Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Correct program parallelisations

International Journal on Software Tools for Technology Transfer - A commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives...     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

Pervaporative removal of acrylonitrile from aqueous streams through polydimethylsiloxane membrane

This study describes the successful separation of acrylonitrile (ACN) from dilute aqueous streams using pervaporation process. The influences of ACN feed concentration, permeate pressure, operating temperature, feed flow rate and membrane thickness on the membrane separation performance were investigated. The results showed that with an increase in ACN concentration in the feed solution, the permeation flux of ACN increased while the enrichment factor decreased. It was also indicated that increasing the permeate pressure reduced the driving force for mass transfer and consequently the permeation flux dropped while the enrichment factor enhanced. Polydimethylsiloxane membranes used in this study showed very good properties in the separation process, leading to enrichment factors in the range of 70-140. Furthermore, the activation energy for pervaporation of both ACN and water calculated from Arrhenius plot indicated that the permeation of water through the membrane was more temperature dependant than ACN.     

A Model for Identifying the Behavior of Alzheimer’s Disease Patients in Smart Homes

Wireless Personal Communications - In recent years, Smart Cities and Smart Homes have been studied as an important field of research. The design and construction of smart homes have flourished so...     

A hybrid capacitive pressure and temperature sensor fabricated by adhesive bonding technique for harsh environment of kraft pulp digesters

We have developed a compensated capacitive pressure and temperature sensor for kraft pulp digesters (pH 13.5, temperatures 25–175°C reaching a local maximum of 180°C and pressures up to 2 MPa). The gauge capacitive pressure sensor was fabricated by bonding silicon and Pyrex chips using a high temperature, low viscosity UV (ultraviolent) adhesive as the gap-controlling layer and heat curing adhesive as the bonding agent. A simple chip bonding technique, involving insertion of the adhesive into the gap between two chips was developed. A platinum thin-film wire was patterned on top of a silicon chip to form a resistance temperature detector (RTD) with a nominal resistance of 1,500 Ω. A silicon dioxide layer and a thin layer of Parylene were deposited to passivate the pressure sensor diaphragm and the sensors were embedded into epoxy for protection against the caustic environment in kraft digesters. The sensors were tested up to 2 MPa and 170°C in an environment chamber. The maximum thermal error of ±1% (absolute value of ±20 kPa) full scale output (FSO) and an average sensitivity of 0.554 fF/kPa were measured. Parylene-coated silicon chips were tested for a full kraft pulping cycle with no signs of corrosion.     

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.