Showing papers by "São Paulo Federal Institute of Education, Science and Technology published in 2019"

Notes, outline and divergence times of Basidiomycota

Abstract: The Basidiomycota constitutes a major phylum of the kingdom Fungi and is second in species numbers to the Ascomycota. The present work provides an overview of all validly published, currently used basidiomycete genera to date in a single document. An outline of all genera of Basidiomycota is provided, which includes 1928 currently used genera names, with 1263 synonyms, which are distributed in 241 families, 68 orders, 18 classes and four subphyla. We provide brief notes for each accepted genus including information on classification, number of accepted species, type species, life mode, habitat, distribution, and sequence information. Furthermore, three phylogenetic analyses with combined LSU, SSU, 5.8s, rpb1, rpb2, and ef1 datasets for the subphyla Agaricomycotina, Pucciniomycotina and Ustilaginomycotina are conducted, respectively. Divergence time estimates are provided to the family level with 632 species from 62 orders, 168 families and 605 genera. Our study indicates that the divergence times of the subphyla in Basidiomycota are 406–430 Mya, classes are 211–383 Mya, and orders are 99–323 Mya, which are largely consistent with previous studies. In this study, all phylogenetically supported families were dated, with the families of Agaricomycotina diverging from 27–178 Mya, Pucciniomycotina from 85–222 Mya, and Ustilaginomycotina from 79–177 Mya. Divergence times as additional criterion in ranking provide additional evidence to resolve taxonomic problems in the Basidiomycota taxonomic system, and also provide a better understanding of their phylogeny and evolution.

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Abstract: It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Juvenile hormone signaling in insect oogenesis.

Abstract: Juvenile hormone (JH) plays a crucial role in insect reproduction, but its molecular mode of action only became clear within the last decade. We here review recent findings revealing the intricate crosstalk between JH and ecdysone signaling with nutrient sensing pathways in Drosophila melanogaster, Aedes aegypti, Tribolium castaneum and Locusta migratoria. The finding for a critical role of ecdysis triggering hormone (ETH) in both molting and ooogenesis now also highlights the importance of an integrated view of development and reproduction. Furthermore, insights from non-model insects, especially so social Hymenoptera and termites, where JH function gradually becomes decoupled from reproduction and plays a role in division of labor, emphasize the need to consider life cycle and life history strategies when studying insect reproductive physiology.

Priors and Posteriors in Bayesian Timing of Divergence Analyses: The Age of Butterflies Revisited.

Abstract: The need for robust estimates of times of divergence is essential for downstream analyses, yet assessing this robustness is still rare. We generated a time-calibrated genus-level phylogeny of butterflies (Papilionoidea), including 994 taxa, up to 10 gene fragments and an unprecedented set of 12 fossils and 10 host-plant node calibration points. We compared marginal priors and posterior distributions to assess the relative importance of the former on the latter. This approach revealed a strong influence of the set of priors on the root age but for most calibrated nodes posterior distributions shifted from the marginal prior, indicating significant information in the molecular data set. Using a very conservative approach we estimated an origin of butterflies at 107.6 Ma, approximately equivalent to the latest Early Cretaceous, with a credibility interval ranging from 89.5 Ma (mid Late Cretaceous) to 129.5 Ma (mid Early Cretaceous). In addition, we tested the effects of changing fossil calibration priors, tree prior, different sets of calibrations and different sampling fractions but our estimate remained robust to these alternative assumptions. With 994 genera, this tree provides a comprehensive source of secondary calibrations for studies on butterflies.

Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

Abstract: It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Contribution for minimization the usage of cutting fluids in CFRP grinding

Abstract: Composite materials are becoming essential and widely used in modern industry, mainly in aeronautics, aerospace, and naval sectors. The reason for its increasing use is their structural composition, a combination of two different materials, resulting in a low weight, extremely rigid, and resistant. Due to the material’s anisotropy, it tends to present residual stresses or structural distortions. Recent researches show that the finishing machining process called grinding is the most recommended for eliminating these structural problems. In grinding process, there needs to be a great amount of cutting fluid (flood cooling), and the surface wear is high. The abundant application of these fluids has become a factor of concern for the modern industries, due to the issues related to occupational health and environmental hazard because of their toxic compounds. In reference to these concerns, arises a new methods of application as well the optimized cooling, the minimum quantity lubrication (MQL) technique and dry grinding. This way, this work analyzed the behavior of the surface grinding of carbon fiber reinforced plastic (CFRP) composites using optimized cooling, MQL, and dry cutting as an alternatives to the conventional coolant technique by SEM images of workpiece surface. Surface roughness, grinding force, specific grinding energy, and G ratio were also analyzed. SEM images showed the difference on fiber surface which is produced by the increase of the depth of cut and different lubrication methods adopted. With the results obtained, the MQL technique generated the lowest grinding values and grinding specific energy. The optimized and flood methods provided the lowest wear of the grinding wheel, as well as the better surface finishing.

Green Extraction Approaches for Carotenoids and Esters: Characterization of Native Composition from Orange Peel.

Abstract: Orange peel is a by-product produced in large amounts that acts as a source of natural pigments such as carotenoids. Xanthophylls, the main carotenoid class found in citrus fruit, can be present in its free form or esterified with fatty acids, forming esters. This esterification modifies the compound’s chemical properties, affecting their bioavailability in the human body, and making it important to characterize the native carotenoid composition of food matrices. We aimed to evaluate the non-saponified carotenoid extracts of orange peel (cv. Pera) obtained using alternative green approaches: extraction with ionic liquid (IL), analyzed by high performance liquid chromatography coupled to a diode array detector with atmospheric pressure chemical ionization and mass spectrometry HPLC-DAD-APCI-MS, and supercritical fluid extraction (SFE), followed by supercritical fluid chromatography with atmospheric pressure chemical ionization and triple quadrupole mass spectrometry detection (SFC-APCI/QqQ/MS) in an online system. Both alternative green methods were successfully applied, allowing the total identification of five free carotenoids, one apocarotenoid, seven monoesters, and 11 diesters in the extract obtained with IL and analyzed by HPLC-DAD-APCI-MS, and nine free carotenoids, six carotenoids esters, 19 apocarotenoids, and eight apo-esters with the SFE-SFC-APCI/QqQ/MS approach, including several free apocarotenoids and apocarotenoid esters identified for the first time in oranges, and particularly in the Pera variety, which could be used as a fruit authenticity parameter.

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Abstract: Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events.

Dynamic Capabilities for Sustainability: Revealing the Systemic Key Factors

Abstract: By applying systems thinking theory to capabilities literature, this paper examines the factors that support the development of dynamic capabilities towards sustainable management. For such, we conducted an in-depth single case study using Soft System Methodology (SSM) in an energy organisation from an emerging economy. Our analysis of the last twenty years of operation revealed that the organisation has developed new ways to change and adapt in a disturbing environment by integrating sustainability into three factors: (1) integrative strategy (green products, biodiversity, organic processes and self-sufficient electricity), (2) sustainable culture (sustainable mindset, environmental awareness, learning orientation and decision-making processes) and (3) organisational routines for innovation (new green processes and products, partnerships/alliances and knowledge management). Our results extend the literature by raising a conceptual framework of the fundamental dimensions of dynamic capabilities for sustainability. This is the first study that connects systems thinking and dynamic capabilities theories applied to sustainable management.

High-performing heuristics to minimize flowtime in no-idle permutation flowshop

Abstract: In this article, the issue of production scheduling in a no-idle flowshop environment is addressed. An extensive literature review has shown that there are no heuristics specifically proposed for this problem, especially when it comes to constructive heuristic methods. In this context, this article proposes a highly efficient simple constructive heuristic to minimize the total flowtime criterion. The proposed heuristic was embedded in the high performance iterated greedy algorithm. Computational results and statistical analysis show that the proposed heuristic overperformed the main constructive methods found up to now. In addition, it is observed that the integration of the proposed heuristic with the iterated greedy algorithm provides the most efficient metaheuristic for the problem.

AVARAC: An Availability-Based Resource Allocation Scheme for Vehicular Cloud

Abstract: Intelligent transportation systems (ITSs) are comprised of multiple technologies that are applied to improve the quality of transport, offering services and applications that will monitor, manage the transportation systems, and increase the level of comfort and safety for passengers and drivers. ITSs services are available for vehicular users through the infrastructure, based on the vehicular network. Furthermore, they can use a vehicular cloud to take advantage of all the resources that a cloud can provide. To achieve this, the ITSs require a mechanism that will aggregate and manage all the available resources provided by the vehicles. Moreover, the aggregation and allocation resource schemes must address the characteristics of the vehicular network to attempt all the quality of service requirements. Therefore, one of the greatest challenges lies in managing the allocation and aggregation of vehicle resources when there is no external infrastructure that will support the system. Hence, we propose an aggregate and allocate resource approach to maximize the availability of service. For this, we formulate the problem through the semi-Markov decision process (SMDP) that will provide an optimal solution for the aggregation and allocation problem. Moreover, we use an average reward function and iterative algorithm to solve the SMDP. The results show that the proposed approach showed stable behavior regardless of the frequency of receiving requests for service. Furthermore, the proposed solution has high average reward when compared to other work in the paper.

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Abstract: Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning

Abstract: Machine learning algorithms have been shown to be suitable for securing platforms for IT systems. However, due to the fundamental differences between the industrial internet of things (IIoT) and regular IT networks, a special performance review needs to be considered. The vulnerabilities and security requirements of IIoT systems demand different considerations. In this paper, we study the reasons why machine learning must be integrated into the security mechanisms of the IIoT, and where it currently falls short in having a satisfactory performance. The challenges and real-world considerations associated with this matter are studied in our experimental design. We use an IIoT testbed resembling a real industrial plant to show our proof of concept.

Quantum decoherence effects in neutrino oscillations at DUNE

Abstract: In this work, we analyze quantum decoherence in neutrino oscillations considering the open quantum system framework and oscillations through matter for three-neutrino families. Taking the Deep Underground Neutrino Experiment as a case study, we performed sensitivity analyses for two neutrino flux configurations, finding sensitivity limits for the decoherence parameters. We also offer a physical interpretation for a new peak which arises at the ${\ensuremath{ u}}_{e}$ appearance probability with decoherence. The sensitivity limits found for the decoherence parameters are ${\mathrm{\ensuremath{\Gamma}}}_{21}\ensuremath{\le}1.2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}23}\text{ }\text{ }\mathrm{GeV}$ and ${\mathrm{\ensuremath{\Gamma}}}_{32}\ensuremath{\le}7.7\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}25}\text{ }\text{ }\mathrm{GeV}$ at 90% C.L.

Gluten-free cookies elaborated with buckwheat flour, millet flour and chia seeds

Abstract: The aim of this study was to obtain an optimized gluten-free cookie formulation using alternative flours. For this, a 22 central composite rotatable design (CCRD), with varying concentrations of millet flour (MF) and chia seeds (CS), on a base of buckwheat flour (BF), was used. Control cookies were elaborated with 100% wheat flour (WF). The cookies were characterized for texture and other physical tests and by scanning electron microscopy of their internal structure. The response surfaces for the quality parameters of the cookies showed that the higher the proportion of MF used in the formulations, the lower the height and the greater the diameter, expansion factor, and hardness of the cookies. The addition of up to 10% CS showed no influence on the responses. The optimum point was defined as that with diameter, expansion factor, thickness, and hardness closer to the control cookie: 7.5% CS, 40% MF, and 52.5% BF. The substitution of wheat flour by buckwheat flour, millet flour, and chia seeds can be considered a suitable alternative for the preparation of gluten-free cookies.

How urbanization affects multiple dimensions of biodiversity in tropical butterfly assemblages

Abstract: We evaluated how the taxonomic, phylogenetic and functional diversities of butterflies and their community-weighted traits are affected by urbanization in the southeastern Brazilian Atlantic Forest. For this purpose, a dataset of Nymphalidae species distributed across 15 urban, semiurban, and rural fragments was analyzed. Urbanization was defined by a set of environmental variables. Furthermore, the total area of each fragment was also considered in the analyses but did not influence the results, in which disturbance level and patch connectivity drove the environmental variation across the urban matrix. Species diversity increased towards the more connected fragments, while phylogenetic and functional diversity did not vary in relation to urbanization. A high forewing:hindwing ratio and the frequency of tiger-like wings were positively related to the urban fragments, while a low forewing:hindwing ratio and iridescent wings were related to the semiurban and rural fragments. The suitability of highly interconnected rural habitats for the maintenance of butterfly diversity was corroborated as expected. Nonetheless, our results also showed that semiurban fragments preserved the ecologically relevant traits of butterflies related to forested habitats, expressed in butterfly groups possibly linked with dispersal capability to avoid predation. Careful management of semiurban fragments and urban landscaping, including highly structured and native vegetation outside urban parks, may increase the functional and taxonomic diversities or at least maintain the current levels of functionality in the urban matrix. Thus, it is possible to preserve the biological diversity of native fauna and flora and recover relevant ecosystem services, ensuring the conservation of Neotropical urban centers.

A novel fog-based resource allocation policy for vehicular clouds in the highway environment

Abstract: Vehicular networks has unique characteristics compared to other wireless networks, such as, highly dynamic topology, short transmission time, among others. These characteristics becomes a challenge for management and allocation of vehicle cloud resources. In this paper, we propose a allocatioN and mANgement resourCe policY for vehicular cloud, called of NANCY. For this, we formulate the problem of allocation of resources through the mathematical method Multiple Attribute Decision, which will decide whether NANCY will allocate the resources available to attend or not the service requested by the vehicle. Simulation results showed that NANCY provides a larger amount of service to be provided, providing a reduction in the number of service locks due to its accuracy, as well as a reduction in the amount of services discarded due to its load balancing in resource allocation.