Search Results (7,646)

Soil monitoring is a key component of precision agriculture and environmental sensing systems, where reliable measurements support irrigation management and crop monitoring. Although high-end sensing platforms provide accurate measurements, their cost limits widespread adoption, particularly in resource-constrained agricultural environments. Low-cost soil sensors, such as widely available 7-in-1 probes capable of measuring soil moisture, temperature, electrical conductivity, and pH, offer a scalable alternative for distributed monitoring; however, their limited accuracy raises concerns regarding their reliability for decision-support systems. This study investigates whether measurements from a single low-cost 7-in-1 soil sensor contain sufficient information to reconstruct the outputs of a commercial high-end sensing platform (CropX), specifically volumetric water content (VWC) and pore-water electrical conductivity (ECpw). Field data were collected in a tropical Hass avocado orchard in Colombia, and four machine learning models were evaluated to reconstruct CropX measurements from low-cost sensor signals at three soil depths (20, 41, and 66 cm). Random Forest achieved the highest reconstruction performance, with coefficient of determination R2 values between 0.9965 and 0.9986 and consistently low root mean square error (RMSE) and mean absolute error (MAE) across depths. Out-of-bag validation and multi-seed stability analyses confirmed the robustness of the models despite the limited dataset size. A chronological validation (80–20%) showed substantially reduced performance, indicating that the proposed approach is more suitable for reconstructing high-end sensor signals under concurrent measurement conditions than for strict temporal extrapolation. Therefore, the framework should be interpreted as a virtual sensing strategy for reconstructing simultaneous CropX measurements from low-cost sensor observations rather than as a standalone model for predicting future soil conditions without periodic recalibration. These results demonstrate that low-cost multi-parameter sensors can support high-fidelity virtual reconstruction of high-end soil measurements, contributing to the development of scalable and cost-effective soil monitoring systems for precision agriculture. Full article

Bridge pier scour is one of the primary causes of bridge foundation failure, making accurate prediction of final scour depth essential for the safe and economical design of hydraulic structures. This study develops interpretable ensemble machine learning models for predicting final scour depth around collar-protected circular bridge piers using a laboratory dataset comprising 48 experimental observations. Four dimensionless hydraulic and geometric parameters, namely (b/b_c, b: bridge pier diameter, b_c: collar diameter), (z/d₅₀, z: collar elevation, d₅₀: median diameter of bed particles), (z/b_c), and (U/U_c, U: time average velocity, U_c: critical velocity of bed particles), were employed as model inputs, while the normalized final scour depth (d_sf/b_c) was considered as the target variable. Three ensemble learning algorithms, namely XGBoost (XGB), Random Forest (RF), and Extra Trees (ET), were developed and evaluated using training, testing, and 5-fold cross-validation procedures. The predictive performance of the models was assessed using the coefficient of determination (R²), Kling–Gupta Efficiency (KGE), root mean square error (RMSE), and mean absolute error (MAE). Among the investigated models, XGBoost demonstrated the highest predictive accuracy, achieving an R² of 0.987, a KGE of 0.982, and an RMSE of 0.0117 on the training dataset. Furthermore, SHapley Additive exPlanations (SHAP) were employed to interpret the influence of individual input variables, revealing that hydraulic intensity and collar-related parameters exert the greatest influence on equilibrium scour prediction, consistent with established scour mechanics. The proposed framework combines high predictive accuracy with model interpretability, providing a reliable decision-support tool for bridge scour assessment and demonstrating the potential of explainable machine learning to support the design and management of scour protection measures under controlled hydraulic conditions. Full article

To address the difficulty of accurately capturing the dynamic patterns of carbon emission time series—characterized by nonlinearity, non-stationarity, and complex fluctuations—this paper proposes a carbon emission prediction model based on an elite-guided collaborative social spider learning optimization algorithm (EGC-SSLO) integrated with a Long short-term memory (LSTM) network. First, considering the limitations of the standard stochastic social learning optimization (SSLO) algorithm in complex high-dimensional optimization problems, such as insufficient elite information guidance, weak local exploitation in the later stages, and a tendency to become trapped in local optima, three complementary improvement strategies are introduced. The adaptive elite mean-guided search strategy enhances the search directionality by incorporating the cooperative information of the best individual and the elite mean. The worst-individual hybrid Cauchy–Lévy search mechanism achieves a dynamic balance between early-stage global exploration and late-stage local exploitation through long-range Lévy flights and fine-grained Cauchy perturbations. The quadratic directional exploitation strategy further refines the search trajectory of candidate solutions, thereby improving convergence accuracy. These three strategies significantly enhance the optimization performance without increasing the time complexity order of the algorithm. Experimental results on the CEC2017 (30-dimensional), CEC2020 (20-dimensional), and CEC2022 (20-dimensional) benchmark suites demonstrate that EGC-SSLO consistently outperforms classical algorithms such as PSO, GWO, and HHO, as well as their improved variants, in terms of convergence accuracy, convergence speed, and robustness. Furthermore, the Wilcoxon rank-sum test and Friedman test confirm that the observed improvements are statistically significant. Finally, an EGC-SSLO-LSTM carbon emission prediction model is constructed and applied to daily carbon emission data in China from 2019 to 2025 for empirical analysis. The experimental findings show that the EGC-SSLO-LSTM model markedly outperforms both the standard LSTM and SSLO-LSTM approaches across key evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²). In particular, the MAE is decreased by 39.9% and 4.64% compared with the two benchmark models, respectively, which highlights the strong effectiveness and practical potential of the proposed method in real-world carbon emission forecasting applications. Full article

Accurate modeling of deformable object dynamics is critical for robotic manipulation but remains challenging due to complex physics and strict physical constraints. In this paper, PhysCon-Deform is introduced, which is a mixed framework for specific tasks, combining residual neurodynamics learning and a differential augmented Lagrangian projection layer. Using a grid-based graph representation, PhysCon-Deform integrates a physics-based neurodynamics operator (PINDO) and a differentiable constraint projection module to achieve the deployment of residual correction, grid-based neurodynamics and model predictive control (MPC). Based on standard simulation benchmarks (Cloth3D, SoftGym and SoftMAC), our framework is always superior to the existing baselines in clean and disturbed environments. Specifically, it reduces long-term constraint violations by over 50%, demonstrates high robustness to end-effector trajectory noise, and enables efficient real-time trajectory optimization within an MPC pipeline. Extensive ablation and bias studies reveal that removing PINDO increases the prediction mean squared error (MSE) from 0.28 cm² to 0.68 cm² (a 143% increase), while omitting the constraint projection layer leads to a fourfold increase in violation rates. Furthermore, the robustness analysis of a colored noise and random walk drift model verifies its elasticity to non-ideal sensing. Although the deformation mechanism with a moderate rate-dependent effect in the simulation environment is optimized at present, PhysCon-Deform provides a very practical method to balance the precision-constraint trade-off in the control of deformable objects with physical constraints. Full article

Ground settlement induced by shield tunnel boring machine (TBM) excavation is a major geotechnical concern in urban tunneling because it may affect the safety of adjacent structures and underground infrastructure. In this study, machine learning models were developed to predict the maximum settlement induced by shield TBM excavation using a three-dimensional numerical analysis database comprising 320 simulation cases generated from combinations of tunnel diameter (D), ground elastic modulus (E), face pressure (FP), and backfill pressure (BP). Random forest (RF) and extreme gradient boosting (XGBoost) models were developed and compared with an existing regression-based settlement prediction equation. Predictive performance and generalization capability were evaluated using random split and GroupKFold validation techniques. Under random split validation, RF achieved the highest predictive performance, with a coefficient of determination of 0.997 and a root mean square error of 0.438 mm, followed by XGBoost. Both machine learning models outperformed the existing settlement prediction equation. However, model performance decreased substantially under GroupKFold validation, indicating limited generalization capability under unseen D–E grouped conditions. The results demonstrate that the developed machine learning models provide accurate predictions within the range of tunnel–ground conditions represented by the adopted numerical analysis database. The findings highlight the importance of evaluating both predictive performance and generalization capability, particularly when machine learning models developed from numerical analysis databases are applied beyond the conditions represented in the training database. Full article

Integrating high-resolution hyperspectral remote sensing with deep generative artificial intelligence (AI) offers a promising method for accurate soil mapping under limited sampling conditions. While the EnMAP satellite provides hyperspectral data for mapping soil properties, its coarse spatial resolution (30 m) restricts its applications in digital soil mapping (DSM). This study investigates the potential of an integrated framework that combines hyperspectral–multispectral satellite data fusion and deep generative AI for high-resolution DSM. A total of 110 surface soil samples (0–30 cm) were collected from an agricultural farm in Ismailia (Egypt) and were analysed for soil organic matter (OM), electrical conductivity (EC), and available phosphorus (P). EnMAP hyperspectral and SuperDove multispectral images were pre-processed and fused using a 1D U-Net-based convolutional neural network (CNN) to generate a hyperspectral high-resolution (3 m) image. A conditional Wasserstein generative adversarial network (GAN) with gradient penalty (cWGAN-GP) was used to generate soil spectra at different levels of augmentation. The generated spectra were combined with 70% of real spectra to create different calibration datasets that were filtered to preserve spectral diversity and avoid spectral duplication. Two predictive models, random forest (RF) and CNN, were developed based on the optimal combined calibration datasets. The prediction results based on the independent prediction dataset (30%) showed that GAN–CNN outperformed GAN–RF at the highest augmentation level (5×), with increases in coefficient of determination (R²) by 31.3, 25.8, and 9.0%, and reductions in root mean square error (RMSE) by 33.2, 22.1 and 8.2% for EC, OM, and P, respectively. The optimal GAN–CNN model was used to produce soil maps at 3 m resolution based on the fused high-resolution hyperspectral image. The results indicate the potential of fusing hyperspectral and multispectral data combined with deep generative AI to overcome limited soil sampling and advance DSM for precision agriculture applications. Full article

The waste-to-energy process relies on the recovery and use of energy produced by the combustion of waste, which allows for waste treatment while ensuring energy production. To improve the prediction accuracy of energy recovered from waste combustion, a new prediction model based on orthogonal Laguerre polynomials is proposed in this work. Exploiting the orthogonality of Laguerre polynomials reduces approximation errors and facilitates parameter estimation through the calculation of orthogonal moments. To validate the proposed method, the propose prediction model was trained on a dataset published online and then used to predict the higher heating value (HHV) of municipal solid waste (MSW), in order to recover energy from this waste. A comparative study was conducted with predictive methods used in the field of energy recovery from waste. Evaluation metrics such as the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute percentage error $\left(MAPE\right)$ were used in this comparative study. The results obtained show that the proposed model achieves its best performance for a polynomial of degree $n=5,$ with an optimal value of $\alpha =0.9$ determined by the Artificial Bee Colony (ABC) algorithm. In this configuration, the model obtains a minimum RMSE of 1.09, compared to 5.12 for the LSTM-MLP model, which is the highest-performing reference method among those compared. These results demonstrate the ability of the proposed model to predict the higher calorific value HHV of municipal solid waste with significantly greater accuracy. Full article

Background: The high costs, long development timelines, and low clinical success rates in oncology highlight an urgent need for reliable computational strategies for drug repositioning. Current machine learning approaches often integrate heterogeneous pharmacogenomic datasets, which may lose biological specificity and limit model interpretability. Methods: In this study, we propose Separate XAI, an explainable artificial intelligence framework that retains dataset-specific biological features by adopting separate preprocessing and training pipelines for the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. Different deep learning architectures such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) were used to predict the drug response in the cancer cell lines. We also used SHapley Additive exPlanations (SHAP) to improve interpretability and identify biologically relevant features. Results: The developed framework showed good predictions with 94.49% accuracy in the CCLE dataset and a mean squared error of 0.0725 in the GDSC dataset. Explainability analysis identified important biomarkers and signaling pathways such as TP53 and KRAS, providing mechanistic insights into drug sensitivity and therapeutic response. Conclusions: The distinct XAI presented here offers an interpretable, biologically grounded framework for cancer drug repositioning by integrating dataset-specific modeling and explainable artificial intelligence. However, integration-based approaches often suffer from confounding effects of experimental and biological heterogeneity, but the proposed framework explicitly preserves dataset-specific characteristics, which potentially could lead to more robust predictions and higher interpretability for precision oncology and translational cancer research. Full article

Accurate atmospheric temperature and humidity profiles are fundamental to weather monitoring and prediction. Geostationary imagers such as the Advanced Meteorological Imager (AMI) provide continuous observations and enable profile retrievals through radiative transfer–based algorithms; however, these products remain affected by systematic biases associated with the limited number of spectral channels and reliance on background fields from numerical weather prediction models. This study presents a data-driven post-processing framework to generate reanalysis-consistent profiles by refining AMI-retrieved temperature, mixing ratio, and relative humidity profiles using Light Gradient Boosting Machine (LGBM) models trained with ERA5 reanalysis data. Using four years (2020–2023) of hourly observations, the refined profiles were evaluated against both ERA5 and independent radiosonde measurements. Relative to ERA5, the refinement yields modest but consistent reductions in root mean square error (RMSE), including approximately 0.04 g kg⁻¹ (6–7%) for mixing ratio and 1.9 percentage points (≈14%) for relative humidity, while temperature shows a smaller error reduction of about 0.02 K (2–3%). When compared with radiosondes, temperature RMSE shows a marginal increase overall (<1%) with a larger increase in the lower troposphere, whereas improvements are observed for mixing ratio (2–3%) and relative humidity (6–7%). Seasonal and diurnal analyses reveal systematic error structures in the original AMI profiles, particularly wet-bias patterns in summer moisture fields, which are partially mitigated by the refinement. Feature-importance analysis using Shapley Additive Explanations (SHAP) identifies the dominant contribution of AMI water vapor channels, consistent with their known vertical sensitivity. Overall, this long-term evaluation demonstrates the feasibility of machine learning-based refinement for geostationary imager atmospheric profiles, while also highlighting inherent limitations related to the information content of current-generation imagers. Full article

This paper proposes a frequency-estimation method based on an iterative midpoint search applied to the discrete Fourier transform (MS-DFT). The method is designed to reduce the sensitivity of classical interpolation-based estimators to fractional-bin offsets and noise variations. The proposed approach exploits the unimodal structure of the spectral magnitude and performs iterative interval contraction using midpoint evaluation and magnitude ordering. This mechanism enables consistent estimation behavior across different spectral alignments without relying on interpolation formulas. Extensive simulations show that the method achieves a nearly constant ratio between the root mean square error (RMSE) and the Cramér–Rao lower bound (CRLB) over a wide range of signal-to-noise ratios (−7.5 dB to 65 dB). This behavior indicates stable relative efficiency with respect to the theoretical limit, rather than optimization at isolated operating points. In addition, the method reaches near-optimal accuracy within a small number of iterations (typically 8–9), resulting in low and predictable computational complexity. These properties make the MS-DFT estimator suitable for real-time and resource-constrained applications such as embedded sensing and biosignal processing. Full article

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and validation, three machine learning architectures, namely two-dimensional convolutional long short-term memory (ConvLSTM2D), hybrid convolutional neural network–long short-term memory (hybrid CNN-LSTM), and hybrid convolutional neural network–bidirectional long short-term memory (hybrid CNN-BiLSTM), were developed to further analyze ground deformation and make future predictions. It was found that from SBAS-InSAR, the deformation rates for the whole Dongli District, Tianjin, ranged from −40.98 to 27.18 mm/year, with a mean of −2.41 mm/year from 2019 to 2024. Model performance was evaluated using held-out validation samples derived from the InSAR deformation dataset. The ConvLSTM2D model achieved the best performance, with an R² value of 0.99 and root mean squared error (RMSE) of 1.37 mm, compared with the hybrid CNN-LSTM (R² = 0.99, RMSE = 2.16 mm) and hybrid CNN-BiLSTM (R² = 0.99, RMSE = 2.19 mm). This optimized ConvLSTM2D model was applied to estimate the predictions of the ground deformation rate with −43.71 mm/year in the high-deformation zone between 2025 and 2028. These findings predict a continuing trend of land instability, highlighting the necessity for urgent geohazard mitigation and urban planning strategies in the affected regions. Full article

Background/Objectives: Total or partial pancreatectomy is associated with significant metabolic stress and high risk of postoperative malnutrition. Accurate estimation of resting energy expenditure (REE) is essential, as predictive equations may not reflect true energy needs. Methods: A prospective study among patients undergoing total or partial pancreatectomy for pancreatic tumors was conducted. REE was measured by indirect calorimetry (mREE) and compared with the Harris–Benedict and Schofield equations and the weight-based approaches (25 and 30 kcal/kg). Agreement was assessed using linear regression and Bland–Altman analysis; accuracy indices included ±10%, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Results: In 26 patients (mean age, 66.7 ± 8.7 years; 53.8% male) undergoing pancreatic resection (17 pancreaticoduodenectomies, 8 distal pancreatectomies, 1 total pancreatectomy), 60% were at preoperative malnutrition risk. The median measured REE was 1484 kcal/day, rising to 1706 kcal/day after activity adjustment (×1.15) within 14 postoperative days. At 3–6 months postoperatively, patients demonstrated significant declines in nutritional status with a median body weight reduction of −7.3% and a decrease in BMI of −2 kg/m². The 30 kcal/kg method showed the lowest accuracy (MAPE 23.2%, RMSE 416 kcal/day) and overestimated energy needs. Harris–Benedict underestimated mREE in 61.5% of cases, while the 25 kcal/kg approach showed more balanced performance. Conclusions: Postoperative energy expenditure in patients undergoing pancreatic resection appeared elevated relative to predictive equations. Predictive equations lack reliability, favoring indirect calorimetry for precision. Sustained weight loss underscores the need for prolonged nutritional surveillance. Full article

Accurate state-of-health (SoH) prediction is vital for safe and efficient battery management, enabling extended lifespan and improving technologies such as electric vehicles and stationary energy storage systems. In this work, a graph neural network-based deep learning framework is proposed to predict the SoH of a commercial nickel–manganese–cobalt oxide (NMC) lithium-ion technology. Health indicators obtained from the in-house-generated experimental aging dataset are used to train and validate the model across batteries subjected to diverse operating conditions. The hybrid architecture combines graph neural networks (GraphSAGE) with convolutional neural networks (CNN) and long short-term memory (LSTM) blocks, capturing both local structural relationships and temporal patterns in the battery data. Evaluation results show strong predictive performance, achieving an R² of 0.989 and a mean squared error of 3.23 × 10⁻⁶. These findings suggest that the proposed methodology could be deployed as a useful diagnostic tool. Full article

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing. Full article

This study investigates the effectiveness of common thermal, climate, and envelope features in predicting annual site energy use intensity (site EUI) for different types of residential buildings in the USA. A proposed multi-level data approach that consists of regression algorithms and feature analysis has been implemented to derive models from different sets of features related to thermal, envelope, and climate, respectively. Feature set analysis is conducted using correlation analysis methods besides chi-square testing (CHI) and gain ratio (GR) methods to offer interpretable global features rankings. Models were developed using regression-based algorithms (linear, lasso, and ridge) under a 10-fold cross-validation on different distinct sets of features besides permutation feature importance (PFI) analyses to validate the models in terms of root mean squared error (RMSE). The novelty of this study lies in the comparison of feature groups and the evaluation of their individual and incremental contributions to site EUI prediction. Results against the WiDS Datathon 2022 building energy dataset demonstrate consistently ranked climate and thermal indicators (accumulated annual heating degree days (AAH) and accumulated annual cooling degree days (AAC), and heating dominance (HD), cooling dominance (CD), snowfall, and extreme temperature days) as the most informative predictors among the evaluated feature groups. The model with the best performance has an RMSE value of about 38.68; however, from the low Coefficient of determination (R²) values, it can be noted that yearly climatic conditions and building envelope characteristics cannot be only used to account for the variation in site EUIs on their own, thus showing the need to consider other factors. Full article