Sustainability Performance Evaluation of Faceshield Bracket Manufacturing by Using the Analytic Hierarchy Process

Faceshield brackets were among highly relevant products manufactured in various workshops, including Sigma Clermont, during the outbreak of COVID-19 to tackle acute shortages of personal protective equipment. However, many of these products are yet to be checked for appropriateness in design, quality, safety, comfort, and overall sustainability performance which might lead to unknown damages to human health and the environment. Thus, the main objective of this study is to evaluate the sustainable performance of Faceshield bracket manufacturing. Appropriate data are collected from designers, machine operators, and supplementary data are utilized from experts’ opinions and literature. A total of 38 carefully selected indicator sets are utilized while applying multicriteria decision-making methodology (MCDM) of the analytic hierarchy process (AHP) to evaluate the sustainability performance of these manufacturing processes. Evaluation is performed to examine the processes’ sustainability performance and associated indicator contribution based on the three dimensions. Results disclosed that 3D printing (score = 0.108) showed better performance for the environment, and injection molding showed better economic and social performance, with scores of 0.1922 and 0.160, respectively. A detailed contribution of each subcriteria for each sustainability dimension and each of the three processes as well as major contributing indicators are identified and discussed. Finally, overall performance was assessed using the total sum approach, and Borda count methodology and injection molding showed better sustainability performance in the manufacturing of Faceshield brackets considered with scores of 43 and 87, respectively. The authors believe that indicator selection approaches and overall methodology followed for this study will be relevant for future sustainability evaluation of engineering products.

Novel World University Rankings Combining Academic, Environmental and Resource Indicators

World university rankings are regarded as an important tool to assess higher education quality. There are several media sources that publish world university rankings every year. These ranking results are mainly based on academic indicators, including research and teaching, with different weightings. However, some of these academic indicators are questionable, which affects the objectivity of the ranking results. In addition, conducting more medical-related studies could enhance the research impact scores. Some universities that devote themselves to enhancing these academic indicators lose sight of their original development goals and directions. To make the rankings more comprehensive, it is necessary to consider different viewpoints in the assessment. In other words, the research question of this paper is: whether considering different kinds of indicators can provide better ranking results? Therefore, in this paper, we introduce a novel ranking approach that combines academic, environmental, and resource indicators based on the Borda count method. The top 100 world universities from the Academic Ranking of World Universities, QS World University Rankings, Times Higher Education World Universities, and U.S. News & World Report are chosen for the analysis. The comparisons between the original and new rankings show that many universities improve in the rankings, while some universities from particular countries drop in the rankings due to the scores obtained from the environmental and resource indicators.

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.

Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.

A Variable Ranking Method for Machine Learning Models with Correlated Features: In-Silico Validation and Application for Diabetes Prediction

When building a predictive model for predicting a clinical outcome using machine learning techniques, the model developers are often interested in ranking the features according to their predictive ability. A commonly used approach to obtain a robust variable ranking is to apply recursive feature elimination (RFE) on multiple resamplings of the training set and then to aggregate the ranking results using the Borda count method. However, the presence of highly correlated features in the training set can deteriorate the ranking performance. In this work, we propose a variant of the method based on RFE and Borda count that takes into account the correlation between variables during the ranking procedure in order to improve the ranking performance in the presence of highly correlated features. The proposed algorithm is tested on simulated datasets in which the true variable importance is known and compared to the standard RFE-Borda count method. According to the root mean square error between the estimated rank and the true (i.e., simulated) feature importance, the proposed algorithm overcomes the standard RFE-Borda count method. Finally, the proposed algorithm is applied to a case study related to the development of a predictive model of type 2 diabetes onset.

Creation of a Turkish University League Based on the Contribution of their Economics Departments to Economics Education

This study aims to measure the added value created by the economics departments of the universities in Turkey for students throughout their undergraduate education. For the analysis section, the minimum admission scores of the universities' economics departments for the years from 2000 to 2012 were used as input and the net average scores obtained in the economics tests of Public Personnel Selection Examination (PPSE) for the years from 2004 to 2016 were used as output. The data were normalized using the min-max method and the universities were ranked using the "Borda count" method. According to the results, Ankara University ranked the 1st, Hacettepe University the 2nd and Middle East Technical University the 3rd. The first six positions in the top ten ranking are held by the universities located in the capital, Ankara. This is attributed to various factors such as the long-established character of the universities in the capital city, availability and diversity of training courses and materials for PPSE, permanency of the academic staff, prevalence of standard daytime education in these universities, and the role of the capital as the seat of public institutions, resulting in greater motivation among students as they have more contact with senior government officials.

Risk Perception of Rural Land Supply Reform in China: From the Perspective of Stakeholders

The reform of rural land supply market has a profound impact on the rural management and agricultural development. In this article, we want to explore risk perception of multi-subject land supply reform in China. The perception of potential risks from the perspective of stakeholders can evaluate the effect of a certain behavioral decision and provide a convincing explanation for further risk control. Based on theoretical analysis and practical investigation, we define the five stakeholders, namely collective economic organizations, farmers, local government, banks and land users, as the respondents of our questionnaire survey. Through in-depth interviews and literature review, we obtained the categories of risks with stakeholders. Data were obtained through questionnaire survey, a total of 307 surveys were conducted to analyze the probability of risk occurrence and the severity of consequences. Frequency analysis, risk matrix method, and Borda count method were used to analyze the survey results in order to determine the risk level and key risk. The research finds that the information asymmetry risk perceived by farmers and the market risk perceived by banks are key risks. In terms of stakeholders, famers and banks perceived the highest overall risks. It implies that the information-disadvantaged stakeholder is usually the one with a strong sense of risk.