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