A survey of decision making methods based on two classes of hybrid soft set models

To the best of our knowledge, the tool of soft set theory is a new efficacious technique to dispose uncertainties and it focuses on the parameterization, while fuzzy set theory emphasizes the truth degree and rough set theory as another tool to handle uncertainties, it places emphasis on granular. However, the real-world problems that under considerations are usual very complicated. Consequently, it is very difficult to solve them by a single mathematical tool. It is worth noting that decision making (briefly, DM) in an imprecise environment has been showing more and more role in real-world applications. Researches on the idiographic applications of the above three uncertain theories as well as their hybrid models in DM have attracted many researchers’ widespread interest. DM methods are not yet proposed based on fusions of the above three uncertain theories. In view of the reason, by compromising the above three uncertain theories, we elaborate some reviews to DM methods based on two classes of hybrid soft models: SRF-sets and SFR-sets. We test all algorithms for DM and computation time on data sets produced by soft sets and FS-sets. The numerical experimentation programs are written for given pseudo codes in MATLAB. At the same time, the comparisons of all algorithms are given. Finally, we expatiate on an overview of techniques based on the involved hybrid soft set models.     

Sustainable Green Fog Computing for Smart Agriculture

Wireless Personal Communications - Medical Body Area Networks or MBANs are gaining popularity in healthcare circles because of the convenience they provide to patients and caregivers and assist in...     

Enhancing robustness and perturbation tolerance of cognitive radio engines with metacognition

A cognitive radio engine (CE) is an intelligent agent which observes the radio environment and chooses the best communication settings that best meet the application’s goal. In this process, providing reliable performance is one of the major tasks in designing CEs for wireless communication systems. The main purpose of this work is providing predictable performance and controlling the cost of intelligent algorithms based on the CE’s experience and complexity analysis respectively. In this work, we extend our meta-CE design to control the cost of computations and provide more reliable performance for providing the minimum requirement of the radio applications in different scenarios. To achieve this, we use robust training algorithm (RoTA) in two different levels alongside of the individual CE algorithms. The RoTA, enables radio to guarantee some minimum output performance based on the learning stages. RoTA uses confidence interval approximation for standard normal distribution to calculate the lower and upper bounds of CE’s expected performance to analyze the reliability of decisions. Moreover, in the case of non-stationary environments, RoTA is facilitated by forgetfulness factor to provide minimum performance guarantees. The second level of RoTA operates in meta-level to control the amount of computation complexity of intelligent algorithms in all levels with respect to the obtained performance and complexity analysis.     

Reinforcement of electroactive characteristics in polyvinylidene fluoride electrospun nanofibers by intercalation of multi-walled carbon nanotubes

This research work reports on development and characterization of multi-walled carbon nanotube (MWCNT)-doped polyvinylidene difluoride (PVDF) nanofibers by the electrospinning method. PVDF is an extensively studied polymer both theoretically and experimentally due to its appealing ferroelectric, piezoelectric, and pyroelectric properties which strongly favors its promising applications in the development of micro/nanostructure devices. The foremost reason for its ferroelectric and piezoelectric behaviors has been attributed to its crystalline structure, specifically the presence of β-phase; however, the existence of the small percentage of β-phase in pristine PVDF limits its applications. To enhance the electroactive features in the PVDF, MWCNTs have been doped in it to prepare electrospun nanofibers, as electrospinning is a single-step approach. These nonwoven nanofibers were prepared at a DC voltage of 20 kV which were subsequently calcined at 100 °C for 12 h. The estimation of crystal structure and phase identification in these nanofibers have been determined by attenuated FT-IR and XRD, while the morphology, microstructure, mean diameter, and length have been examined by FE-SEM. The observed electrical conductivity, capacitance, permittivity (ε), conductivity (δ), and impedance (Z) in these samples have been tailored by doping a range of MWCNT contents and optimizing the experimental conditions.     

Intelligent building energy management system using rule sets

The current study presents an intelligent decision support model using rule sets based on a typical building energy management system. In addition, the model's impact on the energy consumption and indoor quality of a typical office building in Greece is presented. The model can control how the building operational data deviates from the settings as well as carry out diagnosis of internal conditions and optimize building's energy operation. In this context, the integrated “decision support model” can contribute to the management of the daily energy operations of a typical building, related to the energy consumption, by incorporating the following requirements in the best possible way: (a) the guarantee of the desirable levels of living quality in all building's rooms and (b) the necessity for energy savings.     

Modeling of Effective Thermal Conductivity of Dunite Rocks as a Function of Temperature

The thermal conductivity, thermal diffusivity, and heat capacity per unit volume of dunite rocks taken from Chillas near Gilgit, Pakistan, have been measured simultaneously using the transient plane source technique. The temperature dependence of the thermal transport properties was studied in the temperature range from 303 K to 483 K. Different relations for the estimation of the thermal conductivity are applied. A proposed model for the prediction of the thermal conductivity as a function of temperature is also given. It is observed that the values of the effective thermal conductivity predicted by the proposed model are in agreement with the experimental thermal conductivity data within 9%.     

Effect of deflocculants on hardness and densification of YSZ–Al2O3 (whiskers & particulates) composites

In the present study effect of deflocculants like P-Aminobenzoic Acid (PABA) and Cetyltrimethyl ammonium bromide (CTAB) on densification and hardness of 3 mol.% Yttria-stabilized ZrO₂ (abridged as YSZ) + Al₂O₃ (whiskers or particulates) composite have been studied. Maximum hardness & density were achieved at 1 wt% of CTAB or PABA, while further addition (5, 10 and 15 wt%) had no significant affect on the aforementioned properties. It was also observed that alumina addition in form of particulates only improved the density while its addition in form of whiskers significantly increased the hardness of YSZ + alumina composite. The maximum hardness achieved was more than 14 GPa in case of sample containing alumina in form of whiskers.     

Effects of evolution of nanoscale copper precipitation and copper content on mechanical properties of high-strength steel weld metal

Nanoscale copper precipitation in high-strength weld metal was characterised by using atom probe tomography (APT) and transmission electron microscopy. In as-deposited zone, there was no copper precipitation. During welding thermal cycling, nanoscale copper of 2–3?nm in radius precipitated with face-centred cubic (FCC) and body-centred cubic (BCC) structure. During post-weld heat treatment (PWHT), FCC copper precipitate became coarsen and BCC transformed into FCC. The highest hardness was in the reheated zone which subjected to the peak temperature below 500°C; while the lowest hardness was both in deposited zone and the reheated zone which subjected to the peak temperature greater than 810°C. After PWHT, the hardness became homogenous as well as decreased. With increasing copper content, the hardness was increased and the toughness was slightly decreased. After PWHT, the toughness decreased due to the change in the structure of copper precipitates.