Hybrid Event

Event program

Tuesday, 5/21/2024 9:00 AM - 1:00 PM, Camelia 1, Grand hotel Adriatic, Opatija 9:00 AM - 1:00 PM Papers  1. R. Alfasfos , M. Ullah, J. Sillman, P. Nardelli, R. Soukka (LUT univeristy , Lappeenranta, Finland) Recommendation on Cybersecurity and Safety in the Hydrogen Economy  This study examines the cybersecurity implications of the hydrogen economy, analyzing cyber incidents since 2010. It suggests a cybersecurity framework based on Internet of Things (IoT) and Edge computing to enhance infrastructure resilience against evolving threats. The digital transformation of energy systems, including smart grids and meters, has provided efficiency and clean energy benefits but also heightened cybersecurity risks. The research aims to bridge the cybersecurity gap in the hydrogen economy while acknowledging historical safety concerns related to hydrogen handling. Cyberattacks in the energy sector since 2010 have seen a significant upsurge, particularly peaking between 2020 and 2022. The study categorizes common cybersecurity threats, motivations, types, and consequences, and proposes an IoT model and edge computing framework to enhance cybersecurity defenses. This interdisciplinary approach integrates cybersecurity, IoT, Edge computing, and hydrogen technologies to address pressing challenges. Drawing insights from past incidents, this research equips stakeholders to secure the future of the hydrogen economy, emphasizing the dual role of IoT systems in increasing cyber threats and fortifying cybersecurity in this expanding energy sector 2. N. Nelufule, M. Masango, T. Singano (CSIR, Pretoria , South Africa) The Future of Digital Forensic Investigations: Keeping the Pace with Technological Advancements  Digital forensics plays a crucial role in the justice system, providing digital evidence that can be used to prosecute and convict criminals. This field is rapidly evolving due to the increasing complexity of digital devices. These increasing complexities have presented new challenges for digital forensic investigators because the devices are now equipped with multiple processors, advanced operating systems, and complex security features making it more difficult to extract digital evidence. The challenges posed by technological advancements have led to several problems in the field of digital forensics, including Backlog of evidence, Quality of forensics investigations, and Credibility of digital evidence. The problems facing digital forensics have a significant impact on the justice system because a lack of reliable digital evidence can lead to miscarriages of justice, both for the accused and for victims of crime. The problems facing digital forensics have a wide-ranging impact on society because they can lead to the acquittal of criminals, erode trust in law enforcement agencies, and erode trust in the digital world. A comprehensive framework is proposed to address these challenges by emphasizing, Continuous Innovation, Collaboration, Training, and Awareness. 3. N. Nelufule, Pretoria, South Africa), M. Masango, Pretoria, South Africa), T. Singano (Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa) Digital Forensics in Industry 4.0 and Industry 5.0: Major Challenges and Opportunities  The rapid advancements in Industry 4.0 and 5.0, along with the increasing adoption of edge computing, have brought about a significant transformation in industrial landscapes. These advancements have ushered in a new era of interconnected devices, real-time data processing, and decentralized decision making, creating an unprecedented volume of digital data. This surge in data generation has also heightened the need for robust digital forensics capabilities to investigate and respond to cyberattacks, data breaches, and other security incidents. This paper provides an overview of digital forensics in the context of Industry 4.0, Industry 5.0, and edge computing. It discusses the challenges and opportunities associated with forensic investigations in these environments, highlighting the unique characteristics of these technologies and their impact on the collection, preservation, and analysis of digital evidence. The paper also explores the potential applications of digital forensics in these industries, including incident response, fraud detection, and regulatory compliance. 4. D. Borić, T. Hadjina, L. Luttenberger Marić (Končar Digital d.o.o., Zagreb, Croatia) Anomaly Detection for HVAC System Maintenance using Autoencoder Neural Network  The anomaly or outlier detection of HVAC system components enables the detection of system failures and unusual consumption patterns derived from system malfunctions. Prompt and effective anomalies detection of HVAC systems are imperative for initiating repairs, correct maintenance plans and eliminating errors in HVAC system energy consumption forecasts. In this paper publicly available datasets were used together with convolutional autoencoder neural network. In preparation of dataset for training neural network a procedure for preprocessing anomalous data is developed. The developed procedure and the neural network resulted in highly accurate detection of anomalies in HVAC system components. 5. M. Ullah, D. Gutierrez Rojas, G. de Almeida, T. Tynjälä (Lappeenranta-Lahti University of Technology LUT, Lappeenranta, Finland) Unified Framework to Select an IoT Platform for PtX Cogeneration Plants  This study is a systematic analysis of selected research articles about the Power-to-x (P2X) sector. The relevance of this resides in the fact that most of the world energy is made from fossil fuels, which has been led to a huge amount of greenhouse gas emissions that are the source of global warming. One of the most supported actions against this issue is to produce and use the renewable energy resources, some of which are intermittent like solar and wind. This brings the need for large-scale, longer-period energy storage solutions. In this sense, P2X process chain could play this role: renewable energy can be converted into storable hydrogen, chemicals and fuels via electrolysis and subsequent synthesis with CO2. The focus of this study is to highlight the main contributions that are using the advanced data driven methods and latest technologies like Internet of Things (IoT), big data analytics and machine learning for the efficient operation of P2X cogeneration plants. In this case, one key challenge the industries face is the selection of an IoT platform among the hundreds of IoT platforms in the market. In this paper, we adapt our previously proposed general framework to choose an IoT platform focusing here on the requirements of P2X cogeneration plant. The proposed framework provides an objective methodology that can be used to select the most suitable IoT platform for different P2X cogeneration plant based on their particular requirements. 6. M. Kaniški, S. Križanić (Fakultet organizacije i informatike, Varaždin, Croatia) State-of-the-Art Machine Learning Frameworks for Training or Inference on Business Process Dataset  Machine learning is being used to solve complex problems. With the recent emergence of machine and deep learning architectures, the number of available frameworks to choose from has also increased. The different frameworks have different strengths and weaknesses. Therefore, it is crucial to ensure the highest accuracy of ML/DL models by choosing the right framework for the problem and the data. There are two main categories of frameworks. Those which are feature-rich and have been optimized for training, and those which are fast, lightweight, and have been optimized for inference. Training refers to the process of teaching a model to learn from the data it sees. Inference refers to the process of using a trained machine-learning algorithm to make a prediction. Business process data enables the monitoring of events related to the system and is an important source of information for future decisions. The public dataset from a business process that is ready to be processed by machine learning algorithms is used for research. This paper compares and evaluates the features and benefits of various frameworks for training or inference. In this paper, the important metrics are discussed when considering and choosing an ML/DL framework with regard to its limitations. 7. M. Begović, A. Midžić (University of Bihac, Technical Faculty, Bihać, Bosnia and Herzegovina) The Influence of Choosing Sensor Locations Inside Closed Facilities on the Reliability, Adaptability and Energy Efficiency of IOT Systems  This scientific research paper aims to provide insight into how strategic positioning of sensors can affect the reliability, scalability, and energy efficiency of IoT systems. Different algorithms, including those from the field of artificial intelligence, are used to determine the location of sensors. The final, chosen approach to their placement also depends on whether they are used for an internal or external application. Throughout a combination of theoretical analysis, practical experiments, and measurements collected from five sensors in a small household, the connection between IoT system performance and sensor locations is implied. Data collection was performed using an ESP32 microcontroller connected to a WiFi MESH network. Processing, analysis, and presentation of the results were performed using the MQTT protocol, the InFlux database, and Grafana as an open-source analytics and interactive visualization web application. The obtained results showed that the strategic positioning of sensors positively affects the reliability, scalability, and energy efficiency of the analyzed IoT system. 8. M. Qureshi, A. Knutas, A. Wolff (LUT univerisity , Lappeenranta, Finland) Consumer Energy Interface Use and User Behavior Analysis During a Price Hike: A Qualitative Study  Concurrent issues in the supply of fossil fuels are becoming more common in many parts of the world, creating energy price fluctuation. We conducted an interview study to find out how residents in rural Finland handle the recent energy crisis caused by concurrent issues in domestic energy production, a severe winter, and the halt of cheap fossil fuel imports from the east. We used thematic analysis to look for consumers' energy behavior, price monitoring habits, and what type of energy interfaces they used. We also studied their adaptation strategies and evaluated them through the theory of planned behavior. We created a narrative analysis of the condition, context, adaptation strategies, and its consequences based on the consumer's energy behavior. 9. V. Omelyanenko (Sumy State Pedagogical University named after A. S. Makarenko, Sumy, Ukraine), I. Pidorycheva (Institute of Industrial Economics of National Academy of Sciences of Ukraine, Kyiv, Ukraine), O. Omelianenko (Sumy State Pedagogical University named after A. S. Makarenko, Sumy, Ukraine), M. Biloshkurskyi, N. Biloshkurska (Pavlo Tychyna Uman State Pedagogical University, Uman, Ukraine), V. Samoday (Sumy State Pedagogical University named after A. S. Makarenko, Sumy, Ukraine) Strategic Aspects of Application of Smart City Concept for Recovery of Ukrainian Industry  The article is devoted to researching the prospects and possibilities of implementing the concept of a smart city in order to restore the industrial sector of Ukraine. The authors systematically analyze the key aspects of modern smart cities, in particular the use of advanced technologies of the Internet of Things, data analysis, and automation of production processes. The article highlights the potential benefits of implementing a smart grid in Ukrainian industry, such as increasing the efficiency of production processes, optimizing resource use, and creating environmentally friendly technological solutions. In particular, the possibilities of using smart technologies in production, logistics and management, as well as their impact on increasing the competitiveness of enterprises, are considered. The study is based on the study of the experience of implementing a smart grid in other countries and takes into account the specifics of the Ukrainian industrial sector. Thanks to this analysis, the authors provide specific recommendations for the implementation of a smart grid in Ukraine in order to increase its competitiveness and create sustainable economic growth. 10. J. Dobruna, Z. Limani Fazliu, B. Grajçevci (University of Prishtina, Prishtina, Kosovo) A Supervised Learning Method for Anomaly Classification in Cable Networks Based on SINR Patterns  With the continuous expansion of communication networks, the number of electronic devices within these networks has grown substantially. As the number of devices and communication channels continues to rise, signal quality faces increasing threats, resulting in numerous network interruptions. This research paper is focused on cable network anomalies and the application of Machine Learning (ML) techniques to facilitate the maintenance of these networks. This study is based on real-world data collected from one of the largest Internet Service Providers (ISPs) that operates in Kosovo. This dataset is based on SINR (signal-to-interference plus noise ratio) values monitored during 100 different cases of outage incidents within the cable network. The SINR values are analyzed for each case, and factors that lead to such signal degradation are identified. The dataset also includes the necessary maintenance tasks that were required to address the outages. Using this dataset we apply a supervised learning technique to classify anomaly type by analyzing the SINR degradation patterns. The accuracy and reliability of the proposed method is tested using a separated part of the dataset. 11. G. Markovic (Fakultet organizacije i informatike u Varaždinu, Zagreb, Croatia) Extending the Business Modeling Motivation Model for Achieving Business Agility through Enhanced Business-IT Alignment  In the fast-evolving digital landscape, achieving business agility is imperative for organizations seeking to navigate uncertainties and capitalize on emerging opportunities. This research adopts a modeling-centric approach, building upon the Business Motivation Model from the Object Management Group (OMG), to unravel the complexities of business-IT alignment. The primary objective is to extend this foundational model with additional concepts, incorporating determinants that shape alignment dynamics between business and IT, in order to improve business agility . The literature review situates the study within the context of existing modeling approaches, emphasizing the importance of the Business Modeling Motivation Model and exploring its application to the realm of business-IT alignment and agility. The conceptual framework builds upon the OMG model, introducing additional concepts to capture the nuanced relationships between business strategy, IT capabilities, and organizational agility. Acknowledging certain limitations, the study concludes by summarizing its key findings, emphasizing contributions to the field, and suggesting avenues for future research. By shedding light on the intricate dynamics between business strategy and IT, this research aims to inform strategic decision-making and foster a deeper understanding of how organizations can effectively navigate the evolving landscape of technology and business. Tuesday, 5/21/2024 3:00 PM - 6:00 PM, Camelia 1, Grand hotel Adriatic, Opatija 3:00 PM - 6:00 PM Papers  1. P. Linna, A. Halla (Tampere University of Technology – Pori, Pori, Finland), J. Suomalainen (Finnish Geospatial Research Institute of National Land Survey of Finland, Espoo, Finland) Concept for Sharing Drone Data in Agricultural Data Ecosystem  In recent years, drones have been making their way onto farms, but the challenge is still to make it an efficient work tool. These drones offer different applications, such as crop monitoring, pest identification or optimized fertilizer use. This paper introduces the formation of the drone concept, where the farmer collects data for his own use, but at the same time it creates the possibility to share and license data for the use of others. This enables the multi-use of data, in which case it creates a new business opportunity for the farmer and, on the other hand, also a new source of data for researchers or other data users to perform data analyses. The other parts of the concept, enables for example data sharing and visualization of authorized data, or sharing to closed community. Finally, the next steps of the concept have been considered, which would enable better discoverability and analysis of the data produced with this concept. The drone ecosystem in agriculture is not just about technology. It's a blend of technologies, data analysis, and sustainable farming practices. 2. M. Milić, J. Borocki, A. Vekić (Faculty of Technical Sciences , Novi Sad, Serbia) The Power of ICT Infrastructure in Fostering Innovation Development  In an era characterized by rapid technological progress, the pivotal role of Information and Communication Technology (ICT) infrastructure in catalyzing innovation has become increasingly evident. This research explores the diverse ways in which robust ICT infrastructure shapes, accelerates, and sustains innovation ecosystems. The impact assessment incorporates the Global Innovation Index (GII) as a crucial benchmark, providing a quantitative measure to compare the innovation capabilities and ranks of different countries. This paper presents a comparative analysis of Southeast European countries (SEE countries) concerning their positions in the GII reports and a parallel comparison of their ICT infrastructure development. As we stand at the crossroads of digital evolution and global progress, understanding the influential role of ICT infrastructure in fostering innovation is imperative for policymakers, industry leaders, and researchers. This study aims to contribute valuable insights to the discourse on technological empowerment by offering a deeper understanding. It seeks to guide future initiatives and endeavors directed at unlocking the full potential of ICT infrastructure for sustained and inclusive innovation development. 3. J. Ćelić (University of Rijeka Faculty of Maritime Studies, Rijeka, Croatia), T. Bronzin (CITUS, Zagreb, Croatia), M. Horvat (University of Zagreb Faculty of Electrical Engineering and Computing, Department of Applied Computin, Zagreb, Croatia), A. Jović (University of Zagreb Faculty of Electrical Engineering and Computing, Dept. of. El.,Microel., Comp. , Zagreb, Croatia), A. Stipić, B. Prole (CITUS, Zagreb, Croatia), M. Maričević, I. Pavlović, K. Pap, M. Mikota, N. Jelača (University of Zagreb Faculty of Graphic Arts, Zagreb, Croatia) Generative AI in E-maintenance: Myth or Reality?  With increasing requirements for reliability, availability, efficiency, effectiveness, productivity and security of the system, the importance of diagnostics and maintenance is also increasing. E-maintenance as a leading concept for maintenance management has so far primarily involved the use of domain-specific technical language processing (TLP) techniques on historical case data. Due to its popularity, generative AI (GAI) with large language models (LLMs) is starting to be used more and more in various technical areas, thus starting to take an increasingly important place in diagnostics and maintenance. Starting from the fact that the rapid development of information and communication technologies (ICT) was the main factor in the emergence and development of the concept of e-maintenance, the importance of the potential more serious application of all forms of generative AI in the aforementioned context is clear. This is especially pronounced in cases of difficult or impossible access to the location of components or an uncertain situation related to the type of process (eg nuclear, aeronautical, space, offshore). Autonomous vehicles, vessels and aircraft, which are an indispensable part of today's intelligent transport systems, are certainly a leading example of these cases. Regardless of the level of autonomy, these systems are extremely complex and difficult to maintain and represent a clear challenge for the application of new approaches. Therefore, the authors of the paper propose the use of middleware that would enable the integration of various GAI tools, algorithms and models in order to increase the effectiveness of diagnostics and maintenance as close as possible to real time. However, the exact extent of the possibilities and limitations of this approach has yet to be determined. 4. D. Pavković (University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, Croatia), K. Kvaternik (AVL-AST d.o.o., Zagreb, Croatia), Z. Kljaić (Ericsson Nikola Tesla d.d., Zagreb, Croatia), M. Cipek (University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, Croatia) Conventional Approach versus State-of-Charge Feedback for Lithium Titanate Battery Cell Charging – A Comparative Simulation Study  Lithium-titanate currently represents state-of-the-art technology in lithium secondary batteries that is characterized by exceptional thermal stability and operational safety levels. This makes them particularly interesting for highly demanding stationary energy storage applications. In practical field operations, minimizing the recharging time from a partially discharged state is often crucial, while still adhering to technological constraints such as battery terminal voltage and continuous charging current. To address this, the present work outlines the design of a dynamic battery charging system, leveraging state-of-charge estimation obtained from a nonlinear state estimator, specifically an extended Kalman filter (EKF). The above approach is used to enhance the conventional constant current/constant-voltage charging system, which traditionally relies solely on battery voltage feedback. To evaluate the novel charging system performance, a comparative analysis is conducted through extensive simulations employing the nonlinear equivalent circuit model of the lithium titanate battery cell. The presented mathematical model can be successfully used in the design of digital twins of battery systems to augment the related charging strategies for electric vehicles (EVs) through straightforward retrofitting. 5. A. Halla (Tampere University, Pori, Finland), S. Jaakkola (Pyhäjärvi Institute, Eura, Finland), R. Tupi (Tupi Farm, Harjavalta, Finland), P. Linna (Tampere University, Pori, Finland) Collaborative Data Collection in Agriculture – Case Sub-Irrigation On-Farm Experiment  Agriculture is increasingly data-intensive. As farmers aim for more informed decisions, they produce growing data volumes that are valuable for the wider farm data ecosystem, particularly in research. Data collection in on-farm experiments benefits the farmer by improving confidence in the experiment results but also provides researchers with data from production-scale environments. Artificial drainage systems can be used for sub-irrigation of crops in open-field farming, in addition to their original use. The effectiveness of this kind of irrigation depends on local soil characteristics and natural groundwater level patterns, necessitating on-site measurements. However, developing a monitoring system required for validating the effect of sub irrigation can be out of reach of an individual farmer. Over three years, a system for this purpose was collaboratively designed and built during a split-field experiment. This collaboration included the farmer, researchers, extension practitioners and companies. The system proved effective in validating the irrigation’s impact on groundwater levels. The experiment helped develop collaboration in the region and provided insight into the requirements and challenges of developing a farm data ecosystem. The system itself provides a basis for long-term monitoring and supports further research, including the use of the data in simulation and AI models for predictive analytics and optimization. 6. D. Topalović, D. Gabrijelčič (Institute "Jožef Stefan", Ljubljana, Slovenia) Estimating Household’s Physical Parameters Using Neural Ordinary Differential Equations  Integrating renewable energy sources into power grids introduces challenges due to the decentralization and variability of power generation. Demand-side flexibility (DSF) is one solution for optimizing power consumption. Buildings in particular offer significant DSF potential due to their large thermal mass and controllable HVAC (Heating, ventilation, and air conditioning) systems. Maximizing DSF benefits requires accurate energy consumption and heat demand prediction. Therefore, the development of robust thermal models for consumer/prosumer households that adhere to international energy standards, is needed. Thermal models are based on Ordinary Differential Equations (ODE) and explain the thermal behavior in view of the household’s physical parameters, e.g. floor area or thermal capacity. Since measuring these parameters is often impractical, this paper introduces a novel approach for household’s parameters identification. Our methodology involves adapting the model’s ODEfor air temperature observations and enhancing param eter estimation through a comprehensive synthetic dataset. We then classify households into parameter ranges based on collected data, facilitating Neural ODEs training to fit measured temperatures to the ODE for parameter inference. The major contribution of our work is in providing a scalable solution that eliminates the need for individual parameter measurements, enhancing the feasibility of implementing DSF strategies in a broader context. 7. Z. Kljaic (Ericsson Nikola Tesla d.d., Zagreb, Croatia), M. Grdic (Ericsson Nikola Tesla d.d., Rijeka, Croatia), T. Mlinaric, M. Niksic (Fakultet prometnih znanosti, Zagreb, Croatia), D. Pavkovic, M. Cipek (Fakultet strojarstva i brodogradnje, Zagreb, Croatia) Digital Twin Models with ESG Methodology as a Tool for the Transformation of Cities in the Area of Transport and Energy  Advances in transport technology and the transition from fossil to renewable energy sources have also changed the way the transport system and wider infrastructure is managed. This is most evident in cities due to their rapid growth. Scientists are trying to answer such new challenges through virtual mathematical models in which expert knowledge is integrated, all with the goal of more successful predictive management. Such mathematical models appear under the common name "digital twins". Digital twins are becoming an indispensable tool for creating enhanced virtual models of real-world applications, systems, and objects especially for AI-driven systems. With the accelerated technological development in the field of transport and energy, issues of accompanying technological investments with ESG (environmental, social, governmental) methodology were also raised. The application of digital twin technology with integrated ESG requirements opens new possibilities in decision-making support during the implementation of new transport and energy systems with an ecological background. This paper analyzes the possibilities of applying such new virtual models in the areas of transport and energy. 8. S. Stankovic (The Academy of Applied Technical and Preschool Studies, Deparetment of Nis, Nis, Serbia), N. Milutinovic (The Academy of Applied Technical and Preschool Studies, Department of Nis, Nis, Serbia), M. Ivanovic (The Academy of Applied Technical Studies Belgrade, Belgrade, Serbia), M. Milenkovic (PUC Mediana, Nis, Serbia) Integration of Smart Waste Management Solutions: A Case Study of QR Code-Based Recyclable Waste Monitoring System   This research presents a case study on a waste management system implementation developed by Public Utility Company (PUC) Mediana in Nis, Serbia. The system leverages the ReCollect application, a custom Android-based solution for efficient, recyclable waste tracking and monitoring. Utilizing QR code scanning technology, waste collection personnel can swiftly identify and document the separation status of recyclable materials within blue bins dedicated to plastics, glass, metal, and paper. The application facilitates real-time data input after scanning, allowing workers to add comments and record the percentage of properly segregated recyclable waste. It integrates GPS technology, linking it to the PUC database. This connectivity ensures accurate location tracking and enables streamlined communication between field workers and the central waste management system. This system enhances waste management efficiency by providing immediate feedback on waste separation practices, fostering environmental responsibility among citizens. The collected real-time data not only aids in optimizing waste collection routes but also facilitates strategic planning for community education initiatives. This approach amalgamates QR code technology, mobile applications, and real-time data analytics to transform traditional waste management practices. The implementation of such a system holds promise for cities worldwide, offering a scalable and adaptable solution for promoting sustainable waste management practices.

