Optimistic Selection of Cluster Heads Based on Facility Location Problem in Cluster-Based Routing Protocols

Cluster-based routing protocols are one of the most favorable approaches for energy management in wireless sensor networks. The selection of the best cluster heads (CHs), as well as the formation of optimal clusters, is an NP-hard problem. The present study proposes an optimal solution for CHs selection to generate a network topology with optimized network performance. The problem is formulated as facility location problem and a linear programming model is used to solve the optimization problem. Results of analysis o the network simulator (NS2) indicate that applying this method in cluster-based routing protocols prolongs 16 % of the network lifetime, increases 15.5 % of data transmission and improves 5.5 % of throughput, as compared to the results of current heuristic methods such as LEACH, DEEC and EDFCM protocols.     

A nonlinear model for context-dependent modulation of the binocular VOR

Studies on the behavior of the vestibulo-ocular reflex (VOR) reveal that the monocular reflex gain is adjusted according to target position relative to each eye. In this paper, we present a nonlinear approach in modeling the viewing-context dependency of the slow-phase angular VOR. We show that including appropriate nonlinearities in the responses of premotor neurons in the brainstem is sufficient to account for the online modulation of the VOR with target position. This approach allows very complex behaviors in response to sensory patterns without resorting to currently assumed cortical computations. A local premotor topology with nonlinear properties has repercussions in the study of all ocular reflexes, since it implies context dependent dynamics in all behavioral responses (pursuit, optokinetic, VOR, saccades, etc.) that share this network. Local nonlinearities in spinal circuits could similarly influence the context dependence of other motor systems (such as stretch reflex modulation during rhythmic walking).     

Fault Tolerance and Energy Efficient Clustering Algorithm in Wireless Sensor Networks: FTEC

Wireless Personal Communications - The beacon order and Superframe order parameter values, as defined by the IEEE 802.15.4 standard specification, constrain the number of data packets that can be...     

Surface Decoration of Peptide Nanoparticles Enables Efficient Therapy toward Osteoporosis and Diabetes

A versatile surface decoration strategy to efficiently encapsulate water-soluble peptides is developed. By assembling peptide molecules into nanoparticles, diverse physiochemical properties of these compacted molecules are equalized to the surface properties of nanoparticles. Primarily driven by the generic electrostatic attractions, the surface of as-prepared peptide nanoparticles is decorated with charged amino acids-grafted poly(lactic-co-glycolic acid). This adsorbed polymer layer versatilely blocks the phase transfer of peptide nanoparticles by increasing their affinity to the dispersed phase solvent molecules. Attributed to the ultrahigh encapsulation efficiencies (> 96%), the peptide mass fraction inside the obtained microcomposites is higher than 48%. The plasma calcium level has been efficiently reduced for ≈3 weeks with only one single injection of salmon calcitonin-encapsulated microcomposite in osteoporotic rats. Similarly, one single injection of exenatide-encapsulated microcomposites efficiently controls the glycemic level in type 2 diabetic rats for up to 3 weeks. Overall, the developed versatile surface decoration strategy efficiently encapsulates peptides and improves their pharmacokinetic features, regardless of the molecular structure of peptide cargos.     

IAM: an improved mapping on a 2-D network on chip to reduce communication cost and energy consumption

Photonic Network Communications - Based on the recent research, the communication cost has been the most important open issue in network on chip (NoC). In other words, the lower the communication...     

Bioavailability of nutraceuticals: Role of the food matrix,processing conditions,the gastrointestinal tract,and nanodelivery systems

Nowadays, many consumers prefer foods with a high content of nutraceuticals that contribute to the prevention or healing of chronic diseases. Therefore, in recent years, more and more researchers have studied the bioefficiency, safety, and toxicity of nutraceutical‐enriched foods. The key stage of nutraceutical bioefficiency is oral bioavailability, which involves the following processes: the release of nutraceuticals from food matrices or nanocarriers in gastrointestinal fluids, the solubilization of nutraceuticals and their interaction with other components of gastrointestinal fluids, the absorption of nutraceuticals by the epithelial layer, and the chemical and biochemical transformations into epithelial cells. These processes are endogenous factors that greatly influence the bioavailability of nutraceuticals. In addition to endogenous factors, the bioavailability of nutraceuticals is also affected by exogenous factors, such as: physicochemical properties of nutraceuticals, food matrix, food processing and storage, and so forth. Both the endogenous and exogenous factors are comprehensively analyzed in this review. Thus, the physicochemical and enzymatic processes involved in food digestion are described, highlighting the role of each stage of gastrointestinal tract (mouth, stomach, and intestine) in nutraceuticals bioaccessibility. The structure and functions of the mucus and epithelial layers, the mechanisms involved in the active and passive transport of nutraceuticals through the cell membrane, and phase I and phase II metabolism reactions are also discussed. Finally, this review focuses on several types of bioactive‐loaded nanocarriers such as lipid‐based, surfactant‐based, and biopolymeric nanocarriers that improve the bioavailability of nutraceuticals.     

Effect of enzymatic and mechanical treatment on combined desizing and bio-polishing of cotton fabrics

The main objective of this study was to improve the enzymatic effect by mechanical treatment on cotton fabric in combined desizing and bio-polishing. The purpose is to create a commercially and environmentally friendly process for water and energy saving. The effect of mechanical treatment and different enzyme concentration was studied. Both mechanical and enzymatic treatment effect were evaluated through measuring weight loss and light measurement of fabric surface. Fabric surface evaluation also was done via photo micrographing. Fabric hairiness was illustrated on captured photos of treated and untreated fabric. Finally, optimized data of treatment were obtained.     

Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network

Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25?±?2 °C and 20?±?3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p?<?0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively.