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Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. The sensitivity of spring/summer-time coastal upwelling systems to climate change has recently received a lot of attention. Several studies have suggested that their intensity may increase in the future while other authors have shown decreasing intensity in their equatorward portions. Yet, recent observations do not show robust evidence of this intensification. The Senegalo-Mauritanian upwelling system (SMUS) located at the southern edge of the north Atlantic system (12°N–20°N) and most active in winter/spring has been largely excluded from these studies. Here, the seasonal cycle of the SMUS and its response to climate change is investigated in the database of the Coupled Models Inter comparison Project Phase 5 (CMIP5). Upwelling magnitude and surface signature are characterized by several sea surface temperature and wind stress indices. We highlight the ability of the climate models to reproduce the system, as well as their biases. The simulations suggest that the intensity of the SMUS winter/spring upwelling will moderately decrease in the future, primarily because of a reduction of the wind forcing linked to a northward shift of Azores anticyclone and a more regional modulation of the low pressures found over Northwest Africa. The implications of such an upwelling reduction on the ecosystems and local communities exploiting them remains very uncertain.

The most severe failures in power grids are often characterized as cascading failures where an initial event triggers consequent failures all along the grid often leading to blackouts. Upon identifying a failure and its cascade potential, timely control actions should be performed by the grid operators to mitigate the effect of the cascade. These actions have to be delivered to one or more control devices, creating a dependency between the power grid and its control network. This paper examines the dependency of the operation of the power grid on the control network. Different from literature studies on failure control, our dependency model captures the impact of networking parameters. We formulate an algorithmic model that describes the impact of this dependency on cascade control. Based on this model, we propose an efficient cascade control algorithm using load shedding with consideration of delays in the communication network for power grids. Finally, we evaluate the impact of the power-communication network dependency with uncontrolled grids, ideal/simple control grids and our proposed control scheme. The results demonstrate that the proposed algorithm can significantly reduce the failure of power lines while sustaining larger power demand for users.

In Cameroon, as in other countries of the Congo basin region, policy processes and activities related to climate change have been hitherto geared mostly towards mitigation and related questions, with limited concern about adaptation issues. However, the increasing vulnerability of Cameroon to climate variability and change makes adaptation significant to its national climate-change policy. Nonetheless, it remains a challenge to make both adaptation and mitigation occupy the same policy space in Cameroon. This paper builds partly on studies carried out in two community forest carbon initiatives in the southern rainforest of Cameroon. It also argues, supported by existing literature on adaptation and mitigation, that mitigation activities have the potential to produce adaptation outcomes; a situation which avoids duplication of efforts and waste of financial and technical resources, if synergetic options are anticipated and planned. However, whether such integrated approaches succeed and are subsequently reflected in national-level climate policy depends on how actors across different sectors and at different levels engage and carry out their roles. The paper discusses these roles and how they can support each other in pursuing integrated initiatives – a context which is vital for Cameroon.

Abstract The integrity of PWR pressure vessels is assured by keeping the crack tip stress intensity factor below the toughness of the material under monotonic isothermal loading. To study the effects of sudden cooling associated with a thermal gradient, a specially modified compact specimen has been developed. This has been used to carry out tests in the transition zone with different loading-temperature sequences liable to call the conventional criteria into question. The test is described in detail in Part I of this article [Chapuliot S, et al. Thermomechanical analysis of thermal shock fracture in the brittle/ductile transition zone. Part I: Description of the tests. Engng Fract Mech, 72, 2005, 661–73]. The second part describes numerical investigations to estimate the local mechanical fields at the crack tip and the overall parameters of the fracture mechanics. Finite element thermomechanical calculations are used to interpret the results of these new thermal shock tests using the master curve concept [ASTM E 1921–1997. Standard test method for determination of reference temperature To for ferritic steels in the transition range, 1997] and the Beremin statistical model [Beremin FM. A local criterion for cleavage fracture of a nuclear pressure vessel steel. Metall Trans A, 14A, November 1983, 2287–777].

This paper reports the generalized results of computer simulation of physical processes at a rotor-disk film evaporating plant. Optimization of the operation mode cannot be achieved without establishing patterns in the course of physical processes. We have proposed a computer model of hydrodynamics that accounts for all the features, initial and boundary conditions. The results of computer simulations make it possible to adequately assess the effectiveness of using a rotor-disk film evaporating plant (RDFVP) for the concentration of heat-labile materials. We have established patterns in the course of physical processes within a structure of RDFVP by using computer simulation of hydrodynamics in the programming environment ANSYS and applying a k-e turbulence model. The result of simulation is the derived velocity fields of the concentrated fluid (w max =0.413 m/s) and the gas phase (w max =8.176 m/s), as well as the magnitude of values for shear stress τ=0.94·10 -6 Pa. It was established that the gas heat-carrier is characterized by the highly-turbulent flows with maximum values for kinetic energy TKE max =8.985·10 -1 m 2 /s 2 . The reliability of results is ensured by the correctness, completeness, and adequacy of physical assumptions when stating the problem and while solving it using the computer aided design system ANSYS. It has been established that the proposed structure is an effective alternative to equipment for the concentration of solutions. The data obtained could be used when designing heat-and-mass-exchange equipment for the highly efficient dehydration of thermolabile materials

International audience ; This paper proposes a Deep Reinforcement Learning approach for optimally managing multi-energy systems in smart grids. The optimal control problem of the production and storage units within the smart grid is formulated as a Partially Observable Markov Decision Process (POMDP), and is solved using an actor-critic Deep Reinforcement Learning algorithm. The framework is tested on a novel multi-energy residential microgrid model that encompasses electrical, heating and cooling storage as well as thermal production systems and renewable energy generation. One of the main challenges faced when dealing with real-time optimal control of such multi-energy systems is the need to take multiple continuous actions simultaneously. The proposed Deep Deterministic Policy Gradient (DDPG) agent has shown to handle well the continuous state and action spaces and learned to simultaneously take multiple actions on the production and storage systems that allow to jointly optimize the electrical, heating and cooling usages within the smart grid. This allows the approach to be applied for the real-time optimal energy management of larger scale multi-energy Smart Grids like eco-distrits and smart cities where multiple continuous actions need to be taken simultaneously.

Methodology: To carry out the calculation of these agroclimatic indicators, daily data of the following climatic variables were used at a resolution of 5 km: Maximum and minimum temperatures (source: CHIRTS), precipitation (source: CHIRPS), solar radiation (AGMERRA), data of soil (SoilGrids). The indicators were calculated for each month during a period of 33 years (1983 - 2016). With the above, the indicators were calculated per month during 1983 -2016 and finally, in order to summarize the calculated indicators, an aggregation of data was carried out, calculating the average in the following time periods: 1983 - 2016, 1990 - 2016, 1995 - 2016, 2000 – 2016, 2005 – 2016, 2010 – 2016 The purpose for which these indicators were created is to group or characterize the different accessions available in the Genesys database, considering climatic data from where they were collected. For the above, it is necessary to carry out a characterization of zones based on agroclimatic indicators, which are framed in evaluating the following stresses: Heat, Drought and Flooding, in addition to including indicators that capture the behavior of the photoperiod.