78 research outputs found

    A preliminary fishery quality index for Portuguese streams

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    There is a need to quantify the multivariate quality of a recreational fishery at the site scale to better communicate the relative quality among sites to the public and anglers. Borrowing on the general approach of multimetric indices of biotic integrity (IBIs), we developed fishery quality indices (FQIs) from species quality indices (SQIs) based on measures of fish abundance and size structure for northern and central Portuguese streams. Our FQIs showed regional patterns indicating a range in fishery quality. Higher coldwater FQI scores were mostly found in the northwestern (Minho and Lima), northeastern Douro, and northern Tagus basins. Higher warmwater FQI scores occurred in the eastern Tagus basin. The species that contributed the most to warmwater FQI scores were largemouth bass Micropterus salmoides, pumpkinseed Lepomis gibbosus, the cyprinid Luciobarbus bocagei, chubs Squalius carolitertii and S. pyrenaicus, and nases Pseudochondrostoma duriense and P. polylepis. The chubs, nases, and brown trout Salmo trutta contributed the most to coldwater FQI scores. As expected, our indices were correlated with river size and with disturbance at the catchment, segment, and site scales. Regression models for separate coldwater and warmwater FQIs were stronger than those for the individual SQIs and for an all-site FQI. The correlation was positive between the coldwater FQI and a coldwater IBI but negative between the warmwater FQI and warmwater IBIs. The proposed FQIs offer a quantitative approach for assessing relative fishery quality among sites and for making regional assessments given an appropriate study design. The component SQIs and SQI metrics of the FQIs can be disassociated to determine the population and species characteristics most affected by various environmental variables

    Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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    The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.Comment: 27 pages, 11 figures, AI4EIC workshop, tutorials and hackatho

    The Effectiveness of Incarceration-Based Drug Treatment on Criminal Behavior: A Systematic Review

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    Many, if not most, incarcerated offenders have substance abuse problems. Without effective treatment, these substance-abusing offenders are likely to persist in non-drug offending. The period of incarceration offers an opportunity to intervene in the cycle of drug abuse and crime. Although many types of incarceration-based drug treatment programs are available (e.g., therapeutic communities and group counseling), the effectiveness of these programs is unclear. The objective of this research synthesis is to systematically review quasi-experimental and experimental (RCT) evaluations of the effectiveness of incarceration-based drug treatment programs in reducing post-release recidivism and drug relapse. A secondary objective of this synthesis is to examine variation in effectiveness by programmatic, sample, and methodological features. In this update of the original 2006 review (see Mitchell, Wilson, and MacKenzie, 2006), studies made available since the original review were included in an effort to keep current with emerging research. This synthesis of evaluations of incarceration-based drug treatment programs found that such programs are modestly effective in reducing recidivism. These findings most strongly support the effectiveness of therapeutic communities, as these programs produced relatively consistent reductions in recidivism and drug use. Both counseling and incarceration-based narcotic maintenance programs had mixed effects. Counseling programs were associated with reductions in recidivism but not drug use; whereas, incarceration-based narcotic maintenance programs were associated with reductions in drug use but not recidivism. Note that our findings regarding the effectiveness of incarceration-based narcotic maintenance programs differ from a larger review of community-based narcotic maintenance programs (see Egli, Pina, Christensen, Aebi, and Killias, 2009). Finally, boot camp programs for drug offenders had negligible effects on both recidivism and drug use

    Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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    The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R and D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments
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