Search Results (67)

Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage. Full article

In the field of deep learning, the design of optimization algorithms and neural network structures is crucial for improving model performance. Recent advances in medical image analysis have revealed that many pathological features exhibit fractal-like characteristics in their spatial distribution and morphological patterns. This observation has opened new possibilities for developing fractal-inspired deep learning approaches. In this study, we propose the following: (1) a novel Region-Module Adam (RMA) optimizer that incorporates fractal-inspired region-weighting to prioritize areas with higher fractal dimensionality, and (2) an ECA-Enhanced Shuffle MobileNet (ESM) architecture designed to capture multi-scale fractal patterns through its enhanced feature extraction modules. Our experiments demonstrate that this fractal-informed approach significantly improves classification accuracy compared to conventional methods. On gastrointestinal image datasets, the RMA algorithm achieved accuracies of 83.60%, 81.60%, and 87.30% with MobileNetV2, ShuffleNetV2, and ESM networks, respectively. For glaucoma fundus images, the corresponding accuracies reached 84.90%, 83.60%, and 92.73%. These results suggest that explicitly considering fractal properties in medical image analysis can lead to more effective diagnostic tools. Full article

In the present study, the coaxial waterjet-assisted nanosecond laser drilling of micro-holes in C_f/SiC composites, coupled with nanosecond laser drilling in air for fabricating micro-holes with high aspect ratios, were investigated. The surface morphology, reaction products, and micro-hole shapes were thoroughly examined. The results reveal that, for the coaxial waterjet-assisted nanosecond laser drilling of micro-holes in the C_f/SiC composite, the increasing of waterjet velocity enhances the material removal rate and micro-hole depth, but reduces the micro-hole diameter and taper angle. The coaxial waterjet isolates the laser-ablated region and cools down the corresponding region rapidly, leading to the formation of a mixture of SiC, SiO₂, and Si on the surface. As the coaxial waterjet velocity increases, the morphology of residual surface products changes from a net-like structure to individual spheres. Coaxial waterjet-assisted nanosecond laser drilling, with a waterjet velocity of 9.61 m/s, achieves micro-holes with a good balance between efficiency and quality. For the fabrication of micro-holes with a high aspect ratio in C_f/SiC composites, micro-holes fabricated by nanosecond laser drilling in air exhibit obvious taper features, which should be ascribed to the combined effects of spattering slag, plasma, and energy dissipation. The application of coaxial waterjet-assisted nanosecond laser drilling on micro-holes fabricated by laser drilling in air effectively expands the hole diameter. The fabricated micro-holes have very small taper angles, with clean wall surfaces and almost no reaction products. This approach, combining nanosecond laser drilling in air followed by coaxial waterjet-assisted nanosecond laser drilling, offers a promising technique for fabricating high-quality micro-holes with high aspect ratios in C_f/SiC composites. Full article

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. Full article

This study investigated how Leucaena leucocephala, a dry forest plant, copes with soil copper and herbivory caused by Schistocerca piceifrons, crucial for understanding species adaptation in stressed environments. A 33-day factorial experiment with three copper and two herbivory treatments assessed seedling growth rates (relative growth rate of biomass—RGR_B, and leaf area—RGR_LA), morphology, net assimilation rate (NAR), biomass allocation, and survival. Seedlings demonstrated compensatory growth in terms of RGR_B and RGR_LA under high copper and herbivory. Although copper decreased overall survival, surviving individuals effectively compensated for herbivory damage. These tolerance responses, primarily driven by an increased NAR (accounting for 98% of compensation), aligned with the limiting resource model. While most morphological components remained stable, herbivory specifically increased the root–shoot ratio. These findings indicate L. leucocephala possesses significant resilience through physiological adjustments, like enhancing NAR, and biomass reallocation strategies, allowing it to persist despite multiple stressors common in dry forests. Full article

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose a dual-path hybrid deep learning framework based on CNN-LSTM and MLP, integrating anatomical features from cine CMR with biomechanical markers derived from intraventricular pressure gradients (IVPGs), significantly enhancing NICM subtype classification by capturing subtle biomechanical dysfunctions overlooked by traditional morphological models. Our dual-path architecture combines a CNN-LSTM encoder for cine CMR analysis and an MLP encoder for IVPG time-series data, followed by feature fusion and dense classification layers. Trained on a multicenter dataset of 1196 patients and externally validated on 137 patients from a distinct institution, the model achieved a superior performance (internal AUC: 0.974; external AUC: 0.962), outperforming ResNet50, VGG16, and radiomics-based SVM. Ablation studies confirmed IVPGs’ significant contribution, while gradient saliency and gradient-weighted class activation mapping (Grad-CAM) visualizations proved the model pays attention to physiologically relevant cardiac regions and phases. The framework maintained robust generalizability across imaging protocols and institutions with minimal performance degradation. By synergizing biomechanical insights with deep learning, our approach offers an interpretable, data-efficient solution for early NICM detection and subtype differentiation, holding strong translational potential for clinical practice. Full article

Diet, morphological traits, and other ecological characteristics may influence the composition of bird communities. The southern slopes of the Himalayas are one of the global hotspots for avian species diversity. However, systematic research on the distribution patterns of birds in this region and the intrinsic links between these patterns and ecological characteristics has not yet been reported. This research gap limits our comprehensive understanding of the avian ecosystem in this area and affects the formulation of targeted conservation strategies. Using standard transect methods and mist-netting, we surveyed bird species, their numbers, as well as the habitats in four 300 m elevation bands during the breeding season (May–June 2024) in the middle elevations of the Gyirong Valley, a typical valley on the southern slope of the Himalayas. We analyzed the bird species composition, habitat distribution, and the influence of ecological characteristics on bird distribution patterns using R 4.4. During the field survey of the breeding season in the middle elevations of the Gyirong Valley, a total of 76 bird species were recorded. Among them, birds from the families Muscicapidae and Phylloscopidae within the order Passeriformes constitute the dominant groups. Insectivorous and omnivorous birds were the main groups in the Gyirong Valley. Birds with different diets and morphological traits show distinct differentiation in habitat selection. The higher the specialization rate of ecological traits, the smaller the population size of the birds, and the more likely they are to become endangered species. Moreover, morphological traits significantly influenced the distribution patterns of birds in the middle elevations of the Gyirong Valley. Therefore, when formulating conservation strategies for birds in the Gyirong Valley, it is essential to fully consider the differences in habitat requirements for birds with different ecological traits. Full article

Current in vitro methods for intestinal barrier assessment predominantly utilize two-dimensional (2D) membrane inserts in standard culture plates, which are widely recognized for their inability to replicate the microenvironment critical to intestinal barrier functionality. Our study focuses on creating an alternative method for intestinal barrier function by integrating a 3D-printed transwell device with a paper-based membrane. Caco-2 cells were grown on a Matrigel-modified paper membrane, in which the tight junction formation was evaluated using TEER measurements. Neutrophil-like dHL-60 cells were employed for neutrophil extracellular trap (NET) formation experiments. Furthermore, intestinal barrier dysfunction was demonstrated using NET-isolated and Staurosporine interventions. Intestinal barrier characteristics were investigated through immunofluorescence staining of specific proteins and scanning electron microscopy (SEM). Our paper-based intestinal barrier exhibited an increased resistance in a time-dependent manner, consistent with immunofluorescence images of Zonulin Occludens-1 (ZO-1) expression. Interestingly, immunofluorescence analysis revealed changes in the morphology of the intestinal barrier and the formation of surface villi. These disruptions were found to alter the localization of tight junctions, impacting epithelial polarization and surface functionality. Moreover, we successfully demonstrated the permeability of a paper-based intestinal barrier using FITC-dextran assay. Hence, the 3D-printed transwell device integrated with a paper membrane insert presents a straightforward, cost-effective, and sustainable platform for an in vitro cell model to evaluate intestinal barrier function. Full article

Ancient introgression is an infrequent evolutionary process often associated with conflicts between nuclear and organellar phylogenies. Determining whether such conflicts arise from introgression, incomplete lineage sorting (ILS), or other processes is essential to understanding plant diversification. Previous studies have reported phylogenetic discordance in the placement of Xanthoceras, but its causes remain unclear. Here, we analyzed transcriptome data from 41 Sapindaceae samples to reconstruct phylogenies and investigate this discordance. While nuclear phylogenies consistently placed Xanthoceras as sister to subfam. Hippocastanoideae, plastid data positioned it as the earliest-diverging lineage within Sapindaceae. Our coalescent simulations suggest that this cyto-nuclear discordance is unlikely to be explained by ILS alone. HyDe and PhyloNet analyses provided strong evidence that Xanthoceras experienced ancient introgression, incorporating approximately 16% of its genetic material from ancestral subfam. Sapindoideae lineages. Morphological traits further support this evolutionary history, reflecting characteristics of both contributing subfamilies. Likely occurring during the Paleogene, this introgression represents a rare instance of cross-subfamily gene flow shaping the evolutionary trajectory of a major plant lineage. Our findings clarify the evolutionary history of Xanthoceras and underscore the role of ancient introgression in driving phylogenetic conflicts, offering a rare example of introgression-driven diversification in angiosperms. Full article

A novel all-in-one catalytic electrode containing a Ni₃S₂@NiS heterostructure (Ni₃S₂@NiS/Ni-Net) was in situ synthesized on an industrial nickel net (Ni-Net) using a one-step solvothermal method, in which ethanol was the solvent and thioacetamide was the sulfur source, respectively. The effects of the addition amount of the sulfur source on the composition, morphology, and electronic structure of the Ni₃S₂@NiS heterostructures and their electrocatalytic hydrogen evolution reaction (HER) activities were investigated. When 2 mmol of sulfur source was introduced, the prepared Ni₃S₂@NiS/Ni-Net electrode with a nanorod-like structure required overpotentials of 207 and 322 mV to drive the current densities of 100 and 500 mA/cm², respectively, in 1 M KOH solution, and only needed the overpotential of 429 mV to deliver 1000 mA/cm². Meanwhile, the Ni₃S₂@NiS/Ni-Net electrode can operate stably at a high current density of 90 mA/cm² under harsh alkaline conditions for at least 100 h. The results show that the Ni₃S₂@NiS/Ni-Net electrode has high activity and stable HER performance at a high current density, which provides a new idea for the development of high-efficiency electrodes for industrial alkaline hydrogen production. Full article

Leukemia is a life-threatening blood cancer that affects a large cross-section of the population, which underscores the great need for timely, accurate, and efficient diagnostic solutions. Traditional methods are time-consuming, subject to human vulnerability, and do not always grasp the subtle morphological differences that form the basic discriminatory features among different leukemia subtypes. The proposed residual vision transformer (ResViT) model breaks these limitations by combining the advantages of ResNet-50 for high dimensional feature extraction and a vision transformer for global attention to the spatial features. ResViT can extract low-level features like texture and edges as well as high-level features like patterns and shapes from the leukemia cell images. Furthermore, we designed a dual-stream ViT with a convolution stream for local details and a transformer stream for capturing the global dependencies, which enables ResViT to pay attention to multiple image regions simultaneously. The evaluation results of the proposed model on the two datasets were more than 99%, which makes it an excellent candidate for clinical diagnostics. Full article

Tomato is an important crop worldwide. Commonly, the production process is initiated in nurseries that provide seedlings to greenhouse growers. Many factors influence crop production, one of which is the seedlings’ quality. Light has an enormous effect on seedlings; however, in passive greenhouses, its control is quite difficult. In this situation, plants are usually affected by low or high light intensities which induces poor growth on plants. On the other hand, there is some evidence that sucrose applications could compensate for the adverse effects caused by low light intensities and other abiotic factors like salinity, drought, and temperature. In this way, this research aimed to assess the impact of exogenous sucrose on the morphology, quality, and growth of tomato seedlings cultivated under low-tech greenhouse conditions commonly observed in tropical and subtropical commercial nurseries. Four sucrose treatments were proposed (0, 1, 10, and 100 mM). On days 28, 32, 36, 40, and 44 after sowing, several morphological, physiological and growth measurements were evaluated. Sucrose-treated plants displayed higher leaf areas and chlorophyll contents, facilitating light absorption. Therefore, the relative growth rate (RGR) was enhanced and better explained by a higher net assimilation rate (NAR). Consequently, a higher dry matter accumulation and Dixon quality index (DQI) were achieved. Plants under treatment at 100 mM exhibited the best performance. Full article

The emerging usage of digitalized histopathological images is leading to a novel possibility for data analysis. With the help of artificial intelligence algorithms, it is now possible to detect certain structures and morphological features on whole slide images automatically. This enables algorithms to count, measure, or evaluate those areas when trained properly. To achieve suitable training, datasets must be annotated and curated by users in programs like QuPath. The extraction of this data for artificial intelligence algorithms is still rather tedious and needs to be saved on a local hard drive. We developed a toolkit for integration into existing pipelines and tools, like U-net, for the on-the-fly extraction of annotation tiles from existing QuPath projects. The tiles can be directly used as input for artificial intelligence algorithms, and the results are directly transferred back to QuPath for visual inspection. With the toolkit, we created a convenient way to incorporate QuPath into existing AI workflows. Full article

With the intensifying global warming trend, extreme heat and drought are becoming more frequent, seriously impacting potato yield and quality. To maintain sustainable potato production, it is necessary to breed new potato varieties that are adaptable to environmental changes and tolerant to adversity. Despite its importance, there is a significant gap in research focused on the potential mechanisms of potato resistance to abiotic stresses like drought and high temperatures. This article provides a comprehensive review of the recent research available in academic databases according to subject keywords about potato drought tolerance and high temperature tolerance with a view to providing an important theoretical basis for the study of potato stress mechanism and the selection and breeding of potato varieties with drought and high-temperature resistance. The suitable relative soil moisture content for potato growth and development is 55% to 85%, and the suitable temperature is 15 °C to 25 °C. The growth and development of potato plants under drought and high-temperature stress conditions are inhibited, and plant morphology is altered, which affects the process of potato stolon formation, tuberization and expansion, ultimately leading to a significant reduction in potato tuber yields and a remarkable degradation of the market grade of tubers, the specific gravity of tubers, and the processing quality of tubers. In addition, stress also adversely affects potato physiological and biochemical characteristics, such as reduction in root diameter and leaf area, decrease in net photosynthetic rate of leaves, production of reactive oxygen species (ROS), and increase in membrane lipid peroxidation. In addition, various types of genes and transcription factors are involved in the response to drought and heat at the molecular level in potato. This paper illustrates the effects of stress on potato growth and development and the molecular mechanisms of potato response to adversity in detail, which is intended to reduce the damage caused by drought and high temperature to potato in the context of global warming and frequent occurrence of extreme weather to ensure potato yield and quality and to further safeguard food security. Full article