Fitting Method Research Articles

No Redshift Evolution in the Fe ii/Mg ii Flux Ratios of Quasars across Cosmic Time

The Fe ii/Mg ii emission line flux ratio in quasar spectra serves as a proxy for the relative Fe to α-element abundances in the broad-line regions of quasars. Due to the expected different enrichment timescales of the two elements, they can be used as a cosmic clock in the early Universe. We present a study of the Fe ii/Mg ii ratios in a sample of luminous quasars exploiting high-quality near-IR spectra taken primarily by the XQR-30 program with VLT XSHOOTER. These quasars have a median bolometric luminosity of log(L bol[erg s−1]) ∼ 47.3 and cover a redshift range of z = 6.0–6.6. The median value of the measured Fe ii/Mg ii ratios is ∼7.9 with a normalized median absolute deviation of ∼2.2. In order to trace the cosmic evolution of Fe ii/Mg ii in an unbiased manner, we select two comparison samples of quasars with similar luminosities and high-quality spectra from the literature, one at intermediate redshifts (z = 3.5–4.8) and the other at low redshifts (z = 1.0–2.0). We perform the same spectral analysis for all these quasars, including the usage of the same iron template, the same spectral fitting method, and the same wavelength fitting windows. We find no significant redshift evolution in the Fe ii/Mg ii ratio over the wide redshift range from z = 1 to 6.6. The result is consistent with previous studies and supports the scenario of a rapid iron enrichment in the vicinity of accreting supermassive black holes at high redshift.

Estimating the Spin of MAXI J1348–630 from Intermediate and Soft States Using Insight-HXMT Data

Abstract We present a broadband spectral analysis of the soft-intermediate and soft states using Insight-HXMT observations of the black hole binary MAXI J1348–630 during its outburst in 2019. We employ a combination of reflection and continuum fitting methods to measure the spin of the black hole and determine a spin of a = 0.79 ± 0.13, which is consistent with most of the previous results. Additionally, we investigate some sources of systematic uncertainty that could bias the measurement. The valid spectral hardening factor can partially account for the spin evolution observed during the outburst. Other potential factors, such as assumptions about the corona’s structure and their interaction with reflected photons, can also affect the accuracy of spin measurements. Furthermore, our analysis reveals that MAXI J1348–630 exhibits a significantly high disk density (∼1022 cm−3), with a moderate iron abundance of approximately 2.5 times solar.

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. To this end, we built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters AV and Teff, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars.

Accurate molecular recognition from the lowest unoccupied molecular orbital

The quantification of the lowest unoccupied molecular orbital level (LUMO) for molecular semiconductors is of great importance, because it determines the charge transport process and hence the performances of diverse electronic devices. Unfortunately, there is always lack of a convenient technique to determine the intrinsic LUMO. This work provides a reliable electrical spectroscopy by employing an easy-operating hot electron transistor, to make an accurate measurement. By taking advantage of a novel method, named the first derivative-assisted linear fitting method, the determination of the intrinsic LUMO becomes more scientific. Here, four kinds of molecular semiconductors are selected as the research objects and the values can be precisely decided even with a quite small difference in LUMO, which demonstrate the universality and the accuracy of our method. As expected, all the measured values are highly repeatable and it further confirms that we have provided a practical technique for the LUMO detection.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Improving Accuracy and Reproducibility of Cartilage T2 Mapping in the OAI Dataset Through Extended Phase Graph Modeling.

The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T2 relaxation times. Mono-exponential models are used in the OAI for T2 fitting, which neglects stimulated echoes and B1 inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset. To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T2 in the OAI dataset. Retrospective. From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren-Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61 years). 3-T, two-dimensional (2D) MESE sequence. Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T2 maps were obtained using three EPG-based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono-exponential approaches (linear least squares, nonlinear least squares, and noise-corrected exponential). Average T2 values within sub-regions were obtained. Pair-wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's T2 accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations. Bland-Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at P-value <0.05. EPG-based methods demonstrated superior T2 accuracy (mean absolute error below 0.5 msec at SNR > 100) compared to mono-exponential methods (error > 7 msec). EPG-based approaches had better reproducibility, with limits of agreement 1.5-5 msec narrower than exponential-based methods. T2 values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting. EPG modeling improved agreement and reproducibility of cartilage T2 mapping in subjects from the OAI dataset. 3 TECHNICAL EFFICACY: Stage 1.

Photometric redshift estimation for CSST survey with LSTM neural networks

ABSTRACT Accurate estimation of photometric redshifts (photo-zs) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on recurrent neural networks with long short-term memory (LSTM) to predict photo-z. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-z estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalogue, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-z estimation. For instance, when trained on the same data set and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers $f_{\mathrm{out}}$ observed with a multi-layer perceptron neural network (MLP) model, while the normalized median absolute deviation $\rm \sigma _{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-zs of galaxies using photometric data from large-scale survey projects.

Flame temperature and emissivity distribution measurement of solid propellant based on clustering analysis of multispectral radiation images.

To measure the combustion parameters of a solid propellant, this Letter researches the fitting method for flame temperature and emissivity based on multispectral images and proposes the particle swarm optimization-K-means (PSO-K-means) clustering optimization algorithm of a flame multispectral image. Considering the difference in flame radiation characteristics in different regions, the flame multispectral image is clustered, and spectra in different regions are analyzed and selected in different fitting bands to inverse temperature and emissivity. On this basis, the method is applied to measure solid propellant combustion parameters with different formulations. The measurement shows that the flame temperature is between 1700 and 2100 K, and the emissivity is concentrated in 0.1-0.5. Compared with temperature measurements obtained from tungsten-rhenium thermocouples, the relative deviation of multispectral imaging thermometry is less than 5%. The distribution characteristics of solid propellant combustion parameters with different formulations were analyzed, which provided important data support for evaluating combustion conditions and optimizing solid propellant formulations.

Associations between the waist-to-height ratio index and migraine: A cross-section study of the NHANES 1999-2004.

The importance of obesity as a factor that increases the probability of migraine episodes is increasingly acknowledged. Thus, this study aimed to explore the potential correlation between central obesity and migraine, emphasizing the waist-to-height ratio (WHtR) as a key measure in assessing this relationship. This cross-sectional analysis included 13,344 individuals who participated in the National Health and Nutrition Examination Survey (NHANES) from 1999-2004. To investigate the association associations between WHtR and migraine, we utilized refined multivariate logistic regression models, smoothing curve fitting methods, subpopulation analysis, and interactive testing. Of the 13,344 participants, 2,764 (20.72%) had migraines. A significant positive correlation was observed between the WHtR and migraine incidence in both the partially adjusted model (3.08 [95% CI: 1.92-4.94]) and the crude model (1.95 [95% CI: 1.23-3.08]). The participants in the highest quartile of the WHtR had a 13% greater incidence of migraine than those in the lowest quartile [1.13(0.99,1.28)]. The interaction analysis revealed a statistically significant difference (p<0.01) in this relationship among the subgroups. Notably, the correlation between WHtR and migraine risk was not significant and negative in patients ≥60 years, indicating that obesity has a mitigating role in preventing migraine in this elderly population. The incidence of migraine increased concomitantly with increased WHtR. However, central obesity has a protective effect against migraine occurrence in individuals ≥60 years. Thus, our findings underscore the importance of WHtR in migraine prevention and management strategies and highlight its potential as a critical biomarker for mitigating migraine incidence.

Seismic behavior of resilient concrete column-base connection with SMA bolts and RSPs

This paper presented a novel resilient concrete column-base connection with shaped memory alloy (SMA) bolts and rectangular slotted plates (RSPs) to achieve the demountable and recoverable capabilities in precast concrete columns. Quasi-static tests were conducted on one monolithic and three resilient concrete connections to investigate the effects of RSP number and yield strength on seismic behavior. Results showed that the resilient connection with both flange and web RSPs had superior lateral capacity, ultimate drift ratio, and energy dissipation compared to the monolithic connection. The resilient connection also exhibited minimal concrete damage and smaller residual deformation. However, low-yield-strength flange RSPs significantly reduced lateral capacity, initial stiffness, and energy dissipation, which should be considered in engineering design. Additionally, adjustment coefficients for rocking interface rotations were determined from digital image correlation (DIC) data using a fitting method. A formula was proposed to predict the flexural capacity of resilient specimens, assuming a plane section in the compression zone. The predictions closely matched the test data, with differences within 10 %, demonstrating the formula's effectiveness. The length, diameter, and material yield strength of the anti-shear steel bar (ASB) had a significant impact on the shear capacity of the interface.

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was &gt;99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level.

Deciphering the properties of UV upturn galaxies in the Virgo cluster

Abstract The UV upturn refers to the increase in UV flux at wavelengths shorter than 3000 Å observed in quiescent early-type galaxies (ETGs), which still remains a puzzle. In this study, we aim to identify ETGs showing the UV upturn phenomenon within the Virgo galaxy cluster. We utilized a color-color diagram to identify all potential possible UV upturn galaxies. The Spectral Energy Distributions (SED) of these galaxies were then analyzed using the CIGALE software; we confirmed the presence of UV upturn in galaxies within the Virgo cluster. We found that the SED fitting method is the best tool to visualize and confirm the UV upturn phenomenon in ETGs. Our findings reveal that the population distributions regarding stellar mass and star formation rate properties are similar between UV upturn and red sequence galaxies. We suggest that the UV contribution originates from old stellar populations and can be modeled effectively without a burst model. Moreover, by estimating the temperature of the stellar population responsible for the UV emission, we determined it to be 13,000 K to 18,000 K. These temperature estimates support the notion that the UV upturn likely arises from the contribution of low mass evolved stellar populations (extreme horizontal branch stars). Furthermore, the Mg2 index, a metallicity indicator, in the confirmed upturn galaxies shows higher strength and follows a similar trend to previous studies. This study sheds light on the nature of UV upturn galaxies within the Virgo cluster and provides evidence that low-mass evolved stellar populations are the possible mechanisms driving the UV upturn phenomenon.

Vision-based target localization and online error correction for high-precision robotic drilling

Abstract This article presents a detailed examination of circular target localization techniques for measuring robot pose and performing online pose correction. The investigated target localization methods include centroiding, ellipse fitting with point data and gradient information, and ellipse fitting methods with augmented and corrected input data. The performance of each method is evaluated in terms of accuracy and precision of measurements through experimental comparison with a laser tracker. This study provides technical and practical insights for selecting an appropriate target localization method in robotic applications. It also introduces a vision-based solution for robot relative error correction, comprising the calibration procedure and a closed-loop control with a proportional–integral-derivative controller for pose correction. Results show enhanced accuracy in robot positioning relative to workpiece, highlighting the effectiveness of the proposed solution in robotic drilling applications.

The long-term intervention effect and persistence analysis of the baduanjin mental sub -health of college students

In recent years, with the rapid development of society and the increase in academic and living pressure of college students, the psychological sub-health problems of college students have become increasingly prominent. Psychological sub -health refers to the state of the state of health and disease, manifested as a diagnostic criteria for poor individual mental state but not reaching the diagnosis of mental illness. As a special group, college students are in a critical period of psychological and physiological development, and facing multiple pressures such as academics, employment, and social networking. Long -term psychological health state will not only affect its academic and life quality, but also may lead to serious psychological problems such as emotional disorders, anxiety, and depression. Therefore, how to effectively intervene and improve the mental sub -health status of college students have become an important issue for the current psychological health education of colleges and universities. The eight -segment brocade of traditional fitness exercises, as part of ancient Chinese qigong, has gradually been widely used in modern health management in recent years. Baduanjin helps individuals to achieve physical and mental balance by regulating physical movements, breathing and mentality, and have the potential to improve mental health. Existing studies have shown that Baduanjin has certain effects on psychological problems such as relieving stress, improving anxiety, and depression, but there are still fewer long -term intervention effects and continuity research on the healthy sub -healthy health of college students. Therefore, this study aims to discuss the long -term intervention effect and its continuity of the eight -stage brocade of the psychological sub -health of college students, and provide new theoretical support and practical basis for colleges and universities' mental health intervention. This study adopts a random control experimental design. The research object is the college student of the School of Huanghe Science and Technology college. It is included in the standard of the SCL-90 (Symptom Self-Evaluation Table). The research objects are divided into the experimental group and control group. The experimental group participated in the two month Baduanjin, 2 times a week, 60 minutes each time; the control group does not perform Baduanjin to maintain daily life. After the experiment, the SCL-90 symptoms of SCL-90 symptoms and CPSS pressure perception table were performed for the experimental group for 1 month and 2 months to evaluate the continuity of the intervention effect. At the end of the intervention, the psychological sub-health symptoms of the experimental group improved significantly, and the overall scores of the SCL-90 decreased significantly. Compared with the control group, the experimental group improved greater improvement in anxiety, depression, and pressure perception. Especially in terms of anxiety and depression, the improvement of the experimental group is particularly significant. In summary, this study provides a wealth of experimental data and theoretical basis for the long -term intervention effect and sustainability of the Baduanjin for the healthy sub -health of college students through random control experiments. The results of the research not only provide an effective means of intervention for colleges and universities, but also open up a new direction for the modern application of traditional fitness methods. In the future practice, Baduanjin is hoping to become an important means of psychological and health intervention in colleges and universities, and provide theoretical basis for college students' physical and mental health.

