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.

     

A charge sharing–based switching scheme for SAR ADCs

This paper presents an energy-efficient switching scheme for successive approximation register (SAR) analogue-to-digital converter (ADC). The proposed scheme employs charge recycling method to keep the capacitor arrays free of transitional energy between bit generations except reset phase. In comparison with the conventional switching scheme, the proposed one achieves 100% transitional energy saving without considering reset phase. In addition, configuration of a 10-bit SAR ADC shows that the proposed switching scheme reduces the capacitor area by 25% compared with the conventional switching scheme.     

K-ras gene mutations: an unfavorable prognostic marker in stage I lung adenocarcinoma

Activation of K-ras gene by point mutations, a common finding in lung adenocarcinomas, has been suggested to decrease patient survival. We investigated 109 lung adenocarcinomas, mostly small, peripheral, stage I tumours (81/109) for presence of K-ras gene mutations at codons 12 and 13. Mutations were detected by denaturing gradient gel electrophoresis analysis of specific sequences amplified by polymerase chain reaction from DNA extracted from archival pathological material. Thirty-three of 109 (30.3%) tumours showed mutations at codon 12 (28/33, 84.8%) or 13 (5/33, 15.2%) of the gene. Mutations and type of nucleotide substitutions were differently distributed among cytological subtypes, being more prevalent among less differentiated (G2 and G3) tumours and among bronchial than bronchiolo-alveolar type adenocarcinomas. Survival analysis showed an adverse effect of K-ras mutation on survival, restricted to stage I tumours. Median survival for 81 stage I patients was 30 months for non-mutated tumours versus 20 months for mutated tumours (p = 0.016). Multivariate analysis showed that age of patient (p = 0.001) and K-ras mutation status (p = 0.04) were the only independent factors influencing survival significantly. These data strengthen the hypothesis that K-ras gene mutations may be useful in identifying a subgroup of patients with poor outcome.     

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.     

A study on the mechanical strength of Fe2O3/Cr2O3/CuO catalyst for high temperature water gas shift reaction

Journal of Porous Materials - The present study aims to investigate the effects of iron (hydr)oxide phases formed during precipitation and the addition of different binders on the mechanical and...     

A novel design of 8-bit adder/subtractor by quantum-dot cellular automata

Application of quantum-dot is a promising technology for implementing digital systems at nano-scale. QCA supports the new devices with nanotechnology architecture. This technique works based on electron interactions inside quantum-dots leading to emergence of quantum features and decreasing the problem of future integrated circuits in terms of size. In this paper, we will successfully design, implement and simulate a new full adder based on QCA with the minimum delay, area and complexities. Also, new XOR gates will be presented which are used in 8-bit controllable inverter in QCA. Furthermore, a new 8-bit full adder is designed based on the majority gate in the QCA, with the minimum number of cells and area which combines both designs to implement an 8-bit adder/subtractor in the QCA. This 8-bit adder/subtractor circuit has the minimum delay and complexity. Being potentially pipeline, the QCA technology calculates the maximum operating speed.     

Natural killer or T-lymphocyte cells: Which is the best immune therapeutic agent for cancer? An optimal control approach

Therapeutic vaccines are being developed as a promising new approach to treatment for cancer patients. There are still many unanswered questions about which kind of therapeutic vaccines are the best for the cancer treatments? In this paper we consider a mathematical model, in the form of a system of ordinary differential equations (ODE), this system is an example from a class of mathematical models for immunotherapy of the tumor that were derived from a biologically validated model by Lisette G. de Pillis. The problem how to schedule a variable amount of which vaccines to achieve a maximum reduction in the primary cancer volume is consider as an optimal control problem and it is shown that optimal control is quadratic with 0 denoting a trajectory corresponding to no treatment and 1 a trajectory with treatment at maximum dose along that all therapeutics are being exhausted. The ODE system dynamics characterized by locating equilibrium points and stability properties are determined by using appropriate Lyapunov functions. Especially we attend a parametric sensitivity analysis, which indicates the dependency of the optimal solution with respect to disturbances in model 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...     

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.