Software assurance practices for mobile applications

Mobile software applications have to cope with a particular environment that involves small size, limited resources, high autonomy requirements, competitive business models and many other challenges. To provide development guidelines that respond to these needs, several practices have been introduced; however, it is not clear how these guidelines may contribute to solve the issues present in the mobile domain. Furthermore, the rapid evolution of the mobile ecosystem challenges many of the premises upon which the proposed practices were designed. In this paper, we present a survey of the literature on software assurance practices for mobile applications, with the objective of describing them and assessing their contribution and success. We identified, organized and reviewed a body of research that spans in three levels: software development processes, software product assurance practices, and software implementation practices. By carrying out this literature survey, we reviewed the different approaches that researchers on Software Engineering have provided to address the needs that raise in the mobile software development arena. Moreover, we review the evolution of these practices, identifying how the constant changes and modernization of the mobile execution environment has impacted the methods proposed in the literature. Finally, we introduced discussion on the application of these practices in a real productive setting, opening an area for further research that may determine if practitioners have followed the proposed assurance paradigms.     

The effect of co-encapsulation of Lactobacillus rhamnosus GG ATCC 53103 with inulin on alginate/chitosan matrix: the viability in fermented soy blend and simulated digestive system

This research verified the ability of Lactobacillus rhamnosus encapsulated with inulin to tolerate the simulated digestive system and their viability in a soy blend. Probiotic encapsulated in alginate-chitosan matrix without inulin presented a better encapsulation efficiency (80.92%) than encapsulation with inulin (57.39%). On the 28th day, the count of probiotics decreased by 3.42 and 1.99 logarithmic cycles of free and encapsulated cells without inulin, respectively. In contrast, the microorganisms encapsulated with inulin showed an increase of 1.26 logs CFU g⁻¹. During gastrointestinal simulation, cell counts decreased by 0.78, 1.55 and 1.95 CFU g⁻¹ logs for encapsulated cells without inulin, free and encapsulated with inulin, respectively. Sensory panellists liked the fermented soy blend with encapsulated lactobacilli, and this result shows the possibility to create new probiotic foods of plant origin. Therefore, the alginate/chitosan matrix can be considered adequate for the encapsulation of L. rhamnosus. The inulin reduces the encapsulation efficiency and increases the cell loss in gastrointestinal simulation. Considering cellular losses, the best option for preparing a fermented soy blend is to use L. rhamnosus encapsulated without inulin.     

Functionalized-radiolabeled hydroxyapatite/tenorite nanoparticles as theranostic agents for osteosarcoma

Hydroxyapatite (HA) nanoparticles (NPs) doped with different radioisotopes for use as theranostic systems play an important role in scientific research nowadays due to their ability to simultaneously act in the treatment and diagnosis of various types of cancers. In this work, we describe the synthesis and characterization of a hydroxyapatite/tenorite nanocomposite functionalized with folic acid, representing a nanotheranostic material with potential for application as an agent in positron emission tomography imaging systems and to act specifically in the treatment and diagnosis of osteosarcoma. ⁶⁴Cu and ³²P were produced by nuclear activation in the TRIGA reactor at CDTN. The obtained samples were characterized by XRD with Rietveld refinement, XAFS, SEM, BET, TGA, FTIR, CHN, ICP-AES, XPS and gamma spectroscopy. We investigated how CuO grows in HA NPs, the stability of the interactions between CuO and HA constituents and the interactions between folic acid and the surface of the HA NPs. The results indicate the formation of a second phase (tenorite) besides hydroxyapatite, and that the interactions between the two phases are stable, resulting in a nanocomposite. Furthermore, the activation of ⁶⁴Cu and ³²P inside the HA matrix, through the exposition to a neutron flux, produces a theranostic material of interest for biological tests.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.     

Reinforcement learning for electricity dispatch in grids with high intermittent generation and energy storage systems: A case study for the Brazilian grid

Intermittent energy sources such as wind and solar have recently been growing a lot faster than dispatchable energy sources in Brazil, which made investments in energy storage systems become an attractive possibility in the country. Current operational policies for energy dispatch do not consider storage systems and need adjustments to fit this technology. With this motivation, we use reinforcement learning techniques to develop policies for managing storage systems in a grid that can handle time-varying inputs and loads, with rolling forecasts. We use a deterministic lookahead (DLA) policy which has been parametrically modified to perform well in the presence of uncertain forecasts. For realistic simulations, the base model considers important characteristics in a grid that influence the interaction between scheduling and real-time operation such as power and ramping capacities, notification times, and stochastic forecasts. The parametric modification with tunable parameters allows an optimal balance between two conflicting services provided by the storage system: time-shifting and spinning reserves. Optimal reserves ranged from 35% to 100%, depending on the tested dataset, which shows the importance of tuning. Differently from stochastic lookahead policies, which are computationally expensive, parameterized DLA policies can be applied to real-time operation after being optimized in a stochastic base model.     

Microwave heating controlled reactive melt infiltration for graphite-Si-SiC ceramics manufacturing

In this work, we investigated a procedure which exploits microwave ovens to produce SiC- based components by reactive melt infiltration of silicon into graphite preforms. The employed oven is designed to grant optical access to the sample surface, which allows to measure its temperature evolution though a noncontact pyrometer. This signal was used as a feedback to control the power provided to the preform and as an experimental output whose analysis provides insight into the reaction mechanism. Specifically, it is found that complete infiltration is achieved much before the end of the reaction. The latter is not fully self-sustained as the global reaction rate continuously decreases with time until it is no more able to keep the temperature above the silicon solidification value. At that point, the reaction stops. The analysis of the processed samples proved that this procedure allows producing fully infiltrated samples without material failure by adjusting the heat provided during the infiltration stage rather than by tuning the preform structure and composition, which is the usual approach. The proposed method is less time and energy consuming than the standard one.     

Improved graph-based SFA: information preservation complements the slowness principle

Machine Learning - Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a multi-dimensional time series. SFA has been extended to supervised...