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.     

Self-organized porous TiO2 and ZrO2 produced by anodization

The present work investigates the electrochemical formation of self-organized high aspect ratio TiO₂ and ZrO₂ nanotube layers. The formation and growth of a self-organized porous layer can be achieved directly by anodization without any templates in fluoride containing electrolytes. The morphology of the porous layers is affected by the electrochemical conditions such as the electrolyte composition, the pH and the exact polarization treatment (such as the potential sweep rate from the open-circuit potential to the anodizing potential). For Ti, nanotube layers are formed with diameters varying from approx. 20 nm to 100 nm and lengths from approx. 0.25 μm to 2.5 μm depending on the electrolytes and pH. On the other hand, for Zr, tubes of 50 nm in diameter and up to approx. 17 μm in length can be grown—a key parameter in this case is the potential sweep rate. The large difference between Ti and Zr in the achievable thickness of nanotube layers indicates a difference in the growth mechanism which may be based on the different chemical dissolution rates of electrochemically formed oxides.     

Fast intra prediction algorithm based on texture analysis for 3D-HEVC encoders

The advances in display technologies and the growing popularity of 3D video systems have attracted more consumers for 3D viewing experiences, and, consequently, the demand for storage and transmission of 3D video content is increasing. To cope with this demand, a 3D video extension of high-efficiency video coding (HEVC) standard is being developed and near the final standardization stage. The upcoming 3D-HEVC standard is expected to provide higher encoding efficiency than its predecessors, supporting multiple views with high resolution, at a cost of considerable increase in computational complexity, which can be an obstacle to its use in real-time applications. This article proposes a novel complexity reduction algorithm developed to optimize the 3D-HEVC intra mode decision targeting real-time video processing for consumer devices with limited computational power, such as 3D camcorders and smartphones equipped with multiple cameras and depth acquisition capabilities. The proposed algorithm analyzes the texture frames and depth maps to estimate the orientation of edges present in the prediction unit data, speeding up the intra prediction process and reducing the 3D-HEVC encoding processing time. Experimental results demonstrate that the proposed algorithm can save 26 % in computational complexity on average with negligible loss of encoding efficiency. This solution contributes to make more feasible the compression of 3D videos targeting real-time applications in power-constrained devices.     

Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the conformity assessments that providers of high-risk AI systems are expected to conduct, and the post-market monitoring plans that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that the AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks.

     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.     

Performance analysis of available bandwidth estimation tools for grid networks

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.     

APROMORE: An advanced process model repository

Business process models are becoming available in large numbers due to their widespread use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: how can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionality and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.     

Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

An Efficient Solution for Model Checking Graph Transformation Systems

This paper presents an efficient solution for modeling checking graph transformation systems. The approach transforms AGG specifications into Bogor models and supports both attributed typed graphs and layered transformations. Resulting models are amenable to check interesting properties expressed as combinations of LTL (Linear Temporal Logic) and graph transformation rules. The first experimental results are encouraging and show that in most cases our proposal improves existing approaches, both in terms of performance and expressiveness.