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Recently, trans-S-manifolds have been defined as a wide class of metric f-manifolds which includes, for instance, f-Kenmotsu manifolds, S-manifolds and C-manifolds and generalize well-studied trans-Sasakian manifolds. The definition of trans-S-manifolds is formulated using the covariant derivative of the tensor f and although this formulation coincides with the characterization of trans-Sasakian manifolds in such a particular case, this latter type of manifolds were not initially defined in this way but using the Gray-Hervella classification of almost Hermitian manifolds. The aim of this paper is to study how (almost) trans-S-manifolds relate with the Gray-Hervella classification and to establish both similarities and differences with the trans-Sasakian case.

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.

T lymphocytes are key players in adaptive immune responses through the recognition of peptide antigens through the T Cell Receptor (TCR). After TCR engagement, a signaling cascade is activated, leading to T cell activation, proliferation, and differentiation into effector cells. Delicate control of activation signals coupled to the TCR is needed to avoid uncontrolled immune responses involving T cells. It has been previously shown that mice deficient in the expression of the adaptor NTAL (Non-T cell activation linker), a molecule structurally and evolutionarily related to the transmembrane adaptor LAT (Linker for the Activation of T cells), develop an autoimmune syndrome characterized by the presence of autoantibodies and enlarged spleens. In the present work we intended to deepen investigation into the negative regulatory functions of the NTAL adaptor in T cells and its potential relationship with autoimmune disorders. For this purpose, in this work we used Jurkat cells as a T cell model, and we lentivirally transfected them to express the NTAL adaptor in order to analyze the effect on intracellular signals associated with the TCR. In addition, we analyzed the expression of NTAL in primary CD4+ T cells from healthy donors and Rheumatoid Arthritis (RA) patients. Our results showed that NTAL expression in Jurkat cells decreased calcium fluxes and PLC-γ1 activation upon stimulation through the TCR complex. Moreover, we showed that NTAL was also expressed in activated human CD4+ T cells, and that the increase of its expression was reduced in CD4+ T cells from RA patients. Our results, together with previous reports, suggest a relevant role for the NTAL adaptor as a negative regulator of early intracellular TCR signaling, with a potential implication in RA.

The installation of ultra-fast charging stations (UFCSs) is essential to push the adoption of electric vehicles (EVs). Given the high amount of power required by this charging technology, the integration of renewable energy sources (RESs) and energy storage systems (ESSs) in the design of the station represents a valuable option to decrease its impact on the grid and the environment. Therefore, this paper proposes a multi-objective optimization problem for the optimal sizing of photovoltaic (PV) system and battery ESS (BESS) in a UFCS of EVs. The proposed multi-objective function aims to minimize, on one side, the annualized cost of the station, and on the other side, the produced pollutant emissions. The decision variables are the number of PV panels and the capacity of the ESS to be installed. The optimization problem is reduced to a single-objective problem by applying the linear scalarization method. Then the equivalent single-objective function is optimized through a genetic algorithm (GA). The proposed optimization framework is applied to a study case and the results prove that PV and ESS could lead to a significant reduction of both the annualized cost and the pollutant emissions. Finally, a sensitivity analysis is also presented to validate the effectiveness of the proposed solution.

This study presents a new energy management system (EMS) for the optimised operation of power sources of a hybrid charging station for electric vehicles and fuel cell vehicles. It is composed of a photovoltaic (PV) system, a battery and a hydrogen system as energy storage systems (ESSs), a grid connection, six fast charging units and a hydrogen supplier. The proposed EMS is designed to reduce the utilisation costs of the ESS and make them work, as much as possible, around their maximum efficiency points. The optimisation function depends on a cost prediction system that calculates the net present cost of the components from their previous performance and a fuzzy logic system designed for improving their efficiency. Finally, a particle swarm optimisation algorithm is used to solve the optimisation function and obtain the required power for each ESS. The proposed EMS is checked under Simulink environment for long-term simulations (25 years). By comparing the EMS with a simpler one that optimises only the costs, it is proved that the proposed EMS achieves better efficiency of the charging station (+7.35%) and a notable reduction in the loss of power supply probability (-57.32%) without compromising excessively its average utilisation cost (+1.81%).

Finding and tracking maximum power point are two important dynamics in the control of variable-speed wind turbines since they determine the efficiency of wind turbines. The conventional hill climbing possesses the problems of wrong directionality and low performance since it does not take the inertial effect into account. In this paper, a novel hill climbing method is proposed by considering the inertial effect to solve these problems. Besides, employing the exact model knowledge of the generator in the maximum power tracking control deteriorates the efficiency considerably; therefore, it is required to design a parameter independent and robust control system if possible. Thus, the third-order super-twisting sliding mode and continuous integral sliding mode controllers are designed for the control of generator and grid-side converters to track the maximum power trajectory accurately, and they are compared to each other for the chattering in experimental results. A comparison is also performed between the conventional and proposed hill climbing methods based on the captured energy from the wind. Experimental results, with a wind turbine emulator, demonstrate that the proposed hill climbing method relaxes the wrong directionality and sluggish performance of the conventional one.

Photovoltaic (PV) solar generation is gaining an increasing attention due to technological advances such as higher efficiency and life of PV cells and cost reduction. Due to its vast territory, Brazil is composed of regions that can explore renewable energy sources for electricity generation, and the solar resource is found satisfactorily in several areas of the country. This article presents a solar irradiance prediction mechanism developed using data collected in Fortaleza-CE, Brazil. Due to the fact of its characteristic of unpredictability for this resource, many researchers look for several methods to take the generation of this type of energy. The predictions were performed using a Radial Basis Function (RBF) a computational model based on the human nervous system, it is a technical and effective for time series forecasting, which is a relatively complex problem, Artificial Neural Network (ANN) with the advancement of 1 hour. In the ANN performance, a total of 34.4% forecasts underestimated solar energy availability, 7% of the forecasts obtained error 0 and 58.6% of forecasts overestimated the solar resource. A total of 62.33% of forecasts was between -10% and 10% of forecast error. The prediction mean error was 5.93% and the Mean Absolute Percentage Error (MAPE) was 11.43%.

The increasing of photovoltaic plant installations at different scales promotes the development of monitoring systems that facilitate the communication, control and automation of the generating units, allowing to guarantee the predicted energy generation performance. Monitoring systems are composed of different interfaces that involve sensing and capturing data; conversion, treatment, pre-storage and transmission of data; and publishing and final storage through graphic interface. This article focuses on describing the growth of decentralized plants, as well as the increasing demand for monitoring and data acquisition system, commenting the limitations of current commercial models and presenting alternative developed monitoring systems with different platforms.

This work contributes to the research on three-phase photovoltaic systems via a new more robust online adaptive neuro-fuzzy maximum power point tracking (MPPT) control technique considering real partial shading and load conditions. The trapping in local minima and high computational cost in the existing neuro-fuzzy structure are addressed by incorporating B-spline function in the proposed control method. The system parameters are adjusted online via an adaptive neuro-fuzzy inference system rules acquired from the MPPT error. The optimization part of the proposed control law is performed through an online learning gradient-descent back-propagation algorithm. The superiority of the proposed control in terms of energy conversion efficiency, MPPT error, and output power is checked under the same operating conditions with well-known used traditional and intelligent MPPT control algorithms. Finally, the robustness of the proposed control is confirmed through a complete day simulation and comparison indexes.

