Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory

Groundwater vulnerability is assessed by the DRASTIC method, which is more popular among the alternatives available. The challenge taken on board is to treat its inherent subjectivity in estimating the values of the embedded weights as recommended by USEPA for all aquifers. This paper uses a catastrophe based multi-objective evaluation system and applies it to the DRASTIC method to avoid undue preferences by decision maker. Thus, catastrophe fuzzy membership functions are used to treat the dependency of state variables on control parameters and thereby to determine the weights embedded in DRASTIC parameters. The proposed method estimates the weights of DRASTIC parameters with respect to local condition of study area. The Maragheh-Bonab aquifer in Iran is under threat by agricultural and industrial activities and therefore its vulnerability is assessed by the proposed method as well as by the general and pesticide DRASTIC methods. Comparison of results with the special distribution of nitrate-N (NO3-N) shows that the improved DRASTIC has a higher correlation index with respect to the general and pesticide DRASTIC methods. Also in seeking more reliability, the addition of new parameters is possible in the proposed method.     

Optimization of Hydraulic-Hydrologic Complex System of Reservoirs and Connecting Tunnel

Nowadays, population growth, environmental constraints and climate change can adversely affect our water supply systems’ ability to keep up with demand. Due to lack of unsuitable distribution and dispersion of water resources, precipitation, soil resources, etc., inter-basin transfers of water could be a solution in order to balancing between supply and demand water in different areas. In this study, the optimal designing of water conveyance from basin No-1 to basin No-2 is investigated. Water is transferred between these two dams by tunnel structure. Since the water flow through the tunnel is under pressure, increasing dam height will cause the decrease of tunnel diameter for constant water conveyance efficiency. The purpose of this study is transferring 95 % of water flow between two basins after supplying the agriculture consumption and environmental needs. Therefore, the mathematical program was developed first to solve the governing equations of water balance of reservoir and hydraulic of tunnel. Then, various strategies including different diameters of tunnel and dam height were considered and finally the best strategy from economic and technical viewpoint was proposed. The results showed that dam height of 151.2 m and tunnel diameter of 3.2 m are the economic options to convey of 95 % of the water.     

Electrodeposition of silver (Ag) nanoparticles on MnO<Subscript>2</Subscript> nanorods for fabrication of highly conductive and flexible paper electrodes for energy storage application

Metal oxide based electrodes are attractive for energy storage applications with limited characteristics of flexibility due to inherent rigid structure. However, incorporation of flexible insulating matrix within metal oxide composites result in poor electrically conductive and energy storage characteristics. This study presents the fabrication of flexible MnO₂ based composite electrodes prepared by incorporation of lignocelluloses (LC) fibers, directly collected from a self-growing plant, Monochoria Vaginalis. Furthermore electrodeposition of silver (Ag) nanoparticles was performed on LC/MnO₂ in potentiostatic mode to address the electrically conductive characteristics. Morphology, structural, conductive and energy storage properties of fabricated electrodes are analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), impedance analyzer and potentiostat, respectively. SEM images clearly indicate the deposition of Ag nanoparticles on MnO₂ nanorods embedded in LC fibers whereas FTIR results confirm the bonding of the functional groups. Cyclic voltammetry measurements showed efficient kinetics of LC/MnO₂ after electrodeposition of Ag nanoparticles. The effects on electrical properties associated with blending MnO₂ nanorods in lignocelluloses fibers and Ag deposition on MnO₂ in LC/MnO₂ are explored in wide frequency range between 10 Hz and 5 MHz. However, deposition of Ag nanoparticles on MnO₂ nanorods surfaces acts as a conductive path and reduces the associated resistance. Incorporated flexibility in rigid structure of MnO₂ and further improvements in conductive and energy storage characteristics will open the possibilities to be used as electrode in modern bendable energy storage devices.     

Development &amp; Characterization of Green Composites Using Novel 3D Woven Preforms

Green composites are the emerging materials made using natural fibers and environmentally degradable matrix such as green epoxy. Natural fiber composites are the motivation of researchers for low to medium impact applications as well as structural applications like automobiles. In this research work, 3D orthogonal layer to layer (LL) and through the thickness (TT) woven structures with different interlocking patterns, used as preforms in composites are presented. The mechanical properties of preform as well as associated composites are studied on equivalent fiber volume fraction. Jute yarn was woven into four layered 3D woven structures. The use of bridgeable and sustainable fiber, with its prospective use with the biodegradable matrix, is the objective of this work. The focus of this study is to improve mechanical performance by changing weave pattern, so that the resulting composite is robust in design.     

Pricing and ordering decisions of two competing supply chains with different composite policies: a Stackelberg game-theoretic approach

In today’s global highly competitive markets, competition happens among supply chains instead of companies, as the members of supply chains. So, the partners of the chains seek to apply efficient coordinating strategies like discount, return, refund, buyback, or the other coordinating policies to abate the operation costs of the chains and subsequently increase market shares. Hence, because of the importance and application of these strategies in the current non-exclusive markets, in this study, we introduce different composite coordinating strategies to enhance the coordination of the supply chains. Here, we consider two competing supply chains where both chains launch the same product under different brands to the market by applying different composite coordinating strategies. Each supply chain comprises one manufacturer and a group of non-competing retailers where the manufacturer receives raw materials from an outside supplier and transforms them into a finished product; then, the products are sold to the retailers to satisfy the demands of market. In the first chain, a composite (QFF) policy, which is the combination of quantity and freight discount, as well as free shipping quantity policies, are considered between upstream and downstream members while in the second one, different composite polices are considered between upstream and downstream members such that the supplier offers a composite policy, as the first chain, to the manufacturer and the manufacturer proposes a composite (QPR) policy, which is the combination of quantity discount and partial-refund customer return policies, to the retailers. The main objective of the paper is to determine the optimal selling prices and the order quantities of the manufacturer and the retailers in each chain in presence of different composite coordinating strategies. A Stackelberg game-theoretic approach is employed between the members of each chain where the manufacturer is a follower and the retailers are leaders. The concavity of profit functions is proved. Finally, the applicability of the models is justified by presented numerical examples. Moreover, the effects of these strategies on the decisions of the chains’ partners are examined.     

Artificial neural network and dataset optimization for implementation of linear system models in resource-constrained embedded systems

On-board training of artificial neural network (ANN) is important in instances where real time data are required for model training. Provision of on-board intelligence enables the developed systems to self-recalibrate and enhances their efficiencies. In this work, investigations have been performed to determine optimized parameters of ANN model for linear systems. The performance parameters that is, model parameters, memory requirements, accuracy and processing time are chosen by considering the model to be installed on commercially available microcontrollers that have very limited on-board memory. Minimum data requirements for training ANN models of linear systems are also explored for better performance. All dataset ranges are normalized in order to exclude the effects of range differences. It is shown that for linear systems, 1–3–1 architecture produces best results against ≤100 data points when Bayesian Regularization (BR) training function is used along with Log Sigmoid Activation function. Simulations for 1–3–1 architecture are then performed for datasets having 10, 25, 50 and 100 data points. The results show that training with 25 data points produces over-all better performance than other datasets. A large dataset utilizes more training time and memory whereas a smaller dataset produces relatively lesser accuracy. The effects of clustered data and uniformly distributed data are also explored. It is found that total epochs in case of clustered data are significantly higher than uniformly distributed data. The combination of these optimized parameters that is, 1–3–1 architecture, with BR and Log function, for ≤100 data points can be used for the development and implementation of linear components or systems in resource-constrained embedded systems.     

Application layer classification of Internet traffic using ensemble learning models

Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service-level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade-offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)-based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.     

Secure and efficient data transfer using spreading and assimilation in MANET

Mobile ad hoc Network (MANET) is a cluster of moveable devices connected through a wireless medium to design network with rapidly changing topologies due to mobility. MANETs are applicable in variety of innovative application scenarios where smart devices exchange data among each other. In this case, security of data is the major concern to provide dependable solution to users. This article presents a secure mechanism for data transfer where sender splits the data into fragments and receiver gets the actual data by assimilating the data fragments. We have presented an Enhanced Secured Lempel-Ziv-Welch (ES-LZW) algorithm that provides cryptographic operations for secure data transfer. In proposed model, we have utilized the disjoint paths to transfer the data fragments from sender side and assimilate these fragments at receiver to get the original data. The messages containing data fragments are compressed and encrypted as well. Our scheme ensures confidentiality, integrity, efficient memory utilization, and resilience against node compromising attacks. We have validated our work through extensive simulations in NS-2.35 using TCL and C language. Results prove that our scheme reduces memory consumption along with less encryption and decryption cost as compared to blowfish especially when plaintext has more repetitive data. We have also analyzed the impact of creating data fragments, fraction of communication compromised, and probability to compromise the data fragments by subverting intermediaries.     

Energy-efficient RRH-association and resource allocation in D2D enabled multi-tier 5G C-RAN

The device-to-device (D2D) enabled cloud radio access network (C-RAN) is considered as a promising network model which provides high data rate and energy efficiency. In this paper, we formulate a joint mode selection, subchannel assignment (SA), power allocation (PA) and remote radio head (RRH)-association problem in D2D enabled single carrier frequency division multiple access based C-RAN in the uplink. This problem is mixed-integer non-linear problem which is extremely difficult to solve in its original form. To solve this problem, we propose an iterative technique which solves this problem in two stages; mode selection stage and joint SA, PA and RRH-association (SAPARA) stage. For mode selection, a link quality based technique is presented while, for joint SAPARA, we developed an iterative technique that solves this this problem in three steps such that SA and PA are carried out in the first and second step, respectively, while RRH-association is performed in the third step. Our results show the efficiency of the presented techniques.