Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.     

Multiple model bank selection based on nonlinearity measure and H-gap metric

This paper provides a systematic method for model bank selection in multi-linear model analysis for nonlinear systems by presenting a new algorithm which incorporates a nonlinearity measure and a modified gap based metric. This algorithm is developed for off-line use, but can be implemented for on-line usage. Initially, the nonlinearity measure analysis based on the higher order statistic (HOS) and the linear cross correlation methods are used for decomposing the total operating space into several regions with linear models. The resulting linear models are then used to construct the primary model bank. In order to avoid unnecessary linear local models in the primary model bank, a gap based metric is introduced and applied in order to merge similar linear local models. In order to illustrate the usefulness of the proposed algorithm, two simulation examples are presented: a pH neutralization plant and a continuous stirred tank reactor (CSTR).     

Flow resistance in a compound gravel-bed bend

In this paper, the effect of a gravel-bed in a compound bend (similar to sinusoidal top view) of a natural river (Zayandehrud River flowing through Isfahan, Iran) has been investigated for flow resistance analysis, measuring the velocity with a micro current meter. The data were analysed and the following observations were made. In a compound bend, the law of the wall can be valid for up to 66% of the flow depth from the bed. The parabolic law is the most effective method for the determination of shear velocity. Based on the existing criteria for verifying the equilibrium boundary layer, the flow cannot be in equilibrium. The shear stress distribution and the sediment transport parameters have considerable influence on resistance to flow. Froude number and the flow depth relative to the representative gravel size have little effect on the flow resistance estimation.     

Prediction of the Failure Time of Glass Fiber Reinforced Plastic Reinforced Concrete Beams under Fire Conditions

A model is proposed to predict the time to failure of reinforced concrete beams in a fire. The model is developed specifically to predict the lifetime of beams reinforced with glass fiber reinforced plastic rebar, but is applicable to beams with any form of reinforcement. The model is based on the calculations for flexural capacity and shear capacity of beams embedded within ACI design codes where time and temperature dependent values for rebar modulus and strength and concrete strength replace the static design values. The base equations are modified to remove safety factors and where necessary the temperature induced reductions in strength for concrete and steel are derived using the equations presented by EUROCODE 2. In order to validate the model it was used to predict the failure times of steel rebar reinforced beams that had been documented in the literature. There was excellent agreement between the model and the reported lifetimes for these conventional beams. The model was applied to predict the lifetimes of two beams that had been manufactured and tested for destruction in a fire by the research group. The model predicted that the failure mode of the beams would be because of rebar rupture as opposed to the design condition of concrete crushing and this was confirmed by the experimental test results. The model provided reasonable agreement with experimental results with a lifetime of 108?min predicted based on flexural failure and 94 and 128?min observed in the experiments.     

Comparative study on stress–strain hysteresis response of SAC solder joints under thermal cycles

The present study attempts to evaluate the stress-strain hysteresis responses of SAC solder joints in Resistor and FleXBGA144 packages subjected to thermal cyclic loading using several constitutive models. The total deformation of the solder material consists of elastic, rate-independent plastic and rate-dependent creep components. The constitutive models discussed in this study each weighted elastic, plastic and creep deformations differently. At low stresses SAC solder alloys were found to be creep resistant, where at higher stresses, the influence of different microstructures disappears as matrix-creep dominates in this region. Thus, the proper constitutive model requires all the three ingredients of the elastic, the creep, and the time-independent plastic data for different stress levels to effectively predict the hysteresis behavior of the SAC solder alloys. The hysteresis loops predicted by constitutive models were also found in close agreement with the loops generated by FEM for the SAC solder joint subjected to thermal cycling.     

Exponentially Weighted Moving Average Chart and Two‐Component Measurement Error

The two‐component measurement error (TCME) model effectively quantifies both the additive and multiplicative errors in analytical measurements. The performance of control charts in presence of TCME model has been investigated by researchers in recent past, but these studies are based on only considering fixed levels for the two‐component model parameters. In this study, we investigated the exponentially weighted moving average chart's performance in presence of TCME for varying levels of two‐component model parameters. A cost function analysis is also presented to determine the optimal number of multiple measurements and the sample size for reducing the effect of TCME. Copyright © 2015 John Wiley & Sons, Ltd.     

Association between antiinflammatory cytokine,IL‐10, and sleep quality in patients on maintenance hemodialysis

Elevated proinflammatory cytokines have been attributed to poor sleep quality in patients receiving hemodialysis. This is the first investigation about the relationship between sleep quality and circulating levels of antiinflammatory markers in these patients. A total of 72 patients who were receiving maintenance hemodialysis were enrolled in this cross‐sectional study. The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality. Patients were divided into two groups: good sleepers (PSQI score < 5) and poor sleepers (PSQI score ≥ 5). Assessments were made for serum biochemical parameters (albumin, parathyroid hormone), inflammatory (interleukin [IL]‐6, tumor necrosis factor‐alpha [TNF‐α], and high‐sensitivity c‐reactive protein [hs‐CRP] ) and antiinflammatory (IL‐10) markers. Fifty‐four patients (75%) were classified as poor sleepers. Poor sleepers showed significantly lower levels of serum IL‐10 and higher serum triglyceride and parathyroid hormone concentrations. These patients were more likely to have more comorbidities. The global PSQI score was significantly correlated with serum IL‐10 (p = 0.03) and triglyceride levels (p = 0.01). Multivariate logistic regression analysis showed a direct correlation between PSQI and having comorbidities (p = 0.011, odds ratio [OR] = 3.918; confidence interval 95% [CI] = 2.742–19.031), between PSQI and serum triglyceride (p = 0.027, OR = 1.027 [95% CI = 1.007–1.048] ), and an inverse correlation between PSQI and serum IL‐10 level (p = 0.021, OR = 0.424 [95% CI = 0.195–0.922]). Reduced circulating levels of the antiinflammatory cytokine IL‐10 were significantly associated with poor sleep quality in hemodialysis patients. Factors including serum IL‐10 and triglyceride concentrations and having comorbidities may predict patients prone to poor sleep quality.     

Experimental investigation of the flow-induced motion of a square-section cylinder

The flow-induced motion(FIM)of an elastically mounted square-section cylinder is experimentally investigated over a wide range of Reynolds numbers(1.5×104＜Re＜7....     

Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms

With the rapid growth of laser applications and the introduction of high efficiency lasers (e.g. fiber lasers), laser material processing has gained increasing importance in a variety of industries. Among the applications of laser technology, laser cladding has received significant attention due to its high potential for material processing such as metallic coating, high value component repair, prototyping, and even low-volume manufacturing. In this paper, two optimization methods have been applied to obtain optimal operating parameters of Laser Solid Freeform Fabrication Process (LSFF) as a real world engineering problem. First, Particle Swarm Optimization (PSO) algorithm was implemented for real-time prediction of melt pool geometry. Then, a hybrid evolutionary algorithm called Self-organizing Pareto based Evolutionary Algorithm (SOPEA) was proposed to find the optimal process parameters. For further assurance on the performance of the proposed optimization technique, it was compared to some well-known vector optimization algorithms such as Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA 2). Thereafter, it was applied for simultaneous optimization of clad height and melt pool depth in LSFF process. Since there is no exact mathematical model for the clad height (deposited layer thickness) and the melt pool depth, the authors developed two Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to estimate these two process parameters. Optimization procedure being done, the archived non-dominated solutions were surveyed to find the appropriate ranges of process parameters with acceptable dilutions. Finally, the selected optimal ranges were used to find a case with the minimum rapid prototyping time. The results indicate the acceptable potential of evolutionary strategies for controlling and optimization of LSFF process as a complicated engineering problem.