Fuzzy soft subnear-rings and (∈,∈∨q)-fuzzy soft subnear-rings

Our aim in this paper is to introduce and study the fuzzy soft subnear-ring and $(\in ,\in \vee q)$-fuzzy soft subnear-ring which is a generalization of fuzzy soft subnear-ring. We also study some of their basic properties and give several examples.     

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

There is growing demand on datacenters to serve more clients with reasonable response times, demanding more hardware resources, and higher energy consumption. Energy-aware datacenters have thus been amongst the forerunners to deploy virtualization technology to multiplex their physical machines (PMs) to as many virtual machines (VMs) as possible in order to utilize their hardware resources more effectively and save power. The achievement of this objective strongly depends on how smart VMs are consolidated. In this paper, we show that blind consolidation of VMs not only does not reduce the power consumption of datacenters but it can lead to energy wastage. We present four models, namely the target system model, the application model, the energy model, and the migration model, to identify the performance interferences between processor and disk utilizations and the costs of migrating VMs. We also present a consolidation fitness metric to evaluate the merit of consolidating a number of known VMs on a PM based on the processing and storage workloads of VMs. We then propose an energy-aware scheduling algorithm using a set of objective functions in terms of this consolidation fitness metric and presented power and migration models. The proposed scheduling algorithm assigns a set of VMs to a set of PMs in a way to minimize the total power consumption of PMs in the whole datacenter. Empirical results show nearly 24.9% power savings and nearly 1.2% performance degradation when the proposed scheduling algorithm is used compared to when other scheduling algorithms are used.     

Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP

Failure mode and effects analysis (FMEA) is a widely used engineering technique for designing, identifying and eliminating known and/or potential failures, problems, errors and so on from system, design, process, and/or service before they reach the customer (Stamatis, 1995). In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study a fuzzy approach, allowing experts to use linguistic variables for determining S, O, and D, is considered for FMEA by applying fuzzy ‘technique for order preference by similarity to ideal solution’ (TOPSIS) integrated with fuzzy ‘analytical hierarchy process’ (AHP). The hypothetical case study demonstrated the applicability of the model in FMEA under fuzzy environment.     

DDC: distance-based decision classifier

This paper presents a new classification method utilizing distance-based decision surface with nearest neighbor projection approach, called DDC. Kernel type of DDC has been extended to take into account the effective nonlinear structure of the data. DDC has some properties: (1) does not need conventional learning procedure (as k-NN algorithm), (2) does not need searching time to locate the k-nearest neighbors, and (3) does not need optimization process unlike some classification methods such as Support Vector Machine (SVM). In DDC, we compute the weighted average of distances to all the training samples. Unclassified sample will be classified as belonging to a class that has the minimum obtained distance. As a result, by such a rule we can derive a formula that can be used as the decision surface. DDC is tested on both synthetic and real-world data sets from the UCI repository, and the results were compared with k-NN, RBF Network, and SVM. The experimental results indicate DDC outperforms k-NN in the most experiments and the results are comparable to or better than SVM with some data sets.     

RETRACTED ARTICLE: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

     

A distributed resource management model for Virtual Private Networks: Tit-for-Tat strategies

In this paper, we address the autonomic resource management problem for Virtual Private Networks (VPNs) in the presence of stochastic and selfish VPN operators. Resource management is one of the most important problems that faces Internet Service Providers. In the literature, the Autonomic Service Architecture is proposed to provide a resource management model that allows systems to manage themselves and aiming to utilize optimally the unused resources. Unfortunately, this model suffers from two major limitations. First, unused resources from underloaded VPNs (lenders) are utilized over the overloaded ones (borrowers) without considering the unexpected changes of the VPNs’ state, which may often happen in the case of multimedia transmissions. This may affect negatively the Quality-of-Service (QoS) of the lenders while improving the QoS of the borrowers. Second, underloaded VPNs’ operators might behave selfishly and refuse to lend their unused (spare) resources to other overloaded VPNs. To overcome these limitations, we propose a distributed autonomic resource management, which is modeled as a repeated non-cooperative game with stochastic and selfish players. The classical Tit-for-Tat strategy is modified to cope with VPN operators who are not always able to lend some resources to others. Four different strategies are derived from Tit-for-Tat to motivate VPN operators to lend their resources to others. As far as we know, our work is among the first efforts that uses repeated non-cooperative game theory to motivate selfish participants to cooperate and to distinguish between stochastic and purely selfish VPNs’ operators. In our setting, this results in cooperative sharing of unused resources among VPNs. Simulation results show that Tit-for-Tat strategy leads to deadlocks, while our strategies assure good gains to cooperative VPN operators and punish the selfish ones.     

Ferrofluid actuation with varying magnetic fields for micropumping applications

Magnetic nanoparticle suspensions and their manipulation are becoming an alternative research line. They have vital applications in the field of microfluidics such as microscale flow control in microfluidic circuits, actuation of fluids in microscale, and drug delivery mechanisms. In microscale, it is possible and beneficial to use magnetic fields as actuators of such ferrofluids, where these fluids could move along a dynamic gradient of magnetic field so that a micropump could be generated with this technique. Thus, magnetically actuated ferrofluids could have the potential to be used as an alternative micro pumping system. Magnetic actuation of nanofluids is becoming an emergent field that will open up new possibilities in various fields of engineering. Different families of devices actuating ferrofluids were designed and developed in this study to reveal this potential. A family of these devices actuates discrete plugs, whereas a second family of devices generates continuous flows in tubes of inner diameters ranging from 254?μm to 1.56?mm. The devices were first tested with minitubes to prove the effectiveness of the proposed actuation method. The setups were then adjusted to conduct experiments on microtubes. Promising results were obtained from the experiments. Flow rates up to 120 and 0.135?μl/s were achieved in minitubes and microtubes with modest maximum magnetic field magnitudes of 300?mT for discontinuous and continuous actuation, respectively. The proposed magnetic actuation method was proven to work as intended and is expected to be a strong alternative to the existing micropumping methods such as electromechanical, electrokinetic, and piezoelectric actuation. The results suggest that ferrofluids with magnetic nanoparticles merit more research efforts in micro pumping.     

A real-options-based analysis for supply chain decisions

Flexibility allows firms to compete more effectively in a world of short product life cycles, rapid product development, and substantial demand and/or price uncertainty. We develop a supply chain model in which a manufacturing firm can have the flexibility to select different suppliers, plant locations, and market regions and there can be an implementation time lag for the supply chain operations. We use a real options approach to estimate the value of flexibility and to determine the optimum strategy to manage the flexibility under uncertainty in the currency exchange rate. To price the operational flexibility, we develop a Monte Carlo simulation technique that is able to incorporate a large number of variables into the valuation. We show that without considering time lag impact, the value of the operational flexibility can be significantly overestimated.     

Reliability evaluation of a natural circulation system

This paper discusses a reliability study performed with reference to a passive thermohydraulic natural circulation (NC) system, named TTL-1. A methodology based on probabilistic techniques has been applied with the main purpose to optimize the system design. The obtained results have been adopted to estimate the thermal-hydraulic reliability (TH-R) of the same system.A total of 29 relevant parameters (including nominal values and plausible ranges of variations) affecting the design and the NC performance of the TTL-1 loop are identified and a probability of occurrence is assigned for each value based on expert judgment. Following procedures established for the uncertainty evaluation of thermal-hydraulic system codes results, 137 system configurations have been selected and each configuration has been analyzed via the Relap5 best-estimate code. The reference system configuration and the failure criteria derived from the “mission” of the passive system are adopted for the evaluation of the system TH-R.Four different definitions of a less-than-unity “reliability-values” (where unity represents the maximum achievable reliability) are proposed for the performance of the selected passive system. This is normally considered fully reliable, i.e. reliability-value equal one, in typical Probabilistic Safety Assessment (PSA) applications in nuclear reactor safety. The two ‘point’ TH-R values for the considered NC system were found equal to 0.70 and 0.85, i.e. values comparable with the reliability of a pump installed in an “equivalent” forced circulation (active) system having the same “mission.” The design optimization study was completed by a regression analysis addressing the output of the 137 calculations: heat losses, undetected leakage, loop length, riser diameter, and equivalent diameter of the test section have been found as the most important parameters bringing to the optimal system design and affecting the TH-R.As added values for this work, the comparison has been made between results from this study and results from a previous analysis where the same methodology was adopted for the evaluation of the TH-R of a different passive system named Isolation Condenser (IC). The comparison shows that the current single-phase NC system is ‘more reliable’ than the two-phase IC system. This constitutes a proof of qualification and of consistency for the adopted methodology.     

Associative Memory Design via Path Embedding into a Graph

A graph theoretical procedure for storing a set of n-dimensional binary vectors as asymptotically stable equilibrium points of a discrete Hopfield neural network is presented. The method gives an auto-associative memory which stores an arbitrary memory set completely. Spurious memories might occur only in a small neighborhood of the original memory vectors, so cause small errors.