Using infrared imagery to estimate white-tailed deer populations on the Pine Bluff Arsenal

Military installations present unique challenges to natural resource managers managing wildlife populations. For those species that are hunted or trapped, it is important to provide data to these installations for achieving sustainable harvests. Pine Bluff Arsenal (PBA), a military installation in southeastern Arkansas, manages for a host of wildlife species including white-tailed deer (Odocoileus virginianus). However, baseline data regarding population size for deer are lacking. We used infrared technology and distance sampling to estimate the size of the winter, post-harvest deer population on PBA. We identified 9 competing models. The best model provided an estimate of density of 0.245 deer/ha (CV = 43%) with a mean group size of 3.3 deer. This density estimate will serve as a baseline value for evaluating future management actions

De Symbolo Apostolico, Praeside M. Wilhelmo Ernesto Tentzelio, Ordinis Philosoph. Adiuncto, publice disputabit Joannes Techentin, Rostochio-Meklenburg. Ad d. XXIX. Decembr. MDCXXCIII. In Auditorio Maiori

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This thematic study was run as a portion of the second term of design studies in a first professional Masters of Architecture degree program in Spring of 1998

Implementing Iterative Algorithms with SPARQL

The SPARQL declarative query language includes innovative capabilities to match subgraph patterns within a semantic graph database, providing a powerful base upon which to implement complex graph algorithms for very large data. Iterative algorithms are useful in a wide variety of domains, in particular in the data-mining and machine-learning domains relevant to graph analytics. In this paper we describe a general mechanism for implementing iterative algorithms via SPARQL queries, illustrate that mechanism with implementation of three algorithms (peer-pressure clustering, graph di↵usion, and label propagation) that are valuable for graph analytics, and observe the strengths and weaknesses of this approach. We find that writing iterative algorithms in this style is straightforward to implement, with scalability to very large data and good performance

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions