Source Model Research Articles

Practical experience with initial quality assurance of a high dose rate brachytherapy grid-based Boltzmann solver algorithm.

The purpose of this study was to validate the use of a model-based dose calculation algorithm (MBDCA), Acuros BV, for high dose rate brachytherapy treatment planning for a community-based hospital with a Bravos afterloader. Based on published AAPM recommendations, this work details a practical approach for community-based clinics to complete initial validation of Acuros BV, in order to add a MBDCA to a TG-43 based brachytherapy treatment planning program. Source dimensions and materials used in Acuros BV and TG-43 source models were compared to the physical source. TG-186 testing was completed with standardized test cases externally calculated with Monte Carlo compared to locally calculated with Acuros BV. Point doses calculated using TG-43 were compared to those calculated with Acuros BV in water at various dose grid settings. Secondary dose check software was used to evaluate dose distributions resembling clinical patient plans, both in water and on CT datasets representative of patient anatomy. The major source of discrepancy of source models was the length of modeled steel cable. TG-186 testing showed that the largest differences between Monte Carlo and Acuros BV dose distributions were located along the source axis for cases calculated in water, as well as located in regions of high dose gradients and within the applicator for the case calculated with a generic shielded applicator. An audit of point doses calculated with both TG-43 and Acuros BV in water found that dose grid settings significantly affected agreement. Secondary dose check software indicated that Acuros BV functioned satisfactorily, and a 5% threshold was adopted for secondary dose checks on gynecologic plans. This validation process indicated that Acuros BV met expected standards and affirmed its suitability for integration into this clinical practice's brachytherapy treatment planning.

New source model for the 1771 Meiwa tsunami along the southern Ryukyu Trench inferred from high-resolution tsunami calculation

The 1771 Meiwa tsunami which struck the southern Ryukyu Islands (Sakishima Islands) had greater than 22 m run-up height, leaving about 12,000 casualties in its wake. At many places, the tsunami inundation or lack of inundation is well recorded in historical documents. Several tsunami source models have been proposed for this event using historical records as constraints of tsunami calculations. Nevertheless, the source model remains under discussion. This study re-evaluated the tsunami wave source model of the 1771 Meiwa tsunami using high-resolution (10 m mesh) bathymetric and topographical data for tsunami calculation, the latest historical record dataset, and seismological knowledge. Results demonstrated that a tsunami earthquake along the southern Ryukyu Trench was the likely cause of the 1771 event. However, it is noteworthy that assumption of a large slip with 30 m is necessary for a shallow and narrow region (fault depth = 5 km, fault width = 30 km, Mw = 8.49) of the plate boundary in the Ryukyu Trench, which is far larger than previously thought. This requirement of very large initial water level change at the source might involve not only the fault rupture along the plate boundary but also deformation by splay faults, inelastic deformation of unconsolidated sediments near the trench axis, and/or giant submarine landslides. Results also show that the effects of fault parameters on the run-up were quite different depending on the offshore coral reef width. This phenomenon strongly constrained the fault width to 30 km. Our tsunami ray tracing analysis further revealed the effects of bathymetry on tsunami propagation. It is noteworthy that meter-long huge tsunami boulders tend to be distributed along the specific coasts at which the tsunami was concentrated by bathymetric effects. This finding suggests that past tsunamis, including the 1771 event, might have affected the specific coral reefs on Sakishima Islands repeatedly, which is crucially important for understanding the heterogeneous distribution of tsunami boulders. This feature might also be useful to elucidate the effects of large tsunamis on the corals and reefs because a direct comparison of coral reefs that are damaged and not damaged by tsunami waves is testable in narrow areas in the case of the Sakishima Islands.

Tsunami waveform inversion using Green’s functions with advection effects: application to the 2003 Tokachi–Oki earthquake

We explored nonlinear effects within the context of tsunami waveform inversion, wherein Green's functions were linearly superimposed to estimate earthquake slips. We focused on these effects while developing a source model for the 2003 Tokachi–Oki earthquake off Hokkaido, Japan. A source model for this earthquake was developed based on linear tsunami waveform inversion using Green’s functions and tsunami waveforms observed at tide gauge stations. Subsequently, tsunami waveforms from the source were simulated at the stations using nonlinear long-wave theory and compared with those estimated by inversion. The comparisons demonstrated that the waveforms had a non-negligible discrepancy that was attributed to advection effects, even for the primary wave used in the inversion at the two stations. This result strongly suggests that advection effects should be considered in the source modeling of the 2003 earthquake based on tsunami waveforms observed by tide gauges. Based on these results, a new tsunami waveform inversion technique that incorporates linearly approximated advection effects and maintain the framework of linear tsunami waveform inversion using Green’s functions is proposed and applied. The proposed method successfully mimicked the advection effects during the 2003 tsunami, reproduced better tsunami waveforms, and developed a source model for the 2003 earthquake using these effects. The peak slip amount and seismic moment were greater in the source model with advection effects than those without the effects. This finding suggests that the values in the source models developed for other earthquake events without considering these effects may have been underestimated.Graphical abstract

TL‐GNN: Android Malware Detection Using Transfer Learning

ABSTRACTMalware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

Research on Discriminant Model of Water Inrush Source By Principal Component-cluster Analysis in Jiaozuo Mining Area

This paper takes Jiaozuo Mining Area as the research object, based on the analysis of the hydrogeological conditions of the mining area, expounds the characteristics of coal strata, aquifers and groundwater recharge, runoff and discharge in the mining area; analyses the water inrush sources and water diversion channels in the mining area, analyses the routine water chemical data of each aquifer in the mining area, draws the piper three-line diagram and confirms it. To determine the hydrochemical type of groundwater in aquifer, six common water chemical ions are used as discriminant factors, a discriminant model of water inrush source is established by combining principal component analysis and cluster analysis, and the principal component of conventional water chemical ions is extracted as cluster variable to discriminate Water Inrush Source in Jiaozuo Mining Area. In order to prove the rationality and validity of the model, another group of inrush sources in Jiaozuo Mining Area is discussed. The water sample data are discriminated and the results obtained by cluster analysis are compared to verify the accuracy of the model. The results show that the model is feasible. To sum up, the identification model of inrush water source by principal component-cluster analysis can accurately and effectively identify the source of inrush water in Jiaozuo Mining Area.

Time-dependent probabilistic tsunami risk assessment: application to Tofino, British Columbia, Canada, subjected to Cascadia subduction earthquakes

A new time-dependent probabilistic tsunami risk model is developed to facilitate the long-term risk management strategies for coastal communities. The model incorporates the time-dependency of earthquake occurrence and considers numerous heterogeneous slip distributions via a stochastic source modeling approach. Tidal level effects are examined by considering different baseline sea levels. The model is applied to Tofino, British Columbia, Canada within the Cascadia subduction zone. High-resolution topography and high-quality exposure data are utilized to accurately evaluate tsunami damage and economic loss to buildings. The results are tsunami loss curves accounting for different elapsed times since the last major event. The evolutionary aspects of Tofino’s time-dependent tsunami risk profiles show that the current tsunami risk is lower than the tsunami risk based on the conventional time-independent Poisson occurrence model. In contrast, the future tsunami risk in 2100 will exceed the time-independent tsunami risk estimate.

Super-ballistic diffusion induced by nonlinear interactions in a one-dimensional quasiperiodic lattice

We numerically investigate the spreading dynamics of wave packets in a one-dimensional lattice with quasiperiodic disorders and nonlinear interactions. Under moderate disorder strength, we reveal an intriguing super-ballistic diffusion driven by increasing the interaction strength from the ballistic diffusion in the non- and weakly interacting cases. We obtain a wide parameter region for such a faster-than-ballistic spreading of wave packets in the quasiperiodic lattice, although the nonlinear self-trapping is dominated under strong interactions. The super-ballistic diffusion is further analyzed based on the point source model. We also show the interaction-induced sub-diffusive spreading in the Anderson localized regime. The super-ballistic and sub-diffusive dynamics are exhibited in the spreading that starts from both the single-site and Gaussian wave packets. Our results showcase the interaction-enabled super-ballistic diffusion, which is scarce and experimentally realizable in artificial atomic or photonic lattices.

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Different from Unsupervised Domain Adaptation (UDA), Source-Free Unsupervised Domain Adaptation (SFUDA) transfers source knowledge to target domain without accessing the source data, using only the source model, has attracted much attention recently. One mainstream SFUDA method fine-tunes the source model by self-training to generate pseudo-labels of the target data. However, due to the significant differences between different domains, these target pseudo-labels often contain some noise, and it will inevitably degenerates the target performance. For this purpose, we propose an innovative SFUDA method with adaptive pseudo-labels learning named Dual Classifier Adaptation (DCA). In DCA, a dual classifier structure is introduced to adaptively learn target pseudo-labels by cooperation between source and target classifiers. Simultaneously, the minimax entropy is introduced for target learning, in order to adapt target data to source model, while capture the intrinsic cluster structure in target domain as well. After compared our proposed method DCA with a range of UDA and SFUDA methods, DCA achieves far ahead performance on several benchmark datasets.

Improving the transferability of adversarial examples through neighborhood attribution

Adversarial examples, which add carefully planned perturbations to images, pose a serious threat to neural network applications. Transferable adversarial attacks, in which adversarial examples generated on the source model can successfully attack the target model, provide a realistic and undetectable method. Existing transfer-based attacks tend to improve the transferability of adversarial examples by destroying their intrinsic features. They destabilized features differentially by assessing their importance, thus rendering the model incapable of inference. However, the existing methods generate feature-importance assessments that are overly dependent on the source model, leading to inaccurate importance guidance and insufficient feature destruction. In this paper, we propose neighborhood expectancy attribution attacks (NEAA) that accurately guide the destruction of deep features, leading to highly transferable adversarial examples. First, we design a highly versatile attribution tool called neighborhood attribution to represent the importance of features that attribute highly similar results to various source models. Specifically, we discard the imputation of a single baseline and adopt the imputed expectation of a baseline within the neighborhood of the image. Subsequently, we generalize the neighborhood attribution to the middle layer of the model and simplify the computation by assuming linear independence. Finally, the attribution result guides the attack to destroy the intrinsic features of the image and obtain highly transferable adversarial examples. Numerous experiments demonstrate the effectiveness of the proposed method. Code is available at Github: https://github.com/KWPCCC/NEAA.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

A dynamic ventilation strategy for industrial buildings based on weight factors

In industrial buildings, pollution sources are often multi-point and unstable. This study proposes a dynamic exhaust strategy (DES) that aims to balance energy utilization efficiency and system performance without the need for monitoring indoor environmental parameters. Firstly, the DES system only monitors the local pollutant concentration in each outlet to determine its "exhaust potential". Weight factors are then assigned to adjust the total exhaust flowrate, achieving real-time dynamic weight allocation (DWA) and improving the system performance in removing pollutants. Secondly, the system monitors the total pollutants removal from all outlets to enable dynamic total exhaust flowrate adjustment (DTA) to reduce energy consumption. A stochastic release model of multiple pollution sources was established in a typical thermally polluted workshop, and numerical simulations were conducted to investigate the effects of factors such as the stochastic non-uniformity of sources release, frequency and range of dynamic parameter adjustments, and energy-saving potential of the DES system. The results indicate that the DES system demonstrates increased effectiveness in pollutants removal as the randomness of the sources release increases, with a 12% improvement in capture efficiency compared to the unregulated system. Additionally, by utilizing 80% of the total exhaust flowrate to adjust it at a frequency of 5 seconds per time in the DWA process, the system can meet requirements for pollutants removal, performance, and stability. Furthermore, the co-regulation of the DWA and DTA processes can reduce energy consumption by 78% while maintaining an 81% capture efficiency compared to using the DWA alone.

A Generic Model for a Persistent Radio Source around Fast Radio Bursts

The repeated fast radio bursts FRB 121102A and FRB 190520B have been reported, along with a spatially coincident, compact, persistent radio emission. In this paper, we present a parameterized one-zone model, with the basic scenario that a relativistic magnetized wind from the pulsar sweeps up the surroundings, e.g., freely expanding supernova ejecta, giving rise to a power-law distribution of electrons between the forward shock and the termination shock. We show that via appropriate adjustment of the model parameters, we can obtain synchrotron radio emission properties from the one-zone model bright enough to account for the observation, simply and analytically fitting the observed spectra well. Through dynamical evolution of the model, we can also obtain time-varying relevant properties. This parameterized model does not depend on concrete physical models such as a central engine; instead, we can constraint the physical model via comparison between parameters and observations, indicating the information about the central engine and surroundings. We also discuss the synchrotron self-Compton emission in our scenario in the end but find no clue about the counterparts at other wave bands.

A Joint Microwave and Hard X-Ray Study toward Understanding the Transport of Accelerated Electrons During an Eruptive Solar Flare

The standard flare model, despite its success, is limited in comprehensively explaining the various processes involving nonthermal particles. One such missing ingredient is a detailed understanding of the various processes involved during the transport of accelerated electrons from their site of acceleration to different parts of the flare region. Here we use simultaneous radio and X-ray observations from the Expanded Owens Valley Solar Array and the Spectrometer/Telescope for Imaging X-rays on board the Solar Orbiter, respectively, from two distinct viewing perspectives, to study the electron transport processes. Through detailed spectral modeling of the coronal source using radio data and footpoint sources using X-ray spectra, we compare the nonthermal electron distribution at the coronal and footpoint sources. We find that the flux of the nonthermal electrons precipitated at the footpoint is an order of magnitude greater than that trapped in the looptop, consistent with earlier works that primarily used X-ray for their studies. In addition, we find that the electron spectral indices obtained from X-ray footpoints are significantly softer than the spectral hardness of the nonthermal electron distribution in the corona. We interpret these differences based on transport effects and the difference in sensitivity of microwave and X-ray observations to different regimes of electron energies. Such an understanding is crucial for leveraging different diagnostic methods of nonthermal electrons simultaneously to achieve a more comprehensive understanding of the electron acceleration and transport processes of solar flares.

Harmonizing financial futures: AI enhancement and methodical fusion in banking risk oversight

The paper will deal with the link between finance and artificial intelligence (AI) in the context of banking, including how AI was introduced from simple tasks to more complex ones involving risk management and fraud detection. The phenomenon displays use of AI by banks as exemplified by First Financial Bank, which was able to open new accounts through intelligent digital assistants. The article focuses on the question of ethical AI that aims to create trust and compliance. This article has explored the position that big language models (LLMs) hold in enhancing virtual assistants in banking and the significance of open source models, as they contribute to collaboration and innovation in the financial industry.

Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River

The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows.

Transfer‐Learning‐Enabled 3D reconfigurable broadband solar metamaterial absorbers design

Research on metamaterials shows excellent potential in the field of solar energy harvesting. In recent years, the design of broadband solar metamaterial absorbers (SMAs) has attracted significant interest with the wide application of deep learning methods. This paper proposes a deep neural network (DNN) to realize forward prediction and inverse design of reconfigurable 3D SMAs. In the inverse design, a polarization-insensitive broadband SMA with an absorption bandwidth of 2.7 μm and an average absorption rate of 97.6% with an adjustable bandwidth range of 369 nm is successfully designed. The design of SMAs with different structures is also realized by a transfer learning method to improve the training speed and performance further. Using the transfer learning approach, the training speed of the neural network target model can be accelerated, and its training performance can be improved on small datasets by utilizing the trained neural network source model. Meanwhile, using the trained inverse design target model, a polarization-insensitive broadband SMA was designed with an absorption bandwidth of 2.7 μm, an average absorption of 97.9%, and an adjustable bandwidth range of 141 nm. Finally, we verified the solar energy harvesting capability of the designed broadband SMAs under real-world conditions using air mass (AM) 1.5, and they were calculated to be capable of harvesting the vast majority of the energy. The method is instructive in the design process of SMAs and can be effectively used to explore multifunctional complex nanophotonic devices.

Neutron Activation Analysis Based on AB-BNCT Treatment Room.

Boron neutron capture therapy (BNCT) is an ideal binary targeted radiotherapy for treating refractory tumors. An accelerator-based BNCT (AB-BNCT) neutron source has attracted more and more attention due to its advantages such as higher neutron yield in the keV energy region, less gamma radiation, and higher safety. In addition to 10B, neutrons also react with other elements in the treatment room during BNCT to produce many activation products. Due to the long half-life of some activation products, there will be residual radiation after the end of treatment and the shutdown of the accelerator, which has adverse effects on radiation workers. Therefore, the ambient dose equivalent rate in the treatment room needs to be evaluated. The AB-BNCT neutron source model proposed by Li is studied in this paper. Based on the Monte Carlo method, the Geant4 platform was used to simulate the dose induced by radionuclides near the Beam Shaping Assembly (BSA) of the source. It is concluded that the concrete wall contributed the most to the radiation dose. The dose rate of 2.45 μSv h-1 after 13 min of shutdown meets the dose rate limit of 2.5 μSv h-1, at which point it is safe for workers to enter the treatment room area.

Study on thermal penetration process of oil casing in shallow water environment by plasma arc based on heat source simulation and experiments

Abandoned offshore oil wellheads must be removed and plugged to protect the marine environment. Underwater plasma arc cutting holds the promise of playing a significant role in this work. However, much remains unknown about the thermal burn-off process of underwater plasma arcs for oil well casing. This work investigated the thermal penetration process of oil casing in a shallow water environment through experiments and heat source modeling simulations. A workpiece made of mild steel plate and cement block was considered as a small segment of the oil casing to be penetrated. The hole diameters, depths, and removal volumes of the workpieces were investigated experimentally. A combined heat source consisting of an elliptical cone heat source and a semi-ellipsoidal heat source was established based on the hole shape. The simulated and experimental hole profiles matched well. The results showed that the evolution of the hole shape of the workpiece was related to the motion state of the plasma arc and could be reflected by the variation of the arc voltage. Compared to steel plates, cement materials were easily removed by heat. These findings are favorable to promote the development of underwater plasma arc cutting in oil casing removal.