This paper presents a new method, based on the Smart Energy Systems concept. The aim is to increase the share of renewable energy penetration on islands. The method is applied to the island of Gran ...Canaria (Spain), considering the entire energy system of the island. Several smart renewable energy strategies are proposed following a cross-sectoral approach between the electricity, heating/cooling, desalination, transport and gas sectors. The different smart renewable energy strategies were applied in a series of steps, while looking for a transition from the current energy system to a nearly 100% renewable energy system. Based on the results, the study concludes that the suggested method is applicable for increasing renewable integration on islands and can potentially be used in helping energy planners to take decisions about priorities in development of the sector to improve such integration. The results indicate that, for the case of Gran Canaria, a 75.9% renewable energy system could be attained with technologies that can be implemented at present. Furthermore, it is shown that a nearly 100% renewable energy system in Gran Canaria is technically feasible and could be achieved if certain technologies acquire greater maturity.
•A new Smart Renewable Energy approach for islands is presented.•A cross-sectoral method is proposed to increase the renewable energy on islands.•A new strategy is used to select the optimal energy configuration for each scenario.•The more demand becomes manageable, the more the proposed method increases PV power.
So-called Measure-Correlate-Predict (MCP) methods have been extensively proposed in renewable energy related literature to estimate the wind resources that represent the long-term conditions at a ...target site where a short-term wind data measurement campaign has been held.
The main differences between the various MCP methods lie fundamentally in the type of relationship established between the wind data (speed and direction) recorded at the target site and the wind data recorded simultaneously at one or various nearby weather stations which serve as reference stations and for which long-term data series are also available.
This paper reviews a wide range of MCP methods that have been proposed in the context of wind energy analysis, a number of which have been implemented in wind energy industry software applications. This review includes the initial methods first proposed in the 1940s which generally attempted only to estimate the long-term mean annual wind speed from a single reference station, and extends up to the most recent methods proposed in the present century based on automatic learning techniques which use several reference stations.
In addition to offering a description of the linear, non-linear and probabilistic transfer functions used by the different algorithms, the hypotheses on which these functions are based and the data format with which the various methods work (time series or frequency distributions), this review will also cover limitations in the use of MCP methods, the uncertainty associated with them and the different reference data sources that have been studied. In this sense, the extensive collection of MCP methods which is brought together and reviewed in this paper, ranging from the simplest and easiest-to-use models to the most complicated computational ones which require specific user experience, comprises an extremely useful catalogue when it comes to choosing the best predictor method.
The probability density function (PDF) of wind speed is important in numerous wind energy applications. A large number of studies have been published in scientific literature related to renewable ...energies that propose the use of a variety of PDFs to describe wind speed frequency distributions. In this paper a review of these PDFs is carried out. The flexibility and usefulness of the PDFs in the description of different wind regimes (high frequencies of null winds, unimodal, bimodal, bitangential regimes, etc.) is analysed for a wide collection of models. Likewise, the methods that have been used to estimate the parameters on which these models depend are reviewed and the degree of complexity of the estimation is analysed in function of the model selected: these are the method of moments (MM), the maximum likelihood method (MLM) and the least squares method (LSM). In addition, a review is conducted of the statistical tests employed to see whether a sample of wind data comes from a population with a particular probability distribution. With the purpose of cataloguing the various PDFs, a comparison is made between them and the two parameter Weibull distribution (W.pdf), which has been the most widely used and accepted distribution in the specialised literature on wind energy and other renewable energy sources. This comparison is based on: (a) an analysis of the degree of fit of the continuous cumulative distribution functions (CDFs) for wind speed to the cumulative relative frequency histograms of hourly mean wind speeds recorded at weather stations located in the Canarian Archipelago; (b) an analysis of the degree of fit of the CDFs for wind power density to the cumulative relative frequency histograms of the cube of hourly mean wind speeds recorded at the aforementioned weather stations. The suitability of the distributions is judged from the coefficient of determination R2. Amongst the various conclusions obtained, it can be stated that the W.pdf presents a series of advantages with respect to the other PDFs analysed. However, the W.pdf cannot represent all the wind regimes encountered in nature such as, for example, those with high percentages of null wind speeds, bimodal distributions, etc. Therefore, its generalised use is not justified and it will be necessary to select the appropriate PDF for each wind regime in order to minimise errors in the estimation of the energy produced by a WECS (wind energy conversion system). In this sense, the extensive collection of PDFs proposed in this paper comprises a valuable catalogue.
