Search Results (7,583)

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Outlier detection in time series is traditionally formulated as the identification of rare or extreme observations with respect to global statistical properties. While effective for stationary processes, this perspective becomes insufficient in complex and non-stationary systems, where atypical behavior may manifest as disruptions of stable relationships rather than numerical extremeness. This paper proposes a rule-based framework for detecting structural outliers in non-stationary time series. Regular system behavior is represented by an interpretable set of deterministic IF–THEN rules describing stable relational patterns between features. Each rule defines a logical context and an admissible range of a diagnostic quantity, estimated nonparametrically from historical observations satisfying the rule condition. For a given observation, the set of active rules is identified and a structural inconsistency score is computed as the fraction of violated rule consequences. Additionally, observations lacking support from high-frequency contexts are treated as candidates for structural atypicality. The method is deterministic and avoids the need for explicit probabilistic modeling or iterative parameter learning, which simplifies interpretation and implementation. The framework is illustrated on daily EUR/USD data (2010–2022) using technical indicators (EMA, RSI) and absolute log-returns as the diagnostic measure. Results provide evidence that structurally atypical events can be identified even when global statistical thresholds remain unviolated, suggesting the practical relevance of relational analysis for non-stationary time series monitoring contexts. Full article

Aiming at the problem that complex underwater environments induce outliers in Doppler Velocity Log (DVL) measurements, which degrade the navigation accuracy of the Strapdown Inertial Navigation System (SINS)/DVL integrated system, this paper proposes a soft-gating Gaussian–Student’s t mixture robust Kalman filter (MRKF). Firstly, the measurement noise is modeled as a mixture of Gaussian and Student’s t distributions to adapt to normal stationary noise and abrupt outliers, respectively. Secondly, a logistic soft-gating weight is constructed based on the innovation Mahalanobis distance to adaptively balance the output contributions of the standard Kalman Filter (KF) and the variational Bayesian Student’s t filter. Finally, moment matching is adopted to realize the weighted fusion of two-branch posterior distributions, and an equivalent Gaussian posterior estimation is obtained. Simulation results under the considered SINS/DVL integrated navigation scenarios show that the proposed MRKF maintains estimation accuracy close to the standard KF under nominal Gaussian measurement noise. In the designed DVL outlier-injection scenario, the proposed MRKF achieves a position RMSE of $53.39\mathrm{m}$, compared with $878.75\mathrm{m}$, $58.84\mathrm{m}$, and $56.49\mathrm{m}$ for the nominal KF, Huber KF (HKF), and Student’s-t variational Bayesian KF (STVBKF), respectively. These results indicate that the proposed MRKF can improve robustness against DVL outliers while maintaining competitive estimation accuracy under the simulated conditions. Full article

Suburban areas increasingly shape contemporary urbanisation, yet public-space dynamics in these environments are weakly represented by conventional urban indicators. This study examines suburban public-space use as a behavioural dimension of urban metabolism, understood here as the observable patterns of human movement, activity, and co-presence occurring within suburban public spaces. It addresses the limited ability of density- or infrastructure-based measures to capture everyday spatial practices in dispersed, car-oriented settings. While urban metabolism research has expanded beyond material and energy flows, empirical evidence linking configurational accessibility with directly observed public-space behaviour in suburban contexts remains limited. To address this gap, we integrate district-scale space syntax analysis with site-scale UAV-based observation across five public spaces in and around Gdańsk, Poland. Based on a dataset comprising 30 standard observation sessions conducted in September and October 2024, spatial syntax indicators (integration and choice) were used to characterise configurational accessibility and support location selection, while UAV monitoring captured traffic intensity, stationary presence, diversity of activities, and temporal rhythms of use. The results reveal distinct behavioural metabolic profiles shaped by interactions between spatial configuration, functional programming, and temporal dynamics. These profiles vary depending on the function of public spaces and dominant modes of movement (pedestrian or vehicular). The study demonstrates that suburban urban metabolism cannot be interpreted through configurational accessibility or residential density alone. By linking space syntax measures with a repeatable UAV observation protocol, the proposed framework supports comparative assessment of suburban public-space performance and informs planning interventions aimed at suburban transformation and improved accessibility. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

