Data Treatment Method Research Articles

All-MXene electronic tongue for neurotransmitters detection

Neurotransmitters (NTs) are molecules produced by neurons that act as the body's chemical messengers. Their abnormal levels in the human system have been associated with many disorders and neurodegenerative diseases, which makes the monitoring of NTs fundamentally important. Specifically for clinical analysis and understanding of brain behavior, simultaneous detection of NTs at low levels quickly and reliably is imperative for disease prevention and early diagnosis. However, the methods currently employed are usually invasive or inappropriate for multiple NTs detection. Herein, we developed a MXene-based impedimetric electronic tongue (e-tongue) for sensitive NT monitoring, using Nb2C, Nb4C3, Mo2C, and Mo2Ti2C3 MXenes as sensing units of the e-tongue, and Principal Component Analysis (PCA) as the data treatment method. The high specific surface area, distinct electrical properties, and chemical stability of the MXenes gave rise to high sensitivity and good reproducibility of the sensor array toward NT detection. Specifically, the e-tongue detected and differentiated multiple NTs (acetylcholine, dopamine, glycine, glutamate, histamine, and tyrosine) at concentrations as low as 1 nmol L−1 and quantified NTs present in a mixture. Besides, analyses performed with interferents and actual samples confirmed the system's potential to be used in clinical diagnostics. The results demonstrate that the MXene-based e-tongue is a suitable, rapid, and simple method for NT monitoring with high accuracy and sensitivity.

A statistical method for treating the “negative-age” and “negative-atom-number” conundrums in radioisotope dating

In radioisotope dating, statistical fluctuations of atom counts or decay counts can sometimes lead to unphysical results, such as negative counts after subtracting the background or negative apparent ages near the boundaries of the dating range. How to treat these boundary cases with a unified approach and give proper estimates on age limits and confidence intervals is an important issue in radioisotope dating. In this work, we combine the Feldman-Cousins likelihood-ratio ordering method for interval estimation, a Frequentist approach to make consistent transitions from the choice of one-sided limits to two-sided confidence intervals, and a Bayesian method to treat the background uncertainties. This data treatment method naturally eliminates the unphysical results. We use 81Kr dating and Atom Trap Trace Analysis as an example to illustrate the advantages. This method can be generalized to other radioisotopes and ultra-sensitive analysis techniques.

The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout

BackgroundDual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time. MethodsSimulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout. ResultsNo data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes. ConclusionsNormalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.

Acoustic Stall Detection of Variable Pitch Propeller for Unmanned Aerial Vehicles

This paper presents an analysis of acoustic emission and performance data of a UAV rotor equipped with a variable pitch propeller. The proposed study aims to show propeller noise features that indicate stall flow regime on the blade. Analysis of the noise characteristics around the propeller in terms of power spectral density allow to detect the stall. The study shows that a microphone located at different angles around the propeller can provide data sufficient to determine if the blade angle of attack has forced the propeller into the stall regime. In this range, the propeller’s efficiency in hover decreases and leads to an increase in power consumption. The reresearch is a suggests a method of data treatment to obtain a single parameter indicating a blade stall.

Application of blood purification technology in severe fever with thrombocytopenia syndrome

ABSTRACT Objective: The purpose of this paper is to summarize the blood purification technology applied in patients with severe fever with thrombocytopenia syndrome (SFTS) in the clinical treatment effect. Methods: The medical records of 96 patients with severe SFTS admitted to Weihai Municipal Hospital affiliated to Shandong University from May 2014 to November 2019 were retrospectively analyzed, and they were divided into survival group and death group. The differences in basic data test indexes and treatment method selection during intensive care unit (ICU) admission between the two groups were significantly analyzed, and the indexes with statistically significant differences were included in the multivariate logistic regression analysis related to prognosis. Results: There were no statistically significant differences in age, sex composition, white blood cell count, platelet count, creatine kinase (CK), activated partial thromboplastin time (APTT), serum creatinine and hemofiltration renal replacement therapy between the survival group and the death group. There were statistically significant differences between the two groups in viral load bilirubin and the treatment methods of plasma exchange (PE) or hemoperfusion (HP). Plasma exchange group (78 cases), hemofiltration group (12 cases), hemoperfusion group (6 cases), plasma exchange and hemoperfusion and other blood purification treatment of the prognosis were statistically different. Conclusions: Compared with the three blood purification methods, plasmapheresis has a significant effect on virus removal, improvement of coagulation function and survival rate in patients with severe SFTS. Hemofiltration plays a role in removing inflammatory mediators, replacing renal function, maintaining electrolytes and acid-base balance, but not in removing viruses.

Sensitive and accurate determination of nitrogen in simulated Martian soil and environment with LIBS spectrum fusion and regression based on neural network

Nitrogen is a key element necessary for the emergence and development of life. It is one of the elements targeted by the landed missions on Mars in accordance with their scientific goal of investigation of habitability and search for traces of life. A gas chromatography mass spectrometer (GCMS) instrument on board the Mars Science Laboratory Curiosity rover has revealed the existence of oxidized nitrogen-bearing compounds on Martian surface with an equivalent nitrogen concentration up to 0.01 wt%. Although the detection with laser-induced breakdown spectroscopy (LIBS) also on board the Curiosity rover is desirable, the current performance of LIBS for nitrogen analysis does not show the capacity in terms of limit of detection (LOD) and accuracy. Research on a suitable method should therefore be first engaged in laboratory in order to guide further improvements of LIBS instrument on board Mars rover, as well as the data treatment method. Beyond the sensitivity issue, matrix effect also affects LIBS determination of nitrogen, especially due to its various chemical speciation in geological materials. Method research should answer thus double requirements of improving the sensitivity and reinforcing the robustness with respect to different nitrogen-bearing compounds. An experimental configuration of double detections with a narrow bandwidth Czerny-Tuner (CT) spectrometer and a broad bandwidth Echelle spectrometer, was implemented in this work, in such way that the first ensured a sensitive detection of emission lines from nitrogen and the second complemented with those from major elements in the sample. The fusion of the simultaneously acquired spectra took into account the emission characteristics of the both two types of elements, necessary for an effective and robust multivariate regression based on a neural network. In addition, for a better treatment of different chemical speciation of nitrogen in samples, generalized spectrum was used for training of regression models, after an unsupervised clustering having assigned a type label to each training spectrum. The trained model was tested by collections of independent test samples, resulting in a limit of detection (LOD) of 0.18 wt%, and a root mean square error of prediction (RMSEP) of 0.041 wt%, representing a step forward to nitrogen detection using LIBS on Mars.

Handling Missing Values in Surveys With Complex Study Design: A Simulation Study

Abstract The inverse probability weighting (IPW) method is commonly used to deal with missing-at-random outcome (response) data collected by surveys with complex sampling designs. However, IPW methods generally assume that fully observed predictor variables are available for all sampled units, and it is unclear how to appropriately implement these methods when one or more independent variables are subject to missing values. Multiple imputation (MI) methods are well suited for a variety of missingness patterns but are not as easily adapted to complex sampling designs. In this case study, we consider the National Survey of Morbidity and Risk Factors (EMENO), a multistage probability sample survey. To understand the strengths and limitations of using either missing data treatment method for the EMENO, we present an extensive simulation study modeled on the EMENO health survey, with the target analysis being the estimation of population prevalence of hypertension as well as the association between hypertension and income. Both variables are subject to missingness. We test a variety of IPW and MI methods in simulation and on empirical data from the survey, assessing robustness by varying missingness mechanisms, proportions of missingness, and strengths of fitted response propensity models.

A Qualitative Study on the Motivation to Go to Work by Workers in Crisis Areas Like the Southwest Region of Cameroon

This research is motivated by the desire to understand what motivates people to go to work despite the prevalence of the ongoing socio-political upheavals plaguing some regions of Cameroon and the southwest region in particular. The achievement of this main objective led us, through the interpretavist research design, to adopt the qualitative method. Through the interpretavist research design and based on the qualitative research method We did carry out our empirical data using a semi-structured interview guide administered to 16 teachers working in the said region through the purposive random sampling method and based on proximity. Our data was analysed using the manual data treatment method. The results of our analysis indicated that, there are various reasons that motivate workers to go to work. Prominent among the reasons is the learners’ future, self-satisfaction (intrinsic motivation) as well as administrative constraints (extrinsic motivation).

Post-measurement compressed calibration for ICP-MS-based metal quantification in mine residues bioleaching.

Bioleaching is an actual economical alternative to treat residues, which allows, depending on the chosen strategy, two possible outcomes: (1) a leachate enriched with target metals, or (2) a residue enriched in target metals through the leaching of interfering components (IC). This work aimed to study the metals released by bioprocessing the Panasqueira mine tailings, as a strategy to increase critical metals' relative concentration in residues. Biostimulation of the local microbiota was compared to a bioaugmentation approach using the autochthonous Diaphorobacter polyhydroxybutyrativorans strain B2A2W2. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was selected to study the metals released in the leachate through multi-element external standards. A new data treatment method was developed to use a preliminary sweep of intensities to quantify the non-initial target metals concentration in the leachate, based on preliminary ICP-MS intensity measurements. The results demonstrated that biostimulation was an efficient bioleaching strategy for the IC silicon, aluminium, magnesium, selenium, manganese, zinc, iron, and copper, by decreasing concentration, resulting in a relative increase in the gallium and yttrium (10x) levels in the treated residue. The strategy followed to quantify a large number of elements with ICP-MS using a reduced number of data points for calibration proved valid and speeded up the analytical process.

Second-order derivative of square-wave voltammetry for determination of vanillin at platinum electrode

A simple and sensitive method of data treatment by second-order derivative square wave voltammetry (SD-SWV) was developed for the determination of vanillin at a platinum electrode. It was shown that the irreversible oxidation reaction is controlled by the adsorption and occurs following a mechanism involving two electrons, similar to other phenolic derivatives. The experimental parameters of SWV which exert influence on vanillin determination, such as frequency, pulse amplitude, or step potential, were optimized. The calibration curve shows a linear range between 50 and 430 nM vanillin with a detection limit of about 19 nM (signal/noise = 3). The mathematical treatment of experimental data leads to enhances the sensitivity of the determination and was successfully used for the estimation of vanillin in commercial food products.

The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks

The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.

Analytical method of odor compounds, sampling and data treatment

Analytical method of odor compounds, sampling and data treatment

Estimating a Stoichiometric Solid’s Gibbs Free Energy Model by Means of a Constrained Evolutionary Strategy

Modeling of thermodynamic properties, like heat capacities for stoichiometric solids, includes the treatment of different sources of data which may be inconsistent and diverse. In this work, an approach based on the covariance matrix adaptation evolution strategy (CMA-ES) is proposed and described as an alternative method for data treatment and fitting with the support of data source dependent weight factors and physical constraints. This is applied to a Gibb’s Free Energy stoichiometric model for different magnesium sulfate hydrates by means of the NASA9 polynomial. Its behavior is proved by: (i) The comparison of the model to other standard methods for different heat capacity data, yielding a more plausible curve at high temperature ranges; (ii) the comparison of the fitted heat capacity values of MgSO4·7H2O against DSC measurements, resulting in a mean relative error of a 0.7% and a normalized root mean square deviation of 1.1%; and (iii) comparing the Van’t Hoff and proposed Stoichiometric model vapor-solid equilibrium curves to different literature data for MgSO4·7H2O, MgSO4·6H2O, and MgSO4·1H2O, resulting in similar equilibrium values, especially for MgSO4·7H2O and MgSO4·6H2O. The results show good agreement with the employed data and confirm this method as a viable alternative for fitting complex physically constrained data sets, while being a potential approach for automatic data fitting of substance data.

Geometrically Corrected 50 Megapixel Thermal Imaging with Full Radiometric Information

Thermal imaging has become a valuable method for quality control, damage detection and the identification of energy losses. As in every other method, the resolution is crucial for the outcome of analyses and experiments. State of the art thermography cameras have sensors with 1024 $$\times $$ 768 pixels, which can record thermal images up to 3.1 megapixels via sensor motion and interpolation. Being mobile devices, these cameras cost several ten thousand euros. In this article, we use a comparably cheap 640 $$\times $$ 512 pixel thermography camera for stationary applications, in combination with a panorama head, to create geometrically corrected thermal images of around 50 megapixels with full radiometric information. The panorama head is an optical pivotal point adapter, which can rotate a mounted camera to previously defined angular positions and record images automatically. These single images get geometrically corrected and combined to a whole picture. The technique presented in this article can be applied to almost every thermography camera and is therefore flexible in respect to particular applications or experimental questions. With a novel method of data treatment, the complete radiometric information can be maintained, allowing for a better analysis and processing in thermographic software. With this technique, even full 360 $$^\circ $$ $$\times $$ 180 $$^\circ $$ thermal image panoramas are possible, which can be used for the inventory of buildings. Due to the geometric calibration, these pictures can be combined with dimensionally stable measurements such as e.g. 3D point clouds from laser scanning.

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.

Analysis of methane hydrate nucleation in water-in-oil emulsions: Isothermal vs constant cooling ramp method and new method for data treatment

Temperature dependence of methane hydrate nucleation rate in 50 wt% water in oil emulsion was studied within the temperature range from +2 to −6 °C (supercooling values: 13–21 °C). It turned out that the temperature dependence of nucleation rate in this temperature interval cannot be described by the equations of the classical nucleation theory. This point is discussed with use of available literature data. An approach to the analysis of isothermal survival curves is suggested. Using the approach allows us to establish the amount of nucleation centers in the system and their relative activity. A probable number of nucleation centers and the effect of temperature on the manifestation activity of these centers were determined for the obtained experimental data.

Mixed Poiseuille-Knudsen flow model for Gas Liquid Displacement porometry data treatment

A comprehensive methodology has been developed for treating Gas Liquid Displacement (GLD) porometry data with a flow model called Weber model (WM) describing mixed Poiseuille-Knudsen flow regime. The model has been applied in two options: i) considering that the gas viscosity in porometry experiments is the same as that available in reference books for Poiseuille flow regime and ii) equating the expression for Darcy coefficient in gas flow to that obtained in additional liquid permeability experiments and thus leaving the gas viscosity to be an adaptable parameter. In the analysis of GLD porometry data for a range of different microfiltration membranes, it is found that with the WM in both options identical relative pore-number distribution is estimated; and this distribution satisfactorily reproduces both dry and wet flow data from the GLD experiments. The absolute pore-number distributions obtained by the two options are quite similar, but differ in the absolute value of the pore numbers. The pore-number distribution obtained by the second option describes the liquid permeability well, while the first option fails. The WM as a method of GLD porometry data treatment is quite similar to the earlier introduced variable viscosity Poiseuille model (VVPM), and the variable viscosity from the latter model appears to be a combined effect of an uncertainty about actual gas viscosity and the contribution of Knudsen flow. It is concluded that a standard test method for determining pore-size distribution by GLD porometry must include prediction or description of liquid permeability of the membrane. Then, any acceptable gas flow model with adjusted Darcy coefficient obtained from liquid permeability experiment will be suitable for advanced GLD porometry data treatment, beyond the methods typically implemented in gas flow-based porometers, currently used in academia and industry.

Quantifying Optical Absorption of Single Plasmonic Nanoparticles and Nanoparticle Dimers Using Microstring Resonators.

The wide and ever-increasing applications of thermoplasmonics demand the need for sensitive and reliable tools to probe optical absorptions of individual nanoparticles. However, most of the currently available techniques focus only on measuring the surface temperature of nanostructures in a particular medium and are either invasive or suffer from low sensitivity, lengthy calibration, or the inability to probe single structures with nanogaps. Here, we present for the first time the use of micromechanical SiN string resonators for quantifying optical absorption cross sections of individual plasmonic nanostructures. Monomers and dimers of nanospheres, nanostars, shell-isolated nanoparticles, and nanocubes are probed. A reliable data treatment method is developed to obtain the absorption cross sections as a function of responsivity across a string. The presented method exhibits an excellent sensitivity of ∼89 Hz/K. This allows quantification of optical absorption cross sections of individual plasmonic structures even when their plasmon resonance wavelengths are far from the laser excitation wavelength. The experimentally obtained optical absorption cross sections agree well with the simulations. Influencing factors including polarization, surface morphology, and nanogap size are discussed. The developed method and the obtained optical absorption profiles facilitate future development and optimization of thermoplasmonic applications.

Cryo-XPS analysis reveals surface composition of microalgae

Green microalgae are widely used for wastewater treatment, food/feed and pigment production, as well as for aquaculture. However, very little is known about their surface composition, which can determine algae behavior in nature and influence their processing. In this work, we use cryo-XPS to characterize the surface chemical composition of three different microalgae species. We investigate the applicability of a data treatment method, developed for bacteria, deconvoluting C 1s with respect to protein, lipid and polysaccharide. We discuss how substances unique to algae contribute to the C 1s spectra and how this affects the transferability of the method to microalgae. Two species of green microalgae isolated from Northern Sweden, Chlorella vulgaris 13-1 and Coelastrella sp. 3-4, were compared to reference culture collection strain, Scenedesmus obliquus RISE (UTEX 417). Cryo-XPS data indicate that surfaces of Coelastrella sp. 3-4 and C. vulgaris 13-1 cells are dominated by protein and carbohydrates. However, the carbohydrate content was higher for C. vulgaris. The reference strain shows the highest content of proteins and has an increased amount of lipids compared to the other two. Estimation of the cell surface hydrophobicity suggests that the hydrophobicity increases in the order: C. vulgaris < Coelastrella sp. < S. obliquus.

Instantaneous decay rate analysis of time resolved photoluminescence (TRPL): Application to nitrides and nitride structures

An analysis of the main recombination modes in nitrides, based on new method of data treatment is proposed for the determination of the carrier recombination processes in optically excited matter measured by time-resolved photoluminescence (PL). The analysis includes basic recombination modes: nonradiative Shockley-Read-Hall (SRH), radiative and Auger recombination in relation to monomolecular, bi-molecular, and tri-molecular processes of optical relaxation. The method is based on the introduction of instantaneous PL decay rate rPL plotted as a function of the PL intensity or of the time. Such an approach provides deep insight into the time evolution of the recombination of the optically excited semiconductor systems and can be applied to the time evolution of a variety of optically excited systems. The demonstration of its strength is given by the application to III-nitride based systems, including nitride highly doped and semi-insulating thick layers, polar and non-polar multi-quantum wells (MQWs). At low temperatures (5 K), the mono- and bi-molecular processes determine the carrier relaxation, and the tri-molecular Auger recombination contribution is negligible. At room temperature the data indicate an important contribution of Auger processes. It is also shown that asymptotic (low excitation), one-exponential recombination rate has different character depending on the presence of the electric fields across the structure.