Physicochemical and sensory analysis of dry bean landraces in conjunction with multivariate analysis: an exploratory approach

Five landraces of Greek common dry bean (Phaseolus vulgaris L.), were analysed for thirteen physico‐chemical and eighteen sensory variables. Brightness, acceptance, odour and hardness were the most important sensory variables for the overall acceptability of the cooked product. Application of principal component analysis revealed a rather weak relationship between the sensory attributes and the physico‐chemical variables. However, a certain grouping of cultivars with corresponding properties [(PC1 vs. PC2, Kastoria with saltiness, hardness, grassiness, intensity, protein and metal; Byzitsa bushy with fat, odour and sourness; Ksanthi and Belestino with tenaciousness, chroma, pH and overall impression), (PC1 vs. PC3 Byzitsa bushy with odour and acridness; Ksanthi with metal, hardness and pH; Kastoria with colour, protein and intensity)] was made. On the contrary, cluster analysis showed a much more satisfactory grouping of dry bean varieties based on their geographical origin. The multiple regression equation explained 72.3% of the variation in total acceptance of dry beans. The relative importance of brightness, acceptance, smell and hardness in predicting the overall impression of dry beans was 13%, 13.7%, 26% and 10%, respectively. These results may indicate that smell, brightness and acceptance are the most important factors determining dry bean total acceptability, so effort should be directed at improving these attributes in an attempt to satisfy consumer experience.     

A sparse version of the ridge logistic regression for large-scale text categorization

The ridge logistic regression has successfully been used in text categorization problems and it has been shown to reach the same performance as the Support Vector Machine but with the main advantage of computing a probability value rather than a score. However, the dense solution of the ridge makes its use unpractical for large scale categorization. On the other side, LASSO regularization is able to produce sparse solutions but its performance is dominated by the ridge when the number of features is larger than the number of observations and/or when the features are highly correlated. In this paper, we propose a new model selection method which tries to approach the ridge solution by a sparse solution. The method first computes the ridge solution and then performs feature selection. The experimental evaluations show that our method gives a solution which is a good trade-off between the ridge and LASSO solutions.     

Electric vehicle charging in stochastic smart microgrid operation with fuel cell and RES units

Plug-in electric vehicles increasingly augment their share in the global market as they appear to be an economic and emission-free alternative to modern means of transportation. As their presence strengthens, ways that will ensure economic charge along with uninterrupted grid operation are necessary to be found. This paper aims to approach the economic optimization problem that includes several Electric Vehicles (EVs) within a Low Voltage (LV) network comprising various Distributed Energy Resources (DER) as fuel cell, Renewable Energy Sources (RES), (photovoltaics, wind turbine) etc. via a scenario based simulation. The purpose is to investigate the main variables of the grid, such as its operating cost, charging patterns, power injection from the upstream network, resulting from the coordinated control of DER in Smart Microgrid operation in conjunction to the flexible load the controlled EV charging introduces. The base case study is that of absence of EVs, and therefore the demand is met only by the upstream network and the DER units. Subsequently, EVs are introduced as controllable loads and finally as dispatchable storage units incorporating a Vehicle to Grid (V2G) capability to the Smart Microgrid. Furthermore, the problem is not tackled deterministically and although forecasts for all network parameters are assumed to be known, forecasting errors and stochastic driver patterns cannot be ignored. Thus, for each imposed policy, a scenario based approach is implemented to determine operating cost in various cases along to DER utilization and the effect EVs bear on these results.     

Heat transfer and performance characteristics of axial cooling fans with downstream guide vanes

This study examines experimentally the effect of stators on the performance and heat transfer characteristics of small axial cooling fans. A single fan impeller, followed by nine stator blades in the case of a complete stage, was used for all the experimental configurations. Performance measurements were carried out in a constant speed stage performance test rig while the transient liquid crystal technique was used for the heat transfer measurements. Full surface heat transfer coefficient distributions were obtained by recording the temperature history of liquid crystals on a target plate. The experimental data indicated that the results are highly affected by the flow conditions at the fan outlet. Stators can be beneficial in terms of pressure drop and efficiency, and thus more economical operation, as well as, in the local heat transfer distribution at the wake of the stator blades if the fan is installed very close to the cooling object. However, as the separation distance increases, enhanced heat transfer rate in the order of 25% is observed in the case of the fan impeller.     

Mineralogy,geochemistry and leachability of ashes produced after lignite combustion in Amyntaio Power Station,northern Greece

The Kozani–Ptolemais–Amyntaio basin constitutes the principal coal field of Greece. Approximately 50% of the total power of Greece is generated by five power stations operating in the area. Pulverized lignite, a bottom ash, and a fly ash sample from the power plant of Amyntaio were subjected to a leaching procedure. The relative mass leached (RML) of three potentially toxic trace elements (Cu, Cr, Ni) was determined by use of AAS. Results indicate that the determined elements are more readily available in fly ash than in bottom ash or pulverized lignite. Mineralogical examination of the samples has revealed that calcite, gypsum, quartz, and feldspar are the constituents of lignite; quartz and calcite are found in fly ash particles; while gypsum, quartz, and calcite are found in bottom ash.     

Evaluating Free Rides and Observational Advantages in Set Visualizations

Free rides and observational advantages occur in visualizations when they reveal facts that must be inferred from an alternative representation. Understanding whether these concepts correspond to cognitive advantages is important: do they facilitate information extraction, saving the ‘deductive cost’ of making inferences? This paper presents the first evaluations of free rides and observational advantages in visualizations of sets compared to text. We found that, for Euler and linear diagrams, free rides and observational advantages yielded significant improvements in task performance. For Venn diagrams, whilst their observational advantages yielded significant performance benefits over text, this was not universally true for free rides. The consequences are two-fold: more research is needed to establish when free rides are beneficial, and the results suggest that observational advantages better explain the cognitive advantages of diagrams over text. A take-away message is that visualizations with observational advantages are likely to be cognitively advantageous over competing representations.

     

Optimal management of hydrogen storage in stochastic smart microgrid operation

This paper presents an energy management and reserve scheduling scheme in order to optimally operate a 17-bus Low Voltage (LV) grid-tied microgrid, powered by photovoltaics, a wind turbine and a Fuel Cell (FC) utilizing on site hydrogen production and storage. Since high Renewable Energy Resources (RES) penetration is assumed, the expected deviations due to their intermittency are accounted for by the reserve provision by the FC system, in order to deviate as little as possible from the scheduled energy demand injected by the upstream grid. All these are incorporated into the operating cost of the microgrid assuming penalization of unscheduled power injections from the grid. The intermittency of RES and load are incorporated in the model by assuming known probability density functions for the forecasting errors. Then, energy and reserve scheduling is performed utilizing the Harmony Search algorithm in order to minimize the expected operating costs of the examined system by optimal reserve and energy provision from the stored/generated hydrogen. For that purpose, hourly optimizations are performed for a given year to assess the value on-site hydrogen generation and FC technologies add to microgrid operation and Distributed Generation (DG) in general. The purpose is to prove the use of hydrogen storage systems in effective uncertainty balancing. The hydrogen storage system appears to effectively counter the intermittency of renewables in moderate penetration, reducing the uncertainty costs. In higher renewable penetrations, due to uncertainty being already accounted for by the storage, the benefits of the RES penetration are even greater.