Search Results (8,398)

Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID) data, making it challenging to balance data heterogeneity and privacy protection. To address this, we propose a privacy-preserving federated learning method based on cloud–edge–end collaboration. Our method fully considers the three-tier architecture of cloud–edge–end systems and the non-IID nature of terminal node data. It enhances model accuracy while protecting the privacy of terminal node data. The proposed method groups terminal nodes based on the similarity of their data distributions and constructs edge subnetworks for training in collaboration with edge nodes, thereby mitigating the negative impact of non-IID data. Furthermore, we enhance WGAN-GP with attention mechanism to generate balanced synthetic data while preserving key patterns from original datasets, reducing the adverse effects of non-IID data on global model accuracy while preserving data privacy. In addition, we introduce data resampling and loss function weighting strategies to mitigate model bias caused by imbalanced data distribution. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms existing approaches in terms of model accuracy, F1-score, and other metrics. Full article

As the demand for efficient energy management increases, the need for extensive, high-quality energy data becomes critical. However, privacy concerns and insufficient data volume pose significant challenges. To address these issues, data synthesis techniques are employed to augment and replace real data. This paper introduces Doubly Structured Data Synthesis (${\mathrm{DS}}^2$), a novel method to tackle privacy concerns in time-series energy-use data. ${\mathrm{DS}}^2$ synthesizes rate changes to maintain longitudinal information and uses calibration techniques to preserve the cross-sectional mean structure at each time point. Numerical analyses reveal that ${\mathrm{DS}}^2$ surpasses existing methods, such as Conditional Tabular GAN (CTGAN) and Transformer-based Time-Series Generative Adversarial Network (TTS-GAN), in capturing both time-series and cross-sectional characteristics. We evaluated our proposed method using metrics for data similarity, utility, and privacy. The results indicate that ${\mathrm{DS}}^2$ effectively retains the underlying characteristics of real datasets while ensuring adequate privacy protection. ${\mathrm{DS}}^2$ is a valuable tool for sharing and utilizing energy data, significantly enhancing energy demand prediction and management. Full article

Land surface temperature (LST) plays a pivotal role in many environmental sectors. Unfortunately, thermal bands produced by instruments that are onboard satellites have limited spatial resolutions; this seriously impairs their potential usefulness. In this study, we propose an automatic procedure for the spatial downscaling of the two 100 m thermal infrared (TIR) bands of LandSat 8/9, captured by the TIR spectrometer (TIRS), by exploiting the bands of the optical instrument. The problem of fusion of heterogeneous data is approached as hypersharpening: each of the two sharpening images is synthesized following data assimilation concepts, with the linear combination of 30 m optical bands and the 15 m panchromatic (Pan) image that maximizes the correlation with each thermal channel at its native 100 m scale. The TIR bands resampled at 15 m are sharpened, each by its own synthetic Pan. On two different scenes of an OLI-TIRS image, the proposed approach is compared with 100 m to 15 m pansharpening, carried out uniquely by means of the Pan image of OLI and with the two high-resolution assimilated thermal images that are used for hypersharpening the two TIRS bands. Besides visual evaluations of the temperature maps, statistical indexes measuring radiometric and spatial consistencies are provided and discussed. The superiority of the proposed approach is highlighted: the classical pansharpening approach is radiometrically accurate but weak in the consistency of spatial enhancement. Conversely, the assimilated TIR bands, though adequately sharp, lose more than 20% of radiometric consistency. Our proposal trades off the benefits of its counterparts in a unique method. Full article

This study explores the application of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a critical resource for sustainable energy and material production. Two optimization approaches are evaluated, as follows: a base MILP model designed for scenarios with single processing points and an advanced model incorporating intermediate processing steps to enhance logistical efficiency. Using synthetic datasets that simulate vineyard regions, the models demonstrate potential cost reductions of up to 30%, showcasing significant improvements in operational efficiency and resource utilization. This study underscores the scalability and real-world feasibility of the proposed models, highlighting their alignment with circular bioeconomy principles. Additionally, it addresses key limitations such as computational complexity and adaptability to dynamic environments. Future research directions are outlined, focusing on real-time data integration, dynamic updates, and multi-objective optimization to further enhance model robustness and applicability in diverse supply chain scenarios. Full article

This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN’s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness. Full article

Modern dentistry is turning towards natural sources to overcome the immunological, toxicological, aesthetic, and durability drawbacks of synthetic materials. Among the first biomaterials used as endosseous dental implants, mollusk shells also display unique features, such as high mechanical strength, superior toughness, hierarchical architecture, and layered, microporous structure. This review focusses on hydroxyapatite—a bioactive, osteoconductive, calcium-based material crucial for bone healing and regeneration. Mollusk-derived hydroxyapatite is widely available, cost-effective, sustainable, and a low-impact biomaterial. Thermal treatment coupled with wet chemical precipitation and hydrothermal synthesis are the most common methods used for its recovery since they provide efficiency, scalability, and the ability to produce highly crystalline and pure resulting materials. Several factors, such as temperature, pH, and sintering parameters, modulate the size, purity, and crystallinity of the final product. Experimental and clinical data support that mollusk shell-derived hydroxyapatite and its carbonated derivatives, especially their nanocrystaline forms, display notable bioactivity, osteoconductivity, and osteoinductivity without causing adverse immune reactions. These biomaterials are therefore highly relevant for specific dental applications, such as bone graft substitutes or dental implant coatings. However, continued research and clinical validation is needed to optimize the synthesis of mollusk shell-derived hydroxyapatite and determine its applicability to regenerative dentistry and beyond. Full article

This paper introduces a robust bi-orthogonal projection (RBOP) learning method for dimensionality reduction (DR). The proposed RBOP enhances the flexibility, robustness, and sparsity of the embedding framework, extending beyond traditional DR methods such as principal component analysis (PCA), neighborhood preserving embedding (NPE), and locality preserving projection (LPP). Unlike conventional approaches that rely on a single type of projection, RBOP innovates by employing two types of projections: the “true” projection and the “counterfeit” projection. These projections are crafted to be orthogonal, offering enhanced flexibility for the “true” projection and facilitating more precise data transformation in the process of subspace learning. By utilizing sparse reconstruction, the acquired true projection has the capability to map the data into a low-dimensional subspace while efficiently maintaining sparsity. Observing that the two projections share many similar data structures, the method aims to maintain the similarity structure of the data through distinct reconstruction processes. Additionally, the incorporation of a sparse component allows the method to address noise-corrupted data, compensating for noise during the DR process. Within this framework, a number of new unsupervised DR techniques have been developed, such as RBOP_PCA, RBOP_NPE, and RBO_LPP. Experimental results from both natural and synthetic datasets indicate that these proposed methods surpass existing, well-established DR techniques. Full article

Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex and variable nature of tea cultivation landscapes. This study presents a high-precision approach to mapping tea plantations in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data and advanced deep learning models. We employed a combination of Sentinel-2 optical imagery, Sentinel-1 synthetic aperture radar imagery, and digital elevation models to capture the rich spatial, spectral, and temporal characteristics of tea plantations. Three deep learning models, namely U-Net, SE-UNet, and Swin-UNet, were constructed and trained for the semantic segmentation of tea plantations. Cross-validation and point-based accuracy assessment methods were used to evaluate the performance of the models. The results demonstrated that the Swin-UNet model, a transformer-based approach capturing long-range dependencies and global context for superior feature extraction, outperformed the others, achieving an overall accuracy of 0.993 and an F1-score of 0.977 when using multi-temporal Sentinel-2 data. The integration of Sentinel-1 data with optical data slightly improved the classification accuracy, particularly in areas affected by cloud cover, highlighting the complementary nature of Sentinel-1 imagery for all-weather monitoring. The study also analyzed the influence of terrain factors, such as elevation, slope, and aspect, on the accuracy of tea plantation mapping. It was found that tea plantations at higher altitudes or on north-facing slopes exhibited higher classification accuracy, and that accuracy improves with increasing slope, likely due to simpler land cover types and tea’s preference for shade. The findings of this research not only provide valuable insights into the precision mapping of tea plantations but also contribute to the broader application of deep learning in remote sensing for agricultural monitoring. Full article

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (${\sigma }_{vv}^0$), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage. Full article

This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R² = 74%) than Sentinel-1 (R² = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts. Full article

Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. Full article

Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. In this study, we used Monte Carlo dropout (MCDO) to characterize the uncertainty of deep learning image classification algorithms, including feature fusion models, on simulated synthetic aperture radar (SAR) images of persistent ship wakes. Comparing to a baseline, we used the distribution of predictions from dropout with simple mean value ensembling and the Kolmogorov—Smirnov (KS) test to classify in-domain and out-of-domain (OOD) test samples, created by rotating images to angles not present in the training data. Our objective was to improve the classification robustness and identify OOD images during the test time. The mean value ensembling did not improve the performance over the baseline, in that there was a –1.05% difference in the Matthews correlation coefficient (MCC) from the baseline model averaged across all SAR bands. The KS test, by contrast, saw an improvement of +12.5% difference in MCC and was able to identify the majority of OOD samples. Leveraging the full distribution of predictions improved the classification robustness and allowed labeling test images as OOD. The feature fusion models, however, did not improve the performance over the single SAR-band models, demonstrating that it is best to rely on the highest quality data source available (in our case, C-band). Full article

Pests and diseases have caused significant problems since the domestication of crops, resulting in economic loss and hunger. To overcome these problems, synthetic pesticides were developed to control pests; however, there are significant detrimental side effects of synthetic pesticides on the environment and human health. There is an urgent need to develop safer and more sustainable pesticides. Industrial hemp is a reservoir of compounds that could potentially replace some synthetic bactericides, fungicides, and insecticides. We determined the efficacy of industrial hemp extracts against Pseudomonas syringae pv. tabaci (PSTA), Pseudomonas syringae pv. tomato (PSTO), and Erwinia carotovora (EC). The study revealed a minimum inhibitory concentration (MIC) of 2.05 mg/mL and a non-inhibitory concentration (NIC) of 1.2 mg/mL for PSTA, an MIC of 5.7 mg/mL and NIC of 0.66 mg/mL for PSTO, and an MIC of 12.04 mg/mL and NIC of 5.4 mg/mL for EC. Time-kill assays indicated the regrowth of E. carotovora at 4 × MIC after 15 h and P. syringae pv. tomato at 2 × MIC after 20 h; however, P. syringae pv. tabaci had no regrowth. The susceptibility of test bacteria to hemp extract can be ordered from the most susceptible to the least susceptible, as follows: P. syringae pv. tabaci > P. syringae pv. tomato > E. carotovora. Overall, the data indicate hemp extract is a potential source of sustainable and safe biopesticides against these major plant pathogens. Full article

Data storage on DNA has emerged as a molecular approach to safeguarding digital information. Microarrays are an excellent source of complex DNA sequence libraries and are playing a central role in the development of this technology. However, the amount of DNA recovered from microarrays is often too small, and a PCR amplification step is usually required. Primer information can be conveyed alongside the DNA library itself in the form of readable barcodes made of DNA on the array surface. Here, we present a synthetic method to pattern QR and data matrix barcodes using DNA photolithography, phosphoramidite chemistry and fluorescent labeling. Patterning and DNA library synthesis occur simultaneously and on the same surface. We manipulate the chemical composition of the barcodes to make them indelible, erasable or hidden, and a simple chemical treatment under basic conditions can reveal or degrade the pattern. In doing so, information crucial to retrieval and amplification can be made available by the user at the appropriate stage. The code and its data contained within are intimately linked to the library as they are synthesized simultaneously and on the same surface. This process is, in principle, applicable to any in situ microarray synthesis method, for instance, inkjet or electrochemical DNA synthesis. Full article

Traditional studies on Transient Electromagnetic Method (TEM) typically assume that the subsurface medium is non-magnetic. However, in regions with igneous rock accumulations or where the subsurface is rich in ferromagnetic minerals, neglecting the magnetic properties of the underground medium may lead to erroneous interpretations for TEM data. This paper conducts a 3-D TEM forward modeling and inversion study considering the non-uniformity cases of magnetic permeability. 3-D TEM forward modeling employs an edge-based finite element method using unstructured grids and a second-order implicit backward Euler method, achieving a modeling algorithm that simultaneously considers non-uniform models of magnetic permeability and resis-tivity. The accuracy of the modeling algorithm is verified by comparing it with the analytical solution of a homogeneous half-space model and the solution of a 1-D TEM forward modeling algorithm. 3-D TEM inversion employs the L-BFGS algorithm and synthetic examples considering non-uniform magnetic permeability are presented. The inversion results show good recovery for the resistivity and magnetic permeability models. Comparisons with the inversion results that neglect the non-uniformity of magnetic permeability validate the importance of considering the variation of permeability in 3-D TEM forward modeling and inversion. Full article