New solutions in the ferrates(VI) process with the use of SnО₂–modified electrodes

Изучены особенности образования ферратов(VI) из соединений Fe(III) в растворах с различным ионным составом на инертных SnО₂-электродах, легированных Ru, Pt, Pd и Sb. Установлено, что изменением природы и содержания легирующего металла можно целенаправленно регулировать электро-каталитические свойства анодов, в частности величину перенапряжения выделения О₂. Показана принципиальная возможность электрохимического окисления на поверхности электрода и химического окисления в объеме раствора частиц Fe(ОН)₃ и Fe(ОН)₄. Разработаны рекомендации для синтеза ферратов(VI) с использованием анодов, обеспечивающих длительный режим работы без ухудшения их эксплуатационных характеристик.Disadvantages of traditional synthesis methods of ferrates (VI) - promising green oxidants - stimulate the search of new technological solutions which meet the requirements of modern production. The purpose of this work was to study the ferrates (VI) formation from Fe (III) compounds in solutions with different pH on inert SnО₂ electrodes doped with Pt, Ru, Pd, and Sb. The influence of the nature and the content of the alloying metal on the electrocatalytic properties of the electrode was studied by the stationary voltammetry method, as well as by determining the current yields of hypochlorite and sodium chlorate during the electrolysis of a slightly alkaline NaCl solution. Coatings based on SnО₂, doped with palladium and platinum, show maximal electrocatalytic activity according to ClO – synthesis. It has been established that the oxygen evolution overvoltage on the electrodes with comparable dopant concentrations increases in the Ru-Pd-Pt-Sb series. It has been shown that for effective synthesis of ferrates (VI), flat Ti anodes of a large area with an electroactive layer based on SnО₂-Sb2О₃ should be used. It is noted that electrochemical oxidation of Fe (III) in Fe (VI) is more energetically favorable on these electrodes than О₂ evolution, which opens up new possibilities for these processes in ferrate (VI) synthesis technology. We have shown the principal possibility of increasing the productivity of the Fe (VI) process due to the direct interaction of the Fe(ОН)₃ and Fe(ОН)₄− particles in the solution volume with ClO− anions generated on an inert electrode when Сl− anions are preliminarily added to the system. Technological solutions have been proposed to increase the life of inert electrodes when 5-10% TiO2 is introduced into the SnО₂ matrix, providing a long-term operating mode without degradation of their performance characteristics

Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning

Starting from 2021, more demanding NOx emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to prioritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship NO2 estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI/S5P images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a methodology towards the automated and scalable selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data. It is based on a proposed regression model predicting the amount of NO2 that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced NO2 are integrated over observations of the ship in time and are used as a measure of the inspection worthiness of a ship. To add further evidence, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.Computer Systems, Imagery and Medi

The eXPose approach to crosslier detection

Algorithms and the Foundations of Software technolog

Supervised temporal link prediction in large-scale real-world networks

Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link prediction. This paper presents a systematic investigation of supervised temporal link prediction on 26 temporal, structurally diverse, real-world networks ranging from thousands to a million nodes and links. We analyse the relation between global structural properties of each network and the obtained temporal link prediction performance, employing a set of well-established topological features commonly used in the link prediction literature. We report on four contributions. First, using temporal information, an improvement of prediction performance is observed. Second, our experiments show that degree disassortative networks perform better in temporal link prediction than assortative networks. Third, we present a new approach to investigate the distinction between networks modelling discrete events and networks modelling persistent relations. Unlike earlier work, our approach utilises information on all past events in a systematic way, resulting in substantially higher link prediction performance. Fourth, we report on the influence of the temporal activity of the node or the edge on the link prediction performance, and show that the performance differs depending on the considered network type. In the studied information networks, temporal information on the node appears most important. The findings in this paper demonstrate how link prediction can effectively be improved in temporal networks, explicitly taking into account the type of connectivity modelled by the temporal edge. More generally, the findings contribute to a better understanding of the mechanisms behind the evolution of networks.Algorithms and the Foundations of Software technolog

Improving evaluation of NO2 emission from ships using spatial association on TROPOMI satellite data

As of 2021, more demanding NOx emission requirements entered into force for newly built ships operating on the North and Baltic Sea. Even though various methods are used to assess ships’ pollution in ports and off the coastal areas, monitoring over the open sea has been infeasible until now. In this work, we present a novel automated method for evaluation of NO2 emissions produced by individual seagoing ships. We use the spatial association statistic local Moran’s in order to improve the distinguishability between the plume and the background. Using the Automatic Identification Signal (AIS) data of ship locations as well as incorporated uncertainties in wind speed and wind direction, we automatically associate the detected plumes with individual ships. We evaluate the quality of ship-plume matching by calculating the Pearson correlation coefficient between the values of a model-based emission proxy and the estimated NO2 concentrations. For five of the six analyzed areas, our method yields results that are an improvement over the baseline approach used in a previous study.Computer Systems, Imagery and Medi

Fair automated assessment of noncompliance in cargo ship networks

Cargo ships navigating global waters are required to be sufficiently safe and compliant with international treaties. Governmental inspectorates currently assess in a rule-based manner whether a ship is potentially noncompliant and thus needs inspection. One of the dominant ship characteristics in this assessment is the ‘colour’ of the flag a ship is flying, where countries with a positive reputation have a so-called ‘white flag’. The colour of a flag may disproportionately influence the inspector, causing more frequent and stricter inspections of ships flying a non-white flag, resulting in confirmation bias in historical inspection data.In this paper, we propose an automated approach for the assessment of ship noncompliance, realising two important contributions. First, we reduce confirmation bias by using fair classifiers that decorrelate the flag from the risk classification returned by the model. Second, we extract mobility patterns from a cargo ship network, allowing us to derive meaningful features for ship classification. Crucially, these features model the behaviour of a ship, rather than its static properties. Our approach shows both a higher overall prediction performance and improved fairness with respect to the flag. Ultimately, this work enables inspectorates to better target noncompliant ships, thereby improving overall maritime safety and environmental protection.Algorithms and the Foundations of Software technolog

Data-driven risk assessment in infrastructure networks

Algorithms and the Foundations of Software technolog

Local search heuristics for the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) (abbreviated s-AP in the case of s dimensions) is an extension of the well-known assignment problem. The most studied case of MAP is 3-AP, though the problems with larger values of s also have a large number of applications. We consider several known neighborhoods, generalize them and propose some new ones. The heuristics are evaluated both theoretically and experimentally and dominating algorithms are selected. We also demonstrate that a combination of two neighborhoods may yield a heuristics which is superior to both of its components