Timeout Period Analysis to Detect Black Hole Attack in Multihop Wireless Ad Hoc Networks

A novel approach is proposed to estimate the timeout period used in wireless ad hoc networks in order to detect misbehaving nodes that make black hole attacks. Timeout period is an acceptable time frame for a node to forward a packet and is used to judge if the node is behaving properly. To avoid misjudgment and false alarms, the accuracy of the estimate of the timeout period is very important. Our method is based on the IEEE 802.11 MAC protocol and Dynamic Source Routing protocol. The main contribution of this paper is proposing a queuing analysis to calculate the mean and maximum delay per hop implementing the 95-percentile of medium access waiting time. In addition, a new technique is introduced that can be applied to determine the mean number of hops in flooding-based ad hoc networks, taking into account edge effects. For each proposed model, analytical results are compared with results obtained from simulations and the validity of the models is confirmed by observing the close relationship between the results.     

Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.     

Application of shifted Jacobi pseudospectral method for solving (in)finite-horizon min–max optimal control problems with uncertainty

The difficulty of solving the min–max optimal control problems (M-MOCPs) with uncertainty using generalised Euler–Lagrange equations is caused by the combination of split boundary conditions, nonlinear differential equations and the manner in which the final time is treated. In this investigation, the shifted Jacobi pseudospectral method (SJPM) as a numerical technique for solving two-point boundary value problems (TPBVPs) in M-MOCPs for several boundary states is proposed. At first, a novel framework of approximate solutions which satisfied the split boundary conditions automatically for various boundary states is presented. Then, by applying the generalised Euler–Lagrange equations and expanding the required approximate solutions as elements of shifted Jacobi polynomials, finding a solution of TPBVPs in nonlinear M-MOCPs with uncertainty is reduced to the solution of a system of algebraic equations. Moreover, the Jacobi polynomials are particularly useful for boundary value problems in unbounded domain, which allow us to solve infinite- as well as finite and free final time problems by domain truncation method. Some numerical examples are given to demonstrate the accuracy and efficiency of the proposed method. A comparative study between the proposed method and other existing methods shows that the SJPM is simple and accurate.     

Effects of coating layer and release medium on release profile from coated capsules with Eudragit FS 30D: an in vitro and in vivo study

The aim of the present research was to evaluate the impact of coating layers on release profile from enteric coated dosage forms. Capsules were coated with Eudragit FS 30D using dipping method. The drug profile was evaluated in both phosphate buffer and Hank’s solutions. Utilization X-ray imaging, gastrointestinal transmission of enteric coated capsules was traced in rats. According to the results, no release of the drug was found at pH 1.2, and the extent of release drug in pH 6.8 medium was decreased by adding the coating layers. The results indicated single-layer coated capsules in phosphate buffer were significantly higher than that in Hank’s solution. However, no significant difference was observed from capsules with three coating layers in two different dissolution media. X-ray imaging showed that enteric coated capsules were intact in the stomach and in the small intestine, while disintegrated in the colon.     

Nanoethosomal formulation of gammaoryzanol for skin-aging protection and wrinkle improvement: a histopathological study

Background: Free radical scavengers and antioxidants, with the main focus on enhanced targeting to the skin layers, can provide protection against skin ageing.

Objective: The aim of the present study was to prepare nanoethosomal formulation of gammaoryzanol (GO), a water insoluble antioxidant, for its dermal delivery to prevent skin aging.

Methods: Nanoethosomal formulation was prepared by a modified ethanol injection method and characterized by using laser light scattering, scanning electronic microscope (SEM) and X-ray diffraction (XRD) techniques. The effects of formulation parameters on nanoparticle size, encapsulation efficiency percent (EE%) and loading capacity percent (LC%) were investigated. Antioxidant activity of GO-loaded formulation was investigated in vitro using normal African green monkey kidney fibroblast cells (Vero). The effect of control and GO-loaded nanoethosomal formulation on superoxide dismutase (SOD) and malondialdehyde (MDA) content of rat skin was also probed. Furthermore, the effect of GO-loaded nanoethosomes on skin wrinkle improvement was studied by dermoscopic and histological examination on healthy humans and UV-irradiated rats, respectively.

Results: The optimized nanoethosomal formulation showed promising characteristics including narrow size distribution 0.17?±?0.02, mean diameter of 98.9?±?0.05?nm, EE% of 97.12?±?3.62%, LC% of 13.87?±?1.36% and zeta potential value of –15.1?±?0.9?mV. The XRD results confirmed uniform drug dispersion in the nanoethosomes structure. In vitro and in vivo antioxidant studies confirmed the superior antioxidant effect of GO-loaded nanoethosomal formulation compared with control groups (blank nanoethosomes and GO suspension).

