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.

     

In vivo evaluation of a mucoadhesive polymeric caplet for intravaginal anti-HIV-1 delivery and development of a molecular mechanistic model for thermochemical characterization

Context and objective: The aim of this study was to develop, characterize and evaluate a mucoadhesive caplet resulting from a polymeric blend (polymeric caplet) for intravaginal anti-HIV-1 delivery.

Materials and methods: Poly(lactic-co-glycolic) acid, ethylcellulose, poly(vinylalcohol), polyacrylic acid and modified polyamide 6, 10 polymers were blended and compressed to a caplet-shaped device, with and without two model drugs 3′-azido-3′-deoxythymidine (AZT) and polystyrene sulfonate (PSS). Thermal analysis, infrared spectroscopy and microscopic analysis were carried out on the caplets employing temperature-modulated DSC (TMDSC), Fourier transform infra-red (FTIR) spectrometer and scanning electron microscope, respectively. In vitro and in vivo drug release analyses as well as the histopathological toxicity studies were carried out on the drug-loaded caplets. Furthermore, molecular mechanics (MM) simulations were carried out on the drug-loaded caplets to corroborate the experimental findings.

Results and discussion: There was a big deviation between the T_g of the polymeric caplet from the T_g's of the constituent polymers indicating a strong interaction between constituent polymers. FTIR spectroscopy confirmed the presence of specific ionic and non-ionic interactions within the caplet. A controlled near zero-order drug release was obtained for AZT (20 d) and PSS (28 d). In vivo results, i.e. the drug concentration in plasma ranged between 0.012–0.332?mg/mL and 0.009–0.256?mg/mL for AZT and PSS over 1–28 d.

Conclusion: The obtained results, which were corroborated by MM simulations, attested that the developed system has the potential for effective delivery of anti-HIV-agents.     

Influence of fretting on the fatigue strength at the vise clamp-specimen interface

Fretting fatigue is one of the most important phenomena for inducing a significant reduction of fatigue strength and consequently, leading to unexpected failure accidents of the engineering structures even at very low stresses. In the present study, both plain and fretting fatigue tests with zero mean stress were carried out on two different types of steel, low-carbon steel and martensitic stainless steel, by means of a reversed bending fatigue testing machine. The drop in the fatigue strengths through fretting at vise clamp-specimen interface were significant for both tested steels. The fretting processes produced a reduction in fatigue strength of about 27% for low-carbon steel and 16% for martensitic stainless steel.     

Simulation of Natural Depletion and Miscible Gas Injection Effects on Asphaltene Stability in Petroleum Reservoir Fluids

Natural depletion of petroleum reservoirs as well as gas injection for enhance oil recovery, are unavoidable processes in the oil industry. Foremost, prediction of the problems due to these two processes is very necessary and important. So many field and experimental experiences have shown that heavy organic depositions, especially asphaltene deposition, are principal results during these processes. Results of laboratory simulation of asphaltene deposition during the natural depletion of petroleum reservoirs and also during gas injection and enhanced oil recovery (EOR) processes are reported here. This is achieved through the design of a new experimental setup for the investigation of pressure and composition effects on asphaltene deposition in petroleum fluids at high pressure and high temperature conditions. In this work, asphaltene deposition during decreasing pressure, from pressures greater than reservoir pressure to pressures below the bubble point pressure (natural depletion) and also asphaltene deposition during natural gas injection in reservoir conditions, are studied for three samples—one recombined sample and two bottomhole samples. All of the obtained results from this work conform to theoretical and other experimental works.     

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.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty

Organizations have recently become interested in applying new approaches to reduce fuel consumptions, aiming at decreasing green house gases emission due to their harmful effects on environment and human health; however, the large difference between practical and theoretical experiments grows the concern about significant changes in the transportation environment, including fuel consumptions, carbon dioxide (CO₂) emissions cost and vehicles velocity, that it encourages researchers to design a near-reality and robust pollution routing problem. This paper addresses a new time window pickup-delivery pollution routing problem (TWPDPRP) to deal with uncertain input data for the first time in the literature. For this purpose, a new mixed integer linear programming (MILP) approach is presented under uncertainty by taking green house emissions into consideration. The objective of the model is to minimize not only the travel distance and number of available vehicles along with the capacity and aggregated route duration restrictions but also the amount of fuel consumptions and green house emissions along with their total costs. Moreover, a robust counterpart of the MILP is introduced by applying the recent robust optimization theory. Computational results for several test problems indicate the capability and suitability of the presented MILP model in saving costs and reducing green house gases concurrently for the TWPDPRP problem. Finally, both deterministic and robust mathematical programming are compared and contrasted by a number of nominal and realizations under these test problems to judge the robustness of the solution achieved by the presented robust optimization model.