Search Results (332)

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R²), with R² reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production. Full article

Background/Objectives: Blood glucose prediction (BGP) for individuals with type 1 diabetes (T1D) is a clinically essential yet highly challenging task in time series forecasting (TSF) and an important problem in personalised medicine. Accurate bespoke BGP is crucial for individualised T1D management, reducing complications, and supporting patient-specific glycaemic risk mitigation. However, the pronounced volatility of glycaemic fluctuations in T1D, combined with the need for mathematical rigor and clinical relevance, hampers reliable prediction. This complexity underscores the demand to explore and enhance more advanced techniques. While adversarial learning is adept at modelling intricate data variability, its potential for BGP remains largely untapped. Methods: This work presents a novel approach for BGP by addressing a key limitation in conventional adversarial learning when applied to this task. Typically, these methods optimise prediction accuracy within a set horizon by minimising adversarial loss. This focus overlooks how predictions align with longer-term patterns, which are critical for clinical relevance in BGP, thereby yielding suboptimal results. To overcome this limitation, we introduce collaborative augmented adversarial learning, designed to improve the model’s temporal awareness. Incorporating collaborative interaction optimisation, this approach enables the model to reflect extended time dependencies beyond the immediate horizon, thereby improving both the clinical reliability of predictions and overall predictive performance. We develop and evaluate four learning systems for BGP: independent learning, adversarial learning, collaborative learning, and adversarial collaborative learning. The proposed systems were evaluated for two clinically relevant prediction horizons, namely 30 min and 60 min ahead. Results: The interdependent collaboratively augmented learning frameworks, validated using the well-established Ohio T1D datasets, demonstrate statistically significant superior performance in both clinical and mathematical evaluations. Conclusions: Beyond advancing BGP accuracy and clinical reliability, the proposed approach supports personalised medicine by improving subject-specific glucose forecasting from CGM data, with potential relevance for more individualised diabetes monitoring and decision support. The proposed approach also opens new avenues for advancements in other complex TSF domains, as outlined in our future work. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Battery datasets, whether gathered experimentally or through simulation, are typically high-dimensional and complex, which complicates the direct interpretation of degradation behavior or anomaly detection. To overcome these limitations, this study introduces a framework that compresses battery signals into a low-dimensional representation using an autoencoder, enabling the extraction of informative features for state analysis. A central component of this work is the systematic comparison of latent representations obtained from two fundamentally different data sources: frequency-domain impedance data and time-domain voltage-current data. The close agreement of aging trajectories in both representations suggests that information traditionally derived from impedance analysis can also be captured directly from raw time-series signals. To better approximate real operating conditions, synthetic datasets are augmented with stochastic perturbations. In this context, latent spaces learned from idealized periodic inputs are contrasted with those derived from permuted and noise-contaminated signals. The resulting low-dimensional features are subsequently evaluated through a support vector machine with both linear and nonlinear kernel functions, allowing the categorization of battery states into fresh, aged and damaged conditions. The results demonstrate that the progression of battery degradation is consistently reflected in the latent space, independent of the input domain or signal quality. This robustness indicates that the proposed approach can effectively capture essential aging characteristics even under non-ideal conditions. Consequently, this framework provides a basis for developing advanced diagnostic strategies, including the design of pseudo-random excitation profiles for improved battery state assessment and optimized operational control. Full article

