Search Results (5,801)

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. Full article

This work contextualizes the possibility of deriving a unifying artificial intelligence framework by walking in the footsteps of General, Explainable, and Verified Artificial Intelligence (GEVAI): by considering explainability not only at the level of the results produced by a specification but also considering the explicability of the inference process as well as the one related to the data processing step, we can not only ensure human explainability of the process leading to the ultimate results but also mitigate and minimize machine faults leading to incorrect results. This, on the other hand, requires the adoption of automated verification processes beyond system fine-tuning, which are essentially relevant in a more interconnected world. The challenges related to full automation of a data processing pipeline, mostly requiring human-in-the-loop approaches, forces us to tackle the framework from a different perspective: while proposing a preliminary implementation of GEVAI mainly used as an AI test-bed having different state-of-the-art AI algorithms interconnected, we propose two other data processing pipelines, LaSSI and EMeriTAte+DF, being a specific instantiation of GEVAI for solving specific problems (Natural Language Processing, and Multivariate Time Series Classifications). Preliminary results from our ongoing work strengthen the position of the proposed framework by showcasing it as a viable path to improve current state-of-the-art AI algorithms. Full article

Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Machine learning and artificial intelligence have transformed fault detection and maintenance strategies for industrial machinery. This study applies well-established data-driven techniques to a rarely explored industrial application—the condition monitoring of high-precision paper cutting machines—enhancing condition-based maintenance to improve operational efficiency, safety, and cost-effectiveness. A key element of the proposed approach is the integration of an infrared pyrometer into vibration monitoring, utilizing accelerometer data to evaluate the state of health of machinery. Unlike traditional fault detection studies that focus on extreme degradation states, this work successfully identifies subtle deviations from optimal, which even expert technicians struggle to detect. Building on a feasibility study conducted with Tecnau SRL, a comprehensive diagnostic system suitable for industrial deployment is developed. Endurance tests pave the way for continuous monitoring under various operating conditions, enabling real-time industrial diagnostic applications. Multi-scale signal analysis highlights the significance of transient and steady-state phase detection, improving the effectiveness of real-time monitoring strategies. Despite the physical similarity of the classified states, simple time-series statistics combined with machine learning algorithms demonstrate high sensitivity to early-stage deviations, confirming the reliability of the approach. Additionally, a systematic analysis to downgrade acquisition system specifications identifies cost-effective sensor configurations, ensuring the feasibility of industrial implementation. Full article

This study proposes a novel Deep Reinforcement Learning (DRL) policy network structure for portfolio management called PortRSMs. PortRSMs employs stacked State-Space Models (SSMs) for the modeling of multi-scale continuous regime shifts in financial time series, striking a balance between exploring consistent distribution properties over short periods and maintaining sensitivity to sudden shocks in price sequences. PortRSMs also performs cross-asset regime fusion through hypergraph attention mechanisms, providing a more comprehensive state space for describing changes in asset correlations and co-integration. Experiments conducted on two different trading frequencies in the stock markets of the United States and Hong Kong show the superiority of PortRSMs compared to other approaches in terms of profitability, risk–return balancing, robustness, and the ability to handle sudden market shocks. Specifically, PortRSMs achieves up to a 0.03 improvement in the annual Sharpe ratio in the U.S. market, and up to a 0.12 improvement for the Hong Kong market compared to baseline methods. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

Air pollution threatens human health and ecosystems, making timely forecasting essential. The spatiotemporal dynamics of pollutants, shaped by various factors, challenge traditional methods. Therefore, spatiotemporal graph-based deep learning has gained attention for its ability to capture spatial and temporal dependencies within monitoring networks. However, many existing models, despite their high predictive accuracy, face computational complexity and scalability challenges. This study introduces clustered and lightweight spatio-temporal graph convolutional network with gated recurrent unit (ClusLite-STGCN-GRU), a hybrid model that integrates spatial clustering based on pollutant time series for graph construction, Copula-based dependency analysis for selecting relevant pollutants to predict PM_2.5, and graph convolution combined with gated recurrent units to extract spatiotemporal features. Unlike conventional approaches that require learning or dynamically updating adjacency matrices, ClusLite-STGCN-GRU employs a fixed, simple cluster-based structure. Experimental results on Tehran air quality data demonstrate that the proposed model not only achieves competitive predictive performance compared to more complex models, but also significantly reduces computational cost—by up to 66% in training time, 83% in memory usage, and 84% in number of floating-point operations—making it suitable for real-time applications and offering a practical balance between accuracy, interpretability, and efficiency. Full article

Background: Endonasal endoscopic approaches to the skull base are still under investigation, with research aiming to achieve minimally invasive procedures that maximize resection while minimizing complications. This study shares our experience with a mononostril technique and compares it with the existing literature on mononostril approaches for sellar lesions. Methods: A systematic review of eight large series, totaling 1520 patients who underwent endoscopic mononostril transsphenoidal surgery, was performed. The surgical technique was detailed, and parameters such as resection completeness, operative time, complications, and nasal symptoms were analyzed. Results: Gross total resection ranged from 56% to 100% for non-functioning adenomas, 54% to 89% for hormone-secreting adenomas, and 83% to 100% for other sellar lesions. The most common complications were CSF leaks (1.5–4.1%) and nasal issues, such as epistaxis or sinusitis (0–6%). Internal carotid artery injury occurred in 0–1% of cases. The average surgical duration was 87 to 168 min. Conclusions: The mononostril approach offers comparable resection rates, CSF leak risks, and morbidity to binostril or microsurgical methods. The mononostril approach is fast, minimally invasive, and preserves the nasal mucosa, making it a viable option for many sellar lesions. Full article

The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. Two primary research questions are addressed: whether machine learning models outperform traditional techniques, and whether time series modelling improves prediction accuracy. The analysis includes an evaluation of a range of models, including statistical regressions (OLS, LASSO, ridge), regression trees, neural networks, LSTM, and random forests, which are applied to physical modelling and time series approaches. The results reveal that although machine learning methods can outperform statistical models, particularly with the inclusion of exogenous weather features, they are not universally superior across all forecasting horizons. Furthermore, pure time series approach models yield lower performance. However, a hybrid approach in which physical models are integrated with machine learning demonstrates significantly improved accuracy. These findings highlight the value of hybrid models for photovoltaic forecasting and suggest strategic directions for operational implementation. Full article

Multivariate Time Series Forecasting (MTSF) has been innovated with a series of models in the last two decades, ranging from traditional statistical approaches to RNN-based models. However, recent contributions from deep learning to time series problems have made huge progress with a series of Transformer-based models. Despite the breakthroughs by attention mechanisms applied to deep learning areas, many challenges remain to be solved with more sophisticated models. Existing Transformers known as attention-based models outperform classical models with abilities to capture temporal dependencies and better strategies for learning dependencies among variables as well as in the time domain in an efficient manner. Aiming to solve those issues, we propose a novel Transformer—SegmentedCrossformer (SCF), a Transformer-based model that considers both time and dependencies among variables in an efficient manner. The model is built upon the encoder–decoder architecture in different scales and compared with the previous state of the art. Experimental results on different datasets show the effectiveness of SCF with unique advantages and efficiency. Full article

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R² of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K₀ and K₁ and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article

Background/Objectives: Radial head arthroplasty is a commonly preferred treatment for complex, unreconstructable radial head fractures. Recent studies have raised the question of whether factors such as bone resorption may be related to failure. This observational, retrospective, multicenter, spontaneous, and non-profit study aims to assess radiological outcomes, focusing on bone resorption around the stem, for radial head replacement using a modular, cementless radial head prosthesis with interconnected porosity. Methods: A series of 42 cases was available for review. Patients underwent radial head arthroplasty using a three-dimensional-printed radial head prosthesis. Patients were eligible for inclusion if they had undergone at least one follow-up between 6 and 15 months post-operatively. A scoring system to detect bone resorption was developed and administered by two independent evaluators. Results: Forty-two patients (14 males, 28 females), with an average age of 59 ± 11 years (range: 39–80 years), were analyzed with a minimum of six months’ and a maximum of 32 months’ follow-up. At follow-up, 50 radiological evaluations were collected, with 29 showing ≤3 mm and 12 showing 3–6 mm resorption around the stem. The average resorption was 3.5 mm ± 2.3. No correlation was found between the extent of resorption and the time of follow-up. The developed scoring system allowed for a high level of correlation between the evaluators’ measurements of bone resorption. Conclusions: Radial head prosthesis with interconnected porosity provided a low stem resorption rate for patients after a radial head fracture at short-to-mid-term follow-up after the definition of a reliable and easy-to-use radiological-based classification approach. (Level of Evidence: Level IV). Full article