High temperature water gas shift reaction over Fe-Cr-Cu nanocatalyst fabricated by a novel method

Fe-Cr-Cu nanocatalyst was synthesized through an inorganic-precursor thermolysis approach and exploited for high temperature water gas shift reaction. The results demonstrated that the method used for the nanocatalyst fabrication led to smaller crystallite size (32.9 nm) and higher BET surface area (127.3 m²/g) compared to those of a reference sample (65.5 nm, 78.6 m²/g) prepared by co-precipitation conventional method. Furthermore, the obtained data for catalytic activity showed that the catalyst prepared via inorganic precursor has better activity than the reference sample in all studied temperatures (350-500 °C) and also exhibited higher catalytic activity than a commercial Fe-Cr-Cu catalyst in higher temperatures (more than 450 °C).     

GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non‐linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti‐angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti‐angiogenic therapy is more efficient than single chemotherapy. Two different non‐linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti‐angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed‐loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.     

Improving the functionality of biodegradable food packaging materials via porous nanomaterials

Non-biodegradability and disposal problems are the major challenges associated with synthetic plastic packaging. This review article discusses a new generation of biodegradable active and smart packaging based on porous nanomaterials (PNMs), which maintains the quality and freshness of food products while meeting biodegradability requirements. PNMs have recently gained significant attention in the field of food packaging due to their large surface area, peculiar structures, functional flexibility, and thermal stability. We present for the first time the recently published literature on the incorporation of various PNMs into renewable materials to develop advanced, environmentally friendly, and high-quality packaging technology. Various emerging packaging technologies are discussed in this review, along with their advantages and disadvantages. Moreover, it provides general information about PNMs, their characterization, and fabrication methods. It also briefly describes the effects of different PNMs on the functionality of biopolymeric films. Furthermore, we examined how smart packaging loaded with PNMs can improve food shelf life and reduce food waste. The results indicate that PNMs play a critical role in improving the antimicrobial, thermal, physicochemical, and mechanical properties of natural packaging materials. These tailor-made materials can simultaneously extend the shelf life of food while reducing plastic usage and food waste.     

Simulation-Based Traceability Analysis of RFID Authentication Protocols

Nowadays low-cost RFID systems have moved from obscurity into mainstream applications which cause growing security and privacy concerns. The lightweight cryptographic primitives and authentication protocols are indispensable requirements for these devices to grow pervasive. In recent years, there has been an increasing interest in intuitive analysis of RFID protocols. This concept has recently been challenged by formal privacy models. This paper investigates how to analyse and solve privacy problems in formal model. First, we highlight some vague drawbacks especially in forward and backward traceability analysis and extend it in the simulation-based privacy model family. Then, the privacy weaknesses of three new-found RFID authentication protocols are analysed in formal privacy models and three improved protocols are proposed to prevent the aforementioned attacks.     

Reverse iodine transfer polymerization of vinyl acetate and vinyl benzoate: synthesis and characterization of homo‐ and copolymers

Homo‐ and copolymers of vinyl esters including vinyl acetate (VAc) and vinyl benzoate (VBz) were synthesized via the reverse iodine transfer radical polymerization technique. Polymerization was carried out in the presence of iodine as the in situ generator of the transfer agent and 2,2′‐azobis(isobutyronitrile) as the initiator at 70 °C. Reverse iodine transfer radical homopolymerization of VAc and VBz led to conversions of 76 and 57%, number‐average molecular weights of 8266 and 9814 g mol^?1 and molecular weight distributions of 1.58 and 1.49, respectively. The microstructure of the synthesized polymers was investigated in detail using gel permeation chromatography, ¹H NMR, ¹³C NMR and distortionless enhancement of polarization transfer (135° decoupler pulse) techniques. Relatively narrow molecular weight distribution and controlled and predictable trend of molecular weight versus conversion were observed for the synthesized polymers, showing that reverse iodine transfer radical homo‐ and copolymerization of VAc and VBz proceeded with controlled characteristics. Results of molecular weight and its distribution along with the ¹H NMR spectra recorded for homo‐ and copolymers indicated that side reactions can occur during the course of polymerization with a significant contribution when VAc, even in a small amount, was present in the reaction mixture. This can result in polymer chains with aldehyde dead end and broadening of the molecular weight distribution. © 2015 Society of Chemical Industry     

Monitoring the aging of beers using a bioelectronic tongue

This paper deals with the implementation and the application of a bioelectronic tongue including three enzymatic biosensors based on tyrosinase and phthalocyanines as electron mediators, to evaluate the changes that occur during the aging of beers. For this purpose, alcoholic and non alcoholic beers, packaged in can and bottle, have been analyzed using cyclic voltammetry. The electrochemical signals showed significant changes during the aging process. The features extracted from the cyclic voltammograms have been used to perform Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Data have revealed a clear discrimination among the beer classes in the aging process and the results were confirmed by Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The bioelectronic tongue has demonstrated a good capability to discriminate and classify the beer types satisfactorily in such a way, for all beer treatments, full classification accuracy was found.     

Sensitivity analysis of the OWA operator.

The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree, and, therefore, it has better computation efficiency. Since maximizing the combined goodness measure and minimizing its sensitivity to optimism degree are conflicting objectives, a new composite measure of goodness will be defined to have more reliability in obtaining optimal solutions. The theoretical results will be illustrated in a water resources management problem.