A dynamic programming method for analysis of bridges under multiple moving loads

A dynamic programming (DP) method is presented for analysis of statically determinate and indeterminate bridges subjected to moving loads such as those specified by the American Association of State Highway and Transportation Officials (AASHTO) specifications. These loads consist of uniform lane loading, two-axle truck loading and two-axle truck plus one-axle semitrailer loading. The solution of the DP problem is based on the use of influence line diagrams and extremum functions. Recurrence formulae have been developed for various types of AASHTO live loads. The procedure presented in this article is quite general and can be applied to any type of structures with linear behaviour and any type of moving loads. Six examples are presented: a simply-supported beam, a simply-supported Pratt truss, an arch bridge, a continuous Pratt truss, a continuous frame and a cable-stayed bridge.     

Design,Facile Synthesis and Characterization of Porphyrin-Zirconium-Ferrite@SiO2 Core-Shell and Catalytic Application in Cyclohexane Oxidation

Silicon - In this study, a new magnetic ZrFe2O4@SiO2-TCPP nanocatalyst with high efficiency was used for the oxidation of cyclohexane to cyclohexanone (Ke) and cyclohexanol (Al). The mesoporous...     

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.

     

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...     

Wavelet-Hybrid Feedback Linear Mean Squared Algorithm for Robust Control of Cable-Stayed Bridges

Cable-stayed bridges are flexible structures, and control of their vibrations is an important consideration and a challenging problem. In this paper, the wavelet-hybrid feedback least mean squared algorithm recently developed by the writers is used for vibration control of cable-stayed bridges under various seismic excitations. The effectiveness of the algorithm is investigated through numerical simulation using a benchmark control problem created based on an actual semifan-type cable-stayed bridge design. The performance of the algorithm is compared with that of a sample linear quadratic Gaussian (LQG) controller using three different earthquake records: the El Centro (California, 1940), Mexico City (Mexico, 1985), and Gebze (Turkey, 1999) earthquakes. Simulation results demonstrate that the new algorithm is consistently more effective than the sample LQG controller for all three earthquake records. Additional numerical simulations are performed to evaluate the sensitivity of the new control algorithm. It is concluded that the algorithm is robust against the uncertainties existing in modeling structures.     

A parallel genetic/neural network learning algorithm for MIMDshared memory machines

A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.     

Antibacterial activity of zinc oxide nanoparticle suspensions on food‐borne pathogens

Nanoparticles (NPs) are increasingly recognised for their utility in biological applications including nanomedicine and food safety. The present study investigated the antibacterial activity of zinc oxide (ZnO) when tested against the Gram‐negative bacteria Escherichia coli as well as the Gram‐positive bacterium Staphylococcus aureus, and the effect was more pronounced with the Gram‐positive than with the Gram‐negative bacteria. ZnO NPs also exhibited a preferential ability to suppress growth of E. coli and S. aureus in milk. This study suggested that the application of ZnO NPs as antibacterial agents in food systems and medicine may be effective at inhibiting certain pathogens.     

Investigation of the Effect of Foaming Agent on the Fabrication of NiTi Foams Using the Self-Propagating,High-Temperature Synthesis Process

Metallurgical and Materials Transactions B - Fabrication of NiTi intermetallic foams using the self-propagating, high-temperature synthesis (SHS) process was investigated. Weighed amounts of...     

Microstructural and microwave dielectric properties of LZT (Li2ZnTi3O8) ceramics sintered in presence of bismuth borate glass for LTCC applications

In the present research, the Li₂ZnTi₃O₈(LZT) ceramics were synthesized throughout solid-state ceramic processing, then mixed with bismuth borate (BiBO) glass prepared based on conventional melt quenching method. Wetting behavior of BiBO glass on the LZT ceramic substrate was monitored by hot stage microscopy. Afterward, dielectric LZT ceramics containing different amounts of BiBO glass (0.25–6 wt%) were sintered at various temperatures. X-ray diffraction and electron back scatter diffraction examinations revealed the presence of two crystalline phases of Li₂ZnTi₃O₈ and Bi₂Ti₂O₇. The maximum value of relative density (above 95%) was obtained in the case of specimens contained more than 5 wt% glass. The microwave dielectric properties of the finally sintered BiBO glass containing LZT ceramics were as follows: dielectric constant (ε_r) = 21.44–25.09, quality factor (Q × f) = 10839–54708 GHz and temperature coefficient of resonant frequency (τ_f) = (? 15.58) ? (? 12.86)ppm/°C.