Search Results (1,682)

Traditional analyses of transport phenomena rely on prescribed geometric boundaries, yet natural flow systems dynamically evolve their architecture to maximize access to currents. To address this disparity, we propose a field-theoretic framework for the constructal law that treats physical geometry as a dynamic state variable, represented by a time-dependent conductivity tensor. Using a variational approach grounded in non-equilibrium thermodynamics, we derive a general tensor evolution equation. Within this framework, macroscopic flow architecture emerges deterministically from the continuous competition between non-linear flux-induced accretion, linear entropic relaxation, and spatial smoothing. Scaling analysis reduces this dynamic to a tri-parameter dimensionless phase space: a morphogenic number driving structural growth, a structural diffusion number governing spatial coherence, and a stochastic intensity number providing the microscopic seeds for symmetry breaking. Our principal result is the analytical prediction of a critical bifurcation. When the local morphogenic number strictly exceeds unity, the system escapes its stable, isotropic configuration and branches into highly conductive, anisotropic architectures. We demonstrate the predictive validity and trans-scalar applicability of this continuum theory by mapping it to highly diverse phase transitions, successfully capturing phenomena ranging from microscopic aerosol agglomeration and microbial resistance, to macroscopic coral plasticity and crystal growth instabilities, and finally to the astrophysical launching of relativistic jets from black holes. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

Political polarization and policy uncertainty have become increasingly consequential for regional economic adjustment, yet their joint role in shaping socioeconomic resilience remains underdeveloped in the literature. This study advances the debate by conceptualizing regional resilience as the outcome of a multi-layer socioeconomic system in which external policy disturbances, institutional fragmentation, and structural adaptive capacity interact over time. Using balanced panel data for 16 Korean regions from 2004 to 2023, the analysis develops an integrated empirical framework that combines panel local projections, threshold estimation, structural moderation tests, dynamic robustness checks, and forward-looking machine-learning prediction. The results show that policy uncertainty is associated with lower regional socioeconomic resilience and that this effect persists over time. More importantly, political polarization does not simply accompany weaker resilience; it amplifies the transmission of uncertainty shocks, especially once institutional fragmentation crosses a critical threshold. Structural conditions further shape this process. Digital transformation, industrial diversification, and financial depth reduce vulnerability, whereas trade exposure intensifies it. These findings indicate that resilience is not determined by economic structure alone, nor by institutional instability in isolation. It emerges from the interaction between disturbance, amplification, and adaptive capacity within a regional system. The predictive analysis reinforces this interpretation. Variables identified as central in the econometric models also carry forward-looking information about future vulnerability states. This study therefore contributes not only by combining methods, but by linking explanation and prediction within a single systems-oriented account of regional resilience. The Korean case shows how institutional coherence and structural adaptability jointly condition resilience under uncertainty. Full article

Electronic coupling within InAs/InP quantum dots (QDs) influences carrier lifetime and thus QD laser performance. In this work, vertical electronic coupling between QDs is theoretically investigated based on a structure of five-layer QD stacks. This analysis illustrates that the resonant tunneling, a consequence of coherent coupling between QDs, should be considered for carrier redistribution. The carrier tunneling time of ground states is estimated by studying two structures of uniform and chirped five-layer QD stacks. The impact of resonant tunneling on optical properties of InAs/InP QD Fabery–Perot (FP) lasers, such as threshold current, light power-current temperature dependence, and relative intensity noise, is investigated through a comparison of uniform and chirped QD lasers. It is found that the carrier resonant tunneling leads to an increase in the threshold current, low characteristic temperature, and high relative intensity noise. By using the chirped QD stacks, the optical properties are improved thanks to less resonant tunneling. Full article

Background: The goal of automated radiology report generation is to help radiologists in their task of creating descriptive reports from chest radiographs. However, the process of creating coherent and contextually accurate reports has been challenging, mainly due to the intricacies of medical language and the need to correlate visual data with textual descriptions. Methods: This study presents LLaMA-XR, a novel framework that integrates Meta LLaMA 3.1 Large Language Model with DenseNet-121-based image embeddings and Quantized Low-Rank Adaptation (QLoRA) fine-tuning. Results: The experiment conducted on the IU X-ray dataset demonstrates that LLaMA-XR outperforms a range of state-of-the-art methods. It achieves an ROUGE-L score of 0.433 and a METEOR score of 0.336, establishing new performance benchmarks in the domain. Conclusions: These results underscore LLaMA-XR’s potential as an effective artificial intelligence system for automated radiology reporting, offering enhanced performance. Full article

Based on the thermodynamic design of metallurgical reduction, this paper investigates the thermodynamic principles and reaction regulation mechanism of aluminothermic self-propagating reduction for the in situ synthesis of a Ti₄₅Al₈Nb (at%) titanium–aluminum–niobium alloy. The influence of the aluminum distribution coefficient (ADC) on the self-propagating reaction process was verified via high-temperature thermal state experiments. The results show that the thermodynamically predicted trends of phase composition and alloy composition are consistent with the experimental results, with only a ~20% lateral offset in the ADC. When the ADC is set to 0.8, the mass fractions of Ti, Al, Nb, O, and N in the alloy are 51.8%, 29.5%, 17.4%, 1.2%, and 0.0016%, respectively, with a homogeneous microstructure and inclusion size no larger than 8 µm. The alloy presents a typical coarse-grained structure, where 83.1% of the total grain boundary length is low-angle grain boundaries, and the <111> orientation is dominant. A low-energy coherent interface is formed between the Ti-enriched and Nb-enriched regions by TiAl, TiAl₃ and Al₃Nb phases, which enhances the structural stability of the alloy. Full article

Ocean surface currents (OSCs) are central to upper ocean dynamics and air–sea exchange, yet their retrieval from spaceborne synthetic aperture radar (SAR) is limited by wave-induced bias (WB). WB arises from the inherent motion of the scattering facets and from long-wave hydrodynamic and tilt modulations, and is therefore jointly controlled by sea state and radar viewing geometry. This study develops an observation geometry optimization framework. Five years of hourly ERA5 wind and wave reanalysis data over the Kuroshio are used as a representative ensemble of sea states to drive the KaDOP model, and an exhaustive grid search over line-of-sight (LOS) azimuth (0–360°) and incidence angle (20–60°) is performed to identify, for each location and season, the viewing geometry that minimizes the time-mean WB. These local optima are then summarized as mission-level metrics, including the minimum achievable WB, the coverage meeting prescribed WB thresholds, and the spatial coherence of the preferred LOS azimuth and incidence angle. Finally, the theoretical minima are compared with the fixed left-looking geometry of the Luojia-2 (LJ-2) satellite along a 213 km × 6 km observation corridor and with Gaofen-3 (GF-3) viewing geometries at four representative locations in the Kuroshio. Across these validation cases, the optimized geometry reduces mean absolute WB by about 20–60% for LJ-2 and 20–80% for GF-3, providing quantitative constraints for future SAR mission design targeting OSCs. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article

Classical homogenization theory treats critical exponents as universal quantities depending only on spatial dimension, but recent evidence shows that this assumption fails for continuum composites once the mechanism of randomness generation is taken into account. We synthesize three complementary frameworks—structural approximation, structural sums, and self-similar renormalization—to develop a unified geometric theory of criticality in random composites. Dilute-regime expansions for the effective conductivity and shear modulus are expressed in terms of structural sums whose ensemble statistics depend sensitively on the randomness protocol. To bridge the dilute and critical regimes, we employ self-similar factor approximants, iterated-root approximants, additive approximants, and renormalization schemes based on minimal-difference and minimal-sensitivity conditions, combined with Borel summation. For maximally disordered protocols $\mathcal{P}\left(\tau \right)$, the conductivity index s and the elasticity index S fall within comparable numerical ranges, indicating a shared geometric origin and spectral response to the continuous breaking of translational symmetry. A regular periodic arrangement of inclusions ($\tau =0$) possesses full discrete translational symmetry; as a stochastic protocol $\mathcal{P}\left(\tau \right)$ is applied ($\tau$ increases), this symmetry is gradually degraded until statistical chaos is reached. For instance, the parameter $\tau$ can be considered as a time of stirring. During this evolution, the system traverses a continuous spectrum of critical indices, $s=s\left[\mathcal{P}\right(\tau \left)\right],$ which encodes the geometric and topological memory of the initial ordered state. It is established that the classical “universality” of percolation corresponds to a fixed point $\tau$ within a broader manifold of protocol-dependent critical behaviors. The framework developed here provides a coherent basis for inverse design, diagnostics, and classification of random composites by their disorder history, offering a geometric alternative to the universality paradigm. Full article

Electrical load time series exhibit strong heterogeneity across daily patterns driven by calendar effects and behavioral variability, leading many forecasting models to favor dominant weekday profiles while degrading on weekends, holidays, and transition days. This paper proposes an imbalance-aware spatiotemporal forecasting framework via a cluster-conditioned state space model. Daily load patterns are identified via time-series clustering and incorporated as conditioning covariates within a sequence-continuous selective state space models (Mamba), preserving temporal coherence without explicit sequence partitioning. A cluster-weighted training objective further mitigates pattern imbalance while avoiding future-information leakage. The resulting cluster-conditioned Time Series Mamba (TSMamba) consistently improves forecasting robustness across both frequent and infrequent profiles, achieving weighted absolute percentage error (WAPE) reductions of approximately 15% on weekdays, 42% on weekends, and 39% on holidays relative to the vanilla TSMamba, with similar gains in mean absolute error (MAE) and coefficient of variation of the root mean square error (CVRMSE). These results demonstrate that conditioning state dynamics on latent load patterns yields stable and computationally efficient short-term load forecasts under profile transitions. Full article

The rotational evolution of pulsars is governed by torque mechanisms whose mathematical structure encodes fundamental symmetries of the underlying physics. We demonstrate that the standard spin-down equation $\dot{f}=-sf-r{f}^3-g{f}^5$ derives from a discrete antisymmetry requirement, namely invariance of the torque under reversal of rotation sense, which restricts the frequency dependence to odd integer powers. We show that physically motivated plasma processes systematically break this symmetry, introducing fractional frequency exponents: viscous Ekman pumping at the crust–superfluid boundary layer ($f^{3/2}$), magnetohydrodynamic turbulent dissipation via Kolmogorov and Sweet–Parker cascades ($f^{10/3}$, $f^{11/3}$), non-linear superfluid vortex dynamics ($f^{5/2}$), and saturated r-mode oscillations ($f^{7-2\beta }$). The central result is an exact analytical resolution of the long-standing Crab pulsar braking index puzzle: the observed $n=2.51\pm 0.01$, which has defied explanation for nearly four decades, emerges naturally from the superposition of magnetic dipole radiation ($\dot{f}\propto f^3$) and boundary layer Ekman pumping ($\dot{f}\propto f^{3/2}$), with analytically derived coefficients yielding a dipole-component surface field $B_p=6.2\times {10}^{12}$ G—higher than the standard $\sqrt{P\dot{P}}$ estimate of $3.8\times {10}^{12}$ G, because that formula conflates dipole and non-dipole torques, but lower than applying the Larmor formula to the full spin-down rate ($7.6\times {10}^{12}$ G), since $32.7\%$ of the total torque is non-radiative boundary-layer dissipation. We develop the Riemann–Liouville fractional calculus formalism for these equations, showing that fractional derivatives break time-translation symmetry through intrinsic memory effects, with solutions expressed in terms of Mittag-Leffler and Fox H-functions that interpolate continuously between exponential (fully symmetric) and power-law (scale-free symmetric) relaxation. Lambert–Tsallis $W_q$ functions with non-extensive parameter q encoding broken statistical symmetry enable equation-of-state-independent inference of neutron star compactness and tidal deformability. Our framework establishes a unified symmetry-based classification of pulsar spin-down mechanisms and predicts frequency-dependent braking indices evolving at rate $\mathrm{d}n/\mathrm{d}t\sim 2\times {10}^{-4}$ yr⁻¹, yielding $\mathrm{\Delta }n\approx 0.01$ over 50 years—testable with current pulsar timing programmes. The formalism provides a coherent theoretical foundation connecting plasma microphysics at the neutron star interior to macroscopic observables in electromagnetic and gravitational wave channels. Full article

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge. Full article

Background/Objectives: AP-2δ, encoded by TFAP2D, is one of the least characterized members of the AP-2 transcription factor family, although available evidence suggests biologically relevant roles in lung cancer that have not yet been thoroughly examined. The aim of the present study was to provide an integrated characterization of AP-2δ/TFAP2D in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Methods: LUAD and LUSC data were obtained from The Cancer Genome Atlas. The analysis comprised the expression profiling of AP-2δ target genes, survival-guided TFAP2D stratification, clinical profiling, differential expression and intersection analyses, methylation-derived chromatin compartment profiling, TFAP2D-associated cofactor rewiring, and genome-wide enrichment of AP-2δ targets. In parallel, pocket prioritization was performed using an AlphaFold model of AP-2δ with cross-tool consensus mapping. Results: TFAP2D stratification delineated biologically-distinct states in both histological subtypes (LUAD and LUSC). AP-2δ target genes showed subtype-specific expression patterns and functional organization. The consistent survival association was observed for progression-free interval rather than uniformly across all endpoints. Clinical profiling was more closely associated with molecular subtype composition than broad clinicopathological differences. Differential expression analyses identified both shared and histology-dependent programs associated with TFAP2D. In the chromatin-compartment analysis, LUSC showed a broader and more coherent footprint, whereas LUAD displayed more selective cofactor rewiring. Structure-based analysis prioritized a small set of reproducible candidate pockets concentrated within ordered regions of the TF_AP-2 domain. Conclusions: AP-2δ marks biologically meaningful but histologically non-uniform regulatory states in lung cancer. These findings provide an integrated framework for understanding TFAP2D-dependent regulation in LUAD and LUSC, highlighting AP-2δ as a candidate for future mechanistic and translational investigation. Full article

Achieving selective cleavage of specific chemical bonds using ultrafast laser pulses remains a central challenge in ultrafast strong-field molecular physics. Here, we theoretically investigate the coherent control of strong-field dissociation of the heteronuclear molecular ion HD⁺ initially prepared in vibrationally excited states driven by an ultrashort pulse with a quadratic spectral phase. Our results reveal a pronounced sensitivity of both the total dissociation probability and the branching ratio (H⁺ + D vs. H + D⁺) to the chirp rate of the laser pulse. To uncover the underlying physical mechanism, we analyze the population dynamics in the coupled $1s\sigma$ and $2p\sigma$ electronic states and identify pronounced Rabi oscillations arising from the coherent interplay between multiphoton excitation and field-induced stimulated emission. By tuning the laser chirp rate, these oscillations can be suppressed via quantum interference, thereby reshaping the dissociation dynamics and significantly enhancing the dissociation probability of the H + D⁺ channel. These findings demonstrate that spectral-phase engineering provides a robust and versatile strategy for selective control of branching ratios in strong-field molecular dissociation. Full article

Hydrogen energy has emerged as a strategic pathway for decarbonization, industrial transformation, and energy security across major economies. This study does not directly evaluate ex post policy outcomes. Instead, it develops a Four-Dimensional Governance Framework to assess the structural effectiveness and implementation-oriented capacity embedded within national hydrogen policy frameworks. The analysis examines G20 countries through four dimensions, namely policy objectives, policy intensity, policy tools, and policy subjects. Using the entropy weighted TOPSIS method, the study compares the relative coherence of hydrogen governance architectures across countries. The results show that countries such as the United States, the United Kingdom, Germany, France, Canada and Japan consistently rank among the leading group in the comparative evaluation, while other countries occupy intermediate or lower positions according to the composite index results. Policy subjects and policy objectives receive relatively higher weights in the empirical analysis, indicating their stronger contribution to cross-national differentiation within the constructed index. The study provides a structured basis for comparing hydrogen governance frameworks and offers a replicable method for future research linking policy design to implementation evidence. Full article