A Framework for Ground Water Management Based on Bayesian Network and MCDM Techniques

Groundwater resources are steadily subjected to increasing water demands. The aquifers are considered as the most accessible source of fresh water. In recent years, they have been faced with severe water withdrawal in arid and semi-arid countries like Iran and thus some aquifers was considered as forbidden aquifers that it means the water withdrawal from these aquifers is unauthorized. Given a critical situation, groundwater resources management in the form of tools such as monitoring the level of the aquifers and developing the restoring scenarios is essential. Therefore, for this purpose, a framework has been developed based on prediction of groundwater level using Bayesian Networks (BNs) model. Furthermore, Multi Criteria Decision Making methods (MCDM) techniques proposed and employed for ranking of proposed groundwater management scenarios. This framework was evaluated for restoring the Birjand aquifer in Iran in different hydrological conditions. A probabilistic Dynamic BN was proposed for groundwater level prediction under uncertainties. After analyzing the obtained results, the applicable short term scenarios for groundwater management as well as appropriate economic, social and technical criteria were defined for decision making procedure. Then, using elicitation of decision makers’ opinions on the relative importance and performance of criteria, SAW, TOPSIS and PROMETHEE-II techniques were applied to rank the scenarios and the obtained results were aggregated by Borda method for final ranking of the scenarios. Lastly, the final results demonstrates the capability of the proposed framework for groundwater resources planning and management which can be employed for reducing the risk of aquifer level declining.     

Photocatalytic decomposition of VOCs by AC–TiO2 and EG–TiO2 nanocomposites

Clean Technologies and Environmental Policy - In this study, TiO2 nanoparticles (NPs)-based catalysts were prepared for the photocatalytic removal of toluene as a model VOC from air under UV light....     

Damping vibration behavior of visco-elastically coupled double-layered graphene sheets based on nonlocal strain gradient theory

Microsystem Technologies - This paper develops a nonlocal strain gradient plate model for damping vibration analysis of visco-elastically coupled double-layered graphene sheets. For more accurate...     

A comparative study to assess structure–properties relationships in (acrylonitrile butadiene rubber)‐based composites: Recycled microfillers versus nanofillers

This paper reports on the reinforcing effects of different types of fillers, namely, nanoclay (NC), micron size calcium carbonate (MCC), and micron size recycled powder coating waste (MPCW), on the ultimate properties of acrylonitrile butadiene rubber (NBR) compounds. The microcomposites and nanocomposites were characterized by X‐ray diffraction and scanning electron microscopy, implying enlargement of d‐spacing of NC or intercalation of NBR chains and formation of exfoliated structure, while some agglomerates of MCC were detected. Curing characteristics of the studied composites showed that incorporation of the fillers into the NBR, in particular the NC, causes an increase in the torque, indicating a higher degree of crosslinking. Furthermore, different from micron size MPCW and MCC, the NC accelerated the vulcanization reaction. It was also found that the use of NC and MPCW results in a remarkable increase in the mechanical and rheological properties compared with pure NBR. All in all, variations in the aforementioned criteria were attributable to the extent of matrix/filler interaction reflected by scanning electron micrographs. The correlation established between the microstructure and characteristics of the prepared NBR composites can shed some light on how to develop composites with enhanced properties by incorporating waste materials into the polymers. J. VINYL ADDIT. TECHNOL., 23:13–20, 2017. © 2015 Society of Plastics Engineers     

JPEG Pleno: Providing representation interoperability for holographic applications and devices

Guaranteeing interoperability between devices and applications is the core role of standards organizations. Since its first JPEG standard in 1992, the Joint Photographic Experts Group (JPEG) has published several image coding standards that have been successful in a plethora of imaging markets. Recently, these markets have become subject to potentially disruptive innovations owing to the rise of new imaging modalities such as light fields, point clouds, and holography. These so‐called plenoptic modalities hold the promise of facilitating a more efficient and complete representation of 3D scenes when compared to classic 2D modalities. However, due to the heterogeneity of plenoptic products that will hit the market, serious interoperability concerns have arisen. In this paper, we particularly focus on the holographic modality and outline how the JPEG committee has addressed these tremendous challenges. We discuss the main use cases and provide a preliminary list of requirements. In addition, based on the discussion of real‐valued and complex data representations, we elaborate on potential coding technologies that range from approaches utilizing classical 2D coding technologies to holographic content‐aware coding solutions. Finally, we address the problem of visual quality assessment of holographic data covering both visual quality metrics and subjective assessment methodologies.     

Design of RF MEMS Based Oscillatory Neural Network for Ultra High Speed Associative Memories

Microelectromechanical systems are utilized alongside with transistor amplifiers and resistive connections for implementing of oscillatory associative memories. Phase locking is studied in such a network and all requirements of the circuit level implementation are satisfied. A very high gain trans-impedance amplifier operating in 1 GHz in addition to a novel automatic amplitude control circuit is employed to remove amplitude dynamics of the system. Requiring resonator characteristics are extracted and calculated as well. A new method for initialization of the network is proposed. Each neuron consumes 1.08 mW from a 1.8 V power supply. The convergence time of a typical network trained by Hebbian rule is less than 1.5 ns which results in an ultra high speed analog signal processing system.     

Magnetic Properties of Zinc Ferrite Nanoparticles Synthesized by Coprecipitation Method

The influence of the precipitant and ferric concentration on the magnetic properties of coprecipitated zinc ferrite nanoparticles has been investigated. The nanoparticles were characterized using X-ray diffraction, scanning and transmission electron microscope, and vibrating sample magnetometer techniques. The results showed that the single-phase zinc ferrite with partially inverse spinel structures can be formed at high concentrations. The inversion coefficient calculated by the Rietveld method decreases with increasing of the concentrations, may be due to the crystal growth. The magnetic measurements exhibited that the coprecipitated zinc ferrite nanoparticles were superparamagnet and magnetization decreases with increasing of the concentrations through decreasing of inversion coefficient.