University of Rijeka

About: University of Rijeka is a education organization based out in Rijeka, Croatia. It is known for research contribution in the topics: Population & Tourism. The organization has 3471 authors who have published 7993 publications receiving 110386 citations. The organization is also known as: Rijeka University & Sveučilište u Rijeci.

Change in Steam Generators Main and Auxiliary Energy Flow Streams During the Load Increase of LNG Carrier Steam Propulsion System

Abstract: In this paper is presented an analysis of main and auxiliary steam energy flow streams from steam generators during the increase in steam system load at conventional LNG carrier. During the steam system load increase was presented differences in steam pressure and temperature between main and auxiliary steam flow streams. Energy power of the auxiliary flow stream is higher than energy power of the main flow stream only at the lowest steam system loads after which main flow stream takes over primacy at middle and high steam system loads. Cumulative auxiliary energy flow stream was divided on energy flow streams to each auxiliary device and energy power consumption of each auxiliary device was also investigated throughout number steam system loads. Analysis of steam production from marine steam generators presented in this paper provides insight into the operation dynamics of the entire steam propulsion system.

Creep behavior of high-strength low-alloy steel at elevated temperatures

Abstract: In this paper experimental data about the creep behavior of high-strength low-alloy (HSLA, ASTM A618) steel are presented. For different elevated temperatures tensile tests are carried out and also for some of the mentioned elevated temperatures uniaxial creep tests for different constant loads are made. The results are useful for determining, e.g., the load-bearing capacity of a structure under fire conditions as well as for its lifetime prediction.

Marine Objects Recognition Using Convolutional Neural Networks

Abstract: One of the challenges of maritime aff airs is automatic object recognition from aerial imagery. This can be achieved by utilizing a Convolutional Neural Network (CNN) based algorithm. For purposes of these research a dataset of 5608 marine object images is collected by using Google satellite imagery and Google Image Search. The dataset is divided in two main classes (“Vessels” and “Other objects”) and each class is divided into four sub-classes (“Vessels” sub-classes are “Cargo ships”, “Cruise ships”, “War ships” and “Boats”, while “Other objects” sub-classes are “Waves”, “Marine animals”, “Garbage patches” and “Oil spills”). For recognition of marine objects, an algorithm constructed with three CNNs is proposed. The fi rst CNN for classifi cation on the main classes achieves accuracy of 92.37 %. The CNN used for vessels recognition achieves accuracies of 94.12 % for cargo ships recognition, 98.82 % for cruise ships recognition, 97.64 % for war ships recognition and 95.29 % for boats recognition. The CNN used for recognition of other objects achieves accuracies of 88.56 % for waves and marine animals recognition, 96.92 % for garbage patches recognition and 89.21 % for oil spills recognition. This research has shown that CNN is appropriate artifi cial intelligence (AI) method for marine object recognition from aerial imagery.

Interaction of spin-labeled HPMA-based nanoparticles with human blood plasma proteins - the introduction of protein-corona-free polymer nanomedicine.

Abstract: In this paper, we revised the current understanding of the protein corona that is created on the surface of nanoparticles in blood plasma after an intravenous injection. We have focused on nanoparticles that have a proven therapeutic outcome. These nanoparticles are based on two types of biocompatible amphiphilic copolymers based on N-(2-hydroxypropyl)methacrylamide (HPMA): a block copolymer, poly(e-caprolactone) (PCL)-b-poly(HPMA), and a statistical HPMA copolymer bearing cholesterol moieties, which have been tested both in vitro and in vivo. We studied the interaction of nanoparticles with blood plasma and selected blood plasma proteins by electron paramagnetic resonance (EPR), isothermal titration calorimetry, dynamic light scattering, and cryo-transmission electron microscopy. The copolymers were labeled with TEMPO radicals at the end of hydrophobic PCL or along the hydrophilic HPMA chains to monitor changes in polymer chain dynamics caused by protein adsorption. By EPR and other methods, we were able to probe specific interactions between nanoparticles and blood proteins, specifically low- and high-density lipoproteins, immunoglobulin G, human serum albumin (HSA), and human plasma. It was found that individual proteins and plasma have very low binding affinity to nanoparticles. We observed no hard corona around HPMA-based nanoparticles; with the exception of HSA the proteins showed no detectable binding to the nanoparticles. Our study confirms that a classical "hard corona-soft corona" paradigm is not valid for all types of nanoparticles and each system has a unique protein corona that is determined by the nature of the NP material.