Density Model Research Articles

Effect of Einasto spike on the gravitationally decoupled self-gravitating dark matter halos

Abstract In this work, we consider the possibility of constructing gravitationally bound, charged dark matter halos motivated by Einasto density model. This model generalizes the concept of charged, non-commutative mini-compact objects by including dark matter as its primary component through the principles of the minimal geometric deformation strategy. We point out that the coupling of the non-commutativity inspired Einasto spike density model with a non-isotropic fluid, within the context of gravitational decoupling allows the formation of different minimally deformed dark matter halos, corresponding to each value of the deformation parameter. We assume the Tolman-Kuchowicz cosmological model as a seed metric within the geometric deformation scheme to initially generate an electrically charged isotropic solution. Subsequently, we employ a density-like constraint to produce a second anisotropic charged dark matter stellar model via the Einasto density profile. A complete graphical analysis of the structural variables and stability of both models indicate that, for the considered choice of parameters, both cosmological models are well-behaved, exhibiting expected physical behavior.

Dating the Egyptian Old Kingdom: The reign of Djedkare (5th dynasty)

Abstract This study aims to discuss the chronology of the Egyptian 5th dynasty of the Old Kingdom and the tentative date of accession of king Djedkare based on material from his royal necropolis at South Saqqara and non-royal cemetery of Abusir South, Egypt. A series of radiocarbon (14C) dates were established through analysis of archaeological material from several monuments at the necropolis, including the king’s pyramid complex, pyramid complex of his queen, and two elite tombs (Isesiankh and Khuwy). In addition, two samples from non-royal tombs in the Abusir South cemetery, were taken into consideration for further precision during the modeling, associated with king Huni (end 3rd dynasty) and king Niuserre (5th dynasty). The contextualized 14C dates together with re-evaluation of historical evidence on Djedkare’s rule, results in a new model of temporal probability density which can be further refined with any new data from archaeological research. It shows that Djedkare’s reign can be currently modelled between 2503 and 2449 BCE (95.4%), thus slightly older than expected by literature. This presented model provides a more precise chronological frame for the late 5th dynasty period of Egyptian history, which was period of a significant socio-economic transformation.

Localized Laser Heat Treatment on AISI 1045 Steel Using a LPBF Laser

ABSTRACTLaser heat treatment (LHT) can provide local microstructure modification for metallic materials. This study investigates the viability of LHT on AISI 1045 steel using a laser designed for laser powder bed processing (LPBF) with a very small beam diameter compared to existing literature. Energy density models from the literature are analyzed and adapted to improve consistency across varying laser properties, mainly focusing on spot size and laser–material interaction time. Fifteen different laser parameter schematics are evaluated, comparing hardness versus depth relations as well as laser‐affected zone (LAZ) profiles. Through adapting the energy density model, new laser parameters are calculated and demonstrated to produce a valid localized heat treatment.

3Density estimation using spatial capture‐recapture analyses: application to vaccination of prairie dogs against sylvatic plague

AbstractPrairie dogs are notoriously difficult to enumerate, with previously methods including visual counts, mark‐resight, burrow counts, and catch per unit effort. Unlike those methods, spatial capture‐recapture (SCR) analyses allow for formal estimation of density along with associated estimates of uncertainty, detection probability, and the size of the average area over which an individual was detected during the study period (referred to as an activity center). Using SCR analyses, we compared density estimates as part of a field trial evaluating the effectiveness of an oral sylvatic plague vaccine in black‐tailed prairie dogs (Cynomys ludovicianus), Gunnison's prairie dogs (C. gunnisoni), white‐tailed prairie dogs (C. leucurus), and Utah prairie dogs (C. parvidens) at 11 study areas in the western United States. The study was designed as a matched pairs analysis that included 27 individual paired plots (54 plots), each consisting of a plot treated with vaccine baits and a plot treated with placebo baits. Overall, we captured >3,000 individuals each year on these plots, and recapture rates ranged from 5–87%. For black‐tailed prairie dogs, density estimates ranged from 2.7 individuals/ha (95% CI = 2.2–3.3/ha) to 77.3/ha (63.2–94.4/ha), and for Gunnison's prairie dogs, estimates ranged from 11.7/ha (10.6–12.8/ha) to 15.4/ha (14.4–16.7/ha). White‐tailed prairie dogs were at their lowest density (3.3/ha, 95% CI = 2.9–3.8/ha) during the first year of the study and their highest density (14.5/ha; 13.5–15.6/ha) during the last year of the study. Utah prairie dog density estimates ranged from a low of 4.0/ha (95% CI = 3.55–4.6/ha) to a high of 20.8/ha (16.8–25.8/ha). Best‐fitting models of prairie dog density indicated increasing patterns of density over time on most study plots, negative effects of plague, and positive effects of vaccination. Finally, we found low correlations between catch per unit effort estimates from previous published literature at these sites and our densities estimates. Spatial capture‐recapture estimates allowed us to consistently compare treatment effects across space and time, although some exceptions are noted where we observed significant movement between plots within a pair (3 pairs) and when trapping effort between plots or years was not consistent.

Follow-up Timing of 12 Pulsars Discovered in Commensal Radio Astronomy FAST Survey

We present phase-connected timing ephemerides, polarization pulse profiles, and Faraday rotation measurements of 12 pulsars discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in the Commensal Radio Astronomy FAST Survey. The observational data for each pulsar span at least 1 yr. Among them, PSR J1840+2843 shows subpulse drifting, and five pulsars are detected to exhibit pulse nulling phenomena. PSR J0640−0139 and PSR J2031−1254 are isolated millisecond pulsars (MSPs) with stable spin-down rates ( Ṗ ) of 4.8981(6) × 10−20 s s−1 and 6.01(2) × 10−21 s s−1, respectively. Additionally, one pulsar (PSR J1602−0611) is in a neutron star−white dwarf (WD) binary system with an 18.23-day orbit and a companion of ≤0.65 M ⊙. PSR J1602−0611 has a spin period, companion mass, and orbital eccentricity that are consistent with the theoretical expectations for MSP−helium WD (He WD) systems. Therefore, we believe that it might be an MSP−He WD binary system. The locations of PSR J1751−0542 and PSR J1840+2843 on the P−Ṗ diagram are beyond the traditional death line. This indicates that FAST has discovered some low- Ė pulsars, contributing new samples for testing pulsar radiation theories. We estimated the distances of these 12 pulsars based on NE2001 and YMW16 electron density models, and our work enhances the data set for investigating the electron density model of the Galaxy.

Probabilistic Prediction and Assessment of Train-Induced Vibrations Based on Mixture Density Model

This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper. A benchmark example is conducted to demonstrate and evaluate the prediction performance of the DNN-RMDN model. Subsequently, the model is applied to a case study to investigate and compare the uncertainties of train-induced vibrations in the throat area and testing line area of a metro depot. After training, the model is capable of accurately predicting the probability density function (PDF) of train-induced vibrations at different distances from the track and at different frequencies. Utilizing the predicted PDF, probabilistic assessments can be performed to ascertain the likelihood of surpassing predefined limits. By employing a mixture density model instead of a single Gaussian distribution, the DNN-RMDN model achieves more accurate prediction of the PDF for train-induced vibrations. The proposed probabilistic assessment framework can effectively assist in vibration screening during the planning phase and in selecting and designing vibration mitigation measures of appropriate levels.

Prediction of Rubber Fatigue Life Using an Assimilation‐based Learning Approach and Incremental Crack Propagation Model

ABSTRACTAccurately and efficiently predicting the fatigue life of rubber materials has been a long‐standing challenge due to limited understanding of the fatigue mechanism. In this study, a variational assimilation‐based machine learning method assisted with incremental crack propagation model is proposed to predict the fatigue life of rubber materials. Firstly, according to the fracture mechanics theory, a new rubber fatigue life prediction model based on incremental crack propagation and sparse experimental data is established, which owns higher accuracy than the classical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method is introduced to generate a dense fatigue life dataset of rubber materials with high accuracy. Finally, the artificial neural network model is trained, cross‐validated, and tested using the dense dataset, and the three‐dimensional variational assimilation model is employed to merge the predicted values of artificial neural network with experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified; thereby, we offer an accurate and efficient approach to predict the rubber fatigue life.

INTEGRAL YIELDS OF PHOTONEUTRON REACTIONS ON TIN ISOTOPES 118Sn AND 124Sn IN THE NEAR-THRESHOLD ENERGY REGION

The integral yields of the photonuclear reactions 118Sn(,n)117mSn and 124Sn(,n)123mSn, which are of interest for studying scenarios of the formation of atomic nuclei in stars, are measured in the near-threshold energy region. The experimental data are compared with the results of theoretical calculations performed using the TALYS nuclear reaction code. The dependence of theoretical predictions on the choice of models of the nuclear level density and the radiative strength function is investigated.