This paper describes and evaluates a hybrid propulsion system based on diesel generator and supercapacitors (SCs) as energy storage system (ESS) for a rubber tyre gantry (RTG) container crane, which currently operates within the yard of the Algeciras port terminal (Spain) powered by diesel electric generator for supplying the electric drives and motors (hoist and trolley). The SCs, which are connected to the DC bus through a bidirectional DC/DC converter, are controlled by a control strategy based on the DC-bus voltage. The SCs reference current is limited depending on their state-of-charge (SOC). All main components and control strategy of the RTG crane are modelled and simulated in SimPowerSystems. The current and hybrid configuration are simulated and compared under the real working cycle of the RTG crane. The results show the technical viability, the validity of the proposed control strategy, the improvements in the energy efficiency and diesel fuel consumption, and the economic viability of the hybrid propulsion system for the RTG crane.

This paper presents an energy management system (EMS) based on a novel approach using model predictive control (MPC) for the optimized operation of power sources in a hybrid charging station for electric vehicles (EVs). The hybrid charging station is composed of a photovoltaic (PV) system, a battery, a complete hydrogen system based on a fuel cell (FC), electrolyzer (EZ), and tank as an energy storage system (ESS), grid connection, and six fast charging units, all of which are connected to a common MVDC bus through Z-source converters (ZSC). The MPC-based EMS is designed to control the power flow among the energy sources of the hybrid charging station and reduce the utilization costs of the ESS and the dependency on the grid. The viability of the EMS was proved under a long-term simulation of 25 years in Simulink, using real data for the sun irradiance and a European load profile for EVs. Furthermore, this EMS is compared with a simpler alternative that is used as a benchmark, which pursues the same objectives, although using a states-based strategy. The results prove the suitability of the EMS, achieving a lower utilization cost (−25.3%), a notable reduction in grid use (−60% approximately) and an improvement in efficiency.

Mycobacterium kansasii extrapulmonary infections are infrequent in immunocompetent adults. Rifampin (RIF), clarithromycin (CLR), isoniazid (INH) and ethambutol (EMB) are included in all the standard regimens against M.kansasii. We report a case of a healthy 65-year-old male farmer who presented with isolated right supraclavicular lymphadenopathy. The lymph node FNA showed acid-fast-bacilli and granulomatous inflammation. Quantiferon TB Gold test, HIV serology, and functional immunological studies were all negative or normal. He was put on a standard 4 drugs anti-tuberculous regimen that was switched to RIF + CLR+ INH after the Microbiology lab demonstrated an EMB–resistant Mycobacterium kansasii isotype I strain. The patient was cured after 12 months of therapy. This is the 6th reported case of M. kansasii extrapulmonary lymphadenitis in an immunocompetent adult and the 2nd showing EMB resistance in the world literature. Antimycobacterial regimens against M. kansasii, classically resistant to pyrazinamide (PZA) might also exclude EMB due to its increasing resistance in Europe. A 612 months therapy with at least 2 effective antimycobacterial drugs including RIF + CLR might be enough to treat extrapulmonary M. kansasii infections in immunocompetents. Keywords: Mycobacterium kansasii, Cervical lymphadenitis, Immunocompetent, Resistance, Ethambutol, Quantiferon

The use of a battery energy-stored quasi-Z-source inverter (BES-qZSI) for large-scale PV power plants exhibits promising features due to the combination of qZSI and battery as energy storage system, such as single-stage power conversion (without additional DC/DC boost converter), improvements in the output waveform quality (due to the elimination of switching dead time), and continuous and smooth delivery of energy to the grid (through the battery energy storage system). This paper presents a new simplified model of a BES-qZSI to represent the converter dynamics with sufficient accuracy while using a less complex model than the detailed model (including the modelling of all switches and switching pulses). It is based on averaged values of the variables, voltage/current sources, and the same control circuit than the detailed model, except for the switching pulses generation. The simplified model enables faster time-domain simulation and is useful for control design and dynamic analysis purposes. Additionally, an energy management system has been developed to govern the power supply to grid under two possible scenarios: 1) System operator command following; or 2) economic dispatch of the stored energy. The results obtained from simulations and experimental hardware-in-the-loop (HIL) setup for different operating conditions of the grid-connected large-scale PV power plant with battery energy storage under study demonstrate the validity of the proposed simplified model to represent the dynamics of the converter and PV power plant for steady-state stability studies, long-term simulations, or large electric power systems.

Rural electrification is one of the most significant issues faced by electricity companies. For this reason, these companies are choosing alternative sources to generate energy in isolated regions. Furthermore, hybrid generation systems are an effective option for supplying protected areas. In this context, this research aims at designing an autonomous hybrid system to meet the annual electricity demand of the inhabitants of a national park. Fluvial and solar energies are the best options to reduce environmental impact and to ensure the conservation of the endemic fauna and flora of the island at a low carbon footprint. The system comprises a series of subsystems modeled using commercial software for sizing and optimization. The main generation subsystem contains a hydrokinetic turbine and photovoltaic panels, the storage subsystem contains a battery bank, and the backup subsystem consists of a diesel generator used in case of lack of energy from the rest of suppliers of the hybrid system. The main results of the simulation show an optimized system that fulfills the energy demand while minimizing the use of the diesel generator to 5668 kWh/year (14.3%) of thorough generation. The hydrokinetic generator supplies 20,330 kWh/year (51.4% of the total generation) and the solar generator supplies 13,580 kWh/year (34.3%).

An ever-increasing penetration of electric vehicles (EVs) on the roads inevitably leads to an ever-stringent need for an adequate charging infrastructure. The emerging ultra-fast charging (UFC) technology has the potential to provide a refueling experience similar to that of gasoline vehicles; hence, it has a key role in enabling the adoption of EVs for medium-long distance travels. From the perspective of the UFC station, the differences existing in the EVs currently on the market make the sizing problem more challenging. A suitably conceived charging strategy can help to address these concerns. In this paper, we present a smart charging station concept that, through a modular DC/DC stage design, allows the split of the output power among the different charging ports. We model the issue of finding the optimal charging station as a single-objective optimization problem, where the goal is to find the number of modular shared DC/DC converters, and where the power rate of each module ensures the minimum charging time and charging cost. Simulation results show that the proposed solution could significantly reduce the required installed power. In particular, they prove that with an installed power of 800 kW it is possible to satisfy the needs of a UFC station composed of 10 charging spots.

Currently, the technology associated with charging stations for electric vehicles (EV) needs to be studied and improved to further encourage its implementation. This paper presents a new energy management system (EMS) based on a Biogeography-Based Optimization (BBO) algorithm for a hybrid EV charging station with a configuration that integrates Z-source converters (ZSC) into medium voltage direct current (MVDC) grids. The EMS uses the evolutionary BBO algorithm to optimize a fitness function defining the equivalent hydrogen consumption/generation. The charging station consists of a photovoltaic (PV) system, a local grid connection, two fast charging units and two energy storage systems (ESS), a battery energy storage (BES) and a complete hydrogen system with fuel cell (FC), electrolyzer (LZ) and hydrogen tank. Through the use of the BBO algorithm, the EMS manages the energy flow among the components to keep the power balance in the system, reducing the equivalent hydrogen consumption and optimizing the equivalent hydrogen generation. The EMS and the configuration of the charging station based on ZSCs are the main contributions of the paper. The behaviour of the EMS is demonstrated with three EV connected to the charging station under different conditions of sun irradiance. In addition, the proposed EMS is compared with a simpler EMS for the optimal management of ESS in hybrid configurations. The simulation results show that the proposed EMS achieves a notable improvement in the equivalent hydrogen consumption/generation with respect to the simpler EMS. Thanks to the proposed configuration, the output voltage of the components can be upgraded to MVDC, while reducing the number of power converters compared with other configurations without ZSC., This work was partially supported by Spain's Ministerio de Ciencia, Innovaci ' on y Universidades (MCIU), Agencia Estatal de Investigaci ' on (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) Uni ' on Europea (UE) (grant number RTI2018-095720-B-C32), by the Federal Center for Technological Education of Minas Gerais, Brazil (process number 23062-010087/2017-51) and by the National Council of Technological and Scientific Development (CNPq-Brazil).

