Search Results (2,658)

Background: The HPV16 E2 gene plays a crucial role in viral replication and oncogene regulation. This study aimed to assess the genetic variability of the E2 gene and to identify immunogenic epitopes of the E2 protein. Methods: The E2 gene was amplified and sequenced. T-cell epitope prediction and evaluation were performed using IEDB, NetMHCpan v4.0, NetMHCIIpan v4.1, VaxiJen, ToxNet, and pLM4Alg. Results: Phylogenetic analysis of 47 E2 sequences demonstrated co-circulation of the D (n = 4) and A (n = 43) HPV16 lineages in Cajamarca. Twenty-eight Single Nucleotide Polymorphism (SNPs) were identified in E2, 21 of which were nonsynonymous. Seventeen variations were associated with positive Papanicolaou (Pap) test results. Epitope prediction identified 2 MHC class I and 27 MHC class II epitopes classified as potentially antigenic, non-toxic, and non-allergenic, with an overall global population coverage across both MHC classes of 99.78%. Conclusions: The A HPV16 lineage predominated among the women studied. The identified SNPs indicate substantial variability in the E2 gene and a relationship with endocervical lesions. In total, 29 E2-derived T-cell epitopes with immunogenic potential were identified. Full article

Image classification is central to computer vision, supporting applications from autonomous driving to medical imaging, yet state-of-the-art convolutional neural networks remain constrained by heavy floating-point operations (FLOPs) and parameter counts on edge devices. To address this accuracy–efficiency trade-off, we propose a unified lightweight framework built on a pruning-aware coordinate attention block (PACB). PACB integrates coordinate attention (CA) with L1-regularized channel pruning, enriching feature representation while enabling structured compression. Applied to MobileNetV3 and RepVGG, the framework achieves substantial efficiency gains. On GTSRB, MobileNetV3 parameters drop from 16.239 M to 9.871 M (–6.37 M) and FLOPs from 11.297 M to 8.552 M (–24.3%), with accuracy improving from 97.09% to 97.37%. For RepVGG, parameters fall from 7.683 M to 7.093 M (–0.59 M) and FLOPs from 31.264 M to 27.918 M (–3.35 M), with only ~0.51% average accuracy loss across CIFAR-10, Fashion-MNIST, and GTSRB. Complexity analysis further confirms PACB does not increase asymptotic order, since the additional CA operations contribute only lightweight lower-order terms. These results demonstrate that coupling CA with structured pruning yields a scalable accuracy–efficiency trade-off under hardware-agnostic metrics, making PACB a promising, deployment-ready solution for mobile and edge applications. Full article

In the semantic segmentation tasks of polarimetric SAR images, deep learning has become an important end-to-end method that uses convolutional neural networks (CNNs) and other advanced network architectures to extract features and classify the target region pixel by pixel. However, applying original networks used to optical images for PolSAR image segmentation directly will result in the loss of rich phase information in PolSAR data, which leads to unsatisfactory classification results. In order to make full use of polarization information, the complex-valued BiSeNetV2 with a bilateral-segmentation structure is studied and expanded in this work. Then, considering further improving the ability to extract semantic features in the complex domain and alleviating the imbalance of polarization channel response, the complex-valued BiSeNetV2 with a lightweight attention module (LAM-CV-BiSeNetV2) is proposed for the semantic segmentation of PolSAR images. LAM-CV-BiSeNetV2 supports complex-valued operations, and a lightweight attention module (LAM) is designed and introduced at the end of the Semantic Branch to enhance the extraction of detailed features. Compared with the original BiSeNetV2, the LAM-CV-BiSeNetV2 can not only more fully extract the phase information from polarimetric SAR data, but also has stronger semantic feature extraction capabilities. The experimental results on the Flevoland and San Francisco datasets demonstrate that the proposed LAM has better and more stable performance than other commonly used attention modules, and the proposed network can always obtain better classification results than BiSeNetV2 and other known real-valued networks. Full article

The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over fiber identification for composition purposes. This protocol can be invasive and destructive for the artifacts under study, time-consuming, and it often relies on personal expertise. In this preliminary study, an alternative, macroscopic approach is proposed, based on texture and surface textile characteristics, using low-magnification images and deep learning models. Under this scope, a publicly available, imbalanced textile image dataset was used to pretrain and evaluate six computer vision architectures (ResNet50, EfficientNetV2, ViT, ConvNeXt, Swin Transformer, and MaxViT). In addition to accuracy, energy efficiency and ecological footprint of the process were assessed using the CodeCarbon tool. The results indicate that two of the convolutional neural network models, Swin and EfficientNetV2, both deliver competitive accuracies together with low carbon emissions, in comparison to the transformer and hybrid models. This alternative, promising, sustainable, and non-invasive approach for textile classification demonstrates the feasibility of developing a custom, heritage-based image dataset. Full article

One-dimensional (1D) nanowires represent a critical class of nanomaterials with extensive applications in biosensing, biomedicine, bioelectronics, and energy harvesting. In materials science, accurately extracting their morphological and structural features is essential for effective image segmentation. However, 1D nanowires frequently appear in dispersed or entangled configurations, often with blurred backgrounds and indistinct boundaries, which significantly complicates the segmentation process. Traditional threshold-based methods struggle to segment these structurally complex nanowires with high precision. To address this challenge, we propose a wavelet-based Bilateral Segmentation Network named WaveBiSeNet, to which a Dual Wavelet Convolution Module (DWCM) and a Flexible Upsampling Module (FUM) are introduced to enhance feature representation and improve segmentation accuracy. In this study, we benchmarked WaveBiSeNet against ten segmentation models on a peptide nanowire image dataset. Experimental results demonstrate that WaveBiSeNet achieves, mIoU of 77.59%, an accuracy of 89.95%, an F1 score of 87.22%, and a Kappa coefficient of 74.13%, respectively. Compared to other advanced models, our proposed model achieves better segmentation performance. These findings demonstrate that WaveBiSeNet is an end-to-end deep segmentation network capable of accurately analyzing complex 1D nanowire structures. Full article

Soil moisture (SM) is a critical component of the global water cycle, profoundly influencing carbon fluxes and energy exchanges between the land surface and the atmosphere. NASA’s Soil Moisture Active/Passive (SMAP) mission provides soil moisture products at the global scale; however, validation of SMAP faces significant challenges due to scale mismatches between in situ measurements and satellite pixels, particularly in highly heterogeneous regions such as the Qinghai–Tibet Plateau. This study leverages high-spatiotemporal-resolution Harmonized Landsat–Sentinel-2 (HLS v2.0) data and the QLB-NET observation network, employing multiple machine learning models to generate pixel-scale ground-truth soil moisture from in situ measurements. The results indicate that XGBoost performs best (R = 0.941, RMSE = 0.047 m³/m³), and SHAP analysis identifies elevation and DOY as the primary drivers of the spatial patterns and dynamics of soil moisture. The XGBoost-upscaled soil moisture was employed as a validation benchmark to assess the accuracy of the SMAP 9 km and 36 km products, with the following key findings: (1) the proposed upscaling method effectively bridges the scale gap, yielding a correlation of 0.858 between the 36 km SMAP product and the pixel-scale soil moisture reference derived from XGBoost, surpassing the 0.818 correlation obtained using the traditional in situ averaging approach; (2) descending-orbit data generally outperform ascending-orbit data. In the 9 km SMAP product, 15 descending-orbit grids meet the scientific standard, compared to 10 ascending-orbit grids. For the 36 km product, only descending orbits satisfy the scientific standard. Full article

The rising dependence on fossil fuels has intensified greenhouse gas emissions, necessitating the development of renewable energy alternatives. Biogas is a sustainable fuel source; however, its low energy density hinders direct commercial application. This study explores the potential of upgrading biogas to biomethane (Bio-CNG) via CO₂ methanation, using Aspen Plus v14.0 simulations and techno-economic analysis. Equilibrium studies revealed that optimal conditions of 300–400 °C, 1–5 bar, and a H₂/CO₂ ratio of 4 achieve CO₂ conversion above 99%, methane selectivity exceeding 92%, and near-complete suppression of CO formation. The developed process flowsheet delivered a methane-rich stream (>92% CH₄ + H₂) with high yield. Economic evaluation showed that at optimal conditions, the process achieves a positive net present value (NPV) of $12.2 million (1 bar) and $1.7 million (5 bar) and a payback period as low as 0.92 years (1 bar) or 5.6 years (5 bar), depending on the pressure scenario. These results demonstrate that biogas upgrading through CO₂ methanation is not only technically feasible but also economically competitive, supporting its integration into existing energy systems and contributing to the transition toward renewable fuels. Full article

Background/Objectives: Strawberry crops are vulnerable to fungal diseases that severely affect yield and quality. Deep learning has shown strong potential for plant disease recognition; however, most architectures rely on tens of millions of parameters, limiting their use in low-resource agricultural settings. This study presents Light-MobileBerryNet, a lightweight and interpretable model designed to achieve accurate strawberry disease classification while remaining computationally efficient for potential use on mobile and edge devices. Methods: The model, inspired by MobileNetV3-Small, integrates inverted residual blocks, depthwise separable convolutions, squeeze-and-excitation modules, and Swish activation to enhance efficiency. A publicly available dataset was processed using CLAHE and data augmentation, and split into training, validation, and test subsets under consistent conditions. Performance was benchmarked against state-of-the-art CNNs. Results: Light-MobileBerryNet achieved 96.6% accuracy, precision, recall, and F1-score, with a Matthews correlation coefficient of 0.96, while requiring fewer than one million parameters (~2 MB). Grad-CAM confirmed that predictions focused on biologically relevant lesion regions. Conclusions: Light-MobileBerryNet approaches state-of-the-art performance with a fraction of the computational cost, providing a practical and interpretable solution for precision agriculture. Full article

Medical imaging, particularly chest X-rays, plays a vital role in radiological diagnosis. However, interpreting these images and generating detailed diagnostic reports is a time-consuming task for clinicians. To address this challenge, this study proposes an automated image captioning framework for chest X-ray images, aiming to reduce clinical workload and enhance diagnostic efficiency. The proposed approach employs convolutional neural networks (CNNs) for visual feature extraction and a modified Transformer architecture—referred to as the Medical Transformer—for structured report generation. Three CNN models, namely InceptionV3, ResNet152V2, and Inception–ResNetV2, were evaluated as feature extractors. The attention mechanisms, Bahdanau, Luong, and scaled dot product, were activated by ReLU or Tanh functions to identify the optimal configuration, i.e., the maximum attention is used. Experiments were conducted using the Indiana University Chest X-ray dataset, which contains 7466 images paired with corresponding diagnostic reports. The proposed approach employs image augmentation to accommodate input variability, utilizes Inception–ResNetV2 for feature extraction, and integrates the Medical Transformer with maximum attention mechanisms to achieve optimal performance in medical report generation. Evaluation metrics include BLEU (BLEU-1 to BLEU-4 scores of 0.720, 0.669, 0.648, and 0.600, respectively), METEOR (0.741), and BERTScore (F_BERT = 0.787), demonstrating superior performance compared to baseline models and the state of the art. These results validate the effectiveness of the proposed Medical Transformer framework in generating accurate and clinically relevant medical image captions. Full article

Tunneling through weak rock masses under shallow urban overburdens is critically constrained by stand-up time. Conventional models for hydraulic impact hammers prioritize the excavation rate (Net Breaking Rate—NBR) but overlook a key operational bottleneck: the mucking process. This study introduces a paradigm shift from “how fast can we excavate?” to “how can we excavate to facilitate rapid muck clearance?”; it presents a novel, data-driven framework that, for the first time, quantitatively links impact hammer operation to mucking efficiency via the resulting particle size distribution (P₅₀). Field data from metro line excavations in very weak rock (RMR 18-33) were used to develop empirical models via multiple linear regression. The analysis produced (1) a model predicting mucking duration (tₘ) from muck volume (V) and post-excavation mean particle size (P₅₀) and (2) a model predicting NBR from rock mass rating (RMR) and the rock size reduction ratio (F). These models are synthesized into a comprehensive operational equation, enabling engineers to select an impact hammer based on its ability to produce a target P₅₀ that ensures mucking can be completed within the project’s critical stability window. This transforms rock fragmentation from an incidental byproduct into a central, controllable factor in equipment selection and proactive risk management. Full article

Background: Automatic adventitious lung sound classification using deep learning is a promising strategy for objective respiratory disease screening. Evaluating model performance is challenging, particularly with imbalanced clinical datasets. This study compares CNN architectures and proposes a dual-stream classification approach. Methods: Using the public ICBHI 2017 dataset, we compared five pre-trained architectures: VGG16, VGG19, InceptionV3, MobileNetV2, and ResNet152V2. To mitigate class imbalance, we implemented pitch shifting, random shifting, and mixup data augmentation. We also developed and evaluated a novel VGGish-dual-stream network. The primary endpoint was the Average Score (AS), the arithmetic mean of Sensitivity and Specificity. Results: Among benchmarked models, ResNet152V2 achieved the highest AS ($0.541$), approaching the state-of-the-art range (0.56–0.58). This performance was characterised by a high Specificity ($0.67$) but low Sensitivity ($0.41$). Our proposed dual-stream network yielded a more balanced, albeit slightly lower, performance with an AS of $0.508$. Conclusions: Standard CNN architectures like ResNet152V2 can achieve competitive classification performance but may exhibit a clinically significant bias towards high specificity at the expense of sensitivity. This trade-off poses a risk of missing pathological events (false negatives). To ensure clinical safety and utility, future work must prioritise strategies that explicitly improve model sensitivity. Full article

Underground pipelines form a critical part of urban infrastructure, yet their complex configurations and fragmented documentation hinder efficient maintenance and risk management. Ground-penetrating radar provides a non-invasive method for subsurface inspection; however, traditional interpretation of B-scan data relies heavily on manual analysis, which is time-consuming and prone to error. This research proposes a two-step automated system for the detection and quantitative characterization of underground pipelines from GPR B-scans. In the first step, hyperbolic reflections are automatically detected and localized using state-of-the-art object detection algorithms, where YOLOv11x achieved superior stability compared to RT-DETR-X. In the second step, detected hyperbolic reflections are processed in order to estimate key parameters, including two-way travel time, burial depth, pipeline diameter, and the angle between GPR survey line and pipeline. Experimental results from 5-fold cross-validation demonstrate that TWTT and burial depth can be estimated with high performance, while pipeline diameter and angle exhibit moderate performance, reflecting their higher complexity and sensitivity to noise. According to the experimental results, EfficientNetV2L consistently produced the best overall performance. The proposed automated system reduces reliance on manual inspection, improves efficiency, and establishes a foundation for real-time, autonomous GPR-based underground infrastructure assessment. Full article

Agricultural robots operating in greenhouse environments face substantial challenges in detecting tomato stems, including fluctuating lighting, cluttered backgrounds, and the stems’ inherently slender morphology. This study introduces EfficientV1-C2fDWR-IRMB-YOLO (EDI-YOLO), an enhanced model built on YOLOv8n-seg. First, the original backbone is replaced with EfficientNetV1, yielding a 2.3% increase in mAP50 and a 2.6 G reduction in FLOPs. Second, we design a C2f-DWR module that integrates multi-branch dilations with residual connections, enlarging the receptive field and strengthening long-range dependencies; this improves slender-object segmentation by 1.4%. Third, an Inverted Residual Mobile Block (iRMB) is inserted into the neck to apply spatial attention and dual residual paths, boosting key-feature extraction by 1.5% with only +0.7GFLOPs. On a custom tomato-stem dataset, EDI-YOLO achieves 79.3% mAP50 and 33.9% mAP50-95, outperforming the baseline YOLOv8n-seg (75.1%, 31.4%) by 4.2% and 2.6%, and YOLOv5s-seg (66.7%), YOLOv7tiny-seg (75.4%), and YOLOv12s-seg (75.4%) by 12.6%, 3.9%, and 3.9% in mAP50, respectively. Significant improvement is achieved in lateral branch segmentation (60.4% → 65.2%). Running at 86.2 FPS with only 10.4GFLOPs and 8.0 M parameters, EDI-YOLO demonstrates an optimal trade-off between accuracy and efficiency. Full article

Precise extrinsic calibration is a fundamental prerequisite for data fusion in multi-LiDAR systems. However, conventional methods are often encumbered by dependencies on initial estimates, auxiliary sensors, or manual feature selection, which renders them complex, time-consuming, and limited in adaptability across diverse environments. To address these limitations, this paper proposes a novel, high-precision extrinsic calibration method for multi-LiDAR systems with a narrow Field of View (FoV), achieved through the synergistic use of circular and planar features. Our approach commences with the automatic segmentation of the calibration target’s point cloud using an improved VoxelNet. Subsequently, a denoising step, combining RANSAC and a Gaussian Mean Intensity Filter (GMIF), is applied to ensure high-quality feature extraction. From the refined point cloud, planar and circular features are robustly extracted via Principal Component Analysis (PCA) and least-squares fitting, respectively. Finally, the extrinsic parameters are optimized by minimizing a nonlinear objective function formulated with joint constraints from both geometric features. Simulation results validate the high precision of our method, with rotational and translational errors contained within 0.08° and 0.8 cm. Furthermore, real-world experiments confirm its effectiveness and superiority, outperforming conventional point-cloud registration techniques. Full article

Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for automated, accurate, and personalized solutions. This study presents a novel methodology for diagnosing DME and categorizing choroidal neovascularization (CNV), drusen, and normal conditions from fundus images through the application of transfer learning models and bio-inspired optimization methodologies. The methodology utilizes advanced transfer learning architectures, including VGG16, VGG19, ResNet50, EfficientNetB7, EfficientNetV2-S, InceptionV3, and InceptionResNetV2, for analyzing both binary and multi-class Optical Coherence Tomography (OCT) datasets. We combined the OCT datasets OCT2017 and OCTC8 to create a new dataset for our study. The parameters, including learning rate, batch size, and dropout layer of the fully connected network, are further adjusted using the bio-inspired Particle Swarm Optimization (PSO) method, in conjunction with thorough preprocessing. Explainable AI approaches, especially Shapley additive explanations (SHAP), provide transparent insights into the model’s decision-making processes. Experimental findings demonstrate that our bio-inspired optimized transfer learning Inception V3 significantly surpasses conventional deep learning techniques for DME classification, as evidenced by enhanced metrics including the accuracy, precision, recall, F1-score, misclassification rate, Matthew’s correlation coefficient, intersection over union, and kappa coefficient for both binary and multi-class scenarios. The accuracy achieved is approximately 98% in binary classification and roughly 90% in multi-class classification with the Inception V3 model. The integration of contemporary transfer learning architectures with nature-inspired PSO enhances diagnostic precision to approximately 95% in multi-class classification, while also improving interpretability and reliability, which are crucial for clinical implementation. This research promotes the advancement of more precise, personalized, and timely diagnostic and therapeutic strategies for Diabetic Macular Edema, aiming to avert vision loss and improve patient outcomes. Full article