SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face.
SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka.
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The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.
Decades of regional polarity in Serbia conditioned the adoption of a polycentric territorial development as one of the main strategic goals of the country's spatial development. Politics of polycentrism specifically highlights the role and importance of small urban centers in generating a more balanced regional development. In constellation with their rural surroundings, they can serve as strong agents of change towards sustainable spatial development, functional cohesion, and transformation of the rural hinterland, and thus, contribute to the balanced spatial development. The main aim of the chapter is to examine the potential of small urban centers to generate spatial and functional integration, that is, to explore the possibility for small towns to integrate and transform their rural surroundings. The chapter analyses the field of functional influence of small towns on their rural surroundings on the example of Blace Municipality through determining the intensity of daily migrations which are a well-known indicator of spatial and functional integration of rural areas and urban centers.
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