A progress in the inverse matrix method in QCD sum rules

In traditional QCD sum rules, the simple hadron spectral density model of the “delta-function-type ground state + theta-function-type continuous spectrum” determines that there is no perfect parameter selection. In recent years, inverse problem methods, particularly the inverse matrix method, have shown better handling of QCD sum rules. This work continues to develop the inverse matrix method. Considering that the narrow-width approximation may still be a good approximation, we separate the ground state from the spectral density. We then follow the general steps of the inverse matrix method to extract physical quantities such as decay constants that we may be more interested in.

Do gravity data justify a rifted “Liguro-Provençal Basin”?

The geodynamic evolution of the Liguro-Provençal Basin and its crust and upper mantle structure remain debated, especially regarding the role of rifting in continental break-up and seafloor spreading. Our study incorporates updated datasets, including new gravity maps from the AlpArray Gravity Working Group (complete Bouguer, free air, and isostatic anomalies) for 3D modeling and gravity field analysis, seismic data from Lobster offshore campaigns for direct comparison, and geodynamic models, supplemented by seismic profiles from previous French and Italian campaigns to constrain the interpretation. We used GFZ’s IGMAS + software for interactive 3D modeling, creating a density model extending to 300 km depth that includes crustal and upper mantle inhomogeneities based on prior geodynamic models. This hybrid approach, with polygonal structures for the crust and voxels for the upper mantle, clarifies individual contributions to the gravity field. Extending initial gravity modeling from the SPP MB4D project INTEGRATE, our work provides a consistent 3D density model for the Alps and Ligurian Basin. The constrained 3D modeling and numerical analyses (terracing, clustering, filtering, curvature), along with vertical stress and gravitational potential energy calculations, suggest that rifting has significantly influenced the basin’s geological evolution.

Uncertainty of the white dwarf astrophysical gravitational wave background

The astrophysical gravitational wave background (AGWB) is a stochastic gravitational wave (GW) signal emitted by different populations of in-spiralling binary systems containing compact objects throughout the Universe . In the frequency range between 10$^ $ and 10$^ $ hertz (Hz), it will be detected by future space-based gravitational wave detectors, such as Laser Interferometer Space Antenna (LISA). In a recent work, we concluded that the white dwarf (WD) contribution to the AGWB dominates that of black holes (BHs) and neutron stars (NSs) We aim to investigate the uncertainties of the WD AGWB that arise from the use of different stellar metallicities, star formation rate density (SFRD) models, and binary evolution models. We used the code we previously developed to determine the WD component of the AGWB. We used a metallicity-dependent SFRD based on an earlier work to construct five different SFRD models. We used four different population models based on a range of common-envelope treatments and six different metallicities for each model. For all possible combinations, the WD component of the AGWB is dominant over other populations of compact objects. The effects of metallicity and population model are less significant than the effect of a (metallicity dependent) SFRD model. We find a range of about a factor of 5 in the level of the WD AGWB around a value of $ WD $ at 1 mHz and a shape that is weakly dependent on the model. We find the uncertainty for the WD component of the AGWB to be about a factor of 5. We note that there are other uncertainties that have an effect on this signal as well. We discuss whether the turnover of the WD AGWB at 10 mHz will be detectable by LISA and find it to be likely. We confirm our previous findings asserting that the WD component of the AGWB dominates over other populations, in particular, BHs.

Personalized 3D Printing of Artificial Vertebrae: A Predictive Bone Density Modeling Approach for Robotic Cutting Applications

Robotic vertebral plate cutting poses significant challenges due to the complex bone structures of the lumbar spine, which consist of varying densities in cortical and cancellous regions. This study addresses these challenges by developing a predictive model for robotic vertebral plate cutting force and bone quality recognition through the fabrication of artificial vertebrae with controlled, consistent bone density. To address the variability in bone density between cortical and cancellous regions, CT data are utilized to predict target bone density, serving as a foundation for determining the optimal 3D printing process parameters. The proposed methodology integrates a Response Surface Methodology (RSM), Back Propagation (BP) neural network, and genetic algorithm (GA) to systematically evaluate the effects of key process parameters, such as the filling density, material flow rate, and layer thickness, on the printed vertebrae’s density. A one-factor experimental approach and RSM-based central composite design are applied to build an initial bone density prediction model, followed by Sobol’s sensitivity analysis to quantify the influence of each parameter. The GA-BP neural network model is then employed to rapidly and accurately identify optimal printing parameters for different bone layer densities. The resulting optimized models are used to fabricate personalized artificial lumbar vertebrae, which are subsequently validated through robotic cutting experiments. This research not only contributes to the advancement in personalized 3D printing technology but also provides a reliable framework for developing patient-specific surgical planning models in robot-assisted orthopedic surgery.

