Search Results (44)

Runoff time series often exhibit nonlinear and fluctuating characteristics, and their complexity has further increased with the intensification of global climate change; high-precision daily-scale forecasting remains a core challenge in the field of hydrological forecasting. Addressing the shortcomings of existing methods in terms of runoff feature extraction capabilities and limited forecasting accuracy, this paper aims to improve the accuracy of daily runoff forecasting in small watersheds by constructing a hybrid forecasting model that integrates optimization algorithms, signal decomposition, and deep learning models. Specifically, the original runoff data is first preliminarily decomposed using a variational mode decomposition (VMD) method optimized by the grey wolf optimization (GWO) algorithm. The mode components obtained from the decomposition are evaluated using Fuzzy Entropy (FE), and the selected high-entropy components (IMFs) are then input into a second-order decomposition using an optimized Wavelet Transform (WT) to further extract latent features. After decomposition, the mode components are reassembled; second, a bidirectional long short-term memory (BiLSTM) model for daily runoff prediction is constructed for each subcomponent, and the model’s hyperparameters are optimized using an optimization algorithm; finally, the prediction results are reconstructed to obtain the final output. Case studies were conducted using three hydrological stations—Nanfeng, Baiquan, and Shaziling—in the Xujiang River basin of the Fuhe River. The experimental results indicate that by incorporating an optimization mechanism and a two-stage decomposition strategy, the proposed model achieved an NSE of over 0.95 at all three stations. Compared to the baseline BiLSTM model, the proposed model reduced the RMSE by 76.69%, 75.82%, and 65.92% at the three stations, respectively, and reduced the MAE by 64.77%, 73.54%, and 50.46%, and NSE increased by 27.82%, 40.06%, and 38.02%, respectively. This demonstrates that the model exhibits excellent reliability and superiority in daily-scale runoff forecasting for small watersheds. Full article

Background/Objectives: The difficulties caused by breast cancer have been addressed in a number of ways. Since it is said to be the second most common cause of death from cancer among women, early intervention is crucial. Early detection is difficult because of the existing detection tools’ shortcomings in objectivity and accuracy. Quadra Sense, a fusion of deep learning (DL) classifiers for mitosis detection in breast cancer histopathology, is proposed to address the shortcomings of current approaches. It demonstrates a greater capacity to produce more accurate results. Methods: Initially, the raw dataset is preprocessed by using a normalization by means of color channel normalization (zero-mean normalization) and stain normalization (Macenko Stain Normalization), and the artifact can be removed via median filtering and contrast enhancement using histogram equalization; ROI identification is performed using modified Fully Convolutional Networks (FCNs) followed by the feature extraction (FE) with Modified InceptionV4 (M-IV4), by which the deep features are retrieved and the feature are selected by means of a Self-Improved Seagull Optimization Algorithm (SA-SOA), and finally, classification is performed using Mito-Quartet. Results: Ultimately, using a performance evaluation, the suggested approach achieved a higher accuracy of 99.2% in comparison with the current methods. Conclusions: From the outcomes, the recommended technique performs well. Full article

In order to explore the co-evolutionary relationship between the functions of microbial communities and the chemical composition of groundwater in a hyporheic zone affected by groundwater exploitation along riverbank, we have taken the Huangjia water source area on the Liao River main stream in Shenyang as an example. DNA was extracted from microorganisms in the hyporheic zone affected by groundwater exploitation along the riverbank, and we conducted high-throughput sequencing to select the dominant bacterial strains from the indigenous bacteria. They are classified as the Proteobacteria phylum, the Actinobacteria phylum, the Firmicutes phylum, the Bacteroidetes phylum, the Chloroflexi phylum, and the Acidobacteria phylum. The dominant bacteria have a good correlation with Fe, Mn, and environmental factors (such as DO—dissolved oxygen, Eh—oxidation-reduction potential, etc.) in the hyporheic zone. The functions and activities of the superior bacterial strains exhibit a feature of co-evolution with the water’s chemical environment, which has certain response characteristics to redox zoning. Studying the co-evolution relationship between the microbial community structure and function in the hyporheic zone and the chemical composition of the groundwater can provide a microbiological theoretical basis for the redox zonation. It also offers reference for understanding the process of Fe and Mn migration and transformation in the hyporheic zone under the hydrodynamic conditions of groundwater exploitation along the riverbank. Full article

Construction sites are complex environments, and unsafe behaviors by workers, such as not wearing safety helmets or reflective vests, can easily lead to accidents. When using target detection technology to detect unsafe behaviors, the results are often unsatisfactory due to the complexity of the background and the small size of the targets. This paper proposes an unsafe behavior detection algorithm based on dual adaptive feature fusion. The algorithm is based on YOLOv5, introducing a front-end adaptive feature fusion module (FE-AFFM) at the head of the backbone network for deep data processing, improving the model’s feature extraction capability in complex backgrounds. Simultaneously, a back-end adaptive feature fusion module (BE-AFFM) is introduced at the tail of the network to strengthen feature fusion. In the experimental verification phase, this paper selects a self-made laboratory dataset and verifies the effectiveness of the improved algorithm through ablation experiments, algorithm comparisons, and heatmap analysis. The average accuracy of the improved algorithm is 3.6% higher than the baseline model, and the detection effect on small targets is significantly improved, meeting the actual needs of construction sites. This paper also selects the publicly available dataset SHWD for algorithm comparison experiments. The results show that the improved algorithm still has a significant advantage over mainstream algorithms, verifying the generalization ability of the improved model. Full article

Advancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. Full article

Ore mining and smelting are often related to environmental pollution. This study provides information about the geochemical features of Technosols at historical mining and metallurgical sites in the Tatra Mountains, southern Poland, evaluating the contents of potentially toxic trace elements (PTTE) and their behaviours in soils, as well as the influence of soil properties on PTTE mobility. Thirteen soil profiles were studied in eight abandoned mining and smelting sites. PTTE concentrations, including rare earth elements (REE), were measured using ICP-MS and ICP-OES. Selected elements (Cu, Zn, Pb, Cd, As, Sb, Ba, Sr, Co, Ni, Mn and Cr) were fractionated using the modified European Community Bureau of Reference (BCR) four-step sequential extraction. Contamination of soils with PTTE was compared against Polish regulatory limits, which were exceeded for Cu, Zn, Pb, Mo, Hg, As, Co, Ni and Ba, with concentrations exceeding limits by 16, 18, 34 and 160 times for Cu, Hg, As and Ba, respectively, in some profiles. Based on geochemical features depending on parent material properties, the soils examined were divided into three groups. Group I Technosols (near-neutral soils developed from Fe/Mn-ore and carbonate-bearing mining waste) were particularly enriched in Co, Ni, Mn and REE. Group II Technosols (acidic soils developed from polymetallic ore-bearing aluminosilicate mining waste) contained elevated concentrations of Cu, Zn, Hg, As, Sb, Bi, Co, Ag, Ba, Sr, U and Th; they contained lower contents of REE than Group I Technosols. Group III Technosols (soils developed in smelting-affected areas and containing metallurgical waste) were rich in Cu, As, Sb, Ba, Hg, Co and Ag and contained the lowest REE contents among the studied soils. Sequential BCR extraction revealed that PTTE mobility varied strongly according to soil group, with higher mobility of Mn, Cu and Zn in acidic polymetallic ore-derived soils (Group II), while carbonate-rich soils (Group I) showed mainly immobile forms. Metallurgical slag-derived soils (Group III) exhibited complex PTTE behaviour controlled by organic matter and Fe/Mn oxides. Soil properties (pH, carbonates and TOC) seem to control PTTE mobility. Full article

Lubricating oil reflects mechanical component aging and wear. Accurate quantification of its wear metals is essential for equipment safety and intelligent maintenance. This study introduces a rapid, non-destructive method for detecting wear metal content in lubricating oil using hyperspectral technology to overcome limitations such as bulky, expensive instruments and destructive testing in current spectroscopic techniques. Absorption spectra of 98 marine gearbox oil samples were acquired using Hach UV-Vis and GLT optical fiber spectrometers. We propose a multi-head attention mechanism enhanced genetic algorithm (MHA-GA) for deep feature extraction, integrating attention weights into band selection and fitness evaluation to identify key features under multi-element interference. Wear metal prediction models were constructed using random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results demonstrate MHA-GA outperformed traditional genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) in feature selection. The MHA-GA-XGBoost model performed best. Fe prediction R² reached 0.96 (Hach) and 0.93 (GLT), with RPDs of 5.33 and 3.90. For Cu, R² reached 0.91 and 0.83, with RPDs of 3.35 and 2.42. The results indicate that hyperspectral technology combined with machine learning enables effective non-destructive wear metal quantification, offering a promising strategy for intelligent maintenance and condition monitoring of lubricating oil. 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

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

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

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

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R², root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management. Full article

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson’s Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Models—GMMs, Expectation Maximization—EM, Logistic Regression—LoR, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data. Full article

The need to identify secondary sources of REEs and their recovery has led to the search for new methods and materials. In this study, a novel type of ion-imprinted adsorption membranes based on modified chitosan was synthesized. Their application for the recovery of chosen REEs from synthetic coal fly ash extracts was analyzed. The examined membranes were analyzed in terms of adsorption kinetics, isotherms, selectivity, reuse, and their separation abilities. The experimental data obtained were analyzed with two applications, namely, REE 2.0 and REE_isotherm. It was found that the adsorption of Nd³⁺ and Y³⁺ ions in the obtained membranes took place according to the chemisorption mechanism and was significantly controlled by film diffusion. The binding sites on the adsorbent surface were uniformly distributed; the examined ions showed the features of regular monolayer adsorption; and the adsorbents showed a strong affinity to the REE ions. The high values of K_d (900–1472.8 mL/g) demonstrate their high efficiency in the recovery of REEs. After five subsequent adsorption–desorption processes, approximately 85% of the value of one cycle was reached. The synthesized membranes showed a high rejection of the matrix components (Na, Mg, Ca, Al, Fe, and Si) in the extracts of the coal fly ashes, and the retention ratio for these Nd and Y ions was 90.11% and 80.95%, respectively. Full article

The features for assessing the authenticity of wines by region of origin are studied, based on the relationship between the mineral composition of the wine, the grapes, and the soil profile (0 to 160 cm) from the place of growth of Cabernet Sauvignon grapes. Soil, grape, and wine samples were taken from the territories of six vineyards in the Anapa district of Krasnodar Territory, Russia. Using the methods of ICP-OES, thermal, and X-ray phase analysis, the soils were differentiated into three groups, differing in mineralogical and mineral compositions. The soil samples of the first group contained up to 31% quartz, the second group up to 25% quartz and 19% mixed calcite, and the third group up to 32% calcite and 15% quartz. The formation of the elemental image of the grapes was studied, taking into account the total content and mobile forms of metals in the soil. The territorial proximity of the vineyards did not affect the extraction of elements from the soil into the grape berry, and the migration of metals for each territory was selective. According to the values of the biological absorption coefficient, the degree of transition of metals from the soil to a berry was estimated. For K, Ti, Zn, Rb, Cu, and Fe in all berries, the coefficient was higher than 1.00, which means that the berry extracts contained not only mobile-form, but also difficult-to-dissolve metal compounds. The migration of macro-components from the soil to the berry was low, and amounted to 6–7% for Ca, 0.8–3.0% for Na, and 25–70% for Mg of the concentration of their mobile forms. For all territories, the maximum correlation between metal concentrations in grapes and soil was observed for samples from a depth of 0–40 cm. The discriminant model based on concentrations of Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni showed the formation of clusters in the territories of vineyard cultivation. The developed model allow the problems of identifying wines by region to be solved with high accuracy, using their elemental image. Full article