Abstract Thiazolidinedione (TZD) class of peroxisome proliferator receptor gamma (PPAR-γ) agonists display neuroprotective effects in experimental Parkinson's disease (PD) models. Neurons and ...microglia express PPAR-γ, therefore both of them are potential targets for neuroprotection, although the role of each cell type is not clear. Moreover, receptor-dependent as well as receptor-independent mechanisms have been involved. This study further investigated mechanisms of TZD-mediated neuroprotection in PD. We investigated the rosiglitazone effect in the progressive MPTP/probenecid (MPTPp) model of PD. C57BL/6J mice received MPTP (25 mg/kg) plus probenecid (100 mg/kg) twice per week for 5 weeks. Rosiglitazone (10 mg/kg) was given daily until sacrifice, starting on the fourth week of MPTPp treatment, in presence of an ongoing neurodegeneration with microgliosis. Changes in PPAR-γ levels were measured by immunofluorescence and confocal microscopy in tyrosine hydroxylase (TH)-positive neurons and CD11b-positive microglia of the substantia nigra pars compacta (SNc). Chronic MPTPp treatment induced a PPAR-γ overexpression in both TH-positive neurons and microglia (139.9% and 121.7% over vehicle, respectively). Rosiglitazone administration to MPTPp-treated mice, reverted PPAR-γ overexpression in microglia without affecting TH-positive neurons. Thereafter, changes in CD11b and tumor necrosis factor α (TNF-α) immunoreactivity in microglia were evaluated in the SNc. MPTPp progressively increased CD11b immunoreactivity, conferring to microglia a highly activated morphology. Moreover, TNF-α levels were increased (457.38% over vehicle) after MPTPp. Rosiglitazone administration counteracted the increase in CD11b immunoreactivity caused by MPTPp. Moreover, rosiglitazone reverted TNF-α expression to control levels. Nigrostriatal degeneration was assessed by high pressure liquid chromatography (HPLC) measurement of striatal dopamine, and counting of TH-positive neurons in the SNc. MPTPp treatment caused a severe decline of striatal dopamine and a partial degeneration of the SNc. Rosiglitazone arrested the degenerative process in both areas. Results suggest that PPAR-γ expression in microglia and TNF-α production by these cells are crucial changes by which rosiglitazone exerts neuroprotection in PD.
For the purpose of managing the operation of a small-scale prototype of a sea water reverse osmosis desalination plant installed on the island of Gran Canaria (Spain) and enabling it to function with ...fluctuating power input, artificial neural network (ANN) models were incorporated into its control system. The ANN models were developed to generate feed flow and operating pressure setpoints (with the restriction of having to maintain the permeate recovery rate within a certain range) after taking into account not only the available electrical power but also the temperature and conductivity of the feedwater.
It is concluded that the ANN models that were used after training and validation were able to successfully manage the random and widely varying available electrical power. The statistical hypothesis testing that was also performed showed no significant statistical differences (at 5% level) between the errors (both MAE and MAPE) committed when adapting power consumption of the plant to the available electrical power in the various operational tests using different feedwater characteristics.
•ANNs are modelled to manage the variable operation of a prototype SWRO desalination plant.•The ANNs generate appropriate pressure and feed flow setpoints which are used by the control loops to regulate the process.•Genetic algorithms are used to select the number of hidden layers and the number of neurons in each layer.•ANNs adapt the power consumed by the SWRO plant to the variation in the power supplied and to the characteristics of the feed water.•The errors generated in the tests are analysed using statistical hypothesis testing.
Discovered in late 1960, azoles are heterocyclic compounds class which constitute the largest group of available antifungal drugs. Particularly, the imidazole ring is the chemical component that ...confers activity to azoles. Triazoles are obtained by a slight modification of this ring and similar or improved activities as well as less adverse effects are reported for triazole derivatives. Consequently, it is not surprising that benzimidazole/benzotriazole derivatives have been found to be biologically active. Since benzimidazole has been widely investigated, this review is focused on defining the place of benzotriazole derivatives in biomedical research, highlighting their versatile biological properties, the mode of action and Structure Activity Relationship (SAR) studies for a variety of antimicrobial, antiparasitic, and even antitumor, choleretic, cholesterol-lowering agents.
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•We report the versatile biological properties of benzotriazole derivatives.•Benzotriazole is evaluated as lonely pharmacophore or fused in polycyclic systems.•Benzotriazole is often used as bioisosteric replacement of some triazolic systems.•Fusion of benzotriazole with quinolones modified their drug mode of action.•Benzotriazol acrylonitriles demonstrated a potent tubulin inhibition.
Healthcare workers exposed to coronavirus 2019 (COVID-19) patients could be psychologically distressed. This study aims to assess the magnitude of psychological distress and associated factors among ...hospital staff during the COVID-19 pandemic in a large tertiary hospital located in north-east Italy.
All healthcare and administrative staff working in the Verona University Hospital (Veneto, Italy) during the COVID-19 pandemic were asked to complete a web-based survey from 21 April to 6 May 2020. Symptoms of post-traumatic distress, anxiety and depression were assessed, respectively, using the Impact of Event Scale (IES-R), the Self-rating Anxiety Scale (SAS) and the Patient Health Questionnaire (PHQ-9). Personal socio-demographic information and job characteristics were also collected, including gender, age, living condition, having pre-existing psychological problems, occupation, length of working experience, hospital unit (ICUs and sub-intensive COVID-19 units vs. non-COVID-19 units). A multivariable logistic regression analysis was performed to identify factors associated with each of the three mental health outcomes.
A total of 2195 healthcare workers (36.9% of the overall hospital staff) participated in the study. Of the participants, 35.7% were nurses, 24.3% other healthcare staff, 16.4% residents, 13.9% physicians and 9.7% administrative staff. Nine per cent of healthcare staff worked in ICUs, 8% in sub-intensive COVID-19 units and 7.6% in other front-line services, while the remaining staff worked in hospital units not directly engaged with COVID-19 patients. Overall, 63.2% of participants reported COVID-related traumatic experiences at work and 53.8% (95% CI 51.0%-56.6%) showed symptoms of post-traumatic distress; moreover, 50.1% (95% CI 47.9%-52.3%) showed symptoms of clinically relevant anxiety and 26.6% (95% CI 24.7%-28.5%) symptoms of at least moderate depression. Multivariable logistic regressions showed that women, nurses, healthcare workers directly engaged with COVID-19 patients and those with pre-existing psychological problems were at increased risk of psychopathological consequences of the pandemic.
The psychological impact of the COVID-19 pandemic on healthcare staff working in a highly burdened geographical of north-east Italy is relevant and to some extent greater than that reported in China. The study provides solid grounds to elaborate and implement interventions pertaining to psychology and occupational health.
•Eight measure-correlate-predict (MCP) models used to estimate the wind power densities (WPDs) at a target site are compared.•Support vector regressions are used as the main prediction techniques in ...the proposed MCPs.•The most precise MCP uses two sub-models which predict wind speed and air density in an unlinked manner.•The most precise model allows to construct a bivariable (wind speed and air density) WPD probability density function.•MCP models trained to minimise wind speed prediction error do not minimise WPD prediction error.
