Search Results (602)

We introduce a notion of curvature based on informational holonomy. Let $(M,g)$ be a smooth Riemannian manifold and let $\pi :\mathcal{P}\to M$ be a bundle of state spaces equipped fibrewise with a smooth divergence $D_x$ inducing an information metric $g^{{\mathcal{P}}_x}$. Assuming a connection on $\mathcal{P}$ compatible with this fibrewise information geometry, we measure the deviation of holonomy around small geodesic triangles by transporting a reference state ${\mu }_x$ and comparing it to its image via the induced informational distance $d_x=\sqrt{2D_x}$. Normalizing the resulting distance defect by the geometric area yields a continuous informational holonomy (sectional) curvature$K_{\mathrm{hol}}^{\mathrm{cont}}(x,\mathsf{\Pi })$. We prove that this limit exists for all $(x,\mathsf{\Pi })$ and equals the norm of a vector $W_x(\mathsf{\Pi };{\mu }_x)\in T_{{\mu }_x}{\mathcal{P}}_x$ depending linearly on the curvature of the connection along $\mathsf{\Pi }$. In geometric models induced from the Levi–Civita connection via an isometric representation, $K_{\mathrm{hol}}^{\mathrm{cont}}$ becomes a scalar invariant of $R^g{|}_{\mathsf{\Pi }}$ and, on spaces of constant sectional curvature, reduces to a constant multiple of $|{\sec }_g|$. On the discrete side, we consider quasi-uniform sampling graphs whose edges carry channels approximating parallel transport. Discrete triangle holonomies define a curvature estimator, and under explicit sampling, area-approximation, and channel-consistency assumptions, we establish a discrete-to-continuum convergence theorem with a quantitative error bound controlled by the sampling scale. Full article

Many machine-learning tasks involve structured data whose geometry, local feature distributions, and global organization interact in ways that are not well captured by existing methods based on vectorization, graph metrics, or homological signatures. We introduce Fiber Bundle Learning (FBL), a topological framework that represents each data sample as a discrete fiber bundle and extracts a classification signature combining persistent homology, local feature geometry, and gluing structure. FBL builds a base space from the coarse geometry of each object, models local feature patches as fibers, and estimates transition maps between neighboring fibers to construct a discrete connection. From this representation, FBL computes a set of invariants: persistent homology of the base, fibers, and total space; holonomy obtained by transporting fiber states along cycles; curvature-like quantities measuring transition inconsistency; and discrete analogues of characteristic classes. These components are assembled into a fixed-length feature vector that can be used with any standard classifier. We show that FBL yields a signature with three desirable theoretical properties: stability under perturbations of geometry and local features, invariance under isometries and global fiber reparameterizations, and robustness to sampling noise. Our synthetic experiments show that FBL distinguishes twisted from untwisted bundles with identical homology, a distinction classical topological methods fail to capture. Additional tests quantify the system’s resistance to noise, its invariance to geometric transformations, and the contribution of each signature component. Taken together, our results indicate that representing data through fiber bundle structure may provide an effective tool for classifying complex, multi-level objects. Full article

This work formulates semiclassical gravity within a causal-diamond framework where a finite-resolution boundary provides the edge structure for a local Wheeler–DeWitt description. Because the diffeomorphism-invariant Hilbert space does not factorize, each diamond is equipped with a boundary-completed algebra ${\mathfrak{A}}_O$, ensuring the operational state ${\rho }_O$ and the semiclassical reference family ${\sigma }_O[\Lambda ]$ share identical operator content. Dynamics are posed as local statistical inference: the relative-entropy functional $S_{\mathrm{rel}}({\rho }_O\parallel {\sigma }_O[\Lambda ])$ quantifies the mismatch between data and reference. This yields the minimal operational axioms defining subsystems, intrinsic clocks, and regulated observables in a finite-resolution, background-independent setting. The topology-locked boundary capacity budget fixes an effective channel multiplicity $N\approx 1.23\times {10}^{11}$. Calibrating its coherent fraction to Newton’s constant determines a matching scale $M_s\approx 3.02\times {10}^{13}\mathrm{GeV}$. In the modular/KMS regime, the relative-entropy Hessian (Kubo–Mori metric) block-diagonalizes into orthogonal tensor, vector, and scalar response sectors. A heat-kernel expansion on the fixed $S^3\times S^1$ history manifold maps this near-equilibrium response to a matching-scale effective field theory, yielding the Einstein–Hilbert tensor structure, Yang–Mills susceptibilities, and leading mass deformations. Vector and scalar responses remain intensive, while the tensor response scales extensively with coherent channel multiplicity. The fixed modular protocol and quantized boundary currents imply ${\alpha }^{-1}\left(M_s\right)=4\pi k$ at integer levels k, while the reduced $R^2$ plateau sector yields linked cosmological targets: $n_s\simeq 0.965$, $r\simeq 0.0038$, and $A_s\simeq 2.1\times {10}^{-9}$. Translations between causal diamonds act as completely positive trace-preserving (CPTP) updates. The resulting open-modular Walsh filtration selects the three-dimensional degree-one sector as the algebraic basis for family structure. Treating continuum fields as the structured response of a finite boundary, the framework yields correlated, falsifiable relations for gravitational stiffness, gauge response, plateau cosmology, and threefold matter-sector organization from one minimal operational architecture. Full article

In recent years, the integration of renewable energy sources and the widespread use of nonlinear loads have increased dynamic uncertainties in modern power systems, making real-time and synchronized monitoring essential. Accurate M-class synchrophasor estimation under these nonstationary and spectrally uncertain conditions remains a challenging problem due to dynamic variations, harmonics/interharmonics, out-of-band interference, and measurement noise. This study proposes a suitably constrained optimization-based framework for M-class synchrophasor estimation, in which a hybrid structure integrating an ESPRIT-based subspace method with the Adaptive Fitness Distance Balance Artificial Rabbit Optimization (ES-AFDB-ARO) algorithm is employed. In this framework, the optimization stage is guided by spectral information obtained via the subspace stage to narrow the search space and improve convergence stability. Performance is evaluated under IEEE C37.118 steady-state and dynamic conditions via Monte Carlo simulations, showing that total vector error, frequency error, and rate-of-change-of-frequency error values remain within standard limits. Comparative analyses at 60 dB and 40 dB SNR demonstrate that the ES-AFDB-ARO method exhibits improved and more stable performance than the widely used interpolated discrete Fourier transform, Taylor weighted least squares and Taylor–Kalman filter methods. The results show that the proposed framework offers a reliable solution for synchrophasor estimation under dynamic operating conditions. Full article

This paper addresses the flexible job-shop scheduling problem (FJSP) as a constrained combinatorial optimization task with a large discrete action space. Although action-masked reinforcement learning has shown promise for such problems, the effect of structured vector-state encoding in scheduling has received less attention. The main contribution of this work is a structured block-wise state representation and a multi-branch feature extraction module for action-masked Proximal Policy Optimization (PPO). The proposed representation decomposes the scheduling state into three heterogeneous components capturing resource availability, operation readiness, and temporal attributes of operation–machine alternatives. Instead of flattening these signals into a single vector, the proposed encoder processes each block separately before aggregation, with the aim of preserving semantic structure during policy learning. To isolate the effect of representation design, we compare the proposed multi-branch encoder with a baseline single-branch multilayer perceptron under identical PPO hyperparameters and training conditions. Experiments on the Brandimarte MK benchmark suite show that the proposed architecture yields a lower best-achieved makespan on nine of ten instances and improves the best baseline result by up to 27.84%. Additional validation on selected Behnke and Geiger instances indicates that the BR encoder’s advantage extends to larger FJSP cases while preserving sub-second inference. Full article

We propose a structural framework for interpreting quantum computational advantage in terms of admissible continuation of configurations. In this framework, a quantum computation is described not only as a sequence of gates acting on a state vector but also as the organization of admissible histories whose phase contributions combine coherently in a manner related to sum-over-histories and path-integral formulations of quantum mechanics. We identify three structural features that are relevant to quantum advantage: the multiplicity of admissible histories, the degree of phase coherence among them, and the non-factorizable structure of continuation constraints corresponding to entanglement-like global dependence. To make these features explicit, we introduce the notion of effective coherent multiplicity, which measures the coherently usable portion of an admissible-history space before probability normalization. We then formulate a structural speedup conjecture: substantial quantum advantage requires not merely a large number of possible histories but scalable coherent multiplicity supported by non-factorizable constraints whose instability remains bounded. We also introduce a coherent-fiber criterion, which identifies phase-alignable families of histories selected by compact computational relations as a structural source of coherent amplification. This formulation does not replace standard complexity-theoretic measures such as circuit size, query complexity, or BQP membership. Rather, it provides a complementary structural language for relating those measures to interference, entanglement, decoherence, and the organization of computational history space. The framework clarifies, at a structural level, why raw branching alone is insufficient for speedup, why unstructured search yields only a limited advantage, and why problems with compact global regularities, such as Simon’s problem and period finding, can support stronger coherent amplification. The paper also discusses how the proposed quantities relate to standard notions, including success amplitudes, entanglement measures, tensor-network simulability, and fault-tolerance constraints. In this way, admissible-history structure is presented as a diagnostic viewpoint for understanding both the power and limitations of quantum computation. Full article

