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181
First Detection of Low-frequency Striae in Interplanetary Type III Radio Bursts
Published 2025-01-01“…By combining high-resolution radio observations with well-calibrated in situ electron velocity distribution function data from the Wind spacecraft, we characterized the plasma properties of the burst source region near 0.32 au. …”
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182
Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification
Published 2024-01-01“…The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. …”
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183
Sustainable Valorization of Jackfruit Peel Waste: Bio‐Functional and Structural Characterization
Published 2025-03-01“…In conclusion, this study identified the potential utility of A. heterophyllus peel as a valuable source of phytochemical compounds, polyphenolic antioxidants, and the antimicrobial additives that can be used in wide agri‐food‐pharma industries.…”
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184
Effect of cochlear implant surgery on vestibular function: meta-analysis study
Published 2017-06-01“…No significant effect of CI surgery was detected in HIT, posturography, or DHI scores. Overall, the clinical effect of CI surgery on the vestibular function was found to be insignificant. …”
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185
Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images
Published 2025-02-01“…The purpose of this research is to determine whether or not a deep learning model called VGG16 can automatically identify bone fractures in X-ray pictures. The dataset, sourced from Kaggle, includes 10,522 images of human hand and foot bones, which underwent preprocessing steps such as normalization and resizing to 224x224 pixels to enhance data quality. …”
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186
Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs
Published 2023-12-01“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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187
A Contrast-Enhanced Approach for Aerial Moving Target Detection Based on Distributed Satellites
Published 2025-03-01“…This method compensates for the range difference rather than the target range. In the detection period, we develop two weighting functions, i.e., the Doppler frequency rate (DFR) variance function and smooth spatial filtering function, to extract prominent areas and make efficient detection, respectively. …”
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188
Detecting and routing of dust event using remote sensing and numerical modeling in Isfahan Province
Published 2020-03-01“…In addition, numerical weather models alone are not capable of storm detection, which requires the use of dust detection methods based on data remote sensing. …”
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189
The proteome of circulating extracellular vesicles and their functional effect on platelets vary with the isolation method
Published 2025-07-01“…Abstract Extracellular vesicles (EVs) play a crucial role in cell-to-cell communication and serve as a source of biomarkers in several pathologies. In this study, we aimed to characterize plasma-derived EVs isolated by ultracentrifugation (UC) or size exclusion chromatography (SEC) to define the best method for proteomic and functional studies. …”
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190
FsDAOD: Few-shot domain adaptation object detection for heterogeneous SAR image
Published 2025-06-01“…Heterogeneous Synthetic Aperture Radar (SAR) image object detection task with inconsistent joint probability distributions is occurring more and more frequently in practical applications. …”
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191
DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES)
Published 2019-06-01“…The functionality of stereotypes of perception and «memory of the genre» is briefly presented, which manifests itself in the history of understanding a domestic detective story as a constant appeal to the foreign sources of the genre. …”
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192
Network security traffic detection and legal supervision based on adaptive metric learning algorithm
Published 2025-09-01Get full text
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193
App-DDoS detection method using partial binary tree based SVM algorithm
Published 2018-03-01“…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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Self-Powered Microsystem for Ultra-Fast Crash Detection via Prestressed Triboelectric Sensing
Published 2025-01-01“…We further developed a self-powered, compact (<4.5 cm3) microsystem that integrates the shock sensor, a signal processing module, airbag triggering circuitry, and a high-g-resistant supercapacitor as a backup power source. The microsystem achieves ultra-fast shock detection and airbag activation with a delay of less than 0.2 ms. …”
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196
A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1
Published 2025-03-01“…The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.…”
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197
Specific detection of tartaric acid chiral isomers based on centrosymmetric terahertz metamaterial sensors
Published 2025-01-01“…Tartaric acid (C4H6O6) is a common food additive with two mutually symmetrical chiral carbons, which is a very important class of four-carbon organic chiral sources. L-, D-, DL-tartaric acids have different uses in food additives and pharmaceutical fields. …”
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198
Study on Point Spread Function of Perovskite Fast Neutron Scintillation Imaging Screen
Published 2025-02-01“…Additionally, the limited availability of experimental machines for fast neutron imaging and the high cost of imaging systems hinders the efficient detection of large number of materials by using common fast neutron sources. …”
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199
The Relevance of Osteoscintigraphy Technique in Early Detection of Bone Metastatic Lesions: a Systematic Review
Published 2023-06-01“…OSG is an effective and informative technique for early detection of bone metastases, allowing to assess the functional state of the tumor and its surrounding tissues, even before the appearance of structural disorders visible by other diagnostic methods. …”
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