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Painéis sensoriais são muito utilizados na indústria de alimentos para controle de qualidade, classificação de produtos em categorias e pesquisas com diversos fins. Um painel sensorial é composto por um grupo de degustadores que confere notas a diferentes atributos sensoriais de amostras de produtos; em tal processo, espera-se que o painel seja composto por membros que avaliem produtos iguais de forma semelhante e consigam discriminar produtos diferentes. O objetivo deste artigo é aplicar e comparar três abordagens de treinamento de degustadores, com o objetivo de melhorar o desempenho do painel, medido através do Índice Alfa de Ledauphin. As três abordagens comparadas foram (i) treinamento de alguns degustadores para todos os atributos (treinamento de degustadores), (ii) treinamento de alguns atributos para todos os degustadores (treinamento de atributos), e (iii) combinação dessas duas técnicas (alguns atributos para alguns degustadores). O método combinado foi o que apresentou os melhores resultados, com uma necessidade de treinamento, para um mesmo desempenho do painel, 18,7% menor que o treinamento de degustadores e 51,4% menor que o treinamento de atributos.

Nanoflowers are a recent class of nanomaterials that show structural similarity to plant flowers. Among these structures, carbon nanoflowers (CNFs) stand out as promising materials for application in electrochemistry, gas adsorption and absorption, batteries and supercapacitors. This paper innovates by presenting a method for CNFs production using electric arc (EA) discharge in water. The main advantage of using water instead of other liquid media is the possibility of reusing that water several times, reducing its waste in an eco-friendly process. X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) show that the yielded CNFs present high quality (ID/IG = 1.03), homogeneity and purity. The low-cost and simplicity of CNFs synthesis by EA in water make their production on an industrial scale attractive.

This paper proposes a novel framework to identify the most informative variables for clustering bus priority corridors according to their similarities regarding system and operational aspects. Although each bus corridor has its peculiarities, understanding the similarities (e.g., system, physical and operational aspects) between corridors of different regions of the world can help researchers and transit specialists to draw up strategies tailored to improving the traffic in the cities. For that matter, we integrate a novel metric for measuring clustering quality to the omit-one-variable-out-at-a-time selection procedure. The proposed method relies on three steps: (i) collect and preprocess data describing bus corridors; (ii) define the number of clusters to be generated based on a hierarchical approach; and (iii) iteratively group bus corridors, and eliminate less relevant clustering variables. When applied to a dataset comprised of 296 bus priority corridors from 45 countries and described by 44 variables, the proposed framework retained only four variables (i.e., brand and/or logo, station spacing, enhanced stations, and operating speed) responsible for the best stratification of corridors. Four clusters were formed and qualitatively assessed regarding their similarities in terms of system, physical and operational aspects. Corridors were grouped into basic corridors (cluster 1), improved corridors (cluster 2), Bus Rapid Transit (BRT) and Bus with High Level of Service (BHLS) systems (cluster 3), and express, limited-stop services (cluster 4).

Sterilization plants process all reusable materials used in hospitals during different stages of healthcare provision. Most items sterilized come from the surgical theater in the form of individual...

The main purpose of fault detection in batch process monitoring is to identify batches displaying atypical behavior in comparison to normal operating data. The current growth in the number of measurable variables due to process automation yields datasets in which the number of variables is much larger than the number of batches. That may compromise the performance of Multiway Principal Component Analysis (MPCA), which is the most popular quality control approach used in batch processes. To overcome that, new strategies to handle high-dimensional datasets become necessary. In this paper we propose the Pareto Variable Selection (PVS) – MPCA method to monitor batch processes described by high-dimensional datasets. The main idea of PVS-MPCA is to select process variables that promote the best classification of production batches in conforming or non-conforming classes, prior to the construction of T 2 and Q control charts used to monitor batch performance. Our proposition was applied to a real dataset from a chocolate conching batch operation and compared to classical MPCA-based monitoring. PVS-MPCA promoted a reduction of 85.18% in false alarm rate retaining only 5 unfolded variables, in opposition to 2,864 unfolded variables used in classical MPCA. The missed detection rate was null, ensuring that only conforming batches were released to the production line.