The quantum motion of a photon in an arbitrary medium was considered within the framework of the gauge symmetry group $SU\left(2\right)\otimes U\left(1\right)$ using the Yang–Mills (Y-M) equations for Abelian fields. A system of second-order partial differential equations (PDEs) for the vector wave function of a photon is derived using the first-order Y-M equations as identities. The full wave function of a photon was defined as the arithmetic mean of the components of the wave function. In a particular case, an equation is obtained for its full wave function, taking into account the structure of space-time in a plane perpendicular to the direction of propagation of the photon. The quantum state of a photon in a nanowaveguide was investigated, and it is shown that under certain conditions, it is reduced to the problem of two coupled 1D quantum harmonic oscillators (QHO) with variable frequencies. An explicit expression is obtained for the wave function of a photon, which is characterized by two vibrational quantum numbers. A quantum theory of a photon for a dissipative medium has been developed taking into account the processes of absorption and emission of photons. The mathematical expectation (ME) of the photon wave function is constructed as the product of two 2D integral representations in which the integrand is the solution of a system of two coupled second-order PDEs. The ME of the probability amplitude of the transition of a single-photon state into one of the two-photon entangled Bell states is constructed. Finally, it was proven that, in addition to frequency, spin, momentum and polarization, the photon also has a spatial structure responsible for the cross sections of processes in which this massless fundamental particle participates. Full article

Direct power control (DPC) is widely recognized for its simplicity and fast dynamic response; however, conventional implementations based on hysteresis comparators suffer from critical limitations, including variable switching frequency and pronounced active power oscillations, which hinder their applicability in renewable and hybrid energy systems. To address these challenges, this study proposes a fractional-order predictive DPC strategy incorporating a fractional-order proportional–integral (FOPI) regulator to enhance dynamic performance and robustness. The proposed method is systematically evaluated against both a conventional proportional–integral-based DPC (PI-DPC) and existing fractional-order DPC approaches under identical operating conditions using MATLAB simulations. The results demonstrate that the proposed controller achieves a stabilized switching frequency while significantly improving DC-link voltage performance. Specifically, the proposed method reduces voltage ripples to 0.027 V compared to 0.094 V and 0.104 V for PI-DPC and FOPI-FOPI-DPC with space vector modulation (SVM), corresponding to improvements of 71.27% and 74.03%, respectively. The overshoot is also reduced to 0.75%, outperforming PI-DPC (1.25%) and FOPI-FOPI-DPC-SVM (1%), with improvements of 40% and 25%. In terms of dynamic response, the proposed approach achieves a fast response time of 0.06 s, representing a 40% improvement over PI-DPC, while maintaining comparable performance with other fractional-order methods. Additionally, the steady-state error is reduced to 0.04 V, achieving improvements of 60% and 50% compared to PI-DPC and FOPI-FOPI-DPC-SVM, respectively. Although the settling time shows marginal variation, the overall system exhibits enhanced stability and robustness. These outcomes highlight the effectiveness of integrating fractional-order control with predictive strategies, offering a robust and practically viable solution for real-world hybrid power systems that integrate renewable generation and energy storage. Full article

Resource-constrained Multi-Access Edge Computing (MEC) nodes cannot fully replace cloud infrastructure, yet existing service placement models treat edge hosting as an all-or-nothing decision. This paper proposes a queueing-theoretic framework for split-user offloading in hybrid MEC–cloud environments. The system is modeled as a Continuous-Time Markov Chain (CTMC) over a load-vector state space that admits a product-form stationary distribution. A delay-aware greedy orchestration policy determines, at every arrival and departure event, which service occupies the MEC node and how many of its users are offloaded from the cloud. Closed-form expressions are derived for average end-to-end (E2E) delay, MEC occupancy and saturation probabilities, per-service hosting probabilities, and delay-saving indicators. Numerical analysis of a five-service industrial scenario shows that the proposed split-user mechanism keeps the MEC node occupied for most of the observation time (around 97% at the baseline load), naturally prioritizes services with the largest aggregate latency benefit, and substantially reduces the average delay compared with a cloud-only configuration. The analytical results are validated by discrete-event simulation, which matches the CTMC values with relative discrepancy below 1% under the Poisson/exponential assumptions; additional simulations quantify the sensitivity to alternative arrival and service-time distributions. The framework provides analytically tractable, interpretable decision logic with negligible runtime overhead, making it a suitable analytical foundation for cloud service orchestration platforms that must meet strict QoS targets in next-generation edge networks. Full article

