Emission Model Research Articles

Morphologies of Bright Complex Fast Radio Bursts with CHIME/FRB Voltage Data

We present the discovery of 12 apparently nonrepeating fast radio burst (FRB) sources, detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. These sources, only one of which has been presented previously in the first CHIME/FRB catalog, were selected from a database comprising O(103) CHIME/FRB full-array raw voltage data recordings, based on their large signal-to-noise ratios and complex morphologies. Our study examines the time-frequency characteristics of these bursts, including drifting, microstructure, and periodicities. The events in this sample display a variety of unique drifting phenomenologies that deviate from the linear negative drifting phenomenon seen in many repeating FRBs, and motivate a possible new framework for classifying drifting archetypes. Additionally, we detect microstructure features of duration ≲50 μs in seven events, with some as narrow as ≃7 μs. We find no evidence of significant periodicities between subburst components. Furthermore, we report the polarization characteristics of seven events, including their polarization fractions and Faraday rotation measures (RMs). The observed ∣RM∣ values span a wide range of 17.24(2)–328.06(2) rad m−2, with apparent linear polarization fractions between 0.340(1) and 0.946(3). The morphological properties of the bursts in our sample appear broadly consistent with predictions from both relativistic shock and magnetospheric models of FRB emission, as well as propagation through discrete ionized plasma structures. We address these models and discuss how they can be tested using our improved understanding of morphological archetypes.

Process‐Based Modeling of Ecosystem‐Level Monoterpene From a Japanese Larch (Larix kaempferi) Forest

AbstractGlobally, the emission of biogenic volatile organic compounds (BVOC) by plants represents the dominant source of volatile organic compounds emitted to the atmosphere. Monoterpenes, as the major BVOC group, can contribute to forming secondary organic aerosols and influence cloud properties. In this study, we developed a process‐based monoterpene module in the Vegetation Integrative SImulator for Trace gases (VISIT) model by considering the production, storage, and emission of monoterpene as three main processes. We further evaluated the modeled monoterpene emissions against the ecosystem‐level observation data at a half‐hour scale at a Japanese larch (Larix kaempferi) forest site on Mt. Fuji, Japan. The VISIT model performed with relatively higher accuracy with a Willmott's index of agreement at 0.61, a mean bias error (MBE) at 0.29, and a root mean squared error (RMSE) at 0.43, comparable to that of Model of Emissions of Gases and Aerosols from Nature model with a Willmott's index of agreement at 0.63, a MBE at 0.40, and a RMSE at 0.54. In a long‐term simulation under high CO2 emission scenarios, the ratio between monoterpene emission and gross primary production exhibited a stronger correlation with CO2 concentration than temperature. Our study provides a process‐based modeling approach for more accurately simulating monoterpene emissions from Japanese larch.

Multispectral two-dimensional temperature field reconstruction using particle swarm optimization and the Broyden–Fletcher–Goldfarb–Shanno algorithm with multiple extreme value optimization

The flame temperature is a crucial parameter in combustion, indicating heat generation and transfer. However, determining the correct temperature becomes challenging when flame emissivity is unknown. This article presents a multispectral 2D temperature field reconstruction method using particle swarm optimization and the Broyden–Fletcher–Goldfarb–Shanno algorithm, combined with multivariate extreme-value optimization. This method establishes an objective function based on the correlation between the true temperature and spectral data. The objective function was minimized by adjusting the emissivity, enabling the reconstruction of 2D temperature and emissivity images without assuming an emissivity model. Calibration with a multispectral camera yielded the spectral response coefficients, and three-time spline interpolation was used to verify the calibration data. The reconstructed 2D temperature field showed a mean absolute error (MAE) of 4.61 and structural similarity (SSIM) of 0.995. The reconstructed emissivity image had an MAE range of 0.04 to 0.053 and an SSIM range of 0.897 to 0.915. The system, tested on a butane flame, showed a temperature range of 1001 K to 1356 K, with an average temperature of 1194 K and emissivity values ranging from 0.3166 to 0.8621. The results align with the expected combustion process and radiation characteristics distribution of the butane flame.

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO2, CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R2: 0.76), while polynomial regression excelled for CO2 (R2: 0.93). For warm engines, polynomial regression performed best for CO2 (R2: 0.95), and gradient boosting delivered results for THC (R2: 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

Supernova Remnants in Clumpy Medium: A Model of Hydrodynamic and Radio Synchrotron Evolution

We present an analytical model of the Σ–D relation for supernova remnants (SNRs) evolving in a clumpy medium. The model and its approximations were developed using the hydrodynamic simulations of SNRs in environments of low-density bubbles and clumpy media with different densities and volume filling factors. For calculation of SNR luminosities we developed the synchrotron emission model, implying the test-particle approximation. The goal of this work is to explain the flattened part of the Σ–D relation for Galactic SNRs at D ≈ 14–50 pc. Our model shows that the shock collision with the clumpy medium initially enhances the brightness of individual SNRs, which is followed by a steeper fall of their Σ–D curve. We used the analytical model to generate large SNR samples on the Σ–D plane, within a span of different densities and distances to the clumpy medium, keeping the observed distribution of diameters. After comparison with the Galactic sample, we conclude that the observed Σ–D flattening and scatter originate in sporadic emission jumps of individual SNRs while colliding with the dense clumps. Statistically, the significant impact of the clumps starts at diameters of ≈14 pc, up to ∼70 pc, with the average density jump at the clumpy medium of ∼220 times, roughly depending on the low density of the circumstellar region. However, additional analysis considering the selection effects is needed, as well as improvement of the model, considering radiation losses and thermal conduction.

A composite laser ablation diagnosis method based on multiple spectroscopic and imaging analyses

This study proposes a novel diagnostic approach for target ablation to comprehensively elucidate the physical mechanisms of laser ablation in aluminium alloys and stainless steel, precisely measure sample temperatures, and predict the ablation state. The method utilizes a spatially weighted emissivity model in conjunction with multispectral thermometry techniques to analyze spatial variations in temperature and emissivity distributions, facilitating the evaluation of target ablation status. Through a series of experiments, temperature data obtained using an enhanced weighted radiative spectral inversion technique were compared with temperatures recorded by thermal imaging cameras, confirming the effectiveness and accuracy of the weighted radiative spectral inversion method in multispectral thermometry. Additionally, a detailed examination of laser ablation in aluminium alloys and stainless steel was conducted to elucidate the underlying damage mechanisms. This refined approach establishes a solid groundwork for further investigation into the characteristics and dynamic Evolution of laser-damaged regions.

Searching for new cataclysmic variables in the Chandra Source Catalog

Aims. Cataclysmic variables (CVs) are compact binary systems in which a white dwarf accretes matter from a Roche-lobe-filling companion star. For this study we searched for new CVs in the Milky Way in the Chandra Source Catalog v2.0, cross-matched with Gaia Data Release 3 (DR3). Methods. We identified new CV candidates by combining X-ray and optical data in a color-color diagram called the X-ray main sequence. We used two different cuts in this diagram to compile pure and optically variable samples of CV candidates. We undertook optical spectroscopic follow-up observations with the Keck and Palomar Observatories to confirm the nature of these sources. Results. We assembled a sample of 25 887 Galactic X-ray sources and found 14 new CV candidates. Seven objects show X-ray and/or optical variability. All sources show X-ray luminosity in the 1029 − 1032 erg s−1 range, and their X-ray spectra can be approximated by a power-law model with photon indices in the Γ ∼ 1 − 3 range or an optically thin thermal emission model in the kT ∼ 1 − 70 keV range. We spectroscopically confirmed four CVs, discovering two new polars, one low accretion rate polar and a WZ Sge-like low accretion rate CV. X-ray and optical properties of the other nine objects suggest that they are also CVs (likely magnetic or dwarf novae), and one other object could be an eclipsing binary, but revealing their true nature requires further observations. Conclusions. These results show that a joint X-ray and optical analysis can be a powerful tool for finding new CVs in large X-ray and optical catalogs. X-ray observations such as those by Chandra are particularly efficient at discovering magnetic and low accretion rate CVs, which could be missed by purely optical surveys.

