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The study of neutron-induced reactions is of high relevance in a wide variety of fields, ranging from stellar nucleosynthesis and fundamental nuclear physics to applications of nuclear technology. In nuclear energy, high accuracy neutron data are needed for the development of Generation IV fast reactors and accelerator driven systems, these last aimed specifically at nuclear waste incineration, as well as for research on innovative fuel cycles. In this context, a high luminosity Neutron Time Of Flight facility, n_TOF, is operating at CERN since more than a decade, with the aim of providing new, high accuracy and high resolution neutron cross-sections. Thanks to the features of the neutron beam, a rich experimental program relevant to nuclear technology has been carried out so far. The program will be further expanded in the near future, thanks in particular to new highflux experimental area, now under construction.

The feed-in tariff regulation is the widest spread instrument used to promote electricity generation from renewable energy sources in the EU, with the costs of resources devoted to this promotion usually being borne by final consumers. Two components of the electricity retail price are expected to be influenced by the feed-in tariff regulation: the incentive to those firmsproducing electricity fromrenewable energy sources and thewholesale price of electricity. In this studywe analyze the effects that the feed-in tariff regulation has on the electricity retail price for industrial consumers. Weestimate the relative intensity of the impact of the cost of support electricity generation under the feed-in tariff and the electricity wholesale price on the Spanish industrial retail price. Special attention is devoted to technology-specific considerations, as well as short and long run effects. The results show that there is not a strong link between the retail and wholesale market for Spanish industrial consumers. Moreover, the results indicate that an increase of solar generation leads to a higher increase in the industrial retail price than in the case of a proportional increase of wind generation. This suggests that, when evaluating the feed-in tariff regulation impact on the retail price, the cost of incentives effect prevails over the wholesale price effect, and this is stronger for solar than for wind generation.

This paper proposes a hybrid renewable energy system (HRES) consisting of photovoltaic (PV), wind and forest wood biomass power for cogeneration, and applies a multi-objective optimization methodology to study the trade-offs between life-cycle cost and environmental impact (EI) of such a system. The optimization is achieved by applying an operation strategy that maximizes the efficiency of the biomass power subsystem coupled with an optimization model based on the use of genetic algorithm (GA) to obtain the optimal system sizing. The system is designed to supply the electricity demand of a rural township and the thermal demand – both heating and sanitary hot water (SHW) – of a neighborhood in a district heating (DH) scheme. Indigenously available renewable energy sources (RES) are used, taking special care in the case of biomass to not exceed the self-growth rate of local tree species. Results show that by taking advantage of the thermal energy produced, the payback time of the investment required to install the system is significantly reduced, being profitable after 9 years. Furthermore, it is also observed that layouts with low costs have greater EI and vice versa. However, it is shown that moderate cost increases have great returns on EI reduction.

In the low-end mobile processor market, power, energy, and area budgets are significantly lower than in the server/desktop/laptop/high-end mobile markets. It has been shown that vector processors are a highly energy-efficient way to increase performance; however, adding support for them incurs area and power overheads that would not be acceptable for low-end mobile processors. In this work, we propose an integrated vector-scalar design for the ARM architecture that mostly reuses scalar hardware to support the execution of vector instructions. The key element of the design is our proposed block-based model of execution that groups vector computational instructions together to execute them in a coordinated manner. We implemented a classic vector unit and compare its results against our integrated design. Our integrated design improves the performance (more than 6×) and energy consumption (up to 5×) of a scalar in-order core with negligible area overhead (only 4.7% when using a vector register with 32 elements). In contrast, the area overhead of the classic vector unit can be significant (around 44%) if a dedicated vector floating-point unit is incorporated. Our block-based vector execution outperforms the classic vector unit for all kernels with floating-point data and also consumes less energy. We also complement the integrated design with three energy/performance-efficient techniques that further reduce power and increase performance. The first proposal covers the design and implementation of chaining logic that is optimized to work with the cache hierarchy through vector memory instructions, the second proposal reduces the number of reads/writes from/to the vector register file, and the third idea optimizes complex memory access patterns with the memory shape instruction and unified indexed vector load.

In this paper, an energy management strategy based on microgrid power forecasting is applied to a residential grid-connected electro-thermal microgrid with the aim of smoothing the power profile exchanged with the grid. The microgrid architecture under study considers electrical and thermal renewable generation, energy storage system (ESS), and loads. The proposed strategy manages the energy stored in the ESS to cover part of the energy required by the thermal generation system for supplying domestic hot water to the residence. The simulation results using real data and the comparison with previous strategy have demonstrated the effectiveness of the proposed strategy. This work is part of the project 2016-PIC-044 and 2018-REV-003 from the Research Group of Propagation, Electronic Control, and Networking (PROCONET) of Universidad de las Fuerzas Armadas ESPE. This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities: grants DPI2013-41224-P, DPI2015-67292, DPI2016-80641-R, and DPI2017-85404-P.