A lean six sigma framework for continuous and incremental improvement in the oil and gas sector

This article aims to explore synergies between Lean Production (LP) and Six Sigma principles in order to propose a Lean Six Sigma (LSS) framework for continuous and incremental improvement in the oil and gas sector. The Three-Dimensional LSS Framework seeks to provide various combinations about the integration between LP principles, DMAIC cycle and PDCA cycle to support operations management needs. Design/methodology/approach - The research method is composed of two main steps: (i) diagnostic of current problems and proposition of a conceptual framework that qualitatively integrates synergistic aspects of LP and Six Sigma; and (ii) analysis of the application of the construct through semi-structured interviews with leaders from oil and gas companies to assess and validate the proposed framework. Findings - As a result, a conceptual framework of LSS is developed contemplating the integration of LP and Six Sigma and providing a systemic and holistic approach to problemsolving through continuous and incremental improvement in the oil and gas sector. Originality/value - This research is different from previous studies because it integrates LP principles, DMAIC and PDCA cycles into a unique framework that fulfils a specific need of oil and gas sector. It presents a customized LSS framework that guides wastes and costs reduction, while enhances quality and reduces process variability to elevate efficiency in operations management of this sector. The paper type is an original research that present new and original scientific findings.N/

Some of the challenges in implementing Education for Sustainable Development: perspectives from Brazilian engineering students

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article aims to analyse some of the main challenges evidenced in the insertion of sustainability in engineering courses, according to the view from a sample of Brazilian students. Through a systematic literature review, a set of 10 challenges were structured to base the research instrument (questionnaire). These challenges were evaluated by 91 engineering students who participate in sustainable action programs promoted by Enactus Brazil. The collected data were analysed in terms of the averages assigned and via the multi-criteria decision technique TOPSIS, which allowed ranking the challenges. The averages were higher than 5.0 on the scale used, indicating that the students notice the existence of the challenges in the courses in which they are enrolled. The ranking via TOPSIS presented the most evident challenges: ‘Sustainable issues debated only in specific disciplines in a limited extent’; ‘Difficulty to integrate disciplines for the broad teaching of sustainability’; ‘Lack of practical and real examples of how sustainability can be embedded in the specific context of the course’; and ‘Activities and examples presented focus exclusively on environmental issues’. The results presented here may be useful for course coordinators to improve their curriculum; educators to enrich their disciplines from the findings reported here; and researchers interested in the subject can use these findings as a starting point for proposing new teaching techniques. No similar publications were found in the literature, which indicates its originality and contribution to the knowledge base

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge

MIQuant – Semi-Automation of Infarct Size Assessment in Models of Cardiac Ischemic Injury

BACKGROUND: The cardiac regenerative potential of newly developed therapies is traditionally evaluated in rodent models of surgically induced myocardial ischemia. A generally accepted key parameter for determining the success of the applied therapy is the infarct size. Although regarded as a gold standard method for infarct size estimation in heart ischemia, histological planimetry is time-consuming and highly variable amongst studies. The purpose of this work is to contribute towards the standardization and simplification of infarct size assessment by providing free access to a novel semi-automated software tool. The acronym MIQuant was attributed to this application. METHODOLOGY/PRINCIPAL FINDINGS: Mice were subject to permanent coronary artery ligation and the size of chronic infarcts was estimated by area and midline-length methods using manual planimetry and with MIQuant. Repeatability and reproducibility of MIQuant scores were verified. The validation showed high correlation (r(midline length) = 0.981; r(area) = 0.970 ) and agreement (Bland-Altman analysis), free from bias for midline length and negligible bias of 1.21% to 3.72% for area quantification. Further analysis demonstrated that MIQuant reduced by 4.5-fold the time spent on the analysis and, importantly, MIQuant effectiveness is independent of user proficiency. The results indicate that MIQuant can be regarded as a better alternative to manual measurement. CONCLUSIONS: We conclude that MIQuant is a reliable and an easy-to-use software for infarct size quantification. The widespread use of MIQuant will contribute towards the standardization of infarct size assessment across studies and, therefore, to the systematization of the evaluation of cardiac regenerative potential of emerging therapies