The efficiency of airport resource allocation is improved through the establishment of a scientific multi-granularity configuration scheme for flight slot coordination parameters. In this study, a collaborative configuration method for hourly and 15 min coordination parameters is proposed, with Beijing Capital International Airport serving as a case study. Short-term traffic clusters are frequently omitted by traditional hourly parameters, thereby leading to sudden delay surges. First, local delays were extracted from March 2024 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data. Subsequently, a delay prediction model was constructed through the integration of a non-stationary queuing model and a gradient boosting regression tree. Second, simulated timetables were generated via a Monte Carlo method under various parameter combinations. With a constant daily flight volume utilized as the experimental baseline, a mapping relationship was established between parameter combinations and expected local delays. Finally, feasible delay regions were delineated and interpretable configuration rules were extracted via a decision tree to maximize schedule flexibility. It was indicated by the results that at an hourly parameter of 70 flights, the target delay is maintained below 8 min by tightening the 15 min parameter to 19 flights. The findings suggest that average load is controlled by hourly parameters, while traffic clustering in high-load scenarios is effectively suppressed by 15 min parameters. A quantitative reference is provided by this method for the configuration of multi-granularity time parameters at hub airports. Full article

Dynamic Wireless Power Transfer (DWPT) is attracting increasing interest as a promising solution to extend the operating range of battery electric vehicles while reducing stationary charging needs. In this study, a DWPT system for Electric Vehicle charging is investigated through a comparative simulation-based case study focused on the Italian A4 highway, a strategic transport corridor characterized by high traffic intensity and long-distance mobility demand. The proposed system is based on a segmented magnetic coupling architecture with planar circular coils installed along the roadway and a vehicle-side pickup coil. Under common roadway, vehicle, and magnetic coupling assumptions, a benchmark Tesla Model 3 Long Range traveling at a constant speed of 90 km/h and characterized by an estimated energy consumption of 0.129 kWh/km is considered. Two compensation solutions are comparatively assessed, namely the Series–Series (SS) topology and the Inductor-Capacitor-Capacitor (LCC) topology. The methodology evaluates the two topologies under the same benchmark conditions in terms of peak power, average transferred power, transferred energy per kilometer, and effect on vehicle State Of Charge (SOC). The SS topology provides a peak power of 22.52 kW, an average power of 12.30 kW, and an energy transfer of 0.14 kWh/km, whereas the LCC topology reaches a peak power of 20.44 kW, an average power of 13.47 kW, and an energy transfer of 0.15 kWh/km. Starting from an initial SOC of 30%, the final SOC after traveling through the usable electrified highway section reaches 37.48% with SS compensation and 44.28% with LCC compensation. The results show that both topologies enable effective dynamic charging, with the LCC solution exhibiting better energy transfer capability and higher operational stability, while the SS topology delivers higher instantaneous power peaks. From a comparative simulation perspective, the study supports the technical feasibility of DWPT deployment in highway environments and provides useful design insights for selecting compensation topologies in dynamic electric vehicle charging applications. Full article

Exhaust emission assessment of heavy-duty diesel engines is commonly based on complementary steady-state and transient procedures, represented by the World Harmonized Steady-State Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC). However, under transient operation, tailpipe NO_x and CO₂ signals cannot be directly assigned to instantaneous engine operating states because the measured response is affected by transport delay, analyser dynamics, and signal dispersion within the measurement chain. This paper proposes a machine-learning-assisted dynamic synchronization framework for aligning engine operating signals with tailpipe emissions under transient conditions. The method uses actual engine torque as the primary dynamic reference and determines local effective alignment between emission readings and the engine operating states that generated them. The synchronized data are then evaluated using an XGBoost-based modelling approach to assess whether emission characteristics obtained from WHSC steady-state operation can be transferred to WHTC transient operation. The results show that the proposed synchronization improves the physical consistency of transient emission data and provides a more reliable basis for comparing stationary and dynamic test outcomes. The transferability analysis indicates good predictive consistency for CO₂, whereas NO_x shows only partial transferability, reflecting stronger transient sensitivity and more complex formation dynamics. The proposed framework supports intelligent emission-data preprocessing, data-driven interpretation of heavy-duty engine tests, and assessment of the representativeness of steady-state tests under transient operating conditions. Full article

This study proposes a practical framework for estimating instantaneous stay-cable tension in cable-stayed bridges based on the first-order frequency moment (FFM). The proposed framework combines cepstrum-guided modal decomposition, FFM-based instantaneous frequency estimation, windowed cepstrum-based consistency assessment, and energy-weighted multi-modal averaging to estimate instantaneous cable tension from measured vibration responses. Unlike conventional time–frequency analysis methods that rely on local peak extraction in the time–frequency domain, the proposed approach directly estimates instantaneous frequency from the local time–frequency energy distribution, thereby improving tracking robustness while maintaining computational efficiency under operational conditions. Numerical validation demonstrates reliable instantaneous frequency tracking under noisy and non-stationary vibration conditions while maintaining low computational cost. Field validation using acceleration- and displacement-based measurements from an in-service bridge further confirms the capability of the proposed framework to capture vehicle-induced transient tension variations. The results indicate that the framework provides reliable and physically consistent cable tension information under real operational conditions. These characteristics, together with computational efficiency and compatibility with existing monitoring systems, indicate strong potential for near-real-time structural health monitoring applications. Full article

The work is a continuation of studies , focused on the development of fundamental principles of aluminum activation by low-melting metals forming eutectic alloys with fine-grained structure and limited solid solubility. The aim of this work is to investigate the mechanism and kinetics of the interaction of aluminum-based eutectic alloys with water and aqueous electrolytes. Analysis of phase diagrams of binary systems (Al–Ga, Al–In, In–Ga, Al–Sn, Sn–Ga, Al–Zn, Zn–Ga) shows that alloy composition governs surface heterogeneity and reactivity. Ternary and quaternary systems (Al–In–Ga, Al–Sn–Ga, Al–In–Sn–Ga) exhibit enhanced interaction with water due to increased heterogeneity, leading to the formation of numerous microgalvanic couples and accelerated aluminum dissolution. The process is characterized by the stationary potential of aluminum and involves coupled chemical, electrochemical, and topochemical stages described by the Avrami–Erofeev equation, with n ≈ 1.27–2.07. An increase in the In–Ga or In–Sn–Ga fraction reduces the activation energy: 9.1 kcal/mol (82% Al–9% Ga–9% Sn), 11.4 kcal/mol (92% Al–4% Ga–4% In), and 15.5 kcal/mol (91% Al–3% Ga–3% In–3% Sn). Full article

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power. Full article

To address the non-stationary and non-linear characteristics of vibration signals collected by sensors in pumped-storage units, as well as their susceptibility to strong background noise interference, this paper proposes a joint signal denoising method combining parameter-adaptive Variational Mode Decomposition (VMD) and wavelet thresholding. First, the Improved Particle Swarm Optimization (IPSO) algorithm is utilized to adaptively optimize the key parameters of VMD using a comprehensive fitness function as the objective, thereby achieving the optimal decomposition of the signal. Subsequently, a cross-correlation analysis method is introduced to screen the decomposed components, followed by a secondary denoising process using a wavelet threshold to accomplish the final signal denoising. Experimental validations using simulated run-out signals and field-measured sensor data from a pumped-storage power station, along with comparisons against other methods, demonstrate that the proposed method can eliminate noise more effectively. It significantly improves the signal-to-noise ratio (SNR) and reduces the root mean square error (RMSE). Consequently, this study provides a reliable data foundation for the subsequent research and analysis of the units, demonstrating substantial practical engineering significance. Full article

Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (GWO) can improve one-step-ahead Bitcoin forecasting within a controlled model family. We compare four configurations, LSTM, SLSTM, GWO-LSTM, and GWO-SLSTM, on 4039 daily BTC–USD closing prices from 17 September 2014 to 9 October 2025 using Min–Max normalization, strict chronological splitting, windowed regime-based robustness analysis across three distinct market regimes, and repeated-run testing. The proposed SLSTM replaces the conventional hidden-state recurrence with leaky integrate-and-fire-inspired synaptic, membrane, and adaptive-threshold dynamics, functioning as a spiking-inspired recurrent model with thresholded event gating (reset = `none’, learnable threshold). On the primary hold-out split, GWO-SLSTM achieved a test RMSE of 1840.97 and a test MAPE of 1.76%, compared with 2217.24 and 2.46% for GWO-LSTM, 3501.48 and 3.86% for SLSTM, and 4030.10 and 4.40% for LSTM. Both GWO-optimized models exhibited substantial improvements over their non-optimized counterparts, while the SLSTM baseline also outperformed the plain LSTM, indicating gains from both spiking-inspired recurrence and evolutionary hyperparameter optimization. Both optimized models exhibited near-zero bias (PBIAS 0.11% for GWO-LSTM and 0.36% for GWO-SLSTM). Within the present implementation, GWO-SLSTM also trained faster than GWO-LSTM (39.71 s vs. 137.28 s), although this runtime difference should be interpreted as setup-specific because the model families were implemented in different frameworks and stopped after different numbers of epochs. Overall, within the expanded univariate BTC–USD setting, the results support GWO-SLSTM as a strong within-family candidate for one-step-ahead forecasting under non-stationary conditions. Full article

Taking the Yellow River Basin (YRB) as an example, this study explores the non-stationary drought evolution features in large river basins under climate change. This study utilized precipitation and multiple climate factor data to establish the non-stationary standardized precipitation index (NSPI) through the GAMLSS model. Combined with the run theory, Copula function and a cascaded RF-LSTM machine learning model, the drought characteristics and retrospective predictive patterns were systematically assessed. The results show that: (1) The Arctic Oscillation, the Pacific Decadal Oscillation, the Southern Oscillation and the North Pacific Index are the primary climate drivers of non-stationary precipitation variation in the YRB, with the former three being selected most frequently and NPI additionally influencing April–June and September, and their effects are both different and lagging. Compared with the traditional SPI, the NSPI assigned higher drought grades and greater severity to typical drought years (e.g., the 1974 event was rated D3 with a severity of 17.935 by NSPI versus D2 with 11.733 by SPI), and thus better captured non-stationary drought evolution. (2) The duration of droughts exhibited a decreasing trend that was not statistically significant (p > 0.05), whereas drought intensity and severity decreased significantly (p < 0.05); the peak severity showed a significant upward trend (p = 0.0078). Spatially, the northwest of the Loess Plateau was a compound core area with high severity, high frequency and long duration of droughts, while the upper reaches were mainly characterized by low severity, short duration and sudden droughts. (3) The drought risk in the YRB shows a higher frequency in the lower reaches and a lower frequency in the upper reaches. The middle and lower reaches were high-risk areas, with shorter AND-type joint exceedance return periods for moderate drought (2.46–5.83 years) and severe drought (3.77–9.15 years). The upper reaches were low-risk areas, with longer return periods reaching up to 5.83 years for moderate drought and 9.15 years for severe drought. The study shows that the NSPI, considering the driving of multiple climate factors, can more effectively identify and assess non-stationary drought risks, providing a scientific basis for drought prevention and control in river basins. Full article

This study determines the size of the means-of-production markets (MMP) generated by Bulgarian crop production and assesses their comparability with crop product markets (PM). Eight component markets are analyzed: fertilizers, irrigation water, plant protection products, seeds and planting material, buildings and stationary equipment, agricultural machinery, technical and other services, and energy. The methodology integrates firm-level financial data from domestic producers, international trade statistics, and official national data. A detailed market reconstruction based on quantities, prices, and absolute and relative shares is conducted for the reference years 2023 and 2024, constituting the core analytical layer of the study. To test the structural stability of the symmetry relationship across a broader price cycle, the symmetry analysis is extended to the period 2021–2024 using official data aggregate for all markets. The symmetry coefficient (MMP/PM) shows structural comparability ranging from ½ to over ¾ between the two market systems over the full period, averaging 0.72 for 2023–2024. Price dynamics exert a stronger influence on market symmetry than volume changes. Crop product markets exhibit substantially greater price volatility than means-of-production markets. The combined economic contribution of Bulgarian crop production—integrating direct output value with the means-of-production markets it generates—amounted to over EUR 5756.3 million in 2024, substantially exceeding the total agricultural sector output reported in national accounts and implying a real contribution to GDP well above the officially recorded 3% share. Full article