Search Results (158)

Groundwater fluoride contamination poses a significant threat to sustainable water resources and public health, yet conventional water quality analysis is both time-consuming and costly, making large-scale, sustainable monitoring challenging. Machine learning methods offer a promising, cost-effective, and sustainable alternative for assessing the spatial distribution of fluoride. This study aimed to develop and compare the performance of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models for predicting groundwater fluoride contamination in the Datong Basin with the help of satellite embeddings from the AlphaEarth Foundation. Data from 391 groundwater sampling points were utilized, with the dataset partitioned into training (80%) and testing (20%) sets. The ANOVA F-value of each feature was calculated for feature selection, identifying surface elevation, pollution, population, evaporation, vertical distance to the rivers, distance to the Sanggan river, and nine extra bands from the satellite embeddings as the most relevant input variables. Model performance was evaluated using the confusion matrix and the area under the receiver operating characteristic curve (ROC-AUC). The results showed that the SVM model demonstrated the highest ROC-AUC (0.82), outperforming the RF (0.80) and MLP (0.77) models. The introduction of satellite embeddings improved the performance of all three models significantly, with the prediction errors decreasing by 13.8% to 23.3%. The SVM model enhanced by satellite embeddings proved to be a robust and reliable tool for predicting groundwater fluoride contamination, highlighting its potential for use in sustainable groundwater management. Full article

Background/Objectives: The aim was to provide a comprehensive, systematic review on the Lateral Extra-articular Tenodesis (LET) methods used in anterior cruciate ligament (ACL) reconstruction in the light of recent data. Methods: To identify all of the essential studies that reported relevant data concerning primary outcomes: indications for surgery, surgical technique, graft type, fixation method, and tibial fixation location, an extensive search of the major and significant electronic databases (PubMed, Cochrane Central, ScienceDirect, Web of Science, Embase) was performed by three independent authors. A systematic investigation was conducted in November 2023, with no limits regarding the year of publication. After the database search, three independent reviewers screened all the papers, which followed strictly the inclusion and exclusion criteria, identifying a title, abstract, and full text concerning LET, surgical technique, femoral attachment, tibial attachment, graft type, fixation method, knee angle during fixation, and graft tension at fixation in ACL reconstruction. A systematic review of the collected literature was carried out according to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Study quality was assessed using the Cochrane Risk of Bias Tool. Results: Of the 35 papers reviewed, seven surgical techniques of LET differing in the way the procedure was performed were separated. The majority of papers were from Italy (n = 11), USA (n = 3), France (n = 3), and Canada (n = 3). The number of total participants across all studies was 6253. The majority of studies (17 papers) used the Lemaire modified procedure, and 10 papers used the MacIntosh technique modified by the Coker–Arnold approach. Most of the papers mentioned fixation location on the lateral distal part of the femur including six articles referring directly to lateral femoral epicondyle. Most authors (25 papers) defined tibial attachment as Gerdy’s tubercle. The most common graft was the iliotibial band and fixation method was sutures. The types of fixation in the surgical techniques of the collected papers were Sutures, Staples, Anchor, Interference screw, K-wire, Bioabsorbable Screw and Titanium Screw with a serrated polyethylene washer. Conclusions: Despite variability in technique, the Lemaire-modified procedure emerged as the preferred approach for Lateral Extra-articular Tenodesis, suggesting a general consensus around its reliability and reproducibility in clinical practice. The frequent use of the iliotibial band as graft material reflects its accessibility and suitability for reinforcing anterolateral stability. Similarly, the consistent use of sutures and fixation at Gerdy’s tubercle may indicate a favorable balance between technical ease and biomechanical strength. The variability in femoral fixation points—either at the lateral femoral condyle or epicondyle—highlights the ongoing debate or surgeon preference, underscoring the need for further comparative studies to establish optimal fixation strategy. Collectively, these patterns may help guide surgical decision-making, particularly when tailoring procedures to individual patient anatomy or surgical expertise. Full article

