Search Results (20)

Fingertrap is a network encryption and authentication protocol that extends the X3DH and Double Ratchet frameworks with three novel mechanisms inspired by the Chinese finger trap (zhĭ wăng): a friction ratchet that exponentially increases computational cost for each failed authentication attempt; a recursive annihilation protocol that irreversibly destroys all cryptographic state after a configurable failure threshold; and a commit-then-challenge handshake that requires a counterintuitive “inward” action for legitimate authentication. A bidirectional weave hash extends the Double Ratchet’s transcript binding to cover every message in both directions. Together, these mechanisms provide per-message forward secrecy, post-compromise security (self-healing), clock-free operation, and a self-destruct capability. The individual ingredients-client puzzles, key erasure, and ratcheting-each build on established lines of work; their combination into a single stateful protocol, in which failed authentication attempts cryptographically tighten the session state and ultimately destroy it, is not to our knowledge offered by deployed transport protocols such as TLS 1.3, Signal, or WireGuard. The design targets deployments in which interception or capture of a device implies endpoint compromise, such as Unmanned Aerial Vehicle (UAV) telemetry links and body-worn sensors, where denial of exploitation requires guaranteed loss of past and future session material. We describe the full protocol, provide game-based security arguments under an explicit adversarial model, give analytic cost estimates for the friction mechanism, analyse the denial-of-service surface and a two-layer mitigation strategy, and specify a post-quantum extension using hybrid $\mathsf{X25519}\text{/}\mathsf{ML}\text{-}\mathsf{KEM}\text{-}\mathsf{768}$ ratcheting. Full article

Accurate electricity price forecasting is essential for market participants seeking to optimise bidding and arbitrage strategies. This paper presents a week-ahead (168 h) hourly electricity price forecasting study for the Spanish day-ahead market. Nine competing models—two naïve baselines (a Seasonal Naïve and a Day-of-Week persistence), a Lasso-estimated auto-regressive (LEAR) statistical benchmark, and six machine- and deep-learning models (CatBoost, Random Forest, LSTM, GRU, CNN, and a hybrid CNN–LSTM)—are benchmarked; the two leading models, CNN–LSTM and CatBoost, are then compared under exogenous-feature configurations. The analysis is complemented by an ex-post Add-One-In and Leave-One-Out feature-importance analysis, a controlled comparison of weather-input scenarios, and a rolling battery-arbitrage backtest that translates forecast quality into economic value. Under an endogenous benchmark of weekly rolling origins across 2024 (with a rotating start weekday) and Diebold–Mariano testing, a recursive CatBoost and the hybrid CNN–LSTM are statistically indistinguishable and both significantly outperform a direct multi-horizon CatBoost; once an operational (forecasted) weather input is added, recursive CatBoost becomes significantly the most accurate while remaining simpler and more stable to train, a ranking confirmed on a fully out-of-sample 2025 year. Operational weather forecasts are found to be the best weather input, recovering about 84% of the perfect-foresight weather improvement over a no-weather baseline, with the advantage concentrated at longer lead times. Natural-gas-fired generation emerged as the dominant explanatory feature, consistent with the marginal-pricing mechanism governing the Spanish market. In a rolling battery-arbitrage backtest on the out-of-sample 2025 year, a deployable forecast-driven 4-h grid-scale unit (200 MW/800 MWh) captured about 89% of perfect-foresight value at a 168 h optimisation horizon and about 87% at 24 h; extending the horizon from 24 h to 168 h added about 2.4% of profit, an optimisation-horizon (look-ahead) effect bounded at +4.5% under perfect foresight. Full article

Electricity load forecasting is essential for balancing energy supply and demand, reducing energy waste, and maintaining power grid stability. Accurate forecasts enable power utilities to optimize energy dispatch and mitigate the risk of supply shortages or outages. However, conventional forecasting methods often struggle to capture highly nonlinear local fluctuations in electricity consumption and long-term temporal dependencies. To address these challenges, this study proposes MSCNN-ResLSTM, a hybrid model for multi-step electricity load forecasting. The proposed model integrates Multi-Scale Convolutional Neural Networks (MSCNNs) to extract local time-series features at multiple temporal scales, residual blocks (ResBlocks) to enhance feature representation through residual connections, and Long Short-Term Memory (LSTM) networks to model long-range temporal dependencies. To comprehensively evaluate its effectiveness, a cross-paradigm experimental framework is established in which MSCNN-ResLSTM is compared with seven representative benchmark models from three methodological categories: traditional machine learning (Extreme Gradient Boosting-XGBoost), classical recurrent and convolutional neural networks (LSTM, Temporal Convolutional Network-TCN, CNN-LSTM, MSCNN-LSTM, and Direct LSTM (Seq2Seq)), and self-attention-based architectures (Transformer). Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation. Full article

