Analysis of quality in Brazilian e-commerce (B2C)

The business world has changed the way how people think and act on products and services. In this context, the most recent amendment of the scenarios of retail operations has been the use of technology in sales and distribution. The internet has revolutionized the way people communicate, and moreover as they purchase their goods and services. Thus, the e-commerce, specifically the relation business to customer, or simply B2C, has acted so convincingly in this change of paradigm, namely the purchases in the physical location for the virtual site. Quotes online, ease of payment, price, speed of delivery, have become real order winners of applications for companies that compete in this segment. With the focus on quality of services on e-commerce, the research examines the dimension related to the quality of services, and looks for what of these factors are winners of applications. © 2010 IFIP International Federation for Information Processing.

Effects of dredging operations on sediment quality: contaminant mobilization in dredged sediments from the Port of Santos, SP, Brazil

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

Evaluation of mango loss (Mangifera indica L.) in the retail market of Botucatu-SP

Losses in the mango commercialization process in Brazil has reduced its offer to the consumer. The present study aims at determining these losses in different purchase sites of the retail market, its causes and suggestions for reducing them. Twenty two retail points, including supermarkets, greengroceries and free fair were selected in Botucatu, state of São Paulo, Brazil. The total amount commercialized was 114 ton/year. The following average losses were verified for each mango variety: 'Tommy Atkins'(11, 5%), Haden (12, 4%) and 12, 7% for other varieties. The total loss in retail market reached US$ 25.231,00 corresponding to 14 tons. The average loss percentage observed is compatible with previous studies running in other cities. The results suggest the need of better management, the exposure of the fruit to the consumer, technology in the transportation of the fruits and most appropriate storage for maintaining the quality and the reduction of losses. The results show the need of higher investment in technical personnel reskilling in fruit and vegetable sector.

Effects of feed restriction on yield, retail cuts and tissue composition of carcass of Saanen kids

Com o objetivo de estudar o efeito da restrição alimentar sobre as características de carcaça de caprinos leiteiros, realizou-se um experimento utilizando 27 cabritos machos Saanen distribuídos nos tratamentos 0 (alimentação à vontade), 30 ou 60% de restrição. Os animais apresentavam 5 kg de PV inicial e foram abatidos quando atingiram 20 kg de PV. Foram avaliados os rendimentos comercial e biológico, os cortes comerciais, a composição tecidual da perna, a área de olho-de-lombo (AOL) e a compacidade da carcaça. Utilizaram-se o delineamento inteiramente ao acaso e a análise de regressão em função da restrição alimentar. A restrição alimentar provocou redução do peso da carcaça e dos cortes comerciais, aumento da proporção do pescoço e diminuição da proporção do lombo. A proporção de ossos aumentou e a do tecido muscular e da gordura total diminuiu com o aumento da restrição. A proporção de gordura subcutânea diminuiu com o aumento da restrição alimentar. A área de olho-de-lombo (AOL) e a compacidade da carcaça foram afetadas pela restrição alimentar, mas ambas as medidas podem ser utilizadas para predizer a proporção de músculo da carcaça. O tratamento com 30% de restrição alimentar não prejudica a qualidade da carcaça de cabritos leiteiros.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bovine pericardium retail preserved in glutaraldehyde and used as a vascular patch

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

Hazards in non-pasteurized milk on retail sale in Brazil: prevalence of Salmonella spp, Listeria monocytogenes and chemical residues

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

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Predicting beef carcass retail products of Mediterranean buffaloes by real-time ultrasound measures

Twenty eight Mediterranean buffaloes bulls were scanned with real-time ultrasound (RTU), slaughtered, and fabricated into retail cuts to determine the potential for ultrasound measures to predict carcass retail yield. Ultrasound measures of fat thickness, ribeye area and rump fat thickness were recorded three to five days prior to slaughter. Carcass measurements were taken, and one side of each carcass was fabricated into retail cuts. Stepwise regression analysis was used to compare possible models for prediction of either kilograms or percent retail product from carcass mesaurements and ultrasound measures. Results indicate that possible prediction models for percent or kilograms of retail products using RTU measures were similar in their predictive power and accuracy when compared to models derived from carcass measurements. Both fat thickness and ribeye area were over-predicted when measured ultrasonically compared to measurements taken on the carcass in the cooler. The mean absolute differences for both traits are larger than the mean differences, indicating that some images were interpreted to be larger and some smaller than actual carcass measurements. Ultrasound measurements of REA and FT had positive correlations with carcass measures of the same traits (r=.96 for REA and r=.99 for FT). Standard errors of prediction currently are being used as the standard to certify ultrasound technicians for accuracy. Regression equations using live weight (LW), rib eye area (REAU) and subcutaneous fat thickness (FTU) between 12(th) and 13 (th) ribs and also over the biceps femoris muscle (FTP8) by ultrasound explained 95% of the variation in the hot carcass weight when measure immediately before slaughter.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.