In high-speed spindle motors operating above 10,000 rpm, vibration analysis is essential for detecting mechanical anomalies. However, data scarcity and imbalance, especially for rare fault conditions, limit the performance of deep learning-based anomaly detection models. In this study, we define sample scarcity as the limited availability of real labeled vibration sequences for model training, i.e., only 5000 normal and 5000 faulty samples collected from three spindle motors (10,000 real samples in total). We propose a Transformer-based Variational Autoencoder (T-VAE) to generate realistic triaxial acceleration sequences for spindle motor health monitoring. The model integrates positional encoding and multi-head self-attention to capture long-range temporal dependencies in multivariate time-series data, and applies a KL annealing strategy to improve training stability. Using 5000 normal and 5000 faulty vibration samples collected from three spindle motors, the model generates 100,000 synthetic samples per class, which are used to augment training for a downstream CNN–LSTM classifier. Without augmentation, the classifier achieved 95.73% pass detection on normal samples and 81.40% fail detection on faulty samples. After augmentation with Transformer-VAE, performance increased to 98.07% pass detection for normal data and 97.99% fail detection for faulty data. For prediction, we evaluate on an independent dataset of 25,000 normal and 25,000 faulty sequences obtained from eleven different spindle motors not used in training (cross-spindle). The results demonstrate that the T-VAE effectively alleviates the data scarcity problem and significantly improves anomaly detection accuracy for high-speed spindle motor vibration signals. This approach can be directly applied to predictive maintenance systems in real-world manufacturing environments. Full article

Accurate rice detection is essential for food security, sustainable agriculture, and environmental monitoring. Satellite time series observations provide scalable capabilities for rice detection; however, their application in tropical regions is challenged by persistent cloud contamination, asynchronous crop development cycles, and temporal misalignment among multisensor observations, which reduce classification reliability. This study introduces Multivariate Robust Time Series Boosting (MRTS-Boosting), a quality-aware framework for multivariate time series classification (TSC) designed to improve robustness under noisy and irregular observational conditions. The framework integrates quality-weighted feature construction, joint extraction of full-series and interval-based temporal features, and a flexible multivariate formulation that accommodates heterogeneous satellite inputs without strict temporal alignment. Performance was evaluated using synthetic datasets with controlled cloud contamination, 103 benchmark datasets from the University of California, Riverside (UCR) TSC Archive, and 3261 real-world rice field observations from Indonesia. Comparisons were conducted against representative whole-series, interval-based, shapelet-based, kernel-based, and ensemble classifiers. MRTS-Boosting achieved up to 87% accuracy under severe cloud contamination, an average rank of 2.7 on noise-augmented UCR datasets, and 93% accuracy with Cohen’s kappa of 0.76 for Indonesian rice detection, while maintaining moderate computational cost. These results demonstrate that MRTS-Boosting provides a robust, scalable, and computationally efficient framework for satellite-based rice detection. The framework remains competitive in univariate settings while benefiting from multisensor integration, indicating that performance gains arise from both methodological design and the effective use of heterogeneous data. MRTS-Boosting is therefore well-suited for precision agriculture applications under challenging observational conditions. Full article

Accurate forecasting of mining-induced surface deformation is critical for coal-mine safety assessment and hazard mitigation. InSAR deformation time series are often short, temporally sparse, and strongly nonlinear. These characteristics can make purely data-driven predictors unreliable in small-sample settings. To address this issue, we propose a generation–prediction–interpretation framework that combines generative augmentation with physics-informed forecasting. We first develop a TCN-TimeGAN model to synthesize high-fidelity deformation sequences and expand the training set. Recurrent modules in the generator and discriminator are replaced with causal TCN residual blocks, and a temporal self-attention layer is further stacked on top of the TCN backbone to adaptively reweight informative time steps. We then construct a physics-informed Kolmogorov–Arnold Network, termed PI-KAN. Subsidence-consistency and smoothness priors are embedded in the learning objective to promote physically plausible predictions while retaining spline-based interpretability. Experiments on SBAS-InSAR deformation series from the Guqiao coal mine show that the framework achieves an RMSE of 0.825 mm and an R² of 0.968. It outperforms TGAN-KAN, CNN-BiGRU, and BiGRU under the same evaluation protocol. Visualizations of the learned spline-based edge functions further reveal stronger nonlinear responses for lagged inputs closer to the forecast horizon, providing interpretable evidence of short-term temporal sensitivity under sparse observations. Full article

(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article

Timely assessment of macroeconomic conditions is essential because official gross domestic product (GDP) statistics are released with substantial delays and are often revised. This study examines whether high-frequency highway traffic volumes, disaggregated by vehicle type, improve short-term GDP nowcasting in the Republic of Korea. Using nationwide expressway traffic data from 328 toll plazas over the period from September 2008 to September 2025, we integrate traffic series with conventional macroeconomic indicators into a mixed-frequency dynamic factor model and evaluate pseudo-real-time nowcasting performance against official quarterly GDP releases. Time-series diagnostics indicate that traffic volumes contain short-horizon predictive information for GDP and satisfy stationarity requirements after appropriate transformation. In the full evaluation sample, the macro-only benchmark records an RMSE of 1.0258 and an MAE of 0.8716. Adding aggregated traffic changes these metrics only marginally (RMSE = 1.0269, MAE = 0.8696), whereas the model augmented with the heaviest freight class (Vehicle Type 6) performs best, lowering RMSE to 1.0179 and MAE to 0.8652. During the COVID-19 period, forecast accuracy deteriorates across specifications: aggregated traffic increases RMSE and MAE to 1.3456 and 1.2096 relative to the macro-only benchmark (RMSE = 1.3082, MAE = 1.2020), while Vehicle Type 6 lowers MAE to 1.1683 but still records a higher RMSE of 1.3198. These findings show that aggregate mobility measures add limited value, whereas freight-oriented vehicle-type disaggregation provides the most informative highway traffic signal for real-time GDP nowcasting. Full article

Environmental protection and the reduction of carbon dioxide (CO₂) emissions are central priorities within European climate policy. This study analyses and forecasts annual CO₂ emissions in Greece using a univariate time-series framework. Annual data from 1960 to 2024, sourced from Our World in Data, enable the analysis to capture both the historical expansion of emissions and the recent decarbonization phase of the Greek energy system. Using the Box–Jenkins methodology, multiple ARIMA specifications were evaluated based on information criteria and diagnostic tests. To examine the stationarity properties of the series, the Augmented Dickey–Fuller (ADF) unit root test is applied. The findings indicate that the ARIMA (1,1,1) model most accurately represents the stochastic dynamics of the emissions series. The estimated autoregressive and moving-average coefficients, 0.9404 and −0.7165, respectively, are statistically significant at the 1% level. Residual diagnostics confirm the absence of serial correlation, approximate normality, and no significant heteroskedasticity. Forecast evaluation for the 2020–2024 holdout period demonstrates satisfactory predictive performance, with a mean absolute percentage error (MAPE) of approximately 6%. Dynamic forecasts for 2025 to 2030 indicate a gradual decline in national CO₂ emissions, reaching an estimated 45.5 million tonnes by 2030. Overall, the study demonstrates that parsimonious ARIMA models offer a transparent and empirically reliable benchmark for national emissions forecasting. These models provide a reproducible tool for monitoring climate policy outcomes and for supporting evidence-based environmental decision-making. This study contributes to the environmental forecasting literature by providing an updated, diagnostically rigorous univariate benchmark model for Greece’s CO₂ emissions that encompasses both the pre- and post-decarbonization phases of the national energy transition. Full article

In developing countries, sustainable development strategies are increasingly shifting toward a green economy that integrates economic, social, and environmental dimensions. Despite the growing importance of green economic growth, comparative empirical studies examining its determinants in Turkey and Romania remain limited. This study investigates the dynamic relationships between environmentally sustainable growth, carbon emissions, life expectancy, renewable energy consumption, education, and technological innovation in Turkey and Romania over the period 1980–2023. Using annual time series data, the analysis applies the Augmented Dickey–Fuller and Zivot–Andrews unit root tests to examine stationarity and potential structural breaks. The empirical framework is based on the Autoregressive Distributed Lag (ARDL) bounds testing approach, which allows the estimation of both long-run equilibrium relationships and short-run dynamics. The results provide partial evidence of long-run relationships among the variables. Although the ARDL bounds test results fall within the inconclusive region, the negative and statistically significant error correction terms indicate that deviations from long-run equilibrium are corrected over time. The findings also reveal heterogeneous short-run causal interactions across the two countries, suggesting that the drivers of environmentally sustainable growth differ between Turkey and Romania. Overall, the results highlight the importance of country-specific policy frameworks, institutional structures, and energy transition pathways in promoting green economic growth. Full article

Background: While hydroxyapatite (HA) is considered stable and non-resorbable, other calcium phosphate phases such as Tricalcium Phosphate (TCP), Brushite, and Monetite are characterized by higher solubility and biodegradation rates. This review aims to map the clinical evidence of these resorbable phases. Objective: The aim of this scoping review was to map and synthesize the available clinical evidence on resorbable calcium phosphate phases, focusing on TCP-, brushite-, and monetite-based biomaterials in alveolar bone regeneration. The review evaluates clinical indications, surgical protocols, reported outcomes, and existing knowledge gaps. Methods: This scoping review was conducted in accordance with the PRISMA-ScR guidelines. A comprehensive literature search was performed in PubMed, MEDLINE, Scopus, and SCI Clarivate databases without language or time restrictions (from June 2025 to August 2025) using terms related to brushite, monetite, dicalcium phosphate anhydrous, ridge augmentation, bone regeneration, and dental implants. Clinical studies involving brushite- or monetite-based biomaterials used for alveolar bone regeneration were eligible, including randomized controlled trials, prospective cohort studies, and case series. Data were charted descriptively with respect to study design, patient characteristics, clinical scenario, biomaterials used, surgical approach, healing time, outcome measures, and reported complications. No meta-analysis or formal assessment of comparative clinical effectiveness was undertaken, in line with scoping review methodology. Results: Seven clinical studies were included. The identified evidence encompassed heterogeneous clinical scenarios, including post-extraction alveolar ridge preservation, localized ridge augmentation, and periodontal or intraosseous defects with relevance to future implant placement. Study designs, defect characteristics, biomaterial formulations, and outcome measures varied substantially. Across studies, brushite- and monetite-based materials were associated with new bone formation and progressive graft resorption, as assessed by clinical, radiographic, and histological outcomes. Direct comparisons between studies were not feasible due to methodological and clinical heterogeneity. Conclusions: The available literature on brushite- and monetite-based biomaterials in alveolar bone regeneration is limited and heterogeneous. Current evidence supports their biocompatibility and resorbable nature across different clinical contexts, but does not allow conclusions regarding comparative clinical effectiveness. This scoping review highlights important gaps in the literature, particularly the need for well-designed randomized clinical trials with standardized indications and outcome measures. Full article

GARCH models play a fundamental role in modeling time-varying volatility in financial return series. In practice, financial returns are also well known to exhibit heavy-tailed distributions, which naturally motivates the use of quasi-maximum exponential likelihood estimation (QMELE) for accurately capturing tail behavior and risk measures such as Value-at-Risk. At the same time, the increasing availability of intraday high-frequency data has led to the development of high-frequency augmented GARCH models, which incorporate intraday information into conventional low-frequency volatility frameworks. By exploiting transaction-level data recorded at very fine time scales, these models are able to capture intraday volatility dynamics and market microstructure effects that are not reflected in standard low-frequency observations. Against this background, this paper studies conditional quantile estimation for high-frequency augmented GARCH models. We develop QMELE-based estimators for both model parameters and conditional quantiles, and construct an adjusted test statistic for assessing model adequacy. The asymptotic properties of the proposed estimators and test statistic are established, and their finite-sample performance is examined through extensive simulation studies. Empirical applications to three major stock indices demonstrate that augmenting GARCH models with high-frequency information leads to substantial improvements in conditional quantile estimation compared with traditional low-frequency approaches. Full article

Introduction: Major depressive disorder (MDD) is a leading cause of disability worldwide and contributes significantly to the global burden of disease. Recent data show an increasing prevalence of treatment-resistant depression (TRD). Patients with autism spectrum disorder (ASD) often exhibit MDD as a comorbidity and it is often resistant to conventional treatments. ASD determines emotional dysregulation and a reduced ability to understand mental states (mentalization). These features can lead to suicidal ideation and/or behavior. Intranasal esketamine may offer a novel therapeutic option for this population. Methods: This case series focuses on the clinical response to intranasal esketamine in patients with autism and TRD; esketamine is approved in Italy as an add-on therapy in TRD, so our case study is based on an in-label treatment. Three young patients (n = 3, F/M 2:1, age range 20–25 y) with light to moderate autism (Level 1 or 2) were treated. Esketamine was administered in augmentation with selective serotonin reuptake inhibitors (SSRIs) or serotonin-norepinephrine reuptake inhibitors (SNRIs) in accordance with EMA/AIFA guidelines. A structured follow-up protocol was set to monitor depressive symptoms, social cognition, and mentalization. Follow-up during treatment was maintained for six months, and psychometric evaluations were performed at six time points: baseline (T0), 1 week (T1), 1 month (T2), 2 months (T3), 3 months (T4), and 6 months (T5). Also, subjective quality of life was investigated before and after the observation period. Results: Despite differences in clinical profile, all patients showed good efficacy of esketamine in reducing depressive symptoms: two patients experienced clinical remission at T5 (MADRS < 10), one patient showed partial response (dMADRS = 43.24%). No major side effects were reported. Significant improvements were observed after the first week of treatment (P1: MADRS_T0 = 37, MADRS_T1 = 12; P2: MADRS_T0 = 32, MADRS_T1 = 21; P3: MADRS_T0 = 25, MADRS_T1 = 12). Depressive relapses occurred (e.g., P1, T3–T4), but they were not associated with hospitalizations and/or suicidal attempts. Suicidal ideation, when present, decreased by the end of the follow-up period. Lack of mentalization and in social cognition was noted, with just mild improvements during therapy. Subjective quality of life improved significantly for all patients (P1: 28% at T0, 73% at T5. P2: 25% at T0, 71% at T5. P3: 35% at T0, 80% at T5). Conclusions: Intranasal esketamine showed a favorable efficacy and safety in these three cases of TRD in comorbidity with ASD (at six months: total remission = 66.66%, partial remission = 33.33%, inefficacy = 0%, drop-out = 0, severe adverse events = 0). Besides improvements in depressive symptoms, esketamine was associated with a constant decrease in suicidal thoughts. A case series is unfit to form statistical conclusions; preliminary data warrant further investigation in randomized controlled studies to validate the therapeutic potential of esketamine in this population. Full article

To address the challenges of insufficient transformer winding fault samples and the effective fusion of heterogeneous multi-source data, this study proposes an intelligent fault diagnosis method based on a time–frequency diffusion model and ConvNeXt-1D. First, data augmentation is performed on the original signals using the time–frequency diffusion model. Through a forward noise injection and reverse denoising process, the limited time-series samples are expanded. By alternately applying time-domain noise addition and frequency-domain blurring, the signals are jointly enhanced in the time–frequency domain, improving sample diversity and feature representation. Next, a ConvNeXt-1D network is constructed for multi-scale feature extraction and fault classification, incorporating an attention mechanism to efficiently fuse multi-source features and achieve precise fault identification. Finally, the proposed method is validated using dynamic model experiments. The results indicate that under typical fault conditions—such as inter-turn short circuits, winding deformation, and arc discharge—the proposed method achieves a diagnostic accuracy of 99.23 ± 0.29%. Compared with other classical models, the proposed approach demonstrates stronger classification capability and higher stability under small-sample data conditions. Full article