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

How to rapidly and accurately evaluate the cooling performance of radiative cooling materials?

Radiative cooling materials exhibit great potential for reducing energy consumption in numerous areas. However, owing to the lack of standardized experimental methods and the complexity of model calculation, rapidly and accurately evaluating the cooling performance of radiative cooling materials remains challenging. To address this issue, we propose a new evaluation method that relies solely on the material's radiative properties. First, the calculation accuracies of the hemispherical and spectral-directional emissivity models were compared using the results of three-day field measurements for both radiative cooling and white coatings. The hemispherical emissivity model exhibited slightly higher accuracy and considerably simpler calculation steps than the spectral-directional emissivity model, with the daily, daytime, and nighttime average mean absolute errors between the calculated and measured cooling temperatures all within 2.5 °C. Subsequently, using the hemispherical emissivity model, the cooling performances of radiative cooling materials with shortwave reflectivity (ρsr) and hemispherical emissivity (εlr,he) varied in the range of 0.80–1.00 under different meteorological conditions were calculated. Based on the calculation results, a new concept termed the cooling potential index (CPI) was proposed and calculated as CPI = ρsr + εlr,he/18. The index can be used to rapidly evaluate and compare the cooling performances of different radiative cooling materials.

Quantification and sensitivity assessment of Chinese provincial ecological compensation in the perspective of carbon deficit redistribution

The ecological compensation mechanism is a tool for managing regional development and promoting the green economy. This paper proposes a revised model for carbon emissions and absorption based on IPCC, then analyses the spatial and temporal variations of carbon emissions, absorption, and deficit in 31 provinces of China from 2001 to 2019. A model was developed to redistribute carbon deficits and compensate for ecological imbalances, with the aim of eliminating background differences between regions. The concept of ecological compensation sensitivity was proposed, and a redundancy analysis (RDA) was conducted to detect possible influencing factors. Results indicate that: (1) The carbon deficits were relatively substantial in the centre and developed coastal regions, which were the subject regions of China’s ecological compensation. Meanwhile, the northwest and southwest regions were the object regions of ecological compensation, and the compensated object regions tended to extend towards the southeast. The majority of compensated subject areas are generally less sensitive than compensated object regions. (2) The graph of carbon deficit volume over time shows that China’s two carbon targets are closely aligned. The distribution of China’s ecological compensation amounts follows the pattern of provincial economic development levels, with higher compensation amounts concentrated in the central and coastal regions. There is a positive correlation between GDP, year, and population size with carbon emission and carbon deficit. Additionally, there is a positive correlation between year, region, and carbon absorption. (3) This compensation model can strongly incentivise the compensation subject area to proactively adjust its economic development model to cope with the significant compensation pressure. Additionally, it can fully encourage the compensation recipient area to continue adhering to the green economic development model. The conclusions of the study hold significant reference value for promoting the development of a green, low-carbon economy.

Numerical Simulation of Spectral Radiation for Hypersonic Vehicles

The spectral radiation of hypersonic vehicles and surrounding flow fields is crucial for optical target detection. A novel approach combines Low-Discrepancy Sequences with the Reverse Monte Carlo Method to simulate the spectral radiation of hypersonic missiles. The effects of chemical non-equilibrium reactions and high-emissivity coating failures on the spectral radiation of a typical biconical hypersonic missile were investigated. Significant differences were found among chemical equilibrium, non-equilibrium, non-reactive, and catalytic wall models. High-emissivity coating failures occur mainly in the high-temperature regions of the nose cone and fins. The non-equilibrium model shows a transition peak distribution of NO in the head shock layer within the ultraviolet gamma band, with integrated ultraviolet radiation (200–300 nm) three times higher than the equilibrium model. In the 1–3 μm band, the non-equilibrium model’s radiation intensity at a 120° horizontal detection angle is about 1.46 times that of the equilibrium model. Using real coating emissivity, the 3–5 μm band radiation intensity is about 5% higher, and the 8–14 μm band is about 8.51% lower than the uniform emissivity model. When high-emissivity coating emissivity fails by 40%, the 3–5 μm band intensity decreases by about 15.38%, and the 8–14 μm band intensity decreases by about 12.67%.

Waste to energy: Enhancing biogas utilization in dual-fuel engines using machine learning based prognostic analysis

Alternative fuels derived from organic matter like biomass can be a viable solution in the present scenario of increasing greenhouse gases. In the present study, waste food and animal refuse-derived biogas were tested as fuel in a diesel engine. To further enhance the waste-to-energy perspective waste cooking oil biodiesel − diesel blends were used as pilot fuel. A comprehensive set of operational settings including fuel injection timing, pressure, compression ratios, different flow rates of biogas, and engine loads were tested. Four machine learning approaches Linear Regression (LR), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) were employed to develop prognostic models. The developed models for engine performance and emission were predicted with high accuracy with R2 values as high as 0.9962 for brake-specific fuel consumption and 0.9948 for brake thermal efficiency. The prediction errors were low 0.06 for BTE during model training and 0.18 during the model testing phase while it was almost negligible in the case of BSFC. All models were compared using statistical metrics and violin plots. The DT-based forecasting models were observed to be the best among all the models both based on statistical measures as well as violin plots.

Accelerable adaptive cepstrum and L2-Dual Net for acoustic emission-based quality monitoring in laser shock peening

Acoustic emission monitoring in laser shock peening facilitates real-time detection of potential quality issues arising from variations in industrial parameters, enabling iterative optimization of the manufacturing process through material behavior analysis. However, existing research still lacks a comprehensive understanding of the time-varying time-frequency characteristics in dynamic acoustic emission and efficient corresponding models. Therefore, this study proposes an innovative monitoring approach that integrates accelerable adaptive cepstrum (AAC) and L2-Dual Net. Specifically, AAC first employs variable frames and filters to map time-varying features in the signal, and then obtains representative frame length distributions and filter weights for different operating conditions based on statistical information. AAC not only unveils time-varying features in signals but also boasts an efficient computational process. L2-Dual Net is a novel quality assessment model with robust feature extraction and local spatial feature interactions. The incorporation of L2 norm equips the model with robust interference immunity, while the dual spatial attention mechanism helps the model to interact with spatial features exhibiting different time-frequencies. Variable process parameter experiments for aluminum alloy 7075 and titanium alloy TC4 were conducted to validate the reliability of the proposed method. Results demonstrate that AAC showcases optimal computational efficiency and higher feature resolution. When compared with state-of-the-art network architectures, L2-Dual Net exhibits superior information flow, along with higher recognition accuracy and robustness. Moreover, various variants of L2-Dual Net are explored and the code is accessible at https://github.com/Qinr1026/L2-Dual-Net. The proposed method holds promising potential for application in other areas of acoustic emission monitoring.

Analysis of Low-Carbon, Energy-Saving, and Emission Reduction Models Based on Rail Transit

It is difficult to define scientific applicability conditions when dealing with different evaluation objects and scope. This article is based on multi-source big data such as the Automatic Fare Collection System (AFC) of urban rail transit, comprehensively considering the multidimensional impact of urban rail transit on the urban transportation system, and conducting a quantitative analysis of the energy-saving and emission reduction effects of urban rail transit. This study adopts a traffic emission model based on specific driving forces and a traffic demand prediction model, coupling the model and data to establish an urban rail transit energy conservation and emission reduction evaluation model suitable for different urban rail transit setting scenarios. Finally, this study selected six districts in Beijing as model application cases and used a combination of RP (display preference) survey and SP (state preference) survey to complete model parameter calibration for application cases.