Z-source inverters (ZSIs) provide single-stage power conversion with inherent voltage boost capability through shoot-through (ST) states achieved using specialized PWM methods. This study compares various ST PWM strategies, Simple Boost PWM, Maximum Boost PWM, Constant Boost Third Harmonic Injection PWM, and Space Vector PWM, for high-voltage boost ZSI (HVB-ZSI) applications. A MATLAB/Simulink 2024a model was developed to assess their performance in terms of output-voltage quality, THD, capacitor-voltage stress, switch stress, and inductor–current ripple. Results indicate that while all techniques enable ST operation effectively, their voltage stress and harmonic performance differ notably, guiding optimal PWM selection for advanced ZSI-based systems. Full article

Fatigue seriously affects work efficiency and brings potential safety hazards, and electroencephalogram (EEG) serves as a valuable physiological indicator for fatigue monitoring, as it directly reflects underlying brain neural activity. A key characteristic in EEG fatigue research is that the feature spaces of pre-fatigue and post-fatigue EEG signals exhibit obvious spatial separation—this separation is caused by significant changes in brain electrical activity when the human body transitions from a normal awake state to a fatigue state. Existing EEG-based fatigue judgment methods mostly focus on binary classification, which fails to fully leverage the inherent spatial separation characteristic of pre-fatigue and post-fatigue feature spaces, making it difficult to achieve simple, efficient, and accurate fatigue judgment. To address this problem, this paper proposes an EEG fatigue judgment method based on feature space spatial separation and Approximate Nearest Neighbor Search (ANNS). The 16-channel pre-fatigue (Group A) and post-fatigue (Group B) EEG signals acquired from seven subjects are segmented and subjected to feature extraction, projecting the signals into a unified feature space. An ANNS index is constructed using feature vectors from both Group A and Group B, with each vector annotated by its corresponding class label. A separate test set (Group C) is utilized, and the k-nearest neighbors of each test feature vector are retrieved from the built ANNS index. The mental fatigue state is then identified via majority voting according to the class labels of the k-nearest neighbors. Experimental results demonstrate that the proposed method can effectively exploit the spatial separation between pre-fatigue and post-fatigue feature distributions, yielding an average single-subject classification accuracy of approximately 90%. Full article

We study the projected sensitivities of the Jiangmen Underground Neutrino Observatory (JUNO) and the Deep Underground Neutrino Experiment (DUNE) to cosmic-ray-produced dark mesons in a confining dark sector with a leptophobic vector portal. Using the same atmospheric dark meson flux framework as in our previous JUNO study, which includes proton bremsstrahlung, Standard Model meson decays, and Drell–Yan production followed by dark hadronization described by a modified Quark Combination Model, we perform a controlled comparison between JUNO and DUNE within a common source-side setup. Our results indicate that JUNO achieves stronger overall sensitivity across most of the parameter space, primarily because its inclusive event-level visible-energy criterion efficiently retains soft elastic recoils. In contrast, DUNE demonstrates systematically larger visible effective cross sections in the deep-inelastic scattering (DIS) channel, where energetic final states readily exceed its particle-level hadronic thresholds. Moreover, kinematic hardening of elastic recoils at heavier mediator masses ($m_{Z^{\prime }}\gtrsim 1$ GeV) and higher incident energies ($E_{K_D}\gtrsim 1$ GeV) further enhances DUNE’s elastic acceptance. Nevertheless, over most of the benchmark parameter space considered here, JUNO yields a larger total signal rate because the incident dark meson flux peaks sharply at low energies, favoring the soft elastic regime. Consequently, this interplay between flux distribution and detector thresholds causes the sensitivity gap between JUNO and DUNE to narrow significantly in the heavy-mediator regime. Full article