We introduce a new general class of metric f-manifolds which we call (nearly) trans-S-manifolds and includes S- manifolds, C-manifolds, s-th Sasakian manifolds and generalized Kenmotsu manifold studied previously. We prove their main properties and we present many examples which justify their study., Comment: 15 pages

Mycobacterium kansasii extrapulmonary infections are infrequent in immunocompetent adults. Rifampin (RIF), clarithromycin (CLR), isoniazid (INH) and ethambutol (EMB) are included in all the standard regimens against M.kansasii. We report a case of a healthy 65-year-old male farmer who presented with isolated right supraclavicular lymphadenopathy. The lymph node FNA showed acid-fast-bacilli and granulomatous inflammation. Quantiferon TB Gold test, HIV serology, and functional immunological studies were all negative or normal. He was put on a standard 4 drugs anti-tuberculous regimen that was switched to RIF + CLR+ INH after the Microbiology lab demonstrated an EMB–resistant Mycobacterium kansasii isotype I strain. The patient was cured after 12 months of therapy. This is the 6th reported case of M. kansasii extrapulmonary lymphadenitis in an immunocompetent adult and the 2nd showing EMB resistance in the world literature. Antimycobacterial regimens against M. kansasii, classically resistant to pyrazinamide (PZA) might also exclude EMB due to its increasing resistance in Europe. A 612 months therapy with at least 2 effective antimycobacterial drugs including RIF + CLR might be enough to treat extrapulmonary M. kansasii infections in immunocompetents.

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro?fuzzy wavelet?based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top?level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro?fuzzy wavelet?based controller is applied to the inverter. The new proposed wavelet?based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro?fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018?095720?B?C32; Deutsche Forschungsgemeinschaft, DFG: 350746631; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 5190011 Funding: The work of Murat Karabacak and Tariq Kamal is sponsored by the Scientific and Technological Research Council of Turkey under project number 5190011. The work of Vedran S. Peri? is supported by the Bavarian Government and Deutsche Forschungsgemeinschaft (DFG) under project number 350746631. The work of Luis M. Fernández?Ramírez and Tariq Kamal is sponsored by the Spanish Ministry of Science, Innovation, and Universities under project number RTI2018?095720?B?C32.

This paper proposes an efficient bridgeless power factor corrected (PFC) modified single ended primary inductor converter (SEPIC) for arc welding power supplies (AWPS). The overall configuration is composed of two converters: (1) a modified bridgeless SEPIC PFC converter, which is controlled by a PI controller to achieve a high power factor and fast response; and (2) a full bridge buck converter with high-frequency transformer for high-frequency isolation to ensure arc welding stability. The proposed system is simulated under different operating conditions of an AWPS. It is also tested in real time by a hardware-in-the-loop system based on a dSPACE DS1103 control board. The system performances are evaluated based on power quality indices such as power factor, total harmonic distortions of the AC grid current, and voltage regulation. The obtained results show that the proposed controller enhances the weld bead quality by keeping a constant current at the output and a stable arc, meet the international power quality standards and robustness for voltage regulation.

Kılıç, Fuat (Balikesir Author), In this research, a new nonlinear and adaptive state feedback controller with a fast-adaptive robust differentiator is presented for grid-tied inverters. All parameters and external disturbances are taken as uncertain in the design of the proposed controller without the disadvantages of singularity and over-parameterization. A robust differentiator based on the second order sliding mode is also developed with a fast-adaptive structure to be able to consider the time derivative of the virtual control input. Unlike the conventional backstepping, the proposed differentiator overcomes the problem of explosion of complexity. In the closed-loop control system, the three phase source currents and direct current (DC) bus voltage are assumed to be available for feedback. Using the Lyapunov stability theory, it is proven that the overall control system has the global asymptotic stability. In addition, a new simple L filter design method based on the total harmonic distortion approach is also proposed. Simulations and experimental results show that the proposed controller assurances drive the tracking errors to zero with better performance, and it is robust against all uncertainties. Moreover, the proposed L filter design method matches the total harmonic distortion (THD) aim in the design with the experimental result.

The global energy system is changing, mainly to achieve sustainable transport technologies and clean electrical generation based on renewable sources. Thus, as fuels, electricity and hydrogen are the most promising transport technologies in order to reduce greenhouse emissions. On the other hand, photovoltaic and wind energies, including energy storage, have become the main sources of distributed generation. This study proposes a new optimal-technical sizing method based on the Simulink Design Optimization of a stand-alone microgrid with renewable energy sources and energy storage to provide energy to a wireless power transfer system to charge electric vehicles along a motorway and to a hydrogen charging station for fuel cell-powered buses. The results show that the design system can provide energy for the charging of electric vehicles along the motorway and produce the hydrogen consumed by the fuel cell-buses plus a certain tank reserve. The flexibility of the study allows the analysis of other scenarios, design requirements, configurations or types of microgrids.

Control focused on optimizing the lifecycle costs of a stand-alone hybrid system.Combination of renewable sources, battery and hydrogen systems.The modeling includes the electric models of the components.The system assures reliable electricity support for stand-alone applications. This paper presents an Energy Management System (EMS) for hybrid systems (HS) composed by a combination of renewable sources with the support of different storage devices (battery and hydrogen system) that allow its operation without the necessity of grid connection (i.e. a stand-alone system).The importance of the proposed EMS lies in taking into account economic issues that affect to the decision of which device of the HS must operate in each moment. Linear programming was used to meet the objective of minimizing the net present value of the operation cost of the HS for its whole lifespan. The total operation costs depend largely on the reposition costs of its components. Instead of considering predefined reposition years for each component and calculate their net present cost from them (as is commonly considered in other works), in this work it was proposed to use lifetime degradation models - based on the well-known statement that the lifetime depends on the hours of operation and the power profiles that the components are subjected to- from which the repositions are made to check how they affect to the cost calculation and, consequently, to the EMS performance.The behavior of the proposed control is checked under a long term simulation, in MATLAB-Simulink environment, whose duration is the expected lifespan of the HS (25 years). A conventional state-machine EMS is used as a case study to validate and compare the results obtained. The results demonstrate that the proposed HS and EMS combination assures reliable electricity support for stand-alone applications subject to different techno-economic criteria (generation cost and sustenance of battery SOC and hydrogen levels), achieving to minimize the operation cost of the system and extend their lifespan.

Wireless power transfer technology is being widely used in electric vehicle wireless-charging applications, and foreign object detection (FOD) is an important module that is needed to satisfy the transmission and safety requirements. FOD mostly includes two key parts: metal object detection (MOD) and living object detection (LOD), which should be implemented during the charging process. In this paper, equivalent circuit models of a metal object and a living object are proposed, and the FOD methods are reviewed and analyzed within a unified framework based on the proposed FOD models. A comparison of these detection methods and future challenges is also discussed. Based on these analyses, detection methods that employ an additional circuit for detection are recommended for FOD in electric vehicle wireless-charging applications.

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.

Solar energy has been consolidated as one of the main renewable energy sources capable of contributing to supply global energy demand. However, the solar resource has intermittent feature in electricity production, making it difficult to manage the electrical system. Hence, we propose the application of Deep Learning (DL), one of the emerging themes in the field of Artificial Intelligence (AI), as a solar predictor. To attest its capacity, the technique is compared with other consolidated solar forecasting strategies such as Multilayer Perceptron, Radial Base Function and Support Vector Regression. Additionally, integration of AI methods in a new adaptive topology based on the Portfolio Theory (PT) is proposed hereby to improve solar forecasts. PT takes advantage of diversified forecast assets: when one of the assets shows prediction errors, these are offset by another asset. After testing with data from Spain and Brazil, results show that the Mean Absolute Percentage Error (MAPE) for predictions using DL is 6.89% and for the proposed integration (called PrevPT) is 5.36% concerning data from Spain. For the data from Brazil, MAPE for predictions using DL is 6.08% and 4.52% for PrevPT. In both cases, DL and PrevPT results are better than the other techniques being used.

Large mammal communities, integral components of Neotropical ecosystems, are increasingly threatened by habitat fragmentation and degradation due to anthropogenic pressures and climate change. Lowland Neotropical palm forests harbor diverse mammal communities, including threatened and endangered species. We monitored three transects in the Barra del Colorado Wildlife Refuge in Northeast Costa Rica from February 2014 until April 2015 documenting large terrestrial mammal and primate detections. Following established large mammal survey methodology, each transect was walked weekly alternating between early morning and late afternoon to maximize detection. The three transects represent three levels of anthropogenic disturbance with highest disturbance at the Cerro, followed by Caño Palma Biological Station (CPBS), and lowest levels in Tortuguero National Park (TNP). Overall we found higher diversity and increased presence of endangered species in TNP and the lowest diversity and lower presence of those species in the Cerro. Overall we detected 25 species; 19 at the Cerro, 22 in TNP and 23 at CPBS. TNP had the highest diversity as calculated by Simpson index (28.3) followed by CBPS (27.9), and the Cerro had the lowest diversity of mammals (2.27). The most influential species driving the differences of communities between the three transects included the white-lipped and collared peccaries, which were both present in higher densities in transects with lower disturbance and higher levels of protection, and domestic dogs which were present in higher densities at the Cerro. This study has documented baseline densities of large mammal communities in the wildlife refuge which can be used as preliminary data for future comparisons. As development is continuing in the region, including construction of a tourist trail at the Cerro, a continued monitoring is recommended to understand the effect of development on mammal communities.

Background: At present, much research effort has been devoted to investigate overall (average) responses of parasite populations to specific factors, e.g., density-dependence in fecundity or mortality. However, studies on parasite populations usually pay little attention to individual variation (inequality) in reproductive success. A previous study on the acanthocephalan Corynosoma cetaceum in franciscana dolphins, Pontoporia blainvillei, revealed no overall intensity-dependent, or microhabitat effects, on mass and fecundity of worms. In this study, we investigated whether the same factors could influence mass inequalities for this species of acanthocephalan.Methods: A total of 10,138 specimens of C. cetaceum were collected from 10 franciscana dolphins accidentally caught in Buenos Aires Province between 1988-1990. To investigate mass inequalities, all the specimens were sexed, and females were classified according to their developmental stage and weighted. Additionally, the relationship between biomass and fecundity (estimated as the number of acanthors) was investigated for some females. Inequalities in fecundity and biomass were assessed using standard methods, i.e. the Lorenz curve and the Gini coefficient (G).Results: We found a modest, but highly significant linear relationship between mass and fecundity. The G was very low (0.314) compared with that from other helminth species. G values were significantly lower in gravid females, which presumably exhibit a slow rate of growth. Also, G values significantly increased with total intensity, but only for gravid females, and the effect was more predictable considering only the number of gravid females.Conclusions: Apparently, competition between reproducing females increases inequality without producing crowding effects. Although the mechanism whereby this occurs is unclear, gravid females, at higher intensities, expanded their distribution and occupied gut chambers with contrasting environmental conditions, which might result in greater variability in body size. The observed inequalities are not expected to strongly influence the population genetics of C. cetaceum, but they reveal subtle individual effects beyond an overall population impact. Fil: Aznar Avendaño, Francisco Javier. Universidad de Valencia; España Fil: Hernández Orts, Jesús Servando. Universidad Nacional del Comahue. Centro de Investigación Aplicada y Transferencia Tecnológica en Recursos Marinos "Almirante Storni". - Provincia de Río Negro. Ministerio de Agricultura, Ganadería y Pesca. Centro de Investigación Aplicada y Transferencia Tecnológica en Recursos Marinos "Almirante Storni". Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet Centro Nacional Patagónico. Centro de Investigación Aplicada y Transferencia Tecnológica en Recursos Marinos "Almirante Storni"; Argentina Fil: Vélez Rubio, Gabriela Manuela. Universidad de Valencia; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de la República; Uruguay Fil: Fernández, Luis M.. Ceiba Foundation for Tropical Conservation; Estados Unidos Fil: Muriel, Nadia T.. No especifíca; Fil: Raga, Juan Antonio. Universidad de Valencia; España

