Density Model Research Articles

Impact of nuclear level density and radiative strength function models on the S factors of (α,γ) reactions relevant to p-nuclei production

Abstract 
Abstract. The astrophysical S factors of (α,γ) reactions are crucial inputs to nuclear reaction network simulations for studying p-nuclei production. Previous work revealed considerable discrepancies between theoretical and experimental S factors using different nuclear level densities (NLD) and radiative strength functions (RSF). Accordingly, in this article, we studied the influence of six RSF and four NLD models on the astrophysical S factors of (α, γ) reactions on 16 target nuclei. The microscopic NLD and RSF models under investigation are based on the Hartree-Fock + Bardeen-Cooper- Schrieffer (HFBCS) and Hartree-Fock-Bogulubov (HFB) models. The obtained results have shown that within the α-OMP proposed in Ref. [1], RSF and NLD models used in the S factor computation yield root mean square (rms) deviations of less than 0.750, and phenomenological NLD and RSF models predict astrophysical S better than microscopic ones in all cases considered. More research is needed on NLD and RSF models to improve prediction accuracy and reduce deviations in calculations of the S factors of radiative α captures.

Addressing bias in national population density models: Focusing on rural Senegal

Knowing where people are is crucial for policymakers, particularly for the efficient allocation of resources in their country and the development of effective, people-centred policies. However, rural population distribution maps suffer from biases related to the type of dataset used to predict population density, such as the use of nighttime lights datasets in areas without electricity. This renders widely used datasets irrelevant in rural areas and biases nationwide models towards urban areas. To compensate for such biases, we aim at understanding the importance and relationship between water-related covariates and population densities in a random forest model across the urban-rural gradient. By extending a recursive feature elimination framework, we show that commonly used covariates are only selected when modelling the whole country. However, once the highest density areas are removed, water-related characteristics (especially distance to boreholes) become important covariates of population density outside of densely populated areas. This has important implications for modelling population in rural areas, including for a better estimation of the size of remote communities. When seeking to produce country-level population maps, we encourage further studies to explicitly account for rural areas by considering the urban-rural gradient and encourage the use of water-related datasets.

Identification of optimum scopes of environmental drivers for schistosome-transmitting Oncomelania hupensis using agent-based model in Dongting Lake Region, China

Abstract Oncomelania hupensis (O. hupensis), the sole intermediate host of Schistosoma japonicum, greatly influence the prevalence and distribution of schistosomiasis japonica. The distribution area of O. hupensis has remained extensive for numerous years. This study aimed to establish a valid agent-based model of snail density and further explore the environmental conditions suitable for snail breeding. A marshland with O. hupensis was selected as a study site in Dongting Lake Region, and snail surveys were monthly conducted from 2007 to 2016. Combined with the data from historical literature, an agent-based model of snail density was constructed in NetLogo 6.2.0 and validated with the collected survey data. BehaviorSpace was used to identify the optimal ranges of soil temperature, pH, soil water content, and vegetation coverage for snail growth, development and reproduction. An agent-based model of snail density was constructed and showed a strong agreement with the monthly average snail density from the field surveys. As soil temperature increased, the snail density initially rose before declining, reaching its peak at around 21°C. There were similar variation patterns for other environmental factors. The findings from the model suggested that the optimum ranges of soil temperature, pH, soil water content and vegetation coverage were 19°C to 23 °C, 6.4 to 7.6, 42% to 75%, and 70% to 93%, respectively. A valid agent-based model of snail density was constructed, providing more objective information about the optimum ranges of environmental factors for snail growth, development and reproduction.

3-D intrinsic attenuation tomography using ambient seismic noise applied to La Palma Island (Canary Islands)

The potential of the island of La Palma (Canary Islands) to host geothermal resources is very high, mainly due to its high volcanic activity. The primary goal of this study is to get a tridimensional image of the seismic intrinsic attenuation using ambient seismic noise and to identify anomalies that may be linked to active geothermal reservoirs on La Palma island. For this purpose, we developed a new Ambient Noise Attenuation Tomography (ANAT) technique, which uses seismic ambient noise for imaging intrinsic attenuation in 3-D at a local scale down to 5 km depth. Our research identifies two areas with high attenuation in the island’s southern region. One area could be associated with hydrothermal alteration zones beneath the Cumbre Vieja volcanic complex. Another high-attenuation zone was observed in the island’s southern part, which could be associated with extensively fractured rocks that might facilitate the circulation of heated fluids. Furthermore, we discuss the geothermal relevance of such anomalies, making a comparison with previous resistivity, S-wave velocity and density models. This study confirms that intrinsic attenuation retrieved from the coda of Rayleigh waves is more sensitive to the presence of fluids than velocity. Fluids being a key component of active geothermal reservoirs, it is reasonable to expect intrinsic attenuation anomalies in these systems. Therefore, we conclude that ANAT can be useful for geothermal exploration.

Machine Learning Models for High Explosive Crystal Density and Performance

Machine Learning Models for High Explosive Crystal Density and Performance

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.

Investigation of radiogenic heat production in granites of the Goiás Tin Province, Central Brazil

This study defines the radiogenic heat production of A-type granites in the Goiás Tin Province (GTP), Central Brazil, using airborne gamma-ray spectrometry. Pedra Branca Massif and Serra Dourada Granite are rich in tin, rare earth elements, and they exhibit anomalous radiogenic heat (5.5–15 µW/m³). They are therefore classified as high heat production granites (HHPG). By integrating radiogenic heat data, RGB imaging, magnetometry, density model and geological information, we associated anomalous radiogenic heat with mineralized regions present in the granites found in the GTP. Our methodology was validated using geological information, density model and other granites worldwide. It proved to be effective for targeting HHP granites.

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

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.