Contextual classification of multispectral image data

There are no author-identified significant results in this report

An Introduction to Statistical Issues and Methods in Metrology for Physical Science and Engineering

This article provides an overview of the interplay between statistics and measurement. Measurement quality affects inference from data collected and analyzed using statistical methods while appropriate data analysis quantifies the quality of measurements. This article brings material on statistics and measurement together in one place as a resource for practitioners. Both frequentist and Bayesian methods are discussed

An Exact Formula for the Average Run Length to False Alarm of the Generalized Shiryaev-Roberts Procedure for Change-Point Detection under Exponential Observations

We derive analytically an exact closed-form formula for the standard minimax Average Run Length (ARL) to false alarm delivered by the Generalized Shiryaev-Roberts (GSR) change-point detection procedure devised to detect a shift in the baseline mean of a sequence of independent exponentially distributed observations. Specifically, the formula is found through direct solution of the respective integral (renewal) equation, and is a general result in that the GSR procedure's headstart is not restricted to a bounded range, nor is there a "ceiling" value for the detection threshold. Apart from the theoretical significance (in change-point detection, exact closed-form performance formulae are typically either difficult or impossible to get, especially for the GSR procedure), the obtained formula is also useful to a practitioner: in cases of practical interest, the formula is a function linear in both the detection threshold and the headstart, and, therefore, the ARL to false alarm of the GSR procedure can be easily computed.Comment: 9 pages; Accepted for publication in Proceedings of the 12-th German-Polish Workshop on Stochastic Models, Statistics and Their Application

Imagining the Lives of Others: Empathy in Public Relations

This paper asks how we might theorise empathy in public relations (PR) in the light of a widespread ‘turn’ towards emotion in the academy, as well as in popular discourse. Two distinct notions of empathy are explored: ‘true’empathy as discussed in intercultural communication, is driven by a human concern for the other in order to understand experiences, feelings and situations that may be different from our own; whereas ‘instrumental’ empathy, reflecting a self orientation, is said to characterise much neoliberal market discourse in which corporations are urged to understand their customers better. Thus, while empathy may seem highly desirable as a means to enter into dialogue with an organisation’s publics, particularly during times of social upheaval and crisis, it is important to pay attention to empathy in public relations discourses including whose goals are served by empathetic engagement; and the type(s) of empathy called upon within a PR context. A literature review identified a socio-cultural definition of empathy as ‘imaginary effort’. A review of the public relations literature, however, found that while empathy is considered an important principle and personal attribute, notions of empathy, with a few exceptions, are under-explored. Nonfunctionalist, socio-cultural research which examines the meanings that practitioners associate with empathy is distinctly lacking; therefore in order to gain further insight into empathy, two sources of data were explored. The analysis of a popular online practitioner blog showed that other-centred empathic skill is discursively framed as instrumental in achieving clients’ business objectives. The analysis of three empathy statements drawn from 12 in-depth interviews with practitioners revealed complex empathic discourse in practitioner-client relationships. While the findings are limited to illustrative analyses only, this paper challenges researchers to develop conceptualisations and perspectives of empathy as imaginary effort in public relations

Applying Quality Control Charts to the Analysis of Single-Subject Data Sequences

Techniques from the field of quality control can be used to classify the quality of individual samples of physical or cognitive performance. After stable baselines have been established for an individual, deviations in performance can be evaluated using control charts. The effectiveness of this approach in evaluating cognitive performance was tested using databases collected under a variety of risk factors. The sensitivity and specificity characteristics of Shewhart, cumulativesum (CUSUM), and exponentially weighted moving average (EWMA) control charts were determined for a total of 174 trials involving 10 participants and 23 cognitive performance assessment measures. The most effective technique in each case was typically a function of the specific performance measure and the type of performance change being evaluated. Sensitivity and specificity for the best techniques were as high as 100%. This study demonstrated the usefulness of quality control charts as a tool to evaluate individual participant performance over time. Actual or potential applications of this research include readiness-to-perform screening of industrial workers in order to improve the health and safety of the workforce.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR-NEON system Version 1

Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR&ndash;NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR&ndash;NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR&ndash;NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change. &nbsp;</p

Bioactivity of diatomaceous earth to Sitophilus zeamais (Coleoptera: Curculionidae) in different application conditions

The objective of this study was to evaluate the insecticidal activity of diatomaceous earth (DE) at different ambient temperatures on adult Sitophilus zeamais and progeny, using different doses and exposure periods. The experiments were performed in Petri dishes containing 40 g of the whole corn kernel, treated with DE at doses of 0, 0.25, 0.5 and 1.0 kg Mg-1. Each dish was infested with 25 S. zeamais adults and kept at climatic chambers under temperatures of 20, 25, 30, 35 and 40 ºC. The insect mortality was recorded after six and 15 days from the beginning of the bioassays. The grains evaluated at 15 days were separated from insects and kept in the dishes for another 75 days under the same temperature conditions. After this period the effect of ambient temperature and of diatomaceous earth doses on the emergence of S. zeamais in the F1 generation was evaluated. It was found that the mortality of S. zeamais increased with the higher dose and temperature during the exposure period of six and 15 days. The number of insects emerged reduced with increasing temperature in these two exposure periods. The increase of temperature and exposure period favored the efficacy of DE in lower doses for control of S. zeamais.O objetivo deste trabalho foi avaliar a atividade inseticida da terra de diatomácea (TD) em diferentes temperaturas ambiente sobre adultos de Sitophilus zeamais e sua progênie, utilizando-se diferentes doses e períodos de exposição. Os experimentos foram realizados em placas de Petri contendo 40 g de grãos inteiros de milho, tratados com TD nas doses de 0; 0,25; 0,5 e 1,0 kg Mg-1. Cada placa foi infestada com 25 adultos de S. zeamais e mantida em câmaras climáticas nas temperaturas de 20, 25, 30, 35 e 40 ºC. A mortalidade dos insetos foi contabilizada após seis e 15 dias do início dos bioensaios. Os grãos avaliados aos 15 dias foram separados dos insetos e mantidos nas placas por mais 75 dias sob as mesmas condições de temperatura. Após este período avaliou-se o efeito da temperatura ambiente e das doses da TD sobre a emergência de S. zeamais na geração F1. Verificou-se que a mortalidade de S. zeamais aumentou com o incremento da dose e da temperatura nos períodos de exposição de seis e 15 dias; já o número de insetos emergidos reduziu com o aumento da temperatura nesses dois períodos de exposição. O aumento da temperatura e do período de exposição favoreceu a eficácia da TD em menores doses, para controle de S. zeamais

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology