Size-related Hooking Mortality of Incidentally Caught Chinook Salmon, Oncorhynchus tshawytscha

Mortality associated with the incidental catch and release by commercial trollers of two size classes of chinook salmon, Oncorhynchus tshawytscha, was assessed. Observed cumulative mortality 4-6 days after hooking was 18.3 percent for sublegal-sizefish « 66 cm FL) and 19.0 percent for legal-sizefish. Size of fish was not significantly related to mortality; however, when the results were combined with data from a previous experiment, there was a significant inverse relationship between fish length and mortality. Hooking mortality estimates calculated from tagging experiments and observed relative mortality of legal-and sublegal-size fish held in net pens, were used to derive a range for total hooking mortality of 22.0-26.4 percent for sublegal-size chinook salmon and 18.5-26.4 percent for legal-size chinook salmon

Controls on debris-flow avulsions: White Mountains of California and Nevada

The process by which debris flows shift from an active channel and branch out into new transport or depositional areas is termed “avulsion.” They pose serious risks for structures and populations on debris-flow fans, yet avulsion mechanisms are relatively unknown and unaccounted for in hazard assessments, as compared to avulsions of rivers and streams, which are better understood. This study analyzes six debris-flow fans in the White Mountains of California and Nevada to identify relationships between avulsion locations and channel characteristics, constrain the controlling factors on avulsion, assess the probability that avulsion will occur at specified locations, and develop a method to predict avulsion locations. A database of avulsion locations and their channel characteristics was compiled in the field. These were compared to the characteristics of other positions on the fan surface that show evidence of debris flows that did not avulse through stepwise, binary logistic regression. Results indicate that two-thirds of avulsion likelihood can be attributed to the percentage of boulders at the site, slope angle, channel width, and the ratio between flow thickness and average slope at the avulsion location. The accuracy of this model can be improved when it accounts for the presence of a coarse channel plug, which increases the likelihood of avulsion. Application of the model is demonstrated by runout simulations with forced avulsions from modeled channel plugs

The AI challenge

In this article, Ian Herbert and Alex Zarifis from Loughborough University look at a typical industry disruption scenario through the eyes of a hypothetical division that has been tasked by head office with planning a digital transformation. The current context and challenges of the hypothetical insurer are outlined first and then a plan to utilise AI is discussed

Changing Farming Systems – Financial Implications for Farming Businesses

Future prosperity of farming businesses depends not only on immediate prospects, but also on the capability to adapt to changing circumstances. In looking to the future, farm managers need to assess where the current farming system is taking them, and whether changing to an alternative farming system might be more profitable. There are various techniques for assessing the profitability of alternative farming systems, but frequently the cost of transition is overlooked. The financial consequences of transition to a new farming system are assessed for two case study farms using a spreadsheet tool (STEP), developed by the authors. The tool assists farm managers in assessing the risk of transition strategies as well as comparing rotations.Farm Management,

BONNSAI: a Bayesian tool for comparing stars with stellar evolution models

Powerful telescopes equipped with multi-fibre or integral field spectrographs combined with detailed models of stellar atmospheres and automated fitting techniques allow for the analysis of large number of stars. These datasets contain a wealth of information that require new analysis techniques to bridge the gap between observations and stellar evolution models. To that end, we develop BONNSAI (BONN Stellar Astrophysics Interface), a Bayesian statistical method, that is capable of comparing all available observables simultaneously to stellar models while taking observed uncertainties and prior knowledge such as initial mass functions and distributions of stellar rotational velocities into account. BONNSAI can be used to (1) determine probability distributions of fundamental stellar parameters such as initial masses and stellar ages from complex datasets, (2) predict stellar parameters that were not yet observationally determined and (3) test stellar models to further advance our understanding of stellar evolution. An important aspect of BONNSAI is that it singles out stars that cannot be reproduced by stellar models through $\chi^{2}$ hypothesis tests and posterior predictive checks. BONNSAI can be used with any set of stellar models and currently supports massive main-sequence single star models of Milky Way and Large and Small Magellanic Cloud composition. We apply our new method to mock stars to demonstrate its functionality and capabilities. In a first application, we use BONNSAI to test the stellar models of Brott et al. (2011a) by comparing the stellar ages inferred for the primary and secondary stars of eclipsing Milky Way binaries. Ages are determined from dynamical masses and radii that are known to better than 3%. We find that the stellar models reproduce the Milky Way binaries well. BONNSAI is available through a web-interface at http://www.astro.uni-bonn.de/stars/bonnsai.Comment: Accepted for publication in A&A; 15 pages, 10 figures, 4 tables; BONNSAI is available through a web-interface at http://www.astro.uni-bonn.de/stars/bonnsa

First Rites—A Spiritual History Case Study

My patient was playing high school tennis when she noticed a painful bump on her right hand. She had heard about some players getting bone spurs, but the bump became bigger and more painful over the next few weeks. Soon it was marching band season, and she had trouble playing her trumpet. Her parents brought her to a pediatrician, who noted her symptoms of back pain, trouble breathing, and the hand mass. A chest radiographic image was taken, and she was found to have pleural effusions, fluid accumulating between the lungs and a saran wrap–like covering for the lungs (the pleura). A chest computed tomography scan was done, and the pediatrician then sent the patient to my pediatric oncology clinic

Data technologies and next generation insurance operations

This article uses insights from knowledge management to describe and contrast two approaches to the application of artificial intelligence and data technologies in insurance operations. The first focuses on the automation of existing processes using robotic processing intervention (RPA). Knowledge is codified, routinezed, and embedded in systems. The second focuses on using cognitive computing (AI) to support data driven human decision making based on tacit knowledge. These approaches are complementary, and their successful execution depends on a fully developed organizational data strategy. Four cases are presented to illustrate specific applications and data that are being used by insurance firms to effect change of this kind

Patching Neural Barrier Functions Using Hamilton-Jacobi Reachability

Learning-based control algorithms have led to major advances in robotics at the cost of decreased safety guarantees. Recently, neural networks have also been used to characterize safety through the use of barrier functions for complex nonlinear systems. Learned barrier functions approximately encode and enforce a desired safety constraint through a value function, but do not provide any formal guarantees. In this paper, we propose a local dynamic programming (DP) based approach to "patch" an almost-safe learned barrier at potentially unsafe points in the state space. This algorithm, HJ-Patch, obtains a novel barrier that provides formal safety guarantees, yet retains the global structure of the learned barrier. Our local DP based reachability algorithm, HJ-Patch, updates the barrier function "minimally" at points that both (a) neighbor the barrier safety boundary and (b) do not satisfy the safety condition. We view this as a key step to bridging the gap between learning-based barrier functions and Hamilton-Jacobi reachability analysis, providing a framework for further integration of these approaches. We demonstrate that for well-trained barriers we reduce the computational load by 2 orders of magnitude with respect to standard DP-based reachability, and demonstrate scalability to a 6-dimensional system, which is at the limit of standard DP-based reachability.Comment: 8 pages, submitted to IEEE Conference on Decision and Control (CDC), 202