The deployment of Motor Imagery Brain–Computer Interfaces (MI-BCI) is constrained by the inherent physiological variabilities of Electroencephalography (EEG) and parametric opacity. This paper presents a targeted technical audit of ten high-density MI-BCI computational pipelines, evaluating how existing literature addresses low Signal-to-Noise Ratio (SNR), intra-subject variability, and session-to-session instability. The investigation focuses on the contamination of data by ocular and muscular artifacts that overlap with the spectral components of Mu and Beta rhythms, often leading to algorithmic overfitting. Furthermore, the paper evaluates the impact of manifold drift where fluctuations in user state necessitate frequent recalibration as a primary hurdle for BCI portability. By applying a forensic evaluation framework to standardize the analysis across the ten selected studies, this paper identifies a high-performance landscape within standardized benchmarks, with classification accuracies reaching peak values of 95.42%. The audit specifically identifies a performance-reporting gap; while hybrid architectures demonstrate superior noise-rejection, they are frequently characterized by undocumented computational overhead. Additionally, while Neighborhood Component Analysis (NCA) emerges as a stable feature selection algorithm across the sampled literature, the systemic absence of reported execution times prevents a verified assessment of its low-latency viability. A critical technical finding is the widespread issue of Parametric Opacity, particularly regarding the omission of essential deterministic variables such as filter orders, windowing constants, and the final dimensionality of feature vectors. The audit reveals that the frequent failure to report the exact number of features utilized for classification masks potential overfitting and prevents an accurate assessment of the system’s generalization capabilities. Furthermore, only a specialized subset of the reviewed literature validates performance through formal statistical testing, such as Friedman ANOVA or Wilcoxon Signed-Rank tests, with most studies relying on peak accuracy metrics that may disguise filtered artifact residuals. This lack of granular documentation disguises the computational complexity of proposed methods and complicates their feasibility for hardware-in-the-loop validation. The findings establish that standardizing the reporting of preprocessing variables and feature-space dimensions is a prerequisite for overcoming current performance plateaus in universal BCI architectures. Full article

In this paper we study photon entanglement in the framework of Maxwell–Schwartz field theory. The ambient state space is the complex Maxwellian distribution space $W={\mathcal{S}}^{\prime }({\mathbb{M}}_4,{\mathbb{C}}^3)$, whose elements are fields of the form $F=\overrightarrow{E}+ic\overrightarrow{B}$. Polarization is realized as a two-dimensional complex subspace of W, generated by suitable linearly polarized Maxwellian solutions associated with opposite propagation directions. This yields canonical polarization sectors $P_A$ and $P_B$, each naturally isomorphic to ${\mathbb{C}}^2$. Within this setting, the Bell singlet state is represented by a non-factorizable tensorial Maxwellian field in $P_A\otimes P_B\subset W\otimes W$. By means of the induced rotated polarization bases, the standard joint probabilities of the photon polarization experiment are recovered exactly, and the correlation law $E(a,b)=-cos\left(2\right(a-b\left)\right)$ is obtained. Consequently, the usual CHSH value $2\sqrt{2}$ is reproduced in the Maxwell–Schwartz framework. To clarify the meaning of this violation, we first formulate the CHSH inequality in a purely measure-theoretic form, as a theorem about four correlators represented on a single probability space by bounded measurable functions. We then show that the correlators produced by the intrinsic Maxwellian Bell state do not admit such a common representation. The obstruction is structural: the ontic state is a global non-product field configuration, and the four correlations arise from different polarization resolutions of the same tensorial Maxwellian state. A second main result concerns the Born rule. For $L^2$ scalar quantum states in the domain of the Maxwellian correspondence, we prove that the squared Hilbert norm, times the constant ${\epsilon }_0$, coincides with the electromagnetic energy of the associated field. This leads to an energy interpretation of the Born rule: the Born probability density is identified with the normalized electromagnetic energy density up to an interference term depending on the chosen Maxwell–Schwartz isomorphism, which assumes the role of a quantum context. In the context of the Aspect and collaborators’ experiment, we prove that, on the other hand, the polarization probabilities become energy contributions of the corresponding field components. These results show that photon entanglement, Bell inequality violation, and the Born rule admit a coherent interpretation within Maxwell–Schwartz field theory, where the basic ontological objects are electromagnetic-like fields rather than abstract state vectors. Full article

Urban nature and nature-based solutions are increasingly promoted to enhance public space experience and urban climate resilience. In Public and semi-public indoor settings, biophilic design is considered beneficial for stress reduction and mental health restoration through the introduction of natural elements such as plants. However, research focusing on the specific visual features of plants and the underlying mechanisms remains limited. Based on 200 indoor greenery images and their multi-dimensional feature vectors, and combined with questionnaire data from 253 valid participants, this study developed a quantitative framework of plant visual features and adopted a two-level analytical approach. At the image level, linear mixed-effects models (LMMs) were used to identify how plant features influenced immediate responses. At the group level, partial least squares structural equation modelling (PLS-SEM) was employed to examine how cumulative restorative experience translated into affective states, perceived restorativeness, and behavioural intention. The results showed that Green View Index (GVI) and species richness were the most stable positive features, while plant health status, certain planting modes, and spatial layer-related features also showed significant effects. Restorative experience influenced behavioural intention mainly through positive affect and perceived restorativeness. These findings provide evidence for biophilic design, offering quantitative support for incorporating indoor public space into broader urban nature and public space framework. Full article