Effects of Oxide Roughness at Metal Oxide Interface: MgO on Ag(001)

Defects in thin oxide films on metal substrates affect metal work function and determine the chemical and physical properties of an oxide. However, accurately predicting properties of these heterogeneous systems is still challenging. Here we use a new approach to treat a mixed metal/metal oxide system within density functional theory, which is based on the application of the auxiliary density matrix method (J. Chem. Theory Comput.2010, 6, 2348) to calculate the exchange interaction at a sharp interface between the two materials, as implemented in the CP2K code. This method is used to calculate the shift of the Ag work function in the MgO/Ag(001) system as a function of the MgO film morphology as well as charge state, position, and density of oxygen vacancies. An accurate band alignment between metal and oxide allows us to predict the relative stabilities of different charge states of oxygen vacancies in MgO as a function of their position with respect to the interface with Ag. Our results confirm that F+ centers are the most stable defects at terrace sites of MgO clusters and show that F0 and F+ centers can have comparable energies at low-coordinated sites, such as steps and corners. They show how thin oxide film roughness as well as oxygen deficiency can affect the metal work function

Relation between image charge and potential alignment corrections for charged defects in periodic boundary conditions

Charged defects are often studied within the periodic density functional theory (DFT), but this introduces strong finite-size artifacts. In this work, we develop an electrostatic image interaction correction (IIC) method based on the direct solution of the Poisson equation for charge models constructed directly from DFT calculations. These IICs are found to be detail-insensitive, depending almost entirely on bulk dielectric properties. As these IICs are not able to fully explain the observed finite-size scaling, we explore potential alignment in detail and introduce a novel decomposition to separate out different contributions. We find that the two main sources of potential alignment are defect image interactions and changes in the number of atoms present in the supercell. This first effect is accurately predicted by the periodic part of our IIC. The second contribution is unrelated to the IIC and justifies the common observation that the magnitude of finite-size dependence can strongly vary between vacancy and interstitial defects. It can be approximately predicted using atomic radius, but is strongly sensitive to the pseudopotential employed. Combined, these developments provide a new justification for known finite-size scaling rules. Our results suggest that for cubic supercells, the Lany-Zunger IIC, combined with simplified potential alignment between neutral systems, can yield accurate corrections in spite of the simplicity of the approach

Towards more sustainable urban transportation for NetZero cities: assessing air quality and risk for e-scooter users using sensor fusion and artificial intelligence

The need to develop smart and NetZero cities and reduce carbon emission is driving innovation in cities around the world to use electric transportation technologies. Among that the use of e-scooters. Nottingham (UK) is one of the cities that has an e-scooter scheme where people could rent e-scooters to travel around the city. However, in the current situation, to ensure pedestrian safety e-scooters need to be ridden on the road amongst cars, most of them are fossil fuelled. This gives rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, where drivers may not be familiar with seeing e-scooters on the road. This paper uses a mixed methods approach by conducting surveys to drivers and e-scooter users, jointly with an experimental work to monitor the journey of e-scooter users combining air quality, GPS data and 360 degrees camera footage to assess the risk to e-scooter riders using sensor fusion and artificial intelligence. The results indicate that the suggested novel methodology is effective in understanding the current limitations and the potential air quality and physical risks to e-scooter users

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Client abuse to public welfare workers: theoretical framework and critical incident case study

We analyse a case study of workers’ experience of client abuse in a Danish public welfare organisation. We make an original contribution by putting forward two different theoretical expectations of the case. One expectation is that the case follows a pattern of customer abuse processes in a social market economy – in which worker are accorded power and resources, in which workers tend to frame the abuse as the outcome of a co-citizen caught in system failure, and in which workers demonstrate some resilience to abuse. Another expectation is that New Public Management reforms push the case to follow patterns of customer abuse associated with a liberal market economy – in which the customer is treated as sovereign against the relatively powerless worker, and in which workers bear heavy emotional costs of abuse. Our findings show a greater match to the social processes of abuse within a social market economy

Building collaboration in multi-agent systems using reinforcement learning

© Springer Nature Switzerland AG 2018. This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm

Genotype at the P554L Variant of the Hexose-6 Phosphate Dehydrogenase Gene Is Associated with Carotid Intima-Medial Thickness

Objective: The combined thickness of the intima and media of the carotid artery (carotid intima-medial thickness, CIMT) is associated with cardiovascular disease and stroke. Previous studies indicate that carotid intima-medial thickness is a significantly heritable phenotype, but the responsible genes are largely unknown. Hexose-6 phosphate dehydrogenase (H6PDH) is a microsomal enzyme whose activity regulates corticosteroid metabolism in the liver and adipose tissue; variability in measures of corticosteroid metabolism within the normal range have been associated with risk factors for cardiovascular disease. We performed a genetic association study in 854 members of 224 families to assess the relationship between polymorphisms in the gene coding for hexose-6 phosphate dehydrogenase (H6PD) and carotid intima-medial thickness. Methods: Families were ascertained via a hypertensive proband. CIMT was measured using B-mode ultrasound. Single nucleotide polymorphisms (SNPs) tagging common variation in the H6PD gene were genotyped. Association was assessed following adjustment for significant covariates including "classical" cardiovascular risk factors. Functional studies to determine the effect of particular SNPs on H6PDH were performed. Results: There was evidence of association between the single nucleotide polymorphism rs17368528 in exon five of the H6PD gene, which encodes an amino-acid change from proline to leucine in the H6PDH protein, and mean carotid intima-medial thickness (p = 0.00065). Genotype was associated with a 5% (or 0.04 mm) higher mean carotid intima-medial thickness measurement per allele, and determined 2% of the population variability in the phenotype. Conclusions: Our results suggest a novel role for the H6PD gene in atherosclerosis susceptibility

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns