Traditional Fitting Research Articles

Modeling the crystallization kinetics of polymers displaying high levels of secondary crystallization

The isothermal crystallization of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was evaluated using a range of models, namely, Avrami, simplified Hillier, Tobin, Malkin, Urbanovici–Segal, Velisaris–Seferis, and Hay. Two methods of model evaluation were used: determination of the parameters through traditional double log plots and curve fitting via nonlinear, multivariable regression. Visual inspection of the cumulative crystallization curves, calculation of the R2 value and standard error of the regression, and evaluation of the returned parameters were used to assess which model best describes the experimental data. The Hay model was found to generate the best fit, closely followed by the Velisaris–Seferis parallel model, suggesting that primary and secondary crystallization occur concurrently. The Avrami, Malkin, and Tobin models were found to perform well when the data is restricted to the region where primary crystallization dominates; however, they could not be used to successfully model the entire crystallization process. This work highlights the importance of selecting the most appropriate model for analyzing kinetics, especially when high levels of lamellar thickening and infilling occur during crystallization.

Structural Discrimination in Physical Education. The "Encounter" Between the (White) Norwegian Teaching Content in Physical Education Lessons and Female Students of Color's Movements and Expressions.

Background: Throughout society, including in the field of sports and physical education (PE), there are extensive debates on racism and structural racism. Researchers have found that students of color experience racial stereotyping and discrimination in PE. Studies also show that PE teaching practices reflect a masculine culture, emphasizing traditional (Western) competitive male sports and physical fitness practices, while marginalizing female students of color. Simultaneously these students also negotiate, resist, and operate as visible agents within these practices. While previous research has tended toward focusing on the experiences (of discrimination) from the perspective of students of color, numerous scholars today argue for a shift in research focus from the inadequacies of the racialized “others” toward how (white) everyday pedagogical practices privilege some people, and conversely, marginalize and discriminate against students of color. We believe, however, that it is crucial to examine and direct attention toward the students who “encounter” discriminatory practices in PE.The Purpose of the Paper is Twofold: (i) To investigate what kind of movements, knowledge/skills and bodies are valued and privileged, or ignored/discriminated in teaching practices in PE. (ii) To depict the “encounter” between the teaching content in physical education lessons and the female students of color's movements and expressions.Research Design and Production of Empirical Material: Our sample consisted of institutionalized pedagogical practices enacted by four white PE teachers in an upper secondary school located in eastern Oslo, Norway. The empirical material consisted of observations and informal conversations extracted from 42 teaching lessons. Students from a great diversity of backgrounds participated in the lessons during our study. We chose a critical whiteness perspective and critical feminist theory as analytical lenses for the analyses.Findings: Strenuous exercise as a means of becoming physically fit and “healthy” was central during the PE lessons under observation. The teacher's instructions draw on healthism discourses influenced by knowledge from exercise physiology and biomechanics, which produce a normative ideal for the body and movements in PE teaching practice. The movements and expressions of female students of color did not meet the expected norm of intensity, activity and exercises from the health and sport discourses. However, we identified female students of color as competent movers when performing “their own” movements, with which they had more of a cultural connection. These students also expressed vitality, joy and social interaction. Thus, they created new movements and expressions that contrasted with the norm of working out with a high intensity level in PE lessons.Conclusion: Hegemonic “truths” about the body, physical exercise and health make teachers “blind” to the ways that institutionalized pedagogical practices in PE privilege majority ethnic white (male) students. Refusing to adhere to the institutionalized movement/exercise practices enacted by the teachers, the female students of color are competent dancers and express liveliness, spontaneity, and joy. In the future, incorporating these types of expressions to teaching practices can add value to PE.

A PERANCANGAN REBRANDING JAVA LOENPIA SEMARANG

Lunpia is a legendary cuisine from Semarang, capital city if Central Java. Lunpia is a real fusion cuisine from Tionghoa culture and Java which already passed by generation and well known by people especially in Semarang city, so it’s unfortunate if this special cuisine is being forgotten by young people of Semarang nowadays. We need to raise brand awareness by talking the authentic traditional concept but fit to current era. This rebranding also needed so it can be more known not only by the elders but also youn people. The data is collected by qualitative method and some studies by S.W.O.T method. The design is done to create new look for the new target audience with keeping the auntheticity and making it more modern by new logo or new design package.
 Keywords: Traditional food, lunpia, rebranding, design package

Phase reconstruction of a Cu(001) seed layer from in situ Polarized Neutron Reflectometry data using Fe reference layers

The reconstruction of the complex reflection coefficient obtained by in situ Polarized Neutron Reflectometry is presented. Using the reference layer method, with a magnetic Fe layer, the phase information of the underlying Cu(001) seed layer sample structure is successfully retrieved and its scattering length density is calculated and compared with results obtained from traditional fitting. Two different reference layer approaches for retrieving the phase information are compared.

Q2DW-Tide and -Ionosphere Interactions as Observed From ICON and Ground-Based Radars.

A quasi-2-day wave (Q2DW) event during January-February, 2020, is investigated in terms of its propagation from 96 to 250 km as a function of latitude (10°S to 30°N), its nonlinear interactions with migrating tides to produce 16 and 9.6-h secondary waves (SWs), and the plasma drift and density perturbations that it produces in the topside F-region (590-607 km) between magnetic latitudes 18°S and 18°N. This is accomplished through analysis of coincident Ionospheric Connections Explorer (ICON) measurements of neutral winds, plasma drifts and ion densities, and wind measurements from four low-latitude (±15°) specular meteor radars (SMRs). The Q2DW westward-propagating components that existed during this period consist of zonal wavenumbers s = 2 and s = 3, that is, Q2DW+2 and Q2DW+3 (e.g., He, Chau et al., 2021, https://doi.org/10.1029/93jd00380). SWs in the ICON measurements are inferred from Q2DW+2 and Q2DW+3 characteristics derived from traditional longitude-UT fits that potentially contain aliasing contributions from SWs ("apparent" Q2DWs), from fits to space-based zonal wavenumbers that each reflect the aggregate signature of either Q2DW+2 or Q2DW+3 and its SWs combined ("effective" Q2DWs), and based on information contained in published numerical simulations. The total Q2DW ionospheric responses consists of F-region field-aligned and meridional drifts of order ±25 ms-1 and ±5-7 ms-1, respectively, and total ion density perturbations of order (±10%-25%). It is shown that the SWs can sometimes make substantial contributions to the Q2DW winds, drifts, and plasma densities.

Effects of Three Traditional Chinese Fitness Exercises Combined with Antihypertensive Drugs on Patients with Essential Hypertension: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

Objective To compare the efficacy of three different traditional Chinese exercises (Tai Chi, Baduanjin, and Wuqinxi) combined with antihypertensive drugs (AHD) on patients with essential hypertension (EH). Method Eight electronic databases were searched to identify randomized controlled trials (RCTs) comparing the effects of traditional Chinese fitness exercises combined with AHD and AHD alone. The analysis mainly consists of network meta-analysis (NMA) and pairwise meta-analysis. The Cochrane assessment tool was adopted to assess the risk of bias of included literatures. This study used STATA/SE 15.1 (StataCorp, 2017), R software (version 4.0.1), and Cochrane's Review Manager software (version 5.4) to conduct data analysis and figures generation. Results A total of 30 RCTs were included in this study, of which 16 evaluated Tai Chi plus AHD versus AHD, 11 evaluated Baduanjin plus AHD versus AHD, and 3 evaluated Wuqinxi plus AHD versus AHD. No RCT compared directly among the three traditional Chinese fitness exercises. Pairwise meta-analysis showed that Tai Chi plus AHD was significantly superior to AHD alone in reducing systolic blood pressure (SBP) and diastolic blood pressure (DBP). BDJ plus AHD was statistically superior to AHD alone in reducing SBP, DBP, and endothelin (ET) and increasing nitric oxide (NO). NMA results indicated that Tai Chi plus AHD (WMD −12.42 mmHg, 95% CI: −15.29 to −9.55) and Baduanjin plus AHD (WMD −7.03 mmHg, 95% CI: −9.80 to −4.26) were superior to AHD, and Tai Chi was more effective than other traditional exercises in lowering SBP, Tai Chi plus AHD (WMD −7.56 mmHg, 95% CI: −10.15 to −4.96) and Baduanjin plus AHD (WMD −4.51 mmHg, 95% CI: −7.38 to −1.65) were superior to AHD in reducing DBP, Baduanjin plus AHD (WMD 4.26 μmol/L, 95%CI: 2.68 to 5.83) was statistically superior to AHD in increasing NO, and Tai Chi plus AHD (WMD −7.64 pg/ml, 95% CI: −10.46 to −4.83) and Baduanjin plus AHD (WMD −9.23 pg/ml, 95% CI: −10.85 to −7.61) were superior to AHD in lowering ET. Conclusion Compared with AHD alone, both Tai Chi plus AHD and Baduanjin plus AHD showed significant benefit in regulating SBP, DBP, and ET. Among the three traditional Chinese fitness exercises, Tai Chi may be the best as an adjunctive therapy for SBP reduction. These findings provided evidence for the therapeutic benefit of either Tai Chi or Baduanjin exercise as an adjunct therapy for patients with EH. Limited by the methodological quality and quantity of included studies, results need to be interpreted with caution, and it is necessary to carry out further high-quality RCTs on traditional Chinese fitness exercise-assisted treatment of EH in the future.

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.

Artificial neural networks used to retrieve effective properties of metamaterials.

We propose using deep neural networks for the fast retrieval of effective properties of metamaterials based on their angular-dependent reflection and transmission spectra from thin slabs. While we noticed that non-uniqueness is an issue for a successful application, we propose as a solution an automatic algorithm to subdivide the entire parameter space. Then, in each sub-space, the mapping between the optical response (complex reflection and transmission coefficients) and the corresponding material parameters (dielectric permittivity and permeability) is unique. We show that we can easily train one neural network per sub-space. For the final parameter retrieval, predictions from the different sub-networks are compared, and the one with the smallest error expresses the desired effective properties. Our approach allows a significant reduction in run-time, compared to more traditional least-squares fitting. Using deep neural networks to retrieve effective properties of metamaterials is a significant showcase for the application of AI technology to nanophotonic problems. Once trained, the nets can be applied to retrieve properties of a larger number of different metamaterials.

Effectiveness of a Self-Fitting Tool for User-Driven Fitting of Hearing Aids.

Hearing aids can be effective devices to compensate for age- or non-age-related hearing losses. Their overall adoption in the affected population is still low, especially in underdeveloped countries in the subpopulation experiencing milder hearing loss. One of the major reasons for low adoption is the need for repeated complex fitting by professional audiologists, which is often not completed for various reasons. As a result, self-fitting procedures have been appearing as an alternative. Key open questions with these digital tools are linked to their effectiveness, utilized algorithms, and achievable end-results. A digital self-fitting prototype tool with a novel quick four-step fitting workflow was evaluated in a study on 19 individuals with moderate hearing loss. The tool was evaluated in a double-blinded, randomized study, having two study aims: comparing traditional audiological fitting with the new self-fitting tool, which can also be used as a remote tool. The main reported results show moderately high usability and user satisfaction obtained during self-fitting, and quasi-equivalence of the performance of the classical audiological fitting approach. The digital self-fitting tool enables multiple sessions and easy re-fitting, with the potential to outperform the classical fitting approach.

Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern

Optical metrology has experienced a fast development in recent years—cross laser-pattern has become a common cooperative measuring marker in optical metrology equipment, such as infrared imaging equipment or visual 3D measurement system. The rapid and accurate extraction of the center point and attitude of the cross-marker image is the first prerequisite to ensure the measurement speed and accuracy. In this paper, a cross laser-pattern is used as a cooperative marker, in view of the high resolution of the cross laser-pattern image in the project and the vulnerability to adverse environmental effects, such as stray light, smoke, water mist and other interference in the environment, resulting in poor contrast, low signal-to-noise ratio (SNR), uneven energy distribution. As a result, a method is proposed to detect the center point and attitude of cross laser-pattern image based on Gaussian fitting and least square fitting. Firstly, the distortion of original image is corrected in real time, the corrected image is smoothed by median filter, and the noise is suppressed while preserving the edge sharpness and detail of the image. In order to adapt to different environments, the maximum inter-class variance method of threshold automatic selection is used to determine the threshold of image segmentation to eliminate the background interference caused by different illumination intensities. To improve the real-time performance of the algorithm, the four cross laser edge pixels are obtained by line search, and then fitted by least square. With the edge lines, the transverse and portrait line of the cross-laser image are separated, then we calculate Gaussian center points of all Gaussian sections of transverse and portrait lines based on Gaussian fitting method, respectively. Based on the traditional line fitting method, the sub-pixel center of the transverse and portrait laser strip images are fitted by removing the Outlying Points, and the center coordinates and attitude information of the cross laser-pattern are calculated by using the center equation of the laser strip, realizing cross laser-pattern center and attitude accurate positioning. The results show that the method is robust, the center positioning accuracy is better than 0.6 pixels, the attitude positioning accuracy is better than ±15” under smoke and water mist environment and the processing speed is better than 0.1 s, which meets the real-time requirements of the project.

Developing a hybrid data-fitting procedure and a case study for patient service time

ABSTRACT The validity of input data plays a significant role in achieving valid output for simulating modeling systems. This study developed a systematic procedure to deal with the stochastic input data that cannot lead to a good probabilistic distribution using traditional data-fitting techniques. The proposed procedures used hybrid data-fitting techniques, including traditional data fitting, changes to the number of classes of a histogram, F3 distribution, and empirical distribution, in order to obtain a corresponding probabilistic distribution for those ill-fitting input data. Employing one year of historical data from the case image center, this research focused on the service time of computed tomography (CT) outpatients, classifying the CT outpatients into six categories based on their scan type. The results showed that the proposed data-fitting procedure could determine an appropriate probabilistic distribution for the stochastic scan time for each CT scan type. Therefore, the proposed data-fitting procedure could help decision makers construct a validated simulation model in order to evaluate different alternatives and determine a better one in the future.

Estimating ecosystem maximum light use efficiency based on the water use efficiency principle

Light use efficiency (LUE) defines the vegetation efficiency of converting radiative energy into biochemical energy through photosynthesis. Estimating the maximum LUE (ε max) is critical yet challenging for quantifying gross primary production (GPP) using LUE-based models. This study describes an analytical method for estimating ε max based on water use efficiency (WUE) as determined by plant water use and carbon gain. Unlike other complex parameterization schemes, this WUE-based method is simple and requires four variables relatively easy to acquire. The WUE-based ε max estimates compare favorably well with values based on traditional curve fitting method and that reported in the literature, and clearly distinguished ε max between C3 (1.48 ± 0.33 ) and C4 (2.63 ± 0.21 ) dominated ecosystems. The range in ε max estimates was narrow across different years and sites within a biome. The WUE-based ε max estimate is theoretically constrained by vegetation water use and can be directly incorporated into LUE models for GPP estimation across ecosystems.

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.

A Novel Approach for Permittivity Estimation of Lunar Regolith Using the Lunar Penetrating Radar Onboard Chang’E-4 Rover

Accurate relative permittivity is essential to the further analysis of lunar regolith. The traditional hyperbola fitting method for the relative permittivity estimation using the lunar penetrating radar generally ignored the effect of the position and geometry of antennas. This paper proposed a new approach considering the antenna mounting height and spacing in more detail. The proposed method is verified by numerical simulations of the regolith models. Hence the relative permittivity of the lunar regolith is calculated using the latest high-frequency radar image obtained by the Yutu-2 rover within the first 24 lunar days. The simulation results show that the relative permittivity is underestimated when derived by the traditional method, especially at the shallow depth. The proposed method has improved the accuracy of the estimated lunar regolith relative permittivity at a depth of 0–3 m, 3–6 m, and 6–10 m by 35%, 14%, and 9%, respectively. The thickness of the lunar regolith at the Chang’E 4 landing site is reappraised to be 11.1 m, which improved by ~8% compared with previous studies.

Reconstruction of distorted structures in the fault shadow zone based on the fully connected network

Lateral changes in velocity about faults can give rise to fault shadow (FS) zones on time-migrated data volumes, which can result in structural interpretation artifacts in the fault trap reservoir. To address this issue, we have adopted a new reconstruction method of FS distortion structures based on a deep learning fully connected network (FCN). We use the 3D stratigraphic dip attributes to quantitatively delineate the extent of the FS zone. Then, we train a model to construct a nonlinear trend surface based on the structures of the stratigraphic reflectors that fall outside of the shadow zone. Finally, we use this nonlinear trend surface to compensate for the distorted structure within the FS zone. We calibrate our method using synthetic data and find that the method can accurately recover structural data within the FS distortion zone. We then test the effectiveness of our workflow by applying it to recover real FS distortion structures in the Pearl River Mouth Basin of the South China Sea. The results confirm that our method significantly reduces drilling depth errors in the FS zone. Compared to the traditional polynomial fitting method, the multilayer, multiparameter, and flexible nonlinear activation function of FCN is more capable of reconstructing nonlinear geologic structures in the FS zone. We find the FCN-based geologic reconstruction method to be efficient and effective for exploring potential structures in the FS zone and thereby in avoiding the risks of structural failure.

Simulation of gas sensing mechanism of porous metal oxide semiconductor sensor based on finite element analysis

In recent years, finite element analysis is increasingly adopted to simulate the mechanism of metal oxide semiconductor (MOS) resistive gas sensors. In this article, the chemical reaction engineering module in the COMSOL Multiphysics tool is used to describe the dynamic equilibrium process of oxygen ions in the sensor. The boundary conditions of temperature transfer, conductivity model, and mass transfer are applied to simulate the convection, diffusion, and penetration processes. The response of the sensor at different temperatures (445 K–521 K) and different target gas concentrations (1–500 ppm) is simulated. In this paper, the dynamic model of oxygen ions is used creatively as a bridge between gas concentration and sensor response instead of the traditional direct parameter fitting method. The simulated result of the surface oxygen ion control and permeability control model of the MOS gas sensor shows a good agreement with the real sensor. For explaining the principle of metal oxide semiconductor gas sensors simulations has been performed on COMSOL Multiphysics software. The proposed method in this paper is based on the underlying transfer logic of the sensor signal, it is expected to predict the sensor signal and assist the sensor design.

Quantitative analysis of internal polarization dynamics for polymer electrolyte membrane fuel cell by distribution of relaxation times of impedance

Investigating and interpreting each internal polarization dynamics that occurs in the polymer electrolyte membrane fuel cell is significant. Traditional equivalent circuit model fitting by nonlinear least-squares relies on prior model assumptions and initial value selection of components. In this paper, the distribution of relaxation times methodology with powerful separating ability is applied to reveal a more precise analysis of polarization processes. First, the electrochemical impedance spectroscopy under a broad of operating conditions is carried out. Four polarization dynamics related to oxygen transfer, charge transfer of the oxygen reduction, proton transfer inside cathode ionomer, and interface contact process between catalyst layer and membrane (perhaps, including anode oxidation reaction) are effectively extracted. Then, a fourth-order equivalent circuit model established via distribution of relaxation times results is introduced to quantify the loss of each polarization process. Based on this, for the first time, the sensitivity of each polarization loss against operating conditions is analyzed by the multiple stepwise regression analysis, and its application on vehicular fuel cell system control is discussed. Afterward, the distribution of relaxation times is also first to explore the loss and variation trend of each polarization process under flooding, membrane drying, and air starvation fault, where each failure type contains at least eight test sequences. These efforts represent a comprehensive and systematic guideline for fuel cells using distribution of relaxation times, which can also guide the study of degradation mechanisms, optimization design of materials, and even other electrochemical energy sources.

