Model-fitting Approach Research Articles

Radio galaxy shape measurement with Hamiltonian Monte Carlo in the visibility domain

Radio weak lensing, while a highly promising complementary probe to optical weak lensing, will require incredible precision in the measurement of galaxy shape parameters. In this paper, we extend the Bayesian Inference for Radio Observations model fitting approach to measure galaxy shapes directly from visibility data of radio continuum surveys, instead of from image data. We apply a Hamiltonian Monte Carlo (HMC) technique for sampling the posterior, which is more efficient than the standard Monte Carlo Markov Chain method when dealing with a large dimensional parameter space. Adopting the exponential profile for galaxy model fitting allows us to analytically calculate the likelihood gradient required by HMC, allowing a faster and more accurate sampling. The method is tested on SKA1-MID simulated observations at 1.4 GHz of a field containing up to 1000 star-forming galaxies. It is also applied to a simulated observation of the weak lensing precursor survey SuperCLASS. In both cases we obtain reliable measurements of the galaxies' ellipticity and size for all sources with SNR $\ge 10$, and we also find relationships between the convergence properties of the HMC technique and some source parameters. Direct shape measurement in the visibility domain achieves high accuracy at the expected source number densities of the current and next SKA precursor continuum surveys. The proposed method can be easily extended for the fitting of other galaxy and scientific parameters, as well as simultaneously marginalising over systematic and instrumental effects.

Thermoplastic processing and debinding behavior of NbC-M2 high speed steel cemented carbide

The aim of this study is to evaluate the thermoplastic processing on a new generation of hard metals based on NbC bonded with high speed steel. The conventional technique in producing cemented carbide parts is dry pressing with paraffin binder and post production. To overcome shape restriction in pressing and post production, thermoplastic shaping processes can be used. For this purpose, a previously known binder system for ceramic feedstocks containing paraffin wax and ethylene vinyl acetate was selected and used for the first time with cemented carbides. The stated organic binders were mixed with NbC-12 wt.% M2 high speed steel powder and feedstocks with solid loading content from 50 to 60 vol.% were obtained. The rheological behavior of the feedstocks as well as the critical solid loading content were assessed using a capillary rheometer. A model fitting approach was used for the first time in hardmetal feedstocks to estimate the critical solid loading. Experimental values were fitted to a model from Chong (1971) which is based on bimodally disperse powder mixtures and a critical powder loading of 70 vol.% could be calculated. However, based on the abrasion problems during mixing at high filler content, feedstock with 57.5 vol.% powder loading had the optimum processability characteristics, and was therefore selected for further shaping processes. Thermo gravimetric analysis on green samples was performed after shaping in order to develop an optimum thermal debinding profile. The thermal debinding program was adjusted with special attention on decomposition onset, maximum mass loss rate and decomposition offset temperatures. Initial trials using decomposition offset temperature as holding steps, resulted in fragmented samples after debinding. The final designed heating profile resulted in a consistent mass loss rate according to thermo gravimetric analysis which limited crack formation during debinding. Debinding under a forming gas (95N2-5H2) atmosphere using optimized heating cycles resulted in sound sintered bodies only when green parts were embedded in a powder bed during debinding.

Signed difference analysis: Testing for structure under monotonicity

Signed difference analysis (SDA), introduced by Dunn and James (2003), is used to derive testable consequences from a psychological model in which each dependent variable is presumed to be a monotonically increasing function of a linear or nonlinear combination of latent variables. SDA is based on geometric properties of the combination of latent variables that are preserved under arbitrary monotonic transformation and requires estimation neither of these variables nor of the monotonic functions. The aim of the present paper is to connect SDA to the mathematical theory of oriented matroids. This serves to situate SDA within an existing formalism, to clarify its conceptual foundation, and to solve outstanding conjectures. We describe the theory of oriented matroids as it applies to SDA and derive tests for both linear and nonlinear models. In addition, we show that state-trace analysis is a special case of SDA which we extend to models such as additive conjoint measurement where each dependent variable is the same unspecified monotonic function of a linear combination of latent variables. Lastly, we show how measurement error can be accommodated based on the model-fitting approach developed by Kalish et al. (2016).

Kinetic modelling of thermal degradation and non-isothermal crystallization of silk nano-discs reinforced poly (lactic acid) bionanocomposites

The present work investigates the effect of novel biofiller, ‘crystalline silk nano-discs (CSNs)’, on thermal stability and crystallization behaviour of melt-processed poly (lactic acid) (PLA)/CSN nanobiocomposites which are very much important for polymer processing. The PLA/CSN nanobiocomposites are obtained through melt extrusion technique. Thermogravimetric (TGA) data revealed that incorporation of ~ 1 wt.% CSNs in PLA enhanced onset degradation temperature (Tonset) ~ 10 °C, and this improvement in thermal stability is due to the formation of network-like structures. Activation energies (Ea) of nanobiocomposites, determined from model-free (Flynn–Wall–Ozawa, Kissinger–Akahira–Sunose methods) and model fitting (Coats–Redfern method) approaches, increased in comparison with PLA, indicating that CSNs impede thermal degradation process. Possible mechanism is proposed for thermal degradation process of nanobiocomposites using Criado’s method. Hyphenated TGA-Fourier transform infrared is utilized to identify evolved gaseous products during degradation of silk, CSNs, PLA and nanobiocomposites. Differential calorimetry data was analysed by Jeziorny, Liu-Mo and Tobin models. It was found that CSNs act as heterogeneous nucleating agents, hence improve non-isothermal crystallization kinetics of PLA.

A full ARMA model for counts with bounded support and its application to rainy-days time series

Motivated by a large dataset containing time series of weekly number of rainy days collected over two thousand locations across Europe and Russia for the period 2000–2010, we propose a new class of ARMA-like model for time series of bounded counts, which can also handle extra-binomial variation. We abbreviate this model as bvARMA, as it is based upon a novel operation referred to as binomial variation. After having discussed important stochastic properties and proposed a model-fitting approach relying on maximum likelihood estimation, we apply the bvARMA model family to the rainy-days time series. Results show that both bvAR and bvMA models are adequate and exhibit a similar performance. Furthermore, bvARMA results outperform those obtained by fitting ordinary discrete ARMA (NDARMA) models of the same order.

Use of Proteomic and Hematology Biomarkers for Prediction of Hematopoietic Acute Radiation Syndrome Severity in Baboon Radiation Models.

Use of plasma proteomic and hematological biomarkers represents a promising approach to provide useful diagnostic information for assessment of the severity of hematopoietic acute radiation syndrome. Eighteen baboons were evaluated in a radiation model that underwent total-body and partial-body irradiations at doses of Co gamma rays from 2.5 to 15 Gy at dose rates of 6.25 cGy min and 32 cGy min. Hematopoietic acute radiation syndrome severity levels determined by an analysis of blood count changes measured up to 60 d after irradiation were used to gauge overall hematopoietic acute radiation syndrome severity classifications. A panel of protein biomarkers was measured on plasma samples collected at 0 to 28 d after exposure using electrochemiluminescence-detection technology. The database was split into two distinct groups (i.e., "calibration," n = 11; "validation," n = 7). The calibration database was used in an initial stepwise regression multivariate model-fitting approach followed by down selection of biomarkers for identification of subpanels of hematopoietic acute radiation syndrome-responsive biomarkers for three time windows (i.e., 0-2 d, 2-7 d, 7-28 d). Model 1 (0-2 d) includes log C-reactive protein (p < 0.0001), log interleukin-13 (p < 0.0054), and procalcitonin (p < 0.0316) biomarkers; model 2 (2-7 d) includes log CD27 (p < 0.0001), log FMS-related tyrosine kinase 3 ligand (p < 0.0001), log serum amyloid A (p < 0.0007), and log interleukin-6 (p < 0.0002); and model 3 (7-28 d) includes log CD27 (p < 0.0012), log serum amyloid A (p < 0.0002), log erythropoietin (p < 0.0001), and log CD177 (p < 0.0001). The predicted risk of radiation injury categorization values, representing the hematopoietic acute radiation syndrome severity outcome for the three models, produced least squares multiple regression fit confidences of R = 0.73, 0.82, and 0.75, respectively. The resultant algorithms support the proof of concept that plasma proteomic biomarkers can supplement clinical signs and symptoms to assess hematopoietic acute radiation syndrome risk severity.

Optimization of CO2 Capture from Simulated Flue Gas Using K2CO3/Al2O3 in a Micro Fluidized Bed Reactor

The cost-effective dry regenerable K2CO3/Al2O3 seems to be a promising sorbent for CO2 removal from the flue gas of fossil fuel power plants. In this work, the characterization of the carbonation reaction and process optimization were performed in a so-called micro fluidized bed reactor (MFBR), which has recently been applied to study gas–solid reactions. The sorbent was also characterized by BET and SEM techniques. In addition, the most important gas–solid heterogeneous models were evaluated, and the kinetic parameters were determined by the model fitting approach. Based on the kinetic study results, the homogeneous model (HM) and the shrinking core model (SCM) were selected as the reaction models. Also, the effects of the independent variables including temperature, gas flow rate, and vapor pretreatment amount on the responses (adsorption capacity and reaction rate constant) were investigated by the response surface methodology (RSM) coupled with Box–Behnken design (BBD). Regarding to the analysis of va...

Oxidation and reduction kinetic of YBaCo4O7+δ and substituted oxygen carriers

Kinetic analysis of YBaCo4O7+δ, Y0.95Ti0.05BaCo4O7+δ and Y0.15Zr0.1Dy0.75BaCo4O7+δ oxygen carriers for CLAS system has been performed with TG data. The materials are characterized with XRD for phase composition, SEM for particle morphology, and temperature-programmed thermogravimetry for oxidation and reduction reaction analysis. TG experiments are conducted with heating rates of 0.5, 1, and 2 K min−1 in thermal analyzer. The model-fitting approaches, Li Chung-Hsiung and Malek method, are used to estimate the most probable mechanism function. Different processes have different models, for the oxidation process, A1 is selected as the most probable mechanism function for the all samples under different heating rates. And for reduction process, A2/3 is selected as the most probable mechanism function for all samples under different heating rates. That is, nuclei and nuclei growth or their combination are the rate-determining step. In mechanism, activation energy E and pre-exponential factor A are obtained. And the obtained kinetic parameters can reasonably express the oxidation and reduction process.

Comparison of the thermal degradation behaviors and kinetics of palm oil waste under nitrogen and air atmosphere in TGA-FTIR with a complementary use of model-free and model-fitting approaches

Palm oil waste from palm oil industry is a promising biomass resource for bio-fuels and bio-chemicals production by biomass thermochemical conversion technology. In this paper, thermogravimetric analyzer coupled with fourier transform infrared spectrometry (TGA-FTIR) were used to investigate the difference of the thermal degradation behaviors and kinetics of palm kernel shell under nitrogen and air atmosphere. Based on the TG analysis, the weight loss in the third stage with the air atmosphere was higher than the nitrogen atmosphere, presenting as weight loss of 12.4wt.% in the carbonization stage of nitrogen atmosphere and 36.9wt.% in combustion stage of air atmosphere. Based on the 3D-FTIR analysis, the dominant components in the nitrogen atmosphere was CO2, CO bond contained organics, and CH or CO bond contained organics, while CO2 and H2O in the air atmosphere. The evolution and formation mechanism of non-condensable inorganic gaseous components (H2O, CH4, CO2 and CO) in the two atmospheres were remarkably different. The H2O and CO2 were mainly contributed by the breakage of OH group and the cracking/reforming reaction of CO and COOH groups in the nitrogen, however higher contents of H2O and CO2 were observed because of the combustion reaction in the air. The CH4 and CO were mainly contributed by the decomposition of OCH3, CH3, CH2 groups and the cracking of carboxyl group in the nitrogen, while the content of CH4 decreased by the diluting of H2O and CO2, and the CO increased by the incomplete combustion reaction in the air. The kinetic triple (mechanism function, activation energy, and pre-exponential factor) was estimated by the combined use of model-free (Ozawa and Kissinger) and model-fitting (Šatava-Šesták) method.

Confirmatory Validation and Measurement Equivalence of the Eating Loss of Control Scale in Binge Eating and Non-Clinical Samples

Although Loss of Control (LOC) is a transdiagnostic factor in eating pathology, there are few standalone assessments of LOC. The objective of this study was to evaluate the uni-dimensionality and measurement equivalence of the Eating Loss of Control Scale (ELOCS). Confirmatory factor analyses were used to achieve a well-fitting uni-dimensional model in clinical (N = 226) and non-clinical (N = 476) samples. Measurement equivalence was tested in a factor analytic framework, and effect sizes were computed to evaluate the impact of non-equivalence. A well-fitting model was achieved in both samples after the removal of 4 items. The instrument showed configural equivalence but not metric equivalence. Results suggest that the ELOCS is a reliable and valid measure of LOC in clinical and non-clinical samples. However, while the nature of the LOC construct is similar across binge eating and non-clinical participants, comparisons of ELOCS across these groups are affected by measurement non-equivalence. This research also revealed novel insights into the relative sensitivity of model fitting and effect size approaches to investigating measurement equivalence.

An Electronic Health Record–based Algorithm to Ascertain the Date of Second Breast Cancer Events

Studies of cancer recurrences and second primary tumors require information on outcome dates. Little is known about how well electronic health record-based algorithms can identify dates or how errors in dates can bias analyses. We assessed rule-based and model-fitting approaches to assign event dates using a previously published electronic health record-based algorithm for second breast cancer events (SBCE). We conducted a simulation study to assess bias due to date assignment errors in time-to-event analyses. From a cohort of 3152 early-stage breast cancer patients, 358 women accurately identified as having had an SBCE served as the basis for this analysis. Percent of predicted SBCE dates identified within ±60 days of the true date was the primary measure of accuracy. In the simulation study, bias in hazard ratios (HRs) was estimated by averaging the difference between HRs based on algorithm-assigned dates and the true HR across 1000 simulations each with simulated N=4000. The most accurate date algorithm had a median difference between the true and predicted dates of 0 days with 82% of predicted dates falling within 60 days of the true date. Bias resulted when algorithm sensitivity and specificity varied by exposure status, but was minimal when date assignment errors were of the magnitude observed for our date assignment method. SBCE date can be relatively accurately assigned based on a previous algorithm. While acceptable in many scenarios, algorithm-assigned dates are not appropriate to use when operating characteristics are likely to vary by the study exposure.

Frequency response and activation energy of palm-fatty acids at microwave region

Microwave technology has the potential to significantly enhance chemical reaction and knowledge of dielectric properties of materials plays a major role in microwave design for any process. In this paper, the dielectric properties (permittivity and conductivity) of palm-fatty acids were investigated. Palm-fatty acids were chosen being the most versatile and sustainable source of raw materials, also considered as the “gate-way” to other oleochemicals derivatives. Thus, the dielectric properties of commercial palm-fatty acids from various sources in flakes and liquid states were determined within the frequency range of 0.2 GHz to 4.5 GHz at temperature range of 303 K and 443 K (stabilized within 1 °C). The effects of temperature, moisture and unsaturation structure of palm-fatty acids on dielectric properties were also investigated. Real part of dielectric permittivity (εr′) of all the palm-fatty acids increased with increasing temperature from 303 K to 443 K with correlation coefficient, R2 > 0.95 to linear function; depending on temperature and moisture content of the palm-fatty acids. The conductivity increased with increase in temperature and following the model-fitting approach of Arrhenius equation with correlation coefficient (R2 > 0.85) for all the measured samples. The calculated activation energy (Ea) for all measured samples was in the range between 0.06207 eV and 0.16442 eV. Hence, the data calculated would be useful to determine the quality of the palm-fatty acids and to specify their requirement for transportation, storage and process. Moreover, the information will also be useful in the design of a microwave (MW) reactor system for oleochemical downstream process as it is dependent on the dielectric properties of the materials being processed.

Probing the relationship between external and internal human exposure of organophosphate flame retardants using pharmacokinetic modelling

Human external exposure (i.e. intake) of organophosphate flame retardants (PFRs) has recently been quantified, but no link has yet been established between external and internal exposure. In this study, we used a pharmacokinetic (PK) model to probe the relationship between external and internal exposure data for three PFRs (EHDPHP, TNBP and TPHP) available for a Norwegian cohort of 61 individuals from 61 different households. Using current literature on metabolism of PFRs, we predicted the metabolite serum/urine concentrations and compared it to measured data from the study population. Unavailable parameters were estimated using a model fitting approach (least squares method) after assigning reasonable constraints on the ranges of fitted parameters. Results showed an acceptable comparison between PK model estimates and measurements (<10-fold deviation) for EHDPHP. However, a deviation of 10–1000 was observed between PK model estimates and measurements for TNBP and TPHP. Sensitivity and uncertainty analysis on the PK model revealed that EHDPHP results showed higher uncertainty than TNBP or TPHP. However, there are indications that (1) current biomarkers of exposure (i.e. assumed metabolites) for TNBP and TPHP chemicals might not be specific and ultimately affecting the outcome of the modelling and (2) some exposure pathways might be missing. Further research, such as in vivo laboratory metabolism experiments of PFRs including identification of better biomarkers will reduce uncertainties in human exposure assessment.

Predictive RANSAC: Effective model fitting and tracking approach under heavy noise and outliers

In this paper, we introduce a robust and efficient algorithm, Predictive RANSAC, to fit and track a model in the presence of a large number of outlier measurements and heavy noise. Our algorithm works in two stages. In the model fitting stage, the algorithm first ranks the measurements from most likely to be inliers to least likely. It searches for the best model fit from the top-ranked to the lower-ranked measurements using a similar, but more effective, procedure to the RANSAC algorithm. In the model tracking stage, we use a Kalman filter to predict the location of the model and generate the confidence interval using the prediction error. To update the Kalman filter, the fitting process begins again with an initial guess using the Kalman filter prediction result and searches for inliers in the confidence interval. We also estimate the parameter of the Kalman filter such as noise covariance using the fitting algorithm and a Gaussian-Uniform mixture model. In this way, the two components work together to improve overall performance. To show the performance of the Predictive RANSAC algorithm, we compare our approach with previous algorithms based on RANSAC frameworks over several synthetic data sets and real world data sets, which include road mark fitting, homography estimation and multiple target tracking. The Predictive RANSAC algorithm shows better results in estimation accuracy, and consumes significantly less computational power.

Two complementary approaches to quantify variability in heat resistance of spores of Bacillus subtilis

Realistic prediction of microbial inactivation in food requires quantitative information on variability introduced by the microorganisms. Bacillus subtilis forms heat resistant spores and in this study the impact of strain variability on spore heat resistance was quantified using 20 strains. In addition, experimental variability was quantified by using technical replicates per heat treatment experiment, and reproduction variability was quantified by using two biologically independent spore crops for each strain that were heat treated on different days. The fourth-decimal reduction times and z-values were estimated by a one-step and two-step model fitting procedure. Grouping of the 20 B. subtilis strains into two statistically distinguishable groups could be confirmed based on their spore heat resistance. The reproduction variability was higher than experimental variability, but both variabilities were much lower than strain variability. The model fitting approach did not significantly affect the quantification of variability. Remarkably, when strain variability in spore heat resistance was quantified using only the strains producing low-level heat resistant spores, then this strain variability was comparable with the previously reported strain variability in heat resistance of vegetative cells of Listeria monocytogenes, although in a totally other temperature range. Strains that produced spores with high-level heat resistance showed similar temperature range for growth as strains that produced low-level heat resistance. Strain variability affected heat resistance of spores most, and therefore integration of this variability factor in modelling of spore heat resistance will make predictions more realistic.

Misfortune may be a blessing in disguise: Fairness perception and emotion modulate decision making.

Fairness perception and equality during social interactions frequently elicit affective arousal and affect decision making. By integrating the dictator game and a probabilistic gambling task, this study aimed to investigate the effects of a negative experience induced by perceived unfairness on decision making using behavioral, model fitting, and electrophysiological approaches. Participants were randomly assigned to the neutral, harsh, or kind groups, which consisted of various asset allocation scenarios to induce different levels of perceived unfairness. The monetary gain was subsequently considered the initial asset in a negatively rewarded, probabilistic gambling task in which the participants were instructed to maintain as much asset as possible. Our behavioral results indicated that the participants in the harsh group exhibited increased levels of negative emotions but retained greater total game scores than the participants in the other two groups. Parameter estimation of a reinforcement learning model using a Bayesian approach indicated that these participants were more loss aversive and consistent in decision making. Data from simultaneous ERP recordings further demonstrated that these participants exhibited larger feedback-related negativity to unexpected outcomes in the gambling task, which suggests enhanced reward sensitivity and signaling of reward prediction error. Collectively, our study suggests that a negative experience may be an advantage in the modulation of reward-based decision making.

A systematic review of early modelling studies of Ebola virus disease in West Africa.

Phenomenological and mechanistic models are widely used to assist resource planning for pandemics and emerging infections. We conducted a systematic review, to compare methods and outputs of published phenomenological and mechanistic modelling studies pertaining to the 2013-2016 Ebola virus disease (EVD) epidemics in four West African countries - Sierra Leone, Liberia, Guinea and Nigeria. We searched Pubmed, Embase and Scopus databases for relevant English language publications up to December 2015. Of the 874 articles identified, 41 met our inclusion criteria. We evaluated these selected studies based on: the sources of the case data used, and modelling approaches, compartments used, population mixing assumptions, model fitting and calibration approaches, sensitivity analysis used and data bias considerations. We synthesised results of the estimated epidemiological parameters: basic reproductive number (R 0), serial interval, latent period, infectious period and case fatality rate, and examined their relationships. The median of the estimated mean R 0 values were between 1·30 and 1·84 in Sierra Leone, Liberia and Guinea. Much higher R 0 value of 9·01 was described for Nigeria. We investigated several issues with uncertainty around EVD modes of transmission, and unknown observation biases from early reported case data. We found that epidemic models offered R 0 mean estimates which are country-specific, but these estimates are not associating with the use of several key disease parameters within the plausible ranges. We find simple models generally yielded similar estimates of R 0 compared with more complex models. Models that accounted for data uncertainty issues have offered a higher case forecast compared with actual case observation. Simple model which offers transparency to public health policy makers could play a critical role for advising rapid policy decisions under an epidemic emergency.

Characterization of the gas releasing behaviors of catalytic pyrolysis of rice husk using potassium over a micro-fluidized bed reactor

Influence of potassium on the gas releasing behaviors during rice husk high-temperature pyrolysis was investigated under isothermal conditions in a two stage micro-fluidized bed reactor. Reaction kinetics for generating H2, CO, CO2 and CH4 was investigated based on the Friedman and model-fitting approaches. Results indicated that different gas species had different times to start and end the gas release process, particularly at 600°C, representing different chemical routes and mechanics for generating these gas components. The resulting apparent activation energies for H2, CO, and CO2 decreased from 23.10 to 12.00kJ/mol, 15.48 to 14.03kJ/mol and 10.14 to 7.61kJ/mol respectively with an increase in potassium concentration from 0 to 0.5mol/kg, while that for CH4 increased from 16.85 to 19.40kJ/mol. The results indicated that the addition of potassium could promote the generation reactions of H2, CO and CO2 while hinder the generation reactions of CH4. The pyrolysis reaction was further found to be subject to the three-dimensional diffusion model for all the samples.

Crystallization kinetics mechanism investigation of sol–gel-derived NaYF4:(Yb,Er) up-converting phosphors

The crystallization mechanism of sol–gel-derived NaYF4:(Yb,Er) up-converting phosphors has been studied by differential scanning calorimetry analysis using both model-free and model fitting approaches.

Pyrolysis of pellets made with biomass and glycerol: Kinetic analysis and evolved gas analysis

Glycerol is a co-product compound of biodiesel production with an interesting heating value. In this work pyrolysis kinetic parameters for a pellet made with a mass fraction of 90% sawdust and a mass fraction of 10% glycerol are derived through thermogravimetric analysis. A new parallel reaction scheme with four components (cellulose, hemicellulose, lignin and glycerol) is adopted and the kinetic triplet for each component is derived using a model fitting approach applied to this particular kind of pellet. The isoconversional method Kissinger-Akahira-Sunose is employed both to provide initial values for model fitting simulations and to check final results. Results show that activation energies and pre-exponential factors are respectively: 149.7 kJ mol−1 and 1.98*1011 s−1 for hemicellulose, 230.1 kJ mol−1 and 1.84*1017 s−1 for cellulose, 154.3 kJ mol−1 and 5.14*109 s−1 for lignin, 74.5 kJ mol−1 and 2.17*105 s−1 for glycerol with a first reaction order for all components, except for lignin (n = 2.6). Through evolved gas analysis it was demonstrated that the thermal degradation of glycerol contained in the pellet can increase hydrogen content in pyrolysis gases.