Basic information:

Neven Vrček (Croatia), Slavko Vidović (Croatia), Ernest Vlačić (Croatia), Ranko Smokvina (Croatia)

Darinko Bago (Croatia), Hrvoje Balen (Croatia), Vladimir Olujić (Croatia), Nedjeljko Perić (Croatia), Tadej Slapnik (Slovenia), Marija Šćulac Domac (Croatia), Ivan Vidaković (Croatia), Slavko Vidović (Croatia), Ernest Vlačić (Croatia)

Zoran Aralica (Croatia), Boris Blumenschein (Croatia), Darko Bosnar (Croatia), Bojan Jerbić (Croatia), Goran Marković (Croatia), Mladen Mrvelj (Croatia), Matija Srbić (Croatia), Marija Šutina (Croatia), Domen Verdnik (Croatia), Marko Vidović (Croatia)

Neven Vrček
University of Zagreb
Faculty of Organization and Informatics
Pavlinska 2
HR-42000 Varaždin, Croatia

Phone: +385 42 390 830
E-mail: nvrcek@foi.unizg.hr

Accepted papers will be published in the ISSN registered conference proceedings. Papers in English presented at the conference will be submitted for inclusion in the IEEE Xplore Digital Library. 
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There is a possibility that the selected scientific papers with some further modification and refinement are being published in the following journals: Journal of Computing and Information Technology (CIT), MDPI Applied Science, MDPI Information Journal, Frontiers and EAI Endorsed Transaction on Scalable Information Systems.

Opatija is the leading seaside resort of the Eastern Adriatic and one of the most famous tourist destinations on the Mediterranean. With its aristocratic architecture and style, Opatija has been attracting artists, kings, politicians, scientists, sportsmen, as well as business people, bankers and managers for more than 170 years.

The tourist offer in Opatija includes a vast number of hotels, excellent restaurants, entertainment venues, art festivals, superb modern and classical music concerts, beaches and swimming pools – this city satisfies all wishes and demands.

Opatija, the Queen of the Adriatic, is also one of the most prominent congress cities in the Mediterranean, particularly important for its ICT conventions, one of which is MIPRO, which has been held in Opatija since 1979, and attracts more than a thousand participants from over forty countries. These conventions promote Opatija as one of the most desirable technological, business, educational and scientific centers in South-eastern Europe and the European Union in general.

For more details, please visit www.opatija.hr and visitopatija.com.