Career Planning and Progression for Engineering Management Graduates: An Exploratory Study

Abstract:

When faced with the decision of selecting an advanced degree, many engineers opt for management related studies rather than further specialization in a technical field. This article attempts to highlight the reasons behind such choices, and explores the role that a Master's degree in Engineering Management (MEM) plays in career planning and progression. A survey of 58 MEM graduates, who completed their studies at a prominent university in Lebanon between 1992 and 2009, reveals that the majority of the respondents follow a linear career path, rapidly paving the way towards managerial positions. For most of the respondents, earning a graduate degree in engineering management played a primary role in, or at least contributed to, making this shift. The article concludes with a diagram sketching the possible career paths for MEM graduates. By showing the number of years spent at different career stages, the diagram serves as a career-planning tool for MEM graduates, engineers, managers, and researchers.     

Eco-City Projects: Incorporating Sustainability Requirements during Pre-Project Planning

International standards exist for evaluating building or neighborhood sustainability. Nonetheless, they are still not available for large-scale developments. Of special interest to practitioners is how to ensure that sustainability requirements of large-scale projects are well integrated in a master plan. This paper provides design managers of new eco-cities with a framework to integrate sustainability in the pre-project planning phase. A case study of a planned eco-city is investigated to delineate its pre-project planning practices, compare them to the proposed framework, and infer lessons learned. The case study highlights the importance of regular interactions between business planners and master planners to incorporate sustainability requirements at early planning phases.     

Effect of RC wall on the development of plastic rotation in the beams of RC frame structures

The objective of this study is, to interpret the influence of reinforced concrete walls addition in reinforced concrete frame structures considering behavior laws that reflects the actual behavior of such structures, by means of Castem2000computer code (pushover analysis). A finite element model is proposed in this study, using the TAKEDA modified behavior model with Timoshenko beams elements. This model is validated initially on experimental model. Then the work has focused on the behavior of a RC frame with 3 levels and three bays to better visualize the behavior of plastic hinges. Once the plastic hinge control parameters are identified (plastic rotation, ultimate curvature), a strengthening by introduction of reinforced concrete walls (RC/wall) at the ends of the reinforced concrete frame (RC/frame) has been performed. The results show that these RC walls significantly improve the behavior, by a relocation of efforts towards the central part of the beams.     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.     

L-p Fuzzy ARTMAP neural network architecture

In this paper, an L-p based Fuzzy ARTMAP neural network is presented. The category choice of this network is based on the L-p norm. Geometrical properties of this architecture are presented. Comparisons between this category choice and the category choice of the Fuzzy ARTMAP are illustrated. And simulation results on the databases taken from the UCI repository are performed. It will be shown that using the L-p norm is geometrically more attractive. It will operate directly on the input patterns without the need for doing any preprocessing. It should be noted that the Fuzzy ARTMAP architecture requires two preprocessing steps: normalization and complement coding. Simulation results on different databases show the good generalization performance of the L-p Fuzzy ARTMAP compared to the performance of Fuzzy ARTMAP.     

Roebling Suspension Bridge. II: Ambient Testing and Live-Load Response

This is the second part of a two-part paper on the evaluation of the historic Roebling suspension bridge using dynamic-analysis techniques. Dynamic properties are determined using ambient field testing under natural excitation. The finite-element (FE) model described in the first part of this two-part paper is modified to more accurately represent current bridge properties. Modifications of the model are based on correlating the FE model frequencies with ambient test frequencies by adjusting the FE model stiffness parameters. The updated 3D FE model is subsequently subjected to an extreme live-load condition to evaluate static safety margins. In addition, cable areas are reduced by 10 to 40% to simulate further deterioration and corrosion. The safety margin of the main cables is demonstrated to be good even when assuming a very conservative 40% cable area reduction, and truss member forces remain within the maximum load-carrying capacity even when the cable areas are reduced by 40%.     

Quadratic programming based data assimilation with passive drifting sensors for shallow water flows

We present a method for assimilating Lagrangian sensor measurement data into a shallow water equation model. The underlying estimation problem (in which the dynamics of the system are represented by a system of partial differential equations) relies on the formulation of a minimisation of an error functional, which represents the mismatch between the estimate and the measurements. The corresponding so-called variational data assimilation problem is formulated as a quadratic programming problem with linear constraints. For the hydrodynamics application of interest, data is obtained from drifting sensors that gather position and velocity. The data assimilation method refines the estimate of the initial conditions of the hydrodynamic system. The method is implemented using a new sensor network hardware platform for gathering flow information from a river, which is presented in this article for the first time. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors, some of which were deployed as part of our field experiments.     

Fuzzy ART-based prototype classifier

Prototype classifier is based on representing every cluster by a prototype. All the input patterns that belong to that cluster will have the same label as the prototype. It should be noted that a prototype does not have to be only one data. A cluster could be represented by more than one data. In this paper, the M-dimensional rectangle of the Fuzzy ART is used as a prototype. A new tree clustering structure replaces the training phase of Fuzzy ARTMAP. The obtained clusters are used to form the prototype rectangles. These rectangles will be used in the test phase of the Fuzzy ARTMAP. This algorithm is compared to the Nearest Neighbor classifier, the Fuzzy ARTMAP, C4.5, and the fuzzy ART-Var algorithms for different values of the vigilance parameter. Databases from the UCI repository will be used for comparison. Experimental results show the good generalization capability of this new algorithm.     

Participatory scenario planning for sustainable irrigated agriculture when actors seldom communicate: an experiment in Morocco

ABSTRACT

In Morocco, agricultural activities based on groundwater use increasingly face risks of aquifer overdraft and market saturation. However, farmers and public organizations responsible for agriculture and water resources rarely communicate to identify how these risks could be overcome. A participatory scenario-planning process was organized in a small region to identify a pathway towards agricultural activities that are sustainable in terms of groundwater resource use and profitability. Actors jointly determined this pathway thanks to the organization of preparatory workshops held separately with each actor before they met together, and the progressive integration of agriculture development and groundwater use in scenario design.