scholarly journals The use of canid tooth marks on bone for the identification of livestock predation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
José Yravedra ◽  
Miguel Ángel Maté-González ◽  
Lloyd A. Courtenay ◽  
Diego González-Aguilera ◽  
Maximiliano Fernández Fernández
Keyword(s):  
Rural Areas ◽  
Large Scale ◽  
Current Issue ◽  
Machine Learning Algorithms ◽  
Government Officials ◽  
Government Programs ◽  
Livestock Predation ◽  
Alternative Approach ◽  
Depth Analysis ◽  
Tooth Marks

Abstract Historically wolves and humans have had a conflictive relationship which has driven the wolf to extinction in some areas across Northern America and Europe. The last decades have seen a rise of multiple government programs to protect wolf populations. Nevertheless, these programs have been controversial in rural areas, product of the predation of livestock by carnivores. As a response to such issues, governments have presented large scale economic plans to compensate the respected owners. The current issue lies in the lack of reliable techniques that can be used to detect the predator responsible for livestock predation. This has led to complications when obtaining subsidies, creating conflict between landowners and government officials. The objectives of this study therefore are to provide a new alternative approach to differentiating between tooth marks of different predators responsible for livestock predation. Here we present the use of geometric morphometrics and Machine Learning algorithms to discern between different carnivores through in depth analysis of the tooth marks they leave on bone. These results present high classification rates with up to 100% accuracy in some cases, successfully differentiating between wolves, dogs and fox tooth marks.

Training Aides to Screen Children for Speech and Language Problems

1976 ◽  
Vol 7 (4) ◽  
pp. 236-241 ◽  
Author(s):  
Marisue Pickering ◽  
William R. Dopheide
Keyword(s):  
Rural Areas ◽  
Large Scale ◽  
School Personnel ◽  
School Age Children ◽  
Speech And Language ◽  
Elementary School Age ◽  
Evaluation Procedures ◽  
Language Problems ◽  
Competency Based ◽  
Large Scale Screening

This report deals with an effort to begin the process of effectively identifying children in rural areas with speech and language problems using existing school personnel. A two-day competency-based workshop for the purpose of training aides to conduct a large-scale screening of speech and language problems in elementary-school-age children is described. Training strategies, implementation, and evaluation procedures are discussed.

Why Has There Been No People’s Power Rebellion in North Korea?

2018 ◽  
pp. 1-34
Author(s):  
Andrew Jackson
Keyword(s):  
Rural Areas ◽  
North Korea ◽  
Large Scale ◽  
Political Elite ◽  
Information Control ◽  
Al Jazeera ◽  
The North ◽  
New Information ◽  
Effective System ◽  
The Right

One scenario put forward by researchers, political commentators and journalists for the collapse of North Korea has been a People’s Power (or popular) rebellion. This paper analyses why no popular rebellion has occurred in the DPRK under Kim Jong Un. It challenges the assumption that popular rebellion would happen because of widespread anger caused by a greater awareness of superior economic conditions outside the DPRK. Using Jack Goldstone’s theoretical expla-nations for the outbreak of popular rebellion, and comparisons with the 1989 Romanian and 2010–11 Tunisian transitions, this paper argues that marketi-zation has led to a loosening of state ideological control and to an influx of infor-mation about conditions in the outside world. However, unlike the Tunisian transitions—in which a new information context shaped by social media, the Al-Jazeera network and an experience of protest helped create a sense of pan-Arab solidarity amongst Tunisians resisting their government—there has been no similar ideology unifying North Koreans against their regime. There is evidence of discontent in market unrest in the DPRK, although protests between 2011 and the present have mostly been in defense of the right of people to support themselves through private trade. North Koreans believe this right has been guaranteed, or at least tacitly condoned, by the Kim Jong Un government. There has not been any large-scale explosion of popular anger because the state has not attempted to crush market activities outright under Kim Jong Un. There are other reasons why no popular rebellion has occurred in the North. Unlike Tunisia, the DPRK lacks a dissident political elite capable of leading an opposition movement, and unlike Romania, the DPRK authorities have shown some flexibility in their anti-dissent strategies, taking a more tolerant approach to protests against economic issues. Reduced levels of violence during periods of unrest and an effective system of information control may have helped restrict the expansion of unrest beyond rural areas.

scholarly journals Optimal Design of Novel Blade Profile for Savonius Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3484
Author(s):  
Tai-Lin Chang ◽  
Shun-Feng Tsai ◽  
Chun-Lung Chen
Keyword(s):  
Wind Energy ◽  
Drag Force ◽  
Wind Turbines ◽  
Rural Areas ◽  
Large Scale ◽  
Design Method ◽  
Angular Position ◽  
Vertical Axis ◽  
Blade Profile ◽  
Main Concept

Since the affirming of global warming, most wind energy projects have focused on the large-scale Horizontal Axis Wind Turbines (HAWTs). In recent years, the fast-growing wind energy sector and the demand for smarter grids have led to the use of Vertical Axis Wind Turbines (VAWTs) for decentralized energy generation systems, both in urban and remote rural areas. The goals of this study are to improve the Savonius-type VAWT’s efficiency and oscillation. The main concept is to redesign a Novel Blade profile using the Taguchi Robust Design Method and the ANSYS-Fluent simulation package. The convex contour of the blade faces against the wind, creating sufficient lift force and minimizing drag force; the concave contour faces up to the wind, improving or maintaining the drag force. The result is that the Novel Blade improves blade performance by 65% over the Savonius type at the best angular position. In addition, it decreases the oscillation and noise accordingly. This study achieved its two goals.

The Economic Imperative: Cities Need Migrants

2021 ◽  
pp. 001946622110132
Author(s):  
Astha Agarwalla ◽  
Errol D’Souza
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Market Failure ◽  
Jel Classification ◽  
Industry Concentration ◽  
Wage Curve ◽  
Economic Agents ◽  
Reverse Migration ◽  
Local Labour

The policy responses to Covid-19 have triggered large-scale reverse migration from cities to rural areas in developing countries, exposing the vulnerability of migrants living precarious lives in cities, giving rise to debates asserting to migration as undesirable and favouring policy options to discourage the process. However, the very basis of spatial concentration and formation of cities is presence of agglomeration economies, benefits accruing to economic agents operating in cities. Presence of these agglomeration benefits in local labour markets manifests themselves in the form of an upward sloping wage curve in urban areas. We estimate the upward sloping wage curve for various size classes of cities in Indian economy and establish the presence of positive returns to occupation and industry concentration at urban locations. Controlling for worker-specific characteristics influencing wages, we establish that higher the share of an industry or an occupation in local employment as compared to national economy, the desirability of firms to pay higher wages increases. For casual labourers, occupational concentration results in higher wages. However, impact of industry concentration varies across sectors. Results supporting presence of upward sloping urban wage curve, therefore, endorse policies to correct the market failure in cities and promote migration as a desirable process. JEL Classification Codes: J2, R2

scholarly journals Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

2021 ◽  
Vol 13 (11) ◽  
pp. 2074
Author(s):  
Ryan R. Reisinger ◽  
Ari S. Friedlaender ◽  
Alexandre N. Zerbini ◽  
Daniel M. Palacios ◽  
Virginia Andrews-Goff ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Predictive Models ◽  
Regional Variation ◽  
Large Scale ◽  
Predictive Performance ◽  
Humpback Whale ◽  
Machine Learning Algorithms ◽  
Humpback Whales ◽  
Environmental Covariates ◽  
Animal Habitat

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

scholarly journals Large-scale transcriptomics to dissect 2 years of the life of a fungal phytopathogen interacting with its host plant

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elise J. Gay ◽  
Jessica L. Soyer ◽  
Nicolas Lapalu ◽  
Juliette Linglin ◽  
Isabelle Fudal ◽  
...  
Keyword(s):  
Host Plant ◽  
Large Scale ◽  
Field Experiments ◽  
Plant Residues ◽  
Plant Infection ◽  
Effector Genes ◽  
Controlled Conditions ◽  
Niche Adaptation ◽  
Depth Analysis ◽  
Fungal Genes

Abstract Background The fungus Leptosphaeria maculans has an exceptionally long and complex relationship with its host plant, Brassica napus, during which it switches between different lifestyles, including asymptomatic, biotrophic, necrotrophic, and saprotrophic stages. The fungus is also exemplary of “two-speed” genome organisms in the genome of which gene-rich and repeat-rich regions alternate. Except for a few stages of plant infection under controlled conditions, nothing is known about the genes mobilized by the fungus throughout its life cycle, which may last several years in the field. Results We performed RNA-seq on samples corresponding to all stages of the interaction of L. maculans with its host plant, either alive or dead (stem residues after harvest) in controlled conditions or in field experiments under natural inoculum pressure, over periods of time ranging from a few days to months or years. A total of 102 biological samples corresponding to 37 sets of conditions were analyzed. We show here that about 9% of the genes of this fungus are highly expressed during its interactions with its host plant. These genes are distributed into eight well-defined expression clusters, corresponding to specific infection lifestyles or to tissue-specific genes. All expression clusters are enriched in effector genes, and one cluster is specific to the saprophytic lifestyle on plant residues. One cluster, including genes known to be involved in the first phase of asymptomatic fungal growth in leaves, is re-used at each asymptomatic growth stage, regardless of the type of organ infected. The expression of the genes of this cluster is repeatedly turned on and off during infection. Whatever their expression profile, the genes of these clusters are enriched in heterochromatin regions associated with H3K9me3 or H3K27me3 repressive marks. These findings provide support for the hypothesis that part of the fungal genes involved in niche adaptation is located in heterochromatic regions of the genome, conferring an extreme plasticity of expression. Conclusion This work opens up new avenues for plant disease control, by identifying stage-specific effectors that could be used as targets for the identification of novel durable disease resistance genes, or for the in-depth analysis of chromatin remodeling during plant infection, which could be manipulated to interfere with the global expression of effector genes at crucial stages of plant infection.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

scholarly journals Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images

2021 ◽  
Vol 13 (9) ◽  
pp. 1837
Author(s):  
Eve Laroche-Pinel ◽  
Sylvie Duthoit ◽  
Mohanad Albughdadi ◽  
Anne D. Costard ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Potential ◽  
Large Scale ◽  
Water Status ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Severe Drought ◽  
Stem Water Potential ◽  
Stem Water ◽  
Sentinel 2

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).

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