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Categoria:Revistas dos Estados Unidos
No To Hattatsu. In press Livro, com organizador, editor, compilador como autor: Brigth MA, editor. Holistic nursing and healing. Philadelphia: FA Davis Company; In: Ghilhem D, Zicker F, editors. Livro com tradutor: Stein E. Anorectal and colon diseases: textbook and color atlas of proctology. Meanwhile, anaerobic digesters represent a good alternative for renewable energy produc- tion in Brazil.
Usually, such tools are made for a specific sce- nario and may not fit with particular and general needs.
Páginas na categoria "Revistas dos Estados Unidos"
Our goal was to cre- ate a SaaS for biogas data analytics by using a neural network. The results have shown the SaaS application is able to perform pre- dictions.
The user interface demonstrates to be intuitive, and the predictions were accurate when providing the training algorithm with sufficient data. In addition, the file processing and network training time were good enough under traditional workload conditions. Introduction Anaerobic digestion is a process of degradation that breaks multi-molecular substances and produces a gas composed of methane, carbon dioxide and others.
This gas is also known as biogas, which is often used to produce electric energy [Aslanzadeh ]. Research on biogas is important in developing countries, because the implementation of anaerobic digesters is often less costly than comparable renewable technologies e. Moreover, production of electric energy through biomass shows great potential in Brazil due to the large agricultural and livestock activity.
Meanwhile, cloud computing technology is helping to deliver software and infras- tructure. Cloud Computing is an umbrella term that describes services characterized by their on-demand offering, per-use cost, elastic capacity and virtualized resources.
SaaS Software as a Service is a model of delivering applications to end users through web portals. Users can access the application through the Internet instead of using a locally installed applica- tion.
IaaS Infrastructure as a Service distributes computational infrastructure without re- quiring the user to download physical equipment [Vogel et al. The biogas technology in the IT field is becoming even more important, and sev- eral SaaS applications are available for different contexts in the anaerobic digestion field, such as monitoring the biogas plant [Gas Data ] and [Green Lagoon ] , evalu- ating the financial gains from biogas operations [BT IT ] and simulating the diges- tion process in laboratory [Bioprocess Control ].
In addition, [Sota Solutions ] designed a software that uses Neural Networks to predict and optimize biogas produc- tion. Scientific publications regarding neural networks and machine learning have also shown that it is possible to use neural networks to extract information such as biogas volume output, methane percentage and others from biogas data [Kusiak and Wei , Qdais et al.
Differently, our work seeks to provide an intuitive user interface to apply machine learning algorithms for anaerobic digester data sets. Machine learning may help users to improve the biogas production process by per- forming predictions based on historical data.
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These predictions might suggest improve- ments in the biogas production process, generating more financial gains or producing more energy. However, the application of such algorithms often requires the user to have prior knowledge about programming. Also, to allow laypersons on machine learning to extract smart information from data sets, it is important to abstract the low-level details of these algorithms training, modeling, implementing and processing.
In this paper, our goal is to provide a general idea of a SaaS application for anaerobic digester data analyt- ics. This SaaS seeks to serve users with the necessary tools without requiring knowledge on machine learning and programming.
The user will be able to perform predictions for biogas or methane. This paper is organized as follows: Section 2 presents related publications in the area of machine learning applied to agricultural contexts, as well as SaaS applications for biogas and anaerobic digesters.
Section 3 presents the developed SaaS application. Section 5 shows the results of experiments performed with the application and with the neural network.
Fi- nally, Section 6 presents the conclusion of this work as well as future works. Related Work In this section, several scientific publications regarding machine learning applied to biogas and other agricultural contexts are going be presented. A survey of SaaS applications for biogas was performed, and its results are also presented afterwards. The work tried to create a small decision tree that evaluates whether a given animal should be culled or not. In order to achieve good results with the C4.
When the original data set was used, the algorithm generated a very complex decision tree, taking into account several irrelevant parameters for the problem.
After improving the quality of the data and removing unnecessary features, the C4. The authors found gaps in the literature about soil drying prediction related to information availability, mostly caused because land owners might not install soil sensors due to financial limitations, limited accessibility or technological complexity.
The information used by the work is likely to become available globally from satellite sensors in near future, making the research important in order to improve information accessibility. The work shows that even in a scenario where information is limited, machine learning algorithms may help to solve problems when no other alternative is suitable. The work of [Kusiak and Wei ] tried to predict methane production in a waste water treatment facility. Another data set with records was used as a test data set.
The authors also tested the K-Nearest-Neighbors algorithm. However, the authors concluded that the ANFIS algorithm yielded the best results, because the predictions were closer to the actual observations than the other algo- rithms, achieving a correlation coefficient of 0. The K-Nearest-Neighbors yielded the worst results, since the predictions do not fit the observed data very well.
The work of [Qdais et al. The data analyzed contains records and several features, such as Total Solids, Total Volatile Solids, pH and and temperature.
The resulting neural net- work was tested against another data set with 50 records. To optimize the methane output, the authors developed a genetic algorithm that uses the neural network as a fitness function.
The research used a data set of 71 records and 8 parameters, but not all of these parameters were meaningful, since they did not con- tribute much to the variation of the output variable. PCA was used to improve prediction accuracy. The research found out that a simple feedforward neural network with a single hidden layer did not provide satisfactory results. Before using PCA, the neural network achieved a correlation index of 0.
The research indicates that neural networks can represent highly nonlinear relationships, and also shows that preprocessing the data with techniques such as PCA can lead to improvements on neural network performance. Despite the fact that the studied works did not use large amounts, they have had good results using machine learning algorithms, including neural networks. Furthermore, some of these works had to deal with several problems, including lack of data and poor data set quality.
Significant effort is done ensuring that the training data set has good quality in order to reduce mispredictions. The works also suggest that neural networks often perform well in the context of biogas prediction. The software presented here are focusing on monitoring the biogas production process.
An example is Click! System from Gas Data [Gas Data ]. System, allowing to control the equipment installed in the biogas plant and visualize data, monitoring and notifying via email and SMS. BOGIS evaluates the profit obtained from biogas operations as well as energy and biogas production in a given time period. BOGIS is also independent of biogas plant manufacturers, and receives data directly from biogas plant interfaces.
In respect to the current work, Click! System and Carbon Cloud are able to get data from sensors and present them to the final users.
The technical team working on the biogas plant can get more insight about the digestion process, helping on the diagnostic of problems and optimizations of the production process. Therefore, a biogas system should display general information about the biogas processes. However, discovering methods to improve the biogas production can be done with alternative methods, as shown by [Sota Solutions ].
Tuberculosis Edinb , v. Acesso em: 5 dez. Diverse humoral immune responses and changes in IgG antibody levels against mycobacterial lipid antigens in active tuberculosis. Microbiology, [s. GAW, A. Comparative analysis of the apo a gene, apo a glycoprotein, and plasma concentrations of Lp a in three ethnic groups.
Evidence for no common "null" allele at the apo a locus. Journal of Clinical Investigation, [s. Cell Research, v. Granzyme A as a potential biomarker of Mycobacterium tuberculosis infection and disease.
Immunology Letters, [s. Impact of human immunodeficiency virus and CD4 count on tuberculosis diagnosis: Analysis of city-wide data from Cape Town, South Africa. International Journal of Tuberculosis and Lung Disease, [s.
IgG subclass antibody response to mycobacterial serine protease at different stages of pulmonary tuberculosis. Medical science monitor: international medical journal of experimental and clinical research, [s.
CR, A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics, [s.
Immunity, [s. HONG, J. Efficacy of IP as a biomarker for monitoring tuberculosis treatment.
IMAZ, M. Antibody response to culture filtrate antigens of Mycobacterium tuberculosis during and after treatment of tuberculosis patients.
Tuberculosis therapy: past, present and future. Supplement 36, p. Serum biomarkers of treatment response within a randomized clinical trial for pulmonary tuberculosis.
International Journal of Cancer, [s. Analysis of immune responses against a wide range of Mycobacterium tuberculosis antigens in patients with active pulmonary tuberculosis. KIM, C. Tuberculosis and Respiratory Diseases, [s. Dynamic antibody responses to the Mycobacterium tuberculosis proteome.
LEE, K. CXCR3 ligands as clinical markers for pulmonary tuberculosis. Scandinavian Journal of Immunology, [s. LIU, Q. Inflammation responses in patients with pulmonary tuberculosis in an intensive care unit. Experimental and Therapeutic Medicine, [s.
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Biology of Reproduction, [s. Archives of Medical Research, [s. Complement component C1q as serum biomarker to detect active tuberculosis. Frontiers in Immunology, [s. LUO, F. LUO, L. Antigens Rvc and Rvc are promising novel biomarkers for the diagnosis of Mycobacterium tuberculosis infection.
Emerging Microbes and Infections, [s. Heterogeneous antibody responses in tuberculosis. International Immunology, [s. Detection of IgG1 antibodies against Mycobacterium tuberculosis DosR and Rpf antigens in tuberculosis patients before and after chemotherapy. MAY, Y.
Cross-reactive immunity to Mycobacterium tuberculosis DosR regulon-encoded antigens in individuals infected with environmental, nontuberculous mycobacteria. The Journal of Immunology, [s. Low serum albumin as a risk factor for infection-related in-hospital death among hemodialysis patients hospitalized on suspicion of infectious disease : a Japanese multicenter retrospective cohort study.
Renal Replacement Therapy, [s. Treatment of tuberculosis: A historical perspective. Annals of the American Thoracic Society, [s. NIKI, M.Some papers show that. Software Experiments Prophet Service implements a neural network to process biogas data. However, WEKA could perform better by performing some changes in the data set and configurations, as WEKA chooses between sigmoid and linear activation functions depending on the output variable, which is a dis- crete numeric value representing the handwritten digit from 0 to 9 , but WEKA might be interpreting it as a continuous value.
As can be seen in the picture above, Brazilian call centers have the highest percentage of VoIP implementation which means, in addition to the HR advantage, a cost-based strategy can be implemented and it can mean be profitable outsourcing call center services from Brazil to other countries. When used with data sets as large as , rows and several columns, the training process should not take a long time to run around 10 seconds.
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