Search Results (461)

Classical Colonel Blotto models predict smooth and monotone allocation patterns under a homogeneous-resource benchmark. This paper studies how heterogeneous technologies and partially irreversible deployment reshape equilibrium structure through a unifying reduced-form mechanism, which we call the induced cost geometry. In the compatible linear case developed in the main text, heterogeneous technologies and irreversibility generate battlefield-specific effective marginal costs $M{C}_i^g\left(\mu \right)={\kappa }_i^g+\mu {\beta }_i^g$, where $\mu$ is the endogenous shadow price of the global budget. We distinguish results that rely on this compatible linear representation from those that extend directly to the more general lower-envelope induced cost object. The induced cost object serves as the central organizing device for the equilibrium analysis and yields three main structural implications. First, under binding budgets, equilibrium effort can be sparse: positive effort is concentrated on battlefields whose effective marginal costs remain sufficiently low relative to their values, generating selective participation even under smooth contest success functions. Second, within a class of local fixed-support equilibrium configurations with binding budgets, an increase in a battlefield’s value can reduce equilibrium effort on that battlefield via the shadow-price channel—a mechanism-identification result for a specific structural regime rather than a general comparative-statics claim. Third, along durability paths, contestability and equilibrium support can exhibit discrete regime changes generated by technology switching within a finite lower-envelope cost structure. We develop a unified reduced-form characterization of equilibrium structure via induced costs and KKT conditions, highlighting how induced cost geometry reshapes both the intensive and extensive margins of strategic allocation within the equilibrium regimes characterized in the paper. Full article

This paper introduces a rigorous class of two-dimensional Markov-switching autoregressive moving-average (2D MS-ARMA) models for spatial lattice data exhibiting regime-dependent dynamics. The switching mechanism is governed by a latent causal Markov random field that drives spatial transitions between regime-specific autoregressive and moving-average structures. We provide sufficient conditions for the existence of a strictly stationary solution through the top Lyapunov exponent associated with a sequence of random matrices obtained from a state-space representation constructed along the lexicographic order. For the first-order bidirectional specification, we derive explicit spectral conditions linking stationarity to the regime-dependent spectral radii. Sufficient conditions ensuring the existence of finite second-order moments are also provided. Parameter estimation is carried out using a variational expectation–maximization (VEM) algorithm based on a mean-field approximation of the posterior distribution of the hidden regimes. The E-step yields closed-form coordinate ascent updates, while the M-step relies on gradient-based numerical optimization with derivatives computed via recursive differentiation. Under increasing-domain asymptotics, we discuss the consistency and asymptotic behavior of the variational estimator. The proposed framework fills a methodological gap between classical one-dimensional Markov-switching ARMA models and spatial autoregressive structures by extending regime-switching theory to multi-indexed processes with rigorous probabilistic foundations. It provides a comprehensive basis for statistical inference, model diagnostics, and prediction in spatially heterogeneous environments. Full article

Adaptive graph learning has become a widely adopted paradigm for multivariate time series forecasting when explicit physical topology is unavailable. In these approaches, node embeddings are typically used to construct dense adjacency matrices based on pairwise similarity, implicitly coupling representation learning with relational modeling. However, we observe that under identical training settings but different random initializations, the learned adjacency matrices can vary substantially while predictive performance remains nearly unchanged, indicating that the relational structure is often underdetermined by the forecasting objective. This observation suggests a mismatch between similarity-based structural learning and the forecasting objective. In this work, we revisit node embeddings from a sequence approximation perspective and propose a Diagonal Adaptive Graph (DiAG) module that restricts adaptive learning to diagonal elements. The diagonal coefficients are derived from channel-independent predictions, while off-diagonal interactions are constructed from the similarity of input sequences. This design decouples representation learning from relational modeling, allowing variables to adaptively switch between channel-independent and channel-dependent regimes. Experiments on multiple datasets show that DiAG improves forecasting performance without modifying the channel-independent backbones. These results indicate that channel-dependent forecasting can be achieved as a prediction-driven refinement over channel-independent backbones, without requiring fully learned dense relational structures. Full article

The building façade serves as the primary interface between the built environment and external climate, marking the transition from static regulation to dynamic response in climate-adaptive design. While existing research predominantly addresses periodic climatic elements such as temperature and solar radiation, the highly stochastic wind environment and its potential for internal acoustic problems remain systematically unexplored. This study investigates the acoustic modulation mechanism of building façades under dynamic wind conditions through a simulation-based methodology. The primary aim is to demonstrate the use of active control to mitigate the influence of fluctuating wind on the internal acoustic environment of buildings with open windows or semi-open boundaries, focusing on the coupling between stochastic wind fields and architectural acoustics in humid subtropical climates. We propose a wind-responsive adaptive acoustic façade system employing fractal geometry and configurable delay strategies, and develop a high-fidelity simulation framework to quantify how façade geometry and activation logic regulate acoustic parameters under varying wind conditions (1–8 m/s). Results indicate that: (1) support vector regression-based mapping of wind speed to delay strategies maintains key sound-field parameters (Lateral Fraction (LF), Speech Clarity (C₅₀), and Early Decay Time to Reverberation Time ratio (EDT/RT₃₀)) within 10% fluctuation across wind regimes; (2) fractal configurations achieve balanced wide-band (125 Hz–8 kHz) performance, with SPL fluctuation <3 dB, spectral tilt (+0.3 dB), and reverberation time slope <0.3; (3) configurational switching between column (high LF) and row (high C₅₀) arrangements enables dynamic trade-off between spatial impression and speech clarity. This work establishes an integrated framework coupling wind dynamics, façade morphology, and acoustic modulation to regulate objective indoor acoustic parameters. Based on the simulated omnidirectional point-source model, the results show that key acoustic indicators remain stable across varying wind conditions, providing a theoretical and quantifiable basis for climate-responsive acoustic envelope design. Future work will include empirical prototype testing and listening tests to determine whether these simulated acoustic parameters translate into improved comfort and well-being for occupants. Full article

A rapid quasi-one-dimensional (quasi-1D) reacting-flow analysis code was developed for the preliminary assessment of rocket-based combined cycle engines over a broad flight envelope. The internal flow was modeled as steady and quasi-1D in a variable-area duct by solving the coupled conservation equations together with species transport, and finite-rate chemical kinetics were included to represent combustion-induced heat release and composition change. To incorporate configuration-dependent mixing effects that affect RBCC heat release evolution and thermal choking tendencies, a streamwise mixing efficiency distribution was extracted from non-reacting 3D CFD and prescribed as an input to the quasi-1D formulation to represent the progressive availability of reactable fuel along the flowpath. A mode-dependent solution strategy was established by separating the computation into scramjet mode and ramjet mode procedures with a switching criterion based on whether a sonic condition occurs within the combustor, allowing thermal choking and mode transition behavior to be addressed within a single framework. The numerical solver was implemented in Python 3.12.2 and integrated using a stiff ordinary differential equation (ODE) scheme to ensure robust convergence in the presence of reaction-induced stiffness. Verification against previously published hydrogen-fueled scramjet results reproduced the overall streamwise trends of key quantities including Mach number, pressure, temperature, and density. The developed code was then applied to an RBCC configuration under operating conditions representative of ERJ and ESJ regimes, and the quasi-1D predictions were compared with cross-section-averaged 3D RANS CFD results, showing consistent mode identification and comparable axial behavior at a level suitable for preliminary analysis with substantially reduced computational cost. Full article

This paper examines whether government debt financing crowds out private consumption in South Africa or whether household behaviour is consistent with Ricardian equivalence. Using quarterly data from 1960Q1 to 2025Q1, the study employs a Bayesian time-varying parameter framework that accommodates non-stationarity, structural change, and evolving fiscal transmission mechanisms, and is complemented by a Markov-switching Bayesian VAR as a robustness check. All variables are expressed relative to GDP to avoid scale effects, and inference is based on posterior distributions. The results reveal pronounced state dependence in the debt–consumption relationship. In earlier decades, increases in the debt-to-GDP ratio are associated with statistically meaningful declines in the private consumption share, consistent with crowding-out or precautionary behaviour under weaker fiscal credibility. Over time, however, this negative association weakens and converges toward neutrality, with post-2010 estimates indicating no significant effect of debt on consumption. Conditioning on fiscal stance and financial conditions shows that debt does not exert an independent influence on consumption once government expenditure, tax revenue, and interest rates are taken into account. A constant-parameter Bayesian benchmark masks these dynamics, producing an average effect close to zero. Evidence from a Markov-switching Bayesian VAR similarly finds no persistent regime-specific crowding-out effects. Overall, the findings suggest that observed debt–consumption linkages in South Africa operate primarily through broader fiscal and macroeconomic conditions rather than debt accumulation itself, highlighting the importance of fiscal credibility and policy composition. Full article

In this article, we numerically investigate the effects of noise and heterogeneity on a model of immune–tumor cell interactions. We focus on stochastic dynamics and simulation-based analysis of the time required for tumor elimination. We identify the existence of a bistable response, which is disrupted by the introduction of intrinsic noise into the system. In particular, we characterize noise-induced transitions using first-passage time statistics and waiting-time distributions. We discuss various scenarios of tumor elimination, including the impact of vitamin intake and chemotherapy on tumor cell count, mean elimination time, and the duration of tumor dominance. Our results show that increasing chemotherapy reduces the maximum tumor count and decreases the average tumor elimination time, while intrinsic noise promotes memoryless switching toward the tumor-free state. This behavior is explained by the emergence of a quasi-stationary distribution governing the metastable tumor-present regime, leading to exponentially distributed extinction times. Furthermore, this framework enables the decay rate $\lambda$ to be estimated from simulation data and related to treatment parameters $({\beta }_1,\gamma )$. These findings provide a theoretical and statistical justification for memoryless tumor elimination dynamics and offer quantitative insights into stochastic treatment outcomes. Full article

Accurate estimation of the mean-reversion speed $\alpha$ in the AR(1) process $X_{t+1}=(1-\alpha )X_t+{\epsilon }_t$ is central to energy-commodity modelling. Classical estimators such as GARCH, jump-diffusion, and regime-switching produce model-conditioned estimates by embedding $\alpha$ within distributional assumptions, so that different model choices yield different $\widehat{\alpha }$ values from the same series without a principled criterion to adjudicate. We propose a distribution-free neural estimator based on a Temporal Convolutional Network (TCN) trained on synthetic AR(1) series with Sinh-ArcSinh (SAS) innovations. Distribution-free here means that no parametric family is assumed for the innovation distribution at inference time: the estimator imposes no distributional hypothesis when processing a new series. The SAS family serves as a training vehicle—not a model for the real data—chosen for its ability to span a broad range of tail weights and asymmetry profiles. The theoretical foundation is spectral invariance: the Yule–Walker equations establish that the autocorrelation structure ${\rho }_k={(1-\alpha )}^k$ depends on $\alpha$ alone, provided innovations are uncorrelated across lags—a condition satisfied not only by i.i.d. innovations but also by conditionally heteroscedastic processes such as GARCH. The TCN therefore generalises to volatility-clustering environments without modification, learning to extract $\alpha$ from temporal dependence alone, independently of the marginal innovation distribution and of the temporal variance structure. On held-out test series the estimator outperforms all classical competitors, with the advantage growing monotonically with non-Gaussianity. A robustness analysis on three out-of-distribution innovation families and on AR(1)-GARCH(1,1) processes empirically validates the spectral invariance guarantee across both marginal and temporal variance structure, including near-integrated GARCH processes where innovation kurtosis far exceeds the training range. The distribution-free $\widehat{\alpha }$ enables a two-stage pipeline in which $\alpha$ and the innovation distribution are characterised independently—a decoupling structurally impossible in classical likelihood-based approaches. Once trained, the TCN acts as a universal mean-reversion estimator applicable to any price series without re-fitting. Applied to four energy markets—Italian natural gas (PSV price), Italian electricity (PUN price), US Henry Hub, and US PJM West Hub—spanning log-return kurtosis from near-Gaussian to strongly heavy-tailed, the TCN yields robust, distribution-free estimates of mean-reversion speed. Full article

The proliferation of intelligent edge devices demands compact, low-power hardware capable of dynamically switching between sensing, logic, and learning tasks—a versatility that traditional multi-chip solutions fundamentally lack. Here, we demonstrate a reconfigurable spin–orbit torque (SOT) device based on an FeTb/Ru/Co synthetic antiferromagnetic (SAF) heterostructure. By modulating the input current amplitude, the device dynamically switches between two distinct operating modes: saturation and activation. In the saturation regime (>80 mA), deterministic magnetization reversal enables Boolean logic operations (AND, NOR). In the activation regime (<80 mA), gradual, non-volatile conductance modulation emulates synaptic plasticity. Benefiting from the strong antiferromagnetic coupling and near-zero net magnetization of the SAF structure, all operations are achieved without external magnetic fields. This single-device, dual-mode reconfigurable architecture establishes a new paradigm for high-density, low-power, multifunctional in-memory computing units, with promise for advancing adaptive edge computing chips. Full article

