Search Results (315)

Multivariate long-term forecasting requires deciding how much cross-channel structure can be exploited reliably, yet existing architectures fix this decision by design, from no exchange at all to unrestricted mixing. We argue that the amount should instead be selected from data, and propose CLOC (Controlled Local–Global Channel Communication), a framework in which it becomes an explicit operating point with an exact zero-communication endpoint; setting the residual scales to zero recovers the channel-independent model within the same family. Local grouped attention restricts exchange through group size g, while K learnable prototypes mediate global context and the residual scales $\alpha$ and $\beta$ control message strength. Operating points are selected on validation data under a stated rule and fixed before testing. Across eight benchmarks under a unified 720-step lookback, validation selects qualitatively different regimes, from full suppression on the four ETT datasets to local–global communication on Electricity and Solar-Energy. CLOC attains the best or tied-best result on 50 of 64 horizon-level metrics and a mean rank of 1.45 among eleven models, against 3.73 for the next best, while dense mixing runs out of memory at 862 channels. Thus, forecasting quality depends on matching communication to the structure a dataset can reliably support, not on maximizing it. Full article

This study investigates how non-ideal electromechanical actuation influences the emergence and modulation of complex dynamics in dissipative nonlinear systems. A hybrid four-dimensional model is formulated by coupling a continuous stirred tank reactor (CSTR) with Haldane substrate-inhibition kinetics to a non-ideal electromechanical power source, explicitly accounting for the bidirectional interaction between the mechanical driver and the biochemical process. Numerical simulations and Lyapunov spectrum analysis are employed to characterize the resulting nonlinear dynamics and synchronization properties. The results show that the mechanical subsystem can evolve toward a high-energy chaotic regime with non-ideal rotational velocity pulsations, while the reactor subsystem retains a negative conditional Lyapunov exponent over a broad parameter range despite the presence of global chaos. This dynamical configuration characterizes generalized synchronization, in which the dissipative reactor response becomes functionally constrained by the chaotic mechanical attractor. In addition, the parametric analysis demonstrates that variations in coupling strength can either transmit complex oscillatory behavior or suppress chaos, depending on the operating regime. These findings indicate that aperiodic oscillations in process variables may originate from deterministic electromechanical coupling rather than intrinsic chemical instabilities and highlight the dual role of non-ideal actuation as both a source of nonlinear complexity and a potential mechanism for stabilization and control in hybrid engineering systems. Full article

Portfolio optimization models are highly sensitive to estimation errors in expected returns and covariance matrices, often resulting in unstable allocations. Robust optimization mitigates parameter uncertainty by optimizing against worst-case realizations within a specified uncertainty set, whose construction critically determines the effectiveness of the approach. This paper proposes a data-driven framework for constructing polyhedral uncertainty sets that integrates Gaussian mixture models (GMMs) to identify heterogeneous distributional components and ARIMA-GARCH models to capture time-varying volatility dynamics. The construction proceeds in three stages. First, ARIMA-GARCH filters remove serial dependence and volatility clustering. Second, GMM clustering applied to the standardized residuals identifies latent market regimes. Third, convex hulls of observations lying within a Mahalanobis distance threshold form component-wise polyhedral sets, which are aggregated into a global convex uncertainty set. The robust counterpart is derived via linear programming duality, transforming the robust constraint into a tractable quadratic program that preserves convexity and polyhedrality. We prove that the constructed sets are convex and polyhedral, establish probabilistic coverage guarantees under mild regularity conditions, and analyze the computational complexity of the framework. Empirical analysis of Indonesian equity data confirms heavy tails and volatility clustering, with GMM identifying three distinct regimes of approximately equal proportions. Controlled synthetic experiments show that uncertainty set geometry fundamentally influences portfolio outcomes: overlapping clusters yield stable allocations across regimes, whereas well-separated clusters reveal that convex hull aggregation introduces conservatism that masks regime distinctions. Rolling window backtests demonstrate that the proposed approach produces economically higher Sharpe ratios than standard uncertainty set formulations in the reported out-of-sample period, although statistical significance is limited by the small number of independent rebalancing periods (18 quarterly events). The practical advantage should therefore be interpreted as conditional on moderate transaction costs and manageable turnover. These findings provide a statistically grounded, geometrically faithful, and computationally tractable methodology for practical implementation, contributing to financial resilience and sustainable economic growth with broader implications for stable capital markets. Full article

Fire regimes are changing globally, yet dominant fire governance remains centred on emergency response and suppression, often neglecting fire’s ecological functions and cultural significance. Although community-based burning practices persist across many fire-dependent landscapes, particularly in Africa, they rarely receive institutional recognition or appropriate governance. Integrated Fire Management and Community-Based Fire Management frameworks have called for bottom-up approaches that integrate biological, environmental, and social dimensions while prioritising local governance and customary fire practices. However, practical mechanisms for eliciting socio-cultural values, operationalising participation, and addressing power asymmetries in fire decision-making remain limited. This article documents the application of Legislative Theatre as a participatory approach to co-develop an Integrated Fire Management plan in eastern Angola. Through 37 stories based on lived experiences and collective, performance-based exercises exploring fire-related problems across seven villages, communities articulated diverse fire uses, values, and shared norms for improving local fire governance. Our findings show that theatre-based methods enabled the expression of embodied, emotional, and experiential knowledge often overlooked by conventional engagement approaches, while supporting knowledge co-production and participant agency. We argue that Legislative Theatre and storytelling provide practical tools for strengthening the socio-cultural dimensions of Integrated Fire Management, provided they are carefully facilitated, culturally adapted, and supported through long-term engagement. Full article

Accurate calibration of the Preston–Tonks–Wallace (PTW) model is a nonlinear, multimodal optimization problem. Root mean square error (RMSE) is the sole optimization objective, with average absolute relative error (AARE) used only as a diagnostic. The primary comparison jointly calibrates one seven-parameter vector across all temperatures within each fixed strain-rate group; the 0.1, 3000, and 5000 s⁻¹ groups are calibrated separately. A secondary transfer assessment imposes the stricter constraint of one unchanged vector across all 11 temperature-strain-rate conditions. The adaptive GA-BO strategy combines diversity-dependent mutation, periodic and stagnation safeguards, elite-envelope Bayesian optimization (BO), and BO-to-population reinjection. Genetic algorithm (GA), BO, and GA-BO each receive exactly 1000 constitutive-model evaluations and 20 paired seeds. In the primary comparison, GA-BO gives median RMSE values of 8.06, 7.94, and 21.69 MPa and significantly improves on GA at all three rates, while remaining statistically comparable to BO and requiring substantially less serial time in the present implementation. The shared-vector assessment gives a GA-BO median global RMSE of 45.54 MPa and quantifies the accuracy-transfer trade-off created by enforcing one vector over the full rate range. Bootstrap, held-out-temperature, ablation, and one-factor setting analyses further quantify parameter coupling, calibration-transfer uncertainty, component contributions, and setting robustness. The resulting workflow therefore improves the accuracy-efficiency balance of reproducible PTW calibration while explicitly reporting the parameter-transfer cost across rate regimes. Full article

Forest management strongly influences understory plant communities, yet their responses to management remain context-dependent and scale-sensitive. This review synthesizes current knowledge on understory vegetation in managed forests by combining a bibliometric analysis of global research trends (1986–2025) with a comprehensive qualitative evaluation of empirical studies. The bibliometric assessment identified 726 publications, with a marked increase in output after 2017 and a predominance of research articles within Environmental Sciences, forestry, and biodiversity conservation. Research activity is geographically concentrated in North America and Europe, while other biomes remain comparatively underrepresented. The classical review highlights canopy structure and light availability as primary drivers of understory diversity. Management interventions such as thinning, harvesting, gap creation, and prescribed fire frequently increase local (α) species richness and vegetation cover in the short term by promoting early-seral and light-demanding species. However, these gains are often accompanied by compositional shifts toward ruderal and generalist taxa, reductions in bryophytes and forest specialists, and declines in spatial heterogeneity at broader scales. Consequently, β-diversity and landscape-level distinctiveness may decrease despite stable or elevated plot-level richness. Management intensity, retention of structural legacies (e.g., large trees and deadwood), soil and hydrological conditions, landscape context, and interactions with global change drivers (e.g., nitrogen deposition and climate warming) strongly mediate vegetation responses. Low-to-moderate-intensity systems that maintain structural heterogeneity and approximate natural disturbance regimes appear most effective at balancing production objectives with biodiversity conservation. Understory vegetation responses to forest management reflect trade-offs between short-term richness increases and long-term risks of compositional homogenization and specialist decline. Evaluating biodiversity outcomes therefore requires multi-scale, trait-informed approaches that move beyond species richness alone. This synthesis provides an integrative framework to support evidence-based, biodiversity-oriented forest management in increasingly human-modified landscapes. Full article

Claims of AI sovereignty are increasingly invoked but operational control remains uneven. Claims to control are made through national models, sovereign clouds, data localization mandates, and procurement rules; however, whether such claims translate into demonstrable control over how AI systems are run, inspected, and contested remains poorly understood. This paper introduces credible sovereignty, the gap between declared and demonstrable control in deployment, as a conceptual lens for analyzing AI governance to examine how this gap is opened and closed across infrastructure, data, and model supply chains. Using a PRISMA-guided social-science corpus and machine learning-based BERTopic modeling, validated through topic diversity and topic separation diagnostics and triangulated through close reading, the analysis identifies four governance logics through which sovereignty is contested: data infrastructure and legitimacy frameworks; techno-bloc diplomacy and infrastructure politics; European regulatory sovereignty; and community-driven sovereignty in the Global South. Across these logics, sovereignty is enacted less through national capabilities than through proxy mechanisms—certification regimes, procurement clauses, cloud governance, and deployment architectures—each carrying trade-offs between autonomy, dependence, and accountability. Rereading the corpus through an Antecedents–Decisions–Outcomes lens yields a testable research agenda: antecedents that push actors toward sovereignty seeking; design and governance choices that translate ambition into implementation; and outcomes—resilience, inclusion, accountability—against which sovereign AI programs should be assessed. This paper reframes sovereignty as a layered operational capability rather than a discursive claim and links computational synthesis to a normative construct that applies across jurisdictions and scales. Full article

Global hydrological models (GHMs) are essential for assessing water resources and flood risk, yet systematic evaluations of ISIMIP3a models remain limited. We evaluated eight standard ISIMIP3a GHM configurations against observed discharge at four principal Yangtze mainstem gauges using a multi-metric framework covering daily and monthly discharge, the seasonal cycle (defined as the mean annual cycle of monthly discharge), flow percentiles, and annual peak discharge. Metric-based model performance generally increased from daily to monthly and seasonal-cycle scales, partly reflecting the smoothing of short-term errors through temporal aggregation, whereas annual peak discharge remained more difficult to reproduce. WaterGAP2-2e achieved the strongest overall performance across the daily, monthly, and seasonal-cycle evaluations. However, this result was not fully independent of its inherited global discharge calibration, because three evaluation gauges (Yichang, Hankou, and Datong) spatially coincide with gauges in its calibration-station dataset. WaterGAP2-2e also systematically overestimated peak flows, with peak-flow Relative Bias (RBpeak) reaching 30.14%. Among the remaining configurations, WEB-DHM-SG showed the strongest overall performance and a relatively small station-averaged absolute peak-flow bias (|RB_peak| = 15.52%). However, this advantage did not extend to low-flow conditions. H08 and ORCHIDEE-MICT showed the weakest overall performance, with negative NSE values down to −3.61 and peak-flow biases reaching −99.10%, whereas MIROC-INTEG-LAND, CWatM, HydroPy, and JULES-W2-DDM30 showed intermediate performance. Overall, model suitability depended on temporal scale and flow regime, and apparent rankings should be interpreted in light of inherited calibration and configuration differences. These results provide a diagnostic basis for model selection and targeted improvement in regional discharge simulation and peak-flow-related applications. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

Lithium, as a Li-ion battery constituent, is pivotal for the transition to clean energy. Western Australia (WA) has become a global leader in hard-rock lithium mining, realising 10-fold growth from 2010 to 2024 and with royalty receipts to the WA government surpassing $1 billion AUD. Given the sector’s economic significance, we analyse key performance metrics including resource/reserve build, production growth, cumulative capital deployed, capital intensity, and development timelines for the new-generation lithium mines. Several enabling factors supported the rapid build-out of capacity. These include an efficient mine permitting process to manage environmental impacts and competing land use issues, a stable royalty regime, energy and logistics infrastructure, availability of a skilled workforce, and mining services capability. Contrary to the standard industry narrative that new mineral projects are constrained by legislative delay, the new lithium projects achieved development timelines of 7 years or less from first resource to production. This has broader implications for critical mineral projects where success is likely to depend less on strategic classification and more on project quality, financing, and regional capability. Full article

This study investigated the dynamic volatility connectedness among financial technology (FinTech), green indices, and global uncertainty metrics between June 2018 and May 2025. The research was conducted to understand how technological innovation and sustainability indices interact with systemic risk during periods of extreme global stress, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the market disruptions of early 2025. The analysis employed a time-varying parameter vector autoregression (TVP-VAR) framework to capture time-varying interdependencies and risk spillovers across multiple market regimes. Key findings indicated that total dynamic connectedness intensified significantly during crisis events, with major spikes occurring during the 2020 pandemic onset and the 2025 shocks possibly related to the “DeepSeek” AI disruption and the US tariff announcements. FinTech indices and green assets consistently functioned as net transmitters of shocks, while uncertainty indices, particularly the VIX, served as net recipients. Notably, the Alternative Finance Index (AFI) exhibited regime-dependent behaviour, transitioning from a transmitter to a recipient during the COVID-19 pandemic. These results imply that innovative and sustainable sectors have evolved into systemic drivers of global market sentiment rather than mere recipients of external shocks. The findings provide critical insights for stakeholders in financial markets, helping them to rethink their current approaches and prevent financial losses amid market upheaval. Full article

This study proposes MSATE-Net for next-day stock index forecasting. The model combines parallel one-dimensional convolutions with receptive fields of 3, 7, and 15 trading days, a bidirectional LSTM operating entirely inside a historical lookback window, sample-dependent temporal attention, and a residual regularized prediction head. Here, “bidirectional” denotes paired processing of the same observed window; it does not assert time-reversal invariance of financial prices or access to observations after the forecast origin. The globally learned attention temperature controls overall selectivity and is not described as a regime-specific adaptive parameter. Experiments use S&P 500, CSI 300, and Nikkei 225 data; persistence and drift benchmarks; recent forecasting architectures; five-seed uncertainty estimates; expanding-window tests; return and directional metrics; and Diebold–Mariano comparisons. The revised evidence supports lower price-level errors, while directional and significance results are mixed across markets. Because a separate model is fitted in each market, the findings establish cross-market consistency rather than transfer learning. Full article

This paper studies short-horizon trajectory forecasting through a coordinate-conditioned multiscale architecture, termed AeroMixer. The framework combines segment-wise local Cartesian re-representation, multiscale decomposition, bidirectional trend mixing, scale-specific prediction heads, and direct scale aggregation for the one-step prediction of highly dynamic aerial trajectories. The mathematical part of the paper establishes a local tangent-plane approximation bound for the geodetic-to-local map, a horizontal metric-scale characterization related to latitude-dependent distortion, and smoothness as well as spectral perturbation characterizations for the weighted and kinematically regularized objective. The analysis quantifies the local approximation error and objective regularity for the proposed representation and loss. Numerical experiments on 72,000 simulated trajectory samples compare five deep learning models under trajectory-level splits, three seeds, and batch size 256. AeroMixer achieves the lowest batch-wise and pooled global position RMSE in this matched set. Regime-wise and ablation analyses further identify the conditions under which the observed differences arise. Full article

Integrated Modular Avionics (IMA) integrates safety-critical functions on shared computing and network resources, creating coupling channels through which a local fault may escalate into a catastrophic system-level scenario. This study develops a graph-based fractional-order model for cascade escalation in IMA architectures with communication delays and reconfiguration failures. The architecture is represented as a weighted directed graph of core processing modules, network switches, and remote data concentrators, where each node carries functional degradation and queue-backlog states. The proposed delayed Caputo fractional-order dynamics incorporate degradation propagation, backlog spillover, mixed-criticality priority conflict, and a state-dependent reconfiguration-failure mechanism. We establish well-posedness and positive invariance of the feasible state domain, derive a sufficient cascade threshold that separates a delay-independent, globally Mittag–Leffler stable nominal regime from a supercritical regime in which bistability and catastrophic attractors may occur, and characterize delay-induced oscillatory instability together with a memory-stabilization effect. Numerical experiments on a synthetic 22-node IMA configuration show fault absorption below the threshold, reconfiguration-contained cascades under sufficient supervisory capacity, and global escalation when reconfiguration collapses under load. The results indicate that backlog growth is an early warning signal and that maintaining the cascade threshold below unity while provisioning reconfiguration capacity above the tipping point can support safer reconfiguration-policy design in certifiable avionics. Full article

Agronomic modulation of secondary metabolism may influence not only crop resilience but also the biological activity of fruit-derived phytochemicals. In this study, we evaluated the impact of exogenous methyl jasmonate (MeJA) application under contrasting water regimes on the selected phenolic compounds and vascular bioactivity of Vaccinium corymbosum L. cv. Legacy. Antioxidant capacity was assessed by FRAP and DPPH assays, phytochemical composition was characterized by HPLC-DAD, and antiplatelet activity was evaluated through inhibition of TRAP-6–induced P-selectin (CD62P) expression in human platelets. Selected phenolic constituents were further examined using molecular docking and molecular dynamics simulations against a platelet receptor model. Although MeJA treatment altered the abundance of selected phenolic compounds identified by HPLC-DAD, total antioxidant capacity remained largely unchanged. Blueberry extracts significantly inhibited platelet activation in a concentration-dependent manner without cytotoxic effects, and antiplatelet potency was not strictly related to global antioxidant indices. Computational analyses revealed stable ligand–receptor interactions and favorable binding free energies for selected phenolics, providing a structural explanation for receptor-level modulation. These findings suggest that elicitor-driven responses in blueberries can influence platelet functional responses and highlight the importance of qualitative phytochemical composition in determining vascular bioactivity. This multiscale approach connects plant stress physiology, natural product chemistry, and human platelet biology, underscoring the translational relevance of agronomic strategies for nutraceutical functionality. Full article