This paper presents two novel hourly energy supervisory controls (ESC) for improving long-term operation of off-grid hybrid systems (HS) integrating renewable energy sources (wind turbine and photovoltaic solar panels), hydrogen system (fuel cell, hydrogen tank and electrolyzer) and battery. The first ESC tries to improve the power supplied by the HS and the power stored in the battery and/or in the hydrogen tank, whereas the second one tries to minimize the number of needed elements (batteries, fuel cells and electrolyzers) throughout the expected life of the HS (25 years). Moreover, in both ESC, the battery state-of-charge (SOC) and the hydrogen tank level are controlled and maintained between optimum operating margins. Finally, a comparative study between the controls is carried out by models of the commercially available components used in the HS under study in this work. These ESC are also compared with a third ESC, already published by the authors, and based on reducing the utilization costs of the energy storage devices. The comparative study proves the right performance of the ESC and their differences.

Improving grid connection of wind farms is a relevant issue to be addressed, especially for fixed-speed wind turbines. Certain elements, such as FACTS (Flexible AC Transmission Systems), are able to perform voltage and reactive power regulation in order to support voltage stability of wind farms, and compensate reactive power consumption from the grid. Several devices are grouped under the name of FACTS, which embrace different technologies and operating principles. Here, three of them are evaluated and compared, namely STATCOM (Static Synchronous Compensator), SVC (Static Var Compensator) and SSSC (Static Synchronous Series Compensator). They have been modeled in MATLAB / Simulink, and simulated under various scenarios, regarding both normal operation and grid fault conditions. Their response is studied together with the case when no FACTS are implemented. Results show that SSSC improves the voltage stability of the wind farm, whereas STATCOM and SVC provide additional reactive power.

Hybrid systems comprising battery energy storage systems (BESSs) and wind power generation entail considerable advances on the grid integration of renewable energy. Doubly fed induction generators (DFIGs) stand out among different wind turbine (WT) technologies. On the other hand, electrochemical batteries have proved to be valid for these purposes. In this paper, a comparative analysis is carried out between two alternative configurations for hybrid WT-BESS systems, where the BESS is connected either outside or inside the DFIG. The modeling of these two configurations and the control systems applied for achieving the coordinate operation of the energy sources (DFIG and batteries) are illustrated. The hybrid systems under study are evaluated by simulation under normal operation (wind speed fluctuations and grid demand changes) and grid faults. Simulation results show that both configurations improve the grid integration capability of the WT, although the configuration with external BESS presents better results since it can provide additional active/reactive power injection. Copyright © 2014 John Wiley & Sons, Ltd.

The entry into force of Royal Decree-Law 1/2012 has led to the elimination of the support schemes to promote the use of the energy from renewable energy sources existing to date in Spain, and thus, certain standstill of this sector activity. In this context, some renewable technologies, mainly photovoltaic solar, can find in self-consumption an alternative for promoting their development and use. The self-consumption in the form of net-metering is mainly characterized by the possibility that the consumer can generate in situ part of the consumed electricity and use the network as storage or supply system. This option is still under study by the Government, so that there is a draft of Royal Decree that has not yet been approved. The aim of this paper is to analyze the economic viability of the net-metering mode in dwellings with small power photovoltaic facilities, considering those options that can contribute to make them viable.