Bi-layer gelatin active films with “Pitanga” leaf hydroethanolic extract and/or natamycin in the second layer

Research on biopolymers-based active films produced with natural antioxidants and/or antimicrobials has gained attention over the last few years; however, anti-mold activity has been less studied than those of anti-bacteria. The aim of this work was the development and characterization of bi-layer films based on gelatin with natamycin and/or “Pitanga” leaf hydroethanolic extract in the second thin layer in order to determine the effects of these bioactive compounds on bi-layered film properties. The films were characterized regarding their moisture content and solubility in water, optical properties, microstructure, mechanical and thermal properties, water contact angle, water vapor permeability, UV/visible light transmission, X-ray diffraction, Fourier transform infrared (FTIR) spectroscopy, and antioxidant and anti-mold activities. Active films presented activity against Penicillium spp and Aspergillus niger and demonstrated antioxidant activity, as measured by ABTS ^•+ and DPPH ^• methods. Neither additive used in the films' second layer significantly affected the films' moisture content, thermal properties or the molecular interactions of the polymer matrix, assessed by FTIR, although some mechanical properties were affected, and the water contact angle. In conclusion, bi-layer films have reduced the quantity of additives required to maintain the antioxidant and anti-mold activities, as compared to similar monolayer films of the same thickness.     

pH sensitive phosphate crosslinked films of starch-carboxymethyl cellulose

This work aims to develop hydrogel films of starch and carboxymethyl cellulose (CMC) crosslinked with sodium trimetaphosphate (STMP) and to characterize some of their properties. Starch and STMP (S/T), starch and CMC (S/C), and mixed (S/T/C) films were prepared by casting. The degree of substitution, morphology, swelling degree, FTIR, mechanical properties, and sorption isotherms were studied. Reticulated samples (S/T and S/T/C) showed the same degree of substitution (0.050 ± 0.001). All films presented homogeneous morphology, but the mixed film showed greater roughness. Crosslinking increased the swelling capacity of the mixed hydrogel at pH 7, although it remained decreased concerning the S/T hydrogel. However, this property was sensitive to pH variations. The mixed film (S/T/C) showed greater mechanical resistance. The casting process was efficient to produce hydrogel films of starch/CMC crosslinked with STMP and the general results demonstrated the advantages of the mixed hydrogel.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

Depth-Based Classification Method Underlain by a Remote Concentration Measure for Processing Asymmetric Data

A depth-based classification method underlain by a remote concentration measure for processing asymmetric data is developed and investigated. The motivation for the construction of the method was the inefficient use of affine invariant classifiers in combination with depth functions vanishing outside the convex hull of data. The idea of the proposed method is to map a remote space using a remote concentration measure, the Stahel–Donoho remoteness measure, and a corrected remoteness measure.     

Recognition of Actions of Medical Workers on the Basis of Accelerometer Readings with the Use of a Deep Belief Network

This paper analyzes a real set of large-volume medico-statistical data used for the recognition of actions of medical workers on the basis of accelerometer readings at definite moments of time. During the recognition process, a deep belief network was applied to unlabeled data, and then it was supervisedly learned using the error backpropagation method. The obtained results demonstrate higher recognition accuracy in comparison with basic methods. A considerable improvement in medical staff productivity is also achieved.     

Variational Foundations and Generalized Unified Theory of RVE-Based Multiscale Models

A unified variational theory is proposed for a general class of multiscale models based on the concept of Representative Volume Element. The entire theory lies on three fundamental principles: (1) kinematical admissibility, whereby the macro- and micro-scale kinematics are defined and linked in a physically meaningful way; (2) duality, through which the natures of the force- and stress-like quantities are uniquely identified as the duals (power-conjugates) of the adopted kinematical variables; and (3) the Principle of Multiscale Virtual Power, a generalization of the well-known Hill-Mandel Principle of Macrohomogeneity, from which equilibrium equations and homogenization relations for the force- and stress-like quantities are unequivocally obtained by straightforward variational arguments. The proposed theory provides a clear, logically-structured framework within which existing formulations can be rationally justified and new, more general multiscale models can be rigorously derived in well-defined steps. Its generality allows the treatment of problems involving phenomena as diverse as dynamics, higher order strain effects, material failure with kinematical discontinuities, fluid mechanics and coupled multi-physics. This is illustrated in a number of examples where a range of models is systematically derived by following the same steps. Due to the variational basis of the theory, the format in which derived models are presented is naturally well suited for discretization by finite element-based or related methods of numerical approximation. Numerical examples illustrate the use of resulting models, including a non-conventional failure-oriented model with discontinuous kinematics, in practical computations.     

Hefestos: an intelligent system applied to ubiquitous accessibility

This article proposes Hefestos, an intelligent system applied to ubiquitous accessibility. This model uses ubiquitous computing concepts to manage accessibility resources for people with disabilities. Among the concepts employed, context awareness, user profiles and trails management can be highlighted. The paper proposes an ontology for accessibility and delineates scenarios of its application in everyday life of people with disabilities. Moreover, the implementation of a smart wheelchair prototype and its application in a practical experiment is described. Ten users with a range of disability degrees tried the system and filled out a survey based on the technology acceptance model. This experiment demonstrated the main functionalities and the acceptance of the system. The results showed 96 % of acceptance regarding perceived easy of use and 98 % in perceived usefulness. These results were encouraging and show the potential for implementing Hefestos in real life situations.     

Pose consensus in networks of heterogeneous robots with variable time delays

This paper proposes a novel pose (position and orientation) consensus controller for networks of heterogeneous robots modeled in the operational space. The proposed controller is a distributed proportional plus damping scheme that, with a slight modification, solves both the leader–follower and leaderless consensus problems. A singularity‐free representation, unit quaternion, is used to describe the robots orientation, and the network is represented by an undirected and connected interconnection graph. Furthermore, it is shown that the controller is robust to interconnection variable time delays. Experiments with a network of two 6‐degrees‐of‐freedom robots are presented to illustrate the performance of the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.