Search Results (521)

Electroencephalographic (EEG) signals provide macroscopic observables of complex neural dynamics. We introduce a horizon-inspired framework in which measured EEG signals are modeled as projections of a complex wave-like representation constrained by an effective boundary analogous to an event horizon. In this formulation the signal amplitude obeys a renormalization-group scaling relation while EEG spectral entropy parameterizes the accessibility of observable modes. The resulting solutions generate oscillatory structures whose geometry and spectral signatures can be explored through signal analysis and sonification. This mapping between entropy-based neural observables and wave-like signal representations provides a physically motivated framework linking entropy measures, scale-dependent dynamics, and observable neural oscillations. The work is intentionally conceptual. It provides a falsifiable framework intended to stimulate future empirical investigations. Full article

Large Language Models (LLMs) offer opportunities for processing heterogeneous information streams relevant to energy-market decision-making, but their practical role in forecasting-oriented analytical workflows remains uncertain. This paper presents an exploratory feasibility study of LLM use across four energy-market tasks: geopolitical event monitoring for Dutch Title Transfer Facility (TTF) market context using Global Database of Events, Language, and Tone (GDELT)-based data, structured summarisation of commodity-intelligence articles, prompt-engineered solar-power and grid-load forecasting for Austria, and a short-horizon exploratory TTF price-estimation case. The study is positioned as a pilot investigation and hybrid workflow blueprint rather than as a statistically conclusive forecasting benchmark. A four-layer reference architecture was devised, including structured market data, semi-structured news intelligence, web-scraping concepts, and implemented Twitter/X and GDELT monitoring layers. The empirical cases indicate that LLMs are most useful for text-heavy reasoning, event-context integration, source triage, and structured interpretation. In the 20-article summarisation corpus, Gemini 1.5 Pro achieved higher commodity-direction accuracy than GPT-4, while GPT-4 showed stronger output-format stability. In selected solar case checks, OpenAI models produced plausible generation curves close to the Fraunhofer ISE Energy Charts reference, while Energy Charts remained more accurate for aggregate load estimation in the available benchmark comparison. The two-day TTF experiment illustrated that LLMs can incorporate qualitative geopolitical context into short-horizon reasoning, but it did not establish reliable price-forecasting capability. The Twitter/X monitoring layer is retained as a documented negative pathway, showing the limitations of informal social-media scraping for reproducible market intelligence. Full article

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing data-driven approaches often struggle to balance temporal continuity with meteorological sensitivity, leading to lag effects during rapid fluctuations, and frequently generate predictions that violate physical domain knowledge. To address these limitations, this paper proposes a dual-stream architecture to decouple temporal dependencies and spatial–meteorological mappings, utilizing a Physics-Informed GRU (PI-GRU) and an Enhanced Random Forest (ERF). Both streams are strictly bounded by physical constraints. Furthermore, a scenario-aware adaptive fusion mechanism is introduced to dynamically adjust the model’s reliance on each stream based on real-time wind speed gradients and volatility indices. Extensive experiments were conducted using a comprehensive dataset from three coastal wind farms over 8 months, encompassing stable regimes and extreme weather events. Evaluating across both 1-day and 4-day forecast horizons, the results demonstrate that our method significantly outperforms state-of-the-art baselines, proving its robustness and practical value for grid security and dispatch optimization. Full article

Makerspaces can serve as distributed urban infrastructures for repair, reuse, tool sharing, and peer learning, yet their contributions to circular economy (CE) goals often occur without being explicitly recognized or framed as CE practices. Inspired by practice theory and the literature on quiet sustainability, this study introduces implicit circularity as circular practices enacted without an explicit sustainability/CE framing by participants, and examines how such practices shape bottom-up circular transitions. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we examined three linked questions: which circular practices occur in makerspaces and how they cluster into domains, how these practices vary across makerspace types, and which barriers and governance arrangements shape makerspaces’ consolidation as circular urban infrastructure. A qualitative multi-method design was employed in Czechia, combining field mapping with in-depth qualitative inquiry. Data included 40 semi-structured interviews with makerspace founders and operators, documentary analysis based on websites, social media, event listings, rules, and other documents, and 21 observations. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we analyzed how circular practices cluster into domains, how implicit versus explicit circularity varies across makerspace types, which barriers constrain makerspaces’ consolidation as circular urban infrastructure, and what governance arrangements could mitigate them. Circularity was dominated by implicit, routine practices rather than formal, CE-branded programs. Three practice domains were identified: repair and maintenance, material flows, and learning/education. Explicit programming was comparatively less common and context-dependent. Barriers formed a reinforcing system spanning institutional fragmentation and coordination deficits, capability gaps, infrastructural constraints, and tensions around autonomy and legitimacy, which together kept many circular contributions low-visibility. Makerspaces constitute an under-recognized form of circular micro-infrastructure that couples technical capacity with social learning and can translate CE ambitions into everyday practice. To mobilize these latent capacities, cities need hybrid governance, especially light-touch coordination platforms, long-horizon operational support, and integration of makerspaces into municipal material-flow systems and repair/reuse strategies. The study offers a practice-based framework and a cross-case typology to support comparative research and grounded urban CE policy design. Full article

The efficient scheduling of airport logistics vehicles is crucial for ensuring timely and cost-effective ground operations, particularly under dynamic disturbances such as flight delays, cancellations, and new task arrivals. With the increasing deployment of Internet of Things (IoT) technologies in airport environments, real-time data from sensors and connected devices enables efficient and adaptive scheduling. This paper considers a dynamic Airport Logistics Vehicle Scheduling (ALVS) problem that aims to minimize both vehicle usage and total task waiting time while satisfying task precedence and time window constraints. To address this problem, we propose a hybrid optimization framework, termed Periodic and Event-Driven Rolling Horizon Optimization (PERHO), which integrates periodic updates with event-driven rescheduling to adapt to real-time task variations in airport ground operations. Within PERHO, an Order-aware Adaptive Strategy Selection (OASS) algorithm is developed to dynamically select the most appropriate task sequencing heuristic from a candidate set based on recent performance and order relationships. Extensive experiments across various instance scales and dynamic scenarios demonstrate the effectiveness of the proposed PERHO-OASS approach. In experiments considering dynamic events, PERHO-OASS reduces vehicle usage and task waiting time by an average of 23.55% and 61.95%, respectively, over fixed heuristic algorithms, and by an average of 3.77% and 17.30% over adaptive selection methods, demonstrating strong robustness under uncertainty. The proposed approach can support airport operators in improving the efficiency and reliability of ground logistics operations. Full article

Accurate multi-horizon forecasting of reservoir water levels is essential for effective water resource management and flood risk mitigation. While deep learning models have demonstrated strong predictive capabilities, they often struggle to adequately represent uncertainty and extreme hydrological events, particularly at longer forecast horizons. This study proposes four variants of a Conv1D–LSTM–Temporal Attention (CLTA) architecture for probabilistic multi-horizon forecasting, differing exclusively in loss function design. The models incorporate non-crossing constraints, horizon-aware weighting, and event-aware weighting to address key limitations of standard quantile regression approaches. All models are trained on hourly water level data from May 2021 to October 2022 and evaluated on a fully unseen dataset spanning December 2022 to May 2023. The results demonstrate that horizon-aware weighting achieves the lowest average RMSE (0.0149) and the most stable performance across forecast horizons on unseen data, while event-aware weighting improves representation of extreme hydrological events and achieves the highest coefficient of determination ($R^2=0.9961$). However, a controlled experiment further reveals that model performance is strongly influenced by the data partitioning strategy, even when architecture and loss formulation are held constant. Overall, the findings indicate that loss function design, in interaction with data partitioning strategy, is a key contributing factor to model performance in deep learning-based hydrological forecasting. A Multi-Criteria Decision Analysis (MCDA) framework identifies the horizon-weighted model as the most robust general-purpose solution, while the event-aware model is preferable for applications focused on extreme event representation. These results highlight the importance of integrating domain knowledge into both model design and evaluation strategy, offering a scalable and computationally efficient alternative to increasing architectural complexity. Full article