This is a pioneering research linking two areas of knowledge which attract great attention from academia, industry, and governments: business incubation and smart cities. It is a quantitative exploratory study, whose purpose is to investigate the relationship between incubation mechanisms and local urban development according to the concept of Smart City. Methodologically, canonical correlation analysis was applied between two sets of elements: variables associated to business incubation and variables related to smart city characteristics. The research was concentrated on 157 medium- and large-sized cities from 25 European countries. Based on the findings, clusters analysis was also performed. The article presents, among the main results: (1) evidence of correlation between business incubation and smart urban development, and (2) visual classification of these cities into four clusters, according to their urban development and incubation activity.

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC-MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.

Background In this article, we propose a method that integrates systematic layout planning techniques to lean health care practices aided by multicriteria decision analysis that could be applied to reformulate the layout of health care facilities. Methods We analyze a high-variety sterilization unit of a large public hospital located in Brazil. The unit is currently implementing lean practices, and layout changes are required to provide more efficient materials and information flows. Results Traditional design of health care facilities is not aligned with lean implementation and its underlying practices and principles. We propose the integration of such approaches to enhance their benefits. To rank and select the best layout alternative, a multicriteria decision analysis method (analytic hierarchy process) is adopted. Conclusions There are 3 contributions here: the integration of lean principles into traditional health care facility design practices, the use of multicriteria decision analysis to refine the determination of the best layout solution, and the application of our propositions in a real case study.

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative. In this paper we investigate the performance of the Random Forest (RF) classifier associated with several wavelength importance ranking approaches on the task of classifying product samples into categories, such as quality levels or authenticity. Our propositions are tested using six NIR datasets comprised of two or more classes of food and pharmaceutical products, as well as illegal drugs. Our proposed classification model, an integration of the χ2 ranking score and the RF classifier, substantially reduced the number of wavelengths in the dataset, while increasing the classification accuracy when compared to the use of complete datasets. Our propositions also presented good performance when compared to competing methods available in the literature.

BackgroundThe recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems.ObjectiveCompare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED.MethodsThis was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC).ResultsAll graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77-0.87], 0.80 (95% CI: 0.75-0.85), 0.76 (95% CI: 0.71-0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR.ConclusionsOur data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task.

Background Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists). Methods Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries’ completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling. Results Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries’ completions by 55.5%. A more uniform distribution of patients’ arrivals at the PACU was also observed. Conclusions Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation.

BackgroundThe SARS-CoV-2 virus responsible for COVID-19 poses a significant challenge to healthcare systems worldwide. Despite governmental initiatives aimed at containing the spread of the disease, several countries are experiencing unmanageable increases in the demand for ICU beds, medical equipment, and larger testing capacity. Efficient COVID-19 diagnosis enables healthcare systems to provide better care for patients while protecting caregivers from the disease. However, many countries are constrained by the limited amount of test kits available, lack of equipment and trained professionals. In the case of patients visiting emergency rooms (ERs) with a suspect of COVID-19, prompt diagnosis may improve the outcome and even provide information for efficient hospital management. In such a context, a quick, inexpensive and readily available test to perform an initial triage in ERs could help to smooth patient flow, provide better patient care, and reduce the backlog of exams.MethodsIn this Case-control quantitative study, we developed a strategy backed by artificial intelligence to perform an initial screening of suspect COVID-19 patients. We developed a machine learning classifier that takes widely available simple blood exams as input and classifies samples as likely to be positive (having SARS-CoV-2) or negative (not having SARS-CoV-2). Based on this initial classification, positive cases can be referred for further highly sensitive testing (e.g. CT scan, or specific antibodies). We used publicly available data from the Albert Einstein Hospital in Brazil from 5,644 patients. Focusing on simple blood exam figures as main predictors, a sample of 599 subjects that had the fewest missing values for 16 common exams were selected. From these 599 patients, 81 tested positive for SARS-CoV-2 (determined by RT-PCR). Based on the reduced dataset, we built an artificial intelligence classification framework, ER-CoV, aiming at determining if suspect patients arriving in ER were likely to be negative for SARS-CoV-2, that is, to predict if that suspect patient is negative for COVID-19. The primary goal of this investigation is to develop a classifier with high specificity and high negative predictive values, with reasonable sensitivity.FindingsWe identified that our AI framework achieved an average specificity of 85.98% [95%CI: 84.94 – 86.84] and negative predictive value (NPV) of 94.92% [95%CI: 94.37% – 95.37%]. Those values are completely aligned with our goal of providing an effective low-cost system to triage suspect patients in ERs. As for sensitivity, our model achieved an average of 70.25% [95%CI: 66.57% – 73.12%] and positive predictive value (PPV) of 44.96% [95%CI: 43.15% – 46.87%]. The area under the curve (AUC) of the receiver operating characteristic (ROC) was 86.78% [95%CI: 85.65% – 87.90%]. An error analysis (inspection of which patients were misclassified) identified that, on average, 28% of the false negative results would have been hospitalized anyway; thus the model is making mistakes for severe cases that would not be overlooked, partially mitigating the fact that the test is not highly sensitive. All code for our AI model, called ER-CoV is publicly available at https://github.com/soares-f/ER-CoV.InterpretationBased on the capacity of our model to accurately predict which cases are negative from suspect patients arriving in emergency rooms, we envision that this framework may play an important role in patient triage. Probably the most important outcome is related to testing availability, which at this point is extremely low in many countries. Considering the achieved specificity, we could reduce by at least 90% the number of SARS-CoV-2 tests performed in emergency rooms, with around 5% chance of getting a false negative. The second important outcome is related to patient management in hospitals. Patients predicted as positive by our framework could be immediately separated from other patients while waiting for the results of confirmatory tests. This could reduce the spread rate within hospitals since in many of them all suspect cases are kept in the same ward. In Brazil, where the data was collected, rate infection is starting to quickly spread and the lead time of a SARS-CoV-2 may be up to 2 weeks.

This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0–33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100–4950 cm−1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.

Forecasting of attendance demand to sports events is a common topic of study in the sports economics literature, being traditionally addressed through the use of multivariate regression analysis or structural equation modeling. In recent years, a restricted number of authors have approached the problem using machine learning methods, with promising results. In this article, we investigate the use of analytical techniques from the machine learning toolbox, namely symbolic regression and genetic programming (SR/GP), to determine the best fitting prediction function that relates contextual and panel independent variables to soccer match attendance. For that purpose, we analyze five years of attendance at soccer matches played at a large stadium in Southern Brazil. Two datasets with game-level attendance to matches from two soccer championships are considered, covering the seasons from 2014 to 2019. We also propose the use of expert panels to collect information on relevant candidate independent variables and their interactions to be tested in the prediction models, expediting the feature selection step of the modeling process. From the academic perspective, our study is the first to propose the use of SR/GP to model soccer match attendance, contributing to the limited number of studies that use game-by-game attendance as the dependent variable and develop team-specific attendance models. From the managerial perspective, identifying factors responsible for systematic variations in match attendance levels enables better sport management and marketing plans.

The commerce of falsified drugs has substantially grown in recent years due to facilitated access to technologies needed for copying authentic pharmaceutical products. Attenuated Total Reflectance coupled with Fourier Transform Infrared (ATR-FTIR) spectroscopy has been successfully employed as an analytical tool to identify falsified products and support legal agents in interrupting illegal operations. ATR-FTIR spectroscopy typically yields datasets comprised of hundreds of highly correlated wavenumbers, which may compromise the performance of classical multivariate techniques used for sample classification. In this paper we propose a new wavenumber interval selection method aimed at selecting regions of spectra that best discriminate samples of seized drugs into two classes, authentic or falsified. The discriminative power of spectra regions is represented by an Interval Importance Index (III) based on the Two-Sample Kolmogorov-Smirnov test statistic, which is a novel proposition of this paper. The III guides an iterative forward approach for wavenumber selection; different data mining techniques are used for sample classification. In 100 replications using the best combination of classification technique and wavenumber intervals, we obtained average 99.87% accurate classifications on a Cialis® dataset, while retaining 12.5% of the authentic wavenumbers, and average 99.43% accurate classifications on a Viagra® dataset, while retaining 23.75% of the authentic wavenumbers. Our proposition was compared with alternative approaches for individual and interval wavenumber selection available in the literature, always leading to more consistent and easier to interpret results.

This paper proposes an approach for feature selection aimed at classifying wines samples according to place of origin. The method relies on Kruskal-Wallis non-parametric test to remove non significant features, and Linear Discriminant Analysis to derive a feature importance index. The ranked features according that index are iteratively added and classification performance is assessed after each insertion. The number of selected features is chosen according the maximum accuracy in a repeated 10-fold cross-validation. Aiming at improving categorization accuracy, different classification techniques are tested. When applied to a wine dataset comprised of 53 samples from four South America countries (Argentina, Brazil, Chile, and Uruguay) and 45 chemical elements concentrations determined by ICP-OES and ICP-MS, the proposed framework yielded average 99.9% accurate classifications in the testing set, and retained average 6.73 of the 45 original elements. Retained chemical elements were then qualitatively assessed.

Introduction We analyze the assembly of surgical trays in a hospital's sterile services department. The department assembles 520 different tray setups. However, tray assembly times are unknown, imposing a challenge to production planners. To respond to demand, workers from other departments are often called, leading to higher operational costs and more frequent quality problems due to workers' poor training and inconsistency. Methods Conducting traditional time-motion studies is infeasible in such a high variety production setting. Thus, we used design of experiments to optimize the data acquisition. Assembly times of 36 trays were sampled using a 2-factor nested factorial design. Through regression analysis, we built a model to estimate completion times of trays not sampled in the experiment. Results A prediction model with 90.8% accuracy was obtained from the experimental data. The model was validated with assembly times from several trays not included in the experiment. Predicted assembly times had an absolute error of 7.83% on average compared with observed assembly times. Conclusions Design of experiments and regression analysis combined were able to optimize time data acquisition using a small sample of trays, resulting in a model that predicted assembly times within an acceptable margin of error.

Objective: To report the experience of developing a systematic approach for the rationalization of instruments in surgical trays. Method: Study of the development of a systematic approach for the rationalization of instruments, carried out in 2015, using a qualitative method, in the Central Sterile Supply Department (CSSD) of a federal university hospital in Porto Alegre, Brazil. Results: There was a 10.92% average reduction in the number of instruments in institutional trays, a reduction in the number of trays owned by medical teams — 84.06% belonged to the otorhinolaryngology team — and a definitive inactivation of 369 orthopedic surgery instruments, which represented 72.84% of the total number of inactivated instruments. In addition, improvements were made to the management of instruments, the optimization of preparation time and the reduction of sterilization by expiration date. Conclusion: The relocation of instruments and the addition of items in specific trays allowed for the reappraisal of requests for purchase of instruments and the improvement of relationships between the teams. This systematic approach contributed significantly to the management of instruments, the optimizing processes and the involvement of the surgical teams in the work of the CSSD, thus demonstrating that it can be applied in other institutions.

Although lean production (LP) is widely deemed as a means for influencing the complexity of socio-technical systems, empirical evaluations of this impact have not adopted an explicit complexity theory perspective, nor have they accounted for the multidimensional nature of complexity. This paper presents an investigation of the lean impacts on attributes of complex socio-technical systems (CSS) from several sectors. The assessment was based on a survey with 326 respondents. LP impacts on five bundles of complexity attributes were evaluated, namely: number of elements; interactions; diversity; unexpected variability; and resilience. The analyzed systems were firstly classified into manufacturing and services. Then, a cluster analysis divided each group into high and low lean adopters, based on their adherence to lean principles. Next, ANOVA tests were performed to check for differences in the intensity of complexity attributes between high and low lean adopters. Results indicated that LP in CSS tends to: (i) reduce the number of employees; (ii) reduce diversity of behaviors and beliefs; (iii) reduce disruptions due to information and human-related problems; (iv) increase richness and frequency of interactions; (v) increase functional diversity of elements; and (vi) increase resilience. While impacts (i), (ii) and (iii) reduce complexity, the others imply in its increase, suggesting that LP can be an effective way of balancing complexity attributes. Also, the framework for data analysis can be used for assessing lean impacts on the structure and functioning of socio-technical systems of different natures, thus supporting the understanding of lean systems from a complexity perspective.

This paper assesses the support vector regression (SVR) as a robust alternative to partial least squares (PLS) in multivariate calibration using twelve public domain NIR spectroscopy datasets. It also proposes the use of the support vector regression – recursive feature elimination (SVR-RFE) algorithm to select the most informative wavelengths for SVR models. Models based on full spectra were built using SVR and PLS, while wavelength selection methods were carried out using SVR-RFE, interval PLS (iPLS), backward interval PLS (biPLS), synergy interval PLS (siPLS), and successive projection algorithm PLS (SPA-PLS). The prediction performance of tested methods was measured by means of the root mean squared error (RMSE), index of agreement (d-index) and R2 on the test set. SVR-based models yielded the best results in 8 out of 12 datasets, 4 of them using full spectra and 4 relying on SVR-RFE selected wavelengths. Statistical comparison was carried out for the wavelength selection algorithms using Friedman test, which pointed the SVR-RFE as a competitive technique when compared to the other algorithms. This study revealed SVR as a robust alternative to PLS, especially when SVR-RFE is employed for wavelength selection.

This paper proposes a novel image-based approach to detect counterfeit medicines and identify the most relevant regions of the tablet in the task of classification. Images of medicine tablets undergo an initial pre-processing step which (i) removes the background to find the region of interest, (ii) clusters individual pixels into super-pixels, and (iii) extracts features containing color and texture information. The classification relying on Support Vector Machine (SVM) defines the class the respective image will be inserted into. The task of identifying the relevant regions of the tablets for counterfeiting detection is performed using the concept of support vectors, generating a heat map that indicates the regions that contribute the most to the classification purpose. Two datasets containing images of authentic and counterfeit tablets of Cialis and Viagra were used to validate our propositions, achieving correct classification rates of 100% on both datasets. Regarding the task of identifying the most relevant regions, our proposition outperformed the traditional LIME (Local Interpretable Model-agnostic Explanations) method by yielding more robust explanations.

Soft-magnetic nickel ferrite (NiFe2O4) nanoparticles were synthesized via solution combustion synthesis (SCS). The influence of the fuel concentration and the post-treatment temperature on the structural and magnetic properties of the samples were studied. The samples produced in a fuel-short regime presented the purest NiFe2O4 nanoparticles, with an average diameter of 32 nm. Spinel inversion degree, composition, crystallite size, and surface area were evaluated regarding their influence on the magnetic behavior of the nanoparticles. Interestingly, the magnetic properties of pure NiFe2O4 samples remained unaffected even after heat-treated above 900 °C. This result is promising for industrial applications that require magnetic stability over temperature changes. NiFe2O4 presented a critical size of 40 nm for single-domain to multi-domain transition. Below 40 nm, the discharge of Ni and α-Fe2O3 from the NiFe2O4 structure is the main factor influencing the magnetism. Pure NiFe2O4 samples treated at different temperatures presented a similar spinel inversion and consequently similar coercivities, approximately 170 Oe.

Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effective diagnosis methods are required. Blood fluorescence spectroscopy provides rich information that can be used for cancer identification. The main challenges in analyzing blood fluorescence data for CRC classification are related to its high dimensionality and inherent variability, especially when analyzing a small number of samples. In this paper, we present a hierarchical classification method based on plasma fluorescence to identify not only CRC, but also adenomas and other non-malignant colorectal findings that may require further medical investigation. A feature selection algorithm is proposed to deal with the high dimensionality and select discriminant fluorescence wavelengths. These are used to train a binary support vector machine (SVM) in the first level to identify the CRC samples. The remaining samples are then presented to a one-class SVM trained on healthy subjects to detect deviant samples, and thus non-malignant findings. This hierarchical design, together with the one class-SVM, aims to reduce the effects of small samples and high variability. Using a dataset analyzed in previous studies comprised of 12,341 wavelengths, we achieved much superior results. Sensitivity and specificity are 0.87 and 0.95 for CRC detection, and 0.60 and 0.79 for non-malignant findings, respectively. Compared to related work, the proposed method presented a better accuracy, required fewer features, and provides a unified approach that expands CRC detection to non-malignant findings.

A implementação da Manufatura Enxuta ou Lean Manufacturing (LM) implica mudanças fundamentais nos sistemas gerenciais das empresas. Os líderes desempenham um papel crucial no estabelecimento dessa mudança enxuta, uma vez que são responsáveis por influenciar indivíduos e equipes na execução de objetivos estratégicos. Este artigo tem como objetivo analisar empiricamente a relação entre a orientação comportamental de líderes de diferentes níveis hierárquicos e o estágio do roteiro de implementação de LM, bem como analisar a influência das variáveis contextuais de liderança. O método de pesquisa proposto é composto por aspectos qualitativos e quantitativos, uma vez que combina os resultados de entrevistas semi-estruturadas com especialistas em LM e um levantamento com 225 líderes de diferentes empresas Brasileiras que estão passando por uma implementação de LM. Os resultados indicam que o contexto importa com relação à orientação do estilo de liderança ao longo do roteiro de imeplementação enxuta, embora nem todas as variáveis influenciem na mesma magnitude.

Purpose The purpose of this paper is to empirically examine the relationship between the behavioral orientation of leaders from different hierarchical levels and the implementation phase of the lean manufacturing (LM) roadmap. In addition, it also aims at analyzing the influence of inherent contextual variables related to leadership. Design/methodology/approach The proposed method relies on both qualitative and quantitative approaches, since it combines and numerically assesses the results of semi-structured interviews with lean experts, in addition to evaluating a cross-sectional survey with 225 leaders from different Brazilian companies that are undergoing LM implementation. Findings The findings support the existence of a transient leadership style along the LM implementation, which is evidenced in terms of task and relation orientation and may vary according to the hierarchical level. Furthermore, the context does matter with regard to leadership style orientation along the LM roadmap phases, although not all variables matter to the same extent. Originality/value LM implementation is about changing both technical and socio-cultural aspects, which can be seen as the essence of leadership. The relevant question that arises is whether leaders’ behaviors from different hierarchical levels should be adapted according to the maturity of LM implementation and the contextual variables that surround leadership.

In recent years, spectroscopy techniques such as Near infrared (NIR) and Fourier Transform Infrared (FTIR) have been widely adopted as analytical tools in different fields and with several purposes. NIR and FTIR data are typically comprised of hundreds or even thousands of highly correlated wavenumbers, fact that can jeopardize the accuracy of several statistical techniques. In light of that, wavenumber selection emerges as an important step in prediction and classification tasks based on spectroscopy data. This paper proposes a novel framework for wavenumber selection aimed at classifying samples into proper categories, which is applied to two data sets from the petroleum sector. The method relies on two main stages: determination of intervals based on the distance between the average spectra of the classes and selection of the most suitable intervals through cross-validation. An improvement in the misclassification rate was achieved for a NIR spectra data set of diesel, decreasing that metric from 13.90% to 11.63% after the application of the proposed method while retaining 23.19% of the original wavenumbers. As for the biodiesel FTIR data set, the method yielded a misclassification rate of 1.21% while retaining 4.95% of the original variables; misclassification rate was 4.71% when all wavenumbers were used. The proposed method also outperformed traditional approaches for wavenumber selection.

