Proceedings of Papers 2-nd International Scientific Conference MILCON'19

In front of you is the thematic Proceedings, as a collection of papers presented at the 2nd MILCON’19 Conference ''Contemporary education based on ADL'', organized on November 12th 2019, by the Military Academy "General MihailoApostolski" - Skopje associated member of the University ''Goce Delcev'' - Shtip, within the RADLI Project (Regional Advance Distributive Learning Initiative), supported by the Kingdom of Norway and implemented by the Jefferson Institute, USA. The objective of the Conference was to gather educators and trainers from different countries in order to give us the opportunity to increase both knowledge and cooperation within all aspects of advance distributed learning - ADL. Hence, the Proceedings contain 32 papers focused on the contemporary trends in the use of information technology in a pedagogical way, as well as the best practices both from a theoretical point of view, but also from a practical aspect on the topics related to educational programs using blended learning, emerging learning technologies, multiplatform delivery of courseware, motivational and pedagogical learning strategies and other topics related to ADL. This international scientific conference gives us a wonderful opportunity for exchanging experience and knowledge between the scientific workers and the practitioners from North Macedonia, USA, Serbia, Poland, Slovenia, Bosna and Hercegovina and Norway. The papers published in the Proceedings are written by eminent scholars as well as by members of the security system participating in the educational process of the army, police and other security services from different countries. Each paper has been reviewed by international experts competent for the field to which the paper is related. The data and information gained with the empirical research, as well as theoretical thoughts and comparative analyses in the Proceedings will give a significant contribution to the development of the use of ADL in a pedagogical way. We wish to extend our gratitude to all authors and participants to the Conference, as well as to all those who contributed to, or supported the Conference, especially the Kingdom of Norway and the Jefferson Institute, as well as to the Ministry of Defense and the Armed Forces of the Republic of North Macedoniafor their immense support of the Conference

Prediction of aircraft trajectories for air traffic control using machine learning approaches

Air traffic is facing great challenges for the future. The economic crisis has brought a burden of cost savings, while the increase of traffic requires investments in research and development to find new paradigms for safe operations. One of the most important aspects in all future plans is better trajectory calculation, or better knowledge where the aircraft is going to be at a certain time. When positions are known, the planning can optimize flying paths to be cost efficient and safe, which is very important as the traffic becomes denser every day. Aircraft operators are planning flight paths with minimum costs, but they are not optimizing them for conflicts with other aircraft, and for airspace optimizations. Air traffic control and airspace restrictions are taking care of that. Soon, this present model will not provide enough throughput for all aircraft that want to fly. Our research is putting a stone in the mosaic of trajectory prediction and airspace optimization. In the future, aircraft will share data about their planned paths with air traffic control and aircraft in vicinity. Since air traffic is a highly regulated and expensive business, it takes a very long time before changes are implemented. Until then, we have to find alternative ways for better trajectory predictions, which will allow us to plan and optimize traffic, and to increase throughput. The ground control records the data about actual flight paths acquired by radars. Some weather data can be also acquired with a new generation of Mode-S radars. Pure aircraft performance data are enriched with weather and flight plan data into a joint knowledge database. For every new flight, we search in the database for flights similar to the incoming one. If we know how similar flights behaved in the past, we can predict the performances of a new flight, and can calculate the planned flight trajectory more accurately. Our goal is to predict trajectories better than using static models of aircraft performances. With existing prediction methods we predict for the same type of aircraft on a specific path the same trajectory every time. In that way, we have a prediction that deviates the least from the majority of flights. On the other hand, we predict a trajectory that does not fit any flight. With our approach, we want to take into account other factors such as aircraft operator, final destination, time of flight, etc., and every time predict a different trajectory suited to fit exactly to the considered flight. Operator and similar attributes are all factors that do not influence the flight directly. The destination, for instance, determines the distance of flight and therefore determines, how much fuel is on-board. More fuel means more weight and different flight characteristics. Similarly, we can assume that each operator operates airplanes differently than others, or carries different type of passengers that have usually more or less luggage than others. All these factors are not very well measurable, but they do affect flight performances. We use machine learning to find the flights in the database that are the closest to the one being predicted. With the assumption that flights with similar features flight similarly, we expect to predict more accurate trajectories than with static models and default parameters. We tested many machine learning methods and found the ones that perform the best on our data. We also adapted standard machine learning algorithms for our needs and large amounts of data. We have used machine learning predictions instead of static nominal values in widely used trajectory calculation model. The methods using only aircraft type are widely used in aviation, but they lack the capability to adapt to each flight individually. In our opinion, such rigid and static usage of aircraft type is an important cause for poor predictions. The results show that our predictions methods using individually customized predictions are more accurate than predictions based on aircraft type. We have shown that our methods are comparable with standard machine learning methods. The solution that we propose, is deployed as a web service, to which users can send flight details and get back parameters suited for a particular flight. Because the parameters are in the same form as in the widely used Base of Aircaft Data (BADA) model, legacy air control applications could use this service instead of static BADA database, and improve their trajectory calculations. In that way, a minimal change of the air control applications is needed. Trajectory calculations can remain unchanged, but with better input parameters, they can predict more accurately

