Truck Weight Enforcement Measures of Effectiveness: Development and Software Application

1998 ◽  
Vol 1643 (1) ◽  
pp. 152-160 ◽  
Author(s):  
F. R. Hanscom ◽  
M. W. Goelzer
Keyword(s):  
Software Tool ◽  
Field Evaluation ◽  
Seasonal Effects ◽  
Data Sets ◽  
Activity Data ◽  
Software Application ◽  
Enforcement Effort ◽  
Measures Of Effectiveness ◽  
Truck Weight ◽  
Enforcement Activity

A software tool was developed to determine what is accomplished as the result of truck weight enforcement efforts. Traditionally applied measures (e.g., numbers of trucks weighed and citations issued) have simply provided indications of enforcement effort. These previously applied measures failed to provide results in terms of real enforcement objectives, such as deterring overweight trucks and minimizing pavement wear and tear. Consequently the need exists to develop and validate truck weight enforcement measures of effectiveness (MOE). MOEs were developed via a series of analytical procedures. They were subsequently validated in a comprehensive four-state field evaluation. Matched (weigh-in-motion) (WIM) data sets, collected under controlled baseline and enforcement conditions, were analyzed to determine the sensitivity of candidate MOEs to actual enforcement activity. Data collection conditions were controlled in order to avoid contamination from hour-of-day, day-of-week, and seasonal effects. The following MOEs, were validated on the basis of their demonstrated sensitivity to truck weight enforcement objectives and the presence of enforcement activity: (1) severity of overweight violations, (2) proportion of overweight trucks, (3) average equivalent single-axle load (ESAL), (4) excess ESALs, and (5) bridge formula violations. These measures are sensitive to legal load-limit compliance objectives of truck weight enforcement procedures as well as the potential for overweight trucks to produce pavement deterioration. The software User Guide that statistically compares calculated MOEs between observed enforcement conditions is described in this paper. The User Guide also allows users to conduct an automated pavement design life analysis estimating, the theoretical pavement-life effect resulting from the observed enforcement activity.

scholarly journals EFFECTIVENESS OF GUIDANCE AND COUNSELING SERVICES BY USING THE APPLICATIONS INSTRUMENTATION AND ACTIVITY DATA SET IN SMA NEGERI 1 METRO LESSON 2009/2010

2014 ◽  
Vol 4 (1) ◽  
pp. 35
Author(s):  
Agus Wibowo
Keyword(s):  
Qualitative Research ◽  
Counseling Services ◽  
Guidance And Counseling ◽  
Data Sets ◽  
Research Subjects ◽  
Activity Data ◽  
Data Set ◽  
Research Level ◽  
Data Collection Technique ◽  
High Level

Abstract: Implementation of guidance and counseling services should be based on the needs and problems of students, so the effectiveness of the service will be achieved to the fullest. But the reality is a lot of implementation of guidance and counseling services in schools, do not notice it. So that the completion of the problems experienced by students sama.Berangkat always use the services of this, the research level of effectiveness of guidance and counseling that implementation has been using the application activity instrumentation and data sets as the basis for an implementation of the service. The method used is a qualitative research subjects that teachers BK and Students at SMA Negeri 1 Metro. Data collection technique through interview, observation and documentation. Research results show that by utilizing activity instrumentation applications and data sets, the counseling services have a high level of effectiveness. In carrying out the service, BK teachers can identify problems and needs experienced by students, so that the efforts of the assistance provided to be more precise, and problem students can terentaskan optimally.Keyword: Guidance and Counseling, Instrumentation Applications, Data Association

scholarly journals WONKAandOOMMPPAA: analysis of protein–ligand interaction data to direct structure-based drug design

2017 ◽  
Vol 73 (3) ◽  
pp. 279-285
Author(s):  
Charlotte M. Deane ◽  
Ian D. Wall ◽  
Darren V. S. Green ◽  
Brian D. Marsden ◽  
Anthony R. Bradley
Keyword(s):  
Three Dimensional ◽  
Structural Data ◽  
Data Sets ◽  
Interaction Data ◽  
Activity Data ◽  
Ligand Interaction ◽  
Structure Based Drug Design ◽  
Web Based ◽  
Protein Ligand Interaction ◽  
Ligand Activity

In this work, two freely available web-based interactive computational tools that facilitate the analysis and interpretation of protein–ligand interaction data are described. Firstly,WONKA, which assists in uncovering interesting and unusual features (for example residue motions) within ensembles of protein–ligand structures and enables the facile sharing of observations between scientists. Secondly,OOMMPPAA, which incorporates protein–ligand activity data with protein–ligand structural data using three-dimensional matched molecular pairs.OOMMPPAAhighlights nuanced structure–activity relationships (SAR) and summarizes available protein–ligand activity data in the protein context. In this paper, the background that led to the development of both tools is described. Their implementation is outlined and their utility using in-house Structural Genomics Consortium (SGC) data sets and openly available data from the PDB and ChEMBL is described. Both tools are freely available to use and download at http://wonka.sgc.ox.ac.uk/WONKA/ and http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/.

scholarly journals Mumame: a software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

2019 ◽  
Vol 3 ◽  
Author(s):  
Shruthi Magesh ◽  
Viktor Jonsson ◽  
Johan Bengtsson-Palme
Keyword(s):  
Microbial Communities ◽  
Point Mutations ◽  
Software Tool ◽  
Metagenomic Data ◽  
Data Sets ◽  
Resistance Mutations ◽  
Shotgun Metagenomics ◽  
Key Factor ◽  
Detection Of Mutations ◽  
And Function

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame).

scholarly journals Collecting public RGB-D datasets for human daily activity recognition

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141770907 ◽  
Author(s):  
Hanbo Wu ◽  
Xin Ma ◽  
Zhimeng Zhang ◽  
Haibo Wang ◽  
Yibin Li
Keyword(s):  
Activity Recognition ◽  
Daily Activity ◽  
Visual Cues ◽  
Large Scale ◽  
Hot Spot ◽  
Feature Representation ◽  
Data Sets ◽  
Activity Data ◽  
Data Set ◽  
Depth Motion Maps

Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.

scholarly journals Development of a tool-kit for the detection of healthy and injured cardiac tissue based on MR imaging

2017 ◽  
Vol 3 (2) ◽  
pp. 195-198
Author(s):  
Philip Westphal ◽  
Sebastian Hilbert ◽  
Michael Unger ◽  
Claire Chalopin
Keyword(s):  
Contrast Agent ◽  
Cardiac Tissue ◽  
Software Tool ◽  
Ease Of Use ◽  
Free Software ◽  
Patient Specific ◽  
Initial Contour ◽  
Data Sets ◽  
Heart Model ◽  
Data Set

AbstractPlanning of interventions to treat cardiac arrhythmia requires a 3D patient specific model of the heart. Currently available commercial or free software dedicated to this task have important limitations for routinely use. Automatic algorithms are not robust enough while manual methods are time-consuming. Therefore, the project attempts to develop an optimal software tool. The heart model is generated from preoperative MR data-sets acquired with contrast agent and allows visualisation of damaged cardiac tissue. A requirement in the development of the software tool was the use of semi-automatic functions to be more robust. Once the patient image dataset has been loaded, the user selects a region of interest. Thresholding functions allow selecting the areas of high intensities which correspond to anatomical structures filled with contrast agent, namely cardiac cavities and blood vessels. Thereafter, the target-structure, for example the left ventricle, is coarsely selected by interactively outlining the gross shape. An active contour function adjusts automatically the initial contour to the image content. The result can still be manually improved using fast interaction tools. Finally, possible scar tissue located in the cavity muscle is automatically detected and visualized on the 3D heart model. The model is exported in format which is compatible with interventional devices at hospital. The evaluation of the software tool included two steps. Firstly, a comparison with two free software tools was performed on two image data sets of variable quality. Secondly, six scientists and physicians tested our tool and filled out a questionnaire. The performance of our software tool was visually judged more satisfactory than the free software, especially on the data set of lower quality. Professionals evaluated positively our functionalities regarding time taken, ease of use and quality of results. Improvements would consist in performing the planning based on different MR modalities.

scholarly journals Field Evaluation of Yield and Yield Component Traits of Breeding Lines of Maize over Two Seasons in Derived Savannah Agro-Ecology

2018 ◽  
Vol 10 (4) ◽  
pp. 567-574
Author(s):  
Charles U. UBA ◽  
Christian U. AGBO ◽  
Uchechukwu P. CHUKWUDI ◽  
Andrew A. EFUSIE ◽  
Stella O. MUOJIAMA
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Field Evaluation ◽  
Yield Component ◽  
Seasonal Effects ◽  
International Institute ◽  
Early Season ◽  
Breeding Lines ◽  
Late Season ◽  
Positive Effects

The understanding of yield and the interaction with its components is very important for selection in early generations of crop breeding. Twelve maize genotypes were collected from International Institute for Tropical Agriculture (IITA) along with seven landraces in order to identify the contribution of different traits to yield improvement. The experiments were carried out in two different seasons (March/April-early and July/August- late) in a randomized complete block design with three replications. Early season planting had a higher grain yield than late season planting. The difference in grain yield between early and late season was 3.92 tons/ha. This represents a 27.8% increase in grain yield during the early season over the late season planting. Number of ears per plant and shelling percentage were not influenced by seasonal effects. Ear weight and days to tasselling showed the highest direct positive effects of 0.972 and 0.665, respectively on grain yield, during early season. Furthermore, ear weight, followed by shelling percentage, exerted the highest direct positive effect on grain yield in late season. Higher indirect positive effects were obtained for ear diameter, ear length, ear height and plant height via ear weight in both seasons. Ear weight, days to tasselling and ear length were identified as the major traits affecting yield of maize in both seasons in the derived Savannah agro-ecology.

scholarly journals Constraining models of activity on comet 67P/Churyumov-Gerasimenko with Rosetta trajectory, rotation, and water production measurements

2019 ◽  
Vol 630 ◽  
pp. A18 ◽  
Author(s):  
N. Attree ◽  
L. Jorda ◽  
O. Groussin ◽  
S. Mottola ◽  
N. Thomas ◽  
...  
Keyword(s):  
Momentum Transfer ◽  
Observational Data ◽  
Activity Pattern ◽  
Seasonal Effects ◽  
Data Sets ◽  
Gravitational Acceleration ◽  
Water Production ◽  
Active Fraction ◽  
Trajectory Data ◽  
Comet 67P

Aims. We use four observational data sets, mainly from the Rosetta mission, to constrain the activity pattern of the nucleus of comet 67P/Churyumov-Gerasimenko (67P). Methods. We developed a numerical model that computes the production rate and non-gravitational acceleration of the nucleus of comet 67P as a function of time, taking into account its complex shape with a shape model reconstructed from OSIRIS imagery. We used this model to fit three observational data sets: the trajectory data from flight dynamics; the rotation state as reconstructed from OSIRIS imagery; and the water production measurements from ROSINA of 67P. The two key parameters of our model, adjusted to fit the three data sets all together, are the activity pattern and the momentum transfer efficiency (i.e., the so-called η parameter of the non-gravitational forces). Results. We find an activity pattern that can successfully reproduce the three data sets simultaneously. The fitted activity pattern exhibits two main features: a higher effective active fraction in two southern super-regions (~10%) outside perihelion compared to the northern regions (<4%), and a drastic rise in effective active fraction of the southern regions (~25−35%) around perihelion. We interpret the time-varying southern effective active fraction by cyclic formation and removal of a dust mantle in these regions. Our analysis supports moderate values of the momentum transfer coefficient η in the range 0.6–0.7; values η ≤ 0.5 or η ≥ 0.8 significantly degrade the fit to the three data sets. Our conclusions reinforce the idea that seasonal effects linked to the orientation of the spin axis play a key role in the formation and evolution of dust mantles, and in turn, they largely control the temporal variations of the gas flux.

scholarly journals AUSPEX: a graphical tool for X-ray diffraction data analysis

2017 ◽  
Vol 73 (9) ◽  
pp. 729-737 ◽  
Author(s):  
Andrea Thorn ◽  
James Parkhurst ◽  
Paul Emsley ◽  
Robert A. Nicholls ◽  
Melanie Vollmar ◽  
...  
Keyword(s):  
Data Analysis ◽  
Diffraction Data ◽  
Software Tool ◽  
Data Conversion ◽  
Data Sets ◽  
X Ray Diffraction ◽  
X Ray ◽  
Pixel Detectors ◽  
Graphical Tool ◽  
Data Acquisition And Processing

In this paper,AUSPEX, a new software tool for experimental X-ray data analysis, is presented. Exploring the behaviour of diffraction intensities and the associated estimated uncertainties facilitates the discovery of underlying problems and can help users to improve their data acquisition and processing in order to obtain better structural models. The program enables users to inspect the distribution of observed intensities (or amplitudes) against resolution as well as the associated estimated uncertainties (sigmas). It is demonstrated howAUSPEXcan be used to visually and automatically detect ice-ring artefacts in integrated X-ray diffraction data. Such artefacts can hamper structure determination, but may be difficult to identify from the raw diffraction images produced by modern pixel detectors. The analysis suggests that a significant portion of the data sets deposited in the PDB contain ice-ring artefacts. Furthermore, it is demonstrated how other problems in experimental X-ray data caused, for example, by scaling and data-conversion procedures can be detected byAUSPEX.

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