An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping

The combination of complex, multiple minerogenic stages and mineral superposition during geological processes has resulted in dynamic spatial distributions and nonstationarity of geological variables. For example, geochemical elements exhibit clear spatial variability and trends with coverage type changes. Thus, bias is likely to occur under these conditions when general regression models are applied to mineral prospectivity mapping (MPM). In this study, we used a spatially weighted technique to improve general logistic regression and developed an improved model, i.e., the improved logistic regression model, based on a spatially weighted technique (ILRBSWT, version 1.0). The capabilities and advantages of ILRBSWT are as follows: (1) it is a geographically weighted regression (GWR) model, and thus it has all advantages of GWR when managing spatial trends and nonstationarity; (2) while the current software employed for GWR mainly applies linear regression, ILRBSWT is based on logistic regression, which is more suitable for MPM because mineralization is a binary event; (3) a missing data processing method borrowed from weights of evidence is included in ILRBSWT to extend its adaptability when managing multisource data; and (4) in addition to geographical distance, the differences in data quality or exploration level can be weighted in the new model

Otimização do processo de tomada de decisão para o planeamento de compras

Numa organização onde não há custos de produção porque o produto comercializado é comprado, a única maneira de reduzir custos é minimizando custos de transporte e armazenamento. A empresa em estudo é responsável pelo armazenamento e distribuição de produtos líquidos. O transporte do produto é feito por via marítima. O principal objetivo é ter um sistema automático que apoie a tomada de decisão na determinação das melhores datas para receção do produto e quantidades a encomendar, de acordo com as previsões de venda. Para determinação das quantidades a encomendar e as datas de receção dos navios, têm de ser consideradas diversas restrições, umas associadas ao transporte marítimo (capacidade, dimensões, condições climatéricas, acesso ao porto, etc.), e outras aos clientes, capacidades dos tanques de armazenamento, stocks, contratos, preços produto, etc. A maioria das restrições sofrem alterações diárias que têm de ser consideradas aquando a tomada de decisão. Foi desenhada e desenvolvida uma abordagem que automatiza o planeamento de compras de produto de acordo com as previsões de venda. Esta abordagem foi testada com dados reais da empresa e as soluções obtidas comparadas com um histórico dos planos de compra já desenvolvidos. Conclui-se que o sistema desenvolvido não só ajuda a tomada de decisão no “imediato”, como também obtém um melhor planeamento do que o que a empresa atualmente pratica.In an organization where there is no production cost because the marketed product is purchased, the only way to reduce costs is to minimize transportation and storage costs. The company under study is responsible for the storage and distribution of liquid products. The product is transported by sea. The main objective is to have an automatic system that supports decision making in determining the best dates for receiving the product and quantities to order, according to the sales forecasts. In order to determine the quantities to be ordered and the dates of shipment, a number of restrictions have to be considered, some associated with maritime transport (capacity, dimensions, weather conditions, port access, etc.), and others, tank capacities storage, stocks, contracts, product prices, etc. Most restrictions suffer daily changes that have to be considered when making decisions. An approach that automates the planning of product purchases according to sales forecasts has been designed and developed. This approach has been tested with actual company data and the solutions obtained compared to a history of the purchase plans already developed. It is concluded that the developed system not only helps decision making in the "immediate", but also obtains better planning than what the company currently practices.Mestrado em Engenharia e Gestão Industria

NASA Tech Briefs, April 1997

Topics covered include: Video and Imaging; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

Machine and component residual life estimation through the application of neural networks

Analysis of reliability data plays an important role in the maintenance decision making process. The accurate estimation of residual life in components and systems can be a great asset when planning the preventive replacement of components on machines. Artificial intelligence is a field that has rapidly developed over the last twenty years and practical applications have been found in many diverse areas. The use of such methods in the maintenance field have however not yet been fully explored. With the common availability of condition monitoring data, another dimension has been added to the analysis of reliability data. Neural networks allow for explanatory variables to be incorporated into the analysis process. This is expected to improve the quality of predictions when compared to the results achieved through the use of methods that rely solely on failure time data. Neural networks can therefore be seen as an alternative to the various regression models, such as the proportional hazards model, which also incorporate such covariates into the analysis. For the purpose of investigating their applicability to the problem of predicting the residual life of machines and components, neural networks were trained and tested with the data of two different reliability related datasets. The first dataset represents the renewal case where repair leads to complete restoration of the system. A typical maintenance situation was simulated in the laboratory by subjecting a series of similar test pieces to different loading conditions. Measurements were taken at regular intervals during testing with a number of sensors which provided an indication of the test piece’s condition at the time of measurement. The dataset was split into a training set and a test set and a number of neural network variations were trained using the first set. The networks’ ability to generalize was then tested by presenting the data from the test set to each of these networks. The second dataset contained data collected from a group of pumps working in a coal mining environment. This dataset therefore represented an example of the situation encountered with a repaired system. The performance of different neural network variations was subsequently compared through the use of cross-validation. It was proved that in most cases the use of condition monitoring data as network inputs improved the accuracy of the neural networks’ predictions. The average prediction error of the various neural networks under comparison varied between 431 and 841 seconds on the renewal dataset, where test pieces had a characteristic life of 8971 seconds. When optimized the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum of squares error within 11.1% of each other for the data of the repaired system. This result emphasizes the importance of adjusting parameters, network architecture and training targets for optimal performance The advantage of using neural networks for predicting residual life was clearly illustrated when comparing their performance to the results achieved through the use of the traditional statistical methods. The potential of using neural networks for residual life prediction was therefore illustrated in both cases.Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.Mechanical and Aeronautical EngineeringMEngunrestricte

Advances in Manufacturing Technology XXVII: Proceedings of the 11th International Conference on Manufacturing Research (ICMR2013)

ICMR2013 was organised by Cranfield University on the 19-20 September 2013. The conference focuses on any aspects of product development, manufacturing technology, manufacturing systems, information systems and digital technologies. It provides an excellent avenue for researchers to present state-of-the-art multidisciplinary manufacturing research and exchange ideas. In addition to the four keynote speeches from Airbus and Rolls-Royce and three invited presentations, there are 108 papers in these proceedings. These papers are split into 24 technical sessions. The International Conference on Manufacturing Research is a major event for academics and industrialists engaged in manufacturing research. Held annually in the UK since the late 1970s, the conference is renowned as a friendly and inclusive environment that brings together a broad community of researchers who share a common goal; developing and managing the technologies and operations that are key to sustaining the success of manufacturing businesses. For over two decades, ICMR has been the main manufacturing research conference organised in the UK, successfully bringing researchers, academics and industrialists together to share their knowledge and experiences. Initiated a National Conference by the Consortium of UK University Manufacturing Engineering Heads (COMEH), it became an International Conference in 2003. COMEH is an independent body established in 1978. Its main aim is to promote manufacturing engineering education, training and research. To achieve this, the Consortium maintains a close liaison with government bodies concerned with the training and continuing development of professional engineers, while responding to the appropriate consultative and discussion documents and other initiatives. COMEH is represented on the Engineering Professor’s council (EPC) and it organises and supports national manufacturing engineering education research conferences and symposia

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania