Classifying unlabeled short texts using a fuzzy declarative approach

Web 2.0 provides user-friendly tools that allow persons to create and publish content online. User generated content often takes the form of short texts (e.g., blog posts, news feeds, snippets, etc). This has motivated an increasing interest on the analysis of short texts and, specifically, on their categorisation. Text categorisation is the task of classifying documents into a certain number of predefined categories. Traditional text classification techniques are mainly based on word frequency statistical analysis and have been proved inadequate for the classification of short texts where word occurrence is too small. On the other hand, the classic approach to text categorization is based on a learning process that requires a large number of labeled training texts to achieve an accurate performance. However labeled documents might not be available, when unlabeled documents can be easily collected. This paper presents an approach to text categorisation which does not need a pre-classified set of training documents. The proposed method only requires the category names as user input. Each one of these categories is defined by means of an ontology of terms modelled by a set of what we call proximity equations. Hence, our method is not category occurrence frequency based, but highly depends on the definition of that category and how the text fits that definition. Therefore, the proposed approach is an appropriate method for short text classification where the frequency of occurrence of a category is very small or even zero. Another feature of our method is that the classification process is based on the ability of an extension of the standard Prolog language, named Bousi~Prolog , for flexible matching and knowledge representation. This declarative approach provides a text classifier which is quick and easy to build, and a classification process which is easy for the user to understand. The results of experiments showed that the proposed method achieved a reasonably useful performance.     

Complexity control of HEVC encoders targeting real-time constraints

High Efficiency Video Coding (HEVC) encoders impose several challenges in computing constrained embedded applications, especially under real-time throughput constraints. This paper proposes an adaptive complexity control scheme (CCS) that dynamically adjusts the encoder to the varying computing capabilities of the hardware platform. To design an efficient scheme, an extensive complexity analysis of key HEVC encoding parameters is herein presented. For this analysis, we developed a parameterized complexity model called “arithmetic complexity,” which can be widely applied to any computing platform. Our results demonstrate that the proposed scheme provides time savings ranging from 10 up to 90 % with an average error (between target and effective complexity) of 1.2 %. Our adaptability and control performance analysis show that the scheme rapidly adapts to dynamic set-point adjustments. Compared to state of the art, our complexity control achieves more accurate results and extra features (such as dynamic set-point adjustment) at the cost of minor losses in coding efficiency.     

Poly(ethylene glycol)-interpenetrated genipin-crosslinked chitosan hydrogels: Structure,pH responsiveness,gelation kinetics,and rheology

In the development of pH-responsive chitosan-based hydrogels, achieving reproducible porosity and swelling behavior is essential for the design of hydrogel networks. Herein, we enhance the level of control in hydrogel microarchitecture by incorporating poly(ethylene glycol) (PEG) into the chitosan–genipin matrix. Hydrogels, varied in composition, were synthesized under mild conditions (37°C, 1 atm, 24 hr), yielding microporous structures with a pore diameter ranging from 11 to 57 μm and an average cross-sectional porosity of approximately 40–64%. Compared to chitosan–genipin hydrogels without PEG, presence of PEG in concentrations up to 1.9 mM generated the same effect as would increase in genipin content, yielding structures with a smaller pore diameter, a lower swelling degree in pH 2 buffer and a higher elastic modulus. Considering cost effectiveness and scale-up, reducing genipin content by the addition of PEG is favorable. Importantly, hydrogel samples containing higher concentrations of PEG (2.9 mM and above) showed a sudden increase in the swelling degree accompanied with a decrease in the elastic modulus. Findings showcase the potential variation in the composition of these hydrogels has in yielding scaffolds with significantly different physico-chemical behaviors.     

Event‐triggered optimal tracking control of nonlinear systems

We propose a novel event‐triggered optimal tracking control algorithm for nonlinear systems with an infinite horizon discounted cost. The problem is formulated by appropriately augmenting the system and the reference dynamics and then using ideas from reinforcement learning to provide a solution. Namely, a critic network is used to estimate the optimal cost while an actor network is used to approximate the optimal event‐triggered controller. Because the actor network updates only when an event occurs, we shall use a zero‐order hold along with appropriate tuning laws to encounter for this behavior. Because we have dynamics that evolve in continuous and discrete time, we write the closed‐loop system as an impulsive model and prove asymptotic stability of the equilibrium point and Zeno behavior exclusion. Simulation results of a helicopter, a one‐link rigid robot under gravitation field, and a controlled Van‐der‐Pol oscillator are presented to show the efficacy of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Formation Mechanism of Alkyl Nitrites,Valuable Intermediates in C1-Upgrading Chemistry and Oxidation Processes

In this contribution we use computational tools to investigate the reaction of alcohol substrates with reactive nitrogen oxide species such as N₂O₃ and N₂O₄, leading to the formation of alkyl nitrites. These nitrites are interesting intermediates which can be processed to various valuable chemicals such as ketones/aldehydes and dimethyl oxalate while regenerating NO _x . As such, NO _x is used as an oxidation mediator, converting alcohol substrates to more reactive nitrites which can be selectively converted to more desired compounds, closing a catalytic cycle in NO _x species.     

