Search Results (821)

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

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

El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), ERA5 atmospheric fields, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies ($r=0.94$ in the Andes; $r=0.91$ in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective sample size, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was $-0.374$, with marked December–February and June–August responses. El Niño minus La Niña composites of column water vapor, 850-hPa moisture-flux convergence, 500-hPa vertical velocity, and Convective Available Potential Energy (CAPE) revealed seasonally heterogeneous moisture and convergence responses, but coherent positive $\omega$ anomalies over the Andes in DJF and JJA, consistent with reduced ascent. CAPE was significantly higher in MAM–SON, whereas the positive DJF difference was not statistically significant, showing that thermodynamic instability alone did not determine rainfall. Orinoquia did not exhibit a comparably consistent four-variable atmospheric signature. An elevation-stratified analysis showed a modest lowland-to-upland strengthening that plateaued above approximately 1000 m. A strictly antecedent ONI-lag model retained modest fixed-split skill in the Andes ($R^2=0.138$) but negligible skill in Orinoquia ($R^2=0.003$). The results support regional diagnosis, not causal or operational claims. Full article

Quantum key distribution (QKD) employs quantum states to generate shared cryptographic keys. An attacker interacting with the modeled non-orthogonal quantum signals can affect the monitored statistics, and hence they can be detected under the specified protocol assumptions, but this trait does not inherently authenticate the classical channel, and it does not prevent implementation side channels. In this work, we introduce ModPhase-8 (QUEST), a proposed QKD modulation and adaptive-receiver architecture evaluated through analytical modeling and simulation. Instead of using only a few quantum signal types, our system uses eight carefully designed signal variations created by adjusting the phase between two very short light pulses. The eight phase states are organized into four phase bases, each containing two antipodal states that encode one binary raw-key value. The enlarged signal set diversifies the physical representation of the key bit and changes the state-discrimination problem faced by an eavesdropper, but it does not increase the raw-key payload beyond one bit per successfully sifted signal. On the receiving side, the system adaptively switches between two measurement techniques based on the prevailing channel conditions. This adaptive detection mechanism enhances reliability and helps maintain low error rates even when the communication channel is affected by noise. We provide an analytical security assessment under the stated collective-attack, source, channel, receiver, and trusted-device assumptions, supplemented by attack-specific analyses of intercept–resend, beam-splitting, source-side multi-photon leakage, and selected implementation-related vulnerabilities. Simulation studies were conducted to examine the physical-layer and post-processing behavior of the proposed protocol under explicitly stated channel, receiver, detector, and finite-sample values. Under the adopted simulation model, ModPhase-8 maintains low error rates in the low- and moderate-noise operating regimes and exhibits favorable receiver-level robustness across the investigated channel conditions. The reported rate values are model-based performance estimates rather than rigorously certified secret-key lower bounds. In particular, Qiskit simulation does not establish a composable security proof or an optimal bound on Eve’s information for the exact eight-state time-bin ensemble. A protocol-specific numerical security analysis incorporating the homodyne–heterodyne measurement operators, post-selection, reconciliation efficiency, finite-size effects, and Eve’s Holevo information remains necessary before definitive rate comparisons can be made. ModPhase-8 should therefore be interpreted as a practically motivated receiver and modulation framework whose security-rate performance remains subject to further protocol-specific analysis. Full article

As a key energy-conversion component of tower gravity energy storage systems (T-SGESs), the hoisting structure is susceptible to large-amplitude coupled vibrations when the dominant frequency of a continuous external excitation approaches one of its natural frequencies, potentially compromising operational stability and safety. To characterize this behavior, a two-degree-of-freedom nonlinear dynamic model is developed based on Hamilton’s principle. Eigenvalue and modal analyses are performed to determine the natural frequencies and modal characteristics of the coupled system, while the second-mode primary resonance is further analyzed using the method of multiple scales and validated through numerical frequency-sweep simulations. Near the second-mode primary resonance, the system exhibits a pronounced hardening-type nonlinear response characterized by multistability, saddle-node bifurcations, jump transitions, and hysteresis. Parametric analysis indicates that greater attention should be paid to short-rope and low-payload operating conditions, under which the system tends to exhibit stronger nonlinear responses and larger payload swing amplitudes near the second-mode primary resonance. Meanwhile, the nonlinear resonance response of the hoisting structure can be effectively mitigated through enhanced equivalent stiffness and damping, which substantially narrow the multistable frequency interval. At a damping ratio of 0.04, the system transitions from a multivalued response to a single stable branch, with a marked reduction in payload swing amplitude. These findings identify the second-mode primary resonance as a critical nonlinear operating regime and provide a quantitative basis for resonance avoidance and parameter regulation in T-SGES hoisting systems. Full article

Background: The rabbit (Oryctolagus cuniculus) is a currently indispensable pre-clinical model for fundamental discovery and translational research in glaucoma surgery. While not fully representative of the human eye, the rabbit often serves as the site of the first robust assessment of a new chemical entity in an anatomically and physiologically relevant setting. In later stages of development, the rabbit is also a critical model for regulatory-facing Good Laboratory Practice (GLP)-graded pre-clinical safety studies. However, while guidelines exist for human glaucoma surgeries in terms of patient profile vs. surgery type and success criteria, there is limited collated information available on how rabbit glaucoma filtration surgeries are designed, use and mode of administration of anti-metabolite Mitomycin-C (MMC) and post-operative antibiotic and anti-inflammatory dosing regimes. These disparities limit cross-comparability in studies and encourage sub-optimal study design and incomplete reporting. Methods: This review investigated and collated current procedural and reporting practices based on 100 peer-reviewed rabbit glaucoma filtration surgery publications from the years 2011–2025. Publications meeting acceptance criteria were segmented into pre-operative designs (ethical approval, rabbit characteristics, study duration), intra-operative (surgery type, surgical approach, use of MMC) and post-operative follow-up (antibiotics, anti-inflammatories, analysis and adverse events). Results: It was found that there were clear preferences in animal model selection, study design and intra-/post-operative anti-fibrotic/inflammatory treatment regimes. Additionally, there were several areas identified where reporting was ambiguous or omitted, e.g., rabbit age, post-operative treatments, adverse events. The levels of omission were 16–30% and represent areas for improvement and highlight the need for ongoing review in pre-clinical research. Full article

Five studies are frequently cited as experimental evidence that photobiomodulation (PBM) may stimulate tumour growth or produce phototoxicity, contributing to a documented barrier to clinical adoption in oncology. We reappraise these studies against their own reported dosimetry. None characterised the spatial power distribution of its beam. Each was assessed for arithmetic consistency, beam profile, device category, and biological-model appropriateness, and re-examined against the Arrhenius framework for thermal damage. In four of the five, the exposures delivered lay outside the therapeutic photobiological window on the authors’ own stated parameters: surface temperatures of 55 °C and, in places, 71 °C, with injury the original authors describe as coagulation and necrosis extending to paralysis and death; irradiances approximately 25 times those used clinically; and, in one case, parameters the authors themselves describe as high. The fifth, a xenograft study, used a dose within the therapeutic range, but its immunodeficient host cannot express the immune-mediated response at issue. A sixth study, from the same group and using the same device as one of the five but operated at a therapeutic irradiance, is included as an internal control and reported therapeutic benefit. Injury in these regimes is best understood as oxygen-dependent, with temperature acting as a sensitising variable and as a marker of the regime rather than as the proximate cause, a reading supported by the original authors’ own helium-substitution, cooling and heated-probe controls. We outline a two-tier mechanistic framework, in which correctly dosed PBM may engage systemic anti-tumour immunity, as a hypothesis for prospective testing rather than as a finding of this analysis. None of these studies provides dosimetrically secure evidence of a hazard from therapeutic-range PBM; equally, the absence of such evidence is not a demonstration of safety. Beam-profile reporting should become standard in PBM oncology research. Full article

In the development of carbonate reservoirs via water flooding huff-n-puff, the osmotic pressure effect is frequently overlooked, and existing models inadequately quantify the matrix imbibition and oil expulsion driven by salinity gradients. To address this issue, this study establishes a coupled oil–water two-phase huff-n-puff flow model for carbonate reservoirs that incorporates the interplay between salt concentration and osmotic pressure, which, for the first time, fully couples the van ’t Hoff osmotic pressure equation with solute transport equations for fractured-vuggy carbonate huff-n-puff, filling the gap that prior tight/shale reservoir low-salinity flow models fail to adapt to cyclic injection-soaking production regimes of carbonates. Based on the IMPES (implicit pressure–explicit saturation) numerical simulation method, an equivalent single-nucleus model is adopted to characterize the fractured-vuggy reservoir architecture. The model integrates the osmotic pressure formula, solute transport equation, and two-phase seepage governing equations, enabling a systematic analysis of the mechanisms by which osmotic pressure affects the multi-stage seepage process and the influence of key parameters on development performance. Quantitative simulation reveals three core laws controlled by salinity-induced osmosis: first, osmotic pressure drives water molecules to spontaneously migrate from the high-permeability fracture inner core toward the tight matrix pores, thereby modifying the water saturation distribution, expanding the water sweep region, and smoothing the saturation gradient between the inner and outer cores, which effectively mitigates water channeling in fractured reservoirs. Under the base case (injected water salinity = 1000 mg/L, inner-core permeability = 1000 mD, shut-in time = 80 d), the oil recovery factor with osmotic pressure considered reaches 13.46%, representing a 3.50% increment over the case without osmotic pressure. The recovery factor decreases monotonically with increasing injected water salinity, while it increases with longer shut-in time and higher inner-core permeability, both exhibiting pronounced diminishing marginal returns; the optimal shut-in time is approximately 80 d under the simulated conditions. This work delivers a fully coupled numerical tool and quantitative evaluation standard for osmotic imbibition mechanisms in fractured-vuggy carbonates. The quantified recovery increment and optimal soaking window established herein can directly guide field parameter optimization of injection water salinity, shut-in cycle and fracture reconstruction scale, balancing oil increment revenue and water treatment/well shutdown operation costs for on-site low-salinity huff-n-puff design. Full article

Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such workflows, allowing geographically separated quantum processors to execute joint computations using shared entanglement while preserving data privacy. In this work, we investigate the feasibility of satellite-enabled distributed quantum computing for federated quantum learning. As a representative application, we consider a distributed distance-based quantum classifier in which multiple parties contribute local data through quantum operations. To support this application, we develop a hybrid space-ground quantum network architecture in which satellites distribute entanglement between distant ground stations. The communication layer is combined with a noise-aware neutral-atom processor model, enabling a system-level analysis that captures both network and hardware constraints. Simulation results across varying network sizes, feature dimensions, and coherence regimes show that classifier performance is jointly determined by communication resources, processor noise, and data representation. In low-coherence regimes, decoherence destroys the classifier’s discriminative signal, whereas high-coherence regimes reveal limitations arising from feature-space conditioning and feature redundancy. These results demonstrate that satellite quantum networks could support distributed quantum learning over long distances, while highlighting the importance of coherence time, entanglement-distribution performance, and learning-aware data encoding for future large-scale deployments. Full article

The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R² of 0.868 and the 1 h model an R² of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R² of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R² 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H₂/CH₄ blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO₂ emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications. Full article

This study presents and validates a modular Internet of Farming (IoF) sensing architecture designed for high-fidelity microclimate monitoring to address the spatial biases in reference evapotranspiration (ET₀) estimation for precision viticulture. Deployed in a Mediterranean vineyard in Antalya, Türkiye, the system demonstrated high operational robustness with a 98.6% data completion rate over an annual cycle. Comparative analysis between field-level IoF data and regional meteorological station (TSMS) records revealed a systematic overestimation of ET₀ by regional networks (MBE = −0.695 mm/day, MAPE = 10.56%). Sensitivity analysis identified discrepancies in wind speed (36.6%), net radiation (31%), and relative humidity (30%) as the primary physical drivers of this spatial bias, with air temperature serving as a seasonal index for their integrated intensification rather than as a direct dominant contributor (~2.4%). Results indicate that reliance on regional data would lead to an irrigation surplus of approximately 97.5 mm (11.4%) during the growing season. The findings underscore the critical role of localized sensing layers in resolving microclimatic heterogeneities that regional grids fail to capture within the specific site and year examined. While these results motivate further investigation into IoF-based architectures as a potentially transferable framework for ET₀ bias correction, broader generalisation to other crops, climate regimes, and management systems remains a promising avenue for future multi-site, multi-year validation rather than an established conclusion of the present study. Full article

This study investigates the transformation of energy security in distributed energy systems under growing uncertainty, geopolitical shocks, and fuel market volatility. A risk-based multi-objective optimization framework is proposed that integrates economic performance (LCOE, CAPEX, and OPEX), system reliability, and systemic risk measured by Conditional Value-at-Risk (CVaR). The empirical analysis is based on European electricity market data for 2010–2025 and combines historical analysis with stochastic scenario generation and Monte Carlo simulation to evaluate the impacts of exogenous shocks. The results reveal a structural shift in the European electricity market after 2021, characterized by increased sensitivity to fuel price fluctuations and a transition to a more volatile operating regime. Although a higher share of renewable energy improves economic and environmental performance, it does not ensure system resilience without complementary flexibility measures, including energy storage and demand-side management. The proposed framework demonstrates that integrating these measures substantially reduces systemic risk under crisis conditions. The analysis also identifies a persistent post-crisis risk pattern, reflected in elevated CVaR values after market stabilization, which is consistent with the hypothesis of risk hysteresis. Rather than proving hysteresis, the results indicate sustained risk persistence following major external shocks. The proposed framework extends existing approaches to energy security assessment by integrating economic efficiency, reliability, and risk within a unified optimization model. Its modular structure enables adaptation to different electricity markets through recalibration of local parameters, providing a practical decision-support tool for strategic planning under uncertainty. Full article

Human–robot collaborative flexible job shop scheduling (HRC-FJSP) must coordinate heterogeneous capabilities, mode-dependent processing times, safety feasibility, and carbon constraints. The problem becomes harder when a collaboration mode that is attractive during planning becomes infeasible after a human enters the robot safety separation zone. Unlike conventional dynamic disturbances such as machine breakdown or order insertion, this event changes the feasible collaboration mode of the unfinished operation remainder rather than only delaying a resource or adding a job. This study formulates a carbon-aware dynamic HRC-FJSP and evaluates a carbon-aware multi-agent deep reinforcement learning scheduler (CA-MADRL) with local recovery after safety-proximity-induced collaboration disruption. The objective combines normalized makespan, carbon emission, and human workload imbalance with carbon accounting based on operation energy and time-varying grid carbon intensity. Across the benchmark cases, CA-MADRL obtains the best average global criterion (0.7235), wins nine of 12 cases, and achieves the lowest average carbon emissions among the compared policies (48.991 kg CO₂e). Sensitivity analysis shows that stronger carbon preference reduces emissions but increases makespan and tardiness, while adaptive collaboration outperforms fixed human–robot, human-only, and robot-only regimes. The results indicate that dynamic mode adaptation and local rescheduling improve carbon-aware collaborative schedules under safety disruption. Full article

This paper investigates platform promotional subsidy strategies under price regulation. We construct a dynamic game model of incomplete information. The model involves a regulator, a monopoly platform, and heterogeneous consumers. The regulator sets a subsidy cap. The platform chooses the price and subsidy after observing its cost type. Consumers decide whether to purchase based on their valuation and the perceived subsidy value. We solve the game by backward induction. We characterize the perfect Bayesian equilibria. We derive closed-form solutions for the critical subsidy threshold, separating/self-selection equilibrium conditions, and optimal regulatory policies. The analysis yields three main findings. First, the sign of the net social benefit of the promotional subsidy determines the optimal policy. A positive sign calls for a high subsidy cap. This induces full market coverage. A negative sign calls for a ban on subsidies. Second, when the subsidy cap lies between the two platform types’ critical thresholds, a natural separating/self-selection equilibrium emerges. It operates at no cost. The feasible interval widens linearly in the cost gap. Third, diseconomies of scale have a stronger marginal effect on the critical subsidy than marginal cost. There exists an endogenously determined critical proportion of high-value consumers, which depends on the model parameters, such as $v_H,v_L,c_I,{\eta }_I$. This proportion divides the policy space into two regimes. These results provide a basis for low-cost information screening and differentiated regulation. Full article

Unmanned surface vehicle (USV) formations operating under GNSS denial require accurate relative localization using proprioceptive sensors and inter-vehicle ranging. This paper presents an anomaly-aware adaptive extended Kalman filter for four-USV formations using inertial measurements, compass, and ultra-wideband ranging, and systematically characterizes its operating envelope. Observability analysis establishes that S-curve maneuvering achieves structural rank 24, with only global translation unobservable, while straight-line motion leads to a rank deficiency of exactly seven dimensions All four gyroscope biases remain observable under both trajectories. The proposed filter integrates chi-square testing, cumulative sum (CUSUM) detection, and bias drift rate monitoring to trigger coordinated R adaptation and Q-boost mechanisms. Controlled experiments spanning outlier magnitudes and drift rates reveal three performance regimes, clean conditions with equivalent performance across all variants, moderate outliers $[3{\sigma }_d,10{\sigma }_d]$ where the proposed method achieves 4.8–13.4% improvement, and extreme outliers where all robust methods converge. Critically, pure bias drift experiments expose a structural limitation of single-hypothesis, residual domain robustification within the tested drift range—all variants exhibit equivalent performance across the tested drift rates, analytically attributable to Kalman gain partitioning that distributes innovations between position and bias subspaces. The characterized operating envelope establishes that robust mechanisms provide measurable benefits for transient anomalies but encounter hard boundaries under persistent drift conditions, with all variants converging to equivalent performance across the tested range, necessitating multi-hypothesis or constraint-based approaches. Full article