In the context of mass customization and mixed-model production systems, Master Production Scheduling (MPS), which determines production start dates, plays a critical role. However, in such environments, MPS faces a dual challenge: ensuring due-date adherence under multiple capacity constraints while also reducing operational instability caused by uneven day-to-day consumption of critical components, referred to as Replenishment and Industrial Characteristics (RICs). This paper proposes a new mathematical model for MPS with a Smoothing Mechanism for RICs (MPS-SM). This bi-objective formulation extends a baseline due-date-driven model with an explicit production smoothing/leveling (also known as Heijunka) term, minimizing deviations of RIC usage from weekly ideal levels. By embedding smoothing directly into MPS, the approach provides a pre-leveling effect that can reduce (or ideally eliminate) downstream complexity, specifically related to schedule modifications required in a separate smoothing stage. To reflect changing scheduling priorities, smoothing is weighted through an innovative context-aware non-linear weekly function that assigns lower importance near execution and greater importance farther into the horizon. The models are evaluated in a rolling-horizon simulation-optimization framework using data from a real-world truck manufacturer. Several experiments over 300 discrete-event simulated days show that MPS-SM consistently reduces RIC variability while inducing a controlled increase in lateness penalties. Full article

Background and Aims: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of mortality worldwide, with lipid abnormalities playing a central role in disease development. While the causal role of low-density lipoprotein cholesterol (LDL-C) in ASCVD is well-established, the long-term population impact of combined lipid profiles, particularly the HDL-C/LDL-C ratio, remains less clearly quantified. This study aimed to estimate the lifetime burden of cardiovascular outcomes associated with different lipid risk profiles using a patient-level simulation framework. Methods: The authors analyzed projected lifetime ASCVD events across four HDL-C/LDL-C risk strata, ranging from low (≥0.45) to very high (<0.25), using the National Health Model Database of Projected and Estimated Outcomes (NHM-DPEO)—a digital twin of national healthcare systems built from multiple data sources, including national health and demographic statistics and estimates from the relevant literature. The framework is structured as a patient-level simulation model that projects individual health trajectories over a lifetime horizon. Model outputs were assessed for plausibility by comparison with published epidemiological estimates. Results: The NHM simulation revealed a strong, graded relationship between lipid profiles and cardiovascular survival. Life expectancy declined from 80.2 years in the lowest risk group (HDL-C/LDL-C ≥ 0.45) to 63.0 years in the very-high-risk group (HDL-C/LDL-C < 0.25), a reduction of 17.2 years, with 13.7 fewer QALYs. Similarly, participants with LDL-C > 5.0 mmol/L had a life expectancy 13.4 years shorter than those with LDL-C < 3.1 mmol/L. The burden of ASCVD increased exponentially with worsening lipid ratios: MI events rose from 5000 to 73,090 per 100,000 births, with onset in the highest risk group occurring as early as age 20. Ischaemic heart disease followed a similar pattern, showing up to 92% of events attributable to elevated lipid risk. While ischaemic stroke risk displayed a more complex pattern due to earlier MI mortality in high-risk groups, overall cardiovascular mortality and lifetime event burden were dominated by LDL-driven disease. These findings demonstrate that sustained LDL-C reduction and balanced HDL-C/LDL-C ratios confer substantial survival benefits across both sexes and all age groups. Conclusions: This study shows that lipid balance has a decisive influence on cardiovascular survival. Sustained LDL-C reduction and favourable HDL-C/LDL-C ratios markedly extend life expectancy and delay the onset of MI and IHD. The magnitude of this survival benefit highlights the need for early and continuous lipid control as a cornerstone of ASCVD prevention. The NHM quantifies these lifetime effects, offering valuable insights for targeted strategies that improve both longevity and quality of life. Full article

This paper presents a deployment-oriented longitudinal platoon-control architecture for connected and autonomous vehicles operating under repeated leader hard-braking, cut-ins, and spatially varying road friction. The proposed stack combines four elements: (i) a lightweight scalar Kalman filter (KF) that smooths a friction-related signal and feeds friction-dependent constraint tightening; (ii) a model predictive control (MPC) backbone whose weights and horizon are selected offline using multi-objective GA/NSGA-II tuning; (iii) a bounded proximal policy optimization (PPO) residual policy, trained with the aid of a learned surrogate model, that refines the MPC command during transient events; and (iv) a command-level safety projection that enforces instantaneous actuation and clearance constraints at the fast control tick. The contribution is therefore not a new MPC formulation or a new reinforcement-learning algorithm in isolation, but an integrated and experimentally characterized control stack that keeps the safety-critical structure explicit while using learning to improve transient behavior. The method is evaluated in a CARLA digital twin of a six-vehicle platoon over a 5 km mixed urban–highway route and is further assessed in hardware-in-the-loop (HIL) on an automotive ECU using a multi-rate ROS 2/AUTOSAR implementation (50 Hz estimation/safety loop, 10 Hz MPC/RL refresh). Across 10 held-out disturbance seeds, the full stack improves spacing regulation, maintains non-amplifying disturbance propagation according to the reported string-stability indices, and reduces a route-normalized positive tractive-energy-at-the-wheels proxy by about 12% relative to Manual MPC and by up to 18% relative to a PID-CACC reference. Because the PID-CACC baseline does not enforce hard constraints and can collide under the tested disturbance suite, the main performance comparison is among collision-free controllers. The friction signal used in CARLA is derived from simulator road-surface annotations before filtering, so the present study should be interpreted as a friction-aware control and integration study rather than a validated onboard friction-estimation result. Likewise, the reported energy metric is an effort proxy and is not a calibrated fuel or battery consumption model. Full article

Accurate day-ahead estimation of residential demand response (DR) potential is essential for load aggregators participating in electricity markets. It is also difficult to estimate because public residential datasets rarely contain observed DR event labels and household flexibility is shaped by heterogeneous, weather-sensitive consumption behavior. This paper proposes an appliance-agnostic two-stage framework for forecasting residential DR potential from aggregate hourly load and weather data. In the first stage, a thermal-response model estimates household heating and cooling sensitivities and converts thermostat-setback assumptions into synthetic DR-potential targets. Because these targets are model-derived proxies rather than measured DR events, the reported forecasting errors should be interpreted in terms of accuracy against a physically motivated synthetic target. In the second stage, the synthetic target sequence is forecast using a mixture of KAN experts (MoKE). The architecture combines Wavelet-KAN, Fourier-KAN, and RBF-KAN experts through sparse top-k routing with reversible instance normalization, allowing the model to represent local irregularities, recurrent daily/seasonal structure, and smooth nonlinear response regimes in the same forecasting layer and these forecasting characteristics are absent from traditional deep learning forecasting models. The framework is evaluated on the UMass residential dataset, which contains hourly electricity and meteorological measurements from 114 apartments collected during 2015 and 2016, using a 24 h day-ahead forecasting horizon. Across both winter and summer evaluation windows, the proposed model achieves the lowest error among all benchmark methods, outperforming TimesNet, Informer, N-HiTS, FEDformer, PatchTST, and TCN across MAE, MAPE, RMSE, and sMAPE. In particular, MoKE attains MAE values of 3.19 in winter and 3.18 in summer, demonstrating stable predictive accuracy under seasonally distinct operating conditions. These results show that heterogeneous KAN experts offer a feasible method for residential DR forecasting when appliance-level metering and observed event-level DR measurements are unavailable. Full article

In high-inflation economies, stablecoins are increasingly becoming infrastructural channels through which households and firms access U.S.-dollar value outside traditional financial arrangements. We study Argentina as a fragmented USD access system composed of a regulated official channel, an informal parallel channel (the Blue Dollar), and platform-based USDT channels on Binance and Bitso. Using a quantile time-frequency connectedness framework, we estimate reduced-form dynamic dependence and spillover patterns across these interdependent subsystems under normal and extreme market states and across short- and long-term horizons. Four main findings emerge. First, system-wide connectedness is dominated by short-term transmission and rises sharply during policy regime transitions, particularly around the relaxation of capital controls. Second, under normal conditions, stablecoin markets behave as early-moving net spillover transmitters, whereas the Blue Dollar and the official rate primarily absorb shocks. Third, connectedness exhibits a symmetric U-shaped pattern across quantiles, indicating that tail events intensify cross-channel dependence regardless of shock direction. Fourth, under upper-tail extreme market states, the official rate becomes a net transmitter in the long-term frequency band, implying that major devaluation episodes can temporarily reconfigure the system’s transmission architecture, even though stablecoin channels remain important in overall connectedness. These findings should be interpreted as evidence of dynamic dependence rather than structural causality. They suggest that digital dollarization does not simply add another trading venue; it increases boundary permeability, reshapes information hierarchy, and changes the monitoring problem faced by authorities in fragmented financial systems. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

Climate change and intensifying extreme weather events challenge plant adaptability, making the evaluation of adaptive potential imperative. This study aims to identify climatically stable habitats for Rhododendron dauricum, a nationally protected (Class II) shrub species in China. Species occurrence records were integrated with multiple environmental datasets, and habitat suitability was inferred using a maximum entropy model under current and future climate scenarios. The model outputs indicate that habitat suitability is primarily driven by temperature and moisture, vegetation plays a secondary role, and topographic and soil factors are less influential. Projections show a consistent contraction of suitable habitats, particularly in highly suitable areas, with stronger declines under higher emission scenarios and longer time horizons. Spatial patterns shift from continuous to fragmented distributions, with suitable habitats increasingly concentrated in the northeastern regions and northern mountain ranges. Core areas that remain suitable across scenarios are identified through multi-scenario consistency analysis, representing climatically stable regions. These areas should be prioritized for in situ conservation, while populations maintaining high suitability across scenarios may serve as candidate provenances for ex situ conservation and future landscape deployment. This study elucidates the adaptive potential of R. dauricum under future climate scenarios and identifies key environmental drivers, informing conservation, breeding, and climate-adaptive management. Full article

Empathetic dialogue systems built upon large language models overwhelmingly adopt a monolithic inference paradigm that processes emotion perception, causal reasoning, memory retrieval, and response planning within a single forward pass without architecturally enforced intermediate representations, forfeiting intermediate-state transparency and long-horizon personalization. Drawing on neuroscientific and cognitive–psychological evidence that human empathy is functionally dissociable, we present MOSAIC (Multi-agent Orchestration with Structured Affective memory for Interpretable empathiC dialogue), a training-free framework that operationalizes empathetic dialogue as a four-stage cognitive pipeline: affective perception, causal appraisal, episodic memory retrieval, and response synthesis. Three innovations distinguish MOSAIC from prior work: (1) a cognitively motivated modular architecture whose functionally dissociable stages enable post hoc failure attribution through logged intermediate states; (2) a hierarchical three-tier emotional memory—perceptual, semantic, and episodic—coupled with adaptive three-dimensional retrieval over emotion, situation, and coping-strategy cues; and (3) a heterogeneous model orchestration strategy coordinating open-source and API-accessible models through role-specific chain-of-thought prompts, requiring no task-specific fine-tuning. We note that the EmpatheticDialogues evaluation pre-populates the memory store with 200 training-split episodes prior to test-set interaction, a data-access asymmetry relative to single-model baselines that must be borne in mind when interpreting comparative results. Experiments on EmpatheticDialogues and ESConv show that MOSAIC achieves a 76.4% weighted F1 and an empathy score of 3.87 (on a 1–5 Likert scale) and that it improves over single-model, training-free baselines on aggregate empathy and—most prominently—on human-rated personalization (3.67 vs. 3.24 against Claude-3.5 five-shot, $d=0.48$). We caution that the comparison against training-free baselines is not data access-controlled (see the cold-start discussion in Methods); the personalization advantage, supported by the ablation without the Event Agent, is the result we treat as the primary practical contribution of this work. Full article

We perform three-dimensional general relativistic magnetohydrodynamic (GRMHD) simulations of a near-maximally spinning black hole (spin parameter $a=0.998$) with varying initial magnetic field geometries, systematically exploring the parameter space connecting magnetically arrested disk (MAD), intermediate (INT), and standard and normal evolution (SANE) accretion states. The magnetic flux threading the black hole horizon emerges as the fundamental state variable controlling jet efficiency, flow magnetization, and radiative output across all three states. We introduce complementary diagnostics—broadband spectral energy distributions spanning radio through hard X-ray frequencies and time-resolved X-ray light curves—that together connect simulation dynamics directly to multiwavelength observables. The radiative output follows a clear MAD > INT > SANE hierarchy in time-averaged luminosity, mean X-ray emission, as well as variability. Furthermore, MAD exhibits the highest fractional variability through quasi-periodic magnetic flux eruption events, and INT and SANE show moderate variability driven by episodic reconnection and stochastic MRI turbulence, respectively. Scaling to GRS 1915+105, Cyg X-1, and HLX-1, we demonstrate that all twelve temporal classes of GRS 1915+105 map naturally onto our three magnetic states, Cyg X-1’s persistent hard state is reproduced by a sustained INT configuration, and HLX-1’s extreme luminosities arise through efficient Blandford–Znajek extraction in MAD states scaled to higher black hole mass. Full article