A life-cycle approach for managing road infrastructures in developing countries based on Asset Management

Road infrastructures are very important to economic activity, especially in developing countries where they play an essential role in marketing agricultural products and providing access to health, education, and other services. While economic growth and the investments in road transport have increased heavily in developing countries, the public sector responsible for their life-cycle planning, management and maintenance is struggling to make the necessary reforms to keep up with the pace. The main objective of this dissertation is to understand the patterns that influence the strategic planning of road infrastructures and the successful implementation of the practices of asset management in the regulatory environment and structure of the responsible authorities in the developing countries. These patterns (external drivers), different in each country, if not researched and understood correctly, may affect the outcome of the results for the upcoming decades and jeopardize the entire implementation of asset management processes within the organizational structures of the developing countries. It reviews and analyzes the National regulatory environment and practices in Top Asset management countries (Canada, Australia, New Zealand, Uk, USA) and current social and political situation in the western Balkans region (developing countries region), which is influencing the successful management of primary infrastructures in this region. A significant Case study from Albania (Highway Durres- Kukes - Morine, a segment of European route 7 between Albania and Serbia), is introduced and actual physical Conditions, value, and performance of the highway are taken in consideration. Description of Problems this highway experiences because of lack of life-cycle planning and management are presented and how the mismanagement of the assets on a strategic level leads to tangible problems on the technical level. Transport impacts on the highway in terms of displacement, traffic flows, and forecast, historical traffic data are analyzed in order to analyze capacity/demand patterns and future demand, the influence it has on Road asset management and relate this with different strategies of maintenance

The Global and European Integration

The purpose of the course “The Global and European Integration” is to give students the relevant knowledge about the global integration in general and European integration in particular

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Seeking the Best Master

"The economic crisis of 2008–2009 signaled the end of the post-Washington consensus on restricting the role of the state in economic and development policy. Since then, state ownership and state intervention have increased worldwide. This volume offers a comparative analysis of the evolution of direct state intervention in the economy through state-owned companies in Austria, Brazil, France, Germany, Hungary, Poland, Turkey, Singapore, and Slovenia. Each case study includes substantial explanations of historical, cultural, and institutional contexts. All the contributors point to the complex nature of the current revival in state economic interventions. The few models that are successful cannot hide the potential problems of excessive state intervention, linked to high levels of moral hazard. State-owned enterprises are primary tools of market and price manipulation for political purposes. They can be used outright for rent seeking. Yet state-owned enterprises can also play important roles in prestigious national initiatives, like major public works or high-profile social and sports events. The authors conclude that after the uniform application of democratic market economic principles, the 2000s witnessed a path-dependent departure from standard economic and political operating procedures in developed countries.