Evaluation of Bioanalytical Assays for Toxicity Assessment and Mode of Toxic Action Classification of Reactive Chemicals

2003 ◽  
Vol 37 (21) ◽  
pp. 4962-4970 ◽  
Author(s):  
Angela Harder ◽  
Beate I. Escher ◽  
Paolo Landini ◽  
Nicole B. Tobler ◽  
René P. Schwarzenbach
Keyword(s):  
Toxicity Assessment ◽  
Toxic Action ◽  
Action Classification ◽  
Reactive Chemicals ◽  
Bioanalytical Assays

IRAC: Insecticide Resistance and Mode-of-action Classification of Insecticides

2019 ◽  
pp. 995-1012 ◽  
Author(s):  
Ralf Nauen ◽  
Russell Slater ◽  
Thomas C. Sparks ◽  
Alfred Elbert ◽  
Alan Mccaffery
Keyword(s):  
Insecticide Resistance ◽  
Mode Of Action ◽  
Action Classification

Character Strengths Before and After September 11

2003 ◽  
Vol 14 (4) ◽  
pp. 381-384 ◽  
Author(s):  
Christopher Peterson ◽  
Martin E.P. Seligman
Keyword(s):  
Character Strengths ◽  
September 11 ◽  
Self Report ◽  
Terrorist Attacks ◽  
Action Classification ◽  
Before And After ◽  
On Line ◽  
September 11 Terrorist Attacks ◽  
Positive Traits

Did Americans change following the September 11 terrorist attacks? We provide a tentative answer with respect to the positive traits included in the Values in Action Classification of Strengths and measured with a self-report questionnaire available on-line and completed by 4,817 respondents. When scores for individuals completing the survey in the 2 months immediately after September 11 were compared with scores for those individuals who completed the survey before September 11, seven character strengths showed increases: gratitude, hope, kindness, leadership, love, spirituality, and teamwork. Ten months after September 11, these character strengths were still elevated, although to a somewhat lesser degree than immediately following the attacks.

scholarly journals Positive Professional Practice: A Strength-Based Reflective Practice Teaching Model

2021 ◽  
pp. 165-204
Author(s):  
Mathew A. White
Keyword(s):  
Reflective Practice ◽  
Professional Practice ◽  
Character Strengths ◽  
Positive Education ◽  
Theoretical Frameworks ◽  
Action Classification ◽  
Self Reflection ◽  
Strength Based ◽  
Practice Teaching

AbstractWhile positive education research has grown over the past decade, making strides in measurement, interventions, and applications, it has also been criticised for lacking consistent guiding theoretical frameworks, heavily emphasising psychology over education, and being driven by unacknowledged pedagogical assumptions. This chapter argues that a particular stumbling block has been ignoring the professional practice of positive education; that is, what positive education teachers do and how they know they are having an impact. To addresses this gap, this chapter introduces a strength-based reflective practice model for teachers that integrates the Values in Action classification of character strengths with Brookfield’s four lenses for reflective practice, which consists of: (1) the students’ eyes, (2) colleagues’ perceptions, (3) personal experience, and (4) theory. The model aims to provide a method for critical self-reflection, thereby helping to enable effective professional practice. Through this model, perhaps positive education can become a pedagogy that has found its practice.

scholarly journals Adaptive SVM for Data Stream Classification

2017 ◽  
Vol 29 (1) ◽  
Author(s):  
Isah A. Lawal ◽  
Salihu A. Abdulkarim
Keyword(s):  
Human Action ◽  
Training Data ◽  
Support Vector ◽  
Action Classification ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Model Selection Approach ◽  
Real World Applications ◽  
On Line

In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM) learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

scholarly journals Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4266 ◽  
Author(s):  
Behnaz Rezaei ◽  
Yiorgos Christakis ◽  
Bryan Ho ◽  
Kevin Thomas ◽  
Kelley Erb ◽  
...  
Keyword(s):  
Motor Behavior ◽  
Wearable Technology ◽  
Test Accuracy ◽  
Action Classification ◽  
Video Recordings ◽  
Average Accuracy ◽  
Complex Motor ◽  
Human Motor ◽  
Low Dimensional

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g., involving interactions with the environment). Recent advances in computer vision and deep learning have opened up new possibilities for extracting information from video recordings. In this paper, we present a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera. Our method addresses challenges associated with tracking multiple human actors and classification of actions in videos recorded in changing environments with different fields of view. We implement a cascaded pose tracker that uses temporal relationships between detections for short-term tracking and appearance based tracklet fusion for long-term tracking. Furthermore, for action classification, we use pose evolution maps derived from the cascaded pose tracker as low-dimensional and interpretable representations of the movement sequences for training a convolutional neural network. The cascaded pose tracker achieves an average accuracy of 88% in tracking the target human actor in our video recordings, and overall system achieves average test accuracy of 84% for target-specific action classification in untrimmed video recordings.

Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles

2014 ◽  
Vol 49 ◽  
pp. 23-35 ◽  
Author(s):  
Zhankun Xi ◽  
Swanand Khare ◽  
Aaron Cheung ◽  
Biao Huang ◽  
Tianhong Pan ◽  
...  
Keyword(s):  
Mode Of Action ◽  
Cellular Response ◽  
Time Dependent ◽  
Action Classification ◽  
Concentration Time ◽  
Response Profiles

scholarly journals Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8409
Author(s):  
Rajesh Amerineni ◽  
Lalit Gupta ◽  
Nathan Steadman ◽  
Keshwyn Annauth ◽  
Charles Burr ◽  
...  
Keyword(s):  
Human Performance ◽  
Wearable Sensors ◽  
Human Movement ◽  
Sport Performance ◽  
Time Warping ◽  
Action Classification ◽  
Human Movements ◽  
Non Linear ◽  
Dynamic Time

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.

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