New γ-Ray Sources Identified in All-sky Surveys Based on Fermipy’s Advanced Algorithm

We employ an efficient method for identifying γ-ray sources across the entire sky, leveraging advanced algorithms from Fermipy, and cleverly utilizing the Galactic diffuse background emission model to partition the entire sky into 72 regions, thereby greatly enhancing the efficiency of discovering new sources throughout the sky through multi-threaded parallel computing. After confirming the reliability of the new method, we applied it for the first time to analyze data from the Fermi Large Area Telescope (Fermi-LAT) encompassing approximately 15.41 yr of all-sky surveys. Through this analysis, we successfully identified 1379 new sources with significance levels exceeding 4σ, of which 497 sources exhibited higher significance levels exceeding 5σ. Subsequently, we performed a systematic analysis of the spatial extension, spectra, and light variation characteristics of these newly identified sources. We identified 21 extended sources and 23 sources exhibiting spectral curvature above 10 GeV. Additionally, we identified 44 variable sources above 1 GeV.

Vehicle emission models alone are not sufficient to understand full impact of change in traffic signal timings

Few studies have considered the real-world impact of changes in traffic signal timings on air pollution and pedestrian exposure with most only drawing their conclusion from vehicle emission models alone. Here, we consider two distinct cycle timings at a junction in London, UK, model the impact using a traffic microsimulation and a NOx emissions model, and compare these results with NOx and other air pollution measurements collected during a two-week field study at the junction.Our models predict that extending the cycle time leads to a 23% decrease in NOx emissions within a 15 m radius of the junction itself. When the wind direction was such that our sensors were downwind of the junction a 21% decrease in traffic and background-adjusted NOx concentrations were seen, suggesting that the intervention was successful. However, when the sensors were upwind of the junction, we observed an increase of 23% in adjusted NOx concentrations. Similar patterns were found for the other pollutants NO2, lung deposited surface area, black carbon and CO2 we measured. This indicates that meteorology was by far the greatest determinant of roadside concentrations during our two-week study period.Looking at pedestrian exposure for pedestrians waiting to cross the road, we found that their NOx exposure increased by 46% as waiting times to cross the road increased and that potential small reductions in air pollution were offset by increases in waiting times on the main road.The study demonstrates the need to go beyond assessing the impact of hyper-local traffic interventions on vehicle emissions. Real-world trials over extended periods are required to evaluate the impact of meteorology and changes to air pollution concentrations and pedestrian exposures.

An Untargeted Search for Radio-emitting Tidal Disruption Events in the VAST Pilot Survey

We present a systematic search for tidal disruption events (TDEs) using radio data from the Variables and Slow Transients (VAST) Pilot Survey conducted using the Australian Square Kilometre Array Pathfinder. Historically, TDEs have been identified using observations at X-ray, optical, and ultraviolet wavelengths. After discovery, a few dozen TDEs have been shown to have radio counterparts through follow-up observations. With systematic time-domain radio surveys becoming available, we can now identify new TDEs in the radio regime. A population of radio-discovered TDEs has the potential to provide several key insights including an independent constraint on their volumetric rate. We conducted a search to select variable radio sources with a single prominent radio flare and a position consistent within 2σ of the nucleus of a known galaxy. While TDEs were the primary target of our search, sources identified in this search may also be consistent with active galactic nuclei exhibiting unusual flux density changes at the timescales probed, uncharacteristically bright supernovae, or a population of gamma-ray bursts. We identify a sample of 12 radio-bright candidate TDEs. The timescales and luminosities range from ∼6 to 230 days and ∼1038 to 1041 erg s−1, respectively, consistent with models of radio emission from TDEs that launch relativistic jets. After calculating the detection efficiency of our search using a Monte Carlo simulation of TDEs, and assuming all 12 sources are jetted TDEs, we derive a volumetric rate for jetted TDEs of 0.80−0.23+0.31 Gpc−3 yr−1, consistent with previous empirically estimated rates.

Cooling from aerosol–radiation interaction of anthropogenic coarse particles in China

Climate assessments have largely overlooked the radiative effect of anthropogenic coarse particulate matter (PMcoarse, with an aerodynamic diameter between 2.5 and 10 µm) in China. Despite its similar mass concentration to fine particulate matter (PM2.5), anthropogenic sources of PMcoarse in China have been much less studied and typically underrepresented in models. Here, we present a new model simulation for PMcoarse in China that incorporates various anthropogenic sources. The model successfully captures the magnitude and distribution of observed PMcoarse and recently available aerosol optical depth measurements at near-infrared wavelengths, which are substantially underestimated if anthropogenic PMcoarse is not included. We find that anthropogenic PMcoarse exerts a cooling effect of -0.11 Wm−2 (-0.03 to -0.42 Wm−2) in China by aerosol–radiation interaction, capable of completely offsetting the warming effect from black carbon by 2060 under Dynamic Projection model for Emissions in China (DPEC) 1.1 scenario. We conclude that the radiative effect due to anthropogenic PMcoarse will likely dampen the warming penalty caused by the emission reduction of other aerosols in China and should be incorporated into climate models.

Temperature ratio RT = TL/TH of fully accelerated complementary fragments (used for TXE partition) obtained independently of prompt emission model calculations

Several refined prompt emission model codes, nowadays employed, use the partition of total excitation energy (TXE) according to the temperature ratio RT = TL/TH of fully accelerated fragments. In such codes RT is given as input, either as an unique value for all fragmentations or as a function of AH; This temperature ratio RT being obtained by fitting the experimental ν(A) data with the respective prompt emission model code. This paper proposes a method for obtaining “experimental RT(AH)” (also based on experimental ν(A)), but without resorting to prompt emission model calculations for the fit of ν(A) data. So that this is an independent method providing RT(AH) which can be employed by any prompt emission model code in which the TXE partition is done at the full acceleration according to RT given as input. A procedure which facilitates the parameterization of RT(AH) is proposed, too. A prediction of the RT(AH) shape at very high excitation energies of the fissioning nucleus is also reported. The comparison of RT(AH) ratios obtained from any TXE partition based on modeling at scission with those provided by the present method can constitute a supplementary validation of the respective modeling at scission.

Constraining Wind-driven Accretion onto Gaia BH3 with Chandra

Gaia BH3 is the most massive known stellar-origin black hole in the Milky Way, with a mass M • ≈ 33 M ⊙. Detected from Gaia’s astrometry, this black hole is in the mass range of those observed via gravitational waves, whose nature is still highly debated. Hosted in a binary system with a companion giant star that is too far away for Roche-lobe mass transfer, this black hole could nonetheless accrete at low levels due to wind-driven mass loss from its companion star, thus accreting in advection-dominated accretion flow, or ADAF, mode. Using stellar wind models, we constrain its Eddington ratio in the range 10−9 < f Edd < 10−7, corresponding to radiative efficiencies 5 × 10−5 < ϵ < 10−3, compatible with radiatively inefficient accretion modes. Chandra ACIS-S observed this object and obtained the most sensitive upper bound of its [2–10] keV flux: F X < 3.25 × 10−15 erg s−1 cm−2 at 90% confidence level corresponding to L [2–10] < 2.10 × 1029 erg s−1. Using ADAF emission models, we constrained its accretion rate to f Edd < 4.91 × 10−7 at the apastron, in agreement with our theoretical estimate. At the periastron, we expect fluxes ∼50 times larger. Because of the inferred low rates, accretion did not significantly contribute to black hole growth over the system’s lifetime. Detecting the electromagnetic emission from Gaia BH3 will be fundamental to informing stellar wind and accretion disk models.