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The present work starts from a previous experiment where a culture medium for Dunaliella tertiolecta is developed, aiming its use as biofuel feedstock. The effect of the addition of fertilizer NPK-10: 26: 26, NaCl, NaOH, and the intensity of light incident on algal biomass growth, lipid productivity and CO2 sequestration were analyzed. The experimental data set, is first graphed using the graphical outputs of Engineering Equation Solver (EES), then is adjusted into an Adaptive Neuro Diffuse Inference System (ANFIS), obtaining a simulation of the cultivation process which is an easy to use and very accurate tool for instant evaluation of the process under study. The obtained ANFIS facilitates the analysis of the simultaneous influence of independent variables on the output variables. It is thus shown that the most recent computational facilities are of fundamental interest for the analysis of fermentative processes and in particular to model the cultivation of microalgae to be used as fuel feedstock. The results of the ANFIS model are compared with the experimental data and the effective evaluation of the performed simulation is proved.
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resources from the w.p. 'Uncertainty and stochastic theories on derivatives and risk valuation', by C. Alexander Grajales, Santiago Medina, 2020 * Matlab code * output data * paper figures
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This dataset is about a systematic review of unsupervised learning techniques for software defect prediction (our related paper: "A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction" in Information and Software Technology [accepted in Feb, 2020] ). We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. From each paper we extracted: Title, Year, Journal/conference, 'Predatory' publisher? (Y | N), Count of results reported in paper, Count of inconsistent results reported in paper, Parameter tuning in SDP? (Yes | Default | ?) and SDP references(SDPRefs OrigResults | SDPRefs |SDPNoRefs | OnlyUnSDP). Then from within each paper, we extracted for each experimental result including: Prediction method name (e.g., DTJ48), Project name trained on (e.g., PC4), Project name tested on (e.g., PC4), Prediction type (within-project | cross-project), No. of input metrics (count | NA), Dataset family (e.g., NASA), Dateset fault rate (%), Was cross validation used? (Y | N | ?), Was error checking possible? (Y | N), Inconsistent results? (Y | N | ?), Error reason description (text), Learning type (Supervised | Unsupervised), Clustering method? (Y | N | NA), Machine learning family (e.g., Un-NN), Machine learning technique (e.g., KM), Prediction results (including TP, TN, FP, FN, etc.).
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Results of EMD-based Nonstationary Frequency Analysis over South Korea with Climate Indices for different lags
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Esse modelo foi desenhado no software treeage e foi criado um modelo tipo player onde indivíduos que não possuem o software podem fazer o download no site de uma versão do visualizador e abrir o arquivo. Para tal, é preciso acessar: www.treeage.com, clicar no menu em "Free Trial", preencher o formulário e adquirir uma licença gratuita de visualizador (Viewer license). Foi inserido também uma tabela do Excel com o cálculo dos valores mensais baseado nas posologias de tratamento e nos preços relativos a tabela da CMED de fev/2020 com PMVG de 0%. Ele permite alterar os custos de tratamento a fim de verificar o preço em que cada estrategia de tratamento se tornariam custo-efetiva. É possível realizar análise de custo-efetividade com sensibilidades determinísticas para os custos mensais e probabilística de maneira geral, simulando as distribuições inseridas.
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Smoke Test on 17Jul2019 natscilivecustomer (Dataset-1) Smoke Test on 17Jul2019 natscilivecustomer (Dataset-2)
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Associated research in : Gordon, B. L., Paige, G. B., Miller, S. N., Claes, N., & Parsekian, A. D. (2020). Field scale quantification indicates potential for variability in return flows from flood irrigation in the high altitude western US. Agricultural Water Management, 232, 106062. Readme: The included files are: Calculated Flow, Calculated_Losses, Calculated_Return_Flows, ET_Not_Interpolated, Precipitation, and GIS Database. All the data (except GIS) are in tab delimited ASCII files. GIS data are in standard formats, most site specific information including soils, meadow delineation, instrumentation, etc. can be found in the site_information file. Flow data (Calculated_Flow, Calculated_Losses, Calculated_Return_Flows) were obtained using developed rating curves at each site, where each stilling well was instrumented with a pressure transducer (Level TROLL 500 Data Logger, In-Situ, USA) and manual flow measurements consisting of 25+ individual points for each measurement were made using an electromagnetic current meter (MF Pro, OTT Hydromet, USA). ET data include both measurements from a Large Aperture Scintillometer (LAS MKII, Kipp & Zonen, NLD) and from Penman-Monteith Calculations performed on raw meteorological data collected on site. For Penman-Monteith, we include both raw values and values modified using a crop coefficient from Pochop et al. (1992). Precipitation data were collected using a tipping bucket rain gauge (Rain Collector II, Davis Instruments, USA). All data (except the ET data for the scintillometer) are from May 2015 to October 2015; the ET data from the scintillometer are from June 2015 to October 2015. If you have any questions, or would like raw flow data or unprocessed meterological data, please contact me via email at: beatrice.gordon1@gmail.com
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Data and code for: Time-Varying Causality between Bond and Oil Markets of the United States: Evidence from Over One and Half Centuries of Data
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Batch mesophilic 37oC reactors fed with acetic acid (0.5 mL AC/L every 5-6 days), have been amended with different amount of incineration bottom ash/ and ammonium chloride for 120 days. At the start of the experiments, different mass of IBA was added to the reactors which had been amended with IBA. Then the group of the reactors which amended with NH4CL had received 4 g/L NH4Cl every run of 5-6 days. In parallel batch reactors without IBA/ and NH4Cl were also run. Reactor performance (methane production) and stability (pH drop and VFA accumulation) were investigated. On day end of the experiments i.e. on day 120, a representative digestate sample was collected from each reactor, then sequenced for 16S rRNA gene. The sequence files shown in this data set are fastq files from the illumina sequencing analysis.
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1. Raw data of hydro-distilled ginger essential oil (GEO) weight yeilds 2. Raw data of GLC chemical intensities of chemical compounds in GEO 3. Gas chromatography profiles/ Typical ginger chromatography profile 4. Statistical analysis files 5. Statistical analysis main out puts 6. Clavenger light oil arm illustration
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