Fighting With Wine: Ruin, Resistance and Renewal in a Qom Community of Northern Argentina

This study examines public binge drinking among the Qom (Toba) ex-foragers of Formosa, northern Argentina. Based upon 15 months of ethnographic fieldwork in a peri-urban Qom barrio (Lot 84), this analysis relates binge drinking to Qom ethnohistory, community life, and interactions with the Argentine state. The public, performative nature of Qom binge drinking is explored; intoxication is shown to convey in sometimes violent public spectacle the pathos of their socioeconomic marginality, reinforce non-indigenous Argentines’ entrenched perceptions of violent “Indians”, and paradoxically provide the Qom with vehicle for continued colonial resistance. Many Qom view drinking problems as rooted in Lot 84’s close proximity to the city (Formosa) relative to more rural Qom villages. Thus they reference a continuum of health that runs from urban, non-indigenous spaces to the rural bush country where foods—including home-brewed alcohol—are healthful rather than harmful. In kind, the violence and perceived chaos associated with public binge drinking has led to the development of programs intended to stem alcohol use in the community. Locally, counseling efforts are woven in the missions of evangelical churches and the Catholic chapel, while top-down efforts focus upon state-run psychoanalysis. Psychoanalysis is explored as a paternalistic form of governmental domination and attempted assimilation. Rather than relying upon state-run methods for personal and communal re-integration, many Qom centrally position a period of alcohol use within their personal development narratives, during which alcohol allowed them to find personal responsibility or an improved relationship with God. On a communal level, fighting against public alcohol use among youth has led to increased community solidarity and capacity building through sport, education and indigenous-led program creation. In summary, public binge drinking is manifest in the Qom community not through acculturation or personal pathology, but rather as a multi-valent, ritualesque performance that levies resistance against prevailing social conditions and, despite the profound tax of violence, occasions personal and communal transformation

Evidence - The Pennsylvania Rape Shield Law - Admissibility of Evidence Concerning Sexual Conduct Offered for Purposes of Impeachment

The Pennsylvania Supreme Court held that state courts may order a school district and teachers\u27 association to participate in court-monitored negotiations when the courts grant the Secretary of Education an injunction ending a teachers\u27 strike. Carroll v. Ringgold Educ. Ass\u27n, 680 A.2d 1137 (Pa. 1996)

A Simulation Based Approach to Optimize Berth Throughput Under Uncertainty at Marine Container Terminals

Berth scheduling is a critical function at marine container terminals and determining the best berth schedule depends on several factors including the type and function of the port, size of the port, location, nearby competition, and type of contractual agreement between the terminal and the carriers. In this paper we formulate the berth scheduling problem as a bi-objective mixed-integer problem with the objective to maximize customer satisfaction and reliability of the berth schedule under the assumption that vessel handling times are stochastic parameters following a discrete and known probability distribution. A combination of an exact algorithm, a Genetic Algorithms based heuristic and a simulation post-Pareto analysis is proposed as the solution approach to the resulting problem. Based on a number of experiments it is concluded that the proposed berth scheduling policy outperforms the berth scheduling policy where reliability is not considered

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

On Anomaly-Free Dark Matter Models

We investigate the predictions of anomaly-free dark matter models for direct and indirect detection experiments. We focus on gauge theories where the existence of a fermionic dark matter candidate is predicted by anomaly cancellation, its mass is defined by the new symmetry breaking scale, and its stability is guaranteed by a remnant symmetry after the breaking of the gauge symmetry. We find an upper bound on the symmetry breaking scale by applying the relic density and perturbative constraints. The anomaly-free property of the theories allows us to perform a full study of the gamma lines from dark matter annihilation. We investigate the correlation between predictions for final radiation processes and gamma lines. Furthermore, we demonstrate that the latter can be distinguished from the continuum gamma ray spectrum.Comment: 21 pages, 9 figures. v2: minor changes to the text, references added, version to appear in PR

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models