Recollection, rather than familiarity, seems to play a crucial part in sustaining children’s reading comprehension. However, the roles of recollection and familiarity in both word reading and reading comprehension have yet to be fully understood. In this study, we examined estimates of recollection and familiarity in a working memory updating task using an adaptation of the process dissociation procedure. Our study involved 204 children aged 9–11 years. We administered a keeping track task in which lists of words belonging to various semantic categories (e.g., animals) were presented. The children had to follow two sets of instructions: (a) inclusion, which involved saying whether they had seen a word during the previous learning phase, and b) exclusion, which involved saying whether a word was the last one they had seen that belonged to a given category. Our results showed that recollection contributed to explain reading comprehension, but not word reading, performance. Familiarity, instead, did not predict either of the reading measures (word reading or reading comprehension). We discuss these findings in terms of the importance of considering recollection when studying reading processes during development. Alternative explanations considering the role of WM executive functioning are also considered.
Schizophrenia is a severe mental disorder associated with a wide spectrum of cognitive and neurophysiological dysfunctions. Early diagnosis is still difficult and based on the manifestation of the disorder. In this study, we have evaluated whether machine learning techniques can help in the diagnosis of schizophrenia, and proposed a processing pipeline in order to obtain machine learning classifiers of schizophrenia based on resting state EEG data. We have computed well-known linear and non-linear measures on sliding windows of the EEG data, selected those measures which better differentiate between patients and healthy controls, and combined them through principal component analysis. These components were finally used as features in five standard machine learning algorithms: k-nearest neighbours (kNN), logistic regression (LR), decision trees (DT), random forest (RF) and support vector machines (SVM). Complexity measures showed a high level of ability in differentiating schizophrenia patients from healthy controls. These differences between groups were mainly located in a delimited zone of the right brain hemisphere, corresponding to the opercular area and the temporal pole. Based on the area under the curve parameter in receiver operating characteristic curve analysis, we obtained high classification power in almost all of the machine learning algorithms tested: SVM (0.89), RF (0.87), LR (0.86), kNN (0.86) and DT (0.68). Our results suggest that the proposed processing pipeline on resting state EEG data is able to easily compute and select a set of features which allow standard machine learning algorithms to perform very efficiently in differentiating schizophrenia patients from healthy subjects.
Phenolic compounds derived from olive oil have beneficial health properties against cancer, neurodegenerative, and metabolic diseases. Therefore, there are discrepancies in their impact on mitochondrial function that result in changes in oxidative capacity, mitochondrial respiration, and energetic demands. This review focuses on the versatile role of oleuropein, a potent antioxidant that regulates the AMPK/SIRT1/mTOR pathway to modulate autophagy/mitophagy and maintain metabolic homeostasis.
One of the essential studies in the planning and operation of a microgrid is Power Flow (PF). Traditional PF methods are not applicable for droop-controlled islanded microgrids due to the absence of a slack bus in the system. The steady state characteristics of the system such as frequency, bus voltages, Distributed Generators’ (DG) output power, and actual loads’ demand are obtained based on the droop characteristics of the DGs, and also, the frequency and voltage dependency of the load. Therefore, in this paper, an Enhanced Newton Raphson (ENR) method is proposed for PF in the droop-controlled islanded microgrids. The proposed method is based on the well-known NR method but with a more comprehensive model that considers different droop schemes (resistive, inductive, and complex), load demand dependency on voltage and frequency, π line model, and shunt compensators. Moreover, a new index for selecting proper droop characteristics for any droop-controlled islanded microgrid is proposed. Five test systems with different scales, topologies, droop control strategies, and various load models are considered to evaluate the performance of the proposed method. The results are compared with the recently developed methods and steady-state results of Time-Domain simulations conducted in PLECS software. The results show that the proposed technique has excellent accuracy with low computational time and can be easily integrated into the currently available power system analysis tools.
Background The popularity of consumer-wearable activity trackers has led the scientific community to conduct an increasing number of intervention studies integrating them to promote physical activity (PA) and to reduce sedentary behavior (SB) levels among school-aged children. Therefore, the aim of the present study was to estimate the effects of consumer-wearable activity tracker-based programs on daily objectively measured PA and SB among apparently healthy school-aged children, as well as to compare the influence of participants’ and programs’ characteristics. Methods Eligibility criteria were: (1) participants: apparently healthy school-aged children (< 18 years old); (2) intervention: aimed to promote PA and/or to reduce SB incorporating consumer-wearable activity trackers; (3) comparator: baseline measurements and/or a control/traditional group; (4) outcomes: objectively measured daily PA and/or SB levels; (5) study design: pre-experimental, quasi-experimental, and true-experimental trials. Relevant studies were searched from eight databases up to December 2020, as well as from four alternative modes of searching. Based on the Cochrane Risk-of-bias tool 2, the risk of bias was assessed following four domains: (1) randomization process; (2) missing outcome data; (3) measurement of the outcomes; and (4) selection of the reported results. Based on a comprehensive systematic review, meta-analyses of the Cohen’s standardized mean difference (d) and 95% confidence interval (CI) with a random-effects model were conducted to estimate the overall effects, as well as the within- and between-study subgroups analyses effects, of the programs on daily total steps, moderate-to-vigorous PA (MVPA), total PA and SB. Results Forty-four publications (i.e., 45 studies) were included in the systematic review (5,620 unique participants; mean age = 12.85 ± 2.84 years) and 40 publications (i.e., 41 studies) in the meta-analysis. Programs had a mean length of 11.78 ± 13.17 weeks and most used a waist-worn consumer-wearable activity tracker (77.78% waist-worn; 22.22% wrist-worn). Programs characteristics were: goal-setting strategies (64.06%); participants’ logbooks (56.25%); counseling sessions (62.50%); reminders (28.13%); motivational strategies (42.19%); and exercise routine (17.19%). Results showed a statistically significant moderate favorable effect on daily total steps (d = 0.612, 95% CI 0.477–0.746), small favorable effect on daily MVPA (d = 0.220, 95% CI 0.134–0.307), trivial favorable effect on daily total PA (d = 0.151, 95% CI 0.038–0.264) and trivial unfavorable effect on daily SB (d = 0.172, 95% CI 0.039–0.305). Subgroups analyses showed a higher effect for daily total steps and daily MVPA levels in females and the physically inactive for daily total steps (p = 0.003–0.044). Programs with educational counseling and/or goal-setting strategies, as well as a greater number of strategies, were more effective for improving children’s daily total steps, and wrist-worn activity trackers were more effective than waist-worn trackers for improving their daily MVPA levels (p = 0.001–0.021). Conclusions Consumer-wearable activity tracker-based programs seem to be effective in promoting school-aged children’s daily total steps and MVPA levels, especially for females and those that are physically inactive. These programs should include specific goal-setting, educational counseling, and wrist-worn trackers as especially effective strategies. However, due to the certainty of evidence being from “low” to “moderate”, future well-designed primary research studies about the topic are needed. PROSPERO: CRD42020222363.
Renewable energy and, in particular, solar PV power, will play a crucial role in achieving net-zero emissions scenarios over the coming decades. Increasing the share of renewables in countries’ energy mixes is therefore a major challenge that must be urgently addressed. Within this context, the present work proposes an innovative tool that may accelerate the commissioning process of new solar PV power plants, in the form of an aggregated simulation model to assess a power plant's responses under the technical requirements established in the Spanish grid code. Our analyses, based on the comparison of the proposed model with a detailed model -both representing the same installation, show, from a quantitative and qualitative viewpoint, the accuracy and usefulness of the dynamic simulation model proposed. The aggregated model is shown to be ten times faster than the detailed equivalent, while the errors encountered in the simulation responses of both models are below 0.1% in all cases. It must also be noted that the present paper has allowed for the introduction, in the most recent version of the Spanish grid code and following approval of the Spanish Transmission System Operator (TSO), Red Eléctrica de España (REE), of this new and advantageous PV power plant model. Finally, it is worth mentioning that this study could form the basis for encouraging other countries to also adopt this model in their grid codes as a new tool to help integrate PV power into power grids.
Plastic production and usage increase every year due to its practicality, adaptability, and low-cost production. The problem with plastic arises when it becomes waste and needs to be treated. Most of the plastic we use is produced from petrochemical material that can be used in resource recovery processes like pyrolysis to produce various materials. One of the pyrolysis process products is pyrolytic oil, whose properties are similar to conventional fuels. Minor differences in fuel properties can influence the injection process, in-nozzle flow condition, spray formation and break-up, the combustion process, etc. The presented paper aims to study the influence of pyrolytic oil‘s properties on cavitation formation in the injection nozzle of a common-rail injector. First, the pyrolytic oils were obtained from waste high- and low-density polyethylene using a pyrolysis reactor. Afterwards, the oils were characterized and implemented in the AVL FIRE computation program for studying their influence on cavitation formation in the injection nozzle hole. The obtained results indicate slight differences in fuel properties that influence cavitation formation and spread in the injection hole, which further influence conditions at the exit of the injection hole. The lower lower viscosity of pyrolytic oils influences lower friction in the fuel nozzle flow. The lower densyt and viscosity of pyrolytic oils promotes cavitation formation, advance time of it appearance at injection hole exit and influence the shorter presence of cavitation in the needle closing phase.
In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh, the net present cost (NPC) with 6,170,134 $, the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y.
Nowadays, load serving entities require more active participation from consumers. In this context, demand response programs and home energy management systems play a crucial role in achieving multiple goals such as peak clipping. However, the adoption of demand response initiatives typically has a negative impact on the monetary expenditures of the users. This way, a demand response program should be as effective as possible to make the different goals more easily achievable without compromising the financial requirements of the users. This paper develops a home energy management system that incorporates three novel effective demand response strategies. The effectiveness of the adopted demand response strategies is checked through extensive simulations in a benchmark prosumer environment. To this end, a novel scenario-based approach is developed in order to manage uncertainties. The introduced strategies are compared with other well-known demand response mechanisms. To that end, a novel comparative index, which serves to evaluate the compromise between demand response achievements and energy bills, is introduced. Results obtained demonstrate that the developed strategies are more effective than other approaches. In fact, through the use of the proposed mechanisms, different indicators can be improved until ∼70%, while the electricity bill is only scarcely increased (∼0.11€). Other relevant aspects like the influence of the storage capacity and computational performance of the introduced optimization framework are also analysed.
Photovoltaic and hydrokinetic systems are increasing their penetration in electrical distribution systems. This leads to problems of power fluctuations due to the intermittence of renewable sources that could compromise the stability and quality of the power grid. To address this issue, this paper presents a feasibility study of three power smoothing methods for a photovoltaic-hydrokinetic system using laboratory equipment to optimally replicate the real behavior of this type of hybrid system. The proposed algorithms are based on a hybrid storage system with supercapacitors and lithium-ion batteries, several analyzes are presented based on technical and economic parameters.
Geochemical changes and authigenic clays were detected in the fault rocks of the Padul Fault. An enrichment from the protolith to the ultracataclasites in Si, Al, Fe, Ca, Ti and also Zn but an impoverishment in Mg was detected. Although the protolith (dolostones) and fault rocks are mostly composed of carbonates, the fault rocks are also characterized by the presence of fine-grained clays in the matrix (mainly chlorite and Mg-rich biotite, but also smectite and punctually talc). Neither chlorite nor talc were detected in the protolith. The application of chlorite thermometry gives temperatures around 140-220 • C for chlorites lamellae located on the fault plane. These chlorites are compositionally homogeneous, whereas the chlorites detected in the cataclasites are more variable, although both of them are Mg-rich and have almost no Fe. As well, chlorites from the cataclasites usually include Zn in their composition and, as observed at nanoscale, they are genetically related to biotites, which come from the protolith. Talc grains (< 1 μm) are always found between dolomite and calcite. These observations point to clay mineral reactions in the fault rocks as the chlorite and talc precipitation promoted by circulation of fault-controlled hydrothermal fluids close to 200 • C. Other clays such as smectite are the result of the final step of the hydrothermal activity in the fault. The identification of authigenic clay minerals, which cause weakening mechanisms, is consistent with the geodetic characterization of the Padul Fault, which plays an important role in aseismic deformation.
The aim of this study is to examine the cost management strategies of low-carbon renewable energy projects. This manuscript has important contributions by evaluating different cost-management to increase renewable energy investment projects. Owing to the analysis results of this study, the ways to minimize carbon emission problem can be presented. Additionally, another important contribution of this study is to generate a novel hybrid model based on Pythagorean fuzzy DEMATEL, TOPSIS and Shapley value to find appropriate policies to improve these projects. Furthermore, the accuracy of the proposed model is measured for each cooperative cost management strategy by using the VIKOR method. In addition, sensitivity analysis is also applied with 5 cases for both the TOPSIS and VIKOR methods by changing the weighting results of the criteria consecutively. It is defined that the proposed model is coherent and applicable for the further studies. Moreover, the ranking results of the sensitivity analysis are also consistent with the different cases. The results indicate that internal process has always the lowest costs for the solar energy alternatives. In addition, customer is the lowest cost factor with respect to the wind energy alternative. Thus, it is obvious that improving the qualification of the employees is essential for the improvement of the solar energy projects. Moreover, the effectiveness of the wind energy investments can be increased with the help of giving significance to the customers. Furthermore, it is also concluded that when the level of the cooperation increases, the efficiency of the investments can be higher. Another important point is that if the investors prefer to make weak or strong cooperative cost management strategy, they should primarily focus on solar energy projects because they have lower costs in comparison with other alternatives.
This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.
The integration of new renewable power capacity into the grid constitutes an important challenge. Control issues become more complex, and the behavior of the new installations must be carefully assessed. In this sense, countries are establishing strict technical requirements when integrating new generation facilities into networks. Within this framework, given the great importance of photovoltaic solar energy as a clean electricity generation technology that is experiencing an unstoppable increase, the present paper evaluates the compliance of a real solar photovoltaic power plant with a number of technical requirements established in the new Spanish grid code. The analyses are performed by means of a photovoltaic power plant simulation model representing the actual facility. Thus, the main contribution of the present work consists of mitigating the lack of information about the performance of real experiences on the commissioning process of new renewable power plants, as well as demonstrating the usefulness of simulation models towards the modernization of grid codes. The results reveal the compliance of the renewable facility with the requirements analyzed, showing a maximum deviation of 0.47% in the case of the power–frequency requirements, and very accurate responses of the power plant under the reactive power control requisites. The results also reveal the facility fully covers the reactive power capability requirements. This paper has served not only to deepen the process of compliance with grid codes, but also to obtain approval from the Spanish transmission system operator, Red Eléctrica de España, to commission the power plant under consideration, this being a contribution of special interest to industry.
Olive stones (OS) constitute a waste lignocellulosic material produced by the olive oil industry in great amounts, that currently is only used as a low-value energy source for industrial or domestic boilers. Having in view its full valorization, this work proposes and validates an integrated strategy aiming to obtain three different streams of sugars / lignin-derived compounds. Dilute acid hydrolysis was used to obtain a xylose-rich hydroysate that was chemically converted into furfural with a 48.7 % yield. The resulting acid-pretreated solid biomass that consisted mainly of lignin and cellulose, was subjected to a catalyzed ethanol-based organosolv delignification. Temperature, time, and sulphuric acid concentration were optimized in order to recover added-value lignin products and digestible cellulose. At the optimal conditions (190 ᵒC and 30 min), a 50 % delignification was reached, together with the highest enzymatic hydrolysis yields (190 g glucose/kg of OS). Phenolic compounds content in organosolv liquors reached 41.6 mg GAE/g OS. This extract presented an antioxidant capacity up to 10.9 mg TE/g OS. The pretreated solid fraction was used as a substrate for ethanol production by a pre-saccharification and simultaneous saccharification and fermentation process, enabling to obtain an ethanol concentration of 47 g/L, with a fermentation yield of 61.4 % of the theoretical maximum. Globally, from 100 kg of OS processed according to this experimental scheme, 6.9 kg of furfural, 6.2 kg of ethanol, 7.4 kg of lignin, and 4.2 kg of phenolics compounds can be obtained as main products, thus constituting a way of valorization of renewable material in a multiproduct biorefinery strategy.
In recent years, it has generally been admitted that sustainability is a condition which is supportive of success within internationalization processes. We adopt a model based on antecedents and consequences of internationalization within successful agri-food cooperatives involved in the circular economy in order to offer a comprehensive view about the variables that affect this decision. The methodological design includes two phases. Firstly, supported by a literature review both for social capital and responsible research and innovation, indicators are identified as specific antecedents of the international orientation of cooperatives, and the improvement of reputation and performance, as consequences. Secondly, the use of structural equation modelling is applied to a representative sample of cooperatives practicing the circular economy in Spain to demonstrate the empirical validity of the model. As management implications, cooperative managers can opt to reinforce their social capital and foster responsible research and innovation when a cooperative goes international, as a way to gain better reputation and performance and enter a virtuous circle. Policymakers can also use the set of indicators as a guide for future agri-food regulations.
