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Exploiting quantum chaos diagnostics in QAOA for enhanced hybrid quantum classical deep learning classification
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is repurposed here as a feature map within a hybrid quantum–classical classifier, augmented by a chaos-informed diagnostic. We extract a scalar chaos feature by evaluating an Out-Of-Time-Ordered correlators (OTOC) along parameter-scaling rays through the trained circuit, computing spacings between local minima, and standardizing them via a pre-fitted lognormal model. To probe finite-size effects, we sweep the number of qubits $$n\in \{4,6,8,10\}$$ at fixed depth $$p=2$$ and train two models on a balanced 1,000-sample MNIST subset: a StandardHybrid using the $$n$$ local Pauli- $$Z$$ expectations, and a ChaosAwareHybrid which appends the OTOC-derived scalar. We perform multi-run, 5-fold cross-validation with a paired design (identical seeds/folds across models) and report mean±SD, paired mean differences $$\Delta$$ , 95% t- and bootstrap CIs, exact permutation/sign tests, win-rates (Wilson 95% CI), and paired effect sizes. Across $$N_\text {pairs}=\{50,50,67,50\}$$ for $$n=\{4,6,8,10\}$$ , the chaos-aware variant significantly improves test accuracy at $$n\in \{4,6,8\}$$ with $$\Delta \approx +0.016$$ – $$+0.018$$ , all 95% CIs excluding zero, permutation $$p\approx 0$$ , high win-rates (86–100%), and large paired effects ( $$d_z\approx 1.0$$ –2.3). At $$n=10$$ the effect reverses ( $$\Delta =-0.022$$ , 2% win-rate, $$d_z=-2.20$$ ), indicating over-sensitivity. The best average accuracy occurs at $$n=8$$ ( $$0.9006\pm 0.0069$$ ; $$\Delta =+0.0180$$ ; 100% wins). Per-epoch panels (train/val/test; mean±1 SD) reveal a “Goldilocks” width at which expressivity and sensitivity are balanced. These results show that a calibrated chaos diagnostic can enhance hybrid quantum–classical classifiers in resource-limited regimes and provide a principled knob to match circuit expressivity to many-body sensitivity.
Substrate recognition and allosteric inhibition of human betaine/GABA transporter 1
Mechanical, elastic, and damage behavior of structural green concrete incorporating walnut shell aggregate
Recurrent evolution of ligand-binding domain multiplicity fine-tunes TGFβ signaling in vertebrates
Abstract From sponges to mammals, TGFβ signalling is a central regulator of body plan, cell fate and tissue homeostasis, with receptor architecture highly conserved across metazoans. Here we identify unexpected evolutionary divergence within a key receptor structure: 12 independent ligand-binding domain (LBD) duplications across three receptor classes (ACVR1, BMPR2 and TGFBR2) in diverse vertebrate lineages, including fish, amphibians, birds, and mammals. These duplications reveal previously unrecognized structural and functional plasticity arising from domain-level innovation, including in established model organisms such as zebrafish, African clawed frog and chicken. Recently diverged lineages conserve the membrane-distal LBD and ligand-interacting residues, correlating with enhanced ligand binding, whereas more ancient lineages exhibit elevated evolutionary rates of the membrane-distal LBD associated with inhibitory function. Our findings reveal LBD multimerization as a recurring, lineage-independent mechanism that diversifies and fine-tunes TGFβ signalling, adding a regulatory dimension to one of the best-examined conserved and essential pathways in metazoan biology.
Antithrombin III at admission as a new predictive factors for mortality in ECMO for cardiogenic shock caused by acute myocardial infarction
Genetic determinants of Staphylococcus aureus adhesion shape virulence trade-offs in bacteremia
The effect of boron oxide(B₂O₃) and colemanite(2CaO·3B2O3·5 H2O) additives on the aerating, mechanical, post-fire, and microstructural behavior of perlite-based geopolymer lightweight concrete
Local drivers in accelerating North American heat stress
Abstract Climate change increases heat extremes, threatening human health and economies. Using reanalysis data and climate simulations, we show that since the 1940s, population exposure to extreme heat (wet-bulb globe temperature > 32 °C) has increased by 21% in the U.S. At 2 °C of global warming, exposure increases by 273% because heat-stress frequency increases exponentially with warming. Additionally, 2 °C warming leads to increased nighttime heat stress and decreased work capacity, indicating severe health and economic impacts. Heat stress rises fastest in high-latitude areas, while humid regions experience the greatest exposure increases. In northern regions, heatwave frequency increases with warming, whereas in southern regions, events merge into month-long heatwaves. Increasing temperatures and humidity, along with decreasing wind speed, influence regional heat stress, underscoring the need for tailored adaptation strategies. Overall, heat stress exposure is projected to escalate with additional warming, underscoring the need for mitigation and adaptation to protect vulnerable populations.
A stochastic model for dynamic reconfiguration of multi-microgrid networks under demand and supply uncertainties
Abstract Efficiently operating a single microgrid (MG) is increasingly challenging due to volatile electricity demand and intermittent renewable generation. Traditional static networks often fail to adapt to these fluctuations, compromising reliability. Incorporating these uncertainties into planning is essential for developing resilient optimization models that can withstand the stochastic nature of decentralized energy systems. This study proposes a dynamic reconfiguration strategy for interconnected microgrids that reroutes households based on real-time supply and demand. A stochastic nonlinear optimization model was developed to maximize load factors and flatten peaks while accounting for current-dependent power and distribution losses. The Sample Average Approximation (SAA) method was used to handle uncertainty, converting probabilistic variables into a robust deterministic equivalent that prioritizes electrical proximity during reconfiguration. The model was validated using a composite dataset spanning nearly two years of hourly load and renewable profiles. A total of 600 stochastic scenarios were considered and analyzed to represent an empirical distribution of real-world uncertainty while preserving key temporal correlations. Performance was tested under N-1 and N-2 contingency events, in which one or more microgrids are deactivated, to evaluate system resilience. Results indicate that while a single active MG improves the load factor, it also increases operational instability and objective function variance. Conversely, a three-MG configuration enhances system stability and predictability. Economically, the mesh architecture allows for temporary MG deactivation to reduce maintenance and fuel costs without compromising service. The proposed strategy achieves 100% resilience, ensuring uninterrupted service even under severe constraints.
Compounding hazards increase flood economic losses across Europe
Abstract Compound events-combinations of multiple hazards contributing to societal or environmental risk-can significantly exacerbate disaster impacts, yet their effect on flood-related losses remains poorly quantified. Using a pan-European multi-hazard dataset spanning 1981-2020 at sub-national resolution, we find that more than 70% of recorded flood events involve compounding hazards, including meteorological extremes such as heatwaves and windstorms, alongside anomalous river discharge, with an increasing trend over time. The top 1% of events by economic losses are all compound, with total losses exceeding 167 billion euros above single-hazard floods. We introduce a compound hazard complexity metric and combine it with regional exposure and vulnerability data. Applying an ensemble machine learning model with explainable AI and a Double Machine Learning Causal Forest, we show that regions with higher complexity experience greater losses, even after controlling for flood magnitude and vulnerability, highlighting the importance of compound hazard information in risk modeling.
Availability augmentation of smart grid systems using Markovian approach and nature inspired algorithms
Three in one fluorescent sensing and imaging of chemical warfare agents
Screening for depression risk via smartphone narratives with fully fine-tuned WavLM
Gas separation performance in ultrathin zeolite membranes by topotactic conversion and induction of nanosheets
Deep learning-based tooth axis estimation from 3D tooth crowns using quaternion representation and multi-loss optimization
Climate change exacerbates disparities of energy resilience in New York City
Son preference and regional inequalities in girls undernutrition in India
Bridging quantum noise and classical electrodynamics with stochastic methods
Abstract The development of emerging technologies in quantum optics demands accurate models that faithfully capture genuine quantum effects. Mature semiclassical approaches reach their limits when confronted with quantized electromagnetic fields, while full Hilbert space treatments are often computationally prohibitive. To address these challenges, we develop a framework based on coupled stochastic processes with a common cross-covariance structure that can be easily coupled to various types of Maxwell solvers. Our approach accounts for the non-commutativity in the quantum-to-classical transition in a natural way, and has the ability to capture quantum optical signatures while retaining compatibility with classical electromagnetics. For benchmarking, we compare our simulation results with experimental emission spectra of a strongly driven InGaAs quantum dot, finding excellent agreement. Our results highlight the potential of tailored stochastic processes for simulating non-classical light in complex photonic environments.