Spot Model Research Articles

Hot spots around Sagittarius A*. Joint fits to astrometry and polarimetry

Observations of Sagittarius A* ( in the near-infrared (NIR) show irregular flaring activity. Flares coincide with the astrometric rotation of the brightness centroid and with looping patterns in fractional linear polarization. These signatures can be explained with a model of a bright hot spot, transiently orbiting the black hole. We extend the capabilities of the existing algorithms to perform parameter estimation and model comparison in the Bayesian framework using NIR observations from the GRAVITY instrument, and simultaneously fitting the astrometric and polarimetric data. Using the numerical radiative transfer code we defined several geometric models describing a hot spot orbiting Sgr A*, threaded with a magnetic field, and emitting synchrotron radiation. We then explored the posterior space of our models with a nested sampling code We used Bayesian evidence to make comparisons between the models. We have used 11 models to sharpen our understanding of the importance of various aspects of the orbital model, such as non-Keplerian motion, hot-spot size, and off-equatorial orbit. All considered models converge to realizations that fit the data well, but the equatorial super-Keplerian model is favoured by the currently available NIR dataset. We have inferred an inclination of $ deg, which corroborates previous estimates, a preferred period of $ minutes, and an orbital radius of $ gravitational radii with the orbital velocity of $ times the Keplerian value. A hot spot with a diameter smaller than 5 gravitational radii is favoured. Black hole spin is not constrained well.

Constraining the properties of the thermonuclear burst oscillation source XTE J1814-338 through pulse profile modelling

Abstract Pulse profile modelling (PPM) is a comprehensive relativistic ray-tracing technique employed to determine the properties of neutron stars. In this study, we apply this technique to the Type I X-ray burster and accretion-powered millisecond pulsar J, extracting its fundamental properties using PPM of its thermonuclear burst oscillations. Using data from its 2003 outburst, and a single uniform temperature hot spot model, we infer J to be located at a distance of $7.2^{+0.3}_{-0.4}$ kpc, with a mass of $1.21^{+0.05}_{-0.05}$ M⊙ and an equatorial radius of $7.0^{+0.4}_{-0.4}$ km. Our results also offer insight into the time evolution of the hot spot but point to some potential shortcomings of the single uniform temperature hot spot model. We explore the implications of this result, including what we can learn about thermonuclear burst oscillation mechanisms and the importance of modelling the accretion contribution to the emission during the burst.

Evaluating a motor progression connectivity model across Parkinson's disease stages.

Stimulation of a specific site in the dorsolateral subthalamic nucleus (STN) was recently associated with slower motor progression in Parkinson's Disease (PD), based on the deep brain stimulation (DBS) in early-stage PD pilot clinicaltrial. Here, subject-level visualizations are presented of this early-stage PD dataset to further describe the relationship between active contacts and motor progression. This study also evaluates whether stimulation of the sweet spot and connectivity model associated with slower motor progression is alsoassociated with improvements in long-term motor outcomes in patients with advanced-stage PD. Active contacts of the early-stage PD cohort (N = 14) were analyzed alongside the degree of two-year motor progression. Sweet spot and connectivity models derived from the early-stage PD cohort were then used to determine how well they can estimate the variance inlong-term motor outcomes in an independent STN-DBS cohort of advanced-stage PD patients (N = 29). In early-stage PD, proximity of stimulation to the dorsolateral STN wasassociated with slower motor progression. In advanced-stage PD, stimulation proximity to the early PD connectivity model and sweet spot were associated with better long-term motor outcomes (R = 0.60, P < 0.001; R = 0.37, P = 0.046, respectively). Results suggest stimulation of a specific site in the dorsolateral STN isassociated with both slower motor progression and long-term motor improvements in PD.

Quiet Please: Detrending Radial Velocity Variations from Stellar Activity with a Physically Motivated Spot Model

For solar-type stars, spots and their associated magnetic regions induce radial velocity perturbations through the Doppler rotation signal and the suppression of convective blueshift, collectively known as rotation modulation. We developed the Rotation–Convection (RC) model: a method of detrending and characterizing rotation modulation using only cross–correlation functions or one-dimensional spectra without the need for continuous high-cadence measurements. The RC method uses a simple model for the anomalous radial velocity induced by an active region and has two inputs: stellar flux (or a flux proxy) and the relative radial velocity between strongly and weakly absorbed wavelengths (analogous to the bisector–inverse slope). On NEID solar data (3 month baseline), the RC model lowers the amplitude of rotationally modulated stellar activity to below the meter–per–second level. For the standard star HD 26965, the RC model detrends the activity signal to the meter–per–second level for HARPS, EXPRES, and NEID observations, even though the temporal density and time span of the observations differ by an order of magnitude between the three data sets. In addition to detrending, the RC model also characterizes the rotation–modulation signal. From comparison with the Solar Dynamics Observatory, we confirmed that the model accurately recovers and separates the rotation and convection radial velocity components. We also mapped the amplitude of the rotation and convection perturbations as a function of height within the stellar atmosphere. Probing stellar atmospheres with our revised spot model will fuel future innovations in stellar activity mitigation, enabling robust exoplanet detection.

Bayesian spatio-temporal modeling of the Brazilian fire spots between 2011 and 2022

Wildfires are among the most common natural disasters in many world regions and actively impact life quality. These events have become frequent due to climate change, other local policies, and human behavior. Fire spots are areas where the temperature is significantly higher than in the surrounding areas and are often used to identify wildfires. This study considers the historical data with the geographical locations of all the “fire spots” detected by the reference satellites covering the Brazilian territory between January 2011 and December 2022, comprising more than 2.2 million fire spots. This data was modeled with a spatio-temporal generalized linear mixed model for areal unit data, whose inferences about its parameters are made in a Bayesian framework and use meteorological variables (precipitation, air temperature, humidity, and wind speed) and a human variable (land-use transition and occupation) as covariates. The meteorological variables humidity and air temperature showed the most significant impact on the number of fire spots for each of the six Brazilian biomes.

Safety evaluation of deformed coke drum with nested stress at cold spot

In this article, the strain limit criterion and classical strength theories are used to evaluate the strength at the cold spot with nested effect of the deformed coke drum. First, the geometric model of the cold spot with nested effect before and after the deformation of the drum wall is established in the selected area, and the nested stress at the cold spot is calculated. Then, according to the calculated stress–strain results, the stress analysis of the drum wall at the cold spot is carried out. The results show that the coke drum yields during the cooling stage according to the strain limit criterion. According to classical strength theories, coke drum yields during the coke production and cooling stages. Finally, the paths are selected to calculate the equivalent stress–strain, and the stress–strain curves along the paths are plotted. By comparing the results of stress and strain evaluation, three evaluation methods are compared and studied.

An improved YOLOv5-based apple leaf disease detection method

The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture. Computer vision technology can be effectively identifying and prevent plant diseases and insect pests. However, the lack of consideration of disease diversity and accuracy of existing detection models hinders their application and development in the field of plant pest detection. This paper proposes an efficient detection model of apple leaf disease spot through the improvement of the traditional Yolov5 detection network called A-Net. In order to significantly increase the A-Net's detection speed and accuracy, the A-Net model applies the loss function Wise-IoU, which includes the attention mechanism and the dynamic focusing mechanism, to the Yolov5 network model. The RepVGG module is then used to replace the original model's convolution module. The experimental results show that the improved model effectively suppresses the growth of some error weights. Compared with several object detection models, the improved A-Net model has a Mean Average Precision across IoU threshold 0.5 and an accuracy of 92.7%, which fully proves that the improved A-Net model has more advantages in detecting apple leaf diseases.

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study.

Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

Study on the properties of deuterium ions in a composite cathode vacuum arc discharge

A vacuum arc discharge with a metal-deuteride cathode can generate a supersonic deuterium ion jet, which finds important applications in vacuum arc ion sources. In this study, a 1D3V spherical particle-in-cell direct simulation Monte Carlo cathode spot model with a titanium-deuteride cathode is developed to investigate the ionization and acceleration of deuterium ions from vacuum breakdown to the steady-arc stage. The effects of cathode deuteration concentration on the deuterium ion fraction and kinetic energy are also analyzed. The results show that the released deuterium atoms start to be ionized at around tens of nanometers from the cathode and then become fully ionized at about 0.4μm as the cathode potential drop gradually builds up. The proportion of deuterium ions in the plasma is slightly higher than the proportion of deuterium atoms in the cathode material. Velocity separation of deuterium and titanium ions occurs due to the acceleration of the electric field during the vacuum breakdown stage; however, the predominant ion–ion Coulomb collisions wipe out this separation in the steady-arc stage. Shifting the deuterium atom concentration in the cathode under a constant arc burning voltage produces an approximately equal current density, and increases the velocities of all ion species. A higher ion kinetic energy is gained by reducing the ohmic heating dissipation, facilitated by the lower plasma resistivity under the increased deuterium ion density.

Numerical simulation of the damage and ignition responses of high explosives under low-velocity impact using the SPH method

The damage evolution and ignition mechanism of high explosives are of great importance in assessing the safety of munitions and guiding the design of munitions. In this study, the smoothed particle hydrodynamics (SPH) method is combined with a viscoelastic–viscoplastic-damage constitutive model and a hot spot model to explore the damage and ignition responses of high explosives under low-velocity impact. In particular, the sub-particle method is developed in the SPH method to combine the macroscopic constitutive model with the mesoscopic hot spot model. First, the damage evolution of a compressed plate containing a cavity and the ignition response of a single particle at a given pressure and strain rate are studied separately, and the simulation results are validated by experimental results and existing finite element method simulations. Subsequently, the impact shear and crack extrusion tests of high explosives with complex stress states are explored, respectively. For typical pressed PBX under impact-shear loading, a large damage region is formed in the high explosives due to the tensile stress formed by the meeting of rarefaction waves, and the initial ignition region is mainly located in the region where the outer edge of the rod is in contact with the high explosives and extends in a circular shape to the inside of the explosives. For typical pressed PBX under crack extruded loading, the damage region is mainly concentrated in the materials squeezed into the crack, and the initial ignition region is mainly located around the crack and gradually extends above the crack. The results indicate that the proposed SPH method is capable of modeling the damage and ignition responses of high explosives under complex stress states.

Reducing penumbral blur in computed tomography by learning the inverse finite focal spot model

Penumbral blur is one of the major limitations of the high spatial resolution micro-CT, due to a nonideal large focal spot. Penumbral blur hinders the ability to resolve small features that may only be a few pixels in size. Reducing the focal spot size by decreasing the x-ray tube power is a straightforward solution, but it leads to prolonged scan durations. In this paper, we propose to mitigate the penumbral blur by learning the inverse finite focal spot model. First, we derived the finite focal spot model that builds a relationship from the ideal point source projection to the finite focal spot projection. Based on the derived model, we numerically compute a paired projection dataset. Second, we utilized two neural networks—U-net, and convolution modulation-based U-net (CMU-net) —to learn the inverse finite focal spot model. The goal is to estimate the ideal point source projection from the actual finite focal spot projection. CMU-net, which introduces convolution modulation blocks into the contracting path of the U-net, is proposed to boost the robustness of the U-net. Finally, the standard filtered back-projection (FBP) is employed for reconstruction using the estimated ideal point projection. The experiments show that both U-net and CMU-net can effectively reduce the penumbral blur, whereas CMU-net demonstrates better performance on the real data. Experiments on real measured data demonstrate that CMU-net is more robust than U-net and can effectively resolve fine details. This method has great potential in improving the efficiency of micro-CT acquisition. It allows increasing the tube power since our method can computationally compensate for the blur caused by an increased focal spot size.

Improved automated spot counting and modeling with bias correction

ABSTRACT A complete workflow was presented for estimating the concentration of microorganisms in biological samples by automatically counting spots that represent viral plaque forming units (PFU) bacterial colony forming units (CFU), or spot forming units (SFU) in images, and modeling the counts. The workflow was designed for processing images from dilution series but can also be applied to stand-alone images. The accuracy of the methods was greatly improved by adding a newly developed bias correction method. When the spots in images are densely populated, the probability of spot overlapping increases, leading to systematic undercounting. In this paper, this undercount issue was addressed in an empirical way. The proposed empirical bias correction method utilized synthetic images with known spot sizes and counts as a training set, enabling the development of an effective bias correction function using a thin-plate spline model. Its application focused on the bias correction for the automated spot counting algorithm LoST proposed by Lin et al. Simulation results demonstrated that the empirical bias correction significantly improved spot counts, reducing bias for both fixed and random spot sizes and counts.

Optimal bidding strategy for generation companies alliance in electricity‐carbon‐green certificate markets

AbstractTo promote the profit of generation companies and reduce carbon emissions from the generation sector, a two‐layer decision‐making model based on the cooperative game is proposed. Based on the analysis of a renewable‐fossil energy generation alliance to participate in electricity‐carbon‐green certificate markets, a robust optimization model considering the price uncertainty in the electricity spot market is first established based on the information gap decision‐making theory as the upper part of a two‐layer optimization model. Subsequently, the clearing models of the electricity spot, carbon emission trading, and green certificate markets are established. This two‐layer optimization model is transformed into a single‐layer model based on the well‐established KKT conditions. A profit allocation model for the members of the generation alliance is then presented based on the Shapley value and an improved nucleolus kernel method. Finally, the effectiveness of the proposed model is demonstrated by the IEEE 14‐bus power system.

Geological engineering double sweet spot evaluation of thin coal seam reservoirs based on facies control: A case study of tight sandstone gas in the Shenfu block of the Ordos Basin

Abstract The development of thin coal layers within the Shenfu tight sandstone reservoirs significantly impacts the control of hydraulic fractures over the actual sweet spots, leading to considerable inter-well production variability. This paper establishes a connection between individual well sweet spots and block sweet spots through a refined lithofacies model and conducts a comprehensive double sweet spot secondary optimization in conjunction with the characteristics of thin coal layer development. The research findings indicate the following: Class I sweet spots ≥0.46, 0.37≥ Class II sweet spots &lt;0.46, 0.3≥ Class III sweet spots &lt;0.37; fractures will be captured by the coal layer if the thin coal layer is more than 5 m away from the perforation point or if the coal layer thickness exceeds 3 m; the secondary sweet spot optimization can eliminate the influence of thin coal layers and establish a three-dimensional comprehensive double sweet spot model suitable for the study area.

Simultaneous Photometric and Spectroscopic Analysis of V505 Lacertae, a Photospherically and Chromospherically Active Contact Binary Star

New BVR photometric and high-resolution spectroscopic observations of V505 Lac are presented with Transiting Exoplanet Survey Satellite (TESS) photometric data. The orbital period has experienced a secular decrease during the past 16 yr. A clear anticorrelation in the primary and secondary eclipse timing variation (PSETV) obtained from the TESS data is also identified. A double-lined radial velocity (RV) curve is secured, and the effective temperatures of the less- and more-massive stars (Stars 1 and 2, respectively) are measured. Using a spectral subtraction technique, excess emissions are detected in the time-series Ca ii H and K and Hα lines for Star 2. Simultaneous analysis of the light and RV curves using the Wilson–Devinney (WD) code reveals that V505 Lac is a photospherically and chromospherically active W-subtype contact binary system. The component-star masses and radii are determined to an accuracy of approximately 1%. The WD spot model is individually applied to 221 light curves segmented from the TESS data so as to derive the spot parameters of a cool spot on Star 2. The combined variations in both longitude and colatitude among the spot parameters appear to be strongly associated with those of both the anticorrelation in the PSETV and the O’Connell effect in the TESS light curves. Robust negative linear relationships between the PSETV anticorrelation size and the O’Connell effect magnitude are found for the first time. Mass–radius, mass–luminosity, and mass ratio–mass diagrams of contact binaries, along with the mass ratio frequency distribution, are presented in an attempt to elucidate the evolutionary characteristics of these systems.

Mixed-size spot scanning with a compact large momentum acceptance superconducting (LMA-SC) gantry beamline for proton therapy

Objective. Lowering treatment costs and improving treatment quality are two primary goals for next-generation proton therapy (PT) facilities. This work will design a compact large momentum acceptance superconducting (LMA-SC) gantry beamline to reduce the footprint and expense of the PT facilities, with a novel mixed-size spot scanning method to improve the sparing of organs at risk (OAR). Approach. For the LMA-SC gantry beamline, the movable energy slit is placed in the middle of the last achromatic bending section, and the beam momentum spread of delivered spots can be easily changed during the treatment. Simultaneously, changing the collimator size can provide spots with various lateral spot sizes. Based on the provided large-size and small-size spot models, the treatment planning with mixed spot scanning is optimized: the interior of the target is irradiated with large-size spots (to cover the uniform-dose interior efficiently), while the peripheral of the target is irradiated with small-size spots (to shape the sharp dose falloff at the peripheral accurately). Main results. The treatment plan with mixed-size spot scanning was evaluated and compared with small and large-size spot scanning for thirteen clinical prostate cases. The mixed-size spot plan had superior target dose homogeneities, better protection of OAR, and better plan robustness than the large-size spot plan. Compared to the small-size spot plan, the mixed-size spot plan had comparable plan quality, better plan robustness, and reduced plan delivery time from 65.9 to 40.0 s. Significance. The compact LMA-SC gantry beamline is proposed with mixed-size spot scanning, with demonstrated footprint reduction and improved plan quality compared to the conventional spot scanning method.

Numerical study on the effects of discharge parameters on vacuum arc plasma

Studying the characteristics of vacuum arc plasma within a wide range of discharge parameters is vital for its applications. In this study, specific parameters of vacuum discharge, such as cathode temperature ranging from 6000 to 8000K , emitted electron temperature spanning from 0.69 to 2.5eV , and arc burning voltage within the range of 16−30V , have been selected to analyze the effects of these parameters on the cathode erosion, cathode potential drop (CPD), and ion velocity. A validated one-dimensional spherical PIC–DSMC cathode spot model is employed to simulate the generation and expansion of vacuum arc plasma. The results indicate that increasing the cathode temperature and the emitted electron temperature enhance the cathode eroded mass. However, this mass diminishes as the arc voltage increases due to more Cu ions and Cu atoms returning to the cathode under high CPD and charge exchange collisions, respectively. The cathode current density remains nearly constant within the explored range of cathode temperature, but increases with shifting the emitted electron temperature and arc voltage. A higher cathode current density, coupled with the positive cathode plasma resistance, induces a higher CPD that significantly surpasses the cathode sheath voltage. Furthermore, a higher ion velocity is observed at increased arc voltage due to the higher cathode current density, which generates stronger electron friction for accelerating ions. These findings could be valuable in designing a suitable arc voltage for vacuum electric thrusters to enhance thrust.

Application of an Artificial Neural Networks Model for Prediction of Instability Phenomena in Low Current Vacuum Arc by Cathode Spot Model

Application of an Artificial Neural Networks Model for Prediction of Instability Phenomena in Low Current Vacuum Arc by Cathode Spot Model

Sensitizing Explosives Through Molecular Doping.

Cocrystallization assembles multicomponent crystals in defined ratios that are held together by intermolecular interactions. While cocrystals have seen extensive use in the pharmaceutical industry for solving issues with stability and solubility, extension to the field of energetic materials for improved properties has proven difficult. Predicting successful coformers remains a challenge for systems lacking well-understood synthons that promote reliable intermolecular assembly. Herein, an alternative method is investigated for altering energetic properties that operates in the absence of well-defined interactions by molecular doping. An impact sensitive primary explosive, cyanuric triazide (CTA), was selected as the dopant to test if less impact sensitive secondary explosives could gain increased sensitization to impact when CTA is inserted into their crystal lattices. Molecular doping was successful in sensitizing three melt-castable energetics: 2,4,6-trinitrotoluene (TNT), 2,4-dinitroanisole (DNAN), and 1,3,3-trinitroazetidine (TNAZ). CTA could also be incorporated as a stabilized inclusion to sensitize DNAN further. These results demonstrate how the judicious choice of dopant can lead to specific property improvements, providing a method for creating energetic materials with new properties to access metal-free primary explosives and physical hot spot models for explosive ignition.

The GAPS programme at TNG

Context. The intrinsic variability due to the magnetic activity of young active stars is one of the main challenges in detecting and characterising exoplanets. The stellar activity is responsible for jitter effects observed both in photometric and spectroscopic observations that can impact our planetary detection sensitivity. Aims. We present a method able to model the stellar photosphere and its surface inhomogeneities (starspots) in young, active, and fast-rotating stars based on the cross-correlation function (CCF) technique, and we extract information about the spot configuration of the star. Methods. We developed Spot CCF, a tool able to model the deformation of the CCF profile due to the presence of multiple spots on the stellar surface. Within the Global Architecture of Planetary Systems (GAPS) Project at the Telescopio Nazionale Galileo, we analysed more than 300 spectra of the young planet-hosting star V1298 Tau provided by the HARPS-N high-resolution spectrograph. By applying the SpotCCF model to the CCFs, we extracted the spot configuration (latitude, longitude, and projected filling factor) of this star, and provide a new radial velocity (RV) time series for this target. Results. We find that the features identified in the CCF profiles of V1298 Tau are modulated by the stellar rotation, supporting our assumption that they are caused by starspots. The analysis suggests a differential rotation velocity of the star with lower rotation at higher latitudes. Also, we find that SpotCCF provides an improvement in RV extraction, with a significantly lower dispersion with respect to the commonly used pipelines. This allows mitigation of the stellar activity contribution modulated with stellar rotation. A detection sensitivity test, involving the direct injection of a planetary signal into the data, confirms that the SpotCCF model improves the sensitivity and ability to recover planetary signals. Conclusions. Our method enables us to model the stellar photosphere and extract the spot configuration of young, active, and rapidly rotating stars. It also allows the extraction of optimised RV time series, thereby enhancing our detection capabilities for new exoplanets and advancing our understanding of stellar activity.