James Sloan: Renegade or True Republican?

This paper examines the political career of James Sloan of Gloucester County, New Jersey. Sloan was instrumental in organizing the Democratic Republican party in Gloucester County, and he represented New Jersey in the United States House of Representatives from 1803 to 1809. Sloan was born into a Quaker family, but he supported the Whig cause in the American Revolution and faced disciplinary action from his Quaker meeting for doing so. Later, he would gravitate toward Methodism, but he never seemed comfortable in any religious organization. Historian Carl Prince called Sloan an “anomaly” in New Jersey politics. Sloan’s career certainly did manifest anomalous behavior. He criticized his party after it took control of the state legislature in 1801, viewing its implementation of republican principles as insufficiently zealous. As a member of Congress, he broke with the Jefferson administration over the Embargo. After leaving Congress, he coalesced with New Jersey Federalists in opposition to the War of 1812, helping to elect a “peace ticket” in that year. This paper examines Sloan’s role in state and national politics in the very early nineteenth century. Specifically, it points out some of the tensions within the Democratic Republican party, especially among those who were displeased with the domination of the “Virginia Dynasty.” Sloan led efforts to challenge this domination and to offer alternatives to the New Jersey electorate

The Synthesis of 2-(1-Indenyl)-phenol: A Potential Ligand For Metallocene Polymerization Catalysts; and, ω-Iodoalkyl(methyl) Malonate Esters: Synthesis and Application in Bioconjugate Chemistry

Chapter I: Metallocene catalysis is an important division of polymerization chemistry. The design of appropriate ligands for metallocene catalysts has focused on stereochemical control and structural diversity. The synthesis of the potential ligand 2-(1-indenyl)-phenol has been developed. Indenyl phenol ligands offer advancements over cyclopentadienyl ligands because the steric and electronic properties of the catalyst could be tailored to suit individual polymerization reactions. 2-(1-Indenyl)-phenol was synthesized with an overall yield of 4% and characterized with 1H NMR and 13C NMR. Chapter II: A large percentage of breast malignancies overexpress the nuclear estrogen receptor (ER) protein. Binding to nuclear receptors is a mechanism for concentrating ligands in cells, and this pathway can be expanded to ligand analogues provided the analogue is still recognized by the receptor. Estradiol is a high-affinity ligand for the estrogen receptor, and 7α monosubstituted estradiol derivatives have also been shown to be high-affinity ligands for ER. Therefore an estradiol derivative with a cytotoxic moiety incorporated into a 7α substituent could selectively concentrate in breast malignancies to cause cell death. A generation of linkage molecules designed to incorporate a cytotoxic moiety into 7α substitutents of estradiol has been developed. Since bidentate carboxylate ligands are known to form highly stable complexes with platinum, malonate groups were chosen as the common element in this family of linkage molecules. Alkylating malonate groups with diiodoalkanes afforded ω-iodoalkyl(methyl) malonate esters. One specific ester, diisoamyl-6-iodohexyl(methyl) malonate, was used to alkylate the 7α position of an estradiol derivative

The Synthesis of 2-(1-Indenyl)-phenol: A Potential Ligand For Metallocene Polymerization Catalysts; and, ω-Iodoalkyl(methyl) Malonate Esters: Synthesis and Application in Bioconjugate Chemistry

Chapter I: Metallocene catalysis is an important division of polymerization chemistry. The design of appropriate ligands for metallocene catalysts has focused on stereochemical control and structural diversity. The synthesis of the potential ligand 2-(1-indenyl)-phenol has been developed. Indenyl phenol ligands offer advancements over cyclopentadienyl ligands because the steric and electronic properties of the catalyst could be tailored to suit individual polymerization reactions. 2-(1-Indenyl)-phenol was synthesized with an overall yield of 4% and characterized with 1H NMR and 13C NMR. Chapter II: A large percentage of breast malignancies overexpress the nuclear estrogen receptor (ER) protein. Binding to nuclear receptors is a mechanism for concentrating ligands in cells, and this pathway can be expanded to ligand analogues provided the analogue is still recognized by the receptor. Estradiol is a high-affinity ligand for the estrogen receptor, and 7α monosubstituted estradiol derivatives have also been shown to be high-affinity ligands for ER. Therefore an estradiol derivative with a cytotoxic moiety incorporated into a 7α substituent could selectively concentrate in breast malignancies to cause cell death. A generation of linkage molecules designed to incorporate a cytotoxic moiety into 7α substitutents of estradiol has been developed. Since bidentate carboxylate ligands are known to form highly stable complexes with platinum, malonate groups were chosen as the common element in this family of linkage molecules. Alkylating malonate groups with diiodoalkanes afforded ω-iodoalkyl(methyl) malonate esters. One specific ester, diisoamyl-6-iodohexyl(methyl) malonate, was used to alkylate the 7α position of an estradiol derivative

Crime Mapping through Geo-Spatial Social Media Activity

The presence of crime is one of the major challenges for societies all over the World, especially in metropolitan areas. As indicated by prior research, Information Systems can contribute greatly to cope with the complex factors that influence the emergence and location of delinquencies. In this work, we combine commonly used approaches of static environmental characteristics with Social Media. We expect that blending in such dynamic information of public behavior is a valuable addition to explain and predict criminal activity. Consequently, we employ Zero-Inflated Poisson Regressions and Geographically Weighted Regressions to examine how suitable Social Media data actually is for this purpose. Our results unveil geographic variation of explanatory power throughout a metropolitan area. Furthermore, we find that Social Media works exceptionally well for description of certain crime types and thus is also likely to enhance the accuracy of delinquency prediction

AN OPEN DOOR MAY TEMPT A SAINT – DATA ANALYTICS FOR SPATIAL CRIMINOLOGY

The vast amounts of data that are generated and collected in today’s world bear immense potential for businesses and authorities. Innovative companies already adopt novel analytics methods driven by competition and the urge of constantly gaining new insights into business operations, customer preferences, and strategic decision making. Nonetheless, local authorities have been slow to embrace the opportunities enabled by data analytics. In this paper, we demonstrate and discuss how latent structures unveil valuable information on an aspect of public life and communities we all face: criminal activity. On city-scale, we analyze the spatial correspondence of recorded crime to its physical environment, the public presence, and the demographical structure in its vicinity. Our results show that Big Data in fact is able to identify and quantify the main spatial drivers of criminal activity. At the same time, we are able to maintain interpretability by design, which ultimately allows deep informational insights

Taming Uncertainty in Big Data - Evidence from Social Media in Urban Areas

While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions

INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use Twitter data as a decision support system to detect future crimes. In this work, we attempt to unleash the wisdom of crowds materialized in tweets from Twitter. This requires to look at Tweets that have been sent within a vicinity of each other. Based on the aggregated Tweets traffic we correlate them with crime types. Apparently, crimes such as disturbing the peace or homicide exhibit different Tweet patterns before the crime has been committed. We show that these tweet patterns can strengthen the explanation of criminal activity in urban areas. On top of that, we go beyond pure explanatory approaches and use predictive analytics to provide evidence that Twitter data can improve the prediction of crimes

Reaction rate in an evanescent random walkers system

Diffusion mediated reaction models are particularly ubiquitous in the description of phys-ical, chemical or biological processes. The random walk schema is a useful tool for formu-lating these models. Recently, evanescent random walk models have received attention inorder to include finite lifetime processes. For instance, activated chemical reactions, such as laser photolysis, exhibit a different asymptotic limit when compared with immortal walker models. A diffusion limited reaction model based on a one dimensional continuous time random walk on a lattice with evanescent walkers is presented here. The absorption probability density and the reaction rate are analytically calculated in the Laplace domain.A finite absorption rate is considered, a model usually referred to as imperfect trapping.Short and long time behaviors are analyzed.publishedVersionFil: Ré, Miguel Ángel. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina.Fil: Ré, Miguel Ángel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Bustos, Natalia Carolina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Otras Ciencias Física

Volume Effects on the Glass Transition Dynamics

The role of jamming (steric constraints) and its relationship to the available volume is addressed by examining the effect that certain modifications of a glass-former have on the ratio of its isochoric and isobaric activation enthalpies. This ratio reflects the relative contribution of volume (density) and temperature (thermal energy) to the temperature-dependence of the relaxation times of liquids and polymers. We find that an increase in the available volume confers a stronger volume-dependence to the relaxation dynamics, a result at odds with free volume interpretations of the glass transition.Comment: 9 pages 5 figure