Quantum measurement in a family of hidden-variable theories

The measurement process for hidden-configuration formulations of quantum mechanics is analysed. It is shown how a satisfactory description of quantum measurement can be given in this framework. The unified treatment of hidden-configuration theories, including Bohmian mechanics and Nelson's stochastic mechanics, helps in understanding the true reasons why the problem of quantum measurement can succesfully be solved within such theories.Comment: 16 pages, LaTeX; all special macros are included in the file; a figure is there, but it is processed by LaTe

Resonant charging and stopping power of slow channelling atoms in a crystalline metal

Fast moving ions travel great distances along channels between low-index crystallographic planes, slowing through collisions with electrons, until finally they hit a host atom initiating a cascade of atomic displacements. Statistical penetration ranges of incident particles are reliably used in ion-implantation technologies, but a full, necessarily quantum-mechanical, description of the stopping of slow, heavy ions is challenging and the results of experimental investigations are not fully understood. Using a self-consistent model of the electronic structure of a metal, and explicit treatment of atomic structure, we find by direct simulation a resonant accumulation of charge on a channelling ion analogous to the Okorokov effect but originating in electronic excitation between delocalized and localized valence states on the channelling ion and its transient host neighbours, stimulated by the time-periodic potential experienced by the channelling ion. The charge resonance reduces the electronic stopping power on the channelling ion. These are surprising and interesting new chemical aspects of channelling, which cannot be predicted within the standard framework of ions travelling through homogeneous electron gases or by considering either ion or target in isolation. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft

Observation of confined current ribbon in JET plasmas

we report the identification of a localised current structure inside the JET plasma. It is a field aligned closed helical ribbon, carrying current in the same direction as the background current profile (co-current), rotating toroidally with the ion velocity (co-rotating). It appears to be located at a flat spot in the plasma pressure profile, at the top of the pedestal. The structure appears spontaneously in low density, high rotation plasmas, and can last up to 1.4 s, a time comparable to a local resistive time. It considerably delays the appearance of the first ELM.Comment: 10 pages, 6 figure

IMPLEMENTING the NEARLY ZERO-ENERGY BUILDINGS NOTION in INDUSTRIAL FACILITIES

Buildings have a central role to play in the energy transition driven by the European Commission with the introduction of the nearly zero-energy buildings notion for both new constructions and existing building stocks. Despite the growing interest in improving the energy performance of residential as well as office buildings, a research gap is identified in the field of the renovation of industrial buildings and facilities in terms of energy efficiency. A lack is recognized in terms of analysing the improvement of the work environment in all the areas that are not used as offices. Common decarbonisation strategies are usually adopted, as the electrification of some processes, the replacement of obsolete machinery or the implementation of renewable energy sources. However, reaching the ambitious levels set by the Energy Performance of Buildings Directive which aims at nearly zero-energy buildings, remains a challenge. The present paper aims to analyse the main barriers that hamper the development of highly energy efficient and decarbonised buildings in the productive sector and the effective deep energy renovations of industrial facilities. Moreover, it intends to highlight alternative strategies and opportunities and to underline potential benefits

A Fuzzy Logic Control application to the Cement Industry

A case study on continuous process control based on fuzzy logic and supported by expert knowledge is proposed. The aim is to control the coal-grinding operations in a cement manufacturing plant. Fuzzy logic is based on linguistic variables that emulate human judgment and can solve complex modeling problems subject to uncertainty or incomplete information. Fuzzy controllers can handle control problems when an accurate model of the process is unavailable, ill-defined, or subject to excessive parameter variations. The system implementation resulted in productivity gains and energy consumption reductions of 3% and 5% respectively, in line with the literature related to similar applications

A new generation of real-time systems in the JET tokamak

Recently a new recipe for developing and deploying real-time systems has become increasingly adopted in the JET tokamak. Powered by the advent of x86 multi-core technology and the reliability of the JET’s well established Real-Time Data Network (RTDN) to handle all real-time I/O, an official Linux vanilla kernel has been demonstrated to be able to provide realtime performance to user-space applications that are required to meet stringent timing constraints. In particular, a careful rearrangement of the Interrupt ReQuests’ (IRQs) affinities together with the kernel’s CPU isolation mechanism allows to obtain either soft or hard real-time behavior depending on the synchronization mechanism adopted. Finally, the Multithreaded Application Real-Time executor (MARTe) framework is used for building applications particularly optimised for exploring multicore architectures. In the past year, four new systems based on this philosophy have been installed and are now part of the JET’s routine operation. The focus of the present work is on the configuration and interconnection of the ingredients that enable these new systems’ real-time capability and on the impact that JET’s distributed real-time architecture has on system engineering requirements, such as algorithm testing and plant commissioning. Details are given about the common real-time configuration and development path of these systems, followed by a brief description of each system together with results regarding their real-time performance. A cycle time jitter analysis of a user-space MARTe based application synchronising over a network is also presented. The goal is to compare its deterministic performance while running on a vanilla and on a Messaging Real time Grid (MRG) Linux kernel

A human-machine learning curve for stochastic assembly line balancing problems

The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions

Fernando SEBASTIÁN AGUILAR, Nueva Evangelización. Fe, cultura y política en la España de hoy, Ed. Encuentro, Madrid 1991, 302 pp. [RECENSIÓN]

\u3cp\u3eThe most recent JET campaign has focused on characterizing operation with the «ITER-like» wall. One of the questions that needed to be answered is whether the auxiliary heating methods do not lead to unacceptably high levels of impurity influx, preventing fusion-relevant operation. In view of its high single pass absorption, hydrogen minority fundamental cyclotron heating in a deuterium plasma was chosen as the reference wave heating scheme in the ion cyclotron domain of frequencies. The present paper discusses the plasma behavior as a function of the minority concentration X[H] in L-mode with up to 4MW of RF power. It was found that the tungsten concentration decreases by a factor of 4 when the minority concentration is increased from X[H] ≈ 5% to X[H] % 20% and that it remains at a similar level when X[H] is further increased to 30%; a monotonic decrease in Beryllium emission is simultaneously observed. The radiated power drops by a factor of 2 and reaches a minimum at X[H] ≈ 20%. It is discussed that poor single pass absorption at too high minority concentrations ultimately tailors the avoidance of the RF induced impurity influx. The edge density being different for different minority concentrations, it is argued that the impact ICRH has on the fate of heavy ions is not only a result of core (wave and transport) physics but also of edge dynamics and fueling.\u3c/p\u3

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems