The effect of traffic data source on deterioration rates of heavy-duty flexible pavements

The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.     

An integrated semi-automated framework for domain-based polarity words extraction from an unannotated non-English corpus

The Journal of Supercomputing - Building sentiment analysis resources is a fundamental step before developing any sentiment analysis model. Sentiment lexicons are one of these critical resources....     

High Stable Al-MCM-41: Structural Characterization and Evaluation for Removal of Methylene Blue from Aqueous Solution

Aluminosilicate (Al-MCM-41) was synthesized via a modified wet-method in a Si/Al atomic ratio of 13.64:1.00 and calcination at 500 °C. The structural     

Integrative and multi-disciplinary framework for the 3D rehabilitation of large mandibular defects

The International Journal of Advanced Manufacturing Technology - The restoration of mandibular defects, especially large deformities is regarded as the most challenging surgical procedure owing to...     

Interoperability in Internet of Things: Taxonomies and Open Challenges

Mobile Networks and Applications - In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which...     

Isomeric Carborane Neuromuscular Blocking Agents

We synthesized a family of neuromuscular blocking agents (NMB) based on decamethonium, but containing a carborane cluster in the methylene chain between the two quaternary ammonium groups. The carborane cluster isomers o-NMB, m-NMB, and p-NMB were tested in animals for neuromuscular block and compared with agents used clinically: rocuronium and decamethonium. All three isomers caused reversible muscle weakness in mice as determined by grip strength and inverted screen tests, with a potency rank of p-NMB > rocuronium > decamethonium > m-NMB > o-NMB. The mechanism of action of the compounds was determined by using the in vitro rat phrenic nerve hemi-diaphragm preparation and electrophysiologic measurements in cells. Neostigmine reversed hemi-diaphragm weakness caused by the three isomers and rocuronium, but not succinylcholine. In electrophysiologic recordings of currents through acetylcholine receptor channels, the carborane compounds did not activate channel activity but did inhibit channel activation by acetylcholine. These results demonstrate that the carborane neuromuscular blocking agents are non-depolarizers in contrast to the depolarizing action of the parent compound.     

A Dual-Band Printed Antenna Design Based on Annular Koch Snowflake Slot Structure

Wireless Personal Communications - The design of a fractal based slot antenna, to serve the dual-band communication applications, is proposed in this paper. The structure of the proposed antenna is...     

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R²) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.