This paper proposes a novel wideband high-frequency (HF) communication system supporting video transmission based on non-contiguous orthogonal frequency division multiplexing (NC-OFDM) and probabilistic shaping (PS). The HF spectrum is currently very crowded; to find a free continuous frequency band around 500 KHz for video transmission is almost impossible. So this paper investigates how to exploit spectrum holes in the HF band with NC-OFDM technology. We propose a transmission scheme over a wideband HF channel modeled by the Institute for Telecommunication Sciences (ITS) channel model with valid bandwidth up to 1 MHz. In order to improve the reliability of proposed scheme, this paper further investigates the probabilistic shaping-based coding modulation. Simulation results show that the designed wideband HF NC-OFDM communication system can meet the data rate required for video transmission. In addition, the probabilistic shaping-based coding modulation provides a significant performance improvement over uncoded systems and the probabilistic shaping offers an extra 0.6 dB shaping gain in the wideband HF channel compared to equal probability constellation systems. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

The dynamic response in propane atmospheres at different voltages was investigated for samples made from powders of the semiconductor oxide CoSb₂O₆ synthesized using the microwave-assisted colloidal method. Powders of the compound calcined at 700 °C were studied with X-ray diffraction, confirming the CoSb₂O₆ crystalline phase. The microstructural characteristics of the oxide were analyzed using scanning and transmission electron microscopy (SEM/TEM), revealing a high abundance of nanorods, nanoplates, and irregular nanoparticles. These nanoparticles have an average size of ~21 nm. Using UV-Vis, absorption bands associated with the electronic transitions of the CoSb₂O₆’s characteristic bonds were identified, which yielded a bandgap value of ~1.8 eV. Raman spectroscopy identified vibrational bands corresponding to the oxide’s Sb–O and Co–O bonds. Dynamic sensing tests at 300 °C confirmed the material’s p-type semiconductor behavior, showing an increase in resistance upon exposure to propane. Critically, these tests revealed that the sensor’s baseline resistance and overall response are tunable by the applied voltage (1–12 V), with the highest sensitivity observed at the lowest voltages. This establishes a clear relationship between the electrical operating parameters and the sensing performance. The samples exhibited good operational stability, capacity, and efficiency, along with short response and recovery times. Extra-dry air (1500 cm³/min) was used as the carrier gas to stabilize the films’ surfaces during propane detection. These findings lead us to conclude that the CoSb₂O₆ could serve as an excellent gas detector. Full article

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested. Full article

In this study, the surface photocatalytic activity of an anatase–titanium oxide (TiO_x) film was modified by a thin copper (Cu) layer with the subsequential oxidation annealing process. Through this simple annealing process, the photocatalytic activity of the TiO_x/Cu structure to decompose the methylene blue solution and inhibit the growth of Escherichia coli. could be optimized. With the help of a study on the conductive type required for the oxidation of a single Cu layer, an n/p nanocomposite heterojunction was realized, as this contact system anneals at temperatures of 350 °C and 450 °C. An extra electrical field at the contact interfaces that was be beneficial for separating the photo-generated electron–hole pairs (EHPs) under UV light irradiation was built. The built-in electrical field led to an increase in the structural photocatalytic activity. Moreover, as the p-type cuprous oxide (p-Cu₂O) structure oxidized by the annealed Cu layer could provide a high conduction band that is offset when in contact with the TiO_x film, the photogenerated EHPs on the TiO_x surface could be separated more effectively. Accordingly, the 350 °C-annealed sample, abundant in the nanocomposite TiO_x/Cu₂O heterojunction which could significantly retard the recombination of photo-generated carriers, corresponded to an increase of about 38% in the photocatalytic activity as compared with the single TiO_x film. Full article

We report new medium-resolution spectroscopy covering the wavelength range from 0.6 to 2.4 $\mathsf{\mu }$m, as well as multi-epoch, multi-wavelength photometry, of the Class I high-mass embedded young stellar object Mol 12 (IRAS 05373+2349). It is embedded ($A_V\simeq 12$) in the centre of a dense core at a distance of 1.59 kpc from the Sun and has a total luminosity of $1.74\times {10}^3{L}_{\odot }$. The spectra show a large number of permitted atomic emission lines, mostly for Fe, H, C, N, and Ca, that originate in the inner zones of a very active protoplanetary disc and no photospheric absorption lines. Conspicuously, the He I line at 1.0830 $\mathsf{\mu }$m displays a complex P-Cygni profile. Also, the first overtone CO emission band-heads at 2.3 $\mathsf{\mu }$m are seen in emission. From the strengths of the principal emission lines, we determined the accretion rate and luminosity to be $\dot{M}\sim {10}^{-5}{M}_{\odot }$ y⁻¹ and $L_{\mathrm{acc}}\sim {10}^3{L}_{\odot }$, respectively. Decade-long light curves show a series of irregular brightness dips of more than four magnitudes in r, becoming shallower as the wavelength increases and disappearing at $\lambda >3\mathsf{\mu }$m. The colour–magnitude diagrams suggest the occurrence of a series of eclipses caused by the passage of small dust cloudlets in front of the star, producing more than 10 magnitudes of extra extinction. Full article

