Search Results (2,019)

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

This paper introduces a knowledge–data dual-driven method for predicting groundwater conditions during tunnel construction. Unlike existing methods, our approach effectively integrates trend characteristics of apparent resistivity from detection results with geological distribution characteristics and expert insights. This dual-driven strategy significantly enhances the accuracy of the prediction model. The intelligent prediction process for tunnel groundwater conditions proceeds in the following steps: First, the apparent resistivity data matrix is obtained from transient electromagnetic detection results and standardized. Second, to improve data quality, trend characteristics are extracted from the apparent resistivity data, and outliers are eliminated. Third, expert insights are systematically integrated to fully utilize prior information on groundwater conditions at the construction face, leading to the establishment of robust predictive models tailored to data from various construction surfaces. Finally, the relevant prediction segment is extracted to complete the groundwater condition forecast. Full article

Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain in hyperparameter tuning and spatial generalization. This study explores a novel effective approach for intelligent Hs forecasting for marine operations. Multiple automated machine learning (AutoML) frameworks, namely H2O, PyCaret, AutoGluon, and TPOT, have been systematically evaluated on buoy-based Hs prediction tasks, which reveal their advantages and limitations under various forecast horizons and data quality scenarios. The results indicate that PyCaret achieves superior accuracy in short-term forecasts, while AutoGluon demonstrates better robustness in medium-term and long-term predictions. To address the limitations of single-point prediction models, which often exhibit high dependence on localized data and limited spatial generalization, a multi-point data fusion framework incorporating Principal Component Analysis (PCA) is proposed. The framework utilizes Hs data from two stations near the California coast to predict Hs at another adjacent station. The results indicate that it is possible to realize cross-station predictions based on the data from adjacent (high relevance) stations. Full article

The operational and indoor environmental quality of buildings has a significant impact on global energy consumption and human quality of life. One of the key directions for improving building performance is the optimization of building control systems. In modern buildings, the presence of numerous actuators and monitoring points makes manually designed control algorithms potentially suboptimal due to the complexity and human factors. To address this challenge, model predictive control based on artificial neural networks can be employed. The advantage of this approach lies in the model’s ability to learn and understand the dynamic behavior of the building from monitoring datasets. It should be noted that the effectiveness of such control models is directly dependent on the forecasting accuracy of the neural networks. In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. Furthermore, we propose a novel hybrid architecture that combines the strengths of recurrent and convolutional neural networks. These architectures were compared using real monitoring data collected with a custom-developed device introduced in this work. The results indicate that, under the given experimental conditions, the proposed hybrid architecture outperforms both GRU and TCN models, particularly when processing large sequential input vectors. Full article

Urban air pollution remains a significant environmental and public health issue, especially in European coastal cities such as Klaipėda. However, there is still a lack of local-scale knowledge on how land use structure influences pollutant distribution, highlighting the need to address this gap. This study addresses this by examining the spatial distribution of nitrogen dioxide (NO₂) concentrations in Klaipėda’s seaport city and several inland and coastal resort towns in Lithuania. The research specifically asks how different land cover types and demographic factors affect NO₂ variability and population exposure risk. Data were collected using passive sampling methods and analyzed within a GIS environment. The results revealed clear air quality differences between industrial/port zones and greener resort areas, confirmed by statistically significant associations between land cover types and pollutant levels. Based on these findings, a Land Use Pollution Pressure index (LUPP) and its population-weighted variant (PLUPP) were developed to capture demographic sensitivity. These indices provide a practical decision-support tool for sustainable urban planning, enabling the assessment of pollution risks and the forecasting of air quality changes under different land use scenarios, while contributing to local climate adaptation and urban environmental governance. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Efficient antenna orientation of transmitters is essential for improving wireless signal quality and coverage, especially in large-scale and complex 6G networks. Identifying the best antenna angles is difficult due to the nonlinear interaction among orientation, signal propagation, and interference. This paper introduces a digital twin-based evaluation approach utilizing ray tracing simulations to assess the influence of antenna orientation on critical performance metrics: path gain, received signal strength (RSS), and signal-to-interference-plus-noise ratio (SINR). A thorough array of orientation scenarios was simulated to produce a dataset reflecting varied coverage conditions. The dataset was utilized to investigate antenna configurations that produced the optimal and suboptimal performance for each parameter. Additionally, three machine learning models—k-nearest neighbors (KNN), multi-layer perceptron (MLP), and XGBoost—were developed to forecast ideal configurations. XGBoost had superior prediction accuracy compared to the other models, as evidenced by regression outcomes and cumulative distribution function (CDF) analyses. The proposed workflow demonstrates that learning-based predictors can uncover orientation refinements that conventional grid sweeps overlook, enabling agile, interference-aware optimization. Key contributions include an end-to-end digital twin methodology for rapid what-if analysis and a systematic comparison of lightweight machine learning predictors for antenna orientation. This comprehensive method provides a pragmatic and scalable solution for the data-driven optimization of wireless systems in real-world settings. Full article

Sepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. Literature from 2019 to 2025 was systematically reviewed following PRISMA-ScR guidelines. A total of 129 full-text articles were analyzed, with study quality assessed via the JBI and QUADAS-2 tools. AI-based models demonstrated robust predictive performance for early sepsis detection (AUROC 0.68–0.99), antibiotic stewardship, and resistance prediction. Notable tools, such as InSight and KI.SEP, leveraged multimodal clinical and biomarker data to provide actionable, real-time support and facilitate timely interventions. AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. Key challenges remain regarding data integration, algorithmic bias, and ethical implementation. Future research should prioritize multicenter validation, seamless integration with clinical workflows, and robust ethical frameworks to ensure safe, equitable, and effective adoption. AI and ML hold significant promise to transform sepsis management, but their clinical impact depends on transparent, validated, and user-centered deployment. Full article

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability. Full article

The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

Industrial energy systems are increasingly required to reduce operating costs and CO₂ emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. Full article

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article