Enzyme activity and partial characterization of proteases obtained from Bromelia karatas fruit and compared with Bromelia pinguin proteases

Food Science and Biotechnology - The enzymatic activity and partial characterization of proteases from Bromelia karatas fruits were evaluated and compared with Bromelia pinguin proteases. The...     

Incorporating the Raft consensus protocol in containers managed by Kubernetes: an evaluation

Replication is a technique widely used to improve the reliability of applications. State machine replication is a special approach, where a set of computers are kept synchronised in the same state despite of failures that could occur in the system. The Raft algorithm can be used to implement a total order delivery protocol, delivering requests at the same order at all replicas, which is fundamental since in this approach all replicas must execute the same sequence of requests to present the same evolution in their states. Raft is easy to understand and implement, when compared to the Paxos algorithm. On the other hand, virtualisation can be seen as a technique that helps the development of reliable applications since it maintains each virtual machine (VM) isolated from the others. Virtualisation in data centres is changing from the traditional VMs to containers. In this context, this paper proposes KRaft, an incorporation of Raft in Kubernetes, a system that manages containers. After an evaluation of performance and resource consumption of KRaft, we found that it presents performance close to Raft executing on physical machines. Moreover, KRaft demands more network transmission while Raft executed in physical machines needs more processing power and memory.     

Capture of UAVs Through GPS Spoofing Using Low-Cost SDR Platforms

Wireless Personal Communications - The increased use of unmanned aerial vehicles (UAVs), better known as drones, by civilians has grown exponentially and their autonomous flight control systems...     

Study of nanofiber scaffolds of PAA,PAA/CS,and PAA/ALG for its potential use in biotechnological applications

In recent times, electrospun nanofibers have been widely studied from several biotechnological approaches; in this work, poly(acrylic acid) (PAA) solutions mixed with chitosan and alginate were electrospun and characterized to determine the behavior of these fibers when used in combination with bacteria, different samples were incubated with the bacterial strains: Streptomyces spp., Micromonospora spp., and Escherichia coli and a OD600 test was performed. The formation of nanofibers via electrospinning and the physicochemical properties of the obtained fibers were evaluated. Results showed that the presence of chitosan enhanced the thermal stability of PAA, since PAA/alginate fibers lost 5% of their mass at 41°C, whereas PAA/chitosan lost this amount at around 125°C. The fibers demonstrated suitable characteristics to be used as a bacteria bioreactor.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Mast Cell Chymase and Kidney Disease

A sizable part (~2%) of the human genome encodes for proteases. They are involved in many physiological processes, such as development, reproduction and inflammation, but also play a role in pathology. Mast cells (MC) contain a variety of MC specific proteases, the expression of which may differ between various MC subtypes. Amongst these proteases, chymase represents up to 25% of the total proteins in the MC and is released from cytoplasmic granules upon activation. Once secreted, it cleaves the targets in the local tissue environment, but may also act in lymph nodes infiltrated by MC, or systemically, when reaching the circulation during an inflammatory response. MC have been recognized as important components in the development of kidney disease. Based on this observation, MC chymase has gained interest following the discovery that it contributes to the angiotensin-converting enzyme’s independent generation of angiotensin II, an important inflammatory mediator in the development of kidney disease. Hence, progress regarding its role has been made based on studies using inhibitors but also on mice deficient in MC protease 4 (mMCP-4), the functional murine counterpart of human chymase. In this review, we discuss the role and actions of chymase in kidney disease. While initially believed to contribute to pathogenesis, the accumulated data favor a more subtle view, indicating that chymase may also have beneficial actions.     

Neural Networks as Inference and Learning Engines

Abstract: This paper presents an abridgment of a neural network constructive methodology and applications with real data. The neural network can be considered as the learning core and inference engine of an expert system that produces either different network designs or simulations as output, its input being data sequences. Basically, it consists of additive structural learning, limiting it by a cross-validation technique.
Considerations about uncertainty treatment in neural networks are also presented, including uncertainty in data, in neuron activation, in outputs, and combination of several uncertainty sources.
Applications include three different sets of data, all of them related to the energy field. First, river streamflow estimation is discussed. Then CO₂ concentration prediction from gas injection rate is studied. Finally, the program learns to imitate a feedwater control system in a nuclear reactor. All tests show good results, as can be seen when compared with other standard methods.     

Development of Brazilian steel construction

This article presents a concise short overview of the development of the steel industry in Brazil, with the emphasis on civil construction. The relatively rapid increase in the application of steel structures for both low and tall buildings and the modern bridges in Brazil are the results of an extensive effort of the steel producers and the improvement of steel courses and research programmes in several universities and institutions during the last two decades. The steel industry in collaboration with the universities, which have provided the results of their recent studies, contributed to the improved steel technologies and composite construction. Data, supplied by the Brazilian Institute of Metallurgy, IBS, is presented on the actual steel production and fabrication with regard to application in civil engineering. Case studies of steel structures, such as multi-storey buildings, space structures, light gauge steel construction and bridges, are presented and commented on. Further, steel structures subjected to corrosion or to fire, as well as steel and composite semi-rigid joints, are mentioned and finally the Brazilian experience with steel architecture is introduced.