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This paper presents the result of the investigation of organically modified silica (ORMOSIL)-zirconia coatings used to enhance their protective properties, namely corrosion and scratch resistance. Two different materials, i.e., SiO2/ZrO2 and SiO2/GPTMS/ZrO2, were synthesized, measured, and analyzed to find the difference in the used organosilane precursor (dimethyldiethoxysilane and (3-glycidoxypropyl)trimethoxysilane, respectively). SiO2/ZrO2 coatings showed higher hardness than SiO2/GPTMS/ZrO2. Moreover, the value of polarization resistance (Rp) for SiO2/GPTMS/ZrO2 coated 316L steel relative to the uncoated one was obtained. It was nearly 84 times higher. The coating delamination was observed with load 16N. Additionally, the corrosion mitigation for 316L coated by SiO2/GPTMS/ZrO2 was observed even after extended exposure to corrosion agents.

Funding Information: The Gauss Centre for Supercomputing is acknowledged for providing computational resources on SuperMUC-NG at the Leibniz Supercomputing Centre under the Project IDs pn69mi and pn72pa. We also thank the CSC - IT Center for Science for providing computational resources. J.W. acknowledges funding from DFG SFB 1277 (Project A03). P.S. acknowledges funding by the NCCR MARVEL, funded by the Swiss National Science Foundation. D.G. acknowledges financial support by the Academy of Finland (Grant No. 316168). Publisher Copyright: © 2021 The Authors. Published by American Chemical Society. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. GW is an accurate method for computing electron addition and removal energies of molecules and solids. In a conventional GW implementation, however, its computational cost is O(N4) in the system size N, which prohibits its application to many systems of interest. We present a low-scaling GW algorithm with notably improved accuracy compared to our previous algorithm [J. Phys. Chem. Lett. 2018, 9, 306-312]. This is demonstrated for frontier orbitals using the GW100 benchmark set, for which our algorithm yields a mean absolute deviation of only 6 meV with respect to canonical implementations. We show that also excitations of deep valence, semicore, and unbound states match conventional schemes within 0.1 eV. The high accuracy is achieved by using minimax grids with 30 grid points and the resolution of the identity with the truncated Coulomb metric. We apply the low-scaling GW algorithm with improved accuracy to phosphorene nanosheets of increasing size. We find that their fundamental gap is strongly size-dependent varying from 4.0 eV (1.8 nm × 1.3 nm, 88 atoms) to 2.4 eV (6.9 nm × 4.8 nm, 990 atoms) at the evGW0@PBE level.

Bruken av maskinlæring har vokst kraftig i løpet av de siste tiårene, og de mange suksesshistoriene har kastet lys over dens iboende verdi. Flere av disse suksesshistoriene stammer fra selskaper som allerede er i den digitale økonomien. Energiindustrien har falt etter i bruken av ny teknologi. For eksempel har olje og gass i energibransjen gjennom flere tiår hatt en lav break-even og en høy etterspørsel globalt. Som følge av dette har mindre oppmerksomhet blitt rettet mot optimalisering og det har vært lite bruk av ny teknologi. Dette er fordi den fremtidige kontantstrømmen vil være høy uavhengig av eventuelt nye implementeringer. De siste årene har industrien blitt utviklet som følge av en stadig høyere markedsandel av fornybar energi og mer vektlegging på teknologi. Den stadig voksende industrien har ført til økt digitalisering som følge av nye teknologier som Internet of Things og andre smarte systemer. Følgelig blir data nå samlet inn fra flere kilder og selskaper undersøker deres beredskap til å støtte fremtidig maskinlæring. Ny teknologi, rask utvikling og høy grad av prosjektifisering har gitt prosjektledelsen et nytt sett med utfordringer som de nå står overfor. Prosjektledelse blir ofte sett på som et tradisjonelt yrke som bruker den samme teknologien som de alltid har gjort. Digitaliseringen av prosjektledelse øker, og selskapene anerkjenner verdien av mer avansert teknologi. Likevel er maskinlæring fremdeles et begrep fra fremtiden, og det har ikke vært mye forskning som kombinerer maskinlæring og prosjektledelse. Fra den økte digitaliseringen, både i prosjektledelse og i energibransjen, kan det være et stort potensial i det å bruke den innsamlede dataen til noe nytt. Prognoser og klassifiseringsanalyse drevet av maskinlæring kan vise seg nyttig for prosjektledelsen for å takle morgendagens utfordringer. Derfor er bransjen til prosjektledere moden for å implementere maskinlæring for å øke effektiviteten og suksessgraden for deres prosjekter. Denne oppgaven vil fungere som en indikasjon for hvordan maskinlæring kan være til nytte for prosjektledelsesteamet i industrien. Maskinlæring i oppgaven er implementert på to typer datasett, det første er fra prosjektoppfølging med fremdriftsdata. Det andre er fra et prosjektledelsesverktøy. Begge typene er hentet fra faktiske prosjekter. Fremdriftsdataene aggregeres ukentlig basert på disiplinen til arbeidet, mens prosjektledelsesverktøyet strukturerer hver aktivitet for seg. Sistnevnte er for å opprettholde nøkkelinformasjon om hver aktivitet i prosjektet. I tillegg til prosjektledelsesaspektet, vil oppgaven presentere hvordan man kan formatere dataene for å støtte maskinlæring, hvilke utfordringer det er i renseprosessen, og hvordan dataene kan struktureres av selskapene slik at det blir lettere for dem å implementere maskinlæring senere. Funnene i denne studien viser at maskinlæring kan tilføre verdi til prosjektrelaterte data i energibransjen. Videre gis det beskrivelser for hva som kan gjøres for å øke nøyaktigheten til modellene. De mest lovende resultatene er maskinlæringsmodellens evne til å lære ved å trene på flere prosjekter etter hverandre. Trening på to prosjekter i stedet for kun ett reduserte erroren med 37%. Når det kommer til dataene fra prosjektledelsesverktøyet, oppnådde modellen lav nøyaktighet med en error som var omtrent lik den gjennomsnittlige opptjente verdien for prosjektet den uken. Imidlertid oppnådde analysen av fremdriftsdataene høyere nøyaktighet, og dermed regnes disse dataene som lettere å ta i bruk i praksis. For det første antyder disse funnene hvilke områder som er mest anvendelige for implementering av maskinlæring i skrivende stund. Dataene som frembringes fra prosjektledelsesprogramvaren har høyere oppløsning enn dataene som er logget av operatøren. For det andre støtter funnene viktigheten av det å strukturere innkommende data slik at de er komplette og konsistente. The adoption of machine learning has exploded over the last decade, and the many success stories have shed light on its value. Many of these success stories stem from companies already in the digital economy. The energy industry has lagged in the adoption of new technology. For instance, oil and gas in the energy industry have had low break-even price and globally high demand over several decades. The effect has led to less attention to optimization and low adoption of new technology because the future cash flow will be high regardless of new implementations. An increasingly higher market share of renewable energy and more emphasis on development has evolved the industry over the last decades. The evolving industry has increased its digitization with new technologies such as the Internet of Things and smart systems. Consequently, data is now gathered from multiple sources, and companies investigate their readiness to support machine learning. New technologies, rapid development, and a high degree of projectification have presented the project management team with a new set of challenges. Project management is often considered a traditional profession using old technology. However, the digitization of project management is increasing, and companies acknowledge the value of more advanced technology. Nevertheless, machine learning is still a term for the future, and there has not been conducted much research combining machine learning and project management. From the increased digitization, both in project management and in the energy industry, there could be considerable potential in using the gathered information. Forecasts and classification analysis powered by machine learning could prove to aid the project management team to handle the novel challenges of tomorrow. Therefore, project management in the industry is ripe to implement machine learning to enhance the efficiency and the success rate of projects. This thesis will serve as a pilot for how machine learning can be of use for the project management team in the industry. It is written in collaboration with two companies, each providing datasets. Machine learning is implemented on two types of datasets, the first is from project follow-up with progress data, and the second is from a project management tool, in which both types are from physical projects. The thesis presents how to format the datasets to support machine learning for time series and classification analysis, and the accuracy models today can achieve. The progress data is aggregated weekly based on the discipline of the work done, while the project management tool's data are structured as individual activities. This is done to maintain key information of each activity in the project. In addition to implications for project management, the thesis will present challenges for implementing machine learning and how the data could be structured by the companies to be more readily implemented. The findings of this thesis prove that machine learning might extract more value from project-related data in the energy industry, and it gives directions for what can be done to increase the accuracy. The most promising results are the machine learning model's ability to learn by training on more projects. Training on two projects instead of one decreased the Root Mean Squared Error (RMSE) by 37%. Regarding the data from the project management tool, the model achieved low accuracy with an RMSE approximately equal to the average earned value for the project. However, the analysis of the progress data from follow-up achieved higher accuracy and is thus more readily for practice. First, these findings suggest which areas are most applicable for implementing machine learning at the time of writing. The data generated from the project management software is of higher resolution than the data logged by the operator, and could prove better in the long term. Secondly, the findings support the importance of structuring the incoming data to be complete and consistent.

openaire: EC/H2020/824143/EU//MAX CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

The chapter illustrates how cities can be understood from a system–theory perspective as complex social systems. It argues that the classical and linear intervention methods are often no (longer) suitable for the complex structure, temporal dynamics and multifaceted processuality of urban development. It offers a systemic and systems theory-inspired method as an alternative approach, which allows for extended possibilities that are more appropriate for dealing with urban development processes. The method was developed on the basis of practical experience and theoretical insights. The approach should demonstrate for local decision-makers potential areas of activity for organising urban changes through co-design.

The requirement to search for new plasticizers and coalescents for making water-based dispersion adhesives from poly(vinyl acetate) is of particular importance, especially now, after banning all ki...

The combustion of liquid fuels–first of all, heavy crude oils–leads to the formation of pollutants such as nitrogen oxides (NOx), sulfur dioxide and hydrocarbons. Specific modifiers are used to reduce harmful emissions of products arising in the combustion of liquid fuels. Such modifiers include organic metal salts which are soluble in the fuels: under combustion process conditions they form metal oxides which improve the fuels in terms of oxidation properties. In this paper, an iron modifier based on fatty-acid organic iron salts dissolved in fuel oil is presented. The modifier described in this paper was used in liquid fuel combustion tests, showing a desirable effect on reducing hydrocarbon, CO and NOx emissions. Because iron at high concentrations is potentially harmful to humans, the effect of the iron-based modifier on human health is discussed in the paper. Since ingestion and skin absorption are the two main routes whereby the heavy metals enter the human body, the results of the following tests are presented in this paper: acute oral toxicity tests, acute skin irritation/corrosion tests in rabbit, and isolated chicken eye tests for identifying ocular corrosives and severe irritants. In the acute oral toxicity test the iron-based modifier was found to be a non-hazardous substance, posing no risk to human health. The results of the isolated chicken eye test indicate that the test material is not an ocular corrosive or severe irritant. The results of the acute dermal irritation/corrosion test in rabbits indicate that the iron-based modifier is a skin irritant. This leads us to the conclusion that particular attention in handling the iron-based modifier should be paid to contact with the skin.

The combustion of liquid fuels – first of all, heavy crude oils – leads to the formation of pollutants such as: nitrogen oxides (NOx), sulfur dioxide and hydrocarbons. The amount of such pollutants can be reduced by means of special modifiers, or additives. One such additive is a metallic modifier based on fatty acid ferric salts dissolved in fuel oil. Iron at high concentrations is known to be harmful to the aquatic environment, therefore the effect of the iron-based modifier on the aquatic ecosystem is presented in this paper. Specifically, the effect of its addition on algal growth inhibition, acute toxicity to rainbow trout, and reproduction of daphnia has been examined. According to the results, the concentration capable of reducing the rate of algal growth by 50% after 72 h is higher than 100 mg/L, the mean concentration capable of killing 100% of the trout population after 96 h (lethal concentration for 100%, LC 100 ) is higher than 320 mg/L, and, depending on the concentration, the rate of reproduction of Daphnia magna drops by 0.6%–16.7%, respectively. This means that, although capable of affecting the aquatic environment, the iron-based modifier does so only when it is applied at its maximum concentrations. Since it is used only in small quantities as a fuel additive, it will not threaten the environment.

The technologies of thermal processing of biomass, biowaste or sewage sludge into biochar as well as its potential use in the industry, power industry, housebuilding industry, agriculture or environmental protection attract growing attention. The multi-faceted, unique properties of biochar make it particularly attractive from the point of view of the achievement of sustainable development goals according to which the needs of the present generation should be satisfied in such a way so as not to harm the environment and in such a way that the future generations could use the same natural environment as we do. The EU policy focusing on the implementation of the principles of sustainable development emphasises the need to reduce the exploitation of natural resources, to use effective technologies processing waste and to develop new biodegradable and environmentally friendly products. Due to the wide range of biochar applications in many economy sectors, the ways of production, ensuring the reduction of waste generation, and its economic attractiveness, this product meets the expectations of the sustainable development policy. The aim of this paper is review the biochar-based technologies and the concepts of its application, and description of the disadvantages and advantages each of them..

This research work presents the results of studies on the effect of modifiers comprising the salts CuSO4&middot, 2H2O, NaCl, NH4Cl, MgSO4&middot, 7H2O, CaCl2, and urea at various concentrations on the combustion of fine coal. The tests were carried out in a 12-kW laboratory boiler equipped with a rotary-grate retort furnace. The emission levels and concentrations of CO, CO2, SO2, NOx, and TOC in the flue gas were measured using analyzers. A modifier composed of 350 ppm Cu, Na, Mg, NH4+, Ca, and urea showed particularly high activity in the combustion of fine coal. The flue-gas levels of CO, NOx, and SO2 were reduced by approximately 9%, 12%, and 10%, respectively, in comparison with the modifier-free sample. In this case, the boiler efficiency also increased from 65% in the tests with no modifier to 76% in the tests with the modifier. The proprietary application system, enabling the modifier to be added in exact amounts to the variable flow of fine coal, is also described. It was found that the use of the modifier in coal combustion tests results in lower emissions of harmful fuel components, a higher amount of heat obtained from the fuel unit mass, lower corrosive impact of the fuels, lower boiler maintenance costs, and extended service life.

Resumen: A pesar del enorme impacto de las vacunas en la salud de la población, estas han sido y son objeto de cuestionamientos por grupos que las consideran innecesarias o inseguras y argumentan que las personas tienen el derecho a decidir sobre si estas deben ser administradas o no. Sin embargo el uso de vacunas tiene connotaciones distintas a otras decisiones en salud, ya que no vacunar impacta no solo al individuo, sino también a la comunidad que lo rodea. El inmunizar a un alto porcentaje de la población permite limitar la circulación de los agentes infecciosos, logrando la llamada inmunidad comunitaria que protege a los no vacunados por razones médicas o porque son muy pequeños. Por esta razón muchos países han definido las vacunas como obligatorias. Como Comité Consultivo de Inmunizaciones nos parece que esta estrategia es correcta sin embargo debe ser acompañada por una política de educación de la población y personal de salud sobre los beneficios y riesgos reales de las va cunas. Así mismo es necesario introducir mejoras en el sistema de notificación de reacciones adversas a vacunas haciéndolo más accesible. Adicionalmente se debe dar respuesta oportuna a los afectados por supuestas o reales reacciones a vacunas y en los casos de eventos adversos graves efectivamente asociados a vacunas. Entregar cobertura económica y acompañamiento. Finalmente es esencial la coordinación entre los diferentes actores y comunicadores para transmitir mensajes que generen confianza y respondan a las inquietudes de la población de hoy en día. Abstract: Although vaccines have had a tremendous impact in public health they are questioned by certain groups that consider them unnecessary or unsafe and argue in favor of the right to decide to be vacci nated or not. However vaccines must have special considerations because unlike other medical deci sions, not vaccinating has consequences not only for the individual but also for other members of the community. Immunizing a high proportion of the population limits the circulation of an infectious agent attaining what is called community or herd immunity that protects the susceptible members of the group. For this reason many countries consider vaccination mandatory as a responsibility of every citizen. This committee agrees with this view but thinks other strategies should be implemented as well, such as special educational efforts for the public and parents addressing benefits and real risks of vaccinating. Also health care professionals should be trained in vaccines. The notification system for adverse events currently available should be improved and be more accessible. Persons truly affected by adverse events due to vaccination should receive on time responses and be offered psychological and financial support. Finally all stakeholders should make coordinated efforts to work together to deliver messages that answer concerns on vaccines and bring confidence back to the public.