Conclusions: Nanoethosomes was a promising carrier for dermal delivery of GO and consequently had superior anti-aging effect.     

Evaluating the use of digital image correlation for strain measurement in historic tapestries using representative deformation fields

An analysis technique to assess the viability of digital image correlation (DIC) in tracking the full‐field strains across the surface of hanging historic tapestries is presented. Measurement uncertainty related to the use of the inherent tapestry image in tracking displacements is investigated through use of “synthetic” deformation fields. The latter are generated by mapping the details of a given tapestry image into finite element analyses. The combination of self‐weight loading, material non‐linearity, and image specific heterogeneity (related to slit stitching, damage, and patch‐restorations) serve to generate a bespoke deformation field complex enough to assess the reliability of DIC measurements. Accuracy is evaluated by comparing measured results with the original known deformations. The technique demonstrates that the optimum imaging settings and the choice of subset size for DIC analysis are strongly influenced by the tapestry image and the goal of the measurement, they are found using a compromise between conflicting objectives: minimising measurement error while maximising resolution.     

Robust output regulation of a triaxial MEMS gyroscope via nonlinear active disturbance rejection

This paper presents a nonlinear disturbance rejection–based controller for the robust output regulation of a triaxial microelectromechanical system (MEMS) vibratory gyroscope. In a MEMS gyroscope, parameter variations, mechanical couplings, suspension system nonlinearities, thermal noise, and centripetal/Coriolis forces are the main uncertainty sources. In the dynamical equations of the gyroscope, these uncertainties appear as a matched total disturbance, which does not coincide with the required structure of a standard output regulation problem. More specifically, the total disturbance is not guaranteed to belong to the solution space of a fixed dynamical system. Therefore, we propose a control system that comprises a nominal output regulator equipped with a disturbance rejection loop. On the basis of a suitable reference dynamics of the gyroscope, the control system is developed as the stabilization of a zero‐error invariant manifold in the tracking error space. In the disturbance rejection loop, a nonlinear extended state observer (ESO) is designed to estimate the total disturbance. The convergence of the ESO is analyzed in a Lyapunov‐Lurie framework by linear matrix inequalities (LMIs). In the nominal output regulation loop, the stabilization problem of the desired manifold is tackled by introducing a suitable distance coordinate. Next, to achieve guaranteed attenuation of the ESO estimation errors, an energy‐to‐peak design is pursued. On the basis of the center manifold theory, the stability of the overall closed‐loop system is guaranteed. The efficacy of the proposed control method is assessed through software simulations.     

Improved clustering algorithms for target tracking in wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks because of their potential usage in a wide variety of applications such as remote environmental monitoring and target tracking. Target tracking is a typical and substantial application of wireless sensor networks. Generally, target tracking aims basically at estimating the location of the target while it is moving within an area of interest and consequently report it to the base station in a timely manner. However, achieving a high accuracy of tracking together with energy efficiency in target tracking algorithms is extremely challenging. In this article, we propose two algorithms to enhance the adaptive-head clustering algorithm, formerly lunched, namely, the improved adaptive-head and improved prediction-based adaptive head. Particularly, the first algorithm uses dynamic clustering to achieve impressive tracking quality and energy efficiency through optimally choosing the cluster head that participates in the tracking process. On the other hand, the second algorithm incorporates a prediction mechanism to the first proposed algorithm. Our proposed algorithms are simulated using Matlab considering various network conditions. Simulation results show that our proposed algorithms can accurately track a target, even when random moving speeds are considered and consume much less energy, when compared with the previous algorithm for target tracking, which in turn prolong the network lifetime much more.     

Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Many water resources optimization problems involve conflicting objectives which the main goal is to find a set of optimal solutions on, or near to, Pareto front. In this study a multi-objective water allocation model was developed for optimization of conjunctive use of surface water and groundwater resources to achieve sustainable supply of agricultural water. Here, the water resource allocation model is based on simulation-optimization (SO) modeling approach. Two surrogate models, namely an Artificial Neural Network model for groundwater level simulation and a Genetic Programming model for TDS concentration prediction were coupled with NSGA-II. The objective functions involved: 1) minimizing water shortage relative to the water demand, 2) minimizing the drawdown of groundwater level, and 3) minimizing the groundwater quality changes. According to the MSE and R² criteria, the results showed that the surrogate models for prediction of groundwater level and TDS concentration performed favorably in comparison to the measured values at the number of observation wells. In Najaf Abad plain case study, the average drawdown was limited to 0.18 m and the average TDS concentration also decreased from 1257 mg/lit to 1229 mg/lit under optimal conditions.