Spatial data warehouses and spatial OLAP come towards the cloud: design and performance

Cloud computing systems handle large volumes of data by using almost unlimited computational resources, while spatial data warehouses (SDWs) are multidimensional databases that store huge volumes of both spatial data and conventional data. Cloud computing environments have been considered adequate to host voluminous databases, process analytical workloads and deliver database as a service, while spatial online analytical processing (spatial OLAP) queries issued over SDWs are intrinsically analytical. However, hosting a SDW in the cloud and processing spatial OLAP queries over such database impose novel obstacles. In this article, we introduce novel concepts as cloud SDW and spatial OLAP as a service, and afterwards detail the design of novel schemas for cloud SDW and spatial OLAP query processing over cloud SDW. Furthermore, we evaluate the performance to process spatial OLAP queries in cloud SDWs using our own query processor aided by a cloud spatial index. Moreover, we describe the cloud spatial bitmap index to improve the performance to process spatial OLAP queries in cloud SDWs, and assess it through an experimental evaluation. Results derived from our experiments revealed that such index was capable to reduce the query response time from 58.20 up to 98.89 %.     

Development of composite membrane PBAT: Zeolite Y for application as rhynchophorol release system

Composite membranes consisting of biodegradable poly(butylene adipate‐co‐terephthalate (PBAT) and zeolite Y (0–10 wt %) were produced by extrusion. Zeolite Y is well dispersed in the membrane up to 5 wt %, but tends to agglomerate at higher contents. The presence of zeolite Y in the composite resulted in an improvement of the thermal stability, mechanical properties, and increased the barrier properties. The interaction of the composite membranes with rhynchophorol was investigated by different techniques, showing that the semiochemical progressively lixiviates PBAT monomers, causing thermal and mechanical properties to decrease. However, no interaction seemed to occur between the rhynchophorol and the zeolite. Studies of diffusion of pheromone through membranes have shown that the addition of the zeolite Y has not contributed significantly to a decrease in the release rate of rhynchophorol, but the presence of the zeolite Y helps to increase chemical, mechanical, and thermal stabilities of the membranes. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45757.     

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

Arabinosylated phenolics obtained from SO2‐steam‐pretreated sugarcane bagasse

A pentose‐rich hydrolysate fraction obtained by extraction of steam‐pretreated sugarcane bagasse was analysed with regard to dissolved phenolics. The liquid obtained after steam pretreatment (2% SO₂ (w/w) at 190 °C for 5 min) was divided into two parts: one containing dissolved compounds originating from hemicellulose (with xylose as the dominating compound), and the other containing predominantly dissolved compounds originating from lignin. Using nuclear magnetic resonance, the main dissolved compounds originating from lignin were identified as the glycosylated aromatics, 5‐O‐(trans‐feruloyl)‐L‐Arabinofuranose and 5‐O‐(trans‐coumaroyl)‐L‐Arabinofuranose, together with p‐coumaric acid and small amounts of more common free phenolics such as p‐hydroxybenzaldehyde, p‐hydroxybenzoic acid and vanillin. The phenolic compounds were analysed and quantified using reversed‐phase high‐performance liquid chromatography. The findings show that SO₂ steam explosion opened up new degradation pathways during lignin degradation. Copyright © 2012 Society of Chemical Industry     

Identification of temperature and moisture content fields using a combined neural network and clustering method approach

Studies on the dynamics of temperature and moisture content distributions in porous soils have provided important insight on their effect on the building hygrothermal behavior, where the interaction between both building and soil can contribute to reduce building thermal gains or looses. Hygrothermal aspects can be related to many attributes such as energy consumption, occupants' thermal comfort and health, and material deterioration. Recently, a great variety of mathematical models to predict thermal and moisture content profiles in porous media have been presented in the literature. Most of those models are based on analysis of multilayer measurements or on Fourier analysis. The development and validation of such mathematical models facilitate the understanding of heat and moisture flows at different soil depths. In this research, a radial basis function neural network (RBF-NN) approach, combined with Gath–Geva clustering method in order to predict the temperature and moisture content profiles in soils, has been presented. A set of data obtained from the computation of the coupled heat and moisture transfer in porous soils for the Curitiba city (Paraná State, Brazil) weather data file has been used by the RBF-NN modeling method. Simulation results indicate the potentialities of the RBF-NNs to learn, for the one step ahead identification, the behavior of temperature and moisture content profiles in the media at various depths.