Construction of global IGS-3D electron density (Ne) model by deep learning

In this study, we construct a global IGS-3D Ne model that generates global 3-D electron density (Ne) from International Global Navigation Satellite Systems (GNSS) Service (IGS) total electron content (TEC) data through deep learning. As a first step towards this, we make a model to generate a vertical electron density profile from a TEC value using Multi-Layer Perceptron (MLP). In this process, we use the vertical electron density profiles and the corresponding TEC values of the IRI-2016 model from 2001 to 2008 for training, 2009 and 2014 for validation, and 2010 to 2013 for a test. The next step is to generate global IGS electron density profiles using the global IGS TECs as input data for the model, which is called the global IGS-3D Ne model. We evaluate the IGS-3D Ne model by comparing the electron density profiles from the incoherent scatter radars (ISRs) at three stations with the IGS-3D Ne model from 2010 to 2013. The evaluation shows that the electron density profiles from the IGS-3D Ne model are closer to the ISR data than those of the IRI model, especially at high latitudes. The IGS-3D Ne model shows that the averaged root mean square error (RMSE) values between IGS and ISR electron density profiles are 0.37 log(m−3), 0.22 log(m−3), and 0.34 log(m−3) for all test datasets at Jicamarca, Millstone Hill, and EISCAT stations, respectively. These results suggest that our method has sufficient potential to enhance the ability to predict global electron density profiles.

Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China

Identifying urban functional zones is one of the important foundational activities for urban renewal and the development of high-quality urban areas. Efficient and accurate identification methods for urban functional zones are significant for smart city planning and industrial layout optimization. However, existing studies have not adequately considered the impact of the interactions between human activities and geographical space provision on the delineation of urban functional zones. Therefore, from the perspective of integrating the spatiotemporal characteristics of human activities with the distribution of urban functional facilities, by incorporating mobile signaling, POI (point of interest), and building outline data, we propose a multifactorial weighted kernel density model that integrates ‘human activity–land feature area–public awareness’ to delineate urban functional zones quantitatively. The results show that the urban functional zones in the central city area of Lanzhou are primarily characterized by dominant single functional zones nested within mixed functional zones, forming a spatial pattern of ‘single–mixed’ synergistic development. Mixed function zones are widely distributed in the center of Lanzhou City. However, the area accounted for a relatively small proportion, the overall degree of functional mixing is not high, and the inter-district differences are obvious. The confusion matrix showed 85% accuracy and a Kappa coefficient of 0.83.

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.

Integration of Geological Model and Numerical Simulation Technique to Characterize the Remaining Oil of Fractured Biogenic Limestone Reservoirs

AbstractFine geological modeling leads to accurate reservoirs numerical simulations. Fractured biogenic limestone has abundant storage spaces and flow paths to accumulate oil and gas. The complexity and diversity of fractured biogenic limestone also lead to challenges in accurately characterizing its pore volume and remaining oil. This investigation aimed to enhance the understanding of fractured biotite reservoir properties via geological modeling. Numerical simulations were used to characterize the remaining oil during the late stage of field development. Considering the differences in porosity and permeability between fractures and matrix, a facies-controlled stochastic modeling technique was used to establish a dual-porosity and dual-permeability (DPDP) model for numerical simulation. Core information, logging data, and multiple seismic attributes were combined to guide low-level sequence fault interpretation for tectonic refinement. Based on classified seismic inversion, sedimentary phases were reconstructed. A discrete fracture network (DFN) model was obtained based on fracture occurrences and density models. The optimized discrete adjoint (ODA) algorithm was utilized to calibrate model parameters. The findings revealed that dense tectonic fractures develop in thick biogenic limestone areas. Combined with advanced reservoir simulation technology, these findings suggest that areas of thicker biogenic limestone were consistent with areas of higher fracture matrix conductivity multipliers. The remaining oil distribution patterns were investigated, and to deploy new wells was guided. Therefore, it is essential to better understand the tectonic characteristics of fractured biogenic limestone reservoirs and their remaining oil distribution patterns by integrating multiple sources of information and mastering advanced reservoir simulation technology for oilfield development.

