algorithms by showing its efficiency.
IANRA has decreased average end-to-end packet delay by 31.16%, 58.20% and
48.40% in MANET scenario 52.86%, 64.52% and 62.86% by increasing packet
generation rate in WMNs compared to AntHocNet, AODV and B-AntNet routing
algorithms respectively with increased network load. On the other hand, IANRA
shows the packet delivery ratio of 91.96% and 82.77% in MANET, 97.31% and
92.25% in WMNs for low (1 packet/s) and high (20 packet/s) data load respectively
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