Search Results (13,621)

Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis Full article

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R² = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R² = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts. Full article

Background/Objectives: Accurate localization of fractured endodontic instruments (FEIs) in periapical radiographs (PAs) remains a significant challenge. This study aimed to evaluate the performance of YOLOv8 and Mask R-CNN in detecting FEIs and root canal treatments (RCTs) and compare their diagnostic capabilities with those of experienced endodontists. Methods: A data set of 1050 annotated PAs was used. Mask R-CNN and YOLOv8 models were trained and evaluated for FEI and RCT detection. Metrics including accuracy, intersection over union (IoU), mean average precision at 0.5 IoU (mAP50), and inference time were analyzed. Observer agreement was assessed using inter-class correlation (ICC), and comparisons were made between AI predictions and human annotations. Results: YOLOv8 achieved an accuracy of 97.40%, a mAP50 of 98.9%, and an inference time of 14.6 ms, outperforming Mask R-CNN in speed and mAP50. Mask R-CNN demonstrated an accuracy of 98.21%, a mAP50 of 95%, and an inference time of 88.7 ms, excelling in detailed segmentation tasks. Comparative analysis revealed no statistically significant differences in diagnostic performance between the models and experienced endodontists. Conclusions: Both YOLOv8 and Mask R-CNN demonstrated high diagnostic accuracy and reliability, comparable to experienced endodontists. YOLOv8’s rapid detection capabilities make it particularly suitable for real-time clinical applications, while Mask R-CNN excels in precise segmentation. This study establishes a strong foundation for integrating AI into dental diagnostics, offering innovative solutions to improve clinical outcomes. Future research should address data diversity and explore multimodal imaging for enhanced diagnostic capabilities. Full article

Automating agricultural machinery presents a significant opportunity to lower costs and enhance efficiency in both current and future field operations. The detection and destruction of weeds in agricultural areas via robots can be given as an example of this process. Deep learning algorithms can accurately detect weeds in agricultural fields. Additionally, robotic systems can effectively eliminate these weeds. However, the high computational demands of deep learning-based weed detection algorithms pose challenges for their use in real-time applications. This study proposes a vision-based autonomous agricultural robot that leverages the YOLOv8 model in combination with ByteTrack to achieve effective real-time weed detection. A dataset of 4126 images was used to create YOLO models, with 80% of the images designated for training, 10% for validation, and 10% for testing. Six different YOLO object detectors were trained and tested for weed detection. Among these models, YOLOv8 stands out, achieving a precision of 93.8%, a recall of 86.5%, and a [email protected] detection accuracy of 92.1%. With an object detection speed of 18 FPS and the advantages of the ByteTrack integrated object tracking algorithm, YOLOv8 was selected as the most suitable model. Additionally, the YOLOv8-ByteTrack model, developed for weed detection, was deployed on an agricultural robot with autonomous driving capabilities integrated with ROS. This system facilitates real-time weed detection and destruction, enhancing the efficiency of weed management in agricultural practices. Full article

Background: KMT2A rearrangements occur in ~10% of acute myeloid leukemia (AML) cases and are critical for classification, risk stratification, and use of targeted therapy. However, insertions involving the KMT2A gene can evade detection using chromosomal analysis and/or fluorescence in situ hybridization (FISH). Methods: We present a case of a 22-year-old woman with acute monoblastic leukemia harboring a cryptic KMT2A::AFDN fusion identified by RNA sequencing. Initial FISH showed a 3′ KMT2A deletion, while conventional karyotyping and the automated bioinformatic pipeline for optical genome mapping (OGM) did not identify the canonical translocation. Results: To resolve these discrepancies, metaphase KMT2A FISH (break-apart fusion probe) was performed to assess whether KMT2A was translocated to another chromosome. However, the results did not support this possibility. As the fusion signal remained on the normal chromosome 11, with the 5′ KMT2A signal localized to the derivative chromosome 11. A subsequent manual review of the OGM data revealed a cryptic ~300 kb insertion of AFDN into the 3′ region of KMT2A, reconciling the discrepancies between chromosomal analysis, FISH, and RNA fusion results. Conclusions: This case highlights the importance of integrating multiple testing modalities with expert review when there is a discrepancy. Our findings emphasize the need for a comprehensive approach to genomic assessment to enhance diagnostic accuracy and guide therapeutic decision-making. Full article

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a Kolmogorov–Arnold Network (KAN) layer to enhance classification performance. The experimental results demonstrate significant improvements in classification metrics when using the CNN-SOM and CNN-KAN architectures. Additionally, advanced pretrained models such as MaxViT-S and ResNetV2-50 achieved high accuracy rates, with MaxViT-S achieving 98.9% accuracy. Statistical analyses using the chi-square test confirmed the reliability of the results, emphasizing the importance of validating evaluation metrics statistically. This research represents the first application of SOM in fungal classification and highlights the potential of deep learning in advancing fungal taxonomy. Future work will focus on optimizing the KAN architecture and expanding the dataset to include more fungal classes, further enhancing classification accuracy and ecological understanding. Full article

Land-use change models are used to predict future land-use scenarios. Various methods for predicting changes can be found in the literature, which can be divided into two groups: baseline models and machine-learning-based models. Baseline models use clear change logics, such as proximity or distance from spatial objects. While machine-learning-based models use computational methods and spatial variables to identify patterns that explain the occurrence of changes. Considering these two groups of models, machine-learning-based models are much more widely used, even though their formulation is considerably more complex. However, the lack of studies comparing the performance of models from these two groups makes it impossible to determine the superiority of one over the other. Therefore, this article aims to evaluate and compare the accuracy of baseline and machine-learning-based models for study areas in three Brazilian biomes. Four baseline models (Euclidean distance from anthropic uses, Euclidean distance from vegetation suppressions, null change model, and random change model) and four machine-learning-based models (TerrSet artificial neural network, TerrSet SimWeigth, Weights of Evidence–Dinamica Ego. and Random Forest model) were trained considering the environmental context of the period from 1995 to 2000. The objective was to predict natural vegetation suppression from 2000 to the years 2005, 2010, 2015, and 2020. The predicted maps were evaluated by comparing them with reference land-use maps using rigorous accuracy methods. The results show that, regardless of the underlying method, the models presented similar performance in all situations. The results and discussions provide a contribution to understanding the strengths and weaknesses of various change models in different environmental contexts. Full article

Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable [email protected] and [email protected]:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive [email protected] of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management. Full article

The Ice, Cloud, and Land Elevation 2 (ICESat-2) mission uses a micropulse photon-counting lidar system for mapping, which provides technical support for capturing forest parameters and carbon stocks over large areas. However, the current algorithm is greatly affected by the slope, and the extraction of the forest canopy height in the area with steep terrain is poor. In this paper, an improved algorithm was provided to reduce the influence of topography on canopy height estimation and obtain higher accuracy of forest canopy height. First, the improved clustering algorithm based on ordering points to identify the clustering structure (OPTICS) algorithm was developed and used to remove the noisy photons, and then the photon points were divided into canopy photons and ground photons based on mean filtering and smooth filtering, and the pseudo-signal photons were removed according to the distance between the two photons. Finally, the photon points were classified and interpolated again to obtain the canopy height. The results show that the improved algorithm was more effective in estimating ground elevation and canopy height, and the result was better in areas with less noise. The root mean square error (RMSE) values of the ground elevation estimates are within the range of 1.15 m for daytime data and 0.67 m for nighttime data. The estimated RMSE values for vegetation height ranged from 3.83 m to 2.29 m. The improved algorithm can provide a good basis for forest height estimation, and its DEM and CHM accuracy improved by 36.48% and 55.93%, respectively. Full article

The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this paper proposes a QSA-QConvLSTM model, which integrates quantum convolution circuits and quantum self-attention mechanisms. The quantum self-attention mechanism maps query, key, and value vectors using variational quantum circuits, effectively enhancing the ability to model long-distance dependencies in spatiotemporal data. Additionally, the use of quantum convolution circuits improves the extraction of spatial features. Experiments on the Moving MNIST dataset demonstrate the superiority of the QSA-QConvLSTM model over existing models, including ConvLSTM, TrajGRU, PredRNN, and PredRNN v2, with MSE and SSIM scores of 44.3 and 0.906, respectively. Ablation studies further verify the effectiveness and necessity of the quantum convolution circuits and quantum self-attention modules, providing an efficient and accurate approach to quantized modeling for spatiotemporal prediction tasks. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

Hexahedral projections—mapping the Earth’s surface onto the faces of a circumscribed cube—have drawn scientific interest for over half a century. During this time, numerous projections with diverse characteristics have been developed. This paper provides the most comprehensive review of these projections to date, offering a detailed examination of the processes involved in projecting the Earth onto a cube, with a focus on distortion and accuracy. A numerical and graphical analysis of the characteristics of hexahedral projections is presented, serving as the foundation for a composite hierarchical metric based on ranking. This metric is used to rank hexahedral projections according to individual criteria, groups of criteria, and overall performance. Full article

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions. Full article

A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. Due to the magnetostrictive effect, an optical phase shift induced by magnetic field variation is generated between two orthogonal light waves transmitted in the upper and lower arms of the MZI. The polarization-dependent property of a Mach–Zehnder modulator (MZM) is utilized to transform the magnetostrictive phase shift into the phase difference between the sidebands and optical carrier, which is mapped to the oscillating frequency upon the completion of an OEO loop. High-sensitivity magnetic field sensing is achieved by observing the frequency shift of the radio frequency (RF) signal. Temperature-induced cross-sensitivity is mitigated through precise length matching of the MZI arms. In the experiment, the high magnetic field sensitivity of 6.824 MHz/mT with a range of 25 mT to 25.3 mT is achieved and the sensing accuracy measured by an electrical spectrum analyzer (ESA) at “maxhold” mode is 0.002 mT. The proposed sensing structure has excellent magnetic field detection performance and provides a solution for temperature-insensitive magnetic field detection, which would have broad application prospects. Full article

Addressing the challenge of acquiring depth information in aero-engine assembly scenes using monocular vision, which complicates mixed reality (MR) virtual and real occlusion processing, we propose an ORB-SLAM3-based monocular vision assembly scene virtual and real occlusion processing method. The method proposes optimizing ORB-SLAM3 for matching and depth point reconstruction using the MNSTF algorithm. MNSTF can solve the problems of feature point extraction and matching in weakly textured and texture-less scenes by expressing the structure and texture information of the local images. It is then proposed to densify the sparse depth map using the double-three interpolation method, and the complete depth map of the real scene is created by combining the 3D model depth information in the process model. Finally, by comparing the depth values of each pixel point in the real and virtual scene depth maps, the virtual occlusion relationship of the assembly scene is correctly displayed. Experimental validation was performed with an aero-engine piping connector assembly scenario and by comparing it with Holynski’s and Kinect’s methods. The results showed that in terms of virtual and real occlusion accuracy, the average improvement was 2.2 and 3.4 pixel points, respectively. In terms of real-time performance, the real-time frame rate of this paper’s method can reach 42.4 FPS, an improvement of 77.4% and 87.6%, respectively. This shows that the method in this paper has good performance in terms of the accuracy and timeliness of virtual and real occlusion. This study further demonstrates that the proposed method can effectively address the challenges of virtual and real occlusion processing in monocular vision within the context of mixed reality-assisted assembly processes. Full article