Search Results (3,690)

Under ground-based observation geometry, infrared radiant-intensity sequences of space objects are jointly influenced by object micromotion, thermal radiation, time-varying viewing conditions, and atmospheric propagation. Existing simulation studies often prescribe the line of sight or simplify the coupling between viewing geometry and atmospheric attenuation, which limits long-duration ground-based sequence analysis. This study develops a physics-informed framework for generating atmosphere-attenuated infrared radiant-intensity sequences of space objects undergoing precession or tumbling. The framework reconstructs observation geometry from azimuth–elevation–range trajectories, updates facet normals through a unified micromotion attitude model, computes visible projected area and transient facet temperature, and incorporates MODTRAN-derived elevation-dependent atmospheric transmittance. Using this framework, we construct IRPeriodic, an eight-class simulated dataset for long-duration univariate time-series classification. We further propose LPD-Net, which integrates large-kernel residual feature extraction, prototype-guided dynamic temporal alignment, and differential periodic representation to capture long-range waveform morphology, sample-dependent temporal correspondence, and segment-level local variation. On IRPeriodic, LPD-Net achieves an accuracy of 0.8618 ± 0.0057, a macro-F1 of 0.8615 ± 0.0061, and a Matthews correlation coefficient of 0.8426 ± 0.0065, outperforming the evaluated neural-network and ROCKET-type baselines. Ablation and synthetic-noise sensitivity analyses indicate that the performance gain is mainly associated with long-context feature extraction, with additional improvements from dynamic alignment and differential periodic statistics. Auxiliary experiments on selected public UCR datasets suggest that the representation is also competitive for univariate time-series classification. These results demonstrate the effectiveness of LPD-Net on the proposed physics-informed benchmark for long-duration ground-based infrared radiant-intensity sequence classification. Full article

Long-term displacement in ultra-high rockfill dams is spatially heterogeneous, yet point-based monitoring and single-track InSAR provide only a limited account of its three-dimensional evolution. To address this limitation, we develop a workflow combining multi-track three-dimensional InSAR reconstruction, temporal feature clustering and modified HST/HTT modeling. In the modified models, the conventional linear time term is replaced by a predominant displacement component, allowing nonlinear long-term behavior to be represented alongside hydraulic and thermal responses. Three Sentinel-1 tracks acquired between 2014 and 2023 were used to analyze the Pubugou Dam. Maximum vertical and eastward displacement rates reached 17.70 and 19.41 mm/yr, respectively, while cumulative vertical displacement reached approximately 178 mm. Three coherent displacement zones were resolved: the upper dam was dominated by long-term accumulation, the central section displayed the strongest periodic response, and the lower dam and abutments remained comparatively stable. The modified models reproduced the observed series more closely in sample than the conventional formulations, while the fitted coefficients indicated a stronger and more spatially variable response to reservoir level than to air temperature. This integrated analysis provides a spatially resolved account of long-term displacement and environmental response across an ultra-high rockfill dam. Full article

To address the critical challenge of extracting short-period surface deformation signals induced by extreme floods from InSAR time series, this study focuses on the catastrophic flood that struck the Haihe River Basin in July 2023 (hereinafter referred to as the “23·7” flood, with a total duration of approximately 65 days) and proposes a novel method for transient deformation signal extraction. Using Sentinel-1A satellite data and the PS-InSAR technique, we constructed a multivariate composite fitting function comprising a linear trend term, annual and semi-annual seasonal terms, a step term, and a logarithmic decay term. Through nonlinear least-squares fitting, this approach achieves effective separation of long-term tectonic deformation, seasonal fluctuations, high-frequency noise, and transient flood-related signals. The results show that the W-shaped floodplain east of Xiong’an New Area does not exhibit the expected subsidence induced by water loading but instead features pronounced surface uplift of up to 30 mm. Multi-physics forward modeling reveals the underlying mechanism: the elastic subsidence caused by surface water loading, calculated via the LoadDef spherical loading theory, amounts to only ~2 mm. In contrast, forward modeling based on the GMS three-dimensional groundwater seepage model and the principle of effective stress indicates that the pore water rebound effect can produce surface uplift of up to ~36 mm. The superposition of these two effects is highly consistent with InSAR observations in terms of magnitude, direction, and spatial distribution, confirming that the flood-induced surface deformation is dominated by the pore water rebound effect driven by rapid groundwater recharge, rather than subsidence from water loading. The proposed framework extends the application potential of geodetic techniques for monitoring short-period extreme hydrological events. Full article

Purpose: Excessive drill advancement after cortical breakthrough is a potential safety concern in orthopaedic procedures. We developed a data-driven approach to estimate the drill-tip position relative to the far cortex prior to breakthrough using time-series thrust force and spindle torque signals. Methods: Drilling experiments were performed on 268 porcine cortical bone specimens at a constant feed rate of 0.5 mm/s. A long short-term memory network was trained to estimate the drill-tip position from filtered force and torque signals. The reference position was derived from breakthrough timing confirmed by high-speed imaging and the programmed feed rate. Performance was evaluated using mean absolute error within the −2 to +2 mm peri-breakthrough interval. Two post hoc analyses examined whether model performance exceeded an elapsed-time baseline and whether pre-breakthrough force patterns were more consistent when expressed relative to breakthrough position than to drilling onset time. Results: The combined-input LSTM achieved an MAE of 0.20 mm, compared with 0.23 mm for force alone and 0.24 mm for torque alone. Among the representative architectures evaluated, LSTM showed the lowest regression error. A signal-blind time-only baseline yielded an MAE of 0.54 mm. The association between cortical thickness and force-decline onset was weaker when expressed in spatial coordinates relative to breakthrough than when expressed as time from drilling onset (R² = 23% vs. 74%). These findings suggest that force and torque signals contained information associated with proximity to breakthrough beyond that provided by average drilling duration alone. Conclusion: Converting sensor-derived resistance patterns into spatially anchored positional information may support proactive strategies such as controlled deceleration before penetration. The proposed approach represents a step toward exemplifying the emerging concept of surgeon-assisting Physical AI. Full article

We propose a data-driven procedure, based on convolutional variational autoencoders, to identify the presence of signal pulses in long time series. The dataset consists of synthetic waveforms, each composed of non-Gaussian noise and a log-normal-shaped signal of variable intensity, with a length of 10,000 samples. The model heavily compresses the input waveforms, allowing a direct study of such a reduced representation. After training for 150 epochs on 7500 waveforms, a region in the latent space where the network encodes time-series presenting only background noise emerges, allowing, in turn, to tag as candidates for containing a signal those falling outside. When applied to a test dataset of freshly generated waveforms, 100% of events with signal amplitudes well above the baseline noise are correctly labelled, and this fraction only decreases for amplitudes comparable with accidental noise pulses. This approach was designed to fully exploit the measurements in dual-phase Liquid Argon Time Projection Chambers, as the one of the Recoil Directionality experiment, built in the context of the Darkside project. Full article

Seepage pressure time series of concrete dams are governed by reservoir water level, rainfall, temperature and long-term aging effects, featured by strong nonstationarity and complex multi-scale fluctuations. Existing decomposition–ensemble methods ignore nonlinear coupling among scale components, suffering low prediction accuracy and poor physical interpretability. Current model fusion schemes fail to adapt to differentiated evolution mechanisms of frequency-varying seepage components and cannot fully mine implicit cross-scale nonlinear correlations. To overcome these drawbacks, this study proposes a concrete dam seepage pressure prediction approach integrating ensemble empirical mode decomposition, multi-scale preprocessing, and optimized dual-model selection combining ridge regression and Transformer–BiLSTM. Ensemble empirical mode decomposition adaptively denoises and decouples raw seepage series into high-, medium- and low-frequency IMFs according to oscillation cycles. A normalized Comprehensive Optimization Index is constructed to parallelly train ridge regression and Transformer–BiLSTM for each component and select the optimal submodel dynamically. A fully connected nonlinear fusion layer reconstructs multi-scale predictions to retain inherent component coupling features, replacing traditional simple linear superposition. Engineering cases verify that the proposed model efficiently captures periodic laws of key influencing factors, significantly boosting prediction accuracy and generalization capacity, thus possessing prominent theoretical and practical engineering application values. Full article

Since the implementation of the Grain-for-Green Program (GGP), vegetation across the Loess Plateau (LP) has substantially recovered. However, whether the associated increase in ecosystem carbon gain was accompanied by a proportional increase in water consumption and whether groundwater storage changed synchronously remain unclear. This study integrated multi-source remote sensing products, GLDAS-Noah land-surface assimilation data, GRACE/GRACE-FO satellite gravimetry, irrigation water-use data, provincial water-use statistics, and coal-resource information to examine long-term changes in gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency (WUE), soil moisture (SM), and groundwater storage anomaly (GWSA) during 2002–2023. GPP increased significantly by 10.67 g C m⁻² yr⁻¹ ($p<0.01$), whereas ET increased more modestly by 1.97 mm yr⁻¹ ($p<0.05$). The relative growth rate of GPP (1.66%) was approximately 3.5 times that of ET (0.47%), and WUE increased by 0.018 g C m⁻² mm⁻¹ yr⁻¹ ($p<0.01$). In the XGBoost–SHAP models for 2004–2019, LAI showed the strongest model-based association with GPP and WUE, whereas ET was associated more broadly with LAI, air temperature, and precipitation. SM declined during 2002–2015 but showed an increasing tendency during 2016–2023, particularly in the middle and deep layers. The long-term GWSA slopes derived from CSR and JPL were −8.707 and −9.505 mm yr⁻¹, respectively, and the averaged CSR–JPL GWSA series showed a Sen’s slope of −9.131 mm yr⁻¹. GWSA declined during 2002–2020 and showed only a short-term, nonsignificant increase during 2020–2023 (4.110 mm yr⁻¹, $p>0.05$). These contrasting trajectories indicate that increases in surface carbon uptake and improvements in soil-water conditions were not accompanied by synchronous regional groundwater recovery. Overall, the ecological-restoration period was accompanied by increased carbon gain and WUE without a proportional increase in regional ET, while groundwater storage followed a distinct trajectory. These findings provide regional-scale evidence and a quantitative basis for coordinating sustainable water-resource management with ecological-restoration optimization on the LP. Full article

Background: South Korea reports one of the highest adolescent suicide rates among OECD countries. Both psychological factors (perceived stress, depression) and somatic/behavioral factors (atopic disease, obesity, diet, physical activity) have been proposed as correlates of adolescent suicidality, but the extent to which their co-occurring long-term national trends reflect genuine, independent associations—rather than a shared secular (time) trend—has rarely been tested at the population level. Methods: We compiled national annual prevalence estimates (2005–2025) from the Korea Youth Risk Behavior Survey (KYRBS), disseminated via the Korean Statistical Information Service (KOSIS), for eleven indicators. We computed (1) raw Pearson correlations, (2) year-detrended partial correlations, (3) multiple and hierarchical regression, and (4) an exploratory observed-variable path model on the year-adjusted series. Results: In raw correlations, nearly all indicators were significantly associated with the suicide attempt rate (|r| = 0.30–0.92). After removing the shared time trend, only perceived stress (partial r = 0.82, p < 0.001) and depressive mood (partial r = 0.81, p < 0.001) remained strongly and independently associated with suicide attempts. Detrended residuals were approximately normal (Shapiro–Wilk p > 0.26), and the two psychological associations were confirmed by Spearman rank-based partial correlations and permutation tests (both p ≤ 0.0002). A joint regression (suicide ~ stress + depression, year-adjusted) explained 93.5% of variance (adjusted R² = 0.923), and stress and depression added significant explanatory power beyond calendar year (ΔR² = 0.229, p < 0.001). Conclusions: Among the indicators examined, perceived stress and depressive mood show a robust population-level association with adolescent suicide attempts that is not attributable to a shared secular trend, whereas most somatic and behavioral correlates do not. The path model is exploratory and descriptive only and does not test causal mechanisms. Findings are ecological (population-level) and cannot establish—or exclude—individual-level causation. Full article

Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. Full article

Against the dual strategic backdrop of carbon peaking and carbon neutrality goals and high-quality urbanization development, extant literature exhibits four prominent research gaps: oversimplified evaluation indicator systems, exclusive exploration of the unidirectional carbon impacts exerted by urbanization, a scarcity of long-time-series coupling analyses targeting the Yangtze River Economic Belt (YEB), and functional fragmentation between coupling coordination assessment and predictive simulation tools. Drawing on panel data covering 11 provinces and municipalities within the YEB spanning 2000 to 2021, this study constructs a comprehensive urbanization evaluation framework encompassing four dimensions: population, economy, society, and spatial layout. Meanwhile, an integrated carbon emission assessment system is established from the perspectives of population, economy, energy consumption, and carbon sinks. The entropy-weight method is adopted to assign indicator weights, and a combination of the coupling coordination degree model and system dynamics (SD) model is employed to analyze spatiotemporal evolutionary characteristics and simulate development trends from 2022 to 2032. By organically integrating the coupling coordination model and the SD model, this study establishes an integrated analytical framework that unifies static comprehensive evaluation and driving-mechanism decomposition, thereby compensating for the limitations of time-series forecasting models such as the grey prediction model and ARIMA, which only fit trends from historical data. Empirical results reveal that regional urbanization levels witnessed sustained growth across 2000–2021, with spatial urbanization acting as the core driving pillar. The overall coupling coordination degree maintained a steady upward trajectory, while the east–west regional disparity gradually narrowed. The simulation projections for 2022–2032 demonstrate continuous improvements in coordinated development across the entire basin: the coupling coordination degree ranges from 0.788 to 0.954 for the eastern region, 0.810 to 0.859 for the central region, and 0.752 to 0.865 for the western region. Such spatial differentiation corresponds to distinct practical development pathways: low-carbon stock optimization in the east, low-carbon industrial undertaking in the central zone, and clean energy transition acceleration in the west. All provincial-level administrative regions are projected to achieve an upgrade in their coupling coordination grades by 2032. This study acknowledges several limitations: missing raw data are supplemented via interpolation, only a single baseline scenario is simulated, predictive uncertainty is not quantitatively measured, and subjectivity persists in the weight assignment of coupling subsystems. Ultimately, differentiated low-carbon urbanization governance strategies are proposed for the three sub-regions, offering empirical references for the coordinated realization of dual carbon targets throughout the Yangtze River basin. Full article

Water vapor (H₂O) acts as both an essential thermodynamic driver and a primary source of chemical radicals in the middle atmosphere, playing an irreplaceable role in maintaining Earth’s radiative balance and indicating long-term climate variability. In this study, 24 years (2002–2025) of H₂O measurements from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite are systematically analyzed to characterize the H₂O spatiotemporal distribution throughout the middle atmosphere (specifically within the 20–80 km altitude range), with a focus on elucidating its evolutionary patterns across time, altitude, and latitude. Building upon this analysis, an empirical model for the bimonthly mean water vapor volume mixing ratio (VMR) is constructed based on actual measurements. Employing a nonlinear least-squares fitting algorithm, time-series fitting is performed on the data within distinct altitude and latitude grids. Consequently, a mathematical analytical expression for the time series was derived for each latitudinal band at every altitude grid point, alongside the determination of corresponding fitting parameter sets. By integrating these parameterized formulas and derived parameters, a comprehensive empirical H₂O VMR model spanning multiple altitude layers and a broad latitudinal range was ultimately established. Validation results demonstrate that the empirical model exhibits high consistency with the original observational data. The coefficients of determination ($R^2$) generally exceed 0.7 and strictly remain $\ge$ 0.6 in all cases. Furthermore, the model demonstrates strong linear correlation with actual observations (Pearson correlation coefficients typically exceeding 0.8) and maintains low bias, as evidenced by small root mean square errors (mostly < 0.35 ppmv) and mean absolute errors (mostly < 0.25 ppmv) across diverse spatial grids. These evaluation metrics collectively indicate excellent goodness-of-fit and robust reconstruction capabilities. This model provides a reliable empirical reference for investigating the spatiotemporal evolution of middle atmospheric H₂O VMR and serves as a potential data foundation for future optimizations of relevant radiative transfer models. Full article

Electricity theft remains a critical threat to power distribution infrastructure globally, with annual losses exceeding USD 89 billion and non-technical loss rates reaching 40% in developing economies. While machine learning has emerged as the dominant analytical approach for automated theft detection in smart grid environments, the field lacks a unifying framework that connects algorithm selection to the operational realities of Distribution System Operators (DSOs). Existing reviews catalogue methods and report benchmark metrics without addressing how detection paradigm selection should be aligned with data maturity, regulatory requirements, computational constraints, and institutional capacity. This review addresses that gap by systematically analysing 90 peer-reviewed studies published between 2015 and 2025, identified through structured multi-database searches, screened against explicit eligibility criteria, and graded with a formal five-criterion quality rubric, through a unified adversarial time-series formulation that provides a consistent analytical lens across all major learning paradigms. The analysis covers supervised ensemble methods, unsupervised and semi-supervised anomaly detection, deep learning architectures, including convolutional neural networks, long short-term memory networks and Transformer models, graph neural networks, federated learning, and explainable artificial intelligence. Key findings reveal that no single paradigm achieves optimality across all deployment dimensions simultaneously, that gradient boosting methods deliver near state-of-the-art performance with significantly lower computational overhead than deep learning, and that hybrid architectures achieve AUC-ROC scores of 0.95 to 0.98 on benchmark datasets but require complementary governance mechanisms to satisfy regulatory defensibility requirements. A lifecycle-aligned deployment framework and a layered detection architecture are proposed, offering practitioners a structured pathway from early AMI rollout through to advanced smart grid deployment. The principal outcomes of the review are a formal characterisation of which component of the detection problem each learning paradigm estimates, quality-graded and harmonised benchmark performance ranges, and a quantified illustrative analysis indicating that the proposed layered architecture can improve inspection productivity by roughly an order of magnitude at a fixed field budget. Four priority research challenges are identified: real-time edge detection, continual learning, multi-modal data fusion, and standardised benchmarking. Full article

In the era of intelligence, accurate remaining useful life (RUL) prediction is essential to ensure the reliable operation of smart equipment, particularly for rolling bearings—critical components that are highly susceptible to degradation in rotating machinery. However, as faults progressively develop, the vibration signals of rolling bearings exhibit strong non-stationarity and complex degradation patterns. Existing RUL prediction methods, particularly standard Transformer-based models, often struggle to capture local transient features within non-stationary signals and fail to effectively decouple long-term degradation trends from periodic variations. To overcome these limitations, a novel RUL prediction method that integrates time-series analysis techniques with the FEDformer architecture is proposed, termed TE-FEDformer. Firstly, a feature enhancement module is employed at the input stage to reconstruct and strengthen the original sequence, aiming to strengthen the representation of weak fault features that are often overlooked by global attention mechanisms. Then, deep time-series representations are extracted via the encoder. In the decoding stage, a frequency enhancement mechanism and a sequence decomposition mechanism are jointly utilized to explicitly model the coupling between degradation trends and periodic variations, thus resolving the spectral interference commonly encountered in complex degradation processes. Comparative experimental results on the PHM2012 and XJTU-SY datasets demonstrate that TE-FEDformer outperforms other benchmark models. Ablation studies further validate that each module contributes positively to the overall performance, confirming the effectiveness of the proposed approach for RUL prediction. Full article

Smart-diaper signals collected during routine care are affected by sensor noise, transmission gaps, variable event durations, and limited labels, making normal voiding (NV) and urinary incontinence (UI) difficult to distinguish using threshold-based detection alone. We developed a label-efficient time-series classification framework based on Context-Aware Temporal Contrastive Coding (CA-TCC) using the resistance ($R_{\mathrm{VAL}}$) channel of a smart-diaper sensor. Recordings from 97 older residents across three long-term care facilities were quality-filtered, aggregated at 3 min intervals, screened for candidate events, and interpolated to fixed-length inputs. CA-TCC was pre-trained on an unlabeled candidate-event pool and adapted using 4877 manually labeled events. The linear-probe, full fine-tuning, and class-aware pseudo-label retraining configurations were evaluated using participant-grouped five-fold cross-validation. The selected semi-supervised configuration achieved $82.12\pm 2.33$% accuracy, $82.12\pm 2.32$% macro-F1, and an AUC of $0.895\pm 0.018$ (mean ± 95% confidence interval), exceeding the strongest classical baseline by 4.80 macro-F1 percentage points. Its macro-F1 increased from $79.22\pm 1.84$% with 1000 labeled events to $81.97\pm 1.82$% with the full labeled set, whereas full fine-tuning showed greater fold-to-fold variability. Aggregated LIME analysis over 300 held-out events did not support localization of the model’s evidence to the event onset. These results indicate that contrastive pre-training can support smart-diaper voiding-pattern classification when labeled data are limited. Full article

Water quality time series exhibit multi-scale periodicities, including daily and weekly cycles, driven by solar radiation, tidal forcing, and seasonal variation. Existing deep learning methods typically rely on patch-based attention or adaptive period decomposition, which suffer from parameter redundancy and high computational cost while failing to explicitly align with physical periodicities. To address this challenge, we propose HarmonicFormer, which restructures sequences into phase-period matrices, explicitly injects multi-scale periodic priors via harmonic temporal encoding, and achieves linear-complexity interactions through a lightweight cross-phase routing mechanism. Evaluated on hourly data from 37 monitoring stations in the Pearl River Basin (2020–2026) across nine water quality parameters and six forecast horizons, HarmonicFormer achieves the lowest average MSE of 0.3809 and MAE of 0.3753 over 54 experimental configurations, while maintaining high training efficiency and significantly reducing long-term error accumulation. Ablation studies confirm the effectiveness of harmonic encoding, reversible instance normalization, and key hyperparameters. This work offers an efficient and reliable explicit-periodicity modeling approach for water quality forecasting in the Pearl River Basin. Although the model currently adopts a fixed period length, future work can further enhance its generalization capability by introducing adaptive period discovery mechanisms. Full article