Abstract

Abstract
Road condition has an important role in our daily live. Anomalies in road surface can cause accidents, mechanical failure, stress and discomfort in drivers and passengers. Governments spend millions each year in roads maintenance for maintaining roads in good condition. But extensive maintenance work can lead to traffic jams, causing frustration in road users. In way to avoid problems caused by road anomalies, we propose a system that can detect road anomalies using smartphone sensors. The approach is based in data-mining algorithms to mitigate the problem of hardware diversity. In this work we used scikit-learn, a python module, and Weka, as tools for data-mining. All cleaning data process was made using python language. The fmal results show that it is possible detect road anomalies using only a smartphone. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
Sensory stimulation is used by various brands to induce desired behaviours among their customers. Although its effectiveness is recognised in business contexts, little research has been conducted on sensory marketing. In order to contribute to filling this gap, this study sought to build a model that explains how sensory stimulation influences intentions to purchase a brand. Brand experience and brand equity were expected to mediate this relationship. The empirical validation of the model was conducted by carrying out an online survey with a convenience sample of 302 customers of a brand of the catering industry. The data collected were processed using PLS-SEM methodology. The results reveal that sensory stimulation positively influences brand experience and brand equity, which, in turn, have a positive impact on intentions to purchase the brand in question. The relevant contributions that emerged from this study include not only bridging the aforementioned gap in the literature but also offering significant managerial implications.

Abstract
The spread of rumors, hoaxes and misinformation in online social networks has made urgent the development of tools to help users to verify the credibility of information. Although some developers have already come with solutions, some aspects of human computer interaction in the creation of these tools are still disregarded. This article describes guidelines for development of features to promote auditability of information in social networks. The guidelines were grouped in a guide created from a derivation of a catalog based on information transparency and shows orientations for social network developers on how to avoid the proliferation of false information. © 2003-2012 IEEE.

Abstract
The hierarchical planning concept is commonly used for production planning. Dividing the planning process into subprocesses which are solved separately in the order of the hierarchy decreases the complexity and fits the common organizational structure. However, interaction between planning levels is crucial to avoid infeasibility and inconsistency of plans. Furthermore, optimizing subproblems often leads to suboptimal results for the overall problem. The alternative, a monolithic model integrating all planning levels, has been rejected in the literature because of several reasons. In this study, we show that some of them do not hold for an integrated production planning model combining the planning tasks usually attributed to aggregate production planning and master production scheduling. Therefore, we develop a hierarchical and an integrated model considering both levels, aggregate production planning and master production scheduling. Computational tests show that it is possible to solve the integrated model and that it outperforms the hierarchical approach for all instances. Moreover, an indication is given why and when integration is beneficial.

Abstract
We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.