Machine learning-based network intrusion detection systems are often optimized using aggregate accuracy, although operational security depends on the reliable detection of rare, high-impact attacks. This paper proposes a data-preserving intrusion detection framework that combines hybrid feature selection, heterogeneous ensemble learning and cost-sensitive optimization for imbalanced multi-class attack detection. The method first applies Mutual Information filtering and Recursive Feature Elimination to reduce the NSL-KDD feature space from 122 one-hot encoded attributes to 25 discriminative features. Four classifiers, Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors, are evaluated individually, and a stacking ensemble is constructed using Logistic Regression as a meta-learner. Class imbalance is addressed by balanced class weighting rather than by synthetic oversampling, preserving the original minority-class observations. Experiments on the NSL-KDD benchmark show that the proposed cost-sensitive configuration improves rare attack recognition, most notably increasing U2R recall from 0.00% to 35.82% (24 of 67 test instances) for the stacking ensemble; this improvement, together with the accompanying weighted F1-score change from 0.7120 to 0.7214, is statistically significant under the Wilcoxon signed-rank test across repeated random seeds, and both values are reported with their variability rather than as single point estimates. SVM obtains the largest global gain, with a 7.06 percentage point improvement in weighted F1-score. The results show that cost-sensitive learning is a simple and practical mechanism for improving rare-attack visibility, but also reveal a remaining limitation for R2L detection, where feature overlap with Normal traffic remains substantial. The revised validation design explicitly includes direct resampling baselines, repeated-seed evaluation, statistical significance testing, feature-subset sensitivity analysis, and absolute true-positive counts for R2L and U2R in order to avoid overinterpreting marginal point-estimate gains. All experiments, including the resampling comparison, the component ablation, the feature-subset sensitivity analysis and the repeated-seed statistical evaluation, are executed on the complete KDDTrain+ training set of 125,973 instances under a single unified protocol, so that every reported per-class value refers to the same experimental setting. The revised study additionally reports probability-level evaluation for the primary model, including class-level PR-AUC, precision-recall curves and a U2R threshold and alert-budget analysis, and validates the framework externally on the UNSW-NB15 benchmark, where balanced class weighting raises the recall of the rarest categories (Worms, Shellcode, Backdoor) from near-zero baseline levels to 69–96% under an identical protocol. Full article

Accurate forecasting of power production and consumption is essential for the efficient operation of smart energy grids, enabling stable energy exchange and grid reliability. However, the growing integration of photovoltaics (PVs) and electric vehicles introduces significant uncertainty. This paper evaluates multiple Machine Learning (ML) models for single- and multi-step forecasts of PV generation and household consumption, incorporating uncertainty bounds to inform operator decisions. We use data from two Swedish sites and the CityLearn benchmark dataset to compare direct, recursive, and hybrid multi-step strategies. LightGBM with gradient-boosted quantile regression achieves the best single-step performance, with Mean Absolute Error (MAE) as low as 10.19 W in Halmstad and 16.12 W in Uppsala. For multi-step forecasts, the direct method outperforms others, reaching a 48 h consumption MAE of 71.08 W in Uppsala and 52.05 W in Halmstad, and achieving prediction interval coverage probabilities above 0.95. Moreover, personalized models trained on individual households outperform generalized ones, even with smaller datasets, highlighting the value of tailored approaches for improving forecast accuracy under uncertainty. Full article

Partial discharge (PD) signals acquired from gas-insulated switchgear (GIS) are often severely contaminated by discrete-spectrum interference and periodic narrowband noise, which impairs the accuracy of subsequent fault diagnosis. This paper proposes a hybrid denoising method that integrates Spearman coefficient-optimized variational mode decomposition (S_VMD), spatially related recursive sample entropy (Sdr_SampEn) for intrinsic mode function (IMF) classification, an improved wavelet threshold function, and Savitzky–Golay (SG) filtering. First, the Spearman correlation coefficient between the original signal and the reconstructed signal is used to adaptively determine the optimal mode number K of VMD, avoiding the over- and under-decomposition problems of conventional VMD. Second, Sdr_SampEn, which characterizes signal irregularity along both the Chebyshev distance and spatial direction of a recurrence plot, is employed to classify the obtained IMFs into noise-dominant and PD-dominant components, with the discrimination threshold calibrated as p = 1.94 at 0 dB. Third, an improved wavelet threshold function—continuous at the threshold and asymptotically unbiased—is applied to the noise-dominant components, while SG filtering is applied to the PD-dominant components, after which the denoised signal is reconstructed. The results demonstrate that the proposed method effectively suppresses both white and narrowband noise while preserving the detailed morphology of PD pulses. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

Wind power generation exhibits pronounced volatility and intermittency, and direct grid connection may cause instability in grid frequency. To address this issue, this paper proposes an optimisation strategy for hybrid energy storage systems to mitigate wind power fluctuations, integrating lithium-ion batteries with supercapacitors within wind power systems. Firstly, the grid-connected power of wind turbines and the reference power of the energy storage system are determined through dynamic weight adjustment using a weighted filtering algorithm combining adaptive exponential smoothing and recursive averaging algorithms. Secondly, the fish-eagle optimisation algorithm is employed to refine variational modal decomposition parameters. The modal components derived from decomposing the energy storage system’s reference power are converted into Hilbert marginal spectra. Following determination of the cut-off frequency, high-frequency signal components are managed by supercapacitors, while low-frequency components are handled by lithium-ion batteries. Finally, an optimised configuration model for the hybrid energy storage system is constructed to minimise the annual lifecycle target cost. Case study analysis demonstrates that this approach effectively smooths fluctuations in wind power output while fully leveraging the complementary characteristics of both energy storage types, achieving a balance between system economics and overall performance. Full article

Accurate humidity forecasts are crucial for environmental and operational applications, yet Numerical Weather Prediction systems frequently exhibit systematic and random errors. To address this problem, this study introduces a modified hybrid post-processing approach that extends a previously developed methodology, enabling a direct comparison of computational efficiency and predictive capacity. The proposed framework integrates a quadratic Kalman Filter with a Radial Basis Function Neural Network trained via the Orthogonal Least Squares algorithm and updated online through Recursive Least Squares. This modified method was evaluated via a time-window process, using forecasts from the Weather Research and Forecasting model and recorded observations from stations in northern Greece. The results show substantial improvements in forecast accuracy, as the Bias was reduced by over 85%, and the MAE and RMSE decreased by approximately 65% and 58%, respectively, compared with the baseline model. Furthermore, the proposed framework also demonstrates enhanced computational efficiency, reducing processing time by more than 95% relative to the initial methodology. Full article

This paper proposes a Hybrid Recursive Trigonometric (HRT) technique for FPGA-based direct digital frequency synthesizers. The HRT technique integrates a recursive cosine generator with periodic reinitialization via a second-order Taylor polynomial to reduce cumulative errors without requiring ROMs or iterative CORDIC units. A resource-efficient combinational architecture is implemented and validated on the Lattice iCE40HX1K FPGA. The effectiveness of the proposed HRT technique is evaluated through simulation and FPGA-based experiments, with respect to spectral accuracy and resource efficiency, particularly for fixed-point cosine waveform synthesis in low-resource digital systems. Simulation results show that the system has a spurious-free dynamic range (SFDR) of −86.09 dBc and signal-to-noise ratio of 52.74 dB using 16-bit fixed-point arithmetic. Experimental measurements confirm the feasibility, achieving −58.86 dBc SFDR. Full article

Although there do exist a few aeroacoustic studies on harmful artificial phenomena related to the usage of non-uniform Cartesian grids in lattice Boltzmann methods (LBM), a thorough quantitative comparison between different categories of grid arrangement is still missing in the literature. In this paper, several established schemes for hierarchical grid refinement in lattice Boltzmann simulations are analyzed with respect to spurious aeroacoustic emissions using a weakly compressible model based on a D3Q19 athermal velocity set. In order to distinguish between various sources of spurious phenomena, we deploy both the classical Bhatnagar–Gross–Krook and other more recent collision models like the hybrid recursive-regularization operator, the latter of which is able to filter out detrimental non-hydrodynamic mode contributions, inherently present in the LBM dynamics. We show by means of various benchmark simulations that a cell-centered approach, either with a linear or uniform explosion procedure, as well as a vertex-centered direct-coupling method, proves to be the most suitable with regards to aeroacoustics, as they produce the least amount of spurious noise. Furthermore, it is demonstrated how simple modifications in the selection of distribution functions to be reconstructed during the communication step between fine and coarse grids affect spurious aeroacoustic artifacts in vertex-centered schemes and can thus be leveraged to positively influence stability and accuracy. Full article

Overbounding the integrity risk is a significant challenge for Kalman filter (KF)-based advanced receiver autonomous integrity monitoring (ARAIM) when the measurement error has an uncertain time correlation. Thus, this paper presents a method that addresses this challenge by effectively bounding the integrity risk for KF-based ARAIM while considering the uncertainty in the model of the time-correlated error. Firstly, the recursive equation for covariance is derived, establishing a direct mathematical expression that links the integrity risk and the correlation time constant. Subsequently, a min–max optimization model is constructed, utilizing the obtained expression as the objective function, to simultaneously bound the integrity risk and reduce conservatism. To effectively address the current min–max optimization problem, a hybrid evolutionary algorithm is proposed, which conducts global searching followed by local searching. The simulation result demonstrates that it outperforms other algorithms, enabling rapid attainment of the minimum upper bound on the integrity risk. Full article

The in-plane folding wing is one of the important research directions in the field of morphing or bionic aircraft, showing the unique application value of enhancing aircraft maneuverability and gust resistance. This article provides a structural realization of an in-plane folding wing and an aeroelasticity modeling method for the folding process of the wing. By approximating the change in structural properties in each time step, a method for calculating the structural transient response expressed in recursive form is obtained. On this basis, an aeroelasticity model of the wing is developed by coupling with the aerodynamic model using the unsteady panel/viscous vortex particle hybrid method. A wind-tunnel test is implemented to demonstrate the controllable morphing capability of the wing under aerodynamic loads and to validate the reliability of the wing loads predicted by the method in this paper. The results of the gust simulation show that the gust scale has a significant effect on the response of both the open- and closed-loop systems. When the gust alleviation controller is enabled, the peak bending moment at the wing root can be reduced by 5.5%∼47.3% according to different gust scales. Full article

Accurate aboveground vegetation biomass forecasting is essential for livestock management, climate impact assessments, and ecosystem health. While artificial intelligence (AI) techniques have advanced time series forecasting, a research gap in predicting aboveground biomass time series beyond single values persists. This study introduces RECMO and DirRecMO, two multi-output methods for forecasting aboveground vegetation biomass. Using convolutional neural networks, their efficacy is evaluated across short-, medium-, and long-term horizons on six Kenyan grassland biomass datasets, and compared with that of existing single-output methods (Recursive, Direct, and DirRec) and multi-output methods (MIMO and DIRMO). The results indicate that single-output methods are superior for short-term predictions, while both single-output and multi-output methods exhibit a comparable effectiveness in long-term forecasts. RECMO and DirRecMO outperform established multi-output methods, demonstrating a promising potential for biomass forecasting. This study underscores the significant impact of multi-output size on forecast accuracy, highlighting the need for optimal size adjustments and showcasing the proposed methods’ flexibility in long-term forecasts. Short-term predictions show less significant differences among methods, complicating the identification of the best performer. However, clear distinctions emerge in medium- and long-term forecasts, underscoring the greater importance of method choice for long-term predictions. Moreover, as the forecast horizon extends, errors escalate across all methods, reflecting the challenges of predicting distant future periods. This study suggests advancing hybrid models (e.g., RECMO and DirRecMO) to improve extended horizon forecasting. Future research should enhance adaptability, investigate multi-output impacts, and conduct comparative studies across diverse domains, datasets, and AI algorithms for robust insights. Full article

In this work, we highlight three different techniques for automatically constructing the dataset for a time-series study: the direct multi-step, the recursive multi-step, and the direct–recursive hybrid scheme. The nonlinear autoregressive with exogenous variable support vector regression (NARX SVR) and the Gaussian process regression (GPR), combined with the differential evolution (DE) for parameter tuning, are the two novel hybrid methods used in this study. The hyper-parameter settings used in the GPR and SVR training processes as part of this optimization technique DE significantly affect how accurate the regression is. The accuracy in the prediction of DE/GPR and DE/SVR, with or without NARX, is examined in this article using data on spot gold prices from the New York Commodities Exchange (COMEX) that have been made publicly available. According to RMSE statistics, the numerical results obtained demonstrate that NARX DE/SVR achieved the best results. Full article