Laser pulse annealing of ion-implanted GaAs

GaAs single-crystals wafers are implanted at room temperature with 400-keV Te + ions to a dose of 1×10^15 cm^–2 to form an amorphous surface layer. The recrystallization of this layer is investigated by backscattering spectrometry and transmission electron microscopy after transient annealing by Q-switched ruby laser irradiation. An energy density threshold of about 1.0 J/cm^2 exists above which the layer regrows epitaxially. Below the threshold the layer is polycrystalline; the grain size increases as the energy density approaches threshold. The results are analogous to those reported for the elemental semiconductors, Si and Ge. The threshold value observed is in good agreement with that predicted by the simple model successfully applied previously to Si and Ge

Complexity measurement in two supply chains with different competitive priorities

Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity

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

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

Impact of minority concentration on fundamental (H)D ICRF heating performance in JET-ILW

ITER will start its operation with non-activated hydrogen and helium plasmas at a reduced magnetic field of B-0 = 2.65 T. In hydrogen plasmas, the two ion cyclotron resonance frequency (ICRF) heating schemes available for central plasma heating (fundamental H majority and 2nd harmonic He-3 minority ICRF heating) are likely to suffer from relatively low RF wave absorption, as suggested by numerical modelling and confirmed by previous JET experiments conducted in conditions similar to those expected in ITER's initial phase. With He-4 plasmas, the commonly adopted fundamental H minority heating scheme will be used and its performance is expected to be much better. However, one important question that remains to be answered is whether increased levels of hydrogen (due to e. g. H pellet injection) jeopardize the high performance usually observed with this heating scheme, in particular in a full-metal environment. Recent JET experiments performed with the ITER-likewall shed some light onto this question and the main results concerning ICRF heating performance in L-mode discharges are summarized here

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

Studies of the non-axisymmetric plasma boundary displacement in JET in presence of externally applied magnetic field

Non-axisymmetric plasma boundary displacement is caused by the application of the external magnetic field with low toroidal mode number. Such displacement affects edge stability, power load on the first wall and could affect efficiency of the ICRH coupling in ITER. Studies of the displacement are presented for JET tokamak focusing on the interaction between error field correction coils (EFCCs) and shape control system. First results are shown on the direct measurement of the plasma boundary displacement at different toroidal locations. Both qualitative and quantitative studies of the plasma boundary displacement caused by interaction between EFCCs and shape control system are performed for different toroidal phases of the external field. Axisymmetric plasma boundary displacement caused by the EFCC/shape control system interaction is seen for certain phase values of the external field. The value of axisymmetric plasma boundary displacement caused by interaction can be comparable to the non-axisymmetric plasma boundary displacement value produced by EFCCs

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Replacing fossil fuels with renewable energy sources is considered as an effective means to reduce carbon emissions at the industrial level and it is often supported by local authorities. However, individual firms still encounter technical and financial barriers that hinder the installation of renewables. The eco-industrial park approach aims to create synergies among firms thereby enabling them to share and efficiently use natural and economic resources. It also provides a suitable model to encourage the use of renewable energy sources in the industry sector. Synergies among eco-industrial parks and the adjacent urban areas can lead to the development of optimized energy production plants, so that the excess energy is available to cover some of the energy demands of nearby towns. This study thus provides an overview of the scientific literature on energy synergies within eco-industrial parks, which facilitate the uptake of renewable energy sources at the industrial level, potentially creating urban-industrial energy symbiosis. The literature analysis was conducted by arranging the energy-related content into thematic categories, aimed at exploring energy symbiosis options within eco-industrial parks. It focuses on the urban-industrial energy symbiosis solutions, in terms of design and optimization models, technologies used and organizational strategies. The study highlights four main pathways to implement energy synergies, and demonstrates viable solutions to improve renewable energy sources uptake at the industrial level. A number of research gaps are also identified, revealing that the energy symbiosis networks between industrial and urban areas integrating renewable energy systems, are under-investigated