Bag Model Research Articles

Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil’s thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems.

Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.

Analysis of Landing Buffer Characteristics of Unmanned Airdrop Rescue Vehicle

Taking an unmanned airdrop rescue vehicle as the research object, in order to reduce the impact overload of the airdrop landing, the exhaust type airbag buffer mode suitable for the airdrop vehicle is determined. Firstly, the scheme of the airdrop system is designed. According to the basic assumption of the airdrop process, the thermodynamic analysis model of the buffer bag is derived and the parameters are designed. Secondly, the finite element model of airdrop system with airbag buffer is established based on the airbag finite element algorithm (CV method). Finally, the buffering effect is evaluated by analyzing the change of tire force. The calculation results show that the system overload is reduced from 35g to 22g by using airbag buffer, and the buffer effect is obvious.

Gravitational wave asteroseismology of charged strange stars in the Cowling approximation: the fluid pulsation modes

In this work we study, within the framework of Cowling approximation, the effect of the electric charge on the gravitational wave frequency of fluid oscillation modes of strange quark stars. For this purpose, the dense matter of the stellar fluid is described by the MIT bag model equation of state (EoS), while for the electric charge profile, we consider that the electric charge density is proportional to the energy density. We find that the gravitational wave frequencies change with the increment of electric charge; these effects are more noticeable at higher total mass values. We obtain that the f-mode is very sensitive to the change in the electric charge of the star. Furthermore, in the case of the p1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_1$$\\end{document} mode, the effect of the electric charge is not very significant. Our results reveal that the study of the fundamental pulsation mode of an electrically charged compact star is very important in distinguishing whether compact stars could contain electric charge. We also employ another electric charge distribution profile that follows a power law, and it is found that the f-mode change is more noticeable than the p1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_1$$\\end{document}-mode when the electric charge is incremented.

Effect of dark matter interaction on hybrid star in the light of the recent astrophysical observations

We have explored the effect of dark matter interaction on hybrid star (HS) in the light of recent astrophysical observational constraints. The presence of dark matter is assumed to be there in both the hadron as well as the quark sector. The dark matter particle interacts with both hadron and quark matter through the exchange of a scalar as well as a vector meson. The equation of state (EOS) of the hadron part is computed using the NL3 version of the relativistic mean field(RMF) model, whereas the quark part is taken care of using the well-known MIT Bag model with the vector interaction. We investigate the effect of the dark matter density and the mass of the dark matter particle on various observables like mass, radius, tidal deformability of the dark matter admixed hybrid star(DMAHS).In this study, we have noted an intriguing aspect that is the speed of sound in the DMAHS is insensitive to both the mass as well as the density of dark matter. We also observe a striking similarity in the variation of transition mass and its corresponding radius, as well as the maximum mass of neutron stars, with dark matter density and mass. We employ observational constraints from neutron stars to narrow down the allowed range of the parameters of dark matter.

The Forecasting of the Spread of Infectious Diseases Based on Conditional Generative Adversarial Networks

New epidemics encourage the development of new mathematical models of the spread and forecasting of infectious diseases. Statistical epidemiology data are characterized by incomplete and inexact time series, which leads to an unstable and non-unique forecasting of infectious diseases. In this paper, a model of a conditional generative adversarial neural network (CGAN) for modeling and forecasting COVID-19 in St. Petersburg is constructed. It takes 20 processed historical statistics as a condition and is based on the solution of the minimax problem. The CGAN builds a short-term forecast of the number of newly diagnosed COVID-19 cases in the region for 5 days ahead. The CGAN approach allows modeling the distribution of statistical data, which allows obtaining the required amount of training data from the resulting distribution. When comparing the forecasting results with the classical differential SEIR-HCD model and a recurrent neural network with the same input parameters, it was shown that the forecast errors of all three models are in the same range. It is shown that the prediction error of the bagging model based on three models is lower than the results of each model separately.

Beach nourishment for coastal aquifersimpacted by climate change and population growth using machine learning approaches

Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA, using a 3D SEAWAT model nourishment considering the future sea level rise and freshwater over-pumping. The present study focused on the development and comparative evaluation of seven machine learning (ML) models, i.e., additive regression (AR), support vector machine (SVM), reduced error pruning tree (REPTree), Bagging, random subspace (RSS), random forest (RF), artificial neural network (ANN) to predict the SWI using beach nourishment. The performance of ML models was assessed using statistical indicators such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), means absolute error (MAE), root mean square error (RMSE), and root relative squared error (RRSE) along with the graphical inspection (i.e., Radar and Taylor diagram). The findings indicate that applying SVM, Bagging, RSS, and RF models has great potential in predicting the SWI values with limited data in the study area. The RF model emerged as the best fit and closely matched observed values; it obtained R2 (0.999), NSE (0.999), MAE (0.324), RRSE (0.209), and RMSE (0.416) during the testing process. The present study concludes that the RF model could be a valuable tool for accurate predictions of SWI and effective water management in coastal areas.

Bose–Einstein Condensation dark matter models generated by gravitational decoupling

A study on massive compact objects influenced by Bose–Einstein condensation (BEC) dark matter (DM) with a gravitational decoupling approach is presented within the context of f(Q) gravity, assuming a linear form of f(Q). In order to obtain physically valid solutions to the basic field equations of the decoupled systems, we take the Tolman IV potential ansatz for the seed system and the BEC-DM density profile for the new source into consideration. After that, we apply the boundary conditions on the decoupled system with Schwarzschild-de Sitter spacetime as the exterior metric. Eventually, we obtain a complete non-singular solution to the decoupled system, consisting of a strange stellar configuration with a BEC-DM density profile. All the physical properties, such as the effective energy density, pressure along the radial as well as the tangential direction, and anisotropy in the system, are rigorously investigated. Analyzing the stability conditions of the decoupled stellar configuration, we study mass–radius (M−R) relations with observational constraints related to the supermassive compact stars, such as PSR J0740+6620, PSR J2215+5135, GW190814, and GW200210, with observed masses larger than or equal to 2 M⊙. Notably, we examine the influence of DM on constraining the M−R measurements of the observed stars, which satisfy the MIT bag model equation of state as well as the condition of mimicking, i.e., ρθ=ρDM, in the background of f(Q) gravity. Interestingly, any possible conversion of massive compact stars to smaller black holes can be restrained, as the presence of a DM halo in the strange stellar system reduces the maximum mass effectively, as indicated by the M−R curves. This result regarding the maximum mass of the compact star can be associated with the astrophysical data concerning the mass gap of the events GW190814 and GW200210. Specifically, the present model predicts the radius for PSR J0740+6620 as 13.69−0.10+0.09 km, which nearly matches the NICER and XMM-Newton data.

Chiral-odd GPDs in the bag model

Chiral-odd GPDs in the bag model

Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect

The primary cause of frost damage and early failure in concrete structures is the repeated freezing and thaw cycles (FTC). The evolution of internal fractures and scaling at the surface of concrete can be used to assess the impact of frost damage. Surface scaling mechanisms and interior frost damage are contingent upon numerous environmental factors, including the rate of freezing, low temperature, and duration of the freezing point. However, evaluating the amount of strength loss of concrete material is a challenging factor to consider. This work explores the application of predictive modelling tools, including random forest (RF), multilayer perceptron (MLP), decision tree (DT), and bagging, to evaluate the degraded compressive strength (D-CS) of concrete. The models use five input variables: initial compressive strength (I-CS), water-to-cement ratio (W/C), FTC, minimum temperature (Tmin), and maximum temperature (Tmax), with D-CS as the output. Model performance was compared using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics, with the bagging model achieving the highest predictive performance at 92 %. A 10-fold cross-validation approach validated the model's accuracy. The influence of each variable was assessed using SHapley Additive exPlanations (SHAP) analysis. Importantly, a user-friendly Graphical User Interface (GUI) was developed based on the models, making it easy for researchers and professionals to make predictions and thereby increasing the practicality and accessibility of this research. This study aids the research community in selecting appropriate models to forecast the strength of various concrete types, thereby enhancing the practical application of this research.

Temperature fluctuations in a relativistic gas: Pressure corrections and possible consequences in the deconfinement transition

In this work, we study the effects of random temperature fluctuations on the partition function of a quantum system by means of the . This picture provides a conceptual model for a quantum nonequilibrium system, depicted as an ensemble of subsystems at different temperatures, randomly distributed with respect to a given mean value. We then assume the temperature displays stochastic fluctuations T=T0+δT with respect to its ensemble average value T0, with zero mean δT¯=0 and standard deviation δT2¯=Δ. By means of the replica method, we obtain the average grand canonical potential, leading to the equation of state and the corresponding excess pressure caused by these fluctuations with respect to the equilibrium system at a uniform temperature. Our findings reveal an increase in pressure as the system’s ensemble average temperature T0 rises, consistently exceeding the pressure observed in an equilibrium state. We applied our general formalism to three paradigmatic physical systems; the relativistic Fermi gas, the ideal gas of photons, and a gas of non-Abelian gauge fields (gluons) in the noninteracting limit. Finally, we explore the implications for the deconfinement transition in the context of the simple bag model, where we show that the critical temperature decreases. Published by the American Physical Society 2024

Applicability of modified Gauss–Bonnet gravity models on the existence of stellar structures

In this paper, we explore the existence of spherically symmetric strange quark configurations coupled with anisotropic fluid setup in the framework of modified Gauss–Bonnet theory. In this regard, we adopt two models such as (i)f(G)=βG2, and (ii)f(G)=δ1Gx(δ2Gy+1), and derive the field equations representing a static sphere. We then introduce bag constant in the gravitational equations through the use of MIT bag model, so that the quarks’ interior can be discussed. Further, we work out the modified equations under the use of Tolman IV ansatz to make their solution possible. Junction conditions are also employed to find the constants involved in the considered metric potentials. Afterwards, different values of model parameters and bag constant are taken into account to graphically exploring the resulting solutions. This analysis is done by considering five strange quark objects like Her X-I, LMC X-4, 4U 1820-30, PSR J 1614-2230, and Vela X-I. Certain tests are also applied on the developed models to check their physical feasibility. It is much interesting that this modified gravity under its both considered functional forms yield physically viable and stable results for certain parametric values.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Modeling Compact Object Mergers GW190814 and GW200210 and Other Self-bound Compact Stars with Dark Matter Induced by Gravitational Decoupling and Its Significance to the Mass Gap

We present a rigorous study of compact objects within f(Q) gravity where electrical fields and dark matter are studied and provide novel mass–radius relations with models falling in the mass gap of the events GW190814 and GW200210. After formulating the basic equations and finding their relevant solutions, we impose the boundary conditions on the system under treatment. The decoupled solution for the strange stellar model with the dark matter density profile is obtained. The distribution patterns of the effective energy density and the radial as well as the tangential pressure and anisotropy in the system are intensively examined. The stability properties of the stellar configuration and the influence of dark matter are studied. The recent observations of supermassive compact star candidates such as PSR J1614–2230 and PSR J0952–0607 with observed masses greater than or equal to 2 M ⊙ have been employed in our study. Interestingly, the present study predicts the constraints on mass–radius measurements of the observed stars satisfying the equation of state based on the MIT bag model and employing the condition of mimicking, i.e., ρ θ = χ1ρPI in f(Q) gravity. Our graphical results exhibit that for a particular M–R curve with fixed values of the parameters, neutron stars having mass less than M max exist with larger radii within the context of the f(Q) formalism.

Formation of Single-Species and Multispecies Biofilm by Isolates from Septic Transfusion Reactions in Platelet Bag Model.

During 2018-2021, eight septic transfusion reactions occurred from transfusion of platelet units contaminated with Acinetobacter spp., Staphylococcus saprophyticus, Leclercia adecarboxylata, or a combination of those environmental organisms. Whether biofilm formation contributed to evasion of bacterial risk mitigations, including bacterial culture, point-of-care testing, or pathogen-reduction technology, is unclear. We designed a 12-well plate-based method to evaluate environmental determinants of single-species and multispecies biofilm formation in platelets. We evaluated bacteria isolated from septic transfusion reactions for biofilm formation by using crystal violet staining and enumeration of adherent bacteria. Most combinations of bacteria had enhanced biofilm production compared with single bacteria. Combinations involving L. adecarboxylata had increased crystal violet biofilm production and adherent bacteria. This study demonstrates that transfusion-relevant bacteria can produce biofilms well together. More work is needed to clarify the effect of biofilms on platelet bacterial risk control strategies, but US Food and Drug Administration-recommended strategies remain acceptable.

Online chicken carcass volume estimation using depth imaging and 3-D reconstruction

Variability in the size of slaughtered chickens remains a longstanding challenge in the standardization of the poultry industry. To address this issue, we present a novel approach that uses volume as a grading metric for chicken carcasses. This innovative method, unexplored in existing studies, employs real-time data capture of moving chicken carcasses on a production line using Kinect v2 depth imaging and 3-D reconstruction technologies. The captured depth images are processed into point clouds followed by 3-D reconstruction. Volume is calculated from the reconstructed models using the surface integration method, and additional 2-D and 3-D features are extracted as input parameters for machine learning models. Multiple regression models were evaluated, with the bagged tree model demonstrating superior performance, achieving an R² value of 0.9988, RMSE of 5.335, and ARE of 2.125%. Furthermore, our method showed remarkable efficiency with an average processing time of less than 1.6 seconds per carcass. These results indicate that our novel approach fills a critical gap in existing automated grading methodologies by offering both accuracy and efficiency. This validates the applicability of depth imaging, 3-D reconstruction, and machine learning for estimating chicken carcass volume with high precision, thereby enabling a more comprehensive, efficient, and reliable chicken carcass grading system.

Strange star model in higher dimensions (D ≥ 4) with density-dependent B in pseudo-spheroidal geometry

In this paper, we have developed a class of new solutions for relativistic compact stars in higher-dimensional [Formula: see text] space-time assuming pseudo-spheroidal geometry of the [Formula: see text] metric component. To study the physical properties, we consider equation of state of interior strange matter [Formula: see text], as proposed in MIT bag model in the presence of a density-dependent [Formula: see text] parameter. The interior matter of a quark star may consist of three flavor quarks. We observe some interesting results. The parameter [Formula: see text] depends on the anisotropy [Formula: see text] in the interior, and at the stellar surface, [Formula: see text] attains a constant value independent of [Formula: see text]. At the interior, [Formula: see text] increases with the increase of [Formula: see text]. We also note that the value of B approaches a constant value with increasing spheroidal parameter [Formula: see text]. [Formula: see text] also depends on space-time dimensions and it is interesting to note that [Formula: see text] picks up negative values near core region which limits the number of space-time dimensions available for a stellar model. All the stability criterion and energy conditions hold good for a physically realistic stellar configuration. It is observed that strange quark matter may be stable or metastable or unstable depending upon the value of energy per baryon [Formula: see text]. Strange quark matter will be stable if energy per baryon [Formula: see text]. It is noted that the maximum mass obtainable in this model is [Formula: see text] considering stable strange quark matter when dimension [Formula: see text]. In addition, we observe that the dimensions have some effect on the gross properties of strange stars (SS).

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.