A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes is introduced. The proposed model may accommodate the original single shift setting to the more realistic situation of gradual quality deterioration and allows the incorporation of an expert's opinion on the production process. Based on the number of inspections to be carried out until a defective item is found, the Bayesian operation for the distribution function that represents the increasing sequence of defective fractions during a cycle considering a mixture of Dirichlet processes as prior distribution is performed. Bayes estimates for relevant quantities are also obtained. © 2012 Elsevier B.V.

Using of model updating considering damped frequency response function

The search for better performance in the structural systems has been taken to more refined models, involving the analysis of a growing number of details, which should be correctly formulated aiming at defining a representative model of the real system. Representative models demand a great detailing of the project and search for new techniques of evaluation and analysis. Model updating is one of this technologies, it can be used to improve the predictive capabilities of computer-based models. This paper presents a FRF-based finite element model updating procedure whose the updating variables are physical parameters of the model. It includes the damping effects in the updating procedure assuming proportional and none proportional damping mechanism. The updating parameters are defined at an element level or macro regions of the model. So, the parameters are adjusted locally, facilitating the physical interpretation of the adjusting of the model. Different tests for simulated and experimental data are discussed aiming at defining the characteristics and potentialities of the methodology.

Biocompatibility of a chlorhexidine local delivery system in a subcutaneous mouse model

Objective: This study aimed evaluating histologically and histomorphometrically the response of the conjunctive tissue face to the implant of chlorhexidine chips in the subcutaneous tissues of rats. Study Design: In this research 35 male rats Wistar were used to analyze the biocompatibility and the degradation process of chlorhexidine chip. In each animal, it was made 2 incisions for subcutaneous implantation of chlorhexidine chip (test group) and a polytetrafluorethylene membrane (control group). The morphological changes in subcutaneous implantations were assessed after 1, 3, 5, 7, 10, 14, 21 days. The data were submitted to Friedman nonparametric test to analyze the comparisons among observation periods and to allow the comparison among groups. Results: Differences were found in the analysis of the inflammatory response when comparing the tested materials (p values <= 0.05). In test group was observed hemorrhage, edema and intense inflammatory infiltrate predominantly neutrophilic around material. From 3-day and subsequent periods was verified granulation tissue externally at this infiltrate. From 10-day on was observed crescent area of degradation of chlorhexidine chip, associated with neutrophilic and macrophagic infiltrate, that maintained until 21-day. In the control group, moderate inflammatory infiltrate was observed initially, predominantly polymorphonuclear, edema and granulation tissue 3-day period. The inflammatory infiltrate was gradually replaced for granulation tissue, culminating in a fibrous capsule. Giant multinucleate cells situated at contact interface with the coating was examined since 3-day and persisted until 21-day. Conclusion: The chlorhexidine chip induces an intense acute inflammatory response at subcutaneous tissue of rats. Therefore, at conditions of this study was not biocompatible.

The use of nonparametric contrasts in one-way layouts and random block designs

Nonparametric simple-contrast estimates for one-way layouts based on Hodges-Lehmann estimators for two samples and confidence intervals for all contrasts involving only two treatments are found in the literature.Tests for such contrasts are performed from the distribution of the maximum of the rank sum between two treatments. For random block designs, simple contrast estimates based on Hodges-Lehmann estimators for one sample are presented. However, discussions concerning the significance levels of more complex contrast tests in nonparametric statistics are not well outlined.This work aims at presenting a methodology to obtain p-values for any contrast types based on the construction of the permutations required by each design model using a C-language program for each design type. For small samples, all possible treatment configurations are performed in order to obtain the desired p-value. For large samples, a fixed number of random configurations are used. The program prompts the input of contrast coefficients, but does not assume the existence or orthogonality among them.In orthogonal contrasts, the decomposition of the value of the suitable statistic for each case is performed and it is observed that the same procedure used in the parametric analysis of variance can be applied in the nonparametric case, that is, each of the orthogonal contrasts has a chi(2) distribution with one degree of freedom. Also, the similarities between the p-values obtained for nonparametric contrasts and those obtained through approximations suggested in the literature are discussed.

Transmission network expansion planning considering uncertainty in demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand.

ENERGY GENERATION EXPANSION PLANNING MODEL CONSIDERING EMISSIONS CONSTRAINTS

The generation expansion planning (GEP) problem consists in determining the type of technology, size, location and time at which new generation units must be integrated to the system, over a given planning horizon, to satisfy the forecasted energy demand. Over the past few years, due to an increasing awareness of environmental issues, different approaches to solve the GEP problem have included some sort of environmental policy, typically based on emission constraints. This paper presents a linear model in a dynamic version to solve the GEP problem. The main difference between the proposed model and most of the works presented in the specialized literature is the way the environmental policy is envisaged. Such policy includes: i) the taxation of CO(2) emissions, ii) an annual Emissions Reduction Rate (ERR) in the overall system, and iii) the gradual retirement of old inefficient generation plants. The proposed model is applied in an 11-region to design the most cost-effective and sustainable 10-technology US energy portfolio for the next 20 years.

A comparison of human and porcine acellular dermal tissues in the subcutaneous space of a rat model

The study compared the host response to a human and a porcine acellular dermal tissue implanted in the subcutaneous space of a rat model. The human and porcine acellular grafts were surgically implanted in the subcutaneous tissue of rats (5 rats/group) and the materials were evaluated at 7, 15, 30, 60 and 180 postoperative days (PO). The histological immune response was quantified using a digital image analysis system, which evaluated the number of vessels present in the implants and in the surrounding soft tissue, the area of inflammatory cell infiltration in the grafts, the width of the capsular formation present around the tissues and the area of implants absorbed. The data were submitted to statistical analysis. Light microscopy showed mononuclear cellular infiltration, the presence of a capsular formation surrounding the grafts and the presence of vacuolar structures (optically empty spaces) inside the implants. The image analysis comparing both materials showed significant inflammatory cells in the human graft at 15 and 30 PO, thicker capsular formation in the porcine tissue at 60 PO, increased number of vessels inside the implants and in the surrounding tissues in the porcine graft and a similar absorption pattern in both materials at 180 PO. The histological findings showed that both tissues were well-tolerated when implanted in the subcutaneous tissue of rats, allowing us to consider the porcine acellular dermal graft as a provisional alternative material for reconstructive plastic surgery. Copyright © 2005 Taylor & Francis LLC.

Usage of enterprise modeling processes and information systems design to forecast demand

Managing the great complexity of enterprise system, due to entities numbers, decision and process varieties involved to be controlled results in a very hard task because deals with the integration of its operations and its information systems. Moreover, the enterprises find themselves in a constant changing process, reacting in a dynamic and competitive environment where their business processes are constantly altered. The transformation of business processes into models allows to analyze and redefine them. Through computing tools usage it is possible to minimize the cost and risks of an enterprise integration design. This article claims for the necessity of modeling the processes in order to define more precisely the enterprise business requirements and the adequate usage of the modeling methodologies. Following these patterns, the paper concerns the process modeling relative to the domain of demand forecasting as a practical example. The domain of demand forecasting was built based on a theoretical review. The resulting models considered as reference model are transformed into information systems and have the aim to introduce a generic solution and be start point of better practical forecasting. The proposal is to promote the adequacy of the information system to the real needs of an enterprise in order to enable it to obtain and accompany better results, minimizing design errors, time, money and effort. The enterprise processes modeling are obtained with the usage of CIMOSA language and to the support information system it was used the UML language.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Transmission network expansion planning considering uncertainty in generation and demand

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation. ©2008 IEEE.

A time series sequestration and storage model of atmospheric carbon dioxide

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Organization model for mobile wireless sensor networks inspired in artificial bee colony

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mixed-integer convex model for VAr expansion planning

This paper presents a mixed-integer convex-optimization-based approach for optimum investment reactive power sources in transmission systems. Unlike some convex-optimization techniques for the reactive power planning solution, in the proposed approach the taps settings of under-load tap-changing of transformers are modeled as a mixed-integer linear set equations. Are also considered the continuous and discrete variables for the existing and new capacitive and reactive power sources. The problem is solved for three significant demand scenarios (low demand, average demand and peak demand). Numerical results are presented for the CIGRE-32 electric power system.

Influence of double excitation AC/DC in the reactive power demand of 3-phase transformers

In this work it is discussed the performance of the reactive power demand in three-leg transformer core and three-phase transformer bank, under different conditions of AC/DC double excitation. In order to analyse the influence of double excitation in reactive power theoretically a mathematical model was developed considering the mutual coupling between phases and the magnetic nonlinearity. The validity of the proposed model is verified by means of the experimental and simulated results.

Raloxifene modulates regulators of osteoclastogenesis and angiogenesis in an oestrogen deficiency periapical lesion model

Aim To analyse the local regulatory mechanisms of osteoclastogenesis and angiogenesis during the progression of periapical lesions in female rats with oestrogen deficiency and treatment with raloxifene (RLX). Methodology Female Wistar rats were distributed into groups: SHAM-veh, subjected to sham surgery and treated with a vehicle; OVX-veh, subjected to ovary removal and treated with a vehicle; and OVX-RLX, subjected to ovary removal and treated with RLX. Vehicle or RLX was administered orally for 90 days. During treatment, the dental pulp of mandibular first molars was exposed to the oral environment for induction of periapical lesions, which were analysed after 7 and 30 days. After the experimental periods, blood samples were collected for measurement of oestradiol, calcium, phosphorus and alkaline phosphatase. The rats were euthanized and the mandibles removed and processed for immunohistochemical detection of receptor activator of nuclear factor kappa-B ligand (RANKL), osteoprotegerin (OPG), hypoxia-inducible factor-1 alpha (HIF-1α) and bone-specific alkaline phosphatase (BALP). Data were compared using Kruskal–Wallis followed by Dunn test (nonparametric values) and anova followed by the Tukey's test (parametric values). Results The plasma concentration of oestradiol showed hypo-oestrogenism in the rats subjected to ovary removal. On day 7, alkaline phosphatase activity, calcium and phosphorus were higher in the OVX-RLX group than in the OVX-veh group (P < 0.001), but immunolabelling for RANKL and HIF-1α was lower in OVX-RLX group (P < 0.001). On day 30, the OVX-veh group had higher immunolabelling for RANKL than the OVX-RLX group (P < 0.05). There were no significant differences in the immunoreactivity of OPG and BALP between any groups at either time-point (P > 0.05). Conclusion RLX therapy reversed the increased levels of the local regulators of both osteoclastogenesis and angiogenesis induced by oestrogen deficiency.