Frequency response of an axial-flow compressor exposed to inlet pressure perturbations

Experimental results of a series of engine tests designed to obtain the stage dynamics of an eight-stage axial-flow compressor over the frequency range of 0.5 to 200 hertz are presented. The total pressure at the compressor face was varied by means of a secondary air jet system installed in the engine inlet and positioned to oppose the primary airflow. Total-pressure probes located at each compressor stage were used to obtain the frequency response of each compressor-stage total pressure to the average compressor-inlet total pressure. The engine operating conditions were chosen to illustrate the effects of changing the rotor speed, changing the exhaust nozzle area, and isolating the compressor discharge pressure perturbations from the fuel control and hence, the fuel flow

Study of turbojet combustor dynamics using sweep-frequency data

Sweep frequency nozzle pressure oscillation effects on turbojet combustor dynamic

Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays.

ABSTRACT: The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield

Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space.

Early diagnosis of cattle diseases such as mastitis caused by Staphylococcus aureus (S. aureus) can be made effective if on-site detection methods with portable instruments are available. In this work, we fabricated immunosensors based on a layer-by-layer (LbL) film of chitosan and carbon nanotubes coated with a layer of antibodies to detect S. aureus. Using electrical and electrochemical impedance spectroscopies, detection was possible in buffer solutions and in milk with limits of detection which could be as low as 2.6 CFU/mL for milk,sufficient to detect mastitis at early stages. This high sensitivity is ascribed to the specific interactions involving the antibodies, as demonstrated with polarization-modulated infrared reflection absorption spectroscopy (PMIRRAS). The selectivity of the immunosensor was verified by distinguishing S. aureus-containing samples from possible interferents found in milk, for which the interactive document mapping (IDMAP) was employed.Because the interferents affected the spectra, in spite of this distinguishability, we treated the data with a machine learning technique with decision tree models. A multidimensional calibration space was then obtained with rules that permit interpretability and predictability in detecting S. aureus in matrices with high variability as in milk

Graph Layouts by t‐SNE

We propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts

What Google Maps can do for biomedical data dissemination: examples and a design study

BACKGROUND: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. RESULTS: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. CONCLUSIONS: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations

Nanoarchitectonic E-tongue of electrospun zein/curcumin carbon dots for detecting Staphylococcus aureus in milk.

We report a nanoarchitectonic electronic tongue made with flexible electrodes coated with curcumin carbon dots and zein electrospun nanofibers, which could detect Staphylococcus aureus(S. aureus) in milk using electrical impedance spectroscopy. Electronic tongues are based on the global selectivity concept in which the electrical responses of distinct sensing units are combined to provide a unique pattern, which in this case allowed the detection of S. aureus through non-specific interactions. The electronic tongue used here comprised 3 sensors with electrodes coated with zein nanofibers, carbon dots, and carbon dots with zein nanofibers. The capacitance data obtained with the three sensors were processed with a multidimensional projection technique referred to as interactive document mapping (IDMAP) and analyzed using the machine learning-based concept of multidimensional calibration space (MCS). The concentration of S. aureus could be determined with the sensing units, especially with the one containing zein as the limit of detection was 0.83 CFU/mL (CFU stands for colony-forming unit). This high sensitivity is attributed to molecular-level interactions between the protein zein and C?H groups in S. aureus according to polarization-modulated infrared reflection-absorption spectroscopy (PM-IRRAS) data. Using machine learning and IDMAP, we demonstrated the selectivity of the electronic tongue in distinguishing milk samples from mastitis-infected cows from milk collected from healthy cows, and from milk spiked with possible interferents. Calibration of the electronic tongue can also be reached with the MCS concept employing decision tree algorithms, with an 80.1% accuracy in the diagnosis of mastitis. The low-cost electronic tongue presented here may be exploited in diagnosing mastitis at early stages, with tests performed in the farms without requiring specialized laboratories or personnel