Search Results (5,060)

Ecologically sustainable urban design plays a pivotal role in mitigating climate change. This study develops an indicator group consisting of urban ecological emergy, land use change, population density, ecological services, habitat quality, enhanced vegetation index, carbon emissions, and carbon storage to assess urban sustainability. By leveraging a dataset from 2000 to 2020, we employ a neural network to predict emergy sustainability indicators over a time series, projecting the sustainable status of Xuzhou City from 2020 to 2050. The findings indicate that urbanization has led to significant changes in land use, population distribution, ecological service patterns, habitat quality degradation, vegetation fragmentation, and fluctuating carbon dynamics. Cropland constitutes the predominant land type (90.6%), followed by built-up land (8.49%). The neural network predictions suggest that Xuzhou City’s sustainable status is subject to volatility (15–20%), with stability expected only as the city matures into a developed urban area. This research introduces a novel approach to urban sustainability analysis and provides insights for policy development aimed at fostering sustainable urban growth. Full article

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain. Full article

To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an acquisition function aimed at expected improvement, this framework optimizes hyperparameter settings to enhance model adaptability and precision. Through a regional case study, this method demonstrated improved predictive accuracy and operational efficiency, highlighting its advantages in both runtime and performance. Full article

Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency. Full article

Determining the values of various properties for new bio-inks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted, and a database with more than 1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example. These data are difficult to deal with in terms of determining combinations of conditions to optimize the tests and analyze new options. The best model demonstrated a specificity (Sp) of 88.4% and a sensitivity (Sn) of 86.2% in the training series while achieving an Sp of 85.9% and an Sn of 80.3% in the external validation series. This model utilizes operators based on perturbation theory to analyze the complexity of the data. For comparative purposes, neural networks have been used, and very similar results have been obtained. The developed tool could easily be applied to predict the properties of bioprinting assays in silico. These findings could significantly improve the efficiency and accuracy of predictive models in bioprinting without resorting to trial-and-error tests, thereby saving time and funds. Ultimately, this tool may help pave the way for advances in personalized medicine and tissue engineering. Full article

With the rapid expansion of urban subway networks, vibrations induced by subway operations have become an increasingly significant concern for nearby structures. To assess the influence of subway-induced vibrations on nearby structures, it is essential to predict the vibration effects accurately prior to the construction of the subway system. By combining an improved Long Short-Term Memory (LSTM) model with a spectral analysis, this paper proposes a hybrid method to enhance the accuracy and efficiency of predicting structural vibrations induced by subway operations. The improved LSTM model is composed of BiLSTM, an attention mechanism, and the DBO algorithm. The symmetry inherent in the vibration propagation paths and the structural layouts of subway systems is leveraged to improve the feature extraction and modeling accuracy. Additionally, the hybrid method utilizes the symmetric properties of vibration signals in the spectral domain to enhance prediction robustness and efficiency. Then, the hybrid method is utilized to rapidly achieve highly accurate vibration responses induced by subway operations. The verification results demonstrate the following: (1) The improved LSTM model enhances the ability to recognize patterns in time-series vibration data, leading to improved model convergence and generalization. The improved LSTM mode has a significant improvement in prediction accuracy compared to the standard LSTM network. For numerical simulation and real-world measured signals, values of R² increased by 3% and 49.37%. (2) The proposed hybrid method significantly reduces computational time while ensuring results consistent with those obtained from the time-history analysis method. Applying the proposed hybrid method for data augmentation enhances the accuracy of the spectral analysis. The hybrid method achieves an improvement of 7% for the prediction accuracy. Full article

With the rapid development of industrial automation and intelligence, the consumption of resources and the environmental impact of production processes cannot today be ignored. Today, natural gas, as a commonly used energy source, produces significantly lower emissions of carbon dioxide, sulphur dioxide, and nitrogen oxides from combustion than coal and oil, and can be further purified to remove the small amount of impurities it contains, such as sulphur compounds. Therefore, purified natural gas (hereinafter referred to as purified gas), as a clean energy source, plays an important role in realising sustainable development. At the same time, It becomes more and more important to dispatch purified gas resources reasonably and accurately, and the paramount factor is that the load of purified gas needs to be predicted accurately. Therefore, this paper proposes a Transformer–GRU-based hourly prediction model for purified gas. The model uses the Transformer model for data fusion and feature extraction, and then combines the time series processing capability of the Gate Recurrent Unit (GRU) model to capture long-term dependencies and short-term dynamic changes in time series data. In this paper, the purified gas load data of Chongqing Municipality in 2020 was first preprocessed, and then divided into daily and hourly load datasets according to the measurement step. Meanwhile, considering the influence of temperature factor, the experimental dataset is subdivided according to whether it includes temperature data or not, and then the Transformer–GRU model was built for prediction, respectively. The results show that, compared with the Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) and the Transformer and GRU models alone, the Transformer–GRU model exhibits good performance in terms of the coefficient of determination, the average absolute percentage error, and mean square error, which can well meet the requirement of hourly prediction accuracy and has greater application value. Full article

The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surface roughness. However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. The results show that selected preprocessing methods can help the model learn quickly from the data and reach high accuracy in small epochs. Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an R² of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting. Full article

Objectives: Immune-mediated colitis (IMC) is a common immune-related adverse event during immune checkpoint inhibitor (ICI) therapy. This case series and review aimed to highlight atypical cases of IMC and explore the potential of PET/CT to predict imminent ICI colitis. Methods: Through a descriptive, retrospective study at a tertiary cancer center, we identified adult patients receiving ICIs for any cancer between 2010 and 2022 who also underwent PET/CT for routine cancer surveillance during this time. We included patients who had signs and symptoms of colitis and reviewed their surveillance PET/CT scans obtained 2 to 6 weeks before and up to 3 months after diagnosis. Results: For the 33 included patients, surveillance scans were reviewed in collaboration with a nuclear radiologist. A total of 17 patients (51.5%) received combination therapy, while 14 (42.4%) received anti–PD-1/PD-L1 monotherapy. While ICI therapy has a median duration of 6.5 months, most patients (72.7%) had negative surveillance PET/CT for colitis. Diarrhea and colitis severity were similar among those with positive and negative findings for colitis on surveillance PET/CT. The outcomes of colitis were similar, with an 81.8% resolution in patients with negative PET/CT and 71.4% in patients with positive PET/CT. Conclusions: PET/CT imaging did not appear to assist in predicting IMC. This may be due to the long interval between clinical IMC and surveillance PET/CT imaging. The continued use of clinical criteria combined with laboratory markers, e.g., lactoferrin and calprotectin, and endoscopy/histology will enable more accurate detection and timely treatment of IMC. Full article

In recent years, climate change has increased the frequency of extreme rainfall events, significantly impacting surface water quality (SWQ). This study focuses on Hangzhou, utilizing rainfall data from June 2021 to May 2024 to calculate a series of rainfall extreme indices (REIs). It explores the spatiotemporal variations in these REIs alongside SWQ parameters, including water temperature (WT), dissolved oxygen (DO), pH, total phosphorus (TP), total nitrogen (TN), and turbidity. This research also analyzes the correlations between SWQ parameters and REIs for the first time. The results show that extreme rainfall events primarily occur in July, with increases in both intensity and frequency during the study period. Influenced by human activities, natural conditions, and environmental policies, SWQ parameters in Hangzhou exhibit notable spatiotemporal variability. Correlation analyses reveal significant positive relationships between TP, TN, and turbidity in most areas with REIs. However, the correlations between pH, WT, and turbidity with REIs differ between the eastern and western regions, resulting from variations in land use. These findings will provide a theoretical basis for developing models to predict changes in SWQ based on REIs, contributing to the safeguarding of surface water quality. Full article

Although data-driven machine learning methods have been successfully applied to predict complex nonlinear dynamics, forecasting future evolution based on incomplete past information remains a significant challenge. This paper proposes a novel data-driven approach that leverages the dynamical relationships among variables. By integrating Non-Stationary Transformers with LightGBM, we construct a robust model where LightGBM builds a fitting function to capture and simulate the complex coupling relationships among variables in dynamically evolving chaotic systems. This approach enables the reconstruction of missing data, restoring sequence completeness and overcoming the limitations of existing chaotic time series prediction methods in handling missing data. We validate the proposed method by predicting the future evolution of variables with missing data in both dissipative and conservative chaotic systems. Experimental results demonstrate that the model maintains stability and effectiveness even with increasing missing rates, particularly in the range of 30% to 50%, where prediction errors remain relatively low. Furthermore, the feature importance extracted by the model aligns closely with the underlying dynamic characteristics of the chaotic system, enhancing the method’s interpretability and reliability. This research offers a practical and theoretically sound solution to the challenges of predicting chaotic systems with incomplete datasets. Full article

The placement of a well doublet plays a significant role in geothermal resource sustainable production. The normal well placement optimization method of numerical simulation-based faces a higher computational load with the increasing precision demand. This study proposes a surrogate model-based optimization approach that searches the economically optimal injection well location using the Grey Wolf Optimizer (GWO). The surrogate models trained by the novel Multi-layer Regularized Long Short-Term Memory–Convolution Neural Network concatenation model (MR LSTM-CNN) will relieve the computation load and save the simulation time during the simulation–optimization process. The results showed that surrogate models in a homogenous reservoir and heterogenous reservoir can predict the pressure–temperature evolution time series with the accuracy of 99.80% and 94.03%. Additionally, the optimization result fitted the real economic cost distribution in both reservoir situations. Further comparison figured out that the regularization and convolution process help the Long Short-Term Memory neural network (LSTM) perform better overall than random forest. And GWO owned faster search speed and higher optimization quality than a widely used Genetic Algorithm (GA). The surrogate model-based approach shows the good performance of MR LSTM-CNN and the feasibility in the well placement optimization of GWO, which provides a reliable reference for future study and engineering practice. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5). Full article

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions. Full article