Experimental Design to Optimize High-Volume Ash Grout in the Presence of Welan Gum and Superplasticizer

Applications such as soil, rock and oil-well grouting all require enormous amounts of cement and are good examples of areas where a high volume of fly ash could partially replace cement to produce low-cost, environmentally safe and durable concrete. There is an increasing need to identify the rheological properties of cement grout using a simple test to determine the fluidity, and other properties of underwater grouts such as washout resistance and compressive strength. This paper presents statistical models developed using a fractorial design which was carried out to model the influence of key parameters on properties affecting the performance of underwater grout. Such responses of fluidity included mini-slump and flow time measured by Marsh cone, washout resistance, unit weight and compressive strength. The models are valid for mixes with 0.40 to 0.60 water-to-cementitious materials ratio, 0.02 to 0.08% of anti-washout admixture, by mass of binder, and 0.6 to 1.8% of superplasticizer, by mass of cementitious materials. The grout was made with 50% of pulverized-fuel ash replacement, by mass ofcementitious materials. Also presented are the derived models that enable the identification of underlying primary factors and their interactions that influence the modelled responses of underwater cement grout. Such parameters can be useful to reduce the test protocol needed for proportioning of underwater cement grout. This paper highlighted the influence of W/CM and dosage of antiwashout admixture and superplasticizer on fluidity, washout resistance and compressive strength and attempted also to demonstrate the usefulness of the models to improve understanding of trade-offs between parameters.

Selecting Room-Temperature Ionic Liquids to Optimize Voltammetric Responses: The Oxidation of NADH

The electrochemistry of nicotinamide adenine dinucleotide (NADH) in its reduced form was examined in two room-temperature ionic liquids (RTILs): 1-ethyl-3-methylimidazolium bis-(trifluoromethylsulfonyl)imide ([C(2)mim][NTf2]) and 1-butyl-3-methylimidazolium hexafluorophos-phate ([C(4)mim][PF6]). NADH oxidation has previously been studied in aqueous solution where it follows the pathway: one-electron oxidation to the NADH(center dot+) radical cation, deprotonation to produce the neutral NAD(center dot) radical, then oxidation to the NAD(+) cation. The electrochemistry of NADH was examined in [C(2)mim][NTf2] and [C(4)mim][PF6] at the bare Pt electrode (10 mu m diameter): In [C(2)mim][NTf2], no oxidation was observed; in [C(4)mim][PF6], an oxidative signal was observed, which likely followed the pathway described above, where upon formation of the NADH(center dot+) radical cation, the [PF6](-) anion (unlike the [NTf2](-) anion) reacts with the proton to form HPF6, which decomposes. This demonstrates the tunability of RTILs, whereby the choice of one anion in an RTIL over another can promote a reaction. Poly(vinylferrocene) (PVF) was studied as a mediator for the NADH detection in both RTILs to attempt to lower the potential of NADH detection. The Pt electrode was modified with PVF, and the oxidation of PVF to PVF+ was observed in [C(2)mim][NTf2] and [C(4)mim][PF6], but no mediation of the NADH oxidation was observed.

Strategies for adding features to CAD models in order to optimize performance

This paper presents an approach which enables new parameters to be added to a CAD model for optimization purposes. It aims to remove a common roadblock to CAD based optimization, where the parameterization of the model does not offer the shape sufficient flexibility for a truly optimized shape to be created. A technique has been developed which uses adjoint based sensitivity maps to predict
the sensitivity of performance to the addition to a model of four different feature types, allowing the feature providing the greatest benefit to be selected. The optimum position to add the feature is also discussed. It is anticipated that the approach could be used to iteratively add features to a model, providing greater flexibility to the shape of the model, and allowing the newly-added parameters to be used as design variables in a subsequent shape optimization.

Processing characteristics and mechanical properties of metallocene catalyzed linear low-density polyethylene foams for rotational molding

The object of this work is to assess the suitability of metallocene catalyzed linear low-density polyethylenes for the rotational molding of foams and to link the material and processing conditions to cell morphology and part mechanical properties (flexural and compressive strength). Through adjustments to molding conditions, the significant processing and physical material parameters that optimize metallocene catalyzed linear low-density polyethylene foam structure have been identified. The results obtained from an equivalent conventional grade of Ziegler-Natta catalyzed linear low-density polyethylene are used as a basis for comparison. The key findings of this study are that metallocene catalyzed LLDPE can be used in rotational foam molding to produce a foam that will perform as well as a ZieglerNatta catalyzed foam and that foam density Is by far the most Influential factor over mechanical properties of foam. © 2004 Society of Plastics Engineers.

Applications of statistical models in proportioning medium strength self-compacting concrete

Self-compacting concrete (SCC) is generally designed with a relatively higher content of finer, which includes cement, and dosage of superplasticizer than the conventional concrete. The design of the current SCC leads to high compressive strength, which is already used in special applications, where the high cost of materials can be tolerated. Using SCC, which eliminates the need for vibration, leads to increased speed of casting and thus reduces labour requirement, energy consumption, construction time, and cost of equipment. In order to obtain and gain maximum benefit from SCC it has to be used for wider applications. The cost of materials will be decreased by reducing the cement content and using a minimum amount of admixtures. This paper reviews statistical models obtained from a factorial design which was carried out to determine the influence of four key parameters on filling ability, passing ability, segregation and compressive strength. These parameters are important for the successful development of medium strength self-compacting concrete (MS-SCC). The parameters considered in the study were the contents of cement and pulverised fuel ash (PFA), water-to-powder ratio (W/P), and dosage of superplasticizer (SP). The responses of the derived statistical models are slump flow, fluidity loss, rheological parameters, Orimet time, V-funnel time, L-box, JRing combined to Orimet, JRing combined to cone, fresh segregation, and compressive strength at 7, 28 and 90 days. The models are valid for mixes made with 0.38 to 0.72 W/P ratio, 60 to 216 kg/m3 of cement content, 183 to 317 kg/m3 of PFA and 0 to 1% of SP, by mass of powder. The utility of such models to optimize concrete mixes to achieve good balance between filling ability, passing ability, segregation, compressive strength, and cost is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mix parameters on slump flow, loss of fluidity, flow resistance, segregation, JRing combined to Orimet, and compressive strength at 7 and 28 days. Cost analysis is carried out to show trade-offs between cost of materials and specified consistency levels and compressive strength at 7 and 28 days that can be used to identify economic mixes. The paper establishes the usefulness of the mathematical models as a tool to facilitate the test protocol required to optimise medium strength SCC.

Filling Ability and Passing Ability of Self-Consolidating Concrete

This paper reviews statistical models obtained from a composite factorial design study, which was carried out to determine the influence of three key parameters of mixture composition on filling ability and passing ability of self-consolidating concrete (SCC). This study was a part of the European project “Testing SCC”- GRD2-2000-30024. The parameters considered in this study were the dosages of water and high-range water-reducing admixture (HRWRA), and the volume of coarse aggregates. The responses of the derived statistical models were slump flow, T50 , T60, V-funnel flow time, Orimet flow time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 minutes after adding the first water. The models are valid for mixtures made with 188 to 208 L/m3 (317 to 350 lb/yd3) of water, 3.8 to 5.8 kg/m3 (570 to 970 mL/100 kg of binder) of HRWRA, and 220 to 360 L/m3 (5.97 to 9.76 ft3/yd3) of coarse aggregates. The utility of such models to optimize concrete mixtures and to achieve a good balance between filling ability and passing ability is discussed. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mixture parameters on slump flow, T50 , T60 , V-funnel flow time, Orimet flow time, and blocking ratio. The paper also illustrates the various trade-offs between the mixture parameters on the derived responses that affected the filling and the passing ability.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.

The rotational molding characteristics of metallocene polyethylene skin/foam structures

This article reports on the results of ongoing work in which the foaming characteristics of metallocene-catalyzed linear low density polyethylenes for rotational molding are investigated. Earlier publications related rheological and thermal parameters to the polymer structure and mechanical properties and found that metallocene polyethylene can be used in rotational foam molding to produce a foam that will perform as well as a Ziegler-Natta catalyzed foam. Through adjustments to molding conditions, the significant processing and physical material parameters, which optimize metallocene catalyzed linear low-density polyethylene foam structure, have been identified. This article details the optimum processing route for the production of two layer skin/foam parts using the drop box method. © SAGE Publications 2007.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

The consequences of resistance training for movement control in older adults

Older adults who undertake resistance training are typically seeking to maintain or increase their muscular strength with the goal of preserving or improving their functional capabilities. The extent to which resistance training adaptations lead to improved performance on tasks of everyday living is not particularly well understood. Indeed, studies examining changes in functional task performance experienced by older adults following periods of resistance training have produced equivocal findings. A clear understanding of the principles governing the transfer of resistance training adaptations is therefore critical in seeking to optimize the prescription of training regimes that have as their aim the maintenance and improvement of functional movement capacities in older adults. The degenerative processes that occur in the aging motor system are likely to influence heavily any adaptations to resistance training and the subsequent transfer to functional task performance. The resulting characteristics of motor behavior, such as the substantial decline in the rate of force development and the decreased steadiness of force production, may entail that specialized resistance training strategies are necessary to maximize the benefits for older adults. In this review, we summarize the alterations in the neuromuscular system that are responsible for the declines in strength, power, and force control, and the subsequent deterioration in the everyday movement capabilities of older adults. We examine the literature concerning the neural adaptations that older adults experience in response to resistance training, and consider the readiness with which these adaptations will improve the functional movement capabilities of older adults.