Experimental validation of analytical performance models for IEEE 802.11 networks

We consider the simplest IEEE 802.11 WLAN networks for which analytical models are available and seek to provide an experimental validation of these models. Our experiments include the following cases: (i) two nodes with saturated queues, sending fixed-length UDP packets to each other, and (ii) a TCP-controlled transfer between two nodes. Our experiments are based entirely on Aruba AP-70 access points operating under Linux. We report our observations on certain non-standard behavior of the devices. In cases where the devices adhere to the standards, we find that the results from the analytical models estimate the experimental data with a mean error of 3-5%.

Experimental determination and activity coefficient based models for mixture solubilities of nitrophenol isomers in supercritical carbon dioxide

The experimental solubilities of the mixture of nitrophenol (m- and p-) isomers were determined at 308, 318 and 328 K over a pressure range of 10-17.55 MPa. Compared to the binary solubilities, the ternary solubilities of m-nitrophenol increased at 308, 318 and 328 K. The ternary solubilities of p-nitrophenol increased at 308 K, while the ternary solubilities decreased at lower pressures and increased at higher pressure at 318 and 328 K. The solubilities of the solid mixtures in supercritical carbon dioxide (SCCO2) were correlated with solution models by incorporating the non-idealities using activity coefficient based models. The Wilson and NRTL activity coefficient models were applied to determine the nature of the interactions between the molecules. The equation developed by using the NRTL model has three parameters and correlates mixture solubilities of solid solutes in terms of temperature and cosolute composition. The equation derived from the Wilson model contains five parameters and correlates solubilities in terms of temperature, density and cosolute composition. These two new equations developed in this work were used to correlate the solubilities of 25 binary solid mixtures including the current data. The average AARDs of the model equations derived using the NRTL and Wilson models for the solid mixtures were found to be 7% and 4%, respectively. (C) 2012 Elsevier B.V. All rights reserved.

Experimental analysis of composite single-lap joints using digital image correlation and comparison with theoretical models

The demand for energy efficient, low weight structures has boosted the use of composite structures assembled using increased quantities of structural adhesives. Bonded structures may be subjected to severe working environments such as high temperature and moisture due to which the adhesive gets degraded over a period of time. This reduces the strength of a joint and leads to premature failure. Measurement of strains in the adhesive bondline at any point of time during service may be beneficial as an assessment can be made on the integrity of a joint and necessary preventive actions may be taken before failure. This paper presents an experimental approach of measuring peel and shear strains in the adhesive bondline of composite single-lap joints using digital image correlation. Different sets of composite adhesive joints with varied bond quality were prepared and subjected to tensile load during which digital images were taken and processed using digital image correlation software. The measured peel strain at the joint edge showed a rapid increase with the initiation of a crack till failure of the joint. The measured strains were used to compute the corresponding stresses assuming a plane strain condition and the results were compared with stresses predicted using theoretical models, namely linear and nonlinear adhesive beam models. A similar trend in stress distribution was observed. Further comparison of peel and shear strains also exhibited similar trend for both healthy and degraded joints. Maximum peel stress failure criterion was used to predict the failure load of a composite adhesive joint and a comparison was made between predicted and actual failure loads. The predicted failure loads from theoretical models were found to be higher than the actual failure load for all the joints.

Experimental solubilities of two lipid derivatives in supercritical carbon dioxide and new correlations based on activity coefficient models

The solubilities of two lipid derivatives, geranyl butyrate and 10-undecen-1-ol, in SCCO2 (supercritical carbon dioxide) were measured at different operating conditions of temperature (308.15 to 333.15 K) and pressure (10 to 18 MPa). The solubilities (in mole fraction) ranged from 2.1 x 10(-3) to 23.2 x 10(-3) for geranyl butyrate and 2.2 x 10(-3) to 25.0 x 10(-3) for 10-undecen-1-ol, respectively. The solubility data showed a retrograde behavior in the pressure and temperature range investigated. Various combinations of association and solution theory along with different activity coefficient models were developed. The experimental data for the solubilities of 21 liquid solutes along with geranyl butyrate and 10-undecen-1-ol were correlated using both the newly derived models and the existing models. The average deviation of the correlation of the new models was below 15%.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

Implant healing in experimental animal models of diabetes.

Diabetes mellitus is becoming increasingly prevalent worldwide. Additionally, there is an increasing number of patients receiving implantable devices such as glucose sensors and orthopedic implants. Thus, it is likely that the number of diabetic patients receiving these devices will also increase. Even though implantable medical devices are considered biocompatible by the Food and Drug Administration, the adverse tissue healing that occurs adjacent to these foreign objects is a leading cause of their failure. This foreign body response leads to fibrosis, encapsulation of the device, and a reduction or cessation of device performance. A second adverse event is microbial infection of implanted devices, which can lead to persistent local and systemic infections and also exacerbates the fibrotic response. Nearly half of all nosocomial infections are associated with the presence of an indwelling medical device. Events associated with both the foreign body response and implant infection can necessitate device removal and may lead to amputation, which is associated with significant morbidity and cost. Diabetes mellitus is generally indicated as a risk factor for the infection of a variety of implants such as prosthetic joints, pacemakers, implantable cardioverter defibrillators, penile implants, and urinary catheters. Implant infection rates in diabetic patients vary depending upon the implant and the microorganism, however, for example, diabetes was found to be a significant variable associated with a nearly 7.2% infection rate for implantable cardioverter defibrillators by the microorganism Candida albicans. While research has elucidated many of the altered mechanisms of diabetic cutaneous wound healing, the internal healing adjacent to indwelling medical devices in a diabetic model has rarely been studied. Understanding this healing process is crucial to facilitating improved device design. The purpose of this article is to summarize the physiologic factors that influence wound healing and infection in diabetic patients, to review research concerning diabetes and biomedical implants and device infection, and to critically analyze which diabetic animal model might be advantageous for assessing internal healing adjacent to implanted devices.

Start-up and running forces on bulk solids feeders: experimental findings versus available models

Many different models have been postulated over the years for sizing of feeder drives; these models have different bases, some rationally based and others more rule-of-thumb. Experience of Jenike & Johanson and likewise of The Wolfson Centre in trouble-shooting feeder drives has shown that drive powers are often poorly matched, so there is clearly still some way to go towards establishing a universally-used reliable approach. This paper presents an on-going programme of work designed to measure feeder forces experimentally on a purpose designed testing rig, and to compare these against some of the best known available models, and also against a full size installation. One aspect which is novel is the monitoring of the transition between the “filling stress field” load on the feeder and the “flowing stress field” load.

Detecting the effects of diversity on measures of ecosystem function: experimental design, null models and empirical observations

Here we present a novel experimental approach to examine the relationship between diversity and ecosystem Function. We develop four null predictive models, with which to differentiate between the 'sampling effect' - the chance inclusion of a highly productive species, and 'species complementarity' - the complementary use of resources by species that differ in their niche or resource use. We investigate the effects of manipulating species and functional richness on ecosystem function in marine benthic system and using empirical data from our own experiments we illustrate the application of these models.

Pharmacological evaluation of selective α2c-adrenergic agonists in experimental animal models of nasal congestion

Nasal congestion is one of the most troublesome symptoms of many upper airways diseases. We characterized the effect of selective α2c-adrenergic agonists in animal models of nasal congestion. In porcine mucosa tissue, compound A and compound B contracted nasal veins with only modest effects on arteries. In in vivo experiments, we examined the nasal decongestant dose-response characteristics, pharmacokinetic/pharmacodynamic relationship, duration of action, potential development of tolerance, and topical efficacy of α2c-adrenergic agonists. Acoustic rhinometry was used to determine nasal cavity dimensions following intranasal compound 48/80 (1%, 75 µl). In feline experiments, compound 48/80 decreased nasal cavity volume and minimum cross-sectional areas by 77% and 40%, respectively. Oral administration of compound A (0.1-3.0 mg/kg), compound B (0.3-5.0 mg/kg), and d-pseudoephedrine (0.3 and 1.0 mg/kg) produced dose-dependent decongestion. Unlike d-pseudoephedrine, compounds A and B did not alter systolic blood pressure. The plasma exposure of compound A to produce a robust decongestion (EC(80)) was 500 nM, which related well to the duration of action of approximately 4.0 hours. No tolerance to the decongestant effect of compound A (1.0 mg/kg p.o.) was observed. To study the topical efficacies of compounds A and B, the drugs were given topically 30 minutes after compound 48/80 (a therapeutic paradigm) where both agents reversed nasal congestion. Finally, nasal-decongestive activity was confirmed in the dog. We demonstrate that α2c-adrenergic agonists behave as nasal decongestants without cardiovascular actions in animal models of upper airway congestion.

An experimental evaluation of prediction models for the mechanical behavior of unreinforced, lime-mortar masonry under compression

This paper contributes to the understanding of lime-mortar masonry strength and deformation (which determine durability and allowable stresses/stiffness in design codes) by measuring the mechanical properties of brick bound with lime and lime-cement mortars. Based on the regression analysis of experimental results, models to estimate lime-mortar masonry compressive strength are proposed (less accurate for hydrated lime (CL90s) masonry due to the disparity between mortar and brick strengths). Also, three relationships between masonry elastic modulus and its compressive strength are proposed for cement-lime; hydraulic lime (NHL3.5 and 5); and hydrated/feebly hydraulic lime masonries respectively.

Disagreement between the experimental results and former mathematical prediction models (proposed primarily for cement masonry) is caused by a lack of provision for the significant deformation of lime masonry and the relative changes in strength and stiffness between mortar and brick over time (at 6 months and 1 year, the NHL 3.5 and 5 mortars are often stronger than the brick). Eurocode 6 provided the best predictions for the compressive strength of lime and cement-lime masonry based on the strength of their components. All models vastly overestimated the strength of CL90s masonry at 28 days however, Eurocode 6 became an accurate predictor after 6 months, when the mortar had acquired most of its final strength and stiffness.

The experimental results agreed with former stress-strain curves. It was evidenced that mortar strongly impacts masonry deformation, and that the masonry stress/strain relationship becomes increasingly non-linear as mortar strength lowers. It was also noted that, the influence of masonry stiffness on its compressive strength becomes smaller as the mortar hydraulicity increases.