Search Results (37)

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

Chestnut processing has increasingly grown in recent years. All the processes involved in the chestnut supply chain are characterized by the production of high levels of by-products that cause several environmental and disposal issues. The Castanea spp. fruit production is related to a high number of chestnut episperm. This underutilized agricultural by-product may be evaluated as a good resource for the extraction of health-promoting natural molecules, such as phenolics. This preliminary study aimed to develop and optimize, using a multivariate statistical approach, a sustainable protocol for the ultrasound-assisted extraction (UAE) of the main phenolics from chestnut episperm (cv Marsol, C. sativa × C. crenata). A design of experiment (DoE) approach was employed. This approach focused on the two quantitative UAE process factors: the extraction time (X₁), within a timeframe ranging from 10 to 30 min, and the sample-to-solvent (w/v) ratio (X₂), ranging from 1/30 to 1/10. These variables were investigated to determine their impact on phenol extraction yield. Exploratory analysis, in particular principal component analysis (PCA) and multiple linear regression (MLR), were carried out on the studied responses. The phenolic characterization of ten different extracts was also performed using high-performance liquid chromatography (HPLC), both to define the levels of specific phenolics selected for their health-promoting properties and to evaluate some important features, such as the total antioxidant capacity. The values of total polyphenolic content (TPC) obtained in the different experiments ranged between 97 (extract 4) and 142 (extract 6) mg GAE/g of dried weight (DW). Moreover, results from the ferric reducing antioxidant power (FRAP) test confirmed the high TPC values, highlighting that all the ultrasound extracts contained excellent levels of molecules with good antioxidant properties. In particular, extracts 2 and 3 showed the highest AOC values (about 490–505 mmol Fe²⁺/Kg of dried weight). The proposed optimized protocol allowed for obtaining formulations characterized by high levels of tannins, phenolic acids, and catechins. Indeed, episperm extracts contained high levels of chlorogenic acid (15–25 mg/100 g DW), ferulic acid (80–120 mg/100 g DW), castalagin (20–80 mg/100 g DW), and vescalagin (40–75 mg/100 g). Finally, in this research study, the potential of chestnut episperm as a source of polyphenolic molecules to be extracted by green technologies and used in several food and/or pharmaceutical applications was evaluated to valorize a sustainable reuse strategy of agri-food processing by-products, also reducing the environmental impact of this waste derived from chestnut processing. Full article

Due to the wide variety of network services, many different types of protocols exist, producing various packet features. Some features contain irrelevant and redundant information. The presence of such features increases computational complexity and decreases accuracy. Therefore, this research is designed to reduce the data dimensionality and improve the classification accuracy in the UNSW-NB15 dataset. It proposes a hybrid dimensionality reduction system that does feature selection (FS) and feature extraction (FE). FS was performed using the Recursive Feature Elimination (RFE) technique, while FE was accomplished by transforming the features into principal components. This combined scheme reduced a total of 41 input features into 15 components. The proposed systems’ classification performance was determined using an ensemble of Support Vector Classifier (SVC), K-nearest Neighbor classifier (KNC), and Deep Neural Network classifier (DNN). The system was evaluated using accuracy, detection rate, false positive rate, f1-score, and area under the curve metrics. Comparing the voting ensemble results of the full feature set against the 15 principal components confirms that reduced and transformed features did not significantly decrease the classifier’s performance. We achieved 94.34% accuracy, a 93.92% detection rate, a 5.23% false positive rate, a 94.32% f1-score, and a 94.34% area under the curve when 15 components were input to the voting ensemble classifier. Full article

Stellar parameters are estimated through spectra and are crucial in studying both stellar evolution and the history of the galaxy. To extract features from the spectra efficiently, we present ESNet (encoder selection network for spectra), a novel architecture that incorporates three essential modules: a feature encoder (FE), feature selection (FS), and feature mapping (FM). FE is responsible for extracting advanced spectral features through encoding. The role of FS, on the other hand, is to acquire compressed features by reducing the spectral dimension and eliminating redundant information. FM comes into play by fusing the advanced and compressed features, establishing a nonlinear mapping between spectra and stellar parameters. The stellar spectra used for training and testing are obtained through crossing LAMOST and SDSS. The experimental results demonstrate that for low signal-to-noise spectra (0–10), ESNet achieves excellent performance on the test set, with mean absolute error (MAE) values of 82 K for $T_{eff}$ (effective temperature), 0.20 dex for $logg$ (logarithm of the gravity), and 0.10 dex for $[Fe/H]$ (metallicity). The results indeed indicate that ESNet has an excellent ability to extract spectral features. Furthermore, this paper validates the consistency between ESNet predictions and the SDSS catalog. The experimental results prove that the model can be employed for the evaluation of stellar parameters. Full article

In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance and accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach is threefold: first, a pre-processing of the training dataset is applied using a multiscale scheme that decomposes the data at multiple scales using high-pass/low-pass filters to separate the noise from the informative attributes and prevent the stochastic samples. Second, a principal component analysis (PCA) technique is applied to the newly obtained data to select, extract, and preserve only the more relevant, informative, and uncorrelated attributes; and finally, to distinguish between the diverse conditions, the extracted attributes are utilized to train the NNs classifiers. In this study, an effort is made to take into consideration all potential and frequent faults that might occur in PV systems. Thus, twenty-one faulty scenarios (line-to-line, line-to-ground, connectivity faults, and faults that can affect the normal operation of the bay-pass diodes) have been introduced and treated at different levels and locations; each scenario comprises various and diverse conditions, including the occurrence of simple faults in the $P{V}_1$ array, simple faults in the $P{V}_2$ array, multiple faults in $P{V}_1$, multiple faults in $P{V}_2$, and mixed faults in both PV arrays, in order to ensure a complete and global analysis, thereby reducing the loss of generated energy and maintaining the reliability and efficiency of such systems. The obtained outcomes demonstrate that the proposed approach not only achieves good accuracies but also reduces runtimes during the diagnosis process by avoiding noisy and stochastic data, thereby removing irrelevant and correlated samples from the original dataset. Full article

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang’E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO₂ abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions. Full article

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement. Full article

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO₄ batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery. Full article