Universidade Tecnológica Federal do Paraná

Over the last few years, the fourth industrial revolution has attracted more and more attentions all around the world. In the current literature, there is still a lack of efforts to systematically review the state of the art of this new industrial revolution wave. The aim of this study is to address this gap by investigating the academic progresses in Industry 4.0. A systematic literature review was carried out to analyse the academic articles within the Industry 4.0 topic that were published online until the end of June 2016. In this paper, the obtained results from both the general data analysis of included papers (e.g. relevant journals, their subject areas and categories, conferences, keywords) and the specific data analysis corresponding to four research sub-questions are illustrated and discussed. These results not only summarise the current research activities (e.g. main research directions, applied standards, employed software and hardware), but also indicate existing deficiencies and potential research directions through proposing a research agenda. Findings of this review can be used as the basis for future research in Industry 4.0 and related topics.

The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.

The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time-consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring the design of feature extractors by a domain expert. Convolutional Neural Networks (CNNs) are a particular type of deep, feedforward network that have gained attention from research community and industry, achieving empirical successes in tasks such as speech recognition, signal processing, object recognition, natural language processing and transfer learning. In this paper, we conduct some preliminary experiments using the deep learning approach to classify breast cancer histopathological images from BreaKHis, a publicly dataset available at http://web.inf.ufpr.br/vri/breast-cancer-database. We propose a method based on the extraction of image patches for training the CNN and the combination of these patches for final classification. This method aims to allow using the high-resolution histopathological images from BreaKHis as input to existing CNN, avoiding adaptations of the model that can lead to a more complex and computationally costly architecture. The CNN performance is better when compared to previously reported results obtained by other machine learning models trained with hand-crafted textural descriptors. Finally, we also investigate the combination of different CNNs using simple fusion rules, achieving some improvement in recognition rates.

This paper introduces the use of the voltage multiplier technique applied to the classical non-isolated dc-dc converters in order to obtain high step-up static gain, reduction of the maximum switch voltage, zero current switching turn-on. The diodes reverse recovery current problem is minimized and the voltage multiplier also operates as a regenerative clamping circuit, reducing the problems with layout and the EMI generation. These characteristics allows the operation with high static again and high efficiency, making possible to design a compact circuit for applications where the isolation is not required. The operation principle, the design procedure and practical results obtained from the implemented prototypes are presented for the single-phase and multiphase dc-dc converters. A boost converter was tested with the single-phase technique, for an application requiring an output power of 100 W, operating with 12 V input voltage and 100 V output voltage, obtaining efficiency equal to 93%. The multiphase technique was tested with a boost interleaved converter operating with an output power equal to 400 W, 24 V input voltage and 400 V output voltage, obtaining efficiency equal to 95%.

The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.

Summary Phenolic compounds are secondary metabolites widely found in fruits, mostly represented by flavonoids and phenolic acids. The growing interest in these substances is mainly because of their antioxidant potential and the association between their consumption and the prevention of some diseases. The health benefits of these phytochemicals are directly linked to a regular intake and their bioavailability. Studies have shown the importance of the regular consumption of fruits, especially for preventing diseases associated with oxidative stress. In the present review, the most recent articles dealing with polyphenols in fruits are reviewed, focusing on their occurrence, main methods of extraction, quantification and antioxidant assays. In addition, the health benefits and bioaccessibility/bioavailability of phenolic compounds in fruits are addressed.

Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 56 papers from various domains to understand how social characteristics can benefit human–chatbot interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human–chatbot interactions.

An increasing number of plant scientists, including breeders, agronomists, physiologists and molecular biologists, are working towards the development of new and improved energy crops. Research is increasingly focused on how to design crops specifically for bioenergy production and increased biomass generation for biofuel purposes. The most important biofuel to date is bioethanol produced from sugars (sucrose and starch). Second generation bioethanol is also being targeted for studies to allow the use of the cell wall (lignocellulose) as a source of carbon. If a crop is to be used for bioenergy production, the crop should be high yielding, fast growing, low lignin content and requiring relatively small energy inputs for its growth and harvest. Obtaining high yields in nonprime agricultural land is a key for energy crop development to allow sustainability and avoid competition with food production. Sugarcane is the most efficient bioenergy crop of tropical and subtropical regions, and biotechnological tools for the improvement of this crop are advancing rapidly. We focus this review on the studies of sugarcane genes associated with sucrose content, biomass and cell wall metabolism and the preliminary physiological characterization of cultivars that contrast for sugar and biomass yield.

In this paper, we consider a wireless-powered cooperative communication network consisting of one hybrid access-point (AP), one source, and one relay. In contrast to conventional cooperative networks, the source and relay in the considered network have no embedded energy supply. They need to rely on the energy harvested from the signals broadcasted by the AP for their cooperative information transmission. Based on this three-node reference model, we propose a harvest-then-cooperate (HTC) protocol, in which the source and relay harvest energy from the AP in the downlink and work cooperatively in the uplink for the source's information transmission. Considering a delay-limited transmission mode, the approximate closed-form expression for the average throughput of the proposed protocol is derived over Rayleigh fading channels. Subsequently, this analysis is extended to the multi-relay scenario, where the approximate throughput of the HTC protocol with two popular relay selection schemes is derived. The asymptotic analyses for the throughput performance of the considered schemes at high signal-to-noise radio are also provided. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as time allocation, relay number, and relay position, on the throughput performance are extensively investigated.

Agroecology has played a key role in helping Cuba survive the crisis caused by the collapse of the socialist bloc in Europe and the tightening of the US trade embargo. Cuban peasants have been able to boost food production without scarce and expensive imported agricultural chemicals by first substituting more ecological inputs for the no longer available imports, and then by making a transition to more agroecologically integrated and diverse farming systems. This was possible not so much because appropriate alternatives were made available, but rather because of the Campesino-a-Campesino (CAC) social process methodology that the National Association of Small Farmers (ANAP) used to build a grassroots agroecology movement. This paper was produced in a 'self-study' process spearheaded by ANAP and La Via Campesina, the international agrarian movement of which ANAP is a member. In it we document and analyze the history of the Campesino-to-Campesino Agroecology Movement (MACAC), and the significantly increased contribution of peasants to national food production in Cuba that was brought about, at least in part, due to this movement. Our key findings are (i) the spread of agroecology was rapid and successful largely due to the social process methodology and social movement dynamics, (ii) farming practices evolved over time and contributed to significantly increased relative and absolute production by the peasant sector, and (iii) those practices resulted in additional benefits including resilience to climate change.

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.

Lignocellulosic materials, such as forest, agriculture, and agroindustrial residues, are among the most important resources for biorefineries to provide fuels, chemicals, and materials in such a way to substitute for, at least in part, the role of petrochemistry in modern society. Most of these sustainable biorefinery products can be produced from plant polysaccharides (glucans, hemicelluloses, starch, and pectic materials) and lignin. In this scenario, cellulosic ethanol has been considered for decades as one of the most promising alternatives to mitigate fossil fuel dependence and carbon dioxide accumulation in the atmosphere. However, a pretreatment method is required to overcome the physical and chemical barriers that exist in the lignin-carbohydrate composite and to render most, if not all, of the plant cell wall components easily available for conversion into valuable products, including the fuel ethanol. Hence, pretreatment is a key step for an economically viable biorefinery. Successful pretreatment method must lead to partial or total separation of the lignocellulosic components, increasing the accessibility of holocellulose to enzymatic hydrolysis with the least inhibitory compounds being released for subsequent steps of enzymatic hydrolysis and fermentation. Each pretreatment technology has a different specificity against both carbohydrates and lignin and may or may not be efficient for different types of biomasses. Furthermore, it is also desirable to develop pretreatment methods with chemicals that are greener and effluent streams that have a lower impact on the environment. This paper provides an overview of the most important pretreatment methods available, including those that are based on the use of green solvents (supercritical fluids and ionic liquids).

A good search strategy is essential for a successful systematic literature study. Historically, database searches have been the norm, which was later complemented with snowball searches. Our conjecture is that we can perform even better searches if combining these two search approaches, referred to as a hybrid search strategy. Our main objective was to compare and evaluate a hybrid search strategy. Furthermore, we compared four alternative hybrid search strategies to assess whether we could identify more cost-efficient ways of searching for relevant primary studies. To compare and evaluate the hybrid search strategy, we replicated the search procedure in a systematic literature review (SLR) on industry–academia collaboration in software engineering. The SLR used a more “traditional” approach to searching for relevant articles for an SLR, while our replication was executed using a hybrid search strategy. In our evaluation, the hybrid search strategy was superior in identifying relevant primary studies. It identified 30% more primary studies and even more studies when focusing only on peer-reviewed articles. To embrace individual viewpoints when assessing research articles and minimise the risk of missing primary studies, we introduced two new concepts, wild cards and borderline articles, when performing systematic literature studies. The hybrid search strategy is a strong contender for being used when performing systematic literature studies. Furthermore, alternative hybrid search strategies may be viable if selected wisely in relation to the start set for snowballing. Finally, the two new concepts were judged as essential to cater for different individual judgements and to minimise the risk of excluding primary studies that ought to be included.

We propose that 1 + 1 + 1 triple-junction solar cells can provide an increased efficiency, as well as a higher open circuit voltage, compared to tandem solar cells.

This paper presents the analysis, design, and implementation of a parallel connected maximum power point tracking (MPPT) system for stand-alone photovoltaic power generation. The parallel connection of the MPPT system reduces the negative influence of power converter losses in the overall efficiency because only a part of the generated power is processed by the MPPT system. Furthermore, all control algorithms used in the classical series-connected MPPT can be applied to the parallel system. A simple bidirectional dc-dc power converter is proposed for the MPPT implementation and presents the functions of battery charger and step-up converter. The operation characteristics of the proposed circuit are analyzed with the implementation of a prototype in a practical application.

BACKGROUND: Invasive infection by Fusarium sp. is associated with high mortality in patients with hematologic cancer. Yet to the authors' knowledge, little is known regarding predictors of adverse outcome. METHODS: The authors conducted a retrospective review of the records of patients with hematologic carcinoma and invasive fusariosis who were treated at one institution in the U.S. and at 11 centers in Brazil. RESULTS: The records of 84 patients were evaluated. Neutropenia was present in 83% and 33 patients had undergone stem cell transplantation. Only 18 patients (21%) were alive 90 days after the diagnosis of fusariosis. Multivariate predictors of poor outcome were persistent neutropenia (hazard ratio [HR] of 5.43; 95% confidence interval [95% CI], 2.64-11.11) and use of corticosteroids (HR of 2.18; 95% CI, 1.98-3.96). The actuarial survival rate of patients without any of these factors was 67% compared with 30% for patients who recovered from neutropenia but were receiving corticosteroids and 4% for patients with persistent neutropenia only. None of the patients with both risk factors survived (P<0.0001). CONCLUSIONS: Measures to reduce the duration of neutropenia, as well as the judicious use of corticosteroids, may reduce the high mortality rate of fusariosis in patients with hematologic cancer.

Species distributed across vast continental areas and across major biomes provide unique model systems for studies of biotic diversification, yet also constitute daunting financial, logistic and political challenges for data collection across such regions. The tree frog Dendropsophus minutus (Anura: Hylidae) is a nominal species, continentally distributed in South America, that may represent a complex of multiple species, each with a more limited distribution. To understand the spatial pattern of molecular diversity throughout the range of this species complex, we obtained DNA sequence data from two mitochondrial genes, cytochrome oxidase I (COI) and the 16S rhibosomal gene (16S) for 407 samples of D. minutus and closely related species distributed across eleven countries, effectively comprising the entire range of the group. We performed phylogenetic and spatially explicit phylogeographic analyses to assess the genetic structure of lineages and infer ancestral areas. We found 43 statistically supported, deep mitochondrial lineages, several of which may represent currently unrecognized distinct species. One major clade, containing 25 divergent lineages, includes samples from the type locality of D. minutus. We defined that clade as the D. minutus complex. The remaining lineages together with the D. minutus complex constitute the D. minutus species group. Historical analyses support an Amazonian origin for the D. minutus species group with a subsequent dispersal to eastern Brazil where the D. minutus complex originated. According to our dataset, a total of eight mtDNA lineages have ranges >100,000 km2. One of them occupies an area of almost one million km2 encompassing multiple biomes. Our results, at a spatial scale and resolution unprecedented for a Neotropical vertebrate, confirm that widespread amphibian species occur in lowland South America, yet at the same time a large proportion of cryptic diversity still remains to be discovered.

Zein is a biodegradable and biocompatible material extracted from renewable resources; it comprises almost 80% of the whole protein content in corn. This review highlights and describes some zein and zein-based materials, focusing on biomedical applications. It was demonstrated in this review that the biodegradation and biocompatibility of zein are key parameters for its uses in the food-packing, biomedical and pharmaceutical fields. Furthermore, it was pointed out that the presence of hydrophilic-hydrophobic groups in zein chains is a very important aspect for obtaining material with different hydrophobicities by mixing with other moieties (polymeric or not), but also for obtaining derivatives with different properties. The physical and chemical characteristics and special structure (at the molecular, nano and micro scales) make zein molecules inherently superior to many other polymers from natural sources and synthetic ones. The film-forming property of zein and zein-based materials is important for several applications. The good electrospinnability of zein is important for producing zein and zein-based nanofibers for applications in tissue engineering and drug delivery. The use of zein's hydrolysate peptides for reducing blood pressure is another important issue related to the application of derivatives of zein in the biomedical field. It is pointed out that the biodegradability and biocompatibility of zein and other inherent properties associated with zein's structure allow a myriad of applications of such materials with great potential in the near future.

Newcomers' seamless onboarding is important for online communities that depend upon leveraging the contribution of outsiders. Previous studies investigated aspects of the joining process and motivation in open collaboration communities, but few have focused on identifying and understanding the critical barriers newcomers face when placing their first contribution, a period that frequently leads to dropout. This is important for Open Source Software (OSS) projects, which receive contributions from many one-time contributors. Focusing on OSS, our study qualitatively analyzed social barriers that hindered newcomers' first contributions. We defined a conceptual model composed of 58 barriers including 13 social barriers. The barriers were identified from a qualitative data analysis considering different sources: a systematic literature review; open question responses gathered from OSS projects' contributors; students contributing to OSS projects; and semi-structured interviews with 36 developers from 14 different projects. This paper focuses on social barriers and its contributions include gathering empirical evidence of the barriers faced by newcomers, organizing and better understanding these barriers, surveying the literature from the perspective of the barriers, and identifying new potential research streams.

Circular economy can play an important role towards sustainable business management and it can be seen all throughout an organization. Although the current literature regards the circular economy as a guide for more sustainable business models, it is not clear the main implications to key business areas. Therefore this study aimed to present the key impacts of circular economy practices within different business areas that help guide a sustainable management of businesses. To that end, it was identified, by means of a systematic review of the existing literature, the business areas impacted by circular economy practices within an organization. The business areas identified were strategic planning, cost management, supply chain management, quality management, environmental management, process management, logistics and reverse logistics, service management, and research and development, allowing a discussion on the main contributions of the circular economy to each area. A key-impact map was provided summarizing the most influential changes in each area that assist in the management of businesses towards greater sustainability. It is important that organizations understand and accurately internalize circularity principles within their strategic plan. On that note, adopting a circular thinking might enable an organization to obtain more sustainable (economic) results while reducing impacts.