The long-term annual mean wind power density (WPD) is an important indicator of wind as a power source which is usually included in regional wind resource maps as useful prior information to identify potentially attractive sites for the installation of wind projects. In this paper, a comparison is made of eight proposed Measure-Correlate-Predict (MCP) models to estimate the WPDs at a target site. Seven of these models use the Support Vector Regression (SVR) and the eighth the Multiple Linear Regression (MLR) technique, which serves as a basis to compare the performance of the other models. In addition, a wrapper technique with 10-fold cross-validation has been used to select the optimal set of input features for the SVR and MLR models. Some of the eight models were trained to directly estimate the mean hourly WPDs at a target site. Others, however, were firstly trained to estimate the parameters on which the WPD depends (i.e. wind speed and air density) and then, using these parameters, the target site mean hourly WPDs. The explanatory features considered are different combinations of the mean hourly wind speeds, wind directions and air densities recorded in 2014 at ten weather stations in the Canary Archipelago (Spain).
The conclusions that can be drawn from the study undertaken include the argument that the most accurate method for the long-term estimation of WPDs requires the execution of a specially trained model which considers the variability of the wind speeds of the reference stations, as well as of the wind directions and air densities, and in addition the functional manner in which these variables participate in the proposed MCP models. It is also concluded that it is important to consider the annual variation of air density even in regions at sea level. It is further concluded that, of the eight MCP models under comparison, the one that predicts the WPDs based on two sub-models (which estimate the wind speeds and air densities in an unlinked manner) always provides the best MAE (Mean Absolute Error), MARE (Mean Absolute Relative Error) and R2 (Coefficient of determination) metrics, with the differences being statistically significant (5% significance) for most of the cases assessed. Additionally, the regulatory capacity of the SVR technique was sufficient to manage most of the overfitting problems, and hence the contribution of the wrapper method was not relevant in our study.
This paper proposes the use of a new Measure–Correlate–Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses ...the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (
R
2) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.
► This paper proposes the use of a new Measure–Correlate–Predict method. ► Contingency-type bivariate distributions with specified marginal distributions are used. ► The method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. ► The proposed method is vulnerable to climate change and to the limited dependence between the reference and candidate site. ► The authors of this paper are working on a more complete model that takes into account the influence of the wind direction in long-term estimation.
Dairy sheep have been farmed traditionally in the Mediterranean basin in southern Europe, central Europe, eastern Europe, and in Near East countries. Currently, dairy sheep farming systems vary from ...extensive to intensive according to the economic relevance of the production chain and the specific environment and breed. Modern breeding programs were conceived in the 1960s. The most efficient selection scheme for local dairy sheep breeds is based on pyramidal management of the population with the breeders of nucleus flocks at the top, where pedigree and official milk recording, artificial insemination, controlled natural mating, and breeding value estimation are carried out to generate genetic progress. The genetic progress is then transferred to the commercial flocks through artificial insemination or natural-mating rams. Increasing milk yield is still the most profitable breeding objective for several breeds. Almost all milk is used for cheese production and, consequently, milk content traits are very important. Moreover, other traits are gaining interest for selection: machine milking ability and udder morphology, resistance to diseases (mastitis, internal parasites, scrapie), and traits related to the nutritional value of milk (fatty acid composition). Current breeding programs based on the traditional quantitative approach have achieved appreciable genetic gains for milk yield. In many cases, further selection goals such as milk composition, udder morphology, somatic cell count, and scrapie resistance have been implemented. However, the possibility of including other traits of selective interest is limited by high recording costs. Also, the organizational effort needed to apply the traditional quantitative approach limits the diffusion of current selection programs outside the European Mediterranean area. In this context, the application of selection schemes assisted by molecular information, to improve either traditional dairy traits or traits costly to record, seems to be attractive in dairy sheep. At the moment, the most effective strategy seems to be the strengthening of research projects aimed at finding causal mutations along the genes affecting traits of economic importance. However, genome-wide selection seems to be unfeasible in most dairy sheep breeds.
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