Robust Features Research Articles

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Usability Insights from the BUMPER Cancer Prevention App Pilot Study: A Mixed-Methods Approach

Abstract Introduction As the second leading cause of death in the EU, cancer is expected to see a 55% rise in new cases globally over the next two decades. The European Code Against Cancer (ECAC) and the EU’s Beating Cancer Plan (EBCP) provide resources for understanding and preventing cancer. The BUMPER project aims to develop a cancer prevention App to make the ECAC more accessible to the public. Objectives Understand the factors influencing the use of a cancer prevention app among users with different digital health literacy levels and to evaluate the app’s usability. Methods Mixed-methods approach was used to gather user feedback. FGDs explored user perspectives on app usage, while a cross-sectional survey assessed usability through task completion on an initial app wireframe. The pilot was conducted across seven EU countries in late 2023. Results 77 participants participated in the pilots and FGDs across seven EU countries. The average task completion time was approximately 79 seconds. Participants rated their confidence to complete the tasks at 3.96 and difficulty at 2.28 on a scale of 1 to 5. The average task pass rate was 92.11%. Qualitative findings highlighted the need for evidence-based content, user-friendly design, language accessibility, offline functionality, and data security. Participants also stressed the importance of clear instructions, regular updates, and robust tracking features. Despite some challenges, the app was generally well-received, with users appreciating its educational potential and tracking capabilities. Conclusions The BUMPER project’s pilot study demonstrated the cancer prevention App being developed as a potential tool for cancer education and awareness. Despite navigational and language challenges, the app was favorably received by a diverse user base, underscoring the need for ongoing improvements in user guidance, comprehension, and functionality to enhance accessibility. Key messages • BUMPER project’s cancer prevention app shows promise in pilot study, enhancing cancer education and awareness. • Despite challenges, the app was well-received, highlighting the need for ongoing improvements to enhance accessibility.

Plug-and-play segment anything model improves nnUNet performance.

The automatic segmentation of medical images has widespread applications in modern clinical workflows. The Segment Anything Model (SAM), a recent development of foundational models in computer vision, has become a universal tool for image segmentation without the need for specific domain training. However, SAM's reliance on prompts necessitates human-computer interaction during the inference process. Its performance on specific domains can also be limited without additional adaptation. In contrast, traditional models like nnUNet are designed to perform segmentation tasks automatically during inference and can work well for each specific domain, but they require extensive training on domain-specificdatasets. To leverage the advantages of both foundational and domain-specific models and achieve fully automated segmentation with limited training samples, we propose nnSAM, which combines the robust feature extraction capabilities of SAM with the automatic configuration abilities of nnUNet to enhance the accuracy and robustness of medical image segmentation on smalldatasets. We propose the nnSAM model for small sample medical image segmentation. We made optimizations for this goal via two main approaches: first, we integrated the feature extraction capabilities of SAM with the automatic configuration advantages of nnUNet, which enables robust feature extraction and domain-specific adaptation on small datasets. Second, during the training process, we designed a boundary shape supervision loss based on level set functions and curvature calculations, enabling the model to learn anatomical shape priors from limited annotationdata. We conducted quantitative and qualitative assessments on the performance of our proposed method on four segmentation tasks: brain white matter, liver, lung, and heart segmentation. Our method achieved the best performance across all tasks. Specifically, in brain white matter segmentation using 20 training samples, nnSAM achieved the highest DICE score of 82.77 ( 10.12) % and the lowest average surface distance (ASD) of 1.14 ( 1.03) mm, compared to nnUNet, which had a DICE score of 79.25 ( 17.24) % and an ASD of 1.36 ( 1.63) mm. A sample size study shows that the advantage of nnSAM becomes more prominent under fewer trainingsamples. A comprehensive evaluation of multiple small-sample segmentation tasks demonstrates significant improvements in segmentation performance by nnSAM, highlighting the vast potential of small-samplelearning.

The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model

AbstractEl Niño‐Southern Oscillation (ENSO) is the dominant atmosphere–ocean coupled mode of year‐to‐year variations in the tropical Pacific. It shows diverse spatiotemporal characteristics and casts major influences on seasonal predictions of global weather–climate extrema. Despite numerous dynamical and statistical models for ENSO prediction and predictability studies, they are commonly subjected to one‐to‐three issues among less skillful simulation of El Niño diversity, huge requirements of computational resources and a low robustness in statistics. Here, an efficient deep‐learning model involving nonlinear coupling of multiple variables is independently developed to study the predictability of two types of El Niño events related to initial uncertainty, which is the first kind of predictability problem. The model can skillfully simulate statistically robust features of observed El Niño diversity in terms of periodicity, amplitude, and seasonal phase‐locking. Using this model, we have revealed mathematically several new types of fastest‐growing initial errors in two types of El Niño predictions based on a novel concept of conditional nonlinear optimal perturbation (CNOP), especially including one that can strengthen central Pacific types of events, which is rarely investigated before. Moreover, CNOPs are superimposed into a numerical model, GFDL CM2p1, for comprehensive validation and growth mechanism mining, which demonstrates the consistent dynamical evolution of initial errors in both numerical and AI models. Our study represents the first attempt to explore the first kind of ENSO predictability problem from perspectives of nonlinear error‐evolving dynamics using a data‐driven model. This is of great importance as it offers us sufficient confidence to perform ENSO‐related (such as the Madden–Julian Oscillation, etc.) mechanisms and predictability studies in the future without strongly relying on dynamical numerical models.

Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques

Abstract: Because of the increasing prevalence and sophistication of credit card theft, standard detection measures frequently fail. This study investigates the use of several machine learning algorithms to improve fraud detection in bank credit card transactions. The research aims to develop enhanced fraud detection systems by employing an integrated strategy involving data preprocessing, the application of various classification algorithms, and performance evaluation. The study thoroughly examines algorithms such as Random Forest, Logistic Regression, and Neural Networks, focusing on their predictive capabilities and practical applications. The findings indicate that machine learning provides a robust framework for increasing the accuracy and efficiency of fraud detection systems, consequently assisting financial institutions in protecting customer transactions and strengthening security measures. Moreover, by prioritizing robust model selection and feature engineering, banks can significantly enhance their fraud detection performance. This research could greatly influence how financial institutions handle fraud, potentially reducing losses, improving operational efficiency, and securing transaction environments. The insights gained from this study are also valuable for informing broader financial security strategies and guiding future research in the field.

All Roads Lead to Rome: Isomers with Divergent Cathode Modification Mechanisms toward Ohmic Contact.

Cathode interfacial layers (CILs) hold utmost importance for achieving ohmic contact at the organic semiconductor-cathode interface of organic photovoltaic devices. Delving deep into diverse design principles and working mechanisms is of great significance for designing novel CILs with high performance. Herein, two novel nonamine-based CILs are designed: one featuring a cyclopentadiene unit, designated as CIL-cp; while the other, lacking cyclopentadiene, is referred to as CIL-ph, which is an isomer of CIL-cp. The subtle changes in chemical structures result in distinct modification mechanisms toward ohmic contact. On one hand, the robust electron-withdrawing characteristic of cyclopentadiene endows CIL-cp with lower energy levels, resulting in an interfacial dipole at the active layer-CIL-cp interface due to electron transfer from D18 to CIL-cp. On the other hand, CIL-ph exhibits a strong interfacial dipole at the CIL-Ag interface, which significantly reduces the work function (WF) of the silver electrode. Both CIL-cp and CIL-ph demonstrate excellent interfacial modification capability, whereas CIL-cp possesses a stronger electron extraction ability, thus leading to a high power conversion efficiency of 19.31% in the D18:L8-BO system. Our results reveal the distinctive operational mechanism of cyclopentadiene-based CILs, thus offering innovative design ideas for CIL materials.

A novel integrated prediction method using adaptive mode decomposition, attention mechanism and deep learning for coking products prices

Accurate prediction of coking product prices is crucial for enhancing production efficiency, cost optimization, and profit maximization in smart coking facilities. To address the volatility caused by nonlinear factors such as raw material costs, substitutes, macroeconomic indicators, sudden events, policy changes, and market behaviors, we propose a novel integrated prediction method for coking product price prediction. This method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, Bidirectional Encoder Representations from Transformers (BERT) for natural language processing, attention mechanisms (AT) to weigh feature importance, and an ensemble of Bidirectional Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, abbreviated BBG, for robust feature extraction. We design a feature selection strategy to avoid data leakage and improve the predictive ability of the model, and describe a method to maintain textual data information integrity when combining data from different sources. Experimental results on coke and methanol datasets show that our approach retains multi-source text richness improves predictive capability, and outperforms other state-of-the-art methods, providing an effective tool for developing smart coke plants.

Enhanced semantic-positional feature fusion network via diverse pre-trained encoders for remote sensing image water-body segmentation

ABSTRACT In the era of increasingly advanced Earth Observation (EO) technologies, extracting pertinent information (such as water-bodies) from the Earth’s surface has become a crucial task. Deep Learning, especially via pre-trained models, currently offers a highly promising approach for the semantic segmentation of Remote Sensing Imagery (RSI). However, effectively adapting these pre-trained models to RSI tasks remains challenging. Typically, these models undergo fine-tuning for specialized tasks, involving modifications to their parameters or structure of the original architecture, which may impact their inherent generalization capabilities. Furthermore, robust pre-trained models on nature images are not specifically designed for RSI, presenting challenges in their direct application to RSI tasks. To alleviate these problems, our study introduces a light-weight Enhanced Semantic-positional Feature Fusion Network (ESFFNet), leveraging diverse pre-trained image encoders alongside extensive EO data. The proposed method begins by leveraging pre-trained encoders, specifically Vision Transformer (ViT)-based and Convolutional Neural Network (CNN)-based models, to extract deep semantic and precise positional features respectively, without additional training. Following this, we introduce the Enhanced Semantic-positional Feature Fusion Module (ESFFM). This module adeptly merges semantic features derived from the ViT-based encoder with spatial features extracted from the CNN-based encoder. Such integration is realized via multi-scale feature fusion, local and long-distance feature integration, and dense connectivity strategies, leading to a robust feature representation. Finally, the Primary Segmentation-guided Fine Extraction Module (PSFEM) further bolsters the precision of remote sensing image segmentation. Collectively, these two modules constitute our light-weight decoder, with a parameter size of less than 4 M. Our approach is evaluated on two distinct water-body datasets, indicating superiority over other leading segmentation techniques. In addition, our method also demonstrates exemplary efficacy in diverse remote sensing segmentation tasks, such as building extraction and land cover classification. The source codes will be available at https://github.com/zhilyzhang/ESFFNet.

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid–based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student’s t test and Cohen’s D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values (<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}) and the majority of Cohen’s D values (>0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 0.5$$\\end{document}) showed that MEF-AlloSite’s 1–6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Novel Bacteriophage KG853 Exhibits Potent Lytic Activity and Biofilm Inhibition Against Pseudomonas aeruginosa

This study reports the isolation and characterization of a novel bacteriophage, KG853, specifically targeting Pseudomonas aeruginosa ATCC 27853. Morphological analysis using transmission electron microscopy revealed that bacteriophage KG853 belongs to the Bruynoghevirus genus. The phage demonstrated favorable characteristics for potential therapeutic applications, including a short latent period of 30 minutes and a large burst size of 136 plaque-forming units (PFU) per cell. KG853 exhibited stability across various temperatures and pH values, indicating its robustness under various environmental conditions. Genomic analysis showed that KG853 possesses a circular DNA genome of 45,390 base pairs with a GC content of 52.2%. No lysogenic or virulence genes were detected among the 84 open reading frames annotated in the genome, suggesting its safety for potential therapeutic use. Phylogenetic analysis revealed that phage KG853 is closely related to phage PaP3. Notably, KG853 demonstrated the ability to inhibit the formation of 4-hour biofilms by P. aeruginosa, a critical virulence factor in many infections. Host range analysis showed that KG853 is specific to P. aeruginosa, an important characteristic for targeted therapy. These findings suggest that bacteriophage KG853 represents a promising candidate for combating drug-resistant P. aeruginosa infections. Its specific host range, robust physical characteristics, lack of harmful genes, and anti-biofilm activity make it a potential alternative to conventional antibiotics. Further research is warranted to explore its efficacy in in vivo models and potential clinical applications.

Skin feature point tracking using deep feature encodings

AbstractFacial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson’s disease. While traditional computer vision algorithms like Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK) have been benchmarks due to their efficiency and accuracy, they often struggle with challenges like affine transformations and changes in illumination. In response, we propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained upon. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labelled face and hand videos of small and large motion recorded in our lab. Our evaluation protocol is comprehensive, including quantification of errors in human annotation. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude the fact that they stem from the manual labelling based on a $$\chi ^2$$ χ 2 -test. With a mean error of 0.6–3.3 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one that did not diverge. We also compare our method with the latest state-of-the-art transformer for feature matching by Google—Omnimotion. Our results indicate that our method is superior at tracking different skin features under large motion conditions and that it creates better feature descriptors for tracking, matching, and image registration compared to both traditional algorithms and the latest Omnimotion.

Multi-level feature fusion network for neuronal morphology classification.

Neuronal morphology can be represented using various feature representations, such as hand-crafted morphometrics and deep features. These features are complementary to each other, contributing to improving performance. However, existing classification methods only utilize a single feature representation or simply concatenate different features without fully considering their complementarity. Therefore, their performance is limited and can be further improved. In this paper, we propose a multi-level feature fusion network that fully utilizes diverse feature representations and their complementarity to effectively describe neuronal morphology and improve performance. Specifically, we devise a Multi-Level Fusion Module (MLFM) and incorporate it into each feature extraction block. It can facilitate the interaction between different features and achieve effective feature fusion at multiple levels. The MLFM comprises a channel attention-based Feature Enhancement Module (FEM) and a cross-attention-based Feature Interaction Module (FIM). The FEM is used to enhance robust morphological feature presentations, while the FIM mines and propagates complementary information across different feature presentations. In this way, our feature fusion network ultimately yields a more distinctive neuronal morphology descriptor that can effectively characterize neurons than any singular morphological representation. Experimental results show that our method effectively depicts neuronal morphology and correctly classifies 10-type neurons on the NeuronMorpho-10 dataset with an accuracy of 95.18%, outperforming other approaches. Moreover, our method performs well on the NeuronMorpho-12 and NeuronMorpho-17 datasets and possesses good generalization.

A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine

Abstract. To address the absence of GNSS signals in underground coal mines and the susceptibility of mainstream LiDAR SLAM to degradation due to insufficient feature constraints, this paper proposes a tightly-coupled SLAM algorithm incorporating LiDAR and IMU for use in such environments. The paper initially introduces a dynamic feature point extraction strategy, where the number of corner feature points can be dynamically adjusted based on the occurrence of degradation in the underground coal mine environment. This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. Finally, to validate the effectiveness of the method, experimental analysis is conducted in an underground coal mine. The results demonstrate that LeGO_LOAM exhibits poor robustness in underground coal mines and fails to construct a globally consistent pose estimation and map. Conversely, the pose estimation error of the proposed method in this paper is 50.93% lower in the plane direction and 42.13% lower in the elevation direction compared to LIO_SAM. This underscores the method's significance as crucial technical support for the intelligent perception and positioning of robots in underground coal mines.

A Geometric Significance-Aware Deep Mutual Learning Network for Building Extraction from Aerial Images

Knowledge-driven building extraction method exhibits a restricted adaptability scope and is vulnerable to external factors that affect its extraction accuracy. On the other hand, data-driven building extraction method lacks interpretability, heavily relies on extensive training data, and may result in extraction outcomes with building boundary blur issues. The integration of pre-existing knowledge with data-driven learning is essential for the intelligent identification and extraction of buildings from high-resolution aerial images. To overcome the limitations of current deep learning building extraction networks in effectively leveraging prior knowledge of aerial images, a geometric significance-aware deep mutual learning network (GSDMLNet) is proposed. Firstly, the GeoSay algorithm is utilized to derive building geometric significance feature maps as prior knowledge and integrate them into the deep learning network to enhance the targeted extraction of building features. Secondly, a bi-directional guidance attention module (BGAM) is developed to facilitate deep mutual learning between the building feature map and the building geometric significance feature map within the dual-branch network. Furthermore, the deployment of an enhanced flow alignment module (FAM++) is utilized to produce high-resolution, robust semantic feature maps with strong interpretability. Ultimately, a multi-objective loss function is crafted to refine the network’s performance. Experimental results demonstrate that the GSDMLNet excels in building extraction tasks within densely populated and diverse urban areas, reducing misidentification of shadow-obscured regions and color-similar terrains lacking building structural features. This approach effectively ensures the precise acquisition of urban building information in aerial images.

Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images

Background The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early detection and quick isolation of infections. Several diagnostic tools are currently being used for COVID-19 detection using computed tomography (CT) scan and chest X-ray (CXR) images. Methods In this study, a novel deep learning-based model is proposed for rapid detection of COVID-19 using CT-scan images. The model, called pre-trained quantum convolutional neural network (QCNN), seamlessly combines the strength of quantum computing with the feature extraction capabilities of a pre-trained convolutional neural network (CNN), particularly VGG16. By combining the robust feature learning of classical models with the complex data handling of quantum computing, the combination of QCNN and the pre-trained VGG16 model improves the accuracy of feature extraction and classification, which is the significance of the proposed model compared to classical and quantum-based models in previous works. Results The QCNN model was tested on a SARS-CoV-2 CT dataset, initially without any pre-trained models and then with a variety of pre-trained models, such as ResNet50, ResNet18, VGG16, VGG19, and EfficientNetV2L. The results showed the VGG16 model performs the best. The proposed model achieved 96.78% accuracy, 0.9837 precision, 0.9528 recall, 0.9835 specificity, 0.9678 F1-Score and 0.1373 loss. Conclusion Our study presents pre-trained QCNN models as a viable technique for COVID-19 disease detection, showcasing their effectiveness in reaching higher accuracy and specificity. The current paper adds to the continuous efforts to utilize artificial intelligence to aid healthcare professionals in the diagnosis of COVID-19 patients.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Border Gateway Protocol Route Leak Detection Technique Based on Graph Features and Machine Learning

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

Opto/Thermoresponsive Multimode Luminescence in Eu- and Er-Activated CaF2 Phosphor for High-Security Anticounterfeiting.

As the landscape of information storage and security continues to evolve, the deployment of sophisticated anticounterfeiting strategies with robust security features and multimodal luminescent capabilities becomes imperative. In this work, Eu3+ and Er3+ ions are codoped into CaF2 phosphors to achieve multimodel optical output. The self-reduction of Eu3+ within the CaF2 matrix gives rise to the coexistence of Eu3+ and Eu2+ ions, which manifests as a color transition from orange to blue as the excitation wavelength is varied from 300 to 335 nm. Moreover, the distinct temperature-dependent behaviors of Eu3+ and Eu2+ ions underscore the material's visible temperature-sensitive luminescence properties, characterized by remarkable thermal sensitivity (Sa = 0.0156 K-1, Sr = 0.83%K-1). Additionally, the strategic introduction of Er3+ ions adds an extra dimension, enabling the realization of color-tunable upconversion luminescence through the fine-tuning of Er3+ concentration. This synergistic integration culminates in the establishment of an efficient three-path authentication model within the CaF2 host matrix, facilitating a dynamic multicolor response to changes in the excitation wavelength and temperature. By harnessing these diverse luminescent modalities, the study delivers a versatile and potent anticounterfeiting solution, advancing the frontiers of information security.

MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search.

Liquid chromatography-mass spectrometry (LC-MS) based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition (DDA) mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum (MS2), often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a novel database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each MS2, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate (FDR) control. It is also uniquely suited for wide-window acquisition (WWA) data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Precision agriculture with YOLO-Leaf: advanced methods for detecting apple leaf diseases.

The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection methods face significant challenges. To address these issues, we propose a new model called YOLO-Leaf. Specifically, YOLO-Leaf utilizes Dynamic Snake Convolution (DSConv) for robust feature extraction, employs BiFormer to enhance the attention mechanism, and introduces IF-CIoU to improve bounding box regression for increased detection accuracy and generalization ability. Experimental results on the FGVC7 and FGVC8 datasets show that YOLO-Leaf significantly outperforms existing models in terms of detection accuracy, achieving mAP50 scores of 93.88% and 95.69%, respectively. This advancement not only validates the effectiveness of our approach but also highlights its practical application potential in agricultural disease detection.