Performance degradation modeling of civil aviation engines based on component characteristic map optimization

In order to provide a theoretical basis for the degradation of the air path performance of civil aviation engines at the unit level, the CFM56-3 engine was used as the research object. First, on the basis of using the characteristic map scaling method to obtain the component characteristic equation, the selection process of the scaling reference point of the fan general characteristic map was optimized, and a surface fitting method of the characteristic map was proposed to construct an engine component-level benchmark performance model that meets specific speed conditions under steady-state conditions. Then, by introducing the fault factor to generate the fault coefficient matrix, the deviation of the engine monitoring parameters as the component efficiency decreases was calculated, and compared with the fingerprint map data of the GE training manual, it was verified that the engine steady-state performance model that integrates the fan characteristic map scaling reference point optimization and the characteristic map surface fitting method has good accuracy and use in the analysis of air path performance degradation.

Separating the Spectral Counterparts in NGC 1275/Perseus Cluster in X-Rays

We present a model-independent method for separating the spectral counterparts of the active galactic nucleus (AGN) NGC 1275 from the surrounding emission of the Perseus cluster, as observed by Suzaku/XIS cameras. The Perseus cluster emission extends to higher energies than typically observed in AGN environments, reaching up to 9–10 keV. This necessitates precise separation of AGN and cluster spectra. To circumvent the degeneracy arising from numerous spectral fitting parameters, including elemental abundances, thermal and Compton emissions from the nucleus, and spectral parameters of the jet synchrotron self-Compton/inverse Compton emissions, we avoid traditional spectral fitting methods. Instead, we leverage spatial resolution and employ a double background subtraction approach. We apply this procedure to the complete set of Suzaku/XIS observational data for NGC 1275, resulting in cleaned spectra and a light curve of the AGN emission in this system. To demonstrate the applicability of our method, we also utilize the available XMM-Newton/EPIC data.

Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information

Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method.&#xD;Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. &#xD;Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data.&#xD;Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy.&#xD.

Applying a Comprehensive Model for Single-Ring Infiltration: Assessment of Temporal Changes in Saturated Hydraulic Conductivity and Physical Soil Properties

Modeling agricultural systems, from the point of view of saving and optimizing water, is a challenging task, because it may require multiple soil physical and hydraulic measurements to investigate the entire crop cycle. The Beerkan method was proposed as a quick and easy approach to estimate the saturated soil hydraulic conductivity, Ks. In this study, a new complete three-dimensional model for Beerkan experiments recently proposed was used. It consists of thirteen different calculation approaches that differ in estimating the macroscopic capillary length, initial (θi) and saturated (θs) soil water contents, use transient or steady-state infiltration data, and different fitting methods to transient data. A steady-state version of the simplified method based on a Beerkan infiltration run (SSBI) was used as the benchmark. Measurements were carried out on five sampling dates during a single growing season (from November to June) in a long-term experiment in which two soil management systems were compared, i.e., minimum tillage (MT) and no tillage (NT). The objectives of this work were (i) to test the proposed new model and calculation approaches under real field conditions, (ii) investigate the impact of MT and NT on soil properties, and (iii) obtain information on the seasonal variability of Ks and other main soil physical properties (θi, soil bulk density, ρb, and water retention curve) under MT and NT. The results showed that the model always overestimated Ks compared to SSBI. Indeed, the estimated Ks differed by a factor of 11 when the most data demanding (A1) approach was considered by a factor of 4–8, depending on the transient or steady-state phase use, when A3 was considered and by a practically negligible factor of 1.0–1.9 with A4. A relatively higher seasonal variability was detected for θi at the MT than NT system. Under both MT and NT, ρb did not change between November and April but increased significantly until the end of the season. The selected calculation approaches provided substantially coherent information on Ks seasonal evolution. Regardless of the approach, the results showed a temporal stability of Ks at least from early April to June under NT; conversely, the MT system was, overall, more affected by temporal changes with a relative stability at the beginning and middle of the season. These findings suggest that a common sampling time for determining Ks could be set at early spring. Soil management affected the soil properties, because the NT system was significantly wetter and more compact than MT on four out of five dates. However, only NT showed a significantly increasing correlation between Ks and the modal pore diameter, suggesting the presence of a relatively smaller and better interconnected pore network in the no-tilled soil. This study confirms the need to test infiltration models under real field conditions to evaluate their pros and cons. The Beerkan method was effective for intensive soil sampling and accurate field investigations on the temporal variability of Ks.

