Modelling with measures: Approximation of a mass-emitting object by a point source

We consider a linear diffusion equation on $\Omega:=\mathbb{R}^2\setminus\bar{\Omega_\mathcal{O}}$, where $\Omega_\mathcal{O}$ is a bounded domain. The time-dependent flux on the boundary $\Gamma:=\partial\Omega_\mathcal{O}$ is prescribed. The aim of the paper is to approximate the dynamics by the solution of the diffusion equation on the whole of $\mathbb{R}^2$ with a measure-valued point source in the origin and provide estimates for the quality of approximation. For all time $t$, we derive an $L^2([0,t];L^2(\Gamma))$-bound on the difference in flux on the boundary. Moreover, we derive for all $t>0$ an $L^2(\Omega)$-bound and an $L^2([0,t];H^1(\Omega))$-bound for the difference of the solutions to the two models

Quality utility modelling for multimedia applications for Android mobile devices

With the advances in mobile technologies, smart mobile computing devices have become increasingly affordable and powerful, leading to a significant growth in both the number of advanced mobile users and their bandwidth demands. Moreover multimedia streaming to these high-end mobile devices has become widespread. However, multimedia applications are known to be resource-hungry and in order to cope with this explosion of data traffic, operators have started deploying different, overlapping radio access network technologies. One important challenge in such a heterogeneous wireless environment is to ensure an Always Best Experience to the mobile user, anywhere and anytime. This paper proposes the Quality Utility, a realistic mapping function of the received bandwidth to user satisfaction for multimedia streaming applications. The Quality Utility is mapped to a Google Nexus One Android Mobile device and validated through objective and subjective tests

Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results

A machine learning resource allocation solution to improve video quality in remote education

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing internet traffic on the video quality in an educational context is also presented

STUDIES ON THE CHEMICAL AND SENSORIAL COMPOSITION OF THE GRAPE MARC NARURAL DISTILLATES

In order to investigate the possible relationships, the composition of the volatile aromatic compound of 4 grape distillates obtained from four varieties was correlated with the sensory scores for the basic properties (clarity, odor and taste) obtained by sensory testing. Monoterpenes were confirmed to be responsible for a typical Muscat aroma, as well as for descriptors such as flowery, rose, citrus and spicy/menthol in distillates made from Muscat varieties. Indications were found that vegetative odours of C6-alcohols may act as suppressants of Muscat aroma

Kinematic and dynamic response of a novel engine mechanism design driven by an oscillation arm

The goal of this paper is to highlight the advantage fulfilled by a novel engine mechanism, the concept of which is based on an oscillating arm relative to the classical engine mechanism. Further, the results of this paper demonstrate the benefits of a novel type of mechanism and the major advantages in terms of functioning parameters of an engine. Their performances highly depend on the joint positions of the oscillating arm. The increases in the functional performances rate of success (i.e., piston stroke, volume of the combustion chamber or compression ratio) enable a superior engine power parameter (higher power, torque) and bring some additional improvement on the eco parameters of the engine related to consumption, emission, etc

Sputtering of Oxygen Ice by Low Energy Ions

Naturally occurring ices lie on both interstellar dust grains and on celestial objects, such as those in the outer solar system. These ices are continu- ously subjected to irradiation by ions from the solar wind and/or cosmic rays, which modify their surfaces. As a result, new molecular species may form which can be sputtered off into space or planetary atmospheres. We determined the experimental values of sputtering yields for irradiation of oxygen ice at 10 K by singly (He+, C+, N+, O+ and Ar+) and doubly (C2+, N2+ and O2+) charged ions with 4 keV kinetic energy. In these laboratory experiments, oxygen ice was deposited and irradiated by ions in an ultra high vacuum chamber at low temperature to simulate the environment of space. The number of molecules removed by sputtering was observed by measurement of the ice thickness using laser interferometry. Preliminary mass spectra were taken of sputtered species and of molecules formed in the ice by temperature programmed desorption (TPD). We find that the experimental sputtering yields increase approximately linearly with the projectile ion mass (or momentum squared) for all ions studied. No difference was found between the sputtering yield for singly and doubly charged ions of the same atom within the experimental uncertainty, as expected for a process dominated by momentum transfer. The experimental sputter yields are in good agreement with values calculated using a theoretical model except in the case of oxygen ions. Preliminary studies have shown molecular oxygen as the dominant species sputtered and TPD measurements indicate ozone formation.Comment: to be published in Surface Science (2015

Trade union strategies in the age of austerity: the Romanian public sector in comparative perspective

This article examines the impact of the economic crisis and its aftermath on collective bargaining, by comparing reactions to austerity policies of trade unions in healthcare and education in Romania. We develop an encompassing theoretical framework that links strategies used by trade unions with power resources, costs and union democracy. In a tight labour market generated by the massive emigration of doctors, unions in healthcare have successfully deployed their resources to advance their interests and obtain significant wage increases and better working conditions. We also show that in the aftermath of the crisis, healthcare trade unions have redefined their strategies and adopted a more militant stance based on a combination of local strikes, strike threats and temporary alliances with various stakeholders. By comparison, we find that unions in the education sector have adopted less effective strategies built around negotiations with governments combined with national-level militancy

A mesoscopic lattice model for morphology formation in ternary mixtures with evaporation

We develop a mesoscopic lattice model to study the morphology formation in interacting ternary mixtures with the evaporation of one component. As concrete potential application of our model, we wish to capture morphologies as they are typically arising during the fabrication of organic solar cells. In this context, we consider an evaporating solvent into which two other components are dissolved, as a model for a 2-component coating solution that is drying on a substrate. We propose a 3-spins dynamics to describe the evolution of the three interacting species. As main tool, we use a Monte Carlo Metropolis-based algorithm, with the possibility of varying the system's temperature, mixture composition, interaction strengths, and evaporation kinetics. The main novelty is the structure of the mesoscopic model – a bi-dimensional lattice with periodic boundary conditions, divided into square cells to encode a mesoscopic range interaction among the units. We investigate the effect of the model parameters on the structure of the resulting morphologies. Finally, we compare the results obtained with the mesoscopic model with corresponding ones based on an analogous lattice model with a short range interaction among the units, i.e. when the mesoscopic length scale coincides with the microscopic length scale of the lattice