OptFerm - a computational platform for the optimization of fermentation processes

We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for Bioengineering process optimization that can be used to increase productivity. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Also, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The software was built using a component-based modular development methodology, using Java as the programming language. AlBench. a Model-View-Control based application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Also, this allows the tool to be easily extended with new modules, currently being developed

SMS-I: Intelligent Security for Cyber–Physical Systems

Critical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.This research was funded by the Horizon 2020 Framework Programme under grant agreement No 832969. This output reflects the views only of the author(s), and the European Union cannot be held responsible for any use which may be made of the information contained therein. For more information on the project see: http://satie-h2020.eu/.info:eu-repo/semantics/publishedVersio

A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.The present work was done and funded in the scope of the European Union’s Horizon 2020 research and innovation program, under project SeCoIIA (grant agreement no. 871967). This work has also received funding from UIDP/00760/2020.info:eu-repo/semantics/publishedVersio

Na-Ce-modified-SBA-15 as an effective and reusable bimetallic mesoporous catalyst for the sustainable production of biodiesel

With the purpose to compete with the homogeneous catalytic biodiesel production, a highlyefficient bimetallic solid catalyst with high exposed surface, high basicity, and reusablethroughout several reaction cycles, was developed by doping SBA-15 with sodium and cerium indifferent concentrations. The catalyst with 5 wt% of sodium and 20 wt% of cerium showed goodstructural ordering and had the highest basicity due to the presence of a large amount of mediumand strong sites compared to the other materials. It was effectively used as a solid base catalystfor biodiesel production from sunflower oil and absolute methanol. The highest FAME content(98.9 wt%) was achieved under optimum conditions of 40:1 methanol/oil molar ratio, 10 wt%catalyst loading, 60 °C, stirring speed of 600-700 rpm and 180 min. Further, this material wasreused for five consecutive runs, obtaining FAME contents greater than 90% in each one of them.Fil: Sánchez Faba, Edgar Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Ferrero, Gabriel Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Dias, Joana Maia. Universidad de Porto; PortugalFil: Eimer, Griselda Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentin

Alternative raw materials to produce biodiesel through alkaline heterogeneous catalysis

Recent research focuses on new biodiesel production and purification technologies that seek a carbon neutral footprint, as well as cheap, renewable and abundant raw materials that do not compete with the demand for food. Then, many attractive alternatives arise due to their availability or low cost, such as used cooking oil, Jatropha oil (non-edible) or by-products of vegetable oil refineries. Due to their composition and the presence of moisture, these oils may need a pretreatment to reach the established conditions to be used in the biodiesel production process so that the final product complies with the international quality standards. In this work, a solid catalyst based on 10 wt % sodium oxide supported on mesoporous silica SBA-15 was employed in the transesterification of different feedstocks (commercial sunflower and soybean oil, used cooking oil, acid oil from soapstock and Jatropha hieronymi oil) with absolute methanol in the following reaction conditions: 2?8 wt % catalyst, 14:1 methanol to oil molar ratio, 60 °C, vigorous magnetic stirring and 5 h of reaction. In this way, first and second generation biodiesel were obtained through heterogeneous catalysis with methyl ester yields between 52 and 97 wt %, depending on the FFA and the moisture content of the oils.Fil: Sánchez Faba, Edgar Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Ferrero, Gabriel Orlando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; ArgentinaFil: Maia Moreira Dias, Joana. Universidad de Porto; PortugalFil: Eimer, Griselda Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentin

OPTFERM - a computational platform for the optimization of fermentation processes

Numerous products such as antibiotics, proteins, amino-acids and other chemicals are produced using fermentation processes. These systems are affected by biochemical and chemical phenomena as well as environmental conditions. Consequently, several computational tools have been designed and implemented for modeling, simulation and optimization, sharing a common purpose: increase the production yield of the final product. We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for the improvement of Bioengineering processes. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Furthermore, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The features present in this tool allow the users to analyze the robustness of a fed-batch model, compare simulated with experimental data, determine unknown parameters and optimize feeding profiles. The software was built using a component-based modular development methodology, using Java as the programming language. AIBench, a Model-View-Control based Java application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Moreover, this allows the tool to be easily extended with new modules, which are currently being developed

Fenomenologia nas pesquisas em turismo: análise das dissertações dos programas de pós-graduação no Brasil

Na perspectiva de aferir como se vem procedendo metodologicamente e verificar a exata aplicação do método fenomenológico aos estudos em turismo no Brasil, foi realizada neste presente trabalho uma pesquisa de caráter exploratório-descritivo, onde foram analisadas 570 (quinhentos e setenta) dissertações de mestrados acadêmicos, depositadas entre os anos de 2008 e 2016 em todas as instituições aqui examinadas, pertencentes ao território brasileiro. Em um enfoque da busca da compreensão do caminho metodológico foi feita uma análise de dados, observando-se os processos e técnicas utilizadas pelos pesquisadores em questão, tais como entrevistas em profundidade, observação participante e relatos experienciais. Da análise da discussão dos resultados observou-se que, das 570 (quinhentos e setenta) dissertações analisadas, apenas 13 (treze) adotaram o método fenomenológico. Diante deste fato, conclui-se que, dada a necessidade de um embasamento teórico complexo de seu pesquisador, este método é de pouca aplicação nas instituições envolvidas nesta pesquisa. Ademais, a essencialidade da postura neutra de seu pesquisador dificulta muito a caracterização das pesquisas como adotantes do método

Hippocampal sclerosis and status epilepticus - cause or consequence? A MRI study

Background: Transient imaging abnormalities, including changes on diffusion-weighted imaging (DWI), maybe seen in status epilepticus. These abnormalities can be followed by hippocampal sclerosis. Case report: We report a 15-year-old lady with focal non convulsive status epilepticus (NCSE) and focal slowing on EEG. DWI exhibited abnormal hyperintense signals in bilateral temporal and insular cortices. After 3 weeks, MRI performed a localizated hippocampal atrophy. Conclusion: the MRI findings indicated vasogenic and cytotoxic edema during seizure activity and subsequent loss of brain parenchyma.Fleury Inst, Magnet Resonance Imaging Unit, São Paulo, SP, BrazilUniversidade Federal de São Paulo, Dept Neurol, Div Gen Neurol, São Paulo, SP, BrazilUniversidade Federal de São Paulo, Dept Neurol, Div Gen Neurol, São Paulo, SP, BrazilWeb of Scienc

Análise dos fatores condicionantes da emissão de notificações aos acordos SPS e TBT

The objectives of this research consisted on characterizing and analyzing the regulatory measures issued by Brazil to the SPS and TBT agreements, and determining the reasons that influenced the State institutions on the issuing of notifications to the agribusiness imports, between 1996 and 2008. The methodology consisted of a qualitative approach, through descriptive analysis of the notifications, and a quantitative approach, where were determined the relationships between the economic indicator and the national agribusiness indicators in the issuing of such notifications. The results showed that the Brazilian notifications to these agreements had an ongoing growth in this period with the justification of providing safe food and protection to human, animal, and plant health. Moreover, it showed an inverse relationship between the competitive indicator of Brazilian agribusiness and the investment in this sector, with the issuance of these notifications and, a direct relationship between the growth indicators of the national economy and the issuance of these measures. It can be concluded that these regulatory measures implemented by the government institutions are actually a reflex of the actions and functions of the State, together with the market players, defined in terms of the factors that describe the development of agribusiness.  Os objetivos desse estudo consistiram na caracterização e análise das medidas regulatórias notificadas pelo Brasil aos acordos de barreiras sanitárias e fitossanitárias (SPS) e técnicas (TBT) da OMC; e na determinação de fatores que influenciaram as instituições do Estado na emissão das notificações às importações do agronegócio, no período entre 1996 a 2008. Como metodologia utilizou-se uma abordagem qualitativa, por meio da análise descritiva das notificações, e uma abordagem quantitativa, com a qual foram determinados os relacionamentos entre indicadores econômicos e do agronegócio nacional e a emissão das notificações. Os resultados obtidos mostraram um crescimento contínuo das notificações emitidas pelo Brasil, no período entre 1996 a 2008, sob as justificativas de prover alimentos seguros e proteção à saúde humana, animal e vegetal. Além disso, mostraram um relacionamento inverso entre os indicadores de competitividade do agronegócio brasileiro e dos investimentos no setor, com a emissão das notificações e, um relacionamento direto entre os indicadores de crescimento da economia e a emissão dessas medidas. Conclui-se que as medidas regulatórias implementadas pelas instituições governamentais, são na realidade um reflexo das ações e funções do Estado, junto aos agentes de mercado, definidas em função de fatores que descrevem o desenvolvimento do agronegócio