A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

Enhanced Photoelectrochemistry in CdS/Au Nanoparticle Bilayers

Three different configurations of Au‐nanoparticle/CdS‐nanoparticle arrays are organized on Au/quartz electrodes for enhanced photocurrent generation. In one configuration, Au‐nanoparticles are covalently linked to the electrode and the CdS‐nanoparticles are covalently linked to the bare Au‐nanoparticle assembly. The resulting photocurrent, φ = 7.5 %, is ca. 9‐fold higher than the photocurrent originating from a CdS‐nanoparticle layer that lacks the Au‐nanoparticles, φ = 0.8 %. The enhanced photocurrent in the Au/CdS nanoparticle array is attributed to effective charge separation of the electron–hole pair by the injection of conduction‐band electrons from the CdS‐ to the Au‐nanoparticles. Two other configurations involving electrostatically stabilized bipyridinium‐crosslinked Au/CdS or CdS/Au nanoparticle arrays were assembled on the Au/quartz crystal. The photocurrent quantum yields in the two systems are φ = 10 % and φ = 5 %, respectively. The photocurrents in control systems that include electrostatically bridged Au/CdS or CdS/Au nanoparticles by oligocationic units that lack electron‐acceptor units are substantially lower than the values observed in the analogous bipyridinium‐bridged systems. The enhanced photocurrents in the bipyridinium‐crosslinked systems is attributed to the stepwise electron transfer of conduction‐band electrons to the Au‐nanoparticles by the bipyridinium relay bridge, a process that stabilizes the electron–hole pair against recombination and leads to effective charge separation.     

Mathematical modeling of falling liquid film evaporation process

A mathematical model of evaporation process from a laminar falling liquid film on a vertical plate of constant temperature is presented. The model is developed with and without interfacial shear stress due to the vapor flow at the liquid film surface. The vapor pressure drop, vapor exit velocity and cooling rate are calculated for different liquid mass flow values. It is shown that lower liquid mass flow produces higher cooling rate. The results also show that the interfacial shear stress has a considerable negative effect on the cooling rate. It is proved that there exists an optimum distance between the plates, which gives the maximum volumetric cooling rate.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

Alternating active-phase algorithm for multimaterial topology optimization problems: a 115-line MATLAB implementation

Structural and Multidisciplinary Optimization - A new algorithm for the solution of multimaterial topology optimization problems is introduced in the present study. The presented method is based on...     

Inter-comparative study of quantitative methods of industrial clinker

Impurities from the raw materials, the grinding and the homogenization of the raw materials, the kiln instability and the complexity of the cooling step, all these factors make it difficult to obtain a perfect evaluation of the mineralogical composition of Portland clinker. We studied the limitations of the most commonly used quantitative methods and recommend some procedures to obtain reliable and reproducible results of quantitative analyses. Different clinker samples (provided by the Bizerte Cement Company (Tunisia)) were subjected to an elemental analysis by X-ray fluorescence and the mineralogical composition was determined by the Bogue calculation and by X-ray powder diffraction combined with the Rietveld method (Different softwares were used: XPert High Score Plus version 2.0 and TOPAS version 4.2). We then compared the results obtained by the Rietveld method and the Bogue calculation to the specific peak areas of each phase. The content of each phase, determined by the Rietveld method, varied proportionally to the change in peak area; a significant difference in these results was found by using the elementary Bogue calculation.     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.