Soil density specification ranges to optimise plant water use and root growth for phytocapping and urban greening projects

Soil density or compaction is one of the key variables that need to be specified for designed soil profiles as part of urban greening. Examples include land rehabilitation (mining), waste containment (phytocapping), stormwater infrastructure (bioretention basins) and green infrastructure (green roofs and street trees). This study investigates the impact of a range of specified soil densities on plant water use and root development. Native Australian plant species selected for the study include: the C4 and C3 grasses, Themeda triandra and Microlaena stipoides respectively; the Eucalyptus trees, E. camaldulensis and E. cladocalyx; and the nitrogen-fixing pioneer trees, Acacia mearnsii and Allocasuarina verticillata. The plants were established at four soil density (compaction) levels: 72, 77, 82, and 87 % MDD (maximum dry density) in tall cylinders with weekly plant water use measurement over 8 months. Root growth was analysed using WinRhizo image analysis. The experimental results were used to create generalisable models for root length density (RLD), root diameter and plant water use. The models for RLD and plant water use were parabolic in nature, revealing clear optimum ranges that could be used to guide soil density specification. Root diameter provided additional insight into the allocation of resources to root thickening above a threshold soil density of 87 % MDD, indicating plant allocation of resources towards penetrating highly compacted soils. There were correlations between plant water use and RLD that were moderate and significant, particularly for grasses. Notably, T. triandra had the greatest mean RLD, thickest roots and plant water use at 16.8cm/cm3, 0.18 mm and 10mm/week, respectively. Findings demonstrate that RLD and plant water use can be optimised together within practically achievable soil density specification ranges that are sensitive to the pitfalls of both excessively low and excessively high soil densities. Recommended soil density specification ranges include: 75–82 % MDD with plant performance within 5 % of optimum (considered excellent), 74–84 % MDD with plant performance within 10 % of optimum (considered good) and 73–85 % MDD with plant performance within 15 % of optimum (considered fair). Within these ranges, plant water use and root growth performance is well balanced with practical achievement of the soil density ranges. Due to the use of %MDD, the modelled results can be usefully generalised for any soil type. Implementation of these specifications for urban greening and phytocap projects will optimise plant growth, transpiration and hydrological function while maintaining root networks essential for establishing and maintaining resilient living infrastructure.

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

Enhancing the Performance of Unmanned Aerial Vehicle-Based Estimation of Rape Chlorophyll Content by Reducing the Impact of Crop Coverage

Estimating leaf chlorophyll content (LCC) in a timely manner and accurately is of great significance for the precision management of rape. The spectral index derived from UAV images has been adopted as a non-destructive and efficient way to map LCC. However, soil background impairs the performance of UAV-based LCC estimation, limiting the accuracy and applicability of the LCC estimation model, and this issue remains to be addressed. Thus, this research was conducted to study the influence of soil pixels in UAV RGB images on LCC estimation. UAV campaigns were conducted from overwintering to flowering stages to cover the process of soil background being gradually covered by rapeseed plants. Three planting densities of 11.25, 18.75, and 26.26 g/m2 were chosen to further enrich the different soil background percentage levels, namely, the rape fractional vegetation coverage (FVC) levels. The results showed that, compared to the insignificant difference observed for the ground measured LCC at a certain growth stage, a significant difference was found for most of the spectral indices extracted without soil background removal, indicating the influence of soil background. Removing soil background during the extraction of the spectral index enhanced the LCC estimation accuracy, with the coefficient of determination (R2) increasing from 0.58 to 0.68 and the root mean square error (RMSE) decreasing from 5.19 to 4.49. At the same time, the applicability of the LCC estimation model for different plant densities (FVC levels) was also enhanced. The lower the planting density, the greater the enhancement. R2 increased from 0.53 to 0.70, and the RMSE decreased from 5.30 to 4.81 under a low planting density of 11.25 g/m2. These findings indicate that soil background removal significantly enhances the performance of UAV-based rape LCC estimation, particularly under various FVC conditions.

Assessment of solitary pulmonary nodules using dual-layer spectral detector computed tomography.

We aim to quantitatively investigate the difference between benign and malignant solid pulmonary nodules that appeared on dual-energy spectral computed tomography, and assess the diagnostic accuracy of several parameters derived from computed tomography in differentiating malignant from benign pulmonary nodules. Between September 2021 and December 2022, spectral images of 71 patients (male:female = 44:27, mean age = 71.0 years) confirmed by pathology were retrospectively analyzed in the venous phase. Patients were classified into the malignant group and the benign group. The iodine concentration values of the nodules, normalized iodine concentration of the nodules to the neighboring vessels, virtual monochromatic images of 40 and 80 keV, and slope of the spectral curve were calculated and compared between the benign and malignant groups. Receiver operating characteristic curves and the area under the curve were performed to assess the diagnostic performance of the above parameters. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. The malignant pulmonary nodules have higher iodine density and slope of the spectral curve than the benign lesions. The combined model of iodine density and curve slope with an optimal cutoff of 0.39 (area under the curve = 0.82) yielded a sensitivity of 95% and a specificity of 63%. Contrast-enhanced dual-energy spectral computed tomography allows promising capability of distinguishing malignant from benign lesions, potential for avoiding unnecessary invasive procedure or surgery.