Search Results (26,566)

Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model’s predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth. Full article

This paper presents the synthesis and characterization of graphene–polymer composites, focusing on their mechanical and antibacterial properties. Graphene flakes were obtained via an electrochemical method and integrated into polyamide 6 (PA6) matrices using melt intercalation. Various characterization techniques confirmed the quality of the graphene flakes, including X-ray diffraction (XRD), Raman spectroscopy, and infrared (IR) spectroscopy, as well as scanning and transmission electron microscopy (SEM and TEM) imaging. Mechanical tests showed an increase in the elastic modulus with graphene incorporation, while the impact strength decreased. The SEM analysis highlighted the dispersion of the graphene flakes within the composites and their impact on fracture behavior. Antimicrobial tests demonstrated significant antibacterial properties of the composites, attributed to both oxidative stress and mechanical damage induced by the graphene flakes. The results suggest promising applications for graphene–polymer composites in advanced antimicrobial materials. Full article

We present a meta-analysis of mangrove litterfall across 58 sites in the American Pacific, exploring its variability among geoforms, ecoregions, and provinces. This study contributes to filling the information gap on litter-based primary productivity in American mangroves at the ecoregional level and directly examines the effects of geomorphological and biogeographic factors on mangrove productivity. The objective was to evaluate how geoform, ecoregion, and province factors, along with eight environmental variables, influence litterfall-based primary productivity. Each site was categorized according to its landform through the analysis of satellite images obtained from various sensors on the Google Earth Pro v. 7.3.6 platform. Additionally, it was categorized according to its ecoregion and province by analyzing the occurrence of the sites on biogeographic unit coverage in ArcMap 10.4.1. We then analyzed the effect of each factor and the efficiency of categorization using multivariate methods. Our results showed significant differences in litterfall among the geoforms, with estuaries exhibiting higher litterfall production (11.90 Mg ha⁻¹ year⁻¹) compared to lagoons (7.49 ± 4.13 Mg ha⁻¹ year⁻¹). Differences were also observed among provinces, with the highest average in the Tropical Eastern Pacific (11.19 ± 3.63 Mg ha⁻¹ year⁻¹) and the lowest in the Warm Temperate Northeast Pacific (7.34 ± 4.28 Mg ha⁻¹ year⁻¹). Allocation success analyses indicated that sites classified by dominant species and province were more predictable (>60.34%) for litterfall production. Additionally, the maximum temperature and the precipitation of the wettest month and the driest month explained 34.13% of the variability in mangrove litter-based primary productivity. We conclude that mangrove litterfall production is influenced by coastal geomorphic characteristics and biogeography, which are, in turn, affected by latitude-induced climate variation. Full article

The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research. Full article

Investigation of cities’ spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul’s 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul. Full article

This paper presents a new approach for delineating river coastlines in RGB close-range nadir aerial imagery acquired by unmanned aerial vehicles (UAVs), aimed at facilitating waterline detection through the reduction of the dimensions of a colour space and the use of coarse grids rather than pixels. Since water has uniform brightness, expressed as the value (V) component in the hue, saturation, value (HSV) colour model, the reduction in question is attained by extracting V and investigating its histogram to identify areas where V does not vary considerably. A set of 30 nadir UAV-acquired photos, taken at five different locations in Poland, were used to validate the approach. For 67% of all analysed images (both wide and narrow rivers were photographed), the detection rate was above 50% (with the false hit rate ranged between 5.00% and 61.36%, mean 36.62%). When the analysis was limited to wide rivers, the percentage of images in which detection rate exceeded 50% increased to 80%, and the false hit rates remained similar. Apart from the river width, land cover in the vicinity of the river, as well as uniformity of water colour, were found to be factors which influence the waterline detection performance. Our contribution to the existing knowledge is a rough waterline detection approach based on limited information (only the V band, and grids rather than pixels). Full article

This article presents the results of abrasion wear resistance tests of wear-resistant steel and surfacing under laboratory conditions and natural operation. Abrasion wear resistance determined on the basis of the study by determining geometrical characteristics of the alloying additives using computer image analysis methods, as well as examining the changes occurring on the surface of the workpieces and their wear intensity. Based on the results obtained from laboratory tests, it was noted that AR steel exhibited 14 times greater wear than the padding weld. This wear is affected by alloy additives, which, for the padding weld, are chromium additives. The microstructure image shows that soil mass had a destructive effect mainly on the matrix of the material, whereas in the areas with high concentrations of chromium precipitates, this effect was significantly weaker. The operational test results showed that within the area of the tine subjected to hardfacing, the material loss was lower than that for the same area of the tine in the as-delivered state. For the hardfaced tine, a 7% loss of volume was noted in relation to the operating part before testing and following the friction process. However, for the operating part in the as-delivered state, this difference amounted to 12%. Full article

Assessment of the current state of the near-surface part of the geological environment and understanding of its layered structure play an important role in various scientific and applied fields. The presented work is devoted to the application of phasometric modifications of geoelectric control methods to solve the problem of the detailed complex study of the underground layers of the environment in the process of drilling operations with the use of special equipment. These studies are based on the analysis of variations in phase parameters and characteristics of an artificially excited multiphase electric field to assess poorly distinguishable details and changes in the layered structure of the medium. The proposed method has increased accuracy, sensitivity and noise proofness of measurements, which allows for extracting detailed information about the heterogeneity, composition and stratification of underground geological formations not only in the zone where the drill makes contact with the medium, but also in the entire control zone. This paper considers practical mathematical models of phase images for basic scenarios of drill penetration between the layers of the near-surface part of the geological medium with different characteristics, obtained by means of approximation apparatus based on continuous piecewise linear functions, and also suggests the use of modern machine learning methods for intelligent analysis of its structure. Studying the phase shifts in electrical signals during drilling highlights their value for understanding the dynamics of soil response to the process. The observed signal changes during the drilling cycle reveal in detail the heterogeneity in soil structure and its response to changes caused by drilling. The stability of phase shifts at the last stages of the process indicates a quasi-equilibrium state. The results make a significant contribution to geotechnical science by offering an improved approach to monitoring a layered structure without the need for deep drilling. Full article

Cervical cancer ranks among the leading causes of mortality in women worldwide, underscoring the critical need for early detection to ensure patient survival. While the Pap smear test is widely used, its effectiveness is hampered by the inherent subjectivity of cytological analysis, impacting its sensitivity and specificity. This study introduces an innovative methodology for detecting and tracking precursor cervical cancer cells using SIFT descriptors in video sequences captured with mobile devices. More than one hundred digital images were analyzed from Papanicolaou smears provided by the State Public Health Laboratory of Michoacán, Mexico, along with over 1800 unique examples of cervical cancer precursor cells. SIFT descriptors enabled real-time correspondence of precursor cells, yielding results demonstrating 98.34% accuracy, 98.3% precision, 98.2% recovery rate, and an F-measure of 98.05%. These methods were meticulously optimized for real-time analysis, showcasing significant potential to enhance the accuracy and efficiency of the Pap smear test in early cervical cancer detection. Full article

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents. Full article

This study investigates the effects of a water-guided laser on the cutting performance of AISI 1020 steel sheets of various thicknesses by comparing the results with respect to a conventional laser. For this purpose, a novel nozzle design has been devised enabling the delivery of laser beams to the workpiece conventionally as well as through water guidance. Diverging from prior literature, a fiber laser is used with a high wavelength and a laser power output of 1 kW. Experiments are conducted on steel sheets with thicknesses ranging from 1.5 mm to 3 mm using three different cutting speeds and laser power levels. Analysis focuses on assessing surface roughness, burr formation and heat effects on the cut surfaces for both conventional and waterjet-guided cutting. Surface roughness is evaluated by using a 3D profilometer and cut surfaces are examined through SEM imaging. The results showed that the waterjet-guided laser system greatly reduced surface roughness and minimized problems associated with traditional laser cutting such as kerf, dross adherence and thermal damage. The study revealed that cutting speed had a greater effect on surface roughness reduction than laser power, with the most noticeable differences occurring in thinner sheets. Furthermore, the investigation suggests that the waterjet-guided laser cutting system demonstrates superior performance relative to conventional methods, particularly in surface quality. Full article

The term microplastics (MPs) describes a heterogeneous mixture of particles that can vary in size, color, and shape. Once released into the environment, MPs have various toxicological and physical effects on wildlife. The Song Thrush (Turdus philomelos) is a migratory species, staying in Italy in late autumn and winter. The aim of this study is to assess, quantify, and characterize the presence of microplastics in Song Thrushes hunted in the Apulia region of Italy. The birds (n = 360) were hunted in the Bari countryside and donated for research purposes by hunters. MPs were classified in relation to their shape in fibers, films, fragments, and pellets; then, they were divided according to their color and the length of the particles was measured. Nikon image analysis software was applied to the litter size measurements. Of the total of 360 birds, MPs were detected in the stomachs of 129 birds shot in December and 128 birds shot in January. The majority of ingested MPs were fibers that were observed in all contaminated birds. Film fragments were observed in every contaminated specimen. Among all the MPs found, 31.75% were red, 30.13% were black, and 25.91% were blue, while the other colors were less represented. This study provides the first analysis of MPs bioaccumulation in Song Thrushes wintering in the Apulia region, and the high contamination of thrushes confirmed the ubiquity of MPs in terrestrial ecosystems. Full article

The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as the absence or elongation of ligamentous structures results in changes in the kinematic alignment. The aim of the study was to evaluate the parameters of femorotibial translation in patients with MOA (medial unicompartmental OA). An artificial model was employed to develop a method for calculating femorotibial translation in vitro. In a prospective cohort study, gait data using three-dimensional gait analysis were collected from 11 patients (68.73 ± 9.22 years) with severe OA scheduled for UKA and 29 unmatched healthy participants (22.07 ± 2.23 years). The discrete variables characterising femorotibial translation were compared for statistical significance (p < 0.05) using the Student’s t-test and the Mann–Whitney U-test. The results of the study validated an artificial model to mimic femorotibial translation. The comparison of patients scheduled for UKA and a healthy unmatched control group showed no statistically significant differences concerning femorotibial translation in all three planes (p > 0.05). However, the PROMs (patient-reported outcome measures), spatiotemporal, and kinematic parameters showed statistically significant differences between the groups (p < 0.001). The data presented here demonstrate typical changes in PROMs as well as spatiotemporal and kinematic outcomes for MOA as seen in knee OA. The results of the clinical gait analyses demonstrate individualised femorotibial translation. The extent of individual femorotibial translation may prove to be an important parameter for altered joint kinematics in patients with MOA, especially prior to UKA implantation. Full article

The smelting process of Ferronickel in Indonesia produces a significant amount of waste in the form of Ferronickel Slag (FNS), with an annual accumulation of up to 13 million metric tons. Previous studies have shown promising strength results for concrete utilizing FNS as a fine aggregate. This study aims to analyze the mechanical properties of three reinforced concrete (RC) beams measuring 15 cm × 25 cm × 300 cm, each containing FNS as a 50% substitute for fine aggregate. The RC Beams underwent experimental testing using a four-point loading scheme under semi-cyclic loading conditions. Test results show the beams’ capacity had reached up to 8 ton-f and their load–displacement responses show promising results. Digital Image Correlation (DIC) analysis facilitated the observation of surface deformation evolution due to loading, aiding in the identification of concrete crack patterns. Due to semi-cyclic loading, cracks on the beams’ surface were experiencing a crack opening and closing phenomenon, where the propagations of cracks ceased or reclosed throughout the unloading process. Moreover, the opening of residual cracks was also captured by DIC analysis. The experimental finding was validated by finite element analysis. The RC beam numerical model was created using the Timoshenko Multi-fiber element in CAST3M software version 2022. Mazars concrete and elastoplastic steel damage model were used as constitutive laws for numerical modeling. The model’s load–displacement response demonstrated satisfactory agreement compared to the experimental monotonic loading result. However, the model had limitations regarding the simulation of residual displacements of beams due to semi-cyclic loading. Full article

Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected are then processed through an LSTM-based network, employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain–computer interfaces. Full article