The crystal–chemical behavior of two layered titanosilicate minerals with porous crystal structures, kupletskite, K₂NaMn₇²⁺Ti₂(Si₄O₁₂)₂O₂(OH)₄F, and kupletskite-(Cs), Cs₂NaMn₇²⁺Ti₂(Si₄O₁₂)₂O₂(OH)₄F, was investigated under high-temperature conditions using single-crystal and powder X-ray diffraction; infrared and optical absorption spectroscopy and electron-microprobe analysis. Both minerals undergo topotactic transformation to dehydroxylated and oxidized high-temperature (HT) modifications at temperature above 500 °C while maintaining the basic bond topology of the astrophyllite structure-type. The high-temperature structures show contraction of the unit-cell parameters similar to that of Fe²⁺-dominant astrophyllite, indicating that Mn²⁺ oxidizes along with Fe²⁺ in M(2)–M(4) sites. The oxidation of Mn²⁺ is confirmed by the increase of the Mn³⁺-related absorption (in optical spectra) that is inversely correlated with the intensity of O–H bands in the infrared spectra. The Fe,Mn-oxidation is also evident by the contraction of the M(2), M(3), and M(4)O₆ octahedra. The M(1)–O bond length increases slightly, indicating a preference for mono- and divalent cations to occupy the M(1) site in the heated structure; this may be due to site-selective oxidation and/or migration of unoxidized cations (as previously shown for lobanovite) to this site. The role of extra framework A-site cations (K, Cs) in thermal expansion of these minerals is discussed. Full article

The spatial distribution of equiaxed crystal zones during extra-thick slab solidification exerts a critical influence on the mechanical performance of the final product. This investigation establishes a dual-pathway control framework for solidification structure modulation, differentiating between intrinsic regulation through columnar-to-equiaxed transition (CET) positioning and extrinsic intervention via strand electromagnetic stirring (S-EMS) parameter adjustment. The aim is to improve the internal quality of extra-thick slabs, enabling further investigations into the material properties. To achieve this, a solidification heat transfer model along with a cellular automata–finite element model were developed to characterize the thermal conditions at CET initiation, with experimental validation conducted on a 475 mm extra-thick slab. The systematic analysis identified a significant correlation between continuous casting parameters, alloy concentrations, and CET positioning, while S-EMS experiments further elucidated the distribution patterns of the solidification structure and the formation mechanism of the white band in the mushy zone. This methodology bridges computational metallurgy with process engineering, offering systematic guidance for solidification structure control in extra-thick slabs. Full article

Anterior cruciate ligament reconstruction with concurrent lateral extra-articular tenodesis enhances rotational stability. However, not many studies describe radiological features following the abovementioned procedure. The purpose of this study was to evaluate the visibility and describe the sonographic morphology of the iliotibial band strip harvested during the modified Lemaire technique and to establish a correlation between these findings and clinical outcomes. Thirty-two consecutive patients underwent primary or revision anterior cruciate ligament reconstruction with the addition of lateral extra-articular tenodesis by the mini-open modified Lemaire technique. All individuals completed the following preoperative and postoperative questionnaires: the KOOS-pain, KOOS-symptoms, KOOS-ADL, KOOS-sport, KOOS-quality, IKDC, Lysholm, and WOMAC. Each patient underwent a postoperative ultrasound examination to evaluate the lateral extra-articular procedure, especially the harvested iliotibial band strip. The most common ultrasound findings of the iliotibial band strip were its hyperechoic appearance (87.1%), location at the level of the femur (58.1%), no surrounding effusion (83.9%), and no power Doppler signal (100%). Effusion seen in sonographic images was correlated with KOOS-symptoms postoperative scores (p = 0.0115). However, there were no correlations between other iliotibial band strip sonographic features and clinical outcomes. The functional score value increased in each patient compared to preoperative measurements. This is the first study that evaluated the sonographic features of the iliotibial band strip after the lateral extra-articular procedure by the mini-open modified Lemaire technique. Our study indicates that ultrasonography is a useful tool in identifying the iliotibial band strip after the mentioned procedure. Effusion observed around the strip on ultrasound was significantly associated with worse KOOS-symptoms scores, suggesting potential clinical relevance. Full article

Deepfake technology utilizes deep learning (DL)-based face manipulation techniques to seamlessly replace faces in videos, creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment, misuse of its capabilities may lead to serious risks, including identity theft, cyberbullying, and false information. The integration of DL with visual cognition has resulted in important technological improvements, particularly in addressing privacy risks caused by artificially generated “deepfake” images on digital media platforms. In this study, we propose an efficient and lightweight method for detecting deepfake images and videos, making it suitable for devices with limited computational resources. In order to reduce the computational burden usually associated with DL models, our method integrates machine learning classifiers in combination with keyframing approaches and texture analysis. Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively. Full article

Convolutional Neural Networks (CNNs) have proven to be very effective in image classification due to their status as a powerful feature learning algorithm. Traditional approaches have considered the problem of multiclass classification, where the goal is to classify a set of objects at once. However, co-occurrence can make the discriminative features of the target less salient and may lead to overfitting of the model, resulting in lower performance. To address this, we propose a multi-label classification ensemble model including a Vision Transformer (ViT) and CNN for directly detecting one or multiple objects in an image. First, we improve the MobileNetV2 and DenseNet201 models using extra convolutional layers to strengthen image classification. In detail, three convolution layers are applied in parallel at the end of both models. ViT can learn dependencies among distant positions and local detail, making it an effective tool for multi-label classification. Finally, an ensemble learning algorithm is used to combine the classification predictions of the ViT, the modified MobileNetV2, and DenseNet201 bands for increased image classification accuracy using a voting system. The performance of the proposed model is examined on four benchmark datasets, achieving accuracies of 98.24%, 98.89%, 99.91%, and 96.69% on ASCAL VOC 2007, PASCAL VOC 2012, MS-COCO, and NUS-WIDE 318, respectively, showing that our framework can enhance current state-of-the-art methods. Full article

The great interest in the rapid and reliable differentiation of extra virgin olive oil from other olive oil categories is directly related to its unique sensory characteristics and high market prices. The aim of the present study was to investigate the potential of FTIR as a rapid and non-invasive technique to discriminate extra virgin olive oil (EVOO) from other olive oil categories (virgin olive oil, ordinary, and lampante) based on the acquired spectral profile of olive oil. Spectral data were collected, pre-processed, and correlated by Random Forest (RF) analysis with the sensory category (EVOO vs. other) of olive oil samples, as defined by sensory analysis undertaken previously by trained panelists. The results showed that the application of Savitzky–Golay (S-G) smoothing with a second derivative (d = 2), second- and third-order polynomial (p = 2, p = 3), and window size (w) of 12 and 13 points achieved the highest accuracy (0.91) between the two classes of samples. Characteristic spectral bands of triacylglycerols related to the carbonyl groups present in triacylglycerols (C=O) located near 1744 cm⁻¹ (specific features: 1739, 1748, and 1751 cm⁻¹), the fingerprinting area 1250–1000 cm⁻¹ (specific features: 1088, 1094, 1116, 1123, 1124, 1158, 1162, 1236, 1240, and 1247 cm⁻¹), which correspond to CH bending, and 1680 cm⁻¹, which is associated with unsaturated aldehydes were observed to constitute the main basis of the discrimination of EVOO from the “other” class. The ability of the model to achieve high classification accuracy demonstrates the robustness of the FTIR spectral data combined with advanced machine learning techniques. Due to the lower cost and more rapid analysis time afforded by FTIR, this method provides promising perspectives for industrial olive oil classification. Full article

The objective of this study is to integrate and classify electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) signals from a participant exposed to emotional stimuli—happiness, anger, fear, and sadness. We used the LazyPredict library to identify the most effective classification model, leveraging its simplified implementation and wide range of models and performance metrics. The signals were processed in Python following a detailed workflow: (1) normalization, (2) band-pass filtering, (3) epoch extraction and selection, and (4) relative energy extraction using Discrete Wavelet Transform (DWT). After preprocessing, the data were input into LazyPredict, where the Extra Trees model consistently demonstrated the best performance for binary emotion classification. Our experience with LazyPredict proved to be practical and efficient, facilitating the exploration of high-performing models for emotion classification. Full article