First Detection of Low-frequency Striae in Interplanetary Type III Radio Bursts by Vratislav Krupar, Eduard P. Kontar, Jan Soucek, Lynn B. Wilson III, Adam Szabo, Oksana Kruparova, Hamish A. S. Reid, Mychajlo Hajos, David Pisa, Ondrej Santolik, Milan Maksimovic, Jolene S. Pickett

“…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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Sustainable Valorization of Jackfruit Peel Waste: Bio‐Functional and Structural Characterization by Rangina Brahma, Subhajit Ray, Prakash Kumar Nayak, Kandi Shridhar

“…ABSTRACT The sustainable utilization of agricultural waste holds immense promise in addressing environmental concerns and promoting resource efficiency. In this context, jackfruit (Artocarpus heterophyllus) peel waste emerges as a valuable yet underexplored resource. …”
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Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection by Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin

“…Although code embedding-based systems have been successfully applied to various source code analysis tasks, further research is required to enhance code embedding for better code analysis capabilities, aiming to surpass the performance and functionality of static code analysis tools. …”
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Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions by Aakash Patel, Tianqing Zhang, Camille Avestruz, Jeffrey Regier, The LSST Dark Energy Science Collaboration

“…However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. …”
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Design and analysis of intelligent service chain system for network security resource pool by Zenan WANG, Jiahao LI, Chaohong TAN, Dechang PI

“…The traditional network security architecture ensures network security by directing traffic through hardware based network security function devices.Since the architecture consists of fixed hardware devices, it leads to a single form of network security area deployment and poor scalability.Besides, the architecture cannot be flexibly adjusted when facing network security events, making it difficult to meet the security needs of future networks.The intelligent service chain system for network security resource pool was based on software-defined network and network function virtualization technologies, which can effectively solve the above problems.Network security functions of virtual form were added based on network function virtualization technology, combined with the existing hardware network elements to build a network security resource pool.In addition, the switching equipment connected to the network security elements can be flexibly controlled based on software-defined network technology.Then a dynamically adjustable network security service chain was built.Network security events were detected based on security log detection and a expert library consisting of security rules.This enabled dynamic and intelligent regulation of the service chain by means of centralized control in the face of network security events.The deployment process of the service chain was mathematically modeled and a heuristic algorithm was designed to realize the optimal deployment of the service chain.By building a prototype system and conducting experiments, the results show that the designed system can detect security events in seconds and automatically adjust the security service chain in minutes when facing security events, and the designed heuristic algorithm can reduce the occupation of virtual resources by 65%.The proposed system is expected to be applied to the network security area at the exit of the campus and data center network, simplifying the operation and maintenance of this area and improving the deployment flexibility of this area.…”
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An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models by Yi Sun, Faxiu Ji

“…By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. …”
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The Impact of Differences in Renovation Models of Abandoned Boiler Rooms on Community Vitality—A Case Study of Shenyang, China by Lei Chen, Yahang Cheng, Zixi Zhou, Yibo Wen

“…Unlike prior studies that rely on single data sources or unidimensional metrics, our multi-source approach enhances spatiotemporal resolution, improves the accuracy of subjective perceptions, and enables cross-validation between objective behavioral trajectories and residents’ self-reports, thereby significantly strengthening the comprehensiveness and reliability of community vitality measurement. …”
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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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An investigation of the distribution of gaze estimation errors in head mounted gaze trackers using polynomial functions by Diako Mardanbegi, Andrew T. N. Kurauchi, Carlos H. Morimoto

“…In this paper we investigate the behavior of the gaze estimation error distribution throughout the image of the scene camera when using polynomial functions. Using simulated scenarios, we describe effects of four different sources of error: interpolation, extrapolation, parallax, and radial distortion. …”
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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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Detecting and routing of dust event using remote sensing and numerical modeling in Isfahan Province by Mehdii Jafari, Gholamreza Zehtabian, Hasan Ahmadi, Tayebeh Mesbahzadeh, Ali Akbar Norouzi

“…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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A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1 by Adedayo O. Adeyanju, Patrick J. Rich, Gebisa Ejeta

“…The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.…”
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Specific detection of tartaric acid chiral isomers based on centrosymmetric terahertz metamaterial sensors by Xujun Xu, Zhen Sun, Guocui Liu, Jianjun Liu, Yong Du

“…Traditional detection methods, such as fluorescence detection, have problems such as destructive and non-specific characters. …”
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Study on Point Spread Function of Perovskite Fast Neutron Scintillation Imaging Screen by FENG Zhelin1, LIU Linyue2, , SONG Yan2, DUAN Baojun2, BAO Zizhen3, OUYANG Xiaoping2

“…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. …”

The Relevance of Osteoscintigraphy Technique in Early Detection of Bone Metastatic Lesions: a Systematic Review by E. A. Litvinenko, I. V. Burova

“…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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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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Morphological and functional characteristics of Trichinella sp. larvae in bears and badgers in the Kirov Region by O. B. Zhdanova, I. I. Okulova, A. V. Uspensky, L. A. Napisanova

“…For postmortem diagnosis of trichinellosis in the obtained bears and badgers, we can use trichinelloscopy and peptolysis methods which are aimed at detecting infection sources and preventing zoonosis in humans.…”
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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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Design and investigation of charge plasma-based TMD heterojunction TFET biosensor for ultrasensitive detection by Monika Kumari, Niraj Kumar Singh, Vinay Kantipudi, Manodipan Sahoo

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