Regional subsidence modelling in Murcia city (SE Spain) using 1-D vertical finite element analysis and 2-D interpolation of ground surface displacements

Subsidence is a hazard that may have natural or anthropogenic origin causing important economic losses. The area of Murcia city (SE Spain) has been affected by subsidence due to groundwater overexploitation since the year 1992. The main observed historical piezometric level declines occurred in the periods 1982–1984, 1992–1995 and 2004–2008 and showed a close correlation with the temporal evolution of ground displacements. Since 2008, the pressure recovery in the aquifer has led to an uplift of the ground surface that has been detected by the extensometers. In the present work an elastic hydro-mechanical finite element code has been used to compute the subsidence time series for 24 geotechnical boreholes, prescribing the measured groundwater table evolution. The achieved results have been compared with the displacements estimated through an advanced DInSAR technique and measured by the extensometers. These spatio-temporal comparisons have showed that, in spite of the limited geomechanical data available, the model has turned out to satisfactorily reproduce the subsidence phenomenon affecting Murcia City. The model will allow the prediction of future induced deformations and the consequences of any piezometric level variation in the study area.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Analysis of Protein Turnover by Quantitative SNAP-Based Pulse-Chase Imaging

Assessment of protein dynamics in living cells is crucial for understanding their biological properties and functions. The SNAP-tag, a self labeling suicide enzyme, presents a tool with unique features that can be adopted for determining protein dynamics in living cells. Here we present detailed protocols for the use of SNAP in fluorescent pulse-chase and quench-chase-pulse experiments. These time-slicing methods provide powerful tools to assay and quantify the fate and turnover rate of proteins of different ages. We cover advantages and pitfalls of SNAP-tagging in fixed- and live-cell studies and evaluate the recently developed fast-acting SNAPf variant. In addition, to facilitate the analysis of protein turnover datasets, we present an automated algorithm for spot recognition and quantification.

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.

Fact book : National Priorities List under the original Hazard Ranking System, 1981 - 1991.

Mode of access: Internet.

Analysis of subpoenaed television data /

March 1978.

Automated highway systems precursor systems analyses interim results workshop. Conference proceedings.

Turner-Fairbank Highway Research Center, Office of Safety Research and Development, McLean, Va.

Special occupational hazard review with control recommendations for the use of ethylene oxide as a sterilant in medical facilities /

Mode of access: Internet.

State inspection program evaluation and data analysis: data base E SPSS frequencies tabulations for brake components and explanatory variables. Volume III. Final report.

National Highway Traffic Safety Administration, Office of Vehicle Safety Research, Washington, D.C.

Analysis of truck safety on crest vertical curves. Final report.

Federal Highway Administration, Washington, D.C.

Analysis of on-road failure data. Final report.

National Highway Traffic Safety Administration, Crash Avoidance Division, Washington, D.C.

Analysis of Madison County vehicle data to evaluate the effect of Autocheck program on vehicle accident rates. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Rail-highway crossing hazard prediction research results. Final report.

Transportation Department, Washington, D.C.

Economic analysis of the gypsy moth problem in the Northeast.

Cover title.

Amendment : assessment of ecotoxicological hazard of Waukegan Harbor sediment /

"HWRIC Project no. 87-040."