Impact of minimum distance constraints on sheet metal waste for plasma cutting.

We approached the two-dimensional rectangular strip packing problem (2D-SPP), where the main goal is to pack a given number of rectangles without any overlap to minimize the height of the strip. Real-life constraints must be considered when developing 2D-SPP algorithms to deliver solutions that will improve the cutting processes. In the 2D-SPP literature, a gap related to studies approaching constraints in real-life scenarios was identified. Therefore, the impact of real-life constraints found in the plasma cutting process in sheet metal waste was analyzed. A mathematical model from the literature was modified to obtain packing arrangements with plasma cutting constraints. The combination of size and number of rectangles, as well as strip width, was the main factor that affected the packing arrangement, limiting the allocation of rectangles and generating empty spaces. In summary, considering the sheet metal waste context, instances with smaller widths should be avoided in practical operations for high minimum distance constraint values, returning the worst packing arrangements. For low minimum distance constraint values, smaller width instances can be used in practical operations, as the packing arrangement is acceptable. Finally, this article can reduce material waste and enhance the cutting process in the sheet metal industry, by showing packing characteristics which lead to higher amounts of raw material waste.

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review.

The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR.

This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1-649.9 cm(-1). Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.

Determination of amoxicillin content in powdered pharmaceutical formulations using DRIFTS and PLS

The amount of amoxicillin in pharmaceutical formulations was determined using spectra of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), in association with partial least squares (PLS) regression. The spectra of 24 different samples (17 for the calibration set and 7 for the validation set), which had been registered using a Nicolet Magna 550 spectrophotometer, contained 76.7-94.3 percent of amoxicillin in starch. The PLS models were constructed with auto-scaled or variance-scaled data, and multiplicative scatter correction (MSC). The best model presented R²=0.9936, RMSEC=0.441 and RMSEV=0.790. The analytical method proposed is non-destructive and the cost and time of analysis are very much reduced, allowing for the fast and direct determination of amoxicillin content.

Neste trabalho propõe-se a quantificação de amoxicilina em formulações farmacêuticas empregando espectroscopia por refletância difusa no infravermelho com transformada de Fourier (DRIFTS) e regressão por mínimos quadrados parciais (PLS). Foram coletados em um espectrômetro Nicolet Magna 550 os espectros de 24 amostras (17 para o conjunto de calibração e 7 para o conjunto de validação) contendo de 76,7-94,3 por cento de amoxicilina em amido. Para a construção dos modelos PLS foi empregada a correção do espalhamento de luz (MSC) e os dados foram autoescalados ou escalados pela variância. Foi obtido um excelente modelo para a previsão da amoxicilina o qual apresentou R²=0,9936, RMSEC=0,441 e RMSEV=0,790. O método proposto além de não destrutivo apresenta baixo tempo de análise e baixo custo podendo ser facilmente empregado no controle de qualidade das indústrias farmacêuticas.

Análise exploratória aplicada no estudo de medicamentos contendo piroxicam / Exploratory analysis applied in study of pharmaceutical formulations with piroxicam

A identificação de diferentes formulações de medicamentos manipulados contendo piroxicam foi estudada, empregando epectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS), em associação com a técnica de análise por agrupamentos hierárquicos (AAH). Os espectros de amostras, de 5 diferentes farmácias de manipulação, contendo piroxicam (10 mg e 20 mg) e seus respectivos excipientes, foram adquiridos em um espectrofotômetro NICOLET Magna 550, obtendo-se duas réplicas de cada amostra. Para a análise multivariada, as informações espectrais foram tratadas no programa Pirouette® 2.7 da Infometrix, utilizando-se as regiões espectrais...

Análise multivariada aplicada na identificação de fármacos antidepressivos. Parte II: Análise por componentes principais (PCA) e o método de classificação SIMCA / Multivariate analysis to applied in the identification of antidepressants. Part II: principal components analysis (PCA) and soft independent modeling of class analogies (SIMCA)

Neste trabalho a identificação e a discriminação de dois diferentes fármacos utilizados como antidepressivos foi estudada, empregando os espectros de reflexão difusa no infravermelho médio com transformada de Fourier (DRIFTS), juntamente com a análise de componentes principais (PCA) e o método de classificação SIMCA. Os espectros no infravermelho de amostras contendo diferentes concentrações dos princípios ativos cloridrato de amitriptilina e cloridrato de imipramina, foram coletados em um espectrofotômetro NICOLET Magna 550, sendo realizadas 2 réplicas para cada amostra, com resolução de 4 cm¯¹ e 32 varreduras. A análise de componentes principais confirmou a existência de dois grupos distintos, correspondendo aos dois diferentes princípios ativos utilizados, além de evidenciar a presença de amostras anômalas no conjunto de dados que, possivelmente, iriam interferir na modelagem...