In-plane shear behaviour of thin low reinforced concrete panels for earthquake re-construction

Low reinforced thin concrete panels have been used for the re-construction of living buildings in the devastated zone of L’Aquila. A structural characterization of these types of panels is presented in this paper, paying particular attention to the fact that these panels are subjected mainly to shear forces. Refined compression-field theory (RCFT) has recently been proposed in order to better predict the behaviour of reinforced concrete members subjected to in-plane shear and axial stresses. This theory is based on continuum mechanics, i.e. satisfying compatibility, equilibrium and formulating the constitutive equations in terms of average (i.e. “smeared”) stresses and strains. The improvement of RCFT in comparison with the two most famous theories for reinforced concrete member subjected to shear [i.e. the modified compression-field theory (MCFT), and the rotating-angle softened-truss model (RA-STM)], deals with an embedded bar model based on the tension stiffening model in concrete. After an ad-hoc calibration procedure, the RCFT is employed in order to reproduce the envelope of the experimental load-deflection response of three full-scale thin low reinforced concrete panels subjected to cyclic loading. The predictions provided by RCFT are compared with the experimental data as well as with those provided by MCFT and RA-STM. This paper presents the necessary parameters for the design of thin low reinforced concrete panels using the RCFT. The preliminary numerical validations seem very promising. However, additional experimental data are required for calibrating and validating the parameters of the proposed RCFT theory.     

A multi-objective neural network based method for cover crop identification from remote sensed data

One of the objectives of conservation agriculture to reduce soil erosion in olive orchards is to protect the soil with cover crops between rows. Andalusian and European administrations have developed regulations to subsidise the establishment of cover crops between rows in olive orchards. Current methods to follow-up the cover crops systems by administrations consist of sampling and on ground visits of around 1% of the total olive orchards surface at any time from March to late June. This paper outlines a multi-objective neural network based method for the classification of olive trees (OT), bare soil (BS) and different cover crops (CC), using remote sensing data taken in spring and summer.The main findings of this paper are: (1) the proposed models performed well in all seasons (particularly during the summer, where only 48 pixels of CC are confused with BS and 10 of BS with CC with the best model obtained. This model obtained a 97.80% of global classification, 95.20% in the class with the worst classification rate and 0.9710 in the KAPPA statistics), and (2) the best-performing models could potentially decrease the number of complaints made to the Andalusian and European administrations. The complaints in question concern the poor performance of current on-ground methods to address the presence or absence of cover crops in olive orchards.     

Comprehensive characterization of the functional activities of pressurized liquid and ultrasound-assisted extracts from Chlorella vulgaris

Chlorella vulgaris has been referred as a potential source of bioactive compounds (carotenoids and fatty acids). In this work, the ability of an environmentally friendly extraction technique such as Pressurized Liquid Extraction (PLE) and a traditional extraction technique such as Ultrasound-Assisted Extraction (UAE) to obtain functional compounds from C. vulgaris, at analytical scale, has been demonstrated. Seeked bioactivities were antioxidant and antimicrobial, for their interest in the food industry. Therefore, a methodology including analytical extraction, in-vitro assays and chemical characterization via HPLC-DAD and GC–MS has been used to determine the interest of Chlorella as a source of functional food ingredients. Results demonstrated that PLE provide higher yields than UAE while similar bioactivities were obtained. Important concentration of carotenoids (lutein, neoxanthin, β-carotene, etc.), chlorophylls, sterols, phytols, and fatty acids (among others) have been found in Chlorella extracts that could be correlated to the observed biological activity.     

Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content

Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text’s stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.     

A parallel evolutionary algorithm for technical market indicators optimization

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.     

New possibilistic method for discovering linear local behavior using hyper-Gaussian distributed membership function

This paper presents a method to find a model of a system based on the integration of a set of local models. Mainly, properties are sought for the local models: independence of clusters and interpretability of their validity. This has been achieved through the introduction of a possibilistic clustering for the first property and a pre-fixed shape of the membership functions for the second one. A new cost index for the clustering optimization problem has been defined consisting of two terms: one for global error and another for local errors. By giving higher importance to the local errors term, local models valid regionally can be found. To avoid local optima and numerical issues, the parameters of the models are found using global optimization. This new method has been applied to several data sets, and results show how the desired characteristics can be achieved in the resulting models.     

Renewable polybenzoxazines based in diphenolic acid

The reaction of mixtures of renewable diphenolic acid (DPA) and its methylesterbenzoxazine derivative (MDP-Bz) has been studied. The DPA was introduced to lower the high temperature needed to complete the curing of the pure benzoxazine. In this way, samples with different DPA/MDP-Bz ratio (0, 2, 5, 10 and 25% of DPA) were investigated. Moreover, high performance flame retardant thermosetting resins with phosphorus were prepared through the mixture of MDP-Bz and a DPA-phosphazene derivative (DPA-PPZ). The curing behavior of these materials was studied by differential scanning calorimetry (DSC). Finally, the properties of the materials were evaluated by termogravimetric analysis (TGA), dynamic mechanical analysis (DMTA), tensile measurements, limiting oxygen index (LOI) and UL-94 Burn Test.     

Collective foraging in heterogeneous landscapes

Animals foraging alone are hypothesized to optimize the encounter rates with resources through Lévy walks. However, the issue of how the interactions between multiple foragers influence their search efficiency is still not completely understood. To address this, we consider a model to study the optimal strategy for a group of foragers searching for targets distributed heterogeneously. In our model, foragers move on a square lattice containing immobile but regenerative targets. At any instant, a forager is able to detect only those targets that happen to be in the same site. However, we allow the foragers to have information about the state of other foragers. A forager who has not detected any target walks towards the nearest location, where another forager has detected a target, with a probability exp(−αd), where d is the distance between the foragers and α is a parameter characterizing the propensity of the foragers to aggregate. The model reveals that neither overcrowding (α → 0) nor independent searching (α → ∞) is beneficial for the foragers. For a patchy distribution of targets, the efficiency is maximum for intermediate values of α. In addition, in the limit α → 0, the length of the walks can become scale-free.