The spatial aspects of development in south-eastern Europe

This paper analyses for the first time the spatial structure of south-eastern Europe in an effort to assess regional imbalances, border conditions, urban hierarchies and detect the adjustments of the region to the forces of integration and transition. The analysis is based on a unique data base compiled from national sources and is carried on with the use of statistical, diagrammatic and cartographic methods. The analysis shows that south-eastern Europe is characterized by increasing regional disparities, an increasingly superior performance of the metropolitan regions, serious discontinuities at the borders which have, in most cases, generated over-time border regions with below average performance and finally an urban system with serious deficiencies in medium sized cities. These findings suggest that regional policy should become a permanent ingredient of indigenous and international development initiatives, which need to pay a greater attention to the needs of border regions, encouraging and promoting programs and policies of cross-border cooperation.

Enhancing Decision Support for Secondary Education with OLAP

Decision-making is one of the most critical processes taking place in a modern school. It is a necessary competence for school administrators and managerial staff especially in Education Directorates who often have to make decisions regarding the implementation of education strategies and policies. It is also important for teaching staff and school curriculum designers in order to plan teaching methods and monitor student performance. Nowadays many school functions are supported by dedicated information systems. Business Intelligence (BI) is a widely used set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. They include Online Analytical Processing (OLAP) in order to provide historical, current and predictive views of business operations. Schools in secondary education can be viewed as small organizations where effective decision making is required at many areas and levels. The aim of this project is the research of feasibility of applying OLAP Decision Support Systems in Education and Education Management, any possible benefits as well as possible enhancements. The outcome is the design and implementation of an enhanced OLAP system applied in a specific educational setting based on our case study

Electrostatic generation while tank washing and ignition hazards of fuel air mixtures

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Ocean Engineering.Microfiche copy available in Archives and Engineering.Includes bibliographical references.by George Economou.M.S

Alternate means of power generation and fuel conservation in ship operations.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Ocean Engineering.Microfiche copy available in Archives and Engineering.Includes bibliographical references.M.S

Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains