Fitting Results Research Articles

Tailoring conductive polyvinyl alcohol nanofibers: thermal-induced structural evolution in nitrogen for energy storage device

Carbon fibers from polyvinyl alcohol (PVA) was achieved by the electrospinning technique, which was subsequently followed by the stabilization and carbonization of electrospun PVA fibers. The XRD pattern of PVA fiber confirms the change in the structure of PVA nanofibers and carbonized PVA from temperatures of 200, 300, 400, 500, and 700 °C, resulting in a structural evolution from fibrous to plate-like and dot carbon compounds. The SEM image of the PVA precursor reveals the presence of fibers exhibiting a smooth surface following the stabilization and carbonization processes at temperatures below 500 °C. Furthermore, the image depicts fibers that melt and transform into a carbon layer at a higher carbonization temperature ranging from 600 to 700 °C. TEM results show the formation of carbon sheets and dots, with an average diameter of 8.2 nm. The evolution that occurs from fibers to carbon sheet due to the melting process indicates a change in the morphology of 1D to graphenic carbon. The identification of carbon–oxygen functional groups, such as C = C (carbon with sp2 hybridization), C–C (carbon with sp3 hybridization), and C = O bonds, has been successfully detected and analysed using both XPS and Raman spectroscopy techniques. The results of the XPS profile fitting, as proven by Raman spectra, produce a graphenic carbon containing a 2D structure, with the total sp2 fraction increasing in the increasing carbonization temperature. This is also followed by significantly increasing electrical conductivity. So, the carbonized PVA nanofiber has the potential to be applied in electronic and energy storage device applications. This is also followed by an increase in electrical conductivity due to the formation of more carbon functional groups compared to less oxygen. Thus, carbonized PVA nanofibers have great potential for application in electronic devices and energy storage applications such as batteries and supercapacitors.

Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis

IntroductionClimate change isone of the major challenges facing the world today, causing frequent extreme weather events that significantly impact human production, life, and the ecological environment. Traditional climate prediction models largely rely on the simulation of physical processes. While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships.MethodsIn light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN is utilized to extract local features and capture short-term changes. Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans.Results and DiscussionExperiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. The resuts also show that the Transformer-CNN-LSTM hybrid model outperforms other hybrid models on five evaluation metrics, indicating that the proposed model provides more accurate predictions and more stable fitting results.

Imaging Analyses for Pion and Kaon Sources in Relativistic Heavy-Ion Collisions in a Multiphase Transport Model

In this paper, we describe the study of one- and three-dimension pion and kaon source functions for chaotic and partially coherent sources in Pb-Pb central collisions at sNN=2.76 TeV using the AMPT model. The characteristic source function quantities are calculated and compared with the results obtained by fitting the two-boson correlation functions using the Gaussian source formula. It was found that the imaging results are approximately consistent with the results of the Gaussian source formula fits. The partially coherent pion sources exhibit a high degree of coherence. However, the kaon pairs with high transverse momenta are emitted with a high degree of chaos.

An Automated Baseline Correction Method for Raman Spectra Based on Piecewise Polynomial Fitting With Adaptive Window

ABSTRACTBaseline background tends to cause the Raman spectral signal to be hidden or distorted, making it difficult to accurately acquire spectral information. Although polynomial fitting has been widely proven to be an effective baseline correction method, it is difficult to achieve both high speed and high accuracy. In this paper, we propose a piecewise polynomial fitting method based on a sliding adaptive window(S‐ModPoly). S‐ModPoly relies on the adaptive width selection of a sliding window to automatically split the original spectrum into many different length segments containing complete peak information. Low‐order iterative polynomial fitting is performed for each segment separately, which greatly reduces the computational effort while improving the baseline fitting accuracy. By further correcting the piecewise fitting results, the discontinuities between different intervals after piecewise fitting are eliminated. Furthermore, the true intensity information of the spectral signal is retained. Here, S‐ModPoly is compared with three representative automated methods. The experimental results show that the S‐ModPoly method has lower mean error and higher stability in a very short time (less than 20 ms). Additionally, the experimental results on measured Raman spectra demonstrate the effectiveness of the method in automatically processing various real spectral baselines. It performs well in both low and high intensity background baselines.

The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth

BackgroundRisk preference changes nonlinearly across development. Although extensive developmental research on the neurotypical (NTP) population has shown that risk preference is highest during adolescence, developmental changes in risk preference in autistic (AUT) people, who tend to prefer predictable behaviors, have not been investigated. Here, we aimed to investigate these changes and underlying computational mechanisms.MethodWe ran a game-like risk-sensitive reinforcement learning task on 75 participants aged 6–30 years (AUT group, n = 31; NTP group, n = 44). Focusing on choices between alternatives with the same objective value but different risks, we calculated the risk preference and stay probability of a risky choice after a rewarding or non-rewarding outcome. Analyses using t-tests and multiple regression analyses were conducted. Using the choice-related data of each participant, we fit four reinforcement learning models and compared the fit of each model to the data. Furthermore, we validated the results of model fitting with multiple methods, model recovery, parameter recovery, and posterior predictive check.ResultsWe found a significant difference in nonlinear developmental changes in risk preference between the AUT and NTP groups. The computational modeling approach with reinforcement learning models revealed that individual preferences for surprise modulated such preferences.ConclusionsThese findings indicate that for NTP people, adolescence is a developmental period involving risk preference, possibly due to lower surprise aversion. Conversely, for AUT people, who show opposite developmental change of risk preference, adolescence could be a developmental period involving risk avoidance because of low surprise preference.

Study on the Axial Compressive Behavior and Constitutive Relationship of Lightweight Mixed Ceramic Concrete.

To thoroughly study the stress-strain relationship of lightweight mixed ceramic concrete, this paper conducts axial compressive strength tests on three groups of lightweight mixed ceramic concrete specimens with different types and contents as the basis. It establishes the elastic modulus calculation formula and compressive stress-strain formula for lightweight mixed ceramic concrete by combining with the current standards and related research. The results show that lightweight mixed ceramic concrete, made of a mixture of different types and densities of ceramic grains, has better mechanical properties and deformation properties. The calculation errors of the modulus of elasticity formulas, derived from the experimental results for the three groups of lightweight mixed ceramic concretes, are all controlled within 5%. The average relative errors of the fitting results of stress-strain curves for the three groups of specimens and the measured data are as low as 6.66%, 3.16%, and 3.39%. The errors between the experimental values of the modulus of elasticity of different studies and the predicted values based on the formula in this paper were controlled within 17%, and the average relative errors between the predicted and experimental results of the stress-strain curves for the three groups of specimens were 2.64%, 8.94%, and 17.50%. This paper innovatively constructs a prediction model of key mechanical parameters of lightweight mixed ceramic concrete, which can provide a reference and experimental basis for the structural analysis and application of lightweight mixed ceramic concrete.

A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations

The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness.

Time-series Spectroscopy and TESS Photometry of the Oscillating Eclipsing Algol System V389 Cassiopeiae

Abstract We present the results from a detailed analysis of our time-series spectroscopic observations and the Transiting Exoplanet Survey Satellite (TESS) photometric data of the eclipsing binary V389 Cas. The radial velocities (RVs) of the primary and faint secondary components were obtained from a total of 61 high-resolution spectra made between 2016 and 2019. The disentangling spectra were used to determine the effective temperature and projected rotational velocity of the primary star based on the χ 2 fitting statistic, yielding T eff,1 = 7720 ± 200 K and v 1 sin i rot = 59 ± 7 km s−1, respectively. The fundamental properties of V389 Cas were determined from the simultaneous analysis of our RV curves together with the TESS light curves. The fitting results indicate that V389 Cas is a semidetached eclipsing binary with masses of M 1 = 2.3 8 − 0.08 + 0.06 M ⊙ and M 2 = 0.3 3 − 0.01 + 0.01 M ⊙ , and radii of R 1 = 2.6 6 − 0.08 + 0.06 R ⊙ and R 2 = 2.4 1 − 0.04 + 0.03 R ⊙ . A total of 50 frequency signals are detected from the multifrequency analysis of the residuals of the out-of-eclipse light curves. According to the pulsation constants of two frequencies with an amplitude greater than 1.0 mmag (f 2 = 28.3487 day−1 and f 3 = 25.6069 day−1) and location in the Hertzprung–Russell diagram, the primary component of V389 Cas is a typical δ Sct-type pulsator.

Anisotropic Shock Response of 3,4-Dinitropyrazole Revealed by First-Principles Calculations.

DNP (3,4-dinitropyrazole) has attracted much interest due to its promising melting characteristics and high detonation performances, such as low melting point, high density, high detonation velocity, and low sensitivity. In this work, first-principles molecular dynamics (MD) simulations were performed to investigate the anisotropic shock response of DNP in conjunction with the multiscale shock technique (MSST). The initial decomposition mechanism was revealed through the evolution of the chemical reaction and product analysis. Independent gradients based on the Hirshfeld partition (IGMH) method showed that van der Waals forces mainly exist between the layered structures. Chemical reaction analyses revealed four major initial decomposition reactions for the DNP molecule. At different shock velocities, the molecules in were more inclined to undergo H dissociation reactions, whereas the molecules in were more inclined to undergo nitro-dissociation reactions. Product analysis showed that the faster the shock velocities, the earlier the DNP molecules completely disappeared. Furthermore, N2 and CO2 were mainly produced by the ring-opening reaction, and their numbers in were higher than in , indicating that the ring-opening reaction was more easy to occur in . The ring-opening reaction mainly occurred in , suggesting that was more decomposable than . The fitting results of the state equation showed that the theoretical detonation pressures for and are close to the experimental value. These results could help to increase the understanding of shock-induced anisotropy in energetic materials.

Development and Validation of a Capacitor–Current Circuit Model for Evaporation-Induced Electricity

Evaporation-induced electricity is a promising approach for sustainable energy generation which is particularly suited for off-grid and Internet-of-Things (IoT) applications. Despite significant progress, the mechanism of electricity generation remains debated due to complex factors. In this study, we introduce a simplified capacitor–current circuit model to describe the behavior of evaporation-induced electricity. The primary objective of this work is to provide a framework for understanding the transient and steady-state behavior of this phenomenon. We validated this model using experimental data from wood-based nanogenerators with citric acid modified microchannels. The fitting results revealed a steady-state current of approximately 9.832 μA and an initial peak current of 16.168 μA with a time constant of 621.395 s. These findings were explained by a hybrid model incorporating a capacitor and current source components, and subsequent discharge through internal resistance. This simplified model paves the way for better understanding and optimization of evaporation-induced electricity, highlighting potential improvements in device design for enhanced performance. While improving device performance is beyond the scope of this study, the insights gained from this model offer a foundation for future optimization and the enhanced performance of evaporation-induced electricity generation devices.

Black Hole Mass and Optical Radiation Mechanism of the Tidal Disruption Event AT 2023clx

We present the optical light curves of the tidal disruption event AT 2023clx in the declining phase, observed with Mephisto. Combining our light curve with the ASAS-SN and ATLAS data in the rising phase, and fitting the composite multi-band light curves with MOSFiT, we estimate black hole mass for AT 2023clx is between 105.67 and 105.82 M ⊙. This event may be caused by either a full disruption of a 0.1 M ⊙ star, or a partial disruption of a 0.99 M ⊙ star, depending on the data adopted for the rising phase. Based on those fit results and the non-detection of soft X-ray photons in the first 90 days, we propose that the observed optical radiation is powered by stream-stream collision. We speculate that the soft X-ray photons may gradually emerge in 100–600 days after the optical peak, when the debris is fully circularized into a compact accretion disk.

Quantifying Competitive Fitness in Yeast with High-Throughput Fluorescence Microscopy Imaging.

Competitive fitness is a fundamental concept in evolutionary biology that captures the ability of organisms to survive, reproduce, and compete for resources in their environment. Competitive fitness is typically assessed in the lab by growing two or more competitors together and measuring the frequency of each at multiple time points. Traditional microbial competitive fitness assays are labor intensive and involve plating on solid medium and counting colonies. Here, we describe a method to quantitatively measure competitive fitness using fluorescence microscopic imaging and machine-learning-enabled image analysis to directly count the number of cells from each competitor in the mixed population. This high-throughput, primarily automated, and efficient process gives accurate and reproducible results for competitive fitness. Here, we describe the entire process, from sample preparation through microscopy to quantification, and provide instructions and scripts for the image analysis, fitness calculations, and sample data visualizations. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation Basic Protocol 2: Photographing fluorescing and non-fluorescing cells using an EVOS microscope Basic Protocol 3: Counting fluorescing and non-fluorescing cells with Orbit Image Analysis Basic Protocol 4: Getting the average cell counts per well and changing the file names Basic Protocol 5: Calculating competitive fitness using R.

A Time-dependent Spectral Analysis of γ Cassiopeiae

We investigated the temporal and spectral features of γ Cassiopeiae’s X-ray emission within the context of the white dwarf (WD) accretion hypothesis. We find that the variabilities present in the X-ray data show two different signals, one primarily due to absorption and the other due to flickering like in nonmagnetic cataclysmic variables. We then use this two-component insight to investigate previously unreported simultaneous XMM and NuSTAR data. The results of model fitting find WD properties consistent with optical studies alongside a significant secondary, thermal source. We propose a secondary shock between the Be decretion disk and WD accretion disk as the source. Finally, we analyzed a unique, low-count-rate event of the XMM light curve as potential evidence for the WD encountering Be decretion disk structures.

The Burr distribution as a model for the delay between key events in an individual's infection history.

Understanding the temporal relationship between key events in an individual's infection history is crucial for disease control. Delay data between events, such as infection and symptom onset times, is doubly censored because the exact time at which these key events occur is generally unknown. Current mathematical models for delay distributions are derived from heuristic justifications. Here, we derive a new model for delay distributions, specifically for incubation periods, motivated by bacterial-growth dynamics that lead to the Burr family of distributions being a valid modelling choice. We also incorporate methods within these models to account for the doubly censored data. Our approach provides biological justification in the derivation of our delay distribution model, the results of fitting to data highlighting the superiority of the Burr model compared to currently used models when the mode of the distribution is clearly defined or when the distribution tapers off. Under these conditions, our results indicate that the derived Burr distribution is a better-performing model for incubation-period data than currently used methods, with the derived Burr distribution being 13 times more likely to be a better-performing model than the gamma distribution for Legionnaires' disease based on data from a known outbreak.

Shear behavior of metal plate joints of laminated bamboo lumber at different angles or sizes

ABSTRACT To determine the shear behavior and slip resistance of metal plate joints of laminated bamboo lumber (LBL), considering the influence of sizes (length and width) and angles of metal plates, 100 specimens were tested. When the angle of the metal plate is different, the force modes can be divided into three types: pure shear, shear-tension, and shear-compression modes. According to the test results, the shear-tension modes of metal plate joints have the strongest bearing capacity, followed by the pure shear mode in second place and the shear-compression mode in last. The change in the size of the joints dramatically influences the ultimate load. Based on the test, the load-slip curves of the shear metal plate connected joints of LBL were fitted by the Foschi formula, and the fitting results were in good agreement with the test results. Finally, the ultimate shear strength of LBL joints was compared with that of other wood/bamboo composites. The results show that the shear performance of metal plate joints of LBL is better than that of ordinary wood materials. The test provided a reference for the engineering structures and finite element numerical simulation analysis of metal plate connected joints of LBL.

3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction.

To implement a low-rank and subspace model-based reconstruction for 3D deuterium metabolic imaging (DMI) and compare its performance against Fourier transform-based (FFT) reconstruction in terms of spectral fitting reliability. Both reconstruction methods were applied on simulated and experimental DMI data. Numerical simulations were performed to evaluate the effect of increasing acceleration factors. The impact on spectral fitting results, SNR, and the overall normalized root mean squareerror (NRMSE) compared to ground-truth data were calculated. A comparative analysis was performed on DMI data acquired from the human liver, including both natural abundance and post-deuterated glucose intake data at 7 T. Simulation showed the Cramer-Rao lower bound [%] of water, glucose, sum of glutamate and glutamine (Glx), and lipid signals for the low-rank and subspace model-based reconstruction at R = 1.0 was 12.4, 14.7, 17.3, and 11.0 times lower than FFT. At R = 1.1, NRMSE was 1.4%, 1.3%, 0.8%, and 4.2% lower for the water, glucose, Glx, and lipid, respectively, compared to FFT. However, the NRMSE of the Glx and lipid increased by 0.4% and 3.2% at R = 1.3. For the in vivo DMI experiment, SNR was 2.5-3.0 times higher compared to FFT. The fitted amplitude of water and glucose peaks showed Cramer-Rao lower bound [%] values that were approximately 2.3 times lower than FFT. Simulations and in vivo experiments on the human liver demonstrate that low-rank and subspace model-based reconstruction with undersampled data mitigates noise and enhances spectral fitting quality.

The Redshift-space Momentum Power Spectrum. III. Measuring the Growth Rate from the SDSSv Survey Using the Auto- and Cross-power Spectrum of the Galaxy Density and Momentum Fields

The large-scale structure of the Universe and its evolution over time contains an abundance of cosmological information. One way to unlock this is by measuring the density and momentum power spectrum from the positions and peculiar velocities of galaxies and fitting the cosmological parameters from these power spectra. In this paper, we will explore the cross-power spectrum between the density and momentum fields of galaxies. We derive the estimator of the density–momentum cross-power spectrum multipoles. The growth rate of the large-scale structure, f σ 8, is measured from fitting the combined density monopole, momentum monopole, and cross-dipole power spectrum. The estimators and models of the power spectrum as well as our fitting method have been tested using mock catalogs; we find that they perform well in recovering the fiducial values of the cosmological parameters of the simulations, and we also find that the errors of the parameters can be largely reduced by including the cross-power spectrum in the fit. We measure the autodensity, automomentum, and cross-power spectrum using the Sloan Digital Sky Survey Data Release 14 peculiar velocity catalog. The fit result of the growth rate f σ 8 is fσ8=0.413−0.058+0.050 at effective redshift z eff = 0.073, and our measurement is consistent with the prediction of the Lambda cold dark matter cosmological model, assuming general relativity.

COVID-19 REINFECTION PROCESSES: MATHEMATICAL MODELING, DATA FITTING WITH TIME-VARYING PARAMETERS, AND OPTIMAL CONTROL STUDIES

In this paper, we construct a COVID-19 dynamic model that included both the initial and reinfected population compartments, and conduct a structural identifiability analysis of the model parameters to ensure the robustness of the parameter fitting results. We use some actual statistical data from North Carolina to fit the model and estimate the values of some important parameters. In order to accurately fit the parameters in the model, we improve the physics-informed neural networks (PINNs) method in this paper, so that the fitting results can be reproduced on Matlab. The results of this study show that the transmission capacity of the virus in the reinfected person is only slightly lower than that of the first infected person, and vaccination is not effective in reducing the transmission rate of the virus. The death rate of the reinfected is much higher than that of the first infected person. Finally, we conduct a cost-effectiveness study using optimal control methods and found that, while it is easier to reduce reinfection by combining multiple strategies, the most effective strategy for reducing reinfection is to increase treatment cure rates and reduce direct or indirect contact among those who have recovered.

Thermal Decomposition Kinetics Analysis and Its Application to Cigarette Flavouring of Methyl Cyclopentenolone Succinate Menthol Diester

ABSTRACTMethyl cyclopentenolone succinate menthol diester (compound 5) was synthesised from methyl cyclopentenolone, menthol and succinic anhydride using dicyclohexylcarbodiimide (DCC) and 4‐dimethylaminopyridine (DMAP) methods. The thermal decomposition process and kinetic behaviour of the compounds were studied at various heating rates (10°C, 20°C, 40°C, 60°C and 80°C min−1) using thermogravimetry‐derivative thermogravimetry (TG‐DTG), Kissinger‐Akahira‐Sunose (KAS) method, Flynn‐Wall‐Ozawa (FWO) method and Coats–Redfern method. The research revealed that TG curves approached the high‐temperature end with increasing heating rates, and the peak temperature of heat loss rate on the DTG curves tended to rise, but the magnitude of the change was smaller than that of heating rate. The apparent activation energy (Ea) of thermal decomposition increased with the conversion rate. The Ea of compound 5 ranged from 77.65 to 154.05 kJ mol−1 and from 82.56 to 156.43 kJ mol−1 calculated by KAS and FWO, respectively. The thermal decomposition process models of the compounds were found to be one‐dimensional diffusion models (D1). The results of the kinetic compensation effect showed that the fitting result of the KAS method was better than that of the FWO method. Pyrolysis results indicated the production of aromatic substances such as methyl cyclopentenolone, menthol and esters. Additionally, the results of cigarette flavouring showed that the smoking quality of cigarettes was improved by adding compound 5. In summary, the findings of this study not only offer insights into the thermal decomposition and development of slow‐release flavours under heated conditions, but also provide valuable references for assessing the thermal stability and utilisation potential of various substances, including biomass.

Parameters identification of magnetorheological damper based on improved grey wolf optimization algorithm

Abstract Parameter identification of the mechanical model of magnetorheological dampers is usually carried out through a combination of optimization algorithms, but there is little research on the impact of the optimization algorithm itself on the identification results. In order to improve the accuracy of parameter identification results, the influence of the parameters of the optimization algorithm itself on the fitting results was investigated in this study, and the optimization algorithm adopted the improved grey wolf algorithm. The effects of different wolf pack numbers and iteration times on recognition results were examined in this study. The various parameters in the mechanical model of magnetorheological dampers are determined utilizing the optimal combination of algorithm parameters. Finally, the validity of the identification results was verified by evaluating the consistency between the identified damping force and the experimental damping force. The results indicate that when the optimal combination is used, the accuracy of parameter identification can be improved.