Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

DINC-COVID : a webserver for ensemble docking with flexible SARS-CoV-2 proteins

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins

Use of CREAMS model in experimental designs for shallow land burial of low level wastes

A state-of-the art model developed by the US Department of Agriculture called CREAMS (A Field Scale Model for Chemicals, Runoff, and Erosion from Agricultural Management Systems) is used to simulate the hydrologic processes in soil and rock covers at shallow land waste disposal sites. Application of the CREAMS model in management of soil moisture and percolation at waste disposal sites is discussed for diverse topsoil-backfill-cobble-gravel trench cap designs tested at different field scales. 8 references, 7 figures, 3 tables

Variability of Infiltration within Large Runoff Plots on Rangelands

In this study we investigated the variability of infiltration on native rangeland sites. A rainfall simulator was used to collect data on runoff from small (0.37 m2) plots located within large plot boundaries (32.5 m2). Three range sites were sampled and data were collected from unfenced, fenced, and rototilled conditions on each site. In addition data were collected on vegetation, antecedent moisture, bulk density, soil texture, and organic matter as possible explanations for variations in hydrologic response on small and large plots. The field study demonstrated large variability in measured infiltration and soil physical properties on relatively uniform rangeland sites, suggesting that inherent variability patterns need to be examined to provide appropriate confidence intervals for single parameter values that may be applied to larger areas. No set of factors consistently explained the observed variability within large plots.This material was digitized as part of a cooperative project between the Society for Range Management and the University of Arizona Libraries.The Journal of Range Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Integrating environmental restoration management within DOE using an alternative identification and evaluation procedure: A methodology and a case history

The process of identifying and evaluating alternative corrective measures is a fundamental integrating part of Environmental Restoration (ER) activities. The process used in the Los Alamos National Laboratory (the Laboratory) ER Program is based on principles and tools from multiattribute decision analysis, a well-developed and proven method for evaluating options in decision situations involving multiple objectives, uncertainty, multiple interested stakeholders in the final decision, and the need for technical input from disparate disciplines. The process provides a methodology that has been extensively developed and reviewed over the past five decades; it provides a methodological structure for incorporating the concepts espoused in the streamlined approach as well as more specific guidelines such as data quality objectives (DQOs). The application of this methodology to the ER Program at Los Alamos is described in this paper

Integrating environmental restoration management within DOE using an alternative identification and evaluation procedure: A methodology and a case history

The process of identifying and evaluating alternative corrective measures is a fundamental integrating part of Environmental Restoration (ER) activities. The process used in the Los Alamos National Laboratory (the Laboratory) ER Program is based on principles and tools from multiattribute decision analysis, a well-developed and proven method for evaluating options in decision situations involving multiple objectives, uncertainty, multiple interested stakeholders in the final decision, and the need for technical input from disparate disciplines. The process provides a methodology that has been extensively developed and reviewed over the past five decades; it provides a methodological structure for incorporating the concepts espoused in the streamlined approach as well as more specific guidelines such as data quality objectives (DQOs). The application of this methodology to the ER Program at Los Alamos is described in this paper

Prediction methodology for contaminant transport from rangeland watersheds

Weather on arid and semiarid lands can be extremely variable. Runoff is generally emphermeral, and high intensity, short-duration rainfall events are the major stimulus for runoff events. Transport of sediment and associated contaminants occurs with these infrequent events. Incorporation of variability in weather into any prediction technology is essential to provide accurate representations of climate-induced uncertainty in predictions of hydrologic response. The objective of this study is to investigate a method for including short-term climatic variations in analyses for contaminant transport from rangeland watersheds in arid/semiarid regions. Short term is defined here as a twenty to fifty time frame and it is assumed that lone term climatic fluctuations are not observed during this time. Also, most weather records are available for this time period; predictions of greater length are extrapolations of existing records unless corroborative data for longer term trends are collected. Predictions are being made with condensable uncertainty in the weather inputs even if the models for water, sediment, and contaminant transport are perfectly unknown. This study will incorporate uncertainty in weather inputs into the prediction process and address the ramifications of this uncertainty. Uncertainty introduced by improper model or parameter specification is only briefly addressed

Motion Planning

International audienceThis chapter presents a kinodynamic motion planner for computing agile motions of quad-rotor-like aerial robots in constrained environments. Based on a simple dynamic model of the UAV, a computationally-efficient local planner is proposed to generate flyable trajectories of minimal time. This local planner is then integrated as a key component for global motion planning using different approaches. The good performance of the proposed methods is illustrated with results in simulation , as well as a preliminary experimentation with a real quad-rotor

Advancements for A* and RRT in 3D path planning of UAVs

Advancements in Unmanned Aerial Vehicles (UAVs) design, actuator and sensory systems and control are making such devices financially available to a wide spectrum of users with various demands and expectations. To mitigate with this ever increasing demand robust, efficient and application–specific path planning is important. This paper presents advancements over the A* and the smoothing algorithms presented in, 1 utilising the same test scenarios. Analysis of results in 1 showed a ripple in path length as the resolution changes for all scenarios considered and less than 0.1% path length improvements after certain amount of smoothing iterates. To attenuate the path length ripple, the A* ripple reduction algorithm was developed. Results show a reduction of more than 46% in terms of standard deviation with respect to the original A* algorithm without any increase in the mean path length for all scenarios. Secondly, the smoothing algorithm developed in 1 was improved to stop smoothing based on the rate of smoothing of previous iterates. Results show more than 10 multiple less path smoothing time maintaining a path length reduction especially for simple scenarios. These advancements further portray the discussed path planning algorithms as candidates to the realisation of online 3D UAV path planning. Control & Simulatio