Fluid mechanics approach to acoustic liner design

Fluid mechanics approach to acoustic liner desig

Beauty and the Beast-Hybrid Prosecution Externships in a Non-Urban Setting

This article examines and demonstrates how two components -- hybrid and a non-urban setting -- work together to create quality external prosecution clinics. Part I describes the non-urban setting of the University of Montana School of Law and explores the emotional and political characteristics of the non-urban setting. Part II discusses the definition of a hybrid clinic as it is understood at the University of Montana School of Law and as it is described in the published scholarship. Part III examines the history of clinical education at the University of Montana School of Law and describes the evolution of the current prosecution externships. Part IV assesses the strengths and weaknesses of the hybrid model and suggests ways to bolster the strengths and ameliorate the weaknesses

HTself2: Combining p-values to Improve Classification of Differential Gene Expression in HTself

HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression for low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. The method was previously described in Vêncio et al, (DNA Res. 12: 211- e 214, 2005). In this work we consider an extension of HTself by calculating p-values instead of using a fixed credibility level α. As before, the statistic used to compute single spots p-values is obtained from the gaussian Kernel Density Estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent p-values which can be combined by well known statistical methods. The combined p-value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses the idea of p-values combination. It was implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure

Signed harmonic sums of integers with k distinct prime factors

We give some theoretical and computational results on “random” harmonic sums with prime numbers, and more generally, for integers with a fixed number of prime factors

Forest patch isolation drives local extinctions of Amazonian orchid bees in a 26 years old archipelago

Major hydroelectric dams are among key emergent agents of habitat loss and fragmentation in lowland tropical forests. Orchid bees (Apidae, Euglossini) are one of the most important groups of specialized pollinators of flowering plants in Neotropical forests. Here, we investigate how an entire assemblage of orchid bees responded to the effects of forest habitat loss, isolation and forest canopy degradation induced by a hydroelectric reservoir of Central Brazilian Amazonia. Built in 1986, the Balbina Dam resulted in a vast archipelagic landscape containing 3546 primary forest islands of varying sizes and isolation, surrounded by 3129 km2 of freshwater. Using scent traps, we sampled 34 islands, 14 open-water matrix sites, and three mainland continuous forests, yielding 2870 male orchid bees representing 25 species. Local orchid bee species richness was affected by forest patch area but particularly by site isolation. Distance to forest edges, either within forest areas or into the open-water matrix, was the most important predictor of species richness and composition. Variation in matrix dispersal of individual species to increasingly isolated sites was a key determinant of community structure. Given the patterns of patch persistence and matrix movements of orchid bees in increasingly fragmented forest landscapes, we outline how forest bees respond to the landscape alteration induced by major hydroelectric dams. These results should be considered in environmental impact studies prior to the approval of new dams

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model

3_D modeling using TLS and GPR techniques to characterize above and below-ground wood distribution in pyroclastic deposits along the Blanco River (Chilean Patagonia)

To date, the study of in-stream wood in rivers has been focused mainly on quantifying wood pieces deposited above the ground. However, in some particular river systems, the presence of buried dead wood can also represent an important component of wood recruitment and budgeting dynamics. This is the case of the Blanco River (Southern Chile) severely affected by the eruption of Chait\ue9n Volcano occurred between 2008 and 2009. The high pyroclastic sediment deposition and transport affected the channel and the adjacent forest, burying wood logs and standing trees. The aim of this contribution is to assess the presence and distribution of wood in two study areas (483 m2 and 1989 m2, respectively) located along the lower streambank of the Blanco River, and covered by thick pyroclastic deposition up to 5 m. The study areas were surveyed using two different devices, a Terrestrial Laser Scanner (TLS) and a Ground Penetrating Radar (GPR). The first was used to scan the above surface achieving a high point cloud density ( 48 2000 points m-2) which allowed us to identify and measure the wood volume. The second, was used to characterize the internal morphology of the volcanic deposits and to detect the presence and spatial distribution of buried wood up to a depth of 4 m. Preliminary results have demonstrated differences in the numerousness and volume of above wood between the two study areas. In the first one, there were 43 wood elements, 33 standing trees and 10 logs, with a total volume of 2.96 m3 (109.47 m3 km-1), whereas the second one was characterized by the presence of just 7 standing trees and 11 wood pieces, for a total amount of 0.77 m3 (7.73 m3 km-1). The dimensions of the wood elements vary greatly according to the typology, standing trees show the higher median values in diameter and length (0.15 m and 2.91 m, respectively), whereas the wood logs were smaller (0.06 m and 1.12 m, respectively). The low dimensions of deposited wood can be probably connected to their origin, suggesting that these elements were generated by toppling and breaking of surrounding dead trees. Results obtained with the GPR confirm the ability of this instrument to localize the presence and distribution of buried wood. From the 3- D analysis it was possible to assess the spatial distribution and to estimate, as first approach, the volume of the buried wood which represents approximately 0.04% of the entire volcanic deposit. Further analysis will focus on additional GPR calibration with different wood sizes for a more accurate estimation of the volume. The knowledge of the overall wood amount stored in a fluvial system that can be remobilized over time, represent an essential factor to ensure better forest and river management actions

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Influence of wall material concentration and core-to-wall material ratio on the encapsulation of pomegranate seed oil by complex coacervation.

Pomegranate seed oil (PSO) is rich in bioactive compounds, such as conjugated linolenic acids (CLnA). Encapsulation is a packing technology that enables the application of highly oxidizable oils in food matrices. Technological properties of particles produced by this method are highly influenced by wall material (WM) concentration and core:WM ratio. The objective of this study was to evaluate the effects of the WM concentration and the core:WM ratio in the microencapsulation of PSO by complex coacervation.SLACA, 12. De 4 a 7 de Novembro de 2017. Ref. 71235

Grain Sorghum Response to Band Applied Zinc Fertilizer

Zinc (Zn) is one of the micronutrients found to be deficient in Kansas. The objective of this study was to evaluate the response of grain sorghum to Zn fertilization using strip trials. The experiment was set up in Manhattan, KS, in 2015. The experimental design consisted of two strips, one with Zn fertilizer and the other without, with five replications. Zn fertilizer was applied as starter in combination with ammonium polyphosphate at the rate of 0.5 lb Zn/a. Plant tissue samples were collected to determine Zn content. Grain yield was recorded by combine equipped with yield monitor. No significant differences were found for sorghum grain yield. Grain Zn content increased with Zn fertilization. Zn fertilization may be considered for future studies in food biofortification