Europe and North America have numerous studies on 100% renewable power systems. South America, however, lacks research on zero-carbon energy systems, especially understanding South America as an interconnected region, despite its great renewable energy sources, increasing population, and economic productivity. This work extends the cost-optimization energy planning model LEELO and applies it to South America. This results in the to-date most complete model for planning South America’s power sector, with a high temporal (8760 time steps per year) and spatial (over 40 nodes) resolution, and 30 technologies involved. Besides the base case, we study how varying spatial resolution for South America impacted investment results (43, 30, 16, 1 node). Finally, we also evaluate green hydrogen export scenarios, from 0% to 20% on top of the electricity demand. Our study reveals that South America’s energy transition will rely, in decreasing order, on solar photovoltaic, wind, gas as bridging technology, and also on some concentrated solar power. Storage technologies equal to about 10% of the total installed power capacity would be required, aided by the existing hydropower fleet. Not only is the transition to renewables technically possible, but it is also the most cost-efficient solution: electricity costs are expected to reach 32 €/MWh from the year 2035 onwards without the need for further fossil fuels. Varying the spatial resolution, the most-resolved model (43 nodes) reveals 11% and 6% more costs than the one-node and one-node-per-country (16) models, respectively, with large differences in investment recommendations, especially in concentrated solar and wind power. The difference between 43 and 30 nodes is negligible in terms of total costs, energy storage, and technology mix, indicating that 30 nodes are an adequate resolution for this region. We then use the 30-node model to analyze hydrogen export scenarios. The electricity costs drop, as hydrogen is not only a load but also a flexibility provider. Most green hydrogen is produced in Chile, Argentina, and northeast Brazil. For future work, we propose to do an integrated energy plan, including transport and heat, for the region, as well as modeling local hydrogen demands. This work aims to inform policymakers of sustainable transitions, and the energy community.
In certain situations, employing a movable base acting as the excitation of a mechanical system is the best or even the only way to determine the response model for modal analysis. However, the obtained transmissibility functions must be modified prior to modal identification with a conventional procedure based on frequency response functions. Moreover, when employing vision techniques, the response curves are noisier and even poorly defined as the sensitivity is significantly lower than traditional sensors. Using the right model for curve-fitting is particularly relevant in this case. The current study performs an analysis of the adaptation of transmissibility functions, obtained by a vision technique, to improve the accuracy of the modal data estimation with conventional procedures. Two sets of transmissibility functions were evaluated: the originally obtained in the experiment, and the adapted one. After modal identification, significant differences were found concerning mode shapes and curve synthesis. The adaptation improved the accuracy of the identification in all the measurement points, proved by statistical indicators of the curve-fitting procedure like the correlation coefficient and the error between the synthesised and the experimental curves.
The accumulation of waste materials in old, abandoned mining districts without prior sealing of the substrate currently poses a significant risk of contamination to soils and surrounding waters. Some of these mining dams have undergone a reclamation and sealing process in recent years to alleviate this problem. The current study is an analysis of the effectiveness of using different geophysical techniques for characterising these structures and monitoring the isolation performed. This study was carried out in the old mining district of Linares-La Carolina (southern Spain). The following techniques were investigated in this study: direct current (DC) resistivity, induced polarization (IP), ground-penetrating radar (GPR) and the time domain electromagnetic method (TDEM). Combining DC and IP made it possible to characterise the internal structure of the abandoned mining dams, whereby the geometry of these deposits was determined and the corresponding potential risks were assessed. In addition, percolation zones of mining leachates were detected that indicate defects in the sealing stage. TDEM was a good complementary technique for calculating the depth of the bedrock on which the mining dam is located. GPR could only be used to investigate shallow depths at all of the antenna frequencies used (100, 250 and 500 MHz) but was useful for obtained detailed information about the last stages of filling and characterising the encapsulation performed during sealing.
This article introduces a novel hybrid adaptation algorithm comprising both continuous mixed p-norm (CMPN) strategy and the block-sparse Bayesian (BSB) technology to online adapt all the proportional plus integral (PI) controller gains of the power converters of flywheel energy storage units (FESUs). The FESU is based on the doubly fed induction machine (DFIM). The principal target is to improve the transient stability of grid-connected wind farms. To obtain a realistic study, the two-drive train model is utilized in the wind farm modeling affecting the transient analyses. The cascaded control scheme is implemented on controlling both the generator side converter and the grid side inverter of the FESU using the proposed adaptive CMPN-BSB PI controller. The FESU is tied to the point of common coupling of the wind farm, which is tied to the standard IEEE 39 bus system. The performance of the proposed technology is tested under subject the system to different severe faults. The validity of adaptive CMPN-BSB PI controlled FESU is tested by comparing the numerical results with that achieved by least mean square adaptive controlled FESU. The numerical results are performed by PSCAD software. The proposed controlled FESUs will help in improving the transient stability of wind farms and their power quality concerns will be enhanced.
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