Comparison of terrestrial exospheric hydrogen 3D distributions at solar minimum and maximum using TWINS Lyman-α observations

Remote sensing observations of far-ultraviolet (FUV) emissions have been used to estimate 3D neutral hydrogen (H) density models of the terrestrial exosphere under solar minimum (2008) and solar maximum (2013 and 2015) conditions. Specifically, we used Lyman-alpha (Lyman-α, FUV at 121.6 nm) radiance data acquired by the Lyman-alpha detectors (LADs) onboard NASA’s TWINS satellites, collected over several days for each of the 3 years to provide sufficient coverage of the exospheric region. The datasets included Lyman-α measurements taken only above Earth radii (Re) of 3.75, assumed to be the optically thin region of the exosphere, where the measured Lyman-α intensity along a line of sight (LOS) is linearly proportional to the atomic hydrogen column density. Based on the calibration using multiple UV-bright stars, a significant decrease in TWINS1 LAD1/2 sensitivities was found for 2013 (LAD2 ∼1/2, LAD ∼1/3) and 2015 (LAD2 ∼1/3, LAD ∼1/7) compared to 2008. The calibration uncertainty was derived to be, on average, 5%. We estimated the 3D global hydrogen density distributions from these radiance data using two different tomographic inversion methods: first, a parametric fitting method based on a spherical harmonic function of order 3 and second, a high degree-of-freedom retrieval approach to validate our retrievals of H density. Both inversion methods consistently incorporate the effects of Lyman-α absorption within the exosphere and Lyman-α re-emission from Earth’s albedo. Our results reveal that H densities during solar maximum conditions are, on average, 30%–40% higher than those during solar minimum. All models showed a high concentration of atomic H on the dayside and nightside near the Sun–Earth line, which determines a nose/geotail structure consistent with theoretical effects from the solar radiation pressure. Furthermore, we identified that H-density enhancements during solar maximum with respect to solar minimum conditions occur at mid-to-high latitudes, particularly on the dusk side, while no significant enhancement seems to occur near the dayside nose.

Study on infrared thermal imaging for surface deterioration investigation and evaluation of stone cultural relics based on temperature variation

This study leverages infrared thermal imaging technology to advance the field of stone cultural relics preservation by introducing a novel evaluation index—temperature variation (Tv)—that quantifies the degree of surface deterioration. Unlike previous studies that have focused primarily on detecting a single deterioration type, this study pioneered a comprehensive analysis of the interactions of multiple deterioration in stone cultural relic. A deeper understanding of the weathering mechanisms of stone cultural relics has been gained by examining the Tv on the surface of stone cultural relics during heating and cooling, and successfully relating these changes to the degree of weathering of the cultural relic. This study developed a linear fitting method for systematically assessing the degree of weathering of stone cultural relic, which is a significant improvement over traditional assessment techniques. Our approach not only enhances the accuracy and applicability of Tv as an evaluation index but also integrates environmental temperature considerations, ensuring a more reliable assessment under varying conditions. The study's findings validate the effectiveness of infrared thermal imaging in identifying deterioration types and evaluating their severity, setting a new standard for the protection and restoration of stone cultural relics.