Prediction of sediment resuspension in Lake Taihu using support vector regression considering cumulative effect of wind speed

Sediment resuspension is critical to ecosystem function in shallow lakes. Turbidity is one of the most commonly used indicators of sediment resuspension and has proven to be strongly related with wind. However, it is still difficult to predict sediment resuspension due to its complicated mechanisms. In this study, a support vector regression (SVR) model considering the cumulative effect of wind speed was trained to predict sediment resuspension based on intensified field observations at two sites in Lake Taihu. The accuracy of the SVR model was evaluated, and the initial turbidity was introduced to the model to illustrate its contribution to sediment resuspension. The critical wind speed was also evaluated based on this model. The results indicate that the SVR model considering the cumulative effect of wind speed can increase the accuracy of prediction in comparison with traditional fitting methods. The root-mean-square error (RMSE) of the predicted turbidity dropped to 11.36 NTU at one site and 16.78 NTU at the other, and the maximal information coefficient (cimax) for the relationship between wind speed and turbidity increased to 0.56 for both observation sites. The introduction of initial turbidity significantly improved the performance of the SVR model, with an RMSE value lower than 8.00 NTU and a cimax value higher than 0.95. Analysis of the critical wind speed using the SVR model shows that the critical wind speed generally increased with the rise of initial turbidity. The critical wind speeds at initial turbidities of 30, 40, 50, and 60 NTU were 5, 6, 7, and 7 m/s, respectively.

An eco-friendly modified methodology for the resolution of binary pharmaceutical mixtures based on self-deconvolution of the UV spectrophotometric spectra in the Fourier domain: Application of Fourier self-deconvolution in UV spectroscopy

UV spectrophotometry is a rapid and robust technique in resolving several challenging pharmaceutical combinations. Several mathematical treatments are available for the resolution of complex multicomponent UV spectra as; wavelet transformation, derivatization, and deconvolution-curve fitting models. Fourier self deconvolution (FSD) is a mathematical computational methodology for resolving interfering signals in many disciplines and applications. In the current work, we describe a modified FSD based methodology in resolving different binary pharmaceutical mixtures, which overcome the complexity of applying the traditional deconvolution-curve fitting technique on UV spectroscopic spectral data. The current approach differs from the conventional FSD by using the individual spectra of each component as a probing tool to avoid artifacts or errors on the deconvoluted spectra for accuracy of determinations. The utilized approach managed to resolve the binary mixtures of telmisartan/hydrochlorothiazide and ramipril/hydrochlorothiazide in their pharmaceutical dosage forms. The advantage of the current methodology over the traditional deconvolution-curve fitting is the simplicity of application, less time consuming, no need for sophisticated software, and higher sensitivity as revealed by the limit of detection (LOD). The linear ranges for telmisartan, ramipril, and hydrochlorothiazide were 1–25 µg/ml, 5–35 µg/ml, and 1–10 µg/ml, respectively, and the LOD values were in the ranges of 0.067–0.747 µg/ml. The developed FSD approach was validated as per the ICH recommendations regarding the accuracy, precision, linearity, selectivity, and limits of detection and quantitation. The recoveries obtained from the proposed approach were statistically compared with the corresponding reported methods and found no statistical difference between the obtained results.

Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions.

In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, I(q) vs q] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile.