Search Results (807)

We develop a general framework for quantum field theory in curved spacetime based on Local Minkowski Coordinates (LMC), which incorporates curvature effects into local Feynman diagrammatics. Gravitational influence enters through a curvature-dependent normalization function $B\left(x\right)$, derived from covariant current conservation, and a gravitational phase $S\left(x\right)$, obtained via the WKB approximation. These quantities enter through local phase accumulation and observer-dependent normalization of external states, without modifying globally conserved fluxes. As a first application, we analyze the local redshift normalization and phase structure of quantum amplitudes in the vicinity of a Schwarzschild black hole. Within their range of validity, the curvature-dependent factors $B\left(x\right)$ and $S\left(x\right)$ reproduce the expected gravitational redshift of field amplitudes in general relativity. When amplitudes are propagated to asymptotic infinity and evaluated in a standard global quantum state (such as the Unruh state), the resulting flux is consistent with the standard Hawking result. The framework refines the local WKB structure and clarifies the separation between local normalization effects and globally conserved fluxes. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, an adaptive transfer function and two adaptive learning coefficients are introduced to achieve a better balance between exploration and exploitation during the search process. Second, a hybrid learning mechanism that integrates personal best, global best, and elite solutions is utilized to enhance population diversity. Finally, a simulated annealing (SA)–based local search strategy is employed to further refine candidate solutions and improve convergence behavior. Experimental results demonstrate that ABPSO outperforms binary PSO (BPSO), harris hawks optimization (HHO), whale optimization algorithm (WOA), and ant colony optimization (ACO) in classification accuracy. In particular, ABPSO achieves the lowest classification error rates on the Dermatology (0.0106), Ionosphere (0.0705), Lung (0.1521), Sonar (0.0996), Spambase (0.0758), Statlog (0.1446), and Wine (0.0280) datasets. Full article

This paper proposes a Black Kite Algorithm (BKA)-based hyperparameter optimization method for Artificial Neural Network (ANN) training, mitigating local minimum issues associated with conventional training techniques. The resulting BKA-ANN model is then employed to estimate PID controller parameters for DC motor speed regulation. A large-scale dataset of 100,000 samples was generated via MATLAB simulation, with reference speed and load torque stochastically varied, and optimal PID parameters determined by minimizing the ITAE criterion for each operating condition. The optimized controller was evaluated under various operating conditions including transient response, frequency domain analysis (phase margin and bandwidth), parametric robustness, and load disturbance suppression, along with control effort and energy consumption assessments. The proposed BKA-ANN approach was benchmarked against nine algorithms: hybrid atom search optimization-simulated annealing (hASO-SA), harris hawks optimization (HHO), Henry gas solubility optimization with opposition-based learning (OBL/HGSO), atom search optimization (ASO), henry gas solubility op-timization (HGSO), stochastic fractal search(SFS), grey wolf optimization (GWO), sine–cosine algorithm (SCA), and Standard ANN. Simulation results indicate that BKA-ANN achieves stable performance across all tested scenarios, with minimal oscillation and competitive settling time compared to the evaluated algorithms. Full article

This investigation is to address the aerodynamic noise generated from laminar flow over a D-shaped cylinder at a low Reynolds number (Re). Proposed is a novel assembly of arc-shaped splitter plates to effectively reduce the aeolian tone for the D-shaped cylinder. The two-dimensional flow field is simulated at an Re of 160 to investigate the mechanism of reducing the sound of the arc-shaped plates. The radiated sound has been predicted by Ffowcs Williams and Hawkings (FW-H) acoustic analogy. To verify calculations, the predicted results of a circular cylinder have been compared with the data in the literature. The results reveal that the introduction of the arc plates decreases the lift and drag fluctuations as well as the vortex shedding frequency in comparison with the no-arc plate case. The pressure and velocity fluctuations in the wake zone are reduced by the arc plates due to vortex shedding suppression. The application of the arc plates shows an effective control of sound, leading to a maximum reduction in sound pressure level (SPL) by almost 34 dB. Full article

In fire emergency ventilation systems, EC (Electronically Commutated) internal-rotor axial fans are critical devices, but their high-speed operation generates aerodynamic noise often exceeding 90 dB (A). While struts are core structural components regulating flow field stability, their specific geometric impact on trailing-edge vortex shedding and noise generation mechanisms remains unclear. This study investigates three strut configurations: a hexagonal annular type, a hexagonal double-ring type, and a three-pronged type. A coupled numerical model was established using Large Eddy Simulation (LES) and the Ffowcs Williams and Hawkings (FW-H) acoustic analogy. The Q-criterion was employed to analyze vortical structures, with numerical predictions validated against experimental measurements in a semi-anechoic chamber. The results quantitatively demonstrate that optimizing the strut geometry significantly mitigates unsteady flow separation. The three-pronged strut (Model C) effectively dispersed high-velocity airflow, reducing the peak turbulent kinetic energy (TKE) at the inlet by 30% compared to the original design (Model a). Furthermore, Model C achieved a 6.7 dB reduction in the sound pressure level at the blade-passing frequency (BPF), alongside a 14.1% reduction in pressure pulsation amplitude near the blade tip. Structural optimization of struts enables synergistic control over turbulence distribution and pressure fluctuations. By disrupting the phase coherence of shed vortices, the optimized design fundamentally suppresses aerodynamic noise, advancing axial fan design toward precise quantitative aeroacoustic optimization. Full article

It is shown that transient trapped surfaces form in a class of emerging globally hyperbolic spacetimes, within punctured Planck-scale neighbourhoods of the geon supported on intersecting singular supports whose intersection forms a characteristic core in a non-strongly causal setting. These neighbourhoods shrink towards the intersecting singular support in the distributional geometry. In particular, the trapped surfaces occur near the characteristic limit corresponding to the unstable equilibrium of the self-gravitating geon. They act as an effective classical barrier for descriptions formulated purely within smooth differential geometry. The area of these trapped-surface configurations, computed on Planck-referenced neighbourhoods, is shown to tend to zero both in the asymptotically flat limit of the emerging spacetime and in the geon limit. Thus, transient trapped surfaces evaporate in the sense that their area vanishes as classical and asymptotically flat spacetime emerges within the quantum foam framework. A state-counting generating function for the transient trapped surfaces is constructed from the coherent-state density operator. This generating function maps microscopic occupation-number sectors to macroscopic data and thereby allows a definition of Boltzmann entropy (not to be confused with the von Neumann entropy, which is zero for any pure coherent state). Since the coherent state is constructed to implement the correspondence principle, expectation values of the relevant quantised observables reproduce their classical values. In particular, the expectation value of the bosonic occupation-number operator serves as a microstate-counting variable in the coherent sector. The generating function takes the form of an exponential of this expectation value, leading to an entropy–area relation consistent with the Hawking–Bekenstein scaling. Full article

Background/Objectives: Management of bipolar disorder is marked by variability in lithium response, with responders constituting a distinct clinical subgroup. Although pharmacogenetic studies implicate polymorphisms in neuroplasticity-related genes (BDNF) and hypothalamic–pituitary–adrenal (HPA) axis regulators (NR3C1), the underlying biophysical mechanisms remain poorly characterized. This study aims to bridge this structural–mechanistic gap by quantifying the atomic-level effects of key lithium-response polymorphisms on protein–protein interaction stability and conformational dynamics. Methods: Variant sequences for BDNF rs6265 and NR3C1 rs56149945 were generated and structurally modeled with SWISS-MODEL. Protein–protein interaction analyses focused on the BDNF–TrkB and NR3C1–FKBP5 systems. Structural alignment and conformational comparisons were performed with ChimeraX and US-align, while interaction energetics were evaluated with PRODIGY and HawkDock. Conformational flexibility was assessed using CABS-flex through RMSF analysis. Results: Structural validation showed acceptable model quality. Binding analyses indicated stronger interactions in the variant complexes. In the BDNF–TrkB complex, binding affinity shifted from −13.8 to −15.1 kcal/mol with an ~8.5-fold lower dissociation constant, while the NR3C1–FKBP5 variant complex shifted from −16.3 to −18.8 kcal/mol with an ~65-fold lower dissociation constant. MM/GBSA calculations supported increased stability, with binding energies changing from −61.98 to −83.91 kcal/mol (BDNF–TrkB) and from −18.88 to −31.25 kcal/mol (NR3C1–FKBP5). Structural superposition showed high conservation of global folds (pruned RMSD 0.779 Å and 0.310 Å; TM-scores 0.753 and 0.967). RMSF profiles were largely overlapping, indicating localized interface adjustments rather than global conformational changes. Conclusions: These findings suggest that lithium-response polymorphisms may modulate protein–protein interaction stability while preserving overall structure, providing a structural framework for exploring genetic influences on lithium treatment response. Full article

In this study, a novel hybrid optimization approach is proposed to minimize the fuel consumption of commercial aircraft by taking flight-related and meteorological constraints into account during the cruise phase. The new method, the Decision Tree–Robust Multiple Regression–Harris Hawks Optimization Algorithm (DRHA), incorporates data segmentation based on decision trees, modeling of robust multiple regression, and the Harris Hawks optimization algorithm. In this context, a PID speed controller for a Boeing 737-800 aircraft was developed by employing a Software-in-the-Loop (SIL) framework that establishes real-time data exchange between MATLAB/Simulink and the FAA-approved X-Plane flight simulator. Within this framework, a simulation-based fuel consumption dataset was obtained from 1032 different scenarios encompassing various combinations of altitude, speed, aircraft weight, wind speed, and wind direction, thus aiming to reflect a wide range of realistic flight operating conditions. According to comparative analysis outcomes, the proposed DRHA approach significantly outperformed conventional statistical and machine learning-based methods in modeling fuel consumption equations. Namely, a mean absolute error (MAE) and R² value are achieved with values of 1.24 and 0.90, respectively. Moreover, PID controller parameters are optimized under varying conditions thanks to the DRHA method, yielding between 0.07% and 5.33% fuel savings compared to manually tuned controllers. Tests performed under different altitudes, aircraft weights, and wind conditions confirm the algorithm’s robustness and adaptability. The proposed method is anticipated to offer scalable and adaptable solutions for various types of aircraft and real-time control systems. Full article

Considering the dynamics of spin-1/2 fermion in higher-dimensional static multi-charge black holes in gauged supergravity theory, taking into account Lorentz breaking and quantum perturbation theory, this study investigates new expressions for the Hawking temperature and Bekenstein-Hawking entropy of such black holes based on WKB theory and quantum tunneling radiation theory, as well as the laws of black hole thermodynamics. The physical significance of the research methods used in this paper and the related results obtained are analyzed. Furthermore, an in-depth discussion is provided regarding the implications of the research content for addressing relevant issues in high-dimensional curved spacetime. Full article

In this paper, we investigate the inter-event times of frequent words observed in 17 academic books, where the inter-event time is defined as the number of sentences between two successive appearances of a given word. Our results show that the distributions of inter-event times for frequent words can be classified into three types: the exponential distribution, the q-exponential distribution, and the power-law distribution. To examine the generative mechanisms underlying these three types, we conducted text generation simulations and found that combining two mechanisms for word selection—priority-based selection and randomized selection—is sufficient to reproduce the observed three distribution types. In particular, the priority-based selection mechanism, in which words for a constructed sentence are chosen mechanically according to predefined priorities assigned to each word, is identified as the underlying mechanism of the power-law distribution of inter-event times. We also discuss in detail the relationship between the priority-based selection mechanism and the multivariate Hawkes process, which effectively captures mutual correlations among occurrences of important words. Full article

In this work, we investigate the shadow properties of the Kerr–Newman–Anti-de Sitter black hole coupled to nonlinear electrodynamics. The shadow is constructed by employing the celestial coordinate approach for an observer located at a finite distance, which is required due to the non-asymptotically flat structure of the spacetime. The size, distortion, area, and oblateness of the shadow are analyzed in terms of the black hole parameters, namely, the spin, the effective charge, and the nonlinearity parameter. We show that the nonlinear electrodynamics significantly modifies the photon region and therefore changes the shadow observables, while the rotation mainly controls the deformation of the silhouette. We further confront the theoretical results with the Event Horizon Telescope observations of M87* and Sgr A* in order to constrain the parameter space of the model. The allowed ranges of the effective charge depend sensitively on the nonlinearity parameter, and the combination of both sources leads to tighter and physically more consistent bounds. In addition, we study the energy emission rate derived from the shadow radius and the Hawking temperature and discuss how it is affected by the rotation and the nonlinear electromagnetic field. Our analysis shows that the considered black hole solution provides a consistent extension of the Kerr geometry in a non-asymptotically flat background and that the shadow observables can be used as an efficient tool to test the effects of nonlinear electrodynamics in strong gravity. Full article

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

Modern financial systems do not exist in isolation but form part of a complex global network of interconnected financial systems. This globalization of financial systems significantly increases the risk of contagion in financial markets, impacting asset prices and other important economic factors, including interest rates and market volatility. This phenomenon informs not only investors’ investment strategies but also the prices of contingent claims. In this article, we present a derivative pricing model in an incomplete and globalized financial market. To appreciate the dynamics and impact of some important market factors, particularly default risks due to contagion, we consider two different financial markets with defaultable assets: in one market, we consider a stock whose price process follows a Heston stochastic volatility model, and in the other, a stock that follows a Hawkes-type jump diffusion model whose intensity is subjected to external systemic shocks. In both markets, we derive an indifference price for a contingent claim that is subject to the risk of default and show the impacts the investor’s risk aversion and external shocks on the price of the contingent claim. Full article

We revisit the black-hole information problem from the viewpoint of a population–coherence decomposition of density-matrix purity. Building on a previously developed formalism for n-dimensional density matrices, we characterize each state by a normalized global purity index and two complementary indices, which quantify the contributions of level populations and coherences. This yields a simple quadratic relation and a geometric representation in a “population–coherence plane”, where different routes to purity can be distinguished. In the two-level case, we construct explicit families of states with identical spectra and global purity but opposite internal structure, realizing population-dominated and coherence-dominated routes. We then apply this framework to a standard Page-type evaporation model without an explicit Hamiltonian, in which a black hole and its Hawking radiation form a bipartite pure state with varying Hilbert-space dimensions. Using known results for typical reduced states in large dimensions, we analyze the behavior of population and coherence components of purity along the evaporation process. Under the physically motivated requirement that, in this energy-free setting, the radiation populations remain nearly uniform in the chosen basis, we show that the late-time recovery of purity must be coherence-dominated: the global purity of the radiation approaches unity while the population index stays small and the coherence index carries essentially all the purity. Full article