Analysis of public administration, effects and impactof digitalization and interoperability in publicadministration
Vol. 13 No. 1 (2023): JITA – APEIRON Jefto Džino, Stefan Džino, Danijela Injac Analysis of public administration, effects and impact of digitalization and interoperability in public administration Original scientific paper DOI: https://doi.org/10.7251/JIT2301048D Download Article PDF Abstract For the purpose of digitization and interoperability of public administration, we researched the organization and challenges in public administration in Bosnia and Herzegovina as well as in general in public administration. We presented parts of public administration as well as the influences of public administration. The effects and influence of digitalization and interoperability in institutions in B&H, strategic approach to the development of public administration, the relationship between Vision and Technology as an indicator of business success in public administration are given. We also presented a view on the provision of digitalized and interoperable public administration services. Keywords: digitalization, interoperability, public administration, Big Data. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Digitalization of Sound Using Pulse Code Modulation(PCM)
Vol. 13 No. 1 (2023): JITA – APEIRON Siniša Tomić, Dalibor Drljača Digitalization of Sound Using Pulse Code Modulation (PCM) Original scientific paper DOI: https://doi.org/10.7251/JIT2301042T Download Article PDF Abstract This paper focuses on Pulse Code Modulation (PCM) as a technology for digitizing analog signals. PCM is a widely used technique that enables precise encoding and transmission of analog information through digital pulse signals. The basic principles of PCM are explained here. PCM converts an analog signal into a digital form through sampling, quantization, and encoding. Sampling refers to the conversion of a continuous analog signal into discrete samples at regular time intervals. Then, quantization is applied to round each sample to the nearest possible quantization value, reducing the continuous range of values to discrete levels. Afterward, each quantized sample is encoded into a digital form. PCM is commonly used in various communication systems as well as in digital audio processing. In communication systems, PCM enables reliable transmission of voice signals, music, and other audio content over digital networks. In digital audio processing, PCM is used for recording, playback, and manipulation of sound, enabling high-quality reproduction and precise processing. Keywords: PCM, Pulse Code Modulation, Analog Signal Digitization, Sampling, Quantization, Pulse Signals. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Panel Analysis in Function of Measuring the Impact ofHigher Education on International Competitiveness of theWestern Balkan Countries
Vol. 13 No. 1 (2023): JITA – APEIRON Mirjana Landika, Željko Račić, Bogdana Kondić Panel Analysis in Function of Measuring the Impact of Higher Education on International Competitiveness of the Western Balkan Countries Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract An important aspect of the development and perspectives of the development of the socio-economic community refers to the level of coverage of the labor market with adequate staff in terms of expertise and competencies, which largely derive from the results of the educational process. Expressing and measuring the results of the educational process is a continuous and complex process, and requires the application of adequate methodology, such as a panel analysis model. The aim of the researchers is to examine the impact of higher education on appropriate macroeconomic indicators countries of the Western Balkans, which are not yet members of the European Union. The practice of such research has been formalized in Western European countries, where researchers have adequate access to the empirical material on which research is based, but also a standardized procedure for presenting appropriate indicators, which is not the case in the selected geographical area. The context of the educational process since the period of introduction and adoption of the determinants defined by the Bologna Declaration, is going through a turbulent process of transformation. The next turning point in the education system is justified by the pandemic caused by the COVID – 19 virus. Keywords: econometric model, economic growth, international competitiveness, panel analysis, crisis management. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Aircraft Performance Modeling with PolynomialFunction using Small Variable Units Technique
Vol. 13 No. 1 (2023): JITA – APEIRON Vladimir Milošević Aircraft Performance Modeling with Polynomial Function using Small Variable Units Technique Original scientific paper DOI: https://doi.org/10.7251/JIT2301029M Download Article PDF Abstract Mathematical methods of Regression analysis, with focus on polynomial regression, are useful analytical methods for trend-line definitions of an aircraft aerodynamics. Nomogram is graphical interpretation of polynomial regression analysis results and aero-dynamical performance according to different environmental parameters and requirements. If diagram defines relations of two variables, where the one is dependable of another one (y=f (x)), the nomogram defines relations among three variables, where the one is resulting and dependable of another two undependable. The Small Variable Units Technique is efficient method to transfer nomograms’ data in polynomial equitation which can give us different mathematical models of an aircraft performance. In combination with time based navigation, digitalization of aerodynamical characteristic will be a step forward to Continuous Climb and Descent Trajectories, as the most optimal one. Keywords: performance optimization, digitalization, composite, regression, nomogram. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Comparative Analysis of Relational and Non-RelationalDatabases
Vol. 13 No. 1 (2023): JITA – APEIRON Pero Ranilović, Dražen Marinković, Nedeljko Šikanjić Comparative Analysis of Relational and Non-Relational Databases Original scientific paper DOI: https://doi.org/10.7251/JIT2301020R Download Article PDF Abstract This paper presents the results of research into the use of relational and non-relational databases, as well as theircomparative analysis. A theoretical overview of the comparative analysis by different segments of relational and non-relational databases is presented. Comparative analysis through the practical application of databases is shown through the use of applications for measuring system performance. Keywords: relational databases; non-relational databases; comparative analysis; MSSQL; MongoDB. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Linear Wireless Sensor Networks as the Physical Layer ofSmart Street Parking Systems
Vol. 13 No. 1 (2023): JITA – APEIRON Almir Ahmetspahic, Goran Popovic, Goran Djukanovic Linear Wireless Sensor Networks as the Physical Layer of Smart Street Parking Systems Original scientific paper DOI: https://doi.org/10.7251/JIT2301011A Download Article PDF Abstract Wireless sensor networks (WSN) represent a set of different technologies that, in cooperation with each other, form the basis for the realization of the physical layer of the concept of smart cities. Miniaturization of sensor devices and decreasing energy consumption, both for data processing and for mutual communication, as well as simple implementation and low cost, make WSN indispensable for a large number of different applications. Many of the applications imply a linear infrastructure that is subject to monitoring, and as such requires a linear deployment of sensor nodes. This form of WSN represents a special class of networks that we call Linear Wireless Sensor Networks LWSN. In this paper, we will describe the characteristics of these networks, the problems that are specific, as well as possible applications, and we will pay special attention to the application of LWSN in smart street parking lots. Keywords: IoT, LWSN, Smart City, Street Parking, WSN Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Basic Mathematical ATM Model for Time Based NavigationIn U-Space Environment
Vol. 13 No. 1 (2023): JITA – APEIRON Vladimir Milosević Basic Mathematical ATM Model for Time Based Navigation In U-Space Environment Original scientific paper DOI:https://doi.org/10.7251/JIT2301005M Download Article PDF Abstract Time Based Navigation Mathematical model is a navigational tool which allows instant management of distance, speed and time on more effective way. Time is a kind of specific imaginary dimension which describes real life dynamicity. Most efficient tool for time measuring, which precisely represents nature of time, is a circle with angles inside, well known as “clock”. Time Based Navigation Mathematical model is only different way of use of this measuring tool where every aircraft on its route has precise navigational time clock for accurate destination arrival. Implementation of this model can offer higher level of navigational precision in longitudinal and lateral domain, effective speed correction calculations and management in time domain, constant identification and recalculation of total time error and also can be used as safety net tool to define conflicts in UTM Air Space. Keywords: Digitalization, propulsion, aircraft, U-Space, Electrification, ATM, environmental friendly, zero emissions, arrival management, artificial intelligence. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.
Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures
Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA – APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https://doi.org/10.7251/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.