Potential for Remote Sensing to Detect and Predict Herbicide Injury on Waterhyacinth (Eichhornia crassipes)

2010 ◽  
Vol 3 (4) ◽  
pp. 440-450 ◽  
Author(s):  
Wilfredo Robles ◽  
John D. Madsen ◽  
Ryan M. Wersal
Keyword(s):  
Large Scale ◽  
Rating Scale ◽  
Spectral Band ◽  
Simulated Data ◽  
Science Research ◽  
Mississippi State University ◽  
State University ◽  
Data Set ◽  
The Relationship ◽  
Reflectance Measurements

AbstractMany large-scale management programs directed toward the control of waterhyacinth rely on maintenance management with herbicides. Improving the implementation of these programs could be achieved through accurately detecting herbicide injury in order to evaluate efficacy. Mesocosm studies were conducted in the fall and summer of 2006 and 2007 at the R. R. Foil Plant Science Research Center, Mississippi State University, to detect and predict herbicide injury on waterhyacinth treated with four different rates of imazapyr and glyphosate. Herbicide rates corresponded to maximum recommended rates of 0.6 and 3.4 kg ae ha−1(0.5 and 3 lb ac−1) for imazapyr and glyphosate, respectively, and three rates lower than recommended maximum. Injury was visually estimated using a phytotoxicity rating scale, and reflectance measurements were collected using a handheld hyperspectral sensor. Reflectance measurements were then transformed into a Landsat 5 Thematic Mapper (TM) simulated data set to obtain pixel values for each spectral band. Statistical analyses were performed to determine if a correlation existed between bands 1, 2, 3, 4, 5, and 7 and phytotoxicity ratings. Simulated data from Landsat 5 TM indicated that band 4 was the most useful band to detect and predict herbicide injury of waterhyacinth by glyphosate and imazapyr. The relationship was negative because pixel values of band 4 decreased when herbicide injury increased. At 2 wk after treatment, the relationship between band 4 and phytotoxicity was best (r2of 0.75 and 0.90 for glyphosate and imazapyr, respectively), which served to predict herbicide injury in the following weeks.

scholarly journals Exploring the relationship between avalanche hazard and large-scale terrain choices at a helicopter skiing operation – Insight from run list ratings

2019 ◽  
Author(s):  
Reto Sterchi ◽  
Pascal Haegeli ◽  
Patrick Mair
Keyword(s):  
Large Scale ◽  
Selection Process ◽  
Decision Aids ◽  
Large Data ◽  
Limited Attention ◽  
Data Set ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Avalanche Hazard ◽  
The Relationship

Abstract. While guides in mechanized skiing operations use a well-established terrain selection process to limit their exposure to avalanche hazard and keep the residual risk at an acceptable level, the relationship between the open/closed status of runs and environmental factors is complex and has so far only received limited attention from research. Using a large data set of over 25 000 operational run list codes from a mechanized skiing operation, we applied a general linear mixed effects model to explore the relationship between acceptable skiing terrain (i.e., status open) and avalanche hazard conditions. Our results show that the magnitude of the effect of avalanche hazard on run list codes depends on the type of terrain that is being assessed by the guiding team. Ski runs in severe alpine terrain with steep lines through large avalanche slopes are much more susceptible to increases in avalanche hazard than less severe terrain. However, our results also highlight the strong effects of recent skiing on the run coding and thus the importance of prior first-hand experience. Expressing these relationships numerically provides an important step towards the development of meaningful decision aids, which can assist commercial operations to manage their avalanche risk more effectively and efficiently.

Turtle, Task Ninja, or Time Waster? Who Cares? Traditional Task-Completion Strategies Are Overrated

2020 ◽  
Vol 31 (3) ◽  
pp. 306-315 ◽  
Author(s):  
Lisa Vangsness ◽  
Michael E. Young
Keyword(s):  
Research Participation ◽  
Task Characteristics ◽  
Task Completion ◽  
State University ◽  
Data Set ◽  
Holistic Approaches ◽  
Diagnostic Approaches ◽  
Kansas State University ◽  
The Relationship ◽  
And Task

Standard approaches for identifying task-completion strategies, such as precrastination and procrastination, reduce behavior to single markers that oversimplify the process of task completion. To illustrate this point, we consider three task-completion strategies and introduce a new method to identify their use. This approach was tested using an archival data set (N = 8,655) of the available electronic records of research participation at Kansas State University. The approach outperformed standard diagnostic approaches and yielded an interesting finding: Several strategies were associated with negative outcomes. Specifically, both procrastinators and precrastinators struggled to finish tasks on time. Together, these findings underscore the importance of using holistic approaches to determine the relationship among task characteristics, individual differences, and task completion.

scholarly journals The Airlift Planning Problem

2019 ◽  
Vol 53 (3) ◽  
pp. 773-795
Author(s):  
Dimitris Bertsimas ◽  
Allison Chang ◽  
Velibor V. Mišić ◽  
Nishanth Mundru
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Simulated Data ◽  
Mixed Integer ◽  
Planning Problem ◽  
Weight Restrictions ◽  
Total Delay ◽  
Data Set ◽  
Scale Optimization ◽  
Airlift Planning

The U.S. Transportation Command (USTRANSCOM) is responsible for planning and executing the transportation of U.S. military personnel and cargo by air, land, and sea. The airlift planning problem faced by the air component of USTRANSCOM is to decide how requirements (passengers and cargo) will be assigned to the available aircraft fleet and the sequence of pickups and drop-offs that each aircraft will perform to ensure that the requirements are delivered with minimal delay and with maximum utilization of the available aircraft. This problem is of significant interest to USTRANSCOM because of the highly time-sensitive nature of the requirements that are typically designated for delivery by airlift, as well as the very high cost of airlift operations. At the same time, the airlift planning problem is extremely difficult to solve because of the combinatorial nature of the problem and the numerous constraints present in the problem (such as weight restrictions and crew rest requirements). In this paper, we propose an approach for solving the airlift planning problem faced by USTRANSCOM based on modern, large-scale optimization. Our approach relies on solving a large-scale mixed-integer programming model that disentangles the assignment decision (which aircraft will pickup and deliver which requirement) from the sequencing decision (in what order the aircraft will pickup and deliver its assigned requirements), using a combination of heuristics and column generation. Through computational experiments with both a simulated data set and a planning data set provided by USTRANSCOM, we show that our approach leads to high-quality solutions for realistic instances (e.g., 100 aircraft and 100 requirements) within operationally feasible time frames. Compared with a baseline approach that emulates current practice at USTRANSCOM, our approach leads to reductions in total delay and aircraft time of 8%–12% in simulated data instances and 16%–40% in USTRANSCOM’s planning instances.

scholarly journals Adapting research to the 21st century – the Swedish Biobank Register

2012 ◽  
Vol 21 (2) ◽  
Author(s):  
L. Norlin ◽  
M. Fransson ◽  
S. Eaker ◽  
G. Elinder ◽  
J.-E. Litton
Keyword(s):  
Health Care ◽  
Health Care Quality ◽  
Statistical Power ◽  
Large Scale ◽  
Science Research ◽  
Population Based ◽  
Care Quality ◽  
Data Set ◽  
Large Patient ◽  
Medical Health Care

<p>In Sweden, there are currently nearly 600 biobanks. The Swedish Biobank Register (SBR) is an on-going national investment by the county councils working to capture information in one database about all biobank samples collected from patients attending the Swedish medical health care. The aim of the SBR is to gather enough information about biobank samples to be able to physically trace the samples.</p><p>The BioBanking and Molecular Resource Infrastructure of Sweden (BBMRI.se) has been given the task of extending the SBR Information System (IS) with functionality useful for research in connection to health care, quality registers and large patient cohorts. The research extension is called BBMRI catalogue over sample collections for research. To achieve this, the SBR-IS will be extended with attributes useful for both epidemiological and clinical research enabling authorized researchers to search for samples stored at non-clinical biobanks nationwide. The Swedish Biobank Register, together with the BBMRI research catalogue, will be a unique resource for research. SBR-IS will contain information about biobank samples collected by both clinical and population-based biobanks specifically established for research purposes but BBMRI.se researchers will only be granted access to data related to population-based biobanks. As BBMRI.se is the Swedish hub of the pan-European biobank project BBMRI, whose goal is to promote excellence and efficacy in European life science research, the BBMRI research catalogue will also be made compatible with the European register by applying its minimum data set describing biobanks and their objects. In this paper we describe the implementation. Our belief is that it will pave the way for connecting biobanks on an international level as well as stimulate collaborations and optimize usage of biobank samples. In the long run, patients and sample donors will benefit as new results with high statistical power emerge from large scale studies.</p>

scholarly journals Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County–Level Policy Data Set (Preprint)

2020 ◽  
Author(s):  
Senan Ebrahim ◽  
Henry Ashworth ◽  
Cray Noah ◽  
Adesh Kadambi ◽  
Asmae Toumi ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Main Tool ◽  
The United States ◽  
Policy Formulation ◽  
Contact Tracing ◽  
Series Data ◽  
County Level ◽  
Data Set ◽  
The Relationship

BACKGROUND Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. OBJECTIVE The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. METHODS Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties. RESULTS This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (<i>P</i>&lt;.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (<i>P</i>=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22). CONCLUSIONS This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

scholarly journals SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES

2016 ◽  
Vol III-7 ◽  
pp. 33-40 ◽  
Author(s):  
A. Le Bris ◽  
N. Chehata ◽  
X. Briottet ◽  
N. Paparoditis
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Near Infrared ◽  
Spectral Band ◽  
Band Selection ◽  
Svm Classifier ◽  
Stochastic Algorithm ◽  
Confidence Measure ◽  
Data Set ◽  
Material Classification

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000&ndash;2400 nm) to material classification was also shown.

scholarly journals A Note on Smoking Behavior and Health Risk Taking

2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Christian Pfeifer
Keyword(s):  
Health Risk ◽  
Risk Taking ◽  
Large Scale ◽  
Smoking Behavior ◽  
Data Set ◽  
Men And Women ◽  
Risk Effects ◽  
Individual Health ◽  
Risk Takers ◽  
The Relationship

This research note presents two economic frameworks to describe the relationship between individual health risk aversion and smoking behavior. Using a large-scale representative data set (GSOEP), direct empirical evidence is found that individuals, who are more health risk taking, are more likely to be smokers and have a higher demand for cigarettes smoked per day. Non-linear specifications of risk taking reveal, however, that the risk effects are only significant for high risk takers. The estimated effects hold also separately for men and women.

scholarly journals Intersectional inequalities in science

2022 ◽  
Vol 119 (2) ◽  
pp. e2113067119
Author(s):  
Diego Kozlowski ◽  
Vincent Larivière ◽  
Cassidy R. Sugimoto ◽  
Thema Monroe-White
Keyword(s):  
Scientific Knowledge ◽  
Large Scale ◽  
White Men ◽  
Science Research ◽  
Scientific Impact ◽  
Research Areas ◽  
Research Organizations ◽  
The Us ◽  
Scientific Workforce ◽  
The Relationship

The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base. However, topic selection comes at a cost to minoritized individuals for whom we observe both between- and within-topic citation disadvantages. To enhance the robustness of science, research organizations should provide adequate resources to historically underfunded research areas while simultaneously providing access for minoritized individuals into high-prestige networks and topics.

scholarly journals Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

2017 ◽  
Vol 29 (5) ◽  
pp. 1293-1316 ◽  
Author(s):  
Yuan Zhao ◽  
Il Memming Park
Keyword(s):  
Gaussian Process ◽  
Large Scale ◽  
Point Processes ◽  
Simulated Data ◽  
Spike Trains ◽  
Single Trial ◽  
Data Set ◽  
Low Dimensional ◽  
Process Observation ◽  
Latent Trajectories

When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded populations of neurons can be explained by a small number of shared factors, or a low-dimensional trajectory. Recovering these latent trajectories, particularly from single-trial population recordings, may help us understand the dynamics that drive neural computation. However, due to the biophysical constraints and noise in the spike trains, inferring trajectories from data is a challenging statistical problem in general. Here, we propose a practical and efficient inference method, the variational latent gaussian process (vLGP). The vLGP combines a generative model with a history-dependent point process observation, together with a smoothness prior on the latent trajectories. The vLGP improves on earlier methods for recovering latent trajectories, which assume either observation models inappropriate for point processes or linear dynamics. We compare and validate vLGP on both simulated data sets and population recordings from the primary visual cortex. In the V1 data set, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space and the noise correlation. These results show that vLGP is a robust method with the potential to reveal hidden neural dynamics from large-scale neural recordings.

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