Search Results (146)

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs. Full article

Hyperspectral imaging has been increasingly used in mining for detailed mineral characterization and enhanced ore–waste discrimination, which is essential for optimizing resource extraction. However, the full deployment of this technology still faces challenges due to the variability of field conditions and the spectral complexity inherent in real-world mining environments. In this study, we compare the performance of two approaches for ore–waste discrimination in both laboratory and actual mine site conditions: (i) a data-driven feature extraction (FE) method and (ii) a knowledge-based mineral mapping method. Rock samples, including ore and waste from an open-pit gold mine, were obtained and scanned using a hyperspectral imaging system under laboratory conditions. The FE method, which quantifies the frequency absorption peaks at different wavelengths for a given rock sample, was used to train three discriminative models using the random forest classifier (RFC), support vector classification (SVC), and K-nearest neighbor classifier (KNNC) algorithms, with RFC achieving the highest performance with an F1-score of 0.95 for the laboratory data. The mineral mapping method, which quantifies the presence of pyrite, calcite, and potassium feldspar based on prior geochemical analysis, yielded an F1-score of 0.78 for the ore class using the RFC algorithm. In the next step, the performance of the developed discriminative models was tested using hyperspectral data of two muck piles scanned in the open-pit gold mine. The results demonstrated the robustness of the mineral mapping method under field conditions compared to the FE method. These results highlight hyperspectral imaging as a valuable tool for improving ore-sorting efficiency in mining operations. Full article

This study aimed to investigate the effect of the type of sweetener used (xylitol, stevia, cane sugar) on the quality of liqueurs made from Vaccinium myrtillus L. and Vaccinium corymbosum L. fruits. The quality assessment was performed based on selected organoleptic and physicochemical features, with particular emphasis on the health-promoting potential of the produced beverages. The liqueurs were assessed in terms of their physicochemical parameters: pH, total acidity, density, total soluble solids, color, ethanol and polyphenol contents, and redox potential. Antioxidant capacities were determined by a 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging capacity assay and ferric reducing antioxidant power (FRAP). The Qualitative Descriptive Analysis method was employed for their sensory assessment. The sensory profiling method was used to determine the intensity of the flavor sensations. The study results showed that the type of sweetener did not affect the antioxidative properties of the liqueur. The ABTS test yielded values from 1081.88 to 1238.13 μmol Tx/100 mL, the DPPH test from 348.8 to 367.88 μmol Tx/100 mL, and the FRAP test from 594.20 to 653.20 μmol FeSO₄/100 mL. However, the sweetening substrate affected the content of polyphenolic compounds in the resulting products, but by no more than 15%. The liqueur sweetened with xylitol had a comparable extract content to that sweetened with cane sugar. All three variants of liqueurs were accepted by the evaluation panel, and their overall qualities were comparable in the sensory assessment. It is, therefore, possible to produce a high-quality liqueur with a reduced caloric value, which will potentially increase its attractiveness for consumers. Full article

Manual geometric modeling of timber structures is time-intensive and error-prone, impeding efficient structural analysis. To overcome this limitation, this study develops an automated framework for the rapid generation of 3D geometric finite element (FE) models directly from LiDAR point clouds. The methodology first employs a region-growing algorithm for component segmentation. This is followed by the integration of geometric feature extraction techniques to robustly determine the position, orientation, boundaries, and dimensions of structural elements. The extracted geometric information is then output as an executable APDL (ANSYS Parametric Design Language) file for parametric geometric modeling, incorporating interfaces for customizing material and connection properties. The proposed framework accurately reconstructs geometries with high fidelity. It effectively addresses challenges arising from occlusions and incomplete point cloud data through boundary inference and contact relationship analysis. This approach demonstrates substantial promise for applications in both heritage conservation and modern timber engineering. Full article

The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising photovoltaic candidates. A dataset of 1053 double perovskites was extracted from the Materials Project database, with 50 feature descriptors generated. Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. Four algorithms, including gradient-boosting regression (GBR), random forest regression (RFR), LightGBM, and XGBoost, were evaluated, with XGBoost demonstrating the best performance (R² = 0.934 for bandgap, R² = 0.959 for formation energy; MAE = 0.211 eV and 0.013 eV/atom). The SHAP (Shapley Additive Explanations) analysis revealed that the X-site electron affinity positively influences the bandgap, while the B″-site first and third ionization energies exhibit strong negative effects. Formation energy is primarily governed by the X-site first ionization energy and the electronegativities of the B′ and B″ sites. To identify optimal photovoltaic materials, 4573 charge-neutral double perovskites were generated via elemental substitution, with 2054 structurally stable candidates selected using tolerance and octahedral factors. The XGBoost model predicted bandgaps, yielding 99 lead-free double perovskites with ideal bandgaps (1.3~1.4 eV). Among them, four candidates are known compounds according to the Materials Project database, namely Ca₂NbFeO₆, Ca₂FeTaO₆, La₂CrFeO₆, and Cs₂YAgBr₆, while the remaining 95 candidate perovskites are unknown compounds. Notably, X-site elements (Se, S, O, C) and B″-site elements (Pd, Ir, Fe, Ta, Pt, Cu) favor narrow bandgap formation. These findings provide valuable guidance for designing high-performance, non-toxic photovoltaic materials. Full article

A majority of hyperspectral super-resolution methods aim to enhance the spatial resolution of hyperspectral imaging data (HSI) by integrating high-resolution multispectral imaging data (MSI), leveraging rich spectral information for various geospatial applications. Key challenges include spectral distortions from high-frequency spatial data, high computational complexity, and limited training data, particularly for new-generation sensors with unique noise patterns. In this contribution, we propose a novel parallel patch-wise sparse residual learning (P²SR) algorithm for resolution enhancement based on fusion of HSI and MSI. The proposed method uses multi-decomposition techniques (i.e., Independent component analysis, Non-negative matrix factorization, and 3D wavelet transforms) to extract spatial and spectral features to form a sparse dictionary. The spectral and spatial characteristics of the scene encoded in the dictionary enable reconstruction through a first-order optimization algorithm to ensure an efficient sparse representation. The final spatially enhanced HSI is reconstructed by combining the learned features from low-resolution HSI and applying an MSI-regulated guided filter to enhance spatial fidelity while minimizing artifacts. P²SR is deployable on a high-performance computing (HPC) system with parallel processing, ensuring scalability and computational efficiency for large HSI datasets. Extensive evaluations on three diverse study sites demonstrate that P²SR consistently outperforms traditional and state-of-the-art (SOA) methods in both quantitative metrics and qualitative spatial assessments. Specifically, P²SR achieved the best average PSNR (25.2100) and SAM (12.4542) scores, indicating superior spatio-spectral reconstruction contributing to sharper spatial features, reduced mixed pixels, and enhanced geological features. P²SR also achieved the best average ERGAS (8.9295) and Q2n (0.5156), which suggests better overall fidelity across all bands and perceptual accuracy with the least spectral distortions. Importantly, we show that P²SR preserves critical spectral signatures, such as Fe²⁺ absorption, and improves the detection of fine-scale environmental and geological structures. P²SR’s ability to maintain spectral fidelity while enhancing spatial detail makes it a powerful tool for high-precision remote sensing applications, including mineral mapping, land-use analysis, and environmental monitoring. Full article

As a key parameter in the sintering process, the ferrous oxide content of sinter can reflect the working condition, energy consumption level, and quality level of the final sintered products in the sintering process. It has become a key problem to realize the prediction of ferrous oxide content in sinter and feedback control of sinter quality accordingly. The two commonly used methods for detecting ferrous oxide content in industrial production currently do not meet real-time requirements and cannot provide timely feedback for production regulation. Therefore, research on real-time prediction technology of ferrous oxide content in sinter was carried out, and an optimized back propagation neural network model was established to realize the mapping between characteristic parameters and the FeO content in sinter. The characteristic parameters include image parameters and process parameters. Through the research on the brightness change trend of the machine tail cross-section image, the best cross-section image acquisition method based on brightness difference is realized, and image parameters are obtained by image processing technology. The process parameters were selected using correlation analysis. Through data processing techniques such as data cleaning, normalization, and feature fusion, feature parameters were obtained as input vectors for the neural network. To improve prediction accuracy and system stability, an adaptive learning rate and genetic algorithm were used to optimize the traditional BP neural network. The average test error of the optimized prediction model was 0.32%. Taking actual data production as an example, test data on the FeO content of sinter were extracted from the laboratory. Compared with the FeO content predicted by the system, the prediction time of the system was about 2 h earlier than the test time. In terms of prediction accuracy, the average absolute error was 0.25%, and the absolute prediction error was not more than ±1%. Full article

This study employs density functional theory (DFT) simulations to systematically investigate the structural and optoelectronic modifications induced by the substitution of metal ions (Mg²⁺, Ca²⁺, Mn²⁺, Fe²⁺/³⁺, Co²⁺, and Ni²⁺) in kaolinite. First-principles calculations reveal distinct substitution behaviors: Na-Ni (II)-1 exhibits the lowest cell energy, indicating superior structural stability, while Na-Mn (II)-1 demonstrates the most favorable substitution energy (−5.44 eV). XRD simulations of divalent substitutions show a positive correlation between atomic number and diffraction intensity at 8.778° and 9.774°, suggesting a spectral marker for substitution detection. Electronic structure analysis identifies significant bandgap reduction, with Na-Fe (II)-4 achieving an ultranarrow gap of 1.014 eV, attributed to spin-polarized d-orbital contributions. X-ray absorption fine-structure (XAFS) simulations further reveal metal-specific bond elongation, with Fe³⁺ substitutions preserving near-pristine coordination distances. These findings establish a comprehensive framework linking metal substitution to structural distortion and optoelectronic response, providing theoretical insights for optimizing kaolinite-based material properties through computational feature extraction. Full article

Traditional object detection methods for meat duck counting suffer from high manual costs, low image quality, and varying object sizes. To address these issues, this paper proposes FE-P Net, an image enhancement-based parallel density estimation network that integrates CNNs with Transformer models. FE-P Net employs a Laplacian pyramid to extract multi-scale features, effectively reducing the impact of low-resolution images on detection accuracy. Its parallel architecture combines convolutional operations with attention mechanisms, enabling the model to capture both global semantics and local details, thus enhancing its adaptability across diverse density scenarios. The Reconstructed Convolution Module is a crucial component that helps distinguish targets from backgrounds, significantly improving feature extraction accuracy. Validated on a meat duck counting dataset in breeding environments, FE-P Net achieved 96.46% accuracy in large-scale settings, demonstrating state-of-the-art performance. The model shows robustness across various densities, providing valuable insights for poultry counting methods in agricultural contexts. Full article

Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, traditional feature engineering (FE) has been enhanced to interface matching–feature engineering (IM-FE). This approach requires considerable effort in feature design, larger sample duration, or a higher upper limit of frequency. In this context, this paper proposes a one-dimensional network design for underwater acoustic target recognition (UATR-ND1D), only combined with fast Fourier transform (FFT), which can effectively alleviate the problem of IM-FE. This method is abbreviated as FFT-UATR-ND1D. FFT-UATR-ND1D was applied to the design of a one-dimensional network, named ResNet1D. Experiments were conducted on two mainstream datasets, using ResNet1D in 4320 and 360 tests, respectively. The lightweight model ResNet1D_S, with only 0.17 M parameters and 3.4 M floating point operations (FLOPs), achieved average accuracies were 97.2% and 95.20%. The larger model, ResNet1D_B, with 2.1 M parameters and 5.0 M FLOPs, both reached optimal accuracies, 98.81% and 98.42%, respectively. Compared to existing methods, those with similar parameter sizes performed 3–5% worse than the methods proposed in this paper. Additionally, methods achieving similar recognition rates require more parameters of 1 to 2 orders of magnitude and FLOPs. Full article

Mössbauer spectroscopy can be advantageous for studying catalysts. In particular, its use in in situ studies can provide unique access to structural features. However, special attention must be paid to the interpretation of data, since in most studies, the samples are not perfectly homogeneous. Balance and compromise should be found between the refinement of evaluations by extracting and interpreting data from spectra, while also considering the presence of possible inhomogeneities in samples. In this review, examples of studies on two types of catalysts are presented, from which, despite possible inhomogeneities, clear statements can be derived. The first example pertains to selected iron-containing microporous zeolites (with ⁵⁷Fe Mössbauer spectroscopy), from which unique information is collected on the coordination of iron ions. The second example is related to studies on supported PtSn alloy particles (with ¹¹⁹Sn probe nuclei), from which reversible modifications of the tin component due to interactions with the reaction partners are revealed. Full article

Zn-Fe alloys are gaining attention for their use as bioabsorbable implants, and their development requires a deeper understanding of the processing–microstructure–property relationships. This study aimed to analyze the influence of microstructural features on the hardness of a Zn-2 wt.%Fe alloy. To achieve this, a casting was fabricated using directional solidification, and samples that experienced various cooling conditions were extracted from it. The results show that the microstructure of the investigated alloy was composed of a Zn-rich phase (matrix) and FeZn₁₃ intermetallic particles. Four different morphological patterns of the microstructure could be formed, depending on the thermal conditions during solidification. For each of these patterns, a reduction in the spacing between FeZn₁₃ particles, a parameter representing the degree of microstructural refinement, did not lead to a considerable increase in the hardness of the Zn-2wt.%Fe alloy. Hardness was shown to be more dependent on the morphology of the FeZn₁₃ intermetallics and Zn-rich matrix than on the degree of refinement of these microstructural phases. Therefore, the present research provides valuable insights into the development of enhanced Zn-Fe alloys by demonstrating how microstructural features can affect their properties, particularly in terms of hardness and morphologies of the microstructural phases. Full article

Nowadays, object detection algorithms are widely used in various scenarios. However, there are further small object detection requirements in some special scenarios. Due to the problems related to small objects, such as their less available features, unbalanced samples, higher positioning accuracy requirements, and fewer data sets, a small object detection algorithm is more complex than a general object detection algorithm. The detection effect of the model for small objects is not ideal. Therefore, this paper takes YOLOXs as the benchmark network and enhances the feature information on small objects by improving the network’s structure so as to improve the detection effect of the model for small objects. This specific research is presented as follows: Aiming at the problem of a neck network based on an FPN and its variants being prone to information loss in the feature fusion of non-adjacent layers, this paper proposes a feature fusion and distribution module, which replaces the information transmission path, from deep to shallow, in the neck network of YOLOXs. This method first fuses and extracts the feature layers used by the backbone network for prediction to obtain global feature information containing multiple-size objects. Then, the global feature information is distributed to each prediction branch to ensure that the high-level semantic and fine-grained information are more efficiently integrated so as to help the model effectively learn the discriminative information on small objects and classify them correctly. Finally, after testing on the VisDrone2021 dataset, which corresponds to a standard image size of 1080p (1920 × 1080), the resolution of each image is high and the video frame rate contained in the dataset is usually 30 frames/second (fps), with a high resolution in time, it can be used to detect objects of various sizes and for dynamic object detection tasks. And when we integrated the module into a YOLOXs network (named the FE-YOLO network) with the three improvement points of the feature layer, channel number, and maximum pool, the mAP and APs were increased by 1.0% and 0.8%, respectively. Compared with YOLOV5m, YOLOV7-Tiny, FCOS, and other advanced models, it can obtain the best performance. Full article

Feature engineering for whole-genome DNA sequences plays a critical role in predicting plant phenotypic traits. However, due to limitations in the models’ analytical capabilities and computational resources, the existing methods are predominantly confined to SNP-based approaches, which typically extract genetic variation sites for dimensionality reduction before feature extraction. These methods not only suffer from incomplete locus coverage and insufficient genetic information but also overlook the relationships between nucleotides, thereby restricting the accuracy of phenotypic trait prediction. Inspired by the parallels between gene sequences and natural language, the emergence of large language models (LLMs) offers novel approaches for addressing the challenge of constructing genome-wide feature representations with nucleotide granularity. This study proposes FE-WDNA, a whole-genome DNA sequence feature engineering method, using HyenaDNA to fine-tune it on whole-genome data from 1000 soybean samples. We thus provide deep insights into the contextual and long-range dependencies among nucleotide sites to derive comprehensive genome-wide feature vectors. We further evaluated the application of FE-WDNA in agronomic trait prediction, examining factors such as the context window length of the DNA input, feature vector dimensions, and trait prediction methods, achieving significant improvements compared to the existing SNP-based approaches. FE-WDNA provides a mode of high-quality DNA sequence feature engineering at nucleotide resolution, which can be transformed to other plants and directly applied to various computational breeding tasks. Full article

This study explores the chemical composition of different macrophyte species and infers their potential in extracting nutrients and some heavy metals from water as well as the use of macrophytes’ biomass as natural fertilizers. It used a dataset obtained from a previous study composed of 445 samples of chemical concentrations in the dried biomass of 16 macrophyte species collected from the Santana Reservoir in Rio de Janeiro, Brazil. Correlation tests, analysis of variance, and factor analysis of mixed data were performed to infer correspondences between the macrophyte species. The results showed that the macrophyte species can be grouped into three different clusters with significantly different profiles of chemical element concentrations (N, P, K⁺, Ca²⁺, Mg²⁺, S, B, Cu²⁺, Fe²⁺, Mn²⁺, Zn²⁺, Cr³⁺, Cd²⁺, Ni²⁺, Pb²⁺) in their biomass (factorial map from PCA). Most marginal macrophytes have a lower concentration of chemical elements (ANOVA p-value < 0.05). Submerged and floating macrophyte species presented a higher concentration of metallic and non-metallic chemical elements in their biomass (ANOVA p-value < 0.05), revealing their potential in phytoremediation and the removal of toxic compounds (such as heavy metal molecules) from water. A cluster of macrophyte species also exhibited high concentrations of macronutrients and micronutrients (ANOVA p-value < 0.05), indicating their potential for use as soil fertilizers. These results reveal that the plant’s location in the reservoir (marginal, floating, or submerged) is a relevant feature associated with macrophytes’ ability to remove chemical components from the water. The obtained results can contribute to planning the management of macrophyte species in large water reservoirs. Full article