Density Variations Research Articles

3D Gravity and magnetic inversion modelling for geothermal assessment and temperature modelling in the central eastern desert and Red Sea, Egypt

The Central Eastern Desert and Red Sea region have emerged as a significant area of interest for geothermal energy exploration, owing to their unique geological characteristics and active tectonic activity. This research aims to enhance our understanding of the region's geothermal potential through a comprehensive analysis of gravity and magnetic data. By utilizing a 3D gravity inversion model, a detailed examination of subsurface structures and density variations was conducted. Similarly, a 3D magnetic inversion model was employed to investigate subsurface magnetic properties. Integration result from the Pygimli library ensured robustness and accuracy in the inversion results. Furthermore, a temperature model was developed using the WINTERC-G model and inversion techniques, shedding light on the thermal structure and potential anomalies in the study area. The analysis of the Bouguer gravity map, 3D gravity inversion model, and magnetic data inversion yielded significant findings. The Red Sea exhibited higher gravity values compared to the onshore Eastern Desert, attributed to the presence of a thinner and denser oceanic crust as opposed to the less dense continental crust in the Eastern Desert. The 3D gravity inversion model revealed distinct variations in density, particularly high-density zones near the surface of the Red Sea, indicating underlying geological structures and processes. Conversely, density gradually decreased with depth along the onshore line, potentially influenced by a higher concentration of crustal fractures. The magnetic data inversion technique provided additional insights, highlighting areas with demagnetized materials, indicative of elevated temperatures. These findings were consistent with the correlation between high-density areas and low magnetic susceptibility values, reinforcing the proposition of increased heat transfer from the Red Sea. Comparative analysis of temperature profiles further confirmed the presence of elevated temperatures in promising zones, emphasizing the geothermal potential associated with heat transfer from the Red Sea.This research contributes to the understanding of the geothermal resources in the Central Eastern Desert and Red Sea region. The results from gravity and magnetic data inversions, combined with temperature profiles, provide valuable information for future geothermal exploration and utilization efforts. The findings underscore the importance of geothermal energy in achieving sustainability and contribute to the global discourse on renewable energy sources.

Influence of habitat on fine-scale space use by brown lemmings (Lemmus trimucronatus) in the High Arctic.

Space use by small mammals should mirror their immediate needs for food and predator shelters but can also be influenced by seasonal changes in biotic and abiotic factors. Lemmings are keystone species of the tundra food web, but information on their spatial distribution in relation to habitat heterogeneity is still scant, especially at a fine scale. In this study, we used spatially explicit capture-recapture methods to determine how topography, hydrology, vegetation, and soil characteristics influence the fine-scale spatial variations in summer density of brown lemmings (Lemmus trimucronatus). Lemmings were monitored throughout the summer in wet and mesic tundra habitats and in a predator exclusion grid, which was also located in mesic tundra. We found that in wet tundra, lemming densities were higher at sites with a rugged topography dominated by hummocks, but only during snow melt. In both mesic tundra sites, lemming densities were higher in sites with poor drainage and low aspect throughout the summer. We found no clear association between lemming densities and any tested vegetation or soil variables. Overall, hydrology and topography appear to play a dominant role in small-scale space use of brown lemmings with a secondary role for predator avoidance and food plant abundance.

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation

Objectives:This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. Materials and methods:A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. Results:For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. Conclusion:Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

Climate as a Driver of Aboveground Biomass Density Variation: A Study of Ten Pine Species in Mexico

Climate as a Driver of Aboveground Biomass Density Variation: A Study of Ten Pine Species in Mexico

Element-based finite volume method applied to autogenous bead-on-plate GTAW process using the enthalpy energy equation

The welding process is the most used technique for metal joining. Understanding the temperature variation along the welded part during the process can prevent the appearance of failures. The experimental investigation process is quite time-consuming and costly. Therefore, numerical simulation processes based on the finite difference method, finite element method, finite volume method, or meshless Element-Free Galerkin (EFG) methods are important tools to optimize the welding process. The main goal of the present study is to show the feasibility of the Element-based Finite-Volume Method (EbFVM) approach for actual engineering applications. To solve the unsteady 2D and 3D thermal energy equation using enthalpy as an independent variable, an in-house Fortran code has been developed based on the EbFVM approach in conjunction with unstructured and structured meshes. The numerical simulations, with four types of different heat sources, were performed for applications of real welding processes with variations in density and enthalpy as a function of temperature. The results are presented in terms of thermal cycles and temperature fields. Furthermore, the developed code was confronted against experimental works from the literature, simulated and lab-controlled experiments with AISI 409 ferritic workpieces, and exact analytical solutions with thermocouples fixed in different positions. In general, the numerical results from the current investigation are in close agreement with the results from the literature and the experimental results performed by the authors. The numerical results also highlighted the differences between the 2D and 3D models for thermal cycles near the bead weld.

Numerical Modeling and Topology Optimization for Designing the Anode Catalyst Layer in Proton Exchange Membrane Water Electrolyzers Considering Mass Transport Limitation

With the escalation of global warming primarily attributed to fossil fuel and other non-renewable energy consumption, the production of green hydrogen emerges as a mitigation strategy to reduce fossil fuel usage and effectively harness renewable energy sources for energy storage. The proton exchange membrane water electrolyzer (PEMWE) stands out as a promising technology, boasting high efficiency and a rapid response to variations in current density. Despite its stellar performance, the reliance on precious materials presents a cost challenge. To address this concern, we developed a numerical model considering mass transport limitations and temperature variation. The topology optimization (TO) method is employed to generate the optimal structure of the electrode by organizing the two primary constituent materials. Additionally, the impact of optimization points representing low (1.73 V) and high (2.03 V) operating voltage characteristics is analyzed. The optimal structure demonstrates a maximum performance improvement of up to 2.7 times at an operating voltage of 2.03 V compared to the homogeneous electrode structure. The gas coverage model influences the rearrangement of constituent materials, particularly the void fraction, creating channels to facilitate the reaction. Optimization at high voltage points yields a more significant improvement compared to the low voltage scenario.

Similarity solution for the magnetogasdynamic shock wave in a self-gravitating and rotating ideal gas under the influence of radiation heat flux

The Lie invariance method is used to analyze the one-dimensional, unsteady flow of a cylindrical shock wave in a rotating, self-gravitating, radiating ideal gas under the influence of an axial or azimuthal magnetic field, with an emphasis on adiabatic conditions. The analysis assumes a stationary environment just ahead of the shock wave and considers variations in fluid velocity, magnetic field, and density within the perturbed medium just behind the shock front. In the governing equations, the impact of thermal radiation under an optically thin limit is integrated into the energy equation. Utilizing the Lie invariance method, the set of partial differential equations governing the flow in this medium is transformed into a system of nonlinear ordinary differential equations (ODEs) using similarity variables. Two distinct cases of similarity solutions are obtained by selecting different values for the arbitrary constants associated with the generators. Among these cases, one yields similarity solutions assuming a power-law shock path and the other an exponential-law shock path. For both cases, the resulting set of nonlinear ODEs are numerically solved using the 4th-order Runge–Kutta method in MATLAB software. The article thoroughly explores the influence of various parameters, including γ (adiabatic index of the gas), Ma−2 (Alfvén–Mach number), σ (ambient density exponent), l1 (rotational parameter), and G0 (gravitational parameter) on the flow properties. The findings are visually presented to offer a comprehensive insight into the effects of these parameters.

Age- and Gender-Related Variations in Cortical Bone Density of the Mandible using Cone-Beam Computed Tomography in Central Indian Population

Abstract Background: “A radiographic imaging technique called cone-beam computed tomography (CBCT) enables precise three-dimensional (3D) visualization of hard tissue features. It is the most significant of the recently created dental diagnostic imaging modalities. There is, however, a lack of information on the correlation among mandibular cortical bone radiography density, including age and sex. Hence, the goal of this project was to use CBCT to estimate the cortical bone density of the mandible for age and sex variations in the population of central India. Materials and Methods: A retrospective research involving 300 patients was carried out. Patients arrived from the radiology and oral medicine divisions. The CBCT scans were divided into three age groups: those between the ages of 31–50, 51–70, and those older than 70.” The picture archiving and communication system was used to retrieve the images from the system. We determine the mandibular cortical bone radiography density on the CBCT using regions of interest. Results: The CBCT-measured cortical bone radiographic density was substantially linked by age. In addition, young males (1600 ± 129.63) had considerably higher cortical bone radiography density than young females (1366.95 ± 197.46), old men (1480.70 ± 189.59), and elderly females (962.38 ± 138.50). Conclusion: Mandibular cortical bone density is higher in males than females. Age-related alterations in the cortical bone of the jaw were examined using CBCT. In addition, CBCT analysis of the mandibular cortical bone could help recognize risk factors for declining bone mineral density. CBCT changes in the cortical bone of the mandible with aging. In addition, employing CBCT to assess the mandibular cortical bone could help identify risk factors for declining bone mineral density.

Long-term Variation of the Solar Polar Magnetic Fields at Different Latitudes

The polar magnetic fields of the Sun play an important role in governing solar activity and powering fast solar wind. However, because our view of the Sun is limited in the ecliptic plane, the polar regions remain largely uncharted. Using the high spatial resolution and polarimetric precision vector magnetograms observed by Hinode from 2012 to 2021, we investigate the long-term variation of the magnetic fields in polar caps at different latitudes. The Hinode magnetic measurements show that the polarity reversal processes in the north and south polar caps are non-simultaneous. The variation of the averaged radial magnetic flux density reveals that, in each polar cap, the polarity reversal is completed successively from the 70° latitude to the pole, reflecting a poleward magnetic flux migration therein. These results clarify the polar magnetic polarity reversal process at different latitudes.

Azimuthal Size Scales of Solar Wind Periodic Density Structures

Periodic density structures (PDSs) are quasiperiodic variations of solar wind density ranging from a few minutes to a few hours. PDSs advect with the solar wind and have radial length scales (L x ) of tens to several thousand megameters, thus belonging to the class of “mesoscale structures.” Current interplanetary multispacecraft observations are not at spatial separations capable of directly measuring the 3D size scale of PDSs or other mesoscale structures. Instead, previous investigations estimated characteristic spatial scales in solar wind parameters using cross-correlation and/or coherence analysis applied to multispacecraft observations. For the solar wind density and interplanetary magnetic field (IMF) intensity, the reported size scales perpendicular to the Sun–Earth line (L y ) ranged between ≈30 and ≈200 Earth Radii (R E). Here, we implemented a similar approach for the same parameters, but focused on high-density, slow-solar-wind intervals with PDSs observed by the Wind and ARTEMIS-P1 spacecraft. Additionally, this is the first statistical study of the IMF intensity periodicities in relation to PDSs. We identified intervals in which the two spacecraft observed the same periodicity, obtaining two PDS groups based on their radial length scale: L x1 ≈ 86R E and L x2 ≈ 35R E. Then, we classified the events based on the periodic variations’ coherence level. Reproducing the results with simulations of the PDSs’ transit, we inferred the L y order of magnitudes for the two PDS groups: L y1 ≈ 340R E and L y2 ≈ 187R E. Knowing the PDSs’ size scales is fundamental for constraining models aimed at reproducing these structures and is critical for better understanding the PDS–magnetosphere coupling.

How to find a wolverine: Factors affecting detection at wolverine (Gulo gulo) bait stations in western Canada

AbstractHigh individual detection success enables precise estimates of density and the ability to monitor trends in abundance for wolverine and other low‐density species, information that is critical for the implementation and assessment of conservation measures. We evaluated a dataset that included six different wolverine capture–recapture studies over a large gradient in wolverine (Gulo gulo) density to provide recommendations for increasing detection. We examined factors related to bait station components, habitat, and seasonal timing. Accounting for variation in wolverine density and trap duration, our results suggest that bait stations setups having a run pole, frame, and camera to photograph unique ventral color patterns, in addition to hair snag devices, identify more individual wolverine than those without. The presence of snow is a habitat feature that also increases individual detection. Female detection rates were lower than male detection rates at the onset of the reproductive denning season in late February and early March compared with January and early February. We found latency to detection was independent of wolverine density, but greater in areas with human influence. Relatively high rates of genotyping success (55%) were predicted by even a single guard hair left at bait stations, while underfur required ~15 hairs for similar success. Longer sampling intervals reduced genotyping success in spring, more so for underfur than guard hair. Hair samples acquired from barbwire were of higher quality than those from either alligator clips or gun brushes. To improve individual detection for wolverine inventory and monitoring, we recommend deploying run pole setups in areas with low human disturbance that will retain snow into late spring. Extending the winter trapping effort into April and May could increase the chances of detecting denning females. Latency to detection suggests that traps should be active for more than a month, especially in human‐influenced areas, but genotyping success suggests that traps should also be cleared of hair samples at smaller intervals of a month or less, during late winter/spring.

OI 630.0 nm Post‐Sunset Emission Enhancement as an Effect of Tidal Activity Over Low‐Latitudes

AbstractThe OI 630.0 nm airglow emission variability provides salient information on the dynamical changes taking place in the upper atmosphere at around 250 km. The emission rates vary with the changes in the ambient electron densities and the neutral constituents that are associated with these emissions. On several occasions, enhancements in these emissions are observed during post‐sunset hours, around 21 local time (LT), as measured from Mt. Abu (24.6°N, 72.7°E, 19°N Mag), a low‐latitude location at Indian longitudes. These enhancements occur following the typical monotonic decrease in emission intensity after sunset. The presence of poleward meridional wind preceded by cessation and reversal of equatorward wind at the post‐sunset hours was shown to be the cause for such observed emission enhancements in an earlier study. In this study, the cause of such reversal in meridional winds during post‐sunset hours has been investigated using the variation in electron densities and meridional winds simulated by the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCM‐X), which also shows enhancements in electron densities similar to those observed in the post‐sunset OI 630.0 nm nightglow emissions, and simultaneous reversal in meridional winds as well. The amplitudes and phases of different components of tides obtained from WACCM‐X meridional winds reveal a significant contribution of higher‐order tides, especially, quarter‐diurnal tides, to the observed reversal in the meridional winds during post‐sunset hours.

Detecting Geothermal Phenomena at Sikidang Crater Area Based on Ground Penetrating Radar Method

Abstract Sikidang Crater (2000 masl) is one of the leading tourist sites in the Dieng area. However, not many people realise that Sikidang Crater is located in the Dieng Volcanic Complex where most of the volcanoes belong to the type A category. Sikidang Crater is one of the main geothermal manifestations occupied by mud pools, solfatara, fumaroles, and altered rocks. These manifestations have potential geological hazards such as toxic gases, base surge, and mud explosions. On the other hand, it has a high level of human activity that increases the risk of disasters. As an effort to prevent and mitigate disasters, it is necessary to know the subsurface geological conditions of the crater. This study aims to determine the subsurface geological conditions of the crater. This research uses the methods of detailed geological mapping, manifestation observation, and Ground Penetrating Radar (GPR) geophysical survey. The tool used is Akula Geoscanner 9000C running at 64.75 Hz frequency, capable of penetrating 50 meters deep, and has a vertical resolution of > 0.85 meters. Sikidang Crater is located at the southeastern foot of Mount Pangonan and it forms semicircular like a horseshoe. According to Van Zuidam, the crater is classified as a solfatara and fumarole field geomorphology unit. The lithology of this area is dominated by the eruption products of Mount Pangonan. In general, radargrams resulting from GPR surveys show various amplitudes of reflected electromagnetic waves. This is consistent when correlated with the variation in rock density of the research area. At some points, there is a decrease in electromagnetic wave velocity due to contrast in dielectric constant values, which is interpreted as geothermal fluid flow towards the surface. The potential level of geothermal fluid flow is classified into high, medium, low, and none based on the density of potential points seen in the radargram.

Three-dimensional internal multiple elimination in complex structures using Marchenko autofocusing theory

Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media. They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image. With the development of seismic exploration into deep and ultradeep events, especially those from complex targets in the western region of China, the internal multiple eliminations become increasingly challenging. Currently, three-dimensional (3D) seismic data are primarily used for oil and gas target recognition and drilling. Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling. In this study, we propose an internal multiples prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory. This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps. Firstly, simulating direct waves with a 3D macroscopic velocity model. Secondly, using direct waves and 3D full seismic acquisition records to obtain the upgoing and downgoing Green’s functions between the virtual source point and surface. Thirdly, constructing internal multiples of the relevant layers by upgoing and downgoing Green’s functions. Finally, utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples. Compared with the two-dimensional (2D) Marchenko algorithm, the performance of the 3D Marchenko algorithm for internal multiple predictions has been significantly enhanced, resulting in higher computational accuracy. Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data, thereby exhibiting important theoretical and industrial application value.

Physics-Informed Machine Learning for Structural Damage Diagnosis in Aluminium Plates

Damage diagnosis plays a crucial role in Structural Health Monitoring (SHM) by facilitating the identification, localization, and estimation of the extent of defects in structures. Lamb waves, known for their sensitivity to defects, are widely employed in SHM methods for thin-walled structures. Most of those traditional methods require extracting damage indices from Lamb wave signals. This operation involves substantial post-processing and implies that part of the diagnostic information is lost. To solve those limitations and improve the damage diagnosis accuracy, machine learning methods have recently been proposed in the literature. However, the reluctance of the industrial sector to adopt conventional black-box models due to their lack of explainability poses a challenge. This study proposes a physics-informed machine-learning approach to address the limitations of standard black-box methods. Particularly, a Physics-Informed Neural Network (PINN) is implemented to predict the density of an aluminium plate based on measurements of plate displacements caused by Lamb wave excitation. This is made possible by the implementation of a specific loss function, which leverages physical knowledge in the form of the partial differential equation governing Lamb waves. Predicting the plate density based on measured displacements eliminates the need for artificial damage indices, utilizing the density variation itself to detect and localize damage. Additionally, the outputs of the PINN, rooted in physics equations, offer enhanced explainability compared to standard black-box models. The versatility of this framework extends to predicting material properties distributions for components, and efforts will be directed towards adapting the method for composite materials, where the approach may pose additional challenges.

Numerical Modeling of Non-Boussinesq Natural Convection in a Square Cavity Containing a Porous Layer

Numerical simulations were conducted to study the fluid flow and heat transfer through a partially filled square enclosure with a horizontal porous layer at the bottom. The vertical walls of the enclosure were subjected to uniform heating and cooling at specified temperatures TH and TC, respectively. The problem formulation was based on the Darcy–Brinkman model, which took into account density variation using the Mach number law approximation and incorporated variable properties. Based on the developed code, a parametric study of fluid flow and heat transfer inside the cavity was carried out to examine the effects of temperature difference, medium permeability, and Darcy number. Additionally, the variation of the Nusselt number on the hot and cold walls was provided to demonstrate the overall nature of heat transfer within the enclosure. The results of this study highlight that fluid flow and heat transfer are influenced by the temperature difference between the vertical walls and the characteristic parameters of the porous layer.

Study on the mechanism of CO2 huff-n-puff enhanced oil recovery and storage in shale porous media considering heterogeneous structure

CO2 possesses several advantages, including strong solubility, effective viscosity reduction ability, and low miscible pressure, making it a promising candidate for enhanced oil recovery (EOR). Additionally, due to its adsorption capture mechanism, shale formations are considered ideal environments for CO2 storage. However, the influence of heterogeneity of shale multi-scale structure on CO2 migration mechanism, EOR, and storage mechanism is not clear. In this study, a heterogeneous shale structure model containing fractures and matrix was designed based on scanning electron microscope. The multiphase–multicomponent–multirelaxation model was used to study the fluid migration mechanism in the process of miscible CO2 huff-n-puff in shale reservoir. By analyzing density variations, velocity changes, and pressure distributions, the effects of diffusion coefficient, adsorption parameters, and fracture size were studied. Furthermore, by changing the matrix structure, the influence of heterogeneity on the law of oil and gas migration was explored. Additionally, a comparison between CO2 and water was performed. Finally, the influence of reservoir heterogeneity on fluid transport mechanism was studied. The results show that EOR and CO2 storage rate (CSR) are proportional to the diffusion coefficient. The main factor affecting the CSR is the adsorption capacity of rock to CO2. The larger CO2–oil contact area between the fracture and the matrix leads to a larger CSR, highlighting the importance of induced fractures. In addition, it was found that CO2 huff-n-puff was superior to water flooding, showing an EOR performance advantage of about 15%. This study is helpful for the practical application of CO2 huff-n-puff technology in the field of unconventional oil and gas development and CO2 storage.

Transient Changes in Cerebral Tissue Oxygen, Glucose, and Temperature by Microstrokes: A Computational Study.

This study focuses on evaluating the disruptions in key physiological parameters during microstroke events to assess their severity. A mathematical model was developed to simulate the changes in cerebral tissue pO2, glucose concentration, and temperature due to blood flow interruptions. The model considers variations in baseline cerebral blood flow (CBF), capillary density, and blood oxygen/glucose levels, as well as ambient temperature changes. Simulations indicate that complete blood flow obstruction still allows for limited glucose availability, supporting nonoxidative metabolism and potentially exacerbating lactate buildup and acidosis. Partial obstructions decrease tissue pO2, with minimal impact on glucose level, which can remain almost unchanged or even slightly increase. Reduced CBF, capillary density, or blood oxygen due to aging or disease enhances hypoxia risk at lower obstruction levels, with capillary density having a significant effect on stroke severity by influencing both pO2 and glucose levels. Conditions could lead to co-occurrence of hypoxia/hypoglycemia or hypoxia/hyperglycemia, each worsening outcomes. Temperature effects were minimal in deep brain regions but varied near the skull by 0.2-0.8°C depending on ambient temperature. The model provides insights into the conditions driving severe stroke outcomes based on estimated levels of hypoxia, hypoglycemia, hyperglycemia, and temperature changes.

Lexical Richness in Essays: A Corpus Based Study among Indonesian Junior High School Students

This study aims at analysing the lexical richness of junior high school students. Additionally, the aspects included in the analysis were lexical variation, lexical sophistication, lexical density, and the numbers of errors. The data were argumentative essays written by junior high school students Grade 9. The students are from one of the junior high schools in Kabupaten Tangerang. The data were analysed using a web concordance English v.9 named lextutor. The analysis reveal that the essays have a moderately high level lexical density and lexical variation. However, the students still relied on the 2000 most frequent words or K1 words with 68.29% of the total words. This study suggests that educators encourage their students to apply more word variation in their writings to improve the quality of their writings.

A Cross-Sectional Study Examining Hyoid Bone Fusion and Density Variation among Patients Receiving Care at a Tertiary Hospital

A Cross-Sectional Study Examining Hyoid Bone Fusion and Density Variation among Patients Receiving Care at a Tertiary Hospital