This paper presents a hybrid econometric and machine-learning framework for forecasting GDP that bridges long-run structure with short-run regime dynamics. Using annual World Bank data spanning 1960 to 2024, the framework combines three complementary components: an ARIMA baseline that captures persistence, a three-state Hidden Markov Model (HMM) that provides probabilistic regime identification, and an LSTM-based extension that learns nonlinear patterns associated with regime transitions. Detailed out-of-sample forecasting evidence is reported for five representative countries (the United States, China, Germany, India, and Greece), chosen to illustrate performance across different volatility profiles and economic environments. Across these case studies, the integrated HMM–LSTM approach often delivers lower forecast errors than the benchmark alternatives, although the magnitude of the gains is not uniform across countries. Beyond point forecasting performance, the regime layer yields an interpretable probabilistic representation of business cycle conditions that can support real-time monitoring and early-warning assessment. By combining transparency with adaptability, the proposed framework contributes to the forecasting literature and provides a practical decision-support tool under heightened macroeconomic uncertainty. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

This paper tests the benefits of using heterogeneous Markov-Switching GARCH (MS-GARCH) models for active trading of tourism (leisure and entertainment) stocks by performing a weekly backtest of the 36 combinations of two-regime MS-GARCH models, given their regime-specific marginal probability (Gaussian and Student-t). Their regime-specific variance model (time-fixed, symmetric GARCH, or asymmetric EGARCH), and by assuming a two-regime context with a low (high)-volatility regime s = 1 (s = 2), the results suggest that using an MS-GARCH model with a Student-t pdf and a symmetric GARCH variance in s = 1, and a Gaussian pdf with a time-fixed variance in s = 2, leads to a better performance than a buy-and-hold strategy (with a compound annual growth rate, or CAGR, of 10.0716% and an annualized Sharpe ratio of 5.0891). This performance reflects the impact of a 0.1% trading fee per traded amount and a 10% tax. This result suggests that, only in the short term, MS-GARCH models are useful for active trading in tourism stocks by portfolio managers and could be used to forecast high-volatility episodes in such companies, which are prone to price declines during sanitary, geopolitical, or consumer-sentiment crises. Despite this in-sample result, it is important to highlight that the results do not hold in the long term, as tested for randomness in the backtest results (data snooping), and that further improvements must be made to the algorithm to generate a significant overperformance of the trading strategy. Full article

In the realm of computational biomechanics, quantifying the reliability of surface-engineered implants is critical yet challenging due to material anisotropy and experimental limitations. Standard deterministic approaches often fail to capture the failure probability of brittle coatings, compromising the accuracy of lifespan predictions. This study’s originality lies in a stochastic framework that addresses titanium boride data scarcity using a geometric decision node (GDN). By autonomously switching between Palmqvist and Radial-Median regimes, the GDN eliminates deterministic bias and provides a failure-probability-based reliability assessment, thereby surpassing the limitations of conventional models. The evaluation was carried out on powder-pack borided Ti6Al4V layers produced at 1000 °C (10, 15, and 20 h). By combining instrumented Berkovich nanoindentation (N = 14, hardness scatter 17.6–34.8 GPa) with a Monte Carlo simulation algorithm (n = 10,000), we successfully modeled the stochastic brittle failure of the coating. The computational model, governed by a multivariate joint probability density function (JPDF), revealed a mixed-mode fracture mechanism where 77.9% of the virtual population developed radial cracks while 22.1% re mained in the Palmqvist regime. Weibull statistical analysis yielded a characteristic toughness of 2.25 MPa·m^1/2 and a low modulus of m = 1.58. This low modulus mathematically quantifies the coating’s sensitivity to microstructural defects, demonstrating that probabilistic algorithms—rather than mean-value deterministic calculations—are essential for ensuring the structural integrity of borided components in biomechanical design applications. Full article

This paper develops a symmetry-oriented regime-level framework for resonant DC-AC inverters that extends classical source duality toward a multidimensional representation of inverter operation. The proposed formulation introduces a compact inverter signature vector and associated symmetry operators to organize source-domain, detuning side, commutation, switch-path, and modal correspondences within a unified hierarchy. On this basis, a symmetry-guided workflow is defined using compact screening metrics for stress/circulation balance, phase displacement, and commutation feasibility, enabling early-stage comparison of operating regimes before topology-specific detailed design closure. The framework is demonstrated through an extended-duality pairing of two resonant DC-AC inverter regimes: an open-input super-resonant ZVS-like corridor and a closed-input sub-resonant ZCS-like corridor. The case studies show how the proposed regime signatures and screening metrics support structured reasoning about soft-switching corridors, stress redistribution, and device-class-dependent implications, including wide-bandgap (WBG) design tendencies. The proposed metrics are intended as low-order screening indicators and regime-selection tools rather than substitutes for detailed circuit, thermal, EMI, and device-level validation. Within this scope, the paper contributes an operational symmetry formalism that links duality-based interpretation to practical early-stage design organization and robustness-oriented comparison. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article