Bag Model Research Articles

ProcGCN: detecting malicious process in memory based on DGCNN.

The combination of memory forensics and deep learning for malware detection has achieved certain progress, but most existing methods convert process dump to images for classification, which is still based on process byte feature classification. After the malware is loaded into memory, the original byte features will change. Compared with byte features, function call features can represent the behaviors of malware more robustly. Therefore, this article proposes the ProcGCN model, a deep learning model based on DGCNN (Deep Graph Convolutional Neural Network), to detect malicious processes in memory images. First, the process dump is extracted from the whole system memory image; then, the Function Call Graph (FCG) of the process is extracted, and feature vectors for the function node in the FCG are generated based on the word bag model; finally, the FCG is input to the ProcGCN model for classification and detection. Using a public dataset for experiments, the ProcGCN model achieved an accuracy of 98.44% and an F1 score of 0.9828. It shows a better result than the existing deep learning methods based on static features, and its detection speed is faster, which demonstrates the effectiveness of the method based on function call features and graph representation learning in memory forensics.

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.

Wormhole geometries supported by strange quark matter and phantom-like generalized Chaplygin gas within [formula omitted] gravity

A crucial aspect of wormhole (WH) physics is the inclusion of exotic matter, which requires violating the null energy condition. Here, we explore the potential for WHs to be sustained by quark matter under conditions of extreme density along with the phantom-like generalized cosmic Chaplygin gas (GCCG) in symmetric teleparallel gravity. Theoretical and experimental studies on baryon structures indicate that strange quark matter, composed of u (up), d (down), and s (strange) quarks, represents the most energy-efficient form of baryonic matter. Drawing from these theoretical insights, we use the Massachusetts Institute of Technology (MIT) bag model equation of state to characterize ordinary quark matter. By formulating specific configurations for the bag parameter, we develop several WH models corresponding to different shape functions for the isotropic and anisotropic cases. Our analysis strongly suggests that an isotropic WH is not theoretically possible. Furthermore, we investigate traversable WH solutions utilizing a phantom-like GCCG, examining their feasibility. This equation of state, capable of violating the null energy condition, can elucidate late-time cosmic acceleration through various beneficial parameters. In this framework, we derive WH solutions for both constant and variable redshift functions. We have employed the volume integral quantifier (VIQ) method for both studies to assess the quantity of exotic matter. Furthermore, we have done the equilibrium analysis through the Tolman–Oppenheimer–Volkoff (TOV) equation, which supports the viability of our constructed WH model.

General backreaction force of cosmological bubble expansion

The gravitational-wave energy-density spectra from cosmological first-order phase transitions crucially depend on the terminal wall velocity of asymptotic bubble expansion when the driving force from the effective potential difference is gradually balanced by the backreaction force from the thermal plasma. Much attention has previously focused on the backreaction force acting on the bubble wall alone but overlooked the backreaction forces on the sound shell and shock-wave front, if any, which have been both numerically and analytically accomplished in our previous studies but only for a bag equation of state. In this paper, we will generalize the backreaction force on bubble expansion beyond the simple bag model. Published by the American Physical Society 2024

Scalarized hybrid neutron stars in scalar tensor gravity

Hybrid neutron stars, the compact objects consisting hadronic matter and strange quark matter, can be considered as the probes for the scalar tensor gravity. In this work, we explore the scalarization of hybrid neutron stars in the scalar tensor gravity. For the hadronic phase, we apply a piecewise polytropic equation of state constrained by the observational data of GW170817 and the data of six low-mass X-ray binaries with thermonuclear burst or the symmetry energy of the nuclear interaction. In addition, to describe the strange quark matter inside the hybrid neutron star, different MIT bag models are employed. We study the effects of the value of bag constant, the mass of s quark, the perturbative quantum chromodynamics correction parameter, and the density jump at the surface of quark-hadronic phase transition on the scalarization of hybrid neutron stars. Our results confirm that the scalarization is more sensitive to the value of bag constant, the mass of s quark, and the density jump compared to the perturbative quantum chromodynamics correction parameter.

Hadron-quark phase transition in the neutron star with vector MIT bag model and Korea-IBS-Daegu-SKKU functional

Employing the Korea-IBS-Daegu-SKKU (KIDS) density functional for the hadron phase and the MIT bag model with vector (vBag) model for the quark phase, we obtain hadron-quark phase transition in neutron stars considering Maxwell construction. The structural properties of the resultant hybrid stars are computed for three different values of bag constant (B) in the range B1/4=145−160 MeV). We study the effects of symmetry energy (J) on the hybrid star properties with the different KIDS model and found that J has important influence not only on the transition properties like the transition mass, transition radius and jump in density due to phase transition, but also on the stability of the hybrid stars. The vector repulsion of the quark phase via the parameter GV has profound influence in obtaining reasonable hybrid star configurations, consistent with the recent astrophysical constraints on the structural properties of compact stars. Within the aforesaid range of B, the value of GV is constrained to be 0.3 ≲GV≲ 0.4 in order to obtain reasonable hybrid star configurations.

Nonrelativistic Limit of Generalized MIT Bag Models and Spectral Inequalities

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Investigating physical existence of charged stellar models

This study aims at the detailed exploration of the internal structure of various anisotropic star configurations interacting with an electromagnetic field within the framework of f(R,T,Q) theory of gravity. We, specifically, consider a standard form of this theory in this regard, represented by a linear function of the Ricci scalar and the factor Q. Our analysis adopts a spherically symmetric, charged metric to describe the internal structure of dense quark models and proceeds to derive the field equations which correspond to the considered scenario. These equations are subsequently solved utilizing the particular metric potentials as well as the bag model. We then adopt the empirical data, specifically the radii and masses, from two quark stars including SMC X-4 and SAX J 1808.4-3658 to investigate the impact of charge and modified correction terms on their physical properties. Furthermore, we assess the physical relevance of the obtained model graphically by varying the modified model parameter. Our findings indicate that the developed models exhibit consistent stability within the modified framework across every parametric choice.

Experimenting the effectiveness of waste materials in improving the compressive strength of plastic-based mortar

The reduction in compressive strength (CS) of cementitious composites incorporating waste plastic is the main concern limiting its applicability in the building sector. Using industrial wastes as cement substitutes to enhance the CS of plastic mortar is a sustainable approach. This study used fine powdered waste materials such as silica fume (SF), marble powder (MP), and glass powder (GP) in plastic-based mortar for their effectiveness in enhancing CS. Plastic mortar specimens were cast using plastic waste in 5–25 % contents as sand replacement by mass, and their 28-day CS was recorded as a reference. SF, GP, and MP were utilized in plastic mortar mixtures separately in proportions of 5–25 %, with a 5 % increment, substituting cement by mass. These waste powders were also used in combinations of two (SF+GP, SF+MP, and GP+MP) and three (SF+GP+MP) in plastic mortar mixtures. Moreover, prediction models were built using the experimental database for the CS of plastic mortar. Gradient boosting and bagging ensemble machine learning (ML) techniques were chosen for model development. The decrease in CS was limited by substituting SF, GP, and MP for cement in plastic mortar. It was determined that the most effective substitution levels for SF, GP, and MP in plastic mortar, according to the strength enhancement, were 15, 10, and 15 wt.% of cement, respectively. The ML models closely matched experimental results, and in terms of R2 and error evaluations, bagging model outputs were more accurate than gradient boosting. The gradient boosting and bagging models had R2 of 0.89 and 0.94, respectively, with average absolute errors of 0.87 and 0.65 MPa.

Gap-filling techniques applied to the GOCI-derived daily sea surface salinity product for the Changjiang diluted water front in the East China Sea

Abstract. The spatial and temporal resolutions of contemporary microwave-based sea surface salinity (SSS) measurements are insufficient. Thus, we developed a gap-free gridded daily SSS product with higher spatial and temporal resolutions, which can provide information on short-term variability in the East China Sea (ECS), such as the front changes by Changjiang diluted water (CDW). Specifically, we conducted gap-filling for daily SSS products based on the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 1 km (0.01°), using a machine learning approach during the summer seasons from 2015 to 2019. The comparison of the Soil Moisture Active Passive (SMAP), Copernicus Marine Environment Monitoring Service (CMEMS), and Hybrid Coordinate Ocean Model (HYCOM) SSS products with the GOCI-derived SSS over the entire SSS range showed that the SMAP SSS was highly consistent, whereas the HYCOM SSS was the least consistent. In the < 31 psu range, the SMAP SSS was still the most consistent with the GOCI-derived SSS (R2=0.46; root mean squared error: RMSE = 2.41 psu); in the > 31 psu range, the CMEMS and HYCOM SSS products showed similar levels of agreement with that of the SMAP SSS. We trained and tested three machine learning models – the fine trees, boosted trees, and bagged trees models – using the daily GOCI-derived SSS as output, including the three SSS products, environmental variables, and geographical data. We combined the three SSS products to construct input datasets for machine learning. Using the test dataset, the bagged trees model showed the best results (mean R2=0.98 and RMSE = 1.31 psu), and the models that used the SMAP SSS as input had the highest level. For the dataset in the > 31 psu range, all the models exhibited similarly reasonable performances (RMSE = 1.25–1.35 psu). The comparison with in situ SSS data, time series analysis, and the spatial SSS distribution derived from models showed that all the models had proper CDW distributions with reasonable RMSE levels (0.91–1.56 psu). In addition, the CDW front derived from the model gap-free daily SSS product clearly demonstrated the daily oceanic mechanism during the summer season in the ECS at a detailed spatial scale. Notably, the CDW front in the zonal direction, as captured by the Ieodo Ocean Research Station (I-ORS), moved approximately 3.04 km d−1 in 2016, which is very fast compared with the cases in other years. Our model yielded a gap-free gridded daily SSS product with reasonable accuracy and enabled the successful recognition of daily SSS fronts at the 1 km level, which was previously not possible with ocean color data. Such successful application of machine learning models can further provide useful information on the long-term variation of daily SSS in the ECS. The gridded gap-free SSS dataset at 0.01°×0.01° spatial resolution is freely available at https://doi.org/10.22808/DATA-2023-2 (Shin et al., 2023).

Convergence of generalized MIT bag models to Dirac operators with zigzag boundary conditions

Convergence of generalized MIT bag models to Dirac operators with zigzag boundary conditions

Pulsars with the masses 2.14M⊙ and 2.27M⊙ as strange star candidates

Strange stars with maximal masses, more than the values of recently precisely measured masses of two radio pulsars: SRJ0740+6620, with the mass 2.14М⊙, and PSRJ2215+5135, with the mass 2.27М⊙, have been studied. For the examination of strange quark matter (SQM), the bag model, developed in Massachusetts Technological Institute was chosen. The observed bag model depends on vacuum pressure B, quark-gluon interaction constant αc, and strange quark mass ms. Since the transition to SQM state takes place at the energy density, not exceeding double density in atomic nuclei, neutron stars with small mass and configuration, consisting of SQM, form one family in the dependence curve of the mass M, of the equilibrium super-dense configurations, on the energy central density ρc (curve M(ρc)). The state equation of strange quark matter was studied when the vacuum pressure was constant. Groups of the values of these parameters were determined; their application in the state equation of SQM leads to the maximal mass of the equilibrium quark configurations Mmax, which are heavier than the aforementioned radio pulsars. For such configurations, the values of mass, radius, the entire number of baryons, red shift from the strange star surface were calculated, depending on the energy central density ρc. For each series with Mmax>2.14M⊙ and Mmax>2.27M⊙ the values of the mentioned integral parameters were calculated as well for super-dense configurations with 2.27and2.14 solar masses that were precisely determined from observations. According to the obtained state equations, these two radio pulsars can be the possible candidates for the strange stars.

Radial Oscillations of Strange Quark Stars Admixed with Dark Matter

We investigate the equilibrium structure and radial oscillations of strange quark stars admixed with fermionic dark matter. For strange quark matter, we employ a stiff equation of state from a color-superconductivity improved bag model. For dark matter, we adopt the cold free Fermi gas model. We rederive and numerically solve the radial oscillation equations of two-fluid stars based on general relativity, in which the dark matter and strange quark matter couple through gravity and oscillate with the same frequency. Our results show that the stellar maximum mass and radius are reduced by inclusion of dark matter. As to the fundamental mode of the radial oscillations, the frequency f0 is also reduced comparing to pure strange stars, and f02 reaches the zero point at the maximum stellar mass with dM/dϵq,c=0. Therefore, the stability criteria f02>0 and dM/dϵq,c>0 are consistent in our dark matter-mixed strange quark stars with a fixed fraction of dark matter. We also find a discontinuity of f0 as functions of the stellar mass, in contrast to the continuous function in pure strange stars. And it is also accompanied with discontinuity of the oscillation amplitudes as well as a discontinuous in-phase-to-out-phase transition between oscillations of dark matter and strange quark matter.

Study of the hidden-heavy pentaquarks and Pcs states

Inspired by the recently observed resonance states PψsΛ(4338)0 and Pcs(4459)0 by LHCb Collaboration in J/ψΛ decay channel, we perform a systematical study of all possible hidden-heavy pentaquarks with strangeness S=0,−1,−2,−3, in unified framework of MIT bag model. The color-spin wave functions in terms of Young-Yamanouchi bases and transformed into baryon-meson couplings are utilized to calculate masses, magnetic moments, and relative partial widths of the hidden-heavy pentaquarks, suggesting that the observed PψsΛ(4338)0 is likely to be a 1/2− compact Pcs pentaquark, and the Pcs(4459)0 favors two-peak structure of 3/2− and 1/2− Pcs states. Our computation indicates that hidden-heavy pentaquarks have the masses around 4.35–4.90 GeV for the Pcs, 11.21–11.67 GeV for the Pbs, 4.73–5.02 GeV for the Pcss, and 11.54–11.77 GeV for the Pbss. Published by the American Physical Society 2024

Impact of energy–momentum conservation violation on the configuration of compact stars and their GW echoes

This work investigates the impacts of energy–momentum conservation violation on the configuration of strange stars constraint with gravitational wave (GW) event GW190814 as well as eight recent observations of compact objects. The GW echoes from these interesting classes of compact objects are also calculated. To describe the matter of strange stars, we have used two different equations of state (EoSs): first an ad-hoc exotic EoS, the stiffer MIT Bag model and next realistic CFL phase of quark matter EoS. We choose Rastall gravity as a simple model with energy–momentum conservation violation with a set of model parameter values. Our results show that this gravity theory permits stable solutions of strange stars and the resulting structures can foster GW echoes. We illustrate the implication of the gravity theory and found that the negative values of the Rastall parameter result in more compact stellar configurations and lower GW echo frequency. With an increase in the Rastall parameter, both the compactness of the stellar configurations and echo time decrease. It is worth mentioning here that with the chosen set of some probable strange star candidates from observational data and also in light of GW 190814, we have evaluated the radii of stellar models. Also, the GW echo frequencies associated with strange stars are found to be in the range of ≈ 9–27 kHz for both cases. From this work, it is also inferred that the assumption regarding the equivalence of Rastall’s theory to Einstein’s theory is refuted as we have noticed many deviations in the physical properties of the considered compact stars.

Damping of density oscillations from bulk viscosity in quark matter

We study the damping of density oscillations in the quark matter phase that might occur in compact stars. To this end we compute the bulk viscosity and the associated damping time in three-flavor quark matter, considering both nonleptonic and semileptonic electroweak processes. We use two different equations of state of quark matter, more precisely, the MIT bag model and perturbative QCD, including the leading-order corrections in the strong coupling constant. We analyze the dependence of our results on the density, temperature and value of strange quark mass in each case. We then find that the maximum of the bulk viscosity is in the range of temperature from 0.01 to 0.1 MeV for frequencies around 1 kHz, while the associated minimal damping times of the density oscillations at those temperatures might be in the range of few to hundreds milliseconds. Our results suggest that bulk viscous damping might be relevant in the postmerger phase after the collision of two neutron stars if deconfined matter is achieved in the process. Published by the American Physical Society 2024

Masses and decays of triply-heavy pentaquarks* *D. J. is Supported by the National Natural Science Foundation of China (12165017)

In this study, we analyzed masses and decays of triply-heavy pentaquarks in the unified MIT bag model. We construct the color-spin wave functions of the triply-heavy pentaquarks and use the numerical variational method to compute all the ground-state masses of these systems. By excluding the scattering states in configuration spsce, we computed the decay width ratios of each decay channel relative to the maximum width for the compact pentaquark states, obtaining the main decay modes of the triply-heavy pentaquark systems.

Driving Style and Traffic Prediction with Artificial Neural Networks Using On-Board Diagnostics and Smartphone Sensors

Driving style and road traffic play pivotal roles in the development of smart cities, influencing traffic flow, safety, and environmental sustainability. This study presents an innovative approach for detecting road traffic conditions and driving styles using On-Board Diagnostics (OBD) data and smartphone sensors. This approach offers an inexpensive implementation of prediction, as it utilizes existing vehicle data without requiring additional setups. Two Artificial Neural Network (ANN) models were employed: the first utilizes a forward neural network architecture, while the second leverages bootstrapping or bagging neural networks to enhance detection accuracy for low-labeled classes. Support Vector Machine (SVM) is implemented to serve as a baseline for comparison. Experimental results demonstrate that ANNs exhibit significant improvements in detection accuracy compared to SVM. Moreover, the neural network with bagging model showcases enhanced recall values and a substantial improvement in accurately detecting instances belonging to low-labeled classes in both driving style road traffic.

Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India

ABSTRACT Accurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi's semi-arid climate, data spanning 31 years (1990–2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash–Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model's robust performance underscores its potential application in water resource management.

Concrete Compressive Strength Prediction by Ensemble Machine Learning Approach

The prediction of concrete compressive strength is a crucial aspect of ensuring the structural integrity and durability of construction projects. In recent years, machine learning approaches have improved upon the limitations of empirical formulas and laboratory testing methods for predicting concrete compressive strength. This study utilizes ensemble machine-learning techniques, such as Bagging, XGBoost, and Stacking models, to enhance the accuracy of concrete compressive strength prediction models. A five-fold cross-validation technique was applied to mitigate the problems of underfitting or overfitting in the regression model. Furthermore, various statistical indices were employed to compare the forecasting performance of these ensemble techniques. The prediction performance of this research revealed that XGBoost achieved the highest R-squared value of 93%, followed by Stacking and Bagging regression models at 92%. Consequently, this research underscores the potential of ensemble techniques as valuable tools in the domain of civil engineering, paving the way for more reliable and efficient construction practices.