Sustainability in manufacturing processes: practices performed in metal forming, casting, heat treatment, welding and electrostatic painting

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article aims to list the main sustainable practices developed in the processes of metal forming, casting, heat treatment, welding and electrostatic painting. When analysed the literature about sustainable manufacturing, a predominance of studies about machining is observed and the processes mentioned are few explored in academic studies. The research strategy used to reach the objective was systematic literature review, conducted for each process cited. Many sustainable practices were identified with prominence of better materials use and energy efficiency. The authors of this article believe that the information presented here can be useful for researches in their future studies and for industry professionals interested in improving manufacturing processes

A prospective survey in European Society of Cardiology member countries of atrial fibrillation management: baseline results of EURO bservational Research Programme Atrial Fibrillation (EORP-AF) Pilot General Registry

Aims: Given the advances in atrial fibrillation (AF) management and the availability of new European Society of Cardiology (ESC) guidelines, there is a need for the systematic collection of contemporary data regarding the management and treatment of AF in ESC member countries. Methods and results: We conducted a registry of consecutive in- and outpatients with AF presenting to cardiologists in nine participating ESC countries. All patients with an ECG-documented diagnosis of AF confirmed in the year prior to enrolment were eligible. We enroled a total of 3119 patients from February 2012 to March 2013, with full data on clinical subtype available for 3049 patients (40.4% female; mean age 68.8 years). Common comorbidities were hypertension, coronary disease, and heart failure. Lone AF was present in only 3.9% (122 patients). Asymptomatic AF was common, particularly among those with permanent AF. Amiodarone was the most common antiarrhythmic agent used (~20%), while beta-blockers and digoxin were the most used rate control drugs. Oral anticoagulants (OACs) were used in 80% overall, most often vitamin K antagonists (71.6%), with novel OACs being used in 8.4%. Other antithrombotics (mostly antiplatelet therapy, especially aspirin) were still used in one-third of the patients, and no antithrombotic treatment in only 4.8%. Oral anticoagulants were used in 56.4% of CHA 2DS2-VASc = 0, with 26.3% having no antithrombotic therapy. A high HAS-BLED score was not used to exclude OAC use, but there was a trend towards more aspirin use in the presence of a high HAS-BLED score. Conclusion: The EURObservational Research Programme Atrial Fibrillation (EORP-AF) Pilot Registry has provided systematic collection of contemporary data regarding the management and treatment of AF by cardiologists in ESC member countries. Oral anticoagulant use has increased, but novel OAC use was still low. Compliance with the treatment guidelines for patients with the lowest and higher stroke risk scores remains suboptimal. © The Author 2013

The role of transformation in learning and education for sustainability

Education research has acknowledged the value of transformation, which offers an opportunity for researching and rethinking how appropriate and successful educational practices may be. However, despite the role of transformation in higher education and particularly in sustainability learning, there is a paucity of studies which examine the extent to which transformation and learning on matters related to sustainable development may be integrated. Based on this perceived research need, the purpose of this article is to present how transformation in learning in education for sustainability requires the commitment of Faculty and the engament of students. To do this, a set of qualitative case studies were used in higher education institutions across seven countries (Brazil, Serbia, Latvia, South Africa, Spain, Syria, UK). The findings revealed that the concept of education for sustainable development has not been sufficiently integrated into the concept of transformation in higher education institutions. It also found that to enhance sustainability in the curricula, academics should develop collaborative approaches, and discuss how to redesign their own disciplines, and how to appreciate the epistemology and multicultural vision of sustainability, both as a topic, and as a field of education research. It was further found that reflections of the academics on their own values are crucial for developing the transformative potential of students as agents of a sustainable future. It is necessary that universities should transform to serve as models of social justice and environmental stewardship, and to foster sustainability learning

Multi-scale Gaussian representation and outline-learning based cell image segmentation

BACKGROUND: High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. METHODS: We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. RESULTS AND CONCLUSIONS: We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks