Search Results (21,565)

Underground gas storage (UGS) facilities are fundamental for national energy security and global decarbonization efforts. However, solid phase production in carbonate reservoirs, such as Qianmi Bridge, poses a significant operational challenge by compromising wellbore integrity and formation permeability. To address this, this study develops a novel, comprehensive methodology for predicting and mitigating solid phase production risk in carbonate UGS under dynamic operating conditions, specifically focusing on the Qianmi Bridge gas storage. This approach systematically integrates qualitative susceptibility assessments (using acoustic time difference, B index, and S index) with quantitative models for critical and ultimate pressure difference forecasting. Crucially, the methodology rigorously accounts for dynamic process parameters, including rock strength degradation due to acidizing, in situ stress variations, and fluid flow dynamics throughout the reservoir’s operational life cycle, a critical aspect often overlooked in conventional models designed for sandstone reservoirs. Analysis reveals that the safe operating pressure window dramatically narrows as formation pressure declines and rock strength is weakened, especially under high-intensity, multi-cycle alternating loads. Specifically, acidizing treatments can reduce the critical pressure difference by over 50% (e.g., from 40.49 MPa to 19.63 MPa), and under depleted conditions (0.6 P₀, 0.8 UCS), the reservoir’s ability to resist solid phase production approaches zero, highlighting an extremely high risk. These findings provide an essential theoretical and technical basis for formulating robust operational control strategies, enabling data-driven decision-making to enhance the long-term safety, efficiency, and overall process integrity of carbonate gas storage operations. Full article

The numerical reservoir simulation is a valuable tool to enhance heavy oil recovery by assessing different production strategies (like SAGD and CSS) and operational scenarios. While numerous studies have developed complex models, a systematic review identifying the most critical parameters for achieving accurate production forecasts is lacking. In this work, diverse studies have been reviewed regarding the numerical models of steam injection technologies by examining various parameters (reservoir properties and operating conditions) employed and their impact on the results obtained. Additionally, the effect of using kinetic models in simulations, as well as the modeling of solvent and catalyst injection, is discussed. The outcomes highlight that oil recovery for steam injection methods requires effective steam chamber management and an understanding of geomechanical changes due to the significant role of thermal convection on energy transfer and oil displacement. Increasing steam injection pressures can enhance energy efficiency and reduce emissions, but controlling the gases generated during the reaction poses difficulties. The gas formation within the reservoir in simulations is crucial to prevent overestimating oil production and improving precision. This can be achieved using simple kinetic models, but it is essential to incorporate gas–water solubilities to mimic actual gas emissions and avoid gas buildup. Crucially, our synthesis of the literature demonstrates that incorporating gas–water solubilities and kinetic models for H₂S production can improve the prediction accuracy of gas trends by up to 20% compared to oversimplified models. Enhanced recovery methods (adding solvent and catalyst injection) provide advantages compared with conventional steam injection methods. However, suitable interaction models between oil components and solid particles are needed to improve steam displacement, decrease water production, and enhance recovery in certain circumstances. The use of complex reaction schemes in numerical modeling remarkably enhances the prediction of experimental reservoir data. Full article

Ensuring ultra-low latency and high reliability in 6G network slices remains a significant challenge, as current NFV orchestration approaches are largely reactive and not designed to anticipate performance degradation. The advent of 6G networks brings forth stringent requirements for ultra-reliable low-latency communication (URLLC), necessitating advanced orchestration mechanisms that go beyond reactive policies in traditional NFV environments. In this paper, we propose a latency-aware, AI-driven NFV slice orchestration framework aligned with ETSI MANO architecture to address the needs of time-sensitive 6G applications. Our framework integrates a predictive AI engine into the NFV Orchestrator (NFVO) to forecast latency violations based on real-time telemetry and historical trends. It enables dynamic scaling, intelligent VNF migration, and infrastructure-level isolation to maintain stringent end-to-end (E2E) latency targets. Experimental results indicate up to 30% reduction in average latency, a 42% improvement in SLA compliance, and 25% lower migration overhead compared to traditional reactive orchestration. The framework provides a scalable and intelligent orchestration solution adaptable to future 6G deployments. Full article

The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure prediction are essential elements of modern fleet management. This paper presents an analysis of the reliability of fuel systems based on operational data from ten bulk carriers operated by Polska Żegluga Morska in Szczecin. The analysis combined classical statistical methods with artificial intelligence algorithms to develop a hybrid diagnostic and forecasting framework. The Weibull lifetime distribution was applied to estimate time-to-failure parameters, revealing mixed failure mechanisms—random failures (k < 1) and aging-related processes (k > 1). Using the k-means algorithm, ships were automatically classified into two reliability groups: high-failure-rate units and stable operational vessels. Individual linear regression models were then developed for each ship to forecast the time to the next failure, achieving satisfactory predictive performance (R² > 0.75 for most vessels). Sensitivity analysis quantified model robustness under different disturbance scenarios, yielding mean Relative Prediction Deviation (RPD) values of approximately 65% for Missing Data, 60% for False Failure, and 26% for Data Noise. These results confirm that the proposed hybrid reliability–AI framework is resistant to random noise but sensitive to incomplete or erroneous historical data. The developed approach provides an interpretable and effective tool for predictive maintenance, supporting reliability management and operational decision-making in marine engine systems. The article presents a hybrid model that has been developed to enable the detailed characterization of emergency processes and the identification of the most important factors that influence damage forecasting. For systems with variable failure risk, it was found that both classical probabilistic models and machine learning methods must be considered to interpret damage patterns correctly. Implementing data filtration and validation procedures before using data in artificial intelligence models has been shown to improve forecast stability and increase the usefulness of forecasts for planning repairs. Full article

Background. West Nile Virus (WNV) represents a significant public health concern in Europe, with Italy—particularly its northern regions—experiencing recurrent outbreaks. Climate variables and vector dynamics are known to significantly influence transmission patterns, highlighting the need for reliable predictive models to enable timely outbreak detection and response. Methods. We integrated epidemiological data on human WNV infections in Italy (2012–2024) with high-resolution climate variables (temperature, humidity, and precipitation). Using advanced feature engineering and a gradient boosting framework (XGBoost), we developed a predictive model optimized through time-series cross-validation. Results. The model achieved high predictive accuracy at the national level (R² = 0.994, MAPE = 5.16%) and maintained robust performance across the five most affected provinces, with R² values ranging from 0.896 to 0.996. SHAP analysis identified minimum temperature as the most influential climate predictor, while maximum temperature and rainfall demonstrated considerably weaker associations with case incidence. Conclusions. This machine learning approach provides a reliable framework for forecasting WNV outbreaks and supports evidence-based public health responses. The integration of climate and epidemiological data enhances surveillance capabilities and enables informed decision-making at regional and local levels. Full article

This study estimates the number of electric vehicles (EVs) and charging stations in Sakarya Province, Türkiye, for 2030 using advanced artificial intelligence time series methods and statistical approaches. The novelty of the work lies in the application of hyperparameter-optimized LSTM and GRU models alongside Multiple Linear Regression (MLR) to a regional dataset, enabling accurate, data-driven forecasting for regional EV planning. Performance was evaluated using multiple metrics, including R², MAE, MSE, DTW, RMSE, and MAPE, with the GRU model achieving the highest reliability and lowest errors (R² = 0.99, MAE = 0.3, MSE = 2.9, DTW = 123.2, RMSE = 3.1, MAPE = 2.8%) under optimized parameters. The predicted EV counts and charging station numbers from GRU informed a neighborhood-level allocation of charging stations using Google Maps API, considering local population ratios. These results demonstrate the practical applicability of deep learning for regional infrastructure planning and provide a replicable framework for similar studies in other provinces. Full article

Food shortages are becoming increasingly urgent due to the growing global population. Enhancing oil crop yields, particularly sunflowers, is key to ensuring food security and the sustainable provision of vegetable fats essential for human nutrition and animal feed. However, sunflower yields are often reduced by diseases, pests, and other factors. Remote sensing technologies, such as unmanned aerial vehicle (UAV) scans and satellite monitoring, combined with machine learning algorithms, provide powerful tools for monitoring crop health, diagnosing diseases, mapping fields, and forecasting yields. These technologies enhance agricultural efficiency and reduce environmental impact, supporting sustainable development in agriculture. This systematic review aims to assess the accuracy of various machine learning technologies, including classification and segmentation algorithms, convolutional neural networks, random forests, and support vector machines. These methods are applied to monitor sunflower crop conditions, diagnose diseases, and forecast yields. It provides a comprehensive analysis of current methods and their potential for precision farming applications. The review also discusses future research directions, including the development of automated systems for crop monitoring and disease diagnostics. Full article

This paper presents an intelligent hybrid framework for short-term energy consumption forecasting and real-time energy management in photovoltaic (PV)-integrated smart homes with Vehicle-to-Home (V2H) systems, tailored to the Tunisian context. The forecasting module employs an Attention-based Long Short-Term Memory (LSTM) neural network, whose hyperparameters (learning rate, hidden units, temporal window size) are optimized using the Harris Hawks Optimization (HHO) algorithm. Simulation results show that the proposed LSTM-HHO model achieves a Root Mean Square Error (RMSE) of 269 Wh, a Mean Absolute Error (MAE) of 187 Wh, and a Mean Absolute Percentage Error (MAPE) of 9.43%, with R² = 0.97, substantially outperforming conventional LSTM (RMSE: 945 Wh, MAPE: 51.05%) and LSTM-PSO (RMSE: 586 Wh, MAPE: 28.72%). These accurate forecasts are exploited by the Energy Management System (EMS) to optimize energy flows through dynamic appliance scheduling, HVAC load shifting, and coordinated operation of home and EV batteries. Compared with baseline operation, PV self-consumption increased by 18.6%, grid reliance decreased by 25%, and household energy costs were reduced by 17.3%. Cost savings are achieved via predictive and adaptive control that prioritizes PV utilization, shifts flexible loads to surplus periods, and hierarchically manages distributed storage (home battery for short-term balancing, EV battery for extended deficits). Overall, the proposed LSTM-HHO-based EMS provides a practical and effective pathway toward smart, sustainable, and cost-efficient residential energy systems, contributing directly to Tunisia’s energy transition goals. Full article

Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Photovoltaic power generation prediction is crucial for the effective integration of renewable energy into the grid, real-time grid balancing, and the optimization of energy storage systems. However, PV power generation is highly dependent on environmental factors such as weather conditions. Effectively integrating environmental information remains a major challenge for photovoltaic power forecasting. This study proposes a hybrid deep learning model that incorporates an adaptive neural network to capture the latent relationships between PV power generation and environmental variables, thereby enhancing forecasting accuracy. The adaptive graph neural network employs a data-driven directed graph structure, where TCN and variable interaction layers are alternately stacked to better model the spatiotemporal coupling among variables for long-term PV output forecasting. The proposed model was evaluated on three sites located in different regions, with a fixed input length of 96 and output horizons ranging from 96 to 768 steps. Compared with state-of-the-art baselines, the model achieved average improvements of 2.19% and 1.57% in MSE and MAE at a 384-step horizon, and 2.81% and 2.47% at a 768-step horizon, respectively, demonstrating superior performance in long-term PV output forecasting tasks. Full article

Data is an important resource for gaining knowledge about the behavior and condition monitoring of machines, enabling the estimation of parameters and the prediction of failures. However, in industrial environments, sensor interruptions often create gaps in the time series, which affects the reliability of the data. To overcome this challenge, this paper proposes an imputation strategy based on recurrent neural networks, in particular long short-term memory (LSTM) models, within a multivariate encoder–decoder architecture. This approach utilizes correlations between variables to reconstruct missing values, resulting in more complete and robust datasets. Experimental results with multivariate time series show that the proposed method achieves accurate imputation, with errors as low as RMSE = 2.33 and $R^2$ = 0.90 for some variables. Comparisons with alternative architectures, including GRU and Dense networks, show that LSTM excels in specific cases (e.g., VL3, $R^2$ = 0.45), while the Dense architecture provides more stable performance across most variables. In particular, the Dense model achieved the best overall balance between accuracy and robustness, reaching RMSE = 2.33 and $R^2$ = 0.90 for the best-performing variables, while the LSTM achieved the lowest error values in targeted scenarios, confirming its suitability for capturing complex temporal dependencies. Overall, this study highlights the feasibility of using recurrent neural networks to exploit temporal correlations for reliable data recovery, even under conditions of signal interruption in factory environments. Full article

Dinoflagellate resting cysts are critical to dinoflagellate ecology, acting as a key seed source for initiating harmful algal blooms (HABs) through their germination. However, the in situ germination dynamics of these cysts remain poorly understood due to technical challenges. To overcome this, we utilized the Germlings Harvester (GEHA), an in situ germination device we designed, to collect water samples containing dinoflagellate cysts germinated from marine sediments in Jiaozhou Bay, China, after 5 and 20 days of incubation. By combining the GEHA with metabarcoding analysis targeting 28S rDNA-specific primers for dinoflagellates, we identified 44 dinoflagellate species spanning 31 genera, 18 families, and 7 orders. Of these, 12 species were linked to HABs or recognized as toxic, including Azadinium poporum, Alexandrium leei, Alexandrium pacificum, Akashiwo sanguinea, Karlodinium veneficum, Stoeckeria algicida, and Luciella masanensis. Additionally, five species were newly identified as cyst producers, and one symbiotic dinoflagellate, Effrenium voratum, was detected. Our results also found that germinated dinoflagellate species increased from 23 to 34 with extended incubation, and the ratio of mixotrophic to heterotrophic species was approximately 2:1 in the samples of in situ sediments and seawater outside GEHA, as well as across germination durations (Sg-5 d vs. Sg-20 d). These findings provide essential field evidence for the role of resting cysts in driving HAB formation in this region and highlight the efficacy of the GEHA-based approach for studying in situ cyst germination dynamics, offering a robust tool for monitoring, early warning, prevention, and forecasting of HABs. Full article

Extended-spectrum beta-lactamase (ESBL)-producing E. coli from broiler farms can reach watersheds used for recreational swimming. We assessed short-term swimmer exposure by extending a modular quantitative microbial risk assessment (QMRA) to the recreational water pathway linking land manure application to in-stream fate and transport with dilution and decay. We modeled single-event exposure doses and estimated loss of disability-adjusted life years (DALYs). We ran sensitivity analyses on several parameters and compared outputs to published recreational water assessments that include ESBL E. coli. Assuming a worst-case scenario, single-event doses were lower for adults (2.95 CFU; UI 0.14–6.11) and higher for children (8.78 CFU; UI 0.56–17.20) on day 1 after land application, then dropped below 0.01 CFU by day 200, with DALY losses from 10⁻⁷ to 10⁻¹⁰. Uncertainty was dominated by fate and transport. Stronger particle binding, faster in-stream decay, and larger effective volumes lowered exposure, while higher shedding, greater flow, and larger wash-off raised it. Estimates fell at the low end of prior studies. Swimmer exposure appears to be extremely low and short-lived. The modular QMRA links farm contamination to bathing-site risk and supports risk-based monitoring (after spreading or storms) and short-term forecasts that focus advisories on short, higher-risk windows after litter application. Full article

Background: Cryptococcosis, a systemic mycosis, remains a neglected disease in Brazil due to the absence of systematic national surveillance. This study developed an interactive dashboard to analyze cryptococcosis-related deaths (2000–2022) and forecast trends through regional ARIMA modeling. Methodology: The Cross-Industry Standard Process for Data Mining framework was employed to extract mortality data from the Brazilian Mortality Information System, utilizing the microdatasus package in R Studio software, with R version 3.4.0. The records were then filtered using the International Classification of Diseases, Tenth Revision codes (B45 series) to identify primary and associated causes of death. After data extraction, a series of data preprocessing steps was implemented, including deduplication, variable recoding, and the management of missing values. The Shiny framework was employed to construct an interactive dashboard, incorporating Plotly and DT packages, with time-series visualizations, demographic variables, and multilingual support (Portuguese/English). Results: Among 12,308 deaths (2227 primary; 10,081 associated causes), most occurred in males aged 21–60 years. Data completeness was high for age/residence (100%) but lower for education (82%). The dashboard enables dynamic exploration of trends, demographic patterns, and open-data downloads. Regional ARIMA models revealed heterogeneous forecasts, with the Southeast projecting a decline (193 deaths in 2025; 95% CI: 146–240) and the South showing stability (141 deaths; 95% CI: 109–173). Conclusions: This tool bridges a critical gap in cryptococcosis surveillance, enabling dynamic mortality trend analysis, identification of high-risk demographics, and regional forecasting to guide public health resource allocation. While the absence of HIV serostatus data limits etiological analysis, the dashboard’s open-source framework supports adaptation for other neglected diseases. Full article

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

The rapid transition to electric vehicles (EVs) requires a charging infrastructure that is both efficient and equitable. Conventional planning approaches, which often deploy chargers in proportion to current EV density, fail to account for the diverse characteristics of EV owners and the evolving patterns of adoption across different regions and time periods. This paper introduces an integrated, data-driven framework that addresses these limitations through three stages: segmentation of the EV market, spatio-temporal adoption forecasting for each segment, and optimizing charger placement through a constrained optimization model. The proposed optimization model incorporates equity constraints to ensure minimum service coverage for all user segments while maximizing overall utilization within a fixed budget. Methodologically, the paper contributes a transparent, reproducible framework that unifies user segmentation, geographically resolved adoption forecasting, and an equity-constrained MILP for charger placement. Applying this approach to a dataset of EV registrations in Washington State from 2010 to 2025 and extending it to projections through 2030 demonstrate important improvements in demand coverage. Overall coverage increases from 76.0% to 96.1% compared to a proportional-allocation baseline. More importantly, the proposed framework ensures a minimum of 70% coverage for all user segments. The presented approach is portable to other regions and budget scenarios. These findings show the potential for strategic, data-informed infrastructure planning that balances efficiency and equity, providing actionable insights for policymakers and network operators in the EV transition. Full article