Research on the detection of obstacles in front of unmanned vehicles in opencast mines based on binocular vision by Shunling RUAN, Huiguo ZHANG, Qinghua GU, Caiwu LU, Di LIU, Jing MAO

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The functions of religion and science in utopian thinking in the middle ages and the early modern period by Hope Steffen

“…The selection of sources includes both widely-known utopian texts and materials that are rarely used in discussions of utopian thinking. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…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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Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers by Ridip Khanal, Wenqin Wu, Joonwhoan Lee

“…Then, a deep learning classifier, enhanced through knowledge distillation, confirms whether the detected stationary object is indeed a chicken. EfficientNet-B0 is employed as the teacher model, while DeiT-Tiny functions as the student model, balancing high accuracy and computational efficiency. …”
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Unlocking the potential: A comprehensive review on blast furnace slag and silica analog adsorbents for sustainable industrial and pharmaceutical pollution control and resource util... by Qing-Guo Dong, Jing Li, Zhi-Qing Kang, Muhammad Imran Anwar, Muhammad Asad, Baoji Miao, Shuanghu Wang, Ayesha Younas

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Metagenomic and phylogenetic analyses reveal gene-level selection constrained by bacterial phylogeny, surrounding oxalate metabolism in the gut microbiota by Sromona D. Mukherjee, Mangesh Suryavanshi, John Knight, Dirk Lange, Aaron W. Miller

“…The frc gene was primarily allocated to the Pseudomonodota phylum, particularly the Bradyrhizobium genus, which is a species capable of utilizing oxalate as a sole carbon and energy source. Collectively evidence provides strong support that, for oxalate metabolism, evolutionary selection occurs at the gene level, through horizontal gene transfer, rather than at the species level.IMPORTANCEA critical function of the gut microbiota is to neutralize dietary toxins, such as oxalate, which is highly prevalent in plant-based foods and is not degraded by host enzymes. …”
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A Contrast-Enhanced Approach for Aerial Moving Target Detection Based on Distributed Satellites by Yu Li, Hansheng Su, Jinming Chen, Weiwei Wang, Yingbin Wang, Chongdi Duan, Anhong Chen

“…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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Tomato Pomace as a Valuable Resource: Characterization of Sunflower and Rapeseed Oil Extracts from Ox Heart Tomato (<i>Lycopersicon esculentum</i>) by Dumitriţa Flaiş, Mariana Spinei, Mircea Oroian

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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Surface-Enhanced Raman Spectroscopy (SERS) substrates offer a promising solution for the sensitive and specific detection of agrochemicals, enabling timely interventions to mitigate their harmful effects on humans and ecosystems. …”
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Effectiveness of canine‐assisted surveillance and human searches for early detection of invasive spotted lanternfly by Angela K. Fuller, Ben C. Augustine, Eric H. Clifton, Ann E. Hajek, Arden Blumenthal, Josh Beese, Aimee Hurt, Carrie J. Brown‐Lima

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Testing the functionality and contact error of a GPS‐based wildlife tracking network by Melanie J. Davis, Sravan Thokala, Xinyu Xing, N. Thompson Hobbs, Michael W. Miller, Richard Han, Shivakant Mishra

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Serum albumin nanoparticles: Ligand functionalization for enhanced targeted therapeutics in precision medicine by Sakshi Shahapurmath, Bhuvaneshwari R. Sharannavar, Rahul Koli

“…This review provides a comprehensive analysis of recent advancements in the functionalization of albumin nanoparticles with diverse ligands to enhance targeting specificity and therapeutic efficacy. …”
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Effectiveness of Robotics in Stroke Rehabilitation to Accelerate Upper Extremity Function: Systematic Review by Cora Carrillo, Devyn Tilley, Kaitlyn Horn, Michelle Gonzalez, Cassidy Coffman, Claudia Hilton, Karthik Mani

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Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images by Bader Alawfi

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The proteome of circulating extracellular vesicles and their functional effect on platelets vary with the isolation method by Eva M. Fernández-Sáez, Susana B. Bravo, Carmen Pena, Ángel García

“…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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DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

“…To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. …”
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FsDAOD: Few-shot domain adaptation object detection for heterogeneous SAR image by Siyuan Zhao, Yong Kang, Hang Yuan, Guan Wang, Hui Wang, Shichao Xiong, Ying Luo

“…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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DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

“…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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Network security traffic detection and legal supervision based on adaptive metric learning algorithm by Liu Zhen

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App-DDoS detection method using partial binary tree based SVM algorithm by Bin ZHANG, Zihao LIU, Shuqin DONG, Lixun LI

“…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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