Yerba mate (Ilex paraguariensis) is used to produce a beverage typically consumed in South America countries, and presents peculiar land-based characteristics due to geographical origin. Such characteristics have recently become a matter of interest for many producers as specific features of yerba mate tend to influence product acceptance in new markets, prices and commercial advantages. This scenario justifies the developing of frameworks tailored to correctly classify products according to their authenticity. This paper uses Near Infrared (NIR) spectroscopy and data describing concentration of chemical elements to classify commercial yerba mate samples according to their place of origin. Aimed at enhancing data interpretability, we propose a novel variable selection method that applies quadratic programming to reduce redundant information among the retained variables and maximize their relationship regarding the sample place of origin; sample categorization is then performed using alternative classification techniques. When applied to the NIR dataset, the proposed method retained average 8.79% of the original wavenumbers, while leading to 1.9% more accurate classifications when compared to categorization using the full spectra. As for the elements dataset, we increased average classification accuracy by 3.5% and retained 47.22% of the original elements. The proposed method also outperformed two other approaches for variable selection from the literature. Our findings suggest that variable selection frameworks help to correctly identify the origin and authenticity of yerba mate samples, making model construction and interpretation easier.

Purpose The purpose of this paper is to propose a multivariate-based method to classify products in replenishment categories based on principal component analysis (PCA) along with two classification algorithms, k-nearest neighbor (KNN) and linear discriminant analysis (LDA). Design/methodology/approach In the propositions, PCA is applied to data describing products’ features and demand behavior, and a variable importance index (VII) is derived based on PCA parameters. Next, products are allocated to inventory replenishment models applying the KNNs to all original variables; the classification accuracy is then assessed. The variable with the smallest VII is removed and a new classification is carried out; this iterative procedure is performed until a single variable is left. The subset yielding the maximum classification accuracy is recommended for future classification. The aforementioned procedure is repeated replacing the KNN by the LDA. Findings When applied to real data from a consulting company, the KNN classification technique led to higher performance levels than LDA, yielding 89.4 percent average accuracy and retaining about 80 percent of the original variables. On the other hand, LDA reached 87.1 percent average accuracy and retained 95 percent of the variables. Based on such results, the authors’ findings suggest that 14 out of the 24 variables are crucial in determining an inventory replenishment model for a product in a specific location replacement. Several of the retained variables were typically used in reorder point estimation or associated to market profile in specific locals. Originality/value The idea of this paper is to remove irrelevant and noisy market metrics that jeopardize the correct allocation of products to the most appropriate replenishment model.

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd.

More than one million people die or suffer non-fatal injuries annually due to road accidents around the world. Understanding the causes that give rise to different types of conflict events, as well as their characteristics, can help researchers and traffic authorities to draw up strategies aimed at mitigating collision risks. This paper proposes a framework for grouping traffic conflicts relying on similar profiles and factors that contribute to conflict occurrence using self-organizing maps (SOM). In order to improve the quality of the formed groups, we developed a novel variable importance index relying on the outputs of the nonlinear principal component analysis (NLPCA) that intends to identify the most informative variables for grouping collision events. Such index guides a backward variable selection procedure in which less relevant variables are removed one-by-one; after each removal, the clustering quality is assessed via the Davies-Bouldin (DB) index. The proposed framework was applied to a real-time dataset collected from a Brazilian highway aimed at allocating traffic conflicts into groups presenting similar profiles. The selected variables suggest that lower average speeds, which are typically verified during congestion events, contribute to conflict occurrence. Higher variability on speed (denoted by high standard deviation, and speed’s coefficient of variation levels on that variable), which are also perceived in the assessed freeway near to congestion periods, also contribute to conflicts.

The cargo routing and empty repositioning problems are important to growth of world market and competitive differentials of the container transporting. This paper aims to evaluate the efficiency of a new approach for an integrated model to cargo routing and empty containers repositioning. We formulated the proposed optimization model as binary-mixed linear programming based on a network multi-commodity model additionally with capacity constraints. Considering the complexity of this problem compared to models available in the literature, the results proved the model efficiency in computational time and main measures of demand and capacity. We understand the model as efficient to solve problems of large instances, assisting real decisions with robustness. The solving time increases significantly with increasing demand, occurring more containers repositioned in order to more profitable cargos can viability a repositioning decision. The main complexity of the model is related to integer variables of cargo routing decisions.

In this paper, we propose a novel framework to select the most relevant X-Ray Fluorescence (XRF) energy values (i.e., features) to enhance the clustering (grouping) of counterfeit and illicit medical tablets. The framework is based on the integration of multidimensional scaling (MDS) and Procrustes analysis (PA) multivariate techniques. MDS provides a projection of the original data into a lower dimension, while PA finds a projection matrix from the original data. Such outputs give rise to a feature importance index that guides an iterative feature selection process; after each feature is inserted in the subset, an optimization procedure based on a greedy search method is carried out to maximize the clustering quality assessed through the Silhouette Index (SI). The inorganic chemical fingerprinting of 41 commercial samples (Viagra®, Cialis®, Lazar®, Libiden®, Maxfil®, Plenovit®, Potent 75®, Rigix®, V-50®, Vimax® and Pramil®) and 56 seized counterfeit samples (Viagra and Cialis) was used to validate the proposed framework. From the original 2048 data points in the full spectra, we identified a subset comprised of 41 energy values that substantially improved clustering quality; the obtained groups were assessed by visual inspection of the PCA plots.

The Scott test is a preliminary colorimetric method to analyze cocaine. A blue color result in the final step denotes a positive indication for cocaine; however, some pharmacological products may lead to false positives when concentration is higher than 1 mg. In order to eliminate these false positives, digital images derived from the Scott test for pure cocaine, adulterants, diluents, and their binary and ternary mixtures were acquired with a commercial scanner, and the histogram of such images were assessed through Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine Discriminant Analysis (SVM-DA). PCA scores indicated a clear separation between a true positive and a false positive group, with exception for the higher concentrations of the adulterants assessed and their mixtures. In addition, we applied two supervised techniques, PLS-DA and SVM-DA, to the histograms of the digital images derived from the Scott test aimed at categorizing samples into true positive and true negative classes. PLS-DA yielded highly satisfactory results as only two samples were not correctly classified; the SVM-DA correctly classified all samples, yielding sensitivity and specificity equal to 1.

Near infrared (NIR) spectroscopy is an efficient, low-cost analytical technique widely applied to identify the origin of food and pharmaceutical products. NIR spectra-based classification strategies typically use thousands of equally spaced wavelengths as input information, some of which may not carry relevant information for product classification. When that is the case, the performance of predictive and exploratory multivariate techniques may be undermined by such noisy information. In this paper, we propose an iterative framework for selecting subsets of NIR wavelengths aimed at classifying samples into categories. For that matter, we integrate Principal Components Analysis (PCA) and three classification techniques: k-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA). PCA is first applied to NIR data, and a wavelength importance index is derived based on the PCA loadings. Samples are then categorized using the wavelength with the highest index and the classification accuracy is calculated; next, the wavelength with the second highest index is inserted into the dataset and a new classification is performed. This forward-based iterative procedure is carried out until all original wavelengths are inserted into the dataset used for classification. The subset of wavelengths leading to the maximum accuracy is chosen as the recommended subset. Our propositions performed remarkably well when applied to four datasets related to food and pharmaceutical products. Copyright © 2016 John Wiley & Sons, Ltd.

During the last decade, there has been a demand for public services to enhance management practices and attain results comparable to those experienced in the private sector. The need for achieving higher quality standards in public services has highlighted the importance of exploring new management techniques to account for the obsolescence in those organisations’ administrative models, with particular emphasis on healthcare services. This paper reports the application of one such new management technique, named value stream mapping (VSM), in the sterilised materials unit of a public healthcare organisation. In the case study presented here, we demonstrate the benefits of analysing healthcare processes using VSM; most notably reduction of wastes, inventory levels, and production lead times. The contribution of this article is twofold. First, it adds to the existing body of knowledge on lean management by examining